diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..235aae80ee1671a828973aa2fcc371e0a00bfe06 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +diffsynth/tokenizer_configs/hunyuan_video/tokenizer_2/tokenizer.json filter=lfs diff=lfs merge=lfs -text +diffsynth/tokenizer_configs/kolors/tokenizer/vocab.txt filter=lfs diff=lfs merge=lfs -text +examples/1774363417.png filter=lfs diff=lfs merge=lfs -text +examples/1774363487.png filter=lfs diff=lfs merge=lfs -text +examples/1774363572.png filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md index beb5c2fd6386dedf46cec4d240d5634f9eb93a50..b25926f86e47a0ceedaf6ba4aa32e27fd924e07d 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,27 @@ --- -title: Echo Memory -emoji: 🐨 -colorFrom: gray -colorTo: gray +title: Echo-Memory Action World Model +emoji: 🧠 +colorFrom: blue +colorTo: indigo sdk: gradio sdk_version: 6.19.0 -python_version: '3.12' app_file: app.py -pinned: false +short_description: Action-conditioned video world model demo +python_version: "3.12" +startup_duration_timeout: 1h --- -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +# Echo-Memory: Action-Conditioned World Model Demo + +Generate a video from an initial frame, a text prompt, and a camera rotation action using the [Echo-Memory](https://huggingface.co/papers/2606.09803) model. + +**Model:** [Echo-Team/Echo-Memory](https://huggingface.co/Echo-Team/Echo-Memory) (Wan 2.1 1.3B backbone, context_k1 memory) +**Paper:** [Echo-Memory: A Controlled Study of Memory in Action World Models](https://arxiv.org/abs/2606.09803) +**Code:** [GitHub](https://github.com/Echo-Team-Joy-Future-Academy-JD/Echo-Memory) + +## How it works + +1. Upload an initial frame (or use one of the provided examples) +2. Enter a text prompt describing the scene +3. Choose a camera rotation direction and angle +4. Click "Generate Video" to produce a 5-second video clip at 640×352 resolution \ No newline at end of file diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..54146d46d179ff738c2703eadb884466d9c94f06 --- /dev/null +++ b/app.py @@ -0,0 +1,250 @@ +import os + +os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True") + +import spaces # MUST come before torch / any CUDA-touching import +import sys +import json +import math +import tempfile +import numpy as np +import torch +from PIL import Image +import gradio as gr + +# ── Ensure repo root is in sys.path so diffsynth / env / src are importable ── +_repo_root = os.path.dirname(os.path.abspath(__file__)) +if _repo_root not in sys.path: + sys.path.insert(0, _repo_root) + +from huggingface_hub import snapshot_download + +# ── Constants ───────────────────────────────────────────────────────────────── +WAN_BASE_MODEL_ID = "Wan-AI/Wan2.1-T2V-1.3B" +ECHO_CKPT_REPO = "Echo-Team/Echo-Memory" +ECHO_CKPT_PATH = "context_k1/epoch-0.safetensors" +DEFAULT_NEGATIVE_PROMPT = "oversaturated colors, overexposed, static, blurry details" +HEIGHT, WIDTH = 352, 640 +NUM_FRAMES = 81 +FPS = 15 + + +# ── Model loading at module scope (ZeroGPU: .to("cuda") is intercepted) ────── +print("[app] Downloading Wan2.1-T2V-1.3B base model...") +_base_dir = snapshot_download(WAN_BASE_MODEL_ID) + +print("[app] Downloading Echo-Memory checkpoint...") +_ckpt_dir = snapshot_download(ECHO_CKPT_REPO) +_ckpt_path = os.path.join(_ckpt_dir, ECHO_CKPT_PATH) + +_dit_path = os.path.join(_base_dir, "diffusion_pytorch_model.safetensors") +_text_encoder_path = os.path.join(_base_dir, "models_t5_umt5-xxl-enc-bf16.pth") +_vae_path = os.path.join(_base_dir, "Wan2.1_VAE.pth") +_tokenizer_path = os.path.join(_base_dir, "google", "umt5-xxl") + +from env.loop_utils import load_pipeline_and_ckpt +from env.run_replay_loop_two_chunk import run_one_chunk, encode_context_frames_per_frame +from env.memory_baseline_runtime import MemoryProfile, infer_memory_profile_spec +from inference.unified_inference import resolve_memory_profile, apply_profile_to_pipe +from diffsynth import save_video +from src.model_training.fov_retrieval import compute_rotation_list + +print("[app] Loading pipeline (DiT -> cuda)...") +pipe = load_pipeline_and_ckpt( + ckpt_path=_ckpt_path, + dit_path=_dit_path, + text_encoder_path=_text_encoder_path, + vae_path=_vae_path, + device="cuda", + add_action_attn=False, + action_use_temporal_attention=True, + tokenizer_path=_tokenizer_path, +) + +# Apply the memory profile for context_k1 +_profile = resolve_memory_profile("context_k1", _ckpt_path) +apply_profile_to_pipe(pipe, _profile) +print("[app] Model loaded and memory profile applied.") + + +def _build_rotation_action(deg: float, clockwise: bool, num_frames: int = 81) -> dict: + """Build a uniform yaw-rotation action dictionary for `num_frames` frames. + + Args: + deg: rotation magnitude in degrees. + clockwise: if True, rotate clockwise (negative yaw); else counter-clockwise. + num_frames: number of frames in the chunk. + + Returns: + dict mapping frame index (str) -> 12-D RT list. + """ + denom = max(1, num_frames - 1) + actions = {} + for i in range(num_frames): + yaw = (i / denom) * (-deg if clockwise else deg) + actions[str(i)] = compute_rotation_list([0.0, 0.0, 0.0, yaw]) + return actions + + +@spaces.GPU(duration=180) +def generate( + context_image: Image.Image | None, + prompt: str, + rotation_direction: str, + rotation_degrees: float, + seed: int, + num_inference_steps: int, + cfg_scale: float, + progress=gr.Progress(track_tqdm=True), +): + """Generate an action-conditioned video from an initial frame and a text prompt. + + Args: + context_image: Initial frame (first image of the video). + prompt: Text description of the scene. + rotation_direction: Camera rotation direction ("Left (CCW)" or "Right (CW)"). + rotation_degrees: Total rotation in degrees (e.g. 45). + seed: RNG seed for reproducibility. + num_inference_steps: Number of diffusion denoising steps. + cfg_scale: Classifier-free guidance scale. + """ + if context_image is None: + return None, "Please provide an initial image." + if not prompt or not prompt.strip(): + return None, "Please provide a text prompt." + + clockwise = "Right" in rotation_direction or "CW" in rotation_direction + deg = float(rotation_degrees) + + # Resize context image to model resolution + ctx_pil = context_image.convert("RGB").resize((WIDTH, HEIGHT), Image.LANCZOS) + + # Encode context frame through VAE + print("[generate] Encoding context image...") + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + context_latents = encode_context_frames_per_frame(pipe, [ctx_pil], pipe.device) + num_context_frames = 1 + identity_rt = [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0] + context_actions_t = torch.tensor([identity_rt], dtype=torch.float32) + + # Build camera rotation action + cam_pose_actions = _build_rotation_action(deg, clockwise, NUM_FRAMES) + + # Generate video + print(f"[generate] Generating {NUM_FRAMES} frames @ {WIDTH}x{HEIGHT}, rotation={deg}° {'CW' if clockwise else 'CCW'}") + frames = run_one_chunk( + pipe=pipe, + prompt=prompt, + use_negative_prompt=DEFAULT_NEGATIVE_PROMPT, + cam_pose_actions=cam_pose_actions, + context_latents=context_latents, + num_context_frames=num_context_frames, + context_actions_t=context_actions_t, + chunk_frames=NUM_FRAMES, + h=HEIGHT, + w=WIDTH, + seed=int(seed), + sigma_shift=15.0, + num_inference_steps=int(num_inference_steps), + cfg_scale=float(cfg_scale), + log_prefix="[generate]", + ) + + # Save to temporary file + tmp = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) + tmp.close() + save_video(frames, tmp.name, fps=FPS, quality=5) + print(f"[generate] Video saved to {tmp.name}") + + return tmp.name, f"Generated {len(frames)} frames with {deg}° {'clockwise' if clockwise else 'counter-clockwise'} rotation." + + +# ── Gradio UI ──────────────────────────────────────────────────────────────── +CSS = """ +#col-container { max-width: 1100px; margin: 0 auto; } +.dark .gradio-container { color: var(--body-text-color); } +""" + +with gr.Blocks(theme=gr.themes.Citrus(), css=CSS) as demo: + gr.Markdown("# 🧠 Echo-Memory: Action-Conditioned World Model") + gr.Markdown( + "Generate a video from an initial frame, a text prompt, and a camera rotation action. " + "Based on the [Echo-Memory](https://huggingface.co/papers/2606.09803) paper — " + "a controlled study of memory in action world models using the Wan 2.1 1.3B backbone." + ) + + with gr.Row(): + with gr.Column(scale=1): + context_image = gr.Image(label="Initial Frame", type="pil", height=300) + prompt = gr.Textbox( + label="Text Prompt", + placeholder="A toy bear on a table, the camera rotates around it", + lines=2, + ) + with gr.Row(): + rotation_direction = gr.Radio( + label="Camera Rotation", + choices=["Left (CCW)", "Right (CW)"], + value="Left (CCW)", + ) + rotation_degrees = gr.Slider( + label="Rotation Degrees", + minimum=5, + maximum=90, + value=45, + step=5, + ) + run_btn = gr.Button("Generate Video", variant="primary") + + with gr.Column(scale=1): + video_output = gr.Video(label="Generated Video", height=300) + status_text = gr.Textbox(label="Status", interactive=False) + + with gr.Accordion("Advanced Settings", open=False): + with gr.Row(): + seed = gr.Number(label="Seed", value=42, precision=0) + num_inference_steps = gr.Slider( + label="Inference Steps", + minimum=10, + maximum=100, + value=50, + step=5, + ) + cfg_scale = gr.Slider( + label="CFG Scale", + minimum=1.0, + maximum=10.0, + value=5.0, + step=0.5, + ) + + gr.Examples( + examples=[ + ["examples/1774363417.png", "A toy bear on a table, the camera rotates around it", "Left (CCW)", 45], + ["examples/1774363487.png", "A decorative object on a surface, rotating view", "Right (CW)", 45], + ["examples/1774363572.png", "A scene with objects on a table, camera pans", "Left (CCW)", 30], + ], + inputs=[context_image, prompt, rotation_direction, rotation_degrees], + outputs=[video_output, status_text], + fn=generate, + cache_examples=True, + cache_mode="lazy", + ) + + gr.Markdown( + "---\n" + "**Model:** [Echo-Team/Echo-Memory](https://huggingface.co/Echo-Team/Echo-Memory) · " + "**Backbone:** [Wan-AI/Wan2.1-T2V-1.3B](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B) · " + "**Paper:** [arXiv:2606.09803](https://arxiv.org/abs/2606.09803) · " + "**Code:** [GitHub](https://github.com/Echo-Team-Joy-Future-Academy-JD/Echo-Memory)" + ) + + run_btn.click( + fn=generate, + inputs=[context_image, prompt, rotation_direction, rotation_degrees, seed, num_inference_steps, cfg_scale], + outputs=[video_output, status_text], + api_name="generate", + ) + +demo.launch(mcp_server=True) \ No newline at end of file diff --git a/diffsynth/__init__.py b/diffsynth/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ae0a45c2e2dc61f8f16354feb1b0c481776b523f --- /dev/null +++ b/diffsynth/__init__.py @@ -0,0 +1,6 @@ +from .data import * +from .models import * +from .prompters import * +from .schedulers import * +from .pipelines import * +from .controlnets import * diff --git a/diffsynth/configs/__init__.py b/diffsynth/configs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/diffsynth/configs/model_config.py b/diffsynth/configs/model_config.py new file mode 100644 index 0000000000000000000000000000000000000000..93399bfef318deca0db16e04f377ea9e99470c1b --- /dev/null +++ b/diffsynth/configs/model_config.py @@ -0,0 +1,830 @@ +from typing_extensions import Literal, TypeAlias + +from ..models.sd_text_encoder import SDTextEncoder +from ..models.sd_unet import SDUNet +from ..models.sd_vae_encoder import SDVAEEncoder +from ..models.sd_vae_decoder import SDVAEDecoder + +from ..models.sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2 +from ..models.sdxl_unet import SDXLUNet +from ..models.sdxl_vae_decoder import SDXLVAEDecoder +from ..models.sdxl_vae_encoder import SDXLVAEEncoder + +from ..models.sd3_text_encoder import SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3 +from ..models.sd3_dit import SD3DiT +from ..models.sd3_vae_decoder import SD3VAEDecoder +from ..models.sd3_vae_encoder import SD3VAEEncoder + +from ..models.sd_controlnet import SDControlNet +from ..models.sdxl_controlnet import SDXLControlNetUnion + +from ..models.sd_motion import SDMotionModel +from ..models.sdxl_motion import SDXLMotionModel + +from ..models.svd_image_encoder import SVDImageEncoder +from ..models.svd_unet import SVDUNet +from ..models.svd_vae_decoder import SVDVAEDecoder +from ..models.svd_vae_encoder import SVDVAEEncoder + +from ..models.sd_ipadapter import SDIpAdapter, IpAdapterCLIPImageEmbedder +from ..models.sdxl_ipadapter import SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder + +from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder +from ..models.hunyuan_dit import HunyuanDiT + +from ..models.flux_dit import FluxDiT +from ..models.flux_text_encoder import FluxTextEncoder2 +from ..models.flux_vae import FluxVAEEncoder, FluxVAEDecoder +from ..models.flux_controlnet import FluxControlNet +from ..models.flux_ipadapter import FluxIpAdapter +from ..models.flux_infiniteyou import InfiniteYouImageProjector + +from ..models.cog_vae import CogVAEEncoder, CogVAEDecoder +from ..models.cog_dit import CogDiT + +from ..models.omnigen import OmniGenTransformer + +from ..models.hunyuan_video_vae_decoder import HunyuanVideoVAEDecoder +from ..models.hunyuan_video_vae_encoder import HunyuanVideoVAEEncoder + +from ..extensions.RIFE import IFNet +from ..extensions.ESRGAN import RRDBNet + +from ..models.hunyuan_video_dit import HunyuanVideoDiT + +from ..models.stepvideo_vae import StepVideoVAE +from ..models.stepvideo_dit import StepVideoModel + +from ..models.wan_video_dit import WanModel +from ..models.wan_video_text_encoder import WanTextEncoder +from ..models.wan_video_image_encoder import WanImageEncoder +from ..models.wan_video_vae import WanVideoVAE +from ..models.wan_video_motion_controller import WanMotionControllerModel +from ..models.wan_video_vace import VaceWanModel + +from ..models.step1x_connector import Qwen2Connector + +from ..models.flux_value_control import SingleValueEncoder + +from ..lora.flux_lora import FluxLoraPatcher +from ..models.flux_lora_encoder import FluxLoRAEncoder + + +model_loader_configs = [ + # These configs are provided for detecting model type automatically. + # The format is (state_dict_keys_hash, state_dict_keys_hash_with_shape, model_names, model_classes, model_resource) + (None, "091b0e30e77c76626b3ba62acdf95343", ["sd_controlnet"], [SDControlNet], "civitai"), + (None, "4a6c8306a27d916dea81263c8c88f450", ["hunyuan_dit_clip_text_encoder"], [HunyuanDiTCLIPTextEncoder], "civitai"), + (None, "f4aec400fe394297961218c768004521", ["hunyuan_dit"], [HunyuanDiT], "civitai"), + (None, "9e6e58043a5a2e332803ed42f6ee7181", ["hunyuan_dit_t5_text_encoder"], [HunyuanDiTT5TextEncoder], "civitai"), + (None, "13115dd45a6e1c39860f91ab073b8a78", ["sdxl_vae_encoder", "sdxl_vae_decoder"], [SDXLVAEEncoder, SDXLVAEDecoder], "diffusers"), + (None, "d78aa6797382a6d455362358a3295ea9", ["sd_ipadapter_clip_image_encoder"], [IpAdapterCLIPImageEmbedder], "diffusers"), + (None, "e291636cc15e803186b47404262ef812", ["sd_ipadapter"], [SDIpAdapter], "civitai"), + (None, "399c81f2f8de8d1843d0127a00f3c224", ["sdxl_ipadapter_clip_image_encoder"], [IpAdapterXLCLIPImageEmbedder], "diffusers"), + (None, "a64eac9aa0db4b9602213bc0131281c7", ["sdxl_ipadapter"], [SDXLIpAdapter], "civitai"), + (None, "52817e4fdd89df154f02749ca6f692ac", ["sdxl_unet"], [SDXLUNet], "diffusers"), + (None, "03343c606f16d834d6411d0902b53636", ["sd_text_encoder", "sd_unet", "sd_vae_decoder", "sd_vae_encoder"], [SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder], "civitai"), + (None, "d4ba77a7ece070679b4a987f58f201e9", ["sd_text_encoder"], [SDTextEncoder], "civitai"), + (None, "d0c89e55c5a57cf3981def0cb1c9e65a", ["sd_vae_decoder", "sd_vae_encoder"], [SDVAEDecoder, SDVAEEncoder], "civitai"), + (None, "3926bf373b39a67eeafd7901478a47a7", ["sd_unet"], [SDUNet], "civitai"), + (None, "1e0c39ec176b9007c05f76d52b554a4d", ["sd3_text_encoder_1", "sd3_text_encoder_2", "sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3TextEncoder1, SD3TextEncoder2, SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"), + (None, "d9e0290829ba8d98e28e1a2b1407db4a", ["sd3_text_encoder_1", "sd3_text_encoder_2", "sd3_text_encoder_3", "sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3, SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"), + (None, "5072d0b24e406b49507abe861cf97691", ["sd3_text_encoder_3"], [SD3TextEncoder3], "civitai"), + (None, "4cf64a799d04260df438c6f33c9a047e", ["sdxl_text_encoder", "sdxl_text_encoder_2", "sdxl_unet", "sdxl_vae_decoder", "sdxl_vae_encoder"], [SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder], "civitai"), + (None, "d9b008a867c498ab12ad24042eff8e3f", ["sdxl_text_encoder", "sdxl_text_encoder_2", "sdxl_unet", "sdxl_vae_decoder", "sdxl_vae_encoder"], [SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder], "civitai"), # SDXL-Turbo + (None, "025bb7452e531a3853d951d77c63f032", ["sdxl_text_encoder", "sdxl_text_encoder_2"], [SDXLTextEncoder, SDXLTextEncoder2], "civitai"), + (None, "298997b403a4245c04102c9f36aac348", ["sdxl_unet"], [SDXLUNet], "civitai"), + (None, "2a07abce74b4bdc696b76254ab474da6", ["svd_image_encoder", "svd_unet", "svd_vae_decoder", "svd_vae_encoder"], [SVDImageEncoder, SVDUNet, SVDVAEDecoder, SVDVAEEncoder], "civitai"), + (None, "c96a285a6888465f87de22a984d049fb", ["sd_motion_modules"], [SDMotionModel], "civitai"), + (None, "72907b92caed19bdb2adb89aa4063fe2", ["sdxl_motion_modules"], [SDXLMotionModel], "civitai"), + (None, "31d2d9614fba60511fc9bf2604aa01f7", ["sdxl_controlnet"], [SDXLControlNetUnion], "diffusers"), + (None, "94eefa3dac9cec93cb1ebaf1747d7b78", ["sd3_text_encoder_1"], [SD3TextEncoder1], "diffusers"), + (None, "1aafa3cc91716fb6b300cc1cd51b85a3", ["flux_vae_encoder", "flux_vae_decoder"], [FluxVAEEncoder, FluxVAEDecoder], "diffusers"), + (None, "21ea55f476dfc4fd135587abb59dfe5d", ["flux_vae_encoder", "flux_vae_decoder"], [FluxVAEEncoder, FluxVAEDecoder], "civitai"), + (None, "a29710fea6dddb0314663ee823598e50", ["flux_dit"], [FluxDiT], "civitai"), + (None, "57b02550baab820169365b3ee3afa2c9", ["flux_dit"], [FluxDiT], "civitai"), + (None, "3394f306c4cbf04334b712bf5aaed95f", ["flux_dit"], [FluxDiT], "civitai"), + (None, "023f054d918a84ccf503481fd1e3379e", ["flux_dit"], [FluxDiT], "civitai"), + (None, "d02f41c13549fa5093d3521f62a5570a", ["flux_dit"], [FluxDiT], "civitai"), + (None, "605c56eab23e9e2af863ad8f0813a25d", ["flux_dit"], [FluxDiT], "diffusers"), + (None, "0629116fce1472503a66992f96f3eb1a", ["flux_value_controller"], [SingleValueEncoder], "civitai"), + (None, "280189ee084bca10f70907bf6ce1649d", ["cog_vae_encoder", "cog_vae_decoder"], [CogVAEEncoder, CogVAEDecoder], "diffusers"), + (None, "9b9313d104ac4df27991352fec013fd4", ["rife"], [IFNet], "civitai"), + (None, "6b7116078c4170bfbeaedc8fe71f6649", ["esrgan"], [RRDBNet], "civitai"), + (None, "61cbcbc7ac11f169c5949223efa960d1", ["omnigen_transformer"], [OmniGenTransformer], "diffusers"), + (None, "78d18b9101345ff695f312e7e62538c0", ["flux_controlnet"], [FluxControlNet], "diffusers"), + (None, "b001c89139b5f053c715fe772362dd2a", ["flux_controlnet"], [FluxControlNet], "diffusers"), + (None, "52357cb26250681367488a8954c271e8", ["flux_controlnet"], [FluxControlNet], "diffusers"), + (None, "0cfd1740758423a2a854d67c136d1e8c", ["flux_controlnet"], [FluxControlNet], "diffusers"), + (None, "7f9583eb8ba86642abb9a21a4b2c9e16", ["flux_controlnet"], [FluxControlNet], "diffusers"), + (None, "43ad5aaa27dd4ee01b832ed16773fa52", ["flux_controlnet"], [FluxControlNet], "diffusers"), + (None, "c07c0f04f5ff55e86b4e937c7a40d481", ["infiniteyou_image_projector"], [InfiniteYouImageProjector], "diffusers"), + (None, "4daaa66cc656a8fe369908693dad0a35", ["flux_ipadapter"], [FluxIpAdapter], "diffusers"), + (None, "51aed3d27d482fceb5e0739b03060e8f", ["sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"), + (None, "98cc34ccc5b54ae0e56bdea8688dcd5a", ["sd3_text_encoder_2"], [SD3TextEncoder2], "civitai"), + (None, "77ff18050dbc23f50382e45d51a779fe", ["sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"), + (None, "5da81baee73198a7c19e6d2fe8b5148e", ["sd3_text_encoder_1"], [SD3TextEncoder1], "diffusers"), + (None, "aeb82dce778a03dcb4d726cb03f3c43f", ["hunyuan_video_vae_decoder", "hunyuan_video_vae_encoder"], [HunyuanVideoVAEDecoder, HunyuanVideoVAEEncoder], "diffusers"), + (None, "b9588f02e78f5ccafc9d7c0294e46308", ["hunyuan_video_dit"], [HunyuanVideoDiT], "civitai"), + (None, "84ef4bd4757f60e906b54aa6a7815dc6", ["hunyuan_video_dit"], [HunyuanVideoDiT], "civitai"), + (None, "68beaf8429b7c11aa8ca05b1bd0058bd", ["stepvideo_vae"], [StepVideoVAE], "civitai"), + (None, "5c0216a2132b082c10cb7a0e0377e681", ["stepvideo_dit"], [StepVideoModel], "civitai"), + (None, "9269f8db9040a9d860eaca435be61814", ["wan_video_dit"], [WanModel], "civitai"), + (None, "aafcfd9672c3a2456dc46e1cb6e52c70", ["wan_video_dit"], [WanModel], "civitai"), + (None, "6bfcfb3b342cb286ce886889d519a77e", ["wan_video_dit"], [WanModel], "civitai"), + (None, "6d6ccde6845b95ad9114ab993d917893", ["wan_video_dit"], [WanModel], "civitai"), + (None, "6bfcfb3b342cb286ce886889d519a77e", ["wan_video_dit"], [WanModel], "civitai"), + (None, "349723183fc063b2bfc10bb2835cf677", ["wan_video_dit"], [WanModel], "civitai"), + (None, "efa44cddf936c70abd0ea28b6cbe946c", ["wan_video_dit"], [WanModel], "civitai"), + (None, "3ef3b1f8e1dab83d5b71fd7b617f859f", ["wan_video_dit"], [WanModel], "civitai"), + (None, "70ddad9d3a133785da5ea371aae09504", ["wan_video_dit"], [WanModel], "civitai"), + (None, "26bde73488a92e64cc20b0a7485b9e5b", ["wan_video_dit"], [WanModel], "civitai"), + (None, "ac6a5aa74f4a0aab6f64eb9a72f19901", ["wan_video_dit"], [WanModel], "civitai"), + (None, "b61c605c2adbd23124d152ed28e049ae", ["wan_video_dit"], [WanModel], "civitai"), + (None, "a61453409b67cd3246cf0c3bebad47ba", ["wan_video_dit", "wan_video_vace"], [WanModel, VaceWanModel], "civitai"), + (None, "7a513e1f257a861512b1afd387a8ecd9", ["wan_video_dit", "wan_video_vace"], [WanModel, VaceWanModel], "civitai"), + (None, "cb104773c6c2cb6df4f9529ad5c60d0b", ["wan_video_dit"], [WanModel], "diffusers"), + # 添加合并后带 action 模块的模型 hash (stage-2-key-14B-merged) + (None, "3296967e8209bf52ffdb9b8414dab595", ["wan_video_dit"], [WanModel], "civitai"), # 合并后带 action 模块的模型（正确的hash） + (None, "9c8818c2cbea55eca56c7b447df170da", ["wan_video_text_encoder"], [WanTextEncoder], "civitai"), + (None, "5941c53e207d62f20f9025686193c40b", ["wan_video_image_encoder"], [WanImageEncoder], "civitai"), + (None, "1378ea763357eea97acdef78e65d6d96", ["wan_video_vae"], [WanVideoVAE], "civitai"), + (None, "ccc42284ea13e1ad04693284c7a09be6", ["wan_video_vae"], [WanVideoVAE], "civitai"), + (None, "dbd5ec76bbf977983f972c151d545389", ["wan_video_motion_controller"], [WanMotionControllerModel], "civitai"), + (None, "d30fb9e02b1dbf4e509142f05cf7dd50", ["flux_dit", "step1x_connector"], [FluxDiT, Qwen2Connector], "civitai"), + (None, "30143afb2dea73d1ac580e0787628f8c", ["flux_lora_patcher"], [FluxLoraPatcher], "civitai"), + (None, "77c2e4dd2440269eb33bfaa0d004f6ab", ["flux_lora_encoder"], [FluxLoRAEncoder], "civitai"), +] +huggingface_model_loader_configs = [ + # These configs are provided for detecting model type automatically. + # The format is (architecture_in_huggingface_config, huggingface_lib, model_name, redirected_architecture) + ("ChatGLMModel", "diffsynth.models.kolors_text_encoder", "kolors_text_encoder", None), + ("MarianMTModel", "transformers.models.marian.modeling_marian", "translator", None), + ("BloomForCausalLM", "transformers.models.bloom.modeling_bloom", "beautiful_prompt", None), + ("Qwen2ForCausalLM", "transformers.models.qwen2.modeling_qwen2", "qwen_prompt", None), + # ("LlamaForCausalLM", "transformers.models.llama.modeling_llama", "omost_prompt", None), + ("T5EncoderModel", "diffsynth.models.flux_text_encoder", "flux_text_encoder_2", "FluxTextEncoder2"), + ("CogVideoXTransformer3DModel", "diffsynth.models.cog_dit", "cog_dit", "CogDiT"), + ("SiglipModel", "transformers.models.siglip.modeling_siglip", "siglip_vision_model", "SiglipVisionModel"), + ("LlamaForCausalLM", "diffsynth.models.hunyuan_video_text_encoder", "hunyuan_video_text_encoder_2", "HunyuanVideoLLMEncoder"), + ("LlavaForConditionalGeneration", "diffsynth.models.hunyuan_video_text_encoder", "hunyuan_video_text_encoder_2", "HunyuanVideoMLLMEncoder"), + ("Step1Model", "diffsynth.models.stepvideo_text_encoder", "stepvideo_text_encoder_2", "STEP1TextEncoder"), + ("Qwen2_5_VLForConditionalGeneration", "diffsynth.models.qwenvl", "qwenvl", "Qwen25VL_7b_Embedder"), +] +patch_model_loader_configs = [ + # These configs are provided for detecting model type automatically. + # The format is (state_dict_keys_hash_with_shape, model_name, model_class, extra_kwargs) + ("9a4ab6869ac9b7d6e31f9854e397c867", ["svd_unet"], [SVDUNet], {"add_positional_conv": 128}), +] + +preset_models_on_huggingface = { + "HunyuanDiT": [ + ("Tencent-Hunyuan/HunyuanDiT", "t2i/clip_text_encoder/pytorch_model.bin", "models/HunyuanDiT/t2i/clip_text_encoder"), + ("Tencent-Hunyuan/HunyuanDiT", "t2i/mt5/pytorch_model.bin", "models/HunyuanDiT/t2i/mt5"), + ("Tencent-Hunyuan/HunyuanDiT", "t2i/model/pytorch_model_ema.pt", "models/HunyuanDiT/t2i/model"), + ("Tencent-Hunyuan/HunyuanDiT", "t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin", "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix"), + ], + "stable-video-diffusion-img2vid-xt": [ + ("stabilityai/stable-video-diffusion-img2vid-xt", "svd_xt.safetensors", "models/stable_video_diffusion"), + ], + "ExVideo-SVD-128f-v1": [ + ("ECNU-CILab/ExVideo-SVD-128f-v1", "model.fp16.safetensors", "models/stable_video_diffusion"), + ], + # Stable Diffusion + "StableDiffusion_v15": [ + ("benjamin-paine/stable-diffusion-v1-5", "v1-5-pruned-emaonly.safetensors", "models/stable_diffusion"), + ], + "DreamShaper_8": [ + ("Yntec/Dreamshaper8", "dreamshaper_8.safetensors", "models/stable_diffusion"), + ], + # Textual Inversion + "TextualInversion_VeryBadImageNegative_v1.3": [ + ("gemasai/verybadimagenegative_v1.3", "verybadimagenegative_v1.3.pt", "models/textual_inversion"), + ], + # Stable Diffusion XL + "StableDiffusionXL_v1": [ + ("stabilityai/stable-diffusion-xl-base-1.0", "sd_xl_base_1.0.safetensors", "models/stable_diffusion_xl"), + ], + "BluePencilXL_v200": [ + ("frankjoshua/bluePencilXL_v200", "bluePencilXL_v200.safetensors", "models/stable_diffusion_xl"), + ], + "StableDiffusionXL_Turbo": [ + ("stabilityai/sdxl-turbo", "sd_xl_turbo_1.0_fp16.safetensors", "models/stable_diffusion_xl_turbo"), + ], + # Stable Diffusion 3 + "StableDiffusion3": [ + ("stabilityai/stable-diffusion-3-medium", "sd3_medium_incl_clips_t5xxlfp16.safetensors", "models/stable_diffusion_3"), + ], + "StableDiffusion3_without_T5": [ + ("stabilityai/stable-diffusion-3-medium", "sd3_medium_incl_clips.safetensors", "models/stable_diffusion_3"), + ], + # ControlNet + "ControlNet_v11f1p_sd15_depth": [ + ("lllyasviel/ControlNet-v1-1", "control_v11f1p_sd15_depth.pth", "models/ControlNet"), + ("lllyasviel/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators") + ], + "ControlNet_v11p_sd15_softedge": [ + ("lllyasviel/ControlNet-v1-1", "control_v11p_sd15_softedge.pth", "models/ControlNet"), + ("lllyasviel/Annotators", "ControlNetHED.pth", "models/Annotators") + ], + "ControlNet_v11f1e_sd15_tile": [ + ("lllyasviel/ControlNet-v1-1", "control_v11f1e_sd15_tile.pth", "models/ControlNet") + ], + "ControlNet_v11p_sd15_lineart": [ + ("lllyasviel/ControlNet-v1-1", "control_v11p_sd15_lineart.pth", "models/ControlNet"), + ("lllyasviel/Annotators", "sk_model.pth", "models/Annotators"), + ("lllyasviel/Annotators", "sk_model2.pth", "models/Annotators") + ], + "ControlNet_union_sdxl_promax": [ + ("xinsir/controlnet-union-sdxl-1.0", "diffusion_pytorch_model_promax.safetensors", "models/ControlNet/controlnet_union"), + ("lllyasviel/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators") + ], + # AnimateDiff + "AnimateDiff_v2": [ + ("guoyww/animatediff", "mm_sd_v15_v2.ckpt", "models/AnimateDiff"), + ], + "AnimateDiff_xl_beta": [ + ("guoyww/animatediff", "mm_sdxl_v10_beta.ckpt", "models/AnimateDiff"), + ], + + # Qwen Prompt + "QwenPrompt": [ + ("Qwen/Qwen2-1.5B-Instruct", "config.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("Qwen/Qwen2-1.5B-Instruct", "generation_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("Qwen/Qwen2-1.5B-Instruct", "model.safetensors", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("Qwen/Qwen2-1.5B-Instruct", "special_tokens_map.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("Qwen/Qwen2-1.5B-Instruct", "tokenizer.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("Qwen/Qwen2-1.5B-Instruct", "tokenizer_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("Qwen/Qwen2-1.5B-Instruct", "merges.txt", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("Qwen/Qwen2-1.5B-Instruct", "vocab.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ], + # Beautiful Prompt + "BeautifulPrompt": [ + ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "generation_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "model.safetensors", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "special_tokens_map.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "tokenizer.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "tokenizer_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ], + # Omost prompt + "OmostPrompt":[ + ("lllyasviel/omost-llama-3-8b-4bits", "model-00001-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("lllyasviel/omost-llama-3-8b-4bits", "model-00002-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("lllyasviel/omost-llama-3-8b-4bits", "tokenizer.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("lllyasviel/omost-llama-3-8b-4bits", "tokenizer_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("lllyasviel/omost-llama-3-8b-4bits", "config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("lllyasviel/omost-llama-3-8b-4bits", "generation_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("lllyasviel/omost-llama-3-8b-4bits", "model.safetensors.index.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("lllyasviel/omost-llama-3-8b-4bits", "special_tokens_map.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ], + # Translator + "opus-mt-zh-en": [ + ("Helsinki-NLP/opus-mt-zh-en", "config.json", "models/translator/opus-mt-zh-en"), + ("Helsinki-NLP/opus-mt-zh-en", "generation_config.json", "models/translator/opus-mt-zh-en"), + ("Helsinki-NLP/opus-mt-zh-en", "metadata.json", "models/translator/opus-mt-zh-en"), + ("Helsinki-NLP/opus-mt-zh-en", "pytorch_model.bin", "models/translator/opus-mt-zh-en"), + ("Helsinki-NLP/opus-mt-zh-en", "source.spm", "models/translator/opus-mt-zh-en"), + ("Helsinki-NLP/opus-mt-zh-en", "target.spm", "models/translator/opus-mt-zh-en"), + ("Helsinki-NLP/opus-mt-zh-en", "tokenizer_config.json", "models/translator/opus-mt-zh-en"), + ("Helsinki-NLP/opus-mt-zh-en", "vocab.json", "models/translator/opus-mt-zh-en"), + ], + # IP-Adapter + "IP-Adapter-SD": [ + ("h94/IP-Adapter", "models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion/image_encoder"), + ("h94/IP-Adapter", "models/ip-adapter_sd15.bin", "models/IpAdapter/stable_diffusion"), + ], + "IP-Adapter-SDXL": [ + ("h94/IP-Adapter", "sdxl_models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion_xl/image_encoder"), + ("h94/IP-Adapter", "sdxl_models/ip-adapter_sdxl.bin", "models/IpAdapter/stable_diffusion_xl"), + ], + "SDXL-vae-fp16-fix": [ + ("madebyollin/sdxl-vae-fp16-fix", "diffusion_pytorch_model.safetensors", "models/sdxl-vae-fp16-fix") + ], + # Kolors + "Kolors": [ + ("Kwai-Kolors/Kolors", "text_encoder/config.json", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model.bin.index.json", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00001-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00002-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00003-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00004-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00005-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00006-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00007-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "unet/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/unet"), + ("Kwai-Kolors/Kolors", "vae/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/vae"), + ], + # FLUX + "FLUX.1-dev": [ + ("black-forest-labs/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"), + ("black-forest-labs/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("black-forest-labs/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("black-forest-labs/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("black-forest-labs/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("black-forest-labs/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"), + ("black-forest-labs/FLUX.1-dev", "flux1-dev.safetensors", "models/FLUX/FLUX.1-dev"), + ], + "InstantX/FLUX.1-dev-IP-Adapter": { + "file_list": [ + ("InstantX/FLUX.1-dev-IP-Adapter", "ip-adapter.bin", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter"), + ("google/siglip-so400m-patch14-384", "model.safetensors", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"), + ("google/siglip-so400m-patch14-384", "config.json", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"), + ], + "load_path": [ + "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/ip-adapter.bin", + "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder", + ], + }, + # RIFE + "RIFE": [ + ("AlexWortega/RIFE", "flownet.pkl", "models/RIFE"), + ], + # CogVideo + "CogVideoX-5B": [ + ("THUDM/CogVideoX-5b", "text_encoder/config.json", "models/CogVideo/CogVideoX-5b/text_encoder"), + ("THUDM/CogVideoX-5b", "text_encoder/model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/text_encoder"), + ("THUDM/CogVideoX-5b", "text_encoder/model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"), + ("THUDM/CogVideoX-5b", "text_encoder/model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"), + ("THUDM/CogVideoX-5b", "transformer/config.json", "models/CogVideo/CogVideoX-5b/transformer"), + ("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/transformer"), + ("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"), + ("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"), + ("THUDM/CogVideoX-5b", "vae/diffusion_pytorch_model.safetensors", "models/CogVideo/CogVideoX-5b/vae"), + ], + # Stable Diffusion 3.5 + "StableDiffusion3.5-large": [ + ("stabilityai/stable-diffusion-3.5-large", "sd3.5_large.safetensors", "models/stable_diffusion_3"), + ("stabilityai/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"), + ("stabilityai/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"), + ("stabilityai/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"), + ], +} +preset_models_on_modelscope = { + # Hunyuan DiT + "HunyuanDiT": [ + ("modelscope/HunyuanDiT", "t2i/clip_text_encoder/pytorch_model.bin", "models/HunyuanDiT/t2i/clip_text_encoder"), + ("modelscope/HunyuanDiT", "t2i/mt5/pytorch_model.bin", "models/HunyuanDiT/t2i/mt5"), + ("modelscope/HunyuanDiT", "t2i/model/pytorch_model_ema.pt", "models/HunyuanDiT/t2i/model"), + ("modelscope/HunyuanDiT", "t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin", "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix"), + ], + # Stable Video Diffusion + "stable-video-diffusion-img2vid-xt": [ + ("AI-ModelScope/stable-video-diffusion-img2vid-xt", "svd_xt.safetensors", "models/stable_video_diffusion"), + ], + # ExVideo + "ExVideo-SVD-128f-v1": [ + ("ECNU-CILab/ExVideo-SVD-128f-v1", "model.fp16.safetensors", "models/stable_video_diffusion"), + ], + "ExVideo-CogVideoX-LoRA-129f-v1": [ + ("ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1", "ExVideo-CogVideoX-LoRA-129f-v1.safetensors", "models/lora"), + ], + # Stable Diffusion + "StableDiffusion_v15": [ + ("AI-ModelScope/stable-diffusion-v1-5", "v1-5-pruned-emaonly.safetensors", "models/stable_diffusion"), + ], + "DreamShaper_8": [ + ("sd_lora/dreamshaper_8", "dreamshaper_8.safetensors", "models/stable_diffusion"), + ], + "AingDiffusion_v12": [ + ("sd_lora/aingdiffusion_v12", "aingdiffusion_v12.safetensors", "models/stable_diffusion"), + ], + "Flat2DAnimerge_v45Sharp": [ + ("sd_lora/Flat-2D-Animerge", "flat2DAnimerge_v45Sharp.safetensors", "models/stable_diffusion"), + ], + # Textual Inversion + "TextualInversion_VeryBadImageNegative_v1.3": [ + ("sd_lora/verybadimagenegative_v1.3", "verybadimagenegative_v1.3.pt", "models/textual_inversion"), + ], + # Stable Diffusion XL + "StableDiffusionXL_v1": [ + ("AI-ModelScope/stable-diffusion-xl-base-1.0", "sd_xl_base_1.0.safetensors", "models/stable_diffusion_xl"), + ], + "BluePencilXL_v200": [ + ("sd_lora/bluePencilXL_v200", "bluePencilXL_v200.safetensors", "models/stable_diffusion_xl"), + ], + "StableDiffusionXL_Turbo": [ + ("AI-ModelScope/sdxl-turbo", "sd_xl_turbo_1.0_fp16.safetensors", "models/stable_diffusion_xl_turbo"), + ], + "SDXL_lora_zyd232_ChineseInkStyle_SDXL_v1_0": [ + ("sd_lora/zyd232_ChineseInkStyle_SDXL_v1_0", "zyd232_ChineseInkStyle_SDXL_v1_0.safetensors", "models/lora"), + ], + # Stable Diffusion 3 + "StableDiffusion3": [ + ("AI-ModelScope/stable-diffusion-3-medium", "sd3_medium_incl_clips_t5xxlfp16.safetensors", "models/stable_diffusion_3"), + ], + "StableDiffusion3_without_T5": [ + ("AI-ModelScope/stable-diffusion-3-medium", "sd3_medium_incl_clips.safetensors", "models/stable_diffusion_3"), + ], + # ControlNet + "ControlNet_v11f1p_sd15_depth": [ + ("AI-ModelScope/ControlNet-v1-1", "control_v11f1p_sd15_depth.pth", "models/ControlNet"), + ("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators") + ], + "ControlNet_v11p_sd15_softedge": [ + ("AI-ModelScope/ControlNet-v1-1", "control_v11p_sd15_softedge.pth", "models/ControlNet"), + ("sd_lora/Annotators", "ControlNetHED.pth", "models/Annotators") + ], + "ControlNet_v11f1e_sd15_tile": [ + ("AI-ModelScope/ControlNet-v1-1", "control_v11f1e_sd15_tile.pth", "models/ControlNet") + ], + "ControlNet_v11p_sd15_lineart": [ + ("AI-ModelScope/ControlNet-v1-1", "control_v11p_sd15_lineart.pth", "models/ControlNet"), + ("sd_lora/Annotators", "sk_model.pth", "models/Annotators"), + ("sd_lora/Annotators", "sk_model2.pth", "models/Annotators") + ], + "ControlNet_union_sdxl_promax": [ + ("AI-ModelScope/controlnet-union-sdxl-1.0", "diffusion_pytorch_model_promax.safetensors", "models/ControlNet/controlnet_union"), + ("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators") + ], + "Annotators:Depth": [ + ("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators"), + ], + "Annotators:Softedge": [ + ("sd_lora/Annotators", "ControlNetHED.pth", "models/Annotators"), + ], + "Annotators:Lineart": [ + ("sd_lora/Annotators", "sk_model.pth", "models/Annotators"), + ("sd_lora/Annotators", "sk_model2.pth", "models/Annotators"), + ], + "Annotators:Normal": [ + ("sd_lora/Annotators", "scannet.pt", "models/Annotators"), + ], + "Annotators:Openpose": [ + ("sd_lora/Annotators", "body_pose_model.pth", "models/Annotators"), + ("sd_lora/Annotators", "facenet.pth", "models/Annotators"), + ("sd_lora/Annotators", "hand_pose_model.pth", "models/Annotators"), + ], + # AnimateDiff + "AnimateDiff_v2": [ + ("Shanghai_AI_Laboratory/animatediff", "mm_sd_v15_v2.ckpt", "models/AnimateDiff"), + ], + "AnimateDiff_xl_beta": [ + ("Shanghai_AI_Laboratory/animatediff", "mm_sdxl_v10_beta.ckpt", "models/AnimateDiff"), + ], + # RIFE + "RIFE": [ + ("Damo_XR_Lab/cv_rife_video-frame-interpolation", "flownet.pkl", "models/RIFE"), + ], + # Qwen Prompt + "QwenPrompt": { + "file_list": [ + ("qwen/Qwen2-1.5B-Instruct", "config.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("qwen/Qwen2-1.5B-Instruct", "generation_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("qwen/Qwen2-1.5B-Instruct", "model.safetensors", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("qwen/Qwen2-1.5B-Instruct", "special_tokens_map.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("qwen/Qwen2-1.5B-Instruct", "tokenizer.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("qwen/Qwen2-1.5B-Instruct", "tokenizer_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("qwen/Qwen2-1.5B-Instruct", "merges.txt", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("qwen/Qwen2-1.5B-Instruct", "vocab.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ], + "load_path": [ + "models/QwenPrompt/qwen2-1.5b-instruct", + ], + }, + # Beautiful Prompt + "BeautifulPrompt": { + "file_list": [ + ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "generation_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "model.safetensors", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "special_tokens_map.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "tokenizer.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "tokenizer_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ], + "load_path": [ + "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd", + ], + }, + # Omost prompt + "OmostPrompt": { + "file_list": [ + ("Omost/omost-llama-3-8b-4bits", "model-00001-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("Omost/omost-llama-3-8b-4bits", "model-00002-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("Omost/omost-llama-3-8b-4bits", "tokenizer.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("Omost/omost-llama-3-8b-4bits", "tokenizer_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("Omost/omost-llama-3-8b-4bits", "config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("Omost/omost-llama-3-8b-4bits", "generation_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("Omost/omost-llama-3-8b-4bits", "model.safetensors.index.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("Omost/omost-llama-3-8b-4bits", "special_tokens_map.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ], + "load_path": [ + "models/OmostPrompt/omost-llama-3-8b-4bits", + ], + }, + # Translator + "opus-mt-zh-en": { + "file_list": [ + ("moxying/opus-mt-zh-en", "config.json", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "generation_config.json", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "metadata.json", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "pytorch_model.bin", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "source.spm", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "target.spm", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "tokenizer_config.json", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "vocab.json", "models/translator/opus-mt-zh-en"), + ], + "load_path": [ + "models/translator/opus-mt-zh-en", + ], + }, + # IP-Adapter + "IP-Adapter-SD": [ + ("AI-ModelScope/IP-Adapter", "models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion/image_encoder"), + ("AI-ModelScope/IP-Adapter", "models/ip-adapter_sd15.bin", "models/IpAdapter/stable_diffusion"), + ], + "IP-Adapter-SDXL": [ + ("AI-ModelScope/IP-Adapter", "sdxl_models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion_xl/image_encoder"), + ("AI-ModelScope/IP-Adapter", "sdxl_models/ip-adapter_sdxl.bin", "models/IpAdapter/stable_diffusion_xl"), + ], + # Kolors + "Kolors": { + "file_list": [ + ("Kwai-Kolors/Kolors", "text_encoder/config.json", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model.bin.index.json", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00001-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00002-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00003-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00004-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00005-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00006-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00007-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "unet/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/unet"), + ("Kwai-Kolors/Kolors", "vae/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/vae"), + ], + "load_path": [ + "models/kolors/Kolors/text_encoder", + "models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors", + "models/kolors/Kolors/vae/diffusion_pytorch_model.safetensors", + ], + }, + "SDXL-vae-fp16-fix": [ + ("AI-ModelScope/sdxl-vae-fp16-fix", "diffusion_pytorch_model.safetensors", "models/sdxl-vae-fp16-fix") + ], + # FLUX + "FLUX.1-dev": { + "file_list": [ + ("AI-ModelScope/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"), + ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("AI-ModelScope/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"), + ("AI-ModelScope/FLUX.1-dev", "flux1-dev.safetensors", "models/FLUX/FLUX.1-dev"), + ], + "load_path": [ + "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", + "models/FLUX/FLUX.1-dev/text_encoder_2", + "models/FLUX/FLUX.1-dev/ae.safetensors", + "models/FLUX/FLUX.1-dev/flux1-dev.safetensors" + ], + }, + "FLUX.1-schnell": { + "file_list": [ + ("AI-ModelScope/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"), + ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("AI-ModelScope/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"), + ("AI-ModelScope/FLUX.1-schnell", "flux1-schnell.safetensors", "models/FLUX/FLUX.1-schnell"), + ], + "load_path": [ + "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", + "models/FLUX/FLUX.1-dev/text_encoder_2", + "models/FLUX/FLUX.1-dev/ae.safetensors", + "models/FLUX/FLUX.1-schnell/flux1-schnell.safetensors" + ], + }, + "InstantX/FLUX.1-dev-Controlnet-Union-alpha": [ + ("InstantX/FLUX.1-dev-Controlnet-Union-alpha", "diffusion_pytorch_model.safetensors", "models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha"), + ], + "jasperai/Flux.1-dev-Controlnet-Depth": [ + ("jasperai/Flux.1-dev-Controlnet-Depth", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Depth"), + ], + "jasperai/Flux.1-dev-Controlnet-Surface-Normals": [ + ("jasperai/Flux.1-dev-Controlnet-Surface-Normals", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Surface-Normals"), + ], + "jasperai/Flux.1-dev-Controlnet-Upscaler": [ + ("jasperai/Flux.1-dev-Controlnet-Upscaler", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler"), + ], + "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha": [ + ("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha", "diffusion_pytorch_model.safetensors", "models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha"), + ], + "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta": [ + ("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", "diffusion_pytorch_model.safetensors", "models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta"), + ], + "Shakker-Labs/FLUX.1-dev-ControlNet-Depth": [ + ("Shakker-Labs/FLUX.1-dev-ControlNet-Depth", "diffusion_pytorch_model.safetensors", "models/ControlNet/Shakker-Labs/FLUX.1-dev-ControlNet-Depth"), + ], + "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro": [ + ("Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", "diffusion_pytorch_model.safetensors", "models/ControlNet/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro"), + ], + "InstantX/FLUX.1-dev-IP-Adapter": { + "file_list": [ + ("InstantX/FLUX.1-dev-IP-Adapter", "ip-adapter.bin", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter"), + ("AI-ModelScope/siglip-so400m-patch14-384", "model.safetensors", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"), + ("AI-ModelScope/siglip-so400m-patch14-384", "config.json", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"), + ], + "load_path": [ + "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/ip-adapter.bin", + "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder", + ], + }, + "InfiniteYou":{ + "file_list":[ + ("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/InfuseNetModel/diffusion_pytorch_model-00001-of-00002.safetensors", "models/InfiniteYou/InfuseNetModel"), + ("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/InfuseNetModel/diffusion_pytorch_model-00002-of-00002.safetensors", "models/InfiniteYou/InfuseNetModel"), + ("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/image_proj_model.bin", "models/InfiniteYou"), + ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/1k3d68.onnx", "models/InfiniteYou/insightface/models/antelopev2"), + ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/2d106det.onnx", "models/InfiniteYou/insightface/models/antelopev2"), + ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/genderage.onnx", "models/InfiniteYou/insightface/models/antelopev2"), + ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/glintr100.onnx", "models/InfiniteYou/insightface/models/antelopev2"), + ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/scrfd_10g_bnkps.onnx", "models/InfiniteYou/insightface/models/antelopev2"), + ], + "load_path":[ + [ + "models/InfiniteYou/InfuseNetModel/diffusion_pytorch_model-00001-of-00002.safetensors", + "models/InfiniteYou/InfuseNetModel/diffusion_pytorch_model-00002-of-00002.safetensors" + ], + "models/InfiniteYou/image_proj_model.bin", + ], + }, + # ESRGAN + "ESRGAN_x4": [ + ("AI-ModelScope/Real-ESRGAN", "RealESRGAN_x4.pth", "models/ESRGAN"), + ], + # RIFE + "RIFE": [ + ("AI-ModelScope/RIFE", "flownet.pkl", "models/RIFE"), + ], + # Omnigen + "OmniGen-v1": { + "file_list": [ + ("BAAI/OmniGen-v1", "vae/diffusion_pytorch_model.safetensors", "models/OmniGen/OmniGen-v1/vae"), + ("BAAI/OmniGen-v1", "model.safetensors", "models/OmniGen/OmniGen-v1"), + ("BAAI/OmniGen-v1", "config.json", "models/OmniGen/OmniGen-v1"), + ("BAAI/OmniGen-v1", "special_tokens_map.json", "models/OmniGen/OmniGen-v1"), + ("BAAI/OmniGen-v1", "tokenizer_config.json", "models/OmniGen/OmniGen-v1"), + ("BAAI/OmniGen-v1", "tokenizer.json", "models/OmniGen/OmniGen-v1"), + ], + "load_path": [ + "models/OmniGen/OmniGen-v1/vae/diffusion_pytorch_model.safetensors", + "models/OmniGen/OmniGen-v1/model.safetensors", + ] + }, + # CogVideo + "CogVideoX-5B": { + "file_list": [ + ("ZhipuAI/CogVideoX-5b", "text_encoder/config.json", "models/CogVideo/CogVideoX-5b/text_encoder"), + ("ZhipuAI/CogVideoX-5b", "text_encoder/model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/text_encoder"), + ("ZhipuAI/CogVideoX-5b", "text_encoder/model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"), + ("ZhipuAI/CogVideoX-5b", "text_encoder/model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"), + ("ZhipuAI/CogVideoX-5b", "transformer/config.json", "models/CogVideo/CogVideoX-5b/transformer"), + ("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/transformer"), + ("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"), + ("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"), + ("ZhipuAI/CogVideoX-5b", "vae/diffusion_pytorch_model.safetensors", "models/CogVideo/CogVideoX-5b/vae"), + ], + "load_path": [ + "models/CogVideo/CogVideoX-5b/text_encoder", + "models/CogVideo/CogVideoX-5b/transformer", + "models/CogVideo/CogVideoX-5b/vae/diffusion_pytorch_model.safetensors", + ], + }, + # Stable Diffusion 3.5 + "StableDiffusion3.5-large": [ + ("AI-ModelScope/stable-diffusion-3.5-large", "sd3.5_large.safetensors", "models/stable_diffusion_3"), + ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"), + ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"), + ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"), + ], + "StableDiffusion3.5-medium": [ + ("AI-ModelScope/stable-diffusion-3.5-medium", "sd3.5_medium.safetensors", "models/stable_diffusion_3"), + ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"), + ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"), + ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"), + ], + "StableDiffusion3.5-large-turbo": [ + ("AI-ModelScope/stable-diffusion-3.5-large-turbo", "sd3.5_large_turbo.safetensors", "models/stable_diffusion_3"), + ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"), + ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"), + ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"), + ], + "HunyuanVideo":{ + "file_list": [ + ("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideo/text_encoder"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00001-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00002-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00003-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00004-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "config.json", "models/HunyuanVideo/text_encoder_2"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model.safetensors.index.json", "models/HunyuanVideo/text_encoder_2"), + ("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/vae/pytorch_model.pt", "models/HunyuanVideo/vae"), + ("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt", "models/HunyuanVideo/transformers") + ], + "load_path": [ + "models/HunyuanVideo/text_encoder/model.safetensors", + "models/HunyuanVideo/text_encoder_2", + "models/HunyuanVideo/vae/pytorch_model.pt", + "models/HunyuanVideo/transformers/mp_rank_00_model_states.pt" + ], + }, + "HunyuanVideoI2V":{ + "file_list": [ + ("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideoI2V/text_encoder"), + ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00001-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"), + ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00002-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"), + ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00003-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"), + ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00004-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"), + ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "config.json", "models/HunyuanVideoI2V/text_encoder_2"), + ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model.safetensors.index.json", "models/HunyuanVideoI2V/text_encoder_2"), + ("AI-ModelScope/HunyuanVideo-I2V", "hunyuan-video-i2v-720p/vae/pytorch_model.pt", "models/HunyuanVideoI2V/vae"), + ("AI-ModelScope/HunyuanVideo-I2V", "hunyuan-video-i2v-720p/transformers/mp_rank_00_model_states.pt", "models/HunyuanVideoI2V/transformers") + ], + "load_path": [ + "models/HunyuanVideoI2V/text_encoder/model.safetensors", + "models/HunyuanVideoI2V/text_encoder_2", + "models/HunyuanVideoI2V/vae/pytorch_model.pt", + "models/HunyuanVideoI2V/transformers/mp_rank_00_model_states.pt" + ], + }, + "HunyuanVideo-fp8":{ + "file_list": [ + ("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideo/text_encoder"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00001-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00002-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00003-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00004-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "config.json", "models/HunyuanVideo/text_encoder_2"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model.safetensors.index.json", "models/HunyuanVideo/text_encoder_2"), + ("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/vae/pytorch_model.pt", "models/HunyuanVideo/vae"), + ("DiffSynth-Studio/HunyuanVideo-safetensors", "model.fp8.safetensors", "models/HunyuanVideo/transformers") + ], + "load_path": [ + "models/HunyuanVideo/text_encoder/model.safetensors", + "models/HunyuanVideo/text_encoder_2", + "models/HunyuanVideo/vae/pytorch_model.pt", + "models/HunyuanVideo/transformers/model.fp8.safetensors" + ], + }, +} +Preset_model_id: TypeAlias = Literal[ + "HunyuanDiT", + "stable-video-diffusion-img2vid-xt", + "ExVideo-SVD-128f-v1", + "ExVideo-CogVideoX-LoRA-129f-v1", + "StableDiffusion_v15", + "DreamShaper_8", + "AingDiffusion_v12", + "Flat2DAnimerge_v45Sharp", + "TextualInversion_VeryBadImageNegative_v1.3", + "StableDiffusionXL_v1", + "BluePencilXL_v200", + "StableDiffusionXL_Turbo", + "ControlNet_v11f1p_sd15_depth", + "ControlNet_v11p_sd15_softedge", + "ControlNet_v11f1e_sd15_tile", + "ControlNet_v11p_sd15_lineart", + "AnimateDiff_v2", + "AnimateDiff_xl_beta", + "RIFE", + "BeautifulPrompt", + "opus-mt-zh-en", + "IP-Adapter-SD", + "IP-Adapter-SDXL", + "StableDiffusion3", + "StableDiffusion3_without_T5", + "Kolors", + "SDXL-vae-fp16-fix", + "ControlNet_union_sdxl_promax", + "FLUX.1-dev", + "FLUX.1-schnell", + "InstantX/FLUX.1-dev-Controlnet-Union-alpha", + "jasperai/Flux.1-dev-Controlnet-Depth", + "jasperai/Flux.1-dev-Controlnet-Surface-Normals", + "jasperai/Flux.1-dev-Controlnet-Upscaler", + "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha", + "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", + "Shakker-Labs/FLUX.1-dev-ControlNet-Depth", + "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", + "InstantX/FLUX.1-dev-IP-Adapter", + "InfiniteYou", + "SDXL_lora_zyd232_ChineseInkStyle_SDXL_v1_0", + "QwenPrompt", + "OmostPrompt", + "ESRGAN_x4", + "RIFE", + "OmniGen-v1", + "CogVideoX-5B", + "Annotators:Depth", + "Annotators:Softedge", + "Annotators:Lineart", + "Annotators:Normal", + "Annotators:Openpose", + "StableDiffusion3.5-large", + "StableDiffusion3.5-medium", + "HunyuanVideo", + "HunyuanVideo-fp8", + "HunyuanVideoI2V", +] diff --git a/diffsynth/controlnets/__init__.py b/diffsynth/controlnets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a3e15add6ab116bf261804b8c83c86ff4d61c41b --- /dev/null +++ b/diffsynth/controlnets/__init__.py @@ -0,0 +1,2 @@ +from .controlnet_unit import ControlNetConfigUnit, ControlNetUnit, MultiControlNetManager, FluxMultiControlNetManager +from .processors import Annotator diff --git a/diffsynth/controlnets/controlnet_unit.py b/diffsynth/controlnets/controlnet_unit.py new file mode 100644 index 0000000000000000000000000000000000000000..fdb4829483d208ec0295d1b5a8f82681b4251ea4 --- /dev/null +++ b/diffsynth/controlnets/controlnet_unit.py @@ -0,0 +1,91 @@ +import torch +import numpy as np +from .processors import Processor_id + + +class ControlNetConfigUnit: + def __init__(self, processor_id: Processor_id, model_path, scale=1.0, skip_processor=False): + self.processor_id = processor_id + self.model_path = model_path + self.scale = scale + self.skip_processor = skip_processor + + +class ControlNetUnit: + def __init__(self, processor, model, scale=1.0): + self.processor = processor + self.model = model + self.scale = scale + + +class MultiControlNetManager: + def __init__(self, controlnet_units=[]): + self.processors = [unit.processor for unit in controlnet_units] + self.models = [unit.model for unit in controlnet_units] + self.scales = [unit.scale for unit in controlnet_units] + + def cpu(self): + for model in self.models: + model.cpu() + + def to(self, device): + for model in self.models: + model.to(device) + for processor in self.processors: + processor.to(device) + + def process_image(self, image, processor_id=None): + if processor_id is None: + processed_image = [processor(image) for processor in self.processors] + else: + processed_image = [self.processors[processor_id](image)] + processed_image = torch.concat([ + torch.Tensor(np.array(image_, dtype=np.float32) / 255).permute(2, 0, 1).unsqueeze(0) + for image_ in processed_image + ], dim=0) + return processed_image + + def __call__( + self, + sample, timestep, encoder_hidden_states, conditionings, + tiled=False, tile_size=64, tile_stride=32, **kwargs + ): + res_stack = None + for processor, conditioning, model, scale in zip(self.processors, conditionings, self.models, self.scales): + res_stack_ = model( + sample, timestep, encoder_hidden_states, conditioning, **kwargs, + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, + processor_id=processor.processor_id + ) + res_stack_ = [res * scale for res in res_stack_] + if res_stack is None: + res_stack = res_stack_ + else: + res_stack = [i + j for i, j in zip(res_stack, res_stack_)] + return res_stack + + +class FluxMultiControlNetManager(MultiControlNetManager): + def __init__(self, controlnet_units=[]): + super().__init__(controlnet_units=controlnet_units) + + def process_image(self, image, processor_id=None): + if processor_id is None: + processed_image = [processor(image) for processor in self.processors] + else: + processed_image = [self.processors[processor_id](image)] + return processed_image + + def __call__(self, conditionings, **kwargs): + res_stack, single_res_stack = None, None + for processor, conditioning, model, scale in zip(self.processors, conditionings, self.models, self.scales): + res_stack_, single_res_stack_ = model(controlnet_conditioning=conditioning, processor_id=processor.processor_id, **kwargs) + res_stack_ = [res * scale for res in res_stack_] + single_res_stack_ = [res * scale for res in single_res_stack_] + if res_stack is None: + res_stack = res_stack_ + single_res_stack = single_res_stack_ + else: + res_stack = [i + j for i, j in zip(res_stack, res_stack_)] + single_res_stack = [i + j for i, j in zip(single_res_stack, single_res_stack_)] + return res_stack, single_res_stack diff --git a/diffsynth/controlnets/processors.py b/diffsynth/controlnets/processors.py new file mode 100644 index 0000000000000000000000000000000000000000..06553e06d1c6d09f5a3deecfd4ea5604c5dd4352 --- /dev/null +++ b/diffsynth/controlnets/processors.py @@ -0,0 +1,62 @@ +from typing_extensions import Literal, TypeAlias + + +Processor_id: TypeAlias = Literal[ + "canny", "depth", "softedge", "lineart", "lineart_anime", "openpose", "normal", "tile", "none", "inpaint" +] + +class Annotator: + def __init__(self, processor_id: Processor_id, model_path="models/Annotators", detect_resolution=None, device='cuda', skip_processor=False): + if not skip_processor: + if processor_id == "canny": + from controlnet_aux.processor import CannyDetector + self.processor = CannyDetector() + elif processor_id == "depth": + from controlnet_aux.processor import MidasDetector + self.processor = MidasDetector.from_pretrained(model_path).to(device) + elif processor_id == "softedge": + from controlnet_aux.processor import HEDdetector + self.processor = HEDdetector.from_pretrained(model_path).to(device) + elif processor_id == "lineart": + from controlnet_aux.processor import LineartDetector + self.processor = LineartDetector.from_pretrained(model_path).to(device) + elif processor_id == "lineart_anime": + from controlnet_aux.processor import LineartAnimeDetector + self.processor = LineartAnimeDetector.from_pretrained(model_path).to(device) + elif processor_id == "openpose": + from controlnet_aux.processor import OpenposeDetector + self.processor = OpenposeDetector.from_pretrained(model_path).to(device) + elif processor_id == "normal": + from controlnet_aux.processor import NormalBaeDetector + self.processor = NormalBaeDetector.from_pretrained(model_path).to(device) + elif processor_id == "tile" or processor_id == "none" or processor_id == "inpaint": + self.processor = None + else: + raise ValueError(f"Unsupported processor_id: {processor_id}") + else: + self.processor = None + + self.processor_id = processor_id + self.detect_resolution = detect_resolution + + def to(self,device): + if hasattr(self.processor,"model") and hasattr(self.processor.model,"to"): + + self.processor.model.to(device) + + def __call__(self, image, mask=None): + width, height = image.size + if self.processor_id == "openpose": + kwargs = { + "include_body": True, + "include_hand": True, + "include_face": True + } + else: + kwargs = {} + if self.processor is not None: + detect_resolution = self.detect_resolution if self.detect_resolution is not None else min(width, height) + image = self.processor(image, detect_resolution=detect_resolution, image_resolution=min(width, height), **kwargs) + image = image.resize((width, height)) + return image + diff --git a/diffsynth/data/__init__.py b/diffsynth/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..de09a29905d289673e40e53278a0a3181232640d --- /dev/null +++ b/diffsynth/data/__init__.py @@ -0,0 +1 @@ +from .video import VideoData, save_video, save_frames diff --git a/diffsynth/data/simple_text_image.py b/diffsynth/data/simple_text_image.py new file mode 100644 index 0000000000000000000000000000000000000000..7a9525e3c8a4d21418c1464fe11fc621450fd0d8 --- /dev/null +++ b/diffsynth/data/simple_text_image.py @@ -0,0 +1,41 @@ +import torch, os, torchvision +from torchvision import transforms +import pandas as pd +from PIL import Image + + + +class TextImageDataset(torch.utils.data.Dataset): + def __init__(self, dataset_path, steps_per_epoch=10000, height=1024, width=1024, center_crop=True, random_flip=False): + self.steps_per_epoch = steps_per_epoch + metadata = pd.read_csv(os.path.join(dataset_path, "train/metadata.csv")) + self.path = [os.path.join(dataset_path, "train", file_name) for file_name in metadata["file_name"]] + self.text = metadata["text"].to_list() + self.height = height + self.width = width + self.image_processor = transforms.Compose( + [ + transforms.CenterCrop((height, width)) if center_crop else transforms.RandomCrop((height, width)), + transforms.RandomHorizontalFlip() if random_flip else transforms.Lambda(lambda x: x), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]), + ] + ) + + + def __getitem__(self, index): + data_id = torch.randint(0, len(self.path), (1,))[0] + data_id = (data_id + index) % len(self.path) # For fixed seed. + text = self.text[data_id] + image = Image.open(self.path[data_id]).convert("RGB") + target_height, target_width = self.height, self.width + width, height = image.size + scale = max(target_width / width, target_height / height) + shape = [round(height*scale),round(width*scale)] + image = torchvision.transforms.functional.resize(image,shape,interpolation=transforms.InterpolationMode.BILINEAR) + image = self.image_processor(image) + return {"text": text, "image": image} + + + def __len__(self): + return self.steps_per_epoch diff --git a/diffsynth/data/video.py b/diffsynth/data/video.py new file mode 100644 index 0000000000000000000000000000000000000000..8eafa66855fa5668d42a65ac205776ed254213cf --- /dev/null +++ b/diffsynth/data/video.py @@ -0,0 +1,148 @@ +import imageio, os +import numpy as np +from PIL import Image +from tqdm import tqdm + + +class LowMemoryVideo: + def __init__(self, file_name): + self.reader = imageio.get_reader(file_name) + + def __len__(self): + return self.reader.count_frames() + + def __getitem__(self, item): + return Image.fromarray(np.array(self.reader.get_data(item))).convert("RGB") + + def __del__(self): + self.reader.close() + + +def split_file_name(file_name): + result = [] + number = -1 + for i in file_name: + if ord(i)>=ord("0") and ord(i)<=ord("9"): + if number == -1: + number = 0 + number = number*10 + ord(i) - ord("0") + else: + if number != -1: + result.append(number) + number = -1 + result.append(i) + if number != -1: + result.append(number) + result = tuple(result) + return result + + +def search_for_images(folder): + file_list = [i for i in os.listdir(folder) if i.endswith(".jpg") or i.endswith(".png")] + file_list = [(split_file_name(file_name), file_name) for file_name in file_list] + file_list = [i[1] for i in sorted(file_list)] + file_list = [os.path.join(folder, i) for i in file_list] + return file_list + + +class LowMemoryImageFolder: + def __init__(self, folder, file_list=None): + if file_list is None: + self.file_list = search_for_images(folder) + else: + self.file_list = [os.path.join(folder, file_name) for file_name in file_list] + + def __len__(self): + return len(self.file_list) + + def __getitem__(self, item): + return Image.open(self.file_list[item]).convert("RGB") + + def __del__(self): + pass + + +def crop_and_resize(image, height, width): + image = np.array(image) + image_height, image_width, _ = image.shape + if image_height / image_width < height / width: + croped_width = int(image_height / height * width) + left = (image_width - croped_width) // 2 + image = image[:, left: left+croped_width] + image = Image.fromarray(image).resize((width, height)) + else: + croped_height = int(image_width / width * height) + left = (image_height - croped_height) // 2 + image = image[left: left+croped_height, :] + image = Image.fromarray(image).resize((width, height)) + return image + + +class VideoData: + def __init__(self, video_file=None, image_folder=None, height=None, width=None, **kwargs): + if video_file is not None: + self.data_type = "video" + self.data = LowMemoryVideo(video_file, **kwargs) + elif image_folder is not None: + self.data_type = "images" + self.data = LowMemoryImageFolder(image_folder, **kwargs) + else: + raise ValueError("Cannot open video or image folder") + self.length = None + self.set_shape(height, width) + + def raw_data(self): + frames = [] + for i in range(self.__len__()): + frames.append(self.__getitem__(i)) + return frames + + def set_length(self, length): + self.length = length + + def set_shape(self, height, width): + self.height = height + self.width = width + + def __len__(self): + if self.length is None: + return len(self.data) + else: + return self.length + + def shape(self): + if self.height is not None and self.width is not None: + return self.height, self.width + else: + height, width, _ = self.__getitem__(0).shape + return height, width + + def __getitem__(self, item): + frame = self.data.__getitem__(item) + width, height = frame.size + if self.height is not None and self.width is not None: + if self.height != height or self.width != width: + frame = crop_and_resize(frame, self.height, self.width) + return frame + + def __del__(self): + pass + + def save_images(self, folder): + os.makedirs(folder, exist_ok=True) + for i in tqdm(range(self.__len__()), desc="Saving images"): + frame = self.__getitem__(i) + frame.save(os.path.join(folder, f"{i}.png")) + + +def save_video(frames, save_path, fps, quality=9, ffmpeg_params=None): + writer = imageio.get_writer(save_path, fps=fps, quality=quality, ffmpeg_params=ffmpeg_params) + for frame in tqdm(frames, desc="Saving video"): + frame = np.array(frame) + writer.append_data(frame) + writer.close() + +def save_frames(frames, save_path): + os.makedirs(save_path, exist_ok=True) + for i, frame in enumerate(tqdm(frames, desc="Saving images")): + frame.save(os.path.join(save_path, f"{i}.png")) diff --git a/diffsynth/distributed/__init__.py b/diffsynth/distributed/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/diffsynth/distributed/xdit_context_parallel.py b/diffsynth/distributed/xdit_context_parallel.py new file mode 100644 index 0000000000000000000000000000000000000000..2c1a2572539aba98ecf900ae264dbaaf29286371 --- /dev/null +++ b/diffsynth/distributed/xdit_context_parallel.py @@ -0,0 +1,129 @@ +import torch +from typing import Optional +from einops import rearrange +from xfuser.core.distributed import (get_sequence_parallel_rank, + get_sequence_parallel_world_size, + get_sp_group) +from xfuser.core.long_ctx_attention import xFuserLongContextAttention + +def sinusoidal_embedding_1d(dim, position): + sinusoid = torch.outer(position.type(torch.float64), torch.pow( + 10000, -torch.arange(dim//2, dtype=torch.float64, device=position.device).div(dim//2))) + x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1) + return x.to(position.dtype) + +def pad_freqs(original_tensor, target_len): + seq_len, s1, s2 = original_tensor.shape + pad_size = target_len - seq_len + padding_tensor = torch.ones( + pad_size, + s1, + s2, + dtype=original_tensor.dtype, + device=original_tensor.device) + padded_tensor = torch.cat([original_tensor, padding_tensor], dim=0) + return padded_tensor + +def rope_apply(x, freqs, num_heads): + x = rearrange(x, "b s (n d) -> b s n d", n=num_heads) + s_per_rank = x.shape[1] + + x_out = torch.view_as_complex(x.to(torch.float64).reshape( + x.shape[0], x.shape[1], x.shape[2], -1, 2)) + + sp_size = get_sequence_parallel_world_size() + sp_rank = get_sequence_parallel_rank() + freqs = pad_freqs(freqs, s_per_rank * sp_size) + freqs_rank = freqs[(sp_rank * s_per_rank):((sp_rank + 1) * s_per_rank), :, :] + + x_out = torch.view_as_real(x_out * freqs_rank).flatten(2) + return x_out.to(x.dtype) + +def usp_dit_forward(self, + x: torch.Tensor, + timestep: torch.Tensor, + context: torch.Tensor, + clip_feature: Optional[torch.Tensor] = None, + y: Optional[torch.Tensor] = None, + use_gradient_checkpointing: bool = False, + use_gradient_checkpointing_offload: bool = False, + **kwargs, + ): + t = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, timestep)) + t_mod = self.time_projection(t).unflatten(1, (6, self.dim)) + context = self.text_embedding(context) + + if self.has_image_input: + x = torch.cat([x, y], dim=1) # (b, c_x + c_y, f, h, w) + clip_embdding = self.img_emb(clip_feature) + context = torch.cat([clip_embdding, context], dim=1) + + x, (f, h, w) = self.patchify(x) + + freqs = torch.cat([ + self.freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1), + self.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), + self.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1) + ], dim=-1).reshape(f * h * w, 1, -1).to(x.device) + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + return custom_forward + + # Context Parallel + x = torch.chunk( + x, get_sequence_parallel_world_size(), + dim=1)[get_sequence_parallel_rank()] + + for block in self.blocks: + if self.training and use_gradient_checkpointing: + if use_gradient_checkpointing_offload: + with torch.autograd.graph.save_on_cpu(): + x = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + x, context, t_mod, freqs, + use_reentrant=False, + ) + else: + x = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + x, context, t_mod, freqs, + use_reentrant=False, + ) + else: + x = block(x, context, t_mod, freqs) + + x = self.head(x, t) + + # Context Parallel + x = get_sp_group().all_gather(x, dim=1) + + # unpatchify + x = self.unpatchify(x, (f, h, w)) + return x + + +def usp_attn_forward(self, x, freqs): + q = self.norm_q(self.q(x)) + k = self.norm_k(self.k(x)) + v = self.v(x) + + q = rope_apply(q, freqs, self.num_heads) + k = rope_apply(k, freqs, self.num_heads) + q = rearrange(q, "b s (n d) -> b s n d", n=self.num_heads) + k = rearrange(k, "b s (n d) -> b s n d", n=self.num_heads) + v = rearrange(v, "b s (n d) -> b s n d", n=self.num_heads) + + x = xFuserLongContextAttention()( + None, + query=q, + key=k, + value=v, + ) + x = x.flatten(2) + + del q, k, v + torch.cuda.empty_cache() + return self.o(x) \ No newline at end of file diff --git a/diffsynth/extensions/ESRGAN/__init__.py b/diffsynth/extensions/ESRGAN/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..94aff4c6fe8d75ff65e30d672dbe3e38a0d919c3 --- /dev/null +++ b/diffsynth/extensions/ESRGAN/__init__.py @@ -0,0 +1,137 @@ +import torch +from einops import repeat +from PIL import Image +import numpy as np + + +class ResidualDenseBlock(torch.nn.Module): + + def __init__(self, num_feat=64, num_grow_ch=32): + super(ResidualDenseBlock, self).__init__() + self.conv1 = torch.nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1) + self.conv2 = torch.nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1) + self.conv3 = torch.nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1) + self.conv4 = torch.nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1) + self.conv5 = torch.nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1) + self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True) + + def forward(self, x): + x1 = self.lrelu(self.conv1(x)) + x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) + x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) + x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) + x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) + return x5 * 0.2 + x + + +class RRDB(torch.nn.Module): + + def __init__(self, num_feat, num_grow_ch=32): + super(RRDB, self).__init__() + self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch) + self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch) + self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch) + + def forward(self, x): + out = self.rdb1(x) + out = self.rdb2(out) + out = self.rdb3(out) + return out * 0.2 + x + + +class RRDBNet(torch.nn.Module): + + def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, **kwargs): + super(RRDBNet, self).__init__() + self.conv_first = torch.nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) + self.body = torch.torch.nn.Sequential(*[RRDB(num_feat=num_feat, num_grow_ch=num_grow_ch) for _ in range(num_block)]) + self.conv_body = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) + # upsample + self.conv_up1 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_up2 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_hr = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_last = torch.nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True) + + def forward(self, x): + feat = x + feat = self.conv_first(feat) + body_feat = self.conv_body(self.body(feat)) + feat = feat + body_feat + # upsample + feat = repeat(feat, "B C H W -> B C (H 2) (W 2)") + feat = self.lrelu(self.conv_up1(feat)) + feat = repeat(feat, "B C H W -> B C (H 2) (W 2)") + feat = self.lrelu(self.conv_up2(feat)) + out = self.conv_last(self.lrelu(self.conv_hr(feat))) + return out + + @staticmethod + def state_dict_converter(): + return RRDBNetStateDictConverter() + + +class RRDBNetStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + return state_dict, {"upcast_to_float32": True} + + def from_civitai(self, state_dict): + return state_dict, {"upcast_to_float32": True} + + +class ESRGAN(torch.nn.Module): + def __init__(self, model): + super().__init__() + self.model = model + + @staticmethod + def from_model_manager(model_manager): + return ESRGAN(model_manager.fetch_model("esrgan")) + + def process_image(self, image): + image = torch.Tensor(np.array(image, dtype=np.float32) / 255).permute(2, 0, 1) + return image + + def process_images(self, images): + images = [self.process_image(image) for image in images] + images = torch.stack(images) + return images + + def decode_images(self, images): + images = (images.permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8) + images = [Image.fromarray(image) for image in images] + return images + + @torch.no_grad() + def upscale(self, images, batch_size=4, progress_bar=lambda x:x): + if not isinstance(images, list): + images = [images] + is_single_image = True + else: + is_single_image = False + + # Preprocess + input_tensor = self.process_images(images) + + # Interpolate + output_tensor = [] + for batch_id in progress_bar(range(0, input_tensor.shape[0], batch_size)): + batch_id_ = min(batch_id + batch_size, input_tensor.shape[0]) + batch_input_tensor = input_tensor[batch_id: batch_id_] + batch_input_tensor = batch_input_tensor.to( + device=self.model.conv_first.weight.device, + dtype=self.model.conv_first.weight.dtype) + batch_output_tensor = self.model(batch_input_tensor) + output_tensor.append(batch_output_tensor.cpu()) + + # Output + output_tensor = torch.concat(output_tensor, dim=0) + + # To images + output_images = self.decode_images(output_tensor) + if is_single_image: + output_images = output_images[0] + return output_images diff --git a/diffsynth/extensions/FastBlend/__init__.py b/diffsynth/extensions/FastBlend/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2bf812c2085082bfa82658dd249ebca89e9fb465 --- /dev/null +++ b/diffsynth/extensions/FastBlend/__init__.py @@ -0,0 +1,63 @@ +from .runners.fast import TableManager, PyramidPatchMatcher +from PIL import Image +import numpy as np +import cupy as cp + + +class FastBlendSmoother: + def __init__(self): + self.batch_size = 8 + self.window_size = 64 + self.ebsynth_config = { + "minimum_patch_size": 5, + "threads_per_block": 8, + "num_iter": 5, + "gpu_id": 0, + "guide_weight": 10.0, + "initialize": "identity", + "tracking_window_size": 0, + } + + @staticmethod + def from_model_manager(model_manager): + # TODO: fetch GPU ID from model_manager + return FastBlendSmoother() + + def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config): + frames_guide = [np.array(frame) for frame in frames_guide] + frames_style = [np.array(frame) for frame in frames_style] + table_manager = TableManager() + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + **ebsynth_config + ) + # left part + table_l = table_manager.build_remapping_table(frames_guide, frames_style, patch_match_engine, batch_size, desc="FastBlend Step 1/4") + table_l = table_manager.remapping_table_to_blending_table(table_l) + table_l = table_manager.process_window_sum(frames_guide, table_l, patch_match_engine, window_size, batch_size, desc="FastBlend Step 2/4") + # right part + table_r = table_manager.build_remapping_table(frames_guide[::-1], frames_style[::-1], patch_match_engine, batch_size, desc="FastBlend Step 3/4") + table_r = table_manager.remapping_table_to_blending_table(table_r) + table_r = table_manager.process_window_sum(frames_guide[::-1], table_r, patch_match_engine, window_size, batch_size, desc="FastBlend Step 4/4")[::-1] + # merge + frames = [] + for (frame_l, weight_l), frame_m, (frame_r, weight_r) in zip(table_l, frames_style, table_r): + weight_m = -1 + weight = weight_l + weight_m + weight_r + frame = frame_l * (weight_l / weight) + frame_m * (weight_m / weight) + frame_r * (weight_r / weight) + frames.append(frame) + frames = [Image.fromarray(frame.clip(0, 255).astype("uint8")) for frame in frames] + return frames + + def __call__(self, rendered_frames, original_frames=None, **kwargs): + frames = self.run( + original_frames, rendered_frames, + self.batch_size, self.window_size, self.ebsynth_config + ) + mempool = cp.get_default_memory_pool() + pinned_mempool = cp.get_default_pinned_memory_pool() + mempool.free_all_blocks() + pinned_mempool.free_all_blocks() + return frames \ No newline at end of file diff --git a/diffsynth/extensions/FastBlend/api.py b/diffsynth/extensions/FastBlend/api.py new file mode 100644 index 0000000000000000000000000000000000000000..2db24330e375ed62065af54613b6ab956c9c64cf --- /dev/null +++ b/diffsynth/extensions/FastBlend/api.py @@ -0,0 +1,397 @@ +from .runners import AccurateModeRunner, FastModeRunner, BalancedModeRunner, InterpolationModeRunner, InterpolationModeSingleFrameRunner +from .data import VideoData, get_video_fps, save_video, search_for_images +import os +import gradio as gr + + +def check_input_for_blending(video_guide, video_guide_folder, video_style, video_style_folder): + frames_guide = VideoData(video_guide, video_guide_folder) + frames_style = VideoData(video_style, video_style_folder) + message = "" + if len(frames_guide) < len(frames_style): + message += f"The number of frames mismatches. Only the first {len(frames_guide)} frames of style video will be used.\n" + frames_style.set_length(len(frames_guide)) + elif len(frames_guide) > len(frames_style): + message += f"The number of frames mismatches. Only the first {len(frames_style)} frames of guide video will be used.\n" + frames_guide.set_length(len(frames_style)) + height_guide, width_guide = frames_guide.shape() + height_style, width_style = frames_style.shape() + if height_guide != height_style or width_guide != width_style: + message += f"The shape of frames mismatches. The frames in style video will be resized to (height: {height_guide}, width: {width_guide})\n" + frames_style.set_shape(height_guide, width_guide) + return frames_guide, frames_style, message + + +def smooth_video( + video_guide, + video_guide_folder, + video_style, + video_style_folder, + mode, + window_size, + batch_size, + tracking_window_size, + output_path, + fps, + minimum_patch_size, + num_iter, + guide_weight, + initialize, + progress = None, +): + # input + frames_guide, frames_style, message = check_input_for_blending(video_guide, video_guide_folder, video_style, video_style_folder) + if len(message) > 0: + print(message) + # output + if output_path == "": + if video_style is None: + output_path = os.path.join(video_style_folder, "output") + else: + output_path = os.path.join(os.path.split(video_style)[0], "output") + os.makedirs(output_path, exist_ok=True) + print("No valid output_path. Your video will be saved here:", output_path) + elif not os.path.exists(output_path): + os.makedirs(output_path, exist_ok=True) + print("Your video will be saved here:", output_path) + frames_path = os.path.join(output_path, "frames") + video_path = os.path.join(output_path, "video.mp4") + os.makedirs(frames_path, exist_ok=True) + # process + if mode == "Fast" or mode == "Balanced": + tracking_window_size = 0 + ebsynth_config = { + "minimum_patch_size": minimum_patch_size, + "threads_per_block": 8, + "num_iter": num_iter, + "gpu_id": 0, + "guide_weight": guide_weight, + "initialize": initialize, + "tracking_window_size": tracking_window_size, + } + if mode == "Fast": + FastModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path) + elif mode == "Balanced": + BalancedModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path) + elif mode == "Accurate": + AccurateModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path) + # output + try: + fps = int(fps) + except: + fps = get_video_fps(video_style) if video_style is not None else 30 + print("Fps:", fps) + print("Saving video...") + video_path = save_video(frames_path, video_path, num_frames=len(frames_style), fps=fps) + print("Success!") + print("Your frames are here:", frames_path) + print("Your video is here:", video_path) + return output_path, fps, video_path + + +class KeyFrameMatcher: + def __init__(self): + pass + + def extract_number_from_filename(self, file_name): + result = [] + number = -1 + for i in file_name: + if ord(i)>=ord("0") and ord(i)<=ord("9"): + if number == -1: + number = 0 + number = number*10 + ord(i) - ord("0") + else: + if number != -1: + result.append(number) + number = -1 + if number != -1: + result.append(number) + result = tuple(result) + return result + + def extract_number_from_filenames(self, file_names): + numbers = [self.extract_number_from_filename(file_name) for file_name in file_names] + min_length = min(len(i) for i in numbers) + for i in range(min_length-1, -1, -1): + if len(set(number[i] for number in numbers))==len(file_names): + return [number[i] for number in numbers] + return list(range(len(file_names))) + + def match_using_filename(self, file_names_a, file_names_b): + file_names_b_set = set(file_names_b) + matched_file_name = [] + for file_name in file_names_a: + if file_name not in file_names_b_set: + matched_file_name.append(None) + else: + matched_file_name.append(file_name) + return matched_file_name + + def match_using_numbers(self, file_names_a, file_names_b): + numbers_a = self.extract_number_from_filenames(file_names_a) + numbers_b = self.extract_number_from_filenames(file_names_b) + numbers_b_dict = {number: file_name for number, file_name in zip(numbers_b, file_names_b)} + matched_file_name = [] + for number in numbers_a: + if number in numbers_b_dict: + matched_file_name.append(numbers_b_dict[number]) + else: + matched_file_name.append(None) + return matched_file_name + + def match_filenames(self, file_names_a, file_names_b): + matched_file_name = self.match_using_filename(file_names_a, file_names_b) + if sum([i is not None for i in matched_file_name]) > 0: + return matched_file_name + matched_file_name = self.match_using_numbers(file_names_a, file_names_b) + return matched_file_name + + +def detect_frames(frames_path, keyframes_path): + if not os.path.exists(frames_path) and not os.path.exists(keyframes_path): + return "Please input the directory of guide video and rendered frames" + elif not os.path.exists(frames_path): + return "Please input the directory of guide video" + elif not os.path.exists(keyframes_path): + return "Please input the directory of rendered frames" + frames = [os.path.split(i)[-1] for i in search_for_images(frames_path)] + keyframes = [os.path.split(i)[-1] for i in search_for_images(keyframes_path)] + if len(frames)==0: + return f"No images detected in {frames_path}" + if len(keyframes)==0: + return f"No images detected in {keyframes_path}" + matched_keyframes = KeyFrameMatcher().match_filenames(frames, keyframes) + max_filename_length = max([len(i) for i in frames]) + if sum([i is not None for i in matched_keyframes])==0: + message = "" + for frame, matched_keyframe in zip(frames, matched_keyframes): + message += frame + " " * (max_filename_length - len(frame) + 1) + message += "--> No matched keyframes\n" + else: + message = "" + for frame, matched_keyframe in zip(frames, matched_keyframes): + message += frame + " " * (max_filename_length - len(frame) + 1) + if matched_keyframe is None: + message += "--> [to be rendered]\n" + else: + message += f"--> {matched_keyframe}\n" + return message + + +def check_input_for_interpolating(frames_path, keyframes_path): + # search for images + frames = [os.path.split(i)[-1] for i in search_for_images(frames_path)] + keyframes = [os.path.split(i)[-1] for i in search_for_images(keyframes_path)] + # match frames + matched_keyframes = KeyFrameMatcher().match_filenames(frames, keyframes) + file_list = [file_name for file_name in matched_keyframes if file_name is not None] + index_style = [i for i, file_name in enumerate(matched_keyframes) if file_name is not None] + frames_guide = VideoData(None, frames_path) + frames_style = VideoData(None, keyframes_path, file_list=file_list) + # match shape + message = "" + height_guide, width_guide = frames_guide.shape() + height_style, width_style = frames_style.shape() + if height_guide != height_style or width_guide != width_style: + message += f"The shape of frames mismatches. The rendered keyframes will be resized to (height: {height_guide}, width: {width_guide})\n" + frames_style.set_shape(height_guide, width_guide) + return frames_guide, frames_style, index_style, message + + +def interpolate_video( + frames_path, + keyframes_path, + output_path, + fps, + batch_size, + tracking_window_size, + minimum_patch_size, + num_iter, + guide_weight, + initialize, + progress = None, +): + # input + frames_guide, frames_style, index_style, message = check_input_for_interpolating(frames_path, keyframes_path) + if len(message) > 0: + print(message) + # output + if output_path == "": + output_path = os.path.join(keyframes_path, "output") + os.makedirs(output_path, exist_ok=True) + print("No valid output_path. Your video will be saved here:", output_path) + elif not os.path.exists(output_path): + os.makedirs(output_path, exist_ok=True) + print("Your video will be saved here:", output_path) + output_frames_path = os.path.join(output_path, "frames") + output_video_path = os.path.join(output_path, "video.mp4") + os.makedirs(output_frames_path, exist_ok=True) + # process + ebsynth_config = { + "minimum_patch_size": minimum_patch_size, + "threads_per_block": 8, + "num_iter": num_iter, + "gpu_id": 0, + "guide_weight": guide_weight, + "initialize": initialize, + "tracking_window_size": tracking_window_size + } + if len(index_style)==1: + InterpolationModeSingleFrameRunner().run(frames_guide, frames_style, index_style, batch_size=batch_size, ebsynth_config=ebsynth_config, save_path=output_frames_path) + else: + InterpolationModeRunner().run(frames_guide, frames_style, index_style, batch_size=batch_size, ebsynth_config=ebsynth_config, save_path=output_frames_path) + try: + fps = int(fps) + except: + fps = 30 + print("Fps:", fps) + print("Saving video...") + video_path = save_video(output_frames_path, output_video_path, num_frames=len(frames_guide), fps=fps) + print("Success!") + print("Your frames are here:", output_frames_path) + print("Your video is here:", video_path) + return output_path, fps, video_path + + +def on_ui_tabs(): + with gr.Blocks(analytics_enabled=False) as ui_component: + with gr.Tab("Blend"): + gr.Markdown(""" +# Blend + +Given a guide video and a style video, this algorithm will make the style video fluent according to the motion features of the guide video. Click [here](https://github.com/Artiprocher/sd-webui-fastblend/assets/35051019/208d902d-6aba-48d7-b7d5-cd120ebd306d) to see the example. Note that this extension doesn't support long videos. Please use short videos (e.g., several seconds). The algorithm is mainly designed for 512*512 resolution. Please use a larger `Minimum patch size` for higher resolution. + """) + with gr.Row(): + with gr.Column(): + with gr.Tab("Guide video"): + video_guide = gr.Video(label="Guide video") + with gr.Tab("Guide video (images format)"): + video_guide_folder = gr.Textbox(label="Guide video (images format)", value="") + with gr.Column(): + with gr.Tab("Style video"): + video_style = gr.Video(label="Style video") + with gr.Tab("Style video (images format)"): + video_style_folder = gr.Textbox(label="Style video (images format)", value="") + with gr.Column(): + output_path = gr.Textbox(label="Output directory", value="", placeholder="Leave empty to use the directory of style video") + fps = gr.Textbox(label="Fps", value="", placeholder="Leave empty to use the default fps") + video_output = gr.Video(label="Output video", interactive=False, show_share_button=True) + btn = gr.Button(value="Blend") + with gr.Row(): + with gr.Column(): + gr.Markdown("# Settings") + mode = gr.Radio(["Fast", "Balanced", "Accurate"], label="Inference mode", value="Fast", interactive=True) + window_size = gr.Slider(label="Sliding window size", value=15, minimum=1, maximum=1000, step=1, interactive=True) + batch_size = gr.Slider(label="Batch size", value=8, minimum=1, maximum=128, step=1, interactive=True) + tracking_window_size = gr.Slider(label="Tracking window size (only for accurate mode)", value=0, minimum=0, maximum=10, step=1, interactive=True) + gr.Markdown("## Advanced Settings") + minimum_patch_size = gr.Slider(label="Minimum patch size (odd number)", value=5, minimum=5, maximum=99, step=2, interactive=True) + num_iter = gr.Slider(label="Number of iterations", value=5, minimum=1, maximum=10, step=1, interactive=True) + guide_weight = gr.Slider(label="Guide weight", value=10.0, minimum=0.0, maximum=100.0, step=0.1, interactive=True) + initialize = gr.Radio(["identity", "random"], label="NNF initialization", value="identity", interactive=True) + with gr.Column(): + gr.Markdown(""" +# Reference + +* Output directory: the directory to save the video. +* Inference mode + +|Mode|Time|Memory|Quality|Frame by frame output|Description| +|-|-|-|-|-|-| +|Fast|■|■■■|■■|No|Blend the frames using a tree-like data structure, which requires much RAM but is fast.| +|Balanced|■■|■|■■|Yes|Blend the frames naively.| +|Accurate|■■■|■|■■■|Yes|Blend the frames and align them together for higher video quality. When [batch size] >= [sliding window size] * 2 + 1, the performance is the best.| + +* Sliding window size: our algorithm will blend the frames in a sliding windows. If the size is n, each frame will be blended with the last n frames and the next n frames. A large sliding window can make the video fluent but sometimes smoggy. +* Batch size: a larger batch size makes the program faster but requires more VRAM. +* Tracking window size (only for accurate mode): The size of window in which our algorithm tracks moving objects. Empirically, 1 is enough. +* Advanced settings + * Minimum patch size (odd number): the minimum patch size used for patch matching. (Default: 5) + * Number of iterations: the number of iterations of patch matching. (Default: 5) + * Guide weight: a parameter that determines how much motion feature applied to the style video. (Default: 10) + * NNF initialization: how to initialize the NNF (Nearest Neighbor Field). (Default: identity) + """) + btn.click( + smooth_video, + inputs=[ + video_guide, + video_guide_folder, + video_style, + video_style_folder, + mode, + window_size, + batch_size, + tracking_window_size, + output_path, + fps, + minimum_patch_size, + num_iter, + guide_weight, + initialize + ], + outputs=[output_path, fps, video_output] + ) + with gr.Tab("Interpolate"): + gr.Markdown(""" +# Interpolate + +Given a guide video and some rendered keyframes, this algorithm will render the remaining frames. Click [here](https://github.com/Artiprocher/sd-webui-fastblend/assets/35051019/3490c5b4-8f67-478f-86de-f9adc2ace16a) to see the example. The algorithm is experimental and is only tested for 512*512 resolution. + """) + with gr.Row(): + with gr.Column(): + with gr.Row(): + with gr.Column(): + video_guide_folder_ = gr.Textbox(label="Guide video (images format)", value="") + with gr.Column(): + rendered_keyframes_ = gr.Textbox(label="Rendered keyframes (images format)", value="") + with gr.Row(): + detected_frames = gr.Textbox(label="Detected frames", value="Please input the directory of guide video and rendered frames", lines=9, max_lines=9, interactive=False) + video_guide_folder_.change(detect_frames, inputs=[video_guide_folder_, rendered_keyframes_], outputs=detected_frames) + rendered_keyframes_.change(detect_frames, inputs=[video_guide_folder_, rendered_keyframes_], outputs=detected_frames) + with gr.Column(): + output_path_ = gr.Textbox(label="Output directory", value="", placeholder="Leave empty to use the directory of rendered keyframes") + fps_ = gr.Textbox(label="Fps", value="", placeholder="Leave empty to use the default fps") + video_output_ = gr.Video(label="Output video", interactive=False, show_share_button=True) + btn_ = gr.Button(value="Interpolate") + with gr.Row(): + with gr.Column(): + gr.Markdown("# Settings") + batch_size_ = gr.Slider(label="Batch size", value=8, minimum=1, maximum=128, step=1, interactive=True) + tracking_window_size_ = gr.Slider(label="Tracking window size", value=0, minimum=0, maximum=10, step=1, interactive=True) + gr.Markdown("## Advanced Settings") + minimum_patch_size_ = gr.Slider(label="Minimum patch size (odd number, larger is better)", value=15, minimum=5, maximum=99, step=2, interactive=True) + num_iter_ = gr.Slider(label="Number of iterations", value=5, minimum=1, maximum=10, step=1, interactive=True) + guide_weight_ = gr.Slider(label="Guide weight", value=10.0, minimum=0.0, maximum=100.0, step=0.1, interactive=True) + initialize_ = gr.Radio(["identity", "random"], label="NNF initialization", value="identity", interactive=True) + with gr.Column(): + gr.Markdown(""" +# Reference + +* Output directory: the directory to save the video. +* Batch size: a larger batch size makes the program faster but requires more VRAM. +* Tracking window size (only for accurate mode): The size of window in which our algorithm tracks moving objects. Empirically, 1 is enough. +* Advanced settings + * Minimum patch size (odd number): the minimum patch size used for patch matching. **This parameter should be larger than that in blending. (Default: 15)** + * Number of iterations: the number of iterations of patch matching. (Default: 5) + * Guide weight: a parameter that determines how much motion feature applied to the style video. (Default: 10) + * NNF initialization: how to initialize the NNF (Nearest Neighbor Field). (Default: identity) + """) + btn_.click( + interpolate_video, + inputs=[ + video_guide_folder_, + rendered_keyframes_, + output_path_, + fps_, + batch_size_, + tracking_window_size_, + minimum_patch_size_, + num_iter_, + guide_weight_, + initialize_, + ], + outputs=[output_path_, fps_, video_output_] + ) + + return [(ui_component, "FastBlend", "FastBlend_ui")] diff --git a/diffsynth/extensions/FastBlend/cupy_kernels.py b/diffsynth/extensions/FastBlend/cupy_kernels.py new file mode 100644 index 0000000000000000000000000000000000000000..70e2790a2c67a2dd537f4188b38ebfc785f1fb34 --- /dev/null +++ b/diffsynth/extensions/FastBlend/cupy_kernels.py @@ -0,0 +1,119 @@ +import cupy as cp + +remapping_kernel = cp.RawKernel(r''' +extern "C" __global__ +void remap( + const int height, + const int width, + const int channel, + const int patch_size, + const int pad_size, + const float* source_style, + const int* nnf, + float* target_style +) { + const int r = (patch_size - 1) / 2; + const int x = blockDim.x * blockIdx.x + threadIdx.x; + const int y = blockDim.y * blockIdx.y + threadIdx.y; + if (x >= height or y >= width) return; + const int z = blockIdx.z * (height + pad_size * 2) * (width + pad_size * 2) * channel; + const int pid = (x + pad_size) * (width + pad_size * 2) + (y + pad_size); + const int min_px = x < r ? -x : -r; + const int max_px = x + r > height - 1 ? height - 1 - x : r; + const int min_py = y < r ? -y : -r; + const int max_py = y + r > width - 1 ? width - 1 - y : r; + int num = 0; + for (int px = min_px; px <= max_px; px++){ + for (int py = min_py; py <= max_py; py++){ + const int nid = (x + px) * width + y + py; + const int x_ = nnf[blockIdx.z * height * width * 2 + nid*2 + 0] - px; + const int y_ = nnf[blockIdx.z * height * width * 2 + nid*2 + 1] - py; + if (x_ < 0 or y_ < 0 or x_ >= height or y_ >= width)continue; + const int pid_ = (x_ + pad_size) * (width + pad_size * 2) + (y_ + pad_size); + num++; + for (int c = 0; c < channel; c++){ + target_style[z + pid * channel + c] += source_style[z + pid_ * channel + c]; + } + } + } + for (int c = 0; c < channel; c++){ + target_style[z + pid * channel + c] /= num; + } +} +''', 'remap') + + +patch_error_kernel = cp.RawKernel(r''' +extern "C" __global__ +void patch_error( + const int height, + const int width, + const int channel, + const int patch_size, + const int pad_size, + const float* source, + const int* nnf, + const float* target, + float* error +) { + const int r = (patch_size - 1) / 2; + const int x = blockDim.x * blockIdx.x + threadIdx.x; + const int y = blockDim.y * blockIdx.y + threadIdx.y; + const int z = blockIdx.z * (height + pad_size * 2) * (width + pad_size * 2) * channel; + if (x >= height or y >= width) return; + const int x_ = nnf[blockIdx.z * height * width * 2 + (x * width + y)*2 + 0]; + const int y_ = nnf[blockIdx.z * height * width * 2 + (x * width + y)*2 + 1]; + float e = 0; + for (int px = -r; px <= r; px++){ + for (int py = -r; py <= r; py++){ + const int pid = (x + pad_size + px) * (width + pad_size * 2) + y + pad_size + py; + const int pid_ = (x_ + pad_size + px) * (width + pad_size * 2) + y_ + pad_size + py; + for (int c = 0; c < channel; c++){ + const float diff = target[z + pid * channel + c] - source[z + pid_ * channel + c]; + e += diff * diff; + } + } + } + error[blockIdx.z * height * width + x * width + y] = e; +} +''', 'patch_error') + + +pairwise_patch_error_kernel = cp.RawKernel(r''' +extern "C" __global__ +void pairwise_patch_error( + const int height, + const int width, + const int channel, + const int patch_size, + const int pad_size, + const float* source_a, + const int* nnf_a, + const float* source_b, + const int* nnf_b, + float* error +) { + const int r = (patch_size - 1) / 2; + const int x = blockDim.x * blockIdx.x + threadIdx.x; + const int y = blockDim.y * blockIdx.y + threadIdx.y; + const int z = blockIdx.z * (height + pad_size * 2) * (width + pad_size * 2) * channel; + if (x >= height or y >= width) return; + const int z_nnf = blockIdx.z * height * width * 2 + (x * width + y) * 2; + const int x_a = nnf_a[z_nnf + 0]; + const int y_a = nnf_a[z_nnf + 1]; + const int x_b = nnf_b[z_nnf + 0]; + const int y_b = nnf_b[z_nnf + 1]; + float e = 0; + for (int px = -r; px <= r; px++){ + for (int py = -r; py <= r; py++){ + const int pid_a = (x_a + pad_size + px) * (width + pad_size * 2) + y_a + pad_size + py; + const int pid_b = (x_b + pad_size + px) * (width + pad_size * 2) + y_b + pad_size + py; + for (int c = 0; c < channel; c++){ + const float diff = source_a[z + pid_a * channel + c] - source_b[z + pid_b * channel + c]; + e += diff * diff; + } + } + } + error[blockIdx.z * height * width + x * width + y] = e; +} +''', 'pairwise_patch_error') diff --git a/diffsynth/extensions/FastBlend/data.py b/diffsynth/extensions/FastBlend/data.py new file mode 100644 index 0000000000000000000000000000000000000000..dcaddd77de9eaf208cd083dd522e5eaa6b58f783 --- /dev/null +++ b/diffsynth/extensions/FastBlend/data.py @@ -0,0 +1,146 @@ +import imageio, os +import numpy as np +from PIL import Image + + +def read_video(file_name): + reader = imageio.get_reader(file_name) + video = [] + for frame in reader: + frame = np.array(frame) + video.append(frame) + reader.close() + return video + + +def get_video_fps(file_name): + reader = imageio.get_reader(file_name) + fps = reader.get_meta_data()["fps"] + reader.close() + return fps + + +def save_video(frames_path, video_path, num_frames, fps): + writer = imageio.get_writer(video_path, fps=fps, quality=9) + for i in range(num_frames): + frame = np.array(Image.open(os.path.join(frames_path, "%05d.png" % i))) + writer.append_data(frame) + writer.close() + return video_path + + +class LowMemoryVideo: + def __init__(self, file_name): + self.reader = imageio.get_reader(file_name) + + def __len__(self): + return self.reader.count_frames() + + def __getitem__(self, item): + return np.array(self.reader.get_data(item)) + + def __del__(self): + self.reader.close() + + +def split_file_name(file_name): + result = [] + number = -1 + for i in file_name: + if ord(i)>=ord("0") and ord(i)<=ord("9"): + if number == -1: + number = 0 + number = number*10 + ord(i) - ord("0") + else: + if number != -1: + result.append(number) + number = -1 + result.append(i) + if number != -1: + result.append(number) + result = tuple(result) + return result + + +def search_for_images(folder): + file_list = [i for i in os.listdir(folder) if i.endswith(".jpg") or i.endswith(".png")] + file_list = [(split_file_name(file_name), file_name) for file_name in file_list] + file_list = [i[1] for i in sorted(file_list)] + file_list = [os.path.join(folder, i) for i in file_list] + return file_list + + +def read_images(folder): + file_list = search_for_images(folder) + frames = [np.array(Image.open(i)) for i in file_list] + return frames + + +class LowMemoryImageFolder: + def __init__(self, folder, file_list=None): + if file_list is None: + self.file_list = search_for_images(folder) + else: + self.file_list = [os.path.join(folder, file_name) for file_name in file_list] + + def __len__(self): + return len(self.file_list) + + def __getitem__(self, item): + return np.array(Image.open(self.file_list[item])) + + def __del__(self): + pass + + +class VideoData: + def __init__(self, video_file, image_folder, **kwargs): + if video_file is not None: + self.data_type = "video" + self.data = LowMemoryVideo(video_file, **kwargs) + elif image_folder is not None: + self.data_type = "images" + self.data = LowMemoryImageFolder(image_folder, **kwargs) + else: + raise ValueError("Cannot open video or image folder") + self.length = None + self.height = None + self.width = None + + def raw_data(self): + frames = [] + for i in range(self.__len__()): + frames.append(self.__getitem__(i)) + return frames + + def set_length(self, length): + self.length = length + + def set_shape(self, height, width): + self.height = height + self.width = width + + def __len__(self): + if self.length is None: + return len(self.data) + else: + return self.length + + def shape(self): + if self.height is not None and self.width is not None: + return self.height, self.width + else: + height, width, _ = self.__getitem__(0).shape + return height, width + + def __getitem__(self, item): + frame = self.data.__getitem__(item) + height, width, _ = frame.shape + if self.height is not None and self.width is not None: + if self.height != height or self.width != width: + frame = Image.fromarray(frame).resize((self.width, self.height)) + frame = np.array(frame) + return frame + + def __del__(self): + pass diff --git a/diffsynth/extensions/FastBlend/patch_match.py b/diffsynth/extensions/FastBlend/patch_match.py new file mode 100644 index 0000000000000000000000000000000000000000..aeb1f7f9e31b4b2ad77ec58ba8d32361315e0390 --- /dev/null +++ b/diffsynth/extensions/FastBlend/patch_match.py @@ -0,0 +1,298 @@ +from .cupy_kernels import remapping_kernel, patch_error_kernel, pairwise_patch_error_kernel +import numpy as np +import cupy as cp +import cv2 + + +class PatchMatcher: + def __init__( + self, height, width, channel, minimum_patch_size, + threads_per_block=8, num_iter=5, gpu_id=0, guide_weight=10.0, + random_search_steps=3, random_search_range=4, + use_mean_target_style=False, use_pairwise_patch_error=False, + tracking_window_size=0 + ): + self.height = height + self.width = width + self.channel = channel + self.minimum_patch_size = minimum_patch_size + self.threads_per_block = threads_per_block + self.num_iter = num_iter + self.gpu_id = gpu_id + self.guide_weight = guide_weight + self.random_search_steps = random_search_steps + self.random_search_range = random_search_range + self.use_mean_target_style = use_mean_target_style + self.use_pairwise_patch_error = use_pairwise_patch_error + self.tracking_window_size = tracking_window_size + + self.patch_size_list = [minimum_patch_size + i*2 for i in range(num_iter)][::-1] + self.pad_size = self.patch_size_list[0] // 2 + self.grid = ( + (height + threads_per_block - 1) // threads_per_block, + (width + threads_per_block - 1) // threads_per_block + ) + self.block = (threads_per_block, threads_per_block) + + def pad_image(self, image): + return cp.pad(image, ((0, 0), (self.pad_size, self.pad_size), (self.pad_size, self.pad_size), (0, 0))) + + def unpad_image(self, image): + return image[:, self.pad_size: -self.pad_size, self.pad_size: -self.pad_size, :] + + def apply_nnf_to_image(self, nnf, source): + batch_size = source.shape[0] + target = cp.zeros((batch_size, self.height + self.pad_size * 2, self.width + self.pad_size * 2, self.channel), dtype=cp.float32) + remapping_kernel( + self.grid + (batch_size,), + self.block, + (self.height, self.width, self.channel, self.patch_size, self.pad_size, source, nnf, target) + ) + return target + + def get_patch_error(self, source, nnf, target): + batch_size = source.shape[0] + error = cp.zeros((batch_size, self.height, self.width), dtype=cp.float32) + patch_error_kernel( + self.grid + (batch_size,), + self.block, + (self.height, self.width, self.channel, self.patch_size, self.pad_size, source, nnf, target, error) + ) + return error + + def get_pairwise_patch_error(self, source, nnf): + batch_size = source.shape[0]//2 + error = cp.zeros((batch_size, self.height, self.width), dtype=cp.float32) + source_a, nnf_a = source[0::2].copy(), nnf[0::2].copy() + source_b, nnf_b = source[1::2].copy(), nnf[1::2].copy() + pairwise_patch_error_kernel( + self.grid + (batch_size,), + self.block, + (self.height, self.width, self.channel, self.patch_size, self.pad_size, source_a, nnf_a, source_b, nnf_b, error) + ) + error = error.repeat(2, axis=0) + return error + + def get_error(self, source_guide, target_guide, source_style, target_style, nnf): + error_guide = self.get_patch_error(source_guide, nnf, target_guide) + if self.use_mean_target_style: + target_style = self.apply_nnf_to_image(nnf, source_style) + target_style = target_style.mean(axis=0, keepdims=True) + target_style = target_style.repeat(source_guide.shape[0], axis=0) + if self.use_pairwise_patch_error: + error_style = self.get_pairwise_patch_error(source_style, nnf) + else: + error_style = self.get_patch_error(source_style, nnf, target_style) + error = error_guide * self.guide_weight + error_style + return error + + def clamp_bound(self, nnf): + nnf[:,:,:,0] = cp.clip(nnf[:,:,:,0], 0, self.height-1) + nnf[:,:,:,1] = cp.clip(nnf[:,:,:,1], 0, self.width-1) + return nnf + + def random_step(self, nnf, r): + batch_size = nnf.shape[0] + step = cp.random.randint(-r, r+1, size=(batch_size, self.height, self.width, 2), dtype=cp.int32) + upd_nnf = self.clamp_bound(nnf + step) + return upd_nnf + + def neighboor_step(self, nnf, d): + if d==0: + upd_nnf = cp.concatenate([nnf[:, :1, :], nnf[:, :-1, :]], axis=1) + upd_nnf[:, :, :, 0] += 1 + elif d==1: + upd_nnf = cp.concatenate([nnf[:, :, :1], nnf[:, :, :-1]], axis=2) + upd_nnf[:, :, :, 1] += 1 + elif d==2: + upd_nnf = cp.concatenate([nnf[:, 1:, :], nnf[:, -1:, :]], axis=1) + upd_nnf[:, :, :, 0] -= 1 + elif d==3: + upd_nnf = cp.concatenate([nnf[:, :, 1:], nnf[:, :, -1:]], axis=2) + upd_nnf[:, :, :, 1] -= 1 + upd_nnf = self.clamp_bound(upd_nnf) + return upd_nnf + + def shift_nnf(self, nnf, d): + if d>0: + d = min(nnf.shape[0], d) + upd_nnf = cp.concatenate([nnf[d:]] + [nnf[-1:]] * d, axis=0) + else: + d = max(-nnf.shape[0], d) + upd_nnf = cp.concatenate([nnf[:1]] * (-d) + [nnf[:d]], axis=0) + return upd_nnf + + def track_step(self, nnf, d): + if self.use_pairwise_patch_error: + upd_nnf = cp.zeros_like(nnf) + upd_nnf[0::2] = self.shift_nnf(nnf[0::2], d) + upd_nnf[1::2] = self.shift_nnf(nnf[1::2], d) + else: + upd_nnf = self.shift_nnf(nnf, d) + return upd_nnf + + def C(self, n, m): + # not used + c = 1 + for i in range(1, n+1): + c *= i + for i in range(1, m+1): + c //= i + for i in range(1, n-m+1): + c //= i + return c + + def bezier_step(self, nnf, r): + # not used + n = r * 2 - 1 + upd_nnf = cp.zeros(shape=nnf.shape, dtype=cp.float32) + for i, d in enumerate(list(range(-r, 0)) + list(range(1, r+1))): + if d>0: + ctl_nnf = cp.concatenate([nnf[d:]] + [nnf[-1:]] * d, axis=0) + elif d<0: + ctl_nnf = cp.concatenate([nnf[:1]] * (-d) + [nnf[:d]], axis=0) + upd_nnf += ctl_nnf * (self.C(n, i) / 2**n) + upd_nnf = self.clamp_bound(upd_nnf).astype(nnf.dtype) + return upd_nnf + + def update(self, source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf): + upd_err = self.get_error(source_guide, target_guide, source_style, target_style, upd_nnf) + upd_idx = (upd_err < err) + nnf[upd_idx] = upd_nnf[upd_idx] + err[upd_idx] = upd_err[upd_idx] + return nnf, err + + def propagation(self, source_guide, target_guide, source_style, target_style, nnf, err): + for d in cp.random.permutation(4): + upd_nnf = self.neighboor_step(nnf, d) + nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf) + return nnf, err + + def random_search(self, source_guide, target_guide, source_style, target_style, nnf, err): + for i in range(self.random_search_steps): + upd_nnf = self.random_step(nnf, self.random_search_range) + nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf) + return nnf, err + + def track(self, source_guide, target_guide, source_style, target_style, nnf, err): + for d in range(1, self.tracking_window_size + 1): + upd_nnf = self.track_step(nnf, d) + nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf) + upd_nnf = self.track_step(nnf, -d) + nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf) + return nnf, err + + def iteration(self, source_guide, target_guide, source_style, target_style, nnf, err): + nnf, err = self.propagation(source_guide, target_guide, source_style, target_style, nnf, err) + nnf, err = self.random_search(source_guide, target_guide, source_style, target_style, nnf, err) + nnf, err = self.track(source_guide, target_guide, source_style, target_style, nnf, err) + return nnf, err + + def estimate_nnf(self, source_guide, target_guide, source_style, nnf): + with cp.cuda.Device(self.gpu_id): + source_guide = self.pad_image(source_guide) + target_guide = self.pad_image(target_guide) + source_style = self.pad_image(source_style) + for it in range(self.num_iter): + self.patch_size = self.patch_size_list[it] + target_style = self.apply_nnf_to_image(nnf, source_style) + err = self.get_error(source_guide, target_guide, source_style, target_style, nnf) + nnf, err = self.iteration(source_guide, target_guide, source_style, target_style, nnf, err) + target_style = self.unpad_image(self.apply_nnf_to_image(nnf, source_style)) + return nnf, target_style + + +class PyramidPatchMatcher: + def __init__( + self, image_height, image_width, channel, minimum_patch_size, + threads_per_block=8, num_iter=5, gpu_id=0, guide_weight=10.0, + use_mean_target_style=False, use_pairwise_patch_error=False, + tracking_window_size=0, + initialize="identity" + ): + maximum_patch_size = minimum_patch_size + (num_iter - 1) * 2 + self.pyramid_level = int(np.log2(min(image_height, image_width) / maximum_patch_size)) + self.pyramid_heights = [] + self.pyramid_widths = [] + self.patch_matchers = [] + self.minimum_patch_size = minimum_patch_size + self.num_iter = num_iter + self.gpu_id = gpu_id + self.initialize = initialize + for level in range(self.pyramid_level): + height = image_height//(2**(self.pyramid_level - 1 - level)) + width = image_width//(2**(self.pyramid_level - 1 - level)) + self.pyramid_heights.append(height) + self.pyramid_widths.append(width) + self.patch_matchers.append(PatchMatcher( + height, width, channel, minimum_patch_size=minimum_patch_size, + threads_per_block=threads_per_block, num_iter=num_iter, gpu_id=gpu_id, guide_weight=guide_weight, + use_mean_target_style=use_mean_target_style, use_pairwise_patch_error=use_pairwise_patch_error, + tracking_window_size=tracking_window_size + )) + + def resample_image(self, images, level): + height, width = self.pyramid_heights[level], self.pyramid_widths[level] + images = images.get() + images_resample = [] + for image in images: + image_resample = cv2.resize(image, (width, height), interpolation=cv2.INTER_AREA) + images_resample.append(image_resample) + images_resample = cp.array(np.stack(images_resample), dtype=cp.float32) + return images_resample + + def initialize_nnf(self, batch_size): + if self.initialize == "random": + height, width = self.pyramid_heights[0], self.pyramid_widths[0] + nnf = cp.stack([ + cp.random.randint(0, height, (batch_size, height, width), dtype=cp.int32), + cp.random.randint(0, width, (batch_size, height, width), dtype=cp.int32) + ], axis=3) + elif self.initialize == "identity": + height, width = self.pyramid_heights[0], self.pyramid_widths[0] + nnf = cp.stack([ + cp.repeat(cp.arange(height), width).reshape(height, width), + cp.tile(cp.arange(width), height).reshape(height, width) + ], axis=2) + nnf = cp.stack([nnf] * batch_size) + else: + raise NotImplementedError() + return nnf + + def update_nnf(self, nnf, level): + # upscale + nnf = nnf.repeat(2, axis=1).repeat(2, axis=2) * 2 + nnf[:,[i for i in range(nnf.shape[0]) if i&1],:,0] += 1 + nnf[:,:,[i for i in range(nnf.shape[0]) if i&1],1] += 1 + # check if scale is 2 + height, width = self.pyramid_heights[level], self.pyramid_widths[level] + if height != nnf.shape[0] * 2 or width != nnf.shape[1] * 2: + nnf = nnf.get().astype(np.float32) + nnf = [cv2.resize(n, (width, height), interpolation=cv2.INTER_LINEAR) for n in nnf] + nnf = cp.array(np.stack(nnf), dtype=cp.int32) + nnf = self.patch_matchers[level].clamp_bound(nnf) + return nnf + + def apply_nnf_to_image(self, nnf, image): + with cp.cuda.Device(self.gpu_id): + image = self.patch_matchers[-1].pad_image(image) + image = self.patch_matchers[-1].apply_nnf_to_image(nnf, image) + return image + + def estimate_nnf(self, source_guide, target_guide, source_style): + with cp.cuda.Device(self.gpu_id): + if not isinstance(source_guide, cp.ndarray): + source_guide = cp.array(source_guide, dtype=cp.float32) + if not isinstance(target_guide, cp.ndarray): + target_guide = cp.array(target_guide, dtype=cp.float32) + if not isinstance(source_style, cp.ndarray): + source_style = cp.array(source_style, dtype=cp.float32) + for level in range(self.pyramid_level): + nnf = self.initialize_nnf(source_guide.shape[0]) if level==0 else self.update_nnf(nnf, level) + source_guide_ = self.resample_image(source_guide, level) + target_guide_ = self.resample_image(target_guide, level) + source_style_ = self.resample_image(source_style, level) + nnf, target_style = self.patch_matchers[level].estimate_nnf( + source_guide_, target_guide_, source_style_, nnf + ) + return nnf.get(), target_style.get() diff --git a/diffsynth/extensions/FastBlend/runners/__init__.py b/diffsynth/extensions/FastBlend/runners/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..078382729690d282436411661693ce22f3dcc033 --- /dev/null +++ b/diffsynth/extensions/FastBlend/runners/__init__.py @@ -0,0 +1,4 @@ +from .accurate import AccurateModeRunner +from .fast import FastModeRunner +from .balanced import BalancedModeRunner +from .interpolation import InterpolationModeRunner, InterpolationModeSingleFrameRunner diff --git a/diffsynth/extensions/FastBlend/runners/accurate.py b/diffsynth/extensions/FastBlend/runners/accurate.py new file mode 100644 index 0000000000000000000000000000000000000000..2e4a47f1981ebc1ec9a034a814dfc1130955c2e1 --- /dev/null +++ b/diffsynth/extensions/FastBlend/runners/accurate.py @@ -0,0 +1,35 @@ +from ..patch_match import PyramidPatchMatcher +import os +import numpy as np +from PIL import Image +from tqdm import tqdm + + +class AccurateModeRunner: + def __init__(self): + pass + + def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config, desc="Accurate Mode", save_path=None): + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + use_mean_target_style=True, + **ebsynth_config + ) + # run + n = len(frames_style) + for target in tqdm(range(n), desc=desc): + l, r = max(target - window_size, 0), min(target + window_size + 1, n) + remapped_frames = [] + for i in range(l, r, batch_size): + j = min(i + batch_size, r) + source_guide = np.stack([frames_guide[source] for source in range(i, j)]) + target_guide = np.stack([frames_guide[target]] * (j - i)) + source_style = np.stack([frames_style[source] for source in range(i, j)]) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + remapped_frames.append(target_style) + frame = np.concatenate(remapped_frames, axis=0).mean(axis=0) + frame = frame.clip(0, 255).astype("uint8") + if save_path is not None: + Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % target)) \ No newline at end of file diff --git a/diffsynth/extensions/FastBlend/runners/balanced.py b/diffsynth/extensions/FastBlend/runners/balanced.py new file mode 100644 index 0000000000000000000000000000000000000000..1c9a2bb7e438b49c89d0786e858ccf03302fab35 --- /dev/null +++ b/diffsynth/extensions/FastBlend/runners/balanced.py @@ -0,0 +1,46 @@ +from ..patch_match import PyramidPatchMatcher +import os +import numpy as np +from PIL import Image +from tqdm import tqdm + + +class BalancedModeRunner: + def __init__(self): + pass + + def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config, desc="Balanced Mode", save_path=None): + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + **ebsynth_config + ) + # tasks + n = len(frames_style) + tasks = [] + for target in range(n): + for source in range(target - window_size, target + window_size + 1): + if source >= 0 and source < n and source != target: + tasks.append((source, target)) + # run + frames = [(None, 1) for i in range(n)] + for batch_id in tqdm(range(0, len(tasks), batch_size), desc=desc): + tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))] + source_guide = np.stack([frames_guide[source] for source, target in tasks_batch]) + target_guide = np.stack([frames_guide[target] for source, target in tasks_batch]) + source_style = np.stack([frames_style[source] for source, target in tasks_batch]) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + for (source, target), result in zip(tasks_batch, target_style): + frame, weight = frames[target] + if frame is None: + frame = frames_style[target] + frames[target] = ( + frame * (weight / (weight + 1)) + result / (weight + 1), + weight + 1 + ) + if weight + 1 == min(n, target + window_size + 1) - max(0, target - window_size): + frame = frame.clip(0, 255).astype("uint8") + if save_path is not None: + Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % target)) + frames[target] = (None, 1) diff --git a/diffsynth/extensions/FastBlend/runners/fast.py b/diffsynth/extensions/FastBlend/runners/fast.py new file mode 100644 index 0000000000000000000000000000000000000000..2ba5731475ab875929b14181e0c22f4fd466c591 --- /dev/null +++ b/diffsynth/extensions/FastBlend/runners/fast.py @@ -0,0 +1,141 @@ +from ..patch_match import PyramidPatchMatcher +import functools, os +import numpy as np +from PIL import Image +from tqdm import tqdm + + +class TableManager: + def __init__(self): + pass + + def task_list(self, n): + tasks = [] + max_level = 1 + while (1<=n: + break + meta_data = { + "source": i, + "target": j, + "level": level + 1 + } + tasks.append(meta_data) + tasks.sort(key=functools.cmp_to_key(lambda u, v: u["level"]-v["level"])) + return tasks + + def build_remapping_table(self, frames_guide, frames_style, patch_match_engine, batch_size, desc=""): + n = len(frames_guide) + tasks = self.task_list(n) + remapping_table = [[(frames_style[i], 1)] for i in range(n)] + for batch_id in tqdm(range(0, len(tasks), batch_size), desc=desc): + tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))] + source_guide = np.stack([frames_guide[task["source"]] for task in tasks_batch]) + target_guide = np.stack([frames_guide[task["target"]] for task in tasks_batch]) + source_style = np.stack([frames_style[task["source"]] for task in tasks_batch]) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + for task, result in zip(tasks_batch, target_style): + target, level = task["target"], task["level"] + if len(remapping_table[target])==level: + remapping_table[target].append((result, 1)) + else: + frame, weight = remapping_table[target][level] + remapping_table[target][level] = ( + frame * (weight / (weight + 1)) + result / (weight + 1), + weight + 1 + ) + return remapping_table + + def remapping_table_to_blending_table(self, table): + for i in range(len(table)): + for j in range(1, len(table[i])): + frame_1, weight_1 = table[i][j-1] + frame_2, weight_2 = table[i][j] + frame = (frame_1 + frame_2) / 2 + weight = weight_1 + weight_2 + table[i][j] = (frame, weight) + return table + + def tree_query(self, leftbound, rightbound): + node_list = [] + node_index = rightbound + while node_index>=leftbound: + node_level = 0 + while (1<=leftbound: + node_level += 1 + node_list.append((node_index, node_level)) + node_index -= 1<0: + tasks = [] + for m in range(index_style[0]): + tasks.append((index_style[0], m, index_style[0])) + task_group.append(tasks) + # middle frames + for l, r in zip(index_style[:-1], index_style[1:]): + tasks = [] + for m in range(l, r): + tasks.append((l, m, r)) + task_group.append(tasks) + # last frame + tasks = [] + for m in range(index_style[-1], n): + tasks.append((index_style[-1], m, index_style[-1])) + task_group.append(tasks) + return task_group + + def run(self, frames_guide, frames_style, index_style, batch_size, ebsynth_config, save_path=None): + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + use_mean_target_style=False, + use_pairwise_patch_error=True, + **ebsynth_config + ) + # task + index_dict = self.get_index_dict(index_style) + task_group = self.get_task_group(index_style, len(frames_guide)) + # run + for tasks in task_group: + index_start, index_end = min([i[1] for i in tasks]), max([i[1] for i in tasks]) + for batch_id in tqdm(range(0, len(tasks), batch_size), desc=f"Rendering frames {index_start}...{index_end}"): + tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))] + source_guide, target_guide, source_style = [], [], [] + for l, m, r in tasks_batch: + # l -> m + source_guide.append(frames_guide[l]) + target_guide.append(frames_guide[m]) + source_style.append(frames_style[index_dict[l]]) + # r -> m + source_guide.append(frames_guide[r]) + target_guide.append(frames_guide[m]) + source_style.append(frames_style[index_dict[r]]) + source_guide = np.stack(source_guide) + target_guide = np.stack(target_guide) + source_style = np.stack(source_style) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + if save_path is not None: + for frame_l, frame_r, (l, m, r) in zip(target_style[0::2], target_style[1::2], tasks_batch): + weight_l, weight_r = self.get_weight(l, m, r) + frame = frame_l * weight_l + frame_r * weight_r + frame = frame.clip(0, 255).astype("uint8") + Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % m)) + + +class InterpolationModeSingleFrameRunner: + def __init__(self): + pass + + def run(self, frames_guide, frames_style, index_style, batch_size, ebsynth_config, save_path=None): + # check input + tracking_window_size = ebsynth_config["tracking_window_size"] + if tracking_window_size * 2 >= batch_size: + raise ValueError("batch_size should be larger than track_window_size * 2") + frame_style = frames_style[0] + frame_guide = frames_guide[index_style[0]] + patch_match_engine = PyramidPatchMatcher( + image_height=frame_style.shape[0], + image_width=frame_style.shape[1], + channel=3, + **ebsynth_config + ) + # run + frame_id, n = 0, len(frames_guide) + for i in tqdm(range(0, n, batch_size - tracking_window_size * 2), desc=f"Rendering frames 0...{n}"): + if i + batch_size > n: + l, r = max(n - batch_size, 0), n + else: + l, r = i, i + batch_size + source_guide = np.stack([frame_guide] * (r-l)) + target_guide = np.stack([frames_guide[i] for i in range(l, r)]) + source_style = np.stack([frame_style] * (r-l)) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + for i, frame in zip(range(l, r), target_style): + if i==frame_id: + frame = frame.clip(0, 255).astype("uint8") + Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % frame_id)) + frame_id += 1 + if r < n and r-frame_id <= tracking_window_size: + break diff --git a/diffsynth/extensions/ImageQualityMetric/BLIP/__init__.py b/diffsynth/extensions/ImageQualityMetric/BLIP/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..885dcf8f76ad77865054f0c033f8541ae08b1e04 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/BLIP/__init__.py @@ -0,0 +1 @@ +from .blip_pretrain import * diff --git a/diffsynth/extensions/ImageQualityMetric/BLIP/blip.py b/diffsynth/extensions/ImageQualityMetric/BLIP/blip.py new file mode 100644 index 0000000000000000000000000000000000000000..6b24c3c17fdeff6949c3692164362abb8d8d0989 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/BLIP/blip.py @@ -0,0 +1,77 @@ +''' + * Adapted from BLIP (https://github.com/salesforce/BLIP) +''' + +import warnings +warnings.filterwarnings("ignore") + +import torch +import os +from urllib.parse import urlparse +from timm.models.hub import download_cached_file +from transformers import BertTokenizer +from .vit import VisionTransformer, interpolate_pos_embed + + +def default_bert(): + current_dir = os.path.dirname(os.path.abspath(__file__)) + project_root = os.path.abspath(os.path.join(current_dir, '../../../../')) + model_path = os.path.join(project_root, 'models', 'QualityMetric') + return os.path.join(model_path, "bert-base-uncased") + + +def init_tokenizer(bert_model_path): + tokenizer = BertTokenizer.from_pretrained(bert_model_path) + tokenizer.add_special_tokens({'bos_token':'[DEC]'}) + tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']}) + tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0] + return tokenizer + + +def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0): + + assert vit in ['base', 'large'], "vit parameter must be base or large" + if vit=='base': + vision_width = 768 + visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12, + num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, + drop_path_rate=0 or drop_path_rate + ) + elif vit=='large': + vision_width = 1024 + visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24, + num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, + drop_path_rate=0.1 or drop_path_rate + ) + return visual_encoder, vision_width + + +def is_url(url_or_filename): + parsed = urlparse(url_or_filename) + return parsed.scheme in ("http", "https") + +def load_checkpoint(model,url_or_filename): + if is_url(url_or_filename): + cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True) + checkpoint = torch.load(cached_file, map_location='cpu') + elif os.path.isfile(url_or_filename): + checkpoint = torch.load(url_or_filename, map_location='cpu') + else: + raise RuntimeError('checkpoint url or path is invalid') + + state_dict = checkpoint['model'] + + state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) + if 'visual_encoder_m.pos_embed' in model.state_dict().keys(): + state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'], + model.visual_encoder_m) + for key in model.state_dict().keys(): + if key in state_dict.keys(): + if state_dict[key].shape!=model.state_dict()[key].shape: + print(key, ": ", state_dict[key].shape, ', ', model.state_dict()[key].shape) + del state_dict[key] + + msg = model.load_state_dict(state_dict,strict=False) + print('load checkpoint from %s'%url_or_filename) + return model,msg + diff --git a/diffsynth/extensions/ImageQualityMetric/BLIP/blip_pretrain.py b/diffsynth/extensions/ImageQualityMetric/BLIP/blip_pretrain.py new file mode 100644 index 0000000000000000000000000000000000000000..ba711e2776fd086190ca940248e022b4e083819a --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/BLIP/blip_pretrain.py @@ -0,0 +1,44 @@ +''' + * Adapted from BLIP (https://github.com/salesforce/BLIP) +''' + +import transformers +transformers.logging.set_verbosity_error() + +from torch import nn +import os +from .med import BertConfig, BertModel +from .blip import create_vit, init_tokenizer + +class BLIP_Pretrain(nn.Module): + def __init__(self, + med_config = "med_config.json", + image_size = 224, + vit = 'base', + vit_grad_ckpt = False, + vit_ckpt_layer = 0, + embed_dim = 256, + queue_size = 57600, + momentum = 0.995, + bert_model_path = "" + ): + """ + Args: + med_config (str): path for the mixture of encoder-decoder model's configuration file + image_size (int): input image size + vit (str): model size of vision transformer + """ + super().__init__() + + self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, 0) + + self.tokenizer = init_tokenizer(bert_model_path) + encoder_config = BertConfig.from_json_file(med_config) + encoder_config.encoder_width = vision_width + self.text_encoder = BertModel(config=encoder_config, add_pooling_layer=False) + + text_width = self.text_encoder.config.hidden_size + + self.vision_proj = nn.Linear(vision_width, embed_dim) + self.text_proj = nn.Linear(text_width, embed_dim) + diff --git a/diffsynth/extensions/ImageQualityMetric/BLIP/med.py b/diffsynth/extensions/ImageQualityMetric/BLIP/med.py new file mode 100644 index 0000000000000000000000000000000000000000..5905a346c8f3a511fc56d1e052467dcef3246e4b --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/BLIP/med.py @@ -0,0 +1,947 @@ +''' + * Adapted from BLIP (https://github.com/salesforce/BLIP) + * Based on huggingface code base + * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert +''' + +import math +from typing import Tuple + +import torch +from torch import Tensor, device, nn +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss + +from transformers.activations import ACT2FN +from transformers.file_utils import ( + ModelOutput, +) +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + NextSentencePredictorOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from transformers.modeling_utils import ( + PreTrainedModel, + apply_chunking_to_forward, + find_pruneable_heads_and_indices, + prune_linear_layer, +) +from transformers.utils import logging +from transformers.models.bert.configuration_bert import BertConfig + + +logger = logging.get_logger(__name__) + + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word and position embeddings.""" + + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + + self.config = config + + def forward( + self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 + ): + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + if position_ids is None: + position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + embeddings = inputs_embeds + + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class BertSelfAttention(nn.Module): + def __init__(self, config, is_cross_attention): + super().__init__() + self.config = config + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + "The hidden size (%d) is not a multiple of the number of attention " + "heads (%d)" % (config.hidden_size, config.num_attention_heads) + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + if is_cross_attention: + self.key = nn.Linear(config.encoder_width, self.all_head_size) + self.value = nn.Linear(config.encoder_width, self.all_head_size) + else: + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) + self.save_attention = False + + def save_attn_gradients(self, attn_gradients): + self.attn_gradients = attn_gradients + + def get_attn_gradients(self): + return self.attn_gradients + + def save_attention_map(self, attention_map): + self.attention_map = attention_map + + def get_attention_map(self): + return self.attention_map + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) + position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) + positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + + if is_cross_attention and self.save_attention: + self.save_attention_map(attention_probs) + attention_probs.register_hook(self.save_attn_gradients) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs_dropped = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs_dropped = attention_probs_dropped * head_mask + + context_layer = torch.matmul(attention_probs_dropped, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + outputs = outputs + (past_key_value,) + return outputs + + +class BertSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertAttention(nn.Module): + def __init__(self, config, is_cross_attention=False): + super().__init__() + self.self = BertSelfAttention(config, is_cross_attention) + self.output = BertSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +class BertIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +class BertOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertLayer(nn.Module): + def __init__(self, config, layer_num): + super().__init__() + self.config = config + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = BertAttention(config) + self.layer_num = layer_num + if self.config.add_cross_attention: + self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention) + self.intermediate = BertIntermediate(config) + self.output = BertOutput(config) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + mode=None, + ): + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + ) + attention_output = self_attention_outputs[0] + + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + + if mode=='multimodal': + assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers" + + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + output_attentions=output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + outputs = (layer_output,) + outputs + + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +class BertEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + mode='multimodal', + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + + next_decoder_cache = () if use_cache else None + + for i in range(self.config.num_hidden_layers): + layer_module = self.layer[i] + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + mode=mode, + ) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + mode=mode, + ) + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +class BertPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class BertPredictionHeadTransform(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if isinstance(config.hidden_act, str): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +class BertLMPredictionHead(nn.Module): + def __init__(self, config): + super().__init__() + self.transform = BertPredictionHeadTransform(config) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` + self.decoder.bias = self.bias + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + hidden_states = self.decoder(hidden_states) + return hidden_states + + +class BertOnlyMLMHead(nn.Module): + def __init__(self, config): + super().__init__() + self.predictions = BertLMPredictionHead(config) + + def forward(self, sequence_output): + prediction_scores = self.predictions(sequence_output) + return prediction_scores + + +class BertPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = BertConfig + base_model_prefix = "bert" + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def _init_weights(self, module): + """ Initialize the weights """ + if isinstance(module, (nn.Linear, nn.Embedding)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + + +class BertModel(BertPreTrainedModel): + """ + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in `Attention is + all you need `__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, + Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. + argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an + input to the forward pass. + """ + + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = BertEmbeddings(config) + + self.encoder = BertEncoder(config) + + self.pooler = BertPooler(config) if add_pooling_layer else None + + self.init_weights() + + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + + def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor: + """ + Makes broadcastable attention and causal masks so that future and masked tokens are ignored. + + Arguments: + attention_mask (:obj:`torch.Tensor`): + Mask with ones indicating tokens to attend to, zeros for tokens to ignore. + input_shape (:obj:`Tuple[int]`): + The shape of the input to the model. + device: (:obj:`torch.device`): + The device of the input to the model. + + Returns: + :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`. + """ + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + if attention_mask.dim() == 3: + extended_attention_mask = attention_mask[:, None, :, :] + elif attention_mask.dim() == 2: + # Provided a padding mask of dimensions [batch_size, seq_length] + # - if the model is a decoder, apply a causal mask in addition to the padding mask + # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] + if is_decoder: + batch_size, seq_length = input_shape + + seq_ids = torch.arange(seq_length, device=device) + causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None] + # in case past_key_values are used we need to add a prefix ones mask to the causal mask + # causal and attention masks must have same type with pytorch version < 1.3 + causal_mask = causal_mask.to(attention_mask.dtype) + + if causal_mask.shape[1] < attention_mask.shape[1]: + prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1] + causal_mask = torch.cat( + [ + torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype), + causal_mask, + ], + axis=-1, + ) + + extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :] + else: + extended_attention_mask = attention_mask[:, None, None, :] + else: + raise ValueError( + "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format( + input_shape, attention_mask.shape + ) + ) + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and -10000.0 for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility + extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 + return extended_attention_mask + + def forward( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + is_decoder=False, + mode='multimodal', + ): + r""" + encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + batch_size, seq_length = input_shape + device = input_ids.device + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size, seq_length = input_shape + device = inputs_embeds.device + elif encoder_embeds is not None: + input_shape = encoder_embeds.size()[:-1] + batch_size, seq_length = input_shape + device = encoder_embeds.device + else: + raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds") + + # past_key_values_length + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if attention_mask is None: + attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, + device, is_decoder) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if encoder_hidden_states is not None: + if type(encoder_hidden_states) == list: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size() + else: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + + if type(encoder_attention_mask) == list: + encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask] + elif encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + if encoder_embeds is None: + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + else: + embedding_output = encoder_embeds + + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + mode=mode, + ) + sequence_output = encoder_outputs[0] + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + + +class BertLMHeadModel(BertPreTrainedModel): + + _keys_to_ignore_on_load_unexpected = [r"pooler"] + _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] + + def __init__(self, config): + super().__init__(config) + + self.bert = BertModel(config, add_pooling_layer=False) + self.cls = BertOnlyMLMHead(config) + + self.init_weights() + + def get_output_embeddings(self): + return self.cls.predictions.decoder + + def set_output_embeddings(self, new_embeddings): + self.cls.predictions.decoder = new_embeddings + + def forward( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + labels=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + return_logits=False, + is_decoder=True, + reduction='mean', + mode='multimodal', + ): + r""" + encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in + ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are + ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]`` + past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + Returns: + Example:: + >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig + >>> import torch + >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') + >>> config = BertConfig.from_pretrained("bert-base-cased") + >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config) + >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + >>> prediction_logits = outputs.logits + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + if labels is not None: + use_cache = False + + outputs = self.bert( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + is_decoder=is_decoder, + mode=mode, + ) + + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + + if return_logits: + return prediction_scores[:, :-1, :].contiguous() + + lm_loss = None + if labels is not None: + # we are doing next-token prediction; shift prediction scores and input ids by one + shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() + labels = labels[:, 1:].contiguous() + loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) + lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + if reduction=='none': + lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ((lm_loss,) + output) if lm_loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=lm_loss, + logits=prediction_scores, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs): + input_shape = input_ids.shape + # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly + if attention_mask is None: + attention_mask = input_ids.new_ones(input_shape) + + # cut decoder_input_ids if past is used + if past is not None: + input_ids = input_ids[:, -1:] + + return { + "input_ids": input_ids, + "attention_mask": attention_mask, + "past_key_values": past, + "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None), + "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None), + "is_decoder": True, + } + + def _reorder_cache(self, past, beam_idx): + reordered_past = () + for layer_past in past: + reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) + return reordered_past diff --git a/diffsynth/extensions/ImageQualityMetric/BLIP/vit.py b/diffsynth/extensions/ImageQualityMetric/BLIP/vit.py new file mode 100644 index 0000000000000000000000000000000000000000..cef7b650a95f56266775cf0f18b28bc0f74987ab --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/BLIP/vit.py @@ -0,0 +1,301 @@ +''' + * Adapted from BLIP (https://github.com/salesforce/BLIP) + * Based on timm code base + * https://github.com/rwightman/pytorch-image-models/tree/master/timm +''' + +import torch +import torch.nn as nn +import torch.nn.functional as F +from functools import partial + +from timm.models.vision_transformer import _cfg, PatchEmbed +from timm.models.registry import register_model +from timm.models.layers import trunc_normal_, DropPath +from timm.models.helpers import named_apply, adapt_input_conv + +# from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper + +class Mlp(nn.Module): + """ MLP as used in Vision Transformer, MLP-Mixer and related networks + """ + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights + self.scale = qk_scale or head_dim ** -0.5 + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.attn_gradients = None + self.attention_map = None + + def save_attn_gradients(self, attn_gradients): + self.attn_gradients = attn_gradients + + def get_attn_gradients(self): + return self.attn_gradients + + def save_attention_map(self, attention_map): + self.attention_map = attention_map + + def get_attention_map(self): + return self.attention_map + + def forward(self, x, register_hook=False): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + if register_hook: + self.save_attention_map(attn) + attn.register_hook(self.save_attn_gradients) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_grad_checkpointing=False): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + # if use_grad_checkpointing: + # self.attn = checkpoint_wrapper(self.attn) + # self.mlp = checkpoint_wrapper(self.mlp) + + def forward(self, x, register_hook=False): + x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook)) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class VisionTransformer(nn.Module): + """ Vision Transformer + A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - + https://arxiv.org/abs/2010.11929 + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None, + drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None, + use_grad_checkpointing=False, ckpt_layer=0): + """ + Args: + img_size (int, tuple): input image size + patch_size (int, tuple): patch size + in_chans (int): number of input channels + num_classes (int): number of classes for classification head + embed_dim (int): embedding dimension + depth (int): depth of transformer + num_heads (int): number of attention heads + mlp_ratio (int): ratio of mlp hidden dim to embedding dim + qkv_bias (bool): enable bias for qkv if True + qk_scale (float): override default qk scale of head_dim ** -0.5 if set + representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set + drop_rate (float): dropout rate + attn_drop_rate (float): attention dropout rate + drop_path_rate (float): stochastic depth rate + norm_layer: (nn.Module): normalization layer + """ + super().__init__() + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) + + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, + use_grad_checkpointing=(use_grad_checkpointing and i>=depth-ckpt_layer) + ) + for i in range(depth)]) + self.norm = norm_layer(embed_dim) + + trunc_normal_(self.pos_embed, std=.02) + trunc_normal_(self.cls_token, std=.02) + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def forward(self, x, register_blk=-1): + B = x.shape[0] + x = self.patch_embed(x) + + cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_tokens, x), dim=1) + + x = x + self.pos_embed[:,:x.size(1),:] + x = self.pos_drop(x) + + for i,blk in enumerate(self.blocks): + x = blk(x, register_blk==i) + x = self.norm(x) + + return x + + @torch.jit.ignore() + def load_pretrained(self, checkpoint_path, prefix=''): + _load_weights(self, checkpoint_path, prefix) + + +@torch.no_grad() +def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''): + """ Load weights from .npz checkpoints for official Google Brain Flax implementation + """ + import numpy as np + + def _n2p(w, t=True): + if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1: + w = w.flatten() + if t: + if w.ndim == 4: + w = w.transpose([3, 2, 0, 1]) + elif w.ndim == 3: + w = w.transpose([2, 0, 1]) + elif w.ndim == 2: + w = w.transpose([1, 0]) + return torch.from_numpy(w) + + w = np.load(checkpoint_path) + if not prefix and 'opt/target/embedding/kernel' in w: + prefix = 'opt/target/' + + if hasattr(model.patch_embed, 'backbone'): + # hybrid + backbone = model.patch_embed.backbone + stem_only = not hasattr(backbone, 'stem') + stem = backbone if stem_only else backbone.stem + stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel']))) + stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale'])) + stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias'])) + if not stem_only: + for i, stage in enumerate(backbone.stages): + for j, block in enumerate(stage.blocks): + bp = f'{prefix}block{i + 1}/unit{j + 1}/' + for r in range(3): + getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel'])) + getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale'])) + getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias'])) + if block.downsample is not None: + block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel'])) + block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale'])) + block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias'])) + embed_conv_w = _n2p(w[f'{prefix}embedding/kernel']) + else: + embed_conv_w = adapt_input_conv( + model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel'])) + model.patch_embed.proj.weight.copy_(embed_conv_w) + model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias'])) + model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False)) + pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False) + if pos_embed_w.shape != model.pos_embed.shape: + pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights + pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size) + model.pos_embed.copy_(pos_embed_w) + model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale'])) + model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias'])) +# if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]: +# model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel'])) +# model.head.bias.copy_(_n2p(w[f'{prefix}head/bias'])) +# if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w: +# model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel'])) +# model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias'])) + for i, block in enumerate(model.blocks.children()): + block_prefix = f'{prefix}Transformer/encoderblock_{i}/' + mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/' + block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale'])) + block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias'])) + block.attn.qkv.weight.copy_(torch.cat([ + _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')])) + block.attn.qkv.bias.copy_(torch.cat([ + _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')])) + block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1)) + block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias'])) + for r in range(2): + getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel'])) + getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias'])) + block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale'])) + block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias'])) + + +def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder): + # interpolate position embedding + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = visual_encoder.patch_embed.num_patches + num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + + if orig_size!=new_size: + # class_token and dist_token are kept unchanged + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + print('reshape position embedding from %d to %d'%(orig_size ** 2,new_size ** 2)) + + return new_pos_embed + else: + return pos_embed_checkpoint \ No newline at end of file diff --git a/diffsynth/extensions/ImageQualityMetric/__init__.py b/diffsynth/extensions/ImageQualityMetric/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..fcfb7c02b0ce2b6a2fbe345d87c31e0d1bb3a128 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/__init__.py @@ -0,0 +1,148 @@ +from modelscope import snapshot_download +from typing_extensions import Literal, TypeAlias +import os +from diffsynth.extensions.ImageQualityMetric.aesthetic import AestheticScore +from diffsynth.extensions.ImageQualityMetric.imagereward import ImageRewardScore +from diffsynth.extensions.ImageQualityMetric.pickscore import PickScore +from diffsynth.extensions.ImageQualityMetric.clip import CLIPScore +from diffsynth.extensions.ImageQualityMetric.hps import HPScore_v2 +from diffsynth.extensions.ImageQualityMetric.mps import MPScore + + +preference_model_id: TypeAlias = Literal[ + "ImageReward", + "Aesthetic", + "PickScore", + "CLIP", + "HPSv2", + "HPSv2.1", + "MPS", +] +model_dict = { + "ImageReward": { + "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", + "allow_file_pattern": [ + "ImageReward/ImageReward.safetensors", + "ImageReward/med_config.json", + "bert-base-uncased/config.json", + "bert-base-uncased/model.safetensors", + "bert-base-uncased/tokenizer.json", + "bert-base-uncased/tokenizer_config.json", + "bert-base-uncased/vocab.txt", + ], + "load_path": { + "imagereward": "ImageReward/ImageReward.safetensors", + "med_config": "ImageReward/med_config.json", + "bert_model_path": "bert-base-uncased", + }, + "model_class": ImageRewardScore + }, + "Aesthetic": { + "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", + "allow_file_pattern": [ + "aesthetic-predictor/sac+logos+ava1-l14-linearMSE.safetensors", + "clip-vit-large-patch14/config.json", + "clip-vit-large-patch14/merges.txt", + "clip-vit-large-patch14/model.safetensors", + "clip-vit-large-patch14/preprocessor_config.json", + "clip-vit-large-patch14/special_tokens_map.json", + "clip-vit-large-patch14/tokenizer.json", + "clip-vit-large-patch14/tokenizer_config.json", + "clip-vit-large-patch14/vocab.json", + ], + "load_path": { + "aesthetic_predictor": "aesthetic-predictor/sac+logos+ava1-l14-linearMSE.safetensors", + "clip-large": "clip-vit-large-patch14", + }, + "model_class": AestheticScore + }, + "PickScore": { + "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", + "allow_file_pattern": [ + "PickScore_v1/*", + "CLIP-ViT-H-14-laion2B-s32B-b79K/config.json", + "CLIP-ViT-H-14-laion2B-s32B-b79K/merges.txt", + "CLIP-ViT-H-14-laion2B-s32B-b79K/preprocessor_config.json", + "CLIP-ViT-H-14-laion2B-s32B-b79K/special_tokens_map.json", + "CLIP-ViT-H-14-laion2B-s32B-b79K/tokenizer.json", + "CLIP-ViT-H-14-laion2B-s32B-b79K/tokenizer_config.json", + "CLIP-ViT-H-14-laion2B-s32B-b79K/vocab.json", + ], + "load_path": { + "pickscore": "PickScore_v1", + "clip": "CLIP-ViT-H-14-laion2B-s32B-b79K", + }, + "model_class": PickScore + }, + "CLIP": { + "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", + "allow_file_pattern": [ + "CLIP-ViT-H-14-laion2B-s32B-b79K/open_clip_pytorch_model.bin", + "bpe_simple_vocab_16e6.txt.gz", + ], + "load_path": { + "open_clip": "CLIP-ViT-H-14-laion2B-s32B-b79K/open_clip_pytorch_model.bin", + "open_clip_bpe": "bpe_simple_vocab_16e6.txt.gz", + }, + "model_class": CLIPScore + }, + "HPSv2": { + "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", + "allow_file_pattern": [ + "HPS_v2/HPS_v2_compressed.safetensors", + "bpe_simple_vocab_16e6.txt.gz", + ], + "load_path": { + "hpsv2": "HPS_v2/HPS_v2_compressed.safetensors", + "open_clip_bpe": "bpe_simple_vocab_16e6.txt.gz", + }, + "model_class": HPScore_v2, + "extra_kwargs": {"model_version": "v2"} + }, + "HPSv2.1": { + "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", + "allow_file_pattern": [ + "HPS_v2/HPS_v2.1_compressed.safetensors", + "bpe_simple_vocab_16e6.txt.gz", + ], + "load_path": { + "hpsv2.1": "HPS_v2/HPS_v2.1_compressed.safetensors", + "open_clip_bpe": "bpe_simple_vocab_16e6.txt.gz", + }, + "model_class": HPScore_v2, + "extra_kwargs": {"model_version": "v21"} + }, + "MPS": { + "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", + "allow_file_pattern": [ + "MPS_overall_checkpoint/MPS_overall_checkpoint_diffsynth.safetensors", + "CLIP-ViT-H-14-laion2B-s32B-b79K/config.json", + "CLIP-ViT-H-14-laion2B-s32B-b79K/merges.txt", + "CLIP-ViT-H-14-laion2B-s32B-b79K/preprocessor_config.json", + "CLIP-ViT-H-14-laion2B-s32B-b79K/special_tokens_map.json", + "CLIP-ViT-H-14-laion2B-s32B-b79K/tokenizer.json", + "CLIP-ViT-H-14-laion2B-s32B-b79K/tokenizer_config.json", + "CLIP-ViT-H-14-laion2B-s32B-b79K/vocab.json", + ], + "load_path": { + "mps": "MPS_overall_checkpoint/MPS_overall_checkpoint_diffsynth.safetensors", + "clip": "CLIP-ViT-H-14-laion2B-s32B-b79K", + }, + "model_class": MPScore + }, +} + + +def download_preference_model(model_name: preference_model_id, cache_dir="models"): + metadata = model_dict[model_name] + snapshot_download(model_id=metadata["model_id"], allow_file_pattern=metadata["allow_file_pattern"], cache_dir=cache_dir) + load_path = metadata["load_path"] + load_path = {key: os.path.join(cache_dir, metadata["model_id"], path) for key, path in load_path.items()} + return load_path + + +def load_preference_model(model_name: preference_model_id, device = "cuda", path = None): + model_class = model_dict[model_name]["model_class"] + extra_kwargs = model_dict[model_name].get("extra_kwargs", {}) + preference_model = model_class(device=device, path=path, **extra_kwargs) + return preference_model diff --git a/diffsynth/extensions/ImageQualityMetric/aesthetic.py b/diffsynth/extensions/ImageQualityMetric/aesthetic.py new file mode 100644 index 0000000000000000000000000000000000000000..13da98a1f45ca7eea0411e18c307cc5d0154488f --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/aesthetic.py @@ -0,0 +1,148 @@ +from typing import List, Optional +from PIL import Image +import torch +from transformers import AutoProcessor, AutoModel +from safetensors.torch import load_file +import os +from typing import Union, List +from .config import MODEL_PATHS + +class MLP(torch.nn.Module): + def __init__(self, input_size: int, xcol: str = "emb", ycol: str = "avg_rating"): + super().__init__() + self.input_size = input_size + self.xcol = xcol + self.ycol = ycol + self.layers = torch.nn.Sequential( + torch.nn.Linear(self.input_size, 1024), + #torch.nn.ReLU(), + torch.nn.Dropout(0.2), + torch.nn.Linear(1024, 128), + #torch.nn.ReLU(), + torch.nn.Dropout(0.2), + torch.nn.Linear(128, 64), + #torch.nn.ReLU(), + torch.nn.Dropout(0.1), + torch.nn.Linear(64, 16), + #torch.nn.ReLU(), + torch.nn.Linear(16, 1), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.layers(x) + + def training_step(self, batch: dict, batch_idx: int) -> torch.Tensor: + x = batch[self.xcol] + y = batch[self.ycol].reshape(-1, 1) + x_hat = self.layers(x) + loss = torch.nn.functional.mse_loss(x_hat, y) + return loss + + def validation_step(self, batch: dict, batch_idx: int) -> torch.Tensor: + x = batch[self.xcol] + y = batch[self.ycol].reshape(-1, 1) + x_hat = self.layers(x) + loss = torch.nn.functional.mse_loss(x_hat, y) + return loss + + def configure_optimizers(self) -> torch.optim.Optimizer: + return torch.optim.Adam(self.parameters(), lr=1e-3) + + +class AestheticScore(torch.nn.Module): + def __init__(self, device: torch.device, path: str = MODEL_PATHS): + super().__init__() + self.device = device + self.aes_model_path = path.get("aesthetic_predictor") + # Load the MLP model + self.model = MLP(768) + try: + if self.aes_model_path.endswith(".safetensors"): + state_dict = load_file(self.aes_model_path) + else: + state_dict = torch.load(self.aes_model_path) + self.model.load_state_dict(state_dict) + except Exception as e: + raise ValueError(f"Error loading model weights from {self.aes_model_path}: {e}") + + self.model.to(device) + self.model.eval() + + # Load the CLIP model and processor + clip_model_name = path.get('clip-large') + self.model2 = AutoModel.from_pretrained(clip_model_name).eval().to(device) + self.processor = AutoProcessor.from_pretrained(clip_model_name) + + def _calculate_score(self, image: torch.Tensor) -> float: + """Calculate the aesthetic score for a single image. + + Args: + image (torch.Tensor): The processed image tensor. + + Returns: + float: The aesthetic score. + """ + with torch.no_grad(): + # Get image embeddings + image_embs = self.model2.get_image_features(image) + image_embs = image_embs / torch.norm(image_embs, dim=-1, keepdim=True) + + # Compute score + score = self.model(image_embs).cpu().flatten().item() + + return score + + @torch.no_grad() + def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str = "") -> List[float]: + """Score the images based on their aesthetic quality. + + Args: + images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). + + Returns: + List[float]: List of scores for the images. + """ + try: + if isinstance(images, (str, Image.Image)): + # Single image + if isinstance(images, str): + pil_image = Image.open(images) + else: + pil_image = images + + # Prepare image inputs + image_inputs = self.processor( + images=pil_image, + padding=True, + truncation=True, + max_length=77, + return_tensors="pt", + ).to(self.device) + + return [self._calculate_score(image_inputs["pixel_values"])] + elif isinstance(images, list): + # Multiple images + scores = [] + for one_image in images: + if isinstance(one_image, str): + pil_image = Image.open(one_image) + elif isinstance(one_image, Image.Image): + pil_image = one_image + else: + raise TypeError("The type of parameter images is illegal.") + + # Prepare image inputs + image_inputs = self.processor( + images=pil_image, + padding=True, + truncation=True, + max_length=77, + return_tensors="pt", + ).to(self.device) + + scores.append(self._calculate_score(image_inputs["pixel_values"])) + return scores + else: + raise TypeError("The type of parameter images is illegal.") + except Exception as e: + raise RuntimeError(f"Error in scoring images: {e}") diff --git a/diffsynth/extensions/ImageQualityMetric/clip.py b/diffsynth/extensions/ImageQualityMetric/clip.py new file mode 100644 index 0000000000000000000000000000000000000000..f70941e0a45db61be87e21c347e97ad8bb390fff --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/clip.py @@ -0,0 +1,97 @@ +from typing import List, Union +from PIL import Image +import torch +from .open_clip import create_model_and_transforms, get_tokenizer +from .config import MODEL_PATHS + +class CLIPScore(torch.nn.Module): + def __init__(self, device: torch.device, path: str = MODEL_PATHS): + super().__init__() + """Initialize the CLIPScore with a model and tokenizer. + + Args: + device (torch.device): The device to load the model on. + """ + self.device = device + + # Create model and transforms + self.model, _, self.preprocess_val = create_model_and_transforms( + "ViT-H-14", + # "laion2B-s32B-b79K", + pretrained=path.get("open_clip"), + precision="amp", + device=device, + jit=False, + force_quick_gelu=False, + force_custom_text=False, + force_patch_dropout=False, + force_image_size=None, + pretrained_image=False, + image_mean=None, + image_std=None, + light_augmentation=True, + aug_cfg={}, + output_dict=True, + with_score_predictor=False, + with_region_predictor=False, + ) + + # Initialize tokenizer + self.tokenizer = get_tokenizer("ViT-H-14", path["open_clip_bpe"]) + self.model = self.model.to(device) + self.model.eval() + + def _calculate_score(self, image: torch.Tensor, prompt: str) -> float: + """Calculate the CLIP score for a single image and prompt. + + Args: + image (torch.Tensor): The processed image tensor. + prompt (str): The prompt text. + + Returns: + float: The CLIP score. + """ + with torch.no_grad(): + # Process the prompt + text = self.tokenizer([prompt]).to(device=self.device, non_blocking=True) + + # Calculate the CLIP score + outputs = self.model(image, text) + image_features, text_features = outputs["image_features"], outputs["text_features"] + logits_per_image = image_features @ text_features.T + clip_score = torch.diagonal(logits_per_image).cpu().numpy() + + return clip_score[0].item() + + @torch.no_grad() + def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]: + """Score the images based on the prompt. + + Args: + images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). + prompt (str): The prompt text. + + Returns: + List[float]: List of CLIP scores for the images. + """ + if isinstance(images, (str, Image.Image)): + # Single image + if isinstance(images, str): + image = self.preprocess_val(Image.open(images)).unsqueeze(0).to(device=self.device, non_blocking=True) + else: + image = self.preprocess_val(images).unsqueeze(0).to(device=self.device, non_blocking=True) + return [self._calculate_score(image, prompt)] + elif isinstance(images, list): + # Multiple images + scores = [] + for one_images in images: + if isinstance(one_images, str): + image = self.preprocess_val(Image.open(one_images)).unsqueeze(0).to(device=self.device, non_blocking=True) + elif isinstance(one_images, Image.Image): + image = self.preprocess_val(one_images).unsqueeze(0).to(device=self.device, non_blocking=True) + else: + raise TypeError("The type of parameter images is illegal.") + scores.append(self._calculate_score(image, prompt)) + return scores + else: + raise TypeError("The type of parameter images is illegal.") diff --git a/diffsynth/extensions/ImageQualityMetric/config.py b/diffsynth/extensions/ImageQualityMetric/config.py new file mode 100644 index 0000000000000000000000000000000000000000..60faadcb1e5554c8f8f29a64fc55c3150d8a8bbe --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/config.py @@ -0,0 +1,23 @@ +import os + +current_dir = os.path.dirname(os.path.abspath(__file__)) +project_root = os.path.abspath(os.path.join(current_dir, '../../../')) +model_path = os.path.join(project_root, 'models', 'QualityMetric') + + +def get_model_path(model_name): + return os.path.join(model_path, model_name) + + +MODEL_PATHS = { + "aesthetic_predictor": get_model_path("aesthetic-predictor/sac+logos+ava1-l14-linearMSE.safetensors"), + "open_clip": get_model_path("CLIP-ViT-H-14-laion2B-s32B-b79K/open_clip_pytorch_model.bin"), + "hpsv2": get_model_path("HPS_v2/HPS_v2_compressed.safetensors"), + "hpsv2.1": get_model_path("HPS_v2/HPS_v2.1_compressed.safetensors"), + "imagereward": get_model_path("ImageReward/ImageReward.safetensors"), + "med_config": get_model_path("ImageReward/med_config.json"), + "clip": get_model_path("CLIP-ViT-H-14-laion2B-s32B-b79K"), + "clip-large": get_model_path("clip-vit-large-patch14"), + "mps": get_model_path("MPS_overall_checkpoint/MPS_overall_checkpoint_diffsynth.safetensors"), + "pickscore": get_model_path("PickScore_v1") +} \ No newline at end of file diff --git a/diffsynth/extensions/ImageQualityMetric/hps.py b/diffsynth/extensions/ImageQualityMetric/hps.py new file mode 100644 index 0000000000000000000000000000000000000000..a4b266bd261a95676ba700d38c3a63b143bbbb40 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/hps.py @@ -0,0 +1,118 @@ +from typing import List, Union +from PIL import Image +import torch +from .open_clip import create_model_and_transforms, get_tokenizer +from safetensors.torch import load_file +import os +from .config import MODEL_PATHS + +class HPScore_v2(torch.nn.Module): + def __init__(self, device: torch.device, path: str = MODEL_PATHS, model_version: str = "v2"): + super().__init__() + """Initialize the Selector with a model and tokenizer. + + Args: + device (torch.device): The device to load the model on. + model_version (str): The version of the model to load. Supports "v2" or "v21". Default is "v2". + """ + self.device = device + + if model_version == "v2": + safetensors_path = path.get("hpsv2") + elif model_version == "v21": + safetensors_path = path.get("hpsv2.1") + else: + raise ValueError(f"Unsupported model version: {model_version}. Choose 'v2' or 'v21'.") + + # Create model and transforms + model, _, self.preprocess_val = create_model_and_transforms( + "ViT-H-14", + # "laion2B-s32B-b79K", + pretrained=path.get("open_clip"), + precision="amp", + device=device, + jit=False, + force_quick_gelu=False, + force_custom_text=False, + force_patch_dropout=False, + force_image_size=None, + pretrained_image=False, + image_mean=None, + image_std=None, + light_augmentation=True, + aug_cfg={}, + output_dict=True, + with_score_predictor=False, + with_region_predictor=False, + ) + + # Load model weights + try: + state_dict = load_file(safetensors_path) + model.load_state_dict(state_dict) + except Exception as e: + raise ValueError(f"Error loading model weights from {safetensors_path}: {e}") + + # Initialize tokenizer and model + self.tokenizer = get_tokenizer("ViT-H-14", path["open_clip_bpe"]) + model = model.to(device) + model.eval() + self.model = model + + def _calculate_score(self, image: torch.Tensor, prompt: str) -> float: + """Calculate the HPS score for a single image and prompt. + + Args: + image (torch.Tensor): The processed image tensor. + prompt (str): The prompt text. + + Returns: + float: The HPS score. + """ + with torch.no_grad(): + # Process the prompt + text = self.tokenizer([prompt]).to(device=self.device, non_blocking=True) + + # Calculate the HPS score + outputs = self.model(image, text) + image_features, text_features = outputs["image_features"], outputs["text_features"] + logits_per_image = image_features @ text_features.T + hps_score = torch.diagonal(logits_per_image).cpu().numpy() + + return hps_score[0].item() + + @torch.no_grad() + def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]: + """Score the images based on the prompt. + + Args: + images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). + prompt (str): The prompt text. + + Returns: + List[float]: List of HPS scores for the images. + """ + try: + if isinstance(images, (str, Image.Image)): + # Single image + if isinstance(images, str): + image = self.preprocess_val(Image.open(images)).unsqueeze(0).to(device=self.device, non_blocking=True) + else: + image = self.preprocess_val(images).unsqueeze(0).to(device=self.device, non_blocking=True) + return [self._calculate_score(image, prompt)] + elif isinstance(images, list): + # Multiple images + scores = [] + for one_images in images: + if isinstance(one_images, str): + image = self.preprocess_val(Image.open(one_images)).unsqueeze(0).to(device=self.device, non_blocking=True) + elif isinstance(one_images, Image.Image): + image = self.preprocess_val(one_images).unsqueeze(0).to(device=self.device, non_blocking=True) + else: + raise TypeError("The type of parameter images is illegal.") + scores.append(self._calculate_score(image, prompt)) + return scores + else: + raise TypeError("The type of parameter images is illegal.") + except Exception as e: + raise RuntimeError(f"Error in scoring images: {e}") diff --git a/diffsynth/extensions/ImageQualityMetric/imagereward.py b/diffsynth/extensions/ImageQualityMetric/imagereward.py new file mode 100644 index 0000000000000000000000000000000000000000..27607904b23fa1691c5a6966eb4030cd813567b0 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/imagereward.py @@ -0,0 +1,212 @@ +import os +import torch +from PIL import Image +from typing import List, Union +from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize +from .BLIP.blip_pretrain import BLIP_Pretrain +from torchvision.transforms import InterpolationMode +from safetensors.torch import load_file +from .config import MODEL_PATHS +BICUBIC = InterpolationMode.BICUBIC + +def _convert_image_to_rgb(image): + return image.convert("RGB") + +def _transform(n_px): + return Compose([ + Resize(n_px, interpolation=BICUBIC), + CenterCrop(n_px), + _convert_image_to_rgb, + ToTensor(), + Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), + ]) + +class MLP(torch.nn.Module): + def __init__(self, input_size): + super().__init__() + self.input_size = input_size + + self.layers = torch.nn.Sequential( + torch.nn.Linear(self.input_size, 1024), + #nn.ReLU(), + torch.nn.Dropout(0.2), + torch.nn.Linear(1024, 128), + #nn.ReLU(), + torch.nn.Dropout(0.2), + torch.nn.Linear(128, 64), + #nn.ReLU(), + torch.nn.Dropout(0.1), + torch.nn.Linear(64, 16), + #nn.ReLU(), + torch.nn.Linear(16, 1) + ) + + # initial MLP param + for name, param in self.layers.named_parameters(): + if 'weight' in name: + torch.nn.init.normal_(param, mean=0.0, std=1.0/(self.input_size+1)) + if 'bias' in name: + torch.nn.init.constant_(param, val=0) + + def forward(self, input): + return self.layers(input) + +class ImageReward(torch.nn.Module): + def __init__(self, med_config, device='cpu', bert_model_path=""): + super().__init__() + self.device = device + + self.blip = BLIP_Pretrain(image_size=224, vit='large', med_config=med_config, bert_model_path=bert_model_path) + self.preprocess = _transform(224) + self.mlp = MLP(768) + + self.mean = 0.16717362830052426 + self.std = 1.0333394966054072 + + def score_grad(self, prompt_ids, prompt_attention_mask, image): + """Calculate the score with gradient for a single image and prompt. + + Args: + prompt_ids (torch.Tensor): Tokenized prompt IDs. + prompt_attention_mask (torch.Tensor): Attention mask for the prompt. + image (torch.Tensor): The processed image tensor. + + Returns: + torch.Tensor: The reward score. + """ + image_embeds = self.blip.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(self.device) + text_output = self.blip.text_encoder( + prompt_ids, + attention_mask=prompt_attention_mask, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_atts, + return_dict=True, + ) + txt_features = text_output.last_hidden_state[:, 0, :] + rewards = self.mlp(txt_features) + rewards = (rewards - self.mean) / self.std + return rewards + + def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str = "") -> List[float]: + """Score the images based on the prompt. + + Args: + prompt (str): The prompt text. + images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). + + Returns: + List[float]: List of scores for the images. + """ + if isinstance(images, (str, Image.Image)): + # Single image + if isinstance(images, str): + pil_image = Image.open(images) + else: + pil_image = images + image = self.preprocess(pil_image).unsqueeze(0).to(self.device) + return [self._calculate_score(prompt, image).item()] + elif isinstance(images, list): + # Multiple images + scores = [] + for one_image in images: + if isinstance(one_image, str): + pil_image = Image.open(one_image) + elif isinstance(one_image, Image.Image): + pil_image = one_image + else: + raise TypeError("The type of parameter images is illegal.") + image = self.preprocess(pil_image).unsqueeze(0).to(self.device) + scores.append(self._calculate_score(prompt, image).item()) + return scores + else: + raise TypeError("The type of parameter images is illegal.") + + def _calculate_score(self, prompt: str, image: torch.Tensor) -> torch.Tensor: + """Calculate the score for a single image and prompt. + + Args: + prompt (str): The prompt text. + image (torch.Tensor): The processed image tensor. + + Returns: + torch.Tensor: The reward score. + """ + text_input = self.blip.tokenizer(prompt, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(self.device) + image_embeds = self.blip.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(self.device) + text_output = self.blip.text_encoder( + text_input.input_ids, + attention_mask=text_input.attention_mask, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_atts, + return_dict=True, + ) + txt_features = text_output.last_hidden_state[:, 0, :].float() + rewards = self.mlp(txt_features) + rewards = (rewards - self.mean) / self.std + return rewards + + def inference_rank(self, prompt: str, generations_list: List[Union[str, Image.Image]]) -> tuple: + """Rank the images based on the prompt. + + Args: + prompt (str): The prompt text. + generations_list (List[Union[str, Image.Image]]): List of image paths or PIL images. + + Returns: + tuple: (indices, rewards) where indices are the ranks and rewards are the scores. + """ + text_input = self.blip.tokenizer(prompt, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(self.device) + txt_set = [] + for generation in generations_list: + if isinstance(generation, str): + pil_image = Image.open(generation) + elif isinstance(generation, Image.Image): + pil_image = generation + else: + raise TypeError("The type of parameter generations_list is illegal.") + image = self.preprocess(pil_image).unsqueeze(0).to(self.device) + image_embeds = self.blip.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(self.device) + text_output = self.blip.text_encoder( + text_input.input_ids, + attention_mask=text_input.attention_mask, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_atts, + return_dict=True, + ) + txt_set.append(text_output.last_hidden_state[:, 0, :]) + txt_features = torch.cat(txt_set, 0).float() + rewards = self.mlp(txt_features) + rewards = (rewards - self.mean) / self.std + rewards = torch.squeeze(rewards) + _, rank = torch.sort(rewards, dim=0, descending=True) + _, indices = torch.sort(rank, dim=0) + indices = indices + 1 + return indices.detach().cpu().numpy().tolist(), rewards.detach().cpu().numpy().tolist() + + +class ImageRewardScore(torch.nn.Module): + def __init__(self, device: Union[str, torch.device], path: str = MODEL_PATHS): + super().__init__() + self.device = device if isinstance(device, torch.device) else torch.device(device) + model_path = path.get("imagereward") + med_config = path.get("med_config") + state_dict = load_file(model_path) + self.model = ImageReward(device=self.device, med_config=med_config, bert_model_path=path.get("bert_model_path")).to(self.device) + self.model.load_state_dict(state_dict, strict=False) + self.model.eval() + + @torch.no_grad() + def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]: + """Score the images based on the prompt. + + Args: + images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). + prompt (str): The prompt text. + + Returns: + List[float]: List of scores for the images. + """ + return self.model.score(images, prompt) diff --git a/diffsynth/extensions/ImageQualityMetric/mps.py b/diffsynth/extensions/ImageQualityMetric/mps.py new file mode 100644 index 0000000000000000000000000000000000000000..d15aad4b81026a743911512bcc569520182b31c5 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/mps.py @@ -0,0 +1,129 @@ +import numpy as np +import torch +from PIL import Image +from io import BytesIO +from tqdm.auto import tqdm +from transformers import CLIPFeatureExtractor, CLIPImageProcessor +from transformers import CLIPConfig +from dataclasses import dataclass +from transformers import CLIPModel as HFCLIPModel +from safetensors.torch import load_file +from torch import nn, einsum + +from .trainer.models.base_model import BaseModelConfig + +from transformers import CLIPConfig +from transformers import AutoProcessor, AutoModel, AutoTokenizer +from typing import Any, Optional, Tuple, Union, List +import torch + +from .trainer.models.cross_modeling import Cross_model +from .trainer.models import clip_model +import torch.nn.functional as F +import gc +import json +from .config import MODEL_PATHS + +class MPScore(torch.nn.Module): + def __init__(self, device: Union[str, torch.device], path: str = MODEL_PATHS, condition: str = 'overall'): + super().__init__() + """Initialize the MPSModel with a processor, tokenizer, and model. + + Args: + device (Union[str, torch.device]): The device to load the model on. + """ + self.device = device + processor_name_or_path = path.get("clip") + self.image_processor = CLIPImageProcessor.from_pretrained(processor_name_or_path) + self.tokenizer = AutoTokenizer.from_pretrained(processor_name_or_path, trust_remote_code=True) + self.model = clip_model.CLIPModel(processor_name_or_path, config_file=True) + state_dict = load_file(path.get("mps")) + self.model.load_state_dict(state_dict, strict=False) + self.model.to(device) + self.condition = condition + + def _calculate_score(self, image: torch.Tensor, prompt: str) -> float: + """Calculate the reward score for a single image and prompt. + + Args: + image (torch.Tensor): The processed image tensor. + prompt (str): The prompt text. + + Returns: + float: The reward score. + """ + def _tokenize(caption): + input_ids = self.tokenizer( + caption, + max_length=self.tokenizer.model_max_length, + padding="max_length", + truncation=True, + return_tensors="pt" + ).input_ids + return input_ids + + text_input = _tokenize(prompt).to(self.device) + if self.condition == 'overall': + condition_prompt = 'light, color, clarity, tone, style, ambiance, artistry, shape, face, hair, hands, limbs, structure, instance, texture, quantity, attributes, position, number, location, word, things' + elif self.condition == 'aesthetics': + condition_prompt = 'light, color, clarity, tone, style, ambiance, artistry' + elif self.condition == 'quality': + condition_prompt = 'shape, face, hair, hands, limbs, structure, instance, texture' + elif self.condition == 'semantic': + condition_prompt = 'quantity, attributes, position, number, location' + else: + raise ValueError( + f"Unsupported condition: {self.condition}. Choose 'overall', 'aesthetics', 'quality', or 'semantic'.") + condition_batch = _tokenize(condition_prompt).repeat(text_input.shape[0], 1).to(self.device) + + with torch.no_grad(): + text_f, text_features = self.model.model.get_text_features(text_input) + + image_f = self.model.model.get_image_features(image.half()) + condition_f, _ = self.model.model.get_text_features(condition_batch) + + sim_text_condition = einsum('b i d, b j d -> b j i', text_f, condition_f) + sim_text_condition = torch.max(sim_text_condition, dim=1, keepdim=True)[0] + sim_text_condition = sim_text_condition / sim_text_condition.max() + mask = torch.where(sim_text_condition > 0.3, 0, float('-inf')) + mask = mask.repeat(1, image_f.shape[1], 1) + image_features = self.model.cross_model(image_f, text_f, mask.half())[:, 0, :] + + image_features = image_features / image_features.norm(dim=-1, keepdim=True) + text_features = text_features / text_features.norm(dim=-1, keepdim=True) + image_score = self.model.logit_scale.exp() * text_features @ image_features.T + + return image_score[0].cpu().numpy().item() + + @torch.no_grad() + def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]: + """Score the images based on the prompt. + + Args: + images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). + prompt (str): The prompt text. + + Returns: + List[float]: List of reward scores for the images. + """ + if isinstance(images, (str, Image.Image)): + # Single image + if isinstance(images, str): + image = self.image_processor(Image.open(images), return_tensors="pt")["pixel_values"].to(self.device) + else: + image = self.image_processor(images, return_tensors="pt")["pixel_values"].to(self.device) + return [self._calculate_score(image, prompt)] + elif isinstance(images, list): + # Multiple images + scores = [] + for one_images in images: + if isinstance(one_images, str): + image = self.image_processor(Image.open(one_images), return_tensors="pt")["pixel_values"].to(self.device) + elif isinstance(one_images, Image.Image): + image = self.image_processor(one_images, return_tensors="pt")["pixel_values"].to(self.device) + else: + raise TypeError("The type of parameter images is illegal.") + scores.append(self._calculate_score(image, prompt)) + return scores + else: + raise TypeError("The type of parameter images is illegal.") diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/__init__.py b/diffsynth/extensions/ImageQualityMetric/open_clip/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1560db0b543b7b8857f39d7de435c834380666ab --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/__init__.py @@ -0,0 +1,14 @@ +from .coca_model import CoCa +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .factory import create_model, create_model_and_transforms, create_model_from_pretrained, get_tokenizer, create_loss +from .factory import list_models, add_model_config, get_model_config, load_checkpoint +from .loss import ClipLoss, DistillClipLoss, CoCaLoss +from .model import CLIP, CustomTextCLIP, CLIPTextCfg, CLIPVisionCfg, \ + convert_weights_to_lp, convert_weights_to_fp16, trace_model, get_cast_dtype +from .openai import load_openai_model, list_openai_models +from .pretrained import list_pretrained, list_pretrained_models_by_tag, list_pretrained_tags_by_model, \ + get_pretrained_url, download_pretrained_from_url, is_pretrained_cfg, get_pretrained_cfg, download_pretrained +from .push_to_hf_hub import push_pretrained_to_hf_hub, push_to_hf_hub +from .tokenizer import SimpleTokenizer +from .transform import image_transform, AugmentationCfg +from .utils import freeze_batch_norm_2d diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/coca_model.py b/diffsynth/extensions/ImageQualityMetric/open_clip/coca_model.py new file mode 100644 index 0000000000000000000000000000000000000000..039453af70d1c865dd7cc6016f732aff2f7dc3d2 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/coca_model.py @@ -0,0 +1,458 @@ +from typing import Optional + +import torch +from torch import nn +from torch.nn import functional as F +import numpy as np +from dataclasses import dataclass + +from .transformer import ( + LayerNormFp32, + LayerNorm, + QuickGELU, + MultimodalTransformer, +) +from .model import CLIPTextCfg, CLIPVisionCfg, _build_vision_tower, _build_text_tower + +try: + from transformers import ( + BeamSearchScorer, + LogitsProcessorList, + TopPLogitsWarper, + TopKLogitsWarper, + RepetitionPenaltyLogitsProcessor, + MinLengthLogitsProcessor, + MaxLengthCriteria, + StoppingCriteriaList + ) + + GENERATION_TYPES = { + "top_k": TopKLogitsWarper, + "top_p": TopPLogitsWarper, + "beam_search": "beam_search" + } + _has_transformers = True +except ImportError as e: + GENERATION_TYPES = { + "top_k": None, + "top_p": None, + "beam_search": "beam_search" + } + _has_transformers = False + + +@dataclass +class MultimodalCfg(CLIPTextCfg): + mlp_ratio: int = 4 + dim_head: int = 64 + heads: int = 8 + n_queries: int = 256 + attn_pooler_heads: int = 8 + + +def _build_text_decoder_tower( + embed_dim, + multimodal_cfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, +): + multimodal_cfg = MultimodalCfg(**multimodal_cfg) if isinstance(multimodal_cfg, dict) else multimodal_cfg + act_layer = QuickGELU if quick_gelu else nn.GELU + norm_layer = ( + LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm + ) + + decoder = MultimodalTransformer( + context_length=multimodal_cfg.context_length, + width=multimodal_cfg.width, + heads=multimodal_cfg.heads, + layers=multimodal_cfg.layers, + ls_init_value=multimodal_cfg.ls_init_value, + output_dim=embed_dim, + act_layer=act_layer, + norm_layer=norm_layer, + ) + + return decoder + + +class CoCa(nn.Module): + def __init__( + self, + embed_dim, + multimodal_cfg: MultimodalCfg, + text_cfg: CLIPTextCfg, + vision_cfg: CLIPVisionCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, + pad_id: int = 0, + ): + super().__init__() + multimodal_cfg = MultimodalCfg(**multimodal_cfg) if isinstance(multimodal_cfg, dict) else multimodal_cfg + text_cfg = CLIPTextCfg(**text_cfg) if isinstance(text_cfg, dict) else text_cfg + vision_cfg = CLIPVisionCfg(**vision_cfg) if isinstance(vision_cfg, dict) else vision_cfg + + self.text = _build_text_tower( + embed_dim=embed_dim, + text_cfg=text_cfg, + quick_gelu=quick_gelu, + cast_dtype=cast_dtype, + ) + + vocab_size = ( + text_cfg.vocab_size # for hf models + if hasattr(text_cfg, "hf_model_name") and text_cfg.hf_model_name is not None + else text_cfg.vocab_size + ) + + self.visual = _build_vision_tower( + embed_dim=embed_dim, + vision_cfg=vision_cfg, + quick_gelu=quick_gelu, + cast_dtype=cast_dtype, + ) + + self.text_decoder = _build_text_decoder_tower( + vocab_size, + multimodal_cfg=multimodal_cfg, + quick_gelu=quick_gelu, + cast_dtype=cast_dtype, + ) + + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + self.pad_id = pad_id + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.visual.set_grad_checkpointing(enable) + self.text.set_grad_checkpointing(enable) + self.text_decoder.set_grad_checkpointing(enable) + + def _encode_image(self, images, normalize=True): + image_latent, tokens_embs = self.visual(images) + image_latent = F.normalize(image_latent, dim=-1) if normalize else image_latent + return image_latent, tokens_embs + + def _encode_text(self, text, normalize=True, embed_cls=True): + text = text[:, :-1] if embed_cls else text # make space for CLS token + text_latent, token_emb = self.text(text) + text_latent = F.normalize(text_latent, dim=-1) if normalize else text_latent + return text_latent, token_emb + + def encode_image(self, images, normalize=True): + image_latent, _ = self._encode_image(images, normalize=normalize) + return image_latent + + def encode_text(self, text, normalize=True, embed_cls=True): + text_latent, _ = self._encode_text(text, normalize=normalize, embed_cls=embed_cls) + return text_latent + + def forward(self, image, text, embed_cls=True, image_latent=None, image_embs=None): + text_latent, token_embs = self._encode_text(text, embed_cls=embed_cls) + if image_latent is None or image_embs is None: + image_latent, image_embs = self._encode_image(image) + + # TODO: add assertion to avoid bugs? + labels = text[:, -token_embs.shape[1]:] + + logits = self.text_decoder(image_embs, token_embs) + return { + "image_features": image_latent, + "text_features": text_latent, + "logits": logits, + "labels": labels, + "logit_scale": self.logit_scale.exp() + } + + def generate( + self, + image, + text=None, + seq_len=30, + max_seq_len=77, + temperature=1., + generation_type="beam_search", + top_p=0.1, # keep tokens in the 1 - top_p quantile + top_k=1, # keeps the top_k most probable tokens + pad_token_id=None, + eos_token_id=None, + sot_token_id=None, + num_beams=6, + num_beam_groups=3, + min_seq_len=5, + stopping_criteria=None, + repetition_penalty=1.0, + fixed_output_length=False # if True output.shape == (batch_size, seq_len) + ): + # taking many ideas and components from HuggingFace GenerationMixin + # https://huggingface.co/docs/transformers/main/en/main_classes/text_generation + assert _has_transformers, "Please install transformers for generate functionality. `pip install transformers`." + assert seq_len > min_seq_len, "seq_len must be larger than min_seq_len" + + with torch.no_grad(): + sot_token_id = 49406 if sot_token_id is None else sot_token_id + eos_token_id = 49407 if eos_token_id is None else eos_token_id + pad_token_id = self.pad_id if pad_token_id is None else pad_token_id + logit_processor = LogitsProcessorList( + [ + MinLengthLogitsProcessor(min_seq_len, eos_token_id), + RepetitionPenaltyLogitsProcessor(repetition_penalty), + ] + ) + + if stopping_criteria is None: + stopping_criteria = [MaxLengthCriteria(max_length=seq_len)] + + stopping_criteria = StoppingCriteriaList( + stopping_criteria + ) + + device = image.device + + if generation_type == "beam_search": + output = self._generate_beamsearch( + image_inputs = image, + pad_token_id=pad_token_id, + eos_token_id=eos_token_id, + sot_token_id=sot_token_id, + num_beams=num_beams, + num_beam_groups=num_beam_groups, + min_seq_len=min_seq_len, + stopping_criteria=stopping_criteria, + logit_processor=logit_processor, + ) + if fixed_output_length and output.shape[1] < seq_len: + return torch.cat( + (output, torch.ones(output.shape[0], seq_len-output.shape[1], device=device, dtype=output.dtype) * self.pad_id), + dim=1 + ) + return output + + elif generation_type == "top_p": + logit_warper = GENERATION_TYPES[generation_type](top_p) + elif generation_type == "top_k": + logit_warper = GENERATION_TYPES[generation_type](top_k) + else: + raise ValueError( + f"generation_type has to be one of " + f"{'| ' + ' | '.join(list(GENERATION_TYPES.keys())) + ' |'}." + ) + + image_latent, image_embs = self._encode_image(image) + + if text is None: + text = torch.ones((image.shape[0], 1), device=device, dtype=torch.long) * sot_token_id + + was_training = self.training + num_dims = len(text.shape) + + if num_dims == 1: + text = text[None, :] + + cur_len = text.shape[1] + self.eval() + out = text + + while True: + x = out[:, -max_seq_len:] + cur_len = x.shape[1] + logits = self(image, x, image_latent=image_latent, image_embs=image_embs, embed_cls=False)["logits"][:, -1] + mask = (out[:, -1] == eos_token_id) | (out[:, -1] == pad_token_id) + sample = torch.ones((out.shape[0], 1), device=device, dtype=torch.long) * pad_token_id + + if mask.all(): + if not fixed_output_length: + break + else: + logits = logits[~mask, :] + filtered_logits = logit_processor(x[~mask, :], logits) + filtered_logits = logit_warper(x[~mask, :], filtered_logits) + probs = F.softmax(filtered_logits / temperature, dim=-1) + + if (cur_len + 1 == seq_len): + sample[~mask, :] = torch.ones((sum(~mask), 1), device=device, dtype=torch.long) * eos_token_id + else: + sample[~mask, :] = torch.multinomial(probs, 1) + + out = torch.cat((out, sample), dim=-1) + + cur_len += 1 + + if stopping_criteria(out, None): + break + + if num_dims == 1: + out = out.squeeze(0) + + self.train(was_training) + return out + + def _generate_beamsearch( + self, + image_inputs, + pad_token_id=None, + eos_token_id=None, + sot_token_id=None, + num_beams=6, + num_beam_groups=3, + min_seq_len=5, + stopping_criteria=None, + logit_processor=None, + logit_warper=None, + ): + device = image_inputs.device + batch_size = image_inputs.shape[0] + image_inputs = torch.repeat_interleave(image_inputs, num_beams, dim=0) + image_latent, image_embs = self._encode_image(image_inputs) + + input_ids = torch.ones((batch_size * num_beams, 1), device=device, dtype=torch.long) + input_ids = input_ids * sot_token_id + beam_scorer = BeamSearchScorer( + batch_size=batch_size, + num_beams=num_beams, + device=device, + num_beam_groups=num_beam_groups, + ) + # instantiate logits processors + logits_processor = ( + LogitsProcessorList([MinLengthLogitsProcessor(min_seq_len, eos_token_id=eos_token_id)]) + if logit_processor is None + else logit_processor + ) + + batch_size = len(beam_scorer._beam_hyps) + num_beams = beam_scorer.num_beams + num_beam_groups = beam_scorer.num_beam_groups + num_sub_beams = num_beams // num_beam_groups + batch_beam_size, cur_len = input_ids.shape + beam_indices = None + + if num_beams * batch_size != batch_beam_size: + raise ValueError( + f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}." + ) + + beam_scores = torch.full((batch_size, num_beams), -1e9, dtype=torch.float, device=device) + # initialise score of first beam of each group with 0 and the rest with 1e-9. This ensures that the beams in + # the same group don't produce same tokens everytime. + beam_scores[:, ::num_sub_beams] = 0 + beam_scores = beam_scores.view((batch_size * num_beams,)) + + while True: + + # predicted tokens in cur_len step + current_tokens = torch.zeros(batch_size * num_beams, dtype=input_ids.dtype, device=device) + + # indices which will form the beams in the next time step + reordering_indices = torch.zeros(batch_size * num_beams, dtype=torch.long, device=device) + + # do one decoder step on all beams of all sentences in batch + model_inputs = prepare_inputs_for_generation(input_ids=input_ids, image_inputs=image_inputs) + outputs = self( + model_inputs['images'], + model_inputs['text'], + embed_cls=False, + image_latent=image_latent, + image_embs=image_embs + ) + + for beam_group_idx in range(num_beam_groups): + group_start_idx = beam_group_idx * num_sub_beams + group_end_idx = min(group_start_idx + num_sub_beams, num_beams) + group_size = group_end_idx - group_start_idx + + # indices of beams of current group among all sentences in batch + batch_group_indices = [] + + for batch_idx in range(batch_size): + batch_group_indices.extend( + [batch_idx * num_beams + idx for idx in range(group_start_idx, group_end_idx)] + ) + group_input_ids = input_ids[batch_group_indices] + + # select outputs of beams of currentg group only + next_token_logits = outputs['logits'][batch_group_indices, -1, :] + vocab_size = next_token_logits.shape[-1] + + next_token_scores_processed = logits_processor( + group_input_ids, next_token_logits, current_tokens=current_tokens, beam_group_idx=beam_group_idx + ) + next_token_scores = next_token_scores_processed + beam_scores[batch_group_indices].unsqueeze(-1) + next_token_scores = next_token_scores.expand_as(next_token_scores_processed) + + # reshape for beam search + next_token_scores = next_token_scores.view(batch_size, group_size * vocab_size) + + next_token_scores, next_tokens = torch.topk( + next_token_scores, 2 * group_size, dim=1, largest=True, sorted=True + ) + + next_indices = torch.div(next_tokens, vocab_size, rounding_mode="floor") + next_tokens = next_tokens % vocab_size + + # stateless + process_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None + beam_outputs = beam_scorer.process( + group_input_ids, + next_token_scores, + next_tokens, + next_indices, + pad_token_id=pad_token_id, + eos_token_id=eos_token_id, + beam_indices=process_beam_indices, + ) + beam_scores[batch_group_indices] = beam_outputs["next_beam_scores"] + beam_next_tokens = beam_outputs["next_beam_tokens"] + beam_idx = beam_outputs["next_beam_indices"] + + input_ids[batch_group_indices] = group_input_ids[beam_idx] + group_input_ids = torch.cat([group_input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1) + current_tokens[batch_group_indices] = group_input_ids[:, -1] + + # (beam_idx // group_size) -> batch_idx + # (beam_idx % group_size) -> offset of idx inside the group + reordering_indices[batch_group_indices] = ( + num_beams * torch.div(beam_idx, group_size, rounding_mode="floor") + group_start_idx + (beam_idx % group_size) + ) + + input_ids = torch.cat([input_ids, current_tokens.unsqueeze(-1)], dim=-1) + + # increase cur_len + cur_len = cur_len + 1 + if beam_scorer.is_done or stopping_criteria(input_ids, None): + break + + final_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None + sequence_outputs = beam_scorer.finalize( + input_ids, + beam_scores, + next_tokens, + next_indices, + pad_token_id=pad_token_id, + eos_token_id=eos_token_id, + max_length=stopping_criteria.max_length, + beam_indices=final_beam_indices, + ) + return sequence_outputs['sequences'] + + +def prepare_inputs_for_generation(input_ids, image_inputs, past=None, **kwargs): + if past: + input_ids = input_ids[:, -1].unsqueeze(-1) + + attention_mask = kwargs.get("attention_mask", None) + position_ids = kwargs.get("position_ids", None) + + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + else: + position_ids = None + return { + "text": input_ids, + "images": image_inputs, + "past_key_values": past, + "position_ids": position_ids, + "attention_mask": attention_mask, + } diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/constants.py b/diffsynth/extensions/ImageQualityMetric/open_clip/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..a670bb3fab442baeb9af53b91c312e6982af57ee --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/constants.py @@ -0,0 +1,2 @@ +OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073) +OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711) diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/factory.py b/diffsynth/extensions/ImageQualityMetric/open_clip/factory.py new file mode 100644 index 0000000000000000000000000000000000000000..5bd51a1bb6b69e0e69147c8b7cb8d7bd4899b349 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/factory.py @@ -0,0 +1,433 @@ +import json +import logging +import os +import pathlib +import re +from copy import deepcopy +from pathlib import Path +# from turtle import forward +from typing import Any, Dict, Optional, Tuple, Union + +import torch + +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .model import CLIP, CustomTextCLIP, convert_weights_to_lp, convert_to_custom_text_state_dict,\ + resize_pos_embed, get_cast_dtype +from .coca_model import CoCa +from .loss import ClipLoss, DistillClipLoss, CoCaLoss +from .openai import load_openai_model +from .pretrained import is_pretrained_cfg, get_pretrained_cfg, download_pretrained, list_pretrained_tags_by_model, download_pretrained_from_hf +from .transform import image_transform, AugmentationCfg +from .tokenizer import HFTokenizer, SimpleTokenizer + + +HF_HUB_PREFIX = 'hf-hub:' +_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"] +_MODEL_CONFIGS = {} # directory (model_name: config) of model architecture configs + + +def _natural_key(string_): + return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())] + + +def _rescan_model_configs(): + global _MODEL_CONFIGS + + config_ext = ('.json',) + config_files = [] + for config_path in _MODEL_CONFIG_PATHS: + if config_path.is_file() and config_path.suffix in config_ext: + config_files.append(config_path) + elif config_path.is_dir(): + for ext in config_ext: + config_files.extend(config_path.glob(f'*{ext}')) + + for cf in config_files: + with open(cf, 'r') as f: + model_cfg = json.load(f) + if all(a in model_cfg for a in ('embed_dim', 'vision_cfg', 'text_cfg')): + _MODEL_CONFIGS[cf.stem] = model_cfg + + _MODEL_CONFIGS = {k: v for k, v in sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))} + + +_rescan_model_configs() # initial populate of model config registry + + +def list_models(): + """ enumerate available model architectures based on config files """ + return list(_MODEL_CONFIGS.keys()) + + +def add_model_config(path): + """ add model config path or file and update registry """ + if not isinstance(path, Path): + path = Path(path) + _MODEL_CONFIG_PATHS.append(path) + _rescan_model_configs() + + +def get_model_config(model_name): + if model_name in _MODEL_CONFIGS: + return deepcopy(_MODEL_CONFIGS[model_name]) + else: + return None + + +def get_tokenizer(model_name, open_clip_bpe_path=None): + if model_name.startswith(HF_HUB_PREFIX): + tokenizer = HFTokenizer(model_name[len(HF_HUB_PREFIX):]) + else: + config = get_model_config(model_name) + tokenizer = HFTokenizer( + config['text_cfg']['hf_tokenizer_name']) if 'hf_tokenizer_name' in config['text_cfg'] else SimpleTokenizer(open_clip_bpe_path) + return tokenizer + + +def load_state_dict(checkpoint_path: str, map_location='cpu'): + checkpoint = torch.load(checkpoint_path, map_location=map_location) + if isinstance(checkpoint, dict) and 'state_dict' in checkpoint: + state_dict = checkpoint['state_dict'] + else: + state_dict = checkpoint + if next(iter(state_dict.items()))[0].startswith('module'): + state_dict = {k[7:]: v for k, v in state_dict.items()} + return state_dict + + +def load_checkpoint(model, checkpoint_path, strict=True): + state_dict = load_state_dict(checkpoint_path) + # detect old format and make compatible with new format + if 'positional_embedding' in state_dict and not hasattr(model, 'positional_embedding'): + state_dict = convert_to_custom_text_state_dict(state_dict) + resize_pos_embed(state_dict, model) + incompatible_keys = model.load_state_dict(state_dict, strict=strict) + return incompatible_keys + + +def create_model( + model_name: str, + pretrained: Optional[str] = None, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_text: bool = False, + force_patch_dropout: Optional[float] = None, + force_image_size: Optional[Union[int, Tuple[int, int]]] = None, + pretrained_image: bool = False, + pretrained_hf: bool = True, + cache_dir: Optional[str] = None, + output_dict: Optional[bool] = None, + require_pretrained: bool = False, +): + has_hf_hub_prefix = model_name.startswith(HF_HUB_PREFIX) + if has_hf_hub_prefix: + model_id = model_name[len(HF_HUB_PREFIX):] + checkpoint_path = download_pretrained_from_hf(model_id, cache_dir=cache_dir) + config_path = download_pretrained_from_hf(model_id, filename='open_clip_config.json', cache_dir=cache_dir) + + with open(config_path, 'r', encoding='utf-8') as f: + config = json.load(f) + pretrained_cfg = config['preprocess_cfg'] + model_cfg = config['model_cfg'] + else: + model_name = model_name.replace('/', '-') # for callers using old naming with / in ViT names + checkpoint_path = None + pretrained_cfg = {} + model_cfg = None + + if isinstance(device, str): + device = torch.device(device) + + if pretrained and pretrained.lower() == 'openai': + logging.info(f'Loading pretrained {model_name} from OpenAI.') + model = load_openai_model( + model_name, + precision=precision, + device=device, + jit=jit, + cache_dir=cache_dir, + ) + + # to always output dict even if it is clip + if output_dict and hasattr(model, "output_dict"): + model.output_dict = True + else: + model_cfg = model_cfg or get_model_config(model_name) + if model_cfg is not None: + logging.info(f'Loaded {model_name} model config.') + else: + logging.error(f'Model config for {model_name} not found; available models {list_models()}.') + raise RuntimeError(f'Model config for {model_name} not found.') + + if force_quick_gelu: + # override for use of QuickGELU on non-OpenAI transformer models + model_cfg["quick_gelu"] = True + + if force_patch_dropout is not None: + # override the default patch dropout value + model_cfg["vision_cfg"]["patch_dropout"] = force_patch_dropout + + if force_image_size is not None: + # override model config's image size + model_cfg["vision_cfg"]["image_size"] = force_image_size + + if pretrained_image: + if 'timm_model_name' in model_cfg.get('vision_cfg', {}): + # pretrained weight loading for timm models set via vision_cfg + model_cfg['vision_cfg']['timm_model_pretrained'] = True + else: + assert False, 'pretrained image towers currently only supported for timm models' + + cast_dtype = get_cast_dtype(precision) + is_hf_model = 'hf_model_name' in model_cfg.get('text_cfg', {}) + custom_text = model_cfg.pop('custom_text', False) or force_custom_text or is_hf_model + + if custom_text: + if is_hf_model: + model_cfg['text_cfg']['hf_model_pretrained'] = pretrained_hf + if "coca" in model_name: + model = CoCa(**model_cfg, cast_dtype=cast_dtype) + else: + model = CustomTextCLIP(**model_cfg, cast_dtype=cast_dtype) + else: + model = CLIP(**model_cfg, cast_dtype=cast_dtype) + + pretrained_loaded = False + if pretrained: + checkpoint_path = '' + pretrained_cfg = get_pretrained_cfg(model_name, pretrained) + if pretrained_cfg: + checkpoint_path = download_pretrained(pretrained_cfg, cache_dir=cache_dir) + elif os.path.exists(pretrained): + checkpoint_path = pretrained + + if checkpoint_path: + logging.info(f'Loading pretrained {model_name} weights ({pretrained}).') + load_checkpoint(model, checkpoint_path) + else: + error_str = ( + f'Pretrained weights ({pretrained}) not found for model {model_name}.' + f'Available pretrained tags ({list_pretrained_tags_by_model(model_name)}.') + logging.warning(error_str) + raise RuntimeError(error_str) + pretrained_loaded = True + elif has_hf_hub_prefix: + logging.info(f'Loading pretrained {model_name} weights ({pretrained}).') + load_checkpoint(model, checkpoint_path) + pretrained_loaded = True + + if require_pretrained and not pretrained_loaded: + # callers of create_model_from_pretrained always expect pretrained weights + raise RuntimeError( + f'Pretrained weights were required for (model: {model_name}, pretrained: {pretrained}) but not loaded.') + + model.to(device=device) + if precision in ("fp16", "bf16"): + convert_weights_to_lp(model, dtype=torch.bfloat16 if precision == 'bf16' else torch.float16) + + # set image / mean metadata from pretrained_cfg if available, or use default + model.visual.image_mean = pretrained_cfg.get('mean', None) or OPENAI_DATASET_MEAN + model.visual.image_std = pretrained_cfg.get('std', None) or OPENAI_DATASET_STD + + # to always output dict even if it is clip + if output_dict and hasattr(model, "output_dict"): + model.output_dict = True + + if jit: + model = torch.jit.script(model) + + return model + + +def create_loss(args): + if args.distill: + return DistillClipLoss( + local_loss=args.local_loss, + gather_with_grad=args.gather_with_grad, + cache_labels=True, + rank=args.rank, + world_size=args.world_size, + use_horovod=args.horovod, + ) + elif "coca" in args.model.lower(): + return CoCaLoss( + caption_loss_weight=args.coca_caption_loss_weight, + clip_loss_weight=args.coca_contrastive_loss_weight, + local_loss=args.local_loss, + gather_with_grad=args.gather_with_grad, + cache_labels=True, + rank=args.rank, + world_size=args.world_size, + use_horovod=args.horovod, + ) + return ClipLoss( + local_loss=args.local_loss, + gather_with_grad=args.gather_with_grad, + cache_labels=True, + rank=args.rank, + world_size=args.world_size, + use_horovod=args.horovod, + ) + +class MLP(torch.nn.Module): + def __init__(self, input_size): + super().__init__() + self.input_size = input_size + self.layers = torch.nn.Sequential( + torch.nn.Linear(self.input_size, 1024), + torch.nn.Dropout(0.2), + torch.nn.Linear(1024, 128), + torch.nn.Dropout(0.2), + torch.nn.Linear(128, 64), + torch.nn.Dropout(0.1), + torch.nn.Linear(64, 16), + torch.nn.Linear(16, 1) + ) + + def forward(self, x): + return self.layers(x) + +# class semantic_head(torch.nn.Module): +# def __init__(self, input_size): +# super().__init__() +# self.input_size = input_size # for ViT-L-14 is 1024 +# self.seg_head = torch.nn.Sequential( +# torch.nn.Linear(input_size, 128), +# torch.nn.Dropout(0.2), +# torch.nn.Linear(128, 64), +# torch.nn.Dropout(0.1), +# torch.nn.Linear(64, 16), +# torch.nn.Linear(16, 1), +# ) +# self.sigmoid = torch.nn.Sigmoid() + +# def forward(self, x): +# return self.sigmoid(self.seg_head(x)) + +def create_model_and_transforms( + model_name: str, + pretrained: Optional[str] = None, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_text: bool = False, + force_patch_dropout: Optional[float] = None, + force_image_size: Optional[Union[int, Tuple[int, int]]] = None, + pretrained_image: bool = False, + pretrained_hf: bool = True, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None, + cache_dir: Optional[str] = None, + light_augmentation = False, + output_dict: Optional[bool] = None, + with_score_predictor: bool = False, + with_region_predictor: bool = False +): + model = create_model( + model_name, + pretrained, + precision=precision, + device=device, + jit=jit, + force_quick_gelu=force_quick_gelu, + force_custom_text=force_custom_text, + force_patch_dropout=force_patch_dropout, + force_image_size=force_image_size, + pretrained_image=pretrained_image, + pretrained_hf=pretrained_hf, + cache_dir=cache_dir, + output_dict=output_dict, + ) + + image_mean = image_mean or getattr(model.visual, 'image_mean', None) + image_std = image_std or getattr(model.visual, 'image_std', None) + + if with_score_predictor: + model.score_predictor = MLP(model.visual.proj.size(1)).to(device=device, dtype=model.visual.proj.dtype) + + if with_region_predictor: + # model.region_predictor = semantic_head(model.visual.proj.size(1)).to(device=device, dtype=model.visual.proj.dtype) + model.region_predictor = torch.nn.Linear(model.visual.proj.size(0), 1).to(device=device, dtype=model.visual.proj.dtype) + # preprocess_train = image_transform_region( + # model.visual.image_size, + # is_train=True, + # mean=image_mean, + # std=image_std + # ) + # preprocess_val = image_transform_region( + # model.visual.image_size, + # is_train=False, + # mean=image_mean, + # std=image_std + # ) + + if light_augmentation: + preprocess_val = image_transform( + model.visual.image_size, + is_train=False, + mean=image_mean, + std=image_std, + resize_longest_max=True, + ) + preprocess_train = preprocess_val + else: + preprocess_train = image_transform( + model.visual.image_size, + is_train=True, + mean=image_mean, + std=image_std + ) + preprocess_val = image_transform( + model.visual.image_size, + is_train=False, + mean=image_mean, + std=image_std + ) + + return model, preprocess_train, preprocess_val + + +def create_model_from_pretrained( + model_name: str, + pretrained: Optional[str] = None, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_text: bool = False, + force_image_size: Optional[Union[int, Tuple[int, int]]] = None, + return_transform: bool = True, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + cache_dir: Optional[str] = None, +): + model = create_model( + model_name, + pretrained, + precision=precision, + device=device, + jit=jit, + force_quick_gelu=force_quick_gelu, + force_custom_text=force_custom_text, + force_image_size=force_image_size, + cache_dir=cache_dir, + require_pretrained=True, + ) + + if not return_transform: + return model + + image_mean = image_mean or getattr(model.visual, 'image_mean', None) + image_std = image_std or getattr(model.visual, 'image_std', None) + preprocess = image_transform( + model.visual.image_size, + is_train=False, + mean=image_mean, + std=image_std, + ) + + return model, preprocess diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/generation_utils.py b/diffsynth/extensions/ImageQualityMetric/open_clip/generation_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/hf_configs.py b/diffsynth/extensions/ImageQualityMetric/open_clip/hf_configs.py new file mode 100644 index 0000000000000000000000000000000000000000..e236222bafce0358445ea16953ca0b2d5a84758a --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/hf_configs.py @@ -0,0 +1,45 @@ +# HF architecture dict: +arch_dict = { + # https://huggingface.co/docs/transformers/model_doc/roberta#roberta + "roberta": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings" + }, + "pooler": "mean_pooler", + }, + # https://huggingface.co/docs/transformers/model_doc/xlm-roberta#transformers.XLMRobertaConfig + "xlm-roberta": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings" + }, + "pooler": "mean_pooler", + }, + # https://huggingface.co/docs/transformers/model_doc/mt5#mt5 + "mt5": { + "config_names": { + # unlimited seqlen + # https://github.com/google-research/text-to-text-transfer-transformer/issues/273 + # https://github.com/huggingface/transformers/blob/v4.24.0/src/transformers/models/t5/modeling_t5.py#L374 + "context_length": "", + "vocab_size": "vocab_size", + "width": "d_model", + "heads": "num_heads", + "layers": "num_layers", + "layer_attr": "block", + "token_embeddings_attr": "embed_tokens" + }, + "pooler": "mean_pooler", + }, +} diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/hf_model.py b/diffsynth/extensions/ImageQualityMetric/open_clip/hf_model.py new file mode 100644 index 0000000000000000000000000000000000000000..fbccc812757bf10b122ff14096980e0e38d1d221 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/hf_model.py @@ -0,0 +1,176 @@ +""" huggingface model adapter + +Wraps HuggingFace transformers (https://github.com/huggingface/transformers) models for use as a text tower in CLIP model. +""" + +import re + +import torch +import torch.nn as nn +from torch import TensorType + +try: + import transformers + from transformers import AutoModel, AutoTokenizer, AutoConfig, PretrainedConfig + from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, \ + BaseModelOutputWithPoolingAndCrossAttentions +except ImportError as e: + transformers = None + + + class BaseModelOutput: + pass + + + class PretrainedConfig: + pass + +from .hf_configs import arch_dict + + +# utils +def _camel2snake(s): + return re.sub(r'(? torch.Tensor: + # calculated ground-truth and cache if enabled + if self.prev_num_logits != num_logits or device not in self.labels: + labels = torch.arange(num_logits, device=device, dtype=torch.long) + if self.world_size > 1 and self.local_loss: + labels = labels + num_logits * self.rank + if self.cache_labels: + self.labels[device] = labels + self.prev_num_logits = num_logits + else: + labels = self.labels[device] + return labels + + def get_logits(self, image_features, text_features, logit_scale): + if self.world_size > 1: + all_image_features, all_text_features = gather_features( + image_features, text_features, + self.local_loss, self.gather_with_grad, self.rank, self.world_size, self.use_horovod) + + if self.local_loss: + logits_per_image = logit_scale * image_features @ all_text_features.T + logits_per_text = logit_scale * text_features @ all_image_features.T + else: + logits_per_image = logit_scale * all_image_features @ all_text_features.T + logits_per_text = logits_per_image.T + else: + logits_per_image = logit_scale * image_features @ text_features.T + logits_per_text = logit_scale * text_features @ image_features.T + + return logits_per_image, logits_per_text + + def forward(self, image_features, text_features, logit_scale, output_dict=False): + device = image_features.device + logits_per_image, logits_per_text = self.get_logits(image_features, text_features, logit_scale) + + labels = self.get_ground_truth(device, logits_per_image.shape[0]) + + total_loss = ( + F.cross_entropy(logits_per_image, labels) + + F.cross_entropy(logits_per_text, labels) + ) / 2 + return total_loss + +class PreferenceLoss(nn.Module): + + def forward(self, logits_per_image, num_images, labels): + + paired_logits_list = [logit[:,i] for i, logit in enumerate(logits_per_image.split(num_images.tolist()))] + paired_logits = pad_sequence(paired_logits_list, batch_first=True, padding_value=-999) + + ce_loss = F.cross_entropy(paired_logits, labels) + return ce_loss + +class HPSLoss(nn.Module): + + def forward(self, text_logits, labels): + + device = text_logits.device + text_0_logits, text_1_logits = text_logits.chunk(2, dim=-1) + label_0, label_1 = labels.chunk(2, dim=-1) + + index = torch.arange(text_0_logits.shape[0], device=device, dtype=torch.long) + text_0_logits = text_0_logits[index, index] + text_1_logits = text_1_logits[index, index] + text_logits = torch.stack([text_0_logits, text_1_logits], dim=-1) + text_0_labels = torch.zeros(text_logits.shape[0], device=device, dtype=torch.long) + text_1_labels = text_0_labels + 1 + + text_0_loss = torch.nn.functional.cross_entropy(text_logits, text_0_labels, reduction="none") + text_1_loss = torch.nn.functional.cross_entropy(text_logits, text_1_labels, reduction="none") + + text_loss = label_0 * text_0_loss + label_1 * text_1_loss + + # absolute_example_weight = 1 / num_per_prompt + # denominator = absolute_example_weight.sum() + # weight_per_example = absolute_example_weight / denominator + # text_loss *= weight_per_example + + text_loss = text_loss.sum() + return text_loss + +class RankingLoss(nn.Module): + + def forward(self, logits_per_image, num_images, labels, margin = 1.0): + paired_logits_list = [logit[:,i] for i, logit in enumerate(logits_per_image.split(num_images.tolist()))] + label_list = [label for label in labels.split(num_images.tolist())] + # ranked_logits = [torch.index_select(paired_logits_list[i], 0, rank) for i, rank in enumerate(label_list)] + + paired_logits = pad_sequence(paired_logits_list, batch_first=True, padding_value=-1) + padded_labels = pad_sequence(label_list, batch_first=True, padding_value=10) + + # regulized_logits = torch.log(torch.sigmoid(paired_logits)) + + diff = paired_logits.unsqueeze(1) - paired_logits.unsqueeze(2) + # diff = paired_logits.unsqueeze(1) - paired_logits.unsqueeze(2) + # diff_label = torch.clamp(padded_labels.unsqueeze(1) - padded_labels.unsqueeze(2), min=-1, max=1) + diff_label = - (padded_labels.unsqueeze(1) - padded_labels.unsqueeze(2)) + mask = torch.triu(torch.ones(diff.shape[1], diff.shape[1]), diagonal=1).bool().detach() + + loss = torch.clamp(margin - torch.mul(diff[:, ~mask],diff_label[:,~mask]), min=0).mean() + return loss + +class CoCaLoss(ClipLoss): + def __init__( + self, + caption_loss_weight, + clip_loss_weight, + pad_id=0, # pad_token for open_clip custom tokenizer + local_loss=False, + gather_with_grad=False, + cache_labels=False, + rank=0, + world_size=1, + use_horovod=False, + ): + super().__init__( + local_loss=local_loss, + gather_with_grad=gather_with_grad, + cache_labels=cache_labels, + rank=rank, + world_size=world_size, + use_horovod=use_horovod + ) + + self.clip_loss_weight = clip_loss_weight + self.caption_loss_weight = caption_loss_weight + self.caption_loss = nn.CrossEntropyLoss(ignore_index=pad_id) + + def forward(self, image_features, text_features, logits, labels, logit_scale, output_dict=False): + clip_loss = super().forward(image_features, text_features, logit_scale) + clip_loss = self.clip_loss_weight * clip_loss + + caption_loss = self.caption_loss( + logits.permute(0, 2, 1), + labels, + ) + caption_loss = caption_loss * self.caption_loss_weight + + if output_dict: + return {"contrastive_loss": clip_loss, "caption_loss": caption_loss} + + return clip_loss, caption_loss + + +class DistillClipLoss(ClipLoss): + + def dist_loss(self, teacher_logits, student_logits): + return -(teacher_logits.softmax(dim=1) * student_logits.log_softmax(dim=1)).sum(dim=1).mean(dim=0) + + def forward( + self, + image_features, + text_features, + logit_scale, + dist_image_features, + dist_text_features, + dist_logit_scale, + output_dict=False, + ): + logits_per_image, logits_per_text = \ + self.get_logits(image_features, text_features, logit_scale) + + dist_logits_per_image, dist_logits_per_text = \ + self.get_logits(dist_image_features, dist_text_features, dist_logit_scale) + + labels = self.get_ground_truth(image_features.device, logits_per_image.shape[0]) + + contrastive_loss = ( + F.cross_entropy(logits_per_image, labels) + + F.cross_entropy(logits_per_text, labels) + ) / 2 + + distill_loss = ( + self.dist_loss(dist_logits_per_image, logits_per_image) + + self.dist_loss(dist_logits_per_text, logits_per_text) + ) / 2 + + if output_dict: + return {"contrastive_loss": contrastive_loss, "distill_loss": distill_loss} + + return contrastive_loss, distill_loss diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/model.py b/diffsynth/extensions/ImageQualityMetric/open_clip/model.py new file mode 100644 index 0000000000000000000000000000000000000000..e347c42fc8df6464ca28e59adadba61e53a38add --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/model.py @@ -0,0 +1,461 @@ +""" CLIP Model + +Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" +from dataclasses import dataclass +import logging +import math +from typing import Optional, Tuple, Union + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn +from torch.utils.checkpoint import checkpoint + +from .hf_model import HFTextEncoder +from .modified_resnet import ModifiedResNet +from .timm_model import TimmModel +from .transformer import LayerNormFp32, LayerNorm, QuickGELU, Attention, VisionTransformer, TextTransformer +from .utils import to_2tuple + + +@dataclass +class CLIPVisionCfg: + layers: Union[Tuple[int, int, int, int], int] = 12 + width: int = 768 + head_width: int = 64 + mlp_ratio: float = 4.0 + patch_size: int = 16 + image_size: Union[Tuple[int, int], int] = 224 + ls_init_value: Optional[float] = None # layer scale initial value + patch_dropout: float = 0. # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results + input_patchnorm: bool = False # whether to use dual patchnorm - would only apply the input layernorm on each patch, as post-layernorm already exist in original clip vit design + global_average_pool: bool = False # whether to global average pool the last embedding layer, instead of using CLS token (https://arxiv.org/abs/2205.01580) + attentional_pool: bool = False # whether to use attentional pooler in the last embedding layer + n_queries: int = 256 # n_queries for attentional pooler + attn_pooler_heads: int = 8 # n heads for attentional_pooling + timm_model_name: str = None # a valid model name overrides layers, width, patch_size + timm_model_pretrained: bool = False # use (imagenet) pretrained weights for named model + timm_pool: str = 'avg' # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '') + timm_proj: str = 'linear' # linear projection for timm model output ('linear', 'mlp', '') + timm_proj_bias: bool = False # enable bias final projection + timm_drop: float = 0. # head dropout + timm_drop_path: Optional[float] = None # backbone stochastic depth + output_tokens: bool = False + + +@dataclass +class CLIPTextCfg: + context_length: int = 77 + vocab_size: int = 49408 + width: int = 512 + heads: int = 8 + layers: int = 12 + ls_init_value: Optional[float] = None # layer scale initial value + hf_model_name: str = None + hf_tokenizer_name: str = None + hf_model_pretrained: bool = True + proj: str = 'mlp' + pooler_type: str = 'mean_pooler' + embed_cls: bool = False + pad_id: int = 0 + output_tokens: bool = False + + +def get_cast_dtype(precision: str): + cast_dtype = None + if precision == 'bf16': + cast_dtype = torch.bfloat16 + elif precision == 'fp16': + cast_dtype = torch.float16 + return cast_dtype + + +def _build_vision_tower( + embed_dim: int, + vision_cfg: CLIPVisionCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None +): + if isinstance(vision_cfg, dict): + vision_cfg = CLIPVisionCfg(**vision_cfg) + + # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more + # memory efficient in recent PyTorch releases (>= 1.10). + # NOTE: timm models always use native GELU regardless of quick_gelu flag. + act_layer = QuickGELU if quick_gelu else nn.GELU + + if vision_cfg.timm_model_name: + visual = TimmModel( + vision_cfg.timm_model_name, + pretrained=vision_cfg.timm_model_pretrained, + pool=vision_cfg.timm_pool, + proj=vision_cfg.timm_proj, + proj_bias=vision_cfg.timm_proj_bias, + drop=vision_cfg.timm_drop, + drop_path=vision_cfg.timm_drop_path, + embed_dim=embed_dim, + image_size=vision_cfg.image_size, + ) + act_layer = nn.GELU # so that text transformer doesn't use QuickGELU w/ timm models + elif isinstance(vision_cfg.layers, (tuple, list)): + vision_heads = vision_cfg.width * 32 // vision_cfg.head_width + visual = ModifiedResNet( + layers=vision_cfg.layers, + output_dim=embed_dim, + heads=vision_heads, + image_size=vision_cfg.image_size, + width=vision_cfg.width, + ) + else: + vision_heads = vision_cfg.width // vision_cfg.head_width + norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm + visual = VisionTransformer( + image_size=vision_cfg.image_size, + patch_size=vision_cfg.patch_size, + width=vision_cfg.width, + layers=vision_cfg.layers, + heads=vision_heads, + mlp_ratio=vision_cfg.mlp_ratio, + ls_init_value=vision_cfg.ls_init_value, + patch_dropout=vision_cfg.patch_dropout, + input_patchnorm=vision_cfg.input_patchnorm, + global_average_pool=vision_cfg.global_average_pool, + attentional_pool=vision_cfg.attentional_pool, + n_queries=vision_cfg.n_queries, + attn_pooler_heads=vision_cfg.attn_pooler_heads, + output_tokens=vision_cfg.output_tokens, + output_dim=embed_dim, + act_layer=act_layer, + norm_layer=norm_layer, + ) + + return visual + + +def _build_text_tower( + embed_dim: int, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, +): + if isinstance(text_cfg, dict): + text_cfg = CLIPTextCfg(**text_cfg) + + if text_cfg.hf_model_name: + text = HFTextEncoder( + text_cfg.hf_model_name, + output_dim=embed_dim, + proj=text_cfg.proj, + pooler_type=text_cfg.pooler_type, + pretrained=text_cfg.hf_model_pretrained, + output_tokens=text_cfg.output_tokens, + ) + else: + act_layer = QuickGELU if quick_gelu else nn.GELU + norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm + + text = TextTransformer( + context_length=text_cfg.context_length, + vocab_size=text_cfg.vocab_size, + width=text_cfg.width, + heads=text_cfg.heads, + layers=text_cfg.layers, + ls_init_value=text_cfg.ls_init_value, + output_dim=embed_dim, + embed_cls=text_cfg.embed_cls, + output_tokens=text_cfg.output_tokens, + pad_id=text_cfg.pad_id, + act_layer=act_layer, + norm_layer=norm_layer, + ) + return text + + +class CLIP(nn.Module): + output_dict: torch.jit.Final[bool] + + def __init__( + self, + embed_dim: int, + vision_cfg: CLIPVisionCfg, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, + output_dict: bool = False, + ): + super().__init__() + self.output_dict = output_dict + self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype) + + text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype) + self.transformer = text.transformer + self.vocab_size = text.vocab_size + self.token_embedding = text.token_embedding + self.positional_embedding = text.positional_embedding + self.ln_final = text.ln_final + self.text_projection = text.text_projection + self.register_buffer('attn_mask', text.attn_mask, persistent=False) + + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): + # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 + self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats) + + def lock_text_tower(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True): + locked_layers = [] + locked_layers.append(self.token_embedding) + self.positional_embedding.requires_grad = False + if unlocked_layers > 0: + locked_layers.append(self.transformer.resblocks[:-unlocked_layers]) + else: + locked_layers.append(self.transformer) + locked_layers.append(self.ln_final) + self.text_projection.requires_grad = False + + # freeze layers + for module in locked_layers: + for n, p in module.named_parameters(): + p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.visual.set_grad_checkpointing(enable) + self.transformer.grad_checkpointing = enable + + def encode_image(self, image, normalize: bool = False): + features = self.visual(image) + return F.normalize(features, dim=-1) if normalize else features + + def encode_text(self, text, normalize: bool = False): + cast_dtype = self.transformer.get_cast_dtype() + + x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding.to(cast_dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x, attn_mask=self.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x) # [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection + return F.normalize(x, dim=-1) if normalize else x + + def forward(self, image, text): + image_features = self.encode_image(image, normalize=True) + text_features = self.encode_text(text, normalize=True) + if self.output_dict: + return { + "image_features": image_features, + "text_features": text_features, + "logit_scale": self.logit_scale.exp() + } + return image_features, text_features, self.logit_scale.exp() + + +class CustomTextCLIP(nn.Module): + output_dict: torch.jit.Final[bool] + + def __init__( + self, + embed_dim: int, + vision_cfg: CLIPVisionCfg, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, + output_dict: bool = False, + ): + super().__init__() + self.output_dict = output_dict + self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype) + self.text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype) + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): + # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 + self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats) + + def lock_text_tower(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True): + self.text.lock(unlocked_layers, freeze_layer_norm) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.visual.set_grad_checkpointing(enable) + self.text.set_grad_checkpointing(enable) + + def encode_image(self, image, normalize: bool = False): + features = self.visual(image) + return F.normalize(features, dim=-1) if normalize else features + + def encode_text(self, text, normalize: bool = False): + features = self.text(text) + return F.normalize(features, dim=-1) if normalize else features + + def forward(self, image, text): + image_features = self.encode_image(image, normalize=True) + text_features = self.encode_text(text, normalize=True) + if self.output_dict: + return { + "image_features": image_features, + "text_features": text_features, + "logit_scale": self.logit_scale.exp() + } + return image_features, text_features, self.logit_scale.exp() + + +def convert_weights_to_lp(model: nn.Module, dtype=torch.float16): + """Convert applicable model parameters to low-precision (bf16 or fp16)""" + + def _convert_weights(l): + if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): + l.weight.data = l.weight.data.to(dtype) + if l.bias is not None: + l.bias.data = l.bias.data.to(dtype) + + if isinstance(l, (nn.MultiheadAttention, Attention)): + for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: + tensor = getattr(l, attr) + if tensor is not None: + tensor.data = tensor.data.to(dtype) + + for name in ["text_projection", "proj"]: + if hasattr(l, name): + attr = getattr(l, name) + if attr is not None: + attr.data = attr.data.to(dtype) + + model.apply(_convert_weights) + + +convert_weights_to_fp16 = convert_weights_to_lp # backwards compat + + +# used to maintain checkpoint compatibility +def convert_to_custom_text_state_dict(state_dict: dict): + if 'text_projection' in state_dict: + # old format state_dict, move text tower -> .text + new_state_dict = {} + for k, v in state_dict.items(): + if any(k.startswith(p) for p in ( + 'text_projection', + 'positional_embedding', + 'token_embedding', + 'transformer', + 'ln_final', + )): + k = 'text.' + k + new_state_dict[k] = v + return new_state_dict + return state_dict + + +def build_model_from_openai_state_dict( + state_dict: dict, + quick_gelu=True, + cast_dtype=torch.float16, +): + vit = "visual.proj" in state_dict + + if vit: + vision_width = state_dict["visual.conv1.weight"].shape[0] + vision_layers = len( + [k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]) + vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] + grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5) + image_size = vision_patch_size * grid_size + else: + counts: list = [ + len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]] + vision_layers = tuple(counts) + vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] + output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5) + vision_patch_size = None + assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0] + image_size = output_width * 32 + + embed_dim = state_dict["text_projection"].shape[1] + context_length = state_dict["positional_embedding"].shape[0] + vocab_size = state_dict["token_embedding.weight"].shape[0] + transformer_width = state_dict["ln_final.weight"].shape[0] + transformer_heads = transformer_width // 64 + transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks"))) + + vision_cfg = CLIPVisionCfg( + layers=vision_layers, + width=vision_width, + patch_size=vision_patch_size, + image_size=image_size, + ) + text_cfg = CLIPTextCfg( + context_length=context_length, + vocab_size=vocab_size, + width=transformer_width, + heads=transformer_heads, + layers=transformer_layers, + ) + model = CLIP( + embed_dim, + vision_cfg=vision_cfg, + text_cfg=text_cfg, + quick_gelu=quick_gelu, # OpenAI models were trained with QuickGELU + cast_dtype=cast_dtype, + ) + + for key in ["input_resolution", "context_length", "vocab_size"]: + state_dict.pop(key, None) + + convert_weights_to_fp16(model) # OpenAI state dicts are partially converted to float16 + model.load_state_dict(state_dict) + return model.eval() + + +def trace_model(model, batch_size=256, device=torch.device('cpu')): + model.eval() + image_size = model.visual.image_size + example_images = torch.ones((batch_size, 3, image_size, image_size), device=device) + example_text = torch.zeros((batch_size, model.context_length), dtype=torch.int, device=device) + model = torch.jit.trace_module( + model, + inputs=dict( + forward=(example_images, example_text), + encode_text=(example_text,), + encode_image=(example_images,) + )) + model.visual.image_size = image_size + return model + + +def resize_pos_embed(state_dict, model, interpolation: str = 'bicubic', antialias: bool = True): + # Rescale the grid of position embeddings when loading from state_dict + old_pos_embed = state_dict.get('visual.positional_embedding', None) + if old_pos_embed is None or not hasattr(model.visual, 'grid_size'): + return + grid_size = to_2tuple(model.visual.grid_size) + extra_tokens = 1 # FIXME detect different token configs (ie no class token, or more) + new_seq_len = grid_size[0] * grid_size[1] + extra_tokens + if new_seq_len == old_pos_embed.shape[0]: + return + + if extra_tokens: + pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:] + else: + pos_emb_tok, pos_emb_img = None, old_pos_embed + old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img)))) + + logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size) + pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2) + pos_emb_img = F.interpolate( + pos_emb_img, + size=grid_size, + mode=interpolation, + antialias=antialias, + align_corners=False, + ) + pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0] + if pos_emb_tok is not None: + new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0) + else: + new_pos_embed = pos_emb_img + state_dict['visual.positional_embedding'] = new_pos_embed diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/model_configs/ViT-H-14.json b/diffsynth/extensions/ImageQualityMetric/open_clip/model_configs/ViT-H-14.json new file mode 100644 index 0000000000000000000000000000000000000000..3e3a7e934e7f02e41f4829996c4950e05f015a74 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/model_configs/ViT-H-14.json @@ -0,0 +1,17 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 32, + "width": 1280, + "head_width": 80, + "patch_size": 14 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24 + } +} \ No newline at end of file diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/modified_resnet.py b/diffsynth/extensions/ImageQualityMetric/open_clip/modified_resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..6a8d3aeda91ecb394303becbbfccc8acd8cddcd9 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/modified_resnet.py @@ -0,0 +1,181 @@ +from collections import OrderedDict + +import torch +from torch import nn +from torch.nn import functional as F + +from .utils import freeze_batch_norm_2d + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1): + super().__init__() + + # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 + self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.act1 = nn.ReLU(inplace=True) + + self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.act2 = nn.ReLU(inplace=True) + + self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() + + self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.act3 = nn.ReLU(inplace=True) + + self.downsample = None + self.stride = stride + + if stride > 1 or inplanes != planes * Bottleneck.expansion: + # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 + self.downsample = nn.Sequential(OrderedDict([ + ("-1", nn.AvgPool2d(stride)), + ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)), + ("1", nn.BatchNorm2d(planes * self.expansion)) + ])) + + def forward(self, x: torch.Tensor): + identity = x + + out = self.act1(self.bn1(self.conv1(x))) + out = self.act2(self.bn2(self.conv2(out))) + out = self.avgpool(out) + out = self.bn3(self.conv3(out)) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.act3(out) + return out + + +class AttentionPool2d(nn.Module): + def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): + super().__init__() + self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5) + self.k_proj = nn.Linear(embed_dim, embed_dim) + self.q_proj = nn.Linear(embed_dim, embed_dim) + self.v_proj = nn.Linear(embed_dim, embed_dim) + self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) + self.num_heads = num_heads + + def forward(self, x): + x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC + x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC + x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC + x, _ = F.multi_head_attention_forward( + query=x, key=x, value=x, + embed_dim_to_check=x.shape[-1], + num_heads=self.num_heads, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + in_proj_weight=None, + in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), + bias_k=None, + bias_v=None, + add_zero_attn=False, + dropout_p=0., + out_proj_weight=self.c_proj.weight, + out_proj_bias=self.c_proj.bias, + use_separate_proj_weight=True, + training=self.training, + need_weights=False + ) + + return x[0] + + +class ModifiedResNet(nn.Module): + """ + A ResNet class that is similar to torchvision's but contains the following changes: + - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. + - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 + - The final pooling layer is a QKV attention instead of an average pool + """ + + def __init__(self, layers, output_dim, heads, image_size=224, width=64): + super().__init__() + self.output_dim = output_dim + self.image_size = image_size + + # the 3-layer stem + self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(width // 2) + self.act1 = nn.ReLU(inplace=True) + self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(width // 2) + self.act2 = nn.ReLU(inplace=True) + self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False) + self.bn3 = nn.BatchNorm2d(width) + self.act3 = nn.ReLU(inplace=True) + self.avgpool = nn.AvgPool2d(2) + + # residual layers + self._inplanes = width # this is a *mutable* variable used during construction + self.layer1 = self._make_layer(width, layers[0]) + self.layer2 = self._make_layer(width * 2, layers[1], stride=2) + self.layer3 = self._make_layer(width * 4, layers[2], stride=2) + self.layer4 = self._make_layer(width * 8, layers[3], stride=2) + + embed_dim = width * 32 # the ResNet feature dimension + self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim) + + self.init_parameters() + + def _make_layer(self, planes, blocks, stride=1): + layers = [Bottleneck(self._inplanes, planes, stride)] + + self._inplanes = planes * Bottleneck.expansion + for _ in range(1, blocks): + layers.append(Bottleneck(self._inplanes, planes)) + + return nn.Sequential(*layers) + + def init_parameters(self): + if self.attnpool is not None: + std = self.attnpool.c_proj.in_features ** -0.5 + nn.init.normal_(self.attnpool.q_proj.weight, std=std) + nn.init.normal_(self.attnpool.k_proj.weight, std=std) + nn.init.normal_(self.attnpool.v_proj.weight, std=std) + nn.init.normal_(self.attnpool.c_proj.weight, std=std) + + for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]: + for name, param in resnet_block.named_parameters(): + if name.endswith("bn3.weight"): + nn.init.zeros_(param) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + assert unlocked_groups == 0, 'partial locking not currently supported for this model' + for param in self.parameters(): + param.requires_grad = False + if freeze_bn_stats: + freeze_batch_norm_2d(self) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + # FIXME support for non-transformer + pass + + def stem(self, x): + x = self.act1(self.bn1(self.conv1(x))) + x = self.act2(self.bn2(self.conv2(x))) + x = self.act3(self.bn3(self.conv3(x))) + x = self.avgpool(x) + return x + + def forward(self, x): + x = self.stem(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.attnpool(x) + + return x diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/openai.py b/diffsynth/extensions/ImageQualityMetric/open_clip/openai.py new file mode 100644 index 0000000000000000000000000000000000000000..cc4e13e876d6a7a3463b457e62c517cb063b1356 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/openai.py @@ -0,0 +1,144 @@ +""" OpenAI pretrained model functions + +Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" + +import os +import warnings +from typing import List, Optional, Union + +import torch + +from .model import build_model_from_openai_state_dict, convert_weights_to_lp, get_cast_dtype +from .pretrained import get_pretrained_url, list_pretrained_models_by_tag, download_pretrained_from_url + +__all__ = ["list_openai_models", "load_openai_model"] + + +def list_openai_models() -> List[str]: + """Returns the names of available CLIP models""" + return list_pretrained_models_by_tag('openai') + + +def load_openai_model( + name: str, + precision: Optional[str] = None, + device: Optional[Union[str, torch.device]] = None, + jit: bool = True, + cache_dir: Optional[str] = None, +): + """Load a CLIP model + + Parameters + ---------- + name : str + A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict + precision: str + Model precision, if None defaults to 'fp32' if device == 'cpu' else 'fp16'. + device : Union[str, torch.device] + The device to put the loaded model + jit : bool + Whether to load the optimized JIT model (default) or more hackable non-JIT model. + cache_dir : Optional[str] + The directory to cache the downloaded model weights + + Returns + ------- + model : torch.nn.Module + The CLIP model + preprocess : Callable[[PIL.Image], torch.Tensor] + A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input + """ + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + if precision is None: + precision = 'fp32' if device == 'cpu' else 'fp16' + + if get_pretrained_url(name, 'openai'): + model_path = download_pretrained_from_url(get_pretrained_url(name, 'openai'), cache_dir=cache_dir) + elif os.path.isfile(name): + model_path = name + else: + raise RuntimeError(f"Model {name} not found; available models = {list_openai_models()}") + + try: + # loading JIT archive + model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval() + state_dict = None + except RuntimeError: + # loading saved state dict + if jit: + warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead") + jit = False + state_dict = torch.load(model_path, map_location="cpu") + + if not jit: + # Build a non-jit model from the OpenAI jitted model state dict + cast_dtype = get_cast_dtype(precision) + try: + model = build_model_from_openai_state_dict(state_dict or model.state_dict(), cast_dtype=cast_dtype) + except KeyError: + sd = {k[7:]: v for k, v in state_dict["state_dict"].items()} + model = build_model_from_openai_state_dict(sd, cast_dtype=cast_dtype) + + # model from OpenAI state dict is in manually cast fp16 mode, must be converted for AMP/fp32/bf16 use + model = model.to(device) + if precision.startswith('amp') or precision == 'fp32': + model.float() + elif precision == 'bf16': + convert_weights_to_lp(model, dtype=torch.bfloat16) + + return model + + # patch the device names + device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]) + device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1] + + def patch_device(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("prim::Constant"): + if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"): + node.copyAttributes(device_node) + + model.apply(patch_device) + patch_device(model.encode_image) + patch_device(model.encode_text) + + # patch dtype to float32 (typically for CPU) + if precision == 'fp32': + float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[]) + float_input = list(float_holder.graph.findNode("aten::to").inputs())[1] + float_node = float_input.node() + + def patch_float(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("aten::to"): + inputs = list(node.inputs()) + for i in [1, 2]: # dtype can be the second or third argument to aten::to() + if inputs[i].node()["value"] == 5: + inputs[i].node().copyAttributes(float_node) + + model.apply(patch_float) + patch_float(model.encode_image) + patch_float(model.encode_text) + model.float() + + # ensure image_size attr available at consistent location for both jit and non-jit + model.visual.image_size = model.input_resolution.item() + return model diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/pretrained.py b/diffsynth/extensions/ImageQualityMetric/open_clip/pretrained.py new file mode 100644 index 0000000000000000000000000000000000000000..87e7e527497d643fdf6ac931ac73b6e887a90d0d --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/pretrained.py @@ -0,0 +1,376 @@ +import hashlib +import os +import urllib +import warnings +from functools import partial +from typing import Dict, Union + +from tqdm import tqdm + +from .version import __version__ + +try: + from huggingface_hub import hf_hub_download + hf_hub_download = partial(hf_hub_download, library_name="open_clip", library_version=__version__) + _has_hf_hub = True +except ImportError: + hf_hub_download = None + _has_hf_hub = False + + +def _pcfg(url='', hf_hub='', mean=None, std=None): + return dict( + url=url, + hf_hub=hf_hub, + mean=mean, + std=std, + ) + + +_RN50 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt"), + yfcc15m=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt"), + cc12m=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt"), +) + +_RN50_quickgelu = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt"), + yfcc15m=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt"), + cc12m=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt"), +) + +_RN101 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt"), + yfcc15m=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt"), +) + +_RN101_quickgelu = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt"), + yfcc15m=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt"), +) + +_RN50x4 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt"), +) + +_RN50x16 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt"), +) + +_RN50x64 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt"), +) + +_VITB32 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"), + laion2b_e16=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-laion2b_e16-af8dbd0c.pth"), + laion2b_s34b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-laion2B-s34B-b79K/') +) + +_VITB32_quickgelu = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"), +) + +_VITB16 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e31-00efa78f.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e32-55e67d44.pt"), + # laion400m_32k=_pcfg( + # url="", + # mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), + # laion400m_64k=_pcfg( + # url="", + # mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), + laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-B-16-laion2B-s34B-b88K/'), +) + +_VITB16_PLUS_240 = dict( + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e31-8fb26589.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e32-699c4b84.pt"), +) + +_VITL14 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e31-69988bb6.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e32-3d133497.pt"), + laion2b_s32b_b82k=_pcfg( + hf_hub='laion/CLIP-ViT-L-14-laion2B-s32B-b82K/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), +) + +_VITL14_336 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt"), +) + +_VITH14 = dict( + laion2b_s32b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-laion2B-s32B-b79K/'), +) + +_VITg14 = dict( + laion2b_s12b_b42k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s12B-b42K/'), + laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s34B-b88K/'), +) + +_VITbigG14 = dict( + laion2b_s39b_b160k=_pcfg(hf_hub='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/'), +) + +_robertaViTB32 = dict( + laion2b_s12b_b32k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-roberta-base-laion2B-s12B-b32k/'), +) + +_xlmRobertaBaseViTB32 = dict( + laion5b_s13b_b90k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-xlm-roberta-base-laion5B-s13B-b90k/'), +) + +_xlmRobertaLargeFrozenViTH14 = dict( + frozen_laion5b_s13b_b90k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-frozen-xlm-roberta-large-laion5B-s13B-b90k/'), +) + +_convnext_base = dict( + laion400m_s13b_b51k=_pcfg(hf_hub='laion/CLIP-convnext_base-laion400M-s13B-b51K/'), +) + +_convnext_base_w = dict( + laion2b_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion2B-s13B-b82K/'), + laion2b_s13b_b82k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg/'), + laion_aesthetic_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion_aesthetic-s13B-b82K/'), +) + +_convnext_base_w_320 = dict( + laion_aesthetic_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K/'), + laion_aesthetic_s13b_b82k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K-augreg/'), +) + +_convnext_large_d = dict( + laion2b_s26b_b102k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg/'), +) + +_convnext_large_d_320 = dict( + laion2b_s29b_b131k_ft=_pcfg(hf_hub='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft/'), + laion2b_s29b_b131k_ft_soup=_pcfg(hf_hub='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup/'), +) + +_convnext_xxlarge = dict( + laion2b_s34b_b82k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg/'), + laion2b_s34b_b82k_augreg_rewind=_pcfg(hf_hub='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-rewind/'), + laion2b_s34b_b82k_augreg_soup=_pcfg(hf_hub='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup/'), +) + +_coca_VITB32 = dict( + laion2b_s13b_b90k=_pcfg(hf_hub='laion/CoCa-ViT-B-32-laion2B-s13B-b90k/'), + mscoco_finetuned_laion2b_s13b_b90k=_pcfg(hf_hub='laion/mscoco_finetuned_CoCa-ViT-B-32-laion2B-s13B-b90k/') +) + +_coca_VITL14 = dict( + laion2b_s13b_b90k=_pcfg(hf_hub='laion/CoCa-ViT-L-14-laion2B-s13B-b90k/'), + mscoco_finetuned_laion2b_s13b_b90k=_pcfg(hf_hub='laion/mscoco_finetuned_CoCa-ViT-L-14-laion2B-s13B-b90k/') +) + + +_PRETRAINED = { + "RN50": _RN50, + "RN50-quickgelu": _RN50_quickgelu, + "RN101": _RN101, + "RN101-quickgelu": _RN101_quickgelu, + "RN50x4": _RN50x4, + "RN50x16": _RN50x16, + "RN50x64": _RN50x64, + "ViT-B-32": _VITB32, + "ViT-B-32-quickgelu": _VITB32_quickgelu, + "ViT-B-16": _VITB16, + "ViT-B-16-plus-240": _VITB16_PLUS_240, + "ViT-L-14": _VITL14, + "ViT-L-14-336": _VITL14_336, + "ViT-H-14": _VITH14, + "ViT-g-14": _VITg14, + "ViT-bigG-14": _VITbigG14, + "roberta-ViT-B-32": _robertaViTB32, + "xlm-roberta-base-ViT-B-32": _xlmRobertaBaseViTB32, + "xlm-roberta-large-ViT-H-14": _xlmRobertaLargeFrozenViTH14, + "convnext_base": _convnext_base, + "convnext_base_w": _convnext_base_w, + "convnext_base_w_320": _convnext_base_w_320, + "convnext_large_d": _convnext_large_d, + "convnext_large_d_320": _convnext_large_d_320, + "convnext_xxlarge": _convnext_xxlarge, + "coca_ViT-B-32": _coca_VITB32, + "coca_ViT-L-14": _coca_VITL14, +} + + +def _clean_tag(tag: str): + # normalize pretrained tags + return tag.lower().replace('-', '_') + + +def list_pretrained(as_str: bool = False): + """ returns list of pretrained models + Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True + """ + return [':'.join([k, t]) if as_str else (k, t) for k in _PRETRAINED.keys() for t in _PRETRAINED[k].keys()] + + +def list_pretrained_models_by_tag(tag: str): + """ return all models having the specified pretrain tag """ + models = [] + tag = _clean_tag(tag) + for k in _PRETRAINED.keys(): + if tag in _PRETRAINED[k]: + models.append(k) + return models + + +def list_pretrained_tags_by_model(model: str): + """ return all pretrain tags for the specified model architecture """ + tags = [] + if model in _PRETRAINED: + tags.extend(_PRETRAINED[model].keys()) + return tags + + +def is_pretrained_cfg(model: str, tag: str): + if model not in _PRETRAINED: + return False + return _clean_tag(tag) in _PRETRAINED[model] + + +def get_pretrained_cfg(model: str, tag: str): + if model not in _PRETRAINED: + return {} + model_pretrained = _PRETRAINED[model] + return model_pretrained.get(_clean_tag(tag), {}) + + +def get_pretrained_url(model: str, tag: str): + cfg = get_pretrained_cfg(model, _clean_tag(tag)) + return cfg.get('url', '') + + +def download_pretrained_from_url( + url: str, + cache_dir: Union[str, None] = None, +): + if not cache_dir: + cache_dir = os.path.expanduser("~/.cache/clip") + os.makedirs(cache_dir, exist_ok=True) + filename = os.path.basename(url) + + if 'openaipublic' in url: + expected_sha256 = url.split("/")[-2] + elif 'mlfoundations' in url: + expected_sha256 = os.path.splitext(filename)[0].split("-")[-1] + else: + expected_sha256 = '' + + download_target = os.path.join(cache_dir, filename) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + if expected_sha256: + if hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256): + return download_target + else: + warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") + else: + return download_target + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm(total=int(source.headers.get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + if expected_sha256 and not hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256): + raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match") + + return download_target + + +def has_hf_hub(necessary=False): + if not _has_hf_hub and necessary: + # if no HF Hub module installed, and it is necessary to continue, raise error + raise RuntimeError( + 'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.') + return _has_hf_hub + + +def download_pretrained_from_hf( + model_id: str, + filename: str = 'open_clip_pytorch_model.bin', + revision=None, + cache_dir: Union[str, None] = None, +): + has_hf_hub(True) + cached_file = hf_hub_download(model_id, filename, revision=revision, cache_dir=cache_dir) + return cached_file + + +def download_pretrained( + cfg: Dict, + force_hf_hub: bool = False, + cache_dir: Union[str, None] = None, +): + target = '' + if not cfg: + return target + + download_url = cfg.get('url', '') + download_hf_hub = cfg.get('hf_hub', '') + if download_hf_hub and force_hf_hub: + # use HF hub even if url exists + download_url = '' + + if download_url: + target = download_pretrained_from_url(download_url, cache_dir=cache_dir) + elif download_hf_hub: + has_hf_hub(True) + # we assume the hf_hub entries in pretrained config combine model_id + filename in + # 'org/model_name/filename.pt' form. To specify just the model id w/o filename and + # use 'open_clip_pytorch_model.bin' default, there must be a trailing slash 'org/model_name/'. + model_id, filename = os.path.split(download_hf_hub) + if filename: + target = download_pretrained_from_hf(model_id, filename=filename, cache_dir=cache_dir) + else: + target = download_pretrained_from_hf(model_id, cache_dir=cache_dir) + + return target diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/push_to_hf_hub.py b/diffsynth/extensions/ImageQualityMetric/open_clip/push_to_hf_hub.py new file mode 100644 index 0000000000000000000000000000000000000000..23c0631c81dcb43829b7374fac09406ecefcb436 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/push_to_hf_hub.py @@ -0,0 +1,243 @@ +import argparse +import json +from pathlib import Path +from tempfile import TemporaryDirectory +from typing import Optional, Tuple + +import torch + +try: + from huggingface_hub import ( + create_repo, + get_hf_file_metadata, + hf_hub_download, + hf_hub_url, + repo_type_and_id_from_hf_id, + upload_folder, + ) + from huggingface_hub.utils import EntryNotFoundError + _has_hf_hub = True +except ImportError: + _has_hf_hub = False + +from .factory import create_model_from_pretrained, get_model_config, get_tokenizer +from .tokenizer import HFTokenizer + + +def save_config_for_hf( + model, + config_path: str, + model_config: Optional[dict] +): + preprocess_cfg = { + 'mean': model.visual.image_mean, + 'std': model.visual.image_std, + } + hf_config = { + 'model_cfg': model_config, + 'preprocess_cfg': preprocess_cfg, + } + + with config_path.open('w') as f: + json.dump(hf_config, f, indent=2) + + +def save_for_hf( + model, + tokenizer: HFTokenizer, + model_config: dict, + save_directory: str, + weights_filename='open_clip_pytorch_model.bin', + config_filename='open_clip_config.json', +): + save_directory = Path(save_directory) + save_directory.mkdir(exist_ok=True, parents=True) + + weights_path = save_directory / weights_filename + torch.save(model.state_dict(), weights_path) + + tokenizer.save_pretrained(save_directory) + + config_path = save_directory / config_filename + save_config_for_hf(model, config_path, model_config=model_config) + + +def push_to_hf_hub( + model, + tokenizer, + model_config: Optional[dict], + repo_id: str, + commit_message: str = 'Add model', + token: Optional[str] = None, + revision: Optional[str] = None, + private: bool = False, + create_pr: bool = False, + model_card: Optional[dict] = None, +): + if not isinstance(tokenizer, HFTokenizer): + # default CLIP tokenizers use https://huggingface.co/openai/clip-vit-large-patch14 + tokenizer = HFTokenizer('openai/clip-vit-large-patch14') + + # Create repo if it doesn't exist yet + repo_url = create_repo(repo_id, token=token, private=private, exist_ok=True) + + # Infer complete repo_id from repo_url + # Can be different from the input `repo_id` if repo_owner was implicit + _, repo_owner, repo_name = repo_type_and_id_from_hf_id(repo_url) + repo_id = f"{repo_owner}/{repo_name}" + + # Check if README file already exist in repo + try: + get_hf_file_metadata(hf_hub_url(repo_id=repo_id, filename="README.md", revision=revision)) + has_readme = True + except EntryNotFoundError: + has_readme = False + + # Dump model and push to Hub + with TemporaryDirectory() as tmpdir: + # Save model weights and config. + save_for_hf( + model, + tokenizer=tokenizer, + model_config=model_config, + save_directory=tmpdir, + ) + + # Add readme if it does not exist + if not has_readme: + model_card = model_card or {} + model_name = repo_id.split('/')[-1] + readme_path = Path(tmpdir) / "README.md" + readme_text = generate_readme(model_card, model_name) + readme_path.write_text(readme_text) + + # Upload model and return + return upload_folder( + repo_id=repo_id, + folder_path=tmpdir, + revision=revision, + create_pr=create_pr, + commit_message=commit_message, + ) + + +def push_pretrained_to_hf_hub( + model_name, + pretrained: str, + repo_id: str, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + commit_message: str = 'Add model', + token: Optional[str] = None, + revision: Optional[str] = None, + private: bool = False, + create_pr: bool = False, + model_card: Optional[dict] = None, +): + model, preprocess_eval = create_model_from_pretrained( + model_name, + pretrained=pretrained, + image_mean=image_mean, + image_std=image_std, + ) + + model_config = get_model_config(model_name) + assert model_config + + tokenizer = get_tokenizer(model_name) + + push_to_hf_hub( + model=model, + tokenizer=tokenizer, + model_config=model_config, + repo_id=repo_id, + commit_message=commit_message, + token=token, + revision=revision, + private=private, + create_pr=create_pr, + model_card=model_card, + ) + + +def generate_readme(model_card: dict, model_name: str): + readme_text = "---\n" + readme_text += "tags:\n- zero-shot-image-classification\n- clip\n" + readme_text += "library_tag: open_clip\n" + readme_text += f"license: {model_card.get('license', 'mit')}\n" + if 'details' in model_card and 'Dataset' in model_card['details']: + readme_text += 'datasets:\n' + readme_text += f"- {model_card['details']['Dataset'].lower()}\n" + readme_text += "---\n" + readme_text += f"# Model card for {model_name}\n" + if 'description' in model_card: + readme_text += f"\n{model_card['description']}\n" + if 'details' in model_card: + readme_text += f"\n## Model Details\n" + for k, v in model_card['details'].items(): + if isinstance(v, (list, tuple)): + readme_text += f"- **{k}:**\n" + for vi in v: + readme_text += f" - {vi}\n" + elif isinstance(v, dict): + readme_text += f"- **{k}:**\n" + for ki, vi in v.items(): + readme_text += f" - {ki}: {vi}\n" + else: + readme_text += f"- **{k}:** {v}\n" + if 'usage' in model_card: + readme_text += f"\n## Model Usage\n" + readme_text += model_card['usage'] + readme_text += '\n' + + if 'comparison' in model_card: + readme_text += f"\n## Model Comparison\n" + readme_text += model_card['comparison'] + readme_text += '\n' + + if 'citation' in model_card: + readme_text += f"\n## Citation\n" + if not isinstance(model_card['citation'], (list, tuple)): + citations = [model_card['citation']] + else: + citations = model_card['citation'] + for c in citations: + readme_text += f"```bibtex\n{c}\n```\n" + + return readme_text + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Push to Hugging Face Hub") + parser.add_argument( + "--model", type=str, help="Name of the model to use.", + ) + parser.add_argument( + "--pretrained", type=str, + help="Use a pretrained CLIP model weights with the specified tag or file path.", + ) + parser.add_argument( + "--repo-id", type=str, + help="Destination HF Hub repo-id ie 'organization/model_id'.", + ) + parser.add_argument( + '--image-mean', type=float, nargs='+', default=None, metavar='MEAN', + help='Override default image mean value of dataset') + parser.add_argument( + '--image-std', type=float, nargs='+', default=None, metavar='STD', + help='Override default image std deviation of of dataset') + args = parser.parse_args() + + print(f'Saving model {args.model} with pretrained weights {args.pretrained} to Hugging Face Hub at {args.repo_id}') + + # FIXME add support to pass model_card json / template from file via cmd line + + push_pretrained_to_hf_hub( + args.model, + args.pretrained, + args.repo_id, + image_mean=args.image_mean, # override image mean/std if trained w/ non defaults + image_std=args.image_std, + ) + + print(f'{args.model} saved.') diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/timm_model.py b/diffsynth/extensions/ImageQualityMetric/open_clip/timm_model.py new file mode 100644 index 0000000000000000000000000000000000000000..dc71a693f9a42ec01fd88d307661bc382b4d05bc --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/timm_model.py @@ -0,0 +1,127 @@ +""" timm model adapter + +Wraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model. +""" +import logging +from collections import OrderedDict + +import torch +import torch.nn as nn + +try: + import timm + from timm.models.layers import Mlp, to_2tuple + try: + # old timm imports < 0.8.1 + from timm.models.layers.attention_pool2d import RotAttentionPool2d + from timm.models.layers.attention_pool2d import AttentionPool2d as AbsAttentionPool2d + except ImportError: + # new timm imports >= 0.8.1 + from timm.layers import RotAttentionPool2d + from timm.layers import AttentionPool2d as AbsAttentionPool2d +except ImportError: + timm = None + +from .utils import freeze_batch_norm_2d + + +class TimmModel(nn.Module): + """ timm model adapter + # FIXME this adapter is a work in progress, may change in ways that break weight compat + """ + + def __init__( + self, + model_name, + embed_dim, + image_size=224, + pool='avg', + proj='linear', + proj_bias=False, + drop=0., + drop_path=None, + pretrained=False, + ): + super().__init__() + if timm is None: + raise RuntimeError("Please `pip install timm` to use timm models.") + + self.image_size = to_2tuple(image_size) + timm_kwargs = {} + if drop_path is not None: + timm_kwargs['drop_path_rate'] = drop_path + self.trunk = timm.create_model(model_name, pretrained=pretrained, **timm_kwargs) + feat_size = self.trunk.default_cfg.get('pool_size', None) + feature_ndim = 1 if not feat_size else 2 + if pool in ('abs_attn', 'rot_attn'): + assert feature_ndim == 2 + # if attn pooling used, remove both classifier and default pool + self.trunk.reset_classifier(0, global_pool='') + else: + # reset global pool if pool config set, otherwise leave as network default + reset_kwargs = dict(global_pool=pool) if pool else {} + self.trunk.reset_classifier(0, **reset_kwargs) + prev_chs = self.trunk.num_features + + head_layers = OrderedDict() + if pool == 'abs_attn': + head_layers['pool'] = AbsAttentionPool2d(prev_chs, feat_size=feat_size, out_features=embed_dim) + prev_chs = embed_dim + elif pool == 'rot_attn': + head_layers['pool'] = RotAttentionPool2d(prev_chs, out_features=embed_dim) + prev_chs = embed_dim + else: + assert proj, 'projection layer needed if non-attention pooling is used.' + + # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used + if proj == 'linear': + head_layers['drop'] = nn.Dropout(drop) + head_layers['proj'] = nn.Linear(prev_chs, embed_dim, bias=proj_bias) + elif proj == 'mlp': + head_layers['mlp'] = Mlp(prev_chs, 2 * embed_dim, embed_dim, drop=(drop, 0), bias=(True, proj_bias)) + + self.head = nn.Sequential(head_layers) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + """ lock modules + Args: + unlocked_groups (int): leave last n layer groups unlocked (default: 0) + """ + if not unlocked_groups: + # lock full model + for param in self.trunk.parameters(): + param.requires_grad = False + if freeze_bn_stats: + freeze_batch_norm_2d(self.trunk) + else: + # NOTE: partial freeze requires latest timm (master) branch and is subject to change + try: + # FIXME import here until API stable and in an official release + from timm.models.helpers import group_parameters, group_modules + except ImportError: + raise RuntimeError( + 'Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`') + matcher = self.trunk.group_matcher() + gparams = group_parameters(self.trunk, matcher) + max_layer_id = max(gparams.keys()) + max_layer_id = max_layer_id - unlocked_groups + for group_idx in range(max_layer_id + 1): + group = gparams[group_idx] + for param in group: + self.trunk.get_parameter(param).requires_grad = False + if freeze_bn_stats: + gmodules = group_modules(self.trunk, matcher, reverse=True) + gmodules = {k for k, v in gmodules.items() if v <= max_layer_id} + freeze_batch_norm_2d(self.trunk, gmodules) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + try: + self.trunk.set_grad_checkpointing(enable) + except Exception as e: + logging.warning('grad checkpointing not supported for this timm image tower, continuing without...') + + def forward(self, x): + x = self.trunk(x) + x = self.head(x) + return x diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/tokenizer.py b/diffsynth/extensions/ImageQualityMetric/open_clip/tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..22ec4880b13ec73594d5c19b3d3be83aadb55aba --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/tokenizer.py @@ -0,0 +1,211 @@ +""" CLIP tokenizer + +Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" +import gzip +import html +import os +from functools import lru_cache +from typing import Union, List + +import ftfy +import regex as re +import torch + +# https://stackoverflow.com/q/62691279 +import os +os.environ["TOKENIZERS_PARALLELISM"] = "false" + + +@lru_cache() +def default_bpe(): + current_dir = os.path.dirname(os.path.abspath(__file__)) + project_root = os.path.abspath(os.path.join(current_dir, '../../../../')) + quality_metric_path = os.path.join(project_root, 'models', 'QualityMetric') + return os.path.join(quality_metric_path, "bpe_simple_vocab_16e6.txt.gz") + + +@lru_cache() +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a significant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1)) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8+n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +def get_pairs(word): + """Return set of symbol pairs in a word. + Word is represented as tuple of symbols (symbols being variable-length strings). + """ + pairs = set() + prev_char = word[0] + for char in word[1:]: + pairs.add((prev_char, char)) + prev_char = char + return pairs + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r'\s+', ' ', text) + text = text.strip() + return text + + +class SimpleTokenizer(object): + def __init__(self, bpe_path: str = default_bpe(), special_tokens=None): + self.byte_encoder = bytes_to_unicode() + self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} + merges = gzip.open(bpe_path).read().decode("utf-8").split('\n') + merges = merges[1:49152-256-2+1] + merges = [tuple(merge.split()) for merge in merges] + vocab = list(bytes_to_unicode().values()) + vocab = vocab + [v+'' for v in vocab] + for merge in merges: + vocab.append(''.join(merge)) + if not special_tokens: + special_tokens = ['', ''] + else: + special_tokens = ['', ''] + special_tokens + vocab.extend(special_tokens) + self.encoder = dict(zip(vocab, range(len(vocab)))) + self.decoder = {v: k for k, v in self.encoder.items()} + self.bpe_ranks = dict(zip(merges, range(len(merges)))) + self.cache = {t:t for t in special_tokens} + special = "|".join(special_tokens) + self.pat = re.compile(special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE) + + self.vocab_size = len(self.encoder) + self.all_special_ids = [self.encoder[t] for t in special_tokens] + + def bpe(self, token): + if token in self.cache: + return self.cache[token] + word = tuple(token[:-1]) + ( token[-1] + '',) + pairs = get_pairs(word) + + if not pairs: + return token+'' + + while True: + bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf'))) + if bigram not in self.bpe_ranks: + break + first, second = bigram + new_word = [] + i = 0 + while i < len(word): + try: + j = word.index(first, i) + new_word.extend(word[i:j]) + i = j + except: + new_word.extend(word[i:]) + break + + if word[i] == first and i < len(word)-1 and word[i+1] == second: + new_word.append(first+second) + i += 2 + else: + new_word.append(word[i]) + i += 1 + new_word = tuple(new_word) + word = new_word + if len(word) == 1: + break + else: + pairs = get_pairs(word) + word = ' '.join(word) + self.cache[token] = word + return word + + def encode(self, text): + bpe_tokens = [] + text = whitespace_clean(basic_clean(text)).lower() + for token in re.findall(self.pat, text): + token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) + bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) + return bpe_tokens + + def decode(self, tokens): + text = ''.join([self.decoder[token] for token in tokens]) + text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('', ' ') + return text + + def __call__(self, texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor: + """ + Returns the tokenized representation of given input string(s) + + Parameters + ---------- + texts : Union[str, List[str]] + An input string or a list of input strings to tokenize + context_length : int + The context length to use; all CLIP models use 77 as the context length + + Returns + ------- + A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] + """ + if isinstance(texts, str): + texts = [texts] + + sot_token = self.encoder[""] + eot_token = self.encoder[""] + all_tokens = [[sot_token] + self.encode(text) + [eot_token] for text in texts] + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + + for i, tokens in enumerate(all_tokens): + if len(tokens) > context_length: + tokens = tokens[:context_length] # Truncate + tokens[-1] = eot_token + result[i, :len(tokens)] = torch.tensor(tokens) + + return result + + + +class HFTokenizer: + """HuggingFace tokenizer wrapper""" + + def __init__(self, tokenizer_name: str): + from transformers import AutoTokenizer + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) + + def save_pretrained(self, dest): + self.tokenizer.save_pretrained(dest) + + def __call__(self, texts: Union[str, List[str]], context_length: int = 77) -> torch.Tensor: + # same cleaning as for default tokenizer, except lowercasing + # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance + if isinstance(texts, str): + texts = [texts] + texts = [whitespace_clean(basic_clean(text)) for text in texts] + input_ids = self.tokenizer( + texts, + return_tensors='pt', + max_length=context_length, + padding='max_length', + truncation=True, + ).input_ids + return input_ids diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/transform.py b/diffsynth/extensions/ImageQualityMetric/open_clip/transform.py new file mode 100644 index 0000000000000000000000000000000000000000..fe4e21fa5b515f2412049f9274bd06fbe77fb9b9 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/transform.py @@ -0,0 +1,216 @@ +import warnings +from dataclasses import dataclass, asdict +from typing import Any, Dict, Optional, Sequence, Tuple, Union + +import torch +import torch.nn as nn +import torchvision.transforms.functional as F +from functools import partial +from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \ + CenterCrop + +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD + + +@dataclass +class AugmentationCfg: + scale: Tuple[float, float] = (0.9, 1.0) + ratio: Optional[Tuple[float, float]] = None + color_jitter: Optional[Union[float, Tuple[float, float, float]]] = None + interpolation: Optional[str] = None + re_prob: Optional[float] = None + re_count: Optional[int] = None + use_timm: bool = False + + +class ResizeMaxSize(nn.Module): + + def __init__(self, max_size, interpolation=InterpolationMode.BICUBIC, fn='max', fill=0): + super().__init__() + if not isinstance(max_size, int): + raise TypeError(f"Size should be int. Got {type(max_size)}") + self.max_size = max_size + self.interpolation = interpolation + self.fn = min if fn == 'min' else min + self.fill = fill + + def forward(self, img): + if isinstance(img, torch.Tensor): + height, width = img.shape[1:] + else: + width, height = img.size + scale = self.max_size / float(max(height, width)) + if scale != 1.0: + new_size = tuple(round(dim * scale) for dim in (height, width)) + img = F.resize(img, new_size, self.interpolation) + pad_h = self.max_size - new_size[0] + pad_w = self.max_size - new_size[1] + img = F.pad(img, padding=[pad_w//2, pad_h//2, pad_w - pad_w//2, pad_h - pad_h//2], fill=self.fill) + return img + + +def _convert_to_rgb_or_rgba(image): + if image.mode == 'RGBA': + return image + else: + return image.convert('RGB') + +# def transform_and_split(merged, transform_fn, normalize_fn): +# transformed = transform_fn(merged) +# crop_img, crop_label = torch.split(transformed, [3,1], dim=0) + +# # crop_img = _convert_to_rgb(crop_img) +# crop_img = normalize_fn(ToTensor()(crop_img)) +# return crop_img, crop_label + +class MaskAwareNormalize(nn.Module): + def __init__(self, mean, std): + super().__init__() + self.normalize = Normalize(mean=mean, std=std) + + def forward(self, tensor): + if tensor.shape[0] == 4: + return torch.cat([self.normalize(tensor[:3]), tensor[3:]], dim=0) + else: + return self.normalize(tensor) + +def image_transform( + image_size: int, + is_train: bool, + mean: Optional[Tuple[float, ...]] = None, + std: Optional[Tuple[float, ...]] = None, + resize_longest_max: bool = False, + fill_color: int = 0, + aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None, +): + mean = mean or OPENAI_DATASET_MEAN + if not isinstance(mean, (list, tuple)): + mean = (mean,) * 3 + + std = std or OPENAI_DATASET_STD + if not isinstance(std, (list, tuple)): + std = (std,) * 3 + + if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]: + # for square size, pass size as int so that Resize() uses aspect preserving shortest edge + image_size = image_size[0] + + if isinstance(aug_cfg, dict): + aug_cfg = AugmentationCfg(**aug_cfg) + else: + aug_cfg = aug_cfg or AugmentationCfg() + normalize = MaskAwareNormalize(mean=mean, std=std) + if is_train: + aug_cfg_dict = {k: v for k, v in asdict(aug_cfg).items() if v is not None} + use_timm = aug_cfg_dict.pop('use_timm', False) + if use_timm: + assert False, "not tested for augmentation with mask" + from timm.data import create_transform # timm can still be optional + if isinstance(image_size, (tuple, list)): + assert len(image_size) >= 2 + input_size = (3,) + image_size[-2:] + else: + input_size = (3, image_size, image_size) + # by default, timm aug randomly alternates bicubic & bilinear for better robustness at inference time + aug_cfg_dict.setdefault('interpolation', 'random') + aug_cfg_dict.setdefault('color_jitter', None) # disable by default + train_transform = create_transform( + input_size=input_size, + is_training=True, + hflip=0., + mean=mean, + std=std, + re_mode='pixel', + **aug_cfg_dict, + ) + else: + train_transform = Compose([ + _convert_to_rgb_or_rgba, + ToTensor(), + RandomResizedCrop( + image_size, + scale=aug_cfg_dict.pop('scale'), + interpolation=InterpolationMode.BICUBIC, + ), + normalize, + ]) + if aug_cfg_dict: + warnings.warn(f'Unused augmentation cfg items, specify `use_timm` to use ({list(aug_cfg_dict.keys())}).') + return train_transform + else: + transforms = [ + _convert_to_rgb_or_rgba, + ToTensor(), + ] + if resize_longest_max: + transforms.extend([ + ResizeMaxSize(image_size, fill=fill_color) + ]) + else: + transforms.extend([ + Resize(image_size, interpolation=InterpolationMode.BICUBIC), + CenterCrop(image_size), + ]) + transforms.extend([ + normalize, + ]) + return Compose(transforms) + + +# def image_transform_region( +# image_size: int, +# is_train: bool, +# mean: Optional[Tuple[float, ...]] = None, +# std: Optional[Tuple[float, ...]] = None, +# resize_longest_max: bool = False, +# fill_color: int = 0, +# aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None, +# ): +# mean = mean or OPENAI_DATASET_MEAN +# if not isinstance(mean, (list, tuple)): +# mean = (mean,) * 3 + +# std = std or OPENAI_DATASET_STD +# if not isinstance(std, (list, tuple)): +# std = (std,) * 3 + +# if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]: +# # for square size, pass size as int so that Resize() uses aspect preserving shortest edge +# image_size = image_size[0] + +# if isinstance(aug_cfg, dict): +# aug_cfg = AugmentationCfg(**aug_cfg) +# else: +# aug_cfg = aug_cfg or AugmentationCfg() +# normalize = Normalize(mean=mean, std=std) +# if is_train: +# aug_cfg_dict = {k: v for k, v in asdict(aug_cfg).items() if v is not None} + +# transform = Compose([ +# RandomResizedCrop( +# image_size, +# scale=aug_cfg_dict.pop('scale'), +# interpolation=InterpolationMode.BICUBIC, +# ), +# ]) +# train_transform = Compose([ +# partial(transform_and_split, transform_fn=transform,normalize_fn=normalize) +# ]) +# return train_transform +# else: +# if resize_longest_max: +# transform = [ +# ResizeMaxSize(image_size, fill=fill_color) +# ] +# val_transform = Compose([ +# partial(transform_and_split, transform_fn=transform,normalize_fn=normalize), +# ]) +# else: +# transform = [ +# Resize(image_size, interpolation=InterpolationMode.BICUBIC), +# CenterCrop(image_size), +# ] +# val_transform = Compose([ +# partial(transform_and_split, transform_fn=transform,normalize_fn=normalize), +# ]) +# return val_transform \ No newline at end of file diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/transformer.py b/diffsynth/extensions/ImageQualityMetric/open_clip/transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..7465c1b20bf388a17e0f4f80f7b8eee3b564af92 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/transformer.py @@ -0,0 +1,727 @@ +from collections import OrderedDict +import math +from typing import Callable, Optional, Sequence, Tuple + +import torch +from torch import nn +from torch.nn import functional as F +from torch.utils.checkpoint import checkpoint + +from .utils import to_2tuple + + +class LayerNormFp32(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back).""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + x = F.layer_norm(x.to(torch.float32), self.normalized_shape, self.weight, self.bias, self.eps) + return x.to(orig_type) + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm (with cast back to input dtype).""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + return x.to(orig_type) + + +class QuickGELU(nn.Module): + # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class LayerScale(nn.Module): + def __init__(self, dim, init_values=1e-5, inplace=False): + super().__init__() + self.inplace = inplace + self.gamma = nn.Parameter(init_values * torch.ones(dim)) + + def forward(self, x): + return x.mul_(self.gamma) if self.inplace else x * self.gamma + + +class PatchDropout(nn.Module): + """ + https://arxiv.org/abs/2212.00794 + """ + + def __init__(self, prob, exclude_first_token=True): + super().__init__() + assert 0 <= prob < 1. + self.prob = prob + self.exclude_first_token = exclude_first_token # exclude CLS token + + def forward(self, x): + if not self.training or self.prob == 0.: + return x + + if self.exclude_first_token: + cls_tokens, x = x[:, :1], x[:, 1:] + else: + cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1]) + + batch = x.size()[0] + num_tokens = x.size()[1] + + batch_indices = torch.arange(batch) + batch_indices = batch_indices[..., None] + + keep_prob = 1 - self.prob + num_patches_keep = max(1, int(num_tokens * keep_prob)) + + rand = torch.randn(batch, num_tokens) + patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices + + x = x[batch_indices, patch_indices_keep] + + if self.exclude_first_token: + x = torch.cat((cls_tokens, x), dim=1) + + return x + + +class Attention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + scaled_cosine=False, + scale_heads=False, + logit_scale_max=math.log(1. / 0.01), + attn_drop=0., + proj_drop=0. + ): + super().__init__() + self.scaled_cosine = scaled_cosine + self.scale_heads = scale_heads + assert dim % num_heads == 0, 'dim should be divisible by num_heads' + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim ** -0.5 + self.logit_scale_max = logit_scale_max + + # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original + self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale) + if qkv_bias: + self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3)) + else: + self.in_proj_bias = None + + if self.scaled_cosine: + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1)))) + else: + self.logit_scale = None + self.attn_drop = nn.Dropout(attn_drop) + if self.scale_heads: + self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1))) + else: + self.head_scale = None + self.out_proj = nn.Linear(dim, dim) + self.out_drop = nn.Dropout(proj_drop) + + def forward(self, x, attn_mask: Optional[torch.Tensor] = None): + L, N, C = x.shape + q, k, v = F.linear(x, self.in_proj_weight, self.in_proj_bias).chunk(3, dim=-1) + q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + + if self.logit_scale is not None: + attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2)) + logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp() + attn = attn.view(N, self.num_heads, L, L) * logit_scale + attn = attn.view(-1, L, L) + else: + q = q * self.scale + attn = torch.bmm(q, k.transpose(-1, -2)) + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype) + new_attn_mask.masked_fill_(attn_mask, float("-inf")) + attn_mask = new_attn_mask + attn += attn_mask + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = torch.bmm(attn, v) + if self.head_scale is not None: + x = x.view(N, self.num_heads, L, C) * self.head_scale + x = x.view(-1, L, C) + x = x.transpose(0, 1).reshape(L, N, C) + x = self.out_proj(x) + x = self.out_drop(x) + return x + + +class AttentionalPooler(nn.Module): + def __init__( + self, + d_model: int, + context_dim: int, + n_head: int = 8, + n_queries: int = 256, + norm_layer: Callable = LayerNorm + ): + super().__init__() + self.query = nn.Parameter(torch.randn(n_queries, d_model)) + self.attn = nn.MultiheadAttention(d_model, n_head, kdim=context_dim, vdim=context_dim) + self.ln_q = norm_layer(d_model) + self.ln_k = norm_layer(context_dim) + + def forward(self, x: torch.Tensor): + x = self.ln_k(x).permute(1, 0, 2) # NLD -> LND + N = x.shape[1] + q = self.ln_q(self.query) + out = self.attn(self._repeat(q, N), x, x, need_weights=False)[0] + return out.permute(1, 0, 2) # LND -> NLD + + def _repeat(self, query, N: int): + return query.unsqueeze(1).repeat(1, N, 1) + + +class ResidualAttentionBlock(nn.Module): + def __init__( + self, + d_model: int, + n_head: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + is_cross_attention: bool = False, + ): + super().__init__() + + self.ln_1 = norm_layer(d_model) + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + if is_cross_attention: + self.ln_1_kv = norm_layer(d_model) + + self.ln_2 = norm_layer(d_model) + mlp_width = int(d_model * mlp_ratio) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, mlp_width)), + ("gelu", act_layer()), + ("c_proj", nn.Linear(mlp_width, d_model)) + ])) + self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + def attention( + self, + q_x: torch.Tensor, + k_x: Optional[torch.Tensor] = None, + v_x: Optional[torch.Tensor] = None, + attn_mask: Optional[torch.Tensor] = None, + ): + k_x = k_x if k_x is not None else q_x + v_x = v_x if v_x is not None else q_x + + attn_mask = attn_mask.to(q_x.dtype) if attn_mask is not None else None + return self.attn( + q_x, k_x, v_x, need_weights=False, attn_mask=attn_mask + )[0] + + def forward( + self, + q_x: torch.Tensor, + k_x: Optional[torch.Tensor] = None, + v_x: Optional[torch.Tensor] = None, + attn_mask: Optional[torch.Tensor] = None, + ): + k_x = self.ln_1_kv(k_x) if hasattr(self, "ln_1_kv") and k_x is not None else None + v_x = self.ln_1_kv(v_x) if hasattr(self, "ln_1_kv") and v_x is not None else None + + x = q_x + self.ls_1(self.attention(q_x=self.ln_1(q_x), k_x=k_x, v_x=v_x, attn_mask=attn_mask)) + x = x + self.ls_2(self.mlp(self.ln_2(x))) + return x + + +class CustomResidualAttentionBlock(nn.Module): + def __init__( + self, + d_model: int, + n_head: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + scale_cosine_attn: bool = False, + scale_heads: bool = False, + scale_attn: bool = False, + scale_fc: bool = False, + ): + super().__init__() + + self.ln_1 = norm_layer(d_model) + self.attn = Attention( + d_model, n_head, + scaled_cosine=scale_cosine_attn, + scale_heads=scale_heads, + ) + self.ln_attn = norm_layer(d_model) if scale_attn else nn.Identity() + self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + self.ln_2 = norm_layer(d_model) + mlp_width = int(d_model * mlp_ratio) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, mlp_width)), + ('ln', norm_layer(mlp_width) if scale_fc else nn.Identity()), + ("gelu", act_layer()), + ("c_proj", nn.Linear(mlp_width, d_model)) + ])) + self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + x = x + self.ls_1(self.ln_attn(self.attn(self.ln_1(x), attn_mask=attn_mask))) + x = x + self.ls_2(self.mlp(self.ln_2(x))) + return x + + +class Transformer(nn.Module): + def __init__( + self, + width: int, + layers: int, + heads: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + ): + super().__init__() + self.width = width + self.layers = layers + self.grad_checkpointing = False + + self.resblocks = nn.ModuleList([ + ResidualAttentionBlock( + width, heads, mlp_ratio, ls_init_value=ls_init_value, act_layer=act_layer, norm_layer=norm_layer) + for _ in range(layers) + ]) + + def get_cast_dtype(self) -> torch.dtype: + return self.resblocks[0].mlp.c_fc.weight.dtype + + def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + for r in self.resblocks: + if self.grad_checkpointing and not torch.jit.is_scripting(): + # TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372 + x = checkpoint(r, x, None, None, attn_mask) + else: + x = r(x, attn_mask=attn_mask) + return x + + +class VisionTransformer(nn.Module): + output_tokens: torch.jit.Final[bool] + + def __init__( + self, + image_size: int, + patch_size: int, + width: int, + layers: int, + heads: int, + mlp_ratio: float, + ls_init_value: float = None, + global_average_pool: bool = False, + attentional_pool: bool = False, + n_queries: int = 256, + attn_pooler_heads: int = 8, + output_dim: int = 512, + patch_dropout: float = 0., + input_patchnorm: bool = False, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + output_tokens: bool = False + ): + super().__init__() + self.output_tokens = output_tokens + image_height, image_width = self.image_size = to_2tuple(image_size) + patch_height, patch_width = self.patch_size = to_2tuple(patch_size) + self.grid_size = (image_height // patch_height, image_width // patch_width) + self.output_dim = output_dim + + # whether to layernorm each patch, as done in dual patchnorm paper - https://arxiv.org/abs/2302.01327v1 + self.input_patchnorm = input_patchnorm + + if input_patchnorm: + patch_input_dim = patch_height * patch_width * 3 + self.patchnorm_pre_ln = LayerNorm(patch_input_dim) + self.conv1 = nn.Linear(patch_input_dim, width) + else: + self.patchnorm_pre_ln = nn.Identity() + self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False) + + # class embeddings and positional embeddings + scale = width ** -0.5 + self.class_embedding = nn.Parameter(scale * torch.randn(width)) + self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width)) + + # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn + self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity() + + self.ln_pre = norm_layer(width) + self.transformer = Transformer( + width, + layers, + heads, + mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + ) + + self.global_average_pool = global_average_pool + if attentional_pool: + self.attn_pool = AttentionalPooler(output_dim, width, n_head=attn_pooler_heads, n_queries=n_queries) + self.ln_post = norm_layer(output_dim) + self.proj = nn.Parameter(scale * torch.randn(output_dim, output_dim)) + else: + self.attn_pool = None + self.ln_post = norm_layer(width) + self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) + + self.init_parameters() + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + for param in self.parameters(): + param.requires_grad = False + + if unlocked_groups != 0: + groups = [ + [ + self.conv1, + self.class_embedding, + self.positional_embedding, + self.ln_pre, + ], + *self.transformer.resblocks[:-1], + [ + self.transformer.resblocks[-1], + self.ln_post, + ], + self.proj, + ] + + def _unlock(x): + if isinstance(x, Sequence): + for g in x: + _unlock(g) + else: + if isinstance(x, torch.nn.Parameter): + x.requires_grad = True + else: + for p in x.parameters(): + p.requires_grad = True + + _unlock(groups[-unlocked_groups:]) + + def init_parameters(self): + # FIXME OpenAI CLIP did not define an init for the VisualTransformer + # TODO experiment if default PyTorch init, below, or alternate init is best. + + # nn.init.normal_(self.class_embedding, std=self.scale) + # nn.init.normal_(self.positional_embedding, std=self.scale) + # + # proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) + # attn_std = self.transformer.width ** -0.5 + # fc_std = (2 * self.transformer.width) ** -0.5 + # for block in self.transformer.resblocks: + # nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + # nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + # nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + # nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + # + # if self.text_projection is not None: + # nn.init.normal_(self.text_projection, std=self.scale) + pass + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.grad_checkpointing = enable + + def _global_pool(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + if self.global_average_pool: + return x.mean(dim=1), x + else: + return x[:, 0], x[:, 1:] + + def forward(self, x: torch.Tensor, skip_pool: bool = False): + + # to patches - whether to use dual patchnorm - https://arxiv.org/abs/2302.01327v1 + if self.input_patchnorm: + # einops - rearrange(x, 'b c (h p1) (w p2) -> b (h w) (c p1 p2)') + x = x.reshape(x.shape[0], x.shape[1], self.grid_size[0], self.patch_size[0], self.grid_size[1], self.patch_size[1]) + x = x.permute(0, 2, 4, 1, 3, 5) + x = x.reshape(x.shape[0], self.grid_size[0] * self.grid_size[1], -1) + x = self.patchnorm_pre_ln(x) + x = self.conv1(x) + else: + x = self.conv1(x) # shape = [*, width, grid, grid] + x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] + x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] + + # class embeddings and positional embeddings + x = torch.cat( + [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), + x], dim=1) # shape = [*, grid ** 2 + 1, width] + x = x + self.positional_embedding.to(x.dtype) + + # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in + x = self.patch_dropout(x) + x = self.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + + if skip_pool: + return x + + if self.attn_pool is not None: + x = self.attn_pool(x) + x = self.ln_post(x) + pooled, tokens = self._global_pool(x) + else: + pooled, tokens = self._global_pool(x) + pooled = self.ln_post(pooled) + + if self.proj is not None: + pooled = pooled @ self.proj + + if self.output_tokens: + return pooled, tokens + + return pooled + + +class TextTransformer(nn.Module): + output_tokens: torch.jit.Final[bool] + + def __init__( + self, + context_length: int = 77, + vocab_size: int = 49408, + width: int = 512, + heads: int = 8, + layers: int = 12, + ls_init_value: float = None, + output_dim: int = 512, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + embed_cls: bool = False, + pad_id: int = 0, + output_tokens: bool = False, + ): + super().__init__() + self.output_tokens = output_tokens + self.num_pos = self.context_length = context_length + self.vocab_size = vocab_size + self.width = width + self.output_dim = output_dim + self.heads = heads + self.pad_id = pad_id + + self.text_projection = nn.Parameter(torch.empty(width, output_dim)) + + if embed_cls: + self.cls_emb = nn.Parameter(torch.empty(width)) + self.num_pos += 1 + else: + self.cls_emb = None + + self.token_embedding = nn.Embedding(vocab_size, width) + self.positional_embedding = nn.Parameter(torch.empty(self.num_pos, width)) + self.transformer = Transformer( + width=width, + layers=layers, + heads=heads, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + ) + self.ln_final = norm_layer(width) + + self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False) + + self.init_parameters() + + def init_parameters(self): + nn.init.normal_(self.token_embedding.weight, std=0.02) + nn.init.normal_(self.positional_embedding, std=0.01) + if self.cls_emb is not None: + nn.init.normal_(self.cls_emb, std=0.01) + + proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) + attn_std = self.transformer.width ** -0.5 + fc_std = (2 * self.transformer.width) ** -0.5 + for block in self.transformer.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + + if self.text_projection is not None: + nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.grad_checkpointing = enable + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.num_pos, self.num_pos) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + def build_cls_mask(self, text, cast_dtype: torch.dtype): + cls_mask = (text != self.pad_id).unsqueeze(1) + cls_mask = F.pad(cls_mask, (1, 0, cls_mask.shape[2], 0), value=1.0) + additive_mask = torch.empty(cls_mask.shape, dtype=cast_dtype, device=cls_mask.device) + additive_mask.fill_(0) + additive_mask.masked_fill_(~cls_mask, float("-inf")) + additive_mask = torch.repeat_interleave(additive_mask, self.heads, 0) + return additive_mask + + def _repeat(self, t, N: int): + return t.reshape(1, 1, -1).repeat(N, 1, 1) + + def forward(self, text): + cast_dtype = self.transformer.get_cast_dtype() + seq_len = text.shape[1] + + x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model] + attn_mask = self.attn_mask + if self.cls_emb is not None: + seq_len += 1 + x = torch.cat([x, self._repeat(self.cls_emb, x.shape[0])], dim=1) + cls_mask = self.build_cls_mask(text, cast_dtype) + attn_mask = attn_mask[None, :seq_len, :seq_len] + cls_mask[:, :seq_len, :seq_len] + + x = x + self.positional_embedding[:seq_len].to(cast_dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x, attn_mask=attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + if self.cls_emb is not None: + pooled, tokens = x[:, -1], x[:, :-1] + pooled = self.ln_final(pooled) + else: + x = self.ln_final(x) + pooled, tokens = x[torch.arange(x.shape[0]), text.argmax(dim=-1)], x + + if self.text_projection is not None: + pooled = pooled @ self.text_projection + + if self.output_tokens: + return pooled, tokens + + return pooled + + +class MultimodalTransformer(Transformer): + def __init__( + self, + width: int, + layers: int, + heads: int, + context_length: int = 77, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + output_dim: int = 512, + ): + + super().__init__( + width=width, + layers=layers, + heads=heads, + mlp_ratio=mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + ) + self.context_length = context_length + self.cross_attn = nn.ModuleList([ + ResidualAttentionBlock( + width, + heads, + mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + is_cross_attention=True, + ) + for _ in range(layers) + ]) + + self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False) + + self.ln_final = norm_layer(width) + self.text_projection = nn.Parameter(torch.empty(width, output_dim)) + + def init_parameters(self): + proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) + attn_std = self.transformer.width ** -0.5 + fc_std = (2 * self.transformer.width) ** -0.5 + for block in self.transformer.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + for block in self.transformer.cross_attn: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + + if self.text_projection is not None: + nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.context_length, self.context_length) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + def forward(self, image_embs, text_embs): + text_embs = text_embs.permute(1, 0, 2) # NLD -> LNDsq + image_embs = image_embs.permute(1, 0, 2) # NLD -> LND + seq_len = text_embs.shape[0] + + for resblock, cross_attn in zip(self.resblocks, self.cross_attn): + if self.grad_checkpointing and not torch.jit.is_scripting(): + # TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372 + text_embs = checkpoint(resblock, text_embs, None, None, self.attn_mask[:seq_len, :seq_len]) + text_embs = checkpoint(cross_attn, text_embs, image_embs, image_embs, None) + else: + text_embs = resblock(text_embs, attn_mask=self.attn_mask[:seq_len, :seq_len]) + text_embs = cross_attn(text_embs, k_x=image_embs, v_x=image_embs) + + x = text_embs.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x) + + if self.text_projection is not None: + x = x @ self.text_projection + + return x + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.grad_checkpointing = enable diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/utils.py b/diffsynth/extensions/ImageQualityMetric/open_clip/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..51e80c5e296b24cae130ab0459baf268e0db7673 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/utils.py @@ -0,0 +1,60 @@ +from itertools import repeat +import collections.abc + +from torch import nn as nn +from torchvision.ops.misc import FrozenBatchNorm2d + + +def freeze_batch_norm_2d(module, module_match={}, name=''): + """ + Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is + itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and + returned. Otherwise, the module is walked recursively and submodules are converted in place. + + Args: + module (torch.nn.Module): Any PyTorch module. + module_match (dict): Dictionary of full module names to freeze (all if empty) + name (str): Full module name (prefix) + + Returns: + torch.nn.Module: Resulting module + + Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 + """ + res = module + is_match = True + if module_match: + is_match = name in module_match + if is_match and isinstance(module, (nn.modules.batchnorm.BatchNorm2d, nn.modules.batchnorm.SyncBatchNorm)): + res = FrozenBatchNorm2d(module.num_features) + res.num_features = module.num_features + res.affine = module.affine + if module.affine: + res.weight.data = module.weight.data.clone().detach() + res.bias.data = module.bias.data.clone().detach() + res.running_mean.data = module.running_mean.data + res.running_var.data = module.running_var.data + res.eps = module.eps + else: + for child_name, child in module.named_children(): + full_child_name = '.'.join([name, child_name]) if name else child_name + new_child = freeze_batch_norm_2d(child, module_match, full_child_name) + if new_child is not child: + res.add_module(child_name, new_child) + return res + + +# From PyTorch internals +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable): + return x + return tuple(repeat(x, n)) + return parse + + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) +to_3tuple = _ntuple(3) +to_4tuple = _ntuple(4) +to_ntuple = lambda n, x: _ntuple(n)(x) diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/version.py b/diffsynth/extensions/ImageQualityMetric/open_clip/version.py new file mode 100644 index 0000000000000000000000000000000000000000..48aa744fb053599044caf0253b889b5cfe5b78e7 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/version.py @@ -0,0 +1 @@ +__version__ = '2.16.0' diff --git a/diffsynth/extensions/ImageQualityMetric/pickscore.py b/diffsynth/extensions/ImageQualityMetric/pickscore.py new file mode 100644 index 0000000000000000000000000000000000000000..7370e099724997d98f1c4ad3fc5f14c861202665 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/pickscore.py @@ -0,0 +1,112 @@ +import torch +from PIL import Image +from transformers import AutoProcessor, AutoModel +from typing import List, Union +import os +from .config import MODEL_PATHS + +class PickScore(torch.nn.Module): + def __init__(self, device: Union[str, torch.device], path: str = MODEL_PATHS): + super().__init__() + """Initialize the Selector with a processor and model. + + Args: + device (Union[str, torch.device]): The device to load the model on. + """ + self.device = device if isinstance(device, torch.device) else torch.device(device) + processor_name_or_path = path.get("clip") + model_pretrained_name_or_path = path.get("pickscore") + self.processor = AutoProcessor.from_pretrained(processor_name_or_path) + self.model = AutoModel.from_pretrained(model_pretrained_name_or_path).eval().to(self.device) + + def _calculate_score(self, image: torch.Tensor, prompt: str, softmax: bool = False) -> float: + """Calculate the score for a single image and prompt. + + Args: + image (torch.Tensor): The processed image tensor. + prompt (str): The prompt text. + softmax (bool): Whether to apply softmax to the scores. + + Returns: + float: The score for the image. + """ + with torch.no_grad(): + # Prepare text inputs + text_inputs = self.processor( + text=prompt, + padding=True, + truncation=True, + max_length=77, + return_tensors="pt", + ).to(self.device) + + # Embed images and text + image_embs = self.model.get_image_features(pixel_values=image) + image_embs = image_embs / torch.norm(image_embs, dim=-1, keepdim=True) + text_embs = self.model.get_text_features(**text_inputs) + text_embs = text_embs / torch.norm(text_embs, dim=-1, keepdim=True) + + # Compute score + score = (text_embs @ image_embs.T)[0] + if softmax: + # Apply logit scale and softmax + score = torch.softmax(self.model.logit_scale.exp() * score, dim=-1) + + return score.cpu().item() + + @torch.no_grad() + def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str, softmax: bool = False) -> List[float]: + """Score the images based on the prompt. + + Args: + images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). + prompt (str): The prompt text. + softmax (bool): Whether to apply softmax to the scores. + + Returns: + List[float]: List of scores for the images. + """ + try: + if isinstance(images, (str, Image.Image)): + # Single image + if isinstance(images, str): + pil_image = Image.open(images) + else: + pil_image = images + + # Prepare image inputs + image_inputs = self.processor( + images=pil_image, + padding=True, + truncation=True, + max_length=77, + return_tensors="pt", + ).to(self.device) + + return [self._calculate_score(image_inputs["pixel_values"], prompt, softmax)] + elif isinstance(images, list): + # Multiple images + scores = [] + for one_image in images: + if isinstance(one_image, str): + pil_image = Image.open(one_image) + elif isinstance(one_image, Image.Image): + pil_image = one_image + else: + raise TypeError("The type of parameter images is illegal.") + + # Prepare image inputs + image_inputs = self.processor( + images=pil_image, + padding=True, + truncation=True, + max_length=77, + return_tensors="pt", + ).to(self.device) + + scores.append(self._calculate_score(image_inputs["pixel_values"], prompt, softmax)) + return scores + else: + raise TypeError("The type of parameter images is illegal.") + except Exception as e: + raise RuntimeError(f"Error in scoring images: {e}") diff --git a/diffsynth/extensions/ImageQualityMetric/trainer/__init__.py b/diffsynth/extensions/ImageQualityMetric/trainer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cf4f59d6c0977e578ab67ec92c916c7e38842715 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/trainer/__init__.py @@ -0,0 +1 @@ +from .models import * \ No newline at end of file diff --git a/diffsynth/extensions/RIFE/__init__.py b/diffsynth/extensions/RIFE/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e76c391f0b085b3628592990a868ac09f37cced7 --- /dev/null +++ b/diffsynth/extensions/RIFE/__init__.py @@ -0,0 +1,242 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from PIL import Image + + +def warp(tenInput, tenFlow, device): + backwarp_tenGrid = {} + k = (str(tenFlow.device), str(tenFlow.size())) + if k not in backwarp_tenGrid: + tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=device).view( + 1, 1, 1, tenFlow.shape[3]).expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1) + tenVertical = torch.linspace(-1.0, 1.0, tenFlow.shape[2], device=device).view( + 1, 1, tenFlow.shape[2], 1).expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3]) + backwarp_tenGrid[k] = torch.cat( + [tenHorizontal, tenVertical], 1).to(device) + + tenFlow = torch.cat([tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0), + tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0)], 1) + + g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1) + return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=True) + + +def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1): + return nn.Sequential( + nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, + padding=padding, dilation=dilation, bias=True), + nn.PReLU(out_planes) + ) + + +class IFBlock(nn.Module): + def __init__(self, in_planes, c=64): + super(IFBlock, self).__init__() + self.conv0 = nn.Sequential(conv(in_planes, c//2, 3, 2, 1), conv(c//2, c, 3, 2, 1),) + self.convblock0 = nn.Sequential(conv(c, c), conv(c, c)) + self.convblock1 = nn.Sequential(conv(c, c), conv(c, c)) + self.convblock2 = nn.Sequential(conv(c, c), conv(c, c)) + self.convblock3 = nn.Sequential(conv(c, c), conv(c, c)) + self.conv1 = nn.Sequential(nn.ConvTranspose2d(c, c//2, 4, 2, 1), nn.PReLU(c//2), nn.ConvTranspose2d(c//2, 4, 4, 2, 1)) + self.conv2 = nn.Sequential(nn.ConvTranspose2d(c, c//2, 4, 2, 1), nn.PReLU(c//2), nn.ConvTranspose2d(c//2, 1, 4, 2, 1)) + + def forward(self, x, flow, scale=1): + x = F.interpolate(x, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) + flow = F.interpolate(flow, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 1. / scale + feat = self.conv0(torch.cat((x, flow), 1)) + feat = self.convblock0(feat) + feat + feat = self.convblock1(feat) + feat + feat = self.convblock2(feat) + feat + feat = self.convblock3(feat) + feat + flow = self.conv1(feat) + mask = self.conv2(feat) + flow = F.interpolate(flow, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * scale + mask = F.interpolate(mask, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) + return flow, mask + + +class IFNet(nn.Module): + def __init__(self, **kwargs): + super(IFNet, self).__init__() + self.block0 = IFBlock(7+4, c=90) + self.block1 = IFBlock(7+4, c=90) + self.block2 = IFBlock(7+4, c=90) + self.block_tea = IFBlock(10+4, c=90) + + def forward(self, x, scale_list=[4, 2, 1], training=False): + if training == False: + channel = x.shape[1] // 2 + img0 = x[:, :channel] + img1 = x[:, channel:] + flow_list = [] + merged = [] + mask_list = [] + warped_img0 = img0 + warped_img1 = img1 + flow = (x[:, :4]).detach() * 0 + mask = (x[:, :1]).detach() * 0 + block = [self.block0, self.block1, self.block2] + for i in range(3): + f0, m0 = block[i](torch.cat((warped_img0[:, :3], warped_img1[:, :3], mask), 1), flow, scale=scale_list[i]) + f1, m1 = block[i](torch.cat((warped_img1[:, :3], warped_img0[:, :3], -mask), 1), torch.cat((flow[:, 2:4], flow[:, :2]), 1), scale=scale_list[i]) + flow = flow + (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2 + mask = mask + (m0 + (-m1)) / 2 + mask_list.append(mask) + flow_list.append(flow) + warped_img0 = warp(img0, flow[:, :2], device=x.device) + warped_img1 = warp(img1, flow[:, 2:4], device=x.device) + merged.append((warped_img0, warped_img1)) + ''' + c0 = self.contextnet(img0, flow[:, :2]) + c1 = self.contextnet(img1, flow[:, 2:4]) + tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1) + res = tmp[:, 1:4] * 2 - 1 + ''' + for i in range(3): + mask_list[i] = torch.sigmoid(mask_list[i]) + merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i]) + return flow_list, mask_list[2], merged + + @staticmethod + def state_dict_converter(): + return IFNetStateDictConverter() + + +class IFNetStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + state_dict_ = {k.replace("module.", ""): v for k, v in state_dict.items()} + return state_dict_ + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict), {"upcast_to_float32": True} + + +class RIFEInterpolater: + def __init__(self, model, device="cuda"): + self.model = model + self.device = device + # IFNet only does not support float16 + self.torch_dtype = torch.float32 + + @staticmethod + def from_model_manager(model_manager): + return RIFEInterpolater(model_manager.fetch_model("rife"), device=model_manager.device) + + def process_image(self, image): + width, height = image.size + if width % 32 != 0 or height % 32 != 0: + width = (width + 31) // 32 + height = (height + 31) // 32 + image = image.resize((width, height)) + image = torch.Tensor(np.array(image, dtype=np.float32)[:, :, [2,1,0]] / 255).permute(2, 0, 1) + return image + + def process_images(self, images): + images = [self.process_image(image) for image in images] + images = torch.stack(images) + return images + + def decode_images(self, images): + images = (images[:, [2,1,0]].permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8) + images = [Image.fromarray(image) for image in images] + return images + + def add_interpolated_images(self, images, interpolated_images): + output_images = [] + for image, interpolated_image in zip(images, interpolated_images): + output_images.append(image) + output_images.append(interpolated_image) + output_images.append(images[-1]) + return output_images + + + @torch.no_grad() + def interpolate_(self, images, scale=1.0): + input_tensor = self.process_images(images) + input_tensor = torch.cat((input_tensor[:-1], input_tensor[1:]), dim=1) + input_tensor = input_tensor.to(device=self.device, dtype=self.torch_dtype) + flow, mask, merged = self.model(input_tensor, [4/scale, 2/scale, 1/scale]) + output_images = self.decode_images(merged[2].cpu()) + if output_images[0].size != images[0].size: + output_images = [image.resize(images[0].size) for image in output_images] + return output_images + + + @torch.no_grad() + def interpolate(self, images, scale=1.0, batch_size=4, num_iter=1, progress_bar=lambda x:x): + # Preprocess + processed_images = self.process_images(images) + + for iter in range(num_iter): + # Input + input_tensor = torch.cat((processed_images[:-1], processed_images[1:]), dim=1) + + # Interpolate + output_tensor = [] + for batch_id in progress_bar(range(0, input_tensor.shape[0], batch_size)): + batch_id_ = min(batch_id + batch_size, input_tensor.shape[0]) + batch_input_tensor = input_tensor[batch_id: batch_id_] + batch_input_tensor = batch_input_tensor.to(device=self.device, dtype=self.torch_dtype) + flow, mask, merged = self.model(batch_input_tensor, [4/scale, 2/scale, 1/scale]) + output_tensor.append(merged[2].cpu()) + + # Output + output_tensor = torch.concat(output_tensor, dim=0).clip(0, 1) + processed_images = self.add_interpolated_images(processed_images, output_tensor) + processed_images = torch.stack(processed_images) + + # To images + output_images = self.decode_images(processed_images) + if output_images[0].size != images[0].size: + output_images = [image.resize(images[0].size) for image in output_images] + return output_images + + +class RIFESmoother(RIFEInterpolater): + def __init__(self, model, device="cuda"): + super(RIFESmoother, self).__init__(model, device=device) + + @staticmethod + def from_model_manager(model_manager): + return RIFEInterpolater(model_manager.fetch_model("rife"), device=model_manager.device) + + def process_tensors(self, input_tensor, scale=1.0, batch_size=4): + output_tensor = [] + for batch_id in range(0, input_tensor.shape[0], batch_size): + batch_id_ = min(batch_id + batch_size, input_tensor.shape[0]) + batch_input_tensor = input_tensor[batch_id: batch_id_] + batch_input_tensor = batch_input_tensor.to(device=self.device, dtype=self.torch_dtype) + flow, mask, merged = self.model(batch_input_tensor, [4/scale, 2/scale, 1/scale]) + output_tensor.append(merged[2].cpu()) + output_tensor = torch.concat(output_tensor, dim=0) + return output_tensor + + @torch.no_grad() + def __call__(self, rendered_frames, scale=1.0, batch_size=4, num_iter=1, **kwargs): + # Preprocess + processed_images = self.process_images(rendered_frames) + + for iter in range(num_iter): + # Input + input_tensor = torch.cat((processed_images[:-2], processed_images[2:]), dim=1) + + # Interpolate + output_tensor = self.process_tensors(input_tensor, scale=scale, batch_size=batch_size) + + # Blend + input_tensor = torch.cat((processed_images[1:-1], output_tensor), dim=1) + output_tensor = self.process_tensors(input_tensor, scale=scale, batch_size=batch_size) + + # Add to frames + processed_images[1:-1] = output_tensor + + # To images + output_images = self.decode_images(processed_images) + if output_images[0].size != rendered_frames[0].size: + output_images = [image.resize(rendered_frames[0].size) for image in output_images] + return output_images diff --git a/diffsynth/extensions/__init__.py b/diffsynth/extensions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/diffsynth/lora/__init__.py b/diffsynth/lora/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..33bd89c21b74ec0bba8b7b69468a3901e45360fd --- /dev/null +++ b/diffsynth/lora/__init__.py @@ -0,0 +1,45 @@ +import torch + + + +class GeneralLoRALoader: + def __init__(self, device="cpu", torch_dtype=torch.float32): + self.device = device + self.torch_dtype = torch_dtype + + + def get_name_dict(self, lora_state_dict): + lora_name_dict = {} + for key in lora_state_dict: + if ".lora_B." not in key: + continue + keys = key.split(".") + if len(keys) > keys.index("lora_B") + 2: + keys.pop(keys.index("lora_B") + 1) + keys.pop(keys.index("lora_B")) + if keys[0] == "diffusion_model": + keys.pop(0) + keys.pop(-1) + target_name = ".".join(keys) + lora_name_dict[target_name] = (key, key.replace(".lora_B.", ".lora_A.")) + return lora_name_dict + + + def load(self, model: torch.nn.Module, state_dict_lora, alpha=1.0): + updated_num = 0 + lora_name_dict = self.get_name_dict(state_dict_lora) + for name, module in model.named_modules(): + if name in lora_name_dict: + weight_up = state_dict_lora[lora_name_dict[name][0]].to(device=self.device, dtype=self.torch_dtype) + weight_down = state_dict_lora[lora_name_dict[name][1]].to(device=self.device, dtype=self.torch_dtype) + if len(weight_up.shape) == 4: + weight_up = weight_up.squeeze(3).squeeze(2) + weight_down = weight_down.squeeze(3).squeeze(2) + weight_lora = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3) + else: + weight_lora = alpha * torch.mm(weight_up, weight_down) + state_dict = module.state_dict() + state_dict["weight"] = state_dict["weight"].to(device=self.device, dtype=self.torch_dtype) + weight_lora + module.load_state_dict(state_dict) + updated_num += 1 + print(f"{updated_num} tensors are updated by LoRA.") diff --git a/diffsynth/lora/flux_lora.py b/diffsynth/lora/flux_lora.py new file mode 100644 index 0000000000000000000000000000000000000000..45baeaa2f0ba87cb7e549a9e4efbd0710b1b9951 --- /dev/null +++ b/diffsynth/lora/flux_lora.py @@ -0,0 +1,278 @@ +import torch, math +from diffsynth.lora import GeneralLoRALoader +from diffsynth.models.lora import FluxLoRAFromCivitai + + +class FluxLoRALoader(GeneralLoRALoader): + def __init__(self, device="cpu", torch_dtype=torch.float32): + super().__init__(device=device, torch_dtype=torch_dtype) + + self.diffusers_rename_dict = { + "transformer.single_transformer_blocks.blockid.attn.to_k.lora_A.weight":"single_blocks.blockid.a_to_k.lora_A.default.weight", + "transformer.single_transformer_blocks.blockid.attn.to_k.lora_B.weight":"single_blocks.blockid.a_to_k.lora_B.default.weight", + "transformer.single_transformer_blocks.blockid.attn.to_q.lora_A.weight":"single_blocks.blockid.a_to_q.lora_A.default.weight", + "transformer.single_transformer_blocks.blockid.attn.to_q.lora_B.weight":"single_blocks.blockid.a_to_q.lora_B.default.weight", + "transformer.single_transformer_blocks.blockid.attn.to_v.lora_A.weight":"single_blocks.blockid.a_to_v.lora_A.default.weight", + "transformer.single_transformer_blocks.blockid.attn.to_v.lora_B.weight":"single_blocks.blockid.a_to_v.lora_B.default.weight", + "transformer.single_transformer_blocks.blockid.norm.linear.lora_A.weight":"single_blocks.blockid.norm.linear.lora_A.default.weight", + "transformer.single_transformer_blocks.blockid.norm.linear.lora_B.weight":"single_blocks.blockid.norm.linear.lora_B.default.weight", + "transformer.single_transformer_blocks.blockid.proj_mlp.lora_A.weight":"single_blocks.blockid.proj_in_besides_attn.lora_A.default.weight", + "transformer.single_transformer_blocks.blockid.proj_mlp.lora_B.weight":"single_blocks.blockid.proj_in_besides_attn.lora_B.default.weight", + "transformer.single_transformer_blocks.blockid.proj_out.lora_A.weight":"single_blocks.blockid.proj_out.lora_A.default.weight", + "transformer.single_transformer_blocks.blockid.proj_out.lora_B.weight":"single_blocks.blockid.proj_out.lora_B.default.weight", + "transformer.transformer_blocks.blockid.attn.add_k_proj.lora_A.weight":"blocks.blockid.attn.b_to_k.lora_A.default.weight", + "transformer.transformer_blocks.blockid.attn.add_k_proj.lora_B.weight":"blocks.blockid.attn.b_to_k.lora_B.default.weight", + "transformer.transformer_blocks.blockid.attn.add_q_proj.lora_A.weight":"blocks.blockid.attn.b_to_q.lora_A.default.weight", + "transformer.transformer_blocks.blockid.attn.add_q_proj.lora_B.weight":"blocks.blockid.attn.b_to_q.lora_B.default.weight", + "transformer.transformer_blocks.blockid.attn.add_v_proj.lora_A.weight":"blocks.blockid.attn.b_to_v.lora_A.default.weight", + "transformer.transformer_blocks.blockid.attn.add_v_proj.lora_B.weight":"blocks.blockid.attn.b_to_v.lora_B.default.weight", + "transformer.transformer_blocks.blockid.attn.to_add_out.lora_A.weight":"blocks.blockid.attn.b_to_out.lora_A.default.weight", + "transformer.transformer_blocks.blockid.attn.to_add_out.lora_B.weight":"blocks.blockid.attn.b_to_out.lora_B.default.weight", + "transformer.transformer_blocks.blockid.attn.to_k.lora_A.weight":"blocks.blockid.attn.a_to_k.lora_A.default.weight", + "transformer.transformer_blocks.blockid.attn.to_k.lora_B.weight":"blocks.blockid.attn.a_to_k.lora_B.default.weight", + "transformer.transformer_blocks.blockid.attn.to_out.0.lora_A.weight":"blocks.blockid.attn.a_to_out.lora_A.default.weight", + "transformer.transformer_blocks.blockid.attn.to_out.0.lora_B.weight":"blocks.blockid.attn.a_to_out.lora_B.default.weight", + "transformer.transformer_blocks.blockid.attn.to_q.lora_A.weight":"blocks.blockid.attn.a_to_q.lora_A.default.weight", + "transformer.transformer_blocks.blockid.attn.to_q.lora_B.weight":"blocks.blockid.attn.a_to_q.lora_B.default.weight", + "transformer.transformer_blocks.blockid.attn.to_v.lora_A.weight":"blocks.blockid.attn.a_to_v.lora_A.default.weight", + "transformer.transformer_blocks.blockid.attn.to_v.lora_B.weight":"blocks.blockid.attn.a_to_v.lora_B.default.weight", + "transformer.transformer_blocks.blockid.ff.net.0.proj.lora_A.weight":"blocks.blockid.ff_a.0.lora_A.default.weight", + "transformer.transformer_blocks.blockid.ff.net.0.proj.lora_B.weight":"blocks.blockid.ff_a.0.lora_B.default.weight", + "transformer.transformer_blocks.blockid.ff.net.2.lora_A.weight":"blocks.blockid.ff_a.2.lora_A.default.weight", + "transformer.transformer_blocks.blockid.ff.net.2.lora_B.weight":"blocks.blockid.ff_a.2.lora_B.default.weight", + "transformer.transformer_blocks.blockid.ff_context.net.0.proj.lora_A.weight":"blocks.blockid.ff_b.0.lora_A.default.weight", + "transformer.transformer_blocks.blockid.ff_context.net.0.proj.lora_B.weight":"blocks.blockid.ff_b.0.lora_B.default.weight", + "transformer.transformer_blocks.blockid.ff_context.net.2.lora_A.weight":"blocks.blockid.ff_b.2.lora_A.default.weight", + "transformer.transformer_blocks.blockid.ff_context.net.2.lora_B.weight":"blocks.blockid.ff_b.2.lora_B.default.weight", + "transformer.transformer_blocks.blockid.norm1.linear.lora_A.weight":"blocks.blockid.norm1_a.linear.lora_A.default.weight", + "transformer.transformer_blocks.blockid.norm1.linear.lora_B.weight":"blocks.blockid.norm1_a.linear.lora_B.default.weight", + "transformer.transformer_blocks.blockid.norm1_context.linear.lora_A.weight":"blocks.blockid.norm1_b.linear.lora_A.default.weight", + "transformer.transformer_blocks.blockid.norm1_context.linear.lora_B.weight":"blocks.blockid.norm1_b.linear.lora_B.default.weight", + } + + self.civitai_rename_dict = { + "lora_unet_double_blocks_blockid_img_mod_lin.lora_down.weight": "blocks.blockid.norm1_a.linear.lora_A.default.weight", + "lora_unet_double_blocks_blockid_img_mod_lin.lora_up.weight": "blocks.blockid.norm1_a.linear.lora_B.default.weight", + "lora_unet_double_blocks_blockid_txt_mod_lin.lora_down.weight": "blocks.blockid.norm1_b.linear.lora_A.default.weight", + "lora_unet_double_blocks_blockid_txt_mod_lin.lora_up.weight": "blocks.blockid.norm1_b.linear.lora_B.default.weight", + "lora_unet_double_blocks_blockid_img_attn_qkv.lora_down.weight": "blocks.blockid.attn.a_to_qkv.lora_A.default.weight", + "lora_unet_double_blocks_blockid_img_attn_qkv.lora_up.weight": "blocks.blockid.attn.a_to_qkv.lora_B.default.weight", + "lora_unet_double_blocks_blockid_txt_attn_qkv.lora_down.weight": "blocks.blockid.attn.b_to_qkv.lora_A.default.weight", + "lora_unet_double_blocks_blockid_txt_attn_qkv.lora_up.weight": "blocks.blockid.attn.b_to_qkv.lora_B.default.weight", + "lora_unet_double_blocks_blockid_img_attn_proj.lora_down.weight": "blocks.blockid.attn.a_to_out.lora_A.default.weight", + "lora_unet_double_blocks_blockid_img_attn_proj.lora_up.weight": "blocks.blockid.attn.a_to_out.lora_B.default.weight", + "lora_unet_double_blocks_blockid_txt_attn_proj.lora_down.weight": "blocks.blockid.attn.b_to_out.lora_A.default.weight", + "lora_unet_double_blocks_blockid_txt_attn_proj.lora_up.weight": "blocks.blockid.attn.b_to_out.lora_B.default.weight", + "lora_unet_double_blocks_blockid_img_mlp_0.lora_down.weight": "blocks.blockid.ff_a.0.lora_A.default.weight", + "lora_unet_double_blocks_blockid_img_mlp_0.lora_up.weight": "blocks.blockid.ff_a.0.lora_B.default.weight", + "lora_unet_double_blocks_blockid_img_mlp_2.lora_down.weight": "blocks.blockid.ff_a.2.lora_A.default.weight", + "lora_unet_double_blocks_blockid_img_mlp_2.lora_up.weight": "blocks.blockid.ff_a.2.lora_B.default.weight", + "lora_unet_double_blocks_blockid_txt_mlp_0.lora_down.weight": "blocks.blockid.ff_b.0.lora_A.default.weight", + "lora_unet_double_blocks_blockid_txt_mlp_0.lora_up.weight": "blocks.blockid.ff_b.0.lora_B.default.weight", + "lora_unet_double_blocks_blockid_txt_mlp_2.lora_down.weight": "blocks.blockid.ff_b.2.lora_A.default.weight", + "lora_unet_double_blocks_blockid_txt_mlp_2.lora_up.weight": "blocks.blockid.ff_b.2.lora_B.default.weight", + "lora_unet_single_blocks_blockid_modulation_lin.lora_down.weight": "single_blocks.blockid.norm.linear.lora_A.default.weight", + "lora_unet_single_blocks_blockid_modulation_lin.lora_up.weight": "single_blocks.blockid.norm.linear.lora_B.default.weight", + "lora_unet_single_blocks_blockid_linear1.lora_down.weight": "single_blocks.blockid.to_qkv_mlp.lora_A.default.weight", + "lora_unet_single_blocks_blockid_linear1.lora_up.weight": "single_blocks.blockid.to_qkv_mlp.lora_B.default.weight", + "lora_unet_single_blocks_blockid_linear2.lora_down.weight": "single_blocks.blockid.proj_out.lora_A.default.weight", + "lora_unet_single_blocks_blockid_linear2.lora_up.weight": "single_blocks.blockid.proj_out.lora_B.default.weight", + } + + def load(self, model: torch.nn.Module, state_dict_lora, alpha=1.0): + super().load(model, state_dict_lora, alpha) + + + def convert_state_dict(self,state_dict): + + def guess_block_id(name,model_resource): + if model_resource == 'civitai': + names = name.split("_") + for i in names: + if i.isdigit(): + return i, name.replace(f"_{i}_", "_blockid_") + if model_resource == 'diffusers': + names = name.split(".") + for i in names: + if i.isdigit(): + return i, name.replace(f"transformer_blocks.{i}.", "transformer_blocks.blockid.") + return None, None + + def guess_resource(state_dict): + for k in state_dict: + if "lora_unet_" in k: + return 'civitai' + elif k.startswith("transformer."): + return 'diffusers' + else: + None + + model_resource = guess_resource(state_dict) + if model_resource is None: + return state_dict + + rename_dict = self.diffusers_rename_dict if model_resource == 'diffusers' else self.civitai_rename_dict + def guess_alpha(state_dict): + for name, param in state_dict.items(): + if ".alpha" in name: + for suffix in [".lora_down.weight", ".lora_A.weight"]: + name_ = name.replace(".alpha", suffix) + if name_ in state_dict: + lora_alpha = param.item() / state_dict[name_].shape[0] + lora_alpha = math.sqrt(lora_alpha) + return lora_alpha + + return 1 + + alpha = guess_alpha(state_dict) + + state_dict_ = {} + for name, param in state_dict.items(): + block_id, source_name = guess_block_id(name,model_resource) + if alpha != 1: + param *= alpha + if source_name in rename_dict: + target_name = rename_dict[source_name] + target_name = target_name.replace(".blockid.", f".{block_id}.") + state_dict_[target_name] = param + else: + state_dict_[name] = param + + if model_resource == 'diffusers': + for name in list(state_dict_.keys()): + if "single_blocks." in name and ".a_to_q." in name: + mlp = state_dict_.get(name.replace(".a_to_q.", ".proj_in_besides_attn."), None) + if mlp is None: + dim = 4 + if 'lora_A' in name: + dim = 1 + mlp = torch.zeros(dim * state_dict_[name].shape[0], + *state_dict_[name].shape[1:], + dtype=state_dict_[name].dtype) + else: + state_dict_.pop(name.replace(".a_to_q.", ".proj_in_besides_attn.")) + if 'lora_A' in name: + param = torch.concat([ + state_dict_.pop(name), + state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")), + state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")), + mlp, + ], dim=0) + elif 'lora_B' in name: + d, r = state_dict_[name].shape + param = torch.zeros((3*d+mlp.shape[0], 3*r+mlp.shape[1]), dtype=state_dict_[name].dtype, device=state_dict_[name].device) + param[:d, :r] = state_dict_.pop(name) + param[d:2*d, r:2*r] = state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")) + param[2*d:3*d, 2*r:3*r] = state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")) + param[3*d:, 3*r:] = mlp + else: + param = torch.concat([ + state_dict_.pop(name), + state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")), + state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")), + mlp, + ], dim=0) + name_ = name.replace(".a_to_q.", ".to_qkv_mlp.") + state_dict_[name_] = param + for name in list(state_dict_.keys()): + for component in ["a", "b"]: + if f".{component}_to_q." in name: + name_ = name.replace(f".{component}_to_q.", f".{component}_to_qkv.") + concat_dim = 0 + if 'lora_A' in name: + param = torch.concat([ + state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")], + state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")], + state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")], + ], dim=0) + elif 'lora_B' in name: + origin = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")] + d, r = origin.shape + # print(d, r) + param = torch.zeros((3*d, 3*r), dtype=origin.dtype, device=origin.device) + param[:d, :r] = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")] + param[d:2*d, r:2*r] = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")] + param[2*d:3*d, 2*r:3*r] = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")] + else: + param = torch.concat([ + state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")], + state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")], + state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")], + ], dim=0) + state_dict_[name_] = param + state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_q.")) + state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k.")) + state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v.")) + return state_dict_ + + +class LoraMerger(torch.nn.Module): + def __init__(self, dim): + super().__init__() + self.weight_base = torch.nn.Parameter(torch.randn((dim,))) + self.weight_lora = torch.nn.Parameter(torch.randn((dim,))) + self.weight_cross = torch.nn.Parameter(torch.randn((dim,))) + self.weight_out = torch.nn.Parameter(torch.ones((dim,))) + self.bias = torch.nn.Parameter(torch.randn((dim,))) + self.activation = torch.nn.Sigmoid() + self.norm_base = torch.nn.LayerNorm(dim, eps=1e-5) + self.norm_lora = torch.nn.LayerNorm(dim, eps=1e-5) + + def forward(self, base_output, lora_outputs): + norm_base_output = self.norm_base(base_output) + norm_lora_outputs = self.norm_lora(lora_outputs) + gate = self.activation( + norm_base_output * self.weight_base \ + + norm_lora_outputs * self.weight_lora \ + + norm_base_output * norm_lora_outputs * self.weight_cross + self.bias + ) + output = base_output + (self.weight_out * gate * lora_outputs).sum(dim=0) + return output + + +class FluxLoraPatcher(torch.nn.Module): + def __init__(self, lora_patterns=None): + super().__init__() + if lora_patterns is None: + lora_patterns = self.default_lora_patterns() + model_dict = {} + for lora_pattern in lora_patterns: + name, dim = lora_pattern["name"], lora_pattern["dim"] + model_dict[name.replace(".", "___")] = LoraMerger(dim) + self.model_dict = torch.nn.ModuleDict(model_dict) + + def default_lora_patterns(self): + lora_patterns = [] + lora_dict = { + "attn.a_to_qkv": 9216, "attn.a_to_out": 3072, "ff_a.0": 12288, "ff_a.2": 3072, "norm1_a.linear": 18432, + "attn.b_to_qkv": 9216, "attn.b_to_out": 3072, "ff_b.0": 12288, "ff_b.2": 3072, "norm1_b.linear": 18432, + } + for i in range(19): + for suffix in lora_dict: + lora_patterns.append({ + "name": f"blocks.{i}.{suffix}", + "dim": lora_dict[suffix] + }) + lora_dict = {"to_qkv_mlp": 21504, "proj_out": 3072, "norm.linear": 9216} + for i in range(38): + for suffix in lora_dict: + lora_patterns.append({ + "name": f"single_blocks.{i}.{suffix}", + "dim": lora_dict[suffix] + }) + return lora_patterns + + def forward(self, base_output, lora_outputs, name): + return self.model_dict[name.replace(".", "___")](base_output, lora_outputs) + + @staticmethod + def state_dict_converter(): + return FluxLoraPatcherStateDictConverter() + + +class FluxLoraPatcherStateDictConverter: + def __init__(self): + pass + + def from_civitai(self, state_dict): + return state_dict diff --git a/diffsynth/models/__init__.py b/diffsynth/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..96707b666371c39d4ba59a839d5ddfeafb5d1d43 --- /dev/null +++ b/diffsynth/models/__init__.py @@ -0,0 +1 @@ +from .model_manager import * diff --git a/diffsynth/models/attention.py b/diffsynth/models/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..eb90e1ed1a28a0541a8d9df8313997a7d3f14da7 --- /dev/null +++ b/diffsynth/models/attention.py @@ -0,0 +1,89 @@ +import torch +from einops import rearrange + + +def low_version_attention(query, key, value, attn_bias=None): + scale = 1 / query.shape[-1] ** 0.5 + query = query * scale + attn = torch.matmul(query, key.transpose(-2, -1)) + if attn_bias is not None: + attn = attn + attn_bias + attn = attn.softmax(-1) + return attn @ value + + +class Attention(torch.nn.Module): + + def __init__(self, q_dim, num_heads, head_dim, kv_dim=None, bias_q=False, bias_kv=False, bias_out=False): + super().__init__() + dim_inner = head_dim * num_heads + kv_dim = kv_dim if kv_dim is not None else q_dim + self.num_heads = num_heads + self.head_dim = head_dim + + self.to_q = torch.nn.Linear(q_dim, dim_inner, bias=bias_q) + self.to_k = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv) + self.to_v = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv) + self.to_out = torch.nn.Linear(dim_inner, q_dim, bias=bias_out) + + def interact_with_ipadapter(self, hidden_states, q, ip_k, ip_v, scale=1.0): + batch_size = q.shape[0] + ip_k = ip_k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + ip_v = ip_v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + ip_hidden_states = torch.nn.functional.scaled_dot_product_attention(q, ip_k, ip_v) + hidden_states = hidden_states + scale * ip_hidden_states + return hidden_states + + def torch_forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None, ipadapter_kwargs=None, qkv_preprocessor=None): + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + + batch_size = encoder_hidden_states.shape[0] + + q = self.to_q(hidden_states) + k = self.to_k(encoder_hidden_states) + v = self.to_v(encoder_hidden_states) + + q = q.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + k = k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + v = v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + + if qkv_preprocessor is not None: + q, k, v = qkv_preprocessor(q, k, v) + + hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) + if ipadapter_kwargs is not None: + hidden_states = self.interact_with_ipadapter(hidden_states, q, **ipadapter_kwargs) + hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) + hidden_states = hidden_states.to(q.dtype) + + hidden_states = self.to_out(hidden_states) + + return hidden_states + + def xformers_forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None): + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + + q = self.to_q(hidden_states) + k = self.to_k(encoder_hidden_states) + v = self.to_v(encoder_hidden_states) + + q = rearrange(q, "b f (n d) -> (b n) f d", n=self.num_heads) + k = rearrange(k, "b f (n d) -> (b n) f d", n=self.num_heads) + v = rearrange(v, "b f (n d) -> (b n) f d", n=self.num_heads) + + if attn_mask is not None: + hidden_states = low_version_attention(q, k, v, attn_bias=attn_mask) + else: + import xformers.ops as xops + hidden_states = xops.memory_efficient_attention(q, k, v) + hidden_states = rearrange(hidden_states, "(b n) f d -> b f (n d)", n=self.num_heads) + + hidden_states = hidden_states.to(q.dtype) + hidden_states = self.to_out(hidden_states) + + return hidden_states + + def forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None, ipadapter_kwargs=None, qkv_preprocessor=None): + return self.torch_forward(hidden_states, encoder_hidden_states=encoder_hidden_states, attn_mask=attn_mask, ipadapter_kwargs=ipadapter_kwargs, qkv_preprocessor=qkv_preprocessor) \ No newline at end of file diff --git a/diffsynth/models/camera_encoder.py b/diffsynth/models/camera_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..b30571c5b366e6c4040be0e375747c42fdeb50c6 --- /dev/null +++ b/diffsynth/models/camera_encoder.py @@ -0,0 +1,203 @@ +""" +Camera Encoder for RT (Rotation-Translation) matrix injection. +CAM paper [2506.03141]: Maps camera pose to DiT hidden dimension for spatial attention conditioning. + +Design notes: +- Primary purpose: dimension alignment (action/RT [12] -> DiT hidden D). One-layer MLP is sufficient; no need for deeper encoder. +- Per-frame MLP: each frame's RT [12] -> hidden_size independently (no temporal context). +- Optional zero-init scale: conditioning starts weak (scale=0) and grows with training for stability. +- shallow: single Linear(12, D) to match CAM "single-layer MLP" wording (ablation). When shallow=True + and separate_t_r=False, the encoder is exactly one layer (merged MLP): RT [12] -> D. +- separate_t_r: encode translation (t) and rotation (R) with separate MLPs then add, for scale balance. + Use shallow=True and separate_t_r=False for merged single-layer MLP (RT not split). +- explicit_yaw: add a signed yaw scalar branch (Z-only) so CW/CCW are explicitly encoded; helps when + the model is insensitive to rotation direction (Zhou et al. CVPR 2019, sign continuity). +- sincos_yaw: add [cos(yaw), sin(yaw)] branch (2D) for direction; sin carries sign explicitly. + +Design limitations / caveats: +- No input normalization: t (translation) and R (rotation) have different scales; one Linear(12,D) may be + sensitive to units. Caller should use consistent RT scale or relative RT; optional input LayerNorm not implemented. +- Per-frame only: no temporal context (each frame encoded independently). Fine for CAM ablation; temporal modeling not supported. +- 16-dim input: layout for flattened 4x4 is unspecified; yaw branches are disabled. Prefer 12-dim in practice. +- explicit_yaw and sincos_yaw can both be True (redundant encoding of yaw); usually use one. +""" + +import torch +import torch.nn as nn +from typing import Optional + +# For Z-only rotation: yaw = atan2(R_21, R_11); R is row-major [R_11,R_12,R_13, R_21,...] +def _yaw_from_rt_12(rt: torch.Tensor) -> torch.Tensor: + """rt [..., 12] -> yaw in [-1, 1] (normalized by pi).""" + R11 = rt[..., 3] + R21 = rt[..., 6] + yaw_rad = torch.atan2(R21, R11) + return yaw_rad / 3.141592653589793 + + +def _sincos_yaw_from_rt_12(rt: torch.Tensor) -> torch.Tensor: + """rt [..., 12] -> [..., 2] (cos(yaw), sin(yaw)).""" + R11 = rt[..., 3] + R21 = rt[..., 6] + yaw_rad = torch.atan2(R21, R11) + return torch.stack([torch.cos(yaw_rad), torch.sin(yaw_rad)], dim=-1) + + +class CameraEncoder(nn.Module): + """ + Encode RT matrices (camera pose) to DiT hidden dimension. + + Input: rt_matrices [B, F, 12] or [B, F, 16] + - 12: [t_x, t_y, t_z, R_11..R_33] (3 translation + 9 rotation), R row-major. No input normalization: + t and R often differ in scale (e.g. t in meters, R in [-1,1]); single Linear(12,D) may be sensitive to units. + - 16: 4x4 matrix flattened (layout/order unspecified; yaw branches disabled when rt_dim=16). + Output: camera_emb [B, F, D] where D = hidden_size (scaled by learnable scale, default 0-init). + """ + + def __init__( + self, + rt_dim: int = 12, + hidden_size: int = 5120, + mlp_hidden_mult: int = 4, + eps: float = 1e-6, + zero_init_scale: bool = False, + full_zero_init: bool = False, + shallow: bool = False, + separate_t_r: bool = False, + explicit_yaw: bool = False, + sincos_yaw: bool = False, + conditioning_scale: float = 1.0, + r_mlp_no_layernorm: bool = False, + ): + super().__init__() + self.rt_dim = rt_dim + self.hidden_size = hidden_size + self.zero_init_scale = zero_init_scale + self.full_zero_init = full_zero_init + self.shallow = shallow + self.separate_t_r = separate_t_r + self.explicit_yaw = explicit_yaw and rt_dim == 12 + self.sincos_yaw = sincos_yaw and rt_dim == 12 + self.conditioning_scale = float(conditioning_scale) + self.r_mlp_no_layernorm = r_mlp_no_layernorm and separate_t_r + dtype = torch.get_default_dtype() + + if separate_t_r: + # Plan B: separate t (3) and R (9) encoders for scale balance; only for rt_dim=12. + assert rt_dim == 12, "separate_t_r only supported for rt_dim=12" + mid = max(hidden_size // 2, 256) + self.t_mlp = nn.Sequential( + nn.Linear(3, mid), + nn.LayerNorm(mid, eps=eps), + nn.GELU(), + nn.Linear(mid, hidden_size), + nn.LayerNorm(hidden_size, eps=eps), + ) + if r_mlp_no_layernorm: + # No LayerNorm on R so sign of R_12/R_21 (yaw direction) is not normalized away. + self.r_mlp = nn.Sequential( + nn.Linear(9, mid), + nn.GELU(), + nn.Linear(mid, hidden_size), + ) + else: + self.r_mlp = nn.Sequential( + nn.Linear(9, mid), + nn.LayerNorm(mid, eps=eps), + nn.GELU(), + nn.Linear(mid, hidden_size), + nn.LayerNorm(hidden_size, eps=eps), + ) + self.mlp = None + elif shallow: + # Merged single-layer MLP: one Linear(rt_dim, hidden_size), no separate t/R. + self.mlp = nn.Linear(rt_dim, hidden_size) + assert isinstance(self.mlp, nn.Linear), "shallow path must be exactly one Linear layer" + if full_zero_init: + nn.init.zeros_(self.mlp.weight) + nn.init.zeros_(self.mlp.bias) + else: + mid_dim = hidden_size * mlp_hidden_mult + self.mlp = nn.Sequential( + nn.Linear(rt_dim, mid_dim), + nn.LayerNorm(mid_dim, eps=eps), + nn.GELU(), + nn.Linear(mid_dim, mid_dim), + nn.LayerNorm(mid_dim, eps=eps), + nn.GELU(), + nn.Linear(mid_dim, hidden_size), + nn.LayerNorm(hidden_size, eps=eps), + ) + + if self.explicit_yaw: + self.yaw_embed = nn.Linear(1, hidden_size) + else: + self.yaw_embed = None + if self.sincos_yaw: + self.sincos_embed = nn.Linear(2, hidden_size) + else: + self.sincos_embed = None + + # Learnable scale: when zero_init_scale=True, init to 0 so conditioning grows with training (stable). + # When full_zero_init=True, skip scale (GF-ICL style: Linear output directly, no extra scale). + if full_zero_init: + self.scale = None # no scale, use 1.0 in forward + else: + self.scale = nn.Parameter(torch.zeros(1) if zero_init_scale else torch.ones(1)) + + def is_single_layer_merged(self) -> bool: + """True if encoder is exactly one Linear(12, D) with no separate t/R (merged MLP).""" + return self.shallow and not self.separate_t_r and self.mlp is not None and isinstance(self.mlp, nn.Linear) + + def forward(self, rt_matrices: torch.Tensor) -> torch.Tensor: + """ + Args: + rt_matrices: [B, F, 12] or [B, F, 16] + Returns: + camera_emb: [B, F, hidden_size], scaled by self.scale. + """ + d = rt_matrices.dtype + if self.separate_t_r: + t = rt_matrices[..., :3].to(d) + r = rt_matrices[..., 3:12].to(d) + out = self.t_mlp(t) + self.r_mlp(r) + else: + out = self.mlp(rt_matrices.to(d)) + if self.yaw_embed is not None and rt_matrices.shape[-1] >= 12: + yaw_norm = _yaw_from_rt_12(rt_matrices[..., :12]).unsqueeze(-1).to(d) + out = out + self.yaw_embed(yaw_norm) + if self.sincos_embed is not None and rt_matrices.shape[-1] >= 12: + sincos = _sincos_yaw_from_rt_12(rt_matrices[..., :12]).to(d) + out = out + self.sincos_embed(sincos) + scale = self.scale.to(d) if self.scale is not None else torch.ones(1, device=out.device, dtype=out.dtype) + return out * scale * self.conditioning_scale + + +def expand_camera_emb_to_tokens( + camera_emb: torch.Tensor, + num_frames: int, + h: int, + w: int, +) -> torch.Tensor: + """ + Expand per-frame camera_emb [B, F, D] to per-token [B, N, D] + where N = F * h * w (tokens ordered as frame0_all_patches, frame1_all_patches, ...). + + Dimension alignment (与 DiT patchify 一致): + - Encoder 输出: 每帧一个向量 [B, F, D]，即相当于 [B, F, 1, D]（F 帧每帧 1 个 embedding）。 + - 对齐方式: 在空间维上把该 1 重复 H×W 次，得到 [B, F, h*w, D]，再展平为 [B, F*h*w, D]。 + - Token 顺序: frame0 的 h*w 个 token 共用 frame0 的 camera_emb，frame1 的 h*w 个 token 共用 frame1 的 camera_emb，与 + wan_video_dit patchify 的 rearrange(..., 'b c f h w -> b (f h w) c') 顺序一致（帧优先，再空间）。 + + Args: + camera_emb: [B, F, D] + num_frames: F (must equal camera_emb.shape[1]; used for assertion only). + h, w: spatial grid (patches per frame) + Returns: + [B, F*h*w, D] + """ + B, F, D = camera_emb.shape + if F != num_frames: + raise ValueError(f"expand_camera_emb_to_tokens: camera_emb has F={F}, num_frames={num_frames}") + # [B, F, D] -> [B, F, 1, D] (每帧 1 个) -> expand 到 [B, F, h*w, D] (每帧重复 H×W 次) -> [B, F*h*w, D] + return camera_emb.unsqueeze(2).expand(B, F, h * w, D).reshape(B, F * h * w, D) diff --git a/diffsynth/models/cog_dit.py b/diffsynth/models/cog_dit.py new file mode 100644 index 0000000000000000000000000000000000000000..e93c4c38684c6815c099774dd4e3c8291462cd78 --- /dev/null +++ b/diffsynth/models/cog_dit.py @@ -0,0 +1,408 @@ +import torch +from einops import rearrange, repeat +from .sd3_dit import TimestepEmbeddings +from .attention import Attention +from .utils import load_state_dict_from_folder +from .tiler import TileWorker2Dto3D +import numpy as np + + + +class CogPatchify(torch.nn.Module): + def __init__(self, dim_in, dim_out, patch_size) -> None: + super().__init__() + self.proj = torch.nn.Conv3d(dim_in, dim_out, kernel_size=(1, patch_size, patch_size), stride=(1, patch_size, patch_size)) + + def forward(self, hidden_states): + hidden_states = self.proj(hidden_states) + hidden_states = rearrange(hidden_states, "B C T H W -> B (T H W) C") + return hidden_states + + + +class CogAdaLayerNorm(torch.nn.Module): + def __init__(self, dim, dim_cond, single=False): + super().__init__() + self.single = single + self.linear = torch.nn.Linear(dim_cond, dim * (2 if single else 6)) + self.norm = torch.nn.LayerNorm(dim, elementwise_affine=True, eps=1e-5) + + + def forward(self, hidden_states, prompt_emb, emb): + emb = self.linear(torch.nn.functional.silu(emb)) + if self.single: + shift, scale = emb.unsqueeze(1).chunk(2, dim=2) + hidden_states = self.norm(hidden_states) * (1 + scale) + shift + return hidden_states + else: + shift_a, scale_a, gate_a, shift_b, scale_b, gate_b = emb.unsqueeze(1).chunk(6, dim=2) + hidden_states = self.norm(hidden_states) * (1 + scale_a) + shift_a + prompt_emb = self.norm(prompt_emb) * (1 + scale_b) + shift_b + return hidden_states, prompt_emb, gate_a, gate_b + + + +class CogDiTBlock(torch.nn.Module): + def __init__(self, dim, dim_cond, num_heads): + super().__init__() + self.norm1 = CogAdaLayerNorm(dim, dim_cond) + self.attn1 = Attention(q_dim=dim, num_heads=48, head_dim=dim//num_heads, bias_q=True, bias_kv=True, bias_out=True) + self.norm_q = torch.nn.LayerNorm((dim//num_heads,), eps=1e-06, elementwise_affine=True) + self.norm_k = torch.nn.LayerNorm((dim//num_heads,), eps=1e-06, elementwise_affine=True) + + self.norm2 = CogAdaLayerNorm(dim, dim_cond) + self.ff = torch.nn.Sequential( + torch.nn.Linear(dim, dim*4), + torch.nn.GELU(approximate="tanh"), + torch.nn.Linear(dim*4, dim) + ) + + + def apply_rotary_emb(self, x, freqs_cis): + cos, sin = freqs_cis # [S, D] + cos = cos[None, None] + sin = sin[None, None] + cos, sin = cos.to(x.device), sin.to(x.device) + x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] + x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3) + out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype) + return out + + + def process_qkv(self, q, k, v, image_rotary_emb, text_seq_length): + q = self.norm_q(q) + k = self.norm_k(k) + q[:, :, text_seq_length:] = self.apply_rotary_emb(q[:, :, text_seq_length:], image_rotary_emb) + k[:, :, text_seq_length:] = self.apply_rotary_emb(k[:, :, text_seq_length:], image_rotary_emb) + return q, k, v + + + def forward(self, hidden_states, prompt_emb, time_emb, image_rotary_emb): + # Attention + norm_hidden_states, norm_encoder_hidden_states, gate_a, gate_b = self.norm1( + hidden_states, prompt_emb, time_emb + ) + attention_io = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1) + attention_io = self.attn1( + attention_io, + qkv_preprocessor=lambda q, k, v: self.process_qkv(q, k, v, image_rotary_emb, prompt_emb.shape[1]) + ) + + hidden_states = hidden_states + gate_a * attention_io[:, prompt_emb.shape[1]:] + prompt_emb = prompt_emb + gate_b * attention_io[:, :prompt_emb.shape[1]] + + # Feed forward + norm_hidden_states, norm_encoder_hidden_states, gate_a, gate_b = self.norm2( + hidden_states, prompt_emb, time_emb + ) + ff_io = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1) + ff_io = self.ff(ff_io) + + hidden_states = hidden_states + gate_a * ff_io[:, prompt_emb.shape[1]:] + prompt_emb = prompt_emb + gate_b * ff_io[:, :prompt_emb.shape[1]] + + return hidden_states, prompt_emb + + + +class CogDiT(torch.nn.Module): + def __init__(self): + super().__init__() + self.patchify = CogPatchify(16, 3072, 2) + self.time_embedder = TimestepEmbeddings(3072, 512) + self.context_embedder = torch.nn.Linear(4096, 3072) + self.blocks = torch.nn.ModuleList([CogDiTBlock(3072, 512, 48) for _ in range(42)]) + self.norm_final = torch.nn.LayerNorm((3072,), eps=1e-05, elementwise_affine=True) + self.norm_out = CogAdaLayerNorm(3072, 512, single=True) + self.proj_out = torch.nn.Linear(3072, 64, bias=True) + + + def get_resize_crop_region_for_grid(self, src, tgt_width, tgt_height): + tw = tgt_width + th = tgt_height + h, w = src + r = h / w + if r > (th / tw): + resize_height = th + resize_width = int(round(th / h * w)) + else: + resize_width = tw + resize_height = int(round(tw / w * h)) + + crop_top = int(round((th - resize_height) / 2.0)) + crop_left = int(round((tw - resize_width) / 2.0)) + + return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width) + + + def get_3d_rotary_pos_embed( + self, embed_dim, crops_coords, grid_size, temporal_size, theta: int = 10000, use_real: bool = True + ): + start, stop = crops_coords + grid_h = np.linspace(start[0], stop[0], grid_size[0], endpoint=False, dtype=np.float32) + grid_w = np.linspace(start[1], stop[1], grid_size[1], endpoint=False, dtype=np.float32) + grid_t = np.linspace(0, temporal_size, temporal_size, endpoint=False, dtype=np.float32) + + # Compute dimensions for each axis + dim_t = embed_dim // 4 + dim_h = embed_dim // 8 * 3 + dim_w = embed_dim // 8 * 3 + + # Temporal frequencies + freqs_t = 1.0 / (theta ** (torch.arange(0, dim_t, 2).float() / dim_t)) + grid_t = torch.from_numpy(grid_t).float() + freqs_t = torch.einsum("n , f -> n f", grid_t, freqs_t) + freqs_t = freqs_t.repeat_interleave(2, dim=-1) + + # Spatial frequencies for height and width + freqs_h = 1.0 / (theta ** (torch.arange(0, dim_h, 2).float() / dim_h)) + freqs_w = 1.0 / (theta ** (torch.arange(0, dim_w, 2).float() / dim_w)) + grid_h = torch.from_numpy(grid_h).float() + grid_w = torch.from_numpy(grid_w).float() + freqs_h = torch.einsum("n , f -> n f", grid_h, freqs_h) + freqs_w = torch.einsum("n , f -> n f", grid_w, freqs_w) + freqs_h = freqs_h.repeat_interleave(2, dim=-1) + freqs_w = freqs_w.repeat_interleave(2, dim=-1) + + # Broadcast and concatenate tensors along specified dimension + def broadcast(tensors, dim=-1): + num_tensors = len(tensors) + shape_lens = {len(t.shape) for t in tensors} + assert len(shape_lens) == 1, "tensors must all have the same number of dimensions" + shape_len = list(shape_lens)[0] + dim = (dim + shape_len) if dim < 0 else dim + dims = list(zip(*(list(t.shape) for t in tensors))) + expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim] + assert all( + [*(len(set(t[1])) <= 2 for t in expandable_dims)] + ), "invalid dimensions for broadcastable concatenation" + max_dims = [(t[0], max(t[1])) for t in expandable_dims] + expanded_dims = [(t[0], (t[1],) * num_tensors) for t in max_dims] + expanded_dims.insert(dim, (dim, dims[dim])) + expandable_shapes = list(zip(*(t[1] for t in expanded_dims))) + tensors = [t[0].expand(*t[1]) for t in zip(tensors, expandable_shapes)] + return torch.cat(tensors, dim=dim) + + freqs = broadcast((freqs_t[:, None, None, :], freqs_h[None, :, None, :], freqs_w[None, None, :, :]), dim=-1) + + t, h, w, d = freqs.shape + freqs = freqs.view(t * h * w, d) + + # Generate sine and cosine components + sin = freqs.sin() + cos = freqs.cos() + + if use_real: + return cos, sin + else: + freqs_cis = torch.polar(torch.ones_like(freqs), freqs) + return freqs_cis + + + def prepare_rotary_positional_embeddings( + self, + height: int, + width: int, + num_frames: int, + device: torch.device, + ): + grid_height = height // 2 + grid_width = width // 2 + base_size_width = 720 // (8 * 2) + base_size_height = 480 // (8 * 2) + + grid_crops_coords = self.get_resize_crop_region_for_grid( + (grid_height, grid_width), base_size_width, base_size_height + ) + freqs_cos, freqs_sin = self.get_3d_rotary_pos_embed( + embed_dim=64, + crops_coords=grid_crops_coords, + grid_size=(grid_height, grid_width), + temporal_size=num_frames, + use_real=True, + ) + + freqs_cos = freqs_cos.to(device=device) + freqs_sin = freqs_sin.to(device=device) + return freqs_cos, freqs_sin + + + def unpatchify(self, hidden_states, height, width): + hidden_states = rearrange(hidden_states, "B (T H W) (C P Q) -> B C T (H P) (W Q)", P=2, Q=2, H=height//2, W=width//2) + return hidden_states + + + def build_mask(self, T, H, W, dtype, device, is_bound): + t = repeat(torch.arange(T), "T -> T H W", T=T, H=H, W=W) + h = repeat(torch.arange(H), "H -> T H W", T=T, H=H, W=W) + w = repeat(torch.arange(W), "W -> T H W", T=T, H=H, W=W) + border_width = (H + W) // 4 + pad = torch.ones_like(h) * border_width + mask = torch.stack([ + pad if is_bound[0] else t + 1, + pad if is_bound[1] else T - t, + pad if is_bound[2] else h + 1, + pad if is_bound[3] else H - h, + pad if is_bound[4] else w + 1, + pad if is_bound[5] else W - w + ]).min(dim=0).values + mask = mask.clip(1, border_width) + mask = (mask / border_width).to(dtype=dtype, device=device) + mask = rearrange(mask, "T H W -> 1 1 T H W") + return mask + + + def tiled_forward(self, hidden_states, timestep, prompt_emb, tile_size=(60, 90), tile_stride=(30, 45)): + B, C, T, H, W = hidden_states.shape + value = torch.zeros((B, C, T, H, W), dtype=hidden_states.dtype, device=hidden_states.device) + weight = torch.zeros((B, C, T, H, W), dtype=hidden_states.dtype, device=hidden_states.device) + + # Split tasks + tasks = [] + for h in range(0, H, tile_stride): + for w in range(0, W, tile_stride): + if (h-tile_stride >= 0 and h-tile_stride+tile_size >= H) or (w-tile_stride >= 0 and w-tile_stride+tile_size >= W): + continue + h_, w_ = h + tile_size, w + tile_size + if h_ > H: h, h_ = max(H - tile_size, 0), H + if w_ > W: w, w_ = max(W - tile_size, 0), W + tasks.append((h, h_, w, w_)) + + # Run + for hl, hr, wl, wr in tasks: + mask = self.build_mask( + value.shape[2], (hr-hl), (wr-wl), + hidden_states.dtype, hidden_states.device, + is_bound=(True, True, hl==0, hr>=H, wl==0, wr>=W) + ) + model_output = self.forward(hidden_states[:, :, :, hl:hr, wl:wr], timestep, prompt_emb) + value[:, :, :, hl:hr, wl:wr] += model_output * mask + weight[:, :, :, hl:hr, wl:wr] += mask + value = value / weight + + return value + + + def forward(self, hidden_states, timestep, prompt_emb, image_rotary_emb=None, tiled=False, tile_size=90, tile_stride=30, use_gradient_checkpointing=False): + if tiled: + return TileWorker2Dto3D().tiled_forward( + forward_fn=lambda x: self.forward(x, timestep, prompt_emb), + model_input=hidden_states, + tile_size=tile_size, tile_stride=tile_stride, + tile_device=hidden_states.device, tile_dtype=hidden_states.dtype, + computation_device=self.context_embedder.weight.device, computation_dtype=self.context_embedder.weight.dtype + ) + num_frames, height, width = hidden_states.shape[-3:] + if image_rotary_emb is None: + image_rotary_emb = self.prepare_rotary_positional_embeddings(height, width, num_frames, device=self.context_embedder.weight.device) + hidden_states = self.patchify(hidden_states) + time_emb = self.time_embedder(timestep, dtype=hidden_states.dtype) + prompt_emb = self.context_embedder(prompt_emb) + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + return custom_forward + + for block in self.blocks: + if self.training and use_gradient_checkpointing: + hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, prompt_emb, time_emb, image_rotary_emb, + use_reentrant=False, + ) + else: + hidden_states, prompt_emb = block(hidden_states, prompt_emb, time_emb, image_rotary_emb) + + hidden_states = torch.cat([prompt_emb, hidden_states], dim=1) + hidden_states = self.norm_final(hidden_states) + hidden_states = hidden_states[:, prompt_emb.shape[1]:] + hidden_states = self.norm_out(hidden_states, prompt_emb, time_emb) + hidden_states = self.proj_out(hidden_states) + hidden_states = self.unpatchify(hidden_states, height, width) + + return hidden_states + + + @staticmethod + def state_dict_converter(): + return CogDiTStateDictConverter() + + + @staticmethod + def from_pretrained(file_path, torch_dtype=torch.bfloat16): + model = CogDiT().to(torch_dtype) + state_dict = load_state_dict_from_folder(file_path, torch_dtype=torch_dtype) + state_dict = CogDiT.state_dict_converter().from_diffusers(state_dict) + model.load_state_dict(state_dict) + return model + + + +class CogDiTStateDictConverter: + def __init__(self): + pass + + + def from_diffusers(self, state_dict): + rename_dict = { + "patch_embed.proj.weight": "patchify.proj.weight", + "patch_embed.proj.bias": "patchify.proj.bias", + "patch_embed.text_proj.weight": "context_embedder.weight", + "patch_embed.text_proj.bias": "context_embedder.bias", + "time_embedding.linear_1.weight": "time_embedder.timestep_embedder.0.weight", + "time_embedding.linear_1.bias": "time_embedder.timestep_embedder.0.bias", + "time_embedding.linear_2.weight": "time_embedder.timestep_embedder.2.weight", + "time_embedding.linear_2.bias": "time_embedder.timestep_embedder.2.bias", + + "norm_final.weight": "norm_final.weight", + "norm_final.bias": "norm_final.bias", + "norm_out.linear.weight": "norm_out.linear.weight", + "norm_out.linear.bias": "norm_out.linear.bias", + "norm_out.norm.weight": "norm_out.norm.weight", + "norm_out.norm.bias": "norm_out.norm.bias", + "proj_out.weight": "proj_out.weight", + "proj_out.bias": "proj_out.bias", + } + suffix_dict = { + "norm1.linear.weight": "norm1.linear.weight", + "norm1.linear.bias": "norm1.linear.bias", + "norm1.norm.weight": "norm1.norm.weight", + "norm1.norm.bias": "norm1.norm.bias", + "attn1.norm_q.weight": "norm_q.weight", + "attn1.norm_q.bias": "norm_q.bias", + "attn1.norm_k.weight": "norm_k.weight", + "attn1.norm_k.bias": "norm_k.bias", + "attn1.to_q.weight": "attn1.to_q.weight", + "attn1.to_q.bias": "attn1.to_q.bias", + "attn1.to_k.weight": "attn1.to_k.weight", + "attn1.to_k.bias": "attn1.to_k.bias", + "attn1.to_v.weight": "attn1.to_v.weight", + "attn1.to_v.bias": "attn1.to_v.bias", + "attn1.to_out.0.weight": "attn1.to_out.weight", + "attn1.to_out.0.bias": "attn1.to_out.bias", + "norm2.linear.weight": "norm2.linear.weight", + "norm2.linear.bias": "norm2.linear.bias", + "norm2.norm.weight": "norm2.norm.weight", + "norm2.norm.bias": "norm2.norm.bias", + "ff.net.0.proj.weight": "ff.0.weight", + "ff.net.0.proj.bias": "ff.0.bias", + "ff.net.2.weight": "ff.2.weight", + "ff.net.2.bias": "ff.2.bias", + } + state_dict_ = {} + for name, param in state_dict.items(): + if name in rename_dict: + if name == "patch_embed.proj.weight": + param = param.unsqueeze(2) + state_dict_[rename_dict[name]] = param + else: + names = name.split(".") + if names[0] == "transformer_blocks": + suffix = ".".join(names[2:]) + state_dict_[f"blocks.{names[1]}." + suffix_dict[suffix]] = param + return state_dict_ + + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict) diff --git a/diffsynth/models/cog_vae.py b/diffsynth/models/cog_vae.py new file mode 100644 index 0000000000000000000000000000000000000000..24ab3b3f37e111ffabf7d56c582637c6dc1c80b8 --- /dev/null +++ b/diffsynth/models/cog_vae.py @@ -0,0 +1,518 @@ +import torch +from einops import rearrange, repeat +from .tiler import TileWorker2Dto3D + + + +class Downsample3D(torch.nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int = 3, + stride: int = 2, + padding: int = 0, + compress_time: bool = False, + ): + super().__init__() + + self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding) + self.compress_time = compress_time + + def forward(self, x: torch.Tensor, xq: torch.Tensor) -> torch.Tensor: + if self.compress_time: + batch_size, channels, frames, height, width = x.shape + + # (batch_size, channels, frames, height, width) -> (batch_size, height, width, channels, frames) -> (batch_size * height * width, channels, frames) + x = x.permute(0, 3, 4, 1, 2).reshape(batch_size * height * width, channels, frames) + + if x.shape[-1] % 2 == 1: + x_first, x_rest = x[..., 0], x[..., 1:] + if x_rest.shape[-1] > 0: + # (batch_size * height * width, channels, frames - 1) -> (batch_size * height * width, channels, (frames - 1) // 2) + x_rest = torch.nn.functional.avg_pool1d(x_rest, kernel_size=2, stride=2) + + x = torch.cat([x_first[..., None], x_rest], dim=-1) + # (batch_size * height * width, channels, (frames // 2) + 1) -> (batch_size, height, width, channels, (frames // 2) + 1) -> (batch_size, channels, (frames // 2) + 1, height, width) + x = x.reshape(batch_size, height, width, channels, x.shape[-1]).permute(0, 3, 4, 1, 2) + else: + # (batch_size * height * width, channels, frames) -> (batch_size * height * width, channels, frames // 2) + x = torch.nn.functional.avg_pool1d(x, kernel_size=2, stride=2) + # (batch_size * height * width, channels, frames // 2) -> (batch_size, height, width, channels, frames // 2) -> (batch_size, channels, frames // 2, height, width) + x = x.reshape(batch_size, height, width, channels, x.shape[-1]).permute(0, 3, 4, 1, 2) + + # Pad the tensor + pad = (0, 1, 0, 1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + batch_size, channels, frames, height, width = x.shape + # (batch_size, channels, frames, height, width) -> (batch_size, frames, channels, height, width) -> (batch_size * frames, channels, height, width) + x = x.permute(0, 2, 1, 3, 4).reshape(batch_size * frames, channels, height, width) + x = self.conv(x) + # (batch_size * frames, channels, height, width) -> (batch_size, frames, channels, height, width) -> (batch_size, channels, frames, height, width) + x = x.reshape(batch_size, frames, x.shape[1], x.shape[2], x.shape[3]).permute(0, 2, 1, 3, 4) + return x + + + +class Upsample3D(torch.nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int = 3, + stride: int = 1, + padding: int = 1, + compress_time: bool = False, + ) -> None: + super().__init__() + self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding) + self.compress_time = compress_time + + def forward(self, inputs: torch.Tensor, xq: torch.Tensor) -> torch.Tensor: + if self.compress_time: + if inputs.shape[2] > 1 and inputs.shape[2] % 2 == 1: + # split first frame + x_first, x_rest = inputs[:, :, 0], inputs[:, :, 1:] + + x_first = torch.nn.functional.interpolate(x_first, scale_factor=2.0) + x_rest = torch.nn.functional.interpolate(x_rest, scale_factor=2.0) + x_first = x_first[:, :, None, :, :] + inputs = torch.cat([x_first, x_rest], dim=2) + elif inputs.shape[2] > 1: + inputs = torch.nn.functional.interpolate(inputs, scale_factor=2.0) + else: + inputs = inputs.squeeze(2) + inputs = torch.nn.functional.interpolate(inputs, scale_factor=2.0) + inputs = inputs[:, :, None, :, :] + else: + # only interpolate 2D + b, c, t, h, w = inputs.shape + inputs = inputs.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w) + inputs = torch.nn.functional.interpolate(inputs, scale_factor=2.0) + inputs = inputs.reshape(b, t, c, *inputs.shape[2:]).permute(0, 2, 1, 3, 4) + + b, c, t, h, w = inputs.shape + inputs = inputs.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w) + inputs = self.conv(inputs) + inputs = inputs.reshape(b, t, *inputs.shape[1:]).permute(0, 2, 1, 3, 4) + + return inputs + + + +class CogVideoXSpatialNorm3D(torch.nn.Module): + def __init__(self, f_channels, zq_channels, groups): + super().__init__() + self.norm_layer = torch.nn.GroupNorm(num_channels=f_channels, num_groups=groups, eps=1e-6, affine=True) + self.conv_y = torch.nn.Conv3d(zq_channels, f_channels, kernel_size=1, stride=1) + self.conv_b = torch.nn.Conv3d(zq_channels, f_channels, kernel_size=1, stride=1) + + + def forward(self, f: torch.Tensor, zq: torch.Tensor) -> torch.Tensor: + if f.shape[2] > 1 and f.shape[2] % 2 == 1: + f_first, f_rest = f[:, :, :1], f[:, :, 1:] + f_first_size, f_rest_size = f_first.shape[-3:], f_rest.shape[-3:] + z_first, z_rest = zq[:, :, :1], zq[:, :, 1:] + z_first = torch.nn.functional.interpolate(z_first, size=f_first_size) + z_rest = torch.nn.functional.interpolate(z_rest, size=f_rest_size) + zq = torch.cat([z_first, z_rest], dim=2) + else: + zq = torch.nn.functional.interpolate(zq, size=f.shape[-3:]) + + norm_f = self.norm_layer(f) + new_f = norm_f * self.conv_y(zq) + self.conv_b(zq) + return new_f + + + +class Resnet3DBlock(torch.nn.Module): + def __init__(self, in_channels, out_channels, spatial_norm_dim, groups, eps=1e-6, use_conv_shortcut=False): + super().__init__() + self.nonlinearity = torch.nn.SiLU() + if spatial_norm_dim is None: + self.norm1 = torch.nn.GroupNorm(num_channels=in_channels, num_groups=groups, eps=eps) + self.norm2 = torch.nn.GroupNorm(num_channels=out_channels, num_groups=groups, eps=eps) + else: + self.norm1 = CogVideoXSpatialNorm3D(in_channels, spatial_norm_dim, groups) + self.norm2 = CogVideoXSpatialNorm3D(out_channels, spatial_norm_dim, groups) + + self.conv1 = CachedConv3d(in_channels, out_channels, kernel_size=3, padding=(0, 1, 1)) + + self.conv2 = CachedConv3d(out_channels, out_channels, kernel_size=3, padding=(0, 1, 1)) + + if in_channels != out_channels: + if use_conv_shortcut: + self.conv_shortcut = CachedConv3d(in_channels, out_channels, kernel_size=3, padding=(0, 1, 1)) + else: + self.conv_shortcut = torch.nn.Conv3d(in_channels, out_channels, kernel_size=1) + else: + self.conv_shortcut = lambda x: x + + + def forward(self, hidden_states, zq): + residual = hidden_states + + hidden_states = self.norm1(hidden_states, zq) if isinstance(self.norm1, CogVideoXSpatialNorm3D) else self.norm1(hidden_states) + hidden_states = self.nonlinearity(hidden_states) + hidden_states = self.conv1(hidden_states) + + hidden_states = self.norm2(hidden_states, zq) if isinstance(self.norm2, CogVideoXSpatialNorm3D) else self.norm2(hidden_states) + hidden_states = self.nonlinearity(hidden_states) + hidden_states = self.conv2(hidden_states) + + hidden_states = hidden_states + self.conv_shortcut(residual) + + return hidden_states + + + +class CachedConv3d(torch.nn.Conv3d): + def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0): + super().__init__(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding) + self.cached_tensor = None + + + def clear_cache(self): + self.cached_tensor = None + + + def forward(self, input: torch.Tensor, use_cache = True) -> torch.Tensor: + if use_cache: + if self.cached_tensor is None: + self.cached_tensor = torch.concat([input[:, :, :1]] * 2, dim=2) + input = torch.concat([self.cached_tensor, input], dim=2) + self.cached_tensor = input[:, :, -2:] + return super().forward(input) + + + +class CogVAEDecoder(torch.nn.Module): + def __init__(self): + super().__init__() + self.scaling_factor = 0.7 + self.conv_in = CachedConv3d(16, 512, kernel_size=3, stride=1, padding=(0, 1, 1)) + + self.blocks = torch.nn.ModuleList([ + Resnet3DBlock(512, 512, 16, 32), + Resnet3DBlock(512, 512, 16, 32), + Resnet3DBlock(512, 512, 16, 32), + Resnet3DBlock(512, 512, 16, 32), + Resnet3DBlock(512, 512, 16, 32), + Resnet3DBlock(512, 512, 16, 32), + Upsample3D(512, 512, compress_time=True), + Resnet3DBlock(512, 256, 16, 32), + Resnet3DBlock(256, 256, 16, 32), + Resnet3DBlock(256, 256, 16, 32), + Resnet3DBlock(256, 256, 16, 32), + Upsample3D(256, 256, compress_time=True), + Resnet3DBlock(256, 256, 16, 32), + Resnet3DBlock(256, 256, 16, 32), + Resnet3DBlock(256, 256, 16, 32), + Resnet3DBlock(256, 256, 16, 32), + Upsample3D(256, 256, compress_time=False), + Resnet3DBlock(256, 128, 16, 32), + Resnet3DBlock(128, 128, 16, 32), + Resnet3DBlock(128, 128, 16, 32), + Resnet3DBlock(128, 128, 16, 32), + ]) + + self.norm_out = CogVideoXSpatialNorm3D(128, 16, 32) + self.conv_act = torch.nn.SiLU() + self.conv_out = CachedConv3d(128, 3, kernel_size=3, stride=1, padding=(0, 1, 1)) + + + def forward(self, sample): + sample = sample / self.scaling_factor + hidden_states = self.conv_in(sample) + + for block in self.blocks: + hidden_states = block(hidden_states, sample) + + hidden_states = self.norm_out(hidden_states, sample) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + + return hidden_states + + + def decode_video(self, sample, tiled=True, tile_size=(60, 90), tile_stride=(30, 45), progress_bar=lambda x:x): + if tiled: + B, C, T, H, W = sample.shape + return TileWorker2Dto3D().tiled_forward( + forward_fn=lambda x: self.decode_small_video(x), + model_input=sample, + tile_size=tile_size, tile_stride=tile_stride, + tile_device=sample.device, tile_dtype=sample.dtype, + computation_device=sample.device, computation_dtype=sample.dtype, + scales=(3/16, (T//2*8+T%2)/T, 8, 8), + progress_bar=progress_bar + ) + else: + return self.decode_small_video(sample) + + + def decode_small_video(self, sample): + B, C, T, H, W = sample.shape + computation_device = self.conv_in.weight.device + computation_dtype = self.conv_in.weight.dtype + value = [] + for i in range(T//2): + tl = i*2 + T%2 - (T%2 and i==0) + tr = i*2 + 2 + T%2 + model_input = sample[:, :, tl: tr, :, :].to(dtype=computation_dtype, device=computation_device) + model_output = self.forward(model_input).to(dtype=sample.dtype, device=sample.device) + value.append(model_output) + value = torch.concat(value, dim=2) + for name, module in self.named_modules(): + if isinstance(module, CachedConv3d): + module.clear_cache() + return value + + + @staticmethod + def state_dict_converter(): + return CogVAEDecoderStateDictConverter() + + + +class CogVAEEncoder(torch.nn.Module): + def __init__(self): + super().__init__() + self.scaling_factor = 0.7 + self.conv_in = CachedConv3d(3, 128, kernel_size=3, stride=1, padding=(0, 1, 1)) + + self.blocks = torch.nn.ModuleList([ + Resnet3DBlock(128, 128, None, 32), + Resnet3DBlock(128, 128, None, 32), + Resnet3DBlock(128, 128, None, 32), + Downsample3D(128, 128, compress_time=True), + Resnet3DBlock(128, 256, None, 32), + Resnet3DBlock(256, 256, None, 32), + Resnet3DBlock(256, 256, None, 32), + Downsample3D(256, 256, compress_time=True), + Resnet3DBlock(256, 256, None, 32), + Resnet3DBlock(256, 256, None, 32), + Resnet3DBlock(256, 256, None, 32), + Downsample3D(256, 256, compress_time=False), + Resnet3DBlock(256, 512, None, 32), + Resnet3DBlock(512, 512, None, 32), + Resnet3DBlock(512, 512, None, 32), + Resnet3DBlock(512, 512, None, 32), + Resnet3DBlock(512, 512, None, 32), + ]) + + self.norm_out = torch.nn.GroupNorm(32, 512, eps=1e-06, affine=True) + self.conv_act = torch.nn.SiLU() + self.conv_out = CachedConv3d(512, 32, kernel_size=3, stride=1, padding=(0, 1, 1)) + + + def forward(self, sample): + hidden_states = self.conv_in(sample) + + for block in self.blocks: + hidden_states = block(hidden_states, sample) + + hidden_states = self.norm_out(hidden_states) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states)[:, :16] + hidden_states = hidden_states * self.scaling_factor + + return hidden_states + + + def encode_video(self, sample, tiled=True, tile_size=(60, 90), tile_stride=(30, 45), progress_bar=lambda x:x): + if tiled: + B, C, T, H, W = sample.shape + return TileWorker2Dto3D().tiled_forward( + forward_fn=lambda x: self.encode_small_video(x), + model_input=sample, + tile_size=(i * 8 for i in tile_size), tile_stride=(i * 8 for i in tile_stride), + tile_device=sample.device, tile_dtype=sample.dtype, + computation_device=sample.device, computation_dtype=sample.dtype, + scales=(16/3, (T//4+T%2)/T, 1/8, 1/8), + progress_bar=progress_bar + ) + else: + return self.encode_small_video(sample) + + + def encode_small_video(self, sample): + B, C, T, H, W = sample.shape + computation_device = self.conv_in.weight.device + computation_dtype = self.conv_in.weight.dtype + value = [] + for i in range(T//8): + t = i*8 + T%2 - (T%2 and i==0) + t_ = i*8 + 8 + T%2 + model_input = sample[:, :, t: t_, :, :].to(dtype=computation_dtype, device=computation_device) + model_output = self.forward(model_input).to(dtype=sample.dtype, device=sample.device) + value.append(model_output) + value = torch.concat(value, dim=2) + for name, module in self.named_modules(): + if isinstance(module, CachedConv3d): + module.clear_cache() + return value + + + @staticmethod + def state_dict_converter(): + return CogVAEEncoderStateDictConverter() + + + +class CogVAEEncoderStateDictConverter: + def __init__(self): + pass + + + def from_diffusers(self, state_dict): + rename_dict = { + "encoder.conv_in.conv.weight": "conv_in.weight", + "encoder.conv_in.conv.bias": "conv_in.bias", + "encoder.down_blocks.0.downsamplers.0.conv.weight": "blocks.3.conv.weight", + "encoder.down_blocks.0.downsamplers.0.conv.bias": "blocks.3.conv.bias", + "encoder.down_blocks.1.downsamplers.0.conv.weight": "blocks.7.conv.weight", + "encoder.down_blocks.1.downsamplers.0.conv.bias": "blocks.7.conv.bias", + "encoder.down_blocks.2.downsamplers.0.conv.weight": "blocks.11.conv.weight", + "encoder.down_blocks.2.downsamplers.0.conv.bias": "blocks.11.conv.bias", + "encoder.norm_out.weight": "norm_out.weight", + "encoder.norm_out.bias": "norm_out.bias", + "encoder.conv_out.conv.weight": "conv_out.weight", + "encoder.conv_out.conv.bias": "conv_out.bias", + } + prefix_dict = { + "encoder.down_blocks.0.resnets.0.": "blocks.0.", + "encoder.down_blocks.0.resnets.1.": "blocks.1.", + "encoder.down_blocks.0.resnets.2.": "blocks.2.", + "encoder.down_blocks.1.resnets.0.": "blocks.4.", + "encoder.down_blocks.1.resnets.1.": "blocks.5.", + "encoder.down_blocks.1.resnets.2.": "blocks.6.", + "encoder.down_blocks.2.resnets.0.": "blocks.8.", + "encoder.down_blocks.2.resnets.1.": "blocks.9.", + "encoder.down_blocks.2.resnets.2.": "blocks.10.", + "encoder.down_blocks.3.resnets.0.": "blocks.12.", + "encoder.down_blocks.3.resnets.1.": "blocks.13.", + "encoder.down_blocks.3.resnets.2.": "blocks.14.", + "encoder.mid_block.resnets.0.": "blocks.15.", + "encoder.mid_block.resnets.1.": "blocks.16.", + } + suffix_dict = { + "norm1.norm_layer.weight": "norm1.norm_layer.weight", + "norm1.norm_layer.bias": "norm1.norm_layer.bias", + "norm1.conv_y.conv.weight": "norm1.conv_y.weight", + "norm1.conv_y.conv.bias": "norm1.conv_y.bias", + "norm1.conv_b.conv.weight": "norm1.conv_b.weight", + "norm1.conv_b.conv.bias": "norm1.conv_b.bias", + "norm2.norm_layer.weight": "norm2.norm_layer.weight", + "norm2.norm_layer.bias": "norm2.norm_layer.bias", + "norm2.conv_y.conv.weight": "norm2.conv_y.weight", + "norm2.conv_y.conv.bias": "norm2.conv_y.bias", + "norm2.conv_b.conv.weight": "norm2.conv_b.weight", + "norm2.conv_b.conv.bias": "norm2.conv_b.bias", + "conv1.conv.weight": "conv1.weight", + "conv1.conv.bias": "conv1.bias", + "conv2.conv.weight": "conv2.weight", + "conv2.conv.bias": "conv2.bias", + "conv_shortcut.weight": "conv_shortcut.weight", + "conv_shortcut.bias": "conv_shortcut.bias", + "norm1.weight": "norm1.weight", + "norm1.bias": "norm1.bias", + "norm2.weight": "norm2.weight", + "norm2.bias": "norm2.bias", + } + state_dict_ = {} + for name, param in state_dict.items(): + if name in rename_dict: + state_dict_[rename_dict[name]] = param + else: + for prefix in prefix_dict: + if name.startswith(prefix): + suffix = name[len(prefix):] + state_dict_[prefix_dict[prefix] + suffix_dict[suffix]] = param + return state_dict_ + + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict) + + + +class CogVAEDecoderStateDictConverter: + def __init__(self): + pass + + + def from_diffusers(self, state_dict): + rename_dict = { + "decoder.conv_in.conv.weight": "conv_in.weight", + "decoder.conv_in.conv.bias": "conv_in.bias", + "decoder.up_blocks.0.upsamplers.0.conv.weight": "blocks.6.conv.weight", + "decoder.up_blocks.0.upsamplers.0.conv.bias": "blocks.6.conv.bias", + "decoder.up_blocks.1.upsamplers.0.conv.weight": "blocks.11.conv.weight", + "decoder.up_blocks.1.upsamplers.0.conv.bias": "blocks.11.conv.bias", + "decoder.up_blocks.2.upsamplers.0.conv.weight": "blocks.16.conv.weight", + "decoder.up_blocks.2.upsamplers.0.conv.bias": "blocks.16.conv.bias", + "decoder.norm_out.norm_layer.weight": "norm_out.norm_layer.weight", + "decoder.norm_out.norm_layer.bias": "norm_out.norm_layer.bias", + "decoder.norm_out.conv_y.conv.weight": "norm_out.conv_y.weight", + "decoder.norm_out.conv_y.conv.bias": "norm_out.conv_y.bias", + "decoder.norm_out.conv_b.conv.weight": "norm_out.conv_b.weight", + "decoder.norm_out.conv_b.conv.bias": "norm_out.conv_b.bias", + "decoder.conv_out.conv.weight": "conv_out.weight", + "decoder.conv_out.conv.bias": "conv_out.bias" + } + prefix_dict = { + "decoder.mid_block.resnets.0.": "blocks.0.", + "decoder.mid_block.resnets.1.": "blocks.1.", + "decoder.up_blocks.0.resnets.0.": "blocks.2.", + "decoder.up_blocks.0.resnets.1.": "blocks.3.", + "decoder.up_blocks.0.resnets.2.": "blocks.4.", + "decoder.up_blocks.0.resnets.3.": "blocks.5.", + "decoder.up_blocks.1.resnets.0.": "blocks.7.", + "decoder.up_blocks.1.resnets.1.": "blocks.8.", + "decoder.up_blocks.1.resnets.2.": "blocks.9.", + "decoder.up_blocks.1.resnets.3.": "blocks.10.", + "decoder.up_blocks.2.resnets.0.": "blocks.12.", + "decoder.up_blocks.2.resnets.1.": "blocks.13.", + "decoder.up_blocks.2.resnets.2.": "blocks.14.", + "decoder.up_blocks.2.resnets.3.": "blocks.15.", + "decoder.up_blocks.3.resnets.0.": "blocks.17.", + "decoder.up_blocks.3.resnets.1.": "blocks.18.", + "decoder.up_blocks.3.resnets.2.": "blocks.19.", + "decoder.up_blocks.3.resnets.3.": "blocks.20.", + } + suffix_dict = { + "norm1.norm_layer.weight": "norm1.norm_layer.weight", + "norm1.norm_layer.bias": "norm1.norm_layer.bias", + "norm1.conv_y.conv.weight": "norm1.conv_y.weight", + "norm1.conv_y.conv.bias": "norm1.conv_y.bias", + "norm1.conv_b.conv.weight": "norm1.conv_b.weight", + "norm1.conv_b.conv.bias": "norm1.conv_b.bias", + "norm2.norm_layer.weight": "norm2.norm_layer.weight", + "norm2.norm_layer.bias": "norm2.norm_layer.bias", + "norm2.conv_y.conv.weight": "norm2.conv_y.weight", + "norm2.conv_y.conv.bias": "norm2.conv_y.bias", + "norm2.conv_b.conv.weight": "norm2.conv_b.weight", + "norm2.conv_b.conv.bias": "norm2.conv_b.bias", + "conv1.conv.weight": "conv1.weight", + "conv1.conv.bias": "conv1.bias", + "conv2.conv.weight": "conv2.weight", + "conv2.conv.bias": "conv2.bias", + "conv_shortcut.weight": "conv_shortcut.weight", + "conv_shortcut.bias": "conv_shortcut.bias", + } + state_dict_ = {} + for name, param in state_dict.items(): + if name in rename_dict: + state_dict_[rename_dict[name]] = param + else: + for prefix in prefix_dict: + if name.startswith(prefix): + suffix = name[len(prefix):] + state_dict_[prefix_dict[prefix] + suffix_dict[suffix]] = param + return state_dict_ + + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict) + diff --git a/diffsynth/models/downloader.py b/diffsynth/models/downloader.py new file mode 100644 index 0000000000000000000000000000000000000000..6c726f628fdbdac4cba79cb4c62475506df76b20 --- /dev/null +++ b/diffsynth/models/downloader.py @@ -0,0 +1,111 @@ +from huggingface_hub import hf_hub_download +from modelscope import snapshot_download +import os, shutil +from typing_extensions import Literal, TypeAlias +from typing import List +from ..configs.model_config import preset_models_on_huggingface, preset_models_on_modelscope, Preset_model_id + + +def download_from_modelscope(model_id, origin_file_path, local_dir): + os.makedirs(local_dir, exist_ok=True) + file_name = os.path.basename(origin_file_path) + if file_name in os.listdir(local_dir): + print(f" {file_name} has been already in {local_dir}.") + else: + print(f" Start downloading {os.path.join(local_dir, file_name)}") + snapshot_download(model_id, allow_file_pattern=origin_file_path, local_dir=local_dir) + downloaded_file_path = os.path.join(local_dir, origin_file_path) + target_file_path = os.path.join(local_dir, os.path.split(origin_file_path)[-1]) + if downloaded_file_path != target_file_path: + shutil.move(downloaded_file_path, target_file_path) + shutil.rmtree(os.path.join(local_dir, origin_file_path.split("/")[0])) + + +def download_from_huggingface(model_id, origin_file_path, local_dir): + os.makedirs(local_dir, exist_ok=True) + file_name = os.path.basename(origin_file_path) + if file_name in os.listdir(local_dir): + print(f" {file_name} has been already in {local_dir}.") + else: + print(f" Start downloading {os.path.join(local_dir, file_name)}") + hf_hub_download(model_id, origin_file_path, local_dir=local_dir) + downloaded_file_path = os.path.join(local_dir, origin_file_path) + target_file_path = os.path.join(local_dir, file_name) + if downloaded_file_path != target_file_path: + shutil.move(downloaded_file_path, target_file_path) + shutil.rmtree(os.path.join(local_dir, origin_file_path.split("/")[0])) + + +Preset_model_website: TypeAlias = Literal[ + "HuggingFace", + "ModelScope", +] +website_to_preset_models = { + "HuggingFace": preset_models_on_huggingface, + "ModelScope": preset_models_on_modelscope, +} +website_to_download_fn = { + "HuggingFace": download_from_huggingface, + "ModelScope": download_from_modelscope, +} + + +def download_customized_models( + model_id, + origin_file_path, + local_dir, + downloading_priority: List[Preset_model_website] = ["ModelScope", "HuggingFace"], +): + downloaded_files = [] + for website in downloading_priority: + # Check if the file is downloaded. + file_to_download = os.path.join(local_dir, os.path.basename(origin_file_path)) + if file_to_download in downloaded_files: + continue + # Download + website_to_download_fn[website](model_id, origin_file_path, local_dir) + if os.path.basename(origin_file_path) in os.listdir(local_dir): + downloaded_files.append(file_to_download) + return downloaded_files + + +def download_models( + model_id_list: List[Preset_model_id] = [], + downloading_priority: List[Preset_model_website] = ["ModelScope", "HuggingFace"], +): + print(f"Downloading models: {model_id_list}") + downloaded_files = [] + load_files = [] + + for model_id in model_id_list: + for website in downloading_priority: + if model_id in website_to_preset_models[website]: + + # Parse model metadata + model_metadata = website_to_preset_models[website][model_id] + if isinstance(model_metadata, list): + file_data = model_metadata + else: + file_data = model_metadata.get("file_list", []) + + # Try downloading the model from this website. + model_files = [] + for model_id, origin_file_path, local_dir in file_data: + # Check if the file is downloaded. + file_to_download = os.path.join(local_dir, os.path.basename(origin_file_path)) + if file_to_download in downloaded_files: + continue + # Download + website_to_download_fn[website](model_id, origin_file_path, local_dir) + if os.path.basename(origin_file_path) in os.listdir(local_dir): + downloaded_files.append(file_to_download) + model_files.append(file_to_download) + + # If the model is successfully downloaded, break. + if len(model_files) > 0: + if isinstance(model_metadata, dict) and "load_path" in model_metadata: + model_files = model_metadata["load_path"] + load_files.extend(model_files) + break + + return load_files diff --git a/diffsynth/models/flux_controlnet.py b/diffsynth/models/flux_controlnet.py new file mode 100644 index 0000000000000000000000000000000000000000..85fccd7da016020b02f0b6480d0b5c97044126df --- /dev/null +++ b/diffsynth/models/flux_controlnet.py @@ -0,0 +1,331 @@ +import torch +from einops import rearrange, repeat +from .flux_dit import RoPEEmbedding, TimestepEmbeddings, FluxJointTransformerBlock, FluxSingleTransformerBlock, RMSNorm +from .utils import hash_state_dict_keys, init_weights_on_device + + + +class FluxControlNet(torch.nn.Module): + def __init__(self, disable_guidance_embedder=False, num_joint_blocks=5, num_single_blocks=10, num_mode=0, mode_dict={}, additional_input_dim=0): + super().__init__() + self.pos_embedder = RoPEEmbedding(3072, 10000, [16, 56, 56]) + self.time_embedder = TimestepEmbeddings(256, 3072) + self.guidance_embedder = None if disable_guidance_embedder else TimestepEmbeddings(256, 3072) + self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(768, 3072), torch.nn.SiLU(), torch.nn.Linear(3072, 3072)) + self.context_embedder = torch.nn.Linear(4096, 3072) + self.x_embedder = torch.nn.Linear(64, 3072) + + self.blocks = torch.nn.ModuleList([FluxJointTransformerBlock(3072, 24) for _ in range(num_joint_blocks)]) + self.single_blocks = torch.nn.ModuleList([FluxSingleTransformerBlock(3072, 24) for _ in range(num_single_blocks)]) + + self.controlnet_blocks = torch.nn.ModuleList([torch.nn.Linear(3072, 3072) for _ in range(num_joint_blocks)]) + self.controlnet_single_blocks = torch.nn.ModuleList([torch.nn.Linear(3072, 3072) for _ in range(num_single_blocks)]) + + self.mode_dict = mode_dict + self.controlnet_mode_embedder = torch.nn.Embedding(num_mode, 3072) if len(mode_dict) > 0 else None + self.controlnet_x_embedder = torch.nn.Linear(64 + additional_input_dim, 3072) + + + def prepare_image_ids(self, latents): + batch_size, _, height, width = latents.shape + latent_image_ids = torch.zeros(height // 2, width // 2, 3) + latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None] + latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[None, :] + + latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape + + latent_image_ids = latent_image_ids[None, :].repeat(batch_size, 1, 1, 1) + latent_image_ids = latent_image_ids.reshape( + batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels + ) + latent_image_ids = latent_image_ids.to(device=latents.device, dtype=latents.dtype) + + return latent_image_ids + + + def patchify(self, hidden_states): + hidden_states = rearrange(hidden_states, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2) + return hidden_states + + + def align_res_stack_to_original_blocks(self, res_stack, num_blocks, hidden_states): + if len(res_stack) == 0: + return [torch.zeros_like(hidden_states)] * num_blocks + interval = (num_blocks + len(res_stack) - 1) // len(res_stack) + aligned_res_stack = [res_stack[block_id // interval] for block_id in range(num_blocks)] + return aligned_res_stack + + + def forward( + self, + hidden_states, + controlnet_conditioning, + timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None, + processor_id=None, + tiled=False, tile_size=128, tile_stride=64, + **kwargs + ): + if image_ids is None: + image_ids = self.prepare_image_ids(hidden_states) + + conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb) + if self.guidance_embedder is not None: + guidance = guidance * 1000 + conditioning = conditioning + self.guidance_embedder(guidance, hidden_states.dtype) + prompt_emb = self.context_embedder(prompt_emb) + if self.controlnet_mode_embedder is not None: # Different from FluxDiT + processor_id = torch.tensor([self.mode_dict[processor_id]], dtype=torch.int) + processor_id = repeat(processor_id, "D -> B D", B=1).to(text_ids.device) + prompt_emb = torch.concat([self.controlnet_mode_embedder(processor_id), prompt_emb], dim=1) + text_ids = torch.cat([text_ids[:, :1], text_ids], dim=1) + image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1)) + + hidden_states = self.patchify(hidden_states) + hidden_states = self.x_embedder(hidden_states) + controlnet_conditioning = self.patchify(controlnet_conditioning) # Different from FluxDiT + hidden_states = hidden_states + self.controlnet_x_embedder(controlnet_conditioning) # Different from FluxDiT + + controlnet_res_stack = [] + for block, controlnet_block in zip(self.blocks, self.controlnet_blocks): + hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb) + controlnet_res_stack.append(controlnet_block(hidden_states)) + + controlnet_single_res_stack = [] + hidden_states = torch.cat([prompt_emb, hidden_states], dim=1) + for block, controlnet_block in zip(self.single_blocks, self.controlnet_single_blocks): + hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb) + controlnet_single_res_stack.append(controlnet_block(hidden_states[:, prompt_emb.shape[1]:])) + + controlnet_res_stack = self.align_res_stack_to_original_blocks(controlnet_res_stack, 19, hidden_states[:, prompt_emb.shape[1]:]) + controlnet_single_res_stack = self.align_res_stack_to_original_blocks(controlnet_single_res_stack, 38, hidden_states[:, prompt_emb.shape[1]:]) + + return controlnet_res_stack, controlnet_single_res_stack + + + @staticmethod + def state_dict_converter(): + return FluxControlNetStateDictConverter() + + def quantize(self): + def cast_to(weight, dtype=None, device=None, copy=False): + if device is None or weight.device == device: + if not copy: + if dtype is None or weight.dtype == dtype: + return weight + return weight.to(dtype=dtype, copy=copy) + + r = torch.empty_like(weight, dtype=dtype, device=device) + r.copy_(weight) + return r + + def cast_weight(s, input=None, dtype=None, device=None): + if input is not None: + if dtype is None: + dtype = input.dtype + if device is None: + device = input.device + weight = cast_to(s.weight, dtype, device) + return weight + + def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None): + if input is not None: + if dtype is None: + dtype = input.dtype + if bias_dtype is None: + bias_dtype = dtype + if device is None: + device = input.device + bias = None + weight = cast_to(s.weight, dtype, device) + bias = cast_to(s.bias, bias_dtype, device) + return weight, bias + + class quantized_layer: + class QLinear(torch.nn.Linear): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self,input,**kwargs): + weight,bias= cast_bias_weight(self,input) + return torch.nn.functional.linear(input,weight,bias) + + class QRMSNorm(torch.nn.Module): + def __init__(self, module): + super().__init__() + self.module = module + + def forward(self,hidden_states,**kwargs): + weight= cast_weight(self.module,hidden_states) + input_dtype = hidden_states.dtype + variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.module.eps) + hidden_states = hidden_states.to(input_dtype) * weight + return hidden_states + + class QEmbedding(torch.nn.Embedding): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self,input,**kwargs): + weight= cast_weight(self,input) + return torch.nn.functional.embedding( + input, weight, self.padding_idx, self.max_norm, + self.norm_type, self.scale_grad_by_freq, self.sparse) + + def replace_layer(model): + for name, module in model.named_children(): + if isinstance(module,quantized_layer.QRMSNorm): + continue + if isinstance(module, torch.nn.Linear): + with init_weights_on_device(): + new_layer = quantized_layer.QLinear(module.in_features,module.out_features) + new_layer.weight = module.weight + if module.bias is not None: + new_layer.bias = module.bias + setattr(model, name, new_layer) + elif isinstance(module, RMSNorm): + if hasattr(module,"quantized"): + continue + module.quantized= True + new_layer = quantized_layer.QRMSNorm(module) + setattr(model, name, new_layer) + elif isinstance(module,torch.nn.Embedding): + rows, cols = module.weight.shape + new_layer = quantized_layer.QEmbedding( + num_embeddings=rows, + embedding_dim=cols, + _weight=module.weight, + # _freeze=module.freeze, + padding_idx=module.padding_idx, + max_norm=module.max_norm, + norm_type=module.norm_type, + scale_grad_by_freq=module.scale_grad_by_freq, + sparse=module.sparse) + setattr(model, name, new_layer) + else: + replace_layer(module) + + replace_layer(self) + + + +class FluxControlNetStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + hash_value = hash_state_dict_keys(state_dict) + global_rename_dict = { + "context_embedder": "context_embedder", + "x_embedder": "x_embedder", + "time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0", + "time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2", + "time_text_embed.guidance_embedder.linear_1": "guidance_embedder.timestep_embedder.0", + "time_text_embed.guidance_embedder.linear_2": "guidance_embedder.timestep_embedder.2", + "time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0", + "time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2", + "norm_out.linear": "final_norm_out.linear", + "proj_out": "final_proj_out", + } + rename_dict = { + "proj_out": "proj_out", + "norm1.linear": "norm1_a.linear", + "norm1_context.linear": "norm1_b.linear", + "attn.to_q": "attn.a_to_q", + "attn.to_k": "attn.a_to_k", + "attn.to_v": "attn.a_to_v", + "attn.to_out.0": "attn.a_to_out", + "attn.add_q_proj": "attn.b_to_q", + "attn.add_k_proj": "attn.b_to_k", + "attn.add_v_proj": "attn.b_to_v", + "attn.to_add_out": "attn.b_to_out", + "ff.net.0.proj": "ff_a.0", + "ff.net.2": "ff_a.2", + "ff_context.net.0.proj": "ff_b.0", + "ff_context.net.2": "ff_b.2", + "attn.norm_q": "attn.norm_q_a", + "attn.norm_k": "attn.norm_k_a", + "attn.norm_added_q": "attn.norm_q_b", + "attn.norm_added_k": "attn.norm_k_b", + } + rename_dict_single = { + "attn.to_q": "a_to_q", + "attn.to_k": "a_to_k", + "attn.to_v": "a_to_v", + "attn.norm_q": "norm_q_a", + "attn.norm_k": "norm_k_a", + "norm.linear": "norm.linear", + "proj_mlp": "proj_in_besides_attn", + "proj_out": "proj_out", + } + state_dict_ = {} + for name, param in state_dict.items(): + if name.endswith(".weight") or name.endswith(".bias"): + suffix = ".weight" if name.endswith(".weight") else ".bias" + prefix = name[:-len(suffix)] + if prefix in global_rename_dict: + state_dict_[global_rename_dict[prefix] + suffix] = param + elif prefix.startswith("transformer_blocks."): + names = prefix.split(".") + names[0] = "blocks" + middle = ".".join(names[2:]) + if middle in rename_dict: + name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]]) + state_dict_[name_] = param + elif prefix.startswith("single_transformer_blocks."): + names = prefix.split(".") + names[0] = "single_blocks" + middle = ".".join(names[2:]) + if middle in rename_dict_single: + name_ = ".".join(names[:2] + [rename_dict_single[middle]] + [suffix[1:]]) + state_dict_[name_] = param + else: + state_dict_[name] = param + else: + state_dict_[name] = param + for name in list(state_dict_.keys()): + if ".proj_in_besides_attn." in name: + name_ = name.replace(".proj_in_besides_attn.", ".to_qkv_mlp.") + param = torch.concat([ + state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_q.")], + state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_k.")], + state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_v.")], + state_dict_[name], + ], dim=0) + state_dict_[name_] = param + state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_q.")) + state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_k.")) + state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_v.")) + state_dict_.pop(name) + for name in list(state_dict_.keys()): + for component in ["a", "b"]: + if f".{component}_to_q." in name: + name_ = name.replace(f".{component}_to_q.", f".{component}_to_qkv.") + param = torch.concat([ + state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")], + state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")], + state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")], + ], dim=0) + state_dict_[name_] = param + state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_q.")) + state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k.")) + state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v.")) + if hash_value == "78d18b9101345ff695f312e7e62538c0": + extra_kwargs = {"num_mode": 10, "mode_dict": {"canny": 0, "tile": 1, "depth": 2, "blur": 3, "pose": 4, "gray": 5, "lq": 6}} + elif hash_value == "b001c89139b5f053c715fe772362dd2a": + extra_kwargs = {"num_single_blocks": 0} + elif hash_value == "52357cb26250681367488a8954c271e8": + extra_kwargs = {"num_joint_blocks": 6, "num_single_blocks": 0, "additional_input_dim": 4} + elif hash_value == "0cfd1740758423a2a854d67c136d1e8c": + extra_kwargs = {"num_joint_blocks": 4, "num_single_blocks": 1} + elif hash_value == "7f9583eb8ba86642abb9a21a4b2c9e16": + extra_kwargs = {"num_joint_blocks": 4, "num_single_blocks": 10} + elif hash_value == "43ad5aaa27dd4ee01b832ed16773fa52": + extra_kwargs = {"num_joint_blocks": 6, "num_single_blocks": 0} + else: + extra_kwargs = {} + return state_dict_, extra_kwargs + + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict) diff --git a/diffsynth/models/flux_dit.py b/diffsynth/models/flux_dit.py new file mode 100644 index 0000000000000000000000000000000000000000..ea5ce215ba142e6a3c3feb974c0149c86a9679cb --- /dev/null +++ b/diffsynth/models/flux_dit.py @@ -0,0 +1,746 @@ +import torch +from .sd3_dit import TimestepEmbeddings, AdaLayerNorm, RMSNorm +from einops import rearrange +from .tiler import TileWorker +from .utils import init_weights_on_device + +def interact_with_ipadapter(hidden_states, q, ip_k, ip_v, scale=1.0): + batch_size, num_tokens = hidden_states.shape[0:2] + ip_hidden_states = torch.nn.functional.scaled_dot_product_attention(q, ip_k, ip_v) + ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, num_tokens, -1) + hidden_states = hidden_states + scale * ip_hidden_states + return hidden_states + + +class RoPEEmbedding(torch.nn.Module): + def __init__(self, dim, theta, axes_dim): + super().__init__() + self.dim = dim + self.theta = theta + self.axes_dim = axes_dim + + + def rope(self, pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor: + assert dim % 2 == 0, "The dimension must be even." + + scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim + omega = 1.0 / (theta**scale) + + batch_size, seq_length = pos.shape + out = torch.einsum("...n,d->...nd", pos, omega) + cos_out = torch.cos(out) + sin_out = torch.sin(out) + + stacked_out = torch.stack([cos_out, -sin_out, sin_out, cos_out], dim=-1) + out = stacked_out.view(batch_size, -1, dim // 2, 2, 2) + return out.float() + + + def forward(self, ids): + n_axes = ids.shape[-1] + emb = torch.cat([self.rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], dim=-3) + return emb.unsqueeze(1) + + + +class FluxJointAttention(torch.nn.Module): + def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False): + super().__init__() + self.num_heads = num_heads + self.head_dim = head_dim + self.only_out_a = only_out_a + + self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3) + self.b_to_qkv = torch.nn.Linear(dim_b, dim_b * 3) + + self.norm_q_a = RMSNorm(head_dim, eps=1e-6) + self.norm_k_a = RMSNorm(head_dim, eps=1e-6) + self.norm_q_b = RMSNorm(head_dim, eps=1e-6) + self.norm_k_b = RMSNorm(head_dim, eps=1e-6) + + self.a_to_out = torch.nn.Linear(dim_a, dim_a) + if not only_out_a: + self.b_to_out = torch.nn.Linear(dim_b, dim_b) + + + def apply_rope(self, xq, xk, freqs_cis): + xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) + xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) + xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] + xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] + return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) + + def forward(self, hidden_states_a, hidden_states_b, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None): + batch_size = hidden_states_a.shape[0] + + # Part A + qkv_a = self.a_to_qkv(hidden_states_a) + qkv_a = qkv_a.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2) + q_a, k_a, v_a = qkv_a.chunk(3, dim=1) + q_a, k_a = self.norm_q_a(q_a), self.norm_k_a(k_a) + + # Part B + qkv_b = self.b_to_qkv(hidden_states_b) + qkv_b = qkv_b.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2) + q_b, k_b, v_b = qkv_b.chunk(3, dim=1) + q_b, k_b = self.norm_q_b(q_b), self.norm_k_b(k_b) + + q = torch.concat([q_b, q_a], dim=2) + k = torch.concat([k_b, k_a], dim=2) + v = torch.concat([v_b, v_a], dim=2) + + q, k = self.apply_rope(q, k, image_rotary_emb) + + hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) + hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) + hidden_states = hidden_states.to(q.dtype) + hidden_states_b, hidden_states_a = hidden_states[:, :hidden_states_b.shape[1]], hidden_states[:, hidden_states_b.shape[1]:] + if ipadapter_kwargs_list is not None: + hidden_states_a = interact_with_ipadapter(hidden_states_a, q_a, **ipadapter_kwargs_list) + hidden_states_a = self.a_to_out(hidden_states_a) + if self.only_out_a: + return hidden_states_a + else: + hidden_states_b = self.b_to_out(hidden_states_b) + return hidden_states_a, hidden_states_b + + + +class FluxJointTransformerBlock(torch.nn.Module): + def __init__(self, dim, num_attention_heads): + super().__init__() + self.norm1_a = AdaLayerNorm(dim) + self.norm1_b = AdaLayerNorm(dim) + + self.attn = FluxJointAttention(dim, dim, num_attention_heads, dim // num_attention_heads) + + self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff_a = torch.nn.Sequential( + torch.nn.Linear(dim, dim*4), + torch.nn.GELU(approximate="tanh"), + torch.nn.Linear(dim*4, dim) + ) + + self.norm2_b = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff_b = torch.nn.Sequential( + torch.nn.Linear(dim, dim*4), + torch.nn.GELU(approximate="tanh"), + torch.nn.Linear(dim*4, dim) + ) + + + def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None): + norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb) + norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb) + + # Attention + attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b, image_rotary_emb, attn_mask, ipadapter_kwargs_list) + + # Part A + hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a + norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a + hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a) + + # Part B + hidden_states_b = hidden_states_b + gate_msa_b * attn_output_b + norm_hidden_states_b = self.norm2_b(hidden_states_b) * (1 + scale_mlp_b) + shift_mlp_b + hidden_states_b = hidden_states_b + gate_mlp_b * self.ff_b(norm_hidden_states_b) + + return hidden_states_a, hidden_states_b + + + +class FluxSingleAttention(torch.nn.Module): + def __init__(self, dim_a, dim_b, num_heads, head_dim): + super().__init__() + self.num_heads = num_heads + self.head_dim = head_dim + + self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3) + + self.norm_q_a = RMSNorm(head_dim, eps=1e-6) + self.norm_k_a = RMSNorm(head_dim, eps=1e-6) + + + def apply_rope(self, xq, xk, freqs_cis): + xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) + xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) + xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] + xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] + return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) + + + def forward(self, hidden_states, image_rotary_emb): + batch_size = hidden_states.shape[0] + + qkv_a = self.a_to_qkv(hidden_states) + qkv_a = qkv_a.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2) + q_a, k_a, v = qkv_a.chunk(3, dim=1) + q_a, k_a = self.norm_q_a(q_a), self.norm_k_a(k_a) + + q, k = self.apply_rope(q_a, k_a, image_rotary_emb) + + hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v) + hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) + hidden_states = hidden_states.to(q.dtype) + return hidden_states + + + +class AdaLayerNormSingle(torch.nn.Module): + def __init__(self, dim): + super().__init__() + self.silu = torch.nn.SiLU() + self.linear = torch.nn.Linear(dim, 3 * dim, bias=True) + self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + + + def forward(self, x, emb): + emb = self.linear(self.silu(emb)) + shift_msa, scale_msa, gate_msa = emb.chunk(3, dim=1) + x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None] + return x, gate_msa + + + +class FluxSingleTransformerBlock(torch.nn.Module): + def __init__(self, dim, num_attention_heads): + super().__init__() + self.num_heads = num_attention_heads + self.head_dim = dim // num_attention_heads + self.dim = dim + + self.norm = AdaLayerNormSingle(dim) + self.to_qkv_mlp = torch.nn.Linear(dim, dim * (3 + 4)) + self.norm_q_a = RMSNorm(self.head_dim, eps=1e-6) + self.norm_k_a = RMSNorm(self.head_dim, eps=1e-6) + + self.proj_out = torch.nn.Linear(dim * 5, dim) + + + def apply_rope(self, xq, xk, freqs_cis): + xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) + xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) + xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] + xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] + return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) + + + def process_attention(self, hidden_states, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None): + batch_size = hidden_states.shape[0] + + qkv = hidden_states.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2) + q, k, v = qkv.chunk(3, dim=1) + q, k = self.norm_q_a(q), self.norm_k_a(k) + + q, k = self.apply_rope(q, k, image_rotary_emb) + + hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) + hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) + hidden_states = hidden_states.to(q.dtype) + if ipadapter_kwargs_list is not None: + hidden_states = interact_with_ipadapter(hidden_states, q, **ipadapter_kwargs_list) + return hidden_states + + + def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None): + residual = hidden_states_a + norm_hidden_states, gate = self.norm(hidden_states_a, emb=temb) + hidden_states_a = self.to_qkv_mlp(norm_hidden_states) + attn_output, mlp_hidden_states = hidden_states_a[:, :, :self.dim * 3], hidden_states_a[:, :, self.dim * 3:] + + attn_output = self.process_attention(attn_output, image_rotary_emb, attn_mask, ipadapter_kwargs_list) + mlp_hidden_states = torch.nn.functional.gelu(mlp_hidden_states, approximate="tanh") + + hidden_states_a = torch.cat([attn_output, mlp_hidden_states], dim=2) + hidden_states_a = gate.unsqueeze(1) * self.proj_out(hidden_states_a) + hidden_states_a = residual + hidden_states_a + + return hidden_states_a, hidden_states_b + + + +class AdaLayerNormContinuous(torch.nn.Module): + def __init__(self, dim): + super().__init__() + self.silu = torch.nn.SiLU() + self.linear = torch.nn.Linear(dim, dim * 2, bias=True) + self.norm = torch.nn.LayerNorm(dim, eps=1e-6, elementwise_affine=False) + + def forward(self, x, conditioning): + emb = self.linear(self.silu(conditioning)) + scale, shift = torch.chunk(emb, 2, dim=1) + x = self.norm(x) * (1 + scale)[:, None] + shift[:, None] + return x + + + +class FluxDiT(torch.nn.Module): + def __init__(self, disable_guidance_embedder=False, input_dim=64, num_blocks=19): + super().__init__() + self.pos_embedder = RoPEEmbedding(3072, 10000, [16, 56, 56]) + self.time_embedder = TimestepEmbeddings(256, 3072) + self.guidance_embedder = None if disable_guidance_embedder else TimestepEmbeddings(256, 3072) + self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(768, 3072), torch.nn.SiLU(), torch.nn.Linear(3072, 3072)) + self.context_embedder = torch.nn.Linear(4096, 3072) + self.x_embedder = torch.nn.Linear(input_dim, 3072) + + self.blocks = torch.nn.ModuleList([FluxJointTransformerBlock(3072, 24) for _ in range(num_blocks)]) + self.single_blocks = torch.nn.ModuleList([FluxSingleTransformerBlock(3072, 24) for _ in range(38)]) + + self.final_norm_out = AdaLayerNormContinuous(3072) + self.final_proj_out = torch.nn.Linear(3072, 64) + + self.input_dim = input_dim + + + def patchify(self, hidden_states): + hidden_states = rearrange(hidden_states, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2) + return hidden_states + + + def unpatchify(self, hidden_states, height, width): + hidden_states = rearrange(hidden_states, "B (H W) (C P Q) -> B C (H P) (W Q)", P=2, Q=2, H=height//2, W=width//2) + return hidden_states + + + def prepare_image_ids(self, latents): + batch_size, _, height, width = latents.shape + latent_image_ids = torch.zeros(height // 2, width // 2, 3) + latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None] + latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[None, :] + + latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape + + latent_image_ids = latent_image_ids[None, :].repeat(batch_size, 1, 1, 1) + latent_image_ids = latent_image_ids.reshape( + batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels + ) + latent_image_ids = latent_image_ids.to(device=latents.device, dtype=latents.dtype) + + return latent_image_ids + + + def tiled_forward( + self, + hidden_states, + timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, + tile_size=128, tile_stride=64, + **kwargs + ): + # Due to the global positional embedding, we cannot implement layer-wise tiled forward. + hidden_states = TileWorker().tiled_forward( + lambda x: self.forward(x, timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None), + hidden_states, + tile_size, + tile_stride, + tile_device=hidden_states.device, + tile_dtype=hidden_states.dtype + ) + return hidden_states + + + def construct_mask(self, entity_masks, prompt_seq_len, image_seq_len): + N = len(entity_masks) + batch_size = entity_masks[0].shape[0] + total_seq_len = N * prompt_seq_len + image_seq_len + patched_masks = [self.patchify(entity_masks[i]) for i in range(N)] + attention_mask = torch.ones((batch_size, total_seq_len, total_seq_len), dtype=torch.bool).to(device=entity_masks[0].device) + + image_start = N * prompt_seq_len + image_end = N * prompt_seq_len + image_seq_len + # prompt-image mask + for i in range(N): + prompt_start = i * prompt_seq_len + prompt_end = (i + 1) * prompt_seq_len + image_mask = torch.sum(patched_masks[i], dim=-1) > 0 + image_mask = image_mask.unsqueeze(1).repeat(1, prompt_seq_len, 1) + # prompt update with image + attention_mask[:, prompt_start:prompt_end, image_start:image_end] = image_mask + # image update with prompt + attention_mask[:, image_start:image_end, prompt_start:prompt_end] = image_mask.transpose(1, 2) + # prompt-prompt mask + for i in range(N): + for j in range(N): + if i != j: + prompt_start_i = i * prompt_seq_len + prompt_end_i = (i + 1) * prompt_seq_len + prompt_start_j = j * prompt_seq_len + prompt_end_j = (j + 1) * prompt_seq_len + attention_mask[:, prompt_start_i:prompt_end_i, prompt_start_j:prompt_end_j] = False + + attention_mask = attention_mask.float() + attention_mask[attention_mask == 0] = float('-inf') + attention_mask[attention_mask == 1] = 0 + return attention_mask + + + def process_entity_masks(self, hidden_states, prompt_emb, entity_prompt_emb, entity_masks, text_ids, image_ids): + repeat_dim = hidden_states.shape[1] + max_masks = 0 + attention_mask = None + prompt_embs = [prompt_emb] + if entity_masks is not None: + # entity_masks + batch_size, max_masks = entity_masks.shape[0], entity_masks.shape[1] + entity_masks = entity_masks.repeat(1, 1, repeat_dim, 1, 1) + entity_masks = [entity_masks[:, i, None].squeeze(1) for i in range(max_masks)] + # global mask + global_mask = torch.ones_like(entity_masks[0]).to(device=hidden_states.device, dtype=hidden_states.dtype) + entity_masks = entity_masks + [global_mask] # append global to last + # attention mask + attention_mask = self.construct_mask(entity_masks, prompt_emb.shape[1], hidden_states.shape[1]) + attention_mask = attention_mask.to(device=hidden_states.device, dtype=hidden_states.dtype) + attention_mask = attention_mask.unsqueeze(1) + # embds: n_masks * b * seq * d + local_embs = [entity_prompt_emb[:, i, None].squeeze(1) for i in range(max_masks)] + prompt_embs = local_embs + prompt_embs # append global to last + prompt_embs = [self.context_embedder(prompt_emb) for prompt_emb in prompt_embs] + prompt_emb = torch.cat(prompt_embs, dim=1) + + # positional embedding + text_ids = torch.cat([text_ids] * (max_masks + 1), dim=1) + image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1)) + return prompt_emb, image_rotary_emb, attention_mask + + + def forward( + self, + hidden_states, + timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None, + tiled=False, tile_size=128, tile_stride=64, entity_prompt_emb=None, entity_masks=None, + use_gradient_checkpointing=False, + **kwargs + ): + if tiled: + return self.tiled_forward( + hidden_states, + timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, + tile_size=tile_size, tile_stride=tile_stride, + **kwargs + ) + + if image_ids is None: + image_ids = self.prepare_image_ids(hidden_states) + + conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb) + if self.guidance_embedder is not None: + guidance = guidance * 1000 + conditioning = conditioning + self.guidance_embedder(guidance, hidden_states.dtype) + + height, width = hidden_states.shape[-2:] + hidden_states = self.patchify(hidden_states) + hidden_states = self.x_embedder(hidden_states) + + if entity_prompt_emb is not None and entity_masks is not None: + prompt_emb, image_rotary_emb, attention_mask = self.process_entity_masks(hidden_states, prompt_emb, entity_prompt_emb, entity_masks, text_ids, image_ids) + else: + prompt_emb = self.context_embedder(prompt_emb) + image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1)) + attention_mask = None + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + return custom_forward + + for block in self.blocks: + if self.training and use_gradient_checkpointing: + hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask, + use_reentrant=False, + ) + else: + hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask) + + hidden_states = torch.cat([prompt_emb, hidden_states], dim=1) + for block in self.single_blocks: + if self.training and use_gradient_checkpointing: + hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask, + use_reentrant=False, + ) + else: + hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask) + hidden_states = hidden_states[:, prompt_emb.shape[1]:] + + hidden_states = self.final_norm_out(hidden_states, conditioning) + hidden_states = self.final_proj_out(hidden_states) + hidden_states = self.unpatchify(hidden_states, height, width) + + return hidden_states + + + def quantize(self): + def cast_to(weight, dtype=None, device=None, copy=False): + if device is None or weight.device == device: + if not copy: + if dtype is None or weight.dtype == dtype: + return weight + return weight.to(dtype=dtype, copy=copy) + + r = torch.empty_like(weight, dtype=dtype, device=device) + r.copy_(weight) + return r + + def cast_weight(s, input=None, dtype=None, device=None): + if input is not None: + if dtype is None: + dtype = input.dtype + if device is None: + device = input.device + weight = cast_to(s.weight, dtype, device) + return weight + + def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None): + if input is not None: + if dtype is None: + dtype = input.dtype + if bias_dtype is None: + bias_dtype = dtype + if device is None: + device = input.device + bias = None + weight = cast_to(s.weight, dtype, device) + bias = cast_to(s.bias, bias_dtype, device) + return weight, bias + + class quantized_layer: + class Linear(torch.nn.Linear): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self,input,**kwargs): + weight,bias= cast_bias_weight(self,input) + return torch.nn.functional.linear(input,weight,bias) + + class RMSNorm(torch.nn.Module): + def __init__(self, module): + super().__init__() + self.module = module + + def forward(self,hidden_states,**kwargs): + weight= cast_weight(self.module,hidden_states) + input_dtype = hidden_states.dtype + variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.module.eps) + hidden_states = hidden_states.to(input_dtype) * weight + return hidden_states + + def replace_layer(model): + for name, module in model.named_children(): + if isinstance(module, torch.nn.Linear): + with init_weights_on_device(): + new_layer = quantized_layer.Linear(module.in_features,module.out_features) + new_layer.weight = module.weight + if module.bias is not None: + new_layer.bias = module.bias + # del module + setattr(model, name, new_layer) + elif isinstance(module, RMSNorm): + if hasattr(module,"quantized"): + continue + module.quantized= True + new_layer = quantized_layer.RMSNorm(module) + setattr(model, name, new_layer) + else: + replace_layer(module) + + replace_layer(self) + + + @staticmethod + def state_dict_converter(): + return FluxDiTStateDictConverter() + + +class FluxDiTStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + global_rename_dict = { + "context_embedder": "context_embedder", + "x_embedder": "x_embedder", + "time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0", + "time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2", + "time_text_embed.guidance_embedder.linear_1": "guidance_embedder.timestep_embedder.0", + "time_text_embed.guidance_embedder.linear_2": "guidance_embedder.timestep_embedder.2", + "time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0", + "time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2", + "norm_out.linear": "final_norm_out.linear", + "proj_out": "final_proj_out", + } + rename_dict = { + "proj_out": "proj_out", + "norm1.linear": "norm1_a.linear", + "norm1_context.linear": "norm1_b.linear", + "attn.to_q": "attn.a_to_q", + "attn.to_k": "attn.a_to_k", + "attn.to_v": "attn.a_to_v", + "attn.to_out.0": "attn.a_to_out", + "attn.add_q_proj": "attn.b_to_q", + "attn.add_k_proj": "attn.b_to_k", + "attn.add_v_proj": "attn.b_to_v", + "attn.to_add_out": "attn.b_to_out", + "ff.net.0.proj": "ff_a.0", + "ff.net.2": "ff_a.2", + "ff_context.net.0.proj": "ff_b.0", + "ff_context.net.2": "ff_b.2", + "attn.norm_q": "attn.norm_q_a", + "attn.norm_k": "attn.norm_k_a", + "attn.norm_added_q": "attn.norm_q_b", + "attn.norm_added_k": "attn.norm_k_b", + } + rename_dict_single = { + "attn.to_q": "a_to_q", + "attn.to_k": "a_to_k", + "attn.to_v": "a_to_v", + "attn.norm_q": "norm_q_a", + "attn.norm_k": "norm_k_a", + "norm.linear": "norm.linear", + "proj_mlp": "proj_in_besides_attn", + "proj_out": "proj_out", + } + state_dict_ = {} + for name, param in state_dict.items(): + if name.endswith(".weight") or name.endswith(".bias"): + suffix = ".weight" if name.endswith(".weight") else ".bias" + prefix = name[:-len(suffix)] + if prefix in global_rename_dict: + state_dict_[global_rename_dict[prefix] + suffix] = param + elif prefix.startswith("transformer_blocks."): + names = prefix.split(".") + names[0] = "blocks" + middle = ".".join(names[2:]) + if middle in rename_dict: + name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]]) + state_dict_[name_] = param + elif prefix.startswith("single_transformer_blocks."): + names = prefix.split(".") + names[0] = "single_blocks" + middle = ".".join(names[2:]) + if middle in rename_dict_single: + name_ = ".".join(names[:2] + [rename_dict_single[middle]] + [suffix[1:]]) + state_dict_[name_] = param + else: + pass + else: + pass + for name in list(state_dict_.keys()): + if "single_blocks." in name and ".a_to_q." in name: + mlp = state_dict_.get(name.replace(".a_to_q.", ".proj_in_besides_attn."), None) + if mlp is None: + mlp = torch.zeros(4 * state_dict_[name].shape[0], + *state_dict_[name].shape[1:], + dtype=state_dict_[name].dtype) + else: + state_dict_.pop(name.replace(".a_to_q.", ".proj_in_besides_attn.")) + param = torch.concat([ + state_dict_.pop(name), + state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")), + state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")), + mlp, + ], dim=0) + name_ = name.replace(".a_to_q.", ".to_qkv_mlp.") + state_dict_[name_] = param + for name in list(state_dict_.keys()): + for component in ["a", "b"]: + if f".{component}_to_q." in name: + name_ = name.replace(f".{component}_to_q.", f".{component}_to_qkv.") + param = torch.concat([ + state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")], + state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")], + state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")], + ], dim=0) + state_dict_[name_] = param + state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_q.")) + state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k.")) + state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v.")) + return state_dict_ + + def from_civitai(self, state_dict): + rename_dict = { + "time_in.in_layer.bias": "time_embedder.timestep_embedder.0.bias", + "time_in.in_layer.weight": "time_embedder.timestep_embedder.0.weight", + "time_in.out_layer.bias": "time_embedder.timestep_embedder.2.bias", + "time_in.out_layer.weight": "time_embedder.timestep_embedder.2.weight", + "txt_in.bias": "context_embedder.bias", + "txt_in.weight": "context_embedder.weight", + "vector_in.in_layer.bias": "pooled_text_embedder.0.bias", + "vector_in.in_layer.weight": "pooled_text_embedder.0.weight", + "vector_in.out_layer.bias": "pooled_text_embedder.2.bias", + "vector_in.out_layer.weight": "pooled_text_embedder.2.weight", + "final_layer.linear.bias": "final_proj_out.bias", + "final_layer.linear.weight": "final_proj_out.weight", + "guidance_in.in_layer.bias": "guidance_embedder.timestep_embedder.0.bias", + "guidance_in.in_layer.weight": "guidance_embedder.timestep_embedder.0.weight", + "guidance_in.out_layer.bias": "guidance_embedder.timestep_embedder.2.bias", + "guidance_in.out_layer.weight": "guidance_embedder.timestep_embedder.2.weight", + "img_in.bias": "x_embedder.bias", + "img_in.weight": "x_embedder.weight", + "final_layer.adaLN_modulation.1.weight": "final_norm_out.linear.weight", + "final_layer.adaLN_modulation.1.bias": "final_norm_out.linear.bias", + } + suffix_rename_dict = { + "img_attn.norm.key_norm.scale": "attn.norm_k_a.weight", + "img_attn.norm.query_norm.scale": "attn.norm_q_a.weight", + "img_attn.proj.bias": "attn.a_to_out.bias", + "img_attn.proj.weight": "attn.a_to_out.weight", + "img_attn.qkv.bias": "attn.a_to_qkv.bias", + "img_attn.qkv.weight": "attn.a_to_qkv.weight", + "img_mlp.0.bias": "ff_a.0.bias", + "img_mlp.0.weight": "ff_a.0.weight", + "img_mlp.2.bias": "ff_a.2.bias", + "img_mlp.2.weight": "ff_a.2.weight", + "img_mod.lin.bias": "norm1_a.linear.bias", + "img_mod.lin.weight": "norm1_a.linear.weight", + "txt_attn.norm.key_norm.scale": "attn.norm_k_b.weight", + "txt_attn.norm.query_norm.scale": "attn.norm_q_b.weight", + "txt_attn.proj.bias": "attn.b_to_out.bias", + "txt_attn.proj.weight": "attn.b_to_out.weight", + "txt_attn.qkv.bias": "attn.b_to_qkv.bias", + "txt_attn.qkv.weight": "attn.b_to_qkv.weight", + "txt_mlp.0.bias": "ff_b.0.bias", + "txt_mlp.0.weight": "ff_b.0.weight", + "txt_mlp.2.bias": "ff_b.2.bias", + "txt_mlp.2.weight": "ff_b.2.weight", + "txt_mod.lin.bias": "norm1_b.linear.bias", + "txt_mod.lin.weight": "norm1_b.linear.weight", + + "linear1.bias": "to_qkv_mlp.bias", + "linear1.weight": "to_qkv_mlp.weight", + "linear2.bias": "proj_out.bias", + "linear2.weight": "proj_out.weight", + "modulation.lin.bias": "norm.linear.bias", + "modulation.lin.weight": "norm.linear.weight", + "norm.key_norm.scale": "norm_k_a.weight", + "norm.query_norm.scale": "norm_q_a.weight", + } + state_dict_ = {} + for name, param in state_dict.items(): + if name.startswith("model.diffusion_model."): + name = name[len("model.diffusion_model."):] + names = name.split(".") + if name in rename_dict: + rename = rename_dict[name] + if name.startswith("final_layer.adaLN_modulation.1."): + param = torch.concat([param[3072:], param[:3072]], dim=0) + state_dict_[rename] = param + elif names[0] == "double_blocks": + rename = f"blocks.{names[1]}." + suffix_rename_dict[".".join(names[2:])] + state_dict_[rename] = param + elif names[0] == "single_blocks": + if ".".join(names[2:]) in suffix_rename_dict: + rename = f"single_blocks.{names[1]}." + suffix_rename_dict[".".join(names[2:])] + state_dict_[rename] = param + else: + pass + if "guidance_embedder.timestep_embedder.0.weight" not in state_dict_: + return state_dict_, {"disable_guidance_embedder": True} + elif "blocks.8.attn.norm_k_a.weight" not in state_dict_: + return state_dict_, {"input_dim": 196, "num_blocks": 8} + else: + return state_dict_ diff --git a/diffsynth/models/flux_infiniteyou.py b/diffsynth/models/flux_infiniteyou.py new file mode 100644 index 0000000000000000000000000000000000000000..861538a4b02fb6a52edee662b6efcd60f78f6916 --- /dev/null +++ b/diffsynth/models/flux_infiniteyou.py @@ -0,0 +1,129 @@ +import math +import torch +import torch.nn as nn + + +# FFN +def FeedForward(dim, mult=4): + inner_dim = int(dim * mult) + return nn.Sequential( + nn.LayerNorm(dim), + nn.Linear(dim, inner_dim, bias=False), + nn.GELU(), + nn.Linear(inner_dim, dim, bias=False), + ) + + +def reshape_tensor(x, heads): + bs, length, width = x.shape + #(bs, length, width) --> (bs, length, n_heads, dim_per_head) + x = x.view(bs, length, heads, -1) + # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head) + x = x.transpose(1, 2) + # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head) + x = x.reshape(bs, heads, length, -1) + return x + + +class PerceiverAttention(nn.Module): + + def __init__(self, *, dim, dim_head=64, heads=8): + super().__init__() + self.scale = dim_head**-0.5 + self.dim_head = dim_head + self.heads = heads + inner_dim = dim_head * heads + + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + + def forward(self, x, latents): + """ + Args: + x (torch.Tensor): image features + shape (b, n1, D) + latent (torch.Tensor): latent features + shape (b, n2, D) + """ + x = self.norm1(x) + latents = self.norm2(latents) + + b, l, _ = latents.shape + + q = self.to_q(latents) + kv_input = torch.cat((x, latents), dim=-2) + k, v = self.to_kv(kv_input).chunk(2, dim=-1) + + q = reshape_tensor(q, self.heads) + k = reshape_tensor(k, self.heads) + v = reshape_tensor(v, self.heads) + + # attention + scale = 1 / math.sqrt(math.sqrt(self.dim_head)) + weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + out = weight @ v + + out = out.permute(0, 2, 1, 3).reshape(b, l, -1) + + return self.to_out(out) + + +class InfiniteYouImageProjector(nn.Module): + + def __init__( + self, + dim=1280, + depth=4, + dim_head=64, + heads=20, + num_queries=8, + embedding_dim=512, + output_dim=4096, + ff_mult=4, + ): + super().__init__() + self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5) + self.proj_in = nn.Linear(embedding_dim, dim) + + self.proj_out = nn.Linear(dim, output_dim) + self.norm_out = nn.LayerNorm(output_dim) + + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append( + nn.ModuleList([ + PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), + FeedForward(dim=dim, mult=ff_mult), + ])) + + def forward(self, x): + + latents = self.latents.repeat(x.size(0), 1, 1) + latents = latents.to(dtype=x.dtype, device=x.device) + + x = self.proj_in(x) + + for attn, ff in self.layers: + latents = attn(x, latents) + latents + latents = ff(latents) + latents + + latents = self.proj_out(latents) + return self.norm_out(latents) + + @staticmethod + def state_dict_converter(): + return FluxInfiniteYouImageProjectorStateDictConverter() + + +class FluxInfiniteYouImageProjectorStateDictConverter: + + def __init__(self): + pass + + def from_diffusers(self, state_dict): + return state_dict['image_proj'] diff --git a/diffsynth/models/flux_ipadapter.py b/diffsynth/models/flux_ipadapter.py new file mode 100644 index 0000000000000000000000000000000000000000..575c75268c30f9c1d6e6b35d11b93bc57d80cb3f --- /dev/null +++ b/diffsynth/models/flux_ipadapter.py @@ -0,0 +1,94 @@ +from .svd_image_encoder import SVDImageEncoder +from .sd3_dit import RMSNorm +from transformers import CLIPImageProcessor +import torch + + +class MLPProjModel(torch.nn.Module): + def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4): + super().__init__() + + self.cross_attention_dim = cross_attention_dim + self.num_tokens = num_tokens + + self.proj = torch.nn.Sequential( + torch.nn.Linear(id_embeddings_dim, id_embeddings_dim*2), + torch.nn.GELU(), + torch.nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens), + ) + self.norm = torch.nn.LayerNorm(cross_attention_dim) + + def forward(self, id_embeds): + x = self.proj(id_embeds) + x = x.reshape(-1, self.num_tokens, self.cross_attention_dim) + x = self.norm(x) + return x + +class IpAdapterModule(torch.nn.Module): + def __init__(self, num_attention_heads, attention_head_dim, input_dim): + super().__init__() + self.num_heads = num_attention_heads + self.head_dim = attention_head_dim + output_dim = num_attention_heads * attention_head_dim + self.to_k_ip = torch.nn.Linear(input_dim, output_dim, bias=False) + self.to_v_ip = torch.nn.Linear(input_dim, output_dim, bias=False) + self.norm_added_k = RMSNorm(attention_head_dim, eps=1e-5, elementwise_affine=False) + + + def forward(self, hidden_states): + batch_size = hidden_states.shape[0] + # ip_k + ip_k = self.to_k_ip(hidden_states) + ip_k = ip_k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + ip_k = self.norm_added_k(ip_k) + # ip_v + ip_v = self.to_v_ip(hidden_states) + ip_v = ip_v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + return ip_k, ip_v + + +class FluxIpAdapter(torch.nn.Module): + def __init__(self, num_attention_heads=24, attention_head_dim=128, cross_attention_dim=4096, num_tokens=128, num_blocks=57): + super().__init__() + self.ipadapter_modules = torch.nn.ModuleList([IpAdapterModule(num_attention_heads, attention_head_dim, cross_attention_dim) for _ in range(num_blocks)]) + self.image_proj = MLPProjModel(cross_attention_dim=cross_attention_dim, id_embeddings_dim=1152, num_tokens=num_tokens) + self.set_adapter() + + def set_adapter(self): + self.call_block_id = {i:i for i in range(len(self.ipadapter_modules))} + + def forward(self, hidden_states, scale=1.0): + hidden_states = self.image_proj(hidden_states) + hidden_states = hidden_states.view(1, -1, hidden_states.shape[-1]) + ip_kv_dict = {} + for block_id in self.call_block_id: + ipadapter_id = self.call_block_id[block_id] + ip_k, ip_v = self.ipadapter_modules[ipadapter_id](hidden_states) + ip_kv_dict[block_id] = { + "ip_k": ip_k, + "ip_v": ip_v, + "scale": scale + } + return ip_kv_dict + + @staticmethod + def state_dict_converter(): + return FluxIpAdapterStateDictConverter() + + +class FluxIpAdapterStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + state_dict_ = {} + for name in state_dict["ip_adapter"]: + name_ = 'ipadapter_modules.' + name + state_dict_[name_] = state_dict["ip_adapter"][name] + for name in state_dict["image_proj"]: + name_ = "image_proj." + name + state_dict_[name_] = state_dict["image_proj"][name] + return state_dict_ + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict) diff --git a/diffsynth/models/flux_lora_encoder.py b/diffsynth/models/flux_lora_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..695640a8c8690f7f20f6942615c400849e9298a8 --- /dev/null +++ b/diffsynth/models/flux_lora_encoder.py @@ -0,0 +1,111 @@ +import torch +from .sd_text_encoder import CLIPEncoderLayer + + +class LoRALayerBlock(torch.nn.Module): + def __init__(self, L, dim_in, dim_out): + super().__init__() + self.x = torch.nn.Parameter(torch.randn(1, L, dim_in)) + self.layer_norm = torch.nn.LayerNorm(dim_out) + + def forward(self, lora_A, lora_B): + x = self.x @ lora_A.T @ lora_B.T + x = self.layer_norm(x) + return x + + +class LoRAEmbedder(torch.nn.Module): + def __init__(self, lora_patterns=None, L=1, out_dim=2048): + super().__init__() + if lora_patterns is None: + lora_patterns = self.default_lora_patterns() + + model_dict = {} + for lora_pattern in lora_patterns: + name, dim = lora_pattern["name"], lora_pattern["dim"] + model_dict[name.replace(".", "___")] = LoRALayerBlock(L, dim[0], dim[1]) + self.model_dict = torch.nn.ModuleDict(model_dict) + + proj_dict = {} + for lora_pattern in lora_patterns: + layer_type, dim = lora_pattern["type"], lora_pattern["dim"] + if layer_type not in proj_dict: + proj_dict[layer_type.replace(".", "___")] = torch.nn.Linear(dim[1], out_dim) + self.proj_dict = torch.nn.ModuleDict(proj_dict) + + self.lora_patterns = lora_patterns + + + def default_lora_patterns(self): + lora_patterns = [] + lora_dict = { + "attn.a_to_qkv": (3072, 9216), "attn.a_to_out": (3072, 3072), "ff_a.0": (3072, 12288), "ff_a.2": (12288, 3072), "norm1_a.linear": (3072, 18432), + "attn.b_to_qkv": (3072, 9216), "attn.b_to_out": (3072, 3072), "ff_b.0": (3072, 12288), "ff_b.2": (12288, 3072), "norm1_b.linear": (3072, 18432), + } + for i in range(19): + for suffix in lora_dict: + lora_patterns.append({ + "name": f"blocks.{i}.{suffix}", + "dim": lora_dict[suffix], + "type": suffix, + }) + lora_dict = {"to_qkv_mlp": (3072, 21504), "proj_out": (15360, 3072), "norm.linear": (3072, 9216)} + for i in range(38): + for suffix in lora_dict: + lora_patterns.append({ + "name": f"single_blocks.{i}.{suffix}", + "dim": lora_dict[suffix], + "type": suffix, + }) + return lora_patterns + + def forward(self, lora): + lora_emb = [] + for lora_pattern in self.lora_patterns: + name, layer_type = lora_pattern["name"], lora_pattern["type"] + lora_A = lora[name + ".lora_A.default.weight"] + lora_B = lora[name + ".lora_B.default.weight"] + lora_out = self.model_dict[name.replace(".", "___")](lora_A, lora_B) + lora_out = self.proj_dict[layer_type.replace(".", "___")](lora_out) + lora_emb.append(lora_out) + lora_emb = torch.concat(lora_emb, dim=1) + return lora_emb + + +class FluxLoRAEncoder(torch.nn.Module): + def __init__(self, embed_dim=4096, encoder_intermediate_size=8192, num_encoder_layers=1, num_embeds_per_lora=16, num_special_embeds=1): + super().__init__() + self.num_embeds_per_lora = num_embeds_per_lora + # embedder + self.embedder = LoRAEmbedder(L=num_embeds_per_lora, out_dim=embed_dim) + + # encoders + self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size, num_heads=32, head_dim=128) for _ in range(num_encoder_layers)]) + + # special embedding + self.special_embeds = torch.nn.Parameter(torch.randn(1, num_special_embeds, embed_dim)) + self.num_special_embeds = num_special_embeds + + # final layer + self.final_layer_norm = torch.nn.LayerNorm(embed_dim) + self.final_linear = torch.nn.Linear(embed_dim, embed_dim) + + def forward(self, lora): + lora_embeds = self.embedder(lora) + special_embeds = self.special_embeds.to(dtype=lora_embeds.dtype, device=lora_embeds.device) + embeds = torch.concat([special_embeds, lora_embeds], dim=1) + for encoder_id, encoder in enumerate(self.encoders): + embeds = encoder(embeds) + embeds = embeds[:, :self.num_special_embeds] + embeds = self.final_layer_norm(embeds) + embeds = self.final_linear(embeds) + return embeds + + @staticmethod + def state_dict_converter(): + return FluxLoRAEncoderStateDictConverter() + + +class FluxLoRAEncoderStateDictConverter: + def from_civitai(self, state_dict): + return state_dict diff --git a/diffsynth/models/flux_text_encoder.py b/diffsynth/models/flux_text_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..bff9d2944e50e42ef9136269b3e0b1c1ea508a79 --- /dev/null +++ b/diffsynth/models/flux_text_encoder.py @@ -0,0 +1,32 @@ +import torch +from transformers import T5EncoderModel, T5Config +from .sd_text_encoder import SDTextEncoder + + + +class FluxTextEncoder2(T5EncoderModel): + def __init__(self, config): + super().__init__(config) + self.eval() + + def forward(self, input_ids): + outputs = super().forward(input_ids=input_ids) + prompt_emb = outputs.last_hidden_state + return prompt_emb + + @staticmethod + def state_dict_converter(): + return FluxTextEncoder2StateDictConverter() + + + +class FluxTextEncoder2StateDictConverter(): + def __init__(self): + pass + + def from_diffusers(self, state_dict): + state_dict_ = state_dict + return state_dict_ + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict) diff --git a/diffsynth/models/flux_vae.py b/diffsynth/models/flux_vae.py new file mode 100644 index 0000000000000000000000000000000000000000..c99c65522c4d0339adcc9aa734445377b9fdd5b5 --- /dev/null +++ b/diffsynth/models/flux_vae.py @@ -0,0 +1,303 @@ +from .sd3_vae_encoder import SD3VAEEncoder, SDVAEEncoderStateDictConverter +from .sd3_vae_decoder import SD3VAEDecoder, SDVAEDecoderStateDictConverter + + +class FluxVAEEncoder(SD3VAEEncoder): + def __init__(self): + super().__init__() + self.scaling_factor = 0.3611 + self.shift_factor = 0.1159 + + @staticmethod + def state_dict_converter(): + return FluxVAEEncoderStateDictConverter() + + +class FluxVAEDecoder(SD3VAEDecoder): + def __init__(self): + super().__init__() + self.scaling_factor = 0.3611 + self.shift_factor = 0.1159 + + @staticmethod + def state_dict_converter(): + return FluxVAEDecoderStateDictConverter() + + +class FluxVAEEncoderStateDictConverter(SDVAEEncoderStateDictConverter): + def __init__(self): + pass + + def from_civitai(self, state_dict): + rename_dict = { + "encoder.conv_in.bias": "conv_in.bias", + "encoder.conv_in.weight": "conv_in.weight", + "encoder.conv_out.bias": "conv_out.bias", + "encoder.conv_out.weight": "conv_out.weight", + "encoder.down.0.block.0.conv1.bias": "blocks.0.conv1.bias", + "encoder.down.0.block.0.conv1.weight": "blocks.0.conv1.weight", + "encoder.down.0.block.0.conv2.bias": "blocks.0.conv2.bias", + "encoder.down.0.block.0.conv2.weight": "blocks.0.conv2.weight", + "encoder.down.0.block.0.norm1.bias": "blocks.0.norm1.bias", + "encoder.down.0.block.0.norm1.weight": "blocks.0.norm1.weight", + "encoder.down.0.block.0.norm2.bias": "blocks.0.norm2.bias", + "encoder.down.0.block.0.norm2.weight": "blocks.0.norm2.weight", + "encoder.down.0.block.1.conv1.bias": "blocks.1.conv1.bias", + "encoder.down.0.block.1.conv1.weight": "blocks.1.conv1.weight", + "encoder.down.0.block.1.conv2.bias": "blocks.1.conv2.bias", + "encoder.down.0.block.1.conv2.weight": "blocks.1.conv2.weight", + "encoder.down.0.block.1.norm1.bias": "blocks.1.norm1.bias", + "encoder.down.0.block.1.norm1.weight": "blocks.1.norm1.weight", + "encoder.down.0.block.1.norm2.bias": "blocks.1.norm2.bias", + "encoder.down.0.block.1.norm2.weight": "blocks.1.norm2.weight", + "encoder.down.0.downsample.conv.bias": "blocks.2.conv.bias", + "encoder.down.0.downsample.conv.weight": "blocks.2.conv.weight", + "encoder.down.1.block.0.conv1.bias": "blocks.3.conv1.bias", + "encoder.down.1.block.0.conv1.weight": "blocks.3.conv1.weight", + "encoder.down.1.block.0.conv2.bias": "blocks.3.conv2.bias", + "encoder.down.1.block.0.conv2.weight": "blocks.3.conv2.weight", + "encoder.down.1.block.0.nin_shortcut.bias": "blocks.3.conv_shortcut.bias", + "encoder.down.1.block.0.nin_shortcut.weight": "blocks.3.conv_shortcut.weight", + "encoder.down.1.block.0.norm1.bias": "blocks.3.norm1.bias", + "encoder.down.1.block.0.norm1.weight": "blocks.3.norm1.weight", + "encoder.down.1.block.0.norm2.bias": "blocks.3.norm2.bias", + "encoder.down.1.block.0.norm2.weight": "blocks.3.norm2.weight", + "encoder.down.1.block.1.conv1.bias": "blocks.4.conv1.bias", + "encoder.down.1.block.1.conv1.weight": "blocks.4.conv1.weight", + "encoder.down.1.block.1.conv2.bias": "blocks.4.conv2.bias", + "encoder.down.1.block.1.conv2.weight": "blocks.4.conv2.weight", + "encoder.down.1.block.1.norm1.bias": "blocks.4.norm1.bias", + "encoder.down.1.block.1.norm1.weight": "blocks.4.norm1.weight", + "encoder.down.1.block.1.norm2.bias": "blocks.4.norm2.bias", + "encoder.down.1.block.1.norm2.weight": "blocks.4.norm2.weight", + "encoder.down.1.downsample.conv.bias": "blocks.5.conv.bias", + "encoder.down.1.downsample.conv.weight": "blocks.5.conv.weight", + "encoder.down.2.block.0.conv1.bias": "blocks.6.conv1.bias", + "encoder.down.2.block.0.conv1.weight": "blocks.6.conv1.weight", + "encoder.down.2.block.0.conv2.bias": "blocks.6.conv2.bias", + "encoder.down.2.block.0.conv2.weight": "blocks.6.conv2.weight", + "encoder.down.2.block.0.nin_shortcut.bias": "blocks.6.conv_shortcut.bias", + "encoder.down.2.block.0.nin_shortcut.weight": "blocks.6.conv_shortcut.weight", + "encoder.down.2.block.0.norm1.bias": "blocks.6.norm1.bias", + "encoder.down.2.block.0.norm1.weight": "blocks.6.norm1.weight", + "encoder.down.2.block.0.norm2.bias": "blocks.6.norm2.bias", + "encoder.down.2.block.0.norm2.weight": "blocks.6.norm2.weight", + "encoder.down.2.block.1.conv1.bias": "blocks.7.conv1.bias", + "encoder.down.2.block.1.conv1.weight": "blocks.7.conv1.weight", + "encoder.down.2.block.1.conv2.bias": "blocks.7.conv2.bias", + "encoder.down.2.block.1.conv2.weight": "blocks.7.conv2.weight", + "encoder.down.2.block.1.norm1.bias": "blocks.7.norm1.bias", + "encoder.down.2.block.1.norm1.weight": "blocks.7.norm1.weight", + "encoder.down.2.block.1.norm2.bias": "blocks.7.norm2.bias", + "encoder.down.2.block.1.norm2.weight": "blocks.7.norm2.weight", + "encoder.down.2.downsample.conv.bias": "blocks.8.conv.bias", + "encoder.down.2.downsample.conv.weight": "blocks.8.conv.weight", + "encoder.down.3.block.0.conv1.bias": "blocks.9.conv1.bias", + "encoder.down.3.block.0.conv1.weight": "blocks.9.conv1.weight", + "encoder.down.3.block.0.conv2.bias": "blocks.9.conv2.bias", + "encoder.down.3.block.0.conv2.weight": "blocks.9.conv2.weight", + "encoder.down.3.block.0.norm1.bias": "blocks.9.norm1.bias", + "encoder.down.3.block.0.norm1.weight": "blocks.9.norm1.weight", + "encoder.down.3.block.0.norm2.bias": "blocks.9.norm2.bias", + "encoder.down.3.block.0.norm2.weight": "blocks.9.norm2.weight", + "encoder.down.3.block.1.conv1.bias": "blocks.10.conv1.bias", + "encoder.down.3.block.1.conv1.weight": "blocks.10.conv1.weight", + "encoder.down.3.block.1.conv2.bias": "blocks.10.conv2.bias", + "encoder.down.3.block.1.conv2.weight": "blocks.10.conv2.weight", + "encoder.down.3.block.1.norm1.bias": "blocks.10.norm1.bias", + "encoder.down.3.block.1.norm1.weight": "blocks.10.norm1.weight", + "encoder.down.3.block.1.norm2.bias": "blocks.10.norm2.bias", + "encoder.down.3.block.1.norm2.weight": "blocks.10.norm2.weight", + "encoder.mid.attn_1.k.bias": "blocks.12.transformer_blocks.0.to_k.bias", + "encoder.mid.attn_1.k.weight": "blocks.12.transformer_blocks.0.to_k.weight", + "encoder.mid.attn_1.norm.bias": "blocks.12.norm.bias", + "encoder.mid.attn_1.norm.weight": "blocks.12.norm.weight", + "encoder.mid.attn_1.proj_out.bias": "blocks.12.transformer_blocks.0.to_out.bias", + "encoder.mid.attn_1.proj_out.weight": "blocks.12.transformer_blocks.0.to_out.weight", + "encoder.mid.attn_1.q.bias": "blocks.12.transformer_blocks.0.to_q.bias", + "encoder.mid.attn_1.q.weight": "blocks.12.transformer_blocks.0.to_q.weight", + "encoder.mid.attn_1.v.bias": "blocks.12.transformer_blocks.0.to_v.bias", + "encoder.mid.attn_1.v.weight": "blocks.12.transformer_blocks.0.to_v.weight", + "encoder.mid.block_1.conv1.bias": "blocks.11.conv1.bias", + "encoder.mid.block_1.conv1.weight": "blocks.11.conv1.weight", + "encoder.mid.block_1.conv2.bias": "blocks.11.conv2.bias", + "encoder.mid.block_1.conv2.weight": "blocks.11.conv2.weight", + "encoder.mid.block_1.norm1.bias": "blocks.11.norm1.bias", + "encoder.mid.block_1.norm1.weight": "blocks.11.norm1.weight", + "encoder.mid.block_1.norm2.bias": "blocks.11.norm2.bias", + "encoder.mid.block_1.norm2.weight": "blocks.11.norm2.weight", + "encoder.mid.block_2.conv1.bias": "blocks.13.conv1.bias", + "encoder.mid.block_2.conv1.weight": "blocks.13.conv1.weight", + "encoder.mid.block_2.conv2.bias": "blocks.13.conv2.bias", + "encoder.mid.block_2.conv2.weight": "blocks.13.conv2.weight", + "encoder.mid.block_2.norm1.bias": "blocks.13.norm1.bias", + "encoder.mid.block_2.norm1.weight": "blocks.13.norm1.weight", + "encoder.mid.block_2.norm2.bias": "blocks.13.norm2.bias", + "encoder.mid.block_2.norm2.weight": "blocks.13.norm2.weight", + "encoder.norm_out.bias": "conv_norm_out.bias", + "encoder.norm_out.weight": "conv_norm_out.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if "transformer_blocks" in rename_dict[name]: + param = param.squeeze() + state_dict_[rename_dict[name]] = param + return state_dict_ + + + +class FluxVAEDecoderStateDictConverter(SDVAEDecoderStateDictConverter): + def __init__(self): + pass + + def from_civitai(self, state_dict): + rename_dict = { + "decoder.conv_in.bias": "conv_in.bias", + "decoder.conv_in.weight": "conv_in.weight", + "decoder.conv_out.bias": "conv_out.bias", + "decoder.conv_out.weight": "conv_out.weight", + "decoder.mid.attn_1.k.bias": "blocks.1.transformer_blocks.0.to_k.bias", + "decoder.mid.attn_1.k.weight": "blocks.1.transformer_blocks.0.to_k.weight", + "decoder.mid.attn_1.norm.bias": "blocks.1.norm.bias", + "decoder.mid.attn_1.norm.weight": "blocks.1.norm.weight", + "decoder.mid.attn_1.proj_out.bias": "blocks.1.transformer_blocks.0.to_out.bias", + "decoder.mid.attn_1.proj_out.weight": "blocks.1.transformer_blocks.0.to_out.weight", + "decoder.mid.attn_1.q.bias": "blocks.1.transformer_blocks.0.to_q.bias", + "decoder.mid.attn_1.q.weight": "blocks.1.transformer_blocks.0.to_q.weight", + "decoder.mid.attn_1.v.bias": "blocks.1.transformer_blocks.0.to_v.bias", + "decoder.mid.attn_1.v.weight": "blocks.1.transformer_blocks.0.to_v.weight", + "decoder.mid.block_1.conv1.bias": "blocks.0.conv1.bias", + "decoder.mid.block_1.conv1.weight": "blocks.0.conv1.weight", + "decoder.mid.block_1.conv2.bias": "blocks.0.conv2.bias", + "decoder.mid.block_1.conv2.weight": "blocks.0.conv2.weight", + "decoder.mid.block_1.norm1.bias": "blocks.0.norm1.bias", + "decoder.mid.block_1.norm1.weight": "blocks.0.norm1.weight", + "decoder.mid.block_1.norm2.bias": "blocks.0.norm2.bias", + "decoder.mid.block_1.norm2.weight": "blocks.0.norm2.weight", + "decoder.mid.block_2.conv1.bias": "blocks.2.conv1.bias", + "decoder.mid.block_2.conv1.weight": "blocks.2.conv1.weight", + "decoder.mid.block_2.conv2.bias": "blocks.2.conv2.bias", + "decoder.mid.block_2.conv2.weight": "blocks.2.conv2.weight", + "decoder.mid.block_2.norm1.bias": "blocks.2.norm1.bias", + "decoder.mid.block_2.norm1.weight": "blocks.2.norm1.weight", + "decoder.mid.block_2.norm2.bias": "blocks.2.norm2.bias", + "decoder.mid.block_2.norm2.weight": "blocks.2.norm2.weight", + "decoder.norm_out.bias": "conv_norm_out.bias", + "decoder.norm_out.weight": "conv_norm_out.weight", + "decoder.up.0.block.0.conv1.bias": "blocks.15.conv1.bias", + "decoder.up.0.block.0.conv1.weight": "blocks.15.conv1.weight", + "decoder.up.0.block.0.conv2.bias": "blocks.15.conv2.bias", + "decoder.up.0.block.0.conv2.weight": "blocks.15.conv2.weight", + "decoder.up.0.block.0.nin_shortcut.bias": "blocks.15.conv_shortcut.bias", + "decoder.up.0.block.0.nin_shortcut.weight": "blocks.15.conv_shortcut.weight", + "decoder.up.0.block.0.norm1.bias": "blocks.15.norm1.bias", + "decoder.up.0.block.0.norm1.weight": "blocks.15.norm1.weight", + "decoder.up.0.block.0.norm2.bias": "blocks.15.norm2.bias", + "decoder.up.0.block.0.norm2.weight": "blocks.15.norm2.weight", + "decoder.up.0.block.1.conv1.bias": "blocks.16.conv1.bias", + "decoder.up.0.block.1.conv1.weight": "blocks.16.conv1.weight", + "decoder.up.0.block.1.conv2.bias": "blocks.16.conv2.bias", + "decoder.up.0.block.1.conv2.weight": "blocks.16.conv2.weight", + "decoder.up.0.block.1.norm1.bias": "blocks.16.norm1.bias", + "decoder.up.0.block.1.norm1.weight": "blocks.16.norm1.weight", + "decoder.up.0.block.1.norm2.bias": "blocks.16.norm2.bias", + "decoder.up.0.block.1.norm2.weight": "blocks.16.norm2.weight", + "decoder.up.0.block.2.conv1.bias": "blocks.17.conv1.bias", + "decoder.up.0.block.2.conv1.weight": "blocks.17.conv1.weight", + "decoder.up.0.block.2.conv2.bias": "blocks.17.conv2.bias", + "decoder.up.0.block.2.conv2.weight": "blocks.17.conv2.weight", + "decoder.up.0.block.2.norm1.bias": "blocks.17.norm1.bias", + "decoder.up.0.block.2.norm1.weight": "blocks.17.norm1.weight", + "decoder.up.0.block.2.norm2.bias": "blocks.17.norm2.bias", + "decoder.up.0.block.2.norm2.weight": "blocks.17.norm2.weight", + "decoder.up.1.block.0.conv1.bias": "blocks.11.conv1.bias", + "decoder.up.1.block.0.conv1.weight": "blocks.11.conv1.weight", + "decoder.up.1.block.0.conv2.bias": "blocks.11.conv2.bias", + "decoder.up.1.block.0.conv2.weight": "blocks.11.conv2.weight", + "decoder.up.1.block.0.nin_shortcut.bias": "blocks.11.conv_shortcut.bias", + "decoder.up.1.block.0.nin_shortcut.weight": "blocks.11.conv_shortcut.weight", + "decoder.up.1.block.0.norm1.bias": "blocks.11.norm1.bias", + "decoder.up.1.block.0.norm1.weight": "blocks.11.norm1.weight", + "decoder.up.1.block.0.norm2.bias": "blocks.11.norm2.bias", + "decoder.up.1.block.0.norm2.weight": "blocks.11.norm2.weight", + "decoder.up.1.block.1.conv1.bias": "blocks.12.conv1.bias", + "decoder.up.1.block.1.conv1.weight": "blocks.12.conv1.weight", + "decoder.up.1.block.1.conv2.bias": "blocks.12.conv2.bias", + "decoder.up.1.block.1.conv2.weight": "blocks.12.conv2.weight", + "decoder.up.1.block.1.norm1.bias": "blocks.12.norm1.bias", + "decoder.up.1.block.1.norm1.weight": "blocks.12.norm1.weight", + "decoder.up.1.block.1.norm2.bias": "blocks.12.norm2.bias", + "decoder.up.1.block.1.norm2.weight": "blocks.12.norm2.weight", + "decoder.up.1.block.2.conv1.bias": "blocks.13.conv1.bias", + "decoder.up.1.block.2.conv1.weight": "blocks.13.conv1.weight", + "decoder.up.1.block.2.conv2.bias": "blocks.13.conv2.bias", + "decoder.up.1.block.2.conv2.weight": "blocks.13.conv2.weight", + "decoder.up.1.block.2.norm1.bias": "blocks.13.norm1.bias", + "decoder.up.1.block.2.norm1.weight": "blocks.13.norm1.weight", + "decoder.up.1.block.2.norm2.bias": "blocks.13.norm2.bias", + "decoder.up.1.block.2.norm2.weight": "blocks.13.norm2.weight", + "decoder.up.1.upsample.conv.bias": "blocks.14.conv.bias", + "decoder.up.1.upsample.conv.weight": "blocks.14.conv.weight", + "decoder.up.2.block.0.conv1.bias": "blocks.7.conv1.bias", + "decoder.up.2.block.0.conv1.weight": "blocks.7.conv1.weight", + "decoder.up.2.block.0.conv2.bias": "blocks.7.conv2.bias", + "decoder.up.2.block.0.conv2.weight": "blocks.7.conv2.weight", + "decoder.up.2.block.0.norm1.bias": "blocks.7.norm1.bias", + "decoder.up.2.block.0.norm1.weight": "blocks.7.norm1.weight", + "decoder.up.2.block.0.norm2.bias": "blocks.7.norm2.bias", + "decoder.up.2.block.0.norm2.weight": "blocks.7.norm2.weight", + "decoder.up.2.block.1.conv1.bias": "blocks.8.conv1.bias", + "decoder.up.2.block.1.conv1.weight": "blocks.8.conv1.weight", + "decoder.up.2.block.1.conv2.bias": "blocks.8.conv2.bias", + "decoder.up.2.block.1.conv2.weight": "blocks.8.conv2.weight", + "decoder.up.2.block.1.norm1.bias": "blocks.8.norm1.bias", + "decoder.up.2.block.1.norm1.weight": "blocks.8.norm1.weight", + "decoder.up.2.block.1.norm2.bias": "blocks.8.norm2.bias", + "decoder.up.2.block.1.norm2.weight": "blocks.8.norm2.weight", + "decoder.up.2.block.2.conv1.bias": "blocks.9.conv1.bias", + "decoder.up.2.block.2.conv1.weight": "blocks.9.conv1.weight", + "decoder.up.2.block.2.conv2.bias": "blocks.9.conv2.bias", + "decoder.up.2.block.2.conv2.weight": "blocks.9.conv2.weight", + "decoder.up.2.block.2.norm1.bias": "blocks.9.norm1.bias", + "decoder.up.2.block.2.norm1.weight": "blocks.9.norm1.weight", + "decoder.up.2.block.2.norm2.bias": "blocks.9.norm2.bias", + "decoder.up.2.block.2.norm2.weight": "blocks.9.norm2.weight", + "decoder.up.2.upsample.conv.bias": "blocks.10.conv.bias", + "decoder.up.2.upsample.conv.weight": "blocks.10.conv.weight", + "decoder.up.3.block.0.conv1.bias": "blocks.3.conv1.bias", + "decoder.up.3.block.0.conv1.weight": "blocks.3.conv1.weight", + "decoder.up.3.block.0.conv2.bias": "blocks.3.conv2.bias", + "decoder.up.3.block.0.conv2.weight": "blocks.3.conv2.weight", + "decoder.up.3.block.0.norm1.bias": "blocks.3.norm1.bias", + "decoder.up.3.block.0.norm1.weight": "blocks.3.norm1.weight", + "decoder.up.3.block.0.norm2.bias": "blocks.3.norm2.bias", + "decoder.up.3.block.0.norm2.weight": "blocks.3.norm2.weight", + "decoder.up.3.block.1.conv1.bias": "blocks.4.conv1.bias", + "decoder.up.3.block.1.conv1.weight": "blocks.4.conv1.weight", + "decoder.up.3.block.1.conv2.bias": "blocks.4.conv2.bias", + "decoder.up.3.block.1.conv2.weight": "blocks.4.conv2.weight", + "decoder.up.3.block.1.norm1.bias": "blocks.4.norm1.bias", + "decoder.up.3.block.1.norm1.weight": "blocks.4.norm1.weight", + "decoder.up.3.block.1.norm2.bias": "blocks.4.norm2.bias", + "decoder.up.3.block.1.norm2.weight": "blocks.4.norm2.weight", + "decoder.up.3.block.2.conv1.bias": "blocks.5.conv1.bias", + "decoder.up.3.block.2.conv1.weight": "blocks.5.conv1.weight", + "decoder.up.3.block.2.conv2.bias": "blocks.5.conv2.bias", + "decoder.up.3.block.2.conv2.weight": "blocks.5.conv2.weight", + "decoder.up.3.block.2.norm1.bias": "blocks.5.norm1.bias", + "decoder.up.3.block.2.norm1.weight": "blocks.5.norm1.weight", + "decoder.up.3.block.2.norm2.bias": "blocks.5.norm2.bias", + "decoder.up.3.block.2.norm2.weight": "blocks.5.norm2.weight", + "decoder.up.3.upsample.conv.bias": "blocks.6.conv.bias", + "decoder.up.3.upsample.conv.weight": "blocks.6.conv.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if "transformer_blocks" in rename_dict[name]: + param = param.squeeze() + state_dict_[rename_dict[name]] = param + return state_dict_ \ No newline at end of file diff --git a/diffsynth/models/flux_value_control.py b/diffsynth/models/flux_value_control.py new file mode 100644 index 0000000000000000000000000000000000000000..69813442269350b8b48861cd938ba95cce5a0f74 --- /dev/null +++ b/diffsynth/models/flux_value_control.py @@ -0,0 +1,60 @@ +import torch +from diffsynth.models.svd_unet import TemporalTimesteps + + +class MultiValueEncoder(torch.nn.Module): + def __init__(self, encoders=()): + super().__init__() + self.encoders = torch.nn.ModuleList(encoders) + + def __call__(self, values, dtype): + emb = [] + for encoder, value in zip(self.encoders, values): + if value is not None: + value = value.unsqueeze(0) + emb.append(encoder(value, dtype)) + emb = torch.concat(emb, dim=0) + return emb + + +class SingleValueEncoder(torch.nn.Module): + def __init__(self, dim_in=256, dim_out=4096, prefer_len=32, computation_device=None): + super().__init__() + self.prefer_len = prefer_len + self.prefer_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0, computation_device=computation_device) + self.prefer_value_embedder = torch.nn.Sequential( + torch.nn.Linear(dim_in, dim_out), torch.nn.SiLU(), torch.nn.Linear(dim_out, dim_out) + ) + self.positional_embedding = torch.nn.Parameter( + torch.randn(self.prefer_len, dim_out) + ) + self._initialize_weights() + + def _initialize_weights(self): + last_linear = self.prefer_value_embedder[-1] + torch.nn.init.zeros_(last_linear.weight) + torch.nn.init.zeros_(last_linear.bias) + + def forward(self, value, dtype): + value = value * 1000 + emb = self.prefer_proj(value).to(dtype) + emb = self.prefer_value_embedder(emb).squeeze(0) + base_embeddings = emb.expand(self.prefer_len, -1) + positional_embedding = self.positional_embedding.to(dtype=base_embeddings.dtype, device=base_embeddings.device) + learned_embeddings = base_embeddings + positional_embedding + return learned_embeddings + + @staticmethod + def state_dict_converter(): + return SingleValueEncoderStateDictConverter() + + +class SingleValueEncoderStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + return state_dict + + def from_civitai(self, state_dict): + return state_dict diff --git a/diffsynth/models/hunyuan_dit.py b/diffsynth/models/hunyuan_dit.py new file mode 100644 index 0000000000000000000000000000000000000000..8e27183d6993e7f64eee6b9b231dcb4d8d1a6bc0 --- /dev/null +++ b/diffsynth/models/hunyuan_dit.py @@ -0,0 +1,451 @@ +from .attention import Attention +from einops import repeat, rearrange +import math +import torch + + +class HunyuanDiTRotaryEmbedding(torch.nn.Module): + + def __init__(self, q_norm_shape=88, k_norm_shape=88, rotary_emb_on_k=True): + super().__init__() + self.q_norm = torch.nn.LayerNorm((q_norm_shape,), elementwise_affine=True, eps=1e-06) + self.k_norm = torch.nn.LayerNorm((k_norm_shape,), elementwise_affine=True, eps=1e-06) + self.rotary_emb_on_k = rotary_emb_on_k + self.k_cache, self.v_cache = [], [] + + def reshape_for_broadcast(self, freqs_cis, x): + ndim = x.ndim + shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape) + + def rotate_half(self, x): + x_real, x_imag = x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1) + return torch.stack([-x_imag, x_real], dim=-1).flatten(3) + + def apply_rotary_emb(self, xq, xk, freqs_cis): + xk_out = None + cos, sin = self.reshape_for_broadcast(freqs_cis, xq) + cos, sin = cos.to(xq.device), sin.to(xq.device) + xq_out = (xq.float() * cos + self.rotate_half(xq.float()) * sin).type_as(xq) + if xk is not None: + xk_out = (xk.float() * cos + self.rotate_half(xk.float()) * sin).type_as(xk) + return xq_out, xk_out + + def forward(self, q, k, v, freqs_cis_img, to_cache=False): + # norm + q = self.q_norm(q) + k = self.k_norm(k) + + # RoPE + if self.rotary_emb_on_k: + q, k = self.apply_rotary_emb(q, k, freqs_cis_img) + else: + q, _ = self.apply_rotary_emb(q, None, freqs_cis_img) + + if to_cache: + self.k_cache.append(k) + self.v_cache.append(v) + elif len(self.k_cache) > 0 and len(self.v_cache) > 0: + k = torch.concat([k] + self.k_cache, dim=2) + v = torch.concat([v] + self.v_cache, dim=2) + self.k_cache, self.v_cache = [], [] + return q, k, v + + +class FP32_Layernorm(torch.nn.LayerNorm): + def forward(self, inputs): + origin_dtype = inputs.dtype + return torch.nn.functional.layer_norm(inputs.float(), self.normalized_shape, self.weight.float(), self.bias.float(), self.eps).to(origin_dtype) + + +class FP32_SiLU(torch.nn.SiLU): + def forward(self, inputs): + origin_dtype = inputs.dtype + return torch.nn.functional.silu(inputs.float(), inplace=False).to(origin_dtype) + + +class HunyuanDiTFinalLayer(torch.nn.Module): + def __init__(self, final_hidden_size=1408, condition_dim=1408, patch_size=2, out_channels=8): + super().__init__() + self.norm_final = torch.nn.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6) + self.linear = torch.nn.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True) + self.adaLN_modulation = torch.nn.Sequential( + FP32_SiLU(), + torch.nn.Linear(condition_dim, 2 * final_hidden_size, bias=True) + ) + + def modulate(self, x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + def forward(self, hidden_states, condition_emb): + shift, scale = self.adaLN_modulation(condition_emb).chunk(2, dim=1) + hidden_states = self.modulate(self.norm_final(hidden_states), shift, scale) + hidden_states = self.linear(hidden_states) + return hidden_states + + +class HunyuanDiTBlock(torch.nn.Module): + + def __init__( + self, + hidden_dim=1408, + condition_dim=1408, + num_heads=16, + mlp_ratio=4.3637, + text_dim=1024, + skip_connection=False + ): + super().__init__() + self.norm1 = FP32_Layernorm((hidden_dim,), eps=1e-6, elementwise_affine=True) + self.rota1 = HunyuanDiTRotaryEmbedding(hidden_dim//num_heads, hidden_dim//num_heads) + self.attn1 = Attention(hidden_dim, num_heads, hidden_dim//num_heads, bias_q=True, bias_kv=True, bias_out=True) + self.norm2 = FP32_Layernorm((hidden_dim,), eps=1e-6, elementwise_affine=True) + self.rota2 = HunyuanDiTRotaryEmbedding(hidden_dim//num_heads, hidden_dim//num_heads, rotary_emb_on_k=False) + self.attn2 = Attention(hidden_dim, num_heads, hidden_dim//num_heads, kv_dim=text_dim, bias_q=True, bias_kv=True, bias_out=True) + self.norm3 = FP32_Layernorm((hidden_dim,), eps=1e-6, elementwise_affine=True) + self.modulation = torch.nn.Sequential(FP32_SiLU(), torch.nn.Linear(condition_dim, hidden_dim, bias=True)) + self.mlp = torch.nn.Sequential( + torch.nn.Linear(hidden_dim, int(hidden_dim*mlp_ratio), bias=True), + torch.nn.GELU(approximate="tanh"), + torch.nn.Linear(int(hidden_dim*mlp_ratio), hidden_dim, bias=True) + ) + if skip_connection: + self.skip_norm = FP32_Layernorm((hidden_dim * 2,), eps=1e-6, elementwise_affine=True) + self.skip_linear = torch.nn.Linear(hidden_dim * 2, hidden_dim, bias=True) + else: + self.skip_norm, self.skip_linear = None, None + + def forward(self, hidden_states, condition_emb, text_emb, freq_cis_img, residual=None, to_cache=False): + # Long Skip Connection + if self.skip_norm is not None and self.skip_linear is not None: + hidden_states = torch.cat([hidden_states, residual], dim=-1) + hidden_states = self.skip_norm(hidden_states) + hidden_states = self.skip_linear(hidden_states) + + # Self-Attention + shift_msa = self.modulation(condition_emb).unsqueeze(dim=1) + attn_input = self.norm1(hidden_states) + shift_msa + hidden_states = hidden_states + self.attn1(attn_input, qkv_preprocessor=lambda q, k, v: self.rota1(q, k, v, freq_cis_img, to_cache=to_cache)) + + # Cross-Attention + attn_input = self.norm3(hidden_states) + hidden_states = hidden_states + self.attn2(attn_input, text_emb, qkv_preprocessor=lambda q, k, v: self.rota2(q, k, v, freq_cis_img)) + + # FFN Layer + mlp_input = self.norm2(hidden_states) + hidden_states = hidden_states + self.mlp(mlp_input) + return hidden_states + + +class AttentionPool(torch.nn.Module): + def __init__(self, spacial_dim, embed_dim, num_heads, output_dim = None): + super().__init__() + self.positional_embedding = torch.nn.Parameter(torch.randn(spacial_dim + 1, embed_dim) / embed_dim ** 0.5) + self.k_proj = torch.nn.Linear(embed_dim, embed_dim) + self.q_proj = torch.nn.Linear(embed_dim, embed_dim) + self.v_proj = torch.nn.Linear(embed_dim, embed_dim) + self.c_proj = torch.nn.Linear(embed_dim, output_dim or embed_dim) + self.num_heads = num_heads + + def forward(self, x): + x = x.permute(1, 0, 2) # NLC -> LNC + x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (L+1)NC + x = x + self.positional_embedding[:, None, :].to(x.dtype) # (L+1)NC + x, _ = torch.nn.functional.multi_head_attention_forward( + query=x[:1], key=x, value=x, + embed_dim_to_check=x.shape[-1], + num_heads=self.num_heads, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + in_proj_weight=None, + in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), + bias_k=None, + bias_v=None, + add_zero_attn=False, + dropout_p=0, + out_proj_weight=self.c_proj.weight, + out_proj_bias=self.c_proj.bias, + use_separate_proj_weight=True, + training=self.training, + need_weights=False + ) + return x.squeeze(0) + + +class PatchEmbed(torch.nn.Module): + def __init__( + self, + patch_size=(2, 2), + in_chans=4, + embed_dim=1408, + bias=True, + ): + super().__init__() + self.proj = torch.nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias) + + def forward(self, x): + x = self.proj(x) + x = x.flatten(2).transpose(1, 2) # BCHW -> BNC + return x + + +def timestep_embedding(t, dim, max_period=10000, repeat_only=False): + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) + * torch.arange(start=0, end=half, dtype=torch.float32) + / half + ).to(device=t.device) # size: [dim/2], 一个指数衰减的曲线 + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + else: + embedding = repeat(t, "b -> b d", d=dim) + return embedding + + +class TimestepEmbedder(torch.nn.Module): + def __init__(self, hidden_size=1408, frequency_embedding_size=256): + super().__init__() + self.mlp = torch.nn.Sequential( + torch.nn.Linear(frequency_embedding_size, hidden_size, bias=True), + torch.nn.SiLU(), + torch.nn.Linear(hidden_size, hidden_size, bias=True), + ) + self.frequency_embedding_size = frequency_embedding_size + + def forward(self, t): + t_freq = timestep_embedding(t, self.frequency_embedding_size).type(self.mlp[0].weight.dtype) + t_emb = self.mlp(t_freq) + return t_emb + + +class HunyuanDiT(torch.nn.Module): + def __init__(self, num_layers_down=21, num_layers_up=19, in_channels=4, out_channels=8, hidden_dim=1408, text_dim=1024, t5_dim=2048, text_length=77, t5_length=256): + super().__init__() + + # Embedders + self.text_emb_padding = torch.nn.Parameter(torch.randn(text_length + t5_length, text_dim, dtype=torch.float32)) + self.t5_embedder = torch.nn.Sequential( + torch.nn.Linear(t5_dim, t5_dim * 4, bias=True), + FP32_SiLU(), + torch.nn.Linear(t5_dim * 4, text_dim, bias=True), + ) + self.t5_pooler = AttentionPool(t5_length, t5_dim, num_heads=8, output_dim=1024) + self.style_embedder = torch.nn.Parameter(torch.randn(hidden_dim)) + self.patch_embedder = PatchEmbed(in_chans=in_channels) + self.timestep_embedder = TimestepEmbedder() + self.extra_embedder = torch.nn.Sequential( + torch.nn.Linear(256 * 6 + 1024 + hidden_dim, hidden_dim * 4), + FP32_SiLU(), + torch.nn.Linear(hidden_dim * 4, hidden_dim), + ) + + # Transformer blocks + self.num_layers_down = num_layers_down + self.num_layers_up = num_layers_up + self.blocks = torch.nn.ModuleList( + [HunyuanDiTBlock(skip_connection=False) for _ in range(num_layers_down)] + \ + [HunyuanDiTBlock(skip_connection=True) for _ in range(num_layers_up)] + ) + + # Output layers + self.final_layer = HunyuanDiTFinalLayer() + self.out_channels = out_channels + + def prepare_text_emb(self, text_emb, text_emb_t5, text_emb_mask, text_emb_mask_t5): + text_emb_mask = text_emb_mask.bool() + text_emb_mask_t5 = text_emb_mask_t5.bool() + text_emb_t5 = self.t5_embedder(text_emb_t5) + text_emb = torch.cat([text_emb, text_emb_t5], dim=1) + text_emb_mask = torch.cat([text_emb_mask, text_emb_mask_t5], dim=-1) + text_emb = torch.where(text_emb_mask.unsqueeze(2), text_emb, self.text_emb_padding.to(text_emb)) + return text_emb + + def prepare_extra_emb(self, text_emb_t5, timestep, size_emb, dtype, batch_size): + # Text embedding + pooled_text_emb_t5 = self.t5_pooler(text_emb_t5) + + # Timestep embedding + timestep_emb = self.timestep_embedder(timestep) + + # Size embedding + size_emb = timestep_embedding(size_emb.view(-1), 256).to(dtype) + size_emb = size_emb.view(-1, 6 * 256) + + # Style embedding + style_emb = repeat(self.style_embedder, "D -> B D", B=batch_size) + + # Concatenate all extra vectors + extra_emb = torch.cat([pooled_text_emb_t5, size_emb, style_emb], dim=1) + condition_emb = timestep_emb + self.extra_embedder(extra_emb) + + return condition_emb + + def unpatchify(self, x, h, w): + return rearrange(x, "B (H W) (P Q C) -> B C (H P) (W Q)", H=h, W=w, P=2, Q=2) + + def build_mask(self, data, is_bound): + _, _, H, W = data.shape + h = repeat(torch.arange(H), "H -> H W", H=H, W=W) + w = repeat(torch.arange(W), "W -> H W", H=H, W=W) + border_width = (H + W) // 4 + pad = torch.ones_like(h) * border_width + mask = torch.stack([ + pad if is_bound[0] else h + 1, + pad if is_bound[1] else H - h, + pad if is_bound[2] else w + 1, + pad if is_bound[3] else W - w + ]).min(dim=0).values + mask = mask.clip(1, border_width) + mask = (mask / border_width).to(dtype=data.dtype, device=data.device) + mask = rearrange(mask, "H W -> 1 H W") + return mask + + def tiled_block_forward(self, block, hidden_states, condition_emb, text_emb, freq_cis_img, residual, torch_dtype, data_device, computation_device, tile_size, tile_stride): + B, C, H, W = hidden_states.shape + + weight = torch.zeros((1, 1, H, W), dtype=torch_dtype, device=data_device) + values = torch.zeros((B, C, H, W), dtype=torch_dtype, device=data_device) + + # Split tasks + tasks = [] + for h in range(0, H, tile_stride): + for w in range(0, W, tile_stride): + if (h-tile_stride >= 0 and h-tile_stride+tile_size >= H) or (w-tile_stride >= 0 and w-tile_stride+tile_size >= W): + continue + h_, w_ = h + tile_size, w + tile_size + if h_ > H: h, h_ = H - tile_size, H + if w_ > W: w, w_ = W - tile_size, W + tasks.append((h, h_, w, w_)) + + # Run + for hl, hr, wl, wr in tasks: + hidden_states_batch = hidden_states[:, :, hl:hr, wl:wr].to(computation_device) + hidden_states_batch = rearrange(hidden_states_batch, "B C H W -> B (H W) C") + if residual is not None: + residual_batch = residual[:, :, hl:hr, wl:wr].to(computation_device) + residual_batch = rearrange(residual_batch, "B C H W -> B (H W) C") + else: + residual_batch = None + + # Forward + hidden_states_batch = block(hidden_states_batch, condition_emb, text_emb, freq_cis_img, residual_batch).to(data_device) + hidden_states_batch = rearrange(hidden_states_batch, "B (H W) C -> B C H W", H=hr-hl) + + mask = self.build_mask(hidden_states_batch, is_bound=(hl==0, hr>=H, wl==0, wr>=W)) + values[:, :, hl:hr, wl:wr] += hidden_states_batch * mask + weight[:, :, hl:hr, wl:wr] += mask + values /= weight + return values + + def forward( + self, hidden_states, text_emb, text_emb_t5, text_emb_mask, text_emb_mask_t5, timestep, size_emb, freq_cis_img, + tiled=False, tile_size=64, tile_stride=32, + to_cache=False, + use_gradient_checkpointing=False, + ): + # Embeddings + text_emb = self.prepare_text_emb(text_emb, text_emb_t5, text_emb_mask, text_emb_mask_t5) + condition_emb = self.prepare_extra_emb(text_emb_t5, timestep, size_emb, hidden_states.dtype, hidden_states.shape[0]) + + # Input + height, width = hidden_states.shape[-2], hidden_states.shape[-1] + hidden_states = self.patch_embedder(hidden_states) + + # Blocks + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + return custom_forward + if tiled: + hidden_states = rearrange(hidden_states, "B (H W) C -> B C H W", H=height//2) + residuals = [] + for block_id, block in enumerate(self.blocks): + residual = residuals.pop() if block_id >= self.num_layers_down else None + hidden_states = self.tiled_block_forward( + block, hidden_states, condition_emb, text_emb, freq_cis_img, residual, + torch_dtype=hidden_states.dtype, data_device=hidden_states.device, computation_device=hidden_states.device, + tile_size=tile_size, tile_stride=tile_stride + ) + if block_id < self.num_layers_down - 2: + residuals.append(hidden_states) + hidden_states = rearrange(hidden_states, "B C H W -> B (H W) C") + else: + residuals = [] + for block_id, block in enumerate(self.blocks): + residual = residuals.pop() if block_id >= self.num_layers_down else None + if self.training and use_gradient_checkpointing: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, condition_emb, text_emb, freq_cis_img, residual, + use_reentrant=False, + ) + else: + hidden_states = block(hidden_states, condition_emb, text_emb, freq_cis_img, residual, to_cache=to_cache) + if block_id < self.num_layers_down - 2: + residuals.append(hidden_states) + + # Output + hidden_states = self.final_layer(hidden_states, condition_emb) + hidden_states = self.unpatchify(hidden_states, height//2, width//2) + hidden_states, _ = hidden_states.chunk(2, dim=1) + return hidden_states + + @staticmethod + def state_dict_converter(): + return HunyuanDiTStateDictConverter() + + + +class HunyuanDiTStateDictConverter(): + def __init__(self): + pass + + def from_diffusers(self, state_dict): + state_dict_ = {} + for name, param in state_dict.items(): + name_ = name + name_ = name_.replace(".default_modulation.", ".modulation.") + name_ = name_.replace(".mlp.fc1.", ".mlp.0.") + name_ = name_.replace(".mlp.fc2.", ".mlp.2.") + name_ = name_.replace(".attn1.q_norm.", ".rota1.q_norm.") + name_ = name_.replace(".attn2.q_norm.", ".rota2.q_norm.") + name_ = name_.replace(".attn1.k_norm.", ".rota1.k_norm.") + name_ = name_.replace(".attn2.k_norm.", ".rota2.k_norm.") + name_ = name_.replace(".q_proj.", ".to_q.") + name_ = name_.replace(".out_proj.", ".to_out.") + name_ = name_.replace("text_embedding_padding", "text_emb_padding") + name_ = name_.replace("mlp_t5.0.", "t5_embedder.0.") + name_ = name_.replace("mlp_t5.2.", "t5_embedder.2.") + name_ = name_.replace("pooler.", "t5_pooler.") + name_ = name_.replace("x_embedder.", "patch_embedder.") + name_ = name_.replace("t_embedder.", "timestep_embedder.") + name_ = name_.replace("t5_pooler.to_q.", "t5_pooler.q_proj.") + name_ = name_.replace("style_embedder.weight", "style_embedder") + if ".kv_proj." in name_: + param_k = param[:param.shape[0]//2] + param_v = param[param.shape[0]//2:] + state_dict_[name_.replace(".kv_proj.", ".to_k.")] = param_k + state_dict_[name_.replace(".kv_proj.", ".to_v.")] = param_v + elif ".Wqkv." in name_: + param_q = param[:param.shape[0]//3] + param_k = param[param.shape[0]//3:param.shape[0]//3*2] + param_v = param[param.shape[0]//3*2:] + state_dict_[name_.replace(".Wqkv.", ".to_q.")] = param_q + state_dict_[name_.replace(".Wqkv.", ".to_k.")] = param_k + state_dict_[name_.replace(".Wqkv.", ".to_v.")] = param_v + elif "style_embedder" in name_: + state_dict_[name_] = param.squeeze() + else: + state_dict_[name_] = param + return state_dict_ + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict) diff --git a/diffsynth/models/hunyuan_dit_text_encoder.py b/diffsynth/models/hunyuan_dit_text_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..33999a8b10a319b736981dd8f3a911bbe9568e8d --- /dev/null +++ b/diffsynth/models/hunyuan_dit_text_encoder.py @@ -0,0 +1,163 @@ +from transformers import BertModel, BertConfig, T5EncoderModel, T5Config +import torch + + + +class HunyuanDiTCLIPTextEncoder(BertModel): + def __init__(self): + config = BertConfig( + _name_or_path = "", + architectures = ["BertModel"], + attention_probs_dropout_prob = 0.1, + bos_token_id = 0, + classifier_dropout = None, + directionality = "bidi", + eos_token_id = 2, + hidden_act = "gelu", + hidden_dropout_prob = 0.1, + hidden_size = 1024, + initializer_range = 0.02, + intermediate_size = 4096, + layer_norm_eps = 1e-12, + max_position_embeddings = 512, + model_type = "bert", + num_attention_heads = 16, + num_hidden_layers = 24, + output_past = True, + pad_token_id = 0, + pooler_fc_size = 768, + pooler_num_attention_heads = 12, + pooler_num_fc_layers = 3, + pooler_size_per_head = 128, + pooler_type = "first_token_transform", + position_embedding_type = "absolute", + torch_dtype = "float32", + transformers_version = "4.37.2", + type_vocab_size = 2, + use_cache = True, + vocab_size = 47020 + ) + super().__init__(config, add_pooling_layer=False) + self.eval() + + def forward(self, input_ids, attention_mask, clip_skip=1): + input_shape = input_ids.size() + + batch_size, seq_length = input_shape + device = input_ids.device + + past_key_values_length = 0 + + if attention_mask is None: + attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) + + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=None, + token_type_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=False, + output_attentions=False, + output_hidden_states=True, + return_dict=True, + ) + all_hidden_states = encoder_outputs.hidden_states + prompt_emb = all_hidden_states[-clip_skip] + if clip_skip > 1: + mean, std = all_hidden_states[-1].mean(), all_hidden_states[-1].std() + prompt_emb = (prompt_emb - prompt_emb.mean()) / prompt_emb.std() * std + mean + return prompt_emb + + @staticmethod + def state_dict_converter(): + return HunyuanDiTCLIPTextEncoderStateDictConverter() + + + +class HunyuanDiTT5TextEncoder(T5EncoderModel): + def __init__(self): + config = T5Config( + _name_or_path = "../HunyuanDiT/t2i/mt5", + architectures = ["MT5ForConditionalGeneration"], + classifier_dropout = 0.0, + d_ff = 5120, + d_kv = 64, + d_model = 2048, + decoder_start_token_id = 0, + dense_act_fn = "gelu_new", + dropout_rate = 0.1, + eos_token_id = 1, + feed_forward_proj = "gated-gelu", + initializer_factor = 1.0, + is_encoder_decoder = True, + is_gated_act = True, + layer_norm_epsilon = 1e-06, + model_type = "t5", + num_decoder_layers = 24, + num_heads = 32, + num_layers = 24, + output_past = True, + pad_token_id = 0, + relative_attention_max_distance = 128, + relative_attention_num_buckets = 32, + tie_word_embeddings = False, + tokenizer_class = "T5Tokenizer", + transformers_version = "4.37.2", + use_cache = True, + vocab_size = 250112 + ) + super().__init__(config) + self.eval() + + def forward(self, input_ids, attention_mask, clip_skip=1): + outputs = super().forward( + input_ids=input_ids, + attention_mask=attention_mask, + output_hidden_states=True, + ) + prompt_emb = outputs.hidden_states[-clip_skip] + if clip_skip > 1: + mean, std = outputs.hidden_states[-1].mean(), outputs.hidden_states[-1].std() + prompt_emb = (prompt_emb - prompt_emb.mean()) / prompt_emb.std() * std + mean + return prompt_emb + + @staticmethod + def state_dict_converter(): + return HunyuanDiTT5TextEncoderStateDictConverter() + + + +class HunyuanDiTCLIPTextEncoderStateDictConverter(): + def __init__(self): + pass + + def from_diffusers(self, state_dict): + state_dict_ = {name[5:]: param for name, param in state_dict.items() if name.startswith("bert.")} + return state_dict_ + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict) + + +class HunyuanDiTT5TextEncoderStateDictConverter(): + def __init__(self): + pass + + def from_diffusers(self, state_dict): + state_dict_ = {name: param for name, param in state_dict.items() if name.startswith("encoder.")} + state_dict_["shared.weight"] = state_dict["shared.weight"] + return state_dict_ + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict) diff --git a/diffsynth/models/hunyuan_video_dit.py b/diffsynth/models/hunyuan_video_dit.py new file mode 100644 index 0000000000000000000000000000000000000000..13155361734f8ec4cb7947941a1824f5064f1642 --- /dev/null +++ b/diffsynth/models/hunyuan_video_dit.py @@ -0,0 +1,920 @@ +import torch +from .sd3_dit import TimestepEmbeddings, RMSNorm +from .utils import init_weights_on_device +from einops import rearrange, repeat +from tqdm import tqdm +from typing import Union, Tuple, List +from .utils import hash_state_dict_keys + + +def HunyuanVideoRope(latents): + def _to_tuple(x, dim=2): + if isinstance(x, int): + return (x,) * dim + elif len(x) == dim: + return x + else: + raise ValueError(f"Expected length {dim} or int, but got {x}") + + + def get_meshgrid_nd(start, *args, dim=2): + """ + Get n-D meshgrid with start, stop and num. + + Args: + start (int or tuple): If len(args) == 0, start is num; If len(args) == 1, start is start, args[0] is stop, + step is 1; If len(args) == 2, start is start, args[0] is stop, args[1] is num. For n-dim, start/stop/num + should be int or n-tuple. If n-tuple is provided, the meshgrid will be stacked following the dim order in + n-tuples. + *args: See above. + dim (int): Dimension of the meshgrid. Defaults to 2. + + Returns: + grid (np.ndarray): [dim, ...] + """ + if len(args) == 0: + # start is grid_size + num = _to_tuple(start, dim=dim) + start = (0,) * dim + stop = num + elif len(args) == 1: + # start is start, args[0] is stop, step is 1 + start = _to_tuple(start, dim=dim) + stop = _to_tuple(args[0], dim=dim) + num = [stop[i] - start[i] for i in range(dim)] + elif len(args) == 2: + # start is start, args[0] is stop, args[1] is num + start = _to_tuple(start, dim=dim) # Left-Top eg: 12,0 + stop = _to_tuple(args[0], dim=dim) # Right-Bottom eg: 20,32 + num = _to_tuple(args[1], dim=dim) # Target Size eg: 32,124 + else: + raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}") + + # PyTorch implement of np.linspace(start[i], stop[i], num[i], endpoint=False) + axis_grid = [] + for i in range(dim): + a, b, n = start[i], stop[i], num[i] + g = torch.linspace(a, b, n + 1, dtype=torch.float32)[:n] + axis_grid.append(g) + grid = torch.meshgrid(*axis_grid, indexing="ij") # dim x [W, H, D] + grid = torch.stack(grid, dim=0) # [dim, W, H, D] + + return grid + + + def get_1d_rotary_pos_embed( + dim: int, + pos: Union[torch.FloatTensor, int], + theta: float = 10000.0, + use_real: bool = False, + theta_rescale_factor: float = 1.0, + interpolation_factor: float = 1.0, + ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + """ + Precompute the frequency tensor for complex exponential (cis) with given dimensions. + (Note: `cis` means `cos + i * sin`, where i is the imaginary unit.) + + This function calculates a frequency tensor with complex exponential using the given dimension 'dim' + and the end index 'end'. The 'theta' parameter scales the frequencies. + The returned tensor contains complex values in complex64 data type. + + Args: + dim (int): Dimension of the frequency tensor. + pos (int or torch.FloatTensor): Position indices for the frequency tensor. [S] or scalar + theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0. + use_real (bool, optional): If True, return real part and imaginary part separately. + Otherwise, return complex numbers. + theta_rescale_factor (float, optional): Rescale factor for theta. Defaults to 1.0. + + Returns: + freqs_cis: Precomputed frequency tensor with complex exponential. [S, D/2] + freqs_cos, freqs_sin: Precomputed frequency tensor with real and imaginary parts separately. [S, D] + """ + if isinstance(pos, int): + pos = torch.arange(pos).float() + + # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning + # has some connection to NTK literature + if theta_rescale_factor != 1.0: + theta *= theta_rescale_factor ** (dim / (dim - 2)) + + freqs = 1.0 / ( + theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim) + ) # [D/2] + # assert interpolation_factor == 1.0, f"interpolation_factor: {interpolation_factor}" + freqs = torch.outer(pos * interpolation_factor, freqs) # [S, D/2] + if use_real: + freqs_cos = freqs.cos().repeat_interleave(2, dim=1) # [S, D] + freqs_sin = freqs.sin().repeat_interleave(2, dim=1) # [S, D] + return freqs_cos, freqs_sin + else: + freqs_cis = torch.polar( + torch.ones_like(freqs), freqs + ) # complex64 # [S, D/2] + return freqs_cis + + + def get_nd_rotary_pos_embed( + rope_dim_list, + start, + *args, + theta=10000.0, + use_real=False, + theta_rescale_factor: Union[float, List[float]] = 1.0, + interpolation_factor: Union[float, List[float]] = 1.0, + ): + """ + This is a n-d version of precompute_freqs_cis, which is a RoPE for tokens with n-d structure. + + Args: + rope_dim_list (list of int): Dimension of each rope. len(rope_dim_list) should equal to n. + sum(rope_dim_list) should equal to head_dim of attention layer. + start (int | tuple of int | list of int): If len(args) == 0, start is num; If len(args) == 1, start is start, + args[0] is stop, step is 1; If len(args) == 2, start is start, args[0] is stop, args[1] is num. + *args: See above. + theta (float): Scaling factor for frequency computation. Defaults to 10000.0. + use_real (bool): If True, return real part and imaginary part separately. Otherwise, return complex numbers. + Some libraries such as TensorRT does not support complex64 data type. So it is useful to provide a real + part and an imaginary part separately. + theta_rescale_factor (float): Rescale factor for theta. Defaults to 1.0. + + Returns: + pos_embed (torch.Tensor): [HW, D/2] + """ + + grid = get_meshgrid_nd( + start, *args, dim=len(rope_dim_list) + ) # [3, W, H, D] / [2, W, H] + + if isinstance(theta_rescale_factor, int) or isinstance(theta_rescale_factor, float): + theta_rescale_factor = [theta_rescale_factor] * len(rope_dim_list) + elif isinstance(theta_rescale_factor, list) and len(theta_rescale_factor) == 1: + theta_rescale_factor = [theta_rescale_factor[0]] * len(rope_dim_list) + assert len(theta_rescale_factor) == len( + rope_dim_list + ), "len(theta_rescale_factor) should equal to len(rope_dim_list)" + + if isinstance(interpolation_factor, int) or isinstance(interpolation_factor, float): + interpolation_factor = [interpolation_factor] * len(rope_dim_list) + elif isinstance(interpolation_factor, list) and len(interpolation_factor) == 1: + interpolation_factor = [interpolation_factor[0]] * len(rope_dim_list) + assert len(interpolation_factor) == len( + rope_dim_list + ), "len(interpolation_factor) should equal to len(rope_dim_list)" + + # use 1/ndim of dimensions to encode grid_axis + embs = [] + for i in range(len(rope_dim_list)): + emb = get_1d_rotary_pos_embed( + rope_dim_list[i], + grid[i].reshape(-1), + theta, + use_real=use_real, + theta_rescale_factor=theta_rescale_factor[i], + interpolation_factor=interpolation_factor[i], + ) # 2 x [WHD, rope_dim_list[i]] + embs.append(emb) + + if use_real: + cos = torch.cat([emb[0] for emb in embs], dim=1) # (WHD, D/2) + sin = torch.cat([emb[1] for emb in embs], dim=1) # (WHD, D/2) + return cos, sin + else: + emb = torch.cat(embs, dim=1) # (WHD, D/2) + return emb + + freqs_cos, freqs_sin = get_nd_rotary_pos_embed( + [16, 56, 56], + [latents.shape[2], latents.shape[3] // 2, latents.shape[4] // 2], + theta=256, + use_real=True, + theta_rescale_factor=1, + ) + return freqs_cos, freqs_sin + + +class PatchEmbed(torch.nn.Module): + def __init__(self, patch_size=(1, 2, 2), in_channels=16, embed_dim=3072): + super().__init__() + self.proj = torch.nn.Conv3d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size) + + def forward(self, x): + x = self.proj(x) + x = x.flatten(2).transpose(1, 2) + return x + + +class IndividualTokenRefinerBlock(torch.nn.Module): + def __init__(self, hidden_size=3072, num_heads=24): + super().__init__() + self.num_heads = num_heads + self.norm1 = torch.nn.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6) + self.self_attn_qkv = torch.nn.Linear(hidden_size, hidden_size * 3) + self.self_attn_proj = torch.nn.Linear(hidden_size, hidden_size) + + self.norm2 = torch.nn.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6) + self.mlp = torch.nn.Sequential( + torch.nn.Linear(hidden_size, hidden_size * 4), + torch.nn.SiLU(), + torch.nn.Linear(hidden_size * 4, hidden_size) + ) + self.adaLN_modulation = torch.nn.Sequential( + torch.nn.SiLU(), + torch.nn.Linear(hidden_size, hidden_size * 2, device="cuda", dtype=torch.bfloat16), + ) + + def forward(self, x, c, attn_mask=None): + gate_msa, gate_mlp = self.adaLN_modulation(c).chunk(2, dim=1) + + norm_x = self.norm1(x) + qkv = self.self_attn_qkv(norm_x) + q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + + attn = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) + attn = rearrange(attn, "B H L D -> B L (H D)") + + x = x + self.self_attn_proj(attn) * gate_msa.unsqueeze(1) + x = x + self.mlp(self.norm2(x)) * gate_mlp.unsqueeze(1) + + return x + + +class SingleTokenRefiner(torch.nn.Module): + def __init__(self, in_channels=4096, hidden_size=3072, depth=2): + super().__init__() + self.input_embedder = torch.nn.Linear(in_channels, hidden_size, bias=True) + self.t_embedder = TimestepEmbeddings(256, hidden_size, computation_device="cpu") + self.c_embedder = torch.nn.Sequential( + torch.nn.Linear(in_channels, hidden_size), + torch.nn.SiLU(), + torch.nn.Linear(hidden_size, hidden_size) + ) + self.blocks = torch.nn.ModuleList([IndividualTokenRefinerBlock(hidden_size=hidden_size) for _ in range(depth)]) + + def forward(self, x, t, mask=None): + timestep_aware_representations = self.t_embedder(t, dtype=torch.float32) + + mask_float = mask.float().unsqueeze(-1) + context_aware_representations = (x * mask_float).sum(dim=1) / mask_float.sum(dim=1) + context_aware_representations = self.c_embedder(context_aware_representations) + c = timestep_aware_representations + context_aware_representations + + x = self.input_embedder(x) + + mask = mask.to(device=x.device, dtype=torch.bool) + mask = repeat(mask, "B L -> B 1 D L", D=mask.shape[-1]) + mask = mask & mask.transpose(2, 3) + mask[:, :, :, 0] = True + + for block in self.blocks: + x = block(x, c, mask) + + return x + + +class ModulateDiT(torch.nn.Module): + def __init__(self, hidden_size, factor=6): + super().__init__() + self.act = torch.nn.SiLU() + self.linear = torch.nn.Linear(hidden_size, factor * hidden_size) + + def forward(self, x): + return self.linear(self.act(x)) + + +def modulate(x, shift=None, scale=None, tr_shift=None, tr_scale=None, tr_token=None): + if tr_shift is not None: + x_zero = x[:, :tr_token] * (1 + tr_scale.unsqueeze(1)) + tr_shift.unsqueeze(1) + x_orig = x[:, tr_token:] * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + x = torch.concat((x_zero, x_orig), dim=1) + return x + if scale is None and shift is None: + return x + elif shift is None: + return x * (1 + scale.unsqueeze(1)) + elif scale is None: + return x + shift.unsqueeze(1) + else: + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +def reshape_for_broadcast( + freqs_cis, + x: torch.Tensor, + head_first=False, +): + ndim = x.ndim + assert 0 <= 1 < ndim + + if isinstance(freqs_cis, tuple): + # freqs_cis: (cos, sin) in real space + if head_first: + assert freqs_cis[0].shape == ( + x.shape[-2], + x.shape[-1], + ), f"freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}" + shape = [ + d if i == ndim - 2 or i == ndim - 1 else 1 + for i, d in enumerate(x.shape) + ] + else: + assert freqs_cis[0].shape == ( + x.shape[1], + x.shape[-1], + ), f"freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}" + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape) + else: + # freqs_cis: values in complex space + if head_first: + assert freqs_cis.shape == ( + x.shape[-2], + x.shape[-1], + ), f"freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}" + shape = [ + d if i == ndim - 2 or i == ndim - 1 else 1 + for i, d in enumerate(x.shape) + ] + else: + assert freqs_cis.shape == ( + x.shape[1], + x.shape[-1], + ), f"freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}" + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return freqs_cis.view(*shape) + + +def rotate_half(x): + x_real, x_imag = ( + x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1) + ) # [B, S, H, D//2] + return torch.stack([-x_imag, x_real], dim=-1).flatten(3) + + +def apply_rotary_emb( + xq: torch.Tensor, + xk: torch.Tensor, + freqs_cis, + head_first: bool = False, +): + xk_out = None + if isinstance(freqs_cis, tuple): + cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first) # [S, D] + cos, sin = cos.to(xq.device), sin.to(xq.device) + # real * cos - imag * sin + # imag * cos + real * sin + xq_out = (xq.float() * cos + rotate_half(xq.float()) * sin).type_as(xq) + xk_out = (xk.float() * cos + rotate_half(xk.float()) * sin).type_as(xk) + else: + # view_as_complex will pack [..., D/2, 2](real) to [..., D/2](complex) + xq_ = torch.view_as_complex( + xq.float().reshape(*xq.shape[:-1], -1, 2) + ) # [B, S, H, D//2] + freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to( + xq.device + ) # [S, D//2] --> [1, S, 1, D//2] + # (real, imag) * (cos, sin) = (real * cos - imag * sin, imag * cos + real * sin) + # view_as_real will expand [..., D/2](complex) to [..., D/2, 2](real) + xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq) + xk_ = torch.view_as_complex( + xk.float().reshape(*xk.shape[:-1], -1, 2) + ) # [B, S, H, D//2] + xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk) + + return xq_out, xk_out + + +def attention(q, k, v): + q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2) + x = torch.nn.functional.scaled_dot_product_attention(q, k, v) + x = x.transpose(1, 2).flatten(2, 3) + return x + + +def apply_gate(x, gate, tr_gate=None, tr_token=None): + if tr_gate is not None: + x_zero = x[:, :tr_token] * tr_gate.unsqueeze(1) + x_orig = x[:, tr_token:] * gate.unsqueeze(1) + return torch.concat((x_zero, x_orig), dim=1) + else: + return x * gate.unsqueeze(1) + + +class MMDoubleStreamBlockComponent(torch.nn.Module): + def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4): + super().__init__() + self.heads_num = heads_num + + self.mod = ModulateDiT(hidden_size) + self.norm1 = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + + self.to_qkv = torch.nn.Linear(hidden_size, hidden_size * 3) + self.norm_q = RMSNorm(dim=hidden_size // heads_num, eps=1e-6) + self.norm_k = RMSNorm(dim=hidden_size // heads_num, eps=1e-6) + self.to_out = torch.nn.Linear(hidden_size, hidden_size) + + self.norm2 = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.ff = torch.nn.Sequential( + torch.nn.Linear(hidden_size, hidden_size * mlp_width_ratio), + torch.nn.GELU(approximate="tanh"), + torch.nn.Linear(hidden_size * mlp_width_ratio, hidden_size) + ) + + def forward(self, hidden_states, conditioning, freqs_cis=None, token_replace_vec=None, tr_token=None): + mod1_shift, mod1_scale, mod1_gate, mod2_shift, mod2_scale, mod2_gate = self.mod(conditioning).chunk(6, dim=-1) + if token_replace_vec is not None: + assert tr_token is not None + tr_mod1_shift, tr_mod1_scale, tr_mod1_gate, tr_mod2_shift, tr_mod2_scale, tr_mod2_gate = self.mod(token_replace_vec).chunk(6, dim=-1) + else: + tr_mod1_shift, tr_mod1_scale, tr_mod1_gate, tr_mod2_shift, tr_mod2_scale, tr_mod2_gate = None, None, None, None, None, None + + norm_hidden_states = self.norm1(hidden_states) + norm_hidden_states = modulate(norm_hidden_states, shift=mod1_shift, scale=mod1_scale, + tr_shift=tr_mod1_shift, tr_scale=tr_mod1_scale, tr_token=tr_token) + qkv = self.to_qkv(norm_hidden_states) + q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num) + + q = self.norm_q(q) + k = self.norm_k(k) + + if freqs_cis is not None: + q, k = apply_rotary_emb(q, k, freqs_cis, head_first=False) + return (q, k, v), (mod1_gate, mod2_shift, mod2_scale, mod2_gate), (tr_mod1_gate, tr_mod2_shift, tr_mod2_scale, tr_mod2_gate) + + def process_ff(self, hidden_states, attn_output, mod, mod_tr=None, tr_token=None): + mod1_gate, mod2_shift, mod2_scale, mod2_gate = mod + if mod_tr is not None: + tr_mod1_gate, tr_mod2_shift, tr_mod2_scale, tr_mod2_gate = mod_tr + else: + tr_mod1_gate, tr_mod2_shift, tr_mod2_scale, tr_mod2_gate = None, None, None, None + hidden_states = hidden_states + apply_gate(self.to_out(attn_output), mod1_gate, tr_mod1_gate, tr_token) + x = self.ff(modulate(self.norm2(hidden_states), shift=mod2_shift, scale=mod2_scale, tr_shift=tr_mod2_shift, tr_scale=tr_mod2_scale, tr_token=tr_token)) + hidden_states = hidden_states + apply_gate(x, mod2_gate, tr_mod2_gate, tr_token) + return hidden_states + + +class MMDoubleStreamBlock(torch.nn.Module): + def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4): + super().__init__() + self.component_a = MMDoubleStreamBlockComponent(hidden_size, heads_num, mlp_width_ratio) + self.component_b = MMDoubleStreamBlockComponent(hidden_size, heads_num, mlp_width_ratio) + + def forward(self, hidden_states_a, hidden_states_b, conditioning, freqs_cis, token_replace_vec=None, tr_token=None, split_token=71): + (q_a, k_a, v_a), mod_a, mod_tr = self.component_a(hidden_states_a, conditioning, freqs_cis, token_replace_vec, tr_token) + (q_b, k_b, v_b), mod_b, _ = self.component_b(hidden_states_b, conditioning, freqs_cis=None) + + q_a, q_b = torch.concat([q_a, q_b[:, :split_token]], dim=1), q_b[:, split_token:].contiguous() + k_a, k_b = torch.concat([k_a, k_b[:, :split_token]], dim=1), k_b[:, split_token:].contiguous() + v_a, v_b = torch.concat([v_a, v_b[:, :split_token]], dim=1), v_b[:, split_token:].contiguous() + attn_output_a = attention(q_a, k_a, v_a) + attn_output_b = attention(q_b, k_b, v_b) + attn_output_a, attn_output_b = attn_output_a[:, :-split_token].contiguous(), torch.concat([attn_output_a[:, -split_token:], attn_output_b], dim=1) + + hidden_states_a = self.component_a.process_ff(hidden_states_a, attn_output_a, mod_a, mod_tr, tr_token) + hidden_states_b = self.component_b.process_ff(hidden_states_b, attn_output_b, mod_b) + return hidden_states_a, hidden_states_b + + +class MMSingleStreamBlockOriginal(torch.nn.Module): + def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4): + super().__init__() + self.hidden_size = hidden_size + self.heads_num = heads_num + self.mlp_hidden_dim = hidden_size * mlp_width_ratio + + self.linear1 = torch.nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim) + self.linear2 = torch.nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size) + + self.q_norm = RMSNorm(dim=hidden_size // heads_num, eps=1e-6) + self.k_norm = RMSNorm(dim=hidden_size // heads_num, eps=1e-6) + + self.pre_norm = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + + self.mlp_act = torch.nn.GELU(approximate="tanh") + self.modulation = ModulateDiT(hidden_size, factor=3) + + def forward(self, x, vec, freqs_cis=None, txt_len=256): + mod_shift, mod_scale, mod_gate = self.modulation(vec).chunk(3, dim=-1) + x_mod = modulate(self.pre_norm(x), shift=mod_shift, scale=mod_scale) + qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) + q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num) + q = self.q_norm(q) + k = self.k_norm(k) + + q_a, q_b = q[:, :-txt_len, :, :], q[:, -txt_len:, :, :] + k_a, k_b = k[:, :-txt_len, :, :], k[:, -txt_len:, :, :] + q_a, k_a = apply_rotary_emb(q_a, k_a, freqs_cis, head_first=False) + q = torch.cat((q_a, q_b), dim=1) + k = torch.cat((k_a, k_b), dim=1) + + attn_output_a = attention(q[:, :-185].contiguous(), k[:, :-185].contiguous(), v[:, :-185].contiguous()) + attn_output_b = attention(q[:, -185:].contiguous(), k[:, -185:].contiguous(), v[:, -185:].contiguous()) + attn_output = torch.concat([attn_output_a, attn_output_b], dim=1) + + output = self.linear2(torch.cat((attn_output, self.mlp_act(mlp)), 2)) + return x + output * mod_gate.unsqueeze(1) + + +class MMSingleStreamBlock(torch.nn.Module): + def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4): + super().__init__() + self.heads_num = heads_num + + self.mod = ModulateDiT(hidden_size, factor=3) + self.norm = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + + self.to_qkv = torch.nn.Linear(hidden_size, hidden_size * 3) + self.norm_q = RMSNorm(dim=hidden_size // heads_num, eps=1e-6) + self.norm_k = RMSNorm(dim=hidden_size // heads_num, eps=1e-6) + self.to_out = torch.nn.Linear(hidden_size, hidden_size) + + self.ff = torch.nn.Sequential( + torch.nn.Linear(hidden_size, hidden_size * mlp_width_ratio), + torch.nn.GELU(approximate="tanh"), + torch.nn.Linear(hidden_size * mlp_width_ratio, hidden_size, bias=False) + ) + + def forward(self, hidden_states, conditioning, freqs_cis=None, txt_len=256, token_replace_vec=None, tr_token=None, split_token=71): + mod_shift, mod_scale, mod_gate = self.mod(conditioning).chunk(3, dim=-1) + if token_replace_vec is not None: + assert tr_token is not None + tr_mod_shift, tr_mod_scale, tr_mod_gate = self.mod(token_replace_vec).chunk(3, dim=-1) + else: + tr_mod_shift, tr_mod_scale, tr_mod_gate = None, None, None + + norm_hidden_states = self.norm(hidden_states) + norm_hidden_states = modulate(norm_hidden_states, shift=mod_shift, scale=mod_scale, + tr_shift=tr_mod_shift, tr_scale=tr_mod_scale, tr_token=tr_token) + qkv = self.to_qkv(norm_hidden_states) + + q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num) + + q = self.norm_q(q) + k = self.norm_k(k) + + q_a, q_b = q[:, :-txt_len, :, :], q[:, -txt_len:, :, :] + k_a, k_b = k[:, :-txt_len, :, :], k[:, -txt_len:, :, :] + q_a, k_a = apply_rotary_emb(q_a, k_a, freqs_cis, head_first=False) + + v_len = txt_len - split_token + q_a, q_b = torch.concat([q_a, q_b[:, :split_token]], dim=1), q_b[:, split_token:].contiguous() + k_a, k_b = torch.concat([k_a, k_b[:, :split_token]], dim=1), k_b[:, split_token:].contiguous() + v_a, v_b = v[:, :-v_len].contiguous(), v[:, -v_len:].contiguous() + + attn_output_a = attention(q_a, k_a, v_a) + attn_output_b = attention(q_b, k_b, v_b) + attn_output = torch.concat([attn_output_a, attn_output_b], dim=1) + + hidden_states = hidden_states + apply_gate(self.to_out(attn_output), mod_gate, tr_mod_gate, tr_token) + hidden_states = hidden_states + apply_gate(self.ff(norm_hidden_states), mod_gate, tr_mod_gate, tr_token) + return hidden_states + + +class FinalLayer(torch.nn.Module): + def __init__(self, hidden_size=3072, patch_size=(1, 2, 2), out_channels=16): + super().__init__() + + self.norm_final = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.linear = torch.nn.Linear(hidden_size, patch_size[0] * patch_size[1] * patch_size[2] * out_channels) + + self.adaLN_modulation = torch.nn.Sequential(torch.nn.SiLU(), torch.nn.Linear(hidden_size, 2 * hidden_size)) + + def forward(self, x, c): + shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift=shift, scale=scale) + x = self.linear(x) + return x + + +class HunyuanVideoDiT(torch.nn.Module): + def __init__(self, in_channels=16, hidden_size=3072, text_dim=4096, num_double_blocks=20, num_single_blocks=40, guidance_embed=True): + super().__init__() + self.img_in = PatchEmbed(in_channels=in_channels, embed_dim=hidden_size) + self.txt_in = SingleTokenRefiner(in_channels=text_dim, hidden_size=hidden_size) + self.time_in = TimestepEmbeddings(256, hidden_size, computation_device="cpu") + self.vector_in = torch.nn.Sequential( + torch.nn.Linear(768, hidden_size), + torch.nn.SiLU(), + torch.nn.Linear(hidden_size, hidden_size) + ) + self.guidance_in = TimestepEmbeddings(256, hidden_size, computation_device="cpu") if guidance_embed else None + self.double_blocks = torch.nn.ModuleList([MMDoubleStreamBlock(hidden_size) for _ in range(num_double_blocks)]) + self.single_blocks = torch.nn.ModuleList([MMSingleStreamBlock(hidden_size) for _ in range(num_single_blocks)]) + self.final_layer = FinalLayer(hidden_size) + + # TODO: remove these parameters + self.dtype = torch.bfloat16 + self.patch_size = [1, 2, 2] + self.hidden_size = 3072 + self.heads_num = 24 + self.rope_dim_list = [16, 56, 56] + + def unpatchify(self, x, T, H, W): + x = rearrange(x, "B (T H W) (C pT pH pW) -> B C (T pT) (H pH) (W pW)", H=H, W=W, pT=1, pH=2, pW=2) + return x + + def enable_block_wise_offload(self, warm_device="cuda", cold_device="cpu"): + self.warm_device = warm_device + self.cold_device = cold_device + self.to(self.cold_device) + + def load_models_to_device(self, loadmodel_names=[], device="cpu"): + for model_name in loadmodel_names: + model = getattr(self, model_name) + if model is not None: + model.to(device) + torch.cuda.empty_cache() + + def prepare_freqs(self, latents): + return HunyuanVideoRope(latents) + + def forward( + self, + x: torch.Tensor, + t: torch.Tensor, + prompt_emb: torch.Tensor = None, + text_mask: torch.Tensor = None, + pooled_prompt_emb: torch.Tensor = None, + freqs_cos: torch.Tensor = None, + freqs_sin: torch.Tensor = None, + guidance: torch.Tensor = None, + **kwargs + ): + B, C, T, H, W = x.shape + + vec = self.time_in(t, dtype=torch.float32) + self.vector_in(pooled_prompt_emb) + if self.guidance_in is not None: + vec += self.guidance_in(guidance * 1000, dtype=torch.float32) + img = self.img_in(x) + txt = self.txt_in(prompt_emb, t, text_mask) + + for block in tqdm(self.double_blocks, desc="Double stream blocks"): + img, txt = block(img, txt, vec, (freqs_cos, freqs_sin)) + + x = torch.concat([img, txt], dim=1) + for block in tqdm(self.single_blocks, desc="Single stream blocks"): + x = block(x, vec, (freqs_cos, freqs_sin)) + + img = x[:, :-256] + img = self.final_layer(img, vec) + img = self.unpatchify(img, T=T//1, H=H//2, W=W//2) + return img + + + def enable_auto_offload(self, dtype=torch.bfloat16, device="cuda"): + def cast_to(weight, dtype=None, device=None, copy=False): + if device is None or weight.device == device: + if not copy: + if dtype is None or weight.dtype == dtype: + return weight + return weight.to(dtype=dtype, copy=copy) + + r = torch.empty_like(weight, dtype=dtype, device=device) + r.copy_(weight) + return r + + def cast_weight(s, input=None, dtype=None, device=None): + if input is not None: + if dtype is None: + dtype = input.dtype + if device is None: + device = input.device + weight = cast_to(s.weight, dtype, device) + return weight + + def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None): + if input is not None: + if dtype is None: + dtype = input.dtype + if bias_dtype is None: + bias_dtype = dtype + if device is None: + device = input.device + weight = cast_to(s.weight, dtype, device) + bias = cast_to(s.bias, bias_dtype, device) if s.bias is not None else None + return weight, bias + + class quantized_layer: + class Linear(torch.nn.Linear): + def __init__(self, *args, dtype=torch.bfloat16, device="cuda", **kwargs): + super().__init__(*args, **kwargs) + self.dtype = dtype + self.device = device + + def block_forward_(self, x, i, j, dtype, device): + weight_ = cast_to( + self.weight[j * self.block_size: (j + 1) * self.block_size, i * self.block_size: (i + 1) * self.block_size], + dtype=dtype, device=device + ) + if self.bias is None or i > 0: + bias_ = None + else: + bias_ = cast_to(self.bias[j * self.block_size: (j + 1) * self.block_size], dtype=dtype, device=device) + x_ = x[..., i * self.block_size: (i + 1) * self.block_size] + y_ = torch.nn.functional.linear(x_, weight_, bias_) + del x_, weight_, bias_ + torch.cuda.empty_cache() + return y_ + + def block_forward(self, x, **kwargs): + # This feature can only reduce 2GB VRAM, so we disable it. + y = torch.zeros(x.shape[:-1] + (self.out_features,), dtype=x.dtype, device=x.device) + for i in range((self.in_features + self.block_size - 1) // self.block_size): + for j in range((self.out_features + self.block_size - 1) // self.block_size): + y[..., j * self.block_size: (j + 1) * self.block_size] += self.block_forward_(x, i, j, dtype=x.dtype, device=x.device) + return y + + def forward(self, x, **kwargs): + weight, bias = cast_bias_weight(self, x, dtype=self.dtype, device=self.device) + return torch.nn.functional.linear(x, weight, bias) + + + class RMSNorm(torch.nn.Module): + def __init__(self, module, dtype=torch.bfloat16, device="cuda"): + super().__init__() + self.module = module + self.dtype = dtype + self.device = device + + def forward(self, hidden_states, **kwargs): + input_dtype = hidden_states.dtype + variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.module.eps) + hidden_states = hidden_states.to(input_dtype) + if self.module.weight is not None: + weight = cast_weight(self.module, hidden_states, dtype=torch.bfloat16, device="cuda") + hidden_states = hidden_states * weight + return hidden_states + + class Conv3d(torch.nn.Conv3d): + def __init__(self, *args, dtype=torch.bfloat16, device="cuda", **kwargs): + super().__init__(*args, **kwargs) + self.dtype = dtype + self.device = device + + def forward(self, x): + weight, bias = cast_bias_weight(self, x, dtype=self.dtype, device=self.device) + return torch.nn.functional.conv3d(x, weight, bias, self.stride, self.padding, self.dilation, self.groups) + + class LayerNorm(torch.nn.LayerNorm): + def __init__(self, *args, dtype=torch.bfloat16, device="cuda", **kwargs): + super().__init__(*args, **kwargs) + self.dtype = dtype + self.device = device + + def forward(self, x): + if self.weight is not None and self.bias is not None: + weight, bias = cast_bias_weight(self, x, dtype=self.dtype, device=self.device) + return torch.nn.functional.layer_norm(x, self.normalized_shape, weight, bias, self.eps) + else: + return torch.nn.functional.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + + def replace_layer(model, dtype=torch.bfloat16, device="cuda"): + for name, module in model.named_children(): + if isinstance(module, torch.nn.Linear): + with init_weights_on_device(): + new_layer = quantized_layer.Linear( + module.in_features, module.out_features, bias=module.bias is not None, + dtype=dtype, device=device + ) + new_layer.load_state_dict(module.state_dict(), assign=True) + setattr(model, name, new_layer) + elif isinstance(module, torch.nn.Conv3d): + with init_weights_on_device(): + new_layer = quantized_layer.Conv3d( + module.in_channels, module.out_channels, kernel_size=module.kernel_size, stride=module.stride, + dtype=dtype, device=device + ) + new_layer.load_state_dict(module.state_dict(), assign=True) + setattr(model, name, new_layer) + elif isinstance(module, RMSNorm): + new_layer = quantized_layer.RMSNorm( + module, + dtype=dtype, device=device + ) + setattr(model, name, new_layer) + elif isinstance(module, torch.nn.LayerNorm): + with init_weights_on_device(): + new_layer = quantized_layer.LayerNorm( + module.normalized_shape, elementwise_affine=module.elementwise_affine, eps=module.eps, + dtype=dtype, device=device + ) + new_layer.load_state_dict(module.state_dict(), assign=True) + setattr(model, name, new_layer) + else: + replace_layer(module, dtype=dtype, device=device) + + replace_layer(self, dtype=dtype, device=device) + + @staticmethod + def state_dict_converter(): + return HunyuanVideoDiTStateDictConverter() + + +class HunyuanVideoDiTStateDictConverter: + def __init__(self): + pass + + def from_civitai(self, state_dict): + origin_hash_key = hash_state_dict_keys(state_dict, with_shape=True) + if "module" in state_dict: + state_dict = state_dict["module"] + direct_dict = { + "img_in.proj": "img_in.proj", + "time_in.mlp.0": "time_in.timestep_embedder.0", + "time_in.mlp.2": "time_in.timestep_embedder.2", + "vector_in.in_layer": "vector_in.0", + "vector_in.out_layer": "vector_in.2", + "guidance_in.mlp.0": "guidance_in.timestep_embedder.0", + "guidance_in.mlp.2": "guidance_in.timestep_embedder.2", + "txt_in.input_embedder": "txt_in.input_embedder", + "txt_in.t_embedder.mlp.0": "txt_in.t_embedder.timestep_embedder.0", + "txt_in.t_embedder.mlp.2": "txt_in.t_embedder.timestep_embedder.2", + "txt_in.c_embedder.linear_1": "txt_in.c_embedder.0", + "txt_in.c_embedder.linear_2": "txt_in.c_embedder.2", + "final_layer.linear": "final_layer.linear", + "final_layer.adaLN_modulation.1": "final_layer.adaLN_modulation.1", + } + txt_suffix_dict = { + "norm1": "norm1", + "self_attn_qkv": "self_attn_qkv", + "self_attn_proj": "self_attn_proj", + "norm2": "norm2", + "mlp.fc1": "mlp.0", + "mlp.fc2": "mlp.2", + "adaLN_modulation.1": "adaLN_modulation.1", + } + double_suffix_dict = { + "img_mod.linear": "component_a.mod.linear", + "img_attn_qkv": "component_a.to_qkv", + "img_attn_q_norm": "component_a.norm_q", + "img_attn_k_norm": "component_a.norm_k", + "img_attn_proj": "component_a.to_out", + "img_mlp.fc1": "component_a.ff.0", + "img_mlp.fc2": "component_a.ff.2", + "txt_mod.linear": "component_b.mod.linear", + "txt_attn_qkv": "component_b.to_qkv", + "txt_attn_q_norm": "component_b.norm_q", + "txt_attn_k_norm": "component_b.norm_k", + "txt_attn_proj": "component_b.to_out", + "txt_mlp.fc1": "component_b.ff.0", + "txt_mlp.fc2": "component_b.ff.2", + } + single_suffix_dict = { + "linear1": ["to_qkv", "ff.0"], + "linear2": ["to_out", "ff.2"], + "q_norm": "norm_q", + "k_norm": "norm_k", + "modulation.linear": "mod.linear", + } + # single_suffix_dict = { + # "linear1": "linear1", + # "linear2": "linear2", + # "q_norm": "q_norm", + # "k_norm": "k_norm", + # "modulation.linear": "modulation.linear", + # } + state_dict_ = {} + for name, param in state_dict.items(): + names = name.split(".") + direct_name = ".".join(names[:-1]) + if direct_name in direct_dict: + name_ = direct_dict[direct_name] + "." + names[-1] + state_dict_[name_] = param + elif names[0] == "double_blocks": + prefix = ".".join(names[:2]) + suffix = ".".join(names[2:-1]) + name_ = prefix + "." + double_suffix_dict[suffix] + "." + names[-1] + state_dict_[name_] = param + elif names[0] == "single_blocks": + prefix = ".".join(names[:2]) + suffix = ".".join(names[2:-1]) + if isinstance(single_suffix_dict[suffix], list): + if suffix == "linear1": + name_a, name_b = single_suffix_dict[suffix] + param_a, param_b = torch.split(param, (3072*3, 3072*4), dim=0) + state_dict_[prefix + "." + name_a + "." + names[-1]] = param_a + state_dict_[prefix + "." + name_b + "." + names[-1]] = param_b + elif suffix == "linear2": + if names[-1] == "weight": + name_a, name_b = single_suffix_dict[suffix] + param_a, param_b = torch.split(param, (3072*1, 3072*4), dim=-1) + state_dict_[prefix + "." + name_a + "." + names[-1]] = param_a + state_dict_[prefix + "." + name_b + "." + names[-1]] = param_b + else: + name_a, name_b = single_suffix_dict[suffix] + state_dict_[prefix + "." + name_a + "." + names[-1]] = param + else: + pass + else: + name_ = prefix + "." + single_suffix_dict[suffix] + "." + names[-1] + state_dict_[name_] = param + elif names[0] == "txt_in": + prefix = ".".join(names[:4]).replace(".individual_token_refiner.", ".") + suffix = ".".join(names[4:-1]) + name_ = prefix + "." + txt_suffix_dict[suffix] + "." + names[-1] + state_dict_[name_] = param + else: + pass + + return state_dict_ diff --git a/diffsynth/models/hunyuan_video_text_encoder.py b/diffsynth/models/hunyuan_video_text_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..ce7a6805a163c709a4f3de82784538b300541119 --- /dev/null +++ b/diffsynth/models/hunyuan_video_text_encoder.py @@ -0,0 +1,68 @@ +from transformers import LlamaModel, LlamaConfig, DynamicCache, LlavaForConditionalGeneration +from copy import deepcopy +import torch + + +class HunyuanVideoLLMEncoder(LlamaModel): + + def __init__(self, config: LlamaConfig): + super().__init__(config) + self.auto_offload = False + + def enable_auto_offload(self, **kwargs): + self.auto_offload = True + + def forward(self, input_ids, attention_mask, hidden_state_skip_layer=2): + embed_tokens = deepcopy(self.embed_tokens).to(input_ids.device) if self.auto_offload else self.embed_tokens + inputs_embeds = embed_tokens(input_ids) + + past_key_values = DynamicCache() + + cache_position = torch.arange(0, inputs_embeds.shape[1], device=inputs_embeds.device) + position_ids = cache_position.unsqueeze(0) + + causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, None, False) + hidden_states = inputs_embeds + + # create position embeddings to be shared across the decoder layers + rotary_emb = deepcopy(self.rotary_emb).to(input_ids.device) if self.auto_offload else self.rotary_emb + position_embeddings = rotary_emb(hidden_states, position_ids) + + # decoder layers + for layer_id, decoder_layer in enumerate(self.layers): + if self.auto_offload: + decoder_layer = deepcopy(decoder_layer).to(hidden_states.device) + layer_outputs = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=False, + use_cache=True, + cache_position=cache_position, + position_embeddings=position_embeddings, + ) + hidden_states = layer_outputs[0] + if layer_id + hidden_state_skip_layer + 1 >= len(self.layers): + break + + return hidden_states + + +class HunyuanVideoMLLMEncoder(LlavaForConditionalGeneration): + + def __init__(self, config): + super().__init__(config) + self.auto_offload = False + + def enable_auto_offload(self, **kwargs): + self.auto_offload = True + + # TODO: implement the low VRAM inference for MLLM. + def forward(self, input_ids, pixel_values, attention_mask, hidden_state_skip_layer=2): + outputs = super().forward(input_ids=input_ids, + attention_mask=attention_mask, + output_hidden_states=True, + pixel_values=pixel_values) + hidden_state = outputs.hidden_states[-(hidden_state_skip_layer + 1)] + return hidden_state diff --git a/diffsynth/models/hunyuan_video_vae_decoder.py b/diffsynth/models/hunyuan_video_vae_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..ae09ff85a9149edd46a36e7bfeb29a372a81e12c --- /dev/null +++ b/diffsynth/models/hunyuan_video_vae_decoder.py @@ -0,0 +1,507 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +import numpy as np +from tqdm import tqdm +from einops import repeat + + +class CausalConv3d(nn.Module): + + def __init__(self, in_channel, out_channel, kernel_size, stride=1, dilation=1, pad_mode='replicate', **kwargs): + super().__init__() + self.pad_mode = pad_mode + self.time_causal_padding = (kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size - 1, 0 + ) # W, H, T + self.conv = nn.Conv3d(in_channel, out_channel, kernel_size, stride=stride, dilation=dilation, **kwargs) + + def forward(self, x): + x = F.pad(x, self.time_causal_padding, mode=self.pad_mode) + return self.conv(x) + + +class UpsampleCausal3D(nn.Module): + + def __init__(self, channels, use_conv=False, out_channels=None, kernel_size=None, bias=True, upsample_factor=(2, 2, 2)): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.upsample_factor = upsample_factor + self.conv = None + if use_conv: + kernel_size = 3 if kernel_size is None else kernel_size + self.conv = CausalConv3d(self.channels, self.out_channels, kernel_size=kernel_size, bias=bias) + + def forward(self, hidden_states): + # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 + dtype = hidden_states.dtype + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(torch.float32) + + # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 + if hidden_states.shape[0] >= 64: + hidden_states = hidden_states.contiguous() + + # interpolate + B, C, T, H, W = hidden_states.shape + first_h, other_h = hidden_states.split((1, T - 1), dim=2) + if T > 1: + other_h = F.interpolate(other_h, scale_factor=self.upsample_factor, mode="nearest") + first_h = F.interpolate(first_h.squeeze(2), scale_factor=self.upsample_factor[1:], mode="nearest").unsqueeze(2) + hidden_states = torch.cat((first_h, other_h), dim=2) if T > 1 else first_h + + # If the input is bfloat16, we cast back to bfloat16 + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(dtype) + + if self.conv: + hidden_states = self.conv(hidden_states) + + return hidden_states + + +class ResnetBlockCausal3D(nn.Module): + + def __init__(self, in_channels, out_channels=None, dropout=0.0, groups=32, eps=1e-6, conv_shortcut_bias=True): + super().__init__() + self.pre_norm = True + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + + self.norm1 = nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) + self.conv1 = CausalConv3d(in_channels, out_channels, kernel_size=3, stride=1) + + self.norm2 = nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True) + self.conv2 = CausalConv3d(out_channels, out_channels, kernel_size=3, stride=1) + + self.dropout = nn.Dropout(dropout) + self.nonlinearity = nn.SiLU() + + self.conv_shortcut = None + if in_channels != out_channels: + self.conv_shortcut = CausalConv3d(in_channels, out_channels, kernel_size=1, stride=1, bias=conv_shortcut_bias) + + def forward(self, input_tensor): + hidden_states = input_tensor + # conv1 + hidden_states = self.norm1(hidden_states) + hidden_states = self.nonlinearity(hidden_states) + hidden_states = self.conv1(hidden_states) + + # conv2 + hidden_states = self.norm2(hidden_states) + hidden_states = self.nonlinearity(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.conv2(hidden_states) + # shortcut + if self.conv_shortcut is not None: + input_tensor = (self.conv_shortcut(input_tensor)) + # shortcut and scale + output_tensor = input_tensor + hidden_states + + return output_tensor + + +def prepare_causal_attention_mask(n_frame, n_hw, dtype, device, batch_size=None): + seq_len = n_frame * n_hw + mask = torch.full((seq_len, seq_len), float("-inf"), dtype=dtype, device=device) + for i in range(seq_len): + i_frame = i // n_hw + mask[i, :(i_frame + 1) * n_hw] = 0 + if batch_size is not None: + mask = mask.unsqueeze(0).expand(batch_size, -1, -1) + return mask + + +class Attention(nn.Module): + + def __init__(self, + in_channels, + num_heads, + head_dim, + num_groups=32, + dropout=0.0, + eps=1e-6, + bias=True, + residual_connection=True): + super().__init__() + self.num_heads = num_heads + self.head_dim = head_dim + self.residual_connection = residual_connection + dim_inner = head_dim * num_heads + self.group_norm = nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=eps, affine=True) + self.to_q = nn.Linear(in_channels, dim_inner, bias=bias) + self.to_k = nn.Linear(in_channels, dim_inner, bias=bias) + self.to_v = nn.Linear(in_channels, dim_inner, bias=bias) + self.to_out = nn.Sequential(nn.Linear(dim_inner, in_channels, bias=bias), nn.Dropout(dropout)) + + def forward(self, input_tensor, attn_mask=None): + hidden_states = self.group_norm(input_tensor.transpose(1, 2)).transpose(1, 2) + batch_size = hidden_states.shape[0] + + q = self.to_q(hidden_states) + k = self.to_k(hidden_states) + v = self.to_v(hidden_states) + + q = q.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + k = k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + v = v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + + if attn_mask is not None: + attn_mask = attn_mask.view(batch_size, self.num_heads, -1, attn_mask.shape[-1]) + hidden_states = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) + hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) + hidden_states = self.to_out(hidden_states) + if self.residual_connection: + output_tensor = input_tensor + hidden_states + return output_tensor + + +class UNetMidBlockCausal3D(nn.Module): + + def __init__(self, in_channels, dropout=0.0, num_layers=1, eps=1e-6, num_groups=32, attention_head_dim=None): + super().__init__() + resnets = [ + ResnetBlockCausal3D( + in_channels=in_channels, + out_channels=in_channels, + dropout=dropout, + groups=num_groups, + eps=eps, + ) + ] + attentions = [] + attention_head_dim = attention_head_dim or in_channels + + for _ in range(num_layers): + attentions.append( + Attention( + in_channels, + num_heads=in_channels // attention_head_dim, + head_dim=attention_head_dim, + num_groups=num_groups, + dropout=dropout, + eps=eps, + bias=True, + residual_connection=True, + )) + + resnets.append( + ResnetBlockCausal3D( + in_channels=in_channels, + out_channels=in_channels, + dropout=dropout, + groups=num_groups, + eps=eps, + )) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + def forward(self, hidden_states): + hidden_states = self.resnets[0](hidden_states) + for attn, resnet in zip(self.attentions, self.resnets[1:]): + B, C, T, H, W = hidden_states.shape + hidden_states = rearrange(hidden_states, "b c f h w -> b (f h w) c") + attn_mask = prepare_causal_attention_mask(T, H * W, hidden_states.dtype, hidden_states.device, batch_size=B) + hidden_states = attn(hidden_states, attn_mask=attn_mask) + hidden_states = rearrange(hidden_states, "b (f h w) c -> b c f h w", f=T, h=H, w=W) + hidden_states = resnet(hidden_states) + + return hidden_states + + +class UpDecoderBlockCausal3D(nn.Module): + + def __init__( + self, + in_channels, + out_channels, + dropout=0.0, + num_layers=1, + eps=1e-6, + num_groups=32, + add_upsample=True, + upsample_scale_factor=(2, 2, 2), + ): + super().__init__() + resnets = [] + for i in range(num_layers): + cur_in_channel = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlockCausal3D( + in_channels=cur_in_channel, + out_channels=out_channels, + groups=num_groups, + dropout=dropout, + eps=eps, + )) + self.resnets = nn.ModuleList(resnets) + + self.upsamplers = None + if add_upsample: + self.upsamplers = nn.ModuleList([ + UpsampleCausal3D( + out_channels, + use_conv=True, + out_channels=out_channels, + upsample_factor=upsample_scale_factor, + ) + ]) + + def forward(self, hidden_states): + for resnet in self.resnets: + hidden_states = resnet(hidden_states) + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states) + return hidden_states + + +class DecoderCausal3D(nn.Module): + + def __init__( + self, + in_channels=16, + out_channels=3, + eps=1e-6, + dropout=0.0, + block_out_channels=[128, 256, 512, 512], + layers_per_block=2, + num_groups=32, + time_compression_ratio=4, + spatial_compression_ratio=8, + gradient_checkpointing=False, + ): + super().__init__() + self.layers_per_block = layers_per_block + + self.conv_in = CausalConv3d(in_channels, block_out_channels[-1], kernel_size=3, stride=1) + self.up_blocks = nn.ModuleList([]) + + # mid + self.mid_block = UNetMidBlockCausal3D( + in_channels=block_out_channels[-1], + dropout=dropout, + eps=eps, + num_groups=num_groups, + attention_head_dim=block_out_channels[-1], + ) + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + output_channel = reversed_block_out_channels[0] + for i in range(len(block_out_channels)): + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + num_spatial_upsample_layers = int(np.log2(spatial_compression_ratio)) + num_time_upsample_layers = int(np.log2(time_compression_ratio)) + + add_spatial_upsample = bool(i < num_spatial_upsample_layers) + add_time_upsample = bool(i >= len(block_out_channels) - 1 - num_time_upsample_layers and not is_final_block) + + upsample_scale_factor_HW = (2, 2) if add_spatial_upsample else (1, 1) + upsample_scale_factor_T = (2,) if add_time_upsample else (1,) + upsample_scale_factor = tuple(upsample_scale_factor_T + upsample_scale_factor_HW) + + up_block = UpDecoderBlockCausal3D( + in_channels=prev_output_channel, + out_channels=output_channel, + dropout=dropout, + num_layers=layers_per_block + 1, + eps=eps, + num_groups=num_groups, + add_upsample=bool(add_spatial_upsample or add_time_upsample), + upsample_scale_factor=upsample_scale_factor, + ) + + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups, eps=eps) + self.conv_act = nn.SiLU() + self.conv_out = CausalConv3d(block_out_channels[0], out_channels, kernel_size=3) + + self.gradient_checkpointing = gradient_checkpointing + + def forward(self, hidden_states): + hidden_states = self.conv_in(hidden_states) + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + # middle + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(self.mid_block), + hidden_states, + use_reentrant=False, + ) + # up + for up_block in self.up_blocks: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(up_block), + hidden_states, + use_reentrant=False, + ) + else: + # middle + hidden_states = self.mid_block(hidden_states) + # up + for up_block in self.up_blocks: + hidden_states = up_block(hidden_states) + # post-process + hidden_states = self.conv_norm_out(hidden_states) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + + return hidden_states + + +class HunyuanVideoVAEDecoder(nn.Module): + + def __init__( + self, + in_channels=16, + out_channels=3, + eps=1e-6, + dropout=0.0, + block_out_channels=[128, 256, 512, 512], + layers_per_block=2, + num_groups=32, + time_compression_ratio=4, + spatial_compression_ratio=8, + gradient_checkpointing=False, + ): + super().__init__() + self.decoder = DecoderCausal3D( + in_channels=in_channels, + out_channels=out_channels, + eps=eps, + dropout=dropout, + block_out_channels=block_out_channels, + layers_per_block=layers_per_block, + num_groups=num_groups, + time_compression_ratio=time_compression_ratio, + spatial_compression_ratio=spatial_compression_ratio, + gradient_checkpointing=gradient_checkpointing, + ) + self.post_quant_conv = nn.Conv3d(in_channels, in_channels, kernel_size=1) + self.scaling_factor = 0.476986 + + + def forward(self, latents): + latents = latents / self.scaling_factor + latents = self.post_quant_conv(latents) + dec = self.decoder(latents) + return dec + + + def build_1d_mask(self, length, left_bound, right_bound, border_width): + x = torch.ones((length,)) + if not left_bound: + x[:border_width] = (torch.arange(border_width) + 1) / border_width + if not right_bound: + x[-border_width:] = torch.flip((torch.arange(border_width) + 1) / border_width, dims=(0,)) + return x + + + def build_mask(self, data, is_bound, border_width): + _, _, T, H, W = data.shape + t = self.build_1d_mask(T, is_bound[0], is_bound[1], border_width[0]) + h = self.build_1d_mask(H, is_bound[2], is_bound[3], border_width[1]) + w = self.build_1d_mask(W, is_bound[4], is_bound[5], border_width[2]) + + t = repeat(t, "T -> T H W", T=T, H=H, W=W) + h = repeat(h, "H -> T H W", T=T, H=H, W=W) + w = repeat(w, "W -> T H W", T=T, H=H, W=W) + + mask = torch.stack([t, h, w]).min(dim=0).values + mask = rearrange(mask, "T H W -> 1 1 T H W") + return mask + + + def tile_forward(self, hidden_states, tile_size, tile_stride): + B, C, T, H, W = hidden_states.shape + size_t, size_h, size_w = tile_size + stride_t, stride_h, stride_w = tile_stride + + # Split tasks + tasks = [] + for t in range(0, T, stride_t): + if (t-stride_t >= 0 and t-stride_t+size_t >= T): continue + for h in range(0, H, stride_h): + if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue + for w in range(0, W, stride_w): + if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue + t_, h_, w_ = t + size_t, h + size_h, w + size_w + tasks.append((t, t_, h, h_, w, w_)) + + # Run + torch_dtype = self.post_quant_conv.weight.dtype + data_device = hidden_states.device + computation_device = self.post_quant_conv.weight.device + + weight = torch.zeros((1, 1, (T - 1) * 4 + 1, H * 8, W * 8), dtype=torch_dtype, device=data_device) + values = torch.zeros((B, 3, (T - 1) * 4 + 1, H * 8, W * 8), dtype=torch_dtype, device=data_device) + + for t, t_, h, h_, w, w_ in tqdm(tasks, desc="VAE decoding"): + hidden_states_batch = hidden_states[:, :, t:t_, h:h_, w:w_].to(computation_device) + hidden_states_batch = self.forward(hidden_states_batch).to(data_device) + if t > 0: + hidden_states_batch = hidden_states_batch[:, :, 1:] + + mask = self.build_mask( + hidden_states_batch, + is_bound=(t==0, t_>=T, h==0, h_>=H, w==0, w_>=W), + border_width=((size_t - stride_t) * 4, (size_h - stride_h) * 8, (size_w - stride_w) * 8) + ).to(dtype=torch_dtype, device=data_device) + + target_t = 0 if t==0 else t * 4 + 1 + target_h = h * 8 + target_w = w * 8 + values[ + :, + :, + target_t: target_t + hidden_states_batch.shape[2], + target_h: target_h + hidden_states_batch.shape[3], + target_w: target_w + hidden_states_batch.shape[4], + ] += hidden_states_batch * mask + weight[ + :, + :, + target_t: target_t + hidden_states_batch.shape[2], + target_h: target_h + hidden_states_batch.shape[3], + target_w: target_w + hidden_states_batch.shape[4], + ] += mask + return values / weight + + + def decode_video(self, latents, tile_size=(17, 32, 32), tile_stride=(12, 24, 24)): + latents = latents.to(self.post_quant_conv.weight.dtype) + return self.tile_forward(latents, tile_size=tile_size, tile_stride=tile_stride) + + @staticmethod + def state_dict_converter(): + return HunyuanVideoVAEDecoderStateDictConverter() + + +class HunyuanVideoVAEDecoderStateDictConverter: + + def __init__(self): + pass + + def from_diffusers(self, state_dict): + state_dict_ = {} + for name in state_dict: + if name.startswith('decoder.') or name.startswith('post_quant_conv.'): + state_dict_[name] = state_dict[name] + return state_dict_ diff --git a/diffsynth/models/hunyuan_video_vae_encoder.py b/diffsynth/models/hunyuan_video_vae_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..faaaeb95f7d688d57fb61a707c7658a89bb2c92a --- /dev/null +++ b/diffsynth/models/hunyuan_video_vae_encoder.py @@ -0,0 +1,307 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange, repeat +import numpy as np +from tqdm import tqdm +from .hunyuan_video_vae_decoder import CausalConv3d, ResnetBlockCausal3D, UNetMidBlockCausal3D + + +class DownsampleCausal3D(nn.Module): + + def __init__(self, channels, out_channels, kernel_size=3, bias=True, stride=2): + super().__init__() + self.conv = CausalConv3d(channels, out_channels, kernel_size, stride=stride, bias=bias) + + def forward(self, hidden_states): + hidden_states = self.conv(hidden_states) + return hidden_states + + +class DownEncoderBlockCausal3D(nn.Module): + + def __init__( + self, + in_channels, + out_channels, + dropout=0.0, + num_layers=1, + eps=1e-6, + num_groups=32, + add_downsample=True, + downsample_stride=2, + ): + + super().__init__() + resnets = [] + for i in range(num_layers): + cur_in_channel = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlockCausal3D( + in_channels=cur_in_channel, + out_channels=out_channels, + groups=num_groups, + dropout=dropout, + eps=eps, + )) + self.resnets = nn.ModuleList(resnets) + + self.downsamplers = None + if add_downsample: + self.downsamplers = nn.ModuleList([DownsampleCausal3D( + out_channels, + out_channels, + stride=downsample_stride, + )]) + + def forward(self, hidden_states): + for resnet in self.resnets: + hidden_states = resnet(hidden_states) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + return hidden_states + + +class EncoderCausal3D(nn.Module): + + def __init__( + self, + in_channels: int = 3, + out_channels: int = 16, + eps=1e-6, + dropout=0.0, + block_out_channels=[128, 256, 512, 512], + layers_per_block=2, + num_groups=32, + time_compression_ratio: int = 4, + spatial_compression_ratio: int = 8, + gradient_checkpointing=False, + ): + super().__init__() + self.conv_in = CausalConv3d(in_channels, block_out_channels[0], kernel_size=3, stride=1) + self.down_blocks = nn.ModuleList([]) + + # down + output_channel = block_out_channels[0] + for i in range(len(block_out_channels)): + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + num_spatial_downsample_layers = int(np.log2(spatial_compression_ratio)) + num_time_downsample_layers = int(np.log2(time_compression_ratio)) + + add_spatial_downsample = bool(i < num_spatial_downsample_layers) + add_time_downsample = bool(i >= (len(block_out_channels) - 1 - num_time_downsample_layers) and not is_final_block) + + downsample_stride_HW = (2, 2) if add_spatial_downsample else (1, 1) + downsample_stride_T = (2,) if add_time_downsample else (1,) + downsample_stride = tuple(downsample_stride_T + downsample_stride_HW) + down_block = DownEncoderBlockCausal3D( + in_channels=input_channel, + out_channels=output_channel, + dropout=dropout, + num_layers=layers_per_block, + eps=eps, + num_groups=num_groups, + add_downsample=bool(add_spatial_downsample or add_time_downsample), + downsample_stride=downsample_stride, + ) + self.down_blocks.append(down_block) + + # mid + self.mid_block = UNetMidBlockCausal3D( + in_channels=block_out_channels[-1], + dropout=dropout, + eps=eps, + num_groups=num_groups, + attention_head_dim=block_out_channels[-1], + ) + # out + self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=num_groups, eps=eps) + self.conv_act = nn.SiLU() + self.conv_out = CausalConv3d(block_out_channels[-1], 2 * out_channels, kernel_size=3) + + self.gradient_checkpointing = gradient_checkpointing + + def forward(self, hidden_states): + hidden_states = self.conv_in(hidden_states) + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + # down + for down_block in self.down_blocks: + torch.utils.checkpoint.checkpoint( + create_custom_forward(down_block), + hidden_states, + use_reentrant=False, + ) + # middle + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(self.mid_block), + hidden_states, + use_reentrant=False, + ) + else: + # down + for down_block in self.down_blocks: + hidden_states = down_block(hidden_states) + # middle + hidden_states = self.mid_block(hidden_states) + # post-process + hidden_states = self.conv_norm_out(hidden_states) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + + return hidden_states + + +class HunyuanVideoVAEEncoder(nn.Module): + + def __init__( + self, + in_channels=3, + out_channels=16, + eps=1e-6, + dropout=0.0, + block_out_channels=[128, 256, 512, 512], + layers_per_block=2, + num_groups=32, + time_compression_ratio=4, + spatial_compression_ratio=8, + gradient_checkpointing=False, + ): + super().__init__() + self.encoder = EncoderCausal3D( + in_channels=in_channels, + out_channels=out_channels, + eps=eps, + dropout=dropout, + block_out_channels=block_out_channels, + layers_per_block=layers_per_block, + num_groups=num_groups, + time_compression_ratio=time_compression_ratio, + spatial_compression_ratio=spatial_compression_ratio, + gradient_checkpointing=gradient_checkpointing, + ) + self.quant_conv = nn.Conv3d(2 * out_channels, 2 * out_channels, kernel_size=1) + self.scaling_factor = 0.476986 + + + def forward(self, images): + latents = self.encoder(images) + latents = self.quant_conv(latents) + latents = latents[:, :16] + latents = latents * self.scaling_factor + return latents + + + def build_1d_mask(self, length, left_bound, right_bound, border_width): + x = torch.ones((length,)) + if not left_bound: + x[:border_width] = (torch.arange(border_width) + 1) / border_width + if not right_bound: + x[-border_width:] = torch.flip((torch.arange(border_width) + 1) / border_width, dims=(0,)) + return x + + + def build_mask(self, data, is_bound, border_width): + _, _, T, H, W = data.shape + t = self.build_1d_mask(T, is_bound[0], is_bound[1], border_width[0]) + h = self.build_1d_mask(H, is_bound[2], is_bound[3], border_width[1]) + w = self.build_1d_mask(W, is_bound[4], is_bound[5], border_width[2]) + + t = repeat(t, "T -> T H W", T=T, H=H, W=W) + h = repeat(h, "H -> T H W", T=T, H=H, W=W) + w = repeat(w, "W -> T H W", T=T, H=H, W=W) + + mask = torch.stack([t, h, w]).min(dim=0).values + mask = rearrange(mask, "T H W -> 1 1 T H W") + return mask + + + def tile_forward(self, hidden_states, tile_size, tile_stride): + B, C, T, H, W = hidden_states.shape + size_t, size_h, size_w = tile_size + stride_t, stride_h, stride_w = tile_stride + + # Split tasks + tasks = [] + for t in range(0, T, stride_t): + if (t-stride_t >= 0 and t-stride_t+size_t >= T): continue + for h in range(0, H, stride_h): + if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue + for w in range(0, W, stride_w): + if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue + t_, h_, w_ = t + size_t, h + size_h, w + size_w + tasks.append((t, t_, h, h_, w, w_)) + + # Run + torch_dtype = self.quant_conv.weight.dtype + data_device = hidden_states.device + computation_device = self.quant_conv.weight.device + + weight = torch.zeros((1, 1, (T - 1) // 4 + 1, H // 8, W // 8), dtype=torch_dtype, device=data_device) + values = torch.zeros((B, 16, (T - 1) // 4 + 1, H // 8, W // 8), dtype=torch_dtype, device=data_device) + + for t, t_, h, h_, w, w_ in tqdm(tasks, desc="VAE encoding"): + hidden_states_batch = hidden_states[:, :, t:t_, h:h_, w:w_].to(computation_device) + hidden_states_batch = self.forward(hidden_states_batch).to(data_device) + if t > 0: + hidden_states_batch = hidden_states_batch[:, :, 1:] + + mask = self.build_mask( + hidden_states_batch, + is_bound=(t==0, t_>=T, h==0, h_>=H, w==0, w_>=W), + border_width=((size_t - stride_t) // 4, (size_h - stride_h) // 8, (size_w - stride_w) // 8) + ).to(dtype=torch_dtype, device=data_device) + + target_t = 0 if t==0 else t // 4 + 1 + target_h = h // 8 + target_w = w // 8 + values[ + :, + :, + target_t: target_t + hidden_states_batch.shape[2], + target_h: target_h + hidden_states_batch.shape[3], + target_w: target_w + hidden_states_batch.shape[4], + ] += hidden_states_batch * mask + weight[ + :, + :, + target_t: target_t + hidden_states_batch.shape[2], + target_h: target_h + hidden_states_batch.shape[3], + target_w: target_w + hidden_states_batch.shape[4], + ] += mask + return values / weight + + + def encode_video(self, latents, tile_size=(65, 256, 256), tile_stride=(48, 192, 192)): + latents = latents.to(self.quant_conv.weight.dtype) + return self.tile_forward(latents, tile_size=tile_size, tile_stride=tile_stride) + + + @staticmethod + def state_dict_converter(): + return HunyuanVideoVAEEncoderStateDictConverter() + + +class HunyuanVideoVAEEncoderStateDictConverter: + + def __init__(self): + pass + + def from_diffusers(self, state_dict): + state_dict_ = {} + for name in state_dict: + if name.startswith('encoder.') or name.startswith('quant_conv.'): + state_dict_[name] = state_dict[name] + return state_dict_ diff --git a/diffsynth/models/kolors_text_encoder.py b/diffsynth/models/kolors_text_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..2e9212141ba1808d9fafeefc905fb4f52eb93fd5 --- /dev/null +++ b/diffsynth/models/kolors_text_encoder.py @@ -0,0 +1,1551 @@ +""" +This model is copied from https://github.com/Kwai-Kolors/Kolors/tree/master/kolors/models. +We didn't modify this model. +The tensor operation is performed in the prompter. +""" + + +""" PyTorch ChatGLM model. """ + +import math +import copy +import warnings +import re +import sys + +import torch +import torch.utils.checkpoint +import torch.nn.functional as F +from torch import nn +from torch.nn import CrossEntropyLoss, LayerNorm +from torch.nn import CrossEntropyLoss, LayerNorm, MSELoss, BCEWithLogitsLoss +from torch.nn.utils import skip_init +from typing import Optional, Tuple, Union, List, Callable, Dict, Any +from copy import deepcopy + +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, + SequenceClassifierOutputWithPast, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.generation.logits_process import LogitsProcessor +from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput +from transformers import PretrainedConfig +from torch.nn.parameter import Parameter +import bz2 +import torch +import base64 +import ctypes +from transformers.utils import logging +from typing import List + + + +logger = logging.get_logger(__name__) + +try: + from cpm_kernels.kernels.base import LazyKernelCModule, KernelFunction, round_up + + + class Kernel: + def __init__(self, code: bytes, function_names: List[str]): + self.code = code + self._function_names = function_names + self._cmodule = LazyKernelCModule(self.code) + + for name in self._function_names: + setattr(self, name, KernelFunction(self._cmodule, name)) + + + quantization_code = "$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" + + kernels = Kernel( + bz2.decompress(base64.b64decode(quantization_code)), + [ + "int4WeightCompression", + "int4WeightExtractionFloat", + "int4WeightExtractionHalf", + "int8WeightExtractionFloat", + "int8WeightExtractionHalf", + ], + ) +except Exception as exception: + kernels = None + + +class W8A16Linear(torch.autograd.Function): + @staticmethod + def forward(ctx, inp: torch.Tensor, quant_w: torch.Tensor, scale_w: torch.Tensor, weight_bit_width): + ctx.inp_shape = inp.size() + ctx.weight_bit_width = weight_bit_width + out_features = quant_w.size(0) + inp = inp.contiguous().view(-1, inp.size(-1)) + weight = extract_weight_to_half(quant_w, scale_w, weight_bit_width) + ctx.weight_shape = weight.size() + output = inp.mm(weight.t()) + ctx.save_for_backward(inp, quant_w, scale_w) + return output.view(*(ctx.inp_shape[:-1] + (out_features,))) + + @staticmethod + def backward(ctx, grad_output: torch.Tensor): + inp, quant_w, scale_w = ctx.saved_tensors + weight = extract_weight_to_half(quant_w, scale_w, ctx.weight_bit_width) + grad_output = grad_output.contiguous().view(-1, weight.size(0)) + grad_input = grad_output.mm(weight) + grad_weight = grad_output.t().mm(inp) + return grad_input.view(ctx.inp_shape), grad_weight.view(ctx.weight_shape), None, None + + +def compress_int4_weight(weight: torch.Tensor): # (n, m) + with torch.cuda.device(weight.device): + n, m = weight.size(0), weight.size(1) + assert m % 2 == 0 + m = m // 2 + out = torch.empty(n, m, dtype=torch.int8, device="cuda") + stream = torch.cuda.current_stream() + + gridDim = (n, 1, 1) + blockDim = (min(round_up(m, 32), 1024), 1, 1) + + kernels.int4WeightCompression( + gridDim, + blockDim, + 0, + stream, + [ctypes.c_void_p(weight.data_ptr()), ctypes.c_void_p(out.data_ptr()), ctypes.c_int32(n), ctypes.c_int32(m)], + ) + return out + + +def extract_weight_to_half(weight: torch.Tensor, scale_list: torch.Tensor, source_bit_width: int): + assert scale_list.dtype in [torch.half, torch.bfloat16] + assert weight.dtype in [torch.int8] + if source_bit_width == 8: + return weight.to(scale_list.dtype) * scale_list[:, None] + elif source_bit_width == 4: + func = ( + kernels.int4WeightExtractionHalf if scale_list.dtype == torch.half else kernels.int4WeightExtractionBFloat16 + ) + else: + assert False, "Unsupported bit-width" + + with torch.cuda.device(weight.device): + n, m = weight.size(0), weight.size(1) + out = torch.empty(n, m * (8 // source_bit_width), dtype=scale_list.dtype, device="cuda") + stream = torch.cuda.current_stream() + + gridDim = (n, 1, 1) + blockDim = (min(round_up(m, 32), 1024), 1, 1) + + func( + gridDim, + blockDim, + 0, + stream, + [ + ctypes.c_void_p(weight.data_ptr()), + ctypes.c_void_p(scale_list.data_ptr()), + ctypes.c_void_p(out.data_ptr()), + ctypes.c_int32(n), + ctypes.c_int32(m), + ], + ) + return out + + +class QuantizedLinear(torch.nn.Module): + def __init__(self, weight_bit_width: int, weight, bias=None, device="cuda", dtype=None, empty_init=False): + super().__init__() + weight = weight.to(device) # ensure the weight is on the cuda device + assert str(weight.device).startswith( + 'cuda'), 'The weights that need to be quantified should be on the CUDA device' + self.weight_bit_width = weight_bit_width + shape = weight.shape + + if weight is None or empty_init: + self.weight = torch.empty(shape[0], shape[1] * weight_bit_width // 8, dtype=torch.int8, device=device) + self.weight_scale = torch.empty(shape[0], dtype=dtype, device=device) + else: + self.weight_scale = weight.abs().max(dim=-1).values / ((2 ** (weight_bit_width - 1)) - 1) + self.weight = torch.round(weight / self.weight_scale[:, None]).to(torch.int8) + if weight_bit_width == 4: + self.weight = compress_int4_weight(self.weight) + + self.weight = Parameter(self.weight.to(device), requires_grad=False) + self.weight_scale = Parameter(self.weight_scale.to(device), requires_grad=False) + self.bias = Parameter(bias.to(device), requires_grad=False) if bias is not None else None + + def forward(self, input): + output = W8A16Linear.apply(input, self.weight, self.weight_scale, self.weight_bit_width) + if self.bias is not None: + output = output + self.bias + return output + + +def quantize(model, weight_bit_width, empty_init=False, device=None): + """Replace fp16 linear with quantized linear""" + for layer in model.layers: + layer.self_attention.query_key_value = QuantizedLinear( + weight_bit_width=weight_bit_width, + weight=layer.self_attention.query_key_value.weight, + bias=layer.self_attention.query_key_value.bias, + dtype=layer.self_attention.query_key_value.weight.dtype, + device=layer.self_attention.query_key_value.weight.device if device is None else device, + empty_init=empty_init + ) + layer.self_attention.dense = QuantizedLinear( + weight_bit_width=weight_bit_width, + weight=layer.self_attention.dense.weight, + bias=layer.self_attention.dense.bias, + dtype=layer.self_attention.dense.weight.dtype, + device=layer.self_attention.dense.weight.device if device is None else device, + empty_init=empty_init + ) + layer.mlp.dense_h_to_4h = QuantizedLinear( + weight_bit_width=weight_bit_width, + weight=layer.mlp.dense_h_to_4h.weight, + bias=layer.mlp.dense_h_to_4h.bias, + dtype=layer.mlp.dense_h_to_4h.weight.dtype, + device=layer.mlp.dense_h_to_4h.weight.device if device is None else device, + empty_init=empty_init + ) + layer.mlp.dense_4h_to_h = QuantizedLinear( + weight_bit_width=weight_bit_width, + weight=layer.mlp.dense_4h_to_h.weight, + bias=layer.mlp.dense_4h_to_h.bias, + dtype=layer.mlp.dense_4h_to_h.weight.dtype, + device=layer.mlp.dense_4h_to_h.weight.device if device is None else device, + empty_init=empty_init + ) + + return model + + + +class ChatGLMConfig(PretrainedConfig): + model_type = "chatglm" + def __init__( + self, + num_layers=28, + padded_vocab_size=65024, + hidden_size=4096, + ffn_hidden_size=13696, + kv_channels=128, + num_attention_heads=32, + seq_length=2048, + hidden_dropout=0.0, + classifier_dropout=None, + attention_dropout=0.0, + layernorm_epsilon=1e-5, + rmsnorm=True, + apply_residual_connection_post_layernorm=False, + post_layer_norm=True, + add_bias_linear=False, + add_qkv_bias=False, + bias_dropout_fusion=True, + multi_query_attention=False, + multi_query_group_num=1, + apply_query_key_layer_scaling=True, + attention_softmax_in_fp32=True, + fp32_residual_connection=False, + quantization_bit=0, + pre_seq_len=None, + prefix_projection=False, + **kwargs + ): + self.num_layers = num_layers + self.vocab_size = padded_vocab_size + self.padded_vocab_size = padded_vocab_size + self.hidden_size = hidden_size + self.ffn_hidden_size = ffn_hidden_size + self.kv_channels = kv_channels + self.num_attention_heads = num_attention_heads + self.seq_length = seq_length + self.hidden_dropout = hidden_dropout + self.classifier_dropout = classifier_dropout + self.attention_dropout = attention_dropout + self.layernorm_epsilon = layernorm_epsilon + self.rmsnorm = rmsnorm + self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm + self.post_layer_norm = post_layer_norm + self.add_bias_linear = add_bias_linear + self.add_qkv_bias = add_qkv_bias + self.bias_dropout_fusion = bias_dropout_fusion + self.multi_query_attention = multi_query_attention + self.multi_query_group_num = multi_query_group_num + self.apply_query_key_layer_scaling = apply_query_key_layer_scaling + self.attention_softmax_in_fp32 = attention_softmax_in_fp32 + self.fp32_residual_connection = fp32_residual_connection + self.quantization_bit = quantization_bit + self.pre_seq_len = pre_seq_len + self.prefix_projection = prefix_projection + super().__init__(**kwargs) + + + +# flags required to enable jit fusion kernels + +if sys.platform != 'darwin': + torch._C._jit_set_profiling_mode(False) + torch._C._jit_set_profiling_executor(False) + torch._C._jit_override_can_fuse_on_cpu(True) + torch._C._jit_override_can_fuse_on_gpu(True) + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM" +_CONFIG_FOR_DOC = "ChatGLM6BConfig" + +CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "THUDM/chatglm3-6b-base", + # See all ChatGLM models at https://huggingface.co/models?filter=chatglm +] + + +def default_init(cls, *args, **kwargs): + return cls(*args, **kwargs) + + +class InvalidScoreLogitsProcessor(LogitsProcessor): + def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: + if torch.isnan(scores).any() or torch.isinf(scores).any(): + scores.zero_() + scores[..., 5] = 5e4 + return scores + + +class PrefixEncoder(torch.nn.Module): + """ + The torch.nn model to encode the prefix + Input shape: (batch-size, prefix-length) + Output shape: (batch-size, prefix-length, 2*layers*hidden) + """ + + def __init__(self, config: ChatGLMConfig): + super().__init__() + self.prefix_projection = config.prefix_projection + if self.prefix_projection: + # Use a two-layer MLP to encode the prefix + kv_size = config.num_layers * config.kv_channels * config.multi_query_group_num * 2 + self.embedding = torch.nn.Embedding(config.pre_seq_len, kv_size) + self.trans = torch.nn.Sequential( + torch.nn.Linear(kv_size, config.hidden_size), + torch.nn.Tanh(), + torch.nn.Linear(config.hidden_size, kv_size) + ) + else: + self.embedding = torch.nn.Embedding(config.pre_seq_len, + config.num_layers * config.kv_channels * config.multi_query_group_num * 2) + + def forward(self, prefix: torch.Tensor): + if self.prefix_projection: + prefix_tokens = self.embedding(prefix) + past_key_values = self.trans(prefix_tokens) + else: + past_key_values = self.embedding(prefix) + return past_key_values + + +def split_tensor_along_last_dim( + tensor: torch.Tensor, + num_partitions: int, + contiguous_split_chunks: bool = False, +) -> List[torch.Tensor]: + """Split a tensor along its last dimension. + + Arguments: + tensor: input tensor. + num_partitions: number of partitions to split the tensor + contiguous_split_chunks: If True, make each chunk contiguous + in memory. + + Returns: + A list of Tensors + """ + # Get the size and dimension. + last_dim = tensor.dim() - 1 + last_dim_size = tensor.size()[last_dim] // num_partitions + # Split. + tensor_list = torch.split(tensor, last_dim_size, dim=last_dim) + # Note: torch.split does not create contiguous tensors by default. + if contiguous_split_chunks: + return tuple(chunk.contiguous() for chunk in tensor_list) + + return tensor_list + + +class RotaryEmbedding(nn.Module): + def __init__(self, dim, original_impl=False, device=None, dtype=None): + super().__init__() + inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim)) + self.register_buffer("inv_freq", inv_freq) + self.dim = dim + self.original_impl = original_impl + + def forward_impl( + self, seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000 + ): + """Enhanced Transformer with Rotary Position Embedding. + + Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/ + transformers/rope/__init__.py. MIT License: + https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license. + """ + # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$ + theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=torch.float, device=device) / n_elem)) + + # Create position indexes `[0, 1, ..., seq_len - 1]` + seq_idx = torch.arange(seq_len, dtype=torch.float, device=device) + + # Calculate the product of position index and $\theta_i$ + idx_theta = torch.outer(seq_idx, theta).float() + + cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1) + + # this is to mimic the behaviour of complex32, else we will get different results + if dtype in (torch.float16, torch.bfloat16, torch.int8): + cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half() + return cache + + def forward(self, max_seq_len, offset=0): + return self.forward_impl( + max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device + ) + + +@torch.jit.script +def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor: + # x: [sq, b, np, hn] + sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3) + rot_dim = rope_cache.shape[-2] * 2 + x, x_pass = x[..., :rot_dim], x[..., rot_dim:] + # truncate to support variable sizes + rope_cache = rope_cache[:sq] + xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2) + rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2) + x_out2 = torch.stack( + [ + xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1], + xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1], + ], + -1, + ) + x_out2 = x_out2.flatten(3) + return torch.cat((x_out2, x_pass), dim=-1) + + +class RMSNorm(torch.nn.Module): + def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs): + super().__init__() + self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype)) + self.eps = eps + + def forward(self, hidden_states: torch.Tensor): + input_dtype = hidden_states.dtype + variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.eps) + + return (self.weight * hidden_states).to(input_dtype) + + +class CoreAttention(torch.nn.Module): + def __init__(self, config: ChatGLMConfig, layer_number): + super(CoreAttention, self).__init__() + + self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling + self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32 + if self.apply_query_key_layer_scaling: + self.attention_softmax_in_fp32 = True + self.layer_number = max(1, layer_number) + + projection_size = config.kv_channels * config.num_attention_heads + + # Per attention head and per partition values. + self.hidden_size_per_partition = projection_size + self.hidden_size_per_attention_head = projection_size // config.num_attention_heads + self.num_attention_heads_per_partition = config.num_attention_heads + + coeff = None + self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) + if self.apply_query_key_layer_scaling: + coeff = self.layer_number + self.norm_factor *= coeff + self.coeff = coeff + + self.attention_dropout = torch.nn.Dropout(config.attention_dropout) + + def forward(self, query_layer, key_layer, value_layer, attention_mask): + pytorch_major_version = int(torch.__version__.split('.')[0]) + if pytorch_major_version >= 2: + query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]] + if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]: + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, + is_causal=True) + else: + if attention_mask is not None: + attention_mask = ~attention_mask + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, + attention_mask) + context_layer = context_layer.permute(2, 0, 1, 3) + new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) + context_layer = context_layer.reshape(*new_context_layer_shape) + else: + # Raw attention scores + + # [b, np, sq, sk] + output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0)) + + # [sq, b, np, hn] -> [sq, b * np, hn] + query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1) + # [sk, b, np, hn] -> [sk, b * np, hn] + key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1) + + # preallocting input tensor: [b * np, sq, sk] + matmul_input_buffer = torch.empty( + output_size[0] * output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype, + device=query_layer.device + ) + + # Raw attention scores. [b * np, sq, sk] + matmul_result = torch.baddbmm( + matmul_input_buffer, + query_layer.transpose(0, 1), # [b * np, sq, hn] + key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk] + beta=0.0, + alpha=(1.0 / self.norm_factor), + ) + + # change view to [b, np, sq, sk] + attention_scores = matmul_result.view(*output_size) + + # =========================== + # Attention probs and dropout + # =========================== + + # attention scores and attention mask [b, np, sq, sk] + if self.attention_softmax_in_fp32: + attention_scores = attention_scores.float() + if self.coeff is not None: + attention_scores = attention_scores * self.coeff + if attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]: + attention_mask = torch.ones(output_size[0], 1, output_size[2], output_size[3], + device=attention_scores.device, dtype=torch.bool) + attention_mask.tril_() + attention_mask = ~attention_mask + if attention_mask is not None: + attention_scores = attention_scores.masked_fill(attention_mask, float("-inf")) + attention_probs = F.softmax(attention_scores, dim=-1) + attention_probs = attention_probs.type_as(value_layer) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.attention_dropout(attention_probs) + # ========================= + # Context layer. [sq, b, hp] + # ========================= + + # value_layer -> context layer. + # [sk, b, np, hn] --> [b, np, sq, hn] + + # context layer shape: [b, np, sq, hn] + output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3)) + # change view [sk, b * np, hn] + value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1) + # change view [b * np, sq, sk] + attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1) + # matmul: [b * np, sq, hn] + context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1)) + # change view [b, np, sq, hn] + context_layer = context_layer.view(*output_size) + # [b, np, sq, hn] --> [sq, b, np, hn] + context_layer = context_layer.permute(2, 0, 1, 3).contiguous() + # [sq, b, np, hn] --> [sq, b, hp] + new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) + context_layer = context_layer.view(*new_context_layer_shape) + + return context_layer + + +class SelfAttention(torch.nn.Module): + """Parallel self-attention layer abstract class. + + Self-attention layer takes input with size [s, b, h] + and returns output of the same size. + """ + + def __init__(self, config: ChatGLMConfig, layer_number, device=None): + super(SelfAttention, self).__init__() + self.layer_number = max(1, layer_number) + + self.projection_size = config.kv_channels * config.num_attention_heads + + # Per attention head and per partition values. + self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads + self.num_attention_heads_per_partition = config.num_attention_heads + + self.multi_query_attention = config.multi_query_attention + self.qkv_hidden_size = 3 * self.projection_size + if self.multi_query_attention: + self.num_multi_query_groups_per_partition = config.multi_query_group_num + self.qkv_hidden_size = ( + self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num + ) + self.query_key_value = nn.Linear(config.hidden_size, self.qkv_hidden_size, + bias=config.add_bias_linear or config.add_qkv_bias, + device=device, **_config_to_kwargs(config) + ) + + self.core_attention = CoreAttention(config, self.layer_number) + + # Output. + self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear, + device=device, **_config_to_kwargs(config) + ) + + def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None): + if self.multi_query_attention: + num_attention_heads = self.num_multi_query_groups_per_partition + else: + num_attention_heads = self.num_attention_heads_per_partition + return torch.empty( + inference_max_sequence_len, + batch_size, + num_attention_heads, + self.hidden_size_per_attention_head, + dtype=dtype, + device=device, + ) + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True + ): + # hidden_states: [sq, b, h] + + # ================================================= + # Pre-allocate memory for key-values for inference. + # ================================================= + # ===================== + # Query, Key, and Value + # ===================== + + # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)] + mixed_x_layer = self.query_key_value(hidden_states) + + if self.multi_query_attention: + (query_layer, key_layer, value_layer) = mixed_x_layer.split( + [ + self.num_attention_heads_per_partition * self.hidden_size_per_attention_head, + self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, + self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, + ], + dim=-1, + ) + query_layer = query_layer.view( + query_layer.size()[:-1] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + key_layer = key_layer.view( + key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) + ) + value_layer = value_layer.view( + value_layer.size()[:-1] + + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) + ) + else: + new_tensor_shape = mixed_x_layer.size()[:-1] + \ + (self.num_attention_heads_per_partition, + 3 * self.hidden_size_per_attention_head) + mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) + + # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn] + (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3) + + # apply relative positional encoding (rotary embedding) + if rotary_pos_emb is not None: + query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb) + key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb) + + # adjust key and value for inference + if kv_cache is not None: + cache_k, cache_v = kv_cache + key_layer = torch.cat((cache_k, key_layer), dim=0) + value_layer = torch.cat((cache_v, value_layer), dim=0) + if use_cache: + kv_cache = (key_layer, value_layer) + else: + kv_cache = None + + if self.multi_query_attention: + key_layer = key_layer.unsqueeze(-2) + key_layer = key_layer.expand( + -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 + ) + key_layer = key_layer.contiguous().view( + key_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + value_layer = value_layer.unsqueeze(-2) + value_layer = value_layer.expand( + -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 + ) + value_layer = value_layer.contiguous().view( + value_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + + # ================================== + # core attention computation + # ================================== + + context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask) + + # ================= + # Output. [sq, b, h] + # ================= + + output = self.dense(context_layer) + + return output, kv_cache + + +def _config_to_kwargs(args): + common_kwargs = { + "dtype": args.torch_dtype, + } + return common_kwargs + + +class MLP(torch.nn.Module): + """MLP. + + MLP will take the input with h hidden state, project it to 4*h + hidden dimension, perform nonlinear transformation, and project the + state back into h hidden dimension. + """ + + def __init__(self, config: ChatGLMConfig, device=None): + super(MLP, self).__init__() + + self.add_bias = config.add_bias_linear + + # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf + self.dense_h_to_4h = nn.Linear( + config.hidden_size, + config.ffn_hidden_size * 2, + bias=self.add_bias, + device=device, + **_config_to_kwargs(config) + ) + + def swiglu(x): + x = torch.chunk(x, 2, dim=-1) + return F.silu(x[0]) * x[1] + + self.activation_func = swiglu + + # Project back to h. + self.dense_4h_to_h = nn.Linear( + config.ffn_hidden_size, + config.hidden_size, + bias=self.add_bias, + device=device, + **_config_to_kwargs(config) + ) + + def forward(self, hidden_states): + # [s, b, 4hp] + intermediate_parallel = self.dense_h_to_4h(hidden_states) + intermediate_parallel = self.activation_func(intermediate_parallel) + # [s, b, h] + output = self.dense_4h_to_h(intermediate_parallel) + return output + + +class GLMBlock(torch.nn.Module): + """A single transformer layer. + + Transformer layer takes input with size [s, b, h] and returns an + output of the same size. + """ + + def __init__(self, config: ChatGLMConfig, layer_number, device=None): + super(GLMBlock, self).__init__() + self.layer_number = layer_number + + self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm + + self.fp32_residual_connection = config.fp32_residual_connection + + LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm + # Layernorm on the input data. + self.input_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + # Self attention. + self.self_attention = SelfAttention(config, layer_number, device=device) + self.hidden_dropout = config.hidden_dropout + + # Layernorm on the attention output + self.post_attention_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + # MLP + self.mlp = MLP(config, device=device) + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True, + ): + # hidden_states: [s, b, h] + + # Layer norm at the beginning of the transformer layer. + layernorm_output = self.input_layernorm(hidden_states) + # Self attention. + attention_output, kv_cache = self.self_attention( + layernorm_output, + attention_mask, + rotary_pos_emb, + kv_cache=kv_cache, + use_cache=use_cache + ) + + # Residual connection. + if self.apply_residual_connection_post_layernorm: + residual = layernorm_output + else: + residual = hidden_states + + layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training) + layernorm_input = residual + layernorm_input + + # Layer norm post the self attention. + layernorm_output = self.post_attention_layernorm(layernorm_input) + + # MLP. + mlp_output = self.mlp(layernorm_output) + + # Second residual connection. + if self.apply_residual_connection_post_layernorm: + residual = layernorm_output + else: + residual = layernorm_input + + output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training) + output = residual + output + + return output, kv_cache + + +class GLMTransformer(torch.nn.Module): + """Transformer class.""" + + def __init__(self, config: ChatGLMConfig, device=None): + super(GLMTransformer, self).__init__() + + self.fp32_residual_connection = config.fp32_residual_connection + self.post_layer_norm = config.post_layer_norm + + # Number of layers. + self.num_layers = config.num_layers + + # Transformer layers. + def build_layer(layer_number): + return GLMBlock(config, layer_number, device=device) + + self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)]) + + if self.post_layer_norm: + LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm + # Final layer norm before output. + self.final_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + self.gradient_checkpointing = False + + def _get_layer(self, layer_number): + return self.layers[layer_number] + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_caches=None, + use_cache: Optional[bool] = True, + output_hidden_states: Optional[bool] = False, + ): + if not kv_caches: + kv_caches = [None for _ in range(self.num_layers)] + presents = () if use_cache else None + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + all_self_attentions = None + all_hidden_states = () if output_hidden_states else None + for index in range(self.num_layers): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer = self._get_layer(index) + if self.gradient_checkpointing and self.training: + layer_ret = torch.utils.checkpoint.checkpoint( + layer, + hidden_states, + attention_mask, + rotary_pos_emb, + kv_caches[index], + use_cache + ) + else: + layer_ret = layer( + hidden_states, + attention_mask, + rotary_pos_emb, + kv_cache=kv_caches[index], + use_cache=use_cache + ) + hidden_states, kv_cache = layer_ret + if use_cache: + presents = presents + (kv_cache,) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # Final layer norm. + if self.post_layer_norm: + hidden_states = self.final_layernorm(hidden_states) + + return hidden_states, presents, all_hidden_states, all_self_attentions + + +class ChatGLMPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and + a simple interface for downloading and loading pretrained models. + """ + + is_parallelizable = False + supports_gradient_checkpointing = True + config_class = ChatGLMConfig + base_model_prefix = "transformer" + _no_split_modules = ["GLMBlock"] + + def _init_weights(self, module: nn.Module): + """Initialize the weights.""" + return + + def get_masks(self, input_ids, past_key_values, padding_mask=None): + batch_size, seq_length = input_ids.shape + full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device) + full_attention_mask.tril_() + past_length = 0 + if past_key_values: + past_length = past_key_values[0][0].shape[0] + if past_length: + full_attention_mask = torch.cat((torch.ones(batch_size, seq_length, past_length, + device=input_ids.device), full_attention_mask), dim=-1) + if padding_mask is not None: + full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1) + if not past_length and padding_mask is not None: + full_attention_mask -= padding_mask.unsqueeze(-1) - 1 + full_attention_mask = (full_attention_mask < 0.5).bool() + full_attention_mask.unsqueeze_(1) + return full_attention_mask + + def get_position_ids(self, input_ids, device): + batch_size, seq_length = input_ids.shape + position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1) + return position_ids + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, GLMTransformer): + module.gradient_checkpointing = value + + +class Embedding(torch.nn.Module): + """Language model embeddings.""" + + def __init__(self, config: ChatGLMConfig, device=None): + super(Embedding, self).__init__() + + self.hidden_size = config.hidden_size + # Word embeddings (parallel). + self.word_embeddings = nn.Embedding( + config.padded_vocab_size, + self.hidden_size, + dtype=config.torch_dtype, + device=device + ) + self.fp32_residual_connection = config.fp32_residual_connection + + def forward(self, input_ids): + # Embeddings. + words_embeddings = self.word_embeddings(input_ids) + embeddings = words_embeddings + # Data format change to avoid explicit transposes : [b s h] --> [s b h]. + embeddings = embeddings.transpose(0, 1).contiguous() + # If the input flag for fp32 residual connection is set, convert for float. + if self.fp32_residual_connection: + embeddings = embeddings.float() + return embeddings + + +class ChatGLMModel(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, device=None, empty_init=True): + super().__init__(config) + if empty_init: + init_method = skip_init + else: + init_method = default_init + init_kwargs = {} + if device is not None: + init_kwargs["device"] = device + self.embedding = init_method(Embedding, config, **init_kwargs) + self.num_layers = config.num_layers + self.multi_query_group_num = config.multi_query_group_num + self.kv_channels = config.kv_channels + + # Rotary positional embeddings + self.seq_length = config.seq_length + rotary_dim = ( + config.hidden_size // config.num_attention_heads if config.kv_channels is None else config.kv_channels + ) + + self.rotary_pos_emb = RotaryEmbedding(rotary_dim // 2, original_impl=config.original_rope, device=device, + dtype=config.torch_dtype) + self.encoder = init_method(GLMTransformer, config, **init_kwargs) + self.output_layer = init_method(nn.Linear, config.hidden_size, config.padded_vocab_size, bias=False, + dtype=config.torch_dtype, **init_kwargs) + self.pre_seq_len = config.pre_seq_len + self.prefix_projection = config.prefix_projection + if self.pre_seq_len is not None: + for param in self.parameters(): + param.requires_grad = False + self.prefix_tokens = torch.arange(self.pre_seq_len).long() + self.prefix_encoder = PrefixEncoder(config) + self.dropout = torch.nn.Dropout(0.1) + + def get_input_embeddings(self): + return self.embedding.word_embeddings + + def get_prompt(self, batch_size, device, dtype=torch.half): + prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device) + past_key_values = self.prefix_encoder(prefix_tokens).type(dtype) + past_key_values = past_key_values.view( + batch_size, + self.pre_seq_len, + self.num_layers * 2, + self.multi_query_group_num, + self.kv_channels + ) + # seq_len, b, nh, hidden_size + past_key_values = self.dropout(past_key_values) + past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2) + return past_key_values + + def forward( + self, + input_ids, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.BoolTensor] = None, + full_attention_mask: Optional[torch.BoolTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + batch_size, seq_length = input_ids.shape + + if inputs_embeds is None: + inputs_embeds = self.embedding(input_ids) + + if self.pre_seq_len is not None: + if past_key_values is None: + past_key_values = self.get_prompt(batch_size=batch_size, device=input_ids.device, + dtype=inputs_embeds.dtype) + if attention_mask is not None: + attention_mask = torch.cat([attention_mask.new_ones((batch_size, self.pre_seq_len)), + attention_mask], dim=-1) + + if full_attention_mask is None: + if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1): + full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask) + + # Rotary positional embeddings + rotary_pos_emb = self.rotary_pos_emb(self.seq_length) + if position_ids is not None: + rotary_pos_emb = rotary_pos_emb[position_ids] + else: + rotary_pos_emb = rotary_pos_emb[None, :seq_length] + rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous() + + # Run encoder. + hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder( + inputs_embeds, full_attention_mask, rotary_pos_emb=rotary_pos_emb, + kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states + ) + + if not return_dict: + return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + def quantize(self, weight_bit_width: int): + # from .quantization import quantize + quantize(self.encoder, weight_bit_width) + return self + + +class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): + super().__init__(config) + + self.max_sequence_length = config.max_length + self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) + self.config = config + self.quantized = False + + if self.config.quantization_bit: + self.quantize(self.config.quantization_bit, empty_init=True) + + def _update_model_kwargs_for_generation( + self, + outputs: ModelOutput, + model_kwargs: Dict[str, Any], + is_encoder_decoder: bool = False, + standardize_cache_format: bool = False, + ) -> Dict[str, Any]: + # update past_key_values + model_kwargs["past_key_values"] = self._extract_past_from_model_output( + outputs, standardize_cache_format=standardize_cache_format + ) + + # update attention mask + if "attention_mask" in model_kwargs: + attention_mask = model_kwargs["attention_mask"] + model_kwargs["attention_mask"] = torch.cat( + [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1 + ) + + # update position ids + if "position_ids" in model_kwargs: + position_ids = model_kwargs["position_ids"] + new_position_id = position_ids[..., -1:].clone() + new_position_id += 1 + model_kwargs["position_ids"] = torch.cat( + [position_ids, new_position_id], dim=-1 + ) + + model_kwargs["is_first_forward"] = False + return model_kwargs + + def prepare_inputs_for_generation( + self, + input_ids: torch.LongTensor, + past_key_values: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + is_first_forward: bool = True, + **kwargs + ) -> dict: + # only last token for input_ids if past is not None + if position_ids is None: + position_ids = self.get_position_ids(input_ids, device=input_ids.device) + if not is_first_forward: + if past_key_values is not None: + position_ids = position_ids[..., -1:] + input_ids = input_ids[:, -1:] + return { + "input_ids": input_ids, + "past_key_values": past_key_values, + "position_ids": position_ids, + "attention_mask": attention_mask, + "return_last_logit": True, + "use_cache": use_cache + } + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[Tuple[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + return_last_logit: Optional[bool] = False, + ): + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.transformer( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = transformer_outputs[0] + if return_last_logit: + hidden_states = hidden_states[-1:] + lm_logits = self.transformer.output_layer(hidden_states) + lm_logits = lm_logits.transpose(0, 1).contiguous() + + loss = None + if labels is not None: + lm_logits = lm_logits.to(torch.float32) + + # Shift so that tokens < n predict n + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss(ignore_index=-100) + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + + lm_logits = lm_logits.to(hidden_states.dtype) + loss = loss.to(hidden_states.dtype) + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + @staticmethod + def _reorder_cache( + past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor + ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]: + """ + This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or + [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct + beam_idx at every generation step. + + Output shares the same memory storage as `past`. + """ + return tuple( + ( + layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)), + layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)), + ) + for layer_past in past + ) + + def process_response(self, output, history): + content = "" + history = deepcopy(history) + for response in output.split("<|assistant|>"): + metadata, content = response.split("\n", maxsplit=1) + if not metadata.strip(): + content = content.strip() + history.append({"role": "assistant", "metadata": metadata, "content": content}) + content = content.replace("[[训练时间]]", "2023年") + else: + history.append({"role": "assistant", "metadata": metadata, "content": content}) + if history[0]["role"] == "system" and "tools" in history[0]: + content = "\n".join(content.split("\n")[1:-1]) + def tool_call(**kwargs): + return kwargs + parameters = eval(content) + content = {"name": metadata.strip(), "parameters": parameters} + else: + content = {"name": metadata.strip(), "content": content} + return content, history + + @torch.inference_mode() + def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, role: str = "user", + max_length: int = 8192, num_beams=1, do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None, + **kwargs): + if history is None: + history = [] + if logits_processor is None: + logits_processor = LogitsProcessorList() + logits_processor.append(InvalidScoreLogitsProcessor()) + gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p, + "temperature": temperature, "logits_processor": logits_processor, **kwargs} + inputs = tokenizer.build_chat_input(query, history=history, role=role) + inputs = inputs.to(self.device) + eos_token_id = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"), + tokenizer.get_command("<|observation|>")] + outputs = self.generate(**inputs, **gen_kwargs, eos_token_id=eos_token_id) + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1] + response = tokenizer.decode(outputs) + history.append({"role": role, "content": query}) + response, history = self.process_response(response, history) + return response, history + + @torch.inference_mode() + def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, role: str = "user", + past_key_values=None,max_length: int = 8192, do_sample=True, top_p=0.8, temperature=0.8, + logits_processor=None, return_past_key_values=False, **kwargs): + if history is None: + history = [] + if logits_processor is None: + logits_processor = LogitsProcessorList() + logits_processor.append(InvalidScoreLogitsProcessor()) + eos_token_id = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"), + tokenizer.get_command("<|observation|>")] + gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p, + "temperature": temperature, "logits_processor": logits_processor, **kwargs} + if past_key_values is None: + inputs = tokenizer.build_chat_input(query, history=history, role=role) + else: + inputs = tokenizer.build_chat_input(query, role=role) + inputs = inputs.to(self.device) + if past_key_values is not None: + past_length = past_key_values[0][0].shape[0] + if self.transformer.pre_seq_len is not None: + past_length -= self.transformer.pre_seq_len + inputs.position_ids += past_length + attention_mask = inputs.attention_mask + attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1) + inputs['attention_mask'] = attention_mask + history.append({"role": role, "content": query}) + for outputs in self.stream_generate(**inputs, past_key_values=past_key_values, + eos_token_id=eos_token_id, return_past_key_values=return_past_key_values, + **gen_kwargs): + if return_past_key_values: + outputs, past_key_values = outputs + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1] + response = tokenizer.decode(outputs) + if response and response[-1] != "�": + response, new_history = self.process_response(response, history) + if return_past_key_values: + yield response, new_history, past_key_values + else: + yield response, new_history + + @torch.inference_mode() + def stream_generate( + self, + input_ids, + generation_config: Optional[GenerationConfig] = None, + logits_processor: Optional[LogitsProcessorList] = None, + stopping_criteria: Optional[StoppingCriteriaList] = None, + prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None, + return_past_key_values=False, + **kwargs, + ): + batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1] + + if generation_config is None: + generation_config = self.generation_config + generation_config = copy.deepcopy(generation_config) + model_kwargs = generation_config.update(**kwargs) + model_kwargs["use_cache"] = generation_config.use_cache + bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id + + if isinstance(eos_token_id, int): + eos_token_id = [eos_token_id] + eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None + + has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None + if has_default_max_length and generation_config.max_new_tokens is None: + warnings.warn( + f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. " + "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we" + " recommend using `max_new_tokens` to control the maximum length of the generation.", + UserWarning, + ) + elif generation_config.max_new_tokens is not None: + generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length + if not has_default_max_length: + logger.warning( + f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(=" + f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. " + "Please refer to the documentation for more information. " + "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)", + UserWarning, + ) + + if input_ids_seq_length >= generation_config.max_length: + input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids" + logger.warning( + f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to" + f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider" + " increasing `max_new_tokens`." + ) + + # 2. Set generation parameters if not already defined + logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList() + stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList() + + logits_processor = self._get_logits_processor( + generation_config=generation_config, + input_ids_seq_length=input_ids_seq_length, + encoder_input_ids=input_ids, + prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, + logits_processor=logits_processor, + ) + + stopping_criteria = self._get_stopping_criteria( + generation_config=generation_config, stopping_criteria=stopping_criteria + ) + logits_warper = self._get_logits_warper(generation_config) + + unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1) + scores = None + while True: + model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs) + # forward pass to get next token + outputs = self( + **model_inputs, + return_dict=True, + output_attentions=False, + output_hidden_states=False, + ) + + next_token_logits = outputs.logits[:, -1, :] + + # pre-process distribution + next_token_scores = logits_processor(input_ids, next_token_logits) + next_token_scores = logits_warper(input_ids, next_token_scores) + + # sample + probs = nn.functional.softmax(next_token_scores, dim=-1) + if generation_config.do_sample: + next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) + else: + next_tokens = torch.argmax(probs, dim=-1) + # update generated ids, model inputs, and length for next step + input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) + model_kwargs = self._update_model_kwargs_for_generation( + outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder + ) + unfinished_sequences = unfinished_sequences.mul( + next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0) + ) + if return_past_key_values: + yield input_ids, outputs.past_key_values + else: + yield input_ids + # stop when each sentence is finished, or if we exceed the maximum length + if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores): + break + + def quantize(self, bits: int, empty_init=False, device=None, **kwargs): + if bits == 0: + return + + # from .quantization import quantize + + if self.quantized: + logger.info("Already quantized.") + return self + + self.quantized = True + + self.config.quantization_bit = bits + + self.transformer.encoder = quantize(self.transformer.encoder, bits, empty_init=empty_init, device=device, + **kwargs) + return self + + +class ChatGLMForSequenceClassification(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): + super().__init__(config) + + self.num_labels = config.num_labels + self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) + + self.classifier_head = nn.Linear(config.hidden_size, config.num_labels, bias=True, dtype=torch.half) + if config.classifier_dropout is not None: + self.dropout = nn.Dropout(config.classifier_dropout) + else: + self.dropout = None + self.config = config + + if self.config.quantization_bit: + self.quantize(self.config.quantization_bit, empty_init=True) + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + full_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + inputs_embeds: Optional[torch.LongTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor, ...], SequenceClassifierOutputWithPast]: + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.transformer( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + full_attention_mask=full_attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = transformer_outputs[0] + pooled_hidden_states = hidden_states[-1] + if self.dropout is not None: + pooled_hidden_states = self.dropout(pooled_hidden_states) + logits = self.classifier_head(pooled_hidden_states) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze().float(), labels.squeeze()) + else: + loss = loss_fct(logits.float(), labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels).float(), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits.float(), labels.view(-1, self.num_labels)) + + if not return_dict: + output = (logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) diff --git a/diffsynth/models/lora.py b/diffsynth/models/lora.py new file mode 100644 index 0000000000000000000000000000000000000000..11b34e36861fefaaa5b01e5ca381fe81e4a04755 --- /dev/null +++ b/diffsynth/models/lora.py @@ -0,0 +1,387 @@ +import torch +from .sd_unet import SDUNet +from .sdxl_unet import SDXLUNet +from .sd_text_encoder import SDTextEncoder +from .sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2 +from .sd3_dit import SD3DiT +from .flux_dit import FluxDiT +from .hunyuan_dit import HunyuanDiT +from .cog_dit import CogDiT +from .hunyuan_video_dit import HunyuanVideoDiT +from .wan_video_dit import WanModel + + + +class LoRAFromCivitai: + def __init__(self): + self.supported_model_classes = [] + self.lora_prefix = [] + self.renamed_lora_prefix = {} + self.special_keys = {} + + + def convert_state_dict(self, state_dict, lora_prefix="lora_unet_", alpha=1.0): + for key in state_dict: + if ".lora_up" in key: + return self.convert_state_dict_up_down(state_dict, lora_prefix, alpha) + return self.convert_state_dict_AB(state_dict, lora_prefix, alpha) + + + def convert_state_dict_up_down(self, state_dict, lora_prefix="lora_unet_", alpha=1.0): + renamed_lora_prefix = self.renamed_lora_prefix.get(lora_prefix, "") + state_dict_ = {} + for key in state_dict: + if ".lora_up" not in key: + continue + if not key.startswith(lora_prefix): + continue + weight_up = state_dict[key].to(device="cuda", dtype=torch.float16) + weight_down = state_dict[key.replace(".lora_up", ".lora_down")].to(device="cuda", dtype=torch.float16) + if len(weight_up.shape) == 4: + weight_up = weight_up.squeeze(3).squeeze(2).to(torch.float32) + weight_down = weight_down.squeeze(3).squeeze(2).to(torch.float32) + lora_weight = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3) + else: + lora_weight = alpha * torch.mm(weight_up, weight_down) + target_name = key.split(".")[0].replace(lora_prefix, renamed_lora_prefix).replace("_", ".") + ".weight" + for special_key in self.special_keys: + target_name = target_name.replace(special_key, self.special_keys[special_key]) + state_dict_[target_name] = lora_weight.cpu() + return state_dict_ + + + def convert_state_dict_AB(self, state_dict, lora_prefix="", alpha=1.0, device="cuda", torch_dtype=torch.float16): + state_dict_ = {} + for key in state_dict: + if ".lora_B." not in key: + continue + if not key.startswith(lora_prefix): + continue + weight_up = state_dict[key].to(device=device, dtype=torch_dtype) + weight_down = state_dict[key.replace(".lora_B.", ".lora_A.")].to(device=device, dtype=torch_dtype) + if len(weight_up.shape) == 4: + weight_up = weight_up.squeeze(3).squeeze(2) + weight_down = weight_down.squeeze(3).squeeze(2) + lora_weight = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3) + else: + lora_weight = alpha * torch.mm(weight_up, weight_down) + keys = key.split(".") + keys.pop(keys.index("lora_B")) + target_name = ".".join(keys) + target_name = target_name[len(lora_prefix):] + state_dict_[target_name] = lora_weight.cpu() + return state_dict_ + + + def load(self, model, state_dict_lora, lora_prefix, alpha=1.0, model_resource=None): + state_dict_model = model.state_dict() + state_dict_lora = self.convert_state_dict(state_dict_lora, lora_prefix=lora_prefix, alpha=alpha) + if model_resource == "diffusers": + state_dict_lora = model.__class__.state_dict_converter().from_diffusers(state_dict_lora) + elif model_resource == "civitai": + state_dict_lora = model.__class__.state_dict_converter().from_civitai(state_dict_lora) + if isinstance(state_dict_lora, tuple): + state_dict_lora = state_dict_lora[0] + if len(state_dict_lora) > 0: + print(f" {len(state_dict_lora)} tensors are updated.") + for name in state_dict_lora: + fp8=False + if state_dict_model[name].dtype == torch.float8_e4m3fn: + state_dict_model[name]= state_dict_model[name].to(state_dict_lora[name].dtype) + fp8=True + state_dict_model[name] += state_dict_lora[name].to( + dtype=state_dict_model[name].dtype, device=state_dict_model[name].device) + if fp8: + state_dict_model[name] = state_dict_model[name].to(torch.float8_e4m3fn) + model.load_state_dict(state_dict_model) + + + def match(self, model, state_dict_lora): + for lora_prefix, model_class in zip(self.lora_prefix, self.supported_model_classes): + if not isinstance(model, model_class): + continue + state_dict_model = model.state_dict() + for model_resource in ["diffusers", "civitai"]: + try: + state_dict_lora_ = self.convert_state_dict(state_dict_lora, lora_prefix=lora_prefix, alpha=1.0) + converter_fn = model.__class__.state_dict_converter().from_diffusers if model_resource == "diffusers" \ + else model.__class__.state_dict_converter().from_civitai + state_dict_lora_ = converter_fn(state_dict_lora_) + if isinstance(state_dict_lora_, tuple): + state_dict_lora_ = state_dict_lora_[0] + if len(state_dict_lora_) == 0: + continue + for name in state_dict_lora_: + if name not in state_dict_model: + break + else: + return lora_prefix, model_resource + except: + pass + return None + + + +class SDLoRAFromCivitai(LoRAFromCivitai): + def __init__(self): + super().__init__() + self.supported_model_classes = [SDUNet, SDTextEncoder] + self.lora_prefix = ["lora_unet_", "lora_te_"] + self.special_keys = { + "down.blocks": "down_blocks", + "up.blocks": "up_blocks", + "mid.block": "mid_block", + "proj.in": "proj_in", + "proj.out": "proj_out", + "transformer.blocks": "transformer_blocks", + "to.q": "to_q", + "to.k": "to_k", + "to.v": "to_v", + "to.out": "to_out", + "text.model": "text_model", + "self.attn.q.proj": "self_attn.q_proj", + "self.attn.k.proj": "self_attn.k_proj", + "self.attn.v.proj": "self_attn.v_proj", + "self.attn.out.proj": "self_attn.out_proj", + "input.blocks": "model.diffusion_model.input_blocks", + "middle.block": "model.diffusion_model.middle_block", + "output.blocks": "model.diffusion_model.output_blocks", + } + + +class SDXLLoRAFromCivitai(LoRAFromCivitai): + def __init__(self): + super().__init__() + self.supported_model_classes = [SDXLUNet, SDXLTextEncoder, SDXLTextEncoder2] + self.lora_prefix = ["lora_unet_", "lora_te1_", "lora_te2_"] + self.renamed_lora_prefix = {"lora_te2_": "2"} + self.special_keys = { + "down.blocks": "down_blocks", + "up.blocks": "up_blocks", + "mid.block": "mid_block", + "proj.in": "proj_in", + "proj.out": "proj_out", + "transformer.blocks": "transformer_blocks", + "to.q": "to_q", + "to.k": "to_k", + "to.v": "to_v", + "to.out": "to_out", + "text.model": "conditioner.embedders.0.transformer.text_model", + "self.attn.q.proj": "self_attn.q_proj", + "self.attn.k.proj": "self_attn.k_proj", + "self.attn.v.proj": "self_attn.v_proj", + "self.attn.out.proj": "self_attn.out_proj", + "input.blocks": "model.diffusion_model.input_blocks", + "middle.block": "model.diffusion_model.middle_block", + "output.blocks": "model.diffusion_model.output_blocks", + "2conditioner.embedders.0.transformer.text_model.encoder.layers": "text_model.encoder.layers" + } + + +class FluxLoRAFromCivitai(LoRAFromCivitai): + def __init__(self): + super().__init__() + self.supported_model_classes = [FluxDiT, FluxDiT] + self.lora_prefix = ["lora_unet_", "transformer."] + self.renamed_lora_prefix = {} + self.special_keys = { + "single.blocks": "single_blocks", + "double.blocks": "double_blocks", + "img.attn": "img_attn", + "img.mlp": "img_mlp", + "img.mod": "img_mod", + "txt.attn": "txt_attn", + "txt.mlp": "txt_mlp", + "txt.mod": "txt_mod", + } + + + +class GeneralLoRAFromPeft: + def __init__(self): + self.supported_model_classes = [SDUNet, SDXLUNet, SD3DiT, HunyuanDiT, FluxDiT, CogDiT, WanModel] + + + def get_name_dict(self, lora_state_dict): + lora_name_dict = {} + for key in lora_state_dict: + if ".lora_B." not in key: + continue + keys = key.split(".") + if len(keys) > keys.index("lora_B") + 2: + keys.pop(keys.index("lora_B") + 1) + keys.pop(keys.index("lora_B")) + if keys[0] == "diffusion_model": + keys.pop(0) + target_name = ".".join(keys) + lora_name_dict[target_name] = (key, key.replace(".lora_B.", ".lora_A.")) + return lora_name_dict + + + def match(self, model: torch.nn.Module, state_dict_lora): + lora_name_dict = self.get_name_dict(state_dict_lora) + model_name_dict = {name: None for name, _ in model.named_parameters()} + matched_num = sum([i in model_name_dict for i in lora_name_dict]) + if matched_num == len(lora_name_dict): + return "", "" + else: + return None + + + def fetch_device_and_dtype(self, state_dict): + device, dtype = None, None + for name, param in state_dict.items(): + device, dtype = param.device, param.dtype + break + computation_device = device + computation_dtype = dtype + if computation_device == torch.device("cpu"): + if torch.cuda.is_available(): + computation_device = torch.device("cuda") + if computation_dtype == torch.float8_e4m3fn: + computation_dtype = torch.float32 + return device, dtype, computation_device, computation_dtype + + + def load(self, model, state_dict_lora, lora_prefix="", alpha=1.0, model_resource=""): + state_dict_model = model.state_dict() + device, dtype, computation_device, computation_dtype = self.fetch_device_and_dtype(state_dict_model) + lora_name_dict = self.get_name_dict(state_dict_lora) + for name in lora_name_dict: + weight_up = state_dict_lora[lora_name_dict[name][0]].to(device=computation_device, dtype=computation_dtype) + weight_down = state_dict_lora[lora_name_dict[name][1]].to(device=computation_device, dtype=computation_dtype) + if len(weight_up.shape) == 4: + weight_up = weight_up.squeeze(3).squeeze(2) + weight_down = weight_down.squeeze(3).squeeze(2) + weight_lora = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3) + else: + weight_lora = alpha * torch.mm(weight_up, weight_down) + weight_model = state_dict_model[name].to(device=computation_device, dtype=computation_dtype) + weight_patched = weight_model + weight_lora + state_dict_model[name] = weight_patched.to(device=device, dtype=dtype) + print(f" {len(lora_name_dict)} tensors are updated.") + model.load_state_dict(state_dict_model) + + + +class HunyuanVideoLoRAFromCivitai(LoRAFromCivitai): + def __init__(self): + super().__init__() + self.supported_model_classes = [HunyuanVideoDiT, HunyuanVideoDiT] + self.lora_prefix = ["diffusion_model.", "transformer."] + self.special_keys = {} + + +class FluxLoRAConverter: + def __init__(self): + pass + + @staticmethod + def align_to_opensource_format(state_dict, alpha=None): + prefix_rename_dict = { + "single_blocks": "lora_unet_single_blocks", + "blocks": "lora_unet_double_blocks", + } + middle_rename_dict = { + "norm.linear": "modulation_lin", + "to_qkv_mlp": "linear1", + "proj_out": "linear2", + + "norm1_a.linear": "img_mod_lin", + "norm1_b.linear": "txt_mod_lin", + "attn.a_to_qkv": "img_attn_qkv", + "attn.b_to_qkv": "txt_attn_qkv", + "attn.a_to_out": "img_attn_proj", + "attn.b_to_out": "txt_attn_proj", + "ff_a.0": "img_mlp_0", + "ff_a.2": "img_mlp_2", + "ff_b.0": "txt_mlp_0", + "ff_b.2": "txt_mlp_2", + } + suffix_rename_dict = { + "lora_B.weight": "lora_up.weight", + "lora_A.weight": "lora_down.weight", + } + state_dict_ = {} + for name, param in state_dict.items(): + names = name.split(".") + if names[-2] != "lora_A" and names[-2] != "lora_B": + names.pop(-2) + prefix = names[0] + middle = ".".join(names[2:-2]) + suffix = ".".join(names[-2:]) + block_id = names[1] + if middle not in middle_rename_dict: + continue + rename = prefix_rename_dict[prefix] + "_" + block_id + "_" + middle_rename_dict[middle] + "." + suffix_rename_dict[suffix] + state_dict_[rename] = param + if rename.endswith("lora_up.weight"): + lora_alpha = alpha if alpha is not None else param.shape[-1] + state_dict_[rename.replace("lora_up.weight", "alpha")] = torch.tensor((lora_alpha,))[0] + return state_dict_ + + @staticmethod + def align_to_diffsynth_format(state_dict): + rename_dict = { + "lora_unet_double_blocks_blockid_img_mod_lin.lora_down.weight": "blocks.blockid.norm1_a.linear.lora_A.default.weight", + "lora_unet_double_blocks_blockid_img_mod_lin.lora_up.weight": "blocks.blockid.norm1_a.linear.lora_B.default.weight", + "lora_unet_double_blocks_blockid_txt_mod_lin.lora_down.weight": "blocks.blockid.norm1_b.linear.lora_A.default.weight", + "lora_unet_double_blocks_blockid_txt_mod_lin.lora_up.weight": "blocks.blockid.norm1_b.linear.lora_B.default.weight", + "lora_unet_double_blocks_blockid_img_attn_qkv.lora_down.weight": "blocks.blockid.attn.a_to_qkv.lora_A.default.weight", + "lora_unet_double_blocks_blockid_img_attn_qkv.lora_up.weight": "blocks.blockid.attn.a_to_qkv.lora_B.default.weight", + "lora_unet_double_blocks_blockid_txt_attn_qkv.lora_down.weight": "blocks.blockid.attn.b_to_qkv.lora_A.default.weight", + "lora_unet_double_blocks_blockid_txt_attn_qkv.lora_up.weight": "blocks.blockid.attn.b_to_qkv.lora_B.default.weight", + "lora_unet_double_blocks_blockid_img_attn_proj.lora_down.weight": "blocks.blockid.attn.a_to_out.lora_A.default.weight", + "lora_unet_double_blocks_blockid_img_attn_proj.lora_up.weight": "blocks.blockid.attn.a_to_out.lora_B.default.weight", + "lora_unet_double_blocks_blockid_txt_attn_proj.lora_down.weight": "blocks.blockid.attn.b_to_out.lora_A.default.weight", + "lora_unet_double_blocks_blockid_txt_attn_proj.lora_up.weight": "blocks.blockid.attn.b_to_out.lora_B.default.weight", + "lora_unet_double_blocks_blockid_img_mlp_0.lora_down.weight": "blocks.blockid.ff_a.0.lora_A.default.weight", + "lora_unet_double_blocks_blockid_img_mlp_0.lora_up.weight": "blocks.blockid.ff_a.0.lora_B.default.weight", + "lora_unet_double_blocks_blockid_img_mlp_2.lora_down.weight": "blocks.blockid.ff_a.2.lora_A.default.weight", + "lora_unet_double_blocks_blockid_img_mlp_2.lora_up.weight": "blocks.blockid.ff_a.2.lora_B.default.weight", + "lora_unet_double_blocks_blockid_txt_mlp_0.lora_down.weight": "blocks.blockid.ff_b.0.lora_A.default.weight", + "lora_unet_double_blocks_blockid_txt_mlp_0.lora_up.weight": "blocks.blockid.ff_b.0.lora_B.default.weight", + "lora_unet_double_blocks_blockid_txt_mlp_2.lora_down.weight": "blocks.blockid.ff_b.2.lora_A.default.weight", + "lora_unet_double_blocks_blockid_txt_mlp_2.lora_up.weight": "blocks.blockid.ff_b.2.lora_B.default.weight", + "lora_unet_single_blocks_blockid_modulation_lin.lora_down.weight": "single_blocks.blockid.norm.linear.lora_A.default.weight", + "lora_unet_single_blocks_blockid_modulation_lin.lora_up.weight": "single_blocks.blockid.norm.linear.lora_B.default.weight", + "lora_unet_single_blocks_blockid_linear1.lora_down.weight": "single_blocks.blockid.to_qkv_mlp.lora_A.default.weight", + "lora_unet_single_blocks_blockid_linear1.lora_up.weight": "single_blocks.blockid.to_qkv_mlp.lora_B.default.weight", + "lora_unet_single_blocks_blockid_linear2.lora_down.weight": "single_blocks.blockid.proj_out.lora_A.default.weight", + "lora_unet_single_blocks_blockid_linear2.lora_up.weight": "single_blocks.blockid.proj_out.lora_B.default.weight", + } + def guess_block_id(name): + names = name.split("_") + for i in names: + if i.isdigit(): + return i, name.replace(f"_{i}_", "_blockid_") + return None, None + state_dict_ = {} + for name, param in state_dict.items(): + block_id, source_name = guess_block_id(name) + if source_name in rename_dict: + target_name = rename_dict[source_name] + target_name = target_name.replace(".blockid.", f".{block_id}.") + state_dict_[target_name] = param + else: + state_dict_[name] = param + return state_dict_ + + +class WanLoRAConverter: + def __init__(self): + pass + + @staticmethod + def align_to_opensource_format(state_dict, **kwargs): + state_dict = {"diffusion_model." + name.replace(".default.", "."): param for name, param in state_dict.items()} + return state_dict + + @staticmethod + def align_to_diffsynth_format(state_dict, **kwargs): + state_dict = {name.replace("diffusion_model.", "").replace(".lora_A.weight", ".lora_A.default.weight").replace(".lora_B.weight", ".lora_B.default.weight"): param for name, param in state_dict.items()} + return state_dict + + +def get_lora_loaders(): + return [SDLoRAFromCivitai(), SDXLLoRAFromCivitai(), FluxLoRAFromCivitai(), HunyuanVideoLoRAFromCivitai(), GeneralLoRAFromPeft()] diff --git a/diffsynth/models/memory/__init__.py b/diffsynth/models/memory/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1c0e0b4658c1cd5497bb13745f94a7f1c0aa7050 --- /dev/null +++ b/diffsynth/models/memory/__init__.py @@ -0,0 +1,14 @@ +from .framepack_length import ( + framepack_align_context_actions_to_latents, + framepack_length_compress_context_latents, +) +from .framepack_weight import apply_framepack_token_weights +from .spatial_grid_memory import ( + SpatialCrossAttnReadout, + SpatialGridMemory, + apply_spatial_cross_attn_readout, + inject_spatial_memory, +) +from .videossm_hybrid import HybridStateSpaceMemory + +from .block_wise_ssm import BlockWiseStateSpaceMemory diff --git a/diffsynth/models/memory/block_wise_ssm.py b/diffsynth/models/memory/block_wise_ssm.py new file mode 100644 index 0000000000000000000000000000000000000000..519b4e4081459f81574940212ee919e5fec98199 --- /dev/null +++ b/diffsynth/models/memory/block_wise_ssm.py @@ -0,0 +1,46 @@ +import torch +import torch.nn as nn + + +class BlockWiseStateSpaceMemory(nn.Module): + """ + Paper-aligned block-wise recurrent SSM. + + This module is intentionally separate from VideoSSM hybrid. It performs a + recurrent state update along the latent time axis for each spatial token + trajectory, and is attached to selected DiT blocks. + """ + + def __init__(self, dim: int): + super().__init__() + self.dim = int(dim) + self.in_proj = nn.Linear(self.dim, self.dim * 2) + self.out_proj = nn.Linear(self.dim, self.dim) + self.decay_logit = nn.Parameter(torch.zeros(self.dim)) + self.gate = nn.Parameter(torch.zeros(1)) + + def forward(self, x: torch.Tensor, f: int, **_kwargs): + # x: (B, F*S, D), where S is spatial tokens per latent frame. + if x is None or x.ndim != 3: + return x + b, n, d = x.shape + f = int(f or 0) + if d != self.dim or f <= 1 or n % f != 0: + return x + + spatial = n // f + x_seq = x.reshape(b, f, spatial, d).permute(0, 2, 1, 3).reshape(b * spatial, f, d) + update, update_gate = self.in_proj(x_seq).chunk(2, dim=-1) + update = torch.tanh(update) + update_gate = torch.sigmoid(update_gate) + decay = torch.sigmoid(self.decay_logit).to(dtype=x.dtype, device=x.device).view(1, d) + + state = torch.zeros(x_seq.shape[0], d, dtype=x.dtype, device=x.device) + outputs = [] + for t in range(f): + state = decay * state + (1.0 - decay) * update[:, t, :] + outputs.append(state * update_gate[:, t, :]) + y = torch.stack(outputs, dim=1) + y = self.out_proj(y) + y = y.reshape(b, spatial, f, d).permute(0, 2, 1, 3).reshape(b, n, d) + return x + torch.tanh(self.gate) * y diff --git a/diffsynth/models/memory/framepack_length.py b/diffsynth/models/memory/framepack_length.py new file mode 100644 index 0000000000000000000000000000000000000000..4f53dfe30963a9a6045bf268a93e06ea0fa6622c --- /dev/null +++ b/diffsynth/models/memory/framepack_length.py @@ -0,0 +1,128 @@ +import torch +import torch.nn.functional as F + + +def _compress_weights(ratio: int, strategy: str = "distance_merge", recent_keep_ratio: float = 0.5, device=None, dtype=None): + if ratio <= 1: + return None + strategy = str(strategy or "distance_merge").lower() + # Baseline-aligned default: non-overlapping mean pool on each r-frame group. + if strategy in ("distance_merge", "mean", "uniform"): + return None + if strategy in ("recent_weighted", "weighted_recent"): + # Optional weighted variant (kept for compatibility experiments). + idx = torch.arange(ratio, device=device, dtype=dtype) + w = (1.0 - float(recent_keep_ratio)) + float(recent_keep_ratio) * ((idx + 1.0) / float(ratio)) + w = w / w.sum() + return w + return torch.full((ratio,), 1.0 / float(ratio), device=device, dtype=dtype) + + +def framepack_length_compress_context_latents( + context_latents: torch.Tensor, + framepack_ratio: int, + strategy: str = "distance_merge", + recent_keep_ratio: float = 0.5, + multiscale_w2: float = 0.25, + multiscale_w4: float = 0.15, +): + # context_latents: (B, C, K, H, W) + if context_latents is None: + return None, 0, 0, 0 + if context_latents.ndim != 5: + raise ValueError(f"context_latents must be 5D (B,C,K,H,W), got {tuple(context_latents.shape)}") + r = int(framepack_ratio) + if r <= 1: + k = int(context_latents.shape[2]) + return context_latents, k, k, k + + b, c, k_orig, h, w = context_latents.shape + pad = (r - (k_orig % r)) % r + if pad > 0: + pad_lat = context_latents[:, :, -1:, :, :].repeat(1, 1, pad, 1, 1) + context_latents = torch.cat([context_latents, pad_lat], dim=2) + k_pad = int(context_latents.shape[2]) + new_k = k_pad // r + + grouped = context_latents.reshape(b, c, new_k, r, h, w) + strategy = str(strategy or "distance_merge").lower() + if strategy in ("packed_multiscale", "multiscale_packed", "multi_scale_packed"): + base = grouped.mean(dim=3) + + # Base-code inspired approximation: aggregate history with extra low-res spatial views + # (1x/2x/4x) and fuse back to the packed latent stream. + x2 = F.avg_pool3d(context_latents, kernel_size=(1, 2, 2), stride=(1, 2, 2)) + x4 = F.avg_pool3d(context_latents, kernel_size=(1, 4, 4), stride=(1, 4, 4)) + x2 = F.interpolate(x2, size=(k_pad, h, w), mode="trilinear", align_corners=False) + x4 = F.interpolate(x4, size=(k_pad, h, w), mode="trilinear", align_corners=False) + b2 = x2.reshape(b, c, new_k, r, h, w).mean(dim=3) + b4 = x4.reshape(b, c, new_k, r, h, w).mean(dim=3) + w2 = float(multiscale_w2 or 0.0) + w4 = float(multiscale_w4 or 0.0) + w1 = max(1e-6, 1.0 - w2 - w4) + s = w1 + w2 + w4 + out = (w1 * base + w2 * b2 + w4 * b4) / s + else: + cw = _compress_weights(r, strategy=strategy, recent_keep_ratio=recent_keep_ratio, device=context_latents.device, dtype=context_latents.dtype) + if cw is None: + out = grouped.mean(dim=3) + else: + out = (grouped * cw.view(1, 1, 1, r, 1, 1)).sum(dim=3) + return out, int(new_k), int(k_pad), int(k_orig) + + +def framepack_align_context_actions_to_latents( + context_actions, + K_orig_latent: int, + K_after_pad: int, + framepack_ratio: int, + device=None, + dtype=None, + strategy: str = "distance_merge", + recent_keep_ratio: float = 0.5, +): + if context_actions is None: + return None + x = context_actions + if not isinstance(x, torch.Tensor): + x = torch.tensor(x, device=device, dtype=dtype or torch.float32) + else: + if device is not None: + x = x.to(device=device) + if dtype is not None: + x = x.to(dtype=dtype) + if x.ndim not in (2, 3): + raise ValueError(f"context_actions must be 2D/3D, got shape {tuple(x.shape)}") + r = int(framepack_ratio) + if r <= 1: + return x + + if x.ndim == 2: + # (K, D) + k, d = x.shape + k_expected = int(K_orig_latent) + if k < k_expected: + raise ValueError(f"context_actions shorter than K_orig_latent: {k} < {k_expected}") + x = x[:k_expected, :] + pad = int(K_after_pad) - k_expected + if pad > 0: + x = torch.cat([x, x[-1:, :].repeat(pad, 1)], dim=0) + new_k = int(K_after_pad) // r + grouped = x.reshape(new_k, r, d) + cw = _compress_weights(r, strategy=str(strategy or "distance_merge").lower(), recent_keep_ratio=recent_keep_ratio, device=x.device, dtype=x.dtype) + return grouped.mean(dim=1) if cw is None else (grouped * cw.view(1, r, 1)).sum(dim=1) + + # (B, K, D) + b, k, d = x.shape + k_expected = int(K_orig_latent) + if k < k_expected: + raise ValueError(f"context_actions shorter than K_orig_latent: {k} < {k_expected}") + x = x[:, :k_expected, :] + pad = int(K_after_pad) - k_expected + if pad > 0: + x = torch.cat([x, x[:, -1:, :].repeat(1, pad, 1)], dim=1) + new_k = int(K_after_pad) // r + grouped = x.reshape(b, new_k, r, d) + cw = _compress_weights(r, strategy=str(strategy or "distance_merge").lower(), recent_keep_ratio=recent_keep_ratio, device=x.device, dtype=x.dtype) + return grouped.mean(dim=2) if cw is None else (grouped * cw.view(1, 1, r, 1)).sum(dim=2) + diff --git a/diffsynth/models/memory/framepack_weight.py b/diffsynth/models/memory/framepack_weight.py new file mode 100644 index 0000000000000000000000000000000000000000..dfccf2cea7b42424c288d688ba2bef535583670b --- /dev/null +++ b/diffsynth/models/memory/framepack_weight.py @@ -0,0 +1,43 @@ +import torch + + +def apply_framepack_token_weights( + x: torch.Tensor, + num_context_frames: int, + f: int, + h: int, + w: int, + context_position: str = "prefix", + use_framepack_memory: bool = False, + context_temporal_decay: float = 1.0, + context_attention_weight: float = 1.0, +): + if x is None or x.ndim != 3: + return x + if not use_framepack_memory or int(num_context_frames) <= 0: + return x + b, n, d = x.shape + f = int(f) + if f <= 0 or n != f * int(h) * int(w): + return x + + hw = int(h) * int(w) + x4 = x.reshape(b, f, hw, d) + k = min(int(num_context_frames), f) + decay = float(context_temporal_decay) + gain = float(context_attention_weight) + if context_position == "suffix": + ctx_start = f - k + ctx_end = f + # Suffix: first context frame is nearest boundary to target. + distances = torch.arange(k, device=x.device, dtype=x.dtype) + else: + ctx_start = 0 + ctx_end = k + # Prefix: last context frame is nearest boundary to target. + distances = torch.arange(k - 1, -1, -1, device=x.device, dtype=x.dtype) + + weights = gain * torch.pow(torch.tensor(decay, device=x.device, dtype=x.dtype), distances) + x4[:, ctx_start:ctx_end, :, :] = x4[:, ctx_start:ctx_end, :, :] * weights.view(1, k, 1, 1) + return x4.reshape(b, n, d) + diff --git a/diffsynth/models/memory/spatial_grid_memory.py b/diffsynth/models/memory/spatial_grid_memory.py new file mode 100644 index 0000000000000000000000000000000000000000..dbbdb29501c91f8835b4fed739d3b6a425f0a73e --- /dev/null +++ b/diffsynth/models/memory/spatial_grid_memory.py @@ -0,0 +1,86 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class SpatialGridMemory(nn.Module): + def __init__(self, dim: int, grid_size: int = 8, num_tokens: int = 64): + super().__init__() + self.dim = int(dim) + self.grid_size = int(grid_size) + self.num_tokens = int(num_tokens) + g2 = self.grid_size * self.grid_size + # Keep key name aligned with ckpt loading in loop_utils.py (spatial_to_tokens). + self.spatial_to_tokens = nn.Parameter(torch.zeros(g2, self.num_tokens)) + nn.init.normal_(self.spatial_to_tokens, std=0.02) + + @property + def mix(self): + # Backward compatibility for code that referenced the old attribute name. + return self.spatial_to_tokens + + def forward(self, x_context: torch.Tensor, num_context_frames: int, h: int, w: int): + # x_context: (B, K*H*W, D) + if x_context is None or x_context.ndim != 3: + return x_context + b, n, d = x_context.shape + if d != self.dim: + raise ValueError(f"SpatialGridMemory dim mismatch: x={d} module={self.dim}") + k = max(int(num_context_frames), 1) + spatial = int(h) * int(w) + if n != k * spatial: + # Best effort fallback: treat x as a flat token map and pool directly. + x_mean = x_context + else: + x_mean = x_context.reshape(b, k, spatial, d).mean(dim=1) # (B, S, D) + + g2 = self.grid_size * self.grid_size + pooled = F.adaptive_avg_pool1d(x_mean.transpose(1, 2), g2).transpose(1, 2) # (B, G2, D) + mix = torch.softmax(self.spatial_to_tokens, dim=0) # (G2, M) + mem = torch.einsum("bgd,gm->bmd", pooled, mix) # (B, M, D) + return mem + + def load_state_dict(self, state_dict, strict: bool = True): + # Compatibility: + # - old local key: mix + # - current/baseline key: spatial_to_tokens + sd = dict(state_dict) + if "mix" in sd and "spatial_to_tokens" not in sd: + sd["spatial_to_tokens"] = sd.pop("mix") + # Ignore deprecated projection keys from prior experiments. + sd.pop("out.weight", None) + sd.pop("out.bias", None) + return super().load_state_dict(sd, strict=False if not strict else strict) + + +class SpatialCrossAttnReadout(nn.Module): + def __init__(self, dim: int, num_heads: int = 8): + super().__init__() + self.attn = nn.MultiheadAttention(embed_dim=int(dim), num_heads=int(num_heads), batch_first=True) + self.gate = nn.Parameter(torch.zeros(1)) + + def forward(self, x_target: torch.Tensor, mem_tokens: torch.Tensor): + if x_target is None or mem_tokens is None: + return x_target + if x_target.numel() == 0 or mem_tokens.numel() == 0: + return x_target + delta, _ = self.attn(x_target, mem_tokens, mem_tokens, need_weights=False) + return x_target + torch.tanh(self.gate) * delta + + +def apply_spatial_cross_attn_readout(x_target: torch.Tensor, mem_tokens: torch.Tensor, module: nn.Module = None): + if module is None: + module = SpatialCrossAttnReadout(dim=int(x_target.shape[-1]), num_heads=8).to(device=x_target.device, dtype=x_target.dtype) + return module(x_target, mem_tokens) + + +def inject_spatial_memory(context: torch.Tensor, mem_tokens: torch.Tensor, mode: str = "concat_text"): + mode = str(mode or "concat_text").lower() + if mem_tokens is None or mode == "none": + return context + if context is None: + return mem_tokens + if mode in ("concat_text", "cross_attn_readout"): + return torch.cat([context, mem_tokens], dim=1) + return context + diff --git a/diffsynth/models/memory/videossm_hybrid.py b/diffsynth/models/memory/videossm_hybrid.py new file mode 100644 index 0000000000000000000000000000000000000000..19ac465aec7528e0ef240bf67bc1e4a4f9fd23a8 --- /dev/null +++ b/diffsynth/models/memory/videossm_hybrid.py @@ -0,0 +1,39 @@ +import torch +import torch.nn as nn + + +class HybridStateSpaceMemory(nn.Module): + """ + Lightweight legacy VideoSSM hybrid block: + depthwise temporal conv over per-spatial token trajectories. + """ + + def __init__(self, dim: int, kernel_size: int = 3, expand: int = 2): + super().__init__() + self.dim = int(dim) + self.kernel_size = int(kernel_size) + hidden = int(dim) * max(int(expand), 1) + pad = self.kernel_size - 1 # causal-like left padding + self.in_proj = nn.Linear(self.dim, hidden) + self.dw = nn.Conv1d(hidden, hidden, kernel_size=self.kernel_size, groups=hidden, padding=pad) + self.out_proj = nn.Linear(hidden, self.dim) + self.gate = nn.Parameter(torch.zeros(1)) + + def forward(self, x: torch.Tensor, f: int, h: int, w: int, **_kwargs): + # x: (B, F*H*W, D) + if x is None or x.ndim != 3: + return x + b, n, d = x.shape + f = int(f) + hw = int(h) * int(w) + if d != self.dim or f <= 1 or n != f * hw: + return x + x4 = x.reshape(b, f, hw, d).permute(0, 2, 1, 3).reshape(b * hw, f, d) # (B*HW, F, D) + y = self.in_proj(x4) + y = y.transpose(1, 2) # (B*HW, hidden, F) + y = self.dw(y)[..., :f] # causal crop + y = y.transpose(1, 2) + y = self.out_proj(y) + y = y.reshape(b, hw, f, d).permute(0, 2, 1, 3).reshape(b, n, d) + return x + torch.tanh(self.gate) * y + diff --git a/diffsynth/models/model_manager.py b/diffsynth/models/model_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..3757a6f6866b29cbfcdd12624b139961c28ee965 --- /dev/null +++ b/diffsynth/models/model_manager.py @@ -0,0 +1,518 @@ +import os, torch, json, importlib, logging +from typing import List + +logger = logging.getLogger(__name__) + +from .downloader import download_models, download_customized_models, Preset_model_id, Preset_model_website + +from .sd_text_encoder import SDTextEncoder +from .sd_unet import SDUNet +from .sd_vae_encoder import SDVAEEncoder +from .sd_vae_decoder import SDVAEDecoder +from .lora import get_lora_loaders + +from .sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2 +from .sdxl_unet import SDXLUNet +from .sdxl_vae_decoder import SDXLVAEDecoder +from .sdxl_vae_encoder import SDXLVAEEncoder + +from .sd3_text_encoder import SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3 +from .sd3_dit import SD3DiT +from .sd3_vae_decoder import SD3VAEDecoder +from .sd3_vae_encoder import SD3VAEEncoder + +from .sd_controlnet import SDControlNet +from .sdxl_controlnet import SDXLControlNetUnion + +from .sd_motion import SDMotionModel +from .sdxl_motion import SDXLMotionModel + +from .svd_image_encoder import SVDImageEncoder +from .svd_unet import SVDUNet +from .svd_vae_decoder import SVDVAEDecoder +from .svd_vae_encoder import SVDVAEEncoder + +from .sd_ipadapter import SDIpAdapter, IpAdapterCLIPImageEmbedder +from .sdxl_ipadapter import SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder + +from .hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder +from .hunyuan_dit import HunyuanDiT +from .hunyuan_video_vae_decoder import HunyuanVideoVAEDecoder +from .hunyuan_video_vae_encoder import HunyuanVideoVAEEncoder + +from .flux_dit import FluxDiT +from .flux_text_encoder import FluxTextEncoder2 +from .flux_vae import FluxVAEEncoder, FluxVAEDecoder +from .flux_ipadapter import FluxIpAdapter + +from .cog_vae import CogVAEEncoder, CogVAEDecoder +from .cog_dit import CogDiT + +from ..extensions.RIFE import IFNet +from ..extensions.ESRGAN import RRDBNet + +from ..configs.model_config import model_loader_configs, huggingface_model_loader_configs, patch_model_loader_configs +from .utils import load_state_dict, init_weights_on_device, hash_state_dict_keys, split_state_dict_with_prefix + + +def load_model_from_single_file(state_dict, model_names, model_classes, model_resource, torch_dtype, device): + loaded_model_names, loaded_models = [], [] + for model_name, model_class in zip(model_names, model_classes): + print(f" model_name: {model_name} model_class: {model_class.__name__}") + state_dict_converter = model_class.state_dict_converter() + if model_resource == "civitai": + state_dict_results = state_dict_converter.from_civitai(state_dict) + elif model_resource == "diffusers": + state_dict_results = state_dict_converter.from_diffusers(state_dict) + if isinstance(state_dict_results, tuple): + model_state_dict, extra_kwargs = state_dict_results + print(f" This model is initialized with extra kwargs: {extra_kwargs}") + else: + model_state_dict, extra_kwargs = state_dict_results, {} + torch_dtype = torch.float32 if extra_kwargs.get("upcast_to_float32", False) else torch_dtype + with init_weights_on_device(): + model = model_class(**extra_kwargs) + if hasattr(model, "eval"): + model = model.eval() + model.load_state_dict(model_state_dict, assign=True) + model = model.to(dtype=torch_dtype, device=device) + loaded_model_names.append(model_name) + loaded_models.append(model) + return loaded_model_names, loaded_models + + +def load_model_from_huggingface_folder(file_path, model_names, model_classes, torch_dtype, device): + loaded_model_names, loaded_models = [], [] + for model_name, model_class in zip(model_names, model_classes): + if torch_dtype in [torch.float32, torch.float16, torch.bfloat16]: + model = model_class.from_pretrained(file_path, torch_dtype=torch_dtype).eval() + else: + model = model_class.from_pretrained(file_path).eval().to(dtype=torch_dtype) + if torch_dtype == torch.float16 and hasattr(model, "half"): + model = model.half() + try: + model = model.to(device=device) + except: + pass + loaded_model_names.append(model_name) + loaded_models.append(model) + return loaded_model_names, loaded_models + + +def load_single_patch_model_from_single_file(state_dict, model_name, model_class, base_model, extra_kwargs, torch_dtype, device): + print(f" model_name: {model_name} model_class: {model_class.__name__} extra_kwargs: {extra_kwargs}") + base_state_dict = base_model.state_dict() + base_model.to("cpu") + del base_model + model = model_class(**extra_kwargs) + model.load_state_dict(base_state_dict, strict=False) + model.load_state_dict(state_dict, strict=False) + model.to(dtype=torch_dtype, device=device) + return model + + +def load_patch_model_from_single_file(state_dict, model_names, model_classes, extra_kwargs, model_manager, torch_dtype, device): + loaded_model_names, loaded_models = [], [] + for model_name, model_class in zip(model_names, model_classes): + while True: + for model_id in range(len(model_manager.model)): + base_model_name = model_manager.model_name[model_id] + if base_model_name == model_name: + base_model_path = model_manager.model_path[model_id] + base_model = model_manager.model[model_id] + print(f" Adding patch model to {base_model_name} ({base_model_path})") + patched_model = load_single_patch_model_from_single_file( + state_dict, model_name, model_class, base_model, extra_kwargs, torch_dtype, device) + loaded_model_names.append(base_model_name) + loaded_models.append(patched_model) + model_manager.model.pop(model_id) + model_manager.model_path.pop(model_id) + model_manager.model_name.pop(model_id) + break + else: + break + return loaded_model_names, loaded_models + + + +class ModelDetectorTemplate: + def __init__(self): + pass + + def match(self, file_path="", state_dict={}): + return False + + def load(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, **kwargs): + return [], [] + + + +class ModelDetectorFromSingleFile: + def __init__(self, model_loader_configs=[]): + self.keys_hash_with_shape_dict = {} + self.keys_hash_dict = {} + for metadata in model_loader_configs: + self.add_model_metadata(*metadata) + + + def add_model_metadata(self, keys_hash, keys_hash_with_shape, model_names, model_classes, model_resource): + self.keys_hash_with_shape_dict[keys_hash_with_shape] = (model_names, model_classes, model_resource) + if keys_hash is not None: + self.keys_hash_dict[keys_hash] = (model_names, model_classes, model_resource) + + + def match(self, file_path="", state_dict={}): + if isinstance(file_path, str) and os.path.isdir(file_path): + return False + if state_dict is None or len(state_dict) == 0: + # Handle list of file paths (for split model files) + if isinstance(file_path, list): + state_dict = {} + for path in file_path: + state_dict.update(load_state_dict(path)) + else: + state_dict = load_state_dict(file_path) + keys_hash_with_shape = hash_state_dict_keys(state_dict, with_shape=True) + if keys_hash_with_shape in self.keys_hash_with_shape_dict: + return True + keys_hash = hash_state_dict_keys(state_dict, with_shape=False) + if keys_hash in self.keys_hash_dict: + return True + # Debug: log hash if it's a list of files (merged model) + if isinstance(file_path, list) and len(state_dict) > 0: + logger.info(f" Debug: ModelDetectorFromSingleFile - hash_with_shape={keys_hash_with_shape}, hash={keys_hash}, keys_count={len(state_dict)}") + logger.info(f" Debug: Available hashes count: {len(self.keys_hash_with_shape_dict)}") + if keys_hash_with_shape in self.keys_hash_with_shape_dict: + logger.info(f" Debug: Hash FOUND in keys_hash_with_shape_dict!") + else: + logger.warning(f" Debug: Hash NOT FOUND in keys_hash_with_shape_dict") + logger.info(f" Debug: Sample hashes in dict: {list(self.keys_hash_with_shape_dict.keys())[:5]}") + return False + + + def load(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, **kwargs): + if state_dict is None or len(state_dict) == 0: + # Handle list of file paths (for split model files) + if isinstance(file_path, list): + state_dict = {} + for path in file_path: + state_dict.update(load_state_dict(path)) + else: + state_dict = load_state_dict(file_path) + + # Load models with strict matching + keys_hash_with_shape = hash_state_dict_keys(state_dict, with_shape=True) + if keys_hash_with_shape in self.keys_hash_with_shape_dict: + model_names, model_classes, model_resource = self.keys_hash_with_shape_dict[keys_hash_with_shape] + loaded_model_names, loaded_models = load_model_from_single_file(state_dict, model_names, model_classes, model_resource, torch_dtype, device) + return loaded_model_names, loaded_models + + # Load models without strict matching + # (the shape of parameters may be inconsistent, and the state_dict_converter will modify the model architecture) + keys_hash = hash_state_dict_keys(state_dict, with_shape=False) + if keys_hash in self.keys_hash_dict: + model_names, model_classes, model_resource = self.keys_hash_dict[keys_hash] + loaded_model_names, loaded_models = load_model_from_single_file(state_dict, model_names, model_classes, model_resource, torch_dtype, device) + return loaded_model_names, loaded_models + + return [], [] + + + +class ModelDetectorFromSplitedSingleFile(ModelDetectorFromSingleFile): + def __init__(self, model_loader_configs=[]): + super().__init__(model_loader_configs) + + + def match(self, file_path="", state_dict={}): + if isinstance(file_path, str) and os.path.isdir(file_path): + return False + if state_dict is None or len(state_dict) == 0: + # Handle list of file paths (for split model files) + if isinstance(file_path, list): + state_dict = {} + for path in file_path: + state_dict.update(load_state_dict(path)) + else: + state_dict = load_state_dict(file_path) + # First try to match the complete state_dict (for merged models) + if super().match(file_path, state_dict): + return True + # If complete match fails, try split matching + splited_state_dict = split_state_dict_with_prefix(state_dict) + for sub_state_dict in splited_state_dict: + if super().match(file_path, sub_state_dict): + return True + return False + + + def load(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, **kwargs): + # Load state_dict if empty + if state_dict is None or len(state_dict) == 0: + # Handle list of file paths (for split model files) + if isinstance(file_path, list): + state_dict = {} + for path in file_path: + state_dict.update(load_state_dict(path)) + else: + state_dict = load_state_dict(file_path) + # First try to load the complete state_dict (for merged models) + if super().match(file_path, state_dict): + loaded_model_names, loaded_models = super().load(file_path, state_dict, device, torch_dtype, **kwargs) + if loaded_model_names: + return loaded_model_names, loaded_models + # If complete load fails, try split loading + splited_state_dict = split_state_dict_with_prefix(state_dict) + valid_state_dict = {} + for sub_state_dict in splited_state_dict: + if super().match(file_path, sub_state_dict): + valid_state_dict.update(sub_state_dict) + if super().match(file_path, valid_state_dict): + loaded_model_names, loaded_models = super().load(file_path, valid_state_dict, device, torch_dtype, **kwargs) + else: + loaded_model_names, loaded_models = [], [] + for sub_state_dict in splited_state_dict: + if super().match(file_path, sub_state_dict): + loaded_model_names_, loaded_models_ = super().load(file_path, valid_state_dict, device, torch_dtype, **kwargs) + loaded_model_names += loaded_model_names_ + loaded_models += loaded_models_ + return loaded_model_names, loaded_models + + + +class ModelDetectorFromHuggingfaceFolder: + def __init__(self, model_loader_configs=[]): + self.architecture_dict = {} + for metadata in model_loader_configs: + self.add_model_metadata(*metadata) + + + def add_model_metadata(self, architecture, huggingface_lib, model_name, redirected_architecture): + self.architecture_dict[architecture] = (huggingface_lib, model_name, redirected_architecture) + + + def match(self, file_path="", state_dict={}): + if not isinstance(file_path, str) or os.path.isfile(file_path): + return False + file_list = os.listdir(file_path) + if "config.json" not in file_list: + return False + with open(os.path.join(file_path, "config.json"), "r") as f: + config = json.load(f) + if "architectures" not in config and "_class_name" not in config: + return False + return True + + + def load(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, **kwargs): + with open(os.path.join(file_path, "config.json"), "r") as f: + config = json.load(f) + loaded_model_names, loaded_models = [], [] + architectures = config["architectures"] if "architectures" in config else [config["_class_name"]] + for architecture in architectures: + huggingface_lib, model_name, redirected_architecture = self.architecture_dict[architecture] + if redirected_architecture is not None: + architecture = redirected_architecture + model_class = importlib.import_module(huggingface_lib).__getattribute__(architecture) + loaded_model_names_, loaded_models_ = load_model_from_huggingface_folder(file_path, [model_name], [model_class], torch_dtype, device) + loaded_model_names += loaded_model_names_ + loaded_models += loaded_models_ + return loaded_model_names, loaded_models + + + +class ModelDetectorFromPatchedSingleFile: + def __init__(self, model_loader_configs=[]): + self.keys_hash_with_shape_dict = {} + for metadata in model_loader_configs: + self.add_model_metadata(*metadata) + + + def add_model_metadata(self, keys_hash_with_shape, model_name, model_class, extra_kwargs): + self.keys_hash_with_shape_dict[keys_hash_with_shape] = (model_name, model_class, extra_kwargs) + + + def match(self, file_path="", state_dict={}): + if not isinstance(file_path, str) or os.path.isdir(file_path): + return False + if state_dict is None or len(state_dict) == 0: + state_dict = load_state_dict(file_path) + keys_hash_with_shape = hash_state_dict_keys(state_dict, with_shape=True) + if keys_hash_with_shape in self.keys_hash_with_shape_dict: + return True + return False + + + def load(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, model_manager=None, **kwargs): + if state_dict is None or len(state_dict) == 0: + state_dict = load_state_dict(file_path) + + # Load models with strict matching + loaded_model_names, loaded_models = [], [] + keys_hash_with_shape = hash_state_dict_keys(state_dict, with_shape=True) + if keys_hash_with_shape in self.keys_hash_with_shape_dict: + model_names, model_classes, extra_kwargs = self.keys_hash_with_shape_dict[keys_hash_with_shape] + loaded_model_names_, loaded_models_ = load_patch_model_from_single_file( + state_dict, model_names, model_classes, extra_kwargs, model_manager, torch_dtype, device) + loaded_model_names += loaded_model_names_ + loaded_models += loaded_models_ + return loaded_model_names, loaded_models + + + +class ModelManager: + def __init__( + self, + torch_dtype=torch.float16, + device="cuda", + model_id_list: List[Preset_model_id] = [], + downloading_priority: List[Preset_model_website] = ["ModelScope", "HuggingFace"], + file_path_list: List[str] = [], + ): + self.torch_dtype = torch_dtype + self.device = device + self.model = [] + self.model_path = [] + self.model_name = [] + downloaded_files = download_models(model_id_list, downloading_priority) if len(model_id_list) > 0 else [] + self.model_detector = [ + ModelDetectorFromSingleFile(model_loader_configs), + ModelDetectorFromSplitedSingleFile(model_loader_configs), + ModelDetectorFromHuggingfaceFolder(huggingface_model_loader_configs), + ModelDetectorFromPatchedSingleFile(patch_model_loader_configs), + ] + self.load_models(downloaded_files + file_path_list) + + + def load_model_from_single_file(self, file_path="", state_dict={}, model_names=[], model_classes=[], model_resource=None): + print(f"Loading models from file: {file_path}") + if state_dict is None or len(state_dict) == 0: + state_dict = load_state_dict(file_path) + model_names, models = load_model_from_single_file(state_dict, model_names, model_classes, model_resource, self.torch_dtype, self.device) + for model_name, model in zip(model_names, models): + self.model.append(model) + self.model_path.append(file_path) + self.model_name.append(model_name) + print(f" The following models are loaded: {model_names}.") + + + def load_model_from_huggingface_folder(self, file_path="", model_names=[], model_classes=[]): + print(f"Loading models from folder: {file_path}") + model_names, models = load_model_from_huggingface_folder(file_path, model_names, model_classes, self.torch_dtype, self.device) + for model_name, model in zip(model_names, models): + self.model.append(model) + self.model_path.append(file_path) + self.model_name.append(model_name) + print(f" The following models are loaded: {model_names}.") + + + def load_patch_model_from_single_file(self, file_path="", state_dict={}, model_names=[], model_classes=[], extra_kwargs={}): + print(f"Loading patch models from file: {file_path}") + model_names, models = load_patch_model_from_single_file( + state_dict, model_names, model_classes, extra_kwargs, self, self.torch_dtype, self.device) + for model_name, model in zip(model_names, models): + self.model.append(model) + self.model_path.append(file_path) + self.model_name.append(model_name) + print(f" The following patched models are loaded: {model_names}.") + + + def load_lora(self, file_path="", state_dict={}, lora_alpha=1.0): + if isinstance(file_path, list): + for file_path_ in file_path: + self.load_lora(file_path_, state_dict=state_dict, lora_alpha=lora_alpha) + else: + print(f"Loading LoRA models from file: {file_path}") + is_loaded = False + if state_dict is None or len(state_dict) == 0: + state_dict = load_state_dict(file_path) + for model_name, model, model_path in zip(self.model_name, self.model, self.model_path): + for lora in get_lora_loaders(): + match_results = lora.match(model, state_dict) + if match_results is not None: + print(f" Adding LoRA to {model_name} ({model_path}).") + lora_prefix, model_resource = match_results + lora.load(model, state_dict, lora_prefix, alpha=lora_alpha, model_resource=model_resource) + is_loaded = True + break + if not is_loaded: + print(f" Cannot load LoRA: {file_path}") + + + def load_model(self, file_path, model_names=None, device=None, torch_dtype=None): + print(f"Loading models from: {file_path}") + if device is None: device = self.device + if torch_dtype is None: torch_dtype = self.torch_dtype + if isinstance(file_path, list): + state_dict = {} + for path in file_path: + state_dict.update(load_state_dict(path)) + logger.info(f" Merged state_dict from {len(file_path)} files, total keys: {len(state_dict)}") + elif os.path.isfile(file_path): + state_dict = load_state_dict(file_path) + else: + state_dict = None + for i, model_detector in enumerate(self.model_detector): + detector_name = model_detector.__class__.__name__ + if model_detector.match(file_path, state_dict): + logger.info(f" Matched by {detector_name}") + model_names, models = model_detector.load( + file_path, state_dict, + device=device, torch_dtype=torch_dtype, + allowed_model_names=model_names, model_manager=self + ) + for model_name, model in zip(model_names, models): + self.model.append(model) + self.model_path.append(file_path) + self.model_name.append(model_name) + print(f" The following models are loaded: {model_names}.") + break + else: + if isinstance(file_path, list) and len(state_dict) > 0: + logger.info(f" {detector_name} did not match") + else: + print(f" We cannot detect the model type. No models are loaded.") + if isinstance(file_path, list) and len(state_dict) > 0: + from .utils import hash_state_dict_keys + actual_hash = hash_state_dict_keys(state_dict, with_shape=True) + logger.warning(f" Debug: Actual hash = {actual_hash}") + logger.warning(f" Debug: First detector has {len(self.model_detector[0].keys_hash_with_shape_dict)} configured hashes") + # Check if hash exists in config + if actual_hash in self.model_detector[0].keys_hash_with_shape_dict: + logger.error(f" Debug: Hash EXISTS in detector but match() returned False!") + else: + logger.warning(f" Debug: Hash does NOT exist in detector config") + logger.info(f" Debug: Sample configured hashes: {list(self.model_detector[0].keys_hash_with_shape_dict.keys())[:10]}") + + + def load_models(self, file_path_list, model_names=None, device=None, torch_dtype=None): + for file_path in file_path_list: + self.load_model(file_path, model_names, device=device, torch_dtype=torch_dtype) + + + def fetch_model(self, model_name, file_path=None, require_model_path=False): + fetched_models = [] + fetched_model_paths = [] + for model, model_path, model_name_ in zip(self.model, self.model_path, self.model_name): + if file_path is not None and file_path != model_path: + continue + if model_name == model_name_: + fetched_models.append(model) + fetched_model_paths.append(model_path) + if len(fetched_models) == 0: + print(f"No {model_name} models available.") + return None + if len(fetched_models) == 1: + print(f"Using {model_name} from {fetched_model_paths[0]}.") + else: + print(f"More than one {model_name} models are loaded in model manager: {fetched_model_paths}. Using {model_name} from {fetched_model_paths[0]}.") + if require_model_path: + return fetched_models[0], fetched_model_paths[0] + else: + return fetched_models[0] + + + def to(self, device): + for model in self.model: + model.to(device) + diff --git a/diffsynth/models/omnigen.py b/diffsynth/models/omnigen.py new file mode 100644 index 0000000000000000000000000000000000000000..571d6c0e71e5bb28cd0c4e56a2d0437dd82be4c0 --- /dev/null +++ b/diffsynth/models/omnigen.py @@ -0,0 +1,803 @@ +# The code is revised from DiT +import os +import torch +import torch.nn as nn +import numpy as np +import math +from safetensors.torch import load_file +from typing import List, Optional, Tuple, Union +import torch.utils.checkpoint +from huggingface_hub import snapshot_download +from transformers.modeling_outputs import BaseModelOutputWithPast +from transformers import Phi3Config, Phi3Model +from transformers.cache_utils import Cache, DynamicCache +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + + +class Phi3Transformer(Phi3Model): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Phi3DecoderLayer`] + We only modified the attention mask + Args: + config: Phi3Config + """ + def prefetch_layer(self, layer_idx: int, device: torch.device): + "Starts prefetching the next layer cache" + with torch.cuda.stream(self.prefetch_stream): + # Prefetch next layer tensors to GPU + for name, param in self.layers[layer_idx].named_parameters(): + param.data = param.data.to(device, non_blocking=True) + + def evict_previous_layer(self, layer_idx: int): + "Moves the previous layer cache to the CPU" + prev_layer_idx = layer_idx - 1 + for name, param in self.layers[prev_layer_idx].named_parameters(): + param.data = param.data.to("cpu", non_blocking=True) + + def get_offlaod_layer(self, layer_idx: int, device: torch.device): + # init stream + if not hasattr(self, "prefetch_stream"): + self.prefetch_stream = torch.cuda.Stream() + + # delete previous layer + torch.cuda.current_stream().synchronize() + self.evict_previous_layer(layer_idx) + + # make sure the current layer is ready + torch.cuda.synchronize(self.prefetch_stream) + + # load next layer + self.prefetch_layer((layer_idx + 1) % len(self.layers), device) + + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + offload_model: Optional[bool] = False, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + # kept for BC (non `Cache` `past_key_values` inputs) + return_legacy_cache = False + if use_cache and not isinstance(past_key_values, Cache): + return_legacy_cache = True + if past_key_values is None: + past_key_values = DynamicCache() + else: + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + logger.warning_once( + "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and " + "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class " + "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)" + ) + + # if inputs_embeds is None: + # inputs_embeds = self.embed_tokens(input_ids) + + # if cache_position is None: + # past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + # cache_position = torch.arange( + # past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device + # ) + # if position_ids is None: + # position_ids = cache_position.unsqueeze(0) + + if attention_mask is not None and attention_mask.dim() == 3: + dtype = inputs_embeds.dtype + min_dtype = torch.finfo(dtype).min + attention_mask = (1 - attention_mask) * min_dtype + attention_mask = attention_mask.unsqueeze(1).to(inputs_embeds.dtype) + else: + raise Exception("attention_mask parameter was unavailable or invalid") + # causal_mask = self._update_causal_mask( + # attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions + # ) + + hidden_states = inputs_embeds + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = None + + layer_idx = -1 + for decoder_layer in self.layers: + layer_idx += 1 + + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + attention_mask, + position_ids, + past_key_values, + output_attentions, + use_cache, + cache_position, + ) + else: + if offload_model and not self.training: + self.get_offlaod_layer(layer_idx, device=inputs_embeds.device) + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + print('************') + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + if return_legacy_cache: + next_cache = next_cache.to_legacy_cache() + + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + def __init__(self, hidden_size, frequency_embedding_size=256): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(frequency_embedding_size, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + def forward(self, t, dtype=torch.float32): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype) + t_emb = self.mlp(t_freq) + return t_emb + + +class FinalLayer(nn.Module): + """ + The final layer of DiT. + """ + def __init__(self, hidden_size, patch_size, out_channels): + super().__init__() + self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 2 * hidden_size, bias=True) + ) + + def forward(self, x, c): + shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + + +def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0, interpolation_scale=1.0, base_size=1): + """ + grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or + [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) + """ + if isinstance(grid_size, int): + grid_size = (grid_size, grid_size) + + grid_h = np.arange(grid_size[0], dtype=np.float32) / (grid_size[0] / base_size) / interpolation_scale + grid_w = np.arange(grid_size[1], dtype=np.float32) / (grid_size[1] / base_size) / interpolation_scale + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) + + grid = grid.reshape([2, 1, grid_size[1], grid_size[0]]) + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + if cls_token and extra_tokens > 0: + pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0) + return pos_embed + + +def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): + assert embed_dim % 2 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) + + emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) + return emb + + +def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): + """ + embed_dim: output dimension for each position + pos: a list of positions to be encoded: size (M,) + out: (M, D) + """ + assert embed_dim % 2 == 0 + omega = np.arange(embed_dim // 2, dtype=np.float64) + omega /= embed_dim / 2. + omega = 1. / 10000**omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product + + emb_sin = np.sin(out) # (M, D/2) + emb_cos = np.cos(out) # (M, D/2) + + emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) + return emb + + +class PatchEmbedMR(nn.Module): + """ 2D Image to Patch Embedding + """ + def __init__( + self, + patch_size: int = 2, + in_chans: int = 4, + embed_dim: int = 768, + bias: bool = True, + ): + super().__init__() + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias) + + def forward(self, x): + x = self.proj(x) + x = x.flatten(2).transpose(1, 2) # NCHW -> NLC + return x + + +class OmniGenOriginalModel(nn.Module): + """ + Diffusion model with a Transformer backbone. + """ + def __init__( + self, + transformer_config: Phi3Config, + patch_size=2, + in_channels=4, + pe_interpolation: float = 1.0, + pos_embed_max_size: int = 192, + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = in_channels + self.patch_size = patch_size + self.pos_embed_max_size = pos_embed_max_size + + hidden_size = transformer_config.hidden_size + + self.x_embedder = PatchEmbedMR(patch_size, in_channels, hidden_size, bias=True) + self.input_x_embedder = PatchEmbedMR(patch_size, in_channels, hidden_size, bias=True) + + self.time_token = TimestepEmbedder(hidden_size) + self.t_embedder = TimestepEmbedder(hidden_size) + + self.pe_interpolation = pe_interpolation + pos_embed = get_2d_sincos_pos_embed(hidden_size, pos_embed_max_size, interpolation_scale=self.pe_interpolation, base_size=64) + self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=True) + + self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels) + + self.initialize_weights() + + self.llm = Phi3Transformer(config=transformer_config) + self.llm.config.use_cache = False + + @classmethod + def from_pretrained(cls, model_name): + if not os.path.exists(model_name): + cache_folder = os.getenv('HF_HUB_CACHE') + model_name = snapshot_download(repo_id=model_name, + cache_dir=cache_folder, + ignore_patterns=['flax_model.msgpack', 'rust_model.ot', 'tf_model.h5']) + config = Phi3Config.from_pretrained(model_name) + model = cls(config) + if os.path.exists(os.path.join(model_name, 'model.safetensors')): + print("Loading safetensors") + ckpt = load_file(os.path.join(model_name, 'model.safetensors')) + else: + ckpt = torch.load(os.path.join(model_name, 'model.pt'), map_location='cpu') + model.load_state_dict(ckpt) + return model + + def initialize_weights(self): + assert not hasattr(self, "llama") + + # Initialize transformer layers: + def _basic_init(module): + if isinstance(module, nn.Linear): + torch.nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + self.apply(_basic_init) + + # Initialize patch_embed like nn.Linear (instead of nn.Conv2d): + w = self.x_embedder.proj.weight.data + nn.init.xavier_uniform_(w.view([w.shape[0], -1])) + nn.init.constant_(self.x_embedder.proj.bias, 0) + + w = self.input_x_embedder.proj.weight.data + nn.init.xavier_uniform_(w.view([w.shape[0], -1])) + nn.init.constant_(self.x_embedder.proj.bias, 0) + + + # Initialize timestep embedding MLP: + nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02) + nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02) + nn.init.normal_(self.time_token.mlp[0].weight, std=0.02) + nn.init.normal_(self.time_token.mlp[2].weight, std=0.02) + + # Zero-out output layers: + nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0) + nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0) + nn.init.constant_(self.final_layer.linear.weight, 0) + nn.init.constant_(self.final_layer.linear.bias, 0) + + def unpatchify(self, x, h, w): + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + c = self.out_channels + + x = x.reshape(shape=(x.shape[0], h//self.patch_size, w//self.patch_size, self.patch_size, self.patch_size, c)) + x = torch.einsum('nhwpqc->nchpwq', x) + imgs = x.reshape(shape=(x.shape[0], c, h, w)) + return imgs + + + def cropped_pos_embed(self, height, width): + """Crops positional embeddings for SD3 compatibility.""" + if self.pos_embed_max_size is None: + raise ValueError("`pos_embed_max_size` must be set for cropping.") + + height = height // self.patch_size + width = width // self.patch_size + if height > self.pos_embed_max_size: + raise ValueError( + f"Height ({height}) cannot be greater than `pos_embed_max_size`: {self.pos_embed_max_size}." + ) + if width > self.pos_embed_max_size: + raise ValueError( + f"Width ({width}) cannot be greater than `pos_embed_max_size`: {self.pos_embed_max_size}." + ) + + top = (self.pos_embed_max_size - height) // 2 + left = (self.pos_embed_max_size - width) // 2 + spatial_pos_embed = self.pos_embed.reshape(1, self.pos_embed_max_size, self.pos_embed_max_size, -1) + spatial_pos_embed = spatial_pos_embed[:, top : top + height, left : left + width, :] + # print(top, top + height, left, left + width, spatial_pos_embed.size()) + spatial_pos_embed = spatial_pos_embed.reshape(1, -1, spatial_pos_embed.shape[-1]) + return spatial_pos_embed + + + def patch_multiple_resolutions(self, latents, padding_latent=None, is_input_images:bool=False): + if isinstance(latents, list): + return_list = False + if padding_latent is None: + padding_latent = [None] * len(latents) + return_list = True + patched_latents, num_tokens, shapes = [], [], [] + for latent, padding in zip(latents, padding_latent): + height, width = latent.shape[-2:] + if is_input_images: + latent = self.input_x_embedder(latent) + else: + latent = self.x_embedder(latent) + pos_embed = self.cropped_pos_embed(height, width) + latent = latent + pos_embed + if padding is not None: + latent = torch.cat([latent, padding], dim=-2) + patched_latents.append(latent) + + num_tokens.append(pos_embed.size(1)) + shapes.append([height, width]) + if not return_list: + latents = torch.cat(patched_latents, dim=0) + else: + latents = patched_latents + else: + height, width = latents.shape[-2:] + if is_input_images: + latents = self.input_x_embedder(latents) + else: + latents = self.x_embedder(latents) + pos_embed = self.cropped_pos_embed(height, width) + latents = latents + pos_embed + num_tokens = latents.size(1) + shapes = [height, width] + return latents, num_tokens, shapes + + + def forward(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, padding_latent=None, past_key_values=None, return_past_key_values=True, offload_model:bool=False): + """ + + """ + input_is_list = isinstance(x, list) + x, num_tokens, shapes = self.patch_multiple_resolutions(x, padding_latent) + time_token = self.time_token(timestep, dtype=x[0].dtype).unsqueeze(1) + + if input_img_latents is not None: + input_latents, _, _ = self.patch_multiple_resolutions(input_img_latents, is_input_images=True) + if input_ids is not None: + condition_embeds = self.llm.embed_tokens(input_ids).clone() + input_img_inx = 0 + for b_inx in input_image_sizes.keys(): + for start_inx, end_inx in input_image_sizes[b_inx]: + condition_embeds[b_inx, start_inx: end_inx] = input_latents[input_img_inx] + input_img_inx += 1 + if input_img_latents is not None: + assert input_img_inx == len(input_latents) + + input_emb = torch.cat([condition_embeds, time_token, x], dim=1) + else: + input_emb = torch.cat([time_token, x], dim=1) + output = self.llm(inputs_embeds=input_emb, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, offload_model=offload_model) + output, past_key_values = output.last_hidden_state, output.past_key_values + if input_is_list: + image_embedding = output[:, -max(num_tokens):] + time_emb = self.t_embedder(timestep, dtype=x.dtype) + x = self.final_layer(image_embedding, time_emb) + latents = [] + for i in range(x.size(0)): + latent = x[i:i+1, :num_tokens[i]] + latent = self.unpatchify(latent, shapes[i][0], shapes[i][1]) + latents.append(latent) + else: + image_embedding = output[:, -num_tokens:] + time_emb = self.t_embedder(timestep, dtype=x.dtype) + x = self.final_layer(image_embedding, time_emb) + latents = self.unpatchify(x, shapes[0], shapes[1]) + + if return_past_key_values: + return latents, past_key_values + return latents + + @torch.no_grad() + def forward_with_cfg(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, cfg_scale, use_img_cfg, img_cfg_scale, past_key_values, use_kv_cache, offload_model): + self.llm.config.use_cache = use_kv_cache + model_out, past_key_values = self.forward(x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, past_key_values=past_key_values, return_past_key_values=True, offload_model=offload_model) + if use_img_cfg: + cond, uncond, img_cond = torch.split(model_out, len(model_out) // 3, dim=0) + cond = uncond + img_cfg_scale * (img_cond - uncond) + cfg_scale * (cond - img_cond) + model_out = [cond, cond, cond] + else: + cond, uncond = torch.split(model_out, len(model_out) // 2, dim=0) + cond = uncond + cfg_scale * (cond - uncond) + model_out = [cond, cond] + + return torch.cat(model_out, dim=0), past_key_values + + + @torch.no_grad() + def forward_with_separate_cfg(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, cfg_scale, use_img_cfg, img_cfg_scale, past_key_values, use_kv_cache, offload_model): + self.llm.config.use_cache = use_kv_cache + if past_key_values is None: + past_key_values = [None] * len(attention_mask) + + x = torch.split(x, len(x) // len(attention_mask), dim=0) + timestep = timestep.to(x[0].dtype) + timestep = torch.split(timestep, len(timestep) // len(input_ids), dim=0) + + model_out, pask_key_values = [], [] + for i in range(len(input_ids)): + temp_out, temp_pask_key_values = self.forward(x[i], timestep[i], input_ids[i], input_img_latents[i], input_image_sizes[i], attention_mask[i], position_ids[i], past_key_values=past_key_values[i], return_past_key_values=True, offload_model=offload_model) + model_out.append(temp_out) + pask_key_values.append(temp_pask_key_values) + + if len(model_out) == 3: + cond, uncond, img_cond = model_out + cond = uncond + img_cfg_scale * (img_cond - uncond) + cfg_scale * (cond - img_cond) + model_out = [cond, cond, cond] + elif len(model_out) == 2: + cond, uncond = model_out + cond = uncond + cfg_scale * (cond - uncond) + model_out = [cond, cond] + else: + return model_out[0] + + return torch.cat(model_out, dim=0), pask_key_values + + + +class OmniGenTransformer(OmniGenOriginalModel): + def __init__(self): + config = { + "_name_or_path": "Phi-3-vision-128k-instruct", + "architectures": [ + "Phi3ForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "eos_token_id": 2, + "hidden_act": "silu", + "hidden_size": 3072, + "initializer_range": 0.02, + "intermediate_size": 8192, + "max_position_embeddings": 131072, + "model_type": "phi3", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 32, + "original_max_position_embeddings": 4096, + "rms_norm_eps": 1e-05, + "rope_scaling": { + "long_factor": [ + 1.0299999713897705, + 1.0499999523162842, + 1.0499999523162842, + 1.0799999237060547, + 1.2299998998641968, + 1.2299998998641968, + 1.2999999523162842, + 1.4499999284744263, + 1.5999999046325684, + 1.6499998569488525, + 1.8999998569488525, + 2.859999895095825, + 3.68999981880188, + 5.419999599456787, + 5.489999771118164, + 5.489999771118164, + 9.09000015258789, + 11.579999923706055, + 15.65999984741211, + 15.769999504089355, + 15.789999961853027, + 18.360000610351562, + 21.989999771118164, + 23.079999923706055, + 30.009998321533203, + 32.35000228881836, + 32.590003967285156, + 35.56000518798828, + 39.95000457763672, + 53.840003967285156, + 56.20000457763672, + 57.95000457763672, + 59.29000473022461, + 59.77000427246094, + 59.920005798339844, + 61.190006256103516, + 61.96000671386719, + 62.50000762939453, + 63.3700065612793, + 63.48000717163086, + 63.48000717163086, + 63.66000747680664, + 63.850006103515625, + 64.08000946044922, + 64.760009765625, + 64.80001068115234, + 64.81001281738281, + 64.81001281738281 + ], + "short_factor": [ + 1.05, + 1.05, + 1.05, + 1.1, + 1.1, + 1.1, + 1.2500000000000002, + 1.2500000000000002, + 1.4000000000000004, + 1.4500000000000004, + 1.5500000000000005, + 1.8500000000000008, + 1.9000000000000008, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.1000000000000005, + 2.1000000000000005, + 2.2, + 2.3499999999999996, + 2.3499999999999996, + 2.3499999999999996, + 2.3499999999999996, + 2.3999999999999995, + 2.3999999999999995, + 2.6499999999999986, + 2.6999999999999984, + 2.8999999999999977, + 2.9499999999999975, + 3.049999999999997, + 3.049999999999997, + 3.049999999999997 + ], + "type": "su" + }, + "rope_theta": 10000.0, + "sliding_window": 131072, + "tie_word_embeddings": False, + "torch_dtype": "bfloat16", + "transformers_version": "4.38.1", + "use_cache": True, + "vocab_size": 32064, + "_attn_implementation": "sdpa" + } + config = Phi3Config(**config) + super().__init__(config) + + + def forward(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, padding_latent=None, past_key_values=None, return_past_key_values=True, offload_model:bool=False): + input_is_list = isinstance(x, list) + x, num_tokens, shapes = self.patch_multiple_resolutions(x, padding_latent) + time_token = self.time_token(timestep, dtype=x[0].dtype).unsqueeze(1) + + if input_img_latents is not None: + input_latents, _, _ = self.patch_multiple_resolutions(input_img_latents, is_input_images=True) + if input_ids is not None: + condition_embeds = self.llm.embed_tokens(input_ids).clone() + input_img_inx = 0 + for b_inx in input_image_sizes.keys(): + for start_inx, end_inx in input_image_sizes[b_inx]: + condition_embeds[b_inx, start_inx: end_inx] = input_latents[input_img_inx] + input_img_inx += 1 + if input_img_latents is not None: + assert input_img_inx == len(input_latents) + + input_emb = torch.cat([condition_embeds, time_token, x], dim=1) + else: + input_emb = torch.cat([time_token, x], dim=1) + output = self.llm(inputs_embeds=input_emb, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, offload_model=offload_model) + output, past_key_values = output.last_hidden_state, output.past_key_values + if input_is_list: + image_embedding = output[:, -max(num_tokens):] + time_emb = self.t_embedder(timestep, dtype=x.dtype) + x = self.final_layer(image_embedding, time_emb) + latents = [] + for i in range(x.size(0)): + latent = x[i:i+1, :num_tokens[i]] + latent = self.unpatchify(latent, shapes[i][0], shapes[i][1]) + latents.append(latent) + else: + image_embedding = output[:, -num_tokens:] + time_emb = self.t_embedder(timestep, dtype=x.dtype) + x = self.final_layer(image_embedding, time_emb) + latents = self.unpatchify(x, shapes[0], shapes[1]) + + if return_past_key_values: + return latents, past_key_values + return latents + + + @torch.no_grad() + def forward_with_separate_cfg(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, cfg_scale, use_img_cfg, img_cfg_scale, past_key_values, use_kv_cache, offload_model): + self.llm.config.use_cache = use_kv_cache + if past_key_values is None: + past_key_values = [None] * len(attention_mask) + + x = torch.split(x, len(x) // len(attention_mask), dim=0) + timestep = timestep.to(x[0].dtype) + timestep = torch.split(timestep, len(timestep) // len(input_ids), dim=0) + + model_out, pask_key_values = [], [] + for i in range(len(input_ids)): + temp_out, temp_pask_key_values = self.forward(x[i], timestep[i], input_ids[i], input_img_latents[i], input_image_sizes[i], attention_mask[i], position_ids[i], past_key_values=past_key_values[i], return_past_key_values=True, offload_model=offload_model) + model_out.append(temp_out) + pask_key_values.append(temp_pask_key_values) + + if len(model_out) == 3: + cond, uncond, img_cond = model_out + cond = uncond + img_cfg_scale * (img_cond - uncond) + cfg_scale * (cond - img_cond) + model_out = [cond, cond, cond] + elif len(model_out) == 2: + cond, uncond = model_out + cond = uncond + cfg_scale * (cond - uncond) + model_out = [cond, cond] + else: + return model_out[0] + + return torch.cat(model_out, dim=0), pask_key_values + + + @staticmethod + def state_dict_converter(): + return OmniGenTransformerStateDictConverter() + + + +class OmniGenTransformerStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + return state_dict + + def from_civitai(self, state_dict): + return state_dict diff --git a/diffsynth/models/qwenvl.py b/diffsynth/models/qwenvl.py new file mode 100644 index 0000000000000000000000000000000000000000..9677488e1a8e7b5b46ed6c84b74bff57bf65bd1e --- /dev/null +++ b/diffsynth/models/qwenvl.py @@ -0,0 +1,168 @@ +import torch + + +class Qwen25VL_7b_Embedder(torch.nn.Module): + def __init__(self, model_path, max_length=640, dtype=torch.bfloat16, device="cuda"): + super(Qwen25VL_7b_Embedder, self).__init__() + self.max_length = max_length + self.dtype = dtype + self.device = device + + from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration + + self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained( + model_path, + torch_dtype=dtype, + ).to(torch.cuda.current_device()) + + self.model.requires_grad_(False) + self.processor = AutoProcessor.from_pretrained( + model_path, min_pixels=256 * 28 * 28, max_pixels=324 * 28 * 28 + ) + + Qwen25VL_7b_PREFIX = '''Given a user prompt, generate an "Enhanced prompt" that provides detailed visual descriptions suitable for image generation. Evaluate the level of detail in the user prompt: +- If the prompt is simple, focus on adding specifics about colors, shapes, sizes, textures, and spatial relationships to create vivid and concrete scenes. +- If the prompt is already detailed, refine and enhance the existing details slightly without overcomplicating.\n +Here are examples of how to transform or refine prompts: +- User Prompt: A cat sleeping -> Enhanced: A small, fluffy white cat curled up in a round shape, sleeping peacefully on a warm sunny windowsill, surrounded by pots of blooming red flowers. +- User Prompt: A busy city street -> Enhanced: A bustling city street scene at dusk, featuring glowing street lamps, a diverse crowd of people in colorful clothing, and a double-decker bus passing by towering glass skyscrapers.\n +Please generate only the enhanced description for the prompt below and avoid including any additional commentary or evaluations: +User Prompt:''' + + self.prefix = Qwen25VL_7b_PREFIX + + @staticmethod + def from_pretrained(path, torch_dtype=torch.bfloat16, device="cuda"): + return Qwen25VL_7b_Embedder(path, dtype=torch_dtype, device=device) + + def forward(self, caption, ref_images): + text_list = caption + embs = torch.zeros( + len(text_list), + self.max_length, + self.model.config.hidden_size, + dtype=torch.bfloat16, + device=torch.cuda.current_device(), + ) + hidden_states = torch.zeros( + len(text_list), + self.max_length, + self.model.config.hidden_size, + dtype=torch.bfloat16, + device=torch.cuda.current_device(), + ) + masks = torch.zeros( + len(text_list), + self.max_length, + dtype=torch.long, + device=torch.cuda.current_device(), + ) + input_ids_list = [] + attention_mask_list = [] + emb_list = [] + + def split_string(s): + s = s.replace("“", '"').replace("”", '"').replace("'", '''"''') # use english quotes + result = [] + in_quotes = False + temp = "" + + for idx,char in enumerate(s): + if char == '"' and idx>155: + temp += char + if not in_quotes: + result.append(temp) + temp = "" + + in_quotes = not in_quotes + continue + if in_quotes: + if char.isspace(): + pass # have space token + + result.append("“" + char + "”") + else: + temp += char + + if temp: + result.append(temp) + + return result + + for idx, (txt, imgs) in enumerate(zip(text_list, ref_images)): + + messages = [{"role": "user", "content": []}] + + messages[0]["content"].append({"type": "text", "text": f"{self.prefix}"}) + + messages[0]["content"].append({"type": "image", "image": imgs}) + + # 再添加 text + messages[0]["content"].append({"type": "text", "text": f"{txt}"}) + + # Preparation for inference + text = self.processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True, add_vision_id=True + ) + + image_inputs = [imgs] + + inputs = self.processor( + text=[text], + images=image_inputs, + padding=True, + return_tensors="pt", + ) + + old_inputs_ids = inputs.input_ids + text_split_list = split_string(text) + + token_list = [] + for text_each in text_split_list: + txt_inputs = self.processor( + text=text_each, + images=None, + videos=None, + padding=True, + return_tensors="pt", + ) + token_each = txt_inputs.input_ids + if token_each[0][0] == 2073 and token_each[0][-1] == 854: + token_each = token_each[:, 1:-1] + token_list.append(token_each) + else: + token_list.append(token_each) + + new_txt_ids = torch.cat(token_list, dim=1).to("cuda") + + new_txt_ids = new_txt_ids.to(old_inputs_ids.device) + + idx1 = (old_inputs_ids == 151653).nonzero(as_tuple=True)[1][0] + idx2 = (new_txt_ids == 151653).nonzero(as_tuple=True)[1][0] + inputs.input_ids = ( + torch.cat([old_inputs_ids[0, :idx1], new_txt_ids[0, idx2:]], dim=0) + .unsqueeze(0) + .to("cuda") + ) + inputs.attention_mask = (inputs.input_ids > 0).long().to("cuda") + outputs = self.model( + input_ids=inputs.input_ids, + attention_mask=inputs.attention_mask, + pixel_values=inputs.pixel_values.to("cuda"), + image_grid_thw=inputs.image_grid_thw.to("cuda"), + output_hidden_states=True, + ) + + emb = outputs["hidden_states"][-1] + + embs[idx, : min(self.max_length, emb.shape[1] - 217)] = emb[0, 217:][ + : self.max_length + ] + + masks[idx, : min(self.max_length, emb.shape[1] - 217)] = torch.ones( + (min(self.max_length, emb.shape[1] - 217)), + dtype=torch.long, + device=torch.cuda.current_device(), + ) + + return embs, masks \ No newline at end of file diff --git a/diffsynth/models/sd3_dit.py b/diffsynth/models/sd3_dit.py new file mode 100644 index 0000000000000000000000000000000000000000..60e6be4a805f29c9d501fdece10a805c75e4662d --- /dev/null +++ b/diffsynth/models/sd3_dit.py @@ -0,0 +1,551 @@ +import torch +from einops import rearrange +from .svd_unet import TemporalTimesteps +from .tiler import TileWorker + + + +class RMSNorm(torch.nn.Module): + def __init__(self, dim, eps, elementwise_affine=True): + super().__init__() + self.eps = eps + if elementwise_affine: + self.weight = torch.nn.Parameter(torch.ones((dim,))) + else: + self.weight = None + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.eps) + hidden_states = hidden_states.to(input_dtype) + if self.weight is not None: + hidden_states = hidden_states * self.weight + return hidden_states + + + +class PatchEmbed(torch.nn.Module): + def __init__(self, patch_size=2, in_channels=16, embed_dim=1536, pos_embed_max_size=192): + super().__init__() + self.pos_embed_max_size = pos_embed_max_size + self.patch_size = patch_size + + self.proj = torch.nn.Conv2d(in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size) + self.pos_embed = torch.nn.Parameter(torch.zeros(1, self.pos_embed_max_size, self.pos_embed_max_size, embed_dim)) + + def cropped_pos_embed(self, height, width): + height = height // self.patch_size + width = width // self.patch_size + top = (self.pos_embed_max_size - height) // 2 + left = (self.pos_embed_max_size - width) // 2 + spatial_pos_embed = self.pos_embed[:, top : top + height, left : left + width, :].flatten(1, 2) + return spatial_pos_embed + + def forward(self, latent): + height, width = latent.shape[-2:] + latent = self.proj(latent) + latent = latent.flatten(2).transpose(1, 2) + pos_embed = self.cropped_pos_embed(height, width) + return latent + pos_embed + + + +class TimestepEmbeddings(torch.nn.Module): + def __init__(self, dim_in, dim_out, computation_device=None): + super().__init__() + self.time_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0, computation_device=computation_device) + self.timestep_embedder = torch.nn.Sequential( + torch.nn.Linear(dim_in, dim_out), torch.nn.SiLU(), torch.nn.Linear(dim_out, dim_out) + ) + + def forward(self, timestep, dtype): + time_emb = self.time_proj(timestep).to(dtype) + time_emb = self.timestep_embedder(time_emb) + return time_emb + + + +class AdaLayerNorm(torch.nn.Module): + def __init__(self, dim, single=False, dual=False): + super().__init__() + self.single = single + self.dual = dual + self.linear = torch.nn.Linear(dim, dim * [[6, 2][single], 9][dual]) + self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + + def forward(self, x, emb): + emb = self.linear(torch.nn.functional.silu(emb)) + if self.single: + scale, shift = emb.unsqueeze(1).chunk(2, dim=2) + x = self.norm(x) * (1 + scale) + shift + return x + elif self.dual: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp, shift_msa2, scale_msa2, gate_msa2 = emb.unsqueeze(1).chunk(9, dim=2) + norm_x = self.norm(x) + x = norm_x * (1 + scale_msa) + shift_msa + norm_x2 = norm_x * (1 + scale_msa2) + shift_msa2 + return x, gate_msa, shift_mlp, scale_mlp, gate_mlp, norm_x2, gate_msa2 + else: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.unsqueeze(1).chunk(6, dim=2) + x = self.norm(x) * (1 + scale_msa) + shift_msa + return x, gate_msa, shift_mlp, scale_mlp, gate_mlp + + + +class JointAttention(torch.nn.Module): + def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False, use_rms_norm=False): + super().__init__() + self.num_heads = num_heads + self.head_dim = head_dim + self.only_out_a = only_out_a + + self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3) + self.b_to_qkv = torch.nn.Linear(dim_b, dim_b * 3) + + self.a_to_out = torch.nn.Linear(dim_a, dim_a) + if not only_out_a: + self.b_to_out = torch.nn.Linear(dim_b, dim_b) + + if use_rms_norm: + self.norm_q_a = RMSNorm(head_dim, eps=1e-6) + self.norm_k_a = RMSNorm(head_dim, eps=1e-6) + self.norm_q_b = RMSNorm(head_dim, eps=1e-6) + self.norm_k_b = RMSNorm(head_dim, eps=1e-6) + else: + self.norm_q_a = None + self.norm_k_a = None + self.norm_q_b = None + self.norm_k_b = None + + + def process_qkv(self, hidden_states, to_qkv, norm_q, norm_k): + batch_size = hidden_states.shape[0] + qkv = to_qkv(hidden_states) + qkv = qkv.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2) + q, k, v = qkv.chunk(3, dim=1) + if norm_q is not None: + q = norm_q(q) + if norm_k is not None: + k = norm_k(k) + return q, k, v + + + def forward(self, hidden_states_a, hidden_states_b): + batch_size = hidden_states_a.shape[0] + + qa, ka, va = self.process_qkv(hidden_states_a, self.a_to_qkv, self.norm_q_a, self.norm_k_a) + qb, kb, vb = self.process_qkv(hidden_states_b, self.b_to_qkv, self.norm_q_b, self.norm_k_b) + q = torch.concat([qa, qb], dim=2) + k = torch.concat([ka, kb], dim=2) + v = torch.concat([va, vb], dim=2) + + hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v) + hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) + hidden_states = hidden_states.to(q.dtype) + hidden_states_a, hidden_states_b = hidden_states[:, :hidden_states_a.shape[1]], hidden_states[:, hidden_states_a.shape[1]:] + hidden_states_a = self.a_to_out(hidden_states_a) + if self.only_out_a: + return hidden_states_a + else: + hidden_states_b = self.b_to_out(hidden_states_b) + return hidden_states_a, hidden_states_b + + + +class SingleAttention(torch.nn.Module): + def __init__(self, dim_a, num_heads, head_dim, use_rms_norm=False): + super().__init__() + self.num_heads = num_heads + self.head_dim = head_dim + + self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3) + self.a_to_out = torch.nn.Linear(dim_a, dim_a) + + if use_rms_norm: + self.norm_q_a = RMSNorm(head_dim, eps=1e-6) + self.norm_k_a = RMSNorm(head_dim, eps=1e-6) + else: + self.norm_q_a = None + self.norm_k_a = None + + + def process_qkv(self, hidden_states, to_qkv, norm_q, norm_k): + batch_size = hidden_states.shape[0] + qkv = to_qkv(hidden_states) + qkv = qkv.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2) + q, k, v = qkv.chunk(3, dim=1) + if norm_q is not None: + q = norm_q(q) + if norm_k is not None: + k = norm_k(k) + return q, k, v + + + def forward(self, hidden_states_a): + batch_size = hidden_states_a.shape[0] + q, k, v = self.process_qkv(hidden_states_a, self.a_to_qkv, self.norm_q_a, self.norm_k_a) + + hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v) + hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) + hidden_states = hidden_states.to(q.dtype) + hidden_states = self.a_to_out(hidden_states) + return hidden_states + + + +class DualTransformerBlock(torch.nn.Module): + def __init__(self, dim, num_attention_heads, use_rms_norm=False): + super().__init__() + self.norm1_a = AdaLayerNorm(dim, dual=True) + self.norm1_b = AdaLayerNorm(dim) + + self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm) + self.attn2 = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm) + + self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff_a = torch.nn.Sequential( + torch.nn.Linear(dim, dim*4), + torch.nn.GELU(approximate="tanh"), + torch.nn.Linear(dim*4, dim) + ) + + self.norm2_b = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff_b = torch.nn.Sequential( + torch.nn.Linear(dim, dim*4), + torch.nn.GELU(approximate="tanh"), + torch.nn.Linear(dim*4, dim) + ) + + + def forward(self, hidden_states_a, hidden_states_b, temb): + norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a, norm_hidden_states_a_2, gate_msa_a_2 = self.norm1_a(hidden_states_a, emb=temb) + norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb) + + # Attention + attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b) + + # Part A + hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a + hidden_states_a = hidden_states_a + gate_msa_a_2 * self.attn2(norm_hidden_states_a_2) + norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a + hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a) + + # Part B + hidden_states_b = hidden_states_b + gate_msa_b * attn_output_b + norm_hidden_states_b = self.norm2_b(hidden_states_b) * (1 + scale_mlp_b) + shift_mlp_b + hidden_states_b = hidden_states_b + gate_mlp_b * self.ff_b(norm_hidden_states_b) + + return hidden_states_a, hidden_states_b + + + +class JointTransformerBlock(torch.nn.Module): + def __init__(self, dim, num_attention_heads, use_rms_norm=False, dual=False): + super().__init__() + self.norm1_a = AdaLayerNorm(dim, dual=dual) + self.norm1_b = AdaLayerNorm(dim) + + self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm) + if dual: + self.attn2 = SingleAttention(dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm) + + self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff_a = torch.nn.Sequential( + torch.nn.Linear(dim, dim*4), + torch.nn.GELU(approximate="tanh"), + torch.nn.Linear(dim*4, dim) + ) + + self.norm2_b = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff_b = torch.nn.Sequential( + torch.nn.Linear(dim, dim*4), + torch.nn.GELU(approximate="tanh"), + torch.nn.Linear(dim*4, dim) + ) + + + def forward(self, hidden_states_a, hidden_states_b, temb): + if self.norm1_a.dual: + norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a, norm_hidden_states_a_2, gate_msa_a_2 = self.norm1_a(hidden_states_a, emb=temb) + else: + norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb) + norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb) + + # Attention + attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b) + + # Part A + hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a + if self.norm1_a.dual: + hidden_states_a = hidden_states_a + gate_msa_a_2 * self.attn2(norm_hidden_states_a_2) + norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a + hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a) + + # Part B + hidden_states_b = hidden_states_b + gate_msa_b * attn_output_b + norm_hidden_states_b = self.norm2_b(hidden_states_b) * (1 + scale_mlp_b) + shift_mlp_b + hidden_states_b = hidden_states_b + gate_mlp_b * self.ff_b(norm_hidden_states_b) + + return hidden_states_a, hidden_states_b + + + +class JointTransformerFinalBlock(torch.nn.Module): + def __init__(self, dim, num_attention_heads, use_rms_norm=False): + super().__init__() + self.norm1_a = AdaLayerNorm(dim) + self.norm1_b = AdaLayerNorm(dim, single=True) + + self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, only_out_a=True, use_rms_norm=use_rms_norm) + + self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff_a = torch.nn.Sequential( + torch.nn.Linear(dim, dim*4), + torch.nn.GELU(approximate="tanh"), + torch.nn.Linear(dim*4, dim) + ) + + + def forward(self, hidden_states_a, hidden_states_b, temb): + norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb) + norm_hidden_states_b = self.norm1_b(hidden_states_b, emb=temb) + + # Attention + attn_output_a = self.attn(norm_hidden_states_a, norm_hidden_states_b) + + # Part A + hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a + norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a + hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a) + + return hidden_states_a, hidden_states_b + + + +class SD3DiT(torch.nn.Module): + def __init__(self, embed_dim=1536, num_layers=24, use_rms_norm=False, num_dual_blocks=0, pos_embed_max_size=192): + super().__init__() + self.pos_embedder = PatchEmbed(patch_size=2, in_channels=16, embed_dim=embed_dim, pos_embed_max_size=pos_embed_max_size) + self.time_embedder = TimestepEmbeddings(256, embed_dim) + self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(2048, embed_dim), torch.nn.SiLU(), torch.nn.Linear(embed_dim, embed_dim)) + self.context_embedder = torch.nn.Linear(4096, embed_dim) + self.blocks = torch.nn.ModuleList([JointTransformerBlock(embed_dim, embed_dim//64, use_rms_norm=use_rms_norm, dual=True) for _ in range(num_dual_blocks)] + + [JointTransformerBlock(embed_dim, embed_dim//64, use_rms_norm=use_rms_norm) for _ in range(num_layers-1-num_dual_blocks)] + + [JointTransformerFinalBlock(embed_dim, embed_dim//64, use_rms_norm=use_rms_norm)]) + self.norm_out = AdaLayerNorm(embed_dim, single=True) + self.proj_out = torch.nn.Linear(embed_dim, 64) + + def tiled_forward(self, hidden_states, timestep, prompt_emb, pooled_prompt_emb, tile_size=128, tile_stride=64): + # Due to the global positional embedding, we cannot implement layer-wise tiled forward. + hidden_states = TileWorker().tiled_forward( + lambda x: self.forward(x, timestep, prompt_emb, pooled_prompt_emb), + hidden_states, + tile_size, + tile_stride, + tile_device=hidden_states.device, + tile_dtype=hidden_states.dtype + ) + return hidden_states + + def forward(self, hidden_states, timestep, prompt_emb, pooled_prompt_emb, tiled=False, tile_size=128, tile_stride=64, use_gradient_checkpointing=False): + if tiled: + return self.tiled_forward(hidden_states, timestep, prompt_emb, pooled_prompt_emb, tile_size, tile_stride) + conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb) + prompt_emb = self.context_embedder(prompt_emb) + + height, width = hidden_states.shape[-2:] + hidden_states = self.pos_embedder(hidden_states) + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + return custom_forward + + for block in self.blocks: + if self.training and use_gradient_checkpointing: + hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, prompt_emb, conditioning, + use_reentrant=False, + ) + else: + hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning) + + hidden_states = self.norm_out(hidden_states, conditioning) + hidden_states = self.proj_out(hidden_states) + hidden_states = rearrange(hidden_states, "B (H W) (P Q C) -> B C (H P) (W Q)", P=2, Q=2, H=height//2, W=width//2) + return hidden_states + + @staticmethod + def state_dict_converter(): + return SD3DiTStateDictConverter() + + + +class SD3DiTStateDictConverter: + def __init__(self): + pass + + def infer_architecture(self, state_dict): + embed_dim = state_dict["blocks.0.ff_a.0.weight"].shape[1] + num_layers = 100 + while num_layers > 0 and f"blocks.{num_layers-1}.ff_a.0.bias" not in state_dict: + num_layers -= 1 + use_rms_norm = "blocks.0.attn.norm_q_a.weight" in state_dict + num_dual_blocks = 0 + while f"blocks.{num_dual_blocks}.attn2.a_to_out.bias" in state_dict: + num_dual_blocks += 1 + pos_embed_max_size = state_dict["pos_embedder.pos_embed"].shape[1] + return { + "embed_dim": embed_dim, + "num_layers": num_layers, + "use_rms_norm": use_rms_norm, + "num_dual_blocks": num_dual_blocks, + "pos_embed_max_size": pos_embed_max_size + } + + def from_diffusers(self, state_dict): + rename_dict = { + "context_embedder": "context_embedder", + "pos_embed.pos_embed": "pos_embedder.pos_embed", + "pos_embed.proj": "pos_embedder.proj", + "time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0", + "time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2", + "time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0", + "time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2", + "norm_out.linear": "norm_out.linear", + "proj_out": "proj_out", + + "norm1.linear": "norm1_a.linear", + "norm1_context.linear": "norm1_b.linear", + "attn.to_q": "attn.a_to_q", + "attn.to_k": "attn.a_to_k", + "attn.to_v": "attn.a_to_v", + "attn.to_out.0": "attn.a_to_out", + "attn.add_q_proj": "attn.b_to_q", + "attn.add_k_proj": "attn.b_to_k", + "attn.add_v_proj": "attn.b_to_v", + "attn.to_add_out": "attn.b_to_out", + "ff.net.0.proj": "ff_a.0", + "ff.net.2": "ff_a.2", + "ff_context.net.0.proj": "ff_b.0", + "ff_context.net.2": "ff_b.2", + + "attn.norm_q": "attn.norm_q_a", + "attn.norm_k": "attn.norm_k_a", + "attn.norm_added_q": "attn.norm_q_b", + "attn.norm_added_k": "attn.norm_k_b", + } + state_dict_ = {} + for name, param in state_dict.items(): + if name in rename_dict: + if name == "pos_embed.pos_embed": + param = param.reshape((1, 192, 192, param.shape[-1])) + state_dict_[rename_dict[name]] = param + elif name.endswith(".weight") or name.endswith(".bias"): + suffix = ".weight" if name.endswith(".weight") else ".bias" + prefix = name[:-len(suffix)] + if prefix in rename_dict: + state_dict_[rename_dict[prefix] + suffix] = param + elif prefix.startswith("transformer_blocks."): + names = prefix.split(".") + names[0] = "blocks" + middle = ".".join(names[2:]) + if middle in rename_dict: + name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]]) + state_dict_[name_] = param + merged_keys = [name for name in state_dict_ if ".a_to_q." in name or ".b_to_q." in name] + for key in merged_keys: + param = torch.concat([ + state_dict_[key.replace("to_q", "to_q")], + state_dict_[key.replace("to_q", "to_k")], + state_dict_[key.replace("to_q", "to_v")], + ], dim=0) + name = key.replace("to_q", "to_qkv") + state_dict_.pop(key.replace("to_q", "to_q")) + state_dict_.pop(key.replace("to_q", "to_k")) + state_dict_.pop(key.replace("to_q", "to_v")) + state_dict_[name] = param + return state_dict_, self.infer_architecture(state_dict_) + + def from_civitai(self, state_dict): + rename_dict = { + "model.diffusion_model.context_embedder.bias": "context_embedder.bias", + "model.diffusion_model.context_embedder.weight": "context_embedder.weight", + "model.diffusion_model.final_layer.linear.bias": "proj_out.bias", + "model.diffusion_model.final_layer.linear.weight": "proj_out.weight", + + "model.diffusion_model.pos_embed": "pos_embedder.pos_embed", + "model.diffusion_model.t_embedder.mlp.0.bias": "time_embedder.timestep_embedder.0.bias", + "model.diffusion_model.t_embedder.mlp.0.weight": "time_embedder.timestep_embedder.0.weight", + "model.diffusion_model.t_embedder.mlp.2.bias": "time_embedder.timestep_embedder.2.bias", + "model.diffusion_model.t_embedder.mlp.2.weight": "time_embedder.timestep_embedder.2.weight", + "model.diffusion_model.x_embedder.proj.bias": "pos_embedder.proj.bias", + "model.diffusion_model.x_embedder.proj.weight": "pos_embedder.proj.weight", + "model.diffusion_model.y_embedder.mlp.0.bias": "pooled_text_embedder.0.bias", + "model.diffusion_model.y_embedder.mlp.0.weight": "pooled_text_embedder.0.weight", + "model.diffusion_model.y_embedder.mlp.2.bias": "pooled_text_embedder.2.bias", + "model.diffusion_model.y_embedder.mlp.2.weight": "pooled_text_embedder.2.weight", + + "model.diffusion_model.joint_blocks.23.context_block.adaLN_modulation.1.weight": "blocks.23.norm1_b.linear.weight", + "model.diffusion_model.joint_blocks.23.context_block.adaLN_modulation.1.bias": "blocks.23.norm1_b.linear.bias", + "model.diffusion_model.final_layer.adaLN_modulation.1.weight": "norm_out.linear.weight", + "model.diffusion_model.final_layer.adaLN_modulation.1.bias": "norm_out.linear.bias", + } + for i in range(40): + rename_dict.update({ + f"model.diffusion_model.joint_blocks.{i}.context_block.adaLN_modulation.1.bias": f"blocks.{i}.norm1_b.linear.bias", + f"model.diffusion_model.joint_blocks.{i}.context_block.adaLN_modulation.1.weight": f"blocks.{i}.norm1_b.linear.weight", + f"model.diffusion_model.joint_blocks.{i}.context_block.attn.proj.bias": f"blocks.{i}.attn.b_to_out.bias", + f"model.diffusion_model.joint_blocks.{i}.context_block.attn.proj.weight": f"blocks.{i}.attn.b_to_out.weight", + f"model.diffusion_model.joint_blocks.{i}.context_block.attn.qkv.bias": [f'blocks.{i}.attn.b_to_q.bias', f'blocks.{i}.attn.b_to_k.bias', f'blocks.{i}.attn.b_to_v.bias'], + f"model.diffusion_model.joint_blocks.{i}.context_block.attn.qkv.weight": [f'blocks.{i}.attn.b_to_q.weight', f'blocks.{i}.attn.b_to_k.weight', f'blocks.{i}.attn.b_to_v.weight'], + f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc1.bias": f"blocks.{i}.ff_b.0.bias", + f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc1.weight": f"blocks.{i}.ff_b.0.weight", + f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc2.bias": f"blocks.{i}.ff_b.2.bias", + f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc2.weight": f"blocks.{i}.ff_b.2.weight", + f"model.diffusion_model.joint_blocks.{i}.x_block.adaLN_modulation.1.bias": f"blocks.{i}.norm1_a.linear.bias", + f"model.diffusion_model.joint_blocks.{i}.x_block.adaLN_modulation.1.weight": f"blocks.{i}.norm1_a.linear.weight", + f"model.diffusion_model.joint_blocks.{i}.x_block.attn.proj.bias": f"blocks.{i}.attn.a_to_out.bias", + f"model.diffusion_model.joint_blocks.{i}.x_block.attn.proj.weight": f"blocks.{i}.attn.a_to_out.weight", + f"model.diffusion_model.joint_blocks.{i}.x_block.attn.qkv.bias": [f'blocks.{i}.attn.a_to_q.bias', f'blocks.{i}.attn.a_to_k.bias', f'blocks.{i}.attn.a_to_v.bias'], + f"model.diffusion_model.joint_blocks.{i}.x_block.attn.qkv.weight": [f'blocks.{i}.attn.a_to_q.weight', f'blocks.{i}.attn.a_to_k.weight', f'blocks.{i}.attn.a_to_v.weight'], + f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc1.bias": f"blocks.{i}.ff_a.0.bias", + f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc1.weight": f"blocks.{i}.ff_a.0.weight", + f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc2.bias": f"blocks.{i}.ff_a.2.bias", + f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc2.weight": f"blocks.{i}.ff_a.2.weight", + f"model.diffusion_model.joint_blocks.{i}.x_block.attn.ln_q.weight": f"blocks.{i}.attn.norm_q_a.weight", + f"model.diffusion_model.joint_blocks.{i}.x_block.attn.ln_k.weight": f"blocks.{i}.attn.norm_k_a.weight", + f"model.diffusion_model.joint_blocks.{i}.context_block.attn.ln_q.weight": f"blocks.{i}.attn.norm_q_b.weight", + f"model.diffusion_model.joint_blocks.{i}.context_block.attn.ln_k.weight": f"blocks.{i}.attn.norm_k_b.weight", + + f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.ln_q.weight": f"blocks.{i}.attn2.norm_q_a.weight", + f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.ln_k.weight": f"blocks.{i}.attn2.norm_k_a.weight", + f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.qkv.weight": f"blocks.{i}.attn2.a_to_qkv.weight", + f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.qkv.bias": f"blocks.{i}.attn2.a_to_qkv.bias", + f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.proj.weight": f"blocks.{i}.attn2.a_to_out.weight", + f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.proj.bias": f"blocks.{i}.attn2.a_to_out.bias", + }) + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "model.diffusion_model.pos_embed": + pos_embed_max_size = int(param.shape[1] ** 0.5 + 0.4) + param = param.reshape((1, pos_embed_max_size, pos_embed_max_size, param.shape[-1])) + if isinstance(rename_dict[name], str): + state_dict_[rename_dict[name]] = param + else: + name_ = rename_dict[name][0].replace(".a_to_q.", ".a_to_qkv.").replace(".b_to_q.", ".b_to_qkv.") + state_dict_[name_] = param + extra_kwargs = self.infer_architecture(state_dict_) + num_layers = extra_kwargs["num_layers"] + for name in [ + f"blocks.{num_layers-1}.norm1_b.linear.weight", f"blocks.{num_layers-1}.norm1_b.linear.bias", "norm_out.linear.weight", "norm_out.linear.bias", + ]: + param = state_dict_[name] + dim = param.shape[0] // 2 + param = torch.concat([param[dim:], param[:dim]], axis=0) + state_dict_[name] = param + return state_dict_, self.infer_architecture(state_dict_) diff --git a/diffsynth/models/sd3_text_encoder.py b/diffsynth/models/sd3_text_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..efe29ca8ae99586ae197ee633a9c3d7f2c074f77 --- /dev/null +++ b/diffsynth/models/sd3_text_encoder.py @@ -0,0 +1,1120 @@ +import torch +from transformers import T5EncoderModel, T5Config +from .sd_text_encoder import SDTextEncoder +from .sdxl_text_encoder import SDXLTextEncoder2, SDXLTextEncoder2StateDictConverter + + +class SD3TextEncoder1(SDTextEncoder): + def __init__(self, vocab_size=49408): + super().__init__(vocab_size=vocab_size) + + def forward(self, input_ids, clip_skip=2, extra_mask=None): + embeds = self.token_embedding(input_ids) + embeds = embeds + self.position_embeds.to(dtype=embeds.dtype, device=input_ids.device) + attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype) + if extra_mask is not None: + attn_mask[:, extra_mask[0]==0] = float("-inf") + for encoder_id, encoder in enumerate(self.encoders): + embeds = encoder(embeds, attn_mask=attn_mask) + if encoder_id + clip_skip == len(self.encoders): + hidden_states = embeds + embeds = self.final_layer_norm(embeds) + pooled_embeds = embeds[torch.arange(embeds.shape[0]), input_ids.to(dtype=torch.int).argmax(dim=-1)] + return pooled_embeds, hidden_states + + @staticmethod + def state_dict_converter(): + return SD3TextEncoder1StateDictConverter() + + + +class SD3TextEncoder2(SDXLTextEncoder2): + def __init__(self): + super().__init__() + + @staticmethod + def state_dict_converter(): + return SD3TextEncoder2StateDictConverter() + + +class SD3TextEncoder3(T5EncoderModel): + def __init__(self): + config = T5Config( + _name_or_path = ".", + architectures = ["T5EncoderModel"], + classifier_dropout = 0.0, + d_ff = 10240, + d_kv = 64, + d_model = 4096, + decoder_start_token_id = 0, + dense_act_fn = "gelu_new", + dropout_rate = 0.1, + eos_token_id = 1, + feed_forward_proj = "gated-gelu", + initializer_factor = 1.0, + is_encoder_decoder = True, + is_gated_act = True, + layer_norm_epsilon = 1e-06, + model_type = "t5", + num_decoder_layers = 24, + num_heads = 64, + num_layers = 24, + output_past = True, + pad_token_id = 0, + relative_attention_max_distance = 128, + relative_attention_num_buckets = 32, + tie_word_embeddings = False, + torch_dtype = torch.float16, + transformers_version = "4.41.2", + use_cache = True, + vocab_size = 32128 + ) + super().__init__(config) + self.eval() + + def forward(self, input_ids): + outputs = super().forward(input_ids=input_ids) + prompt_emb = outputs.last_hidden_state + return prompt_emb + + @staticmethod + def state_dict_converter(): + return SD3TextEncoder3StateDictConverter() + + + +class SD3TextEncoder1StateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + rename_dict = { + "text_model.embeddings.token_embedding.weight": "token_embedding.weight", + "text_model.embeddings.position_embedding.weight": "position_embeds", + "text_model.final_layer_norm.weight": "final_layer_norm.weight", + "text_model.final_layer_norm.bias": "final_layer_norm.bias", + } + attn_rename_dict = { + "self_attn.q_proj": "attn.to_q", + "self_attn.k_proj": "attn.to_k", + "self_attn.v_proj": "attn.to_v", + "self_attn.out_proj": "attn.to_out", + "layer_norm1": "layer_norm1", + "layer_norm2": "layer_norm2", + "mlp.fc1": "fc1", + "mlp.fc2": "fc2", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "text_model.embeddings.position_embedding.weight": + param = param.reshape((1, param.shape[0], param.shape[1])) + state_dict_[rename_dict[name]] = param + elif name.startswith("text_model.encoder.layers."): + param = state_dict[name] + names = name.split(".") + layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1] + name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail]) + state_dict_[name_] = param + return state_dict_ + + def from_civitai(self, state_dict): + rename_dict = { + "text_encoders.clip_l.transformer.text_model.embeddings.position_embedding.weight": "position_embeds", + "text_encoders.clip_l.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm2.weight": "encoders.0.layer_norm2.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.mlp.fc1.bias": "encoders.0.fc1.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.mlp.fc1.weight": "encoders.0.fc1.weight", 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"text_encoders.clip_l.transformer.text_model.encoder.layers.8.mlp.fc1.weight": "encoders.8.fc1.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.8.mlp.fc2.bias": "encoders.8.fc2.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.8.mlp.fc2.weight": "encoders.8.fc2.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.8.self_attn.k_proj.bias": "encoders.8.attn.to_k.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.8.self_attn.k_proj.weight": "encoders.8.attn.to_k.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.8.self_attn.out_proj.bias": "encoders.8.attn.to_out.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.8.self_attn.out_proj.weight": "encoders.8.attn.to_out.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.8.self_attn.q_proj.bias": "encoders.8.attn.to_q.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.8.self_attn.q_proj.weight": "encoders.8.attn.to_q.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.8.self_attn.v_proj.bias": "encoders.8.attn.to_v.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.8.self_attn.v_proj.weight": "encoders.8.attn.to_v.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.layer_norm1.bias": "encoders.9.layer_norm1.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.layer_norm1.weight": "encoders.9.layer_norm1.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.layer_norm2.bias": "encoders.9.layer_norm2.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.layer_norm2.weight": "encoders.9.layer_norm2.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.mlp.fc1.bias": "encoders.9.fc1.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.mlp.fc1.weight": "encoders.9.fc1.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.mlp.fc2.bias": "encoders.9.fc2.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.mlp.fc2.weight": "encoders.9.fc2.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.k_proj.bias": "encoders.9.attn.to_k.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.k_proj.weight": "encoders.9.attn.to_k.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.out_proj.bias": "encoders.9.attn.to_out.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.out_proj.weight": "encoders.9.attn.to_out.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.q_proj.bias": "encoders.9.attn.to_q.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.q_proj.weight": "encoders.9.attn.to_q.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.v_proj.bias": "encoders.9.attn.to_v.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight", + "text_encoders.clip_l.transformer.text_model.final_layer_norm.bias": "final_layer_norm.bias", + "text_encoders.clip_l.transformer.text_model.final_layer_norm.weight": "final_layer_norm.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "text_encoders.clip_l.transformer.text_model.embeddings.position_embedding.weight": + param = param.reshape((1, param.shape[0], param.shape[1])) + state_dict_[rename_dict[name]] = param + elif ("text_encoders.clip_l.transformer." + name) in rename_dict: + param = state_dict[name] + if name == "text_model.embeddings.position_embedding.weight": + param = param.reshape((1, param.shape[0], param.shape[1])) + state_dict_[rename_dict["text_encoders.clip_l.transformer." + name]] = param + return state_dict_ + + + +class SD3TextEncoder2StateDictConverter(SDXLTextEncoder2StateDictConverter): + def __init__(self): + pass + + def from_diffusers(self, state_dict): + return super().from_diffusers(state_dict) + + def from_civitai(self, state_dict): + rename_dict = { + "text_encoders.clip_g.transformer.text_model.embeddings.position_embedding.weight": "position_embeds", + "text_encoders.clip_g.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm2.weight": "encoders.0.layer_norm2.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.mlp.fc1.bias": "encoders.0.fc1.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.mlp.fc1.weight": "encoders.0.fc1.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.mlp.fc2.bias": "encoders.0.fc2.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.mlp.fc2.weight": "encoders.0.fc2.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.k_proj.bias": "encoders.0.attn.to_k.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight": "encoders.0.attn.to_k.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.out_proj.bias": "encoders.0.attn.to_out.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.out_proj.weight": "encoders.0.attn.to_out.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.q_proj.bias": "encoders.0.attn.to_q.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.q_proj.weight": "encoders.0.attn.to_q.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.v_proj.bias": "encoders.0.attn.to_v.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.v_proj.weight": "encoders.0.attn.to_v.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.1.layer_norm1.bias": "encoders.1.layer_norm1.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.1.layer_norm1.weight": "encoders.1.layer_norm1.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.1.layer_norm2.bias": "encoders.1.layer_norm2.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.1.layer_norm2.weight": "encoders.1.layer_norm2.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.1.mlp.fc1.bias": "encoders.1.fc1.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.1.mlp.fc1.weight": "encoders.1.fc1.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.1.mlp.fc2.bias": "encoders.1.fc2.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.1.mlp.fc2.weight": "encoders.1.fc2.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.k_proj.bias": "encoders.1.attn.to_k.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.k_proj.weight": "encoders.1.attn.to_k.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.out_proj.bias": "encoders.1.attn.to_out.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.out_proj.weight": 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"text_encoders.clip_g.transformer.text_model.encoder.layers.9.self_attn.q_proj.weight": "encoders.9.attn.to_q.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.9.self_attn.v_proj.bias": "encoders.9.attn.to_v.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight", + "text_encoders.clip_g.transformer.text_model.final_layer_norm.bias": "final_layer_norm.bias", + "text_encoders.clip_g.transformer.text_model.final_layer_norm.weight": "final_layer_norm.weight", + "text_encoders.clip_g.transformer.text_projection.weight": "text_projection.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "text_encoders.clip_g.transformer.text_model.embeddings.position_embedding.weight": + param = param.reshape((1, param.shape[0], param.shape[1])) + state_dict_[rename_dict[name]] = param + elif ("text_encoders.clip_g.transformer." + name) in rename_dict: + param = state_dict[name] + if name == "text_model.embeddings.position_embedding.weight": + param = param.reshape((1, param.shape[0], param.shape[1])) + state_dict_[rename_dict["text_encoders.clip_g.transformer." + name]] = param + return state_dict_ + + + +class SD3TextEncoder3StateDictConverter(): + def __init__(self): + pass + + def from_diffusers(self, state_dict): + state_dict_ = state_dict + return state_dict_ + + def from_civitai(self, state_dict): + prefix = "text_encoders.t5xxl.transformer." + state_dict_ = {name[len(prefix):]: param for name, param in state_dict.items() if name.startswith(prefix)} + if len(state_dict_) > 0: + return self.from_diffusers(state_dict_) + name_list = [ + "encoder.block.0.layer.0.SelfAttention.k.weight", + "encoder.block.0.layer.0.SelfAttention.o.weight", + "encoder.block.0.layer.0.SelfAttention.q.weight", + "encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight", + "encoder.block.0.layer.0.SelfAttention.v.weight", + 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-0,0 +1,81 @@ +import torch +from .sd_vae_decoder import VAEAttentionBlock, SDVAEDecoderStateDictConverter +from .sd_unet import ResnetBlock, UpSampler +from .tiler import TileWorker + + + +class SD3VAEDecoder(torch.nn.Module): + def __init__(self): + super().__init__() + self.scaling_factor = 1.5305 # Different from SD 1.x + self.shift_factor = 0.0609 # Different from SD 1.x + self.conv_in = torch.nn.Conv2d(16, 512, kernel_size=3, padding=1) # Different from SD 1.x + + self.blocks = torch.nn.ModuleList([ + # UNetMidBlock2D + ResnetBlock(512, 512, eps=1e-6), + VAEAttentionBlock(1, 512, 512, 1, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + # UpDecoderBlock2D + ResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + UpSampler(512), + # UpDecoderBlock2D + ResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + UpSampler(512), + # UpDecoderBlock2D + ResnetBlock(512, 256, eps=1e-6), + ResnetBlock(256, 256, eps=1e-6), + ResnetBlock(256, 256, eps=1e-6), + UpSampler(256), + # UpDecoderBlock2D + ResnetBlock(256, 128, eps=1e-6), + ResnetBlock(128, 128, eps=1e-6), + ResnetBlock(128, 128, eps=1e-6), + ]) + + self.conv_norm_out = torch.nn.GroupNorm(num_channels=128, num_groups=32, eps=1e-6) + self.conv_act = torch.nn.SiLU() + self.conv_out = torch.nn.Conv2d(128, 3, kernel_size=3, padding=1) + + def tiled_forward(self, sample, tile_size=64, tile_stride=32): + hidden_states = TileWorker().tiled_forward( + lambda x: self.forward(x), + sample, + tile_size, + tile_stride, + tile_device=sample.device, + tile_dtype=sample.dtype + ) + return hidden_states + + def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs): + # For VAE Decoder, we do not need to apply the tiler on each layer. + if tiled: + return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride) + + # 1. pre-process + hidden_states = sample / self.scaling_factor + self.shift_factor + hidden_states = self.conv_in(hidden_states) + time_emb = None + text_emb = None + res_stack = None + + # 2. blocks + for i, block in enumerate(self.blocks): + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + + # 3. output + hidden_states = self.conv_norm_out(hidden_states) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + + return hidden_states + + @staticmethod + def state_dict_converter(): + return SDVAEDecoderStateDictConverter() \ No newline at end of file diff --git a/diffsynth/models/sd3_vae_encoder.py b/diffsynth/models/sd3_vae_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..c486866b889093ac501c54e224f67c5428cf81c8 --- /dev/null +++ b/diffsynth/models/sd3_vae_encoder.py @@ -0,0 +1,95 @@ +import torch +from .sd_unet import ResnetBlock, DownSampler +from .sd_vae_encoder import VAEAttentionBlock, SDVAEEncoderStateDictConverter +from .tiler import TileWorker +from einops import rearrange + + +class SD3VAEEncoder(torch.nn.Module): + def __init__(self): + super().__init__() + self.scaling_factor = 1.5305 # Different from SD 1.x + self.shift_factor = 0.0609 # Different from SD 1.x + self.conv_in = torch.nn.Conv2d(3, 128, kernel_size=3, padding=1) + + self.blocks = torch.nn.ModuleList([ + # DownEncoderBlock2D + ResnetBlock(128, 128, eps=1e-6), + ResnetBlock(128, 128, eps=1e-6), + DownSampler(128, padding=0, extra_padding=True), + # DownEncoderBlock2D + ResnetBlock(128, 256, eps=1e-6), + ResnetBlock(256, 256, eps=1e-6), + DownSampler(256, padding=0, extra_padding=True), + # DownEncoderBlock2D + ResnetBlock(256, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + DownSampler(512, padding=0, extra_padding=True), + # DownEncoderBlock2D + ResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + # UNetMidBlock2D + ResnetBlock(512, 512, eps=1e-6), + VAEAttentionBlock(1, 512, 512, 1, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + ]) + + self.conv_norm_out = torch.nn.GroupNorm(num_channels=512, num_groups=32, eps=1e-6) + self.conv_act = torch.nn.SiLU() + self.conv_out = torch.nn.Conv2d(512, 32, kernel_size=3, padding=1) + + def tiled_forward(self, sample, tile_size=64, tile_stride=32): + hidden_states = TileWorker().tiled_forward( + lambda x: self.forward(x), + sample, + tile_size, + tile_stride, + tile_device=sample.device, + tile_dtype=sample.dtype + ) + return hidden_states + + def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs): + # For VAE Decoder, we do not need to apply the tiler on each layer. + if tiled: + return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride) + + # 1. pre-process + hidden_states = self.conv_in(sample) + time_emb = None + text_emb = None + res_stack = None + + # 2. blocks + for i, block in enumerate(self.blocks): + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + + # 3. output + hidden_states = self.conv_norm_out(hidden_states) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + hidden_states = hidden_states[:, :16] + hidden_states = (hidden_states - self.shift_factor) * self.scaling_factor + + return hidden_states + + def encode_video(self, sample, batch_size=8): + B = sample.shape[0] + hidden_states = [] + + for i in range(0, sample.shape[2], batch_size): + + j = min(i + batch_size, sample.shape[2]) + sample_batch = rearrange(sample[:,:,i:j], "B C T H W -> (B T) C H W") + + hidden_states_batch = self(sample_batch) + hidden_states_batch = rearrange(hidden_states_batch, "(B T) C H W -> B C T H W", B=B) + + hidden_states.append(hidden_states_batch) + + hidden_states = torch.concat(hidden_states, dim=2) + return hidden_states + + @staticmethod + def state_dict_converter(): + return SDVAEEncoderStateDictConverter() diff --git a/diffsynth/models/sd_controlnet.py b/diffsynth/models/sd_controlnet.py new file mode 100644 index 0000000000000000000000000000000000000000..910e0dbae8dc6647e6e478f79c239450cefb2027 --- /dev/null +++ b/diffsynth/models/sd_controlnet.py @@ -0,0 +1,589 @@ +import torch +from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, DownSampler +from .tiler import TileWorker + + +class ControlNetConditioningLayer(torch.nn.Module): + def __init__(self, channels = (3, 16, 32, 96, 256, 320)): + super().__init__() + self.blocks = torch.nn.ModuleList([]) + self.blocks.append(torch.nn.Conv2d(channels[0], channels[1], kernel_size=3, padding=1)) + self.blocks.append(torch.nn.SiLU()) + for i in range(1, len(channels) - 2): + self.blocks.append(torch.nn.Conv2d(channels[i], channels[i], kernel_size=3, padding=1)) + self.blocks.append(torch.nn.SiLU()) + self.blocks.append(torch.nn.Conv2d(channels[i], channels[i+1], kernel_size=3, padding=1, stride=2)) + self.blocks.append(torch.nn.SiLU()) + self.blocks.append(torch.nn.Conv2d(channels[-2], channels[-1], kernel_size=3, padding=1)) + + def forward(self, conditioning): + for block in self.blocks: + conditioning = block(conditioning) + return conditioning + + +class SDControlNet(torch.nn.Module): + def __init__(self, global_pool=False): + super().__init__() + self.time_proj = Timesteps(320) + self.time_embedding = torch.nn.Sequential( + torch.nn.Linear(320, 1280), + torch.nn.SiLU(), + torch.nn.Linear(1280, 1280) + ) + self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1) + + self.controlnet_conv_in = ControlNetConditioningLayer(channels=(3, 16, 32, 96, 256, 320)) + + self.blocks = torch.nn.ModuleList([ + # CrossAttnDownBlock2D + ResnetBlock(320, 320, 1280), + AttentionBlock(8, 40, 320, 1, 768), + PushBlock(), + ResnetBlock(320, 320, 1280), + AttentionBlock(8, 40, 320, 1, 768), + PushBlock(), + DownSampler(320), + PushBlock(), + # CrossAttnDownBlock2D + ResnetBlock(320, 640, 1280), + AttentionBlock(8, 80, 640, 1, 768), + PushBlock(), + ResnetBlock(640, 640, 1280), + AttentionBlock(8, 80, 640, 1, 768), + PushBlock(), + DownSampler(640), + PushBlock(), + # CrossAttnDownBlock2D + ResnetBlock(640, 1280, 1280), + AttentionBlock(8, 160, 1280, 1, 768), + PushBlock(), + ResnetBlock(1280, 1280, 1280), + AttentionBlock(8, 160, 1280, 1, 768), + PushBlock(), + DownSampler(1280), + PushBlock(), + # DownBlock2D + ResnetBlock(1280, 1280, 1280), + PushBlock(), + ResnetBlock(1280, 1280, 1280), + PushBlock(), + # UNetMidBlock2DCrossAttn + ResnetBlock(1280, 1280, 1280), + AttentionBlock(8, 160, 1280, 1, 768), + ResnetBlock(1280, 1280, 1280), + PushBlock() + ]) + + self.controlnet_blocks = torch.nn.ModuleList([ + torch.nn.Conv2d(320, 320, kernel_size=(1, 1)), + torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False), + torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False), + torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False), + torch.nn.Conv2d(640, 640, kernel_size=(1, 1)), + torch.nn.Conv2d(640, 640, kernel_size=(1, 1), bias=False), + torch.nn.Conv2d(640, 640, kernel_size=(1, 1), bias=False), + torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)), + torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), + torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), + torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), + torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), + torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), + ]) + + self.global_pool = global_pool + + def forward( + self, + sample, timestep, encoder_hidden_states, conditioning, + tiled=False, tile_size=64, tile_stride=32, + **kwargs + ): + # 1. time + time_emb = self.time_proj(timestep).to(sample.dtype) + time_emb = self.time_embedding(time_emb) + time_emb = time_emb.repeat(sample.shape[0], 1) + + # 2. pre-process + height, width = sample.shape[2], sample.shape[3] + hidden_states = self.conv_in(sample) + self.controlnet_conv_in(conditioning) + text_emb = encoder_hidden_states + res_stack = [hidden_states] + + # 3. blocks + for i, block in enumerate(self.blocks): + if tiled and not isinstance(block, PushBlock): + _, _, inter_height, _ = hidden_states.shape + resize_scale = inter_height / height + hidden_states = TileWorker().tiled_forward( + lambda x: block(x, time_emb, text_emb, res_stack)[0], + hidden_states, + int(tile_size * resize_scale), + int(tile_stride * resize_scale), + tile_device=hidden_states.device, + tile_dtype=hidden_states.dtype + ) + else: + hidden_states, _, _, _ = block(hidden_states, time_emb, text_emb, res_stack) + + # 4. ControlNet blocks + controlnet_res_stack = [block(res) for block, res in zip(self.controlnet_blocks, res_stack)] + + # pool + if self.global_pool: + controlnet_res_stack = [res.mean(dim=(2, 3), keepdim=True) for res in controlnet_res_stack] + + return controlnet_res_stack + + @staticmethod + def state_dict_converter(): + return SDControlNetStateDictConverter() + + +class SDControlNetStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + # architecture + block_types = [ + 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', + 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', + 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', + 'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', + 'ResnetBlock', 'AttentionBlock', 'ResnetBlock', + 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'UpSampler', + 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', + 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', + 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock' + ] + + # controlnet_rename_dict + controlnet_rename_dict = { + "controlnet_cond_embedding.conv_in.weight": "controlnet_conv_in.blocks.0.weight", + "controlnet_cond_embedding.conv_in.bias": "controlnet_conv_in.blocks.0.bias", + "controlnet_cond_embedding.blocks.0.weight": "controlnet_conv_in.blocks.2.weight", + "controlnet_cond_embedding.blocks.0.bias": "controlnet_conv_in.blocks.2.bias", + "controlnet_cond_embedding.blocks.1.weight": "controlnet_conv_in.blocks.4.weight", + "controlnet_cond_embedding.blocks.1.bias": "controlnet_conv_in.blocks.4.bias", + "controlnet_cond_embedding.blocks.2.weight": "controlnet_conv_in.blocks.6.weight", + "controlnet_cond_embedding.blocks.2.bias": "controlnet_conv_in.blocks.6.bias", + "controlnet_cond_embedding.blocks.3.weight": "controlnet_conv_in.blocks.8.weight", + "controlnet_cond_embedding.blocks.3.bias": "controlnet_conv_in.blocks.8.bias", + "controlnet_cond_embedding.blocks.4.weight": "controlnet_conv_in.blocks.10.weight", + "controlnet_cond_embedding.blocks.4.bias": "controlnet_conv_in.blocks.10.bias", + "controlnet_cond_embedding.blocks.5.weight": "controlnet_conv_in.blocks.12.weight", + "controlnet_cond_embedding.blocks.5.bias": "controlnet_conv_in.blocks.12.bias", + "controlnet_cond_embedding.conv_out.weight": "controlnet_conv_in.blocks.14.weight", + "controlnet_cond_embedding.conv_out.bias": "controlnet_conv_in.blocks.14.bias", + } + + # Rename each parameter + name_list = sorted([name for name in state_dict]) + rename_dict = {} + block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1} + last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""} + for name in name_list: + names = name.split(".") + if names[0] in ["conv_in", "conv_norm_out", "conv_out"]: + pass + elif name in controlnet_rename_dict: + names = controlnet_rename_dict[name].split(".") + elif names[0] == "controlnet_down_blocks": + names[0] = "controlnet_blocks" + elif names[0] == "controlnet_mid_block": + names = ["controlnet_blocks", "12", names[-1]] + elif names[0] in ["time_embedding", "add_embedding"]: + if names[0] == "add_embedding": + names[0] = "add_time_embedding" + names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]] + elif names[0] in ["down_blocks", "mid_block", "up_blocks"]: + if names[0] == "mid_block": + names.insert(1, "0") + block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]] + block_type_with_id = ".".join(names[:4]) + if block_type_with_id != last_block_type_with_id[block_type]: + block_id[block_type] += 1 + last_block_type_with_id[block_type] = block_type_with_id + while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: + block_id[block_type] += 1 + block_type_with_id = ".".join(names[:4]) + names = ["blocks", str(block_id[block_type])] + names[4:] + if "ff" in names: + ff_index = names.index("ff") + component = ".".join(names[ff_index:ff_index+3]) + component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component] + names = names[:ff_index] + [component] + names[ff_index+3:] + if "to_out" in names: + names.pop(names.index("to_out") + 1) + else: + raise ValueError(f"Unknown parameters: {name}") + rename_dict[name] = ".".join(names) + + # Convert state_dict + state_dict_ = {} + for name, param in state_dict.items(): + if ".proj_in." in name or ".proj_out." in name: + param = param.squeeze() + if rename_dict[name] in [ + "controlnet_blocks.1.bias", "controlnet_blocks.2.bias", "controlnet_blocks.3.bias", "controlnet_blocks.5.bias", "controlnet_blocks.6.bias", + "controlnet_blocks.8.bias", "controlnet_blocks.9.bias", "controlnet_blocks.10.bias", "controlnet_blocks.11.bias", "controlnet_blocks.12.bias" + ]: + continue + state_dict_[rename_dict[name]] = param + return state_dict_ + + def from_civitai(self, state_dict): + if "mid_block.resnets.1.time_emb_proj.weight" in state_dict: + # For controlnets in diffusers format + return self.from_diffusers(state_dict) + rename_dict = { + "control_model.time_embed.0.weight": "time_embedding.0.weight", + "control_model.time_embed.0.bias": "time_embedding.0.bias", + "control_model.time_embed.2.weight": "time_embedding.2.weight", + "control_model.time_embed.2.bias": "time_embedding.2.bias", + "control_model.input_blocks.0.0.weight": "conv_in.weight", + "control_model.input_blocks.0.0.bias": "conv_in.bias", + "control_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight", + "control_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias", + "control_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight", + "control_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias", + "control_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight", + "control_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias", + "control_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight", + "control_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias", + "control_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight", + "control_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias", + "control_model.input_blocks.1.1.norm.weight": "blocks.1.norm.weight", + "control_model.input_blocks.1.1.norm.bias": "blocks.1.norm.bias", + "control_model.input_blocks.1.1.proj_in.weight": "blocks.1.proj_in.weight", + "control_model.input_blocks.1.1.proj_in.bias": "blocks.1.proj_in.bias", + "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_q.weight": "blocks.1.transformer_blocks.0.attn1.to_q.weight", + "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_k.weight": "blocks.1.transformer_blocks.0.attn1.to_k.weight", + "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_v.weight": "blocks.1.transformer_blocks.0.attn1.to_v.weight", + "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.1.transformer_blocks.0.attn1.to_out.weight", + "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.1.transformer_blocks.0.attn1.to_out.bias", + "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.1.transformer_blocks.0.act_fn.proj.weight", + "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.1.transformer_blocks.0.act_fn.proj.bias", + 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"control_model.middle_block.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.29.transformer_blocks.0.attn2.to_out.bias", + "control_model.middle_block.1.transformer_blocks.0.norm1.weight": "blocks.29.transformer_blocks.0.norm1.weight", + "control_model.middle_block.1.transformer_blocks.0.norm1.bias": "blocks.29.transformer_blocks.0.norm1.bias", + "control_model.middle_block.1.transformer_blocks.0.norm2.weight": "blocks.29.transformer_blocks.0.norm2.weight", + "control_model.middle_block.1.transformer_blocks.0.norm2.bias": "blocks.29.transformer_blocks.0.norm2.bias", + "control_model.middle_block.1.transformer_blocks.0.norm3.weight": "blocks.29.transformer_blocks.0.norm3.weight", + "control_model.middle_block.1.transformer_blocks.0.norm3.bias": "blocks.29.transformer_blocks.0.norm3.bias", + "control_model.middle_block.1.proj_out.weight": "blocks.29.proj_out.weight", + "control_model.middle_block.1.proj_out.bias": "blocks.29.proj_out.bias", + "control_model.middle_block.2.in_layers.0.weight": "blocks.30.norm1.weight", + "control_model.middle_block.2.in_layers.0.bias": "blocks.30.norm1.bias", + "control_model.middle_block.2.in_layers.2.weight": "blocks.30.conv1.weight", + "control_model.middle_block.2.in_layers.2.bias": "blocks.30.conv1.bias", + "control_model.middle_block.2.emb_layers.1.weight": "blocks.30.time_emb_proj.weight", + "control_model.middle_block.2.emb_layers.1.bias": "blocks.30.time_emb_proj.bias", + "control_model.middle_block.2.out_layers.0.weight": "blocks.30.norm2.weight", + "control_model.middle_block.2.out_layers.0.bias": "blocks.30.norm2.bias", + "control_model.middle_block.2.out_layers.3.weight": "blocks.30.conv2.weight", + "control_model.middle_block.2.out_layers.3.bias": "blocks.30.conv2.bias", + "control_model.middle_block_out.0.weight": "controlnet_blocks.12.weight", + "control_model.middle_block_out.0.bias": "controlnet_blocks.7.bias", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if ".proj_in." in name or ".proj_out." in name: + param = param.squeeze() + state_dict_[rename_dict[name]] = param + return state_dict_ diff --git a/diffsynth/models/sd_ipadapter.py b/diffsynth/models/sd_ipadapter.py new file mode 100644 index 0000000000000000000000000000000000000000..8d6ebd7d5e79ccd534aab11d22e046111562ccde --- /dev/null +++ b/diffsynth/models/sd_ipadapter.py @@ -0,0 +1,57 @@ +from .svd_image_encoder import SVDImageEncoder +from .sdxl_ipadapter import IpAdapterImageProjModel, IpAdapterModule, SDXLIpAdapterStateDictConverter +from transformers import CLIPImageProcessor +import torch + + +class IpAdapterCLIPImageEmbedder(SVDImageEncoder): + def __init__(self): + super().__init__() + self.image_processor = CLIPImageProcessor() + + def forward(self, image): + pixel_values = self.image_processor(images=image, return_tensors="pt").pixel_values + pixel_values = pixel_values.to(device=self.embeddings.class_embedding.device, dtype=self.embeddings.class_embedding.dtype) + return super().forward(pixel_values) + + +class SDIpAdapter(torch.nn.Module): + def __init__(self): + super().__init__() + shape_list = [(768, 320)] * 2 + [(768, 640)] * 2 + [(768, 1280)] * 5 + [(768, 640)] * 3 + [(768, 320)] * 3 + [(768, 1280)] * 1 + self.ipadapter_modules = torch.nn.ModuleList([IpAdapterModule(*shape) for shape in shape_list]) + self.image_proj = IpAdapterImageProjModel(cross_attention_dim=768, clip_embeddings_dim=1024, clip_extra_context_tokens=4) + self.set_full_adapter() + + def set_full_adapter(self): + block_ids = [1, 4, 9, 12, 17, 20, 40, 43, 46, 50, 53, 56, 60, 63, 66, 29] + self.call_block_id = {(i, 0): j for j, i in enumerate(block_ids)} + + def set_less_adapter(self): + # IP-Adapter for SD v1.5 doesn't support this feature. + self.set_full_adapter() + + def forward(self, hidden_states, scale=1.0): + hidden_states = self.image_proj(hidden_states) + hidden_states = hidden_states.view(1, -1, hidden_states.shape[-1]) + ip_kv_dict = {} + for (block_id, transformer_id) in self.call_block_id: + ipadapter_id = self.call_block_id[(block_id, transformer_id)] + ip_k, ip_v = self.ipadapter_modules[ipadapter_id](hidden_states) + if block_id not in ip_kv_dict: + ip_kv_dict[block_id] = {} + ip_kv_dict[block_id][transformer_id] = { + "ip_k": ip_k, + "ip_v": ip_v, + "scale": scale + } + return ip_kv_dict + + @staticmethod + def state_dict_converter(): + return SDIpAdapterStateDictConverter() + + +class SDIpAdapterStateDictConverter(SDXLIpAdapterStateDictConverter): + def __init__(self): + pass diff --git a/diffsynth/models/sd_motion.py b/diffsynth/models/sd_motion.py new file mode 100644 index 0000000000000000000000000000000000000000..fb49138e147538537a60fb4a3e2d12a175da4a50 --- /dev/null +++ b/diffsynth/models/sd_motion.py @@ -0,0 +1,199 @@ +from .sd_unet import SDUNet, Attention, GEGLU +import torch +from einops import rearrange, repeat + + +class TemporalTransformerBlock(torch.nn.Module): + + def __init__(self, dim, num_attention_heads, attention_head_dim, max_position_embeddings=32): + super().__init__() + + # 1. Self-Attn + self.pe1 = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, dim)) + self.norm1 = torch.nn.LayerNorm(dim, elementwise_affine=True) + self.attn1 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True) + + # 2. Cross-Attn + self.pe2 = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, dim)) + self.norm2 = torch.nn.LayerNorm(dim, elementwise_affine=True) + self.attn2 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True) + + # 3. Feed-forward + self.norm3 = torch.nn.LayerNorm(dim, elementwise_affine=True) + self.act_fn = GEGLU(dim, dim * 4) + self.ff = torch.nn.Linear(dim * 4, dim) + + + def forward(self, hidden_states, batch_size=1): + + # 1. Self-Attention + norm_hidden_states = self.norm1(hidden_states) + norm_hidden_states = rearrange(norm_hidden_states, "(b f) h c -> (b h) f c", b=batch_size) + attn_output = self.attn1(norm_hidden_states + self.pe1[:, :norm_hidden_states.shape[1]]) + attn_output = rearrange(attn_output, "(b h) f c -> (b f) h c", b=batch_size) + hidden_states = attn_output + hidden_states + + # 2. Cross-Attention + norm_hidden_states = self.norm2(hidden_states) + norm_hidden_states = rearrange(norm_hidden_states, "(b f) h c -> (b h) f c", b=batch_size) + attn_output = self.attn2(norm_hidden_states + self.pe2[:, :norm_hidden_states.shape[1]]) + attn_output = rearrange(attn_output, "(b h) f c -> (b f) h c", b=batch_size) + hidden_states = attn_output + hidden_states + + # 3. Feed-forward + norm_hidden_states = self.norm3(hidden_states) + ff_output = self.act_fn(norm_hidden_states) + ff_output = self.ff(ff_output) + hidden_states = ff_output + hidden_states + + return hidden_states + + +class TemporalBlock(torch.nn.Module): + + def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5): + super().__init__() + inner_dim = num_attention_heads * attention_head_dim + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True) + self.proj_in = torch.nn.Linear(in_channels, inner_dim) + + self.transformer_blocks = torch.nn.ModuleList([ + TemporalTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim + ) + for d in range(num_layers) + ]) + + self.proj_out = torch.nn.Linear(inner_dim, in_channels) + + def forward(self, hidden_states, time_emb, text_emb, res_stack, batch_size=1): + batch, _, height, width = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) + hidden_states = self.proj_in(hidden_states) + + for block in self.transformer_blocks: + hidden_states = block( + hidden_states, + batch_size=batch_size + ) + + hidden_states = self.proj_out(hidden_states) + hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + hidden_states = hidden_states + residual + + return hidden_states, time_emb, text_emb, res_stack + + +class SDMotionModel(torch.nn.Module): + def __init__(self): + super().__init__() + self.motion_modules = torch.nn.ModuleList([ + TemporalBlock(8, 40, 320, eps=1e-6), + TemporalBlock(8, 40, 320, eps=1e-6), + TemporalBlock(8, 80, 640, eps=1e-6), + TemporalBlock(8, 80, 640, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 80, 640, eps=1e-6), + TemporalBlock(8, 80, 640, eps=1e-6), + TemporalBlock(8, 80, 640, eps=1e-6), + TemporalBlock(8, 40, 320, eps=1e-6), + TemporalBlock(8, 40, 320, eps=1e-6), + TemporalBlock(8, 40, 320, eps=1e-6), + ]) + self.call_block_id = { + 1: 0, + 4: 1, + 9: 2, + 12: 3, + 17: 4, + 20: 5, + 24: 6, + 26: 7, + 29: 8, + 32: 9, + 34: 10, + 36: 11, + 40: 12, + 43: 13, + 46: 14, + 50: 15, + 53: 16, + 56: 17, + 60: 18, + 63: 19, + 66: 20 + } + + def forward(self): + pass + + @staticmethod + def state_dict_converter(): + return SDMotionModelStateDictConverter() + + +class SDMotionModelStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + rename_dict = { + "norm": "norm", + "proj_in": "proj_in", + "transformer_blocks.0.attention_blocks.0.to_q": "transformer_blocks.0.attn1.to_q", + "transformer_blocks.0.attention_blocks.0.to_k": "transformer_blocks.0.attn1.to_k", + "transformer_blocks.0.attention_blocks.0.to_v": "transformer_blocks.0.attn1.to_v", + "transformer_blocks.0.attention_blocks.0.to_out.0": "transformer_blocks.0.attn1.to_out", + "transformer_blocks.0.attention_blocks.0.pos_encoder": "transformer_blocks.0.pe1", + "transformer_blocks.0.attention_blocks.1.to_q": "transformer_blocks.0.attn2.to_q", + "transformer_blocks.0.attention_blocks.1.to_k": "transformer_blocks.0.attn2.to_k", + "transformer_blocks.0.attention_blocks.1.to_v": "transformer_blocks.0.attn2.to_v", + "transformer_blocks.0.attention_blocks.1.to_out.0": "transformer_blocks.0.attn2.to_out", + "transformer_blocks.0.attention_blocks.1.pos_encoder": "transformer_blocks.0.pe2", + "transformer_blocks.0.norms.0": "transformer_blocks.0.norm1", + "transformer_blocks.0.norms.1": "transformer_blocks.0.norm2", + "transformer_blocks.0.ff.net.0.proj": "transformer_blocks.0.act_fn.proj", + "transformer_blocks.0.ff.net.2": "transformer_blocks.0.ff", + "transformer_blocks.0.ff_norm": "transformer_blocks.0.norm3", + "proj_out": "proj_out", + } + name_list = sorted([i for i in state_dict if i.startswith("down_blocks.")]) + name_list += sorted([i for i in state_dict if i.startswith("mid_block.")]) + name_list += sorted([i for i in state_dict if i.startswith("up_blocks.")]) + state_dict_ = {} + last_prefix, module_id = "", -1 + for name in name_list: + names = name.split(".") + prefix_index = names.index("temporal_transformer") + 1 + prefix = ".".join(names[:prefix_index]) + if prefix != last_prefix: + last_prefix = prefix + module_id += 1 + middle_name = ".".join(names[prefix_index:-1]) + suffix = names[-1] + if "pos_encoder" in names: + rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name]]) + else: + rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name], suffix]) + state_dict_[rename] = state_dict[name] + return state_dict_ + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict) diff --git a/diffsynth/models/sd_text_encoder.py b/diffsynth/models/sd_text_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..8fe8994a10bc998b8778cae2cbd57b95545166ba --- /dev/null +++ b/diffsynth/models/sd_text_encoder.py @@ -0,0 +1,321 @@ +import torch +from .attention import Attention + + +class CLIPEncoderLayer(torch.nn.Module): + def __init__(self, embed_dim, intermediate_size, num_heads=12, head_dim=64, use_quick_gelu=True): + super().__init__() + self.attn = Attention(q_dim=embed_dim, num_heads=num_heads, head_dim=head_dim, bias_q=True, bias_kv=True, bias_out=True) + self.layer_norm1 = torch.nn.LayerNorm(embed_dim) + self.layer_norm2 = torch.nn.LayerNorm(embed_dim) + self.fc1 = torch.nn.Linear(embed_dim, intermediate_size) + self.fc2 = torch.nn.Linear(intermediate_size, embed_dim) + + self.use_quick_gelu = use_quick_gelu + + def quickGELU(self, x): + return x * torch.sigmoid(1.702 * x) + + def forward(self, hidden_states, attn_mask=None): + residual = hidden_states + + hidden_states = self.layer_norm1(hidden_states) + hidden_states = self.attn(hidden_states, attn_mask=attn_mask) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.fc1(hidden_states) + if self.use_quick_gelu: + hidden_states = self.quickGELU(hidden_states) + else: + hidden_states = torch.nn.functional.gelu(hidden_states) + hidden_states = self.fc2(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +class SDTextEncoder(torch.nn.Module): + def __init__(self, embed_dim=768, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=12, encoder_intermediate_size=3072): + super().__init__() + + # token_embedding + self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim) + + # position_embeds (This is a fixed tensor) + self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim)) + + # encoders + self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size) for _ in range(num_encoder_layers)]) + + # attn_mask + self.attn_mask = self.attention_mask(max_position_embeddings) + + # final_layer_norm + self.final_layer_norm = torch.nn.LayerNorm(embed_dim) + + def attention_mask(self, length): + mask = torch.empty(length, length) + mask.fill_(float("-inf")) + mask.triu_(1) + return mask + + def forward(self, input_ids, clip_skip=1): + embeds = self.token_embedding(input_ids) + self.position_embeds + attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype) + for encoder_id, encoder in enumerate(self.encoders): + embeds = encoder(embeds, attn_mask=attn_mask) + if encoder_id + clip_skip == len(self.encoders): + break + embeds = self.final_layer_norm(embeds) + return embeds + + @staticmethod + def state_dict_converter(): + return SDTextEncoderStateDictConverter() + + +class SDTextEncoderStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + rename_dict = { + "text_model.embeddings.token_embedding.weight": "token_embedding.weight", + "text_model.embeddings.position_embedding.weight": "position_embeds", + "text_model.final_layer_norm.weight": "final_layer_norm.weight", + "text_model.final_layer_norm.bias": "final_layer_norm.bias" + } + attn_rename_dict = { + "self_attn.q_proj": "attn.to_q", + "self_attn.k_proj": "attn.to_k", + "self_attn.v_proj": "attn.to_v", + "self_attn.out_proj": "attn.to_out", + "layer_norm1": "layer_norm1", + "layer_norm2": "layer_norm2", + "mlp.fc1": "fc1", + "mlp.fc2": "fc2", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "text_model.embeddings.position_embedding.weight": + param = param.reshape((1, param.shape[0], param.shape[1])) + state_dict_[rename_dict[name]] = param + elif name.startswith("text_model.encoder.layers."): + param = state_dict[name] + names = name.split(".") + layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1] + name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail]) + state_dict_[name_] = param + return state_dict_ + + def from_civitai(self, state_dict): + rename_dict = { + "cond_stage_model.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight", + "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias", + "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight", + "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias", + "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm2.weight": "encoders.0.layer_norm2.weight", + "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc1.bias": "encoders.0.fc1.bias", + "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc1.weight": "encoders.0.fc1.weight", + "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc2.bias": "encoders.0.fc2.bias", + "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc2.weight": "encoders.0.fc2.weight", + "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.k_proj.bias": "encoders.0.attn.to_k.bias", + "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight": "encoders.0.attn.to_k.weight", + "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.out_proj.bias": "encoders.0.attn.to_out.bias", + "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.out_proj.weight": "encoders.0.attn.to_out.weight", + "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.q_proj.bias": "encoders.0.attn.to_q.bias", + "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.q_proj.weight": "encoders.0.attn.to_q.weight", + "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.v_proj.bias": "encoders.0.attn.to_v.bias", + "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.v_proj.weight": "encoders.0.attn.to_v.weight", + "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm1.bias": "encoders.1.layer_norm1.bias", + "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm1.weight": "encoders.1.layer_norm1.weight", + "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm2.bias": "encoders.1.layer_norm2.bias", + "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm2.weight": "encoders.1.layer_norm2.weight", + "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc1.bias": "encoders.1.fc1.bias", + "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc1.weight": "encoders.1.fc1.weight", + "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc2.bias": "encoders.1.fc2.bias", + "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc2.weight": "encoders.1.fc2.weight", + "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.k_proj.bias": "encoders.1.attn.to_k.bias", + "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.k_proj.weight": "encoders.1.attn.to_k.weight", + "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.out_proj.bias": "encoders.1.attn.to_out.bias", + "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.out_proj.weight": "encoders.1.attn.to_out.weight", + "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.q_proj.bias": "encoders.1.attn.to_q.bias", + "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.q_proj.weight": 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state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "cond_stage_model.transformer.text_model.embeddings.position_embedding.weight": + param = param.reshape((1, param.shape[0], param.shape[1])) + state_dict_[rename_dict[name]] = param + return state_dict_ diff --git a/diffsynth/models/sd_unet.py b/diffsynth/models/sd_unet.py new file mode 100644 index 0000000000000000000000000000000000000000..33363909e4968292fe22e1953dc0c4a12e41d921 --- /dev/null +++ b/diffsynth/models/sd_unet.py @@ -0,0 +1,1108 @@ +import torch, math +from .attention import Attention +from .tiler import TileWorker + + +class Timesteps(torch.nn.Module): + def __init__(self, num_channels): + super().__init__() + self.num_channels = num_channels + + def forward(self, timesteps): + half_dim = self.num_channels // 2 + exponent = -math.log(10000) * torch.arange(start=0, end=half_dim, dtype=torch.float32, device=timesteps.device) / half_dim + timesteps = timesteps.unsqueeze(-1) + emb = timesteps.float() * torch.exp(exponent) + emb = torch.cat([torch.cos(emb), torch.sin(emb)], dim=-1) + return emb + + +class GEGLU(torch.nn.Module): + + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = torch.nn.Linear(dim_in, dim_out * 2) + + def forward(self, hidden_states): + hidden_states, gate = self.proj(hidden_states).chunk(2, dim=-1) + return hidden_states * torch.nn.functional.gelu(gate) + + +class BasicTransformerBlock(torch.nn.Module): + + def __init__(self, dim, num_attention_heads, attention_head_dim, cross_attention_dim): + super().__init__() + + # 1. Self-Attn + self.norm1 = torch.nn.LayerNorm(dim, elementwise_affine=True) + self.attn1 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True) + + # 2. Cross-Attn + self.norm2 = torch.nn.LayerNorm(dim, elementwise_affine=True) + self.attn2 = Attention(q_dim=dim, kv_dim=cross_attention_dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True) + + # 3. Feed-forward + self.norm3 = torch.nn.LayerNorm(dim, elementwise_affine=True) + self.act_fn = GEGLU(dim, dim * 4) + self.ff = torch.nn.Linear(dim * 4, dim) + + + def forward(self, hidden_states, encoder_hidden_states, ipadapter_kwargs=None): + # 1. Self-Attention + norm_hidden_states = self.norm1(hidden_states) + attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None) + hidden_states = attn_output + hidden_states + + # 2. Cross-Attention + norm_hidden_states = self.norm2(hidden_states) + attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=encoder_hidden_states, ipadapter_kwargs=ipadapter_kwargs) + hidden_states = attn_output + hidden_states + + # 3. Feed-forward + norm_hidden_states = self.norm3(hidden_states) + ff_output = self.act_fn(norm_hidden_states) + ff_output = self.ff(ff_output) + hidden_states = ff_output + hidden_states + + return hidden_states + + +class DownSampler(torch.nn.Module): + def __init__(self, channels, padding=1, extra_padding=False): + super().__init__() + self.conv = torch.nn.Conv2d(channels, channels, 3, stride=2, padding=padding) + self.extra_padding = extra_padding + + def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): + if self.extra_padding: + hidden_states = torch.nn.functional.pad(hidden_states, (0, 1, 0, 1), mode="constant", value=0) + hidden_states = self.conv(hidden_states) + return hidden_states, time_emb, text_emb, res_stack + + +class UpSampler(torch.nn.Module): + def __init__(self, channels): + super().__init__() + self.conv = torch.nn.Conv2d(channels, channels, 3, padding=1) + + def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): + hidden_states = torch.nn.functional.interpolate(hidden_states, scale_factor=2.0, mode="nearest") + hidden_states = self.conv(hidden_states) + return hidden_states, time_emb, text_emb, res_stack + + +class ResnetBlock(torch.nn.Module): + def __init__(self, in_channels, out_channels, temb_channels=None, groups=32, eps=1e-5): + super().__init__() + self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) + self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + if temb_channels is not None: + self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels) + self.norm2 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True) + self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + self.nonlinearity = torch.nn.SiLU() + self.conv_shortcut = None + if in_channels != out_channels: + self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=True) + + def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): + x = hidden_states + x = self.norm1(x) + x = self.nonlinearity(x) + x = self.conv1(x) + if time_emb is not None: + emb = self.nonlinearity(time_emb) + emb = self.time_emb_proj(emb)[:, :, None, None] + x = x + emb + x = self.norm2(x) + x = self.nonlinearity(x) + x = self.conv2(x) + if self.conv_shortcut is not None: + hidden_states = self.conv_shortcut(hidden_states) + hidden_states = hidden_states + x + return hidden_states, time_emb, text_emb, res_stack + + +class AttentionBlock(torch.nn.Module): + + def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, cross_attention_dim=None, norm_num_groups=32, eps=1e-5, need_proj_out=True): + super().__init__() + inner_dim = num_attention_heads * attention_head_dim + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True) + self.proj_in = torch.nn.Linear(in_channels, inner_dim) + + self.transformer_blocks = torch.nn.ModuleList([ + BasicTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim, + cross_attention_dim=cross_attention_dim + ) + for d in range(num_layers) + ]) + self.need_proj_out = need_proj_out + if need_proj_out: + self.proj_out = torch.nn.Linear(inner_dim, in_channels) + + def forward( + self, + hidden_states, time_emb, text_emb, res_stack, + cross_frame_attention=False, + tiled=False, tile_size=64, tile_stride=32, + ipadapter_kwargs_list={}, + **kwargs + ): + batch, _, height, width = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) + hidden_states = self.proj_in(hidden_states) + + if cross_frame_attention: + hidden_states = hidden_states.reshape(1, batch * height * width, inner_dim) + encoder_hidden_states = text_emb.mean(dim=0, keepdim=True) + else: + encoder_hidden_states = text_emb + if encoder_hidden_states.shape[0] != hidden_states.shape[0]: + encoder_hidden_states = encoder_hidden_states.repeat(hidden_states.shape[0], 1, 1) + + if tiled: + tile_size = min(tile_size, min(height, width)) + hidden_states = hidden_states.permute(0, 2, 1).reshape(batch, inner_dim, height, width) + def block_tile_forward(x): + b, c, h, w = x.shape + x = x.permute(0, 2, 3, 1).reshape(b, h*w, c) + x = block(x, encoder_hidden_states) + x = x.reshape(b, h, w, c).permute(0, 3, 1, 2) + return x + for block in self.transformer_blocks: + hidden_states = TileWorker().tiled_forward( + block_tile_forward, + hidden_states, + tile_size, + tile_stride, + tile_device=hidden_states.device, + tile_dtype=hidden_states.dtype + ) + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) + else: + for block_id, block in enumerate(self.transformer_blocks): + hidden_states = block( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + ipadapter_kwargs=ipadapter_kwargs_list.get(block_id, None) + ) + if cross_frame_attention: + hidden_states = hidden_states.reshape(batch, height * width, inner_dim) + + if self.need_proj_out: + hidden_states = self.proj_out(hidden_states) + hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + hidden_states = hidden_states + residual + else: + hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + + return hidden_states, time_emb, text_emb, res_stack + + +class PushBlock(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): + res_stack.append(hidden_states) + return hidden_states, time_emb, text_emb, res_stack + + +class PopBlock(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): + res_hidden_states = res_stack.pop() + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + return hidden_states, time_emb, text_emb, res_stack + + +class SDUNet(torch.nn.Module): + def __init__(self): + super().__init__() + self.time_proj = Timesteps(320) + self.time_embedding = torch.nn.Sequential( + torch.nn.Linear(320, 1280), + torch.nn.SiLU(), + torch.nn.Linear(1280, 1280) + ) + self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1) + + self.blocks = torch.nn.ModuleList([ + # CrossAttnDownBlock2D + ResnetBlock(320, 320, 1280), + AttentionBlock(8, 40, 320, 1, 768, eps=1e-6), + PushBlock(), + ResnetBlock(320, 320, 1280), + AttentionBlock(8, 40, 320, 1, 768, eps=1e-6), + PushBlock(), + DownSampler(320), + PushBlock(), + # CrossAttnDownBlock2D + ResnetBlock(320, 640, 1280), + AttentionBlock(8, 80, 640, 1, 768, eps=1e-6), + PushBlock(), + ResnetBlock(640, 640, 1280), + AttentionBlock(8, 80, 640, 1, 768, eps=1e-6), + PushBlock(), + DownSampler(640), + PushBlock(), + # CrossAttnDownBlock2D + ResnetBlock(640, 1280, 1280), + AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), + PushBlock(), + ResnetBlock(1280, 1280, 1280), + AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), + PushBlock(), + DownSampler(1280), + PushBlock(), + # DownBlock2D + ResnetBlock(1280, 1280, 1280), + PushBlock(), + ResnetBlock(1280, 1280, 1280), + PushBlock(), + # UNetMidBlock2DCrossAttn + ResnetBlock(1280, 1280, 1280), + AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), + ResnetBlock(1280, 1280, 1280), + # UpBlock2D + PopBlock(), + ResnetBlock(2560, 1280, 1280), + PopBlock(), + ResnetBlock(2560, 1280, 1280), + PopBlock(), + ResnetBlock(2560, 1280, 1280), + UpSampler(1280), + # CrossAttnUpBlock2D + PopBlock(), + ResnetBlock(2560, 1280, 1280), + AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), + PopBlock(), + ResnetBlock(2560, 1280, 1280), + AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), + PopBlock(), + ResnetBlock(1920, 1280, 1280), + AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), + UpSampler(1280), + # CrossAttnUpBlock2D + PopBlock(), + ResnetBlock(1920, 640, 1280), + AttentionBlock(8, 80, 640, 1, 768, eps=1e-6), + PopBlock(), + ResnetBlock(1280, 640, 1280), + AttentionBlock(8, 80, 640, 1, 768, eps=1e-6), + PopBlock(), + ResnetBlock(960, 640, 1280), + AttentionBlock(8, 80, 640, 1, 768, eps=1e-6), + UpSampler(640), + # CrossAttnUpBlock2D + PopBlock(), + ResnetBlock(960, 320, 1280), + AttentionBlock(8, 40, 320, 1, 768, eps=1e-6), + PopBlock(), + ResnetBlock(640, 320, 1280), + AttentionBlock(8, 40, 320, 1, 768, eps=1e-6), + PopBlock(), + ResnetBlock(640, 320, 1280), + AttentionBlock(8, 40, 320, 1, 768, eps=1e-6), + ]) + + self.conv_norm_out = torch.nn.GroupNorm(num_channels=320, num_groups=32, eps=1e-5) + self.conv_act = torch.nn.SiLU() + self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=3, padding=1) + + def forward(self, sample, timestep, encoder_hidden_states, **kwargs): + # 1. time + time_emb = self.time_proj(timestep).to(sample.dtype) + time_emb = self.time_embedding(time_emb) + + # 2. pre-process + hidden_states = self.conv_in(sample) + text_emb = encoder_hidden_states + res_stack = [hidden_states] + + # 3. blocks + for i, block in enumerate(self.blocks): + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + + # 4. output + hidden_states = self.conv_norm_out(hidden_states) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + + return hidden_states + + @staticmethod + def state_dict_converter(): + return SDUNetStateDictConverter() + + +class SDUNetStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + # architecture + block_types = [ + 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', + 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', + 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', + 'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', + 'ResnetBlock', 'AttentionBlock', 'ResnetBlock', + 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'UpSampler', + 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', + 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', + 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock' + ] + + # Rename each parameter + name_list = sorted([name for name in state_dict]) + rename_dict = {} + block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1} + last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""} + for name in name_list: + names = name.split(".") + if names[0] in ["conv_in", "conv_norm_out", "conv_out"]: + pass + elif names[0] in ["time_embedding", "add_embedding"]: + if names[0] == "add_embedding": + names[0] = "add_time_embedding" + names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]] + elif names[0] in ["down_blocks", "mid_block", "up_blocks"]: + if names[0] == "mid_block": + names.insert(1, "0") + block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]] + block_type_with_id = ".".join(names[:4]) + if block_type_with_id != last_block_type_with_id[block_type]: + block_id[block_type] += 1 + last_block_type_with_id[block_type] = block_type_with_id + while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: + block_id[block_type] += 1 + block_type_with_id = ".".join(names[:4]) + names = ["blocks", str(block_id[block_type])] + names[4:] + if "ff" in names: + ff_index = names.index("ff") + component = ".".join(names[ff_index:ff_index+3]) + component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component] + names = names[:ff_index] + [component] + names[ff_index+3:] + if "to_out" in names: + names.pop(names.index("to_out") + 1) + else: + raise ValueError(f"Unknown parameters: {name}") + rename_dict[name] = ".".join(names) + + # Convert state_dict + state_dict_ = {} + for name, param in state_dict.items(): + if ".proj_in." in name or ".proj_out." in name: + param = param.squeeze() + state_dict_[rename_dict[name]] = param + return state_dict_ + + def from_civitai(self, state_dict): + rename_dict = { + "model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias", + "model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight", + "model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": 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"model.diffusion_model.input_blocks.1.1.proj_in.bias": "blocks.1.proj_in.bias", + "model.diffusion_model.input_blocks.1.1.proj_in.weight": "blocks.1.proj_in.weight", + "model.diffusion_model.input_blocks.1.1.proj_out.bias": "blocks.1.proj_out.bias", + "model.diffusion_model.input_blocks.1.1.proj_out.weight": "blocks.1.proj_out.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_k.weight": "blocks.1.transformer_blocks.0.attn1.to_k.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.1.transformer_blocks.0.attn1.to_out.bias", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.1.transformer_blocks.0.attn1.to_out.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_q.weight": "blocks.1.transformer_blocks.0.attn1.to_q.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_v.weight": 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+ "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.bias": "blocks.1.transformer_blocks.0.ff.bias", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.weight": "blocks.1.transformer_blocks.0.ff.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1.bias": "blocks.1.transformer_blocks.0.norm1.bias", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1.weight": "blocks.1.transformer_blocks.0.norm1.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2.bias": "blocks.1.transformer_blocks.0.norm2.bias", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2.weight": "blocks.1.transformer_blocks.0.norm2.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.bias": "blocks.1.transformer_blocks.0.norm3.bias", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.weight": "blocks.1.transformer_blocks.0.norm3.weight", + 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param.squeeze() + state_dict_[rename_dict[name]] = param + return state_dict_ \ No newline at end of file diff --git a/diffsynth/models/sd_vae_decoder.py b/diffsynth/models/sd_vae_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..93f015a63ededec53507ea73d34e2d904f5bed06 --- /dev/null +++ b/diffsynth/models/sd_vae_decoder.py @@ -0,0 +1,336 @@ +import torch +from .attention import Attention +from .sd_unet import ResnetBlock, UpSampler +from .tiler import TileWorker + + +class VAEAttentionBlock(torch.nn.Module): + + def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5): + super().__init__() + inner_dim = num_attention_heads * attention_head_dim + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True) + + self.transformer_blocks = torch.nn.ModuleList([ + Attention( + inner_dim, + num_attention_heads, + attention_head_dim, + bias_q=True, + bias_kv=True, + bias_out=True + ) + for d in range(num_layers) + ]) + + def forward(self, hidden_states, time_emb, text_emb, res_stack): + batch, _, height, width = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) + + for block in self.transformer_blocks: + hidden_states = block(hidden_states) + + hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + hidden_states = hidden_states + residual + + return hidden_states, time_emb, text_emb, res_stack + + +class SDVAEDecoder(torch.nn.Module): + def __init__(self): + super().__init__() + self.scaling_factor = 0.18215 + self.post_quant_conv = torch.nn.Conv2d(4, 4, kernel_size=1) + self.conv_in = torch.nn.Conv2d(4, 512, kernel_size=3, padding=1) + + self.blocks = torch.nn.ModuleList([ + # UNetMidBlock2D + ResnetBlock(512, 512, eps=1e-6), + VAEAttentionBlock(1, 512, 512, 1, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + # UpDecoderBlock2D + ResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + UpSampler(512), + # UpDecoderBlock2D + ResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + UpSampler(512), + # UpDecoderBlock2D + ResnetBlock(512, 256, eps=1e-6), + ResnetBlock(256, 256, eps=1e-6), + ResnetBlock(256, 256, eps=1e-6), + UpSampler(256), + # UpDecoderBlock2D + ResnetBlock(256, 128, eps=1e-6), + ResnetBlock(128, 128, eps=1e-6), + ResnetBlock(128, 128, eps=1e-6), + ]) + + self.conv_norm_out = torch.nn.GroupNorm(num_channels=128, num_groups=32, eps=1e-5) + self.conv_act = torch.nn.SiLU() + self.conv_out = torch.nn.Conv2d(128, 3, kernel_size=3, padding=1) + + def tiled_forward(self, sample, tile_size=64, tile_stride=32): + hidden_states = TileWorker().tiled_forward( + lambda x: self.forward(x), + sample, + tile_size, + tile_stride, + tile_device=sample.device, + tile_dtype=sample.dtype + ) + return hidden_states + + def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs): + original_dtype = sample.dtype + sample = sample.to(dtype=next(iter(self.parameters())).dtype) + # For VAE Decoder, we do not need to apply the tiler on each layer. + if tiled: + return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride) + + # 1. pre-process + sample = sample / self.scaling_factor + hidden_states = self.post_quant_conv(sample) + hidden_states = self.conv_in(hidden_states) + time_emb = None + text_emb = None + res_stack = None + + # 2. blocks + for i, block in enumerate(self.blocks): + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + + # 3. output + hidden_states = self.conv_norm_out(hidden_states) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + hidden_states = hidden_states.to(original_dtype) + + return hidden_states + + @staticmethod + def state_dict_converter(): + return SDVAEDecoderStateDictConverter() + + +class SDVAEDecoderStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + # architecture + block_types = [ + 'ResnetBlock', 'VAEAttentionBlock', 'ResnetBlock', + 'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler', + 'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler', + 'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler', + 'ResnetBlock', 'ResnetBlock', 'ResnetBlock' + ] + + # Rename each parameter + local_rename_dict = { + "post_quant_conv": "post_quant_conv", + "decoder.conv_in": "conv_in", + "decoder.mid_block.attentions.0.group_norm": "blocks.1.norm", + "decoder.mid_block.attentions.0.to_q": "blocks.1.transformer_blocks.0.to_q", + "decoder.mid_block.attentions.0.to_k": "blocks.1.transformer_blocks.0.to_k", + "decoder.mid_block.attentions.0.to_v": "blocks.1.transformer_blocks.0.to_v", + "decoder.mid_block.attentions.0.to_out.0": "blocks.1.transformer_blocks.0.to_out", + "decoder.mid_block.resnets.0.norm1": "blocks.0.norm1", + "decoder.mid_block.resnets.0.conv1": "blocks.0.conv1", + "decoder.mid_block.resnets.0.norm2": "blocks.0.norm2", + "decoder.mid_block.resnets.0.conv2": "blocks.0.conv2", + "decoder.mid_block.resnets.1.norm1": "blocks.2.norm1", + "decoder.mid_block.resnets.1.conv1": "blocks.2.conv1", + "decoder.mid_block.resnets.1.norm2": "blocks.2.norm2", + "decoder.mid_block.resnets.1.conv2": "blocks.2.conv2", + "decoder.conv_norm_out": "conv_norm_out", + "decoder.conv_out": "conv_out", + } + name_list = sorted([name for name in state_dict]) + rename_dict = {} + block_id = {"ResnetBlock": 2, "DownSampler": 2, "UpSampler": 2} + last_block_type_with_id = {"ResnetBlock": "", "DownSampler": "", "UpSampler": ""} + for name in name_list: + names = name.split(".") + name_prefix = ".".join(names[:-1]) + if name_prefix in local_rename_dict: + rename_dict[name] = local_rename_dict[name_prefix] + "." + names[-1] + elif name.startswith("decoder.up_blocks"): + block_type = {"resnets": "ResnetBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[3]] + block_type_with_id = ".".join(names[:5]) + if block_type_with_id != last_block_type_with_id[block_type]: + block_id[block_type] += 1 + last_block_type_with_id[block_type] = block_type_with_id + while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: + block_id[block_type] += 1 + block_type_with_id = ".".join(names[:5]) + names = ["blocks", str(block_id[block_type])] + names[5:] + rename_dict[name] = ".".join(names) + + # Convert state_dict + state_dict_ = {} + for name, param in state_dict.items(): + if name in rename_dict: + state_dict_[rename_dict[name]] = param + return state_dict_ + + def from_civitai(self, state_dict): + rename_dict = { + "first_stage_model.decoder.conv_in.bias": "conv_in.bias", + "first_stage_model.decoder.conv_in.weight": "conv_in.weight", + "first_stage_model.decoder.conv_out.bias": "conv_out.bias", + "first_stage_model.decoder.conv_out.weight": "conv_out.weight", + "first_stage_model.decoder.mid.attn_1.k.bias": "blocks.1.transformer_blocks.0.to_k.bias", + "first_stage_model.decoder.mid.attn_1.k.weight": "blocks.1.transformer_blocks.0.to_k.weight", + "first_stage_model.decoder.mid.attn_1.norm.bias": "blocks.1.norm.bias", + "first_stage_model.decoder.mid.attn_1.norm.weight": "blocks.1.norm.weight", + "first_stage_model.decoder.mid.attn_1.proj_out.bias": "blocks.1.transformer_blocks.0.to_out.bias", + "first_stage_model.decoder.mid.attn_1.proj_out.weight": "blocks.1.transformer_blocks.0.to_out.weight", + "first_stage_model.decoder.mid.attn_1.q.bias": "blocks.1.transformer_blocks.0.to_q.bias", + "first_stage_model.decoder.mid.attn_1.q.weight": "blocks.1.transformer_blocks.0.to_q.weight", + "first_stage_model.decoder.mid.attn_1.v.bias": "blocks.1.transformer_blocks.0.to_v.bias", + "first_stage_model.decoder.mid.attn_1.v.weight": "blocks.1.transformer_blocks.0.to_v.weight", + "first_stage_model.decoder.mid.block_1.conv1.bias": "blocks.0.conv1.bias", + "first_stage_model.decoder.mid.block_1.conv1.weight": "blocks.0.conv1.weight", + "first_stage_model.decoder.mid.block_1.conv2.bias": "blocks.0.conv2.bias", + "first_stage_model.decoder.mid.block_1.conv2.weight": "blocks.0.conv2.weight", + "first_stage_model.decoder.mid.block_1.norm1.bias": "blocks.0.norm1.bias", + "first_stage_model.decoder.mid.block_1.norm1.weight": "blocks.0.norm1.weight", + "first_stage_model.decoder.mid.block_1.norm2.bias": "blocks.0.norm2.bias", + "first_stage_model.decoder.mid.block_1.norm2.weight": "blocks.0.norm2.weight", + "first_stage_model.decoder.mid.block_2.conv1.bias": "blocks.2.conv1.bias", + "first_stage_model.decoder.mid.block_2.conv1.weight": "blocks.2.conv1.weight", + "first_stage_model.decoder.mid.block_2.conv2.bias": "blocks.2.conv2.bias", + "first_stage_model.decoder.mid.block_2.conv2.weight": "blocks.2.conv2.weight", + "first_stage_model.decoder.mid.block_2.norm1.bias": "blocks.2.norm1.bias", + "first_stage_model.decoder.mid.block_2.norm1.weight": "blocks.2.norm1.weight", + "first_stage_model.decoder.mid.block_2.norm2.bias": "blocks.2.norm2.bias", + "first_stage_model.decoder.mid.block_2.norm2.weight": "blocks.2.norm2.weight", + "first_stage_model.decoder.norm_out.bias": "conv_norm_out.bias", + "first_stage_model.decoder.norm_out.weight": "conv_norm_out.weight", + "first_stage_model.decoder.up.0.block.0.conv1.bias": "blocks.15.conv1.bias", + "first_stage_model.decoder.up.0.block.0.conv1.weight": "blocks.15.conv1.weight", + "first_stage_model.decoder.up.0.block.0.conv2.bias": "blocks.15.conv2.bias", + "first_stage_model.decoder.up.0.block.0.conv2.weight": "blocks.15.conv2.weight", + "first_stage_model.decoder.up.0.block.0.nin_shortcut.bias": "blocks.15.conv_shortcut.bias", + "first_stage_model.decoder.up.0.block.0.nin_shortcut.weight": "blocks.15.conv_shortcut.weight", + "first_stage_model.decoder.up.0.block.0.norm1.bias": "blocks.15.norm1.bias", + "first_stage_model.decoder.up.0.block.0.norm1.weight": "blocks.15.norm1.weight", + "first_stage_model.decoder.up.0.block.0.norm2.bias": "blocks.15.norm2.bias", + "first_stage_model.decoder.up.0.block.0.norm2.weight": "blocks.15.norm2.weight", + "first_stage_model.decoder.up.0.block.1.conv1.bias": "blocks.16.conv1.bias", + "first_stage_model.decoder.up.0.block.1.conv1.weight": "blocks.16.conv1.weight", + "first_stage_model.decoder.up.0.block.1.conv2.bias": "blocks.16.conv2.bias", + "first_stage_model.decoder.up.0.block.1.conv2.weight": "blocks.16.conv2.weight", + "first_stage_model.decoder.up.0.block.1.norm1.bias": "blocks.16.norm1.bias", + "first_stage_model.decoder.up.0.block.1.norm1.weight": "blocks.16.norm1.weight", + "first_stage_model.decoder.up.0.block.1.norm2.bias": "blocks.16.norm2.bias", + "first_stage_model.decoder.up.0.block.1.norm2.weight": "blocks.16.norm2.weight", + "first_stage_model.decoder.up.0.block.2.conv1.bias": "blocks.17.conv1.bias", + "first_stage_model.decoder.up.0.block.2.conv1.weight": "blocks.17.conv1.weight", + "first_stage_model.decoder.up.0.block.2.conv2.bias": "blocks.17.conv2.bias", + "first_stage_model.decoder.up.0.block.2.conv2.weight": "blocks.17.conv2.weight", + "first_stage_model.decoder.up.0.block.2.norm1.bias": "blocks.17.norm1.bias", + "first_stage_model.decoder.up.0.block.2.norm1.weight": "blocks.17.norm1.weight", + "first_stage_model.decoder.up.0.block.2.norm2.bias": "blocks.17.norm2.bias", + "first_stage_model.decoder.up.0.block.2.norm2.weight": "blocks.17.norm2.weight", + "first_stage_model.decoder.up.1.block.0.conv1.bias": "blocks.11.conv1.bias", + "first_stage_model.decoder.up.1.block.0.conv1.weight": "blocks.11.conv1.weight", + "first_stage_model.decoder.up.1.block.0.conv2.bias": "blocks.11.conv2.bias", + "first_stage_model.decoder.up.1.block.0.conv2.weight": "blocks.11.conv2.weight", + "first_stage_model.decoder.up.1.block.0.nin_shortcut.bias": "blocks.11.conv_shortcut.bias", + "first_stage_model.decoder.up.1.block.0.nin_shortcut.weight": "blocks.11.conv_shortcut.weight", + "first_stage_model.decoder.up.1.block.0.norm1.bias": "blocks.11.norm1.bias", + "first_stage_model.decoder.up.1.block.0.norm1.weight": "blocks.11.norm1.weight", + "first_stage_model.decoder.up.1.block.0.norm2.bias": "blocks.11.norm2.bias", + "first_stage_model.decoder.up.1.block.0.norm2.weight": "blocks.11.norm2.weight", + "first_stage_model.decoder.up.1.block.1.conv1.bias": "blocks.12.conv1.bias", + "first_stage_model.decoder.up.1.block.1.conv1.weight": "blocks.12.conv1.weight", + "first_stage_model.decoder.up.1.block.1.conv2.bias": "blocks.12.conv2.bias", + "first_stage_model.decoder.up.1.block.1.conv2.weight": "blocks.12.conv2.weight", + "first_stage_model.decoder.up.1.block.1.norm1.bias": "blocks.12.norm1.bias", + "first_stage_model.decoder.up.1.block.1.norm1.weight": "blocks.12.norm1.weight", + "first_stage_model.decoder.up.1.block.1.norm2.bias": "blocks.12.norm2.bias", + "first_stage_model.decoder.up.1.block.1.norm2.weight": "blocks.12.norm2.weight", + "first_stage_model.decoder.up.1.block.2.conv1.bias": "blocks.13.conv1.bias", + "first_stage_model.decoder.up.1.block.2.conv1.weight": "blocks.13.conv1.weight", + "first_stage_model.decoder.up.1.block.2.conv2.bias": "blocks.13.conv2.bias", + "first_stage_model.decoder.up.1.block.2.conv2.weight": "blocks.13.conv2.weight", + "first_stage_model.decoder.up.1.block.2.norm1.bias": "blocks.13.norm1.bias", + "first_stage_model.decoder.up.1.block.2.norm1.weight": "blocks.13.norm1.weight", + "first_stage_model.decoder.up.1.block.2.norm2.bias": "blocks.13.norm2.bias", + "first_stage_model.decoder.up.1.block.2.norm2.weight": "blocks.13.norm2.weight", + "first_stage_model.decoder.up.1.upsample.conv.bias": "blocks.14.conv.bias", + "first_stage_model.decoder.up.1.upsample.conv.weight": "blocks.14.conv.weight", + "first_stage_model.decoder.up.2.block.0.conv1.bias": "blocks.7.conv1.bias", + "first_stage_model.decoder.up.2.block.0.conv1.weight": "blocks.7.conv1.weight", + "first_stage_model.decoder.up.2.block.0.conv2.bias": "blocks.7.conv2.bias", + "first_stage_model.decoder.up.2.block.0.conv2.weight": "blocks.7.conv2.weight", + "first_stage_model.decoder.up.2.block.0.norm1.bias": "blocks.7.norm1.bias", + "first_stage_model.decoder.up.2.block.0.norm1.weight": "blocks.7.norm1.weight", + "first_stage_model.decoder.up.2.block.0.norm2.bias": "blocks.7.norm2.bias", + "first_stage_model.decoder.up.2.block.0.norm2.weight": "blocks.7.norm2.weight", + "first_stage_model.decoder.up.2.block.1.conv1.bias": "blocks.8.conv1.bias", + "first_stage_model.decoder.up.2.block.1.conv1.weight": "blocks.8.conv1.weight", + "first_stage_model.decoder.up.2.block.1.conv2.bias": "blocks.8.conv2.bias", + "first_stage_model.decoder.up.2.block.1.conv2.weight": "blocks.8.conv2.weight", + "first_stage_model.decoder.up.2.block.1.norm1.bias": "blocks.8.norm1.bias", + "first_stage_model.decoder.up.2.block.1.norm1.weight": "blocks.8.norm1.weight", + "first_stage_model.decoder.up.2.block.1.norm2.bias": "blocks.8.norm2.bias", + "first_stage_model.decoder.up.2.block.1.norm2.weight": "blocks.8.norm2.weight", + "first_stage_model.decoder.up.2.block.2.conv1.bias": "blocks.9.conv1.bias", + "first_stage_model.decoder.up.2.block.2.conv1.weight": "blocks.9.conv1.weight", + "first_stage_model.decoder.up.2.block.2.conv2.bias": "blocks.9.conv2.bias", + "first_stage_model.decoder.up.2.block.2.conv2.weight": "blocks.9.conv2.weight", + "first_stage_model.decoder.up.2.block.2.norm1.bias": "blocks.9.norm1.bias", + "first_stage_model.decoder.up.2.block.2.norm1.weight": "blocks.9.norm1.weight", + "first_stage_model.decoder.up.2.block.2.norm2.bias": "blocks.9.norm2.bias", + "first_stage_model.decoder.up.2.block.2.norm2.weight": "blocks.9.norm2.weight", + "first_stage_model.decoder.up.2.upsample.conv.bias": "blocks.10.conv.bias", + "first_stage_model.decoder.up.2.upsample.conv.weight": "blocks.10.conv.weight", + "first_stage_model.decoder.up.3.block.0.conv1.bias": "blocks.3.conv1.bias", + "first_stage_model.decoder.up.3.block.0.conv1.weight": "blocks.3.conv1.weight", + "first_stage_model.decoder.up.3.block.0.conv2.bias": "blocks.3.conv2.bias", + "first_stage_model.decoder.up.3.block.0.conv2.weight": "blocks.3.conv2.weight", + "first_stage_model.decoder.up.3.block.0.norm1.bias": "blocks.3.norm1.bias", + "first_stage_model.decoder.up.3.block.0.norm1.weight": "blocks.3.norm1.weight", + "first_stage_model.decoder.up.3.block.0.norm2.bias": "blocks.3.norm2.bias", + "first_stage_model.decoder.up.3.block.0.norm2.weight": "blocks.3.norm2.weight", + "first_stage_model.decoder.up.3.block.1.conv1.bias": "blocks.4.conv1.bias", + "first_stage_model.decoder.up.3.block.1.conv1.weight": "blocks.4.conv1.weight", + "first_stage_model.decoder.up.3.block.1.conv2.bias": "blocks.4.conv2.bias", + "first_stage_model.decoder.up.3.block.1.conv2.weight": "blocks.4.conv2.weight", + "first_stage_model.decoder.up.3.block.1.norm1.bias": "blocks.4.norm1.bias", + "first_stage_model.decoder.up.3.block.1.norm1.weight": "blocks.4.norm1.weight", + "first_stage_model.decoder.up.3.block.1.norm2.bias": "blocks.4.norm2.bias", + "first_stage_model.decoder.up.3.block.1.norm2.weight": "blocks.4.norm2.weight", + "first_stage_model.decoder.up.3.block.2.conv1.bias": "blocks.5.conv1.bias", + "first_stage_model.decoder.up.3.block.2.conv1.weight": "blocks.5.conv1.weight", + "first_stage_model.decoder.up.3.block.2.conv2.bias": "blocks.5.conv2.bias", + "first_stage_model.decoder.up.3.block.2.conv2.weight": "blocks.5.conv2.weight", + "first_stage_model.decoder.up.3.block.2.norm1.bias": "blocks.5.norm1.bias", + "first_stage_model.decoder.up.3.block.2.norm1.weight": "blocks.5.norm1.weight", + "first_stage_model.decoder.up.3.block.2.norm2.bias": "blocks.5.norm2.bias", + "first_stage_model.decoder.up.3.block.2.norm2.weight": "blocks.5.norm2.weight", + "first_stage_model.decoder.up.3.upsample.conv.bias": "blocks.6.conv.bias", + "first_stage_model.decoder.up.3.upsample.conv.weight": "blocks.6.conv.weight", + "first_stage_model.post_quant_conv.bias": "post_quant_conv.bias", + "first_stage_model.post_quant_conv.weight": "post_quant_conv.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if "transformer_blocks" in rename_dict[name]: + param = param.squeeze() + state_dict_[rename_dict[name]] = param + return state_dict_ diff --git a/diffsynth/models/sd_vae_encoder.py b/diffsynth/models/sd_vae_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..60965c591c01bd12dce5f0abdbfd121c033c47c6 --- /dev/null +++ b/diffsynth/models/sd_vae_encoder.py @@ -0,0 +1,282 @@ +import torch +from .sd_unet import ResnetBlock, DownSampler +from .sd_vae_decoder import VAEAttentionBlock +from .tiler import TileWorker +from einops import rearrange + + +class SDVAEEncoder(torch.nn.Module): + def __init__(self): + super().__init__() + self.scaling_factor = 0.18215 + self.quant_conv = torch.nn.Conv2d(8, 8, kernel_size=1) + self.conv_in = torch.nn.Conv2d(3, 128, kernel_size=3, padding=1) + + self.blocks = torch.nn.ModuleList([ + # DownEncoderBlock2D + ResnetBlock(128, 128, eps=1e-6), + ResnetBlock(128, 128, eps=1e-6), + DownSampler(128, padding=0, extra_padding=True), + # DownEncoderBlock2D + ResnetBlock(128, 256, eps=1e-6), + ResnetBlock(256, 256, eps=1e-6), + DownSampler(256, padding=0, extra_padding=True), + # DownEncoderBlock2D + ResnetBlock(256, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + DownSampler(512, padding=0, extra_padding=True), + # DownEncoderBlock2D + ResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + # UNetMidBlock2D + ResnetBlock(512, 512, eps=1e-6), + VAEAttentionBlock(1, 512, 512, 1, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + ]) + + self.conv_norm_out = torch.nn.GroupNorm(num_channels=512, num_groups=32, eps=1e-6) + self.conv_act = torch.nn.SiLU() + self.conv_out = torch.nn.Conv2d(512, 8, kernel_size=3, padding=1) + + def tiled_forward(self, sample, tile_size=64, tile_stride=32): + hidden_states = TileWorker().tiled_forward( + lambda x: self.forward(x), + sample, + tile_size, + tile_stride, + tile_device=sample.device, + tile_dtype=sample.dtype + ) + return hidden_states + + def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs): + original_dtype = sample.dtype + sample = sample.to(dtype=next(iter(self.parameters())).dtype) + # For VAE Decoder, we do not need to apply the tiler on each layer. + if tiled: + return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride) + + # 1. pre-process + hidden_states = self.conv_in(sample) + time_emb = None + text_emb = None + res_stack = None + + # 2. blocks + for i, block in enumerate(self.blocks): + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + + # 3. output + hidden_states = self.conv_norm_out(hidden_states) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + hidden_states = self.quant_conv(hidden_states) + hidden_states = hidden_states[:, :4] + hidden_states *= self.scaling_factor + hidden_states = hidden_states.to(original_dtype) + + return hidden_states + + def encode_video(self, sample, batch_size=8): + B = sample.shape[0] + hidden_states = [] + + for i in range(0, sample.shape[2], batch_size): + + j = min(i + batch_size, sample.shape[2]) + sample_batch = rearrange(sample[:,:,i:j], "B C T H W -> (B T) C H W") + + hidden_states_batch = self(sample_batch) + hidden_states_batch = rearrange(hidden_states_batch, "(B T) C H W -> B C T H W", B=B) + + hidden_states.append(hidden_states_batch) + + hidden_states = torch.concat(hidden_states, dim=2) + return hidden_states + + @staticmethod + def state_dict_converter(): + return SDVAEEncoderStateDictConverter() + + +class SDVAEEncoderStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + # architecture + block_types = [ + 'ResnetBlock', 'ResnetBlock', 'DownSampler', + 'ResnetBlock', 'ResnetBlock', 'DownSampler', + 'ResnetBlock', 'ResnetBlock', 'DownSampler', + 'ResnetBlock', 'ResnetBlock', + 'ResnetBlock', 'VAEAttentionBlock', 'ResnetBlock' + ] + + # Rename each parameter + local_rename_dict = { + "quant_conv": "quant_conv", + "encoder.conv_in": "conv_in", + "encoder.mid_block.attentions.0.group_norm": "blocks.12.norm", + "encoder.mid_block.attentions.0.to_q": "blocks.12.transformer_blocks.0.to_q", + "encoder.mid_block.attentions.0.to_k": "blocks.12.transformer_blocks.0.to_k", + "encoder.mid_block.attentions.0.to_v": "blocks.12.transformer_blocks.0.to_v", + "encoder.mid_block.attentions.0.to_out.0": "blocks.12.transformer_blocks.0.to_out", + "encoder.mid_block.resnets.0.norm1": "blocks.11.norm1", + "encoder.mid_block.resnets.0.conv1": "blocks.11.conv1", + "encoder.mid_block.resnets.0.norm2": "blocks.11.norm2", + "encoder.mid_block.resnets.0.conv2": "blocks.11.conv2", + "encoder.mid_block.resnets.1.norm1": "blocks.13.norm1", + "encoder.mid_block.resnets.1.conv1": "blocks.13.conv1", + "encoder.mid_block.resnets.1.norm2": "blocks.13.norm2", + "encoder.mid_block.resnets.1.conv2": "blocks.13.conv2", + "encoder.conv_norm_out": "conv_norm_out", + "encoder.conv_out": "conv_out", + } + name_list = sorted([name for name in state_dict]) + rename_dict = {} + block_id = {"ResnetBlock": -1, "DownSampler": -1, "UpSampler": -1} + last_block_type_with_id = {"ResnetBlock": "", "DownSampler": "", "UpSampler": ""} + for name in name_list: + names = name.split(".") + name_prefix = ".".join(names[:-1]) + if name_prefix in local_rename_dict: + rename_dict[name] = local_rename_dict[name_prefix] + "." + names[-1] + elif name.startswith("encoder.down_blocks"): + block_type = {"resnets": "ResnetBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[3]] + block_type_with_id = ".".join(names[:5]) + if block_type_with_id != last_block_type_with_id[block_type]: + block_id[block_type] += 1 + last_block_type_with_id[block_type] = block_type_with_id + while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: + block_id[block_type] += 1 + block_type_with_id = ".".join(names[:5]) + names = ["blocks", str(block_id[block_type])] + names[5:] + rename_dict[name] = ".".join(names) + + # Convert state_dict + state_dict_ = {} + for name, param in state_dict.items(): + if name in rename_dict: + state_dict_[rename_dict[name]] = param + return state_dict_ + + def from_civitai(self, state_dict): + rename_dict = { + "first_stage_model.encoder.conv_in.bias": "conv_in.bias", + "first_stage_model.encoder.conv_in.weight": "conv_in.weight", + "first_stage_model.encoder.conv_out.bias": "conv_out.bias", + "first_stage_model.encoder.conv_out.weight": "conv_out.weight", + "first_stage_model.encoder.down.0.block.0.conv1.bias": "blocks.0.conv1.bias", + "first_stage_model.encoder.down.0.block.0.conv1.weight": "blocks.0.conv1.weight", + "first_stage_model.encoder.down.0.block.0.conv2.bias": "blocks.0.conv2.bias", + "first_stage_model.encoder.down.0.block.0.conv2.weight": "blocks.0.conv2.weight", + "first_stage_model.encoder.down.0.block.0.norm1.bias": "blocks.0.norm1.bias", + "first_stage_model.encoder.down.0.block.0.norm1.weight": "blocks.0.norm1.weight", + "first_stage_model.encoder.down.0.block.0.norm2.bias": "blocks.0.norm2.bias", + "first_stage_model.encoder.down.0.block.0.norm2.weight": "blocks.0.norm2.weight", + "first_stage_model.encoder.down.0.block.1.conv1.bias": "blocks.1.conv1.bias", + "first_stage_model.encoder.down.0.block.1.conv1.weight": "blocks.1.conv1.weight", + "first_stage_model.encoder.down.0.block.1.conv2.bias": "blocks.1.conv2.bias", + "first_stage_model.encoder.down.0.block.1.conv2.weight": "blocks.1.conv2.weight", + "first_stage_model.encoder.down.0.block.1.norm1.bias": "blocks.1.norm1.bias", + "first_stage_model.encoder.down.0.block.1.norm1.weight": "blocks.1.norm1.weight", + "first_stage_model.encoder.down.0.block.1.norm2.bias": "blocks.1.norm2.bias", + "first_stage_model.encoder.down.0.block.1.norm2.weight": "blocks.1.norm2.weight", + "first_stage_model.encoder.down.0.downsample.conv.bias": "blocks.2.conv.bias", + "first_stage_model.encoder.down.0.downsample.conv.weight": "blocks.2.conv.weight", + "first_stage_model.encoder.down.1.block.0.conv1.bias": "blocks.3.conv1.bias", + "first_stage_model.encoder.down.1.block.0.conv1.weight": "blocks.3.conv1.weight", + "first_stage_model.encoder.down.1.block.0.conv2.bias": "blocks.3.conv2.bias", + "first_stage_model.encoder.down.1.block.0.conv2.weight": "blocks.3.conv2.weight", + "first_stage_model.encoder.down.1.block.0.nin_shortcut.bias": "blocks.3.conv_shortcut.bias", + "first_stage_model.encoder.down.1.block.0.nin_shortcut.weight": "blocks.3.conv_shortcut.weight", + "first_stage_model.encoder.down.1.block.0.norm1.bias": "blocks.3.norm1.bias", + "first_stage_model.encoder.down.1.block.0.norm1.weight": "blocks.3.norm1.weight", + "first_stage_model.encoder.down.1.block.0.norm2.bias": "blocks.3.norm2.bias", + "first_stage_model.encoder.down.1.block.0.norm2.weight": "blocks.3.norm2.weight", + "first_stage_model.encoder.down.1.block.1.conv1.bias": "blocks.4.conv1.bias", + "first_stage_model.encoder.down.1.block.1.conv1.weight": "blocks.4.conv1.weight", + "first_stage_model.encoder.down.1.block.1.conv2.bias": "blocks.4.conv2.bias", + "first_stage_model.encoder.down.1.block.1.conv2.weight": "blocks.4.conv2.weight", + "first_stage_model.encoder.down.1.block.1.norm1.bias": "blocks.4.norm1.bias", + "first_stage_model.encoder.down.1.block.1.norm1.weight": "blocks.4.norm1.weight", + "first_stage_model.encoder.down.1.block.1.norm2.bias": "blocks.4.norm2.bias", + "first_stage_model.encoder.down.1.block.1.norm2.weight": "blocks.4.norm2.weight", + "first_stage_model.encoder.down.1.downsample.conv.bias": "blocks.5.conv.bias", + "first_stage_model.encoder.down.1.downsample.conv.weight": "blocks.5.conv.weight", + "first_stage_model.encoder.down.2.block.0.conv1.bias": "blocks.6.conv1.bias", + "first_stage_model.encoder.down.2.block.0.conv1.weight": "blocks.6.conv1.weight", + "first_stage_model.encoder.down.2.block.0.conv2.bias": "blocks.6.conv2.bias", + "first_stage_model.encoder.down.2.block.0.conv2.weight": "blocks.6.conv2.weight", + "first_stage_model.encoder.down.2.block.0.nin_shortcut.bias": "blocks.6.conv_shortcut.bias", + "first_stage_model.encoder.down.2.block.0.nin_shortcut.weight": "blocks.6.conv_shortcut.weight", + "first_stage_model.encoder.down.2.block.0.norm1.bias": "blocks.6.norm1.bias", + "first_stage_model.encoder.down.2.block.0.norm1.weight": "blocks.6.norm1.weight", + "first_stage_model.encoder.down.2.block.0.norm2.bias": "blocks.6.norm2.bias", + "first_stage_model.encoder.down.2.block.0.norm2.weight": "blocks.6.norm2.weight", + "first_stage_model.encoder.down.2.block.1.conv1.bias": "blocks.7.conv1.bias", + "first_stage_model.encoder.down.2.block.1.conv1.weight": "blocks.7.conv1.weight", + "first_stage_model.encoder.down.2.block.1.conv2.bias": "blocks.7.conv2.bias", + "first_stage_model.encoder.down.2.block.1.conv2.weight": "blocks.7.conv2.weight", + "first_stage_model.encoder.down.2.block.1.norm1.bias": "blocks.7.norm1.bias", + "first_stage_model.encoder.down.2.block.1.norm1.weight": "blocks.7.norm1.weight", + "first_stage_model.encoder.down.2.block.1.norm2.bias": "blocks.7.norm2.bias", + "first_stage_model.encoder.down.2.block.1.norm2.weight": "blocks.7.norm2.weight", + "first_stage_model.encoder.down.2.downsample.conv.bias": "blocks.8.conv.bias", + "first_stage_model.encoder.down.2.downsample.conv.weight": "blocks.8.conv.weight", + "first_stage_model.encoder.down.3.block.0.conv1.bias": "blocks.9.conv1.bias", + "first_stage_model.encoder.down.3.block.0.conv1.weight": "blocks.9.conv1.weight", + "first_stage_model.encoder.down.3.block.0.conv2.bias": "blocks.9.conv2.bias", + "first_stage_model.encoder.down.3.block.0.conv2.weight": "blocks.9.conv2.weight", + "first_stage_model.encoder.down.3.block.0.norm1.bias": "blocks.9.norm1.bias", + "first_stage_model.encoder.down.3.block.0.norm1.weight": "blocks.9.norm1.weight", + "first_stage_model.encoder.down.3.block.0.norm2.bias": "blocks.9.norm2.bias", + "first_stage_model.encoder.down.3.block.0.norm2.weight": "blocks.9.norm2.weight", + "first_stage_model.encoder.down.3.block.1.conv1.bias": "blocks.10.conv1.bias", + "first_stage_model.encoder.down.3.block.1.conv1.weight": "blocks.10.conv1.weight", + "first_stage_model.encoder.down.3.block.1.conv2.bias": "blocks.10.conv2.bias", + "first_stage_model.encoder.down.3.block.1.conv2.weight": "blocks.10.conv2.weight", + "first_stage_model.encoder.down.3.block.1.norm1.bias": "blocks.10.norm1.bias", + "first_stage_model.encoder.down.3.block.1.norm1.weight": "blocks.10.norm1.weight", + "first_stage_model.encoder.down.3.block.1.norm2.bias": "blocks.10.norm2.bias", + "first_stage_model.encoder.down.3.block.1.norm2.weight": "blocks.10.norm2.weight", + "first_stage_model.encoder.mid.attn_1.k.bias": "blocks.12.transformer_blocks.0.to_k.bias", + "first_stage_model.encoder.mid.attn_1.k.weight": "blocks.12.transformer_blocks.0.to_k.weight", + "first_stage_model.encoder.mid.attn_1.norm.bias": "blocks.12.norm.bias", + "first_stage_model.encoder.mid.attn_1.norm.weight": "blocks.12.norm.weight", + "first_stage_model.encoder.mid.attn_1.proj_out.bias": "blocks.12.transformer_blocks.0.to_out.bias", + "first_stage_model.encoder.mid.attn_1.proj_out.weight": "blocks.12.transformer_blocks.0.to_out.weight", + "first_stage_model.encoder.mid.attn_1.q.bias": "blocks.12.transformer_blocks.0.to_q.bias", + "first_stage_model.encoder.mid.attn_1.q.weight": "blocks.12.transformer_blocks.0.to_q.weight", + "first_stage_model.encoder.mid.attn_1.v.bias": "blocks.12.transformer_blocks.0.to_v.bias", + "first_stage_model.encoder.mid.attn_1.v.weight": "blocks.12.transformer_blocks.0.to_v.weight", + "first_stage_model.encoder.mid.block_1.conv1.bias": "blocks.11.conv1.bias", + "first_stage_model.encoder.mid.block_1.conv1.weight": "blocks.11.conv1.weight", + "first_stage_model.encoder.mid.block_1.conv2.bias": "blocks.11.conv2.bias", + "first_stage_model.encoder.mid.block_1.conv2.weight": "blocks.11.conv2.weight", + "first_stage_model.encoder.mid.block_1.norm1.bias": "blocks.11.norm1.bias", + "first_stage_model.encoder.mid.block_1.norm1.weight": "blocks.11.norm1.weight", + "first_stage_model.encoder.mid.block_1.norm2.bias": "blocks.11.norm2.bias", + "first_stage_model.encoder.mid.block_1.norm2.weight": "blocks.11.norm2.weight", + "first_stage_model.encoder.mid.block_2.conv1.bias": "blocks.13.conv1.bias", + "first_stage_model.encoder.mid.block_2.conv1.weight": "blocks.13.conv1.weight", + "first_stage_model.encoder.mid.block_2.conv2.bias": "blocks.13.conv2.bias", + "first_stage_model.encoder.mid.block_2.conv2.weight": "blocks.13.conv2.weight", + "first_stage_model.encoder.mid.block_2.norm1.bias": "blocks.13.norm1.bias", + "first_stage_model.encoder.mid.block_2.norm1.weight": "blocks.13.norm1.weight", + "first_stage_model.encoder.mid.block_2.norm2.bias": "blocks.13.norm2.bias", + "first_stage_model.encoder.mid.block_2.norm2.weight": "blocks.13.norm2.weight", + "first_stage_model.encoder.norm_out.bias": "conv_norm_out.bias", + "first_stage_model.encoder.norm_out.weight": "conv_norm_out.weight", + "first_stage_model.quant_conv.bias": "quant_conv.bias", + "first_stage_model.quant_conv.weight": "quant_conv.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if "transformer_blocks" in rename_dict[name]: + param = param.squeeze() + state_dict_[rename_dict[name]] = param + return state_dict_ diff --git a/diffsynth/models/sdxl_controlnet.py b/diffsynth/models/sdxl_controlnet.py new file mode 100644 index 0000000000000000000000000000000000000000..acddf1cc4109af01bd9c06121f6cd4d8604ce945 --- /dev/null +++ b/diffsynth/models/sdxl_controlnet.py @@ -0,0 +1,318 @@ +import torch +from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, DownSampler +from .sdxl_unet import SDXLUNet +from .tiler import TileWorker +from .sd_controlnet import ControlNetConditioningLayer +from collections import OrderedDict + + + +class QuickGELU(torch.nn.Module): + + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + + +class ResidualAttentionBlock(torch.nn.Module): + + def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): + super().__init__() + + self.attn = torch.nn.MultiheadAttention(d_model, n_head) + self.ln_1 = torch.nn.LayerNorm(d_model) + self.mlp = torch.nn.Sequential(OrderedDict([ + ("c_fc", torch.nn.Linear(d_model, d_model * 4)), + ("gelu", QuickGELU()), + ("c_proj", torch.nn.Linear(d_model * 4, d_model)) + ])) + self.ln_2 = torch.nn.LayerNorm(d_model) + self.attn_mask = attn_mask + + def attention(self, x: torch.Tensor): + self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None + return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] + + def forward(self, x: torch.Tensor): + x = x + self.attention(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + + +class SDXLControlNetUnion(torch.nn.Module): + def __init__(self, global_pool=False): + super().__init__() + self.time_proj = Timesteps(320) + self.time_embedding = torch.nn.Sequential( + torch.nn.Linear(320, 1280), + torch.nn.SiLU(), + torch.nn.Linear(1280, 1280) + ) + self.add_time_proj = Timesteps(256) + self.add_time_embedding = torch.nn.Sequential( + torch.nn.Linear(2816, 1280), + torch.nn.SiLU(), + torch.nn.Linear(1280, 1280) + ) + self.control_type_proj = Timesteps(256) + self.control_type_embedding = torch.nn.Sequential( + torch.nn.Linear(256 * 8, 1280), + torch.nn.SiLU(), + torch.nn.Linear(1280, 1280) + ) + self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1) + + self.controlnet_conv_in = ControlNetConditioningLayer(channels=(3, 16, 32, 96, 256, 320)) + self.controlnet_transformer = ResidualAttentionBlock(320, 8) + self.task_embedding = torch.nn.Parameter(torch.randn(8, 320)) + self.spatial_ch_projs = torch.nn.Linear(320, 320) + + self.blocks = torch.nn.ModuleList([ + # DownBlock2D + ResnetBlock(320, 320, 1280), + PushBlock(), + ResnetBlock(320, 320, 1280), + PushBlock(), + DownSampler(320), + PushBlock(), + # CrossAttnDownBlock2D + ResnetBlock(320, 640, 1280), + AttentionBlock(10, 64, 640, 2, 2048), + PushBlock(), + ResnetBlock(640, 640, 1280), + AttentionBlock(10, 64, 640, 2, 2048), + PushBlock(), + DownSampler(640), + PushBlock(), + # CrossAttnDownBlock2D + ResnetBlock(640, 1280, 1280), + AttentionBlock(20, 64, 1280, 10, 2048), + PushBlock(), + ResnetBlock(1280, 1280, 1280), + AttentionBlock(20, 64, 1280, 10, 2048), + PushBlock(), + # UNetMidBlock2DCrossAttn + ResnetBlock(1280, 1280, 1280), + AttentionBlock(20, 64, 1280, 10, 2048), + ResnetBlock(1280, 1280, 1280), + PushBlock() + ]) + + self.controlnet_blocks = torch.nn.ModuleList([ + torch.nn.Conv2d(320, 320, kernel_size=(1, 1)), + torch.nn.Conv2d(320, 320, kernel_size=(1, 1)), + torch.nn.Conv2d(320, 320, kernel_size=(1, 1)), + torch.nn.Conv2d(320, 320, kernel_size=(1, 1)), + torch.nn.Conv2d(640, 640, kernel_size=(1, 1)), + torch.nn.Conv2d(640, 640, kernel_size=(1, 1)), + torch.nn.Conv2d(640, 640, kernel_size=(1, 1)), + torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)), + torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)), + torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)), + ]) + + self.global_pool = global_pool + + # 0 -- openpose + # 1 -- depth + # 2 -- hed/pidi/scribble/ted + # 3 -- canny/lineart/anime_lineart/mlsd + # 4 -- normal + # 5 -- segment + # 6 -- tile + # 7 -- repaint + self.task_id = { + "openpose": 0, + "depth": 1, + "softedge": 2, + "canny": 3, + "lineart": 3, + "lineart_anime": 3, + "tile": 6, + "inpaint": 7 + } + + + def fuse_condition_to_input(self, hidden_states, task_id, conditioning): + controlnet_cond = self.controlnet_conv_in(conditioning) + feat_seq = torch.mean(controlnet_cond, dim=(2, 3)) + feat_seq = feat_seq + self.task_embedding[task_id] + x = torch.stack([feat_seq, torch.mean(hidden_states, dim=(2, 3))], dim=1) + x = self.controlnet_transformer(x) + + alpha = self.spatial_ch_projs(x[:,0]).unsqueeze(-1).unsqueeze(-1) + controlnet_cond_fuser = controlnet_cond + alpha + + hidden_states = hidden_states + controlnet_cond_fuser + return hidden_states + + + def forward( + self, + sample, timestep, encoder_hidden_states, + conditioning, processor_id, add_time_id, add_text_embeds, + tiled=False, tile_size=64, tile_stride=32, + unet:SDXLUNet=None, + **kwargs + ): + task_id = self.task_id[processor_id] + + # 1. time + t_emb = self.time_proj(timestep).to(sample.dtype) + t_emb = self.time_embedding(t_emb) + + time_embeds = self.add_time_proj(add_time_id) + time_embeds = time_embeds.reshape((add_text_embeds.shape[0], -1)) + add_embeds = torch.concat([add_text_embeds, time_embeds], dim=-1) + add_embeds = add_embeds.to(sample.dtype) + if unet is not None and unet.is_kolors: + add_embeds = unet.add_time_embedding(add_embeds) + else: + add_embeds = self.add_time_embedding(add_embeds) + + control_type = torch.zeros((sample.shape[0], 8), dtype=sample.dtype, device=sample.device) + control_type[:, task_id] = 1 + control_embeds = self.control_type_proj(control_type.flatten()) + control_embeds = control_embeds.reshape((sample.shape[0], -1)) + control_embeds = control_embeds.to(sample.dtype) + control_embeds = self.control_type_embedding(control_embeds) + time_emb = t_emb + add_embeds + control_embeds + + # 2. pre-process + height, width = sample.shape[2], sample.shape[3] + hidden_states = self.conv_in(sample) + hidden_states = self.fuse_condition_to_input(hidden_states, task_id, conditioning) + text_emb = encoder_hidden_states + if unet is not None and unet.is_kolors: + text_emb = unet.text_intermediate_proj(text_emb) + res_stack = [hidden_states] + + # 3. blocks + for i, block in enumerate(self.blocks): + if tiled and not isinstance(block, PushBlock): + _, _, inter_height, _ = hidden_states.shape + resize_scale = inter_height / height + hidden_states = TileWorker().tiled_forward( + lambda x: block(x, time_emb, text_emb, res_stack)[0], + hidden_states, + int(tile_size * resize_scale), + int(tile_stride * resize_scale), + tile_device=hidden_states.device, + tile_dtype=hidden_states.dtype + ) + else: + hidden_states, _, _, _ = block(hidden_states, time_emb, text_emb, res_stack) + + # 4. ControlNet blocks + controlnet_res_stack = [block(res) for block, res in zip(self.controlnet_blocks, res_stack)] + + # pool + if self.global_pool: + controlnet_res_stack = [res.mean(dim=(2, 3), keepdim=True) for res in controlnet_res_stack] + + return controlnet_res_stack + + @staticmethod + def state_dict_converter(): + return SDXLControlNetUnionStateDictConverter() + + + +class SDXLControlNetUnionStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + # architecture + block_types = [ + "ResnetBlock", "PushBlock", "ResnetBlock", "PushBlock", "DownSampler", "PushBlock", + "ResnetBlock", "AttentionBlock", "PushBlock", "ResnetBlock", "AttentionBlock", "PushBlock", "DownSampler", "PushBlock", + "ResnetBlock", "AttentionBlock", "PushBlock", "ResnetBlock", "AttentionBlock", "PushBlock", + "ResnetBlock", "AttentionBlock", "ResnetBlock", "PushBlock" + ] + + # controlnet_rename_dict + controlnet_rename_dict = { + "controlnet_cond_embedding.conv_in.weight": "controlnet_conv_in.blocks.0.weight", + "controlnet_cond_embedding.conv_in.bias": "controlnet_conv_in.blocks.0.bias", + "controlnet_cond_embedding.blocks.0.weight": "controlnet_conv_in.blocks.2.weight", + "controlnet_cond_embedding.blocks.0.bias": "controlnet_conv_in.blocks.2.bias", + "controlnet_cond_embedding.blocks.1.weight": "controlnet_conv_in.blocks.4.weight", + "controlnet_cond_embedding.blocks.1.bias": "controlnet_conv_in.blocks.4.bias", + "controlnet_cond_embedding.blocks.2.weight": "controlnet_conv_in.blocks.6.weight", + "controlnet_cond_embedding.blocks.2.bias": "controlnet_conv_in.blocks.6.bias", + "controlnet_cond_embedding.blocks.3.weight": "controlnet_conv_in.blocks.8.weight", + "controlnet_cond_embedding.blocks.3.bias": "controlnet_conv_in.blocks.8.bias", + "controlnet_cond_embedding.blocks.4.weight": "controlnet_conv_in.blocks.10.weight", + "controlnet_cond_embedding.blocks.4.bias": "controlnet_conv_in.blocks.10.bias", + "controlnet_cond_embedding.blocks.5.weight": "controlnet_conv_in.blocks.12.weight", + "controlnet_cond_embedding.blocks.5.bias": "controlnet_conv_in.blocks.12.bias", + "controlnet_cond_embedding.conv_out.weight": "controlnet_conv_in.blocks.14.weight", + "controlnet_cond_embedding.conv_out.bias": "controlnet_conv_in.blocks.14.bias", + "control_add_embedding.linear_1.weight": "control_type_embedding.0.weight", + "control_add_embedding.linear_1.bias": "control_type_embedding.0.bias", + "control_add_embedding.linear_2.weight": "control_type_embedding.2.weight", + "control_add_embedding.linear_2.bias": "control_type_embedding.2.bias", + } + + # Rename each parameter + name_list = sorted([name for name in state_dict]) + rename_dict = {} + block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1} + last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""} + for name in name_list: + names = name.split(".") + if names[0] in ["conv_in", "conv_norm_out", "conv_out", "task_embedding", "spatial_ch_projs"]: + pass + elif name in controlnet_rename_dict: + names = controlnet_rename_dict[name].split(".") + elif names[0] == "controlnet_down_blocks": + names[0] = "controlnet_blocks" + elif names[0] == "controlnet_mid_block": + names = ["controlnet_blocks", "9", names[-1]] + elif names[0] in ["time_embedding", "add_embedding"]: + if names[0] == "add_embedding": + names[0] = "add_time_embedding" + names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]] + elif names[0] == "control_add_embedding": + names[0] = "control_type_embedding" + elif names[0] == "transformer_layes": + names[0] = "controlnet_transformer" + names.pop(1) + elif names[0] in ["down_blocks", "mid_block", "up_blocks"]: + if names[0] == "mid_block": + names.insert(1, "0") + block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]] + block_type_with_id = ".".join(names[:4]) + if block_type_with_id != last_block_type_with_id[block_type]: + block_id[block_type] += 1 + last_block_type_with_id[block_type] = block_type_with_id + while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: + block_id[block_type] += 1 + block_type_with_id = ".".join(names[:4]) + names = ["blocks", str(block_id[block_type])] + names[4:] + if "ff" in names: + ff_index = names.index("ff") + component = ".".join(names[ff_index:ff_index+3]) + component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component] + names = names[:ff_index] + [component] + names[ff_index+3:] + if "to_out" in names: + names.pop(names.index("to_out") + 1) + else: + print(name, state_dict[name].shape) + # raise ValueError(f"Unknown parameters: {name}") + rename_dict[name] = ".".join(names) + + # Convert state_dict + state_dict_ = {} + for name, param in state_dict.items(): + if name not in rename_dict: + continue + if ".proj_in." in name or ".proj_out." in name: + param = param.squeeze() + state_dict_[rename_dict[name]] = param + return state_dict_ + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict) \ No newline at end of file diff --git a/diffsynth/models/sdxl_ipadapter.py b/diffsynth/models/sdxl_ipadapter.py new file mode 100644 index 0000000000000000000000000000000000000000..d959d3b9249e690c6240670fc191e11e27deaaa2 --- /dev/null +++ b/diffsynth/models/sdxl_ipadapter.py @@ -0,0 +1,122 @@ +from .svd_image_encoder import SVDImageEncoder +from transformers import CLIPImageProcessor +import torch + + +class IpAdapterXLCLIPImageEmbedder(SVDImageEncoder): + def __init__(self): + super().__init__(embed_dim=1664, encoder_intermediate_size=8192, projection_dim=1280, num_encoder_layers=48, num_heads=16, head_dim=104) + self.image_processor = CLIPImageProcessor() + + def forward(self, image): + pixel_values = self.image_processor(images=image, return_tensors="pt").pixel_values + pixel_values = pixel_values.to(device=self.embeddings.class_embedding.device, dtype=self.embeddings.class_embedding.dtype) + return super().forward(pixel_values) + + +class IpAdapterImageProjModel(torch.nn.Module): + def __init__(self, cross_attention_dim=2048, clip_embeddings_dim=1280, clip_extra_context_tokens=4): + super().__init__() + self.cross_attention_dim = cross_attention_dim + self.clip_extra_context_tokens = clip_extra_context_tokens + self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim) + self.norm = torch.nn.LayerNorm(cross_attention_dim) + + def forward(self, image_embeds): + clip_extra_context_tokens = self.proj(image_embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim) + clip_extra_context_tokens = self.norm(clip_extra_context_tokens) + return clip_extra_context_tokens + + +class IpAdapterModule(torch.nn.Module): + def __init__(self, input_dim, output_dim): + super().__init__() + self.to_k_ip = torch.nn.Linear(input_dim, output_dim, bias=False) + self.to_v_ip = torch.nn.Linear(input_dim, output_dim, bias=False) + + def forward(self, hidden_states): + ip_k = self.to_k_ip(hidden_states) + ip_v = self.to_v_ip(hidden_states) + return ip_k, ip_v + + +class SDXLIpAdapter(torch.nn.Module): + def __init__(self): + super().__init__() + shape_list = [(2048, 640)] * 4 + [(2048, 1280)] * 50 + [(2048, 640)] * 6 + [(2048, 1280)] * 10 + self.ipadapter_modules = torch.nn.ModuleList([IpAdapterModule(*shape) for shape in shape_list]) + self.image_proj = IpAdapterImageProjModel() + self.set_full_adapter() + + def set_full_adapter(self): + map_list = sum([ + [(7, i) for i in range(2)], + [(10, i) for i in range(2)], + [(15, i) for i in range(10)], + [(18, i) for i in range(10)], + [(25, i) for i in range(10)], + [(28, i) for i in range(10)], + [(31, i) for i in range(10)], + [(35, i) for i in range(2)], + [(38, i) for i in range(2)], + [(41, i) for i in range(2)], + [(21, i) for i in range(10)], + ], []) + self.call_block_id = {i: j for j, i in enumerate(map_list)} + + def set_less_adapter(self): + map_list = sum([ + [(7, i) for i in range(2)], + [(10, i) for i in range(2)], + [(15, i) for i in range(10)], + [(18, i) for i in range(10)], + [(25, i) for i in range(10)], + [(28, i) for i in range(10)], + [(31, i) for i in range(10)], + [(35, i) for i in range(2)], + [(38, i) for i in range(2)], + [(41, i) for i in range(2)], + [(21, i) for i in range(10)], + ], []) + self.call_block_id = {i: j for j, i in enumerate(map_list) if j>=34 and j<44} + + def forward(self, hidden_states, scale=1.0): + hidden_states = self.image_proj(hidden_states) + hidden_states = hidden_states.view(1, -1, hidden_states.shape[-1]) + ip_kv_dict = {} + for (block_id, transformer_id) in self.call_block_id: + ipadapter_id = self.call_block_id[(block_id, transformer_id)] + ip_k, ip_v = self.ipadapter_modules[ipadapter_id](hidden_states) + if block_id not in ip_kv_dict: + ip_kv_dict[block_id] = {} + ip_kv_dict[block_id][transformer_id] = { + "ip_k": ip_k, + "ip_v": ip_v, + "scale": scale + } + return ip_kv_dict + + @staticmethod + def state_dict_converter(): + return SDXLIpAdapterStateDictConverter() + + +class SDXLIpAdapterStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + state_dict_ = {} + for name in state_dict["ip_adapter"]: + names = name.split(".") + layer_id = str(int(names[0]) // 2) + name_ = ".".join(["ipadapter_modules"] + [layer_id] + names[1:]) + state_dict_[name_] = state_dict["ip_adapter"][name] + for name in state_dict["image_proj"]: + name_ = "image_proj." + name + state_dict_[name_] = state_dict["image_proj"][name] + return state_dict_ + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict) + diff --git a/diffsynth/models/sdxl_motion.py b/diffsynth/models/sdxl_motion.py new file mode 100644 index 0000000000000000000000000000000000000000..268c3e96f006e697eed5ac03fad3f5c995cfa319 --- /dev/null +++ b/diffsynth/models/sdxl_motion.py @@ -0,0 +1,104 @@ +from .sd_motion import TemporalBlock +import torch + + + +class SDXLMotionModel(torch.nn.Module): + def __init__(self): + super().__init__() + self.motion_modules = torch.nn.ModuleList([ + TemporalBlock(8, 320//8, 320, eps=1e-6), + TemporalBlock(8, 320//8, 320, eps=1e-6), + + TemporalBlock(8, 640//8, 640, eps=1e-6), + TemporalBlock(8, 640//8, 640, eps=1e-6), + + TemporalBlock(8, 1280//8, 1280, eps=1e-6), + TemporalBlock(8, 1280//8, 1280, eps=1e-6), + + TemporalBlock(8, 1280//8, 1280, eps=1e-6), + TemporalBlock(8, 1280//8, 1280, eps=1e-6), + TemporalBlock(8, 1280//8, 1280, eps=1e-6), + + TemporalBlock(8, 640//8, 640, eps=1e-6), + TemporalBlock(8, 640//8, 640, eps=1e-6), + TemporalBlock(8, 640//8, 640, eps=1e-6), + + TemporalBlock(8, 320//8, 320, eps=1e-6), + TemporalBlock(8, 320//8, 320, eps=1e-6), + TemporalBlock(8, 320//8, 320, eps=1e-6), + ]) + self.call_block_id = { + 0: 0, + 2: 1, + 7: 2, + 10: 3, + 15: 4, + 18: 5, + 25: 6, + 28: 7, + 31: 8, + 35: 9, + 38: 10, + 41: 11, + 44: 12, + 46: 13, + 48: 14, + } + + def forward(self): + pass + + @staticmethod + def state_dict_converter(): + return SDMotionModelStateDictConverter() + + +class SDMotionModelStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + rename_dict = { + "norm": "norm", + "proj_in": "proj_in", + "transformer_blocks.0.attention_blocks.0.to_q": "transformer_blocks.0.attn1.to_q", + "transformer_blocks.0.attention_blocks.0.to_k": "transformer_blocks.0.attn1.to_k", + "transformer_blocks.0.attention_blocks.0.to_v": "transformer_blocks.0.attn1.to_v", + "transformer_blocks.0.attention_blocks.0.to_out.0": "transformer_blocks.0.attn1.to_out", + "transformer_blocks.0.attention_blocks.0.pos_encoder": "transformer_blocks.0.pe1", + "transformer_blocks.0.attention_blocks.1.to_q": "transformer_blocks.0.attn2.to_q", + "transformer_blocks.0.attention_blocks.1.to_k": "transformer_blocks.0.attn2.to_k", + "transformer_blocks.0.attention_blocks.1.to_v": "transformer_blocks.0.attn2.to_v", + "transformer_blocks.0.attention_blocks.1.to_out.0": "transformer_blocks.0.attn2.to_out", + "transformer_blocks.0.attention_blocks.1.pos_encoder": "transformer_blocks.0.pe2", + "transformer_blocks.0.norms.0": "transformer_blocks.0.norm1", + "transformer_blocks.0.norms.1": "transformer_blocks.0.norm2", + "transformer_blocks.0.ff.net.0.proj": "transformer_blocks.0.act_fn.proj", + "transformer_blocks.0.ff.net.2": "transformer_blocks.0.ff", + "transformer_blocks.0.ff_norm": "transformer_blocks.0.norm3", + "proj_out": "proj_out", + } + name_list = sorted([i for i in state_dict if i.startswith("down_blocks.")]) + name_list += sorted([i for i in state_dict if i.startswith("mid_block.")]) + name_list += sorted([i for i in state_dict if i.startswith("up_blocks.")]) + state_dict_ = {} + last_prefix, module_id = "", -1 + for name in name_list: + names = name.split(".") + prefix_index = names.index("temporal_transformer") + 1 + prefix = ".".join(names[:prefix_index]) + if prefix != last_prefix: + last_prefix = prefix + module_id += 1 + middle_name = ".".join(names[prefix_index:-1]) + suffix = names[-1] + if "pos_encoder" in names: + rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name]]) + else: + rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name], suffix]) + state_dict_[rename] = state_dict[name] + return state_dict_ + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict) diff --git a/diffsynth/models/sdxl_text_encoder.py b/diffsynth/models/sdxl_text_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..5d30c7d4056bf37abbb341b2807aa47a67785023 --- /dev/null +++ b/diffsynth/models/sdxl_text_encoder.py @@ -0,0 +1,759 @@ +import torch +from .sd_text_encoder import CLIPEncoderLayer + + +class SDXLTextEncoder(torch.nn.Module): + def __init__(self, embed_dim=768, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=11, encoder_intermediate_size=3072): + super().__init__() + + # token_embedding + self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim) + + # position_embeds (This is a fixed tensor) + self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim)) + + # encoders + self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size) for _ in range(num_encoder_layers)]) + + # attn_mask + self.attn_mask = self.attention_mask(max_position_embeddings) + + # The text encoder is different to that in Stable Diffusion 1.x. + # It does not include final_layer_norm. + + def attention_mask(self, length): + mask = torch.empty(length, length) + mask.fill_(float("-inf")) + mask.triu_(1) + return mask + + def forward(self, input_ids, clip_skip=1): + embeds = self.token_embedding(input_ids) + self.position_embeds + attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype) + for encoder_id, encoder in enumerate(self.encoders): + embeds = encoder(embeds, attn_mask=attn_mask) + if encoder_id + clip_skip == len(self.encoders): + break + return embeds + + @staticmethod + def state_dict_converter(): + return SDXLTextEncoderStateDictConverter() + + +class SDXLTextEncoder2(torch.nn.Module): + def __init__(self, embed_dim=1280, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=32, encoder_intermediate_size=5120): + super().__init__() + + # token_embedding + self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim) + + # position_embeds (This is a fixed tensor) + self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim)) + + # encoders + self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size, num_heads=20, head_dim=64, use_quick_gelu=False) for _ in range(num_encoder_layers)]) + + # attn_mask + self.attn_mask = self.attention_mask(max_position_embeddings) + + # final_layer_norm + self.final_layer_norm = torch.nn.LayerNorm(embed_dim) + + # text_projection + self.text_projection = torch.nn.Linear(embed_dim, embed_dim, bias=False) + + def attention_mask(self, length): + mask = torch.empty(length, length) + mask.fill_(float("-inf")) + mask.triu_(1) + return mask + + def forward(self, input_ids, clip_skip=2): + embeds = self.token_embedding(input_ids) + self.position_embeds + attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype) + for encoder_id, encoder in enumerate(self.encoders): + embeds = encoder(embeds, attn_mask=attn_mask) + if encoder_id + clip_skip == len(self.encoders): + hidden_states = embeds + embeds = self.final_layer_norm(embeds) + pooled_embeds = embeds[torch.arange(embeds.shape[0]), input_ids.to(dtype=torch.int).argmax(dim=-1)] + pooled_embeds = self.text_projection(pooled_embeds) + return pooled_embeds, hidden_states + + @staticmethod + def state_dict_converter(): + return SDXLTextEncoder2StateDictConverter() + + +class SDXLTextEncoderStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + rename_dict = { + "text_model.embeddings.token_embedding.weight": "token_embedding.weight", + "text_model.embeddings.position_embedding.weight": "position_embeds", + "text_model.final_layer_norm.weight": "final_layer_norm.weight", + "text_model.final_layer_norm.bias": "final_layer_norm.bias" + } + attn_rename_dict = { + "self_attn.q_proj": "attn.to_q", + "self_attn.k_proj": "attn.to_k", + "self_attn.v_proj": "attn.to_v", + "self_attn.out_proj": "attn.to_out", + "layer_norm1": "layer_norm1", + "layer_norm2": "layer_norm2", + "mlp.fc1": "fc1", + "mlp.fc2": "fc2", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "text_model.embeddings.position_embedding.weight": + param = param.reshape((1, param.shape[0], param.shape[1])) + state_dict_[rename_dict[name]] = param + elif name.startswith("text_model.encoder.layers."): + param = state_dict[name] + names = name.split(".") + layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1] + name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail]) + state_dict_[name_] = param + return state_dict_ + + def from_civitai(self, state_dict): + rename_dict = { + "conditioner.embedders.0.transformer.text_model.embeddings.position_embedding.weight": "position_embeds", + "conditioner.embedders.0.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm2.weight": "encoders.0.layer_norm2.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.0.mlp.fc1.bias": "encoders.0.fc1.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.0.mlp.fc1.weight": "encoders.0.fc1.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.0.mlp.fc2.bias": "encoders.0.fc2.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.0.mlp.fc2.weight": "encoders.0.fc2.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.k_proj.bias": "encoders.0.attn.to_k.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight": "encoders.0.attn.to_k.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.out_proj.bias": "encoders.0.attn.to_out.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.out_proj.weight": "encoders.0.attn.to_out.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.q_proj.bias": "encoders.0.attn.to_q.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.q_proj.weight": "encoders.0.attn.to_q.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.v_proj.bias": "encoders.0.attn.to_v.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.v_proj.weight": "encoders.0.attn.to_v.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.1.layer_norm1.bias": "encoders.1.layer_norm1.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.1.layer_norm1.weight": "encoders.1.layer_norm1.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.1.layer_norm2.bias": "encoders.1.layer_norm2.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.1.layer_norm2.weight": "encoders.1.layer_norm2.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.1.mlp.fc1.bias": "encoders.1.fc1.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.1.mlp.fc1.weight": "encoders.1.fc1.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.1.mlp.fc2.bias": "encoders.1.fc2.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.1.mlp.fc2.weight": "encoders.1.fc2.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.1.self_attn.k_proj.bias": "encoders.1.attn.to_k.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.1.self_attn.k_proj.weight": "encoders.1.attn.to_k.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.1.self_attn.out_proj.bias": "encoders.1.attn.to_out.bias", + 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"conditioner.embedders.0.transformer.text_model.encoder.layers.9.layer_norm2.weight": "encoders.9.layer_norm2.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.mlp.fc1.bias": "encoders.9.fc1.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.mlp.fc1.weight": "encoders.9.fc1.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.mlp.fc2.bias": "encoders.9.fc2.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.mlp.fc2.weight": "encoders.9.fc2.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.k_proj.bias": "encoders.9.attn.to_k.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.k_proj.weight": "encoders.9.attn.to_k.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.out_proj.bias": "encoders.9.attn.to_out.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.out_proj.weight": "encoders.9.attn.to_out.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.q_proj.bias": "encoders.9.attn.to_q.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.q_proj.weight": "encoders.9.attn.to_q.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.v_proj.bias": "encoders.9.attn.to_v.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "conditioner.embedders.0.transformer.text_model.embeddings.position_embedding.weight": + param = param.reshape((1, param.shape[0], param.shape[1])) + state_dict_[rename_dict[name]] = param + return state_dict_ + + +class SDXLTextEncoder2StateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + rename_dict = { + "text_model.embeddings.token_embedding.weight": "token_embedding.weight", + "text_model.embeddings.position_embedding.weight": "position_embeds", + "text_model.final_layer_norm.weight": "final_layer_norm.weight", + "text_model.final_layer_norm.bias": "final_layer_norm.bias", + "text_projection.weight": "text_projection.weight" + } + attn_rename_dict = { + "self_attn.q_proj": "attn.to_q", + "self_attn.k_proj": "attn.to_k", + "self_attn.v_proj": "attn.to_v", + "self_attn.out_proj": "attn.to_out", + "layer_norm1": "layer_norm1", + "layer_norm2": "layer_norm2", + "mlp.fc1": "fc1", + "mlp.fc2": "fc2", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "text_model.embeddings.position_embedding.weight": + param = param.reshape((1, param.shape[0], param.shape[1])) + state_dict_[rename_dict[name]] = param + elif name.startswith("text_model.encoder.layers."): + param = state_dict[name] + names = name.split(".") + layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1] + name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail]) + state_dict_[name_] = param + return state_dict_ + + def from_civitai(self, state_dict): + rename_dict = { + "conditioner.embedders.1.model.ln_final.bias": "final_layer_norm.bias", + "conditioner.embedders.1.model.ln_final.weight": "final_layer_norm.weight", + "conditioner.embedders.1.model.positional_embedding": "position_embeds", + "conditioner.embedders.1.model.token_embedding.weight": "token_embedding.weight", + "conditioner.embedders.1.model.transformer.resblocks.0.attn.in_proj_bias": ['encoders.0.attn.to_q.bias', 'encoders.0.attn.to_k.bias', 'encoders.0.attn.to_v.bias'], + "conditioner.embedders.1.model.transformer.resblocks.0.attn.in_proj_weight": ['encoders.0.attn.to_q.weight', 'encoders.0.attn.to_k.weight', 'encoders.0.attn.to_v.weight'], + "conditioner.embedders.1.model.transformer.resblocks.0.attn.out_proj.bias": "encoders.0.attn.to_out.bias", + "conditioner.embedders.1.model.transformer.resblocks.0.attn.out_proj.weight": "encoders.0.attn.to_out.weight", + "conditioner.embedders.1.model.transformer.resblocks.0.ln_1.bias": "encoders.0.layer_norm1.bias", + "conditioner.embedders.1.model.transformer.resblocks.0.ln_1.weight": "encoders.0.layer_norm1.weight", + "conditioner.embedders.1.model.transformer.resblocks.0.ln_2.bias": "encoders.0.layer_norm2.bias", + "conditioner.embedders.1.model.transformer.resblocks.0.ln_2.weight": "encoders.0.layer_norm2.weight", + "conditioner.embedders.1.model.transformer.resblocks.0.mlp.c_fc.bias": "encoders.0.fc1.bias", + "conditioner.embedders.1.model.transformer.resblocks.0.mlp.c_fc.weight": "encoders.0.fc1.weight", + "conditioner.embedders.1.model.transformer.resblocks.0.mlp.c_proj.bias": "encoders.0.fc2.bias", + "conditioner.embedders.1.model.transformer.resblocks.0.mlp.c_proj.weight": "encoders.0.fc2.weight", + "conditioner.embedders.1.model.transformer.resblocks.1.attn.in_proj_bias": ['encoders.1.attn.to_q.bias', 'encoders.1.attn.to_k.bias', 'encoders.1.attn.to_v.bias'], + "conditioner.embedders.1.model.transformer.resblocks.1.attn.in_proj_weight": ['encoders.1.attn.to_q.weight', 'encoders.1.attn.to_k.weight', 'encoders.1.attn.to_v.weight'], + "conditioner.embedders.1.model.transformer.resblocks.1.attn.out_proj.bias": "encoders.1.attn.to_out.bias", + "conditioner.embedders.1.model.transformer.resblocks.1.attn.out_proj.weight": "encoders.1.attn.to_out.weight", + "conditioner.embedders.1.model.transformer.resblocks.1.ln_1.bias": "encoders.1.layer_norm1.bias", + "conditioner.embedders.1.model.transformer.resblocks.1.ln_1.weight": "encoders.1.layer_norm1.weight", + "conditioner.embedders.1.model.transformer.resblocks.1.ln_2.bias": "encoders.1.layer_norm2.bias", + "conditioner.embedders.1.model.transformer.resblocks.1.ln_2.weight": "encoders.1.layer_norm2.weight", + "conditioner.embedders.1.model.transformer.resblocks.1.mlp.c_fc.bias": "encoders.1.fc1.bias", + "conditioner.embedders.1.model.transformer.resblocks.1.mlp.c_fc.weight": "encoders.1.fc1.weight", + "conditioner.embedders.1.model.transformer.resblocks.1.mlp.c_proj.bias": "encoders.1.fc2.bias", + "conditioner.embedders.1.model.transformer.resblocks.1.mlp.c_proj.weight": "encoders.1.fc2.weight", + "conditioner.embedders.1.model.transformer.resblocks.10.attn.in_proj_bias": ['encoders.10.attn.to_q.bias', 'encoders.10.attn.to_k.bias', 'encoders.10.attn.to_v.bias'], + "conditioner.embedders.1.model.transformer.resblocks.10.attn.in_proj_weight": ['encoders.10.attn.to_q.weight', 'encoders.10.attn.to_k.weight', 'encoders.10.attn.to_v.weight'], + "conditioner.embedders.1.model.transformer.resblocks.10.attn.out_proj.bias": "encoders.10.attn.to_out.bias", + "conditioner.embedders.1.model.transformer.resblocks.10.attn.out_proj.weight": "encoders.10.attn.to_out.weight", + "conditioner.embedders.1.model.transformer.resblocks.10.ln_1.bias": "encoders.10.layer_norm1.bias", + "conditioner.embedders.1.model.transformer.resblocks.10.ln_1.weight": "encoders.10.layer_norm1.weight", + "conditioner.embedders.1.model.transformer.resblocks.10.ln_2.bias": "encoders.10.layer_norm2.bias", + "conditioner.embedders.1.model.transformer.resblocks.10.ln_2.weight": "encoders.10.layer_norm2.weight", + "conditioner.embedders.1.model.transformer.resblocks.10.mlp.c_fc.bias": "encoders.10.fc1.bias", + "conditioner.embedders.1.model.transformer.resblocks.10.mlp.c_fc.weight": "encoders.10.fc1.weight", + "conditioner.embedders.1.model.transformer.resblocks.10.mlp.c_proj.bias": "encoders.10.fc2.bias", + "conditioner.embedders.1.model.transformer.resblocks.10.mlp.c_proj.weight": "encoders.10.fc2.weight", + "conditioner.embedders.1.model.transformer.resblocks.11.attn.in_proj_bias": ['encoders.11.attn.to_q.bias', 'encoders.11.attn.to_k.bias', 'encoders.11.attn.to_v.bias'], + "conditioner.embedders.1.model.transformer.resblocks.11.attn.in_proj_weight": ['encoders.11.attn.to_q.weight', 'encoders.11.attn.to_k.weight', 'encoders.11.attn.to_v.weight'], + "conditioner.embedders.1.model.transformer.resblocks.11.attn.out_proj.bias": "encoders.11.attn.to_out.bias", + "conditioner.embedders.1.model.transformer.resblocks.11.attn.out_proj.weight": "encoders.11.attn.to_out.weight", + "conditioner.embedders.1.model.transformer.resblocks.11.ln_1.bias": "encoders.11.layer_norm1.bias", + "conditioner.embedders.1.model.transformer.resblocks.11.ln_1.weight": "encoders.11.layer_norm1.weight", + "conditioner.embedders.1.model.transformer.resblocks.11.ln_2.bias": "encoders.11.layer_norm2.bias", + "conditioner.embedders.1.model.transformer.resblocks.11.ln_2.weight": "encoders.11.layer_norm2.weight", + "conditioner.embedders.1.model.transformer.resblocks.11.mlp.c_fc.bias": "encoders.11.fc1.bias", + "conditioner.embedders.1.model.transformer.resblocks.11.mlp.c_fc.weight": "encoders.11.fc1.weight", + "conditioner.embedders.1.model.transformer.resblocks.11.mlp.c_proj.bias": "encoders.11.fc2.bias", + "conditioner.embedders.1.model.transformer.resblocks.11.mlp.c_proj.weight": "encoders.11.fc2.weight", + "conditioner.embedders.1.model.transformer.resblocks.12.attn.in_proj_bias": ['encoders.12.attn.to_q.bias', 'encoders.12.attn.to_k.bias', 'encoders.12.attn.to_v.bias'], + "conditioner.embedders.1.model.transformer.resblocks.12.attn.in_proj_weight": ['encoders.12.attn.to_q.weight', 'encoders.12.attn.to_k.weight', 'encoders.12.attn.to_v.weight'], + "conditioner.embedders.1.model.transformer.resblocks.12.attn.out_proj.bias": "encoders.12.attn.to_out.bias", + "conditioner.embedders.1.model.transformer.resblocks.12.attn.out_proj.weight": "encoders.12.attn.to_out.weight", + "conditioner.embedders.1.model.transformer.resblocks.12.ln_1.bias": "encoders.12.layer_norm1.bias", + "conditioner.embedders.1.model.transformer.resblocks.12.ln_1.weight": "encoders.12.layer_norm1.weight", + "conditioner.embedders.1.model.transformer.resblocks.12.ln_2.bias": "encoders.12.layer_norm2.bias", + "conditioner.embedders.1.model.transformer.resblocks.12.ln_2.weight": "encoders.12.layer_norm2.weight", + "conditioner.embedders.1.model.transformer.resblocks.12.mlp.c_fc.bias": "encoders.12.fc1.bias", + "conditioner.embedders.1.model.transformer.resblocks.12.mlp.c_fc.weight": "encoders.12.fc1.weight", + "conditioner.embedders.1.model.transformer.resblocks.12.mlp.c_proj.bias": "encoders.12.fc2.bias", + "conditioner.embedders.1.model.transformer.resblocks.12.mlp.c_proj.weight": "encoders.12.fc2.weight", + "conditioner.embedders.1.model.transformer.resblocks.13.attn.in_proj_bias": ['encoders.13.attn.to_q.bias', 'encoders.13.attn.to_k.bias', 'encoders.13.attn.to_v.bias'], + "conditioner.embedders.1.model.transformer.resblocks.13.attn.in_proj_weight": ['encoders.13.attn.to_q.weight', 'encoders.13.attn.to_k.weight', 'encoders.13.attn.to_v.weight'], + "conditioner.embedders.1.model.transformer.resblocks.13.attn.out_proj.bias": "encoders.13.attn.to_out.bias", + "conditioner.embedders.1.model.transformer.resblocks.13.attn.out_proj.weight": "encoders.13.attn.to_out.weight", + "conditioner.embedders.1.model.transformer.resblocks.13.ln_1.bias": "encoders.13.layer_norm1.bias", + "conditioner.embedders.1.model.transformer.resblocks.13.ln_1.weight": "encoders.13.layer_norm1.weight", + "conditioner.embedders.1.model.transformer.resblocks.13.ln_2.bias": "encoders.13.layer_norm2.bias", + "conditioner.embedders.1.model.transformer.resblocks.13.ln_2.weight": "encoders.13.layer_norm2.weight", + "conditioner.embedders.1.model.transformer.resblocks.13.mlp.c_fc.bias": "encoders.13.fc1.bias", + "conditioner.embedders.1.model.transformer.resblocks.13.mlp.c_fc.weight": "encoders.13.fc1.weight", + "conditioner.embedders.1.model.transformer.resblocks.13.mlp.c_proj.bias": "encoders.13.fc2.bias", + "conditioner.embedders.1.model.transformer.resblocks.13.mlp.c_proj.weight": "encoders.13.fc2.weight", + "conditioner.embedders.1.model.transformer.resblocks.14.attn.in_proj_bias": ['encoders.14.attn.to_q.bias', 'encoders.14.attn.to_k.bias', 'encoders.14.attn.to_v.bias'], + "conditioner.embedders.1.model.transformer.resblocks.14.attn.in_proj_weight": ['encoders.14.attn.to_q.weight', 'encoders.14.attn.to_k.weight', 'encoders.14.attn.to_v.weight'], + "conditioner.embedders.1.model.transformer.resblocks.14.attn.out_proj.bias": "encoders.14.attn.to_out.bias", + "conditioner.embedders.1.model.transformer.resblocks.14.attn.out_proj.weight": "encoders.14.attn.to_out.weight", + "conditioner.embedders.1.model.transformer.resblocks.14.ln_1.bias": "encoders.14.layer_norm1.bias", + "conditioner.embedders.1.model.transformer.resblocks.14.ln_1.weight": "encoders.14.layer_norm1.weight", + "conditioner.embedders.1.model.transformer.resblocks.14.ln_2.bias": "encoders.14.layer_norm2.bias", + "conditioner.embedders.1.model.transformer.resblocks.14.ln_2.weight": "encoders.14.layer_norm2.weight", + "conditioner.embedders.1.model.transformer.resblocks.14.mlp.c_fc.bias": "encoders.14.fc1.bias", + "conditioner.embedders.1.model.transformer.resblocks.14.mlp.c_fc.weight": "encoders.14.fc1.weight", + "conditioner.embedders.1.model.transformer.resblocks.14.mlp.c_proj.bias": "encoders.14.fc2.bias", + "conditioner.embedders.1.model.transformer.resblocks.14.mlp.c_proj.weight": "encoders.14.fc2.weight", + "conditioner.embedders.1.model.transformer.resblocks.15.attn.in_proj_bias": ['encoders.15.attn.to_q.bias', 'encoders.15.attn.to_k.bias', 'encoders.15.attn.to_v.bias'], + "conditioner.embedders.1.model.transformer.resblocks.15.attn.in_proj_weight": ['encoders.15.attn.to_q.weight', 'encoders.15.attn.to_k.weight', 'encoders.15.attn.to_v.weight'], + "conditioner.embedders.1.model.transformer.resblocks.15.attn.out_proj.bias": "encoders.15.attn.to_out.bias", + "conditioner.embedders.1.model.transformer.resblocks.15.attn.out_proj.weight": "encoders.15.attn.to_out.weight", + "conditioner.embedders.1.model.transformer.resblocks.15.ln_1.bias": "encoders.15.layer_norm1.bias", + "conditioner.embedders.1.model.transformer.resblocks.15.ln_1.weight": "encoders.15.layer_norm1.weight", + "conditioner.embedders.1.model.transformer.resblocks.15.ln_2.bias": "encoders.15.layer_norm2.bias", + "conditioner.embedders.1.model.transformer.resblocks.15.ln_2.weight": "encoders.15.layer_norm2.weight", + "conditioner.embedders.1.model.transformer.resblocks.15.mlp.c_fc.bias": "encoders.15.fc1.bias", + "conditioner.embedders.1.model.transformer.resblocks.15.mlp.c_fc.weight": "encoders.15.fc1.weight", + "conditioner.embedders.1.model.transformer.resblocks.15.mlp.c_proj.bias": "encoders.15.fc2.bias", + "conditioner.embedders.1.model.transformer.resblocks.15.mlp.c_proj.weight": "encoders.15.fc2.weight", + "conditioner.embedders.1.model.transformer.resblocks.16.attn.in_proj_bias": ['encoders.16.attn.to_q.bias', 'encoders.16.attn.to_k.bias', 'encoders.16.attn.to_v.bias'], + "conditioner.embedders.1.model.transformer.resblocks.16.attn.in_proj_weight": ['encoders.16.attn.to_q.weight', 'encoders.16.attn.to_k.weight', 'encoders.16.attn.to_v.weight'], + "conditioner.embedders.1.model.transformer.resblocks.16.attn.out_proj.bias": "encoders.16.attn.to_out.bias", + "conditioner.embedders.1.model.transformer.resblocks.16.attn.out_proj.weight": "encoders.16.attn.to_out.weight", + "conditioner.embedders.1.model.transformer.resblocks.16.ln_1.bias": "encoders.16.layer_norm1.bias", + "conditioner.embedders.1.model.transformer.resblocks.16.ln_1.weight": "encoders.16.layer_norm1.weight", + "conditioner.embedders.1.model.transformer.resblocks.16.ln_2.bias": "encoders.16.layer_norm2.bias", + "conditioner.embedders.1.model.transformer.resblocks.16.ln_2.weight": "encoders.16.layer_norm2.weight", + "conditioner.embedders.1.model.transformer.resblocks.16.mlp.c_fc.bias": "encoders.16.fc1.bias", + "conditioner.embedders.1.model.transformer.resblocks.16.mlp.c_fc.weight": "encoders.16.fc1.weight", + "conditioner.embedders.1.model.transformer.resblocks.16.mlp.c_proj.bias": "encoders.16.fc2.bias", + "conditioner.embedders.1.model.transformer.resblocks.16.mlp.c_proj.weight": "encoders.16.fc2.weight", + "conditioner.embedders.1.model.transformer.resblocks.17.attn.in_proj_bias": ['encoders.17.attn.to_q.bias', 'encoders.17.attn.to_k.bias', 'encoders.17.attn.to_v.bias'], + "conditioner.embedders.1.model.transformer.resblocks.17.attn.in_proj_weight": ['encoders.17.attn.to_q.weight', 'encoders.17.attn.to_k.weight', 'encoders.17.attn.to_v.weight'], + "conditioner.embedders.1.model.transformer.resblocks.17.attn.out_proj.bias": "encoders.17.attn.to_out.bias", + "conditioner.embedders.1.model.transformer.resblocks.17.attn.out_proj.weight": "encoders.17.attn.to_out.weight", + "conditioner.embedders.1.model.transformer.resblocks.17.ln_1.bias": "encoders.17.layer_norm1.bias", + "conditioner.embedders.1.model.transformer.resblocks.17.ln_1.weight": "encoders.17.layer_norm1.weight", + "conditioner.embedders.1.model.transformer.resblocks.17.ln_2.bias": "encoders.17.layer_norm2.bias", + "conditioner.embedders.1.model.transformer.resblocks.17.ln_2.weight": "encoders.17.layer_norm2.weight", + "conditioner.embedders.1.model.transformer.resblocks.17.mlp.c_fc.bias": "encoders.17.fc1.bias", + "conditioner.embedders.1.model.transformer.resblocks.17.mlp.c_fc.weight": "encoders.17.fc1.weight", + "conditioner.embedders.1.model.transformer.resblocks.17.mlp.c_proj.bias": "encoders.17.fc2.bias", + "conditioner.embedders.1.model.transformer.resblocks.17.mlp.c_proj.weight": "encoders.17.fc2.weight", + 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"conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.weight": "encoders.9.fc2.weight", + "conditioner.embedders.1.model.text_projection": "text_projection.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "conditioner.embedders.1.model.positional_embedding": + param = param.reshape((1, param.shape[0], param.shape[1])) + elif name == "conditioner.embedders.1.model.text_projection": + param = param.T + if isinstance(rename_dict[name], str): + state_dict_[rename_dict[name]] = param + else: + length = param.shape[0] // 3 + for i, rename in enumerate(rename_dict[name]): + state_dict_[rename] = param[i*length: i*length+length] + return state_dict_ \ No newline at end of file diff --git a/diffsynth/models/sdxl_unet.py b/diffsynth/models/sdxl_unet.py new file mode 100644 index 0000000000000000000000000000000000000000..9bc63e63181c999f94421b72843c7b9e03b31d4a --- /dev/null +++ b/diffsynth/models/sdxl_unet.py @@ -0,0 +1,1901 @@ +import torch +from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, PopBlock, DownSampler, UpSampler + + +class SDXLUNet(torch.nn.Module): + def __init__(self, is_kolors=False): + super().__init__() + self.time_proj = Timesteps(320) + self.time_embedding = torch.nn.Sequential( + torch.nn.Linear(320, 1280), + torch.nn.SiLU(), + torch.nn.Linear(1280, 1280) + ) + self.add_time_proj = Timesteps(256) + self.add_time_embedding = torch.nn.Sequential( + torch.nn.Linear(5632 if is_kolors else 2816, 1280), + torch.nn.SiLU(), + torch.nn.Linear(1280, 1280) + ) + self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1) + self.text_intermediate_proj = torch.nn.Linear(4096, 2048) if is_kolors else None + + self.blocks = torch.nn.ModuleList([ + # DownBlock2D + ResnetBlock(320, 320, 1280), + PushBlock(), + ResnetBlock(320, 320, 1280), + PushBlock(), + DownSampler(320), + PushBlock(), + # CrossAttnDownBlock2D + ResnetBlock(320, 640, 1280), + AttentionBlock(10, 64, 640, 2, 2048), + PushBlock(), + ResnetBlock(640, 640, 1280), + AttentionBlock(10, 64, 640, 2, 2048), + PushBlock(), + DownSampler(640), + PushBlock(), + # CrossAttnDownBlock2D + ResnetBlock(640, 1280, 1280), + AttentionBlock(20, 64, 1280, 10, 2048), + PushBlock(), + ResnetBlock(1280, 1280, 1280), + AttentionBlock(20, 64, 1280, 10, 2048), + PushBlock(), + # UNetMidBlock2DCrossAttn + ResnetBlock(1280, 1280, 1280), + AttentionBlock(20, 64, 1280, 10, 2048), + ResnetBlock(1280, 1280, 1280), + # CrossAttnUpBlock2D + PopBlock(), + ResnetBlock(2560, 1280, 1280), + AttentionBlock(20, 64, 1280, 10, 2048), + PopBlock(), + ResnetBlock(2560, 1280, 1280), + AttentionBlock(20, 64, 1280, 10, 2048), + PopBlock(), + ResnetBlock(1920, 1280, 1280), + AttentionBlock(20, 64, 1280, 10, 2048), + UpSampler(1280), + # CrossAttnUpBlock2D + PopBlock(), + ResnetBlock(1920, 640, 1280), + AttentionBlock(10, 64, 640, 2, 2048), + PopBlock(), + ResnetBlock(1280, 640, 1280), + AttentionBlock(10, 64, 640, 2, 2048), + PopBlock(), + ResnetBlock(960, 640, 1280), + AttentionBlock(10, 64, 640, 2, 2048), + UpSampler(640), + # UpBlock2D + PopBlock(), + ResnetBlock(960, 320, 1280), + PopBlock(), + ResnetBlock(640, 320, 1280), + PopBlock(), + ResnetBlock(640, 320, 1280) + ]) + + self.conv_norm_out = torch.nn.GroupNorm(num_channels=320, num_groups=32, eps=1e-5) + self.conv_act = torch.nn.SiLU() + self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=3, padding=1) + + self.is_kolors = is_kolors + + def forward( + self, + sample, timestep, encoder_hidden_states, add_time_id, add_text_embeds, + tiled=False, tile_size=64, tile_stride=8, + use_gradient_checkpointing=False, + **kwargs + ): + # 1. time + t_emb = self.time_proj(timestep).to(sample.dtype) + t_emb = self.time_embedding(t_emb) + + time_embeds = self.add_time_proj(add_time_id) + time_embeds = time_embeds.reshape((add_text_embeds.shape[0], -1)) + add_embeds = torch.concat([add_text_embeds, time_embeds], dim=-1) + add_embeds = add_embeds.to(sample.dtype) + add_embeds = self.add_time_embedding(add_embeds) + + time_emb = t_emb + add_embeds + + # 2. pre-process + height, width = sample.shape[2], sample.shape[3] + hidden_states = self.conv_in(sample) + text_emb = encoder_hidden_states if self.text_intermediate_proj is None else self.text_intermediate_proj(encoder_hidden_states) + res_stack = [hidden_states] + + # 3. blocks + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + return custom_forward + for i, block in enumerate(self.blocks): + if self.training and use_gradient_checkpointing and not (isinstance(block, PushBlock) or isinstance(block, PopBlock)): + hidden_states, time_emb, text_emb, res_stack = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, time_emb, text_emb, res_stack, + use_reentrant=False, + ) + else: + hidden_states, time_emb, text_emb, res_stack = block( + hidden_states, time_emb, text_emb, res_stack, + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride + ) + + # 4. output + hidden_states = self.conv_norm_out(hidden_states) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + + return hidden_states + + @staticmethod + def state_dict_converter(): + return SDXLUNetStateDictConverter() + + +class SDXLUNetStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + # architecture + block_types = [ + 'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', 'DownSampler', 'PushBlock', + 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', + 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', + 'ResnetBlock', 'AttentionBlock', 'ResnetBlock', + 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', + 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', + 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock' + ] + + # Rename each parameter + name_list = sorted([name for name in state_dict]) + rename_dict = {} + block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1} + last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""} + for name in name_list: + names = name.split(".") + if names[0] in ["conv_in", "conv_norm_out", "conv_out"]: + pass + elif names[0] in ["encoder_hid_proj"]: + names[0] = "text_intermediate_proj" + elif names[0] in ["time_embedding", "add_embedding"]: + if names[0] == "add_embedding": + names[0] = "add_time_embedding" + names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]] + elif names[0] in ["down_blocks", "mid_block", "up_blocks"]: + if names[0] == "mid_block": + names.insert(1, "0") + block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]] + block_type_with_id = ".".join(names[:4]) + if block_type_with_id != last_block_type_with_id[block_type]: + block_id[block_type] += 1 + last_block_type_with_id[block_type] = block_type_with_id + while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: + block_id[block_type] += 1 + block_type_with_id = ".".join(names[:4]) + names = ["blocks", str(block_id[block_type])] + names[4:] + if "ff" in names: + ff_index = names.index("ff") + component = ".".join(names[ff_index:ff_index+3]) + component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component] + names = names[:ff_index] + [component] + names[ff_index+3:] + if "to_out" in names: + names.pop(names.index("to_out") + 1) + else: + raise ValueError(f"Unknown parameters: {name}") + rename_dict[name] = ".".join(names) + + # Convert state_dict + state_dict_ = {} + for name, param in state_dict.items(): + if ".proj_in." in name or ".proj_out." in name: + param = param.squeeze() + state_dict_[rename_dict[name]] = param + if "text_intermediate_proj.weight" in state_dict_: + return state_dict_, {"is_kolors": True} + else: + return state_dict_ + + def from_civitai(self, state_dict): + rename_dict = { + "model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias", + "model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight", + "model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias", + "model.diffusion_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight", + "model.diffusion_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias", + "model.diffusion_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight", + "model.diffusion_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias", + "model.diffusion_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight", + "model.diffusion_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias", + "model.diffusion_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight", + "model.diffusion_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias", + "model.diffusion_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight", + "model.diffusion_model.input_blocks.2.0.emb_layers.1.bias": "blocks.2.time_emb_proj.bias", + "model.diffusion_model.input_blocks.2.0.emb_layers.1.weight": "blocks.2.time_emb_proj.weight", + "model.diffusion_model.input_blocks.2.0.in_layers.0.bias": "blocks.2.norm1.bias", + "model.diffusion_model.input_blocks.2.0.in_layers.0.weight": "blocks.2.norm1.weight", + "model.diffusion_model.input_blocks.2.0.in_layers.2.bias": "blocks.2.conv1.bias", + "model.diffusion_model.input_blocks.2.0.in_layers.2.weight": "blocks.2.conv1.weight", + "model.diffusion_model.input_blocks.2.0.out_layers.0.bias": "blocks.2.norm2.bias", + 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"text_intermediate_proj.weight" in state_dict_: + return state_dict_, {"is_kolors": True} + else: + return state_dict_ diff --git a/diffsynth/models/sdxl_vae_decoder.py b/diffsynth/models/sdxl_vae_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..290c7851e3619f61a1baa2ddb3ad77180809297d --- /dev/null +++ b/diffsynth/models/sdxl_vae_decoder.py @@ -0,0 +1,24 @@ +from .sd_vae_decoder import SDVAEDecoder, SDVAEDecoderStateDictConverter + + +class SDXLVAEDecoder(SDVAEDecoder): + def __init__(self, upcast_to_float32=True): + super().__init__() + self.scaling_factor = 0.13025 + + @staticmethod + def state_dict_converter(): + return SDXLVAEDecoderStateDictConverter() + + +class SDXLVAEDecoderStateDictConverter(SDVAEDecoderStateDictConverter): + def __init__(self): + super().__init__() + + def from_diffusers(self, state_dict): + state_dict = super().from_diffusers(state_dict) + return state_dict, {"upcast_to_float32": True} + + def from_civitai(self, state_dict): + state_dict = super().from_civitai(state_dict) + return state_dict, {"upcast_to_float32": True} diff --git a/diffsynth/models/sdxl_vae_encoder.py b/diffsynth/models/sdxl_vae_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..14af09cd33c41452b9777daa6819115eb900b788 --- /dev/null +++ b/diffsynth/models/sdxl_vae_encoder.py @@ -0,0 +1,24 @@ +from .sd_vae_encoder import SDVAEEncoderStateDictConverter, SDVAEEncoder + + +class SDXLVAEEncoder(SDVAEEncoder): + def __init__(self, upcast_to_float32=True): + super().__init__() + self.scaling_factor = 0.13025 + + @staticmethod + def state_dict_converter(): + return SDXLVAEEncoderStateDictConverter() + + +class SDXLVAEEncoderStateDictConverter(SDVAEEncoderStateDictConverter): + def __init__(self): + super().__init__() + + def from_diffusers(self, state_dict): + state_dict = super().from_diffusers(state_dict) + return state_dict, {"upcast_to_float32": True} + + def from_civitai(self, state_dict): + state_dict = super().from_civitai(state_dict) + return state_dict, {"upcast_to_float32": True} diff --git a/diffsynth/models/spatial_grid_memory.py b/diffsynth/models/spatial_grid_memory.py new file mode 100644 index 0000000000000000000000000000000000000000..fbe80ca1d661d0c84fc18857261e3b3928fe0542 --- /dev/null +++ b/diffsynth/models/spatial_grid_memory.py @@ -0,0 +1,2 @@ +from .memory.spatial_grid_memory import * # backward-compat re-export + diff --git a/diffsynth/models/step1x_connector.py b/diffsynth/models/step1x_connector.py new file mode 100644 index 0000000000000000000000000000000000000000..9d5f0d90f4c3627124e487474e60d32549efc4ce --- /dev/null +++ b/diffsynth/models/step1x_connector.py @@ -0,0 +1,683 @@ +from typing import Optional + +import torch, math +import torch.nn +from einops import rearrange +from torch import nn +from functools import partial +from einops import rearrange + + + +def attention(q, k, v, attn_mask, mode="torch"): + q = q.transpose(1, 2) + k = k.transpose(1, 2) + v = v.transpose(1, 2) + x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) + x = rearrange(x, "b n s d -> b s (n d)") + return x + + + +class MLP(nn.Module): + """MLP as used in Vision Transformer, MLP-Mixer and related networks""" + + def __init__( + self, + in_channels, + hidden_channels=None, + out_features=None, + act_layer=nn.GELU, + norm_layer=None, + bias=True, + drop=0.0, + use_conv=False, + device=None, + dtype=None, + ): + super().__init__() + out_features = out_features or in_channels + hidden_channels = hidden_channels or in_channels + bias = (bias, bias) + drop_probs = (drop, drop) + linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear + + self.fc1 = linear_layer( + in_channels, hidden_channels, bias=bias[0], device=device, dtype=dtype + ) + self.act = act_layer() + self.drop1 = nn.Dropout(drop_probs[0]) + self.norm = ( + norm_layer(hidden_channels, device=device, dtype=dtype) + if norm_layer is not None + else nn.Identity() + ) + self.fc2 = linear_layer( + hidden_channels, out_features, bias=bias[1], device=device, dtype=dtype + ) + self.drop2 = nn.Dropout(drop_probs[1]) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop1(x) + x = self.norm(x) + x = self.fc2(x) + x = self.drop2(x) + return x + + +class TextProjection(nn.Module): + """ + Projects text embeddings. Also handles dropout for classifier-free guidance. + + Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py + """ + + def __init__(self, in_channels, hidden_size, act_layer, dtype=None, device=None): + factory_kwargs = {"dtype": dtype, "device": device} + super().__init__() + self.linear_1 = nn.Linear( + in_features=in_channels, + out_features=hidden_size, + bias=True, + **factory_kwargs, + ) + self.act_1 = act_layer() + self.linear_2 = nn.Linear( + in_features=hidden_size, + out_features=hidden_size, + bias=True, + **factory_kwargs, + ) + + def forward(self, caption): + hidden_states = self.linear_1(caption) + hidden_states = self.act_1(hidden_states) + hidden_states = self.linear_2(hidden_states) + return hidden_states + + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + + def __init__( + self, + hidden_size, + act_layer, + frequency_embedding_size=256, + max_period=10000, + out_size=None, + dtype=None, + device=None, + ): + factory_kwargs = {"dtype": dtype, "device": device} + super().__init__() + self.frequency_embedding_size = frequency_embedding_size + self.max_period = max_period + if out_size is None: + out_size = hidden_size + + self.mlp = nn.Sequential( + nn.Linear( + frequency_embedding_size, hidden_size, bias=True, **factory_kwargs + ), + act_layer(), + nn.Linear(hidden_size, out_size, bias=True, **factory_kwargs), + ) + nn.init.normal_(self.mlp[0].weight, std=0.02) # type: ignore + nn.init.normal_(self.mlp[2].weight, std=0.02) # type: ignore + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + + Args: + t (torch.Tensor): a 1-D Tensor of N indices, one per batch element. These may be fractional. + dim (int): the dimension of the output. + max_period (int): controls the minimum frequency of the embeddings. + + Returns: + embedding (torch.Tensor): An (N, D) Tensor of positional embeddings. + + .. ref_link: https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + """ + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) + * torch.arange(start=0, end=half, dtype=torch.float32) + / half + ).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + return embedding + + def forward(self, t): + t_freq = self.timestep_embedding( + t, self.frequency_embedding_size, self.max_period + ).type(t.dtype) # type: ignore + t_emb = self.mlp(t_freq) + return t_emb + + +def apply_gate(x, gate=None, tanh=False): + """AI is creating summary for apply_gate + + Args: + x (torch.Tensor): input tensor. + gate (torch.Tensor, optional): gate tensor. Defaults to None. + tanh (bool, optional): whether to use tanh function. Defaults to False. + + Returns: + torch.Tensor: the output tensor after apply gate. + """ + if gate is None: + return x + if tanh: + return x * gate.unsqueeze(1).tanh() + else: + return x * gate.unsqueeze(1) + + +class RMSNorm(nn.Module): + def __init__( + self, + dim: int, + elementwise_affine=True, + eps: float = 1e-6, + device=None, + dtype=None, + ): + """ + Initialize the RMSNorm normalization layer. + + Args: + dim (int): The dimension of the input tensor. + eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. + + Attributes: + eps (float): A small value added to the denominator for numerical stability. + weight (nn.Parameter): Learnable scaling parameter. + + """ + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + self.eps = eps + if elementwise_affine: + self.weight = nn.Parameter(torch.ones(dim, **factory_kwargs)) + + def _norm(self, x): + """ + Apply the RMSNorm normalization to the input tensor. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The normalized tensor. + + """ + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + """ + Forward pass through the RMSNorm layer. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The output tensor after applying RMSNorm. + + """ + output = self._norm(x.float()).type_as(x) + if hasattr(self, "weight"): + output = output * self.weight + return output + + +def get_norm_layer(norm_layer): + """ + Get the normalization layer. + + Args: + norm_layer (str): The type of normalization layer. + + Returns: + norm_layer (nn.Module): The normalization layer. + """ + if norm_layer == "layer": + return nn.LayerNorm + elif norm_layer == "rms": + return RMSNorm + else: + raise NotImplementedError(f"Norm layer {norm_layer} is not implemented") + + +def get_activation_layer(act_type): + """get activation layer + + Args: + act_type (str): the activation type + + Returns: + torch.nn.functional: the activation layer + """ + if act_type == "gelu": + return lambda: nn.GELU() + elif act_type == "gelu_tanh": + return lambda: nn.GELU(approximate="tanh") + elif act_type == "relu": + return nn.ReLU + elif act_type == "silu": + return nn.SiLU + else: + raise ValueError(f"Unknown activation type: {act_type}") + +class IndividualTokenRefinerBlock(torch.nn.Module): + def __init__( + self, + hidden_size, + heads_num, + mlp_width_ratio: str = 4.0, + mlp_drop_rate: float = 0.0, + act_type: str = "silu", + qk_norm: bool = False, + qk_norm_type: str = "layer", + qkv_bias: bool = True, + need_CA: bool = False, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + ): + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + self.need_CA = need_CA + self.heads_num = heads_num + head_dim = hidden_size // heads_num + mlp_hidden_dim = int(hidden_size * mlp_width_ratio) + + self.norm1 = nn.LayerNorm( + hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs + ) + self.self_attn_qkv = nn.Linear( + hidden_size, hidden_size * 3, bias=qkv_bias, **factory_kwargs + ) + qk_norm_layer = get_norm_layer(qk_norm_type) + self.self_attn_q_norm = ( + qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) + if qk_norm + else nn.Identity() + ) + self.self_attn_k_norm = ( + qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) + if qk_norm + else nn.Identity() + ) + self.self_attn_proj = nn.Linear( + hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs + ) + + self.norm2 = nn.LayerNorm( + hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs + ) + act_layer = get_activation_layer(act_type) + self.mlp = MLP( + in_channels=hidden_size, + hidden_channels=mlp_hidden_dim, + act_layer=act_layer, + drop=mlp_drop_rate, + **factory_kwargs, + ) + + self.adaLN_modulation = nn.Sequential( + act_layer(), + nn.Linear(hidden_size, 2 * hidden_size, bias=True, **factory_kwargs), + ) + + if self.need_CA: + self.cross_attnblock=CrossAttnBlock(hidden_size=hidden_size, + heads_num=heads_num, + mlp_width_ratio=mlp_width_ratio, + mlp_drop_rate=mlp_drop_rate, + act_type=act_type, + qk_norm=qk_norm, + qk_norm_type=qk_norm_type, + qkv_bias=qkv_bias, + **factory_kwargs,) + # Zero-initialize the modulation + nn.init.zeros_(self.adaLN_modulation[1].weight) + nn.init.zeros_(self.adaLN_modulation[1].bias) + + def forward( + self, + x: torch.Tensor, + c: torch.Tensor, # timestep_aware_representations + context_aware_representations + attn_mask: torch.Tensor = None, + y: torch.Tensor = None, + ): + gate_msa, gate_mlp = self.adaLN_modulation(c).chunk(2, dim=1) + + norm_x = self.norm1(x) + qkv = self.self_attn_qkv(norm_x) + q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num) + # Apply QK-Norm if needed + q = self.self_attn_q_norm(q).to(v) + k = self.self_attn_k_norm(k).to(v) + + # Self-Attention + attn = attention(q, k, v, mode="torch", attn_mask=attn_mask) + + x = x + apply_gate(self.self_attn_proj(attn), gate_msa) + + if self.need_CA: + x = self.cross_attnblock(x, c, attn_mask, y) + + # FFN Layer + x = x + apply_gate(self.mlp(self.norm2(x)), gate_mlp) + + return x + + + + +class CrossAttnBlock(torch.nn.Module): + def __init__( + self, + hidden_size, + heads_num, + mlp_width_ratio: str = 4.0, + mlp_drop_rate: float = 0.0, + act_type: str = "silu", + qk_norm: bool = False, + qk_norm_type: str = "layer", + qkv_bias: bool = True, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + ): + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + self.heads_num = heads_num + head_dim = hidden_size // heads_num + + self.norm1 = nn.LayerNorm( + hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs + ) + self.norm1_2 = nn.LayerNorm( + hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs + ) + self.self_attn_q = nn.Linear( + hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs + ) + self.self_attn_kv = nn.Linear( + hidden_size, hidden_size*2, bias=qkv_bias, **factory_kwargs + ) + qk_norm_layer = get_norm_layer(qk_norm_type) + self.self_attn_q_norm = ( + qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) + if qk_norm + else nn.Identity() + ) + self.self_attn_k_norm = ( + qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) + if qk_norm + else nn.Identity() + ) + self.self_attn_proj = nn.Linear( + hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs + ) + + self.norm2 = nn.LayerNorm( + hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs + ) + act_layer = get_activation_layer(act_type) + + self.adaLN_modulation = nn.Sequential( + act_layer(), + nn.Linear(hidden_size, 2 * hidden_size, bias=True, **factory_kwargs), + ) + # Zero-initialize the modulation + nn.init.zeros_(self.adaLN_modulation[1].weight) + nn.init.zeros_(self.adaLN_modulation[1].bias) + + def forward( + self, + x: torch.Tensor, + c: torch.Tensor, # timestep_aware_representations + context_aware_representations + attn_mask: torch.Tensor = None, + y: torch.Tensor=None, + + ): + gate_msa, gate_mlp = self.adaLN_modulation(c).chunk(2, dim=1) + + norm_x = self.norm1(x) + norm_y = self.norm1_2(y) + q = self.self_attn_q(norm_x) + q = rearrange(q, "B L (H D) -> B L H D", H=self.heads_num) + kv = self.self_attn_kv(norm_y) + k, v = rearrange(kv, "B L (K H D) -> K B L H D", K=2, H=self.heads_num) + # Apply QK-Norm if needed + q = self.self_attn_q_norm(q).to(v) + k = self.self_attn_k_norm(k).to(v) + + # Self-Attention + attn = attention(q, k, v, mode="torch", attn_mask=attn_mask) + + x = x + apply_gate(self.self_attn_proj(attn), gate_msa) + + return x + + + +class IndividualTokenRefiner(torch.nn.Module): + def __init__( + self, + hidden_size, + heads_num, + depth, + mlp_width_ratio: float = 4.0, + mlp_drop_rate: float = 0.0, + act_type: str = "silu", + qk_norm: bool = False, + qk_norm_type: str = "layer", + qkv_bias: bool = True, + need_CA:bool=False, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + ): + + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + self.need_CA = need_CA + self.blocks = nn.ModuleList( + [ + IndividualTokenRefinerBlock( + hidden_size=hidden_size, + heads_num=heads_num, + mlp_width_ratio=mlp_width_ratio, + mlp_drop_rate=mlp_drop_rate, + act_type=act_type, + qk_norm=qk_norm, + qk_norm_type=qk_norm_type, + qkv_bias=qkv_bias, + need_CA=self.need_CA, + **factory_kwargs, + ) + for _ in range(depth) + ] + ) + + + def forward( + self, + x: torch.Tensor, + c: torch.LongTensor, + mask: Optional[torch.Tensor] = None, + y:torch.Tensor=None, + ): + self_attn_mask = None + if mask is not None: + batch_size = mask.shape[0] + seq_len = mask.shape[1] + mask = mask.to(x.device) + # batch_size x 1 x seq_len x seq_len + self_attn_mask_1 = mask.view(batch_size, 1, 1, seq_len).repeat( + 1, 1, seq_len, 1 + ) + # batch_size x 1 x seq_len x seq_len + self_attn_mask_2 = self_attn_mask_1.transpose(2, 3) + # batch_size x 1 x seq_len x seq_len, 1 for broadcasting of heads_num + self_attn_mask = (self_attn_mask_1 & self_attn_mask_2).bool() + # avoids self-attention weight being NaN for padding tokens + self_attn_mask[:, :, :, 0] = True + + + for block in self.blocks: + x = block(x, c, self_attn_mask,y) + + return x + + +class SingleTokenRefiner(torch.nn.Module): + """ + A single token refiner block for llm text embedding refine. + """ + def __init__( + self, + in_channels, + hidden_size, + heads_num, + depth, + mlp_width_ratio: float = 4.0, + mlp_drop_rate: float = 0.0, + act_type: str = "silu", + qk_norm: bool = False, + qk_norm_type: str = "layer", + qkv_bias: bool = True, + need_CA:bool=False, + attn_mode: str = "torch", + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + ): + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + self.attn_mode = attn_mode + self.need_CA = need_CA + assert self.attn_mode == "torch", "Only support 'torch' mode for token refiner." + + self.input_embedder = nn.Linear( + in_channels, hidden_size, bias=True, **factory_kwargs + ) + if self.need_CA: + self.input_embedder_CA = nn.Linear( + in_channels, hidden_size, bias=True, **factory_kwargs + ) + + act_layer = get_activation_layer(act_type) + # Build timestep embedding layer + self.t_embedder = TimestepEmbedder(hidden_size, act_layer, **factory_kwargs) + # Build context embedding layer + self.c_embedder = TextProjection( + in_channels, hidden_size, act_layer, **factory_kwargs + ) + + self.individual_token_refiner = IndividualTokenRefiner( + hidden_size=hidden_size, + heads_num=heads_num, + depth=depth, + mlp_width_ratio=mlp_width_ratio, + mlp_drop_rate=mlp_drop_rate, + act_type=act_type, + qk_norm=qk_norm, + qk_norm_type=qk_norm_type, + qkv_bias=qkv_bias, + need_CA=need_CA, + **factory_kwargs, + ) + + def forward( + self, + x: torch.Tensor, + t: torch.LongTensor, + mask: Optional[torch.LongTensor] = None, + y: torch.LongTensor=None, + ): + timestep_aware_representations = self.t_embedder(t) + + if mask is None: + context_aware_representations = x.mean(dim=1) + else: + mask_float = mask.unsqueeze(-1) # [b, s1, 1] + context_aware_representations = (x * mask_float).sum( + dim=1 + ) / mask_float.sum(dim=1) + context_aware_representations = self.c_embedder(context_aware_representations) + c = timestep_aware_representations + context_aware_representations + + x = self.input_embedder(x) + if self.need_CA: + y = self.input_embedder_CA(y) + x = self.individual_token_refiner(x, c, mask, y) + else: + x = self.individual_token_refiner(x, c, mask) + + return x + + +class Qwen2Connector(torch.nn.Module): + def __init__( + self, + # biclip_dim=1024, + in_channels=3584, + hidden_size=4096, + heads_num=32, + depth=2, + need_CA=False, + device=None, + dtype=torch.bfloat16, + ): + super().__init__() + factory_kwargs = {"device": device, "dtype":dtype} + + self.S =SingleTokenRefiner(in_channels=in_channels,hidden_size=hidden_size,heads_num=heads_num,depth=depth,need_CA=need_CA,**factory_kwargs) + self.global_proj_out=nn.Linear(in_channels,768) + + self.scale_factor = nn.Parameter(torch.zeros(1)) + with torch.no_grad(): + self.scale_factor.data += -(1 - 0.09) + + def forward(self, x,t,mask): + mask_float = mask.unsqueeze(-1) # [b, s1, 1] + x_mean = (x * mask_float).sum( + dim=1 + ) / mask_float.sum(dim=1) * (1 + self.scale_factor.to(dtype=x.dtype, device=x.device)) + + global_out=self.global_proj_out(x_mean) + encoder_hidden_states = self.S(x,t,mask) + return encoder_hidden_states,global_out + + @staticmethod + def state_dict_converter(): + return Qwen2ConnectorStateDictConverter() + + +class Qwen2ConnectorStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + return state_dict + + def from_civitai(self, state_dict): + state_dict_ = {} + for name, param in state_dict.items(): + if name.startswith("connector."): + name_ = name[len("connector."):] + state_dict_[name_] = param + return state_dict_ diff --git a/diffsynth/models/stepvideo_dit.py b/diffsynth/models/stepvideo_dit.py new file mode 100644 index 0000000000000000000000000000000000000000..10576e77dbbcd9ac10e1e5b4ed8d52362e6ad82a --- /dev/null +++ b/diffsynth/models/stepvideo_dit.py @@ -0,0 +1,940 @@ +# Copyright 2025 StepFun Inc. All Rights Reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# ============================================================================== +from typing import Dict, Optional, Tuple, Union, List +import torch, math +from torch import nn +from einops import rearrange, repeat +from tqdm import tqdm + + +class RMSNorm(nn.Module): + def __init__( + self, + dim: int, + elementwise_affine=True, + eps: float = 1e-6, + device=None, + dtype=None, + ): + """ + Initialize the RMSNorm normalization layer. + + Args: + dim (int): The dimension of the input tensor. + eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. + + Attributes: + eps (float): A small value added to the denominator for numerical stability. + weight (nn.Parameter): Learnable scaling parameter. + + """ + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + self.eps = eps + if elementwise_affine: + self.weight = nn.Parameter(torch.ones(dim, **factory_kwargs)) + + def _norm(self, x): + """ + Apply the RMSNorm normalization to the input tensor. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The normalized tensor. + + """ + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + """ + Forward pass through the RMSNorm layer. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The output tensor after applying RMSNorm. + + """ + output = self._norm(x.float()).type_as(x) + if hasattr(self, "weight"): + output = output * self.weight + return output + + +ACTIVATION_FUNCTIONS = { + "swish": nn.SiLU(), + "silu": nn.SiLU(), + "mish": nn.Mish(), + "gelu": nn.GELU(), + "relu": nn.ReLU(), +} + + +def get_activation(act_fn: str) -> nn.Module: + """Helper function to get activation function from string. + + Args: + act_fn (str): Name of activation function. + + Returns: + nn.Module: Activation function. + """ + + act_fn = act_fn.lower() + if act_fn in ACTIVATION_FUNCTIONS: + return ACTIVATION_FUNCTIONS[act_fn] + else: + raise ValueError(f"Unsupported activation function: {act_fn}") + + +def get_timestep_embedding( + timesteps: torch.Tensor, + embedding_dim: int, + flip_sin_to_cos: bool = False, + downscale_freq_shift: float = 1, + scale: float = 1, + max_period: int = 10000, +): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings. + + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the + embeddings. :return: an [N x dim] Tensor of positional embeddings. + """ + assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array" + + half_dim = embedding_dim // 2 + exponent = -math.log(max_period) * torch.arange( + start=0, end=half_dim, dtype=torch.float32, device=timesteps.device + ) + exponent = exponent / (half_dim - downscale_freq_shift) + + emb = torch.exp(exponent) + emb = timesteps[:, None].float() * emb[None, :] + + # scale embeddings + emb = scale * emb + + # concat sine and cosine embeddings + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1) + + # flip sine and cosine embeddings + if flip_sin_to_cos: + emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1) + + # zero pad + if embedding_dim % 2 == 1: + emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) + return emb + + +class Timesteps(nn.Module): + def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float): + super().__init__() + self.num_channels = num_channels + self.flip_sin_to_cos = flip_sin_to_cos + self.downscale_freq_shift = downscale_freq_shift + + def forward(self, timesteps): + t_emb = get_timestep_embedding( + timesteps, + self.num_channels, + flip_sin_to_cos=self.flip_sin_to_cos, + downscale_freq_shift=self.downscale_freq_shift, + ) + return t_emb + + +class TimestepEmbedding(nn.Module): + def __init__( + self, + in_channels: int, + time_embed_dim: int, + act_fn: str = "silu", + out_dim: int = None, + post_act_fn: Optional[str] = None, + cond_proj_dim=None, + sample_proj_bias=True + ): + super().__init__() + linear_cls = nn.Linear + + self.linear_1 = linear_cls( + in_channels, + time_embed_dim, + bias=sample_proj_bias, + ) + + if cond_proj_dim is not None: + self.cond_proj = linear_cls( + cond_proj_dim, + in_channels, + bias=False, + ) + else: + self.cond_proj = None + + self.act = get_activation(act_fn) + + if out_dim is not None: + time_embed_dim_out = out_dim + else: + time_embed_dim_out = time_embed_dim + + self.linear_2 = linear_cls( + time_embed_dim, + time_embed_dim_out, + bias=sample_proj_bias, + ) + + if post_act_fn is None: + self.post_act = None + else: + self.post_act = get_activation(post_act_fn) + + def forward(self, sample, condition=None): + if condition is not None: + sample = sample + self.cond_proj(condition) + sample = self.linear_1(sample) + + if self.act is not None: + sample = self.act(sample) + + sample = self.linear_2(sample) + + if self.post_act is not None: + sample = self.post_act(sample) + return sample + + +class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module): + def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False): + super().__init__() + + self.outdim = size_emb_dim + self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0) + self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim) + + self.use_additional_conditions = use_additional_conditions + if self.use_additional_conditions: + self.additional_condition_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0) + self.resolution_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim) + self.nframe_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim) + self.fps_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim) + + def forward(self, timestep, resolution=None, nframe=None, fps=None): + hidden_dtype = timestep.dtype + + timesteps_proj = self.time_proj(timestep) + timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype)) # (N, D) + + if self.use_additional_conditions: + batch_size = timestep.shape[0] + resolution_emb = self.additional_condition_proj(resolution.flatten()).to(hidden_dtype) + resolution_emb = self.resolution_embedder(resolution_emb).reshape(batch_size, -1) + nframe_emb = self.additional_condition_proj(nframe.flatten()).to(hidden_dtype) + nframe_emb = self.nframe_embedder(nframe_emb).reshape(batch_size, -1) + conditioning = timesteps_emb + resolution_emb + nframe_emb + + if fps is not None: + fps_emb = self.additional_condition_proj(fps.flatten()).to(hidden_dtype) + fps_emb = self.fps_embedder(fps_emb).reshape(batch_size, -1) + conditioning = conditioning + fps_emb + else: + conditioning = timesteps_emb + + return conditioning + + +class AdaLayerNormSingle(nn.Module): + r""" + Norm layer adaptive layer norm single (adaLN-single). + + As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3). + + Parameters: + embedding_dim (`int`): The size of each embedding vector. + use_additional_conditions (`bool`): To use additional conditions for normalization or not. + """ + def __init__(self, embedding_dim: int, use_additional_conditions: bool = False, time_step_rescale=1000): + super().__init__() + + self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings( + embedding_dim, size_emb_dim=embedding_dim // 2, use_additional_conditions=use_additional_conditions + ) + + self.silu = nn.SiLU() + self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True) + + self.time_step_rescale = time_step_rescale ## timestep usually in [0, 1], we rescale it to [0,1000] for stability + + def forward( + self, + timestep: torch.Tensor, + added_cond_kwargs: Dict[str, torch.Tensor] = None, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + embedded_timestep = self.emb(timestep*self.time_step_rescale, **added_cond_kwargs) + + out = self.linear(self.silu(embedded_timestep)) + + return out, embedded_timestep + + +class PixArtAlphaTextProjection(nn.Module): + """ + Projects caption embeddings. Also handles dropout for classifier-free guidance. + + Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py + """ + + def __init__(self, in_features, hidden_size): + super().__init__() + self.linear_1 = nn.Linear( + in_features, + hidden_size, + bias=True, + ) + self.act_1 = nn.GELU(approximate="tanh") + self.linear_2 = nn.Linear( + hidden_size, + hidden_size, + bias=True, + ) + + def forward(self, caption): + hidden_states = self.linear_1(caption) + hidden_states = self.act_1(hidden_states) + hidden_states = self.linear_2(hidden_states) + return hidden_states + + +class Attention(nn.Module): + def __init__(self): + super().__init__() + + def attn_processor(self, attn_type): + if attn_type == 'torch': + return self.torch_attn_func + elif attn_type == 'parallel': + return self.parallel_attn_func + else: + raise Exception('Not supported attention type...') + + def torch_attn_func( + self, + q, + k, + v, + attn_mask=None, + causal=False, + drop_rate=0.0, + **kwargs + ): + + if attn_mask is not None and attn_mask.dtype != torch.bool: + attn_mask = attn_mask.to(q.dtype) + + if attn_mask is not None and attn_mask.ndim == 3: ## no head + n_heads = q.shape[2] + attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1, 1) + + q, k, v = map(lambda x: rearrange(x, 'b s h d -> b h s d'), (q, k, v)) + if attn_mask is not None: + attn_mask = attn_mask.to(q.device) + x = torch.nn.functional.scaled_dot_product_attention( + q, k, v, attn_mask=attn_mask, dropout_p=drop_rate, is_causal=causal + ) + x = rearrange(x, 'b h s d -> b s h d') + return x + + +class RoPE1D: + def __init__(self, freq=1e4, F0=1.0, scaling_factor=1.0): + self.base = freq + self.F0 = F0 + self.scaling_factor = scaling_factor + self.cache = {} + + def get_cos_sin(self, D, seq_len, device, dtype): + if (D, seq_len, device, dtype) not in self.cache: + inv_freq = 1.0 / (self.base ** (torch.arange(0, D, 2).float().to(device) / D)) + t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) + freqs = torch.einsum("i,j->ij", t, inv_freq).to(dtype) + freqs = torch.cat((freqs, freqs), dim=-1) + cos = freqs.cos() # (Seq, Dim) + sin = freqs.sin() + self.cache[D, seq_len, device, dtype] = (cos, sin) + return self.cache[D, seq_len, device, dtype] + + @staticmethod + def rotate_half(x): + x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2:] + return torch.cat((-x2, x1), dim=-1) + + def apply_rope1d(self, tokens, pos1d, cos, sin): + assert pos1d.ndim == 2 + cos = torch.nn.functional.embedding(pos1d, cos)[:, :, None, :] + sin = torch.nn.functional.embedding(pos1d, sin)[:, :, None, :] + return (tokens * cos) + (self.rotate_half(tokens) * sin) + + def __call__(self, tokens, positions): + """ + input: + * tokens: batch_size x ntokens x nheads x dim + * positions: batch_size x ntokens (t position of each token) + output: + * tokens after applying RoPE2D (batch_size x ntokens x nheads x dim) + """ + D = tokens.size(3) + assert positions.ndim == 2 # Batch, Seq + cos, sin = self.get_cos_sin(D, int(positions.max()) + 1, tokens.device, tokens.dtype) + tokens = self.apply_rope1d(tokens, positions, cos, sin) + return tokens + + +class RoPE3D(RoPE1D): + def __init__(self, freq=1e4, F0=1.0, scaling_factor=1.0): + super(RoPE3D, self).__init__(freq, F0, scaling_factor) + self.position_cache = {} + + def get_mesh_3d(self, rope_positions, bsz): + f, h, w = rope_positions + + if f"{f}-{h}-{w}" not in self.position_cache: + x = torch.arange(f, device='cpu') + y = torch.arange(h, device='cpu') + z = torch.arange(w, device='cpu') + self.position_cache[f"{f}-{h}-{w}"] = torch.cartesian_prod(x, y, z).view(1, f*h*w, 3).expand(bsz, -1, 3) + return self.position_cache[f"{f}-{h}-{w}"] + + def __call__(self, tokens, rope_positions, ch_split, parallel=False): + """ + input: + * tokens: batch_size x ntokens x nheads x dim + * rope_positions: list of (f, h, w) + output: + * tokens after applying RoPE2D (batch_size x ntokens x nheads x dim) + """ + assert sum(ch_split) == tokens.size(-1); + + mesh_grid = self.get_mesh_3d(rope_positions, bsz=tokens.shape[0]) + out = [] + for i, (D, x) in enumerate(zip(ch_split, torch.split(tokens, ch_split, dim=-1))): + cos, sin = self.get_cos_sin(D, int(mesh_grid.max()) + 1, tokens.device, tokens.dtype) + + if parallel: + pass + else: + mesh = mesh_grid[:, :, i].clone() + x = self.apply_rope1d(x, mesh.to(tokens.device), cos, sin) + out.append(x) + + tokens = torch.cat(out, dim=-1) + return tokens + + +class SelfAttention(Attention): + def __init__(self, hidden_dim, head_dim, bias=False, with_rope=True, with_qk_norm=True, attn_type='torch'): + super().__init__() + self.head_dim = head_dim + self.n_heads = hidden_dim // head_dim + + self.wqkv = nn.Linear(hidden_dim, hidden_dim*3, bias=bias) + self.wo = nn.Linear(hidden_dim, hidden_dim, bias=bias) + + self.with_rope = with_rope + self.with_qk_norm = with_qk_norm + if self.with_qk_norm: + self.q_norm = RMSNorm(head_dim, elementwise_affine=True) + self.k_norm = RMSNorm(head_dim, elementwise_affine=True) + + if self.with_rope: + self.rope_3d = RoPE3D(freq=1e4, F0=1.0, scaling_factor=1.0) + self.rope_ch_split = [64, 32, 32] + + self.core_attention = self.attn_processor(attn_type=attn_type) + self.parallel = attn_type=='parallel' + + def apply_rope3d(self, x, fhw_positions, rope_ch_split, parallel=True): + x = self.rope_3d(x, fhw_positions, rope_ch_split, parallel) + return x + + def forward( + self, + x, + cu_seqlens=None, + max_seqlen=None, + rope_positions=None, + attn_mask=None + ): + xqkv = self.wqkv(x) + xqkv = xqkv.view(*x.shape[:-1], self.n_heads, 3*self.head_dim) + + xq, xk, xv = torch.split(xqkv, [self.head_dim]*3, dim=-1) ## seq_len, n, dim + + if self.with_qk_norm: + xq = self.q_norm(xq) + xk = self.k_norm(xk) + + if self.with_rope: + xq = self.apply_rope3d(xq, rope_positions, self.rope_ch_split, parallel=self.parallel) + xk = self.apply_rope3d(xk, rope_positions, self.rope_ch_split, parallel=self.parallel) + + output = self.core_attention( + xq, + xk, + xv, + cu_seqlens=cu_seqlens, + max_seqlen=max_seqlen, + attn_mask=attn_mask + ) + output = rearrange(output, 'b s h d -> b s (h d)') + output = self.wo(output) + + return output + + +class CrossAttention(Attention): + def __init__(self, hidden_dim, head_dim, bias=False, with_qk_norm=True, attn_type='torch'): + super().__init__() + self.head_dim = head_dim + self.n_heads = hidden_dim // head_dim + + self.wq = nn.Linear(hidden_dim, hidden_dim, bias=bias) + self.wkv = nn.Linear(hidden_dim, hidden_dim*2, bias=bias) + self.wo = nn.Linear(hidden_dim, hidden_dim, bias=bias) + + self.with_qk_norm = with_qk_norm + if self.with_qk_norm: + self.q_norm = RMSNorm(head_dim, elementwise_affine=True) + self.k_norm = RMSNorm(head_dim, elementwise_affine=True) + + self.core_attention = self.attn_processor(attn_type=attn_type) + + def forward( + self, + x: torch.Tensor, + encoder_hidden_states: torch.Tensor, + attn_mask=None + ): + xq = self.wq(x) + xq = xq.view(*xq.shape[:-1], self.n_heads, self.head_dim) + + xkv = self.wkv(encoder_hidden_states) + xkv = xkv.view(*xkv.shape[:-1], self.n_heads, 2*self.head_dim) + + xk, xv = torch.split(xkv, [self.head_dim]*2, dim=-1) ## seq_len, n, dim + + if self.with_qk_norm: + xq = self.q_norm(xq) + xk = self.k_norm(xk) + + output = self.core_attention( + xq, + xk, + xv, + attn_mask=attn_mask + ) + + output = rearrange(output, 'b s h d -> b s (h d)') + output = self.wo(output) + + return output + + +class GELU(nn.Module): + r""" + GELU activation function with tanh approximation support with `approximate="tanh"`. + + Parameters: + dim_in (`int`): The number of channels in the input. + dim_out (`int`): The number of channels in the output. + approximate (`str`, *optional*, defaults to `"none"`): If `"tanh"`, use tanh approximation. + bias (`bool`, defaults to True): Whether to use a bias in the linear layer. + """ + + def __init__(self, dim_in: int, dim_out: int, approximate: str = "none", bias: bool = True): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out, bias=bias) + self.approximate = approximate + + def gelu(self, gate: torch.Tensor) -> torch.Tensor: + return torch.nn.functional.gelu(gate, approximate=self.approximate) + + def forward(self, hidden_states): + hidden_states = self.proj(hidden_states) + hidden_states = self.gelu(hidden_states) + return hidden_states + + +class FeedForward(nn.Module): + def __init__( + self, + dim: int, + inner_dim: Optional[int] = None, + dim_out: Optional[int] = None, + mult: int = 4, + bias: bool = False, + ): + super().__init__() + inner_dim = dim*mult if inner_dim is None else inner_dim + dim_out = dim if dim_out is None else dim_out + self.net = nn.ModuleList([ + GELU(dim, inner_dim, approximate="tanh", bias=bias), + nn.Identity(), + nn.Linear(inner_dim, dim_out, bias=bias) + ]) + + + def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor: + for module in self.net: + hidden_states = module(hidden_states) + return hidden_states + + +def modulate(x, scale, shift): + x = x * (1 + scale) + shift + return x + + +def gate(x, gate): + x = gate * x + return x + + +class StepVideoTransformerBlock(nn.Module): + r""" + A basic Transformer block. + + Parameters: + dim (`int`): The number of channels in the input and output. + num_attention_heads (`int`): The number of heads to use for multi-head attention. + attention_head_dim (`int`): The number of channels in each head. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + num_embeds_ada_norm (: + obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`. + attention_bias (: + obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. + only_cross_attention (`bool`, *optional*): + Whether to use only cross-attention layers. In this case two cross attention layers are used. + double_self_attention (`bool`, *optional*): + Whether to use two self-attention layers. In this case no cross attention layers are used. + upcast_attention (`bool`, *optional*): + Whether to upcast the attention computation to float32. This is useful for mixed precision training. + norm_elementwise_affine (`bool`, *optional*, defaults to `True`): + Whether to use learnable elementwise affine parameters for normalization. + norm_type (`str`, *optional*, defaults to `"layer_norm"`): + The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`. + final_dropout (`bool` *optional*, defaults to False): + Whether to apply a final dropout after the last feed-forward layer. + attention_type (`str`, *optional*, defaults to `"default"`): + The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`. + positional_embeddings (`str`, *optional*, defaults to `None`): + The type of positional embeddings to apply to. + num_positional_embeddings (`int`, *optional*, defaults to `None`): + The maximum number of positional embeddings to apply. + """ + + def __init__( + self, + dim: int, + attention_head_dim: int, + norm_eps: float = 1e-5, + ff_inner_dim: Optional[int] = None, + ff_bias: bool = False, + attention_type: str = 'parallel' + ): + super().__init__() + self.dim = dim + self.norm1 = nn.LayerNorm(dim, eps=norm_eps) + self.attn1 = SelfAttention(dim, attention_head_dim, bias=False, with_rope=True, with_qk_norm=True, attn_type=attention_type) + + self.norm2 = nn.LayerNorm(dim, eps=norm_eps) + self.attn2 = CrossAttention(dim, attention_head_dim, bias=False, with_qk_norm=True, attn_type='torch') + + self.ff = FeedForward(dim=dim, inner_dim=ff_inner_dim, dim_out=dim, bias=ff_bias) + + self.scale_shift_table = nn.Parameter(torch.randn(6, dim) /dim**0.5) + + @torch.no_grad() + def forward( + self, + q: torch.Tensor, + kv: Optional[torch.Tensor] = None, + timestep: Optional[torch.LongTensor] = None, + attn_mask = None, + rope_positions: list = None, + ) -> torch.Tensor: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( + torch.clone(chunk) for chunk in (self.scale_shift_table[None].to(dtype=q.dtype, device=q.device) + timestep.reshape(-1, 6, self.dim)).chunk(6, dim=1) + ) + + scale_shift_q = modulate(self.norm1(q), scale_msa, shift_msa) + + attn_q = self.attn1( + scale_shift_q, + rope_positions=rope_positions + ) + + q = gate(attn_q, gate_msa) + q + + attn_q = self.attn2( + q, + kv, + attn_mask + ) + + q = attn_q + q + + scale_shift_q = modulate(self.norm2(q), scale_mlp, shift_mlp) + + ff_output = self.ff(scale_shift_q) + + q = gate(ff_output, gate_mlp) + q + + return q + + +class PatchEmbed(nn.Module): + """2D Image to Patch Embedding""" + + def __init__( + self, + patch_size=64, + in_channels=3, + embed_dim=768, + layer_norm=False, + flatten=True, + bias=True, + ): + super().__init__() + + self.flatten = flatten + self.layer_norm = layer_norm + + self.proj = nn.Conv2d( + in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias + ) + + def forward(self, latent): + latent = self.proj(latent).to(latent.dtype) + if self.flatten: + latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC + if self.layer_norm: + latent = self.norm(latent) + + return latent + + +class StepVideoModel(torch.nn.Module): + def __init__( + self, + num_attention_heads: int = 48, + attention_head_dim: int = 128, + in_channels: int = 64, + out_channels: Optional[int] = 64, + num_layers: int = 48, + dropout: float = 0.0, + patch_size: int = 1, + norm_type: str = "ada_norm_single", + norm_elementwise_affine: bool = False, + norm_eps: float = 1e-6, + use_additional_conditions: Optional[bool] = False, + caption_channels: Optional[Union[int, List, Tuple]] = [6144, 1024], + attention_type: Optional[str] = "torch", + ): + super().__init__() + + # Set some common variables used across the board. + self.inner_dim = num_attention_heads * attention_head_dim + self.out_channels = in_channels if out_channels is None else out_channels + + self.use_additional_conditions = use_additional_conditions + + self.pos_embed = PatchEmbed( + patch_size=patch_size, + in_channels=in_channels, + embed_dim=self.inner_dim, + ) + + self.transformer_blocks = nn.ModuleList( + [ + StepVideoTransformerBlock( + dim=self.inner_dim, + attention_head_dim=attention_head_dim, + attention_type=attention_type + ) + for _ in range(num_layers) + ] + ) + + # 3. Output blocks. + self.norm_out = nn.LayerNorm(self.inner_dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) + self.scale_shift_table = nn.Parameter(torch.randn(2, self.inner_dim) / self.inner_dim**0.5) + self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels) + self.patch_size = patch_size + + self.adaln_single = AdaLayerNormSingle( + self.inner_dim, use_additional_conditions=self.use_additional_conditions + ) + + if isinstance(caption_channels, int): + caption_channel = caption_channels + else: + caption_channel, clip_channel = caption_channels + self.clip_projection = nn.Linear(clip_channel, self.inner_dim) + + self.caption_norm = nn.LayerNorm(caption_channel, eps=norm_eps, elementwise_affine=norm_elementwise_affine) + + self.caption_projection = PixArtAlphaTextProjection( + in_features=caption_channel, hidden_size=self.inner_dim + ) + + self.parallel = attention_type=='parallel' + + def patchfy(self, hidden_states): + hidden_states = rearrange(hidden_states, 'b f c h w -> (b f) c h w') + hidden_states = self.pos_embed(hidden_states) + return hidden_states + + def prepare_attn_mask(self, encoder_attention_mask, encoder_hidden_states, q_seqlen): + kv_seqlens = encoder_attention_mask.sum(dim=1).int() + mask = torch.zeros([len(kv_seqlens), q_seqlen, max(kv_seqlens)], dtype=torch.bool, device=encoder_attention_mask.device) + encoder_hidden_states = encoder_hidden_states[:,: max(kv_seqlens)] + for i, kv_len in enumerate(kv_seqlens): + mask[i, :, :kv_len] = 1 + return encoder_hidden_states, mask + + + def block_forward( + self, + hidden_states, + encoder_hidden_states=None, + timestep=None, + rope_positions=None, + attn_mask=None, + parallel=True + ): + for block in tqdm(self.transformer_blocks, desc="Transformer blocks"): + hidden_states = block( + hidden_states, + encoder_hidden_states, + timestep=timestep, + attn_mask=attn_mask, + rope_positions=rope_positions + ) + + return hidden_states + + + @torch.inference_mode() + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_hidden_states_2: Optional[torch.Tensor] = None, + timestep: Optional[torch.LongTensor] = None, + added_cond_kwargs: Dict[str, torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + fps: torch.Tensor=None, + return_dict: bool = False, + ): + assert hidden_states.ndim==5; "hidden_states's shape should be (bsz, f, ch, h ,w)" + + bsz, frame, _, height, width = hidden_states.shape + height, width = height // self.patch_size, width // self.patch_size + + hidden_states = self.patchfy(hidden_states) + len_frame = hidden_states.shape[1] + + if self.use_additional_conditions: + added_cond_kwargs = { + "resolution": torch.tensor([(height, width)]*bsz, device=hidden_states.device, dtype=hidden_states.dtype), + "nframe": torch.tensor([frame]*bsz, device=hidden_states.device, dtype=hidden_states.dtype), + "fps": fps + } + else: + added_cond_kwargs = {} + + timestep, embedded_timestep = self.adaln_single( + timestep, added_cond_kwargs=added_cond_kwargs + ) + + encoder_hidden_states = self.caption_projection(self.caption_norm(encoder_hidden_states)) + + if encoder_hidden_states_2 is not None and hasattr(self, 'clip_projection'): + clip_embedding = self.clip_projection(encoder_hidden_states_2) + encoder_hidden_states = torch.cat([clip_embedding, encoder_hidden_states], dim=1) + + hidden_states = rearrange(hidden_states, '(b f) l d-> b (f l) d', b=bsz, f=frame, l=len_frame).contiguous() + encoder_hidden_states, attn_mask = self.prepare_attn_mask(encoder_attention_mask, encoder_hidden_states, q_seqlen=frame*len_frame) + + hidden_states = self.block_forward( + hidden_states, + encoder_hidden_states, + timestep=timestep, + rope_positions=[frame, height, width], + attn_mask=attn_mask, + parallel=self.parallel + ) + + hidden_states = rearrange(hidden_states, 'b (f l) d -> (b f) l d', b=bsz, f=frame, l=len_frame) + + embedded_timestep = repeat(embedded_timestep, 'b d -> (b f) d', f=frame).contiguous() + + shift, scale = (self.scale_shift_table[None].to(dtype=embedded_timestep.dtype, device=embedded_timestep.device) + embedded_timestep[:, None]).chunk(2, dim=1) + hidden_states = self.norm_out(hidden_states) + # Modulation + hidden_states = hidden_states * (1 + scale) + shift + hidden_states = self.proj_out(hidden_states) + + # unpatchify + hidden_states = hidden_states.reshape( + shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) + ) + + hidden_states = rearrange(hidden_states, 'n h w p q c -> n c h p w q') + output = hidden_states.reshape( + shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size) + ) + + output = rearrange(output, '(b f) c h w -> b f c h w', f=frame) + + if return_dict: + return {'x': output} + return output + + @staticmethod + def state_dict_converter(): + return StepVideoDiTStateDictConverter() + + +class StepVideoDiTStateDictConverter: + def __init__(self): + super().__init__() + + def from_diffusers(self, state_dict): + return state_dict + + def from_civitai(self, state_dict): + return state_dict + + + \ No newline at end of file diff --git a/diffsynth/models/stepvideo_text_encoder.py b/diffsynth/models/stepvideo_text_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..9bd710cfbecb53bbbd7f2f6a45d75cbf8a01b2f1 --- /dev/null +++ b/diffsynth/models/stepvideo_text_encoder.py @@ -0,0 +1,554 @@ +# Copyright 2025 StepFun Inc. All Rights Reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# ============================================================================== +import os +from typing import Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +from .stepvideo_dit import RMSNorm +from safetensors.torch import load_file +from transformers.configuration_utils import PretrainedConfig +from transformers.modeling_utils import PreTrainedModel +from einops import rearrange +import json +from typing import List +from functools import wraps +import warnings + + + +class EmptyInitOnDevice(torch.overrides.TorchFunctionMode): + def __init__(self, device=None): + self.device = device + + def __torch_function__(self, func, types, args=(), kwargs=None): + kwargs = kwargs or {} + if getattr(func, '__module__', None) == 'torch.nn.init': + if 'tensor' in kwargs: + return kwargs['tensor'] + else: + return args[0] + if self.device is not None and func in torch.utils._device._device_constructors() and kwargs.get('device') is None: + kwargs['device'] = self.device + return func(*args, **kwargs) + + +def with_empty_init(func): + @wraps(func) + def wrapper(*args, **kwargs): + with EmptyInitOnDevice('cpu'): + return func(*args, **kwargs) + return wrapper + + + +class LLaMaEmbedding(nn.Module): + """Language model embeddings. + + Arguments: + hidden_size: hidden size + vocab_size: vocabulary size + max_sequence_length: maximum size of sequence. This + is used for positional embedding + embedding_dropout_prob: dropout probability for embeddings + init_method: weight initialization method + num_tokentypes: size of the token-type embeddings. 0 value + will ignore this embedding + """ + + def __init__(self, + cfg, + ): + super().__init__() + self.hidden_size = cfg.hidden_size + self.params_dtype = cfg.params_dtype + self.fp32_residual_connection = cfg.fp32_residual_connection + self.embedding_weights_in_fp32 = cfg.embedding_weights_in_fp32 + self.word_embeddings = torch.nn.Embedding( + cfg.padded_vocab_size, self.hidden_size, + ) + self.embedding_dropout = torch.nn.Dropout(cfg.hidden_dropout) + + def forward(self, input_ids): + # Embeddings. + if self.embedding_weights_in_fp32: + self.word_embeddings = self.word_embeddings.to(torch.float32) + embeddings = self.word_embeddings(input_ids) + if self.embedding_weights_in_fp32: + embeddings = embeddings.to(self.params_dtype) + self.word_embeddings = self.word_embeddings.to(self.params_dtype) + + # Data format change to avoid explicit transposes : [b s h] --> [s b h]. + embeddings = embeddings.transpose(0, 1).contiguous() + + # If the input flag for fp32 residual connection is set, convert for float. + if self.fp32_residual_connection: + embeddings = embeddings.float() + + # Dropout. + embeddings = self.embedding_dropout(embeddings) + + return embeddings + + + +class StepChatTokenizer: + """Step Chat Tokenizer""" + + def __init__( + self, model_file, name="StepChatTokenizer", + bot_token="<|BOT|>", # Begin of Turn + eot_token="<|EOT|>", # End of Turn + call_start_token="<|CALL_START|>", # Call Start + call_end_token="<|CALL_END|>", # Call End + think_start_token="<|THINK_START|>", # Think Start + think_end_token="<|THINK_END|>", # Think End + mask_start_token="<|MASK_1e69f|>", # Mask start + mask_end_token="<|UNMASK_1e69f|>", # Mask end + ): + import sentencepiece + + self._tokenizer = sentencepiece.SentencePieceProcessor(model_file=model_file) + + self._vocab = {} + self._inv_vocab = {} + + self._special_tokens = {} + self._inv_special_tokens = {} + + self._t5_tokens = [] + + for idx in range(self._tokenizer.get_piece_size()): + text = self._tokenizer.id_to_piece(idx) + self._inv_vocab[idx] = text + self._vocab[text] = idx + + if self._tokenizer.is_control(idx) or self._tokenizer.is_unknown(idx): + self._special_tokens[text] = idx + self._inv_special_tokens[idx] = text + + self._unk_id = self._tokenizer.unk_id() + self._bos_id = self._tokenizer.bos_id() + self._eos_id = self._tokenizer.eos_id() + + for token in [ + bot_token, eot_token, call_start_token, call_end_token, + think_start_token, think_end_token + ]: + assert token in self._vocab, f"Token '{token}' not found in tokenizer" + assert token in self._special_tokens, f"Token '{token}' is not a special token" + + for token in [mask_start_token, mask_end_token]: + assert token in self._vocab, f"Token '{token}' not found in tokenizer" + + self._bot_id = self._tokenizer.piece_to_id(bot_token) + self._eot_id = self._tokenizer.piece_to_id(eot_token) + self._call_start_id = self._tokenizer.piece_to_id(call_start_token) + self._call_end_id = self._tokenizer.piece_to_id(call_end_token) + self._think_start_id = self._tokenizer.piece_to_id(think_start_token) + self._think_end_id = self._tokenizer.piece_to_id(think_end_token) + self._mask_start_id = self._tokenizer.piece_to_id(mask_start_token) + self._mask_end_id = self._tokenizer.piece_to_id(mask_end_token) + + self._underline_id = self._tokenizer.piece_to_id("\u2581") + + @property + def vocab(self): + return self._vocab + + @property + def inv_vocab(self): + return self._inv_vocab + + @property + def vocab_size(self): + return self._tokenizer.vocab_size() + + def tokenize(self, text: str) -> List[int]: + return self._tokenizer.encode_as_ids(text) + + def detokenize(self, token_ids: List[int]) -> str: + return self._tokenizer.decode_ids(token_ids) + + +class Tokens: + def __init__(self, input_ids, cu_input_ids, attention_mask, cu_seqlens, max_seq_len) -> None: + self.input_ids = input_ids + self.attention_mask = attention_mask + self.cu_input_ids = cu_input_ids + self.cu_seqlens = cu_seqlens + self.max_seq_len = max_seq_len + def to(self, device): + self.input_ids = self.input_ids.to(device) + self.attention_mask = self.attention_mask.to(device) + self.cu_input_ids = self.cu_input_ids.to(device) + self.cu_seqlens = self.cu_seqlens.to(device) + return self + +class Wrapped_StepChatTokenizer(StepChatTokenizer): + def __call__(self, text, max_length=320, padding="max_length", truncation=True, return_tensors="pt"): + # [bos, ..., eos, pad, pad, ..., pad] + self.BOS = 1 + self.EOS = 2 + self.PAD = 2 + out_tokens = [] + attn_mask = [] + if len(text) == 0: + part_tokens = [self.BOS] + [self.EOS] + valid_size = len(part_tokens) + if len(part_tokens) < max_length: + part_tokens += [self.PAD] * (max_length - valid_size) + out_tokens.append(part_tokens) + attn_mask.append([1]*valid_size+[0]*(max_length-valid_size)) + else: + for part in text: + part_tokens = self.tokenize(part) + part_tokens = part_tokens[:(max_length - 2)] # leave 2 space for bos and eos + part_tokens = [self.BOS] + part_tokens + [self.EOS] + valid_size = len(part_tokens) + if len(part_tokens) < max_length: + part_tokens += [self.PAD] * (max_length - valid_size) + out_tokens.append(part_tokens) + attn_mask.append([1]*valid_size+[0]*(max_length-valid_size)) + + out_tokens = torch.tensor(out_tokens, dtype=torch.long) + attn_mask = torch.tensor(attn_mask, dtype=torch.long) + + # padding y based on tp size + padded_len = 0 + padded_flag = True if padded_len > 0 else False + if padded_flag: + pad_tokens = torch.tensor([[self.PAD] * max_length], device=out_tokens.device) + pad_attn_mask = torch.tensor([[1]*padded_len+[0]*(max_length-padded_len)], device=attn_mask.device) + out_tokens = torch.cat([out_tokens, pad_tokens], dim=0) + attn_mask = torch.cat([attn_mask, pad_attn_mask], dim=0) + + # cu_seqlens + cu_out_tokens = out_tokens.masked_select(attn_mask != 0).unsqueeze(0) + seqlen = attn_mask.sum(dim=1).tolist() + cu_seqlens = torch.cumsum(torch.tensor([0]+seqlen), 0).to(device=out_tokens.device,dtype=torch.int32) + max_seq_len = max(seqlen) + return Tokens(out_tokens, cu_out_tokens, attn_mask, cu_seqlens, max_seq_len) + + + +def flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=None, causal=True, + return_attn_probs=False, tp_group_rank=0, tp_group_size=1): + softmax_scale = q.size(-1) ** (-0.5) if softmax_scale is None else softmax_scale + if hasattr(torch.ops.Optimus, "fwd"): + results = torch.ops.Optimus.fwd(q, k, v, None, dropout_p, softmax_scale, causal, return_attn_probs, None, tp_group_rank, tp_group_size)[0] + else: + warnings.warn("Cannot load `torch.ops.Optimus.fwd`. Using `torch.nn.functional.scaled_dot_product_attention` instead.") + results = torch.nn.functional.scaled_dot_product_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), is_causal=True, scale=softmax_scale).transpose(1, 2) + return results + + +class FlashSelfAttention(torch.nn.Module): + def __init__( + self, + attention_dropout=0.0, + ): + super().__init__() + self.dropout_p = attention_dropout + + + def forward(self, q, k, v, cu_seqlens=None, max_seq_len=None): + if cu_seqlens is None: + output = flash_attn_func(q, k, v, dropout_p=self.dropout_p) + else: + raise ValueError('cu_seqlens is not supported!') + + return output + + + +def safediv(n, d): + q, r = divmod(n, d) + assert r == 0 + return q + + +class MultiQueryAttention(nn.Module): + def __init__(self, cfg, layer_id=None): + super().__init__() + + self.head_dim = cfg.hidden_size // cfg.num_attention_heads + self.max_seq_len = cfg.seq_length + self.use_flash_attention = cfg.use_flash_attn + assert self.use_flash_attention, 'FlashAttention is required!' + + self.n_groups = cfg.num_attention_groups + self.tp_size = 1 + self.n_local_heads = cfg.num_attention_heads + self.n_local_groups = self.n_groups + + self.wqkv = nn.Linear( + cfg.hidden_size, + cfg.hidden_size + self.head_dim * 2 * self.n_groups, + bias=False, + ) + self.wo = nn.Linear( + cfg.hidden_size, + cfg.hidden_size, + bias=False, + ) + + assert self.use_flash_attention, 'non-Flash attention not supported yet.' + self.core_attention = FlashSelfAttention(attention_dropout=cfg.attention_dropout) + + self.layer_id = layer_id + + def forward( + self, + x: torch.Tensor, + mask: Optional[torch.Tensor], + cu_seqlens: Optional[torch.Tensor], + max_seq_len: Optional[torch.Tensor], + ): + seqlen, bsz, dim = x.shape + xqkv = self.wqkv(x) + + xq, xkv = torch.split( + xqkv, + (dim // self.tp_size, + self.head_dim*2*self.n_groups // self.tp_size + ), + dim=-1, + ) + + # gather on 1st dimension + xq = xq.view(seqlen, bsz, self.n_local_heads, self.head_dim) + xkv = xkv.view(seqlen, bsz, self.n_local_groups, 2 * self.head_dim) + xk, xv = xkv.chunk(2, -1) + + # rotary embedding + flash attn + xq = rearrange(xq, "s b h d -> b s h d") + xk = rearrange(xk, "s b h d -> b s h d") + xv = rearrange(xv, "s b h d -> b s h d") + + q_per_kv = self.n_local_heads // self.n_local_groups + if q_per_kv > 1: + b, s, h, d = xk.size() + if h == 1: + xk = xk.expand(b, s, q_per_kv, d) + xv = xv.expand(b, s, q_per_kv, d) + else: + ''' To cover the cases where h > 1, we have + the following implementation, which is equivalent to: + xk = xk.repeat_interleave(q_per_kv, dim=-2) + xv = xv.repeat_interleave(q_per_kv, dim=-2) + but can avoid calling aten::item() that involves cpu. + ''' + idx = torch.arange(q_per_kv * h, device=xk.device).reshape(q_per_kv, -1).permute(1, 0).flatten() + xk = torch.index_select(xk.repeat(1, 1, q_per_kv, 1), 2, idx).contiguous() + xv = torch.index_select(xv.repeat(1, 1, q_per_kv, 1), 2, idx).contiguous() + + if self.use_flash_attention: + output = self.core_attention(xq, xk, xv, + cu_seqlens=cu_seqlens, + max_seq_len=max_seq_len) + # reduce-scatter only support first dimension now + output = rearrange(output, "b s h d -> s b (h d)").contiguous() + else: + xq, xk, xv = [ + rearrange(x, "b s ... -> s b ...").contiguous() + for x in (xq, xk, xv) + ] + output = self.core_attention(xq, xk, xv, mask) + output = self.wo(output) + return output + + + +class FeedForward(nn.Module): + def __init__( + self, + cfg, + dim: int, + hidden_dim: int, + layer_id: int, + multiple_of: int=256, + ): + super().__init__() + + hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + def swiglu(x): + x = torch.chunk(x, 2, dim=-1) + return F.silu(x[0]) * x[1] + self.swiglu = swiglu + + self.w1 = nn.Linear( + dim, + 2 * hidden_dim, + bias=False, + ) + self.w2 = nn.Linear( + hidden_dim, + dim, + bias=False, + ) + + def forward(self, x): + x = self.swiglu(self.w1(x)) + output = self.w2(x) + return output + + + +class TransformerBlock(nn.Module): + def __init__( + self, cfg, layer_id: int + ): + super().__init__() + + self.n_heads = cfg.num_attention_heads + self.dim = cfg.hidden_size + self.head_dim = cfg.hidden_size // cfg.num_attention_heads + self.attention = MultiQueryAttention( + cfg, + layer_id=layer_id, + ) + + self.feed_forward = FeedForward( + cfg, + dim=cfg.hidden_size, + hidden_dim=cfg.ffn_hidden_size, + layer_id=layer_id, + ) + self.layer_id = layer_id + self.attention_norm = RMSNorm( + cfg.hidden_size, + eps=cfg.layernorm_epsilon, + ) + self.ffn_norm = RMSNorm( + cfg.hidden_size, + eps=cfg.layernorm_epsilon, + ) + + def forward( + self, + x: torch.Tensor, + mask: Optional[torch.Tensor], + cu_seqlens: Optional[torch.Tensor], + max_seq_len: Optional[torch.Tensor], + ): + residual = self.attention.forward( + self.attention_norm(x), mask, + cu_seqlens, max_seq_len + ) + h = x + residual + ffn_res = self.feed_forward.forward(self.ffn_norm(h)) + out = h + ffn_res + return out + + +class Transformer(nn.Module): + def __init__( + self, + config, + max_seq_size=8192, + ): + super().__init__() + self.num_layers = config.num_layers + self.layers = self._build_layers(config) + + def _build_layers(self, config): + layers = torch.nn.ModuleList() + for layer_id in range(self.num_layers): + layers.append( + TransformerBlock( + config, + layer_id=layer_id + 1 , + ) + ) + return layers + + def forward( + self, + hidden_states, + attention_mask, + cu_seqlens=None, + max_seq_len=None, + ): + + if max_seq_len is not None and not isinstance(max_seq_len, torch.Tensor): + max_seq_len = torch.tensor(max_seq_len, dtype=torch.int32, device="cpu") + + for lid, layer in enumerate(self.layers): + hidden_states = layer( + hidden_states, + attention_mask, + cu_seqlens, + max_seq_len, + ) + return hidden_states + + +class Step1Model(PreTrainedModel): + config_class=PretrainedConfig + @with_empty_init + def __init__( + self, + config, + ): + super().__init__(config) + self.tok_embeddings = LLaMaEmbedding(config) + self.transformer = Transformer(config) + + def forward( + self, + input_ids=None, + attention_mask=None, + ): + + hidden_states = self.tok_embeddings(input_ids) + + hidden_states = self.transformer( + hidden_states, + attention_mask, + ) + return hidden_states + + + +class STEP1TextEncoder(torch.nn.Module): + def __init__(self, model_dir, max_length=320): + super(STEP1TextEncoder, self).__init__() + self.max_length = max_length + self.text_tokenizer = Wrapped_StepChatTokenizer(os.path.join(model_dir, 'step1_chat_tokenizer.model')) + text_encoder = Step1Model.from_pretrained(model_dir) + self.text_encoder = text_encoder.eval().to(torch.bfloat16) + + @staticmethod + def from_pretrained(path, torch_dtype=torch.bfloat16): + model = STEP1TextEncoder(path).to(torch_dtype) + return model + + @torch.no_grad + def forward(self, prompts, with_mask=True, max_length=None, device="cuda"): + self.device = device + with torch.no_grad(), torch.amp.autocast(dtype=torch.bfloat16, device_type=device): + if type(prompts) is str: + prompts = [prompts] + + txt_tokens = self.text_tokenizer( + prompts, max_length=max_length or self.max_length, padding="max_length", truncation=True, return_tensors="pt" + ) + y = self.text_encoder( + txt_tokens.input_ids.to(self.device), + attention_mask=txt_tokens.attention_mask.to(self.device) if with_mask else None + ) + y_mask = txt_tokens.attention_mask + return y.transpose(0,1), y_mask + diff --git a/diffsynth/models/stepvideo_vae.py b/diffsynth/models/stepvideo_vae.py new file mode 100644 index 0000000000000000000000000000000000000000..db244c00de53c29f87de67956e472fabad17256b --- /dev/null +++ b/diffsynth/models/stepvideo_vae.py @@ -0,0 +1,1132 @@ +# Copyright 2025 StepFun Inc. All Rights Reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# ============================================================================== +import torch +from einops import rearrange +from torch import nn +from torch.nn import functional as F +from tqdm import tqdm +from einops import repeat + + +class BaseGroupNorm(nn.GroupNorm): + def __init__(self, num_groups, num_channels): + super().__init__(num_groups=num_groups, num_channels=num_channels) + + def forward(self, x, zero_pad=False, **kwargs): + if zero_pad: + return base_group_norm_with_zero_pad(x, self, **kwargs) + else: + return base_group_norm(x, self, **kwargs) + + +def base_group_norm(x, norm_layer, act_silu=False, channel_last=False): + if hasattr(base_group_norm, 'spatial') and base_group_norm.spatial: + assert channel_last == True + x_shape = x.shape + x = x.flatten(0, 1) + if channel_last: + # Permute to NCHW format + x = x.permute(0, 3, 1, 2) + + out = F.group_norm(x.contiguous(), norm_layer.num_groups, norm_layer.weight, norm_layer.bias, norm_layer.eps) + if act_silu: + out = F.silu(out) + + if channel_last: + # Permute back to NHWC format + out = out.permute(0, 2, 3, 1) + + out = out.view(x_shape) + else: + if channel_last: + # Permute to NCHW format + x = x.permute(0, 3, 1, 2) + out = F.group_norm(x.contiguous(), norm_layer.num_groups, norm_layer.weight, norm_layer.bias, norm_layer.eps) + if act_silu: + out = F.silu(out) + if channel_last: + # Permute back to NHWC format + out = out.permute(0, 2, 3, 1) + return out + +def base_conv2d(x, conv_layer, channel_last=False, residual=None): + if channel_last: + x = x.permute(0, 3, 1, 2) # NHWC to NCHW + out = F.conv2d(x, conv_layer.weight, conv_layer.bias, stride=conv_layer.stride, padding=conv_layer.padding) + if residual is not None: + if channel_last: + residual = residual.permute(0, 3, 1, 2) # NHWC to NCHW + out += residual + if channel_last: + out = out.permute(0, 2, 3, 1) # NCHW to NHWC + return out + +def base_conv3d(x, conv_layer, channel_last=False, residual=None, only_return_output=False): + if only_return_output: + size = cal_outsize(x.shape, conv_layer.weight.shape, conv_layer.stride, conv_layer.padding) + return torch.empty(size, device=x.device, dtype=x.dtype) + if channel_last: + x = x.permute(0, 4, 1, 2, 3) # NDHWC to NCDHW + out = F.conv3d(x, conv_layer.weight, conv_layer.bias, stride=conv_layer.stride, padding=conv_layer.padding) + if residual is not None: + if channel_last: + residual = residual.permute(0, 4, 1, 2, 3) # NDHWC to NCDHW + out += residual + if channel_last: + out = out.permute(0, 2, 3, 4, 1) # NCDHW to NDHWC + return out + + +def cal_outsize(input_sizes, kernel_sizes, stride, padding): + stride_d, stride_h, stride_w = stride + padding_d, padding_h, padding_w = padding + dilation_d, dilation_h, dilation_w = 1, 1, 1 + + in_d = input_sizes[1] + in_h = input_sizes[2] + in_w = input_sizes[3] + in_channel = input_sizes[4] + + + kernel_d = kernel_sizes[2] + kernel_h = kernel_sizes[3] + kernel_w = kernel_sizes[4] + out_channels = kernel_sizes[0] + + out_d = calc_out_(in_d, padding_d, dilation_d, kernel_d, stride_d) + out_h = calc_out_(in_h, padding_h, dilation_h, kernel_h, stride_h) + out_w = calc_out_(in_w, padding_w, dilation_w, kernel_w, stride_w) + size = [input_sizes[0], out_d, out_h, out_w, out_channels] + return size + + + + +def calc_out_(in_size, padding, dilation, kernel, stride): + return (in_size + 2 * padding - dilation * (kernel - 1) - 1) // stride + 1 + + + +def base_conv3d_channel_last(x, conv_layer, residual=None): + in_numel = x.numel() + out_numel = int(x.numel() * conv_layer.out_channels / conv_layer.in_channels) + if (in_numel >= 2**30) or (out_numel >= 2**30): + assert conv_layer.stride[0] == 1, "time split asks time stride = 1" + + B,T,H,W,C = x.shape + K = conv_layer.kernel_size[0] + + chunks = 4 + chunk_size = T // chunks + + if residual is None: + out_nhwc = base_conv3d(x, conv_layer, channel_last=True, residual=residual, only_return_output=True) + else: + out_nhwc = residual + + assert B == 1 + outs = [] + for i in range(chunks): + if i == chunks-1: + xi = x[:1,chunk_size*i:] + out_nhwci = out_nhwc[:1,chunk_size*i:] + else: + xi = x[:1,chunk_size*i:chunk_size*(i+1)+K-1] + out_nhwci = out_nhwc[:1,chunk_size*i:chunk_size*(i+1)] + if residual is not None: + if i == chunks-1: + ri = residual[:1,chunk_size*i:] + else: + ri = residual[:1,chunk_size*i:chunk_size*(i+1)] + else: + ri = None + out_nhwci.copy_(base_conv3d(xi, conv_layer, channel_last=True, residual=ri)) + else: + out_nhwc = base_conv3d(x, conv_layer, channel_last=True, residual=residual) + return out_nhwc + + + +class Upsample2D(nn.Module): + def __init__(self, + channels, + use_conv=False, + use_conv_transpose=False, + out_channels=None): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_conv_transpose = use_conv_transpose + + if use_conv: + self.conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=1) + else: + assert "Not Supported" + self.conv = nn.ConvTranspose2d(channels, self.out_channels, 4, 2, 1) + + def forward(self, x, output_size=None): + assert x.shape[-1] == self.channels + + if self.use_conv_transpose: + return self.conv(x) + + if output_size is None: + x = F.interpolate( + x.permute(0,3,1,2).to(memory_format=torch.channels_last), + scale_factor=2.0, mode='nearest').permute(0,2,3,1).contiguous() + else: + x = F.interpolate( + x.permute(0,3,1,2).to(memory_format=torch.channels_last), + size=output_size, mode='nearest').permute(0,2,3,1).contiguous() + + # x = self.conv(x) + x = base_conv2d(x, self.conv, channel_last=True) + return x + + +class Downsample2D(nn.Module): + def __init__(self, channels, use_conv=False, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.padding = padding + stride = 2 + + if use_conv: + self.conv = nn.Conv2d(self.channels, self.out_channels, 3, stride=stride, padding=padding) + else: + assert self.channels == self.out_channels + self.conv = nn.AvgPool2d(kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[-1] == self.channels + if self.use_conv and self.padding == 0: + pad = (0, 0, 0, 1, 0, 1) + x = F.pad(x, pad, mode="constant", value=0) + + assert x.shape[-1] == self.channels + # x = self.conv(x) + x = base_conv2d(x, self.conv, channel_last=True) + return x + + + +class CausalConv(nn.Module): + def __init__(self, + chan_in, + chan_out, + kernel_size, + **kwargs + ): + super().__init__() + + if isinstance(kernel_size, int): + kernel_size = kernel_size if isinstance(kernel_size, tuple) else ((kernel_size,) * 3) + time_kernel_size, height_kernel_size, width_kernel_size = kernel_size + + self.dilation = kwargs.pop('dilation', 1) + self.stride = kwargs.pop('stride', 1) + if isinstance(self.stride, int): + self.stride = (self.stride, 1, 1) + time_pad = self.dilation * (time_kernel_size - 1) + max((1 - self.stride[0]), 0) + height_pad = height_kernel_size // 2 + width_pad = width_kernel_size // 2 + self.time_causal_padding = (width_pad, width_pad, height_pad, height_pad, time_pad, 0) + self.time_uncausal_padding = (width_pad, width_pad, height_pad, height_pad, 0, 0) + + self.conv = nn.Conv3d(chan_in, chan_out, kernel_size, stride=self.stride, dilation=self.dilation, **kwargs) + self.is_first_run = True + + def forward(self, x, is_init=True, residual=None): + x = nn.functional.pad(x, + self.time_causal_padding if is_init else self.time_uncausal_padding) + + x = self.conv(x) + if residual is not None: + x.add_(residual) + return x + + +class ChannelDuplicatingPixelUnshuffleUpSampleLayer3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + factor: int, + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.factor = factor + assert out_channels * factor**3 % in_channels == 0 + self.repeats = out_channels * factor**3 // in_channels + + def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor: + x = x.repeat_interleave(self.repeats, dim=1) + x = x.view(x.size(0), self.out_channels, self.factor, self.factor, self.factor, x.size(2), x.size(3), x.size(4)) + x = x.permute(0, 1, 5, 2, 6, 3, 7, 4).contiguous() + x = x.view(x.size(0), self.out_channels, x.size(2)*self.factor, x.size(4)*self.factor, x.size(6)*self.factor) + x = x[:, :, self.factor - 1:, :, :] + return x + +class ConvPixelShuffleUpSampleLayer3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + factor: int, + ): + super().__init__() + self.factor = factor + out_ratio = factor**3 + self.conv = CausalConv( + in_channels, + out_channels * out_ratio, + kernel_size=kernel_size + ) + + def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor: + x = self.conv(x, is_init) + x = self.pixel_shuffle_3d(x, self.factor) + return x + + @staticmethod + def pixel_shuffle_3d(x: torch.Tensor, factor: int) -> torch.Tensor: + batch_size, channels, depth, height, width = x.size() + new_channels = channels // (factor ** 3) + new_depth = depth * factor + new_height = height * factor + new_width = width * factor + + x = x.view(batch_size, new_channels, factor, factor, factor, depth, height, width) + x = x.permute(0, 1, 5, 2, 6, 3, 7, 4).contiguous() + x = x.view(batch_size, new_channels, new_depth, new_height, new_width) + x = x[:, :, factor - 1:, :, :] + return x + +class ConvPixelUnshuffleDownSampleLayer3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + factor: int, + ): + super().__init__() + self.factor = factor + out_ratio = factor**3 + assert out_channels % out_ratio == 0 + self.conv = CausalConv( + in_channels, + out_channels // out_ratio, + kernel_size=kernel_size + ) + + def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor: + x = self.conv(x, is_init) + x = self.pixel_unshuffle_3d(x, self.factor) + return x + + @staticmethod + def pixel_unshuffle_3d(x: torch.Tensor, factor: int) -> torch.Tensor: + pad = (0, 0, 0, 0, factor-1, 0) # (left, right, top, bottom, front, back) + x = F.pad(x, pad) + B, C, D, H, W = x.shape + x = x.view(B, C, D // factor, factor, H // factor, factor, W // factor, factor) + x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous() + x = x.view(B, C * factor**3, D // factor, H // factor, W // factor) + return x + +class PixelUnshuffleChannelAveragingDownSampleLayer3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + factor: int, + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.factor = factor + assert in_channels * factor**3 % out_channels == 0 + self.group_size = in_channels * factor**3 // out_channels + + def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor: + pad = (0, 0, 0, 0, self.factor-1, 0) # (left, right, top, bottom, front, back) + x = F.pad(x, pad) + B, C, D, H, W = x.shape + x = x.view(B, C, D // self.factor, self.factor, H // self.factor, self.factor, W // self.factor, self.factor) + x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous() + x = x.view(B, C * self.factor**3, D // self.factor, H // self.factor, W // self.factor) + x = x.view(B, self.out_channels, self.group_size, D // self.factor, H // self.factor, W // self.factor) + x = x.mean(dim=2) + return x + + def __init__( + self, + in_channels: int, + out_channels: int, + factor: int, + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.factor = factor + assert in_channels * factor**3 % out_channels == 0 + self.group_size = in_channels * factor**3 // out_channels + + def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor: + pad = (0, 0, 0, 0, self.factor-1, 0) # (left, right, top, bottom, front, back) + x = F.pad(x, pad) + B, C, D, H, W = x.shape + x = x.view(B, C, D // self.factor, self.factor, H // self.factor, self.factor, W // self.factor, self.factor) + x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous() + x = x.view(B, C * self.factor**3, D // self.factor, H // self.factor, W // self.factor) + x = x.view(B, self.out_channels, self.group_size, D // self.factor, H // self.factor, W // self.factor) + x = x.mean(dim=2) + return x + + + + +def base_group_norm_with_zero_pad(x, norm_layer, act_silu=True, pad_size=2): + out_shape = list(x.shape) + out_shape[1] += pad_size + out = torch.empty(out_shape, dtype=x.dtype, device=x.device) + out[:, pad_size:] = base_group_norm(x, norm_layer, act_silu=act_silu, channel_last=True) + out[:, :pad_size] = 0 + return out + + +class CausalConvChannelLast(CausalConv): + def __init__(self, + chan_in, + chan_out, + kernel_size, + **kwargs + ): + super().__init__( + chan_in, chan_out, kernel_size, **kwargs) + + self.time_causal_padding = (0, 0) + self.time_causal_padding + self.time_uncausal_padding = (0, 0) + self.time_uncausal_padding + + def forward(self, x, is_init=True, residual=None): + if self.is_first_run: + self.is_first_run = False + # self.conv.weight = nn.Parameter(self.conv.weight.permute(0,2,3,4,1).contiguous()) + + x = nn.functional.pad(x, + self.time_causal_padding if is_init else self.time_uncausal_padding) + + x = base_conv3d_channel_last(x, self.conv, residual=residual) + return x + +class CausalConvAfterNorm(CausalConv): + def __init__(self, + chan_in, + chan_out, + kernel_size, + **kwargs + ): + super().__init__( + chan_in, chan_out, kernel_size, **kwargs) + + if self.time_causal_padding == (1, 1, 1, 1, 2, 0): + self.conv = nn.Conv3d(chan_in, chan_out, kernel_size, stride=self.stride, dilation=self.dilation, padding=(0, 1, 1), **kwargs) + else: + self.conv = nn.Conv3d(chan_in, chan_out, kernel_size, stride=self.stride, dilation=self.dilation, **kwargs) + self.is_first_run = True + + def forward(self, x, is_init=True, residual=None): + if self.is_first_run: + self.is_first_run = False + + if self.time_causal_padding == (1, 1, 1, 1, 2, 0): + pass + else: + x = nn.functional.pad(x, self.time_causal_padding).contiguous() + + x = base_conv3d_channel_last(x, self.conv, residual=residual) + return x + +class AttnBlock(nn.Module): + def __init__(self, + in_channels + ): + super().__init__() + + self.norm = BaseGroupNorm(num_groups=32, num_channels=in_channels) + self.q = CausalConvChannelLast(in_channels, in_channels, kernel_size=1) + self.k = CausalConvChannelLast(in_channels, in_channels, kernel_size=1) + self.v = CausalConvChannelLast(in_channels, in_channels, kernel_size=1) + self.proj_out = CausalConvChannelLast(in_channels, in_channels, kernel_size=1) + + def attention(self, x, is_init=True): + x = self.norm(x, act_silu=False, channel_last=True) + q = self.q(x, is_init) + k = self.k(x, is_init) + v = self.v(x, is_init) + + b, t, h, w, c = q.shape + q, k, v = map(lambda x: rearrange(x, "b t h w c -> b 1 (t h w) c"), (q, k, v)) + x = nn.functional.scaled_dot_product_attention(q, k, v, is_causal=True) + x = rearrange(x, "b 1 (t h w) c -> b t h w c", t=t, h=h, w=w) + + return x + + def forward(self, x): + x = x.permute(0,2,3,4,1).contiguous() + h = self.attention(x) + x = self.proj_out(h, residual=x) + x = x.permute(0,4,1,2,3) + return x + +class Resnet3DBlock(nn.Module): + def __init__(self, + in_channels, + out_channels=None, + temb_channels=512, + conv_shortcut=False, + ): + super().__init__() + + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + + self.norm1 = BaseGroupNorm(num_groups=32, num_channels=in_channels) + self.conv1 = CausalConvAfterNorm(in_channels, out_channels, kernel_size=3) + if temb_channels > 0: + self.temb_proj = nn.Linear(temb_channels, out_channels) + + self.norm2 = BaseGroupNorm(num_groups=32, num_channels=out_channels) + self.conv2 = CausalConvAfterNorm(out_channels, out_channels, kernel_size=3) + + assert conv_shortcut is False + self.use_conv_shortcut = conv_shortcut + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = CausalConvAfterNorm(in_channels, out_channels, kernel_size=3) + else: + self.nin_shortcut = CausalConvAfterNorm(in_channels, out_channels, kernel_size=1) + + def forward(self, x, temb=None, is_init=True): + x = x.permute(0,2,3,4,1).contiguous() + + h = self.norm1(x, zero_pad=True, act_silu=True, pad_size=2) + h = self.conv1(h) + if temb is not None: + h = h + self.temb_proj(nn.functional.silu(temb))[:, :, None, None] + + x = self.nin_shortcut(x) if self.in_channels != self.out_channels else x + + h = self.norm2(h, zero_pad=True, act_silu=True, pad_size=2) + x = self.conv2(h, residual=x) + + x = x.permute(0,4,1,2,3) + return x + + +class Downsample3D(nn.Module): + def __init__(self, + in_channels, + with_conv, + stride + ): + super().__init__() + + self.with_conv = with_conv + if with_conv: + self.conv = CausalConv(in_channels, in_channels, kernel_size=3, stride=stride) + + def forward(self, x, is_init=True): + if self.with_conv: + x = self.conv(x, is_init) + else: + x = nn.functional.avg_pool3d(x, kernel_size=2, stride=2) + return x + +class VideoEncoder(nn.Module): + def __init__(self, + ch=32, + ch_mult=(4, 8, 16, 16), + num_res_blocks=2, + in_channels=3, + z_channels=16, + double_z=True, + down_sampling_layer=[1, 2], + resamp_with_conv=True, + version=1, + ): + super().__init__() + + temb_ch = 0 + + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + + # downsampling + self.conv_in = CausalConv(in_channels, ch, kernel_size=3) + self.down_sampling_layer = down_sampling_layer + + in_ch_mult = (1,) + tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append( + Resnet3DBlock(in_channels=block_in, out_channels=block_out, temb_channels=temb_ch)) + block_in = block_out + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + if i_level in self.down_sampling_layer: + down.downsample = Downsample3D(block_in, resamp_with_conv, stride=(2, 2, 2)) + else: + down.downsample = Downsample2D(block_in, resamp_with_conv, padding=0) #DIFF + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = Resnet3DBlock(in_channels=block_in, out_channels=block_in, temb_channels=temb_ch) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = Resnet3DBlock(in_channels=block_in, out_channels=block_in, temb_channels=temb_ch) + + # end + self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in) + self.version = version + if version == 2: + channels = 4 * z_channels * 2 ** 3 + self.conv_patchify = ConvPixelUnshuffleDownSampleLayer3D(block_in, channels, kernel_size=3, factor=2) + self.shortcut_pathify = PixelUnshuffleChannelAveragingDownSampleLayer3D(block_in, channels, 2) + self.shortcut_out = PixelUnshuffleChannelAveragingDownSampleLayer3D(channels, 2 * z_channels if double_z else z_channels, 1) + self.conv_out = CausalConvChannelLast(channels, 2 * z_channels if double_z else z_channels, kernel_size=3) + else: + self.conv_out = CausalConvAfterNorm(block_in, 2 * z_channels if double_z else z_channels, kernel_size=3) + + @torch.inference_mode() + def forward(self, x, video_frame_num, is_init=True): + # timestep embedding + temb = None + + t = video_frame_num + + # downsampling + h = self.conv_in(x, is_init) + + # make it real channel last, but behave like normal layout + h = h.permute(0,2,3,4,1).contiguous().permute(0,4,1,2,3) + + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](h, temb, is_init) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + + if i_level != self.num_resolutions - 1: + if isinstance(self.down[i_level].downsample, Downsample2D): + _, _, t, _, _ = h.shape + h = rearrange(h, "b c t h w -> (b t) h w c", t=t) + h = self.down[i_level].downsample(h) + h = rearrange(h, "(b t) h w c -> b c t h w", t=t) + else: + h = self.down[i_level].downsample(h, is_init) + + h = self.mid.block_1(h, temb, is_init) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb, is_init) + + h = h.permute(0,2,3,4,1).contiguous() # b c l h w -> b l h w c + if self.version == 2: + h = base_group_norm(h, self.norm_out, act_silu=True, channel_last=True) + h = h.permute(0,4,1,2,3).contiguous() + shortcut = self.shortcut_pathify(h, is_init) + h = self.conv_patchify(h, is_init) + h = h.add_(shortcut) + shortcut = self.shortcut_out(h, is_init).permute(0,2,3,4,1) + h = self.conv_out(h.permute(0,2,3,4,1).contiguous(), is_init) + h = h.add_(shortcut) + else: + h = base_group_norm_with_zero_pad(h, self.norm_out, act_silu=True, pad_size=2) + h = self.conv_out(h, is_init) + h = h.permute(0,4,1,2,3) # b l h w c -> b c l h w + + h = rearrange(h, "b c t h w -> b t c h w") + return h + + +class Res3DBlockUpsample(nn.Module): + def __init__(self, + input_filters, + num_filters, + down_sampling_stride, + down_sampling=False + ): + super().__init__() + + self.input_filters = input_filters + self.num_filters = num_filters + + self.act_ = nn.SiLU(inplace=True) + + self.conv1 = CausalConvChannelLast(num_filters, num_filters, kernel_size=[3, 3, 3]) + self.norm1 = BaseGroupNorm(32, num_filters) + + self.conv2 = CausalConvChannelLast(num_filters, num_filters, kernel_size=[3, 3, 3]) + self.norm2 = BaseGroupNorm(32, num_filters) + + self.down_sampling = down_sampling + if down_sampling: + self.down_sampling_stride = down_sampling_stride + else: + self.down_sampling_stride = [1, 1, 1] + + if num_filters != input_filters or down_sampling: + self.conv3 = CausalConvChannelLast(input_filters, num_filters, kernel_size=[1, 1, 1], stride=self.down_sampling_stride) + self.norm3 = BaseGroupNorm(32, num_filters) + + def forward(self, x, is_init=False): + x = x.permute(0,2,3,4,1).contiguous() + + residual = x + + h = self.conv1(x, is_init) + h = self.norm1(h, act_silu=True, channel_last=True) + + h = self.conv2(h, is_init) + h = self.norm2(h, act_silu=False, channel_last=True) + + if self.down_sampling or self.num_filters != self.input_filters: + x = self.conv3(x, is_init) + x = self.norm3(x, act_silu=False, channel_last=True) + + h.add_(x) + h = self.act_(h) + if residual is not None: + h.add_(residual) + + h = h.permute(0,4,1,2,3) + return h + +class Upsample3D(nn.Module): + def __init__(self, + in_channels, + scale_factor=2 + ): + super().__init__() + + self.scale_factor = scale_factor + self.conv3d = Res3DBlockUpsample(input_filters=in_channels, + num_filters=in_channels, + down_sampling_stride=(1, 1, 1), + down_sampling=False) + + def forward(self, x, is_init=True, is_split=True): + b, c, t, h, w = x.shape + + # x = x.permute(0,2,3,4,1).contiguous().permute(0,4,1,2,3).to(memory_format=torch.channels_last_3d) + if is_split: + split_size = c // 8 + x_slices = torch.split(x, split_size, dim=1) + x = [nn.functional.interpolate(x, scale_factor=self.scale_factor) for x in x_slices] + x = torch.cat(x, dim=1) + else: + x = nn.functional.interpolate(x, scale_factor=self.scale_factor) + + x = self.conv3d(x, is_init) + return x + +class VideoDecoder(nn.Module): + def __init__(self, + ch=128, + z_channels=16, + out_channels=3, + ch_mult=(1, 2, 4, 4), + num_res_blocks=2, + temporal_up_layers=[2, 3], + temporal_downsample=4, + resamp_with_conv=True, + version=1, + ): + super().__init__() + + temb_ch = 0 + + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.temporal_downsample = temporal_downsample + + block_in = ch * ch_mult[self.num_resolutions - 1] + self.version = version + if version == 2: + channels = 4 * z_channels * 2 ** 3 + self.conv_in = CausalConv(z_channels, channels, kernel_size=3) + self.shortcut_in = ChannelDuplicatingPixelUnshuffleUpSampleLayer3D(z_channels, channels, 1) + self.conv_unpatchify = ConvPixelShuffleUpSampleLayer3D(channels, block_in, kernel_size=3, factor=2) + self.shortcut_unpathify = ChannelDuplicatingPixelUnshuffleUpSampleLayer3D(channels, block_in, 2) + else: + self.conv_in = CausalConv(z_channels, block_in, kernel_size=3) + + # middle + self.mid = nn.Module() + self.mid.block_1 = Resnet3DBlock(in_channels=block_in, out_channels=block_in, temb_channels=temb_ch) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = Resnet3DBlock(in_channels=block_in, out_channels=block_in, temb_channels=temb_ch) + + # upsampling + self.up_id = len(temporal_up_layers) + self.video_frame_num = 1 + self.cur_video_frame_num = self.video_frame_num // 2 ** self.up_id + 1 + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + block.append( + Resnet3DBlock(in_channels=block_in, out_channels=block_out, temb_channels=temb_ch)) + block_in = block_out + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + if i_level in temporal_up_layers: + up.upsample = Upsample3D(block_in) + self.cur_video_frame_num = self.cur_video_frame_num * 2 + else: + up.upsample = Upsample2D(block_in, resamp_with_conv) + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in) + self.conv_out = CausalConvAfterNorm(block_in, out_channels, kernel_size=3) + + @torch.inference_mode() + def forward(self, z, is_init=True): + z = rearrange(z, "b t c h w -> b c t h w") + + h = self.conv_in(z, is_init=is_init) + if self.version == 2: + shortcut = self.shortcut_in(z, is_init=is_init) + h = h.add_(shortcut) + shortcut = self.shortcut_unpathify(h, is_init=is_init) + h = self.conv_unpatchify(h, is_init=is_init) + h = h.add_(shortcut) + + temb = None + + h = h.permute(0,2,3,4,1).contiguous().permute(0,4,1,2,3) + h = self.mid.block_1(h, temb, is_init=is_init) + h = self.mid.attn_1(h) + h = h.permute(0,2,3,4,1).contiguous().permute(0,4,1,2,3) + h = self.mid.block_2(h, temb, is_init=is_init) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = h.permute(0,2,3,4,1).contiguous().permute(0,4,1,2,3) + h = self.up[i_level].block[i_block](h, temb, is_init=is_init) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + if isinstance(self.up[i_level].upsample, Upsample2D) or (hasattr(self.up[i_level].upsample, "module") and isinstance(self.up[i_level].upsample.module, Upsample2D)): + B = h.size(0) + h = h.permute(0,2,3,4,1).flatten(0,1) + h = self.up[i_level].upsample(h) + h = h.unflatten(0, (B, -1)).permute(0,4,1,2,3) + else: + h = self.up[i_level].upsample(h, is_init=is_init) + + # end + h = h.permute(0,2,3,4,1) # b c l h w -> b l h w c + self.norm_out.to(dtype=h.dtype, device=h.device) # To be updated + h = base_group_norm_with_zero_pad(h, self.norm_out, act_silu=True, pad_size=2) + h = self.conv_out(h) + h = h.permute(0,4,1,2,3) + + if is_init: + h = h[:, :, (self.temporal_downsample - 1):] + return h + + + +def rms_norm(input, normalized_shape, eps=1e-6): + dtype = input.dtype + input = input.to(torch.float32) + variance = input.pow(2).flatten(-len(normalized_shape)).mean(-1)[(...,) + (None,) * len(normalized_shape)] + input = input * torch.rsqrt(variance + eps) + return input.to(dtype) + +class DiagonalGaussianDistribution(object): + def __init__(self, parameters, deterministic=False, rms_norm_mean=False, only_return_mean=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=-3) #N,[X],C,H,W + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + self.deterministic = deterministic + if self.deterministic: + self.var = self.std = torch.zeros_like( + self.mean, + device=self.parameters.device, + dtype=self.parameters.dtype) + if rms_norm_mean: + self.mean = rms_norm(self.mean, self.mean.size()[1:]) + self.only_return_mean = only_return_mean + + def sample(self, generator=None): + # make sure sample is on the same device + # as the parameters and has same dtype + sample = torch.randn( + self.mean.shape, generator=generator, device=self.parameters.device) + sample = sample.to(dtype=self.parameters.dtype) + x = self.mean + self.std * sample + if self.only_return_mean: + return self.mean + else: + return x + + +class StepVideoVAE(nn.Module): + def __init__(self, + in_channels=3, + out_channels=3, + z_channels=64, + num_res_blocks=2, + model_path=None, + weight_dict={}, + world_size=1, + version=2, + ): + super().__init__() + + self.frame_len = 17 + self.latent_len = 3 if version == 2 else 5 + + base_group_norm.spatial = True if version == 2 else False + + self.encoder = VideoEncoder( + in_channels=in_channels, + z_channels=z_channels, + num_res_blocks=num_res_blocks, + version=version, + ) + + self.decoder = VideoDecoder( + z_channels=z_channels, + out_channels=out_channels, + num_res_blocks=num_res_blocks, + version=version, + ) + + if model_path is not None: + weight_dict = self.init_from_ckpt(model_path) + if len(weight_dict) != 0: + self.load_from_dict(weight_dict) + self.convert_channel_last() + + self.world_size = world_size + + def init_from_ckpt(self, model_path): + from safetensors import safe_open + p = {} + with safe_open(model_path, framework="pt", device="cpu") as f: + for k in f.keys(): + tensor = f.get_tensor(k) + if k.startswith("decoder.conv_out."): + k = k.replace("decoder.conv_out.", "decoder.conv_out.conv.") + p[k] = tensor + return p + + def load_from_dict(self, p): + self.load_state_dict(p) + + def convert_channel_last(self): + #Conv2d NCHW->NHWC + pass + + def naive_encode(self, x, is_init_image=True): + b, l, c, h, w = x.size() + x = rearrange(x, 'b l c h w -> b c l h w').contiguous() + z = self.encoder(x, l, True) # 下采样[1, 4, 8, 16, 16] + return z + + @torch.inference_mode() + def encode(self, x): + # b (nc cf) c h w -> (b nc) cf c h w -> encode -> (b nc) cf c h w -> b (nc cf) c h w + chunks = list(x.split(self.frame_len, dim=1)) + for i in range(len(chunks)): + chunks[i] = self.naive_encode(chunks[i], True) + z = torch.cat(chunks, dim=1) + + posterior = DiagonalGaussianDistribution(z) + return posterior.sample() + + def decode_naive(self, z, is_init=True): + z = z.to(next(self.decoder.parameters()).dtype) + dec = self.decoder(z, is_init) + return dec + + @torch.inference_mode() + def decode_original(self, z): + # b (nc cf) c h w -> (b nc) cf c h w -> decode -> (b nc) c cf h w -> b (nc cf) c h w + chunks = list(z.split(self.latent_len, dim=1)) + + if self.world_size > 1: + chunks_total_num = len(chunks) + max_num_per_rank = (chunks_total_num + self.world_size - 1) // self.world_size + rank = torch.distributed.get_rank() + chunks_ = chunks[max_num_per_rank * rank : max_num_per_rank * (rank + 1)] + if len(chunks_) < max_num_per_rank: + chunks_.extend(chunks[:max_num_per_rank-len(chunks_)]) + chunks = chunks_ + + for i in range(len(chunks)): + chunks[i] = self.decode_naive(chunks[i], True).permute(0,2,1,3,4) + x = torch.cat(chunks, dim=1) + + if self.world_size > 1: + x_ = torch.empty([x.size(0), (self.world_size * max_num_per_rank) * self.frame_len, *x.shape[2:]], dtype=x.dtype, device=x.device) + torch.distributed.all_gather_into_tensor(x_, x) + x = x_[:, : chunks_total_num * self.frame_len] + + x = self.mix(x) + return x + + def mix(self, x, smooth_scale = 0.6): + remain_scale = smooth_scale + mix_scale = 1. - remain_scale + front = slice(self.frame_len - 1, x.size(1) - 1, self.frame_len) + back = slice(self.frame_len, x.size(1), self.frame_len) + x[:, front], x[:, back] = ( + x[:, front] * remain_scale + x[:, back] * mix_scale, + x[:, back] * remain_scale + x[:, front] * mix_scale + ) + return x + + def single_decode(self, hidden_states, device): + chunks = list(hidden_states.split(self.latent_len, dim=1)) + for i in range(len(chunks)): + chunks[i] = self.decode_naive(chunks[i].to(device), True).permute(0,2,1,3,4).cpu() + x = torch.cat(chunks, dim=1) + return x + + def build_1d_mask(self, length, left_bound, right_bound, border_width): + x = torch.ones((length,)) + if not left_bound: + x[:border_width] = (torch.arange(border_width) + 1) / border_width + if not right_bound: + x[-border_width:] = torch.flip((torch.arange(border_width) + 1) / border_width, dims=(0,)) + return x + + def build_mask(self, data, is_bound, border_width): + _, _, _, H, W = data.shape + h = self.build_1d_mask(H, is_bound[0], is_bound[1], border_width[0]) + w = self.build_1d_mask(W, is_bound[2], is_bound[3], border_width[1]) + + h = repeat(h, "H -> H W", H=H, W=W) + w = repeat(w, "W -> H W", H=H, W=W) + + mask = torch.stack([h, w]).min(dim=0).values + mask = rearrange(mask, "H W -> 1 1 1 H W") + return mask + + def tiled_decode(self, hidden_states, device, tile_size=(34, 34), tile_stride=(16, 16)): + B, T, C, H, W = hidden_states.shape + size_h, size_w = tile_size + stride_h, stride_w = tile_stride + + # Split tasks + tasks = [] + for t in range(0, T, 3): + for h in range(0, H, stride_h): + if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue + for w in range(0, W, stride_w): + if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue + t_, h_, w_ = t + 3, h + size_h, w + size_w + tasks.append((t, t_, h, h_, w, w_)) + + # Run + data_device = "cpu" + computation_device = device + + weight = torch.zeros((1, 1, T//3*17, H * 16, W * 16), dtype=hidden_states.dtype, device=data_device) + values = torch.zeros((B, 3, T//3*17, H * 16, W * 16), dtype=hidden_states.dtype, device=data_device) + + for t, t_, h, h_, w, w_ in tqdm(tasks, desc="VAE decoding"): + hidden_states_batch = hidden_states[:, t:t_, :, h:h_, w:w_].to(computation_device) + hidden_states_batch = self.decode_naive(hidden_states_batch, True).to(data_device) + + mask = self.build_mask( + hidden_states_batch, + is_bound=(h==0, h_>=H, w==0, w_>=W), + border_width=((size_h - stride_h) * 16, (size_w - stride_w) * 16) + ).to(dtype=hidden_states.dtype, device=data_device) + + target_t = t // 3 * 17 + target_h = h * 16 + target_w = w * 16 + values[ + :, + :, + target_t: target_t + hidden_states_batch.shape[2], + target_h: target_h + hidden_states_batch.shape[3], + target_w: target_w + hidden_states_batch.shape[4], + ] += hidden_states_batch * mask + weight[ + :, + :, + target_t: target_t + hidden_states_batch.shape[2], + target_h: target_h + hidden_states_batch.shape[3], + target_w: target_w + hidden_states_batch.shape[4], + ] += mask + return values / weight + + def decode(self, hidden_states, device, tiled=False, tile_size=(34, 34), tile_stride=(16, 16), smooth_scale=0.6): + hidden_states = hidden_states.to("cpu") + if tiled: + video = self.tiled_decode(hidden_states, device, tile_size, tile_stride) + else: + video = self.single_decode(hidden_states, device) + video = self.mix(video, smooth_scale=smooth_scale) + return video + + @staticmethod + def state_dict_converter(): + return StepVideoVAEStateDictConverter() + + +class StepVideoVAEStateDictConverter: + def __init__(self): + super().__init__() + + def from_diffusers(self, state_dict): + return self.from_civitai(state_dict) + + def from_civitai(self, state_dict): + state_dict_ = {} + for name, param in state_dict.items(): + if name.startswith("decoder.conv_out."): + name_ = name.replace("decoder.conv_out.", "decoder.conv_out.conv.") + else: + name_ = name + state_dict_[name_] = param + return state_dict_ diff --git a/diffsynth/models/svd_image_encoder.py b/diffsynth/models/svd_image_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..4ee79c863ec8da0185bf0e57ac0a286495b049ed --- /dev/null +++ b/diffsynth/models/svd_image_encoder.py @@ -0,0 +1,505 @@ +import torch +from .sd_text_encoder import CLIPEncoderLayer + + +class CLIPVisionEmbeddings(torch.nn.Module): + def __init__(self, embed_dim=1280, image_size=224, patch_size=14, num_channels=3): + super().__init__() + + # class_embeds (This is a fixed tensor) + self.class_embedding = torch.nn.Parameter(torch.randn(1, 1, embed_dim)) + + # position_embeds + self.patch_embedding = torch.nn.Conv2d(in_channels=num_channels, out_channels=embed_dim, kernel_size=patch_size, stride=patch_size, bias=False) + + # position_embeds (This is a fixed tensor) + self.position_embeds = torch.nn.Parameter(torch.zeros(1, (image_size // patch_size) ** 2 + 1, embed_dim)) + + def forward(self, pixel_values): + batch_size = pixel_values.shape[0] + patch_embeds = self.patch_embedding(pixel_values) + patch_embeds = patch_embeds.flatten(2).transpose(1, 2) + class_embeds = self.class_embedding.repeat(batch_size, 1, 1) + embeddings = torch.cat([class_embeds, patch_embeds], dim=1) + self.position_embeds + return embeddings + + +class SVDImageEncoder(torch.nn.Module): + def __init__(self, embed_dim=1280, layer_norm_eps=1e-5, num_encoder_layers=32, encoder_intermediate_size=5120, projection_dim=1024, num_heads=16, head_dim=80): + super().__init__() + self.embeddings = CLIPVisionEmbeddings(embed_dim=embed_dim) + self.pre_layernorm = torch.nn.LayerNorm(embed_dim, eps=layer_norm_eps) + self.encoders = torch.nn.ModuleList([ + CLIPEncoderLayer(embed_dim, encoder_intermediate_size, num_heads=num_heads, head_dim=head_dim, use_quick_gelu=False) + for _ in range(num_encoder_layers)]) + self.post_layernorm = torch.nn.LayerNorm(embed_dim, eps=layer_norm_eps) + self.visual_projection = torch.nn.Linear(embed_dim, projection_dim, bias=False) + + def forward(self, pixel_values): + embeds = self.embeddings(pixel_values) + embeds = self.pre_layernorm(embeds) + for encoder_id, encoder in enumerate(self.encoders): + embeds = encoder(embeds) + embeds = self.post_layernorm(embeds[:, 0, :]) + embeds = self.visual_projection(embeds) + return embeds + + @staticmethod + def state_dict_converter(): + return SVDImageEncoderStateDictConverter() + + +class SVDImageEncoderStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + rename_dict = { + "vision_model.embeddings.patch_embedding.weight": "embeddings.patch_embedding.weight", + "vision_model.embeddings.class_embedding": "embeddings.class_embedding", + "vision_model.embeddings.position_embedding.weight": "embeddings.position_embeds", + "vision_model.pre_layrnorm.weight": "pre_layernorm.weight", + "vision_model.pre_layrnorm.bias": "pre_layernorm.bias", + "vision_model.post_layernorm.weight": "post_layernorm.weight", + "vision_model.post_layernorm.bias": "post_layernorm.bias", + "visual_projection.weight": "visual_projection.weight" + } + attn_rename_dict = { + "self_attn.q_proj": "attn.to_q", + "self_attn.k_proj": "attn.to_k", + "self_attn.v_proj": "attn.to_v", + "self_attn.out_proj": "attn.to_out", + "layer_norm1": "layer_norm1", + "layer_norm2": "layer_norm2", + "mlp.fc1": "fc1", + "mlp.fc2": "fc2", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "vision_model.embeddings.class_embedding": + param = state_dict[name].view(1, 1, -1) + elif name == "vision_model.embeddings.position_embedding.weight": + param = state_dict[name].unsqueeze(0) + state_dict_[rename_dict[name]] = param + elif name.startswith("vision_model.encoder.layers."): + param = state_dict[name] + names = name.split(".") + layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1] + name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail]) + state_dict_[name_] = param + return state_dict_ + + def from_civitai(self, state_dict): + rename_dict = { + "conditioner.embedders.0.open_clip.model.visual.class_embedding": "embeddings.class_embedding", + "conditioner.embedders.0.open_clip.model.visual.conv1.weight": "embeddings.patch_embedding.weight", + "conditioner.embedders.0.open_clip.model.visual.ln_post.bias": "post_layernorm.bias", + "conditioner.embedders.0.open_clip.model.visual.ln_post.weight": "post_layernorm.weight", + "conditioner.embedders.0.open_clip.model.visual.ln_pre.bias": "pre_layernorm.bias", + "conditioner.embedders.0.open_clip.model.visual.ln_pre.weight": "pre_layernorm.weight", + "conditioner.embedders.0.open_clip.model.visual.positional_embedding": "embeddings.position_embeds", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.attn.in_proj_bias": ['encoders.0.attn.to_q.bias', 'encoders.0.attn.to_k.bias', 'encoders.0.attn.to_v.bias'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.attn.in_proj_weight": ['encoders.0.attn.to_q.weight', 'encoders.0.attn.to_k.weight', 'encoders.0.attn.to_v.weight'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.attn.out_proj.bias": "encoders.0.attn.to_out.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.attn.out_proj.weight": "encoders.0.attn.to_out.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.ln_1.bias": "encoders.0.layer_norm1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.ln_1.weight": "encoders.0.layer_norm1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.ln_2.bias": "encoders.0.layer_norm2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.ln_2.weight": "encoders.0.layer_norm2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.mlp.c_fc.bias": "encoders.0.fc1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.mlp.c_fc.weight": "encoders.0.fc1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.mlp.c_proj.bias": "encoders.0.fc2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.mlp.c_proj.weight": "encoders.0.fc2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.attn.in_proj_bias": ['encoders.1.attn.to_q.bias', 'encoders.1.attn.to_k.bias', 'encoders.1.attn.to_v.bias'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.attn.in_proj_weight": ['encoders.1.attn.to_q.weight', 'encoders.1.attn.to_k.weight', 'encoders.1.attn.to_v.weight'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.attn.out_proj.bias": "encoders.1.attn.to_out.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.attn.out_proj.weight": "encoders.1.attn.to_out.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.ln_1.bias": "encoders.1.layer_norm1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.ln_1.weight": "encoders.1.layer_norm1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.ln_2.bias": "encoders.1.layer_norm2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.ln_2.weight": "encoders.1.layer_norm2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.mlp.c_fc.bias": "encoders.1.fc1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.mlp.c_fc.weight": "encoders.1.fc1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.mlp.c_proj.bias": "encoders.1.fc2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.mlp.c_proj.weight": "encoders.1.fc2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.attn.in_proj_bias": ['encoders.10.attn.to_q.bias', 'encoders.10.attn.to_k.bias', 'encoders.10.attn.to_v.bias'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.attn.in_proj_weight": ['encoders.10.attn.to_q.weight', 'encoders.10.attn.to_k.weight', 'encoders.10.attn.to_v.weight'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.attn.out_proj.bias": "encoders.10.attn.to_out.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.attn.out_proj.weight": "encoders.10.attn.to_out.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.ln_1.bias": "encoders.10.layer_norm1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.ln_1.weight": "encoders.10.layer_norm1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.ln_2.bias": "encoders.10.layer_norm2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.ln_2.weight": "encoders.10.layer_norm2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.mlp.c_fc.bias": "encoders.10.fc1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.mlp.c_fc.weight": "encoders.10.fc1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.mlp.c_proj.bias": "encoders.10.fc2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.mlp.c_proj.weight": "encoders.10.fc2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.attn.in_proj_bias": ['encoders.11.attn.to_q.bias', 'encoders.11.attn.to_k.bias', 'encoders.11.attn.to_v.bias'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.attn.in_proj_weight": ['encoders.11.attn.to_q.weight', 'encoders.11.attn.to_k.weight', 'encoders.11.attn.to_v.weight'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.attn.out_proj.bias": "encoders.11.attn.to_out.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.attn.out_proj.weight": "encoders.11.attn.to_out.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.ln_1.bias": "encoders.11.layer_norm1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.ln_1.weight": "encoders.11.layer_norm1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.ln_2.bias": "encoders.11.layer_norm2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.ln_2.weight": "encoders.11.layer_norm2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.mlp.c_fc.bias": "encoders.11.fc1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.mlp.c_fc.weight": "encoders.11.fc1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.mlp.c_proj.bias": "encoders.11.fc2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.mlp.c_proj.weight": "encoders.11.fc2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.attn.in_proj_bias": ['encoders.12.attn.to_q.bias', 'encoders.12.attn.to_k.bias', 'encoders.12.attn.to_v.bias'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.attn.in_proj_weight": ['encoders.12.attn.to_q.weight', 'encoders.12.attn.to_k.weight', 'encoders.12.attn.to_v.weight'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.attn.out_proj.bias": "encoders.12.attn.to_out.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.attn.out_proj.weight": "encoders.12.attn.to_out.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.ln_1.bias": "encoders.12.layer_norm1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.ln_1.weight": "encoders.12.layer_norm1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.ln_2.bias": "encoders.12.layer_norm2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.ln_2.weight": "encoders.12.layer_norm2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.mlp.c_fc.bias": "encoders.12.fc1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.mlp.c_fc.weight": "encoders.12.fc1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.mlp.c_proj.bias": "encoders.12.fc2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.mlp.c_proj.weight": "encoders.12.fc2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.attn.in_proj_bias": ['encoders.13.attn.to_q.bias', 'encoders.13.attn.to_k.bias', 'encoders.13.attn.to_v.bias'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.attn.in_proj_weight": ['encoders.13.attn.to_q.weight', 'encoders.13.attn.to_k.weight', 'encoders.13.attn.to_v.weight'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.attn.out_proj.bias": "encoders.13.attn.to_out.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.attn.out_proj.weight": "encoders.13.attn.to_out.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.ln_1.bias": "encoders.13.layer_norm1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.ln_1.weight": "encoders.13.layer_norm1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.ln_2.bias": "encoders.13.layer_norm2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.ln_2.weight": "encoders.13.layer_norm2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.mlp.c_fc.bias": "encoders.13.fc1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.mlp.c_fc.weight": "encoders.13.fc1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.mlp.c_proj.bias": "encoders.13.fc2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.mlp.c_proj.weight": "encoders.13.fc2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.attn.in_proj_bias": ['encoders.14.attn.to_q.bias', 'encoders.14.attn.to_k.bias', 'encoders.14.attn.to_v.bias'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.attn.in_proj_weight": ['encoders.14.attn.to_q.weight', 'encoders.14.attn.to_k.weight', 'encoders.14.attn.to_v.weight'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.attn.out_proj.bias": "encoders.14.attn.to_out.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.attn.out_proj.weight": "encoders.14.attn.to_out.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.ln_1.bias": "encoders.14.layer_norm1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.ln_1.weight": "encoders.14.layer_norm1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.ln_2.bias": "encoders.14.layer_norm2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.ln_2.weight": "encoders.14.layer_norm2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.mlp.c_fc.bias": "encoders.14.fc1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.mlp.c_fc.weight": "encoders.14.fc1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.mlp.c_proj.bias": "encoders.14.fc2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.mlp.c_proj.weight": "encoders.14.fc2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.attn.in_proj_bias": ['encoders.15.attn.to_q.bias', 'encoders.15.attn.to_k.bias', 'encoders.15.attn.to_v.bias'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.attn.in_proj_weight": ['encoders.15.attn.to_q.weight', 'encoders.15.attn.to_k.weight', 'encoders.15.attn.to_v.weight'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.attn.out_proj.bias": "encoders.15.attn.to_out.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.attn.out_proj.weight": "encoders.15.attn.to_out.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.ln_1.bias": "encoders.15.layer_norm1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.ln_1.weight": "encoders.15.layer_norm1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.ln_2.bias": "encoders.15.layer_norm2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.ln_2.weight": "encoders.15.layer_norm2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.mlp.c_fc.bias": "encoders.15.fc1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.mlp.c_fc.weight": "encoders.15.fc1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.mlp.c_proj.bias": "encoders.15.fc2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.mlp.c_proj.weight": "encoders.15.fc2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.attn.in_proj_bias": ['encoders.16.attn.to_q.bias', 'encoders.16.attn.to_k.bias', 'encoders.16.attn.to_v.bias'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.attn.in_proj_weight": ['encoders.16.attn.to_q.weight', 'encoders.16.attn.to_k.weight', 'encoders.16.attn.to_v.weight'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.attn.out_proj.bias": "encoders.16.attn.to_out.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.attn.out_proj.weight": "encoders.16.attn.to_out.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.ln_1.bias": "encoders.16.layer_norm1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.ln_1.weight": "encoders.16.layer_norm1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.ln_2.bias": "encoders.16.layer_norm2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.ln_2.weight": "encoders.16.layer_norm2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.mlp.c_fc.bias": "encoders.16.fc1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.mlp.c_fc.weight": "encoders.16.fc1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.mlp.c_proj.bias": "encoders.16.fc2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.mlp.c_proj.weight": "encoders.16.fc2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.17.attn.in_proj_bias": ['encoders.17.attn.to_q.bias', 'encoders.17.attn.to_k.bias', 'encoders.17.attn.to_v.bias'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.17.attn.in_proj_weight": ['encoders.17.attn.to_q.weight', 'encoders.17.attn.to_k.weight', 'encoders.17.attn.to_v.weight'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.17.attn.out_proj.bias": "encoders.17.attn.to_out.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.17.attn.out_proj.weight": "encoders.17.attn.to_out.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.17.ln_1.bias": "encoders.17.layer_norm1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.17.ln_1.weight": 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"conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.ln_2.bias": "encoders.9.layer_norm2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.ln_2.weight": "encoders.9.layer_norm2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.mlp.c_fc.bias": "encoders.9.fc1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.mlp.c_fc.weight": "encoders.9.fc1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.mlp.c_proj.bias": "encoders.9.fc2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.mlp.c_proj.weight": "encoders.9.fc2.weight", + "conditioner.embedders.0.open_clip.model.visual.proj": "visual_projection.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "conditioner.embedders.0.open_clip.model.visual.class_embedding": + param = param.reshape((1, 1, param.shape[0])) + elif name == "conditioner.embedders.0.open_clip.model.visual.positional_embedding": + param = param.reshape((1, param.shape[0], param.shape[1])) + elif name == "conditioner.embedders.0.open_clip.model.visual.proj": + param = param.T + if isinstance(rename_dict[name], str): + state_dict_[rename_dict[name]] = param + else: + length = param.shape[0] // 3 + for i, rename in enumerate(rename_dict[name]): + state_dict_[rename] = param[i*length: i*length+length] + return state_dict_ diff --git a/diffsynth/models/svd_unet.py b/diffsynth/models/svd_unet.py new file mode 100644 index 0000000000000000000000000000000000000000..19c540a926914eea4f827cfb1aa460a098c61025 --- /dev/null +++ b/diffsynth/models/svd_unet.py @@ -0,0 +1,2007 @@ +import torch, math +from einops import rearrange, repeat +from .sd_unet import Timesteps, PushBlock, PopBlock, Attention, GEGLU, ResnetBlock, AttentionBlock, DownSampler, UpSampler + + +class TemporalResnetBlock(torch.nn.Module): + def __init__(self, in_channels, out_channels, temb_channels=None, groups=32, eps=1e-5): + super().__init__() + self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) + self.conv1 = torch.nn.Conv3d(in_channels, out_channels, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0)) + if temb_channels is not None: + self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels) + self.norm2 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True) + self.conv2 = torch.nn.Conv3d(out_channels, out_channels, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0)) + self.nonlinearity = torch.nn.SiLU() + self.conv_shortcut = None + if in_channels != out_channels: + self.conv_shortcut = torch.nn.Conv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=True) + + def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): + x = rearrange(hidden_states, "f c h w -> 1 c f h w") + x = self.norm1(x) + x = self.nonlinearity(x) + x = self.conv1(x) + if time_emb is not None: + emb = self.nonlinearity(time_emb) + emb = self.time_emb_proj(emb) + emb = repeat(emb, "b c -> b c f 1 1", f=hidden_states.shape[0]) + x = x + emb + x = self.norm2(x) + x = self.nonlinearity(x) + x = self.conv2(x) + if self.conv_shortcut is not None: + hidden_states = self.conv_shortcut(hidden_states) + x = rearrange(x[0], "c f h w -> f c h w") + hidden_states = hidden_states + x + return hidden_states, time_emb, text_emb, res_stack + + +def get_timestep_embedding( + timesteps: torch.Tensor, + embedding_dim: int, + flip_sin_to_cos: bool = False, + downscale_freq_shift: float = 1, + scale: float = 1, + max_period: int = 10000, + computation_device = None, +): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings. + + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the + embeddings. :return: an [N x dim] Tensor of positional embeddings. + """ + assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array" + + half_dim = embedding_dim // 2 + exponent = -math.log(max_period) * torch.arange( + start=0, end=half_dim, dtype=torch.float32, device=timesteps.device if computation_device is None else computation_device + ) + exponent = exponent / (half_dim - downscale_freq_shift) + + emb = torch.exp(exponent).to(timesteps.device) + emb = timesteps[:, None].float() * emb[None, :] + + # scale embeddings + emb = scale * emb + + # concat sine and cosine embeddings + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1) + + # flip sine and cosine embeddings + if flip_sin_to_cos: + emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1) + + # zero pad + if embedding_dim % 2 == 1: + emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) + return emb + + +class TemporalTimesteps(torch.nn.Module): + def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, computation_device = None): + super().__init__() + self.num_channels = num_channels + self.flip_sin_to_cos = flip_sin_to_cos + self.downscale_freq_shift = downscale_freq_shift + self.computation_device = computation_device + + def forward(self, timesteps): + t_emb = get_timestep_embedding( + timesteps, + self.num_channels, + flip_sin_to_cos=self.flip_sin_to_cos, + downscale_freq_shift=self.downscale_freq_shift, + computation_device=self.computation_device, + ) + return t_emb + + +class TrainableTemporalTimesteps(torch.nn.Module): + def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, num_frames: int): + super().__init__() + timesteps = PositionalID()(num_frames) + embeddings = get_timestep_embedding(timesteps, num_channels, flip_sin_to_cos, downscale_freq_shift) + self.embeddings = torch.nn.Parameter(embeddings) + + def forward(self, timesteps): + t_emb = self.embeddings[timesteps] + return t_emb + + +class PositionalID(torch.nn.Module): + def __init__(self, max_id=25, repeat_length=20): + super().__init__() + self.max_id = max_id + self.repeat_length = repeat_length + + def frame_id_to_position_id(self, frame_id): + if frame_id < self.max_id: + position_id = frame_id + else: + position_id = (frame_id - self.max_id) % (self.repeat_length * 2) + if position_id < self.repeat_length: + position_id = self.max_id - 2 - position_id + else: + position_id = self.max_id - 2 * self.repeat_length + position_id + return position_id + + def forward(self, num_frames, pivot_frame_id=0): + position_ids = [self.frame_id_to_position_id(abs(i-pivot_frame_id)) for i in range(num_frames)] + position_ids = torch.IntTensor(position_ids) + return position_ids + + +class TemporalAttentionBlock(torch.nn.Module): + + def __init__(self, num_attention_heads, attention_head_dim, in_channels, cross_attention_dim=None, add_positional_conv=None): + super().__init__() + + self.positional_embedding_proj = torch.nn.Sequential( + torch.nn.Linear(in_channels, in_channels * 4), + torch.nn.SiLU(), + torch.nn.Linear(in_channels * 4, in_channels) + ) + if add_positional_conv is not None: + self.positional_embedding = TrainableTemporalTimesteps(in_channels, True, 0, add_positional_conv) + self.positional_conv = torch.nn.Conv3d(in_channels, in_channels, kernel_size=3, padding=1, padding_mode="reflect") + else: + self.positional_embedding = TemporalTimesteps(in_channels, True, 0) + self.positional_conv = None + + self.norm_in = torch.nn.LayerNorm(in_channels) + self.act_fn_in = GEGLU(in_channels, in_channels * 4) + self.ff_in = torch.nn.Linear(in_channels * 4, in_channels) + + self.norm1 = torch.nn.LayerNorm(in_channels) + self.attn1 = Attention( + q_dim=in_channels, + num_heads=num_attention_heads, + head_dim=attention_head_dim, + bias_out=True + ) + + self.norm2 = torch.nn.LayerNorm(in_channels) + self.attn2 = Attention( + q_dim=in_channels, + kv_dim=cross_attention_dim, + num_heads=num_attention_heads, + head_dim=attention_head_dim, + bias_out=True + ) + + self.norm_out = torch.nn.LayerNorm(in_channels) + self.act_fn_out = GEGLU(in_channels, in_channels * 4) + self.ff_out = torch.nn.Linear(in_channels * 4, in_channels) + + def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): + + batch, inner_dim, height, width = hidden_states.shape + pos_emb = torch.arange(batch) + pos_emb = self.positional_embedding(pos_emb).to(dtype=hidden_states.dtype, device=hidden_states.device) + pos_emb = self.positional_embedding_proj(pos_emb) + + hidden_states = rearrange(hidden_states, "T C H W -> 1 C T H W") + rearrange(pos_emb, "T C -> 1 C T 1 1") + if self.positional_conv is not None: + hidden_states = self.positional_conv(hidden_states) + hidden_states = rearrange(hidden_states[0], "C T H W -> (H W) T C") + + residual = hidden_states + hidden_states = self.norm_in(hidden_states) + hidden_states = self.act_fn_in(hidden_states) + hidden_states = self.ff_in(hidden_states) + hidden_states = hidden_states + residual + + norm_hidden_states = self.norm1(hidden_states) + attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None) + hidden_states = attn_output + hidden_states + + norm_hidden_states = self.norm2(hidden_states) + attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=text_emb.repeat(height * width, 1)) + hidden_states = attn_output + hidden_states + + residual = hidden_states + hidden_states = self.norm_out(hidden_states) + hidden_states = self.act_fn_out(hidden_states) + hidden_states = self.ff_out(hidden_states) + hidden_states = hidden_states + residual + + hidden_states = hidden_states.reshape(height, width, batch, inner_dim).permute(2, 3, 0, 1) + + return hidden_states, time_emb, text_emb, res_stack + + +class PopMixBlock(torch.nn.Module): + def __init__(self, in_channels=None): + super().__init__() + self.mix_factor = torch.nn.Parameter(torch.Tensor([0.5])) + self.need_proj = in_channels is not None + if self.need_proj: + self.proj = torch.nn.Linear(in_channels, in_channels) + + def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): + res_hidden_states = res_stack.pop() + alpha = torch.sigmoid(self.mix_factor) + hidden_states = alpha * res_hidden_states + (1 - alpha) * hidden_states + if self.need_proj: + hidden_states = hidden_states.permute(0, 2, 3, 1) + hidden_states = self.proj(hidden_states) + hidden_states = hidden_states.permute(0, 3, 1, 2) + res_hidden_states = res_stack.pop() + hidden_states = hidden_states + res_hidden_states + return hidden_states, time_emb, text_emb, res_stack + + +class SVDUNet(torch.nn.Module): + def __init__(self, add_positional_conv=None): + super().__init__() + self.time_proj = Timesteps(320) + self.time_embedding = torch.nn.Sequential( + torch.nn.Linear(320, 1280), + torch.nn.SiLU(), + torch.nn.Linear(1280, 1280) + ) + self.add_time_proj = Timesteps(256) + self.add_time_embedding = torch.nn.Sequential( + torch.nn.Linear(768, 1280), + torch.nn.SiLU(), + torch.nn.Linear(1280, 1280) + ) + self.conv_in = torch.nn.Conv2d(8, 320, kernel_size=3, padding=1) + + self.blocks = torch.nn.ModuleList([ + # CrossAttnDownBlockSpatioTemporal + ResnetBlock(320, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv), PopMixBlock(320), PushBlock(), + ResnetBlock(320, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv), PopMixBlock(320), PushBlock(), + DownSampler(320), PushBlock(), + # CrossAttnDownBlockSpatioTemporal + ResnetBlock(320, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv), PopMixBlock(640), PushBlock(), + ResnetBlock(640, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv), PopMixBlock(640), PushBlock(), + DownSampler(640), PushBlock(), + # CrossAttnDownBlockSpatioTemporal + ResnetBlock(640, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), PushBlock(), + ResnetBlock(1280, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), PushBlock(), + DownSampler(1280), PushBlock(), + # DownBlockSpatioTemporal + ResnetBlock(1280, 1280, 1280, eps=1e-5), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), PushBlock(), + ResnetBlock(1280, 1280, 1280, eps=1e-5), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), PushBlock(), + # UNetMidBlockSpatioTemporal + ResnetBlock(1280, 1280, 1280, eps=1e-5), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), PushBlock(), + AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), + ResnetBlock(1280, 1280, 1280, eps=1e-5), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), + # UpBlockSpatioTemporal + PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), + PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), + PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), + UpSampler(1280), + # CrossAttnUpBlockSpatioTemporal + PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), + PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), + PopBlock(), ResnetBlock(1920, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), + UpSampler(1280), + # CrossAttnUpBlockSpatioTemporal + PopBlock(), ResnetBlock(1920, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv), PopMixBlock(640), + PopBlock(), ResnetBlock(1280, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv), PopMixBlock(640), + PopBlock(), ResnetBlock(960, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv), PopMixBlock(640), + UpSampler(640), + # CrossAttnUpBlockSpatioTemporal + PopBlock(), ResnetBlock(960, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv), PopMixBlock(320), + PopBlock(), ResnetBlock(640, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv), PopMixBlock(320), + PopBlock(), ResnetBlock(640, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv), PopMixBlock(320), + ]) + + self.conv_norm_out = torch.nn.GroupNorm(32, 320, eps=1e-05, affine=True) + self.conv_act = torch.nn.SiLU() + self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + + + def build_mask(self, data, is_bound): + T, C, H, W = data.shape + t = repeat(torch.arange(T), "T -> T H W", T=T, H=H, W=W) + h = repeat(torch.arange(H), "H -> T H W", T=T, H=H, W=W) + w = repeat(torch.arange(W), "W -> T H W", T=T, H=H, W=W) + border_width = (T + H + W) // 6 + pad = torch.ones_like(t) * border_width + mask = torch.stack([ + pad if is_bound[0] else t + 1, + pad if is_bound[1] else T - t, + pad if is_bound[2] else h + 1, + pad if is_bound[3] else H - h, + pad if is_bound[4] else w + 1, + pad if is_bound[5] else W - w + ]).min(dim=0).values + mask = mask.clip(1, border_width) + mask = (mask / border_width).to(dtype=data.dtype, device=data.device) + mask = rearrange(mask, "T H W -> T 1 H W") + return mask + + + def tiled_forward( + self, sample, timestep, encoder_hidden_states, add_time_id, + batch_time=25, batch_height=128, batch_width=128, + stride_time=5, stride_height=64, stride_width=64, + progress_bar=lambda x:x + ): + data_device = sample.device + computation_device = self.conv_in.weight.device + torch_dtype = sample.dtype + T, C, H, W = sample.shape + + weight = torch.zeros((T, 1, H, W), dtype=torch_dtype, device=data_device) + values = torch.zeros((T, 4, H, W), dtype=torch_dtype, device=data_device) + + # Split tasks + tasks = [] + for t in range(0, T, stride_time): + for h in range(0, H, stride_height): + for w in range(0, W, stride_width): + if (t-stride_time >= 0 and t-stride_time+batch_time >= T)\ + or (h-stride_height >= 0 and h-stride_height+batch_height >= H)\ + or (w-stride_width >= 0 and w-stride_width+batch_width >= W): + continue + tasks.append((t, t+batch_time, h, h+batch_height, w, w+batch_width)) + + # Run + for tl, tr, hl, hr, wl, wr in progress_bar(tasks): + sample_batch = sample[tl:tr, :, hl:hr, wl:wr].to(computation_device) + sample_batch = self.forward(sample_batch, timestep, encoder_hidden_states, add_time_id).to(data_device) + mask = self.build_mask(sample_batch, is_bound=(tl==0, tr>=T, hl==0, hr>=H, wl==0, wr>=W)) + values[tl:tr, :, hl:hr, wl:wr] += sample_batch * mask + weight[tl:tr, :, hl:hr, wl:wr] += mask + values /= weight + return values + + + def forward(self, sample, timestep, encoder_hidden_states, add_time_id, use_gradient_checkpointing=False, **kwargs): + # 1. time + timestep = torch.tensor((timestep,)).to(sample.device) + t_emb = self.time_proj(timestep).to(sample.dtype) + t_emb = self.time_embedding(t_emb) + + add_embeds = self.add_time_proj(add_time_id.flatten()).to(sample.dtype) + add_embeds = add_embeds.reshape((-1, 768)) + add_embeds = self.add_time_embedding(add_embeds) + + time_emb = t_emb + add_embeds + + # 2. pre-process + height, width = sample.shape[2], sample.shape[3] + hidden_states = self.conv_in(sample) + text_emb = encoder_hidden_states + res_stack = [hidden_states] + + # 3. blocks + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + return custom_forward + for i, block in enumerate(self.blocks): + if self.training and use_gradient_checkpointing and not (isinstance(block, PushBlock) or isinstance(block, PopBlock) or isinstance(block, PopMixBlock)): + hidden_states, time_emb, text_emb, res_stack = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, time_emb, text_emb, res_stack, + use_reentrant=False, + ) + else: + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + + # 4. output + hidden_states = self.conv_norm_out(hidden_states) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + + return hidden_states + + @staticmethod + def state_dict_converter(): + return SVDUNetStateDictConverter() + + + +class SVDUNetStateDictConverter: + def __init__(self): + pass + + def get_block_name(self, names): + if names[0] in ["down_blocks", "mid_block", "up_blocks"]: + if names[4] in ["norm", "proj_in"]: + return ".".join(names[:4] + ["transformer_blocks"]) + elif names[4] in ["time_pos_embed"]: + return ".".join(names[:4] + ["temporal_transformer_blocks"]) + elif names[4] in ["proj_out"]: + return ".".join(names[:4] + ["time_mixer"]) + else: + return ".".join(names[:5]) + return "" + + def from_diffusers(self, state_dict): + rename_dict = { + "time_embedding.linear_1": "time_embedding.0", + "time_embedding.linear_2": "time_embedding.2", + "add_embedding.linear_1": "add_time_embedding.0", + "add_embedding.linear_2": "add_time_embedding.2", + "conv_in": "conv_in", + "conv_norm_out": "conv_norm_out", + "conv_out": "conv_out", + } + blocks_rename_dict = [ + "down_blocks.0.resnets.0.spatial_res_block", None, "down_blocks.0.resnets.0.temporal_res_block", "down_blocks.0.resnets.0.time_mixer", None, + "down_blocks.0.attentions.0.transformer_blocks", None, "down_blocks.0.attentions.0.temporal_transformer_blocks", "down_blocks.0.attentions.0.time_mixer", None, + "down_blocks.0.resnets.1.spatial_res_block", None, "down_blocks.0.resnets.1.temporal_res_block", "down_blocks.0.resnets.1.time_mixer", None, + "down_blocks.0.attentions.1.transformer_blocks", None, "down_blocks.0.attentions.1.temporal_transformer_blocks", "down_blocks.0.attentions.1.time_mixer", None, + "down_blocks.0.downsamplers.0.conv", None, + "down_blocks.1.resnets.0.spatial_res_block", None, "down_blocks.1.resnets.0.temporal_res_block", "down_blocks.1.resnets.0.time_mixer", None, + "down_blocks.1.attentions.0.transformer_blocks", None, "down_blocks.1.attentions.0.temporal_transformer_blocks", "down_blocks.1.attentions.0.time_mixer", None, + "down_blocks.1.resnets.1.spatial_res_block", None, "down_blocks.1.resnets.1.temporal_res_block", "down_blocks.1.resnets.1.time_mixer", None, + "down_blocks.1.attentions.1.transformer_blocks", None, "down_blocks.1.attentions.1.temporal_transformer_blocks", "down_blocks.1.attentions.1.time_mixer", None, + "down_blocks.1.downsamplers.0.conv", None, + "down_blocks.2.resnets.0.spatial_res_block", None, "down_blocks.2.resnets.0.temporal_res_block", "down_blocks.2.resnets.0.time_mixer", None, + "down_blocks.2.attentions.0.transformer_blocks", None, "down_blocks.2.attentions.0.temporal_transformer_blocks", "down_blocks.2.attentions.0.time_mixer", None, + "down_blocks.2.resnets.1.spatial_res_block", None, "down_blocks.2.resnets.1.temporal_res_block", "down_blocks.2.resnets.1.time_mixer", None, + "down_blocks.2.attentions.1.transformer_blocks", None, "down_blocks.2.attentions.1.temporal_transformer_blocks", "down_blocks.2.attentions.1.time_mixer", None, + "down_blocks.2.downsamplers.0.conv", None, + "down_blocks.3.resnets.0.spatial_res_block", None, "down_blocks.3.resnets.0.temporal_res_block", "down_blocks.3.resnets.0.time_mixer", None, + "down_blocks.3.resnets.1.spatial_res_block", None, "down_blocks.3.resnets.1.temporal_res_block", "down_blocks.3.resnets.1.time_mixer", None, + "mid_block.mid_block.resnets.0.spatial_res_block", None, "mid_block.mid_block.resnets.0.temporal_res_block", "mid_block.mid_block.resnets.0.time_mixer", None, + "mid_block.mid_block.attentions.0.transformer_blocks", None, "mid_block.mid_block.attentions.0.temporal_transformer_blocks", "mid_block.mid_block.attentions.0.time_mixer", + "mid_block.mid_block.resnets.1.spatial_res_block", None, "mid_block.mid_block.resnets.1.temporal_res_block", "mid_block.mid_block.resnets.1.time_mixer", + None, "up_blocks.0.resnets.0.spatial_res_block", None, "up_blocks.0.resnets.0.temporal_res_block", "up_blocks.0.resnets.0.time_mixer", + None, "up_blocks.0.resnets.1.spatial_res_block", None, "up_blocks.0.resnets.1.temporal_res_block", "up_blocks.0.resnets.1.time_mixer", + None, "up_blocks.0.resnets.2.spatial_res_block", None, "up_blocks.0.resnets.2.temporal_res_block", "up_blocks.0.resnets.2.time_mixer", + "up_blocks.0.upsamplers.0.conv", + None, "up_blocks.1.resnets.0.spatial_res_block", None, "up_blocks.1.resnets.0.temporal_res_block", "up_blocks.1.resnets.0.time_mixer", None, + "up_blocks.1.attentions.0.transformer_blocks", None, "up_blocks.1.attentions.0.temporal_transformer_blocks", "up_blocks.1.attentions.0.time_mixer", + None, "up_blocks.1.resnets.1.spatial_res_block", None, "up_blocks.1.resnets.1.temporal_res_block", "up_blocks.1.resnets.1.time_mixer", None, + "up_blocks.1.attentions.1.transformer_blocks", None, "up_blocks.1.attentions.1.temporal_transformer_blocks", "up_blocks.1.attentions.1.time_mixer", + None, "up_blocks.1.resnets.2.spatial_res_block", None, "up_blocks.1.resnets.2.temporal_res_block", "up_blocks.1.resnets.2.time_mixer", None, + "up_blocks.1.attentions.2.transformer_blocks", None, "up_blocks.1.attentions.2.temporal_transformer_blocks", "up_blocks.1.attentions.2.time_mixer", + "up_blocks.1.upsamplers.0.conv", + None, "up_blocks.2.resnets.0.spatial_res_block", None, "up_blocks.2.resnets.0.temporal_res_block", "up_blocks.2.resnets.0.time_mixer", None, + "up_blocks.2.attentions.0.transformer_blocks", None, "up_blocks.2.attentions.0.temporal_transformer_blocks", "up_blocks.2.attentions.0.time_mixer", + None, "up_blocks.2.resnets.1.spatial_res_block", None, "up_blocks.2.resnets.1.temporal_res_block", "up_blocks.2.resnets.1.time_mixer", None, + "up_blocks.2.attentions.1.transformer_blocks", None, "up_blocks.2.attentions.1.temporal_transformer_blocks", "up_blocks.2.attentions.1.time_mixer", + None, "up_blocks.2.resnets.2.spatial_res_block", None, "up_blocks.2.resnets.2.temporal_res_block", "up_blocks.2.resnets.2.time_mixer", None, + "up_blocks.2.attentions.2.transformer_blocks", None, "up_blocks.2.attentions.2.temporal_transformer_blocks", "up_blocks.2.attentions.2.time_mixer", + "up_blocks.2.upsamplers.0.conv", + None, "up_blocks.3.resnets.0.spatial_res_block", None, "up_blocks.3.resnets.0.temporal_res_block", "up_blocks.3.resnets.0.time_mixer", None, + "up_blocks.3.attentions.0.transformer_blocks", None, "up_blocks.3.attentions.0.temporal_transformer_blocks", "up_blocks.3.attentions.0.time_mixer", + None, "up_blocks.3.resnets.1.spatial_res_block", None, "up_blocks.3.resnets.1.temporal_res_block", "up_blocks.3.resnets.1.time_mixer", None, + "up_blocks.3.attentions.1.transformer_blocks", None, "up_blocks.3.attentions.1.temporal_transformer_blocks", "up_blocks.3.attentions.1.time_mixer", + None, "up_blocks.3.resnets.2.spatial_res_block", None, "up_blocks.3.resnets.2.temporal_res_block", "up_blocks.3.resnets.2.time_mixer", None, + "up_blocks.3.attentions.2.transformer_blocks", None, "up_blocks.3.attentions.2.temporal_transformer_blocks", "up_blocks.3.attentions.2.time_mixer", + ] + blocks_rename_dict = {i:j for j,i in enumerate(blocks_rename_dict) if i is not None} + state_dict_ = {} + for name, param in sorted(state_dict.items()): + names = name.split(".") + if names[0] == "mid_block": + names = ["mid_block"] + names + if names[-1] in ["weight", "bias"]: + name_prefix = ".".join(names[:-1]) + if name_prefix in rename_dict: + state_dict_[rename_dict[name_prefix] + "." + names[-1]] = param + else: + block_name = self.get_block_name(names) + if "resnets" in block_name and block_name in blocks_rename_dict: + rename = ".".join(["blocks", str(blocks_rename_dict[block_name])] + names[5:]) + state_dict_[rename] = param + elif ("downsamplers" in block_name or "upsamplers" in block_name) and block_name in blocks_rename_dict: + rename = ".".join(["blocks", str(blocks_rename_dict[block_name])] + names[-2:]) + state_dict_[rename] = param + elif "attentions" in block_name and block_name in blocks_rename_dict: + attention_id = names[5] + if "transformer_blocks" in names: + suffix_dict = { + "attn1.to_out.0": "attn1.to_out", + "attn2.to_out.0": "attn2.to_out", + "ff.net.0.proj": "act_fn.proj", + "ff.net.2": "ff", + } + suffix = ".".join(names[6:-1]) + suffix = suffix_dict.get(suffix, suffix) + rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), "transformer_blocks", attention_id, suffix, names[-1]]) + elif "temporal_transformer_blocks" in names: + suffix_dict = { + "attn1.to_out.0": "attn1.to_out", + "attn2.to_out.0": "attn2.to_out", + "ff_in.net.0.proj": "act_fn_in.proj", + "ff_in.net.2": "ff_in", + "ff.net.0.proj": "act_fn_out.proj", + "ff.net.2": "ff_out", + "norm3": "norm_out", + } + suffix = ".".join(names[6:-1]) + suffix = suffix_dict.get(suffix, suffix) + rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), suffix, names[-1]]) + elif "time_mixer" in block_name: + rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), "proj", names[-1]]) + else: + suffix_dict = { + "linear_1": "positional_embedding_proj.0", + "linear_2": "positional_embedding_proj.2", + } + suffix = names[-2] + suffix = suffix_dict.get(suffix, suffix) + rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), suffix, names[-1]]) + state_dict_[rename] = param + else: + print(name) + else: + block_name = self.get_block_name(names) + if len(block_name)>0 and block_name in blocks_rename_dict: + rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), names[-1]]) + state_dict_[rename] = param + return state_dict_ + + + def from_civitai(self, state_dict, add_positional_conv=None): + rename_dict = { + "model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias", + "model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight", + "model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias", + "model.diffusion_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight", + "model.diffusion_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias", + "model.diffusion_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight", + "model.diffusion_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias", + "model.diffusion_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight", + "model.diffusion_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias", + "model.diffusion_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight", + "model.diffusion_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias", + "model.diffusion_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight", + "model.diffusion_model.input_blocks.1.0.time_mixer.mix_factor": "blocks.3.mix_factor", + "model.diffusion_model.input_blocks.1.0.time_stack.emb_layers.1.bias": "blocks.2.time_emb_proj.bias", + "model.diffusion_model.input_blocks.1.0.time_stack.emb_layers.1.weight": "blocks.2.time_emb_proj.weight", + "model.diffusion_model.input_blocks.1.0.time_stack.in_layers.0.bias": "blocks.2.norm1.bias", + "model.diffusion_model.input_blocks.1.0.time_stack.in_layers.0.weight": "blocks.2.norm1.weight", + "model.diffusion_model.input_blocks.1.0.time_stack.in_layers.2.bias": "blocks.2.conv1.bias", + "model.diffusion_model.input_blocks.1.0.time_stack.in_layers.2.weight": "blocks.2.conv1.weight", + "model.diffusion_model.input_blocks.1.0.time_stack.out_layers.0.bias": "blocks.2.norm2.bias", + "model.diffusion_model.input_blocks.1.0.time_stack.out_layers.0.weight": "blocks.2.norm2.weight", + "model.diffusion_model.input_blocks.1.0.time_stack.out_layers.3.bias": "blocks.2.conv2.bias", + "model.diffusion_model.input_blocks.1.0.time_stack.out_layers.3.weight": "blocks.2.conv2.weight", + "model.diffusion_model.input_blocks.1.1.norm.bias": "blocks.5.norm.bias", + "model.diffusion_model.input_blocks.1.1.norm.weight": "blocks.5.norm.weight", + "model.diffusion_model.input_blocks.1.1.proj_in.bias": "blocks.5.proj_in.bias", + "model.diffusion_model.input_blocks.1.1.proj_in.weight": "blocks.5.proj_in.weight", + "model.diffusion_model.input_blocks.1.1.proj_out.bias": "blocks.8.proj.bias", + "model.diffusion_model.input_blocks.1.1.proj_out.weight": "blocks.8.proj.weight", + "model.diffusion_model.input_blocks.1.1.time_mixer.mix_factor": "blocks.8.mix_factor", + "model.diffusion_model.input_blocks.1.1.time_pos_embed.0.bias": "blocks.7.positional_embedding_proj.0.bias", + "model.diffusion_model.input_blocks.1.1.time_pos_embed.0.weight": "blocks.7.positional_embedding_proj.0.weight", + "model.diffusion_model.input_blocks.1.1.time_pos_embed.2.bias": "blocks.7.positional_embedding_proj.2.bias", + "model.diffusion_model.input_blocks.1.1.time_pos_embed.2.weight": "blocks.7.positional_embedding_proj.2.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn1.to_k.weight": "blocks.7.attn1.to_k.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn1.to_out.0.bias": "blocks.7.attn1.to_out.bias", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn1.to_out.0.weight": "blocks.7.attn1.to_out.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn1.to_q.weight": "blocks.7.attn1.to_q.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn1.to_v.weight": "blocks.7.attn1.to_v.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn2.to_k.weight": "blocks.7.attn2.to_k.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn2.to_out.0.bias": "blocks.7.attn2.to_out.bias", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn2.to_out.0.weight": "blocks.7.attn2.to_out.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn2.to_q.weight": "blocks.7.attn2.to_q.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn2.to_v.weight": "blocks.7.attn2.to_v.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.ff.net.0.proj.bias": "blocks.7.act_fn_out.proj.bias", + "model.diffusion_model.input_blocks.1.1.time_stack.0.ff.net.0.proj.weight": "blocks.7.act_fn_out.proj.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.ff.net.2.bias": "blocks.7.ff_out.bias", + "model.diffusion_model.input_blocks.1.1.time_stack.0.ff.net.2.weight": "blocks.7.ff_out.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.ff_in.net.0.proj.bias": "blocks.7.act_fn_in.proj.bias", + "model.diffusion_model.input_blocks.1.1.time_stack.0.ff_in.net.0.proj.weight": "blocks.7.act_fn_in.proj.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.ff_in.net.2.bias": "blocks.7.ff_in.bias", + "model.diffusion_model.input_blocks.1.1.time_stack.0.ff_in.net.2.weight": "blocks.7.ff_in.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.norm1.bias": "blocks.7.norm1.bias", + "model.diffusion_model.input_blocks.1.1.time_stack.0.norm1.weight": "blocks.7.norm1.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.norm2.bias": "blocks.7.norm2.bias", + "model.diffusion_model.input_blocks.1.1.time_stack.0.norm2.weight": "blocks.7.norm2.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.norm3.bias": "blocks.7.norm_out.bias", + "model.diffusion_model.input_blocks.1.1.time_stack.0.norm3.weight": "blocks.7.norm_out.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.norm_in.bias": "blocks.7.norm_in.bias", + "model.diffusion_model.input_blocks.1.1.time_stack.0.norm_in.weight": "blocks.7.norm_in.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_k.weight": "blocks.5.transformer_blocks.0.attn1.to_k.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.5.transformer_blocks.0.attn1.to_out.bias", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.5.transformer_blocks.0.attn1.to_out.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_q.weight": "blocks.5.transformer_blocks.0.attn1.to_q.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_v.weight": "blocks.5.transformer_blocks.0.attn1.to_v.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight": "blocks.5.transformer_blocks.0.attn2.to_k.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.5.transformer_blocks.0.attn2.to_out.bias", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.5.transformer_blocks.0.attn2.to_out.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_q.weight": "blocks.5.transformer_blocks.0.attn2.to_q.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_v.weight": "blocks.5.transformer_blocks.0.attn2.to_v.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.5.transformer_blocks.0.act_fn.proj.bias", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.5.transformer_blocks.0.act_fn.proj.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.bias": "blocks.5.transformer_blocks.0.ff.bias", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.weight": "blocks.5.transformer_blocks.0.ff.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1.bias": "blocks.5.transformer_blocks.0.norm1.bias", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1.weight": "blocks.5.transformer_blocks.0.norm1.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2.bias": "blocks.5.transformer_blocks.0.norm2.bias", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2.weight": "blocks.5.transformer_blocks.0.norm2.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.bias": "blocks.5.transformer_blocks.0.norm3.bias", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.weight": "blocks.5.transformer_blocks.0.norm3.weight", + "model.diffusion_model.input_blocks.10.0.emb_layers.1.bias": "blocks.66.time_emb_proj.bias", + "model.diffusion_model.input_blocks.10.0.emb_layers.1.weight": "blocks.66.time_emb_proj.weight", + "model.diffusion_model.input_blocks.10.0.in_layers.0.bias": "blocks.66.norm1.bias", + "model.diffusion_model.input_blocks.10.0.in_layers.0.weight": "blocks.66.norm1.weight", + "model.diffusion_model.input_blocks.10.0.in_layers.2.bias": "blocks.66.conv1.bias", + 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"blocks.7.positional_conv", "blocks.17.positional_conv", "blocks.29.positional_conv", "blocks.39.positional_conv", + "blocks.51.positional_conv", "blocks.61.positional_conv", "blocks.83.positional_conv", "blocks.113.positional_conv", + "blocks.123.positional_conv", "blocks.133.positional_conv", "blocks.144.positional_conv", "blocks.154.positional_conv", + "blocks.164.positional_conv", "blocks.175.positional_conv", "blocks.185.positional_conv", "blocks.195.positional_conv", + ] + extra_channels = [320, 320, 640, 640, 1280, 1280, 1280, 1280, 1280, 1280, 640, 640, 640, 320, 320, 320] + for name, channels in zip(extra_names, extra_channels): + weight = torch.zeros((channels, channels, 3, 3, 3)) + weight[:,:,1,1,1] = torch.eye(channels, channels) + bias = torch.zeros((channels,)) + state_dict_[name + ".weight"] = weight + state_dict_[name + ".bias"] = bias + return state_dict_ diff --git a/diffsynth/models/svd_vae_decoder.py b/diffsynth/models/svd_vae_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..a4815961136820dc1b863573a559076b68fd785a --- /dev/null +++ b/diffsynth/models/svd_vae_decoder.py @@ -0,0 +1,578 @@ +import torch +from .attention import Attention +from .sd_unet import ResnetBlock, UpSampler +from .tiler import TileWorker +from einops import rearrange, repeat + + +class VAEAttentionBlock(torch.nn.Module): + + def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5): + super().__init__() + inner_dim = num_attention_heads * attention_head_dim + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True) + + self.transformer_blocks = torch.nn.ModuleList([ + Attention( + inner_dim, + num_attention_heads, + attention_head_dim, + bias_q=True, + bias_kv=True, + bias_out=True + ) + for d in range(num_layers) + ]) + + def forward(self, hidden_states, time_emb, text_emb, res_stack): + batch, _, height, width = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) + + for block in self.transformer_blocks: + hidden_states = block(hidden_states) + + hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + hidden_states = hidden_states + residual + + return hidden_states, time_emb, text_emb, res_stack + + +class TemporalResnetBlock(torch.nn.Module): + + def __init__(self, in_channels, out_channels, groups=32, eps=1e-5): + super().__init__() + self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) + self.conv1 = torch.nn.Conv3d(in_channels, out_channels, kernel_size=(3, 1, 1), stride=1, padding=(1, 0, 0)) + self.norm2 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True) + self.conv2 = torch.nn.Conv3d(out_channels, out_channels, kernel_size=(3, 1, 1), stride=1, padding=(1, 0, 0)) + self.nonlinearity = torch.nn.SiLU() + self.mix_factor = torch.nn.Parameter(torch.Tensor([0.5])) + + def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): + x_spatial = hidden_states + x = rearrange(hidden_states, "T C H W -> 1 C T H W") + x = self.norm1(x) + x = self.nonlinearity(x) + x = self.conv1(x) + x = self.norm2(x) + x = self.nonlinearity(x) + x = self.conv2(x) + x_temporal = hidden_states + x[0].permute(1, 0, 2, 3) + alpha = torch.sigmoid(self.mix_factor) + hidden_states = alpha * x_temporal + (1 - alpha) * x_spatial + return hidden_states, time_emb, text_emb, res_stack + + +class SVDVAEDecoder(torch.nn.Module): + def __init__(self): + super().__init__() + self.scaling_factor = 0.18215 + self.conv_in = torch.nn.Conv2d(4, 512, kernel_size=3, padding=1) + + self.blocks = torch.nn.ModuleList([ + # UNetMidBlock + ResnetBlock(512, 512, eps=1e-6), + TemporalResnetBlock(512, 512, eps=1e-6), + VAEAttentionBlock(1, 512, 512, 1, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + TemporalResnetBlock(512, 512, eps=1e-6), + # UpDecoderBlock + ResnetBlock(512, 512, eps=1e-6), + TemporalResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + TemporalResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + TemporalResnetBlock(512, 512, eps=1e-6), + UpSampler(512), + # UpDecoderBlock + ResnetBlock(512, 512, eps=1e-6), + TemporalResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + TemporalResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + TemporalResnetBlock(512, 512, eps=1e-6), + UpSampler(512), + # UpDecoderBlock + ResnetBlock(512, 256, eps=1e-6), + TemporalResnetBlock(256, 256, eps=1e-6), + ResnetBlock(256, 256, eps=1e-6), + TemporalResnetBlock(256, 256, eps=1e-6), + ResnetBlock(256, 256, eps=1e-6), + TemporalResnetBlock(256, 256, eps=1e-6), + UpSampler(256), + # UpDecoderBlock + ResnetBlock(256, 128, eps=1e-6), + TemporalResnetBlock(128, 128, eps=1e-6), + ResnetBlock(128, 128, eps=1e-6), + TemporalResnetBlock(128, 128, eps=1e-6), + ResnetBlock(128, 128, eps=1e-6), + TemporalResnetBlock(128, 128, eps=1e-6), + ]) + + self.conv_norm_out = torch.nn.GroupNorm(num_channels=128, num_groups=32, eps=1e-5) + self.conv_act = torch.nn.SiLU() + self.conv_out = torch.nn.Conv2d(128, 3, kernel_size=3, padding=1) + self.time_conv_out = torch.nn.Conv3d(3, 3, kernel_size=(3, 1, 1), padding=(1, 0, 0)) + + + def forward(self, sample): + # 1. pre-process + hidden_states = rearrange(sample, "C T H W -> T C H W") + hidden_states = hidden_states / self.scaling_factor + hidden_states = self.conv_in(hidden_states) + time_emb, text_emb, res_stack = None, None, None + + # 2. blocks + for i, block in enumerate(self.blocks): + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + + # 3. output + hidden_states = self.conv_norm_out(hidden_states) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + hidden_states = rearrange(hidden_states, "T C H W -> C T H W") + hidden_states = self.time_conv_out(hidden_states) + + return hidden_states + + + def build_mask(self, data, is_bound): + _, T, H, W = data.shape + t = repeat(torch.arange(T), "T -> T H W", T=T, H=H, W=W) + h = repeat(torch.arange(H), "H -> T H W", T=T, H=H, W=W) + w = repeat(torch.arange(W), "W -> T H W", T=T, H=H, W=W) + border_width = (T + H + W) // 6 + pad = torch.ones_like(t) * border_width + mask = torch.stack([ + pad if is_bound[0] else t + 1, + pad if is_bound[1] else T - t, + pad if is_bound[2] else h + 1, + pad if is_bound[3] else H - h, + pad if is_bound[4] else w + 1, + pad if is_bound[5] else W - w + ]).min(dim=0).values + mask = mask.clip(1, border_width) + mask = (mask / border_width).to(dtype=data.dtype, device=data.device) + mask = rearrange(mask, "T H W -> 1 T H W") + return mask + + + def decode_video( + self, sample, + batch_time=8, batch_height=128, batch_width=128, + stride_time=4, stride_height=32, stride_width=32, + progress_bar=lambda x:x + ): + sample = sample.permute(1, 0, 2, 3) + data_device = sample.device + computation_device = self.conv_in.weight.device + torch_dtype = sample.dtype + _, T, H, W = sample.shape + + weight = torch.zeros((1, T, H*8, W*8), dtype=torch_dtype, device=data_device) + values = torch.zeros((3, T, H*8, W*8), dtype=torch_dtype, device=data_device) + + # Split tasks + tasks = [] + for t in range(0, T, stride_time): + for h in range(0, H, stride_height): + for w in range(0, W, stride_width): + if (t-stride_time >= 0 and t-stride_time+batch_time >= T)\ + or (h-stride_height >= 0 and h-stride_height+batch_height >= H)\ + or (w-stride_width >= 0 and w-stride_width+batch_width >= W): + continue + tasks.append((t, t+batch_time, h, h+batch_height, w, w+batch_width)) + + # Run + for tl, tr, hl, hr, wl, wr in progress_bar(tasks): + sample_batch = sample[:, tl:tr, hl:hr, wl:wr].to(computation_device) + sample_batch = self.forward(sample_batch).to(data_device) + mask = self.build_mask(sample_batch, is_bound=(tl==0, tr>=T, hl==0, hr>=H, wl==0, wr>=W)) + values[:, tl:tr, hl*8:hr*8, wl*8:wr*8] += sample_batch * mask + weight[:, tl:tr, hl*8:hr*8, wl*8:wr*8] += mask + values /= weight + return values + + + @staticmethod + def state_dict_converter(): + return SVDVAEDecoderStateDictConverter() + + +class SVDVAEDecoderStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + static_rename_dict = { + "decoder.conv_in": "conv_in", + "decoder.mid_block.attentions.0.group_norm": "blocks.2.norm", + "decoder.mid_block.attentions.0.to_q": "blocks.2.transformer_blocks.0.to_q", + "decoder.mid_block.attentions.0.to_k": "blocks.2.transformer_blocks.0.to_k", + "decoder.mid_block.attentions.0.to_v": "blocks.2.transformer_blocks.0.to_v", + "decoder.mid_block.attentions.0.to_out.0": "blocks.2.transformer_blocks.0.to_out", + "decoder.up_blocks.0.upsamplers.0.conv": "blocks.11.conv", + "decoder.up_blocks.1.upsamplers.0.conv": "blocks.18.conv", + "decoder.up_blocks.2.upsamplers.0.conv": "blocks.25.conv", + "decoder.conv_norm_out": "conv_norm_out", + "decoder.conv_out": "conv_out", + "decoder.time_conv_out": "time_conv_out" + } + prefix_rename_dict = { + "decoder.mid_block.resnets.0.spatial_res_block": "blocks.0", + "decoder.mid_block.resnets.0.temporal_res_block": "blocks.1", + "decoder.mid_block.resnets.0.time_mixer": "blocks.1", + "decoder.mid_block.resnets.1.spatial_res_block": "blocks.3", + "decoder.mid_block.resnets.1.temporal_res_block": "blocks.4", + "decoder.mid_block.resnets.1.time_mixer": "blocks.4", + + "decoder.up_blocks.0.resnets.0.spatial_res_block": "blocks.5", + "decoder.up_blocks.0.resnets.0.temporal_res_block": "blocks.6", + "decoder.up_blocks.0.resnets.0.time_mixer": "blocks.6", + "decoder.up_blocks.0.resnets.1.spatial_res_block": "blocks.7", + "decoder.up_blocks.0.resnets.1.temporal_res_block": "blocks.8", + "decoder.up_blocks.0.resnets.1.time_mixer": "blocks.8", + "decoder.up_blocks.0.resnets.2.spatial_res_block": "blocks.9", + "decoder.up_blocks.0.resnets.2.temporal_res_block": "blocks.10", + "decoder.up_blocks.0.resnets.2.time_mixer": "blocks.10", + + "decoder.up_blocks.1.resnets.0.spatial_res_block": "blocks.12", + "decoder.up_blocks.1.resnets.0.temporal_res_block": "blocks.13", + "decoder.up_blocks.1.resnets.0.time_mixer": "blocks.13", + "decoder.up_blocks.1.resnets.1.spatial_res_block": "blocks.14", + "decoder.up_blocks.1.resnets.1.temporal_res_block": "blocks.15", + "decoder.up_blocks.1.resnets.1.time_mixer": "blocks.15", + "decoder.up_blocks.1.resnets.2.spatial_res_block": "blocks.16", + "decoder.up_blocks.1.resnets.2.temporal_res_block": "blocks.17", + "decoder.up_blocks.1.resnets.2.time_mixer": "blocks.17", + + "decoder.up_blocks.2.resnets.0.spatial_res_block": "blocks.19", + "decoder.up_blocks.2.resnets.0.temporal_res_block": "blocks.20", + "decoder.up_blocks.2.resnets.0.time_mixer": "blocks.20", + "decoder.up_blocks.2.resnets.1.spatial_res_block": "blocks.21", + "decoder.up_blocks.2.resnets.1.temporal_res_block": "blocks.22", + "decoder.up_blocks.2.resnets.1.time_mixer": "blocks.22", + "decoder.up_blocks.2.resnets.2.spatial_res_block": "blocks.23", + "decoder.up_blocks.2.resnets.2.temporal_res_block": "blocks.24", + "decoder.up_blocks.2.resnets.2.time_mixer": "blocks.24", + + "decoder.up_blocks.3.resnets.0.spatial_res_block": "blocks.26", + "decoder.up_blocks.3.resnets.0.temporal_res_block": "blocks.27", + "decoder.up_blocks.3.resnets.0.time_mixer": "blocks.27", + "decoder.up_blocks.3.resnets.1.spatial_res_block": "blocks.28", + "decoder.up_blocks.3.resnets.1.temporal_res_block": "blocks.29", + "decoder.up_blocks.3.resnets.1.time_mixer": "blocks.29", + "decoder.up_blocks.3.resnets.2.spatial_res_block": "blocks.30", + "decoder.up_blocks.3.resnets.2.temporal_res_block": "blocks.31", + "decoder.up_blocks.3.resnets.2.time_mixer": "blocks.31", + } + suffix_rename_dict = { + "norm1.weight": "norm1.weight", + "conv1.weight": "conv1.weight", + "norm2.weight": "norm2.weight", + "conv2.weight": "conv2.weight", + "conv_shortcut.weight": "conv_shortcut.weight", + "norm1.bias": "norm1.bias", + "conv1.bias": "conv1.bias", + "norm2.bias": "norm2.bias", + "conv2.bias": "conv2.bias", + "conv_shortcut.bias": "conv_shortcut.bias", + "mix_factor": "mix_factor", + } + + state_dict_ = {} + for name in static_rename_dict: + state_dict_[static_rename_dict[name] + ".weight"] = state_dict[name + ".weight"] + state_dict_[static_rename_dict[name] + ".bias"] = state_dict[name + ".bias"] + for prefix_name in prefix_rename_dict: + for suffix_name in suffix_rename_dict: + name = prefix_name + "." + suffix_name + name_ = prefix_rename_dict[prefix_name] + "." + suffix_rename_dict[suffix_name] + if name in state_dict: + state_dict_[name_] = state_dict[name] + + return state_dict_ + + + def from_civitai(self, state_dict): + rename_dict = { + "first_stage_model.decoder.conv_in.bias": "conv_in.bias", + "first_stage_model.decoder.conv_in.weight": "conv_in.weight", + "first_stage_model.decoder.conv_out.bias": "conv_out.bias", + "first_stage_model.decoder.conv_out.time_mix_conv.bias": "time_conv_out.bias", + "first_stage_model.decoder.conv_out.time_mix_conv.weight": "time_conv_out.weight", + "first_stage_model.decoder.conv_out.weight": "conv_out.weight", + "first_stage_model.decoder.mid.attn_1.k.bias": "blocks.2.transformer_blocks.0.to_k.bias", + "first_stage_model.decoder.mid.attn_1.k.weight": "blocks.2.transformer_blocks.0.to_k.weight", + "first_stage_model.decoder.mid.attn_1.norm.bias": "blocks.2.norm.bias", + "first_stage_model.decoder.mid.attn_1.norm.weight": "blocks.2.norm.weight", + "first_stage_model.decoder.mid.attn_1.proj_out.bias": "blocks.2.transformer_blocks.0.to_out.bias", + "first_stage_model.decoder.mid.attn_1.proj_out.weight": "blocks.2.transformer_blocks.0.to_out.weight", + "first_stage_model.decoder.mid.attn_1.q.bias": "blocks.2.transformer_blocks.0.to_q.bias", + "first_stage_model.decoder.mid.attn_1.q.weight": "blocks.2.transformer_blocks.0.to_q.weight", + "first_stage_model.decoder.mid.attn_1.v.bias": "blocks.2.transformer_blocks.0.to_v.bias", + "first_stage_model.decoder.mid.attn_1.v.weight": "blocks.2.transformer_blocks.0.to_v.weight", + "first_stage_model.decoder.mid.block_1.conv1.bias": "blocks.0.conv1.bias", + "first_stage_model.decoder.mid.block_1.conv1.weight": "blocks.0.conv1.weight", + "first_stage_model.decoder.mid.block_1.conv2.bias": "blocks.0.conv2.bias", + "first_stage_model.decoder.mid.block_1.conv2.weight": "blocks.0.conv2.weight", + "first_stage_model.decoder.mid.block_1.mix_factor": "blocks.1.mix_factor", + "first_stage_model.decoder.mid.block_1.norm1.bias": "blocks.0.norm1.bias", + "first_stage_model.decoder.mid.block_1.norm1.weight": "blocks.0.norm1.weight", + "first_stage_model.decoder.mid.block_1.norm2.bias": "blocks.0.norm2.bias", + "first_stage_model.decoder.mid.block_1.norm2.weight": "blocks.0.norm2.weight", + "first_stage_model.decoder.mid.block_1.time_stack.in_layers.0.bias": "blocks.1.norm1.bias", + "first_stage_model.decoder.mid.block_1.time_stack.in_layers.0.weight": "blocks.1.norm1.weight", + "first_stage_model.decoder.mid.block_1.time_stack.in_layers.2.bias": "blocks.1.conv1.bias", + "first_stage_model.decoder.mid.block_1.time_stack.in_layers.2.weight": "blocks.1.conv1.weight", + "first_stage_model.decoder.mid.block_1.time_stack.out_layers.0.bias": "blocks.1.norm2.bias", + "first_stage_model.decoder.mid.block_1.time_stack.out_layers.0.weight": "blocks.1.norm2.weight", + "first_stage_model.decoder.mid.block_1.time_stack.out_layers.3.bias": "blocks.1.conv2.bias", + "first_stage_model.decoder.mid.block_1.time_stack.out_layers.3.weight": "blocks.1.conv2.weight", + "first_stage_model.decoder.mid.block_2.conv1.bias": "blocks.3.conv1.bias", + "first_stage_model.decoder.mid.block_2.conv1.weight": "blocks.3.conv1.weight", + "first_stage_model.decoder.mid.block_2.conv2.bias": "blocks.3.conv2.bias", + "first_stage_model.decoder.mid.block_2.conv2.weight": "blocks.3.conv2.weight", + "first_stage_model.decoder.mid.block_2.mix_factor": "blocks.4.mix_factor", + "first_stage_model.decoder.mid.block_2.norm1.bias": "blocks.3.norm1.bias", + "first_stage_model.decoder.mid.block_2.norm1.weight": "blocks.3.norm1.weight", + "first_stage_model.decoder.mid.block_2.norm2.bias": "blocks.3.norm2.bias", + "first_stage_model.decoder.mid.block_2.norm2.weight": "blocks.3.norm2.weight", + "first_stage_model.decoder.mid.block_2.time_stack.in_layers.0.bias": "blocks.4.norm1.bias", + "first_stage_model.decoder.mid.block_2.time_stack.in_layers.0.weight": "blocks.4.norm1.weight", + "first_stage_model.decoder.mid.block_2.time_stack.in_layers.2.bias": "blocks.4.conv1.bias", + "first_stage_model.decoder.mid.block_2.time_stack.in_layers.2.weight": "blocks.4.conv1.weight", + "first_stage_model.decoder.mid.block_2.time_stack.out_layers.0.bias": "blocks.4.norm2.bias", + "first_stage_model.decoder.mid.block_2.time_stack.out_layers.0.weight": "blocks.4.norm2.weight", + "first_stage_model.decoder.mid.block_2.time_stack.out_layers.3.bias": "blocks.4.conv2.bias", + "first_stage_model.decoder.mid.block_2.time_stack.out_layers.3.weight": "blocks.4.conv2.weight", + "first_stage_model.decoder.norm_out.bias": "conv_norm_out.bias", + "first_stage_model.decoder.norm_out.weight": "conv_norm_out.weight", + "first_stage_model.decoder.up.0.block.0.conv1.bias": "blocks.26.conv1.bias", + "first_stage_model.decoder.up.0.block.0.conv1.weight": "blocks.26.conv1.weight", + "first_stage_model.decoder.up.0.block.0.conv2.bias": "blocks.26.conv2.bias", + "first_stage_model.decoder.up.0.block.0.conv2.weight": "blocks.26.conv2.weight", + "first_stage_model.decoder.up.0.block.0.mix_factor": "blocks.27.mix_factor", + "first_stage_model.decoder.up.0.block.0.nin_shortcut.bias": "blocks.26.conv_shortcut.bias", + "first_stage_model.decoder.up.0.block.0.nin_shortcut.weight": "blocks.26.conv_shortcut.weight", + "first_stage_model.decoder.up.0.block.0.norm1.bias": "blocks.26.norm1.bias", + "first_stage_model.decoder.up.0.block.0.norm1.weight": "blocks.26.norm1.weight", + "first_stage_model.decoder.up.0.block.0.norm2.bias": "blocks.26.norm2.bias", + "first_stage_model.decoder.up.0.block.0.norm2.weight": "blocks.26.norm2.weight", + "first_stage_model.decoder.up.0.block.0.time_stack.in_layers.0.bias": "blocks.27.norm1.bias", + "first_stage_model.decoder.up.0.block.0.time_stack.in_layers.0.weight": "blocks.27.norm1.weight", + "first_stage_model.decoder.up.0.block.0.time_stack.in_layers.2.bias": "blocks.27.conv1.bias", + "first_stage_model.decoder.up.0.block.0.time_stack.in_layers.2.weight": "blocks.27.conv1.weight", + "first_stage_model.decoder.up.0.block.0.time_stack.out_layers.0.bias": "blocks.27.norm2.bias", + "first_stage_model.decoder.up.0.block.0.time_stack.out_layers.0.weight": "blocks.27.norm2.weight", + "first_stage_model.decoder.up.0.block.0.time_stack.out_layers.3.bias": "blocks.27.conv2.bias", + "first_stage_model.decoder.up.0.block.0.time_stack.out_layers.3.weight": "blocks.27.conv2.weight", + "first_stage_model.decoder.up.0.block.1.conv1.bias": "blocks.28.conv1.bias", + "first_stage_model.decoder.up.0.block.1.conv1.weight": "blocks.28.conv1.weight", + "first_stage_model.decoder.up.0.block.1.conv2.bias": "blocks.28.conv2.bias", + "first_stage_model.decoder.up.0.block.1.conv2.weight": "blocks.28.conv2.weight", + "first_stage_model.decoder.up.0.block.1.mix_factor": "blocks.29.mix_factor", + "first_stage_model.decoder.up.0.block.1.norm1.bias": "blocks.28.norm1.bias", + "first_stage_model.decoder.up.0.block.1.norm1.weight": "blocks.28.norm1.weight", + "first_stage_model.decoder.up.0.block.1.norm2.bias": "blocks.28.norm2.bias", + "first_stage_model.decoder.up.0.block.1.norm2.weight": "blocks.28.norm2.weight", + "first_stage_model.decoder.up.0.block.1.time_stack.in_layers.0.bias": "blocks.29.norm1.bias", + "first_stage_model.decoder.up.0.block.1.time_stack.in_layers.0.weight": "blocks.29.norm1.weight", + "first_stage_model.decoder.up.0.block.1.time_stack.in_layers.2.bias": "blocks.29.conv1.bias", + "first_stage_model.decoder.up.0.block.1.time_stack.in_layers.2.weight": "blocks.29.conv1.weight", + "first_stage_model.decoder.up.0.block.1.time_stack.out_layers.0.bias": "blocks.29.norm2.bias", + "first_stage_model.decoder.up.0.block.1.time_stack.out_layers.0.weight": "blocks.29.norm2.weight", + "first_stage_model.decoder.up.0.block.1.time_stack.out_layers.3.bias": "blocks.29.conv2.bias", + "first_stage_model.decoder.up.0.block.1.time_stack.out_layers.3.weight": "blocks.29.conv2.weight", + 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"first_stage_model.decoder.up.3.block.2.norm2.bias": "blocks.9.norm2.bias", + "first_stage_model.decoder.up.3.block.2.norm2.weight": "blocks.9.norm2.weight", + "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.0.bias": "blocks.10.norm1.bias", + "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.0.weight": "blocks.10.norm1.weight", + "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.2.bias": "blocks.10.conv1.bias", + "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.2.weight": "blocks.10.conv1.weight", + "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.0.bias": "blocks.10.norm2.bias", + "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.0.weight": "blocks.10.norm2.weight", + "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.3.bias": "blocks.10.conv2.bias", + "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.3.weight": "blocks.10.conv2.weight", + "first_stage_model.decoder.up.3.upsample.conv.bias": "blocks.11.conv.bias", + "first_stage_model.decoder.up.3.upsample.conv.weight": "blocks.11.conv.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if "blocks.2.transformer_blocks.0" in rename_dict[name]: + param = param.squeeze() + state_dict_[rename_dict[name]] = param + return state_dict_ diff --git a/diffsynth/models/svd_vae_encoder.py b/diffsynth/models/svd_vae_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..04a543a22c2794095d5f56089b2ca60d445fbc4e --- /dev/null +++ b/diffsynth/models/svd_vae_encoder.py @@ -0,0 +1,139 @@ +from .sd_vae_encoder import SDVAEEncoderStateDictConverter, SDVAEEncoder + + +class SVDVAEEncoder(SDVAEEncoder): + def __init__(self): + super().__init__() + self.scaling_factor = 0.13025 + + @staticmethod + def state_dict_converter(): + return SVDVAEEncoderStateDictConverter() + + +class SVDVAEEncoderStateDictConverter(SDVAEEncoderStateDictConverter): + def __init__(self): + super().__init__() + + def from_diffusers(self, state_dict): + return super().from_diffusers(state_dict) + + def from_civitai(self, state_dict): + rename_dict = { + "conditioner.embedders.3.encoder.encoder.conv_in.bias": "conv_in.bias", + "conditioner.embedders.3.encoder.encoder.conv_in.weight": "conv_in.weight", + "conditioner.embedders.3.encoder.encoder.conv_out.bias": "conv_out.bias", + "conditioner.embedders.3.encoder.encoder.conv_out.weight": "conv_out.weight", + "conditioner.embedders.3.encoder.encoder.down.0.block.0.conv1.bias": "blocks.0.conv1.bias", + "conditioner.embedders.3.encoder.encoder.down.0.block.0.conv1.weight": "blocks.0.conv1.weight", + "conditioner.embedders.3.encoder.encoder.down.0.block.0.conv2.bias": "blocks.0.conv2.bias", + "conditioner.embedders.3.encoder.encoder.down.0.block.0.conv2.weight": "blocks.0.conv2.weight", + "conditioner.embedders.3.encoder.encoder.down.0.block.0.norm1.bias": "blocks.0.norm1.bias", + 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"conditioner.embedders.3.encoder.encoder.mid.block_1.conv1.bias": "blocks.11.conv1.bias", + "conditioner.embedders.3.encoder.encoder.mid.block_1.conv1.weight": "blocks.11.conv1.weight", + "conditioner.embedders.3.encoder.encoder.mid.block_1.conv2.bias": "blocks.11.conv2.bias", + "conditioner.embedders.3.encoder.encoder.mid.block_1.conv2.weight": "blocks.11.conv2.weight", + "conditioner.embedders.3.encoder.encoder.mid.block_1.norm1.bias": "blocks.11.norm1.bias", + "conditioner.embedders.3.encoder.encoder.mid.block_1.norm1.weight": "blocks.11.norm1.weight", + "conditioner.embedders.3.encoder.encoder.mid.block_1.norm2.bias": "blocks.11.norm2.bias", + "conditioner.embedders.3.encoder.encoder.mid.block_1.norm2.weight": "blocks.11.norm2.weight", + "conditioner.embedders.3.encoder.encoder.mid.block_2.conv1.bias": "blocks.13.conv1.bias", + "conditioner.embedders.3.encoder.encoder.mid.block_2.conv1.weight": "blocks.13.conv1.weight", + "conditioner.embedders.3.encoder.encoder.mid.block_2.conv2.bias": "blocks.13.conv2.bias", + "conditioner.embedders.3.encoder.encoder.mid.block_2.conv2.weight": "blocks.13.conv2.weight", + "conditioner.embedders.3.encoder.encoder.mid.block_2.norm1.bias": "blocks.13.norm1.bias", + "conditioner.embedders.3.encoder.encoder.mid.block_2.norm1.weight": "blocks.13.norm1.weight", + "conditioner.embedders.3.encoder.encoder.mid.block_2.norm2.bias": "blocks.13.norm2.bias", + "conditioner.embedders.3.encoder.encoder.mid.block_2.norm2.weight": "blocks.13.norm2.weight", + "conditioner.embedders.3.encoder.encoder.norm_out.bias": "conv_norm_out.bias", + "conditioner.embedders.3.encoder.encoder.norm_out.weight": "conv_norm_out.weight", + "conditioner.embedders.3.encoder.quant_conv.bias": "quant_conv.bias", + "conditioner.embedders.3.encoder.quant_conv.weight": "quant_conv.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if "transformer_blocks" in rename_dict[name]: + param = param.squeeze() + state_dict_[rename_dict[name]] = param + return state_dict_ diff --git a/diffsynth/models/tiler.py b/diffsynth/models/tiler.py new file mode 100644 index 0000000000000000000000000000000000000000..dff5ebf2674b504f0b66a6ba7aba800e048f5099 --- /dev/null +++ b/diffsynth/models/tiler.py @@ -0,0 +1,234 @@ +import torch +from einops import rearrange, repeat + + +class TileWorker: + def __init__(self): + pass + + + def mask(self, height, width, border_width): + # Create a mask with shape (height, width). + # The centre area is filled with 1, and the border line is filled with values in range (0, 1]. + x = torch.arange(height).repeat(width, 1).T + y = torch.arange(width).repeat(height, 1) + mask = torch.stack([x + 1, height - x, y + 1, width - y]).min(dim=0).values + mask = (mask / border_width).clip(0, 1) + return mask + + + def tile(self, model_input, tile_size, tile_stride, tile_device, tile_dtype): + # Convert a tensor (b, c, h, w) to (b, c, tile_size, tile_size, tile_num) + batch_size, channel, _, _ = model_input.shape + model_input = model_input.to(device=tile_device, dtype=tile_dtype) + unfold_operator = torch.nn.Unfold( + kernel_size=(tile_size, tile_size), + stride=(tile_stride, tile_stride) + ) + model_input = unfold_operator(model_input) + model_input = model_input.view((batch_size, channel, tile_size, tile_size, -1)) + + return model_input + + + def tiled_inference(self, forward_fn, model_input, tile_batch_size, inference_device, inference_dtype, tile_device, tile_dtype): + # Call y=forward_fn(x) for each tile + tile_num = model_input.shape[-1] + model_output_stack = [] + + for tile_id in range(0, tile_num, tile_batch_size): + + # process input + tile_id_ = min(tile_id + tile_batch_size, tile_num) + x = model_input[:, :, :, :, tile_id: tile_id_] + x = x.to(device=inference_device, dtype=inference_dtype) + x = rearrange(x, "b c h w n -> (n b) c h w") + + # process output + y = forward_fn(x) + y = rearrange(y, "(n b) c h w -> b c h w n", n=tile_id_-tile_id) + y = y.to(device=tile_device, dtype=tile_dtype) + model_output_stack.append(y) + + model_output = torch.concat(model_output_stack, dim=-1) + return model_output + + + def io_scale(self, model_output, tile_size): + # Determine the size modification happened in forward_fn + # We only consider the same scale on height and width. + io_scale = model_output.shape[2] / tile_size + return io_scale + + + def untile(self, model_output, height, width, tile_size, tile_stride, border_width, tile_device, tile_dtype): + # The reversed function of tile + mask = self.mask(tile_size, tile_size, border_width) + mask = mask.to(device=tile_device, dtype=tile_dtype) + mask = rearrange(mask, "h w -> 1 1 h w 1") + model_output = model_output * mask + + fold_operator = torch.nn.Fold( + output_size=(height, width), + kernel_size=(tile_size, tile_size), + stride=(tile_stride, tile_stride) + ) + mask = repeat(mask[0, 0, :, :, 0], "h w -> 1 (h w) n", n=model_output.shape[-1]) + model_output = rearrange(model_output, "b c h w n -> b (c h w) n") + model_output = fold_operator(model_output) / fold_operator(mask) + + return model_output + + + def tiled_forward(self, forward_fn, model_input, tile_size, tile_stride, tile_batch_size=1, tile_device="cpu", tile_dtype=torch.float32, border_width=None): + # Prepare + inference_device, inference_dtype = model_input.device, model_input.dtype + height, width = model_input.shape[2], model_input.shape[3] + border_width = int(tile_stride*0.5) if border_width is None else border_width + + # tile + model_input = self.tile(model_input, tile_size, tile_stride, tile_device, tile_dtype) + + # inference + model_output = self.tiled_inference(forward_fn, model_input, tile_batch_size, inference_device, inference_dtype, tile_device, tile_dtype) + + # resize + io_scale = self.io_scale(model_output, tile_size) + height, width = int(height*io_scale), int(width*io_scale) + tile_size, tile_stride = int(tile_size*io_scale), int(tile_stride*io_scale) + border_width = int(border_width*io_scale) + + # untile + model_output = self.untile(model_output, height, width, tile_size, tile_stride, border_width, tile_device, tile_dtype) + + # Done! + model_output = model_output.to(device=inference_device, dtype=inference_dtype) + return model_output + + + +class FastTileWorker: + def __init__(self): + pass + + + def build_mask(self, data, is_bound): + _, _, H, W = data.shape + h = repeat(torch.arange(H), "H -> H W", H=H, W=W) + w = repeat(torch.arange(W), "W -> H W", H=H, W=W) + border_width = (H + W) // 4 + pad = torch.ones_like(h) * border_width + mask = torch.stack([ + pad if is_bound[0] else h + 1, + pad if is_bound[1] else H - h, + pad if is_bound[2] else w + 1, + pad if is_bound[3] else W - w + ]).min(dim=0).values + mask = mask.clip(1, border_width) + mask = (mask / border_width).to(dtype=data.dtype, device=data.device) + mask = rearrange(mask, "H W -> 1 H W") + return mask + + + def tiled_forward(self, forward_fn, model_input, tile_size, tile_stride, tile_device="cpu", tile_dtype=torch.float32, border_width=None): + # Prepare + B, C, H, W = model_input.shape + border_width = int(tile_stride*0.5) if border_width is None else border_width + weight = torch.zeros((1, 1, H, W), dtype=tile_dtype, device=tile_device) + values = torch.zeros((B, C, H, W), dtype=tile_dtype, device=tile_device) + + # Split tasks + tasks = [] + for h in range(0, H, tile_stride): + for w in range(0, W, tile_stride): + if (h-tile_stride >= 0 and h-tile_stride+tile_size >= H) or (w-tile_stride >= 0 and w-tile_stride+tile_size >= W): + continue + h_, w_ = h + tile_size, w + tile_size + if h_ > H: h, h_ = H - tile_size, H + if w_ > W: w, w_ = W - tile_size, W + tasks.append((h, h_, w, w_)) + + # Run + for hl, hr, wl, wr in tasks: + # Forward + hidden_states_batch = forward_fn(hl, hr, wl, wr).to(dtype=tile_dtype, device=tile_device) + + mask = self.build_mask(hidden_states_batch, is_bound=(hl==0, hr>=H, wl==0, wr>=W)) + values[:, :, hl:hr, wl:wr] += hidden_states_batch * mask + weight[:, :, hl:hr, wl:wr] += mask + values /= weight + return values + + + +class TileWorker2Dto3D: + """ + Process 3D tensors, but only enable TileWorker on 2D. + """ + def __init__(self): + pass + + + def build_mask(self, T, H, W, dtype, device, is_bound, border_width): + t = repeat(torch.arange(T), "T -> T H W", T=T, H=H, W=W) + h = repeat(torch.arange(H), "H -> T H W", T=T, H=H, W=W) + w = repeat(torch.arange(W), "W -> T H W", T=T, H=H, W=W) + border_width = (H + W) // 4 if border_width is None else border_width + pad = torch.ones_like(h) * border_width + mask = torch.stack([ + pad if is_bound[0] else t + 1, + pad if is_bound[1] else T - t, + pad if is_bound[2] else h + 1, + pad if is_bound[3] else H - h, + pad if is_bound[4] else w + 1, + pad if is_bound[5] else W - w + ]).min(dim=0).values + mask = mask.clip(1, border_width) + mask = (mask / border_width).to(dtype=dtype, device=device) + mask = rearrange(mask, "T H W -> 1 1 T H W") + return mask + + + def tiled_forward( + self, + forward_fn, + model_input, + tile_size, tile_stride, + tile_device="cpu", tile_dtype=torch.float32, + computation_device="cuda", computation_dtype=torch.float32, + border_width=None, scales=[1, 1, 1, 1], + progress_bar=lambda x:x + ): + B, C, T, H, W = model_input.shape + scale_C, scale_T, scale_H, scale_W = scales + tile_size_H, tile_size_W = tile_size + tile_stride_H, tile_stride_W = tile_stride + + value = torch.zeros((B, int(C*scale_C), int(T*scale_T), int(H*scale_H), int(W*scale_W)), dtype=tile_dtype, device=tile_device) + weight = torch.zeros((1, 1, int(T*scale_T), int(H*scale_H), int(W*scale_W)), dtype=tile_dtype, device=tile_device) + + # Split tasks + tasks = [] + for h in range(0, H, tile_stride_H): + for w in range(0, W, tile_stride_W): + if (h-tile_stride_H >= 0 and h-tile_stride_H+tile_size_H >= H) or (w-tile_stride_W >= 0 and w-tile_stride_W+tile_size_W >= W): + continue + h_, w_ = h + tile_size_H, w + tile_size_W + if h_ > H: h, h_ = max(H - tile_size_H, 0), H + if w_ > W: w, w_ = max(W - tile_size_W, 0), W + tasks.append((h, h_, w, w_)) + + # Run + for hl, hr, wl, wr in progress_bar(tasks): + mask = self.build_mask( + int(T*scale_T), int((hr-hl)*scale_H), int((wr-wl)*scale_W), + tile_dtype, tile_device, + is_bound=(True, True, hl==0, hr>=H, wl==0, wr>=W), + border_width=border_width + ) + grid_input = model_input[:, :, :, hl:hr, wl:wr].to(dtype=computation_dtype, device=computation_device) + grid_output = forward_fn(grid_input).to(dtype=tile_dtype, device=tile_device) + value[:, :, :, int(hl*scale_H):int(hr*scale_H), int(wl*scale_W):int(wr*scale_W)] += grid_output * mask + weight[:, :, :, int(hl*scale_H):int(hr*scale_H), int(wl*scale_W):int(wr*scale_W)] += mask + value = value / weight + return value \ No newline at end of file diff --git a/diffsynth/models/utils.py b/diffsynth/models/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..86104d043a54350dfaf74eb5626bfcc0eb1d68e2 --- /dev/null +++ b/diffsynth/models/utils.py @@ -0,0 +1,182 @@ +import torch, os +from safetensors import safe_open +from contextlib import contextmanager +import hashlib + +@contextmanager +def init_weights_on_device(device = torch.device("meta"), include_buffers :bool = False): + + old_register_parameter = torch.nn.Module.register_parameter + if include_buffers: + old_register_buffer = torch.nn.Module.register_buffer + + def register_empty_parameter(module, name, param): + old_register_parameter(module, name, param) + if param is not None: + param_cls = type(module._parameters[name]) + kwargs = module._parameters[name].__dict__ + kwargs["requires_grad"] = param.requires_grad + module._parameters[name] = param_cls(module._parameters[name].to(device), **kwargs) + + def register_empty_buffer(module, name, buffer, persistent=True): + old_register_buffer(module, name, buffer, persistent=persistent) + if buffer is not None: + module._buffers[name] = module._buffers[name].to(device) + + def patch_tensor_constructor(fn): + def wrapper(*args, **kwargs): + kwargs["device"] = device + return fn(*args, **kwargs) + + return wrapper + + if include_buffers: + tensor_constructors_to_patch = { + torch_function_name: getattr(torch, torch_function_name) + for torch_function_name in ["empty", "zeros", "ones", "full"] + } + else: + tensor_constructors_to_patch = {} + + try: + torch.nn.Module.register_parameter = register_empty_parameter + if include_buffers: + torch.nn.Module.register_buffer = register_empty_buffer + for torch_function_name in tensor_constructors_to_patch.keys(): + setattr(torch, torch_function_name, patch_tensor_constructor(getattr(torch, torch_function_name))) + yield + finally: + torch.nn.Module.register_parameter = old_register_parameter + if include_buffers: + torch.nn.Module.register_buffer = old_register_buffer + for torch_function_name, old_torch_function in tensor_constructors_to_patch.items(): + setattr(torch, torch_function_name, old_torch_function) + +def load_state_dict_from_folder(file_path, torch_dtype=None): + state_dict = {} + for file_name in os.listdir(file_path): + if "." in file_name and file_name.split(".")[-1] in [ + "safetensors", "bin", "ckpt", "pth", "pt" + ]: + state_dict.update(load_state_dict(os.path.join(file_path, file_name), torch_dtype=torch_dtype)) + return state_dict + + +def load_state_dict(file_path, torch_dtype=None, device="cpu"): + if file_path.endswith(".safetensors"): + return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype, device=device) + else: + return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype, device=device) + + +def load_state_dict_from_safetensors(file_path, torch_dtype=None, device="cpu"): + state_dict = {} + with safe_open(file_path, framework="pt", device=str(device)) as f: + for k in f.keys(): + state_dict[k] = f.get_tensor(k) + if torch_dtype is not None: + state_dict[k] = state_dict[k].to(torch_dtype) + return state_dict + + +def load_state_dict_from_bin(file_path, torch_dtype=None, device="cpu"): + state_dict = torch.load(file_path, map_location=device, weights_only=True) + if torch_dtype is not None: + for i in state_dict: + if isinstance(state_dict[i], torch.Tensor): + state_dict[i] = state_dict[i].to(torch_dtype) + return state_dict + + +def search_for_embeddings(state_dict): + embeddings = [] + for k in state_dict: + if isinstance(state_dict[k], torch.Tensor): + embeddings.append(state_dict[k]) + elif isinstance(state_dict[k], dict): + embeddings += search_for_embeddings(state_dict[k]) + return embeddings + + +def search_parameter(param, state_dict): + for name, param_ in state_dict.items(): + if param.numel() == param_.numel(): + if param.shape == param_.shape: + if torch.dist(param, param_) < 1e-3: + return name + else: + if torch.dist(param.flatten(), param_.flatten()) < 1e-3: + return name + return None + + +def build_rename_dict(source_state_dict, target_state_dict, split_qkv=False): + matched_keys = set() + with torch.no_grad(): + for name in source_state_dict: + rename = search_parameter(source_state_dict[name], target_state_dict) + if rename is not None: + print(f'"{name}": "{rename}",') + matched_keys.add(rename) + elif split_qkv and len(source_state_dict[name].shape)>=1 and source_state_dict[name].shape[0]%3==0: + length = source_state_dict[name].shape[0] // 3 + rename = [] + for i in range(3): + rename.append(search_parameter(source_state_dict[name][i*length: i*length+length], target_state_dict)) + if None not in rename: + print(f'"{name}": {rename},') + for rename_ in rename: + matched_keys.add(rename_) + for name in target_state_dict: + if name not in matched_keys: + print("Cannot find", name, target_state_dict[name].shape) + + +def search_for_files(folder, extensions): + files = [] + if os.path.isdir(folder): + for file in sorted(os.listdir(folder)): + files += search_for_files(os.path.join(folder, file), extensions) + elif os.path.isfile(folder): + for extension in extensions: + if folder.endswith(extension): + files.append(folder) + break + return files + + +def convert_state_dict_keys_to_single_str(state_dict, with_shape=True): + keys = [] + for key, value in state_dict.items(): + if isinstance(key, str): + if isinstance(value, torch.Tensor): + if with_shape: + shape = "_".join(map(str, list(value.shape))) + keys.append(key + ":" + shape) + keys.append(key) + elif isinstance(value, dict): + keys.append(key + "|" + convert_state_dict_keys_to_single_str(value, with_shape=with_shape)) + keys.sort() + keys_str = ",".join(keys) + return keys_str + + +def split_state_dict_with_prefix(state_dict): + keys = sorted([key for key in state_dict if isinstance(key, str)]) + prefix_dict = {} + for key in keys: + prefix = key if "." not in key else key.split(".")[0] + if prefix not in prefix_dict: + prefix_dict[prefix] = [] + prefix_dict[prefix].append(key) + state_dicts = [] + for prefix, keys in prefix_dict.items(): + sub_state_dict = {key: state_dict[key] for key in keys} + state_dicts.append(sub_state_dict) + return state_dicts + + +def hash_state_dict_keys(state_dict, with_shape=True): + keys_str = convert_state_dict_keys_to_single_str(state_dict, with_shape=with_shape) + keys_str = keys_str.encode(encoding="UTF-8") + return hashlib.md5(keys_str).hexdigest() \ No newline at end of file diff --git a/diffsynth/models/videossm_hybrid.py b/diffsynth/models/videossm_hybrid.py new file mode 100644 index 0000000000000000000000000000000000000000..0ad63c83c6da669e4b34562f160f0b1aec216126 --- /dev/null +++ b/diffsynth/models/videossm_hybrid.py @@ -0,0 +1,2 @@ +from .memory.videossm_hybrid import * # backward-compat re-export + diff --git a/diffsynth/models/wan_video_camera_controller.py b/diffsynth/models/wan_video_camera_controller.py new file mode 100644 index 0000000000000000000000000000000000000000..026b5581c4284abace2fcab8db2d33d8e518dd35 --- /dev/null +++ b/diffsynth/models/wan_video_camera_controller.py @@ -0,0 +1,202 @@ +import torch +import torch.nn as nn +import numpy as np +from einops import rearrange +import os +from typing_extensions import Literal + +class SimpleAdapter(nn.Module): + def __init__(self, in_dim, out_dim, kernel_size, stride, num_residual_blocks=1): + super(SimpleAdapter, self).__init__() + + # Pixel Unshuffle: reduce spatial dimensions by a factor of 8 + self.pixel_unshuffle = nn.PixelUnshuffle(downscale_factor=8) + + # Convolution: reduce spatial dimensions by a factor + # of 2 (without overlap) + self.conv = nn.Conv2d(in_dim * 64, out_dim, kernel_size=kernel_size, stride=stride, padding=0) + + # Residual blocks for feature extraction + self.residual_blocks = nn.Sequential( + *[ResidualBlock(out_dim) for _ in range(num_residual_blocks)] + ) + + def forward(self, x): + # Reshape to merge the frame dimension into batch + bs, c, f, h, w = x.size() + x = x.permute(0, 2, 1, 3, 4).contiguous().view(bs * f, c, h, w) + + # Pixel Unshuffle operation + x_unshuffled = self.pixel_unshuffle(x) + + # Convolution operation + x_conv = self.conv(x_unshuffled) + + # Feature extraction with residual blocks + out = self.residual_blocks(x_conv) + + # Reshape to restore original bf dimension + out = out.view(bs, f, out.size(1), out.size(2), out.size(3)) + + # Permute dimensions to reorder (if needed), e.g., swap channels and feature frames + out = out.permute(0, 2, 1, 3, 4) + + return out + + def process_camera_coordinates( + self, + direction: Literal["Left", "Right", "Up", "Down", "LeftUp", "LeftDown", "RightUp", "RightDown"], + length: int, + height: int, + width: int, + speed: float = 1/54, + origin=(0, 0.532139961, 0.946026558, 0.5, 0.5, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0) + ): + if origin is None: + origin = (0, 0.532139961, 0.946026558, 0.5, 0.5, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0) + coordinates = generate_camera_coordinates(direction, length, speed, origin) + plucker_embedding = process_pose_file(coordinates, width, height) + return plucker_embedding + + + +class ResidualBlock(nn.Module): + def __init__(self, dim): + super(ResidualBlock, self).__init__() + self.conv1 = nn.Conv2d(dim, dim, kernel_size=3, padding=1) + self.relu = nn.ReLU(inplace=True) + self.conv2 = nn.Conv2d(dim, dim, kernel_size=3, padding=1) + + def forward(self, x): + residual = x + out = self.relu(self.conv1(x)) + out = self.conv2(out) + out += residual + return out + +class Camera(object): + """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py + """ + def __init__(self, entry): + fx, fy, cx, cy = entry[1:5] + self.fx = fx + self.fy = fy + self.cx = cx + self.cy = cy + w2c_mat = np.array(entry[7:]).reshape(3, 4) + w2c_mat_4x4 = np.eye(4) + w2c_mat_4x4[:3, :] = w2c_mat + self.w2c_mat = w2c_mat_4x4 + self.c2w_mat = np.linalg.inv(w2c_mat_4x4) + +def get_relative_pose(cam_params): + """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py + """ + abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params] + abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params] + cam_to_origin = 0 + target_cam_c2w = np.array([ + [1, 0, 0, 0], + [0, 1, 0, -cam_to_origin], + [0, 0, 1, 0], + [0, 0, 0, 1] + ]) + abs2rel = target_cam_c2w @ abs_w2cs[0] + ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]] + ret_poses = np.array(ret_poses, dtype=np.float32) + return ret_poses + +def custom_meshgrid(*args): + # torch>=2.0.0 only + return torch.meshgrid(*args, indexing='ij') + + +def ray_condition(K, c2w, H, W, device): + """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py + """ + # c2w: B, V, 4, 4 + # K: B, V, 4 + + B = K.shape[0] + + j, i = custom_meshgrid( + torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype), + torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype), + ) + i = i.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW] + j = j.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW] + + fx, fy, cx, cy = K.chunk(4, dim=-1) # B,V, 1 + + zs = torch.ones_like(i) # [B, HxW] + xs = (i - cx) / fx * zs + ys = (j - cy) / fy * zs + zs = zs.expand_as(ys) + + directions = torch.stack((xs, ys, zs), dim=-1) # B, V, HW, 3 + directions = directions / directions.norm(dim=-1, keepdim=True) # B, V, HW, 3 + + rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2) # B, V, 3, HW + rays_o = c2w[..., :3, 3] # B, V, 3 + rays_o = rays_o[:, :, None].expand_as(rays_d) # B, V, 3, HW + # c2w @ dirctions + rays_dxo = torch.linalg.cross(rays_o, rays_d) + plucker = torch.cat([rays_dxo, rays_d], dim=-1) + plucker = plucker.reshape(B, c2w.shape[1], H, W, 6) # B, V, H, W, 6 + # plucker = plucker.permute(0, 1, 4, 2, 3) + return plucker + + +def process_pose_file(cam_params, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu', return_poses=False): + if return_poses: + return cam_params + else: + cam_params = [Camera(cam_param) for cam_param in cam_params] + + sample_wh_ratio = width / height + pose_wh_ratio = original_pose_width / original_pose_height # Assuming placeholder ratios, change as needed + + if pose_wh_ratio > sample_wh_ratio: + resized_ori_w = height * pose_wh_ratio + for cam_param in cam_params: + cam_param.fx = resized_ori_w * cam_param.fx / width + else: + resized_ori_h = width / pose_wh_ratio + for cam_param in cam_params: + cam_param.fy = resized_ori_h * cam_param.fy / height + + intrinsic = np.asarray([[cam_param.fx * width, + cam_param.fy * height, + cam_param.cx * width, + cam_param.cy * height] + for cam_param in cam_params], dtype=np.float32) + + K = torch.as_tensor(intrinsic)[None] # [1, 1, 4] + c2ws = get_relative_pose(cam_params) # Assuming this function is defined elsewhere + c2ws = torch.as_tensor(c2ws)[None] # [1, n_frame, 4, 4] + plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous() # V, 6, H, W + plucker_embedding = plucker_embedding[None] + plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0] + return plucker_embedding + + + +def generate_camera_coordinates( + direction: Literal["Left", "Right", "Up", "Down", "LeftUp", "LeftDown", "RightUp", "RightDown"], + length: int, + speed: float = 1/54, + origin=(0, 0.532139961, 0.946026558, 0.5, 0.5, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0) +): + coordinates = [list(origin)] + while len(coordinates) < length: + coor = coordinates[-1].copy() + if "Left" in direction: + coor[9] += speed + if "Right" in direction: + coor[9] -= speed + if "Up" in direction: + coor[13] += speed + if "Down" in direction: + coor[13] -= speed + coordinates.append(coor) + return coordinates diff --git a/diffsynth/models/wan_video_dit.py b/diffsynth/models/wan_video_dit.py new file mode 100644 index 0000000000000000000000000000000000000000..2e71a914f486734863623658af055363cfa99c78 --- /dev/null +++ b/diffsynth/models/wan_video_dit.py @@ -0,0 +1,803 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import math +from typing import Tuple, Optional +from einops import rearrange +from .utils import hash_state_dict_keys +from .wan_video_camera_controller import SimpleAdapter +try: + import flash_attn_interface + FLASH_ATTN_3_AVAILABLE = True +except ModuleNotFoundError: + FLASH_ATTN_3_AVAILABLE = False + +try: + import flash_attn + FLASH_ATTN_2_AVAILABLE = True +except ModuleNotFoundError: + FLASH_ATTN_2_AVAILABLE = False + +try: + from sageattention import sageattn + SAGE_ATTN_AVAILABLE = True +except ModuleNotFoundError: + SAGE_ATTN_AVAILABLE = False + + +def flash_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, num_heads: int, compatibility_mode=False): + if compatibility_mode: + q = rearrange(q, "b s (n d) -> b n s d", n=num_heads) + k = rearrange(k, "b s (n d) -> b n s d", n=num_heads) + v = rearrange(v, "b s (n d) -> b n s d", n=num_heads) + x = F.scaled_dot_product_attention(q, k, v) + x = rearrange(x, "b n s d -> b s (n d)", n=num_heads) + elif FLASH_ATTN_3_AVAILABLE: + q = rearrange(q, "b s (n d) -> b s n d", n=num_heads) + k = rearrange(k, "b s (n d) -> b s n d", n=num_heads) + v = rearrange(v, "b s (n d) -> b s n d", n=num_heads) + x = flash_attn_interface.flash_attn_func(q, k, v) + if isinstance(x,tuple): + x = x[0] + x = rearrange(x, "b s n d -> b s (n d)", n=num_heads) + elif FLASH_ATTN_2_AVAILABLE: + q = rearrange(q, "b s (n d) -> b s n d", n=num_heads) + k = rearrange(k, "b s (n d) -> b s n d", n=num_heads) + v = rearrange(v, "b s (n d) -> b s n d", n=num_heads) + x = flash_attn.flash_attn_func(q, k, v) + x = rearrange(x, "b s n d -> b s (n d)", n=num_heads) + elif SAGE_ATTN_AVAILABLE: + q = rearrange(q, "b s (n d) -> b n s d", n=num_heads) + k = rearrange(k, "b s (n d) -> b n s d", n=num_heads) + v = rearrange(v, "b s (n d) -> b n s d", n=num_heads) + x = sageattn(q, k, v) + x = rearrange(x, "b n s d -> b s (n d)", n=num_heads) + else: + q = rearrange(q, "b s (n d) -> b n s d", n=num_heads) + k = rearrange(k, "b s (n d) -> b n s d", n=num_heads) + v = rearrange(v, "b s (n d) -> b n s d", n=num_heads) + x = F.scaled_dot_product_attention(q, k, v) + x = rearrange(x, "b n s d -> b s (n d)", n=num_heads) + return x + + +def modulate(x: torch.Tensor, shift: torch.Tensor, scale: torch.Tensor): + return (x * (1 + scale) + shift) + + +def sinusoidal_embedding_1d(dim, position): + sinusoid = torch.outer(position.type(torch.float64), torch.pow( + 10000, -torch.arange(dim//2, dtype=torch.float64, device=position.device).div(dim//2))) + x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1) + return x.to(position.dtype) + + +def precompute_freqs_cis_3d(dim: int, end: int = 1024, theta: float = 10000.0): + # 3d rope precompute + f_freqs_cis = precompute_freqs_cis(dim - 2 * (dim // 3), end, theta) + h_freqs_cis = precompute_freqs_cis(dim // 3, end, theta) + w_freqs_cis = precompute_freqs_cis(dim // 3, end, theta) + return f_freqs_cis, h_freqs_cis, w_freqs_cis + + +def precompute_freqs_cis(dim: int, end: int = 1024, theta: float = 10000.0): + # 1d rope precompute + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2) + [: (dim // 2)].double() / dim)) + freqs = torch.outer(torch.arange(end, device=freqs.device), freqs) + freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 + return freqs_cis + + +def rope_apply(x, freqs, num_heads): + x = rearrange(x, "b s (n d) -> b s n d", n=num_heads) + x_out = torch.view_as_complex(x.to(torch.float64).reshape( + x.shape[0], x.shape[1], x.shape[2], -1, 2)) + x_out = torch.view_as_real(x_out * freqs).flatten(2) + return x_out.to(x.dtype) + + +class RMSNorm(nn.Module): + def __init__(self, dim, eps=1e-5): + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def norm(self, x): + return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps) + + def forward(self, x): + dtype = x.dtype + return self.norm(x.float()).to(dtype) * self.weight + + +class AttentionModule(nn.Module): + def __init__(self, num_heads): + super().__init__() + self.num_heads = num_heads + + def forward(self, q, k, v): + x = flash_attention(q=q, k=k, v=v, num_heads=self.num_heads) + return x + + +class SelfAttention(nn.Module): + def __init__(self, dim: int, num_heads: int, eps: float = 1e-6): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.head_dim = dim // num_heads + + self.q = nn.Linear(dim, dim) + self.k = nn.Linear(dim, dim) + self.v = nn.Linear(dim, dim) + self.o = nn.Linear(dim, dim) + self.norm_q = RMSNorm(dim, eps=eps) + self.norm_k = RMSNorm(dim, eps=eps) + + self.attn = AttentionModule(self.num_heads) + + def forward(self, x, freqs=None, camera_emb: Optional[torch.Tensor] = None): + """ + x: [B, N, D] where N = F*h*w (tokens). camera_emb: [B, N, D] broadcast-ready. + CAM paper: inject RT to spatial attention output: out = attn_out + camera_emb + """ + q = self.norm_q(self.q(x)) + k = self.norm_k(self.k(x)) + v = self.v(x) + if freqs is not None: + q = rope_apply(q, freqs, self.num_heads) + k = rope_apply(k, freqs, self.num_heads) + x = self.attn(q, k, v) + x = self.o(x) + if camera_emb is not None: + x = x + camera_emb.to(x.dtype) + return x + + +class CrossAttention(nn.Module): + def __init__(self, dim: int, num_heads: int, eps: float = 1e-6, has_image_input: bool = False): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.head_dim = dim // num_heads + + self.q = nn.Linear(dim, dim) + self.k = nn.Linear(dim, dim) + self.v = nn.Linear(dim, dim) + self.o = nn.Linear(dim, dim) + self.norm_q = RMSNorm(dim, eps=eps) + self.norm_k = RMSNorm(dim, eps=eps) + self.has_image_input = has_image_input + if has_image_input: + self.k_img = nn.Linear(dim, dim) + self.v_img = nn.Linear(dim, dim) + self.norm_k_img = RMSNorm(dim, eps=eps) + + self.attn = AttentionModule(self.num_heads) + + def forward(self, x: torch.Tensor, y: torch.Tensor): + if self.has_image_input: + img = y[:, :257] + ctx = y[:, 257:] + else: + ctx = y + q = self.norm_q(self.q(x)) + k = self.norm_k(self.k(ctx)) + v = self.v(ctx) + x = self.attn(q, k, v) + if self.has_image_input: + k_img = self.norm_k_img(self.k_img(img)) + v_img = self.v_img(img) + y = flash_attention(q, k_img, v_img, num_heads=self.num_heads) + x = x + y + return self.o(x) + + +class GateModule(nn.Module): + def __init__(self,): + super().__init__() + + def forward(self, x, gate, residual): + return x + gate * residual + +class DiTBlock(nn.Module): + def __init__(self, has_image_input: bool, dim: int, num_heads: int, ffn_dim: int, eps: float = 1e-6): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.ffn_dim = ffn_dim + + self.self_attn = SelfAttention(dim, num_heads, eps) + self.cross_attn = CrossAttention( + dim, num_heads, eps, has_image_input=has_image_input) + self.norm1 = nn.LayerNorm(dim, eps=eps, elementwise_affine=False) + self.norm2 = nn.LayerNorm(dim, eps=eps, elementwise_affine=False) + self.norm3 = nn.LayerNorm(dim, eps=eps) + self.ffn = nn.Sequential(nn.Linear(dim, ffn_dim), nn.GELU( + approximate='tanh'), nn.Linear(ffn_dim, dim)) + self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5) + self.gate = GateModule() + + def forward(self, x, context, t_mod, freqs, actions=None, camera_emb: Optional[torch.Tensor] = None): + # actions parameter added for compatibility with DiTBlock_w_Action + # Standard DiTBlock ignores actions (doesn't use them) + # camera_emb: optional [B, N, D] for CAM RT injection (spatial attention output) + has_seq = len(t_mod.shape) == 4 + chunk_dim = 2 if has_seq else 1 + # msa: multi-head self-attention mlp: multi-layer perceptron + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( + self.modulation.to(dtype=t_mod.dtype, device=t_mod.device) + t_mod).chunk(6, dim=chunk_dim) + if has_seq: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( + shift_msa.squeeze(2), scale_msa.squeeze(2), gate_msa.squeeze(2), + shift_mlp.squeeze(2), scale_mlp.squeeze(2), gate_mlp.squeeze(2), + ) + input_x = modulate(self.norm1(x), shift_msa, scale_msa) + x = self.gate(x, gate_msa, self.self_attn(input_x, freqs, camera_emb=camera_emb)) + x = x + self.cross_attn(self.norm3(x), context) + input_x = modulate(self.norm2(x), shift_mlp, scale_mlp) + x = self.gate(x, gate_mlp, self.ffn(input_x)) + return x + + +class MLP(torch.nn.Module): + def __init__(self, in_dim, out_dim, has_pos_emb=False): + super().__init__() + self.proj = torch.nn.Sequential( + nn.LayerNorm(in_dim), + nn.Linear(in_dim, in_dim), + nn.GELU(), + nn.Linear(in_dim, out_dim), + nn.LayerNorm(out_dim) + ) + self.has_pos_emb = has_pos_emb + if has_pos_emb: + self.emb_pos = torch.nn.Parameter(torch.zeros((1, 514, 1280))) + + def forward(self, x): + if self.has_pos_emb: + x = x + self.emb_pos.to(dtype=x.dtype, device=x.device) + return self.proj(x) + + +class Head(nn.Module): + def __init__(self, dim: int, out_dim: int, patch_size: Tuple[int, int, int], eps: float): + super().__init__() + self.dim = dim + self.patch_size = patch_size + self.norm = nn.LayerNorm(dim, eps=eps, elementwise_affine=False) + self.head = nn.Linear(dim, out_dim * math.prod(patch_size)) + self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5) + + def forward(self, x, t_mod): + if len(t_mod.shape) == 3: + shift, scale = (self.modulation.unsqueeze(0).to(dtype=t_mod.dtype, device=t_mod.device) + t_mod.unsqueeze(2)).chunk(2, dim=2) + x = (self.head(self.norm(x) * (1 + scale.squeeze(2)) + shift.squeeze(2))) + else: + shift, scale = (self.modulation.to(dtype=t_mod.dtype, device=t_mod.device) + t_mod).chunk(2, dim=1) + x = (self.head(self.norm(x) * (1 + scale) + shift)) + return x + + +class WanModel(torch.nn.Module): + def __init__( + self, + dim: int, + in_dim: int, + ffn_dim: int, + out_dim: int, + text_dim: int, + freq_dim: int, + eps: float, + patch_size: Tuple[int, int, int], + num_heads: int, + num_layers: int, + has_image_input: bool, + has_image_pos_emb: bool = False, + has_ref_conv: bool = False, + add_control_adapter: bool = False, + in_dim_control_adapter: int = 24, + seperated_timestep: bool = False, + require_vae_embedding: bool = True, + require_clip_embedding: bool = True, + fuse_vae_embedding_in_latents: bool = False, + ): + super().__init__() + self.dim = dim + self.in_dim = in_dim + self.freq_dim = freq_dim + self.has_image_input = has_image_input + self.patch_size = patch_size + self.seperated_timestep = seperated_timestep + self.require_vae_embedding = require_vae_embedding + self.require_clip_embedding = require_clip_embedding + self.fuse_vae_embedding_in_latents = fuse_vae_embedding_in_latents + self.num_heads = num_heads + self.ffn_dim = ffn_dim + + self.patch_embedding = nn.Conv3d( + in_dim, dim, kernel_size=patch_size, stride=patch_size) + self.text_embedding = nn.Sequential( + nn.Linear(text_dim, dim), + nn.GELU(approximate='tanh'), + nn.Linear(dim, dim) + ) + self.time_embedding = nn.Sequential( + nn.Linear(freq_dim, dim), + nn.SiLU(), + nn.Linear(dim, dim) + ) + self.time_projection = nn.Sequential( + nn.SiLU(), nn.Linear(dim, dim * 6)) + self.blocks = nn.ModuleList([ + DiTBlock(has_image_input, dim, num_heads, ffn_dim, eps) + for _ in range(num_layers) + ]) + self.head = Head(dim, out_dim, patch_size, eps) + head_dim = dim // num_heads + self.freqs = precompute_freqs_cis_3d(head_dim) + + if has_image_input: + self.img_emb = MLP(1280, dim, has_pos_emb=has_image_pos_emb) # clip_feature_dim = 1280 + if has_ref_conv: + self.ref_conv = nn.Conv2d(16, dim, kernel_size=(2, 2), stride=(2, 2)) + self.has_image_pos_emb = has_image_pos_emb + self.has_ref_conv = has_ref_conv + if add_control_adapter: + self.control_adapter = SimpleAdapter(in_dim_control_adapter, dim, kernel_size=patch_size[1:], stride=patch_size[1:]) + else: + self.control_adapter = None + self.camera_encoder = None # Optional CameraEncoder for CAM RT injection (added by train.py when use_camera_encoder) + + def patchify(self, x: torch.Tensor, control_camera_latents_input: Optional[torch.Tensor] = None): + x = self.patch_embedding(x) + if self.control_adapter is not None and control_camera_latents_input is not None: + y_camera = self.control_adapter(control_camera_latents_input) + x = [u + v for u, v in zip(x, y_camera)] + x = x[0].unsqueeze(0) + grid_size = x.shape[2:] + x = rearrange(x, 'b c f h w -> b (f h w) c').contiguous() + return x, grid_size # x, grid_size: (f, h, w) + + def unpatchify(self, x: torch.Tensor, grid_size: torch.Tensor): + return rearrange( + x, 'b (f h w) (x y z c) -> b c (f x) (h y) (w z)', + f=grid_size[0], h=grid_size[1], w=grid_size[2], + x=self.patch_size[0], y=self.patch_size[1], z=self.patch_size[2] + ) + + def forward(self, + x: torch.Tensor, + timestep: torch.Tensor, + context: torch.Tensor, + clip_feature: Optional[torch.Tensor] = None, + y: Optional[torch.Tensor] = None, + use_gradient_checkpointing: bool = False, + use_gradient_checkpointing_offload: bool = False, + context_latents: Optional[torch.Tensor] = None, + num_context_frames: int = 0, + context_mask: Optional[torch.Tensor] = None, + **kwargs, + ): + t = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, timestep)) + t_mod = self.time_projection(t).unflatten(1, (6, self.dim)) + context = self.text_embedding(context) + + # Context Memory: concatenate clean context latents with noisy target latents + # This should be done BEFORE patchify, so x already contains both context and target + # x shape: (B, C, K_context + T_target, H, W) + # where K_context = num_context_frames, T_target = target frames + if context_latents is not None and num_context_frames > 0: + # x should already contain both context and target latents (concatenated in pipeline) + # But if not, we concatenate here + # Note: In Context Memory mode, context_latents are clean (not noised) + # and x contains noisy target latents. They should be concatenated in pipeline + # before calling this forward function. + # Here we just verify the shape and store num_context_frames for position encoding + pass # Context latents should already be concatenated with x in pipeline + + if self.has_image_input: + x = torch.cat([x, y], dim=1) # (b, c_x + c_y, f, h, w) + clip_embdding = self.img_emb(clip_feature) + context = torch.cat([clip_embdding, context], dim=1) + + x, (f, h, w) = self.patchify(x) + + # Position encoding: freqs should be computed in pipeline (model_fn_wan_video) + # Negative encoding for context frames is handled in pipeline layer, not here + # Use standard position encoding (freqs should be passed from pipeline if needed) + freqs = torch.cat([ + self.freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1), + self.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), + self.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1) + ], dim=-1).reshape(f * h * w, 1, -1).to(x.device) + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + return custom_forward + + for block in self.blocks: + if self.training and use_gradient_checkpointing: + if use_gradient_checkpointing_offload: + with torch.autograd.graph.save_on_cpu(): + x = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + x, context, t_mod, freqs, + use_reentrant=False, + ) + else: + x = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + x, context, t_mod, freqs, + use_reentrant=False, + ) + else: + x = block(x, context, t_mod, freqs) + + x = self.head(x, t) + x = self.unpatchify(x, (f, h, w)) + return x + + @staticmethod + def state_dict_converter(): + return WanModelStateDictConverter() + + +class WanModelStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + rename_dict = { + "blocks.0.attn1.norm_k.weight": "blocks.0.self_attn.norm_k.weight", + "blocks.0.attn1.norm_q.weight": "blocks.0.self_attn.norm_q.weight", + "blocks.0.attn1.to_k.bias": "blocks.0.self_attn.k.bias", + "blocks.0.attn1.to_k.weight": "blocks.0.self_attn.k.weight", + "blocks.0.attn1.to_out.0.bias": "blocks.0.self_attn.o.bias", + "blocks.0.attn1.to_out.0.weight": "blocks.0.self_attn.o.weight", + "blocks.0.attn1.to_q.bias": "blocks.0.self_attn.q.bias", + "blocks.0.attn1.to_q.weight": "blocks.0.self_attn.q.weight", + "blocks.0.attn1.to_v.bias": "blocks.0.self_attn.v.bias", + "blocks.0.attn1.to_v.weight": "blocks.0.self_attn.v.weight", + "blocks.0.attn2.norm_k.weight": "blocks.0.cross_attn.norm_k.weight", + "blocks.0.attn2.norm_q.weight": "blocks.0.cross_attn.norm_q.weight", + "blocks.0.attn2.to_k.bias": "blocks.0.cross_attn.k.bias", + "blocks.0.attn2.to_k.weight": "blocks.0.cross_attn.k.weight", + "blocks.0.attn2.to_out.0.bias": "blocks.0.cross_attn.o.bias", + "blocks.0.attn2.to_out.0.weight": "blocks.0.cross_attn.o.weight", + "blocks.0.attn2.to_q.bias": "blocks.0.cross_attn.q.bias", + "blocks.0.attn2.to_q.weight": "blocks.0.cross_attn.q.weight", + "blocks.0.attn2.to_v.bias": "blocks.0.cross_attn.v.bias", + "blocks.0.attn2.to_v.weight": "blocks.0.cross_attn.v.weight", + "blocks.0.ffn.net.0.proj.bias": "blocks.0.ffn.0.bias", + "blocks.0.ffn.net.0.proj.weight": "blocks.0.ffn.0.weight", + "blocks.0.ffn.net.2.bias": "blocks.0.ffn.2.bias", + "blocks.0.ffn.net.2.weight": "blocks.0.ffn.2.weight", + "blocks.0.norm2.bias": "blocks.0.norm3.bias", + "blocks.0.norm2.weight": "blocks.0.norm3.weight", + "blocks.0.scale_shift_table": "blocks.0.modulation", + "condition_embedder.text_embedder.linear_1.bias": "text_embedding.0.bias", + "condition_embedder.text_embedder.linear_1.weight": "text_embedding.0.weight", + "condition_embedder.text_embedder.linear_2.bias": "text_embedding.2.bias", + "condition_embedder.text_embedder.linear_2.weight": "text_embedding.2.weight", + "condition_embedder.time_embedder.linear_1.bias": "time_embedding.0.bias", + "condition_embedder.time_embedder.linear_1.weight": "time_embedding.0.weight", + "condition_embedder.time_embedder.linear_2.bias": "time_embedding.2.bias", + "condition_embedder.time_embedder.linear_2.weight": "time_embedding.2.weight", + "condition_embedder.time_proj.bias": "time_projection.1.bias", + "condition_embedder.time_proj.weight": "time_projection.1.weight", + "patch_embedding.bias": "patch_embedding.bias", + "patch_embedding.weight": "patch_embedding.weight", + "scale_shift_table": "head.modulation", + "proj_out.bias": "head.head.bias", + "proj_out.weight": "head.head.weight", + } + state_dict_ = {} + for name, param in state_dict.items(): + if name in rename_dict: + state_dict_[rename_dict[name]] = param + else: + name_ = ".".join(name.split(".")[:1] + ["0"] + name.split(".")[2:]) + if name_ in rename_dict: + name_ = rename_dict[name_] + name_ = ".".join(name_.split(".")[:1] + [name.split(".")[1]] + name_.split(".")[2:]) + state_dict_[name_] = param + if hash_state_dict_keys(state_dict) == "cb104773c6c2cb6df4f9529ad5c60d0b": + config = { + "model_type": "t2v", + "patch_size": (1, 2, 2), + "text_len": 512, + "in_dim": 16, + "dim": 5120, + "ffn_dim": 13824, + "freq_dim": 256, + "text_dim": 4096, + "out_dim": 16, + "num_heads": 40, + "num_layers": 40, + "window_size": (-1, -1), + "qk_norm": True, + "cross_attn_norm": True, + "eps": 1e-6, + } + else: + config = {} + return state_dict_, config + + def from_civitai(self, state_dict): + state_dict = {name: param for name, param in state_dict.items() if not name.startswith("vace")} + if hash_state_dict_keys(state_dict) == "9269f8db9040a9d860eaca435be61814": + config = { + "has_image_input": False, + "patch_size": [1, 2, 2], + "in_dim": 16, + "dim": 1536, + "ffn_dim": 8960, + "freq_dim": 256, + "text_dim": 4096, + "out_dim": 16, + "num_heads": 12, + "num_layers": 30, + "eps": 1e-6 + } + elif hash_state_dict_keys(state_dict) == "aafcfd9672c3a2456dc46e1cb6e52c70": + config = { + "has_image_input": False, + "patch_size": [1, 2, 2], + "in_dim": 16, + "dim": 5120, + "ffn_dim": 13824, + "freq_dim": 256, + "text_dim": 4096, + "out_dim": 16, + "num_heads": 40, + "num_layers": 40, + "eps": 1e-6 + } + elif hash_state_dict_keys(state_dict) == "6bfcfb3b342cb286ce886889d519a77e": + config = { + "has_image_input": True, + "patch_size": [1, 2, 2], + "in_dim": 36, + "dim": 5120, + "ffn_dim": 13824, + "freq_dim": 256, + "text_dim": 4096, + "out_dim": 16, + "num_heads": 40, + "num_layers": 40, + "eps": 1e-6 + } + elif hash_state_dict_keys(state_dict) == "6d6ccde6845b95ad9114ab993d917893": + config = { + "has_image_input": True, + "patch_size": [1, 2, 2], + "in_dim": 36, + "dim": 1536, + "ffn_dim": 8960, + "freq_dim": 256, + "text_dim": 4096, + "out_dim": 16, + "num_heads": 12, + "num_layers": 30, + "eps": 1e-6 + } + elif hash_state_dict_keys(state_dict) == "6bfcfb3b342cb286ce886889d519a77e": + config = { + "has_image_input": True, + "patch_size": [1, 2, 2], + "in_dim": 36, + "dim": 5120, + "ffn_dim": 13824, + "freq_dim": 256, + "text_dim": 4096, + "out_dim": 16, + "num_heads": 40, + "num_layers": 40, + "eps": 1e-6 + } + elif hash_state_dict_keys(state_dict) == "349723183fc063b2bfc10bb2835cf677": + # 1.3B PAI control + config = { + "has_image_input": True, + "patch_size": [1, 2, 2], + "in_dim": 48, + "dim": 1536, + "ffn_dim": 8960, + "freq_dim": 256, + "text_dim": 4096, + "out_dim": 16, + "num_heads": 12, + "num_layers": 30, + "eps": 1e-6 + } + elif hash_state_dict_keys(state_dict) == "efa44cddf936c70abd0ea28b6cbe946c": + # 14B PAI control + config = { + "has_image_input": True, + "patch_size": [1, 2, 2], + "in_dim": 48, + "dim": 5120, + "ffn_dim": 13824, + "freq_dim": 256, + "text_dim": 4096, + "out_dim": 16, + "num_heads": 40, + "num_layers": 40, + "eps": 1e-6 + } + elif hash_state_dict_keys(state_dict) == "3ef3b1f8e1dab83d5b71fd7b617f859f": + config = { + "has_image_input": True, + "patch_size": [1, 2, 2], + "in_dim": 36, + "dim": 5120, + "ffn_dim": 13824, + "freq_dim": 256, + "text_dim": 4096, + "out_dim": 16, + "num_heads": 40, + "num_layers": 40, + "eps": 1e-6, + "has_image_pos_emb": True + } + elif hash_state_dict_keys(state_dict) == "70ddad9d3a133785da5ea371aae09504": + # 1.3B PAI control v1.1 + config = { + "has_image_input": True, + "patch_size": [1, 2, 2], + "in_dim": 48, + "dim": 1536, + "ffn_dim": 8960, + "freq_dim": 256, + "text_dim": 4096, + "out_dim": 16, + "num_heads": 12, + "num_layers": 30, + "eps": 1e-6, + "has_ref_conv": True + } + elif hash_state_dict_keys(state_dict) == "26bde73488a92e64cc20b0a7485b9e5b": + # 14B PAI control v1.1 + config = { + "has_image_input": True, + "patch_size": [1, 2, 2], + "in_dim": 48, + "dim": 5120, + "ffn_dim": 13824, + "freq_dim": 256, + "text_dim": 4096, + "out_dim": 16, + "num_heads": 40, + "num_layers": 40, + "eps": 1e-6, + "has_ref_conv": True + } + elif hash_state_dict_keys(state_dict) == "ac6a5aa74f4a0aab6f64eb9a72f19901": + # 1.3B PAI control-camera v1.1 + config = { + "has_image_input": True, + "patch_size": [1, 2, 2], + "in_dim": 32, + "dim": 1536, + "ffn_dim": 8960, + "freq_dim": 256, + "text_dim": 4096, + "out_dim": 16, + "num_heads": 12, + "num_layers": 30, + "eps": 1e-6, + "has_ref_conv": False, + "add_control_adapter": True, + "in_dim_control_adapter": 24, + } + elif hash_state_dict_keys(state_dict) == "b61c605c2adbd23124d152ed28e049ae": + # 14B PAI control-camera v1.1 + config = { + "has_image_input": True, + "patch_size": [1, 2, 2], + "in_dim": 32, + "dim": 5120, + "ffn_dim": 13824, + "freq_dim": 256, + "text_dim": 4096, + "out_dim": 16, + "num_heads": 40, + "num_layers": 40, + "eps": 1e-6, + "has_ref_conv": False, + "add_control_adapter": True, + "in_dim_control_adapter": 24, + } + elif hash_state_dict_keys(state_dict) == "1f5ab7703c6fc803fdded85ff040c316": + # Wan-AI/Wan2.2-TI2V-5B + config = { + "has_image_input": False, + "patch_size": [1, 2, 2], + "in_dim": 48, + "dim": 3072, + "ffn_dim": 14336, + "freq_dim": 256, + "text_dim": 4096, + "out_dim": 48, + "num_heads": 24, + "num_layers": 30, + "eps": 1e-6, + "seperated_timestep": True, + "require_clip_embedding": False, + "require_vae_embedding": False, + "fuse_vae_embedding_in_latents": True, + } + elif hash_state_dict_keys(state_dict) == "5b013604280dd715f8457c6ed6d6a626": + # Wan-AI/Wan2.2-I2V-A14B + config = { + "has_image_input": False, + "patch_size": [1, 2, 2], + "in_dim": 36, + "dim": 5120, + "ffn_dim": 13824, + "freq_dim": 256, + "text_dim": 4096, + "out_dim": 16, + "num_heads": 40, + "num_layers": 40, + "eps": 1e-6, + "require_clip_embedding": False, + } + elif hash_state_dict_keys(state_dict) == "2267d489f0ceb9f21836532952852ee5": + # Wan2.2-Fun-A14B-Control + config = { + "has_image_input": False, + "patch_size": [1, 2, 2], + "in_dim": 52, + "dim": 5120, + "ffn_dim": 13824, + "freq_dim": 256, + "text_dim": 4096, + "out_dim": 16, + "num_heads": 40, + "num_layers": 40, + "eps": 1e-6, + "has_ref_conv": True, + "require_clip_embedding": False, + } + elif hash_state_dict_keys(state_dict) == "47dbeab5e560db3180adf51dc0232fb1": + # Wan2.2-Fun-A14B-Control-Camera + config = { + "has_image_input": False, + "patch_size": [1, 2, 2], + "in_dim": 36, + "dim": 5120, + "ffn_dim": 13824, + "freq_dim": 256, + "text_dim": 4096, + "out_dim": 16, + "num_heads": 40, + "num_layers": 40, + "eps": 1e-6, + "has_ref_conv": False, + "add_control_adapter": True, + "in_dim_control_adapter": 24, + "require_clip_embedding": False, + } + elif hash_state_dict_keys(state_dict) == "3296967e8209bf52ffdb9b8414dab595": + # 14B merged model with action modules (stage-2-key-14B-merged) + config = { + "has_image_input": False, + "patch_size": [1, 2, 2], + "in_dim": 16, + "dim": 5120, + "ffn_dim": 13824, + "freq_dim": 256, + "text_dim": 4096, + "out_dim": 16, + "num_heads": 40, + "num_layers": 40, + "eps": 1e-6, + } + else: + config = {} + return state_dict, config \ No newline at end of file diff --git a/diffsynth/models/wan_video_image_encoder.py b/diffsynth/models/wan_video_image_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..5ca878b1fd6ed6dc00420f092f87479fb65ef63a --- /dev/null +++ b/diffsynth/models/wan_video_image_encoder.py @@ -0,0 +1,902 @@ +""" +Concise re-implementation of +``https://github.com/openai/CLIP'' and +``https://github.com/mlfoundations/open_clip''. +""" +import math +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision.transforms as T +from .wan_video_dit import flash_attention + + +class SelfAttention(nn.Module): + + def __init__(self, dim, num_heads, dropout=0.1, eps=1e-5): + assert dim % num_heads == 0 + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.eps = eps + + # layers + self.q = nn.Linear(dim, dim) + self.k = nn.Linear(dim, dim) + self.v = nn.Linear(dim, dim) + self.o = nn.Linear(dim, dim) + self.dropout = nn.Dropout(dropout) + + def forward(self, x, mask): + """ + x: [B, L, C]. + """ + b, s, c, n, d = *x.size(), self.num_heads, self.head_dim + + # compute query, key, value + q = self.q(x).reshape(b, s, n, d).permute(0, 2, 1, 3) + k = self.k(x).reshape(b, s, n, d).permute(0, 2, 1, 3) + v = self.v(x).reshape(b, s, n, d).permute(0, 2, 1, 3) + + # compute attention + p = self.dropout.p if self.training else 0.0 + x = F.scaled_dot_product_attention(q, k, v, mask, p) + x = x.permute(0, 2, 1, 3).reshape(b, s, c) + + # output + x = self.o(x) + x = self.dropout(x) + return x + + +class AttentionBlock(nn.Module): + + def __init__(self, dim, num_heads, post_norm, dropout=0.1, eps=1e-5): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.post_norm = post_norm + self.eps = eps + + # layers + self.attn = SelfAttention(dim, num_heads, dropout, eps) + self.norm1 = nn.LayerNorm(dim, eps=eps) + self.ffn = nn.Sequential( + nn.Linear(dim, dim * 4), nn.GELU(), nn.Linear(dim * 4, dim), + nn.Dropout(dropout)) + self.norm2 = nn.LayerNorm(dim, eps=eps) + + def forward(self, x, mask): + if self.post_norm: + x = self.norm1(x + self.attn(x, mask)) + x = self.norm2(x + self.ffn(x)) + else: + x = x + self.attn(self.norm1(x), mask) + x = x + self.ffn(self.norm2(x)) + return x + + +class XLMRoberta(nn.Module): + """ + XLMRobertaModel with no pooler and no LM head. + """ + + def __init__(self, + vocab_size=250002, + max_seq_len=514, + type_size=1, + pad_id=1, + dim=1024, + num_heads=16, + num_layers=24, + post_norm=True, + dropout=0.1, + eps=1e-5): + super().__init__() + self.vocab_size = vocab_size + self.max_seq_len = max_seq_len + self.type_size = type_size + self.pad_id = pad_id + self.dim = dim + self.num_heads = num_heads + self.num_layers = num_layers + self.post_norm = post_norm + self.eps = eps + + # embeddings + self.token_embedding = nn.Embedding(vocab_size, dim, padding_idx=pad_id) + self.type_embedding = nn.Embedding(type_size, dim) + self.pos_embedding = nn.Embedding(max_seq_len, dim, padding_idx=pad_id) + self.dropout = nn.Dropout(dropout) + + # blocks + self.blocks = nn.ModuleList([ + AttentionBlock(dim, num_heads, post_norm, dropout, eps) + for _ in range(num_layers) + ]) + + # norm layer + self.norm = nn.LayerNorm(dim, eps=eps) + + def forward(self, ids): + """ + ids: [B, L] of torch.LongTensor. + """ + b, s = ids.shape + mask = ids.ne(self.pad_id).long() + + # embeddings + x = self.token_embedding(ids) + \ + self.type_embedding(torch.zeros_like(ids)) + \ + self.pos_embedding(self.pad_id + torch.cumsum(mask, dim=1) * mask) + if self.post_norm: + x = self.norm(x) + x = self.dropout(x) + + # blocks + mask = torch.where( + mask.view(b, 1, 1, s).gt(0), 0.0, + torch.finfo(x.dtype).min) + for block in self.blocks: + x = block(x, mask) + + # output + if not self.post_norm: + x = self.norm(x) + return x + + +def xlm_roberta_large(pretrained=False, + return_tokenizer=False, + device='cpu', + **kwargs): + """ + XLMRobertaLarge adapted from Huggingface. + """ + # params + cfg = dict( + vocab_size=250002, + max_seq_len=514, + type_size=1, + pad_id=1, + dim=1024, + num_heads=16, + num_layers=24, + post_norm=True, + dropout=0.1, + eps=1e-5) + cfg.update(**kwargs) + + # init model + if pretrained: + from sora import DOWNLOAD_TO_CACHE + + # init a meta model + with torch.device('meta'): + model = XLMRoberta(**cfg) + + # load checkpoint + model.load_state_dict( + torch.load( + DOWNLOAD_TO_CACHE('models/xlm_roberta/xlm_roberta_large.pth'), + map_location=device), + assign=True) + else: + # init a model on device + with torch.device(device): + model = XLMRoberta(**cfg) + + # init tokenizer + if return_tokenizer: + from sora.data import HuggingfaceTokenizer + tokenizer = HuggingfaceTokenizer( + name='xlm-roberta-large', + seq_len=model.text_len, + clean='whitespace') + return model, tokenizer + else: + return model + + + +def pos_interpolate(pos, seq_len): + if pos.size(1) == seq_len: + return pos + else: + src_grid = int(math.sqrt(pos.size(1))) + tar_grid = int(math.sqrt(seq_len)) + n = pos.size(1) - src_grid * src_grid + return torch.cat([ + pos[:, :n], + F.interpolate( + pos[:, n:].float().reshape(1, src_grid, src_grid, -1).permute( + 0, 3, 1, 2), + size=(tar_grid, tar_grid), + mode='bicubic', + align_corners=False).flatten(2).transpose(1, 2) + ], + dim=1) + + +class QuickGELU(nn.Module): + + def forward(self, x): + return x * torch.sigmoid(1.702 * x) + + +class LayerNorm(nn.LayerNorm): + + def forward(self, x): + return super().forward(x).type_as(x) + + +class SelfAttention(nn.Module): + + def __init__(self, + dim, + num_heads, + causal=False, + attn_dropout=0.0, + proj_dropout=0.0): + assert dim % num_heads == 0 + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.causal = causal + self.attn_dropout = attn_dropout + self.proj_dropout = proj_dropout + + # layers + self.to_qkv = nn.Linear(dim, dim * 3) + self.proj = nn.Linear(dim, dim) + + def forward(self, x): + """ + x: [B, L, C]. + """ + # compute query, key, value + q, k, v = self.to_qkv(x).chunk(3, dim=-1) + + # compute attention + x = flash_attention(q, k, v, num_heads=self.num_heads, compatibility_mode=True) + + # output + x = self.proj(x) + x = F.dropout(x, self.proj_dropout, self.training) + return x + + +class SwiGLU(nn.Module): + + def __init__(self, dim, mid_dim): + super().__init__() + self.dim = dim + self.mid_dim = mid_dim + + # layers + self.fc1 = nn.Linear(dim, mid_dim) + self.fc2 = nn.Linear(dim, mid_dim) + self.fc3 = nn.Linear(mid_dim, dim) + + def forward(self, x): + x = F.silu(self.fc1(x)) * self.fc2(x) + x = self.fc3(x) + return x + + +class AttentionBlock(nn.Module): + + def __init__(self, + dim, + mlp_ratio, + num_heads, + post_norm=False, + causal=False, + activation='quick_gelu', + attn_dropout=0.0, + proj_dropout=0.0, + norm_eps=1e-5): + assert activation in ['quick_gelu', 'gelu', 'swi_glu'] + super().__init__() + self.dim = dim + self.mlp_ratio = mlp_ratio + self.num_heads = num_heads + self.post_norm = post_norm + self.causal = causal + self.norm_eps = norm_eps + + # layers + self.norm1 = LayerNorm(dim, eps=norm_eps) + self.attn = SelfAttention(dim, num_heads, causal, attn_dropout, + proj_dropout) + self.norm2 = LayerNorm(dim, eps=norm_eps) + if activation == 'swi_glu': + self.mlp = SwiGLU(dim, int(dim * mlp_ratio)) + else: + self.mlp = nn.Sequential( + nn.Linear(dim, int(dim * mlp_ratio)), + QuickGELU() if activation == 'quick_gelu' else nn.GELU(), + nn.Linear(int(dim * mlp_ratio), dim), nn.Dropout(proj_dropout)) + + def forward(self, x): + if self.post_norm: + x = x + self.norm1(self.attn(x)) + x = x + self.norm2(self.mlp(x)) + else: + x = x + self.attn(self.norm1(x)) + x = x + self.mlp(self.norm2(x)) + return x + + +class AttentionPool(nn.Module): + + def __init__(self, + dim, + mlp_ratio, + num_heads, + activation='gelu', + proj_dropout=0.0, + norm_eps=1e-5): + assert dim % num_heads == 0 + super().__init__() + self.dim = dim + self.mlp_ratio = mlp_ratio + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.proj_dropout = proj_dropout + self.norm_eps = norm_eps + + # layers + gain = 1.0 / math.sqrt(dim) + self.cls_embedding = nn.Parameter(gain * torch.randn(1, 1, dim)) + self.to_q = nn.Linear(dim, dim) + self.to_kv = nn.Linear(dim, dim * 2) + self.proj = nn.Linear(dim, dim) + self.norm = LayerNorm(dim, eps=norm_eps) + self.mlp = nn.Sequential( + nn.Linear(dim, int(dim * mlp_ratio)), + QuickGELU() if activation == 'quick_gelu' else nn.GELU(), + nn.Linear(int(dim * mlp_ratio), dim), nn.Dropout(proj_dropout)) + + def forward(self, x): + """ + x: [B, L, C]. + """ + b, s, c, n, d = *x.size(), self.num_heads, self.head_dim + + # compute query, key, value + q = self.to_q(self.cls_embedding).view(1, 1, n*d).expand(b, -1, -1) + k, v = self.to_kv(x).chunk(2, dim=-1) + + # compute attention + x = flash_attention(q, k, v, num_heads=self.num_heads, compatibility_mode=True) + x = x.reshape(b, 1, c) + + # output + x = self.proj(x) + x = F.dropout(x, self.proj_dropout, self.training) + + # mlp + x = x + self.mlp(self.norm(x)) + return x[:, 0] + + +class VisionTransformer(nn.Module): + + def __init__(self, + image_size=224, + patch_size=16, + dim=768, + mlp_ratio=4, + out_dim=512, + num_heads=12, + num_layers=12, + pool_type='token', + pre_norm=True, + post_norm=False, + activation='quick_gelu', + attn_dropout=0.0, + proj_dropout=0.0, + embedding_dropout=0.0, + norm_eps=1e-5): + if image_size % patch_size != 0: + print( + '[WARNING] image_size is not divisible by patch_size', + flush=True) + assert pool_type in ('token', 'token_fc', 'attn_pool') + out_dim = out_dim or dim + super().__init__() + self.image_size = image_size + self.patch_size = patch_size + self.num_patches = (image_size // patch_size)**2 + self.dim = dim + self.mlp_ratio = mlp_ratio + self.out_dim = out_dim + self.num_heads = num_heads + self.num_layers = num_layers + self.pool_type = pool_type + self.post_norm = post_norm + self.norm_eps = norm_eps + + # embeddings + gain = 1.0 / math.sqrt(dim) + self.patch_embedding = nn.Conv2d( + 3, + dim, + kernel_size=patch_size, + stride=patch_size, + bias=not pre_norm) + if pool_type in ('token', 'token_fc'): + self.cls_embedding = nn.Parameter(gain * torch.randn(1, 1, dim)) + self.pos_embedding = nn.Parameter(gain * torch.randn( + 1, self.num_patches + + (1 if pool_type in ('token', 'token_fc') else 0), dim)) + self.dropout = nn.Dropout(embedding_dropout) + + # transformer + self.pre_norm = LayerNorm(dim, eps=norm_eps) if pre_norm else None + self.transformer = nn.Sequential(*[ + AttentionBlock(dim, mlp_ratio, num_heads, post_norm, False, + activation, attn_dropout, proj_dropout, norm_eps) + for _ in range(num_layers) + ]) + self.post_norm = LayerNorm(dim, eps=norm_eps) + + # head + if pool_type == 'token': + self.head = nn.Parameter(gain * torch.randn(dim, out_dim)) + elif pool_type == 'token_fc': + self.head = nn.Linear(dim, out_dim) + elif pool_type == 'attn_pool': + self.head = AttentionPool(dim, mlp_ratio, num_heads, activation, + proj_dropout, norm_eps) + + def forward(self, x, interpolation=False, use_31_block=False): + b = x.size(0) + + # embeddings + x = self.patch_embedding(x).flatten(2).permute(0, 2, 1) + if self.pool_type in ('token', 'token_fc'): + x = torch.cat([self.cls_embedding.expand(b, -1, -1).to(dtype=x.dtype, device=x.device), x], dim=1) + if interpolation: + e = pos_interpolate(self.pos_embedding, x.size(1)) + else: + e = self.pos_embedding + e = e.to(dtype=x.dtype, device=x.device) + x = self.dropout(x + e) + if self.pre_norm is not None: + x = self.pre_norm(x) + + # transformer + if use_31_block: + x = self.transformer[:-1](x) + return x + else: + x = self.transformer(x) + return x + + +class CLIP(nn.Module): + + def __init__(self, + embed_dim=512, + image_size=224, + patch_size=16, + vision_dim=768, + vision_mlp_ratio=4, + vision_heads=12, + vision_layers=12, + vision_pool='token', + vision_pre_norm=True, + vision_post_norm=False, + vocab_size=49408, + text_len=77, + text_dim=512, + text_mlp_ratio=4, + text_heads=8, + text_layers=12, + text_causal=True, + text_pool='argmax', + text_head_bias=False, + logit_bias=None, + activation='quick_gelu', + attn_dropout=0.0, + proj_dropout=0.0, + embedding_dropout=0.0, + norm_eps=1e-5): + super().__init__() + self.embed_dim = embed_dim + self.image_size = image_size + self.patch_size = patch_size + self.vision_dim = vision_dim + self.vision_mlp_ratio = vision_mlp_ratio + self.vision_heads = vision_heads + self.vision_layers = vision_layers + self.vision_pool = vision_pool + self.vision_pre_norm = vision_pre_norm + self.vision_post_norm = vision_post_norm + self.vocab_size = vocab_size + self.text_len = text_len + self.text_dim = text_dim + self.text_mlp_ratio = text_mlp_ratio + self.text_heads = text_heads + self.text_layers = text_layers + self.text_causal = text_causal + self.text_pool = text_pool + self.text_head_bias = text_head_bias + self.norm_eps = norm_eps + + # models + self.visual = VisionTransformer( + image_size=image_size, + patch_size=patch_size, + dim=vision_dim, + mlp_ratio=vision_mlp_ratio, + out_dim=embed_dim, + num_heads=vision_heads, + num_layers=vision_layers, + pool_type=vision_pool, + pre_norm=vision_pre_norm, + post_norm=vision_post_norm, + activation=activation, + attn_dropout=attn_dropout, + proj_dropout=proj_dropout, + embedding_dropout=embedding_dropout, + norm_eps=norm_eps) + self.textual = TextTransformer( + vocab_size=vocab_size, + text_len=text_len, + dim=text_dim, + mlp_ratio=text_mlp_ratio, + out_dim=embed_dim, + num_heads=text_heads, + num_layers=text_layers, + causal=text_causal, + pool_type=text_pool, + head_bias=text_head_bias, + activation=activation, + attn_dropout=attn_dropout, + proj_dropout=proj_dropout, + embedding_dropout=embedding_dropout, + norm_eps=norm_eps) + self.log_scale = nn.Parameter(math.log(1 / 0.07) * torch.ones([])) + if logit_bias is not None: + self.logit_bias = nn.Parameter(logit_bias * torch.ones([])) + + # initialize weights + self.init_weights() + + def forward(self, imgs, txt_ids): + """ + imgs: [B, 3, H, W] of torch.float32. + - mean: [0.48145466, 0.4578275, 0.40821073] + - std: [0.26862954, 0.26130258, 0.27577711] + txt_ids: [B, L] of torch.long. Encoded by data.CLIPTokenizer. + """ + xi = self.visual(imgs) + xt = self.textual(txt_ids) + return xi, xt + + def init_weights(self): + # embeddings + nn.init.normal_(self.textual.token_embedding.weight, std=0.02) + nn.init.normal_(self.visual.patch_embedding.weight, std=0.1) + + # attentions + for modality in ['visual', 'textual']: + dim = self.vision_dim if modality == 'visual' else self.text_dim + transformer = getattr(self, modality).transformer + proj_gain = (1.0 / math.sqrt(dim)) * ( + 1.0 / math.sqrt(2 * len(transformer))) + attn_gain = 1.0 / math.sqrt(dim) + mlp_gain = 1.0 / math.sqrt(2.0 * dim) + for block in transformer: + nn.init.normal_(block.attn.to_qkv.weight, std=attn_gain) + nn.init.normal_(block.attn.proj.weight, std=proj_gain) + nn.init.normal_(block.mlp[0].weight, std=mlp_gain) + nn.init.normal_(block.mlp[2].weight, std=proj_gain) + + def param_groups(self): + groups = [{ + 'params': [ + p for n, p in self.named_parameters() + if 'norm' in n or n.endswith('bias') + ], + 'weight_decay': 0.0 + }, { + 'params': [ + p for n, p in self.named_parameters() + if not ('norm' in n or n.endswith('bias')) + ] + }] + return groups + + +class XLMRobertaWithHead(XLMRoberta): + + def __init__(self, **kwargs): + self.out_dim = kwargs.pop('out_dim') + super().__init__(**kwargs) + + # head + mid_dim = (self.dim + self.out_dim) // 2 + self.head = nn.Sequential( + nn.Linear(self.dim, mid_dim, bias=False), nn.GELU(), + nn.Linear(mid_dim, self.out_dim, bias=False)) + + def forward(self, ids): + # xlm-roberta + x = super().forward(ids) + + # average pooling + mask = ids.ne(self.pad_id).unsqueeze(-1).to(x) + x = (x * mask).sum(dim=1) / mask.sum(dim=1) + + # head + x = self.head(x) + return x + + +class XLMRobertaCLIP(nn.Module): + + def __init__(self, + embed_dim=1024, + image_size=224, + patch_size=14, + vision_dim=1280, + vision_mlp_ratio=4, + vision_heads=16, + vision_layers=32, + vision_pool='token', + vision_pre_norm=True, + vision_post_norm=False, + activation='gelu', + vocab_size=250002, + max_text_len=514, + type_size=1, + pad_id=1, + text_dim=1024, + text_heads=16, + text_layers=24, + text_post_norm=True, + text_dropout=0.1, + attn_dropout=0.0, + proj_dropout=0.0, + embedding_dropout=0.0, + norm_eps=1e-5): + super().__init__() + self.embed_dim = embed_dim + self.image_size = image_size + self.patch_size = patch_size + self.vision_dim = vision_dim + self.vision_mlp_ratio = vision_mlp_ratio + self.vision_heads = vision_heads + self.vision_layers = vision_layers + self.vision_pre_norm = vision_pre_norm + self.vision_post_norm = vision_post_norm + self.activation = activation + self.vocab_size = vocab_size + self.max_text_len = max_text_len + self.type_size = type_size + self.pad_id = pad_id + self.text_dim = text_dim + self.text_heads = text_heads + self.text_layers = text_layers + self.text_post_norm = text_post_norm + self.norm_eps = norm_eps + + # models + self.visual = VisionTransformer( + image_size=image_size, + patch_size=patch_size, + dim=vision_dim, + mlp_ratio=vision_mlp_ratio, + out_dim=embed_dim, + num_heads=vision_heads, + num_layers=vision_layers, + pool_type=vision_pool, + pre_norm=vision_pre_norm, + post_norm=vision_post_norm, + activation=activation, + attn_dropout=attn_dropout, + proj_dropout=proj_dropout, + embedding_dropout=embedding_dropout, + norm_eps=norm_eps) + self.textual = None + self.log_scale = nn.Parameter(math.log(1 / 0.07) * torch.ones([])) + + def forward(self, imgs, txt_ids): + """ + imgs: [B, 3, H, W] of torch.float32. + - mean: [0.48145466, 0.4578275, 0.40821073] + - std: [0.26862954, 0.26130258, 0.27577711] + txt_ids: [B, L] of torch.long. + Encoded by data.CLIPTokenizer. + """ + xi = self.visual(imgs) + xt = self.textual(txt_ids) + return xi, xt + + def param_groups(self): + groups = [{ + 'params': [ + p for n, p in self.named_parameters() + if 'norm' in n or n.endswith('bias') + ], + 'weight_decay': 0.0 + }, { + 'params': [ + p for n, p in self.named_parameters() + if not ('norm' in n or n.endswith('bias')) + ] + }] + return groups + + +def _clip(pretrained=False, + pretrained_name=None, + model_cls=CLIP, + return_transforms=False, + return_tokenizer=False, + tokenizer_padding='eos', + dtype=torch.float32, + device='cpu', + **kwargs): + # init model + if pretrained and pretrained_name: + from sora import BUCKET, DOWNLOAD_TO_CACHE + + # init a meta model + with torch.device('meta'): + model = model_cls(**kwargs) + + # checkpoint path + checkpoint = f'models/clip/{pretrained_name}' + if dtype in (torch.float16, torch.bfloat16): + suffix = '-' + { + torch.float16: 'fp16', + torch.bfloat16: 'bf16' + }[dtype] + if object_exists(BUCKET, f'{checkpoint}{suffix}.pth'): + checkpoint = f'{checkpoint}{suffix}' + checkpoint += '.pth' + + # load + model.load_state_dict( + torch.load(DOWNLOAD_TO_CACHE(checkpoint), map_location=device), + assign=True, + strict=False) + else: + # init a model on device + with torch.device(device): + model = model_cls(**kwargs) + + # set device + output = (model,) + + # init transforms + if return_transforms: + # mean and std + if 'siglip' in pretrained_name.lower(): + mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5] + else: + mean = [0.48145466, 0.4578275, 0.40821073] + std = [0.26862954, 0.26130258, 0.27577711] + + # transforms + transforms = T.Compose([ + T.Resize((model.image_size, model.image_size), + interpolation=T.InterpolationMode.BICUBIC), + T.ToTensor(), + T.Normalize(mean=mean, std=std) + ]) + output += (transforms,) + + # init tokenizer + if return_tokenizer: + from sora import data + if 'siglip' in pretrained_name.lower(): + tokenizer = data.HuggingfaceTokenizer( + name=f'timm/{pretrained_name}', + seq_len=model.text_len, + clean='canonicalize') + elif 'xlm' in pretrained_name.lower(): + tokenizer = data.HuggingfaceTokenizer( + name='xlm-roberta-large', + seq_len=model.max_text_len - 2, + clean='whitespace') + elif 'mba' in pretrained_name.lower(): + tokenizer = data.HuggingfaceTokenizer( + name='facebook/xlm-roberta-xl', + seq_len=model.max_text_len - 2, + clean='whitespace') + else: + tokenizer = data.CLIPTokenizer( + seq_len=model.text_len, padding=tokenizer_padding) + output += (tokenizer,) + return output[0] if len(output) == 1 else output + + +def clip_xlm_roberta_vit_h_14( + pretrained=False, + pretrained_name='open-clip-xlm-roberta-large-vit-huge-14', + **kwargs): + cfg = dict( + embed_dim=1024, + image_size=224, + patch_size=14, + vision_dim=1280, + vision_mlp_ratio=4, + vision_heads=16, + vision_layers=32, + vision_pool='token', + activation='gelu', + vocab_size=250002, + max_text_len=514, + type_size=1, + pad_id=1, + text_dim=1024, + text_heads=16, + text_layers=24, + text_post_norm=True, + text_dropout=0.1, + attn_dropout=0.0, + proj_dropout=0.0, + embedding_dropout=0.0) + cfg.update(**kwargs) + return _clip(pretrained, pretrained_name, XLMRobertaCLIP, **cfg) + + +class WanImageEncoder(torch.nn.Module): + + def __init__(self): + super().__init__() + # init model + self.model, self.transforms = clip_xlm_roberta_vit_h_14( + pretrained=False, + return_transforms=True, + return_tokenizer=False, + dtype=torch.float32, + device="cpu") + + def encode_image(self, videos): + # preprocess + size = (self.model.image_size,) * 2 + videos = torch.cat([ + F.interpolate( + u, + size=size, + mode='bicubic', + align_corners=False) for u in videos + ]) + videos = self.transforms.transforms[-1](videos.mul_(0.5).add_(0.5)) + + # forward + dtype = next(iter(self.model.visual.parameters())).dtype + videos = videos.to(dtype) + out = self.model.visual(videos, use_31_block=True) + return out + + @staticmethod + def state_dict_converter(): + return WanImageEncoderStateDictConverter() + + +class WanImageEncoderStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + return state_dict + + def from_civitai(self, state_dict): + state_dict_ = {} + for name, param in state_dict.items(): + if name.startswith("textual."): + continue + name = "model." + name + state_dict_[name] = param + return state_dict_ + diff --git a/diffsynth/models/wan_video_motion_controller.py b/diffsynth/models/wan_video_motion_controller.py new file mode 100644 index 0000000000000000000000000000000000000000..518c1c66edca1cae11d5f3371af0455808b2a66a --- /dev/null +++ b/diffsynth/models/wan_video_motion_controller.py @@ -0,0 +1,44 @@ +import torch +import torch.nn as nn +from .wan_video_dit import sinusoidal_embedding_1d + + + +class WanMotionControllerModel(torch.nn.Module): + def __init__(self, freq_dim=256, dim=1536): + super().__init__() + self.freq_dim = freq_dim + self.linear = nn.Sequential( + nn.Linear(freq_dim, dim), + nn.SiLU(), + nn.Linear(dim, dim), + nn.SiLU(), + nn.Linear(dim, dim * 6), + ) + + def forward(self, motion_bucket_id): + emb = sinusoidal_embedding_1d(self.freq_dim, motion_bucket_id * 10) + emb = self.linear(emb) + return emb + + def init(self): + state_dict = self.linear[-1].state_dict() + state_dict = {i: state_dict[i] * 0 for i in state_dict} + self.linear[-1].load_state_dict(state_dict) + + @staticmethod + def state_dict_converter(): + return WanMotionControllerModelDictConverter() + + + +class WanMotionControllerModelDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + return state_dict + + def from_civitai(self, state_dict): + return state_dict + diff --git a/diffsynth/models/wan_video_text_encoder.py b/diffsynth/models/wan_video_text_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..c28873722ee92f23914712c9d5b2c3a26fd2adb7 --- /dev/null +++ b/diffsynth/models/wan_video_text_encoder.py @@ -0,0 +1,269 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def fp16_clamp(x): + if x.dtype == torch.float16 and torch.isinf(x).any(): + clamp = torch.finfo(x.dtype).max - 1000 + x = torch.clamp(x, min=-clamp, max=clamp) + return x + + +class GELU(nn.Module): + + def forward(self, x): + return 0.5 * x * (1.0 + torch.tanh( + math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0)))) + + +class T5LayerNorm(nn.Module): + + def __init__(self, dim, eps=1e-6): + super(T5LayerNorm, self).__init__() + self.dim = dim + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def forward(self, x): + x = x * torch.rsqrt(x.float().pow(2).mean(dim=-1, keepdim=True) + + self.eps) + if self.weight.dtype in [torch.float16, torch.bfloat16]: + x = x.type_as(self.weight) + return self.weight * x + + +class T5Attention(nn.Module): + + def __init__(self, dim, dim_attn, num_heads, dropout=0.1): + assert dim_attn % num_heads == 0 + super(T5Attention, self).__init__() + self.dim = dim + self.dim_attn = dim_attn + self.num_heads = num_heads + self.head_dim = dim_attn // num_heads + + # layers + self.q = nn.Linear(dim, dim_attn, bias=False) + self.k = nn.Linear(dim, dim_attn, bias=False) + self.v = nn.Linear(dim, dim_attn, bias=False) + self.o = nn.Linear(dim_attn, dim, bias=False) + self.dropout = nn.Dropout(dropout) + + def forward(self, x, context=None, mask=None, pos_bias=None): + """ + x: [B, L1, C]. + context: [B, L2, C] or None. + mask: [B, L2] or [B, L1, L2] or None. + """ + # check inputs + context = x if context is None else context + b, n, c = x.size(0), self.num_heads, self.head_dim + + # compute query, key, value + q = self.q(x).view(b, -1, n, c) + k = self.k(context).view(b, -1, n, c) + v = self.v(context).view(b, -1, n, c) + + # attention bias + attn_bias = x.new_zeros(b, n, q.size(1), k.size(1)) + if pos_bias is not None: + attn_bias += pos_bias + if mask is not None: + assert mask.ndim in [2, 3] + mask = mask.view(b, 1, 1, + -1) if mask.ndim == 2 else mask.unsqueeze(1) + attn_bias.masked_fill_(mask == 0, torch.finfo(x.dtype).min) + + # compute attention (T5 does not use scaling) + attn = torch.einsum('binc,bjnc->bnij', q, k) + attn_bias + attn = F.softmax(attn.float(), dim=-1).type_as(attn) + x = torch.einsum('bnij,bjnc->binc', attn, v) + + # output + x = x.reshape(b, -1, n * c) + x = self.o(x) + x = self.dropout(x) + return x + + +class T5FeedForward(nn.Module): + + def __init__(self, dim, dim_ffn, dropout=0.1): + super(T5FeedForward, self).__init__() + self.dim = dim + self.dim_ffn = dim_ffn + + # layers + self.gate = nn.Sequential(nn.Linear(dim, dim_ffn, bias=False), GELU()) + self.fc1 = nn.Linear(dim, dim_ffn, bias=False) + self.fc2 = nn.Linear(dim_ffn, dim, bias=False) + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + x = self.fc1(x) * self.gate(x) + x = self.dropout(x) + x = self.fc2(x) + x = self.dropout(x) + return x + + +class T5SelfAttention(nn.Module): + + def __init__(self, + dim, + dim_attn, + dim_ffn, + num_heads, + num_buckets, + shared_pos=True, + dropout=0.1): + super(T5SelfAttention, self).__init__() + self.dim = dim + self.dim_attn = dim_attn + self.dim_ffn = dim_ffn + self.num_heads = num_heads + self.num_buckets = num_buckets + self.shared_pos = shared_pos + + # layers + self.norm1 = T5LayerNorm(dim) + self.attn = T5Attention(dim, dim_attn, num_heads, dropout) + self.norm2 = T5LayerNorm(dim) + self.ffn = T5FeedForward(dim, dim_ffn, dropout) + self.pos_embedding = None if shared_pos else T5RelativeEmbedding( + num_buckets, num_heads, bidirectional=True) + + def forward(self, x, mask=None, pos_bias=None): + e = pos_bias if self.shared_pos else self.pos_embedding( + x.size(1), x.size(1)) + x = fp16_clamp(x + self.attn(self.norm1(x), mask=mask, pos_bias=e)) + x = fp16_clamp(x + self.ffn(self.norm2(x))) + return x + + +class T5RelativeEmbedding(nn.Module): + + def __init__(self, num_buckets, num_heads, bidirectional, max_dist=128): + super(T5RelativeEmbedding, self).__init__() + self.num_buckets = num_buckets + self.num_heads = num_heads + self.bidirectional = bidirectional + self.max_dist = max_dist + + # layers + self.embedding = nn.Embedding(num_buckets, num_heads) + + def forward(self, lq, lk): + device = self.embedding.weight.device + # rel_pos = torch.arange(lk).unsqueeze(0).to(device) - \ + # torch.arange(lq).unsqueeze(1).to(device) + rel_pos = torch.arange(lk, device=device).unsqueeze(0) - \ + torch.arange(lq, device=device).unsqueeze(1) + rel_pos = self._relative_position_bucket(rel_pos) + rel_pos_embeds = self.embedding(rel_pos) + rel_pos_embeds = rel_pos_embeds.permute(2, 0, 1).unsqueeze( + 0) # [1, N, Lq, Lk] + return rel_pos_embeds.contiguous() + + def _relative_position_bucket(self, rel_pos): + # preprocess + if self.bidirectional: + num_buckets = self.num_buckets // 2 + rel_buckets = (rel_pos > 0).long() * num_buckets + rel_pos = torch.abs(rel_pos) + else: + num_buckets = self.num_buckets + rel_buckets = 0 + rel_pos = -torch.min(rel_pos, torch.zeros_like(rel_pos)) + + # embeddings for small and large positions + max_exact = num_buckets // 2 + rel_pos_large = max_exact + (torch.log(rel_pos.float() / max_exact) / + math.log(self.max_dist / max_exact) * + (num_buckets - max_exact)).long() + rel_pos_large = torch.min( + rel_pos_large, torch.full_like(rel_pos_large, num_buckets - 1)) + rel_buckets += torch.where(rel_pos < max_exact, rel_pos, rel_pos_large) + return rel_buckets + +def init_weights(m): + if isinstance(m, T5LayerNorm): + nn.init.ones_(m.weight) + elif isinstance(m, T5FeedForward): + nn.init.normal_(m.gate[0].weight, std=m.dim**-0.5) + nn.init.normal_(m.fc1.weight, std=m.dim**-0.5) + nn.init.normal_(m.fc2.weight, std=m.dim_ffn**-0.5) + elif isinstance(m, T5Attention): + nn.init.normal_(m.q.weight, std=(m.dim * m.dim_attn)**-0.5) + nn.init.normal_(m.k.weight, std=m.dim**-0.5) + nn.init.normal_(m.v.weight, std=m.dim**-0.5) + nn.init.normal_(m.o.weight, std=(m.num_heads * m.dim_attn)**-0.5) + elif isinstance(m, T5RelativeEmbedding): + nn.init.normal_( + m.embedding.weight, std=(2 * m.num_buckets * m.num_heads)**-0.5) + + +class WanTextEncoder(torch.nn.Module): + + def __init__(self, + vocab=256384, + dim=4096, + dim_attn=4096, + dim_ffn=10240, + num_heads=64, + num_layers=24, + num_buckets=32, + shared_pos=False, + dropout=0.1): + super(WanTextEncoder, self).__init__() + self.dim = dim + self.dim_attn = dim_attn + self.dim_ffn = dim_ffn + self.num_heads = num_heads + self.num_layers = num_layers + self.num_buckets = num_buckets + self.shared_pos = shared_pos + + # layers + self.token_embedding = vocab if isinstance(vocab, nn.Embedding) \ + else nn.Embedding(vocab, dim) + self.pos_embedding = T5RelativeEmbedding( + num_buckets, num_heads, bidirectional=True) if shared_pos else None + self.dropout = nn.Dropout(dropout) + self.blocks = nn.ModuleList([ + T5SelfAttention(dim, dim_attn, dim_ffn, num_heads, num_buckets, + shared_pos, dropout) for _ in range(num_layers) + ]) + self.norm = T5LayerNorm(dim) + + # initialize weights + self.apply(init_weights) + + def forward(self, ids, mask=None): + x = self.token_embedding(ids) + x = self.dropout(x) + e = self.pos_embedding(x.size(1), + x.size(1)) if self.shared_pos else None + for block in self.blocks: + x = block(x, mask, pos_bias=e) + x = self.norm(x) + x = self.dropout(x) + return x + + @staticmethod + def state_dict_converter(): + return WanTextEncoderStateDictConverter() + + +class WanTextEncoderStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + return state_dict + + def from_civitai(self, state_dict): + return state_dict diff --git a/diffsynth/models/wan_video_vace.py b/diffsynth/models/wan_video_vace.py new file mode 100644 index 0000000000000000000000000000000000000000..40f38048fe07184084c76e3b1a5abac062bb0ab8 --- /dev/null +++ b/diffsynth/models/wan_video_vace.py @@ -0,0 +1,113 @@ +import torch +from .wan_video_dit import DiTBlock +from .utils import hash_state_dict_keys + +class VaceWanAttentionBlock(DiTBlock): + def __init__(self, has_image_input, dim, num_heads, ffn_dim, eps=1e-6, block_id=0): + super().__init__(has_image_input, dim, num_heads, ffn_dim, eps=eps) + self.block_id = block_id + if block_id == 0: + self.before_proj = torch.nn.Linear(self.dim, self.dim) + self.after_proj = torch.nn.Linear(self.dim, self.dim) + + def forward(self, c, x, context, t_mod, freqs): + if self.block_id == 0: + c = self.before_proj(c) + x + all_c = [] + else: + all_c = list(torch.unbind(c)) + c = all_c.pop(-1) + c = super().forward(c, context, t_mod, freqs) + c_skip = self.after_proj(c) + all_c += [c_skip, c] + c = torch.stack(all_c) + return c + + +class VaceWanModel(torch.nn.Module): + def __init__( + self, + vace_layers=(0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28), + vace_in_dim=96, + patch_size=(1, 2, 2), + has_image_input=False, + dim=1536, + num_heads=12, + ffn_dim=8960, + eps=1e-6, + ): + super().__init__() + self.vace_layers = vace_layers + self.vace_in_dim = vace_in_dim + self.vace_layers_mapping = {i: n for n, i in enumerate(self.vace_layers)} + + # vace blocks + self.vace_blocks = torch.nn.ModuleList([ + VaceWanAttentionBlock(has_image_input, dim, num_heads, ffn_dim, eps, block_id=i) + for i in self.vace_layers + ]) + + # vace patch embeddings + self.vace_patch_embedding = torch.nn.Conv3d(vace_in_dim, dim, kernel_size=patch_size, stride=patch_size) + + def forward( + self, x, vace_context, context, t_mod, freqs, + use_gradient_checkpointing: bool = False, + use_gradient_checkpointing_offload: bool = False, + ): + c = [self.vace_patch_embedding(u.unsqueeze(0)) for u in vace_context] + c = [u.flatten(2).transpose(1, 2) for u in c] + c = torch.cat([ + torch.cat([u, u.new_zeros(1, x.shape[1] - u.size(1), u.size(2))], + dim=1) for u in c + ]) + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + return custom_forward + + for block in self.vace_blocks: + if use_gradient_checkpointing_offload: + with torch.autograd.graph.save_on_cpu(): + c = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + c, x, context, t_mod, freqs, + use_reentrant=False, + ) + elif use_gradient_checkpointing: + c = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + c, x, context, t_mod, freqs, + use_reentrant=False, + ) + else: + c = block(c, x, context, t_mod, freqs) + hints = torch.unbind(c)[:-1] + return hints + + @staticmethod + def state_dict_converter(): + return VaceWanModelDictConverter() + + +class VaceWanModelDictConverter: + def __init__(self): + pass + + def from_civitai(self, state_dict): + state_dict_ = {name: param for name, param in state_dict.items() if name.startswith("vace")} + if hash_state_dict_keys(state_dict_) == '3b2726384e4f64837bdf216eea3f310d': # vace 14B + config = { + "vace_layers": (0, 5, 10, 15, 20, 25, 30, 35), + "vace_in_dim": 96, + "patch_size": (1, 2, 2), + "has_image_input": False, + "dim": 5120, + "num_heads": 40, + "ffn_dim": 13824, + "eps": 1e-06, + } + else: + config = {} + return state_dict_, config diff --git a/diffsynth/models/wan_video_vae.py b/diffsynth/models/wan_video_vae.py new file mode 100644 index 0000000000000000000000000000000000000000..137fd28a83805467dd6d239ad68e09ec46a44b2b --- /dev/null +++ b/diffsynth/models/wan_video_vae.py @@ -0,0 +1,800 @@ +from einops import rearrange, repeat + +import torch +import torch.nn as nn +import torch.nn.functional as F +from tqdm import tqdm + +CACHE_T = 2 + + +def check_is_instance(model, module_class): + if isinstance(model, module_class): + return True + if hasattr(model, "module") and isinstance(model.module, module_class): + return True + return False + + +def block_causal_mask(x, block_size): + # params + b, n, s, _, device = *x.size(), x.device + assert s % block_size == 0 + num_blocks = s // block_size + + # build mask + mask = torch.zeros(b, n, s, s, dtype=torch.bool, device=device) + for i in range(num_blocks): + mask[:, :, + i * block_size:(i + 1) * block_size, :(i + 1) * block_size] = 1 + return mask + + +class CausalConv3d(nn.Conv3d): + """ + Causal 3d convolusion. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._padding = (self.padding[2], self.padding[2], self.padding[1], + self.padding[1], 2 * self.padding[0], 0) + self.padding = (0, 0, 0) + + def forward(self, x, cache_x=None): + padding = list(self._padding) + if cache_x is not None and self._padding[4] > 0: + cache_x = cache_x.to(x.device) + x = torch.cat([cache_x, x], dim=2) + padding[4] -= cache_x.shape[2] + x = F.pad(x, padding) + + return super().forward(x) + + +class RMS_norm(nn.Module): + + def __init__(self, dim, channel_first=True, images=True, bias=False): + super().__init__() + broadcastable_dims = (1, 1, 1) if not images else (1, 1) + shape = (dim, *broadcastable_dims) if channel_first else (dim,) + + self.channel_first = channel_first + self.scale = dim**0.5 + self.gamma = nn.Parameter(torch.ones(shape)) + self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0. + + def forward(self, x): + return F.normalize( + x, dim=(1 if self.channel_first else + -1)) * self.scale * self.gamma + self.bias + + +class Upsample(nn.Upsample): + + def forward(self, x): + """ + Fix bfloat16 support for nearest neighbor interpolation. + """ + return super().forward(x.float()).type_as(x) + + +class Resample(nn.Module): + + def __init__(self, dim, mode): + assert mode in ('none', 'upsample2d', 'upsample3d', 'downsample2d', + 'downsample3d') + super().__init__() + self.dim = dim + self.mode = mode + + # layers + if mode == 'upsample2d': + self.resample = nn.Sequential( + Upsample(scale_factor=(2., 2.), mode='nearest-exact'), + nn.Conv2d(dim, dim // 2, 3, padding=1)) + elif mode == 'upsample3d': + self.resample = nn.Sequential( + Upsample(scale_factor=(2., 2.), mode='nearest-exact'), + nn.Conv2d(dim, dim // 2, 3, padding=1)) + self.time_conv = CausalConv3d(dim, + dim * 2, (3, 1, 1), + padding=(1, 0, 0)) + + elif mode == 'downsample2d': + self.resample = nn.Sequential( + nn.ZeroPad2d((0, 1, 0, 1)), + nn.Conv2d(dim, dim, 3, stride=(2, 2))) + elif mode == 'downsample3d': + self.resample = nn.Sequential( + nn.ZeroPad2d((0, 1, 0, 1)), + nn.Conv2d(dim, dim, 3, stride=(2, 2))) + self.time_conv = CausalConv3d(dim, + dim, (3, 1, 1), + stride=(2, 1, 1), + padding=(0, 0, 0)) + + else: + self.resample = nn.Identity() + + def forward(self, x, feat_cache=None, feat_idx=[0]): + b, c, t, h, w = x.size() + if self.mode == 'upsample3d': + if feat_cache is not None: + idx = feat_idx[0] + if feat_cache[idx] is None: + feat_cache[idx] = 'Rep' + feat_idx[0] += 1 + else: + + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[ + idx] is not None and feat_cache[idx] != 'Rep': + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + if cache_x.shape[2] < 2 and feat_cache[ + idx] is not None and feat_cache[idx] == 'Rep': + cache_x = torch.cat([ + torch.zeros_like(cache_x).to(cache_x.device), + cache_x + ], + dim=2) + if feat_cache[idx] == 'Rep': + x = self.time_conv(x) + else: + x = self.time_conv(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + + x = x.reshape(b, 2, c, t, h, w) + x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), + 3) + x = x.reshape(b, c, t * 2, h, w) + t = x.shape[2] + x = rearrange(x, 'b c t h w -> (b t) c h w') + x = self.resample(x) + x = rearrange(x, '(b t) c h w -> b c t h w', t=t) + + if self.mode == 'downsample3d': + if feat_cache is not None: + idx = feat_idx[0] + if feat_cache[idx] is None: + feat_cache[idx] = x.clone() + feat_idx[0] += 1 + else: + cache_x = x[:, :, -1:, :, :].clone() + x = self.time_conv( + torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2)) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + return x + + def init_weight(self, conv): + conv_weight = conv.weight + nn.init.zeros_(conv_weight) + c1, c2, t, h, w = conv_weight.size() + one_matrix = torch.eye(c1, c2) + init_matrix = one_matrix + nn.init.zeros_(conv_weight) + conv_weight.data[:, :, 1, 0, 0] = init_matrix + conv.weight.data.copy_(conv_weight) + nn.init.zeros_(conv.bias.data) + + def init_weight2(self, conv): + conv_weight = conv.weight.data + nn.init.zeros_(conv_weight) + c1, c2, t, h, w = conv_weight.size() + init_matrix = torch.eye(c1 // 2, c2) + conv_weight[:c1 // 2, :, -1, 0, 0] = init_matrix + conv_weight[c1 // 2:, :, -1, 0, 0] = init_matrix + conv.weight.data.copy_(conv_weight) + nn.init.zeros_(conv.bias.data) + + +class ResidualBlock(nn.Module): + + def __init__(self, in_dim, out_dim, dropout=0.0): + super().__init__() + self.in_dim = in_dim + self.out_dim = out_dim + + # layers + self.residual = nn.Sequential( + RMS_norm(in_dim, images=False), nn.SiLU(), + CausalConv3d(in_dim, out_dim, 3, padding=1), + RMS_norm(out_dim, images=False), nn.SiLU(), nn.Dropout(dropout), + CausalConv3d(out_dim, out_dim, 3, padding=1)) + self.shortcut = CausalConv3d(in_dim, out_dim, 1) \ + if in_dim != out_dim else nn.Identity() + + def forward(self, x, feat_cache=None, feat_idx=[0]): + h = self.shortcut(x) + for layer in self.residual: + if check_is_instance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = layer(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + return x + h + + +class AttentionBlock(nn.Module): + """ + Causal self-attention with a single head. + """ + + def __init__(self, dim): + super().__init__() + self.dim = dim + + # layers + self.norm = RMS_norm(dim) + self.to_qkv = nn.Conv2d(dim, dim * 3, 1) + self.proj = nn.Conv2d(dim, dim, 1) + + # zero out the last layer params + nn.init.zeros_(self.proj.weight) + + def forward(self, x): + identity = x + b, c, t, h, w = x.size() + x = rearrange(x, 'b c t h w -> (b t) c h w') + x = self.norm(x) + # compute query, key, value + q, k, v = self.to_qkv(x).reshape(b * t, 1, c * 3, -1).permute( + 0, 1, 3, 2).contiguous().chunk(3, dim=-1) + + # apply attention + x = F.scaled_dot_product_attention( + q, + k, + v, + #attn_mask=block_causal_mask(q, block_size=h * w) + ) + x = x.squeeze(1).permute(0, 2, 1).reshape(b * t, c, h, w) + + # output + x = self.proj(x) + x = rearrange(x, '(b t) c h w-> b c t h w', t=t) + return x + identity + + +class Encoder3d(nn.Module): + + def __init__(self, + dim=128, + z_dim=4, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_downsample=[True, True, False], + dropout=0.0): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_downsample = temperal_downsample + + # dimensions + dims = [dim * u for u in [1] + dim_mult] + scale = 1.0 + + # init block + self.conv1 = CausalConv3d(3, dims[0], 3, padding=1) + + # downsample blocks + downsamples = [] + for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): + # residual (+attention) blocks + for _ in range(num_res_blocks): + downsamples.append(ResidualBlock(in_dim, out_dim, dropout)) + if scale in attn_scales: + downsamples.append(AttentionBlock(out_dim)) + in_dim = out_dim + + # downsample block + if i != len(dim_mult) - 1: + mode = 'downsample3d' if temperal_downsample[ + i] else 'downsample2d' + downsamples.append(Resample(out_dim, mode=mode)) + scale /= 2.0 + self.downsamples = nn.Sequential(*downsamples) + + # middle blocks + self.middle = nn.Sequential(ResidualBlock(out_dim, out_dim, dropout), + AttentionBlock(out_dim), + ResidualBlock(out_dim, out_dim, dropout)) + + # output blocks + self.head = nn.Sequential(RMS_norm(out_dim, images=False), nn.SiLU(), + CausalConv3d(out_dim, z_dim, 3, padding=1)) + + def forward(self, x, feat_cache=None, feat_idx=[0]): + if feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = self.conv1(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = self.conv1(x) + + ## downsamples + for layer in self.downsamples: + if feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## middle + for layer in self.middle: + if check_is_instance(layer, ResidualBlock) and feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## head + for layer in self.head: + if check_is_instance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = layer(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + return x + + +class Decoder3d(nn.Module): + + def __init__(self, + dim=128, + z_dim=4, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_upsample=[False, True, True], + dropout=0.0): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_upsample = temperal_upsample + + # dimensions + dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]] + scale = 1.0 / 2**(len(dim_mult) - 2) + + # init block + self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1) + + # middle blocks + self.middle = nn.Sequential(ResidualBlock(dims[0], dims[0], dropout), + AttentionBlock(dims[0]), + ResidualBlock(dims[0], dims[0], dropout)) + + # upsample blocks + upsamples = [] + for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): + # residual (+attention) blocks + if i == 1 or i == 2 or i == 3: + in_dim = in_dim // 2 + for _ in range(num_res_blocks + 1): + upsamples.append(ResidualBlock(in_dim, out_dim, dropout)) + if scale in attn_scales: + upsamples.append(AttentionBlock(out_dim)) + in_dim = out_dim + + # upsample block + if i != len(dim_mult) - 1: + mode = 'upsample3d' if temperal_upsample[i] else 'upsample2d' + upsamples.append(Resample(out_dim, mode=mode)) + scale *= 2.0 + self.upsamples = nn.Sequential(*upsamples) + + # output blocks + self.head = nn.Sequential(RMS_norm(out_dim, images=False), nn.SiLU(), + CausalConv3d(out_dim, 3, 3, padding=1)) + + def forward(self, x, feat_cache=None, feat_idx=[0]): + ## conv1 + if feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = self.conv1(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = self.conv1(x) + + ## middle + for layer in self.middle: + if check_is_instance(layer, ResidualBlock) and feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## upsamples + for layer in self.upsamples: + if feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## head + for layer in self.head: + if check_is_instance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = layer(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + return x + + +def count_conv3d(model): + count = 0 + for m in model.modules(): + if check_is_instance(m, CausalConv3d): + count += 1 + return count + + +class VideoVAE_(nn.Module): + + def __init__(self, + dim=96, + z_dim=16, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_downsample=[False, True, True], + dropout=0.0): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_downsample = temperal_downsample + self.temperal_upsample = temperal_downsample[::-1] + + # modules + self.encoder = Encoder3d(dim, z_dim * 2, dim_mult, num_res_blocks, + attn_scales, self.temperal_downsample, dropout) + self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1) + self.conv2 = CausalConv3d(z_dim, z_dim, 1) + self.decoder = Decoder3d(dim, z_dim, dim_mult, num_res_blocks, + attn_scales, self.temperal_upsample, dropout) + + def forward(self, x): + mu, log_var = self.encode(x) + z = self.reparameterize(mu, log_var) + x_recon = self.decode(z) + return x_recon, mu, log_var + + def encode(self, x, scale): + self.clear_cache() + ## cache + t = x.shape[2] + iter_ = 1 + (t - 1) // 4 + + for i in range(iter_): + self._enc_conv_idx = [0] + if i == 0: + out = self.encoder(x[:, :, :1, :, :], + feat_cache=self._enc_feat_map, + feat_idx=self._enc_conv_idx) + else: + out_ = self.encoder(x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :], + feat_cache=self._enc_feat_map, + feat_idx=self._enc_conv_idx) + out = torch.cat([out, out_], 2) + mu, log_var = self.conv1(out).chunk(2, dim=1) + if isinstance(scale[0], torch.Tensor): + scale = [s.to(dtype=mu.dtype, device=mu.device) for s in scale] + mu = (mu - scale[0].view(1, self.z_dim, 1, 1, 1)) * scale[1].view( + 1, self.z_dim, 1, 1, 1) + else: + scale = scale.to(dtype=mu.dtype, device=mu.device) + mu = (mu - scale[0]) * scale[1] + return mu + + def decode(self, z, scale): + self.clear_cache() + # z: [b,c,t,h,w] + if isinstance(scale[0], torch.Tensor): + scale = [s.to(dtype=z.dtype, device=z.device) for s in scale] + z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view( + 1, self.z_dim, 1, 1, 1) + else: + scale = scale.to(dtype=z.dtype, device=z.device) + z = z / scale[1] + scale[0] + iter_ = z.shape[2] + x = self.conv2(z) + for i in range(iter_): + self._conv_idx = [0] + if i == 0: + out = self.decoder(x[:, :, i:i + 1, :, :], + feat_cache=self._feat_map, + feat_idx=self._conv_idx) + else: + out_ = self.decoder(x[:, :, i:i + 1, :, :], + feat_cache=self._feat_map, + feat_idx=self._conv_idx) + out = torch.cat([out, out_], 2) # may add tensor offload + return out + + def reparameterize(self, mu, log_var): + std = torch.exp(0.5 * log_var) + eps = torch.randn_like(std) + return eps * std + mu + + def sample(self, imgs, deterministic=False): + mu, log_var = self.encode(imgs) + if deterministic: + return mu + std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0)) + return mu + std * torch.randn_like(std) + + def clear_cache(self): + self._conv_num = count_conv3d(self.decoder) + self._conv_idx = [0] + self._feat_map = [None] * self._conv_num + # cache encode + self._enc_conv_num = count_conv3d(self.encoder) + self._enc_conv_idx = [0] + self._enc_feat_map = [None] * self._enc_conv_num + + +class WanVideoVAE(nn.Module): + + def __init__(self, z_dim=16): + super().__init__() + + mean = [ + -0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508, + 0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921 + ] + std = [ + 2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743, + 3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160 + ] + self.mean = torch.tensor(mean) + self.std = torch.tensor(std) + self.scale = [self.mean, 1.0 / self.std] + + # init model + self.model = VideoVAE_(z_dim=z_dim).eval().requires_grad_(False) + self.upsampling_factor = 8 + + + def build_1d_mask(self, length, left_bound, right_bound, border_width): + x = torch.ones((length,)) + if not left_bound: + x[:border_width] = (torch.arange(border_width) + 1) / border_width + if not right_bound: + x[-border_width:] = torch.flip((torch.arange(border_width) + 1) / border_width, dims=(0,)) + return x + + + def build_mask(self, data, is_bound, border_width): + _, _, _, H, W = data.shape + h = self.build_1d_mask(H, is_bound[0], is_bound[1], border_width[0]) + w = self.build_1d_mask(W, is_bound[2], is_bound[3], border_width[1]) + + h = repeat(h, "H -> H W", H=H, W=W) + w = repeat(w, "W -> H W", H=H, W=W) + + mask = torch.stack([h, w]).min(dim=0).values + mask = rearrange(mask, "H W -> 1 1 1 H W") + return mask + + + def tiled_decode(self, hidden_states, device, tile_size, tile_stride): + _, _, T, H, W = hidden_states.shape + size_h, size_w = tile_size + stride_h, stride_w = tile_stride + + # Split tasks + tasks = [] + for h in range(0, H, stride_h): + if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue + for w in range(0, W, stride_w): + if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue + h_, w_ = h + size_h, w + size_w + tasks.append((h, h_, w, w_)) + + data_device = "cpu" + computation_device = device + + out_T = T * 4 - 3 + weight = torch.zeros((1, 1, out_T, H * self.upsampling_factor, W * self.upsampling_factor), dtype=hidden_states.dtype, device=data_device) + values = torch.zeros((1, 3, out_T, H * self.upsampling_factor, W * self.upsampling_factor), dtype=hidden_states.dtype, device=data_device) + + for h, h_, w, w_ in tqdm(tasks, desc="VAE decoding"): + hidden_states_batch = hidden_states[:, :, :, h:h_, w:w_].to(computation_device) + hidden_states_batch = self.model.decode(hidden_states_batch, self.scale).to(data_device) + + mask = self.build_mask( + hidden_states_batch, + is_bound=(h==0, h_>=H, w==0, w_>=W), + border_width=((size_h - stride_h) * self.upsampling_factor, (size_w - stride_w) * self.upsampling_factor) + ).to(dtype=hidden_states.dtype, device=data_device) + + target_h = h * self.upsampling_factor + target_w = w * self.upsampling_factor + values[ + :, + :, + :, + target_h:target_h + hidden_states_batch.shape[3], + target_w:target_w + hidden_states_batch.shape[4], + ] += hidden_states_batch * mask + weight[ + :, + :, + :, + target_h: target_h + hidden_states_batch.shape[3], + target_w: target_w + hidden_states_batch.shape[4], + ] += mask + values = values / weight + values = values.clamp_(-1, 1) + return values + + + def tiled_encode(self, video, device, tile_size, tile_stride): + _, _, T, H, W = video.shape + size_h, size_w = tile_size + stride_h, stride_w = tile_stride + + # Split tasks + tasks = [] + for h in range(0, H, stride_h): + if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue + for w in range(0, W, stride_w): + if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue + h_, w_ = h + size_h, w + size_w + tasks.append((h, h_, w, w_)) + + data_device = "cpu" + computation_device = device + + out_T = (T + 3) // 4 + weight = torch.zeros((1, 1, out_T, H // self.upsampling_factor, W // self.upsampling_factor), dtype=video.dtype, device=data_device) + values = torch.zeros((1, 16, out_T, H // self.upsampling_factor, W // self.upsampling_factor), dtype=video.dtype, device=data_device) + + for h, h_, w, w_ in tqdm(tasks, desc="VAE encoding"): + hidden_states_batch = video[:, :, :, h:h_, w:w_].to(computation_device) + hidden_states_batch = self.model.encode(hidden_states_batch, self.scale).to(data_device) + + mask = self.build_mask( + hidden_states_batch, + is_bound=(h==0, h_>=H, w==0, w_>=W), + border_width=((size_h - stride_h) // self.upsampling_factor, (size_w - stride_w) // self.upsampling_factor) + ).to(dtype=video.dtype, device=data_device) + + target_h = h // self.upsampling_factor + target_w = w // self.upsampling_factor + values[ + :, + :, + :, + target_h:target_h + hidden_states_batch.shape[3], + target_w:target_w + hidden_states_batch.shape[4], + ] += hidden_states_batch * mask + weight[ + :, + :, + :, + target_h: target_h + hidden_states_batch.shape[3], + target_w: target_w + hidden_states_batch.shape[4], + ] += mask + values = values / weight + return values + + + def single_encode(self, video, device): + video = video.to(device) + x = self.model.encode(video, self.scale) + return x + + + def single_decode(self, hidden_state, device): + hidden_state = hidden_state.to(device) + video = self.model.decode(hidden_state, self.scale) + return video.clamp_(-1, 1) + + + def encode(self, videos, device, tiled=False, tile_size=(34, 34), tile_stride=(18, 16)): + + videos = [video.to("cpu") for video in videos] + hidden_states = [] + for video in videos: + video = video.unsqueeze(0) + if tiled: + tile_size = (tile_size[0] * 8, tile_size[1] * 8) + tile_stride = (tile_stride[0] * 8, tile_stride[1] * 8) + hidden_state = self.tiled_encode(video, device, tile_size, tile_stride) + else: + hidden_state = self.single_encode(video, device) + hidden_state = hidden_state.squeeze(0) + hidden_states.append(hidden_state) + hidden_states = torch.stack(hidden_states) + return hidden_states + + + def decode(self, hidden_states, device, tiled=False, tile_size=(34, 34), tile_stride=(18, 16)): + if tiled: + video = self.tiled_decode(hidden_states, device, tile_size, tile_stride) + else: + video = self.single_decode(hidden_states, device) + return video + + + @staticmethod + def state_dict_converter(): + return WanVideoVAEStateDictConverter() + + +class WanVideoVAEStateDictConverter: + + def __init__(self): + pass + + def from_civitai(self, state_dict): + state_dict_ = {} + if 'model_state' in state_dict: + state_dict = state_dict['model_state'] + for name in state_dict: + state_dict_['model.' + name] = state_dict[name] + return state_dict_ diff --git a/diffsynth/pipelines/__init__.py b/diffsynth/pipelines/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2c2915b47aeacb3c0a4dcc2af3ab532cdf87a633 --- /dev/null +++ b/diffsynth/pipelines/__init__.py @@ -0,0 +1,16 @@ +from .sd_image import SDImagePipeline +from .sd_video import SDVideoPipeline +from .sdxl_image import SDXLImagePipeline +from .sdxl_video import SDXLVideoPipeline +from .sd3_image import SD3ImagePipeline +from .hunyuan_image import HunyuanDiTImagePipeline +from .svd_video import SVDVideoPipeline +from .flux_image import FluxImagePipeline +from .cog_video import CogVideoPipeline +from .omnigen_image import OmnigenImagePipeline +from .pipeline_runner import SDVideoPipelineRunner +from .hunyuan_video import HunyuanVideoPipeline +from .step_video import StepVideoPipeline +from .wan_video import WanVideoPipeline +from .wan_video_new import WanVideoPipeline as WanVideoPipelineNew, ModelConfig +KolorsImagePipeline = SDXLImagePipeline diff --git a/diffsynth/pipelines/base.py b/diffsynth/pipelines/base.py new file mode 100644 index 0000000000000000000000000000000000000000..2a4f01cff55dc0fcca02dc5234227bd65efc7434 --- /dev/null +++ b/diffsynth/pipelines/base.py @@ -0,0 +1,127 @@ +import torch +import numpy as np +from PIL import Image +from torchvision.transforms import GaussianBlur + + + +class BasePipeline(torch.nn.Module): + + def __init__(self, device="cuda", torch_dtype=torch.float16, height_division_factor=64, width_division_factor=64): + super().__init__() + self.device = device + self.torch_dtype = torch_dtype + self.height_division_factor = height_division_factor + self.width_division_factor = width_division_factor + self.cpu_offload = False + self.model_names = [] + + + def check_resize_height_width(self, height, width): + if height % self.height_division_factor != 0: + height = (height + self.height_division_factor - 1) // self.height_division_factor * self.height_division_factor + print(f"The height cannot be evenly divided by {self.height_division_factor}. We round it up to {height}.") + if width % self.width_division_factor != 0: + width = (width + self.width_division_factor - 1) // self.width_division_factor * self.width_division_factor + print(f"The width cannot be evenly divided by {self.width_division_factor}. We round it up to {width}.") + return height, width + + + def preprocess_image(self, image): + image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0) + return image + + + def preprocess_images(self, images): + return [self.preprocess_image(image) for image in images] + + + def vae_output_to_image(self, vae_output): + image = vae_output[0].cpu().float().permute(1, 2, 0).numpy() + image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8")) + return image + + + def vae_output_to_video(self, vae_output): + video = vae_output.cpu().permute(1, 2, 0).numpy() + video = [Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8")) for image in video] + return video + + + def merge_latents(self, value, latents, masks, scales, blur_kernel_size=33, blur_sigma=10.0): + if len(latents) > 0: + blur = GaussianBlur(kernel_size=blur_kernel_size, sigma=blur_sigma) + height, width = value.shape[-2:] + weight = torch.ones_like(value) + for latent, mask, scale in zip(latents, masks, scales): + mask = self.preprocess_image(mask.resize((width, height))).mean(dim=1, keepdim=True) > 0 + mask = mask.repeat(1, latent.shape[1], 1, 1).to(dtype=latent.dtype, device=latent.device) + mask = blur(mask) + value += latent * mask * scale + weight += mask * scale + value /= weight + return value + + + def control_noise_via_local_prompts(self, prompt_emb_global, prompt_emb_locals, masks, mask_scales, inference_callback, special_kwargs=None, special_local_kwargs_list=None): + if special_kwargs is None: + noise_pred_global = inference_callback(prompt_emb_global) + else: + noise_pred_global = inference_callback(prompt_emb_global, special_kwargs) + if special_local_kwargs_list is None: + noise_pred_locals = [inference_callback(prompt_emb_local) for prompt_emb_local in prompt_emb_locals] + else: + noise_pred_locals = [inference_callback(prompt_emb_local, special_kwargs) for prompt_emb_local, special_kwargs in zip(prompt_emb_locals, special_local_kwargs_list)] + noise_pred = self.merge_latents(noise_pred_global, noise_pred_locals, masks, mask_scales) + return noise_pred + + + def extend_prompt(self, prompt, local_prompts, masks, mask_scales): + local_prompts = local_prompts or [] + masks = masks or [] + mask_scales = mask_scales or [] + extended_prompt_dict = self.prompter.extend_prompt(prompt) + prompt = extended_prompt_dict.get("prompt", prompt) + local_prompts += extended_prompt_dict.get("prompts", []) + masks += extended_prompt_dict.get("masks", []) + mask_scales += [100.0] * len(extended_prompt_dict.get("masks", [])) + return prompt, local_prompts, masks, mask_scales + + + def enable_cpu_offload(self): + self.cpu_offload = True + + + def load_models_to_device(self, loadmodel_names=[]): + # only load models to device if cpu_offload is enabled + if not self.cpu_offload: + return + # offload the unneeded models to cpu + for model_name in self.model_names: + if model_name not in loadmodel_names: + model = getattr(self, model_name) + if model is not None: + if hasattr(model, "vram_management_enabled") and model.vram_management_enabled: + for module in model.modules(): + if hasattr(module, "offload"): + module.offload() + else: + model.cpu() + # load the needed models to device + for model_name in loadmodel_names: + model = getattr(self, model_name) + if model is not None: + if hasattr(model, "vram_management_enabled") and model.vram_management_enabled: + for module in model.modules(): + if hasattr(module, "onload"): + module.onload() + else: + model.to(self.device) + # fresh the cuda cache + torch.cuda.empty_cache() + + + def generate_noise(self, shape, seed=None, device="cpu", dtype=torch.float16): + generator = None if seed is None else torch.Generator(device).manual_seed(seed) + noise = torch.randn(shape, generator=generator, device=device, dtype=dtype) + return noise diff --git a/diffsynth/pipelines/cog_video.py b/diffsynth/pipelines/cog_video.py new file mode 100644 index 0000000000000000000000000000000000000000..f42d295187e718617cc7d4e327067700f2a689fd --- /dev/null +++ b/diffsynth/pipelines/cog_video.py @@ -0,0 +1,135 @@ +from ..models import ModelManager, FluxTextEncoder2, CogDiT, CogVAEEncoder, CogVAEDecoder +from ..prompters import CogPrompter +from ..schedulers import EnhancedDDIMScheduler +from .base import BasePipeline +import torch +from tqdm import tqdm +from PIL import Image +import numpy as np +from einops import rearrange + + + +class CogVideoPipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__(device=device, torch_dtype=torch_dtype, height_division_factor=16, width_division_factor=16) + self.scheduler = EnhancedDDIMScheduler(rescale_zero_terminal_snr=True, prediction_type="v_prediction") + self.prompter = CogPrompter() + # models + self.text_encoder: FluxTextEncoder2 = None + self.dit: CogDiT = None + self.vae_encoder: CogVAEEncoder = None + self.vae_decoder: CogVAEDecoder = None + + + def fetch_models(self, model_manager: ModelManager, prompt_refiner_classes=[]): + self.text_encoder = model_manager.fetch_model("flux_text_encoder_2") + self.dit = model_manager.fetch_model("cog_dit") + self.vae_encoder = model_manager.fetch_model("cog_vae_encoder") + self.vae_decoder = model_manager.fetch_model("cog_vae_decoder") + self.prompter.fetch_models(self.text_encoder) + self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) + + + @staticmethod + def from_model_manager(model_manager: ModelManager, prompt_refiner_classes=[]): + pipe = CogVideoPipeline( + device=model_manager.device, + torch_dtype=model_manager.torch_dtype + ) + pipe.fetch_models(model_manager, prompt_refiner_classes) + return pipe + + + def tensor2video(self, frames): + frames = rearrange(frames, "C T H W -> T H W C") + frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8) + frames = [Image.fromarray(frame) for frame in frames] + return frames + + + def encode_prompt(self, prompt, positive=True): + prompt_emb = self.prompter.encode_prompt(prompt, device=self.device, positive=positive) + return {"prompt_emb": prompt_emb} + + + def prepare_extra_input(self, latents): + return {"image_rotary_emb": self.dit.prepare_rotary_positional_embeddings(latents.shape[3], latents.shape[4], latents.shape[2], device=self.device)} + + + @torch.no_grad() + def __call__( + self, + prompt, + negative_prompt="", + input_video=None, + cfg_scale=7.0, + denoising_strength=1.0, + num_frames=49, + height=480, + width=720, + num_inference_steps=20, + tiled=False, + tile_size=(60, 90), + tile_stride=(30, 45), + seed=None, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + height, width = self.check_resize_height_width(height, width) + + # Tiler parameters + tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} + + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength) + + # Prepare latent tensors + noise = self.generate_noise((1, 16, num_frames // 4 + 1, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype) + + if denoising_strength == 1.0: + latents = noise.clone() + else: + input_video = self.preprocess_images(input_video) + input_video = torch.stack(input_video, dim=2) + latents = self.vae_encoder.encode_video(input_video, **tiler_kwargs, progress_bar=progress_bar_cmd).to(dtype=self.torch_dtype) + latents = self.scheduler.add_noise(latents, noise, self.scheduler.timesteps[0]) + if not tiled: latents = latents.to(self.device) + + # Encode prompt + prompt_emb_posi = self.encode_prompt(prompt, positive=True) + if cfg_scale != 1.0: + prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False) + + # Extra input + extra_input = self.prepare_extra_input(latents) + + # Denoise + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(self.device) + + # Classifier-free guidance + noise_pred_posi = self.dit( + latents, timestep=timestep, **prompt_emb_posi, **tiler_kwargs, **extra_input + ) + if cfg_scale != 1.0: + noise_pred_nega = self.dit( + latents, timestep=timestep, **prompt_emb_nega, **tiler_kwargs, **extra_input + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + # DDIM + latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) + + # Update progress bar + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + video = self.vae_decoder.decode_video(latents.to("cpu"), **tiler_kwargs, progress_bar=progress_bar_cmd) + video = self.tensor2video(video[0]) + + return video diff --git a/diffsynth/pipelines/dancer.py b/diffsynth/pipelines/dancer.py new file mode 100644 index 0000000000000000000000000000000000000000..593b57c8363f94e312debf7c7f69bf6decdb7dbd --- /dev/null +++ b/diffsynth/pipelines/dancer.py @@ -0,0 +1,236 @@ +import torch +from ..models import SDUNet, SDMotionModel, SDXLUNet, SDXLMotionModel +from ..models.sd_unet import PushBlock, PopBlock +from ..controlnets import MultiControlNetManager + + +def lets_dance( + unet: SDUNet, + motion_modules: SDMotionModel = None, + controlnet: MultiControlNetManager = None, + sample = None, + timestep = None, + encoder_hidden_states = None, + ipadapter_kwargs_list = {}, + controlnet_frames = None, + unet_batch_size = 1, + controlnet_batch_size = 1, + cross_frame_attention = False, + tiled=False, + tile_size=64, + tile_stride=32, + device = "cuda", + vram_limit_level = 0, +): + # 0. Text embedding alignment (only for video processing) + if encoder_hidden_states.shape[0] != sample.shape[0]: + encoder_hidden_states = encoder_hidden_states.repeat(sample.shape[0], 1, 1, 1) + + # 1. ControlNet + # This part will be repeated on overlapping frames if animatediff_batch_size > animatediff_stride. + # I leave it here because I intend to do something interesting on the ControlNets. + controlnet_insert_block_id = 30 + if controlnet is not None and controlnet_frames is not None: + res_stacks = [] + # process controlnet frames with batch + for batch_id in range(0, sample.shape[0], controlnet_batch_size): + batch_id_ = min(batch_id + controlnet_batch_size, sample.shape[0]) + res_stack = controlnet( + sample[batch_id: batch_id_], + timestep, + encoder_hidden_states[batch_id: batch_id_], + controlnet_frames[:, batch_id: batch_id_], + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride + ) + if vram_limit_level >= 1: + res_stack = [res.cpu() for res in res_stack] + res_stacks.append(res_stack) + # concat the residual + additional_res_stack = [] + for i in range(len(res_stacks[0])): + res = torch.concat([res_stack[i] for res_stack in res_stacks], dim=0) + additional_res_stack.append(res) + else: + additional_res_stack = None + + # 2. time + time_emb = unet.time_proj(timestep).to(sample.dtype) + time_emb = unet.time_embedding(time_emb) + + # 3. pre-process + height, width = sample.shape[2], sample.shape[3] + hidden_states = unet.conv_in(sample) + text_emb = encoder_hidden_states + res_stack = [hidden_states.cpu() if vram_limit_level>=1 else hidden_states] + + # 4. blocks + for block_id, block in enumerate(unet.blocks): + # 4.1 UNet + if isinstance(block, PushBlock): + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + if vram_limit_level>=1: + res_stack[-1] = res_stack[-1].cpu() + elif isinstance(block, PopBlock): + if vram_limit_level>=1: + res_stack[-1] = res_stack[-1].to(device) + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + else: + hidden_states_input = hidden_states + hidden_states_output = [] + for batch_id in range(0, sample.shape[0], unet_batch_size): + batch_id_ = min(batch_id + unet_batch_size, sample.shape[0]) + hidden_states, _, _, _ = block( + hidden_states_input[batch_id: batch_id_], + time_emb, + text_emb[batch_id: batch_id_], + res_stack, + cross_frame_attention=cross_frame_attention, + ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, {}), + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride + ) + hidden_states_output.append(hidden_states) + hidden_states = torch.concat(hidden_states_output, dim=0) + # 4.2 AnimateDiff + if motion_modules is not None: + if block_id in motion_modules.call_block_id: + motion_module_id = motion_modules.call_block_id[block_id] + hidden_states, time_emb, text_emb, res_stack = motion_modules.motion_modules[motion_module_id]( + hidden_states, time_emb, text_emb, res_stack, + batch_size=1 + ) + # 4.3 ControlNet + if block_id == controlnet_insert_block_id and additional_res_stack is not None: + hidden_states += additional_res_stack.pop().to(device) + if vram_limit_level>=1: + res_stack = [(res.to(device) + additional_res.to(device)).cpu() for res, additional_res in zip(res_stack, additional_res_stack)] + else: + res_stack = [res + additional_res for res, additional_res in zip(res_stack, additional_res_stack)] + + # 5. output + hidden_states = unet.conv_norm_out(hidden_states) + hidden_states = unet.conv_act(hidden_states) + hidden_states = unet.conv_out(hidden_states) + + return hidden_states + + + + +def lets_dance_xl( + unet: SDXLUNet, + motion_modules: SDXLMotionModel = None, + controlnet: MultiControlNetManager = None, + sample = None, + add_time_id = None, + add_text_embeds = None, + timestep = None, + encoder_hidden_states = None, + ipadapter_kwargs_list = {}, + controlnet_frames = None, + unet_batch_size = 1, + controlnet_batch_size = 1, + cross_frame_attention = False, + tiled=False, + tile_size=64, + tile_stride=32, + device = "cuda", + vram_limit_level = 0, +): + # 0. Text embedding alignment (only for video processing) + if encoder_hidden_states.shape[0] != sample.shape[0]: + encoder_hidden_states = encoder_hidden_states.repeat(sample.shape[0], 1, 1, 1) + if add_text_embeds.shape[0] != sample.shape[0]: + add_text_embeds = add_text_embeds.repeat(sample.shape[0], 1) + + # 1. ControlNet + controlnet_insert_block_id = 22 + if controlnet is not None and controlnet_frames is not None: + res_stacks = [] + # process controlnet frames with batch + for batch_id in range(0, sample.shape[0], controlnet_batch_size): + batch_id_ = min(batch_id + controlnet_batch_size, sample.shape[0]) + res_stack = controlnet( + sample[batch_id: batch_id_], + timestep, + encoder_hidden_states[batch_id: batch_id_], + controlnet_frames[:, batch_id: batch_id_], + add_time_id=add_time_id, + add_text_embeds=add_text_embeds, + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, + unet=unet, # for Kolors, some modules in ControlNets will be replaced. + ) + if vram_limit_level >= 1: + res_stack = [res.cpu() for res in res_stack] + res_stacks.append(res_stack) + # concat the residual + additional_res_stack = [] + for i in range(len(res_stacks[0])): + res = torch.concat([res_stack[i] for res_stack in res_stacks], dim=0) + additional_res_stack.append(res) + else: + additional_res_stack = None + + # 2. time + t_emb = unet.time_proj(timestep).to(sample.dtype) + t_emb = unet.time_embedding(t_emb) + + time_embeds = unet.add_time_proj(add_time_id) + time_embeds = time_embeds.reshape((add_text_embeds.shape[0], -1)) + add_embeds = torch.concat([add_text_embeds, time_embeds], dim=-1) + add_embeds = add_embeds.to(sample.dtype) + add_embeds = unet.add_time_embedding(add_embeds) + + time_emb = t_emb + add_embeds + + # 3. pre-process + height, width = sample.shape[2], sample.shape[3] + hidden_states = unet.conv_in(sample) + text_emb = encoder_hidden_states if unet.text_intermediate_proj is None else unet.text_intermediate_proj(encoder_hidden_states) + res_stack = [hidden_states] + + # 4. blocks + for block_id, block in enumerate(unet.blocks): + # 4.1 UNet + if isinstance(block, PushBlock): + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + if vram_limit_level>=1: + res_stack[-1] = res_stack[-1].cpu() + elif isinstance(block, PopBlock): + if vram_limit_level>=1: + res_stack[-1] = res_stack[-1].to(device) + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + else: + hidden_states_input = hidden_states + hidden_states_output = [] + for batch_id in range(0, sample.shape[0], unet_batch_size): + batch_id_ = min(batch_id + unet_batch_size, sample.shape[0]) + hidden_states, _, _, _ = block( + hidden_states_input[batch_id: batch_id_], + time_emb[batch_id: batch_id_], + text_emb[batch_id: batch_id_], + res_stack, + cross_frame_attention=cross_frame_attention, + ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, {}), + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, + ) + hidden_states_output.append(hidden_states) + hidden_states = torch.concat(hidden_states_output, dim=0) + # 4.2 AnimateDiff + if motion_modules is not None: + if block_id in motion_modules.call_block_id: + motion_module_id = motion_modules.call_block_id[block_id] + hidden_states, time_emb, text_emb, res_stack = motion_modules.motion_modules[motion_module_id]( + hidden_states, time_emb, text_emb, res_stack, + batch_size=1 + ) + # 4.3 ControlNet + if block_id == controlnet_insert_block_id and additional_res_stack is not None: + hidden_states += additional_res_stack.pop().to(device) + res_stack = [res + additional_res for res, additional_res in zip(res_stack, additional_res_stack)] + + # 5. output + hidden_states = unet.conv_norm_out(hidden_states) + hidden_states = unet.conv_act(hidden_states) + hidden_states = unet.conv_out(hidden_states) + + return hidden_states \ No newline at end of file diff --git a/diffsynth/pipelines/flux_image.py b/diffsynth/pipelines/flux_image.py new file mode 100644 index 0000000000000000000000000000000000000000..90b196da3c92fb85613ee08796256176a72d66bd --- /dev/null +++ b/diffsynth/pipelines/flux_image.py @@ -0,0 +1,823 @@ +from ..models import ModelManager, FluxDiT, SD3TextEncoder1, FluxTextEncoder2, FluxVAEDecoder, FluxVAEEncoder, FluxIpAdapter +from ..models.step1x_connector import Qwen2Connector +from ..controlnets import FluxMultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator +from ..prompters import FluxPrompter +from ..schedulers import FlowMatchScheduler +from .base import BasePipeline +from typing import List +import torch +from tqdm import tqdm +import numpy as np +from PIL import Image +from ..models.tiler import FastTileWorker +from transformers import SiglipVisionModel +from copy import deepcopy +from transformers.models.t5.modeling_t5 import T5LayerNorm, T5DenseActDense, T5DenseGatedActDense +from ..models.flux_dit import RMSNorm +from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear + + +class FluxImagePipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__(device=device, torch_dtype=torch_dtype, height_division_factor=16, width_division_factor=16) + self.scheduler = FlowMatchScheduler() + self.prompter = FluxPrompter() + # models + self.text_encoder_1: SD3TextEncoder1 = None + self.text_encoder_2: FluxTextEncoder2 = None + self.dit: FluxDiT = None + self.vae_decoder: FluxVAEDecoder = None + self.vae_encoder: FluxVAEEncoder = None + self.controlnet: FluxMultiControlNetManager = None + self.ipadapter: FluxIpAdapter = None + self.ipadapter_image_encoder: SiglipVisionModel = None + self.infinityou_processor: InfinitYou = None + self.qwenvl = None + self.step1x_connector: Qwen2Connector = None + self.model_names = ['text_encoder_1', 'text_encoder_2', 'dit', 'vae_decoder', 'vae_encoder', 'controlnet', 'ipadapter', 'ipadapter_image_encoder', 'qwenvl', 'step1x_connector'] + + + def enable_vram_management(self, num_persistent_param_in_dit=None): + if self.text_encoder_1 is not None: + dtype = next(iter(self.text_encoder_1.parameters())).dtype + enable_vram_management( + self.text_encoder_1, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Embedding: AutoWrappedModule, + torch.nn.LayerNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + if self.text_encoder_2 is not None: + dtype = next(iter(self.text_encoder_2.parameters())).dtype + enable_vram_management( + self.text_encoder_2, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Embedding: AutoWrappedModule, + T5LayerNorm: AutoWrappedModule, + T5DenseActDense: AutoWrappedModule, + T5DenseGatedActDense: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + if self.dit is not None: + dtype = next(iter(self.dit.parameters())).dtype + enable_vram_management( + self.dit, + module_map = { + RMSNorm: AutoWrappedModule, + torch.nn.Linear: AutoWrappedLinear, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cuda", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + max_num_param=num_persistent_param_in_dit, + overflow_module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + if self.vae_decoder is not None: + dtype = next(iter(self.vae_decoder.parameters())).dtype + enable_vram_management( + self.vae_decoder, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv2d: AutoWrappedModule, + torch.nn.GroupNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + if self.vae_encoder is not None: + dtype = next(iter(self.vae_encoder.parameters())).dtype + enable_vram_management( + self.vae_encoder, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv2d: AutoWrappedModule, + torch.nn.GroupNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + self.enable_cpu_offload() + + + def denoising_model(self): + return self.dit + + + def fetch_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[], prompt_extender_classes=[]): + self.text_encoder_1 = model_manager.fetch_model("sd3_text_encoder_1") + self.text_encoder_2 = model_manager.fetch_model("flux_text_encoder_2") + self.dit = model_manager.fetch_model("flux_dit") + self.vae_decoder = model_manager.fetch_model("flux_vae_decoder") + self.vae_encoder = model_manager.fetch_model("flux_vae_encoder") + self.prompter.fetch_models(self.text_encoder_1, self.text_encoder_2) + self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) + self.prompter.load_prompt_extenders(model_manager, prompt_extender_classes) + + # ControlNets + controlnet_units = [] + for config in controlnet_config_units: + controlnet_unit = ControlNetUnit( + Annotator(config.processor_id, device=self.device, skip_processor=config.skip_processor), + model_manager.fetch_model("flux_controlnet", config.model_path), + config.scale + ) + controlnet_units.append(controlnet_unit) + self.controlnet = FluxMultiControlNetManager(controlnet_units) + + # IP-Adapters + self.ipadapter = model_manager.fetch_model("flux_ipadapter") + self.ipadapter_image_encoder = model_manager.fetch_model("siglip_vision_model") + + # InfiniteYou + self.image_proj_model = model_manager.fetch_model("infiniteyou_image_projector") + if self.image_proj_model is not None: + self.infinityou_processor = InfinitYou(device=self.device) + + # Step1x + self.qwenvl = model_manager.fetch_model("qwenvl") + self.step1x_connector = model_manager.fetch_model("step1x_connector") + + + @staticmethod + def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[], prompt_extender_classes=[], device=None, torch_dtype=None): + pipe = FluxImagePipeline( + device=model_manager.device if device is None else device, + torch_dtype=model_manager.torch_dtype if torch_dtype is None else torch_dtype, + ) + pipe.fetch_models(model_manager, controlnet_config_units, prompt_refiner_classes, prompt_extender_classes) + return pipe + + + def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32): + latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + return latents + + + def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): + image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + image = self.vae_output_to_image(image) + return image + + + def encode_prompt(self, prompt, positive=True, t5_sequence_length=512): + if self.text_encoder_1 is not None and self.text_encoder_2 is not None: + prompt_emb, pooled_prompt_emb, text_ids = self.prompter.encode_prompt( + prompt, device=self.device, positive=positive, t5_sequence_length=t5_sequence_length + ) + return {"prompt_emb": prompt_emb, "pooled_prompt_emb": pooled_prompt_emb, "text_ids": text_ids} + else: + return {} + + + def prepare_extra_input(self, latents=None, guidance=1.0): + latent_image_ids = self.dit.prepare_image_ids(latents) + guidance = torch.Tensor([guidance] * latents.shape[0]).to(device=latents.device, dtype=latents.dtype) + return {"image_ids": latent_image_ids, "guidance": guidance} + + + def apply_controlnet_mask_on_latents(self, latents, mask): + mask = (self.preprocess_image(mask) + 1) / 2 + mask = mask.mean(dim=1, keepdim=True) + mask = mask.to(dtype=self.torch_dtype, device=self.device) + mask = 1 - torch.nn.functional.interpolate(mask, size=latents.shape[-2:]) + latents = torch.concat([latents, mask], dim=1) + return latents + + + def apply_controlnet_mask_on_image(self, image, mask): + mask = mask.resize(image.size) + mask = self.preprocess_image(mask).mean(dim=[0, 1]) + image = np.array(image) + image[mask > 0] = 0 + image = Image.fromarray(image) + return image + + + def prepare_controlnet_input(self, controlnet_image, controlnet_inpaint_mask, tiler_kwargs): + if isinstance(controlnet_image, Image.Image): + controlnet_image = [controlnet_image] * len(self.controlnet.processors) + + controlnet_frames = [] + for i in range(len(self.controlnet.processors)): + # image annotator + image = self.controlnet.process_image(controlnet_image[i], processor_id=i)[0] + if controlnet_inpaint_mask is not None and self.controlnet.processors[i].processor_id == "inpaint": + image = self.apply_controlnet_mask_on_image(image, controlnet_inpaint_mask) + + # image to tensor + image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) + + # vae encoder + image = self.encode_image(image, **tiler_kwargs) + if controlnet_inpaint_mask is not None and self.controlnet.processors[i].processor_id == "inpaint": + image = self.apply_controlnet_mask_on_latents(image, controlnet_inpaint_mask) + + # store it + controlnet_frames.append(image) + return controlnet_frames + + + def prepare_ipadapter_inputs(self, images, height=384, width=384): + images = [image.convert("RGB").resize((width, height), resample=3) for image in images] + images = [self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) for image in images] + return torch.cat(images, dim=0) + + + def inpaint_fusion(self, latents, inpaint_latents, pred_noise, fg_mask, bg_mask, progress_id, background_weight=0.): + # inpaint noise + inpaint_noise = (latents - inpaint_latents) / self.scheduler.sigmas[progress_id] + # merge noise + weight = torch.ones_like(inpaint_noise) + inpaint_noise[fg_mask] = pred_noise[fg_mask] + inpaint_noise[bg_mask] += pred_noise[bg_mask] * background_weight + weight[bg_mask] += background_weight + inpaint_noise /= weight + return inpaint_noise + + + def preprocess_masks(self, masks, height, width, dim): + out_masks = [] + for mask in masks: + mask = self.preprocess_image(mask.resize((width, height), resample=Image.NEAREST)).mean(dim=1, keepdim=True) > 0 + mask = mask.repeat(1, dim, 1, 1).to(device=self.device, dtype=self.torch_dtype) + out_masks.append(mask) + return out_masks + + + def prepare_entity_inputs(self, entity_prompts, entity_masks, width, height, t5_sequence_length=512, enable_eligen_inpaint=False): + fg_mask, bg_mask = None, None + if enable_eligen_inpaint: + masks_ = deepcopy(entity_masks) + fg_masks = torch.cat([self.preprocess_image(mask.resize((width//8, height//8))).mean(dim=1, keepdim=True) for mask in masks_]) + fg_masks = (fg_masks > 0).float() + fg_mask = fg_masks.sum(dim=0, keepdim=True).repeat(1, 16, 1, 1) > 0 + bg_mask = ~fg_mask + entity_masks = self.preprocess_masks(entity_masks, height//8, width//8, 1) + entity_masks = torch.cat(entity_masks, dim=0).unsqueeze(0) # b, n_mask, c, h, w + entity_prompts = self.encode_prompt(entity_prompts, t5_sequence_length=t5_sequence_length)['prompt_emb'].unsqueeze(0) + return entity_prompts, entity_masks, fg_mask, bg_mask + + + def prepare_latents(self, input_image, height, width, seed, tiled, tile_size, tile_stride): + if input_image is not None: + self.load_models_to_device(['vae_encoder']) + image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype) + input_latents = self.encode_image(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + noise = self.generate_noise((1, 16, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + latents = self.scheduler.add_noise(input_latents, noise, timestep=self.scheduler.timesteps[0]) + else: + latents = self.generate_noise((1, 16, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + input_latents = None + return latents, input_latents + + + def prepare_ipadapter(self, ipadapter_images, ipadapter_scale): + if ipadapter_images is not None: + self.load_models_to_device(['ipadapter_image_encoder']) + ipadapter_images = self.prepare_ipadapter_inputs(ipadapter_images) + ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images).pooler_output + self.load_models_to_device(['ipadapter']) + ipadapter_kwargs_list_posi = {"ipadapter_kwargs_list": self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)} + ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": self.ipadapter(torch.zeros_like(ipadapter_image_encoding))} + else: + ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": {}}, {"ipadapter_kwargs_list": {}} + return ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega + + + def prepare_controlnet(self, controlnet_image, masks, controlnet_inpaint_mask, tiler_kwargs, enable_controlnet_on_negative): + if controlnet_image is not None: + self.load_models_to_device(['vae_encoder']) + controlnet_kwargs_posi = {"controlnet_frames": self.prepare_controlnet_input(controlnet_image, controlnet_inpaint_mask, tiler_kwargs)} + if len(masks) > 0 and controlnet_inpaint_mask is not None: + print("The controlnet_inpaint_mask will be overridden by masks.") + local_controlnet_kwargs = [{"controlnet_frames": self.prepare_controlnet_input(controlnet_image, mask, tiler_kwargs)} for mask in masks] + else: + local_controlnet_kwargs = None + else: + controlnet_kwargs_posi, local_controlnet_kwargs = {"controlnet_frames": None}, [{}] * len(masks) + controlnet_kwargs_nega = controlnet_kwargs_posi if enable_controlnet_on_negative else {} + return controlnet_kwargs_posi, controlnet_kwargs_nega, local_controlnet_kwargs + + + def prepare_eligen(self, prompt_emb_nega, eligen_entity_prompts, eligen_entity_masks, width, height, t5_sequence_length, enable_eligen_inpaint, enable_eligen_on_negative, cfg_scale): + if eligen_entity_masks is not None: + entity_prompt_emb_posi, entity_masks_posi, fg_mask, bg_mask = self.prepare_entity_inputs(eligen_entity_prompts, eligen_entity_masks, width, height, t5_sequence_length, enable_eligen_inpaint) + if enable_eligen_on_negative and cfg_scale != 1.0: + entity_prompt_emb_nega = prompt_emb_nega['prompt_emb'].unsqueeze(1).repeat(1, entity_masks_posi.shape[1], 1, 1) + entity_masks_nega = entity_masks_posi + else: + entity_prompt_emb_nega, entity_masks_nega = None, None + else: + entity_prompt_emb_posi, entity_masks_posi, entity_prompt_emb_nega, entity_masks_nega = None, None, None, None + fg_mask, bg_mask = None, None + eligen_kwargs_posi = {"entity_prompt_emb": entity_prompt_emb_posi, "entity_masks": entity_masks_posi} + eligen_kwargs_nega = {"entity_prompt_emb": entity_prompt_emb_nega, "entity_masks": entity_masks_nega} + return eligen_kwargs_posi, eligen_kwargs_nega, fg_mask, bg_mask + + + def prepare_prompts(self, prompt, local_prompts, masks, mask_scales, t5_sequence_length, negative_prompt, cfg_scale): + # Extend prompt + self.load_models_to_device(['text_encoder_1', 'text_encoder_2']) + prompt, local_prompts, masks, mask_scales = self.extend_prompt(prompt, local_prompts, masks, mask_scales) + + # Encode prompts + prompt_emb_posi = self.encode_prompt(prompt, t5_sequence_length=t5_sequence_length) + prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False, t5_sequence_length=t5_sequence_length) if cfg_scale != 1.0 else None + prompt_emb_locals = [self.encode_prompt(prompt_local, t5_sequence_length=t5_sequence_length) for prompt_local in local_prompts] + return prompt_emb_posi, prompt_emb_nega, prompt_emb_locals + + + def prepare_infinite_you(self, id_image, controlnet_image, infinityou_guidance, height, width): + if self.infinityou_processor is not None and id_image is not None: + return self.infinityou_processor.prepare_infinite_you(self.image_proj_model, id_image, controlnet_image, infinityou_guidance, height, width) + else: + return {}, controlnet_image + + + def prepare_flex_kwargs(self, latents, flex_inpaint_image=None, flex_inpaint_mask=None, flex_control_image=None, flex_control_strength=0.5, flex_control_stop=0.5, tiled=False, tile_size=64, tile_stride=32): + if self.dit.input_dim == 196: + if flex_inpaint_image is None: + flex_inpaint_image = torch.zeros_like(latents) + else: + flex_inpaint_image = self.preprocess_image(flex_inpaint_image).to(device=self.device, dtype=self.torch_dtype) + flex_inpaint_image = self.encode_image(flex_inpaint_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + if flex_inpaint_mask is None: + flex_inpaint_mask = torch.ones_like(latents)[:, 0:1, :, :] + else: + flex_inpaint_mask = flex_inpaint_mask.resize((latents.shape[3], latents.shape[2])) + flex_inpaint_mask = self.preprocess_image(flex_inpaint_mask).to(device=self.device, dtype=self.torch_dtype) + flex_inpaint_mask = (flex_inpaint_mask[:, 0:1, :, :] + 1) / 2 + flex_inpaint_image = flex_inpaint_image * (1 - flex_inpaint_mask) + if flex_control_image is None: + flex_control_image = torch.zeros_like(latents) + else: + flex_control_image = self.preprocess_image(flex_control_image).to(device=self.device, dtype=self.torch_dtype) + flex_control_image = self.encode_image(flex_control_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) * flex_control_strength + flex_condition = torch.concat([flex_inpaint_image, flex_inpaint_mask, flex_control_image], dim=1) + flex_uncondition = torch.concat([flex_inpaint_image, flex_inpaint_mask, torch.zeros_like(flex_control_image)], dim=1) + flex_control_stop_timestep = self.scheduler.timesteps[int(flex_control_stop * (len(self.scheduler.timesteps) - 1))] + flex_kwargs = {"flex_condition": flex_condition, "flex_uncondition": flex_uncondition, "flex_control_stop_timestep": flex_control_stop_timestep} + else: + flex_kwargs = {} + return flex_kwargs + + + def prepare_step1x_kwargs(self, prompt, negative_prompt, image): + if image is None: + return {}, {} + self.load_models_to_device(["qwenvl", "vae_encoder"]) + captions = [prompt, negative_prompt] + ref_images = [image, image] + embs, masks = self.qwenvl(captions, ref_images) + image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) + image = self.encode_image(image) + return {"step1x_llm_embedding": embs[0:1], "step1x_mask": masks[0:1], "step1x_reference_latents": image}, {"step1x_llm_embedding": embs[1:2], "step1x_mask": masks[1:2], "step1x_reference_latents": image} + + + @torch.no_grad() + def __call__( + self, + # Prompt + prompt, + negative_prompt="", + cfg_scale=1.0, + embedded_guidance=3.5, + t5_sequence_length=512, + # Image + input_image=None, + denoising_strength=1.0, + height=1024, + width=1024, + seed=None, + # Steps + num_inference_steps=30, + # local prompts + local_prompts=(), + masks=(), + mask_scales=(), + # ControlNet + controlnet_image=None, + controlnet_inpaint_mask=None, + enable_controlnet_on_negative=False, + # IP-Adapter + ipadapter_images=None, + ipadapter_scale=1.0, + # EliGen + eligen_entity_prompts=None, + eligen_entity_masks=None, + enable_eligen_on_negative=False, + enable_eligen_inpaint=False, + # InfiniteYou + infinityou_id_image=None, + infinityou_guidance=1.0, + # Flex + flex_inpaint_image=None, + flex_inpaint_mask=None, + flex_control_image=None, + flex_control_strength=0.5, + flex_control_stop=0.5, + # Step1x + step1x_reference_image=None, + # TeaCache + tea_cache_l1_thresh=None, + # Tile + tiled=False, + tile_size=128, + tile_stride=64, + # Progress bar + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + height, width = self.check_resize_height_width(height, width) + + # Tiler parameters + tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} + + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + latents, input_latents = self.prepare_latents(input_image, height, width, seed, tiled, tile_size, tile_stride) + + # Prompt + prompt_emb_posi, prompt_emb_nega, prompt_emb_locals = self.prepare_prompts(prompt, local_prompts, masks, mask_scales, t5_sequence_length, negative_prompt, cfg_scale) + + # Extra input + extra_input = self.prepare_extra_input(latents, guidance=embedded_guidance) + + # InfiniteYou + infiniteyou_kwargs, controlnet_image = self.prepare_infinite_you(infinityou_id_image, controlnet_image, infinityou_guidance, height, width) + + # Entity control + eligen_kwargs_posi, eligen_kwargs_nega, fg_mask, bg_mask = self.prepare_eligen(prompt_emb_nega, eligen_entity_prompts, eligen_entity_masks, width, height, t5_sequence_length, enable_eligen_inpaint, enable_eligen_on_negative, cfg_scale) + + # IP-Adapter + ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = self.prepare_ipadapter(ipadapter_images, ipadapter_scale) + + # ControlNets + controlnet_kwargs_posi, controlnet_kwargs_nega, local_controlnet_kwargs = self.prepare_controlnet(controlnet_image, masks, controlnet_inpaint_mask, tiler_kwargs, enable_controlnet_on_negative) + + # Flex + flex_kwargs = self.prepare_flex_kwargs(latents, flex_inpaint_image, flex_inpaint_mask, flex_control_image, flex_control_strength=flex_control_strength, flex_control_stop=flex_control_stop, **tiler_kwargs) + + # Step1x + step1x_kwargs_posi, step1x_kwargs_nega = self.prepare_step1x_kwargs(prompt, negative_prompt, image=step1x_reference_image) + + # TeaCache + tea_cache_kwargs = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh) if tea_cache_l1_thresh is not None else None} + + # Denoise + self.load_models_to_device(['dit', 'controlnet', 'step1x_connector']) + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(self.device) + + # Positive side + inference_callback = lambda prompt_emb_posi, controlnet_kwargs: lets_dance_flux( + dit=self.dit, controlnet=self.controlnet, step1x_connector=self.step1x_connector, + hidden_states=latents, timestep=timestep, + **prompt_emb_posi, **tiler_kwargs, **extra_input, **controlnet_kwargs, **ipadapter_kwargs_list_posi, **eligen_kwargs_posi, **tea_cache_kwargs, **infiniteyou_kwargs, **flex_kwargs, **step1x_kwargs_posi, + ) + noise_pred_posi = self.control_noise_via_local_prompts( + prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback, + special_kwargs=controlnet_kwargs_posi, special_local_kwargs_list=local_controlnet_kwargs + ) + + # Inpaint + if enable_eligen_inpaint: + noise_pred_posi = self.inpaint_fusion(latents, input_latents, noise_pred_posi, fg_mask, bg_mask, progress_id) + + # Classifier-free guidance + if cfg_scale != 1.0: + # Negative side + noise_pred_nega = lets_dance_flux( + dit=self.dit, controlnet=self.controlnet, step1x_connector=self.step1x_connector, + hidden_states=latents, timestep=timestep, + **prompt_emb_nega, **tiler_kwargs, **extra_input, **controlnet_kwargs_nega, **ipadapter_kwargs_list_nega, **eligen_kwargs_nega, **infiniteyou_kwargs, **flex_kwargs, **step1x_kwargs_nega, + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + # Iterate + latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) + + # UI + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + self.load_models_to_device(['vae_decoder']) + image = self.decode_image(latents, **tiler_kwargs) + + # Offload all models + self.load_models_to_device([]) + return image + + + +class InfinitYou: + def __init__(self, device="cuda", torch_dtype=torch.bfloat16): + from facexlib.recognition import init_recognition_model + from insightface.app import FaceAnalysis + self.device = device + self.torch_dtype = torch_dtype + insightface_root_path = 'models/InfiniteYou/insightface' + self.app_640 = FaceAnalysis(name='antelopev2', root=insightface_root_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) + self.app_640.prepare(ctx_id=0, det_size=(640, 640)) + self.app_320 = FaceAnalysis(name='antelopev2', root=insightface_root_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) + self.app_320.prepare(ctx_id=0, det_size=(320, 320)) + self.app_160 = FaceAnalysis(name='antelopev2', root=insightface_root_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) + self.app_160.prepare(ctx_id=0, det_size=(160, 160)) + self.arcface_model = init_recognition_model('arcface', device=self.device) + + def _detect_face(self, id_image_cv2): + face_info = self.app_640.get(id_image_cv2) + if len(face_info) > 0: + return face_info + face_info = self.app_320.get(id_image_cv2) + if len(face_info) > 0: + return face_info + face_info = self.app_160.get(id_image_cv2) + return face_info + + def extract_arcface_bgr_embedding(self, in_image, landmark): + from insightface.utils import face_align + arc_face_image = face_align.norm_crop(in_image, landmark=np.array(landmark), image_size=112) + arc_face_image = torch.from_numpy(arc_face_image).unsqueeze(0).permute(0, 3, 1, 2) / 255. + arc_face_image = 2 * arc_face_image - 1 + arc_face_image = arc_face_image.contiguous().to(self.device) + face_emb = self.arcface_model(arc_face_image)[0] # [512], normalized + return face_emb + + def prepare_infinite_you(self, model, id_image, controlnet_image, infinityou_guidance, height, width): + import cv2 + if id_image is None: + return {'id_emb': None}, controlnet_image + id_image_cv2 = cv2.cvtColor(np.array(id_image), cv2.COLOR_RGB2BGR) + face_info = self._detect_face(id_image_cv2) + if len(face_info) == 0: + raise ValueError('No face detected in the input ID image') + landmark = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*(x['bbox'][3]-x['bbox'][1]))[-1]['kps'] # only use the maximum face + id_emb = self.extract_arcface_bgr_embedding(id_image_cv2, landmark) + id_emb = model(id_emb.unsqueeze(0).reshape([1, -1, 512]).to(dtype=self.torch_dtype)) + if controlnet_image is None: + controlnet_image = Image.fromarray(np.zeros([height, width, 3]).astype(np.uint8)) + infinityou_guidance = torch.Tensor([infinityou_guidance]).to(device=self.device, dtype=self.torch_dtype) + return {'id_emb': id_emb, 'infinityou_guidance': infinityou_guidance}, controlnet_image + + +class TeaCache: + def __init__(self, num_inference_steps, rel_l1_thresh): + self.num_inference_steps = num_inference_steps + self.step = 0 + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = None + self.rel_l1_thresh = rel_l1_thresh + self.previous_residual = None + self.previous_hidden_states = None + + def check(self, dit: FluxDiT, hidden_states, conditioning): + inp = hidden_states.clone() + temb_ = conditioning.clone() + modulated_inp, _, _, _, _ = dit.blocks[0].norm1_a(inp, emb=temb_) + if self.step == 0 or self.step == self.num_inference_steps - 1: + should_calc = True + self.accumulated_rel_l1_distance = 0 + else: + coefficients = [4.98651651e+02, -2.83781631e+02, 5.58554382e+01, -3.82021401e+00, 2.64230861e-01] + rescale_func = np.poly1d(coefficients) + self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item()) + if self.accumulated_rel_l1_distance < self.rel_l1_thresh: + should_calc = False + else: + should_calc = True + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = modulated_inp + self.step += 1 + if self.step == self.num_inference_steps: + self.step = 0 + if should_calc: + self.previous_hidden_states = hidden_states.clone() + return not should_calc + + def store(self, hidden_states): + self.previous_residual = hidden_states - self.previous_hidden_states + self.previous_hidden_states = None + + def update(self, hidden_states): + hidden_states = hidden_states + self.previous_residual + return hidden_states + + +def lets_dance_flux( + dit: FluxDiT, + controlnet: FluxMultiControlNetManager = None, + step1x_connector: Qwen2Connector = None, + hidden_states=None, + timestep=None, + prompt_emb=None, + pooled_prompt_emb=None, + guidance=None, + text_ids=None, + image_ids=None, + controlnet_frames=None, + tiled=False, + tile_size=128, + tile_stride=64, + entity_prompt_emb=None, + entity_masks=None, + ipadapter_kwargs_list={}, + id_emb=None, + infinityou_guidance=None, + flex_condition=None, + flex_uncondition=None, + flex_control_stop_timestep=None, + step1x_llm_embedding=None, + step1x_mask=None, + step1x_reference_latents=None, + tea_cache: TeaCache = None, + **kwargs +): + if tiled: + def flux_forward_fn(hl, hr, wl, wr): + tiled_controlnet_frames = [f[:, :, hl: hr, wl: wr] for f in controlnet_frames] if controlnet_frames is not None else None + return lets_dance_flux( + dit=dit, + controlnet=controlnet, + hidden_states=hidden_states[:, :, hl: hr, wl: wr], + timestep=timestep, + prompt_emb=prompt_emb, + pooled_prompt_emb=pooled_prompt_emb, + guidance=guidance, + text_ids=text_ids, + image_ids=None, + controlnet_frames=tiled_controlnet_frames, + tiled=False, + **kwargs + ) + return FastTileWorker().tiled_forward( + flux_forward_fn, + hidden_states, + tile_size=tile_size, + tile_stride=tile_stride, + tile_device=hidden_states.device, + tile_dtype=hidden_states.dtype + ) + + + # ControlNet + if controlnet is not None and controlnet_frames is not None: + controlnet_extra_kwargs = { + "hidden_states": hidden_states, + "timestep": timestep, + "prompt_emb": prompt_emb, + "pooled_prompt_emb": pooled_prompt_emb, + "guidance": guidance, + "text_ids": text_ids, + "image_ids": image_ids, + "tiled": tiled, + "tile_size": tile_size, + "tile_stride": tile_stride, + } + if id_emb is not None: + controlnet_text_ids = torch.zeros(id_emb.shape[0], id_emb.shape[1], 3).to(device=hidden_states.device, dtype=hidden_states.dtype) + controlnet_extra_kwargs.update({"prompt_emb": id_emb, 'text_ids': controlnet_text_ids, 'guidance': infinityou_guidance}) + controlnet_res_stack, controlnet_single_res_stack = controlnet( + controlnet_frames, **controlnet_extra_kwargs + ) + + # Flex + if flex_condition is not None: + if timestep.tolist()[0] >= flex_control_stop_timestep: + hidden_states = torch.concat([hidden_states, flex_condition], dim=1) + else: + hidden_states = torch.concat([hidden_states, flex_uncondition], dim=1) + + # Step1x + if step1x_llm_embedding is not None: + prompt_emb, pooled_prompt_emb = step1x_connector(step1x_llm_embedding, timestep / 1000, step1x_mask) + text_ids = torch.zeros((1, prompt_emb.shape[1], 3), dtype=prompt_emb.dtype, device=prompt_emb.device) + + if image_ids is None: + image_ids = dit.prepare_image_ids(hidden_states) + + conditioning = dit.time_embedder(timestep, hidden_states.dtype) + dit.pooled_text_embedder(pooled_prompt_emb) + if dit.guidance_embedder is not None: + guidance = guidance * 1000 + conditioning = conditioning + dit.guidance_embedder(guidance, hidden_states.dtype) + + height, width = hidden_states.shape[-2:] + hidden_states = dit.patchify(hidden_states) + + # Step1x + if step1x_reference_latents is not None: + step1x_reference_image_ids = dit.prepare_image_ids(step1x_reference_latents) + step1x_reference_latents = dit.patchify(step1x_reference_latents) + image_ids = torch.concat([image_ids, step1x_reference_image_ids], dim=-2) + hidden_states = torch.concat([hidden_states, step1x_reference_latents], dim=1) + + hidden_states = dit.x_embedder(hidden_states) + + if entity_prompt_emb is not None and entity_masks is not None: + prompt_emb, image_rotary_emb, attention_mask = dit.process_entity_masks(hidden_states, prompt_emb, entity_prompt_emb, entity_masks, text_ids, image_ids) + else: + prompt_emb = dit.context_embedder(prompt_emb) + image_rotary_emb = dit.pos_embedder(torch.cat((text_ids, image_ids), dim=1)) + attention_mask = None + + # TeaCache + if tea_cache is not None: + tea_cache_update = tea_cache.check(dit, hidden_states, conditioning) + else: + tea_cache_update = False + + if tea_cache_update: + hidden_states = tea_cache.update(hidden_states) + else: + # Joint Blocks + for block_id, block in enumerate(dit.blocks): + hidden_states, prompt_emb = block( + hidden_states, + prompt_emb, + conditioning, + image_rotary_emb, + attention_mask, + ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, None) + ) + # ControlNet + if controlnet is not None and controlnet_frames is not None: + hidden_states = hidden_states + controlnet_res_stack[block_id] + + # Single Blocks + hidden_states = torch.cat([prompt_emb, hidden_states], dim=1) + num_joint_blocks = len(dit.blocks) + for block_id, block in enumerate(dit.single_blocks): + hidden_states, prompt_emb = block( + hidden_states, + prompt_emb, + conditioning, + image_rotary_emb, + attention_mask, + ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id + num_joint_blocks, None) + ) + # ControlNet + if controlnet is not None and controlnet_frames is not None: + hidden_states[:, prompt_emb.shape[1]:] = hidden_states[:, prompt_emb.shape[1]:] + controlnet_single_res_stack[block_id] + hidden_states = hidden_states[:, prompt_emb.shape[1]:] + + if tea_cache is not None: + tea_cache.store(hidden_states) + + hidden_states = dit.final_norm_out(hidden_states, conditioning) + hidden_states = dit.final_proj_out(hidden_states) + + # Step1x + if step1x_reference_latents is not None: + hidden_states = hidden_states[:, :hidden_states.shape[1] // 2] + + hidden_states = dit.unpatchify(hidden_states, height, width) + + return hidden_states diff --git a/diffsynth/pipelines/flux_image_new.py b/diffsynth/pipelines/flux_image_new.py new file mode 100644 index 0000000000000000000000000000000000000000..6525dd48c0155ac25132385808d97a31756ed558 --- /dev/null +++ b/diffsynth/pipelines/flux_image_new.py @@ -0,0 +1,1240 @@ +import torch, warnings, glob, os, types +import numpy as np +from PIL import Image +from einops import repeat, reduce +from typing import Optional, Union +from dataclasses import dataclass +from modelscope import snapshot_download +from einops import rearrange +import numpy as np +from PIL import Image +from tqdm import tqdm +from typing import Optional +from typing_extensions import Literal + +from ..schedulers import FlowMatchScheduler +from ..prompters import FluxPrompter +from ..models import ModelManager, load_state_dict, SD3TextEncoder1, FluxTextEncoder2, FluxDiT, FluxVAEEncoder, FluxVAEDecoder +from ..models.step1x_connector import Qwen2Connector +from ..models.flux_controlnet import FluxControlNet +from ..models.flux_ipadapter import FluxIpAdapter +from ..models.flux_value_control import MultiValueEncoder +from ..models.flux_infiniteyou import InfiniteYouImageProjector +from ..models.flux_lora_encoder import FluxLoRAEncoder, LoRALayerBlock +from ..models.tiler import FastTileWorker +from .wan_video_new import BasePipeline, ModelConfig, PipelineUnitRunner, PipelineUnit +from ..lora.flux_lora import FluxLoRALoader, FluxLoraPatcher + +from ..models.flux_dit import RMSNorm +from ..vram_management import gradient_checkpoint_forward, enable_vram_management, AutoWrappedModule, AutoWrappedLinear + + + +@dataclass +class ControlNetInput: + controlnet_id: int = 0 + scale: float = 1.0 + start: float = 1.0 + end: float = 0.0 + image: Image.Image = None + inpaint_mask: Image.Image = None + processor_id: str = None + + + +class MultiControlNet(torch.nn.Module): + def __init__(self, models: list[FluxControlNet]): + super().__init__() + self.models = torch.nn.ModuleList(models) + + def process_single_controlnet(self, controlnet_input: ControlNetInput, conditioning: torch.Tensor, **kwargs): + model = self.models[controlnet_input.controlnet_id] + res_stack, single_res_stack = model( + controlnet_conditioning=conditioning, + processor_id=controlnet_input.processor_id, + **kwargs + ) + res_stack = [res * controlnet_input.scale for res in res_stack] + single_res_stack = [res * controlnet_input.scale for res in single_res_stack] + return res_stack, single_res_stack + + def forward(self, conditionings: list[torch.Tensor], controlnet_inputs: list[ControlNetInput], progress_id, num_inference_steps, **kwargs): + res_stack, single_res_stack = None, None + for controlnet_input, conditioning in zip(controlnet_inputs, conditionings): + progress = (num_inference_steps - 1 - progress_id) / max(num_inference_steps - 1, 1) + if progress > controlnet_input.start or progress < controlnet_input.end: + continue + res_stack_, single_res_stack_ = self.process_single_controlnet(controlnet_input, conditioning, **kwargs) + if res_stack is None: + res_stack = res_stack_ + single_res_stack = single_res_stack_ + else: + res_stack = [i + j for i, j in zip(res_stack, res_stack_)] + single_res_stack = [i + j for i, j in zip(single_res_stack, single_res_stack_)] + return res_stack, single_res_stack + + + +class FluxImagePipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.bfloat16): + super().__init__( + device=device, torch_dtype=torch_dtype, + height_division_factor=16, width_division_factor=16, + ) + self.scheduler = FlowMatchScheduler() + self.prompter = FluxPrompter() + self.text_encoder_1: SD3TextEncoder1 = None + self.text_encoder_2: FluxTextEncoder2 = None + self.dit: FluxDiT = None + self.vae_decoder: FluxVAEDecoder = None + self.vae_encoder: FluxVAEEncoder = None + self.controlnet: MultiControlNet = None + self.ipadapter: FluxIpAdapter = None + self.ipadapter_image_encoder = None + self.qwenvl = None + self.step1x_connector: Qwen2Connector = None + self.value_controller: MultiValueEncoder = None + self.infinityou_processor: InfinitYou = None + self.image_proj_model: InfiniteYouImageProjector = None + self.lora_patcher: FluxLoraPatcher = None + self.lora_encoder: FluxLoRAEncoder = None + self.unit_runner = PipelineUnitRunner() + self.in_iteration_models = ("dit", "step1x_connector", "controlnet", "lora_patcher") + self.units = [ + FluxImageUnit_ShapeChecker(), + FluxImageUnit_NoiseInitializer(), + FluxImageUnit_PromptEmbedder(), + FluxImageUnit_InputImageEmbedder(), + FluxImageUnit_ImageIDs(), + FluxImageUnit_EmbeddedGuidanceEmbedder(), + FluxImageUnit_Kontext(), + FluxImageUnit_InfiniteYou(), + FluxImageUnit_ControlNet(), + FluxImageUnit_IPAdapter(), + FluxImageUnit_EntityControl(), + FluxImageUnit_TeaCache(), + FluxImageUnit_Flex(), + FluxImageUnit_Step1x(), + FluxImageUnit_ValueControl(), + FluxImageUnit_LoRAEncode(), + ] + self.model_fn = model_fn_flux_image + + + def load_lora( + self, + module: torch.nn.Module, + lora_config: Union[ModelConfig, str], + alpha=1, + hotload=False, + local_model_path="./models", + skip_download=False + ): + if isinstance(lora_config, str): + lora_config = ModelConfig(path=lora_config) + else: + lora_config.download_if_necessary(local_model_path, skip_download=skip_download) + loader = FluxLoRALoader(torch_dtype=self.torch_dtype, device=self.device) + lora = load_state_dict(lora_config.path, torch_dtype=self.torch_dtype, device=self.device) + lora = loader.convert_state_dict(lora) + if hotload: + for name, module in module.named_modules(): + if isinstance(module, AutoWrappedLinear): + lora_a_name = f'{name}.lora_A.default.weight' + lora_b_name = f'{name}.lora_B.default.weight' + if lora_a_name in lora and lora_b_name in lora: + module.lora_A_weights.append(lora[lora_a_name] * alpha) + module.lora_B_weights.append(lora[lora_b_name]) + else: + loader.load(module, lora, alpha=alpha) + + + def enable_lora_patcher(self): + if not (hasattr(self, "vram_management_enabled") and self.vram_management_enabled): + print("Please enable VRAM management using `enable_vram_management()` before `enable_lora_patcher()`.") + return + if self.lora_patcher is None: + print("Please load lora patcher models before `enable_lora_patcher()`.") + return + for name, module in self.dit.named_modules(): + if isinstance(module, AutoWrappedLinear): + merger_name = name.replace(".", "___") + if merger_name in self.lora_patcher.model_dict: + module.lora_merger = self.lora_patcher.model_dict[merger_name] + + + def clear_lora(self): + for name, module in self.named_modules(): + if isinstance(module, AutoWrappedLinear): + if hasattr(module, "lora_A_weights"): + module.lora_A_weights.clear() + if hasattr(module, "lora_B_weights"): + module.lora_B_weights.clear() + + + def training_loss(self, **inputs): + timestep_id = torch.randint(0, self.scheduler.num_train_timesteps, (1,)) + timestep = self.scheduler.timesteps[timestep_id].to(dtype=self.torch_dtype, device=self.device) + + inputs["latents"] = self.scheduler.add_noise(inputs["input_latents"], inputs["noise"], timestep) + training_target = self.scheduler.training_target(inputs["input_latents"], inputs["noise"], timestep) + + noise_pred = self.model_fn(**inputs, timestep=timestep) + + loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float()) + loss = loss * self.scheduler.training_weight(timestep) + return loss + + + def _enable_vram_management_with_default_config(self, model, vram_limit): + if model is not None: + dtype = next(iter(model.parameters())).dtype + enable_vram_management( + model, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Embedding: AutoWrappedModule, + torch.nn.LayerNorm: AutoWrappedModule, + torch.nn.Conv2d: AutoWrappedModule, + torch.nn.GroupNorm: AutoWrappedModule, + RMSNorm: AutoWrappedModule, + LoRALayerBlock: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + + + def enable_lora_magic(self): + if self.dit is not None: + if not (hasattr(self.dit, "vram_management_enabled") and self.dit.vram_management_enabled): + dtype = next(iter(self.dit.parameters())).dtype + enable_vram_management( + self.dit, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + }, + module_config = dict( + offload_dtype=dtype, + offload_device=self.device, + onload_dtype=dtype, + onload_device=self.device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=None, + ) + if self.lora_patcher is not None: + for name, module in self.dit.named_modules(): + if isinstance(module, AutoWrappedLinear): + merger_name = name.replace(".", "___") + if merger_name in self.lora_patcher.model_dict: + module.lora_merger = self.lora_patcher.model_dict[merger_name] + + + def enable_vram_management(self, num_persistent_param_in_dit=None, vram_limit=None, vram_buffer=0.5): + self.vram_management_enabled = True + if num_persistent_param_in_dit is not None: + vram_limit = None + else: + if vram_limit is None: + vram_limit = self.get_vram() + vram_limit = vram_limit - vram_buffer + + # Default config + default_vram_management_models = ["text_encoder_1", "vae_decoder", "vae_encoder", "controlnet", "image_proj_model", "ipadapter", "lora_patcher", "value_controller", "step1x_connector", "lora_encoder"] + for model_name in default_vram_management_models: + self._enable_vram_management_with_default_config(getattr(self, model_name), vram_limit) + + # Special config + if self.text_encoder_2 is not None: + from transformers.models.t5.modeling_t5 import T5LayerNorm, T5DenseActDense, T5DenseGatedActDense + dtype = next(iter(self.text_encoder_2.parameters())).dtype + enable_vram_management( + self.text_encoder_2, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Embedding: AutoWrappedModule, + T5LayerNorm: AutoWrappedModule, + T5DenseActDense: AutoWrappedModule, + T5DenseGatedActDense: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + if self.dit is not None: + dtype = next(iter(self.dit.parameters())).dtype + device = "cpu" if vram_limit is not None else self.device + enable_vram_management( + self.dit, + module_map = { + RMSNorm: AutoWrappedModule, + torch.nn.Linear: AutoWrappedLinear, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + max_num_param=num_persistent_param_in_dit, + overflow_module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + if self.ipadapter_image_encoder is not None: + from transformers.models.siglip.modeling_siglip import SiglipVisionEmbeddings, SiglipEncoder, SiglipMultiheadAttentionPoolingHead + dtype = next(iter(self.ipadapter_image_encoder.parameters())).dtype + enable_vram_management( + self.ipadapter_image_encoder, + module_map = { + SiglipVisionEmbeddings: AutoWrappedModule, + SiglipEncoder: AutoWrappedModule, + SiglipMultiheadAttentionPoolingHead: AutoWrappedModule, + torch.nn.MultiheadAttention: AutoWrappedModule, + torch.nn.Linear: AutoWrappedLinear, + torch.nn.LayerNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + if self.qwenvl is not None: + from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import ( + Qwen2_5_VisionPatchEmbed, Qwen2_5_VLVisionBlock, Qwen2_5_VLPatchMerger, + Qwen2_5_VLDecoderLayer, Qwen2_5_VisionRotaryEmbedding, Qwen2_5_VLRotaryEmbedding, Qwen2RMSNorm + ) + dtype = next(iter(self.qwenvl.parameters())).dtype + enable_vram_management( + self.qwenvl, + module_map = { + Qwen2_5_VisionPatchEmbed: AutoWrappedModule, + Qwen2_5_VLVisionBlock: AutoWrappedModule, + Qwen2_5_VLPatchMerger: AutoWrappedModule, + Qwen2_5_VLDecoderLayer: AutoWrappedModule, + Qwen2_5_VisionRotaryEmbedding: AutoWrappedModule, + Qwen2_5_VLRotaryEmbedding: AutoWrappedModule, + Qwen2RMSNorm: AutoWrappedModule, + torch.nn.Embedding: AutoWrappedModule, + torch.nn.Linear: AutoWrappedLinear, + torch.nn.LayerNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + + + @staticmethod + def from_pretrained( + torch_dtype: torch.dtype = torch.bfloat16, + device: Union[str, torch.device] = "cuda", + model_configs: list[ModelConfig] = [], + tokenizer_config: ModelConfig = ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/*"), + local_model_path: str = "./models", + skip_download: bool = False, + redirect_common_files: bool = True, + use_usp=False, + ): + # Download and load models + model_manager = ModelManager() + for model_config in model_configs: + model_config.download_if_necessary(local_model_path, skip_download=skip_download) + model_manager.load_model( + model_config.path, + device=model_config.offload_device or device, + torch_dtype=model_config.offload_dtype or torch_dtype + ) + + # Initialize pipeline + pipe = FluxImagePipeline(device=device, torch_dtype=torch_dtype) + pipe.text_encoder_1 = model_manager.fetch_model("sd3_text_encoder_1") + pipe.text_encoder_2 = model_manager.fetch_model("flux_text_encoder_2") + pipe.dit = model_manager.fetch_model("flux_dit") + pipe.vae_decoder = model_manager.fetch_model("flux_vae_decoder") + pipe.vae_encoder = model_manager.fetch_model("flux_vae_encoder") + pipe.prompter.fetch_models(pipe.text_encoder_1, pipe.text_encoder_2) + pipe.ipadapter = model_manager.fetch_model("flux_ipadapter") + pipe.ipadapter_image_encoder = model_manager.fetch_model("siglip_vision_model") + pipe.qwenvl = model_manager.fetch_model("qwenvl") + pipe.step1x_connector = model_manager.fetch_model("step1x_connector") + pipe.image_proj_model = model_manager.fetch_model("infiniteyou_image_projector") + if pipe.image_proj_model is not None: + pipe.infinityou_processor = InfinitYou(device=device) + pipe.lora_patcher = model_manager.fetch_model("flux_lora_patcher") + pipe.lora_encoder = model_manager.fetch_model("flux_lora_encoder") + + # ControlNet + controlnets = [] + for model_name, model in zip(model_manager.model_name, model_manager.model): + if model_name == "flux_controlnet": + controlnets.append(model) + if len(controlnets) > 0: + pipe.controlnet = MultiControlNet(controlnets) + + # Value Controller + value_controllers = [] + for model_name, model in zip(model_manager.model_name, model_manager.model): + if model_name == "flux_value_controller": + value_controllers.append(model) + if len(value_controllers) > 0: + pipe.value_controller = MultiValueEncoder(value_controllers) + + return pipe + + + @torch.no_grad() + def __call__( + self, + # Prompt + prompt: str, + negative_prompt: str = "", + cfg_scale: float = 1.0, + embedded_guidance: float = 3.5, + t5_sequence_length: int = 512, + # Image + input_image: Image.Image = None, + denoising_strength: float = 1.0, + # Shape + height: int = 1024, + width: int = 1024, + # Randomness + seed: int = None, + rand_device: str = "cpu", + # Scheduler + sigma_shift: float = None, + # Steps + num_inference_steps: int = 30, + # local prompts + multidiffusion_prompts=(), + multidiffusion_masks=(), + multidiffusion_scales=(), + # Kontext + kontext_images: Union[list[Image.Image], Image.Image] = None, + # ControlNet + controlnet_inputs: list[ControlNetInput] = None, + # IP-Adapter + ipadapter_images: Union[list[Image.Image], Image.Image] = None, + ipadapter_scale: float = 1.0, + # EliGen + eligen_entity_prompts: list[str] = None, + eligen_entity_masks: list[Image.Image] = None, + eligen_enable_on_negative: bool = False, + eligen_enable_inpaint: bool = False, + # InfiniteYou + infinityou_id_image: Image.Image = None, + infinityou_guidance: float = 1.0, + # Flex + flex_inpaint_image: Image.Image = None, + flex_inpaint_mask: Image.Image = None, + flex_control_image: Image.Image = None, + flex_control_strength: float = 0.5, + flex_control_stop: float = 0.5, + # Value Controller + value_controller_inputs: Union[list[float], float] = None, + # Step1x + step1x_reference_image: Image.Image = None, + # LoRA Encoder + lora_encoder_inputs: Union[list[ModelConfig], ModelConfig, str] = None, + lora_encoder_scale: float = 1.0, + # TeaCache + tea_cache_l1_thresh: float = None, + # Tile + tiled: bool = False, + tile_size: int = 128, + tile_stride: int = 64, + # Progress bar + progress_bar_cmd = tqdm, + ): + # Scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift) + + inputs_posi = { + "prompt": prompt, + } + inputs_nega = { + "negative_prompt": negative_prompt, + } + inputs_shared = { + "cfg_scale": cfg_scale, "embedded_guidance": embedded_guidance, "t5_sequence_length": t5_sequence_length, + "input_image": input_image, "denoising_strength": denoising_strength, + "height": height, "width": width, + "seed": seed, "rand_device": rand_device, + "sigma_shift": sigma_shift, "num_inference_steps": num_inference_steps, + "multidiffusion_prompts": multidiffusion_prompts, "multidiffusion_masks": multidiffusion_masks, "multidiffusion_scales": multidiffusion_scales, + "kontext_images": kontext_images, + "controlnet_inputs": controlnet_inputs, + "ipadapter_images": ipadapter_images, "ipadapter_scale": ipadapter_scale, + "eligen_entity_prompts": eligen_entity_prompts, "eligen_entity_masks": eligen_entity_masks, "eligen_enable_on_negative": eligen_enable_on_negative, "eligen_enable_inpaint": eligen_enable_inpaint, + "infinityou_id_image": infinityou_id_image, "infinityou_guidance": infinityou_guidance, + "flex_inpaint_image": flex_inpaint_image, "flex_inpaint_mask": flex_inpaint_mask, "flex_control_image": flex_control_image, "flex_control_strength": flex_control_strength, "flex_control_stop": flex_control_stop, + "value_controller_inputs": value_controller_inputs, + "step1x_reference_image": step1x_reference_image, + "lora_encoder_inputs": lora_encoder_inputs, "lora_encoder_scale": lora_encoder_scale, + "tea_cache_l1_thresh": tea_cache_l1_thresh, + "tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride, + "progress_bar_cmd": progress_bar_cmd, + } + for unit in self.units: + inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega) + + # Denoise + self.load_models_to_device(self.in_iteration_models) + models = {name: getattr(self, name) for name in self.in_iteration_models} + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device) + + # Inference + noise_pred_posi = self.model_fn(**models, **inputs_shared, **inputs_posi, timestep=timestep, progress_id=progress_id) + if cfg_scale != 1.0: + noise_pred_nega = self.model_fn(**models, **inputs_shared, **inputs_nega, timestep=timestep, progress_id=progress_id) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + # Scheduler + inputs_shared["latents"] = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], inputs_shared["latents"]) + + # Decode + self.load_models_to_device(['vae_decoder']) + image = self.vae_decoder(inputs_shared["latents"], device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + image = self.vae_output_to_image(image) + self.load_models_to_device([]) + + return image + + + +class FluxImageUnit_ShapeChecker(PipelineUnit): + def __init__(self): + super().__init__(input_params=("height", "width")) + + def process(self, pipe: FluxImagePipeline, height, width): + height, width = pipe.check_resize_height_width(height, width) + return {"height": height, "width": width} + + + +class FluxImageUnit_NoiseInitializer(PipelineUnit): + def __init__(self): + super().__init__(input_params=("height", "width", "seed", "rand_device")) + + def process(self, pipe: FluxImagePipeline, height, width, seed, rand_device): + noise = pipe.generate_noise((1, 16, height//8, width//8), seed=seed, rand_device=rand_device) + return {"noise": noise} + + + +class FluxImageUnit_InputImageEmbedder(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("input_image", "noise", "tiled", "tile_size", "tile_stride"), + onload_model_names=("vae_encoder",) + ) + + def process(self, pipe: FluxImagePipeline, input_image, noise, tiled, tile_size, tile_stride): + if input_image is None: + return {"latents": noise, "input_latents": None} + pipe.load_models_to_device(['vae_encoder']) + image = pipe.preprocess_image(input_image).to(device=pipe.device, dtype=pipe.torch_dtype) + input_latents = pipe.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + if pipe.scheduler.training: + return {"latents": noise, "input_latents": input_latents} + else: + latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0]) + return {"latents": latents, "input_latents": None} + + + +class FluxImageUnit_PromptEmbedder(PipelineUnit): + def __init__(self): + super().__init__( + seperate_cfg=True, + input_params_posi={"prompt": "prompt", "positive": "positive"}, + input_params_nega={"prompt": "negative_prompt", "positive": "positive"}, + input_params=("t5_sequence_length",), + onload_model_names=("text_encoder_1", "text_encoder_2") + ) + + def process(self, pipe: FluxImagePipeline, prompt, t5_sequence_length, positive) -> dict: + if pipe.text_encoder_1 is not None and pipe.text_encoder_2 is not None: + prompt_emb, pooled_prompt_emb, text_ids = pipe.prompter.encode_prompt( + prompt, device=pipe.device, positive=positive, t5_sequence_length=t5_sequence_length + ) + return {"prompt_emb": prompt_emb, "pooled_prompt_emb": pooled_prompt_emb, "text_ids": text_ids} + else: + return {} + + +class FluxImageUnit_ImageIDs(PipelineUnit): + def __init__(self): + super().__init__(input_params=("latents",)) + + def process(self, pipe: FluxImagePipeline, latents): + latent_image_ids = pipe.dit.prepare_image_ids(latents) + return {"image_ids": latent_image_ids} + + + +class FluxImageUnit_EmbeddedGuidanceEmbedder(PipelineUnit): + def __init__(self): + super().__init__(input_params=("embedded_guidance", "latents")) + + def process(self, pipe: FluxImagePipeline, embedded_guidance, latents): + guidance = torch.Tensor([embedded_guidance] * latents.shape[0]).to(device=latents.device, dtype=latents.dtype) + return {"guidance": guidance} + + + +class FluxImageUnit_Kontext(PipelineUnit): + def __init__(self): + super().__init__(input_params=("kontext_images", "tiled", "tile_size", "tile_stride")) + + def process(self, pipe: FluxImagePipeline, kontext_images, tiled, tile_size, tile_stride): + if kontext_images is None: + return {} + if not isinstance(kontext_images, list): + kontext_images = [kontext_images] + + kontext_latents = [] + kontext_image_ids = [] + for kontext_image in kontext_images: + kontext_image = pipe.preprocess_image(kontext_image) + kontext_latent = pipe.vae_encoder(kontext_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + image_ids = pipe.dit.prepare_image_ids(kontext_latent) + image_ids[..., 0] = 1 + kontext_image_ids.append(image_ids) + kontext_latent = pipe.dit.patchify(kontext_latent) + kontext_latents.append(kontext_latent) + kontext_latents = torch.concat(kontext_latents, dim=1) + kontext_image_ids = torch.concat(kontext_image_ids, dim=-2) + return {"kontext_latents": kontext_latents, "kontext_image_ids": kontext_image_ids} + + + +class FluxImageUnit_ControlNet(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("controlnet_inputs", "tiled", "tile_size", "tile_stride"), + onload_model_names=("vae_encoder",) + ) + + def apply_controlnet_mask_on_latents(self, pipe, latents, mask): + mask = (pipe.preprocess_image(mask) + 1) / 2 + mask = mask.mean(dim=1, keepdim=True) + mask = 1 - torch.nn.functional.interpolate(mask, size=latents.shape[-2:]) + latents = torch.concat([latents, mask], dim=1) + return latents + + def apply_controlnet_mask_on_image(self, pipe, image, mask): + mask = mask.resize(image.size) + mask = pipe.preprocess_image(mask).mean(dim=[0, 1]).cpu() + image = np.array(image) + image[mask > 0] = 0 + image = Image.fromarray(image) + return image + + def process(self, pipe: FluxImagePipeline, controlnet_inputs: list[ControlNetInput], tiled, tile_size, tile_stride): + if controlnet_inputs is None: + return {} + pipe.load_models_to_device(['vae_encoder']) + conditionings = [] + for controlnet_input in controlnet_inputs: + image = controlnet_input.image + if controlnet_input.inpaint_mask is not None: + image = self.apply_controlnet_mask_on_image(pipe, image, controlnet_input.inpaint_mask) + + image = pipe.preprocess_image(image).to(device=pipe.device, dtype=pipe.torch_dtype) + image = pipe.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + + if controlnet_input.inpaint_mask is not None: + image = self.apply_controlnet_mask_on_latents(pipe, image, controlnet_input.inpaint_mask) + conditionings.append(image) + return {"controlnet_conditionings": conditionings} + + + +class FluxImageUnit_IPAdapter(PipelineUnit): + def __init__(self): + super().__init__( + take_over=True, + onload_model_names=("ipadapter_image_encoder", "ipadapter") + ) + + def process(self, pipe: FluxImagePipeline, inputs_shared, inputs_posi, inputs_nega): + ipadapter_images, ipadapter_scale = inputs_shared.get("ipadapter_images", None), inputs_shared.get("ipadapter_scale", 1.0) + if ipadapter_images is None: + return inputs_shared, inputs_posi, inputs_nega + if not isinstance(ipadapter_images, list): + ipadapter_images = [ipadapter_images] + + pipe.load_models_to_device(self.onload_model_names) + images = [image.convert("RGB").resize((384, 384), resample=3) for image in ipadapter_images] + images = [pipe.preprocess_image(image).to(device=pipe.device, dtype=pipe.torch_dtype) for image in images] + ipadapter_images = torch.cat(images, dim=0) + ipadapter_image_encoding = pipe.ipadapter_image_encoder(ipadapter_images).pooler_output + + inputs_posi.update({"ipadapter_kwargs_list": pipe.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)}) + if inputs_shared.get("cfg_scale", 1.0) != 1.0: + inputs_nega.update({"ipadapter_kwargs_list": pipe.ipadapter(torch.zeros_like(ipadapter_image_encoding))}) + return inputs_shared, inputs_posi, inputs_nega + + + +class FluxImageUnit_EntityControl(PipelineUnit): + def __init__(self): + super().__init__( + take_over=True, + onload_model_names=("text_encoder_1", "text_encoder_2") + ) + + def preprocess_masks(self, pipe, masks, height, width, dim): + out_masks = [] + for mask in masks: + mask = pipe.preprocess_image(mask.resize((width, height), resample=Image.NEAREST)).mean(dim=1, keepdim=True) > 0 + mask = mask.repeat(1, dim, 1, 1).to(device=pipe.device, dtype=pipe.torch_dtype) + out_masks.append(mask) + return out_masks + + def prepare_entity_inputs(self, pipe, entity_prompts, entity_masks, width, height, t5_sequence_length=512): + entity_masks = self.preprocess_masks(pipe, entity_masks, height//8, width//8, 1) + entity_masks = torch.cat(entity_masks, dim=0).unsqueeze(0) # b, n_mask, c, h, w + + prompt_emb, _, _ = pipe.prompter.encode_prompt( + entity_prompts, device=pipe.device, t5_sequence_length=t5_sequence_length + ) + return prompt_emb.unsqueeze(0), entity_masks + + def prepare_eligen(self, pipe, prompt_emb_nega, eligen_entity_prompts, eligen_entity_masks, width, height, t5_sequence_length, enable_eligen_on_negative, cfg_scale): + entity_prompt_emb_posi, entity_masks_posi = self.prepare_entity_inputs(pipe, eligen_entity_prompts, eligen_entity_masks, width, height, t5_sequence_length) + if enable_eligen_on_negative and cfg_scale != 1.0: + entity_prompt_emb_nega = prompt_emb_nega['prompt_emb'].unsqueeze(1).repeat(1, entity_masks_posi.shape[1], 1, 1) + entity_masks_nega = entity_masks_posi + else: + entity_prompt_emb_nega, entity_masks_nega = None, None + eligen_kwargs_posi = {"entity_prompt_emb": entity_prompt_emb_posi, "entity_masks": entity_masks_posi} + eligen_kwargs_nega = {"entity_prompt_emb": entity_prompt_emb_nega, "entity_masks": entity_masks_nega} + return eligen_kwargs_posi, eligen_kwargs_nega + + def process(self, pipe: FluxImagePipeline, inputs_shared, inputs_posi, inputs_nega): + eligen_entity_prompts, eligen_entity_masks = inputs_shared.get("eligen_entity_prompts", None), inputs_shared.get("eligen_entity_masks", None) + if eligen_entity_prompts is None or eligen_entity_masks is None: + return inputs_shared, inputs_posi, inputs_nega + pipe.load_models_to_device(self.onload_model_names) + eligen_kwargs_posi, eligen_kwargs_nega = self.prepare_eligen(pipe, inputs_nega, + eligen_entity_prompts, eligen_entity_masks, inputs_shared["width"], inputs_shared["height"], + inputs_shared["t5_sequence_length"], inputs_shared["eligen_enable_on_negative"], inputs_shared["cfg_scale"]) + inputs_posi.update(eligen_kwargs_posi) + if inputs_shared.get("cfg_scale", 1.0) != 1.0: + inputs_nega.update(eligen_kwargs_nega) + return inputs_shared, inputs_posi, inputs_nega + + +class FluxImageUnit_Step1x(PipelineUnit): + def __init__(self): + super().__init__(take_over=True,onload_model_names=("qwenvl","vae_encoder")) + + def process(self, pipe: FluxImagePipeline, inputs_shared: dict, inputs_posi: dict, inputs_nega: dict): + image = inputs_shared.get("step1x_reference_image",None) + if image is None: + return inputs_shared, inputs_posi, inputs_nega + else: + pipe.load_models_to_device(self.onload_model_names) + prompt = inputs_posi["prompt"] + nega_prompt = inputs_nega["negative_prompt"] + captions = [prompt, nega_prompt] + ref_images = [image, image] + embs, masks = pipe.qwenvl(captions, ref_images) + image = pipe.preprocess_image(image).to(device=pipe.device, dtype=pipe.torch_dtype) + image = pipe.vae_encoder(image) + inputs_posi.update({"step1x_llm_embedding": embs[0:1], "step1x_mask": masks[0:1], "step1x_reference_latents": image}) + if inputs_shared.get("cfg_scale", 1) != 1: + inputs_nega.update({"step1x_llm_embedding": embs[1:2], "step1x_mask": masks[1:2], "step1x_reference_latents": image}) + return inputs_shared, inputs_posi, inputs_nega + + +class FluxImageUnit_TeaCache(PipelineUnit): + def __init__(self): + super().__init__(input_params=("num_inference_steps","tea_cache_l1_thresh")) + + def process(self, pipe: FluxImagePipeline, num_inference_steps, tea_cache_l1_thresh): + if tea_cache_l1_thresh is None: + return {} + else: + return {"tea_cache": TeaCache(num_inference_steps=num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh)} + +class FluxImageUnit_Flex(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("latents", "flex_inpaint_image", "flex_inpaint_mask", "flex_control_image", "flex_control_strength", "flex_control_stop", "tiled", "tile_size", "tile_stride"), + onload_model_names=("vae_encoder",) + ) + + def process(self, pipe: FluxImagePipeline, latents, flex_inpaint_image, flex_inpaint_mask, flex_control_image, flex_control_strength, flex_control_stop, tiled, tile_size, tile_stride): + if pipe.dit.input_dim == 196: + if flex_control_stop is None: + flex_control_stop = 1 + pipe.load_models_to_device(self.onload_model_names) + if flex_inpaint_image is None: + flex_inpaint_image = torch.zeros_like(latents) + else: + flex_inpaint_image = pipe.preprocess_image(flex_inpaint_image).to(device=pipe.device, dtype=pipe.torch_dtype) + flex_inpaint_image = pipe.vae_encoder(flex_inpaint_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + if flex_inpaint_mask is None: + flex_inpaint_mask = torch.ones_like(latents)[:, 0:1, :, :] + else: + flex_inpaint_mask = flex_inpaint_mask.resize((latents.shape[3], latents.shape[2])) + flex_inpaint_mask = pipe.preprocess_image(flex_inpaint_mask).to(device=pipe.device, dtype=pipe.torch_dtype) + flex_inpaint_mask = (flex_inpaint_mask[:, 0:1, :, :] + 1) / 2 + flex_inpaint_image = flex_inpaint_image * (1 - flex_inpaint_mask) + if flex_control_image is None: + flex_control_image = torch.zeros_like(latents) + else: + flex_control_image = pipe.preprocess_image(flex_control_image).to(device=pipe.device, dtype=pipe.torch_dtype) + flex_control_image = pipe.vae_encoder(flex_control_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) * flex_control_strength + flex_condition = torch.concat([flex_inpaint_image, flex_inpaint_mask, flex_control_image], dim=1) + flex_uncondition = torch.concat([flex_inpaint_image, flex_inpaint_mask, torch.zeros_like(flex_control_image)], dim=1) + flex_control_stop_timestep = pipe.scheduler.timesteps[int(flex_control_stop * (len(pipe.scheduler.timesteps) - 1))] + return {"flex_condition": flex_condition, "flex_uncondition": flex_uncondition, "flex_control_stop_timestep": flex_control_stop_timestep} + else: + return {} + + + +class FluxImageUnit_InfiniteYou(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("infinityou_id_image", "infinityou_guidance"), + onload_model_names=("infinityou_processor",) + ) + + def process(self, pipe: FluxImagePipeline, infinityou_id_image, infinityou_guidance): + pipe.load_models_to_device("infinityou_processor") + if infinityou_id_image is not None: + return pipe.infinityou_processor.prepare_infinite_you(pipe.image_proj_model, infinityou_id_image, infinityou_guidance, pipe.device) + else: + return {} + + + +class FluxImageUnit_ValueControl(PipelineUnit): + def __init__(self): + super().__init__( + seperate_cfg=True, + input_params_posi={"prompt_emb": "prompt_emb", "text_ids": "text_ids"}, + input_params_nega={"prompt_emb": "prompt_emb", "text_ids": "text_ids"}, + input_params=("value_controller_inputs",), + onload_model_names=("value_controller",) + ) + + def add_to_text_embedding(self, prompt_emb, text_ids, value_emb): + prompt_emb = torch.concat([prompt_emb, value_emb], dim=1) + extra_text_ids = torch.zeros((value_emb.shape[0], value_emb.shape[1], 3), device=value_emb.device, dtype=value_emb.dtype) + text_ids = torch.concat([text_ids, extra_text_ids], dim=1) + return prompt_emb, text_ids + + def process(self, pipe: FluxImagePipeline, prompt_emb, text_ids, value_controller_inputs): + if value_controller_inputs is None: + return {} + if not isinstance(value_controller_inputs, list): + value_controller_inputs = [value_controller_inputs] + value_controller_inputs = torch.tensor(value_controller_inputs).to(dtype=pipe.torch_dtype, device=pipe.device) + pipe.load_models_to_device(["value_controller"]) + value_emb = pipe.value_controller(value_controller_inputs, pipe.torch_dtype) + value_emb = value_emb.unsqueeze(0) + prompt_emb, text_ids = self.add_to_text_embedding(prompt_emb, text_ids, value_emb) + return {"prompt_emb": prompt_emb, "text_ids": text_ids} + + + +class InfinitYou(torch.nn.Module): + def __init__(self, device="cuda", torch_dtype=torch.bfloat16): + super().__init__() + from facexlib.recognition import init_recognition_model + from insightface.app import FaceAnalysis + self.device = device + self.torch_dtype = torch_dtype + insightface_root_path = 'models/ByteDance/InfiniteYou/supports/insightface' + self.app_640 = FaceAnalysis(name='antelopev2', root=insightface_root_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) + self.app_640.prepare(ctx_id=0, det_size=(640, 640)) + self.app_320 = FaceAnalysis(name='antelopev2', root=insightface_root_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) + self.app_320.prepare(ctx_id=0, det_size=(320, 320)) + self.app_160 = FaceAnalysis(name='antelopev2', root=insightface_root_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) + self.app_160.prepare(ctx_id=0, det_size=(160, 160)) + self.arcface_model = init_recognition_model('arcface', device=self.device).to(torch_dtype) + + def _detect_face(self, id_image_cv2): + face_info = self.app_640.get(id_image_cv2) + if len(face_info) > 0: + return face_info + face_info = self.app_320.get(id_image_cv2) + if len(face_info) > 0: + return face_info + face_info = self.app_160.get(id_image_cv2) + return face_info + + def extract_arcface_bgr_embedding(self, in_image, landmark, device): + from insightface.utils import face_align + arc_face_image = face_align.norm_crop(in_image, landmark=np.array(landmark), image_size=112) + arc_face_image = torch.from_numpy(arc_face_image).unsqueeze(0).permute(0, 3, 1, 2) / 255. + arc_face_image = 2 * arc_face_image - 1 + arc_face_image = arc_face_image.contiguous().to(device=device, dtype=self.torch_dtype) + face_emb = self.arcface_model(arc_face_image)[0] # [512], normalized + return face_emb + + def prepare_infinite_you(self, model, id_image, infinityou_guidance, device): + import cv2 + if id_image is None: + return {'id_emb': None} + id_image_cv2 = cv2.cvtColor(np.array(id_image), cv2.COLOR_RGB2BGR) + face_info = self._detect_face(id_image_cv2) + if len(face_info) == 0: + raise ValueError('No face detected in the input ID image') + landmark = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*(x['bbox'][3]-x['bbox'][1]))[-1]['kps'] # only use the maximum face + id_emb = self.extract_arcface_bgr_embedding(id_image_cv2, landmark, device) + id_emb = model(id_emb.unsqueeze(0).reshape([1, -1, 512]).to(dtype=self.torch_dtype)) + infinityou_guidance = torch.Tensor([infinityou_guidance]).to(device=device, dtype=self.torch_dtype) + return {'id_emb': id_emb, 'infinityou_guidance': infinityou_guidance} + + + +class FluxImageUnit_LoRAEncode(PipelineUnit): + def __init__(self): + super().__init__( + take_over=True, + onload_model_names=("lora_encoder",) + ) + + def parse_lora_encoder_inputs(self, lora_encoder_inputs): + if not isinstance(lora_encoder_inputs, list): + lora_encoder_inputs = [lora_encoder_inputs] + lora_configs = [] + for lora_encoder_input in lora_encoder_inputs: + if isinstance(lora_encoder_input, str): + lora_encoder_input = ModelConfig(path=lora_encoder_input) + lora_encoder_input.download_if_necessary() + lora_configs.append(lora_encoder_input) + return lora_configs + + def load_lora(self, lora_config, dtype, device): + loader = FluxLoRALoader(torch_dtype=dtype, device=device) + lora = load_state_dict(lora_config.path, torch_dtype=dtype, device=device) + lora = loader.convert_state_dict(lora) + return lora + + def lora_embedding(self, pipe, lora_encoder_inputs): + lora_emb = [] + for lora_config in self.parse_lora_encoder_inputs(lora_encoder_inputs): + lora = self.load_lora(lora_config, pipe.torch_dtype, pipe.device) + lora_emb.append(pipe.lora_encoder(lora)) + lora_emb = torch.concat(lora_emb, dim=1) + return lora_emb + + def add_to_text_embedding(self, prompt_emb, text_ids, lora_emb): + prompt_emb = torch.concat([prompt_emb, lora_emb], dim=1) + extra_text_ids = torch.zeros((lora_emb.shape[0], lora_emb.shape[1], 3), device=lora_emb.device, dtype=lora_emb.dtype) + text_ids = torch.concat([text_ids, extra_text_ids], dim=1) + return prompt_emb, text_ids + + def process(self, pipe: FluxImagePipeline, inputs_shared, inputs_posi, inputs_nega): + if inputs_shared.get("lora_encoder_inputs", None) is None: + return inputs_shared, inputs_posi, inputs_nega + + # Encode + pipe.load_models_to_device(["lora_encoder"]) + lora_encoder_inputs = inputs_shared["lora_encoder_inputs"] + lora_emb = self.lora_embedding(pipe, lora_encoder_inputs) + + # Scale + lora_encoder_scale = inputs_shared.get("lora_encoder_scale", None) + if lora_encoder_scale is not None: + lora_emb = lora_emb * lora_encoder_scale + + # Add to prompt embedding + inputs_posi["prompt_emb"], inputs_posi["text_ids"] = self.add_to_text_embedding( + inputs_posi["prompt_emb"], inputs_posi["text_ids"], lora_emb) + return inputs_shared, inputs_posi, inputs_nega + + + +class TeaCache: + def __init__(self, num_inference_steps, rel_l1_thresh): + self.num_inference_steps = num_inference_steps + self.step = 0 + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = None + self.rel_l1_thresh = rel_l1_thresh + self.previous_residual = None + self.previous_hidden_states = None + + def check(self, dit: FluxDiT, hidden_states, conditioning): + inp = hidden_states.clone() + temb_ = conditioning.clone() + modulated_inp, _, _, _, _ = dit.blocks[0].norm1_a(inp, emb=temb_) + if self.step == 0 or self.step == self.num_inference_steps - 1: + should_calc = True + self.accumulated_rel_l1_distance = 0 + else: + coefficients = [4.98651651e+02, -2.83781631e+02, 5.58554382e+01, -3.82021401e+00, 2.64230861e-01] + rescale_func = np.poly1d(coefficients) + self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item()) + if self.accumulated_rel_l1_distance < self.rel_l1_thresh: + should_calc = False + else: + should_calc = True + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = modulated_inp + self.step += 1 + if self.step == self.num_inference_steps: + self.step = 0 + if should_calc: + self.previous_hidden_states = hidden_states.clone() + return not should_calc + + def store(self, hidden_states): + self.previous_residual = hidden_states - self.previous_hidden_states + self.previous_hidden_states = None + + def update(self, hidden_states): + hidden_states = hidden_states + self.previous_residual + return hidden_states + + +def model_fn_flux_image( + dit: FluxDiT, + controlnet=None, + step1x_connector=None, + latents=None, + timestep=None, + prompt_emb=None, + pooled_prompt_emb=None, + guidance=None, + text_ids=None, + image_ids=None, + kontext_latents=None, + kontext_image_ids=None, + controlnet_inputs=None, + controlnet_conditionings=None, + tiled=False, + tile_size=128, + tile_stride=64, + entity_prompt_emb=None, + entity_masks=None, + ipadapter_kwargs_list={}, + id_emb=None, + infinityou_guidance=None, + flex_condition=None, + flex_uncondition=None, + flex_control_stop_timestep=None, + step1x_llm_embedding=None, + step1x_mask=None, + step1x_reference_latents=None, + tea_cache: TeaCache = None, + progress_id=0, + num_inference_steps=1, + use_gradient_checkpointing=False, + use_gradient_checkpointing_offload=False, + **kwargs +): + if tiled: + def flux_forward_fn(hl, hr, wl, wr): + tiled_controlnet_conditionings = [f[:, :, hl: hr, wl: wr] for f in controlnet_conditionings] if controlnet_conditionings is not None else None + return model_fn_flux_image( + dit=dit, + controlnet=controlnet, + latents=latents[:, :, hl: hr, wl: wr], + timestep=timestep, + prompt_emb=prompt_emb, + pooled_prompt_emb=pooled_prompt_emb, + guidance=guidance, + text_ids=text_ids, + image_ids=None, + controlnet_inputs=controlnet_inputs, + controlnet_conditionings=tiled_controlnet_conditionings, + tiled=False, + **kwargs + ) + return FastTileWorker().tiled_forward( + flux_forward_fn, + latents, + tile_size=tile_size, + tile_stride=tile_stride, + tile_device=latents.device, + tile_dtype=latents.dtype + ) + + hidden_states = latents + + # ControlNet + if controlnet is not None and controlnet_conditionings is not None: + controlnet_extra_kwargs = { + "hidden_states": hidden_states, + "timestep": timestep, + "prompt_emb": prompt_emb, + "pooled_prompt_emb": pooled_prompt_emb, + "guidance": guidance, + "text_ids": text_ids, + "image_ids": image_ids, + "controlnet_inputs": controlnet_inputs, + "tiled": tiled, + "tile_size": tile_size, + "tile_stride": tile_stride, + "progress_id": progress_id, + "num_inference_steps": num_inference_steps, + } + if id_emb is not None: + controlnet_text_ids = torch.zeros(id_emb.shape[0], id_emb.shape[1], 3).to(device=hidden_states.device, dtype=hidden_states.dtype) + controlnet_extra_kwargs.update({"prompt_emb": id_emb, 'text_ids': controlnet_text_ids, 'guidance': infinityou_guidance}) + controlnet_res_stack, controlnet_single_res_stack = controlnet( + controlnet_conditionings, **controlnet_extra_kwargs + ) + + # Flex + if flex_condition is not None: + if timestep.tolist()[0] >= flex_control_stop_timestep: + hidden_states = torch.concat([hidden_states, flex_condition], dim=1) + else: + hidden_states = torch.concat([hidden_states, flex_uncondition], dim=1) + + # Step1x + if step1x_llm_embedding is not None: + prompt_emb, pooled_prompt_emb = step1x_connector(step1x_llm_embedding, timestep / 1000, step1x_mask) + text_ids = torch.zeros((1, prompt_emb.shape[1], 3), dtype=prompt_emb.dtype, device=prompt_emb.device) + + if image_ids is None: + image_ids = dit.prepare_image_ids(hidden_states) + + conditioning = dit.time_embedder(timestep, hidden_states.dtype) + dit.pooled_text_embedder(pooled_prompt_emb) + if dit.guidance_embedder is not None: + guidance = guidance * 1000 + conditioning = conditioning + dit.guidance_embedder(guidance, hidden_states.dtype) + + height, width = hidden_states.shape[-2:] + hidden_states = dit.patchify(hidden_states) + + # Kontext + if kontext_latents is not None: + image_ids = torch.concat([image_ids, kontext_image_ids], dim=-2) + hidden_states = torch.concat([hidden_states, kontext_latents], dim=1) + + # Step1x + if step1x_reference_latents is not None: + step1x_reference_image_ids = dit.prepare_image_ids(step1x_reference_latents) + step1x_reference_latents = dit.patchify(step1x_reference_latents) + image_ids = torch.concat([image_ids, step1x_reference_image_ids], dim=-2) + hidden_states = torch.concat([hidden_states, step1x_reference_latents], dim=1) + + hidden_states = dit.x_embedder(hidden_states) + + # EliGen + if entity_prompt_emb is not None and entity_masks is not None: + prompt_emb, image_rotary_emb, attention_mask = dit.process_entity_masks(hidden_states, prompt_emb, entity_prompt_emb, entity_masks, text_ids, image_ids) + else: + prompt_emb = dit.context_embedder(prompt_emb) + image_rotary_emb = dit.pos_embedder(torch.cat((text_ids, image_ids), dim=1)) + attention_mask = None + + # TeaCache + if tea_cache is not None: + tea_cache_update = tea_cache.check(dit, hidden_states, conditioning) + else: + tea_cache_update = False + + if tea_cache_update: + hidden_states = tea_cache.update(hidden_states) + else: + # Joint Blocks + for block_id, block in enumerate(dit.blocks): + hidden_states, prompt_emb = gradient_checkpoint_forward( + block, + use_gradient_checkpointing, + use_gradient_checkpointing_offload, + hidden_states, + prompt_emb, + conditioning, + image_rotary_emb, + attention_mask, + ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, None), + ) + # ControlNet + if controlnet is not None and controlnet_conditionings is not None and controlnet_res_stack is not None: + if kontext_latents is None: + hidden_states = hidden_states + controlnet_res_stack[block_id] + else: + hidden_states[:, :-kontext_latents.shape[1]] = hidden_states[:, :-kontext_latents.shape[1]] + controlnet_res_stack[block_id] + + # Single Blocks + hidden_states = torch.cat([prompt_emb, hidden_states], dim=1) + num_joint_blocks = len(dit.blocks) + for block_id, block in enumerate(dit.single_blocks): + hidden_states, prompt_emb = gradient_checkpoint_forward( + block, + use_gradient_checkpointing, + use_gradient_checkpointing_offload, + hidden_states, + prompt_emb, + conditioning, + image_rotary_emb, + attention_mask, + ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id + num_joint_blocks, None), + ) + # ControlNet + if controlnet is not None and controlnet_conditionings is not None and controlnet_single_res_stack is not None: + if kontext_latents is None: + hidden_states[:, prompt_emb.shape[1]:] = hidden_states[:, prompt_emb.shape[1]:] + controlnet_single_res_stack[block_id] + else: + hidden_states[:, prompt_emb.shape[1]:-kontext_latents.shape[1]] = hidden_states[:, prompt_emb.shape[1]:-kontext_latents.shape[1]] + controlnet_single_res_stack[block_id] + hidden_states = hidden_states[:, prompt_emb.shape[1]:] + + if tea_cache is not None: + tea_cache.store(hidden_states) + + hidden_states = dit.final_norm_out(hidden_states, conditioning) + hidden_states = dit.final_proj_out(hidden_states) + + # Step1x + if step1x_reference_latents is not None: + hidden_states = hidden_states[:, :hidden_states.shape[1] // 2] + + # Kontext + if kontext_latents is not None: + hidden_states = hidden_states[:, :-kontext_latents.shape[1]] + + hidden_states = dit.unpatchify(hidden_states, height, width) + + return hidden_states diff --git a/diffsynth/pipelines/hunyuan_image.py b/diffsynth/pipelines/hunyuan_image.py new file mode 100644 index 0000000000000000000000000000000000000000..0c6f6d5dedc6aac50b06a9f10701f7f8ab33117f --- /dev/null +++ b/diffsynth/pipelines/hunyuan_image.py @@ -0,0 +1,288 @@ +from ..models.hunyuan_dit import HunyuanDiT +from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder +from ..models.sdxl_vae_encoder import SDXLVAEEncoder +from ..models.sdxl_vae_decoder import SDXLVAEDecoder +from ..models import ModelManager +from ..prompters import HunyuanDiTPrompter +from ..schedulers import EnhancedDDIMScheduler +from .base import BasePipeline +import torch +from tqdm import tqdm +import numpy as np + + + +class ImageSizeManager: + def __init__(self): + pass + + + def _to_tuple(self, x): + if isinstance(x, int): + return x, x + else: + return x + + + def get_fill_resize_and_crop(self, src, tgt): + th, tw = self._to_tuple(tgt) + h, w = self._to_tuple(src) + + tr = th / tw # base 分辨率 + r = h / w # 目标分辨率 + + # resize + if r > tr: + resize_height = th + resize_width = int(round(th / h * w)) + else: + resize_width = tw + resize_height = int(round(tw / w * h)) # 根据base分辨率，将目标分辨率resize下来 + + crop_top = int(round((th - resize_height) / 2.0)) + crop_left = int(round((tw - resize_width) / 2.0)) + + return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width) + + + def get_meshgrid(self, start, *args): + if len(args) == 0: + # start is grid_size + num = self._to_tuple(start) + start = (0, 0) + stop = num + elif len(args) == 1: + # start is start, args[0] is stop, step is 1 + start = self._to_tuple(start) + stop = self._to_tuple(args[0]) + num = (stop[0] - start[0], stop[1] - start[1]) + elif len(args) == 2: + # start is start, args[0] is stop, args[1] is num + start = self._to_tuple(start) # 左上角 eg: 12,0 + stop = self._to_tuple(args[0]) # 右下角 eg: 20,32 + num = self._to_tuple(args[1]) # 目标大小 eg: 32,124 + else: + raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}") + + grid_h = np.linspace(start[0], stop[0], num[0], endpoint=False, dtype=np.float32) # 12-20 中间差值32份 0-32 中间差值124份 + grid_w = np.linspace(start[1], stop[1], num[1], endpoint=False, dtype=np.float32) + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) # [2, W, H] + return grid + + + def get_2d_rotary_pos_embed(self, embed_dim, start, *args, use_real=True): + grid = self.get_meshgrid(start, *args) # [2, H, w] + grid = grid.reshape([2, 1, *grid.shape[1:]]) # 返回一个采样矩阵 分辨率与目标分辨率一致 + pos_embed = self.get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real) + return pos_embed + + + def get_2d_rotary_pos_embed_from_grid(self, embed_dim, grid, use_real=False): + assert embed_dim % 4 == 0 + + # use half of dimensions to encode grid_h + emb_h = self.get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real) # (H*W, D/4) + emb_w = self.get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real) # (H*W, D/4) + + if use_real: + cos = torch.cat([emb_h[0], emb_w[0]], dim=1) # (H*W, D/2) + sin = torch.cat([emb_h[1], emb_w[1]], dim=1) # (H*W, D/2) + return cos, sin + else: + emb = torch.cat([emb_h, emb_w], dim=1) # (H*W, D/2) + return emb + + + def get_1d_rotary_pos_embed(self, dim: int, pos, theta: float = 10000.0, use_real=False): + if isinstance(pos, int): + pos = np.arange(pos) + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) # [D/2] + t = torch.from_numpy(pos).to(freqs.device) # type: ignore # [S] + freqs = torch.outer(t, freqs).float() # type: ignore # [S, D/2] + if use_real: + freqs_cos = freqs.cos().repeat_interleave(2, dim=1) # [S, D] + freqs_sin = freqs.sin().repeat_interleave(2, dim=1) # [S, D] + return freqs_cos, freqs_sin + else: + freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 # [S, D/2] + return freqs_cis + + + def calc_rope(self, height, width): + patch_size = 2 + head_size = 88 + th = height // 8 // patch_size + tw = width // 8 // patch_size + base_size = 512 // 8 // patch_size + start, stop = self.get_fill_resize_and_crop((th, tw), base_size) + sub_args = [start, stop, (th, tw)] + rope = self.get_2d_rotary_pos_embed(head_size, *sub_args) + return rope + + + +class HunyuanDiTImagePipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__(device=device, torch_dtype=torch_dtype, height_division_factor=16, width_division_factor=16) + self.scheduler = EnhancedDDIMScheduler(prediction_type="v_prediction", beta_start=0.00085, beta_end=0.03) + self.prompter = HunyuanDiTPrompter() + self.image_size_manager = ImageSizeManager() + # models + self.text_encoder: HunyuanDiTCLIPTextEncoder = None + self.text_encoder_t5: HunyuanDiTT5TextEncoder = None + self.dit: HunyuanDiT = None + self.vae_decoder: SDXLVAEDecoder = None + self.vae_encoder: SDXLVAEEncoder = None + self.model_names = ['text_encoder', 'text_encoder_t5', 'dit', 'vae_decoder', 'vae_encoder'] + + + def denoising_model(self): + return self.dit + + + def fetch_models(self, model_manager: ModelManager, prompt_refiner_classes=[]): + # Main models + self.text_encoder = model_manager.fetch_model("hunyuan_dit_clip_text_encoder") + self.text_encoder_t5 = model_manager.fetch_model("hunyuan_dit_t5_text_encoder") + self.dit = model_manager.fetch_model("hunyuan_dit") + self.vae_decoder = model_manager.fetch_model("sdxl_vae_decoder") + self.vae_encoder = model_manager.fetch_model("sdxl_vae_encoder") + self.prompter.fetch_models(self.text_encoder, self.text_encoder_t5) + self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) + + + @staticmethod + def from_model_manager(model_manager: ModelManager, prompt_refiner_classes=[], device=None): + pipe = HunyuanDiTImagePipeline( + device=model_manager.device if device is None else device, + torch_dtype=model_manager.torch_dtype, + ) + pipe.fetch_models(model_manager, prompt_refiner_classes) + return pipe + + + def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32): + latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + return latents + + + def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): + image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + image = self.vae_output_to_image(image) + return image + + + def encode_prompt(self, prompt, clip_skip=1, clip_skip_2=1, positive=True): + text_emb, text_emb_mask, text_emb_t5, text_emb_mask_t5 = self.prompter.encode_prompt( + prompt, + clip_skip=clip_skip, + clip_skip_2=clip_skip_2, + positive=positive, + device=self.device + ) + return { + "text_emb": text_emb, + "text_emb_mask": text_emb_mask, + "text_emb_t5": text_emb_t5, + "text_emb_mask_t5": text_emb_mask_t5 + } + + + def prepare_extra_input(self, latents=None, tiled=False, tile_size=64, tile_stride=32): + batch_size, height, width = latents.shape[0], latents.shape[2] * 8, latents.shape[3] * 8 + if tiled: + height, width = tile_size * 16, tile_size * 16 + image_meta_size = torch.as_tensor([width, height, width, height, 0, 0]).to(device=self.device) + freqs_cis_img = self.image_size_manager.calc_rope(height, width) + image_meta_size = torch.stack([image_meta_size] * batch_size) + return { + "size_emb": image_meta_size, + "freq_cis_img": (freqs_cis_img[0].to(dtype=self.torch_dtype, device=self.device), freqs_cis_img[1].to(dtype=self.torch_dtype, device=self.device)), + "tiled": tiled, + "tile_size": tile_size, + "tile_stride": tile_stride + } + + + @torch.no_grad() + def __call__( + self, + prompt, + local_prompts=[], + masks=[], + mask_scales=[], + negative_prompt="", + cfg_scale=7.5, + clip_skip=1, + clip_skip_2=1, + input_image=None, + reference_strengths=[0.4], + denoising_strength=1.0, + height=1024, + width=1024, + num_inference_steps=20, + tiled=False, + tile_size=64, + tile_stride=32, + seed=None, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + height, width = self.check_resize_height_width(height, width) + + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + if input_image is not None: + self.load_models_to_device(['vae_encoder']) + image = self.preprocess_image(input_image).to(device=self.device, dtype=torch.float32) + latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(self.torch_dtype) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + else: + latents = noise.clone() + + # Encode prompts + self.load_models_to_device(['text_encoder', 'text_encoder_t5']) + prompt_emb_posi = self.encode_prompt(prompt, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=True) + if cfg_scale != 1.0: + prompt_emb_nega = self.encode_prompt(negative_prompt, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=True) + prompt_emb_locals = [self.encode_prompt(prompt_local, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=True) for prompt_local in local_prompts] + + # Prepare positional id + extra_input = self.prepare_extra_input(latents, tiled, tile_size) + + # Denoise + self.load_models_to_device(['dit']) + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = torch.tensor([timestep]).to(dtype=self.torch_dtype, device=self.device) + + # Positive side + inference_callback = lambda prompt_emb_posi: self.dit(latents, timestep=timestep, **prompt_emb_posi, **extra_input) + noise_pred_posi = self.control_noise_via_local_prompts(prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback) + + if cfg_scale != 1.0: + # Negative side + noise_pred_nega = self.dit( + latents, timestep=timestep, **prompt_emb_nega, **extra_input, + ) + # Classifier-free guidance + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) + + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + self.load_models_to_device(['vae_decoder']) + image = self.decode_image(latents.to(torch.float32), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + + # Offload all models + self.load_models_to_device([]) + return image diff --git a/diffsynth/pipelines/hunyuan_video.py b/diffsynth/pipelines/hunyuan_video.py new file mode 100644 index 0000000000000000000000000000000000000000..d8a0411e155f293e86a2b64073fa8b25af3d83d5 --- /dev/null +++ b/diffsynth/pipelines/hunyuan_video.py @@ -0,0 +1,395 @@ +from ..models import ModelManager, SD3TextEncoder1, HunyuanVideoVAEDecoder, HunyuanVideoVAEEncoder +from ..models.hunyuan_video_dit import HunyuanVideoDiT +from ..models.hunyuan_video_text_encoder import HunyuanVideoLLMEncoder +from ..schedulers.flow_match import FlowMatchScheduler +from .base import BasePipeline +from ..prompters import HunyuanVideoPrompter +import torch +import torchvision.transforms as transforms +from einops import rearrange +import numpy as np +from PIL import Image +from tqdm import tqdm + + +class HunyuanVideoPipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__(device=device, torch_dtype=torch_dtype) + self.scheduler = FlowMatchScheduler(shift=7.0, sigma_min=0.0, extra_one_step=True) + self.prompter = HunyuanVideoPrompter() + self.text_encoder_1: SD3TextEncoder1 = None + self.text_encoder_2: HunyuanVideoLLMEncoder = None + self.dit: HunyuanVideoDiT = None + self.vae_decoder: HunyuanVideoVAEDecoder = None + self.vae_encoder: HunyuanVideoVAEEncoder = None + self.model_names = ['text_encoder_1', 'text_encoder_2', 'dit', 'vae_decoder', 'vae_encoder'] + self.vram_management = False + + + def enable_vram_management(self): + self.vram_management = True + self.enable_cpu_offload() + self.text_encoder_2.enable_auto_offload(dtype=self.torch_dtype, device=self.device) + self.dit.enable_auto_offload(dtype=self.torch_dtype, device=self.device) + + + def fetch_models(self, model_manager: ModelManager): + self.text_encoder_1 = model_manager.fetch_model("sd3_text_encoder_1") + self.text_encoder_2 = model_manager.fetch_model("hunyuan_video_text_encoder_2") + self.dit = model_manager.fetch_model("hunyuan_video_dit") + self.vae_decoder = model_manager.fetch_model("hunyuan_video_vae_decoder") + self.vae_encoder = model_manager.fetch_model("hunyuan_video_vae_encoder") + self.prompter.fetch_models(self.text_encoder_1, self.text_encoder_2) + + + @staticmethod + def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None, enable_vram_management=True): + if device is None: device = model_manager.device + if torch_dtype is None: torch_dtype = model_manager.torch_dtype + pipe = HunyuanVideoPipeline(device=device, torch_dtype=torch_dtype) + pipe.fetch_models(model_manager) + if enable_vram_management: + pipe.enable_vram_management() + return pipe + + def generate_crop_size_list(self, base_size=256, patch_size=32, max_ratio=4.0): + num_patches = round((base_size / patch_size)**2) + assert max_ratio >= 1.0 + crop_size_list = [] + wp, hp = num_patches, 1 + while wp > 0: + if max(wp, hp) / min(wp, hp) <= max_ratio: + crop_size_list.append((wp * patch_size, hp * patch_size)) + if (hp + 1) * wp <= num_patches: + hp += 1 + else: + wp -= 1 + return crop_size_list + + + def get_closest_ratio(self, height: float, width: float, ratios: list, buckets: list): + aspect_ratio = float(height) / float(width) + closest_ratio_id = np.abs(ratios - aspect_ratio).argmin() + closest_ratio = min(ratios, key=lambda ratio: abs(float(ratio) - aspect_ratio)) + return buckets[closest_ratio_id], float(closest_ratio) + + + def prepare_vae_images_inputs(self, semantic_images, i2v_resolution="720p"): + if i2v_resolution == "720p": + bucket_hw_base_size = 960 + elif i2v_resolution == "540p": + bucket_hw_base_size = 720 + elif i2v_resolution == "360p": + bucket_hw_base_size = 480 + else: + raise ValueError(f"i2v_resolution: {i2v_resolution} must be in [360p, 540p, 720p]") + origin_size = semantic_images[0].size + + crop_size_list = self.generate_crop_size_list(bucket_hw_base_size, 32) + aspect_ratios = np.array([round(float(h) / float(w), 5) for h, w in crop_size_list]) + closest_size, closest_ratio = self.get_closest_ratio(origin_size[1], origin_size[0], aspect_ratios, crop_size_list) + ref_image_transform = transforms.Compose([ + transforms.Resize(closest_size), + transforms.CenterCrop(closest_size), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]) + ]) + + semantic_image_pixel_values = [ref_image_transform(semantic_image) for semantic_image in semantic_images] + semantic_image_pixel_values = torch.cat(semantic_image_pixel_values).unsqueeze(0).unsqueeze(2).to(self.device) + target_height, target_width = closest_size + return semantic_image_pixel_values, target_height, target_width + + + def encode_prompt(self, prompt, positive=True, clip_sequence_length=77, llm_sequence_length=256, input_images=None): + prompt_emb, pooled_prompt_emb, text_mask = self.prompter.encode_prompt( + prompt, device=self.device, positive=positive, clip_sequence_length=clip_sequence_length, llm_sequence_length=llm_sequence_length, images=input_images + ) + return {"prompt_emb": prompt_emb, "pooled_prompt_emb": pooled_prompt_emb, "text_mask": text_mask} + + + def prepare_extra_input(self, latents=None, guidance=1.0): + freqs_cos, freqs_sin = self.dit.prepare_freqs(latents) + guidance = torch.Tensor([guidance] * latents.shape[0]).to(device=latents.device, dtype=latents.dtype) + return {"freqs_cos": freqs_cos, "freqs_sin": freqs_sin, "guidance": guidance} + + + def tensor2video(self, frames): + frames = rearrange(frames, "C T H W -> T H W C") + frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8) + frames = [Image.fromarray(frame) for frame in frames] + return frames + + + def encode_video(self, frames, tile_size=(17, 30, 30), tile_stride=(12, 20, 20)): + tile_size = ((tile_size[0] - 1) * 4 + 1, tile_size[1] * 8, tile_size[2] * 8) + tile_stride = (tile_stride[0] * 4, tile_stride[1] * 8, tile_stride[2] * 8) + latents = self.vae_encoder.encode_video(frames, tile_size=tile_size, tile_stride=tile_stride) + return latents + + + @torch.no_grad() + def __call__( + self, + prompt, + negative_prompt="", + input_video=None, + input_images=None, + i2v_resolution="720p", + i2v_stability=True, + denoising_strength=1.0, + seed=None, + rand_device=None, + height=720, + width=1280, + num_frames=129, + embedded_guidance=6.0, + cfg_scale=1.0, + num_inference_steps=30, + tea_cache_l1_thresh=None, + tile_size=(17, 30, 30), + tile_stride=(12, 20, 20), + step_processor=None, + progress_bar_cmd=lambda x: x, + progress_bar_st=None, + ): + # Tiler parameters + tiler_kwargs = {"tile_size": tile_size, "tile_stride": tile_stride} + + # Scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # encoder input images + if input_images is not None: + self.load_models_to_device(['vae_encoder']) + image_pixel_values, height, width = self.prepare_vae_images_inputs(input_images, i2v_resolution=i2v_resolution) + with torch.autocast(device_type=self.device, dtype=torch.float16, enabled=True): + image_latents = self.vae_encoder(image_pixel_values) + + # Initialize noise + rand_device = self.device if rand_device is None else rand_device + noise = self.generate_noise((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), seed=seed, device=rand_device, dtype=self.torch_dtype).to(self.device) + if input_video is not None: + self.load_models_to_device(['vae_encoder']) + input_video = self.preprocess_images(input_video) + input_video = torch.stack(input_video, dim=2) + latents = self.encode_video(input_video, **tiler_kwargs).to(dtype=self.torch_dtype, device=self.device) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + elif input_images is not None and i2v_stability: + noise = self.generate_noise((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), seed=seed, device=rand_device, dtype=image_latents.dtype).to(self.device) + t = torch.tensor([0.999]).to(device=self.device) + latents = noise * t + image_latents.repeat(1, 1, (num_frames - 1) // 4 + 1, 1, 1) * (1 - t) + latents = latents.to(dtype=image_latents.dtype) + else: + latents = noise + + # Encode prompts + # current mllm does not support vram_management + self.load_models_to_device(["text_encoder_1"] if self.vram_management and input_images is None else ["text_encoder_1", "text_encoder_2"]) + prompt_emb_posi = self.encode_prompt(prompt, positive=True, input_images=input_images) + if cfg_scale != 1.0: + prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False) + + # Extra input + extra_input = self.prepare_extra_input(latents, guidance=embedded_guidance) + + # TeaCache + tea_cache_kwargs = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh) if tea_cache_l1_thresh is not None else None} + + # Denoise + self.load_models_to_device([] if self.vram_management else ["dit"]) + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(self.device) + print(f"Step {progress_id + 1} / {len(self.scheduler.timesteps)}") + + forward_func = lets_dance_hunyuan_video + if input_images is not None: + latents = torch.concat([image_latents, latents[:, :, 1:, :, :]], dim=2) + forward_func = lets_dance_hunyuan_video_i2v + + # Inference + with torch.autocast(device_type=self.device, dtype=self.torch_dtype): + noise_pred_posi = forward_func(self.dit, latents, timestep, **prompt_emb_posi, **extra_input, **tea_cache_kwargs) + if cfg_scale != 1.0: + noise_pred_nega = forward_func(self.dit, latents, timestep, **prompt_emb_nega, **extra_input) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + # (Experimental feature, may be removed in the future) + if step_processor is not None: + self.load_models_to_device(['vae_decoder']) + rendered_frames = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents, to_final=True) + rendered_frames = self.vae_decoder.decode_video(rendered_frames, **tiler_kwargs) + rendered_frames = self.tensor2video(rendered_frames[0]) + rendered_frames = step_processor(rendered_frames, original_frames=input_video) + self.load_models_to_device(['vae_encoder']) + rendered_frames = self.preprocess_images(rendered_frames) + rendered_frames = torch.stack(rendered_frames, dim=2) + target_latents = self.encode_video(rendered_frames).to(dtype=self.torch_dtype, device=self.device) + noise_pred = self.scheduler.return_to_timestep(self.scheduler.timesteps[progress_id], latents, target_latents) + self.load_models_to_device([] if self.vram_management else ["dit"]) + + # Scheduler + if input_images is not None: + latents = self.scheduler.step(noise_pred[:, :, 1:, :, :], self.scheduler.timesteps[progress_id], latents[:, :, 1:, :, :]) + latents = torch.concat([image_latents, latents], dim=2) + else: + latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) + + # Decode + self.load_models_to_device(['vae_decoder']) + frames = self.vae_decoder.decode_video(latents, **tiler_kwargs) + self.load_models_to_device([]) + frames = self.tensor2video(frames[0]) + + return frames + + + +class TeaCache: + def __init__(self, num_inference_steps, rel_l1_thresh): + self.num_inference_steps = num_inference_steps + self.step = 0 + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = None + self.rel_l1_thresh = rel_l1_thresh + self.previous_residual = None + self.previous_hidden_states = None + + def check(self, dit: HunyuanVideoDiT, img, vec): + img_ = img.clone() + vec_ = vec.clone() + img_mod1_shift, img_mod1_scale, _, _, _, _ = dit.double_blocks[0].component_a.mod(vec_).chunk(6, dim=-1) + normed_inp = dit.double_blocks[0].component_a.norm1(img_) + modulated_inp = normed_inp * (1 + img_mod1_scale.unsqueeze(1)) + img_mod1_shift.unsqueeze(1) + if self.step == 0 or self.step == self.num_inference_steps - 1: + should_calc = True + self.accumulated_rel_l1_distance = 0 + else: + coefficients = [7.33226126e+02, -4.01131952e+02, 6.75869174e+01, -3.14987800e+00, 9.61237896e-02] + rescale_func = np.poly1d(coefficients) + self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item()) + if self.accumulated_rel_l1_distance < self.rel_l1_thresh: + should_calc = False + else: + should_calc = True + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = modulated_inp + self.step += 1 + if self.step == self.num_inference_steps: + self.step = 0 + if should_calc: + self.previous_hidden_states = img.clone() + return not should_calc + + def store(self, hidden_states): + self.previous_residual = hidden_states - self.previous_hidden_states + self.previous_hidden_states = None + + def update(self, hidden_states): + hidden_states = hidden_states + self.previous_residual + return hidden_states + + + +def lets_dance_hunyuan_video( + dit: HunyuanVideoDiT, + x: torch.Tensor, + t: torch.Tensor, + prompt_emb: torch.Tensor = None, + text_mask: torch.Tensor = None, + pooled_prompt_emb: torch.Tensor = None, + freqs_cos: torch.Tensor = None, + freqs_sin: torch.Tensor = None, + guidance: torch.Tensor = None, + tea_cache: TeaCache = None, + **kwargs +): + B, C, T, H, W = x.shape + + vec = dit.time_in(t, dtype=torch.float32) + dit.vector_in(pooled_prompt_emb) + dit.guidance_in(guidance * 1000, dtype=torch.float32) + img = dit.img_in(x) + txt = dit.txt_in(prompt_emb, t, text_mask) + + # TeaCache + if tea_cache is not None: + tea_cache_update = tea_cache.check(dit, img, vec) + else: + tea_cache_update = False + + if tea_cache_update: + print("TeaCache skip forward.") + img = tea_cache.update(img) + else: + split_token = int(text_mask.sum(dim=1)) + txt_len = int(txt.shape[1]) + for block in tqdm(dit.double_blocks, desc="Double stream blocks"): + img, txt = block(img, txt, vec, (freqs_cos, freqs_sin), split_token=split_token) + + x = torch.concat([img, txt], dim=1) + for block in tqdm(dit.single_blocks, desc="Single stream blocks"): + x = block(x, vec, (freqs_cos, freqs_sin), txt_len=txt_len, split_token=split_token) + img = x[:, :-txt_len] + + if tea_cache is not None: + tea_cache.store(img) + img = dit.final_layer(img, vec) + img = dit.unpatchify(img, T=T//1, H=H//2, W=W//2) + return img + + +def lets_dance_hunyuan_video_i2v( + dit: HunyuanVideoDiT, + x: torch.Tensor, + t: torch.Tensor, + prompt_emb: torch.Tensor = None, + text_mask: torch.Tensor = None, + pooled_prompt_emb: torch.Tensor = None, + freqs_cos: torch.Tensor = None, + freqs_sin: torch.Tensor = None, + guidance: torch.Tensor = None, + tea_cache: TeaCache = None, + **kwargs +): + B, C, T, H, W = x.shape + # Uncomment below to keep same as official implementation + # guidance = guidance.to(dtype=torch.float32).to(torch.bfloat16) + vec = dit.time_in(t, dtype=torch.bfloat16) + vec_2 = dit.vector_in(pooled_prompt_emb) + vec = vec + vec_2 + vec = vec + dit.guidance_in(guidance * 1000., dtype=torch.bfloat16) + + token_replace_vec = dit.time_in(torch.zeros_like(t), dtype=torch.bfloat16) + tr_token = (H // 2) * (W // 2) + token_replace_vec = token_replace_vec + vec_2 + + img = dit.img_in(x) + txt = dit.txt_in(prompt_emb, t, text_mask) + + # TeaCache + if tea_cache is not None: + tea_cache_update = tea_cache.check(dit, img, vec) + else: + tea_cache_update = False + + if tea_cache_update: + print("TeaCache skip forward.") + img = tea_cache.update(img) + else: + split_token = int(text_mask.sum(dim=1)) + txt_len = int(txt.shape[1]) + for block in tqdm(dit.double_blocks, desc="Double stream blocks"): + img, txt = block(img, txt, vec, (freqs_cos, freqs_sin), token_replace_vec, tr_token, split_token) + + x = torch.concat([img, txt], dim=1) + for block in tqdm(dit.single_blocks, desc="Single stream blocks"): + x = block(x, vec, (freqs_cos, freqs_sin), txt_len, token_replace_vec, tr_token, split_token) + img = x[:, :-txt_len] + + if tea_cache is not None: + tea_cache.store(img) + img = dit.final_layer(img, vec) + img = dit.unpatchify(img, T=T//1, H=H//2, W=W//2) + return img diff --git a/diffsynth/pipelines/omnigen_image.py b/diffsynth/pipelines/omnigen_image.py new file mode 100644 index 0000000000000000000000000000000000000000..ddb2ae656639550084b7143fe690186602c0387d --- /dev/null +++ b/diffsynth/pipelines/omnigen_image.py @@ -0,0 +1,289 @@ +from ..models.omnigen import OmniGenTransformer +from ..models.sdxl_vae_encoder import SDXLVAEEncoder +from ..models.sdxl_vae_decoder import SDXLVAEDecoder +from ..models.model_manager import ModelManager +from ..prompters.omnigen_prompter import OmniGenPrompter +from ..schedulers import FlowMatchScheduler +from .base import BasePipeline +from typing import Optional, Dict, Any, Tuple, List +from transformers.cache_utils import DynamicCache +import torch, os +from tqdm import tqdm + + + +class OmniGenCache(DynamicCache): + def __init__(self, + num_tokens_for_img: int, offload_kv_cache: bool=False) -> None: + if not torch.cuda.is_available(): + print("No available GPU, offload_kv_cache will be set to False, which will result in large memory usage and time cost when input multiple images!!!") + offload_kv_cache = False + raise RuntimeError("OffloadedCache can only be used with a GPU") + super().__init__() + self.original_device = [] + self.prefetch_stream = torch.cuda.Stream() + self.num_tokens_for_img = num_tokens_for_img + self.offload_kv_cache = offload_kv_cache + + def prefetch_layer(self, layer_idx: int): + "Starts prefetching the next layer cache" + if layer_idx < len(self): + with torch.cuda.stream(self.prefetch_stream): + # Prefetch next layer tensors to GPU + device = self.original_device[layer_idx] + self.key_cache[layer_idx] = self.key_cache[layer_idx].to(device, non_blocking=True) + self.value_cache[layer_idx] = self.value_cache[layer_idx].to(device, non_blocking=True) + + + def evict_previous_layer(self, layer_idx: int): + "Moves the previous layer cache to the CPU" + if len(self) > 2: + # We do it on the default stream so it occurs after all earlier computations on these tensors are done + if layer_idx == 0: + prev_layer_idx = -1 + else: + prev_layer_idx = (layer_idx - 1) % len(self) + self.key_cache[prev_layer_idx] = self.key_cache[prev_layer_idx].to("cpu", non_blocking=True) + self.value_cache[prev_layer_idx] = self.value_cache[prev_layer_idx].to("cpu", non_blocking=True) + + + def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]: + "Gets the cache for this layer to the device. Prefetches the next and evicts the previous layer." + if layer_idx < len(self): + if self.offload_kv_cache: + # Evict the previous layer if necessary + torch.cuda.current_stream().synchronize() + self.evict_previous_layer(layer_idx) + # Load current layer cache to its original device if not already there + original_device = self.original_device[layer_idx] + # self.prefetch_stream.synchronize(original_device) + torch.cuda.synchronize(self.prefetch_stream) + key_tensor = self.key_cache[layer_idx] + value_tensor = self.value_cache[layer_idx] + + # Prefetch the next layer + self.prefetch_layer((layer_idx + 1) % len(self)) + else: + key_tensor = self.key_cache[layer_idx] + value_tensor = self.value_cache[layer_idx] + return (key_tensor, value_tensor) + else: + raise KeyError(f"Cache only has {len(self)} layers, attempted to access layer with index {layer_idx}") + + + def update( + self, + key_states: torch.Tensor, + value_states: torch.Tensor, + layer_idx: int, + cache_kwargs: Optional[Dict[str, Any]] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`. + Parameters: + key_states (`torch.Tensor`): + The new key states to cache. + value_states (`torch.Tensor`): + The new value states to cache. + layer_idx (`int`): + The index of the layer to cache the states for. + cache_kwargs (`Dict[str, Any]`, `optional`): + Additional arguments for the cache subclass. No additional arguments are used in `OffloadedCache`. + Return: + A tuple containing the updated key and value states. + """ + # Update the cache + if len(self.key_cache) < layer_idx: + raise ValueError("OffloadedCache does not support model usage where layers are skipped. Use DynamicCache.") + elif len(self.key_cache) == layer_idx: + # only cache the states for condition tokens + key_states = key_states[..., :-(self.num_tokens_for_img+1), :] + value_states = value_states[..., :-(self.num_tokens_for_img+1), :] + + # Update the number of seen tokens + if layer_idx == 0: + self._seen_tokens += key_states.shape[-2] + + self.key_cache.append(key_states) + self.value_cache.append(value_states) + self.original_device.append(key_states.device) + if self.offload_kv_cache: + self.evict_previous_layer(layer_idx) + return self.key_cache[layer_idx], self.value_cache[layer_idx] + else: + # only cache the states for condition tokens + key_tensor, value_tensor = self[layer_idx] + k = torch.cat([key_tensor, key_states], dim=-2) + v = torch.cat([value_tensor, value_states], dim=-2) + return k, v + + + +class OmnigenImagePipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__(device=device, torch_dtype=torch_dtype) + self.scheduler = FlowMatchScheduler(num_train_timesteps=1, shift=1, inverse_timesteps=True, sigma_min=0, sigma_max=1) + # models + self.vae_decoder: SDXLVAEDecoder = None + self.vae_encoder: SDXLVAEEncoder = None + self.transformer: OmniGenTransformer = None + self.prompter: OmniGenPrompter = None + self.model_names = ['transformer', 'vae_decoder', 'vae_encoder'] + + + def denoising_model(self): + return self.transformer + + + def fetch_models(self, model_manager: ModelManager, prompt_refiner_classes=[]): + # Main models + self.transformer, model_path = model_manager.fetch_model("omnigen_transformer", require_model_path=True) + self.vae_decoder = model_manager.fetch_model("sdxl_vae_decoder") + self.vae_encoder = model_manager.fetch_model("sdxl_vae_encoder") + self.prompter = OmniGenPrompter.from_pretrained(os.path.dirname(model_path)) + + + @staticmethod + def from_model_manager(model_manager: ModelManager, prompt_refiner_classes=[], device=None): + pipe = OmnigenImagePipeline( + device=model_manager.device if device is None else device, + torch_dtype=model_manager.torch_dtype, + ) + pipe.fetch_models(model_manager, prompt_refiner_classes=[]) + return pipe + + + def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32): + latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + return latents + + + def encode_images(self, images, tiled=False, tile_size=64, tile_stride=32): + latents = [self.encode_image(image.to(device=self.device), tiled, tile_size, tile_stride).to(self.torch_dtype) for image in images] + return latents + + + def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): + image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + image = self.vae_output_to_image(image) + return image + + + def encode_prompt(self, prompt, clip_skip=1, positive=True): + prompt_emb = self.prompter.encode_prompt(prompt, clip_skip=clip_skip, device=self.device, positive=positive) + return {"encoder_hidden_states": prompt_emb} + + + def prepare_extra_input(self, latents=None): + return {} + + + def crop_position_ids_for_cache(self, position_ids, num_tokens_for_img): + if isinstance(position_ids, list): + for i in range(len(position_ids)): + position_ids[i] = position_ids[i][:, -(num_tokens_for_img+1):] + else: + position_ids = position_ids[:, -(num_tokens_for_img+1):] + return position_ids + + + def crop_attention_mask_for_cache(self, attention_mask, num_tokens_for_img): + if isinstance(attention_mask, list): + return [x[..., -(num_tokens_for_img+1):, :] for x in attention_mask] + return attention_mask[..., -(num_tokens_for_img+1):, :] + + + @torch.no_grad() + def __call__( + self, + prompt, + reference_images=[], + cfg_scale=2.0, + image_cfg_scale=2.0, + use_kv_cache=True, + offload_kv_cache=True, + input_image=None, + denoising_strength=1.0, + height=1024, + width=1024, + num_inference_steps=20, + tiled=False, + tile_size=64, + tile_stride=32, + seed=None, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + height, width = self.check_resize_height_width(height, width) + + # Tiler parameters + tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} + + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + if input_image is not None: + self.load_models_to_device(['vae_encoder']) + image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype) + latents = self.encode_image(image, **tiler_kwargs) + noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + else: + latents = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + latents = latents.repeat(3, 1, 1, 1) + + # Encode prompts + input_data = self.prompter(prompt, reference_images, height=height, width=width, use_img_cfg=True, separate_cfg_input=True, use_input_image_size_as_output=False) + + # Encode images + reference_latents = [self.encode_images(images, **tiler_kwargs) for images in input_data['input_pixel_values']] + + # Pack all parameters + model_kwargs = dict(input_ids=[input_ids.to(self.device) for input_ids in input_data['input_ids']], + input_img_latents=reference_latents, + input_image_sizes=input_data['input_image_sizes'], + attention_mask=[attention_mask.to(self.device) for attention_mask in input_data["attention_mask"]], + position_ids=[position_ids.to(self.device) for position_ids in input_data["position_ids"]], + cfg_scale=cfg_scale, + img_cfg_scale=image_cfg_scale, + use_img_cfg=True, + use_kv_cache=use_kv_cache, + offload_model=False, + ) + + # Denoise + self.load_models_to_device(['transformer']) + cache = [OmniGenCache(latents.size(-1)*latents.size(-2) // 4, offload_kv_cache) for _ in range(len(model_kwargs['input_ids']))] if use_kv_cache else None + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).repeat(latents.shape[0]).to(self.device) + + # Forward + noise_pred, cache = self.transformer.forward_with_separate_cfg(latents, timestep, past_key_values=cache, **model_kwargs) + + # Scheduler + latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) + + # Update KV cache + if progress_id == 0 and use_kv_cache: + num_tokens_for_img = latents.size(-1)*latents.size(-2) // 4 + if isinstance(cache, list): + model_kwargs['input_ids'] = [None] * len(cache) + else: + model_kwargs['input_ids'] = None + model_kwargs['position_ids'] = self.crop_position_ids_for_cache(model_kwargs['position_ids'], num_tokens_for_img) + model_kwargs['attention_mask'] = self.crop_attention_mask_for_cache(model_kwargs['attention_mask'], num_tokens_for_img) + + # UI + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + del cache + self.load_models_to_device(['vae_decoder']) + image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + + # offload all models + self.load_models_to_device([]) + return image diff --git a/diffsynth/pipelines/pipeline_runner.py b/diffsynth/pipelines/pipeline_runner.py new file mode 100644 index 0000000000000000000000000000000000000000..1b842f9bd7b25edca1c9951e67ebe5c364deca81 --- /dev/null +++ b/diffsynth/pipelines/pipeline_runner.py @@ -0,0 +1,105 @@ +import os, torch, json +from .sd_video import ModelManager, SDVideoPipeline, ControlNetConfigUnit +from ..processors.sequencial_processor import SequencialProcessor +from ..data import VideoData, save_frames, save_video + + + +class SDVideoPipelineRunner: + def __init__(self, in_streamlit=False): + self.in_streamlit = in_streamlit + + + def load_pipeline(self, model_list, textual_inversion_folder, device, lora_alphas, controlnet_units): + # Load models + model_manager = ModelManager(torch_dtype=torch.float16, device=device) + model_manager.load_models(model_list) + pipe = SDVideoPipeline.from_model_manager( + model_manager, + [ + ControlNetConfigUnit( + processor_id=unit["processor_id"], + model_path=unit["model_path"], + scale=unit["scale"] + ) for unit in controlnet_units + ] + ) + textual_inversion_paths = [] + for file_name in os.listdir(textual_inversion_folder): + if file_name.endswith(".pt") or file_name.endswith(".bin") or file_name.endswith(".pth") or file_name.endswith(".safetensors"): + textual_inversion_paths.append(os.path.join(textual_inversion_folder, file_name)) + pipe.prompter.load_textual_inversions(textual_inversion_paths) + return model_manager, pipe + + + def load_smoother(self, model_manager, smoother_configs): + smoother = SequencialProcessor.from_model_manager(model_manager, smoother_configs) + return smoother + + + def synthesize_video(self, model_manager, pipe, seed, smoother, **pipeline_inputs): + torch.manual_seed(seed) + if self.in_streamlit: + import streamlit as st + progress_bar_st = st.progress(0.0) + output_video = pipe(**pipeline_inputs, smoother=smoother, progress_bar_st=progress_bar_st) + progress_bar_st.progress(1.0) + else: + output_video = pipe(**pipeline_inputs, smoother=smoother) + model_manager.to("cpu") + return output_video + + + def load_video(self, video_file, image_folder, height, width, start_frame_id, end_frame_id): + video = VideoData(video_file=video_file, image_folder=image_folder, height=height, width=width) + if start_frame_id is None: + start_frame_id = 0 + if end_frame_id is None: + end_frame_id = len(video) + frames = [video[i] for i in range(start_frame_id, end_frame_id)] + return frames + + + def add_data_to_pipeline_inputs(self, data, pipeline_inputs): + pipeline_inputs["input_frames"] = self.load_video(**data["input_frames"]) + pipeline_inputs["num_frames"] = len(pipeline_inputs["input_frames"]) + pipeline_inputs["width"], pipeline_inputs["height"] = pipeline_inputs["input_frames"][0].size + if len(data["controlnet_frames"]) > 0: + pipeline_inputs["controlnet_frames"] = [self.load_video(**unit) for unit in data["controlnet_frames"]] + return pipeline_inputs + + + def save_output(self, video, output_folder, fps, config): + os.makedirs(output_folder, exist_ok=True) + save_frames(video, os.path.join(output_folder, "frames")) + save_video(video, os.path.join(output_folder, "video.mp4"), fps=fps) + config["pipeline"]["pipeline_inputs"]["input_frames"] = [] + config["pipeline"]["pipeline_inputs"]["controlnet_frames"] = [] + with open(os.path.join(output_folder, "config.json"), 'w') as file: + json.dump(config, file, indent=4) + + + def run(self, config): + if self.in_streamlit: + import streamlit as st + if self.in_streamlit: st.markdown("Loading videos ...") + config["pipeline"]["pipeline_inputs"] = self.add_data_to_pipeline_inputs(config["data"], config["pipeline"]["pipeline_inputs"]) + if self.in_streamlit: st.markdown("Loading videos ... done!") + if self.in_streamlit: st.markdown("Loading models ...") + model_manager, pipe = self.load_pipeline(**config["models"]) + if self.in_streamlit: st.markdown("Loading models ... done!") + if "smoother_configs" in config: + if self.in_streamlit: st.markdown("Loading smoother ...") + smoother = self.load_smoother(model_manager, config["smoother_configs"]) + if self.in_streamlit: st.markdown("Loading smoother ... done!") + else: + smoother = None + if self.in_streamlit: st.markdown("Synthesizing videos ...") + output_video = self.synthesize_video(model_manager, pipe, config["pipeline"]["seed"], smoother, **config["pipeline"]["pipeline_inputs"]) + if self.in_streamlit: st.markdown("Synthesizing videos ... done!") + if self.in_streamlit: st.markdown("Saving videos ...") + self.save_output(output_video, config["data"]["output_folder"], config["data"]["fps"], config) + if self.in_streamlit: st.markdown("Saving videos ... done!") + if self.in_streamlit: st.markdown("Finished!") + video_file = open(os.path.join(os.path.join(config["data"]["output_folder"], "video.mp4")), 'rb') + if self.in_streamlit: st.video(video_file.read()) diff --git a/diffsynth/pipelines/sd3_image.py b/diffsynth/pipelines/sd3_image.py new file mode 100644 index 0000000000000000000000000000000000000000..c6098739b2701d59958ef3fa85b0dc96b5ffe86a --- /dev/null +++ b/diffsynth/pipelines/sd3_image.py @@ -0,0 +1,147 @@ +from ..models import ModelManager, SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3, SD3DiT, SD3VAEDecoder, SD3VAEEncoder +from ..prompters import SD3Prompter +from ..schedulers import FlowMatchScheduler +from .base import BasePipeline +import torch +from tqdm import tqdm + + + +class SD3ImagePipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__(device=device, torch_dtype=torch_dtype, height_division_factor=16, width_division_factor=16) + self.scheduler = FlowMatchScheduler() + self.prompter = SD3Prompter() + # models + self.text_encoder_1: SD3TextEncoder1 = None + self.text_encoder_2: SD3TextEncoder2 = None + self.text_encoder_3: SD3TextEncoder3 = None + self.dit: SD3DiT = None + self.vae_decoder: SD3VAEDecoder = None + self.vae_encoder: SD3VAEEncoder = None + self.model_names = ['text_encoder_1', 'text_encoder_2', 'text_encoder_3', 'dit', 'vae_decoder', 'vae_encoder'] + + + def denoising_model(self): + return self.dit + + + def fetch_models(self, model_manager: ModelManager, prompt_refiner_classes=[]): + self.text_encoder_1 = model_manager.fetch_model("sd3_text_encoder_1") + self.text_encoder_2 = model_manager.fetch_model("sd3_text_encoder_2") + self.text_encoder_3 = model_manager.fetch_model("sd3_text_encoder_3") + self.dit = model_manager.fetch_model("sd3_dit") + self.vae_decoder = model_manager.fetch_model("sd3_vae_decoder") + self.vae_encoder = model_manager.fetch_model("sd3_vae_encoder") + self.prompter.fetch_models(self.text_encoder_1, self.text_encoder_2, self.text_encoder_3) + self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) + + + @staticmethod + def from_model_manager(model_manager: ModelManager, prompt_refiner_classes=[], device=None): + pipe = SD3ImagePipeline( + device=model_manager.device if device is None else device, + torch_dtype=model_manager.torch_dtype, + ) + pipe.fetch_models(model_manager, prompt_refiner_classes) + return pipe + + + def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32): + latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + return latents + + + def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): + image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + image = self.vae_output_to_image(image) + return image + + + def encode_prompt(self, prompt, positive=True, t5_sequence_length=77): + prompt_emb, pooled_prompt_emb = self.prompter.encode_prompt( + prompt, device=self.device, positive=positive, t5_sequence_length=t5_sequence_length + ) + return {"prompt_emb": prompt_emb, "pooled_prompt_emb": pooled_prompt_emb} + + + def prepare_extra_input(self, latents=None): + return {} + + + @torch.no_grad() + def __call__( + self, + prompt, + local_prompts=[], + masks=[], + mask_scales=[], + negative_prompt="", + cfg_scale=7.5, + input_image=None, + denoising_strength=1.0, + height=1024, + width=1024, + num_inference_steps=20, + t5_sequence_length=77, + tiled=False, + tile_size=128, + tile_stride=64, + seed=None, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + height, width = self.check_resize_height_width(height, width) + + # Tiler parameters + tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} + + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + if input_image is not None: + self.load_models_to_device(['vae_encoder']) + image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype) + latents = self.encode_image(image, **tiler_kwargs) + noise = self.generate_noise((1, 16, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + else: + latents = self.generate_noise((1, 16, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + + # Encode prompts + self.load_models_to_device(['text_encoder_1', 'text_encoder_2', 'text_encoder_3']) + prompt_emb_posi = self.encode_prompt(prompt, positive=True, t5_sequence_length=t5_sequence_length) + prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False, t5_sequence_length=t5_sequence_length) + prompt_emb_locals = [self.encode_prompt(prompt_local, t5_sequence_length=t5_sequence_length) for prompt_local in local_prompts] + + # Denoise + self.load_models_to_device(['dit']) + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(self.device) + + # Classifier-free guidance + inference_callback = lambda prompt_emb_posi: self.dit( + latents, timestep=timestep, **prompt_emb_posi, **tiler_kwargs, + ) + noise_pred_posi = self.control_noise_via_local_prompts(prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback) + noise_pred_nega = self.dit( + latents, timestep=timestep, **prompt_emb_nega, **tiler_kwargs, + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + + # DDIM + latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) + + # UI + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + self.load_models_to_device(['vae_decoder']) + image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + + # offload all models + self.load_models_to_device([]) + return image diff --git a/diffsynth/pipelines/sd_image.py b/diffsynth/pipelines/sd_image.py new file mode 100644 index 0000000000000000000000000000000000000000..c22c3fe69578f28925be900036bf21afeb750f17 --- /dev/null +++ b/diffsynth/pipelines/sd_image.py @@ -0,0 +1,191 @@ +from ..models import SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder, SDIpAdapter, IpAdapterCLIPImageEmbedder +from ..models.model_manager import ModelManager +from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator +from ..prompters import SDPrompter +from ..schedulers import EnhancedDDIMScheduler +from .base import BasePipeline +from .dancer import lets_dance +from typing import List +import torch +from tqdm import tqdm + + + +class SDImagePipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__(device=device, torch_dtype=torch_dtype) + self.scheduler = EnhancedDDIMScheduler() + self.prompter = SDPrompter() + # models + self.text_encoder: SDTextEncoder = None + self.unet: SDUNet = None + self.vae_decoder: SDVAEDecoder = None + self.vae_encoder: SDVAEEncoder = None + self.controlnet: MultiControlNetManager = None + self.ipadapter_image_encoder: IpAdapterCLIPImageEmbedder = None + self.ipadapter: SDIpAdapter = None + self.model_names = ['text_encoder', 'unet', 'vae_decoder', 'vae_encoder', 'controlnet', 'ipadapter_image_encoder', 'ipadapter'] + + + def denoising_model(self): + return self.unet + + + def fetch_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): + # Main models + self.text_encoder = model_manager.fetch_model("sd_text_encoder") + self.unet = model_manager.fetch_model("sd_unet") + self.vae_decoder = model_manager.fetch_model("sd_vae_decoder") + self.vae_encoder = model_manager.fetch_model("sd_vae_encoder") + self.prompter.fetch_models(self.text_encoder) + self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) + + # ControlNets + controlnet_units = [] + for config in controlnet_config_units: + controlnet_unit = ControlNetUnit( + Annotator(config.processor_id, device=self.device), + model_manager.fetch_model("sd_controlnet", config.model_path), + config.scale + ) + controlnet_units.append(controlnet_unit) + self.controlnet = MultiControlNetManager(controlnet_units) + + # IP-Adapters + self.ipadapter = model_manager.fetch_model("sd_ipadapter") + self.ipadapter_image_encoder = model_manager.fetch_model("sd_ipadapter_clip_image_encoder") + + + @staticmethod + def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[], device=None): + pipe = SDImagePipeline( + device=model_manager.device if device is None else device, + torch_dtype=model_manager.torch_dtype, + ) + pipe.fetch_models(model_manager, controlnet_config_units, prompt_refiner_classes=[]) + return pipe + + + def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32): + latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + return latents + + + def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): + image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + image = self.vae_output_to_image(image) + return image + + + def encode_prompt(self, prompt, clip_skip=1, positive=True): + prompt_emb = self.prompter.encode_prompt(prompt, clip_skip=clip_skip, device=self.device, positive=positive) + return {"encoder_hidden_states": prompt_emb} + + + def prepare_extra_input(self, latents=None): + return {} + + + @torch.no_grad() + def __call__( + self, + prompt, + local_prompts=[], + masks=[], + mask_scales=[], + negative_prompt="", + cfg_scale=7.5, + clip_skip=1, + input_image=None, + ipadapter_images=None, + ipadapter_scale=1.0, + controlnet_image=None, + denoising_strength=1.0, + height=512, + width=512, + num_inference_steps=20, + tiled=False, + tile_size=64, + tile_stride=32, + seed=None, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + height, width = self.check_resize_height_width(height, width) + + # Tiler parameters + tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} + + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + if input_image is not None: + self.load_models_to_device(['vae_encoder']) + image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype) + latents = self.encode_image(image, **tiler_kwargs) + noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + else: + latents = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + + # Encode prompts + self.load_models_to_device(['text_encoder']) + prompt_emb_posi = self.encode_prompt(prompt, clip_skip=clip_skip, positive=True) + prompt_emb_nega = self.encode_prompt(negative_prompt, clip_skip=clip_skip, positive=False) + prompt_emb_locals = [self.encode_prompt(prompt_local, clip_skip=clip_skip, positive=True) for prompt_local in local_prompts] + + # IP-Adapter + if ipadapter_images is not None: + self.load_models_to_device(['ipadapter_image_encoder']) + ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images) + self.load_models_to_device(['ipadapter']) + ipadapter_kwargs_list_posi = {"ipadapter_kwargs_list": self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)} + ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": self.ipadapter(torch.zeros_like(ipadapter_image_encoding))} + else: + ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": {}}, {"ipadapter_kwargs_list": {}} + + # Prepare ControlNets + if controlnet_image is not None: + self.load_models_to_device(['controlnet']) + controlnet_image = self.controlnet.process_image(controlnet_image).to(device=self.device, dtype=self.torch_dtype) + controlnet_image = controlnet_image.unsqueeze(1) + controlnet_kwargs = {"controlnet_frames": controlnet_image} + else: + controlnet_kwargs = {"controlnet_frames": None} + + # Denoise + self.load_models_to_device(['controlnet', 'unet']) + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(self.device) + + # Classifier-free guidance + inference_callback = lambda prompt_emb_posi: lets_dance( + self.unet, motion_modules=None, controlnet=self.controlnet, + sample=latents, timestep=timestep, + **prompt_emb_posi, **controlnet_kwargs, **tiler_kwargs, **ipadapter_kwargs_list_posi, + device=self.device, + ) + noise_pred_posi = self.control_noise_via_local_prompts(prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback) + noise_pred_nega = lets_dance( + self.unet, motion_modules=None, controlnet=self.controlnet, + sample=latents, timestep=timestep, **prompt_emb_nega, **controlnet_kwargs, **tiler_kwargs, **ipadapter_kwargs_list_nega, + device=self.device, + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + + # DDIM + latents = self.scheduler.step(noise_pred, timestep, latents) + + # UI + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + self.load_models_to_device(['vae_decoder']) + image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + + # offload all models + self.load_models_to_device([]) + return image diff --git a/diffsynth/pipelines/sd_video.py b/diffsynth/pipelines/sd_video.py new file mode 100644 index 0000000000000000000000000000000000000000..4337beb4f7a2d4a08c5955fdbd5f528ea328b39e --- /dev/null +++ b/diffsynth/pipelines/sd_video.py @@ -0,0 +1,269 @@ +from ..models import SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder, SDIpAdapter, IpAdapterCLIPImageEmbedder, SDMotionModel +from ..models.model_manager import ModelManager +from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator +from ..prompters import SDPrompter +from ..schedulers import EnhancedDDIMScheduler +from .sd_image import SDImagePipeline +from .dancer import lets_dance +from typing import List +import torch +from tqdm import tqdm + + + +def lets_dance_with_long_video( + unet: SDUNet, + motion_modules: SDMotionModel = None, + controlnet: MultiControlNetManager = None, + sample = None, + timestep = None, + encoder_hidden_states = None, + ipadapter_kwargs_list = {}, + controlnet_frames = None, + unet_batch_size = 1, + controlnet_batch_size = 1, + cross_frame_attention = False, + tiled=False, + tile_size=64, + tile_stride=32, + device="cuda", + animatediff_batch_size=16, + animatediff_stride=8, +): + num_frames = sample.shape[0] + hidden_states_output = [(torch.zeros(sample[0].shape, dtype=sample[0].dtype), 0) for i in range(num_frames)] + + for batch_id in range(0, num_frames, animatediff_stride): + batch_id_ = min(batch_id + animatediff_batch_size, num_frames) + + # process this batch + hidden_states_batch = lets_dance( + unet, motion_modules, controlnet, + sample[batch_id: batch_id_].to(device), + timestep, + encoder_hidden_states, + ipadapter_kwargs_list=ipadapter_kwargs_list, + controlnet_frames=controlnet_frames[:, batch_id: batch_id_].to(device) if controlnet_frames is not None else None, + unet_batch_size=unet_batch_size, controlnet_batch_size=controlnet_batch_size, + cross_frame_attention=cross_frame_attention, + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, device=device + ).cpu() + + # update hidden_states + for i, hidden_states_updated in zip(range(batch_id, batch_id_), hidden_states_batch): + bias = max(1 - abs(i - (batch_id + batch_id_ - 1) / 2) / ((batch_id_ - batch_id - 1 + 1e-2) / 2), 1e-2) + hidden_states, num = hidden_states_output[i] + hidden_states = hidden_states * (num / (num + bias)) + hidden_states_updated * (bias / (num + bias)) + hidden_states_output[i] = (hidden_states, num + bias) + + if batch_id_ == num_frames: + break + + # output + hidden_states = torch.stack([h for h, _ in hidden_states_output]) + return hidden_states + + + +class SDVideoPipeline(SDImagePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16, use_original_animatediff=True): + super().__init__(device=device, torch_dtype=torch_dtype) + self.scheduler = EnhancedDDIMScheduler(beta_schedule="linear" if use_original_animatediff else "scaled_linear") + self.prompter = SDPrompter() + # models + self.text_encoder: SDTextEncoder = None + self.unet: SDUNet = None + self.vae_decoder: SDVAEDecoder = None + self.vae_encoder: SDVAEEncoder = None + self.controlnet: MultiControlNetManager = None + self.ipadapter_image_encoder: IpAdapterCLIPImageEmbedder = None + self.ipadapter: SDIpAdapter = None + self.motion_modules: SDMotionModel = None + + + def fetch_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): + # Main models + self.text_encoder = model_manager.fetch_model("sd_text_encoder") + self.unet = model_manager.fetch_model("sd_unet") + self.vae_decoder = model_manager.fetch_model("sd_vae_decoder") + self.vae_encoder = model_manager.fetch_model("sd_vae_encoder") + self.prompter.fetch_models(self.text_encoder) + self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) + + # ControlNets + controlnet_units = [] + for config in controlnet_config_units: + controlnet_unit = ControlNetUnit( + Annotator(config.processor_id, device=self.device), + model_manager.fetch_model("sd_controlnet", config.model_path), + config.scale + ) + controlnet_units.append(controlnet_unit) + self.controlnet = MultiControlNetManager(controlnet_units) + + # IP-Adapters + self.ipadapter = model_manager.fetch_model("sd_ipadapter") + self.ipadapter_image_encoder = model_manager.fetch_model("sd_ipadapter_clip_image_encoder") + + # Motion Modules + self.motion_modules = model_manager.fetch_model("sd_motion_modules") + if self.motion_modules is None: + self.scheduler = EnhancedDDIMScheduler(beta_schedule="scaled_linear") + + + @staticmethod + def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): + pipe = SDVideoPipeline( + device=model_manager.device, + torch_dtype=model_manager.torch_dtype, + ) + pipe.fetch_models(model_manager, controlnet_config_units, prompt_refiner_classes) + return pipe + + + def decode_video(self, latents, tiled=False, tile_size=64, tile_stride=32): + images = [ + self.decode_image(latents[frame_id: frame_id+1], tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + for frame_id in range(latents.shape[0]) + ] + return images + + + def encode_video(self, processed_images, tiled=False, tile_size=64, tile_stride=32): + latents = [] + for image in processed_images: + image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) + latent = self.encode_image(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + latents.append(latent.cpu()) + latents = torch.concat(latents, dim=0) + return latents + + + @torch.no_grad() + def __call__( + self, + prompt, + negative_prompt="", + cfg_scale=7.5, + clip_skip=1, + num_frames=None, + input_frames=None, + ipadapter_images=None, + ipadapter_scale=1.0, + controlnet_frames=None, + denoising_strength=1.0, + height=512, + width=512, + num_inference_steps=20, + animatediff_batch_size = 16, + animatediff_stride = 8, + unet_batch_size = 1, + controlnet_batch_size = 1, + cross_frame_attention = False, + smoother=None, + smoother_progress_ids=[], + tiled=False, + tile_size=64, + tile_stride=32, + seed=None, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + height, width = self.check_resize_height_width(height, width) + + # Tiler parameters, batch size ... + tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} + other_kwargs = { + "animatediff_batch_size": animatediff_batch_size, "animatediff_stride": animatediff_stride, + "unet_batch_size": unet_batch_size, "controlnet_batch_size": controlnet_batch_size, + "cross_frame_attention": cross_frame_attention, + } + + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + if self.motion_modules is None: + noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype).repeat(num_frames, 1, 1, 1) + else: + noise = self.generate_noise((num_frames, 4, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype) + if input_frames is None or denoising_strength == 1.0: + latents = noise + else: + latents = self.encode_video(input_frames, **tiler_kwargs) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + + # Encode prompts + prompt_emb_posi = self.encode_prompt(prompt, clip_skip=clip_skip, positive=True) + prompt_emb_nega = self.encode_prompt(negative_prompt, clip_skip=clip_skip, positive=False) + + # IP-Adapter + if ipadapter_images is not None: + ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images) + ipadapter_kwargs_list_posi = {"ipadapter_kwargs_list": self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)} + ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": self.ipadapter(torch.zeros_like(ipadapter_image_encoding))} + else: + ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": {}}, {"ipadapter_kwargs_list": {}} + + # Prepare ControlNets + if controlnet_frames is not None: + if isinstance(controlnet_frames[0], list): + controlnet_frames_ = [] + for processor_id in range(len(controlnet_frames)): + controlnet_frames_.append( + torch.stack([ + self.controlnet.process_image(controlnet_frame, processor_id=processor_id).to(self.torch_dtype) + for controlnet_frame in progress_bar_cmd(controlnet_frames[processor_id]) + ], dim=1) + ) + controlnet_frames = torch.concat(controlnet_frames_, dim=0) + else: + controlnet_frames = torch.stack([ + self.controlnet.process_image(controlnet_frame).to(self.torch_dtype) + for controlnet_frame in progress_bar_cmd(controlnet_frames) + ], dim=1) + controlnet_kwargs = {"controlnet_frames": controlnet_frames} + else: + controlnet_kwargs = {"controlnet_frames": None} + + # Denoise + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(self.device) + + # Classifier-free guidance + noise_pred_posi = lets_dance_with_long_video( + self.unet, motion_modules=self.motion_modules, controlnet=self.controlnet, + sample=latents, timestep=timestep, + **prompt_emb_posi, **controlnet_kwargs, **ipadapter_kwargs_list_posi, **other_kwargs, **tiler_kwargs, + device=self.device, + ) + noise_pred_nega = lets_dance_with_long_video( + self.unet, motion_modules=self.motion_modules, controlnet=self.controlnet, + sample=latents, timestep=timestep, + **prompt_emb_nega, **controlnet_kwargs, **ipadapter_kwargs_list_nega, **other_kwargs, **tiler_kwargs, + device=self.device, + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + + # DDIM and smoother + if smoother is not None and progress_id in smoother_progress_ids: + rendered_frames = self.scheduler.step(noise_pred, timestep, latents, to_final=True) + rendered_frames = self.decode_video(rendered_frames) + rendered_frames = smoother(rendered_frames, original_frames=input_frames) + target_latents = self.encode_video(rendered_frames) + noise_pred = self.scheduler.return_to_timestep(timestep, latents, target_latents) + latents = self.scheduler.step(noise_pred, timestep, latents) + + # UI + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + output_frames = self.decode_video(latents, **tiler_kwargs) + + # Post-process + if smoother is not None and (num_inference_steps in smoother_progress_ids or -1 in smoother_progress_ids): + output_frames = smoother(output_frames, original_frames=input_frames) + + return output_frames diff --git a/diffsynth/pipelines/sdxl_image.py b/diffsynth/pipelines/sdxl_image.py new file mode 100644 index 0000000000000000000000000000000000000000..499c4bbce707fa7cfd026c66af8c8dca3e554127 --- /dev/null +++ b/diffsynth/pipelines/sdxl_image.py @@ -0,0 +1,226 @@ +from ..models import SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder, SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder +from ..models.kolors_text_encoder import ChatGLMModel +from ..models.model_manager import ModelManager +from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator +from ..prompters import SDXLPrompter, KolorsPrompter +from ..schedulers import EnhancedDDIMScheduler +from .base import BasePipeline +from .dancer import lets_dance_xl +from typing import List +import torch +from tqdm import tqdm +from einops import repeat + + + +class SDXLImagePipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__(device=device, torch_dtype=torch_dtype) + self.scheduler = EnhancedDDIMScheduler() + self.prompter = SDXLPrompter() + # models + self.text_encoder: SDXLTextEncoder = None + self.text_encoder_2: SDXLTextEncoder2 = None + self.text_encoder_kolors: ChatGLMModel = None + self.unet: SDXLUNet = None + self.vae_decoder: SDXLVAEDecoder = None + self.vae_encoder: SDXLVAEEncoder = None + self.controlnet: MultiControlNetManager = None + self.ipadapter_image_encoder: IpAdapterXLCLIPImageEmbedder = None + self.ipadapter: SDXLIpAdapter = None + self.model_names = ['text_encoder', 'text_encoder_2', 'text_encoder_kolors', 'unet', 'vae_decoder', 'vae_encoder', 'controlnet', 'ipadapter_image_encoder', 'ipadapter'] + + + def denoising_model(self): + return self.unet + + + def fetch_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): + # Main models + self.text_encoder = model_manager.fetch_model("sdxl_text_encoder") + self.text_encoder_2 = model_manager.fetch_model("sdxl_text_encoder_2") + self.text_encoder_kolors = model_manager.fetch_model("kolors_text_encoder") + self.unet = model_manager.fetch_model("sdxl_unet") + self.vae_decoder = model_manager.fetch_model("sdxl_vae_decoder") + self.vae_encoder = model_manager.fetch_model("sdxl_vae_encoder") + + # ControlNets + controlnet_units = [] + for config in controlnet_config_units: + controlnet_unit = ControlNetUnit( + Annotator(config.processor_id, device=self.device), + model_manager.fetch_model("sdxl_controlnet", config.model_path), + config.scale + ) + controlnet_units.append(controlnet_unit) + self.controlnet = MultiControlNetManager(controlnet_units) + + # IP-Adapters + self.ipadapter = model_manager.fetch_model("sdxl_ipadapter") + self.ipadapter_image_encoder = model_manager.fetch_model("sdxl_ipadapter_clip_image_encoder") + + # Kolors + if self.text_encoder_kolors is not None: + print("Switch to Kolors. The prompter and scheduler will be replaced.") + self.prompter = KolorsPrompter() + self.prompter.fetch_models(self.text_encoder_kolors) + self.scheduler = EnhancedDDIMScheduler(beta_end=0.014, num_train_timesteps=1100) + else: + self.prompter.fetch_models(self.text_encoder, self.text_encoder_2) + self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) + + + @staticmethod + def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[], device=None): + pipe = SDXLImagePipeline( + device=model_manager.device if device is None else device, + torch_dtype=model_manager.torch_dtype, + ) + pipe.fetch_models(model_manager, controlnet_config_units, prompt_refiner_classes) + return pipe + + + def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32): + latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + return latents + + + def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): + image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + image = self.vae_output_to_image(image) + return image + + + def encode_prompt(self, prompt, clip_skip=1, clip_skip_2=2, positive=True): + add_prompt_emb, prompt_emb = self.prompter.encode_prompt( + prompt, + clip_skip=clip_skip, clip_skip_2=clip_skip_2, + device=self.device, + positive=positive, + ) + return {"encoder_hidden_states": prompt_emb, "add_text_embeds": add_prompt_emb} + + + def prepare_extra_input(self, latents=None): + height, width = latents.shape[2] * 8, latents.shape[3] * 8 + add_time_id = torch.tensor([height, width, 0, 0, height, width], device=self.device).repeat(latents.shape[0]) + return {"add_time_id": add_time_id} + + + @torch.no_grad() + def __call__( + self, + prompt, + local_prompts=[], + masks=[], + mask_scales=[], + negative_prompt="", + cfg_scale=7.5, + clip_skip=1, + clip_skip_2=2, + input_image=None, + ipadapter_images=None, + ipadapter_scale=1.0, + ipadapter_use_instant_style=False, + controlnet_image=None, + denoising_strength=1.0, + height=1024, + width=1024, + num_inference_steps=20, + tiled=False, + tile_size=64, + tile_stride=32, + seed=None, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + height, width = self.check_resize_height_width(height, width) + + # Tiler parameters + tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} + + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + if input_image is not None: + self.load_models_to_device(['vae_encoder']) + image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype) + latents = self.encode_image(image, **tiler_kwargs) + noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + else: + latents = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + + # Encode prompts + self.load_models_to_device(['text_encoder', 'text_encoder_2', 'text_encoder_kolors']) + prompt_emb_posi = self.encode_prompt(prompt, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=True) + prompt_emb_nega = self.encode_prompt(negative_prompt, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=False) + prompt_emb_locals = [self.encode_prompt(prompt_local, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=True) for prompt_local in local_prompts] + + # IP-Adapter + if ipadapter_images is not None: + if ipadapter_use_instant_style: + self.ipadapter.set_less_adapter() + else: + self.ipadapter.set_full_adapter() + self.load_models_to_device(['ipadapter_image_encoder']) + ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images) + self.load_models_to_device(['ipadapter']) + ipadapter_kwargs_list_posi = {"ipadapter_kwargs_list": self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)} + ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": self.ipadapter(torch.zeros_like(ipadapter_image_encoding))} + else: + ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": {}}, {"ipadapter_kwargs_list": {}} + + # Prepare ControlNets + if controlnet_image is not None: + self.load_models_to_device(['controlnet']) + controlnet_image = self.controlnet.process_image(controlnet_image).to(device=self.device, dtype=self.torch_dtype) + controlnet_image = controlnet_image.unsqueeze(1) + controlnet_kwargs = {"controlnet_frames": controlnet_image} + else: + controlnet_kwargs = {"controlnet_frames": None} + + # Prepare extra input + extra_input = self.prepare_extra_input(latents) + + # Denoise + self.load_models_to_device(['controlnet', 'unet']) + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(self.device) + + # Classifier-free guidance + inference_callback = lambda prompt_emb_posi: lets_dance_xl( + self.unet, motion_modules=None, controlnet=self.controlnet, + sample=latents, timestep=timestep, **extra_input, + **prompt_emb_posi, **controlnet_kwargs, **tiler_kwargs, **ipadapter_kwargs_list_posi, + device=self.device, + ) + noise_pred_posi = self.control_noise_via_local_prompts(prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback) + + if cfg_scale != 1.0: + noise_pred_nega = lets_dance_xl( + self.unet, motion_modules=None, controlnet=self.controlnet, + sample=latents, timestep=timestep, **extra_input, + **prompt_emb_nega, **controlnet_kwargs, **tiler_kwargs, **ipadapter_kwargs_list_nega, + device=self.device, + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + # DDIM + latents = self.scheduler.step(noise_pred, timestep, latents) + + # UI + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + self.load_models_to_device(['vae_decoder']) + image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + + # offload all models + self.load_models_to_device([]) + return image diff --git a/diffsynth/pipelines/sdxl_video.py b/diffsynth/pipelines/sdxl_video.py new file mode 100644 index 0000000000000000000000000000000000000000..308590ca6a874c5803da95db1d90fced26126893 --- /dev/null +++ b/diffsynth/pipelines/sdxl_video.py @@ -0,0 +1,226 @@ +from ..models import SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder, SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder, SDXLMotionModel +from ..models.kolors_text_encoder import ChatGLMModel +from ..models.model_manager import ModelManager +from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator +from ..prompters import SDXLPrompter, KolorsPrompter +from ..schedulers import EnhancedDDIMScheduler +from .sdxl_image import SDXLImagePipeline +from .dancer import lets_dance_xl +from typing import List +import torch +from tqdm import tqdm + + + +class SDXLVideoPipeline(SDXLImagePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16, use_original_animatediff=True): + super().__init__(device=device, torch_dtype=torch_dtype) + self.scheduler = EnhancedDDIMScheduler(beta_schedule="linear" if use_original_animatediff else "scaled_linear") + self.prompter = SDXLPrompter() + # models + self.text_encoder: SDXLTextEncoder = None + self.text_encoder_2: SDXLTextEncoder2 = None + self.text_encoder_kolors: ChatGLMModel = None + self.unet: SDXLUNet = None + self.vae_decoder: SDXLVAEDecoder = None + self.vae_encoder: SDXLVAEEncoder = None + # self.controlnet: MultiControlNetManager = None (TODO) + self.ipadapter_image_encoder: IpAdapterXLCLIPImageEmbedder = None + self.ipadapter: SDXLIpAdapter = None + self.motion_modules: SDXLMotionModel = None + + + def fetch_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): + # Main models + self.text_encoder = model_manager.fetch_model("sdxl_text_encoder") + self.text_encoder_2 = model_manager.fetch_model("sdxl_text_encoder_2") + self.text_encoder_kolors = model_manager.fetch_model("kolors_text_encoder") + self.unet = model_manager.fetch_model("sdxl_unet") + self.vae_decoder = model_manager.fetch_model("sdxl_vae_decoder") + self.vae_encoder = model_manager.fetch_model("sdxl_vae_encoder") + self.prompter.fetch_models(self.text_encoder) + self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) + + # ControlNets (TODO) + + # IP-Adapters + self.ipadapter = model_manager.fetch_model("sdxl_ipadapter") + self.ipadapter_image_encoder = model_manager.fetch_model("sdxl_ipadapter_clip_image_encoder") + + # Motion Modules + self.motion_modules = model_manager.fetch_model("sdxl_motion_modules") + if self.motion_modules is None: + self.scheduler = EnhancedDDIMScheduler(beta_schedule="scaled_linear") + + # Kolors + if self.text_encoder_kolors is not None: + print("Switch to Kolors. The prompter will be replaced.") + self.prompter = KolorsPrompter() + self.prompter.fetch_models(self.text_encoder_kolors) + # The schedulers of AniamteDiff and Kolors are incompatible. We align it with AniamteDiff. + if self.motion_modules is None: + self.scheduler = EnhancedDDIMScheduler(beta_end=0.014, num_train_timesteps=1100) + else: + self.prompter.fetch_models(self.text_encoder, self.text_encoder_2) + + + @staticmethod + def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): + pipe = SDXLVideoPipeline( + device=model_manager.device, + torch_dtype=model_manager.torch_dtype, + ) + pipe.fetch_models(model_manager, controlnet_config_units, prompt_refiner_classes) + return pipe + + + def decode_video(self, latents, tiled=False, tile_size=64, tile_stride=32): + images = [ + self.decode_image(latents[frame_id: frame_id+1], tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + for frame_id in range(latents.shape[0]) + ] + return images + + + def encode_video(self, processed_images, tiled=False, tile_size=64, tile_stride=32): + latents = [] + for image in processed_images: + image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) + latent = self.encode_image(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + latents.append(latent.cpu()) + latents = torch.concat(latents, dim=0) + return latents + + + @torch.no_grad() + def __call__( + self, + prompt, + negative_prompt="", + cfg_scale=7.5, + clip_skip=1, + num_frames=None, + input_frames=None, + ipadapter_images=None, + ipadapter_scale=1.0, + ipadapter_use_instant_style=False, + controlnet_frames=None, + denoising_strength=1.0, + height=512, + width=512, + num_inference_steps=20, + animatediff_batch_size = 16, + animatediff_stride = 8, + unet_batch_size = 1, + controlnet_batch_size = 1, + cross_frame_attention = False, + smoother=None, + smoother_progress_ids=[], + tiled=False, + tile_size=64, + tile_stride=32, + seed=None, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + height, width = self.check_resize_height_width(height, width) + + # Tiler parameters, batch size ... + tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} + + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + if self.motion_modules is None: + noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype).repeat(num_frames, 1, 1, 1) + else: + noise = self.generate_noise((num_frames, 4, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype) + if input_frames is None or denoising_strength == 1.0: + latents = noise + else: + latents = self.encode_video(input_frames, **tiler_kwargs) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + latents = latents.to(self.device) # will be deleted for supporting long videos + + # Encode prompts + prompt_emb_posi = self.encode_prompt(prompt, clip_skip=clip_skip, positive=True) + prompt_emb_nega = self.encode_prompt(negative_prompt, clip_skip=clip_skip, positive=False) + + # IP-Adapter + if ipadapter_images is not None: + if ipadapter_use_instant_style: + self.ipadapter.set_less_adapter() + else: + self.ipadapter.set_full_adapter() + ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images) + ipadapter_kwargs_list_posi = {"ipadapter_kwargs_list": self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)} + ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": self.ipadapter(torch.zeros_like(ipadapter_image_encoding))} + else: + ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": {}}, {"ipadapter_kwargs_list": {}} + + # Prepare ControlNets + if controlnet_frames is not None: + if isinstance(controlnet_frames[0], list): + controlnet_frames_ = [] + for processor_id in range(len(controlnet_frames)): + controlnet_frames_.append( + torch.stack([ + self.controlnet.process_image(controlnet_frame, processor_id=processor_id).to(self.torch_dtype) + for controlnet_frame in progress_bar_cmd(controlnet_frames[processor_id]) + ], dim=1) + ) + controlnet_frames = torch.concat(controlnet_frames_, dim=0) + else: + controlnet_frames = torch.stack([ + self.controlnet.process_image(controlnet_frame).to(self.torch_dtype) + for controlnet_frame in progress_bar_cmd(controlnet_frames) + ], dim=1) + controlnet_kwargs = {"controlnet_frames": controlnet_frames} + else: + controlnet_kwargs = {"controlnet_frames": None} + + # Prepare extra input + extra_input = self.prepare_extra_input(latents) + + # Denoise + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(self.device) + + # Classifier-free guidance + noise_pred_posi = lets_dance_xl( + self.unet, motion_modules=self.motion_modules, controlnet=None, + sample=latents, timestep=timestep, + **prompt_emb_posi, **controlnet_kwargs, **ipadapter_kwargs_list_posi, **extra_input, **tiler_kwargs, + device=self.device, + ) + noise_pred_nega = lets_dance_xl( + self.unet, motion_modules=self.motion_modules, controlnet=None, + sample=latents, timestep=timestep, + **prompt_emb_nega, **controlnet_kwargs, **ipadapter_kwargs_list_nega, **extra_input, **tiler_kwargs, + device=self.device, + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + + # DDIM and smoother + if smoother is not None and progress_id in smoother_progress_ids: + rendered_frames = self.scheduler.step(noise_pred, timestep, latents, to_final=True) + rendered_frames = self.decode_video(rendered_frames) + rendered_frames = smoother(rendered_frames, original_frames=input_frames) + target_latents = self.encode_video(rendered_frames) + noise_pred = self.scheduler.return_to_timestep(timestep, latents, target_latents) + latents = self.scheduler.step(noise_pred, timestep, latents) + + # UI + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + output_frames = self.decode_video(latents, **tiler_kwargs) + + # Post-process + if smoother is not None and (num_inference_steps in smoother_progress_ids or -1 in smoother_progress_ids): + output_frames = smoother(output_frames, original_frames=input_frames) + + return output_frames diff --git a/diffsynth/pipelines/step_video.py b/diffsynth/pipelines/step_video.py new file mode 100644 index 0000000000000000000000000000000000000000..56140178e9d6cdaf5efeca77ea061f8232836f11 --- /dev/null +++ b/diffsynth/pipelines/step_video.py @@ -0,0 +1,209 @@ +from ..models import ModelManager +from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder +from ..models.stepvideo_text_encoder import STEP1TextEncoder +from ..models.stepvideo_dit import StepVideoModel +from ..models.stepvideo_vae import StepVideoVAE +from ..schedulers.flow_match import FlowMatchScheduler +from .base import BasePipeline +from ..prompters import StepVideoPrompter +import torch +from einops import rearrange +import numpy as np +from PIL import Image +from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear +from transformers.models.bert.modeling_bert import BertEmbeddings +from ..models.stepvideo_dit import RMSNorm +from ..models.stepvideo_vae import CausalConv, CausalConvAfterNorm, Upsample2D, BaseGroupNorm + + + +class StepVideoPipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__(device=device, torch_dtype=torch_dtype) + self.scheduler = FlowMatchScheduler(sigma_min=0.0, extra_one_step=True, shift=13.0, reverse_sigmas=True, num_train_timesteps=1) + self.prompter = StepVideoPrompter() + self.text_encoder_1: HunyuanDiTCLIPTextEncoder = None + self.text_encoder_2: STEP1TextEncoder = None + self.dit: StepVideoModel = None + self.vae: StepVideoVAE = None + self.model_names = ['text_encoder_1', 'text_encoder_2', 'dit', 'vae'] + + + def enable_vram_management(self, num_persistent_param_in_dit=None): + dtype = next(iter(self.text_encoder_1.parameters())).dtype + enable_vram_management( + self.text_encoder_1, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + BertEmbeddings: AutoWrappedModule, + torch.nn.LayerNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=torch.float32, + computation_device=self.device, + ), + ) + dtype = next(iter(self.text_encoder_2.parameters())).dtype + enable_vram_management( + self.text_encoder_2, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + RMSNorm: AutoWrappedModule, + torch.nn.Embedding: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + dtype = next(iter(self.dit.parameters())).dtype + enable_vram_management( + self.dit, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv2d: AutoWrappedModule, + torch.nn.LayerNorm: AutoWrappedModule, + RMSNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=self.device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + max_num_param=num_persistent_param_in_dit, + overflow_module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + dtype = next(iter(self.vae.parameters())).dtype + enable_vram_management( + self.vae, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv3d: AutoWrappedModule, + CausalConv: AutoWrappedModule, + CausalConvAfterNorm: AutoWrappedModule, + Upsample2D: AutoWrappedModule, + BaseGroupNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + self.enable_cpu_offload() + + + def fetch_models(self, model_manager: ModelManager): + self.text_encoder_1 = model_manager.fetch_model("hunyuan_dit_clip_text_encoder") + self.text_encoder_2 = model_manager.fetch_model("stepvideo_text_encoder_2") + self.dit = model_manager.fetch_model("stepvideo_dit") + self.vae = model_manager.fetch_model("stepvideo_vae") + self.prompter.fetch_models(self.text_encoder_1, self.text_encoder_2) + + + @staticmethod + def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None): + if device is None: device = model_manager.device + if torch_dtype is None: torch_dtype = model_manager.torch_dtype + pipe = StepVideoPipeline(device=device, torch_dtype=torch_dtype) + pipe.fetch_models(model_manager) + return pipe + + + def encode_prompt(self, prompt, positive=True): + clip_embeds, llm_embeds, llm_mask = self.prompter.encode_prompt(prompt, device=self.device, positive=positive) + clip_embeds = clip_embeds.to(dtype=self.torch_dtype, device=self.device) + llm_embeds = llm_embeds.to(dtype=self.torch_dtype, device=self.device) + llm_mask = llm_mask.to(dtype=self.torch_dtype, device=self.device) + return {"encoder_hidden_states_2": clip_embeds, "encoder_hidden_states": llm_embeds, "encoder_attention_mask": llm_mask} + + + def tensor2video(self, frames): + frames = rearrange(frames, "C T H W -> T H W C") + frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8) + frames = [Image.fromarray(frame) for frame in frames] + return frames + + + @torch.no_grad() + def __call__( + self, + prompt, + negative_prompt="", + input_video=None, + denoising_strength=1.0, + seed=None, + rand_device="cpu", + height=544, + width=992, + num_frames=204, + cfg_scale=9.0, + num_inference_steps=30, + tiled=True, + tile_size=(34, 34), + tile_stride=(16, 16), + smooth_scale=0.6, + progress_bar_cmd=lambda x: x, + progress_bar_st=None, + ): + # Tiler parameters + tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} + + # Scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Initialize noise + latents = self.generate_noise((1, max(num_frames//17*3, 1), 64, height//16, width//16), seed=seed, device=rand_device, dtype=self.torch_dtype).to(self.device) + + # Encode prompts + self.load_models_to_device(["text_encoder_1", "text_encoder_2"]) + prompt_emb_posi = self.encode_prompt(prompt, positive=True) + if cfg_scale != 1.0: + prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False) + + # Denoise + self.load_models_to_device(["dit"]) + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device) + print(f"Step {progress_id + 1} / {len(self.scheduler.timesteps)}") + + # Inference + noise_pred_posi = self.dit(latents, timestep=timestep, **prompt_emb_posi) + if cfg_scale != 1.0: + noise_pred_nega = self.dit(latents, timestep=timestep, **prompt_emb_nega) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + # Scheduler + latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) + + # Decode + self.load_models_to_device(['vae']) + frames = self.vae.decode(latents, device=self.device, smooth_scale=smooth_scale, **tiler_kwargs) + self.load_models_to_device([]) + frames = self.tensor2video(frames[0]) + + return frames diff --git a/diffsynth/pipelines/svd_video.py b/diffsynth/pipelines/svd_video.py new file mode 100644 index 0000000000000000000000000000000000000000..b71597efa73783f7e3746a2bcf6b7be5c70c360e --- /dev/null +++ b/diffsynth/pipelines/svd_video.py @@ -0,0 +1,300 @@ +from ..models import ModelManager, SVDImageEncoder, SVDUNet, SVDVAEEncoder, SVDVAEDecoder +from ..schedulers import ContinuousODEScheduler +from .base import BasePipeline +import torch +from tqdm import tqdm +from PIL import Image +import numpy as np +from einops import rearrange, repeat + + + +class SVDVideoPipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__(device=device, torch_dtype=torch_dtype) + self.scheduler = ContinuousODEScheduler() + # models + self.image_encoder: SVDImageEncoder = None + self.unet: SVDUNet = None + self.vae_encoder: SVDVAEEncoder = None + self.vae_decoder: SVDVAEDecoder = None + + + def fetch_models(self, model_manager: ModelManager): + self.image_encoder = model_manager.fetch_model("svd_image_encoder") + self.unet = model_manager.fetch_model("svd_unet") + self.vae_encoder = model_manager.fetch_model("svd_vae_encoder") + self.vae_decoder = model_manager.fetch_model("svd_vae_decoder") + + + @staticmethod + def from_model_manager(model_manager: ModelManager, **kwargs): + pipe = SVDVideoPipeline( + device=model_manager.device, + torch_dtype=model_manager.torch_dtype + ) + pipe.fetch_models(model_manager) + return pipe + + + def encode_image_with_clip(self, image): + image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) + image = SVDCLIPImageProcessor().resize_with_antialiasing(image, (224, 224)) + image = (image + 1.0) / 2.0 + mean = torch.tensor([0.48145466, 0.4578275, 0.40821073]).reshape(1, 3, 1, 1).to(device=self.device, dtype=self.torch_dtype) + std = torch.tensor([0.26862954, 0.26130258, 0.27577711]).reshape(1, 3, 1, 1).to(device=self.device, dtype=self.torch_dtype) + image = (image - mean) / std + image_emb = self.image_encoder(image) + return image_emb + + + def encode_image_with_vae(self, image, noise_aug_strength, seed=None): + image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) + noise = self.generate_noise(image.shape, seed=seed, device=self.device, dtype=self.torch_dtype) + image = image + noise_aug_strength * noise + image_emb = self.vae_encoder(image) / self.vae_encoder.scaling_factor + return image_emb + + + def encode_video_with_vae(self, video): + video = torch.concat([self.preprocess_image(frame) for frame in video], dim=0) + video = rearrange(video, "T C H W -> 1 C T H W") + video = video.to(device=self.device, dtype=self.torch_dtype) + latents = self.vae_encoder.encode_video(video) + latents = rearrange(latents[0], "C T H W -> T C H W") + return latents + + + def tensor2video(self, frames): + frames = rearrange(frames, "C T H W -> T H W C") + frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8) + frames = [Image.fromarray(frame) for frame in frames] + return frames + + + def calculate_noise_pred( + self, + latents, + timestep, + add_time_id, + cfg_scales, + image_emb_vae_posi, image_emb_clip_posi, + image_emb_vae_nega, image_emb_clip_nega + ): + # Positive side + noise_pred_posi = self.unet( + torch.cat([latents, image_emb_vae_posi], dim=1), + timestep, image_emb_clip_posi, add_time_id + ) + # Negative side + noise_pred_nega = self.unet( + torch.cat([latents, image_emb_vae_nega], dim=1), + timestep, image_emb_clip_nega, add_time_id + ) + + # Classifier-free guidance + noise_pred = noise_pred_nega + cfg_scales * (noise_pred_posi - noise_pred_nega) + + return noise_pred + + + def post_process_latents(self, latents, post_normalize=True, contrast_enhance_scale=1.0): + if post_normalize: + mean, std = latents.mean(), latents.std() + latents = (latents - latents.mean(dim=[1, 2, 3], keepdim=True)) / latents.std(dim=[1, 2, 3], keepdim=True) * std + mean + latents = latents * contrast_enhance_scale + return latents + + + @torch.no_grad() + def __call__( + self, + input_image=None, + input_video=None, + mask_frames=[], + mask_frame_ids=[], + min_cfg_scale=1.0, + max_cfg_scale=3.0, + denoising_strength=1.0, + num_frames=25, + height=576, + width=1024, + fps=7, + motion_bucket_id=127, + noise_aug_strength=0.02, + num_inference_steps=20, + post_normalize=True, + contrast_enhance_scale=1.2, + seed=None, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + height, width = self.check_resize_height_width(height, width) + + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength) + + # Prepare latent tensors + noise = self.generate_noise((num_frames, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + if denoising_strength == 1.0: + latents = noise.clone() + else: + latents = self.encode_video_with_vae(input_video) + latents = self.scheduler.add_noise(latents, noise, self.scheduler.timesteps[0]) + + # Prepare mask frames + if len(mask_frames) > 0: + mask_latents = self.encode_video_with_vae(mask_frames) + + # Encode image + image_emb_clip_posi = self.encode_image_with_clip(input_image) + image_emb_clip_nega = torch.zeros_like(image_emb_clip_posi) + image_emb_vae_posi = repeat(self.encode_image_with_vae(input_image, noise_aug_strength, seed=seed), "B C H W -> (B T) C H W", T=num_frames) + image_emb_vae_nega = torch.zeros_like(image_emb_vae_posi) + + # Prepare classifier-free guidance + cfg_scales = torch.linspace(min_cfg_scale, max_cfg_scale, num_frames) + cfg_scales = cfg_scales.reshape(num_frames, 1, 1, 1).to(device=self.device, dtype=self.torch_dtype) + + # Prepare positional id + add_time_id = torch.tensor([[fps-1, motion_bucket_id, noise_aug_strength]], device=self.device) + + # Denoise + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + + # Mask frames + for frame_id, mask_frame_id in enumerate(mask_frame_ids): + latents[mask_frame_id] = self.scheduler.add_noise(mask_latents[frame_id], noise[mask_frame_id], timestep) + + # Fetch model output + noise_pred = self.calculate_noise_pred( + latents, timestep, add_time_id, cfg_scales, + image_emb_vae_posi, image_emb_clip_posi, image_emb_vae_nega, image_emb_clip_nega + ) + + # Forward Euler + latents = self.scheduler.step(noise_pred, timestep, latents) + + # Update progress bar + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + latents = self.post_process_latents(latents, post_normalize=post_normalize, contrast_enhance_scale=contrast_enhance_scale) + video = self.vae_decoder.decode_video(latents, progress_bar=progress_bar_cmd) + video = self.tensor2video(video) + + return video + + + +class SVDCLIPImageProcessor: + def __init__(self): + pass + + def resize_with_antialiasing(self, input, size, interpolation="bicubic", align_corners=True): + h, w = input.shape[-2:] + factors = (h / size[0], w / size[1]) + + # First, we have to determine sigma + # Taken from skimage: https://github.com/scikit-image/scikit-image/blob/v0.19.2/skimage/transform/_warps.py#L171 + sigmas = ( + max((factors[0] - 1.0) / 2.0, 0.001), + max((factors[1] - 1.0) / 2.0, 0.001), + ) + + # Now kernel size. Good results are for 3 sigma, but that is kind of slow. Pillow uses 1 sigma + # https://github.com/python-pillow/Pillow/blob/master/src/libImaging/Resample.c#L206 + # But they do it in the 2 passes, which gives better results. Let's try 2 sigmas for now + ks = int(max(2.0 * 2 * sigmas[0], 3)), int(max(2.0 * 2 * sigmas[1], 3)) + + # Make sure it is odd + if (ks[0] % 2) == 0: + ks = ks[0] + 1, ks[1] + + if (ks[1] % 2) == 0: + ks = ks[0], ks[1] + 1 + + input = self._gaussian_blur2d(input, ks, sigmas) + + output = torch.nn.functional.interpolate(input, size=size, mode=interpolation, align_corners=align_corners) + return output + + + def _compute_padding(self, kernel_size): + """Compute padding tuple.""" + # 4 or 6 ints: (padding_left, padding_right,padding_top,padding_bottom) + # https://pytorch.org/docs/stable/nn.html#torch.nn.functional.pad + if len(kernel_size) < 2: + raise AssertionError(kernel_size) + computed = [k - 1 for k in kernel_size] + + # for even kernels we need to do asymmetric padding :( + out_padding = 2 * len(kernel_size) * [0] + + for i in range(len(kernel_size)): + computed_tmp = computed[-(i + 1)] + + pad_front = computed_tmp // 2 + pad_rear = computed_tmp - pad_front + + out_padding[2 * i + 0] = pad_front + out_padding[2 * i + 1] = pad_rear + + return out_padding + + + def _filter2d(self, input, kernel): + # prepare kernel + b, c, h, w = input.shape + tmp_kernel = kernel[:, None, ...].to(device=input.device, dtype=input.dtype) + + tmp_kernel = tmp_kernel.expand(-1, c, -1, -1) + + height, width = tmp_kernel.shape[-2:] + + padding_shape: list[int] = self._compute_padding([height, width]) + input = torch.nn.functional.pad(input, padding_shape, mode="reflect") + + # kernel and input tensor reshape to align element-wise or batch-wise params + tmp_kernel = tmp_kernel.reshape(-1, 1, height, width) + input = input.view(-1, tmp_kernel.size(0), input.size(-2), input.size(-1)) + + # convolve the tensor with the kernel. + output = torch.nn.functional.conv2d(input, tmp_kernel, groups=tmp_kernel.size(0), padding=0, stride=1) + + out = output.view(b, c, h, w) + return out + + + def _gaussian(self, window_size: int, sigma): + if isinstance(sigma, float): + sigma = torch.tensor([[sigma]]) + + batch_size = sigma.shape[0] + + x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1) + + if window_size % 2 == 0: + x = x + 0.5 + + gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0))) + + return gauss / gauss.sum(-1, keepdim=True) + + + def _gaussian_blur2d(self, input, kernel_size, sigma): + if isinstance(sigma, tuple): + sigma = torch.tensor([sigma], dtype=input.dtype) + else: + sigma = sigma.to(dtype=input.dtype) + + ky, kx = int(kernel_size[0]), int(kernel_size[1]) + bs = sigma.shape[0] + kernel_x = self._gaussian(kx, sigma[:, 1].view(bs, 1)) + kernel_y = self._gaussian(ky, sigma[:, 0].view(bs, 1)) + out_x = self._filter2d(input, kernel_x[..., None, :]) + out = self._filter2d(out_x, kernel_y[..., None]) + + return out diff --git a/diffsynth/pipelines/wan_video.py b/diffsynth/pipelines/wan_video.py new file mode 100644 index 0000000000000000000000000000000000000000..8ac1e4e4290ed82db2eb28c2befe17ab1e6bebac --- /dev/null +++ b/diffsynth/pipelines/wan_video.py @@ -0,0 +1,617 @@ +import types +from ..models import ModelManager +from ..models.wan_video_dit import WanModel +from ..models.wan_video_text_encoder import WanTextEncoder +from ..models.wan_video_vae import WanVideoVAE +from ..models.wan_video_image_encoder import WanImageEncoder +from ..models.wan_video_vace import VaceWanModel +from ..schedulers.flow_match import FlowMatchScheduler +from .base import BasePipeline +from ..prompters import WanPrompter +import torch, os +from einops import rearrange +import numpy as np +from PIL import Image +from tqdm import tqdm +from typing import Optional + +from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear +from ..models.wan_video_text_encoder import T5RelativeEmbedding, T5LayerNorm +from ..models.wan_video_dit import RMSNorm, sinusoidal_embedding_1d +from ..models.wan_video_vae import RMS_norm, CausalConv3d, Upsample +from ..models.wan_video_motion_controller import WanMotionControllerModel + + + +class WanVideoPipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16, tokenizer_path=None): + super().__init__(device=device, torch_dtype=torch_dtype) + self.scheduler = FlowMatchScheduler(shift=5, sigma_min=0.0, extra_one_step=True) + self.prompter = WanPrompter(tokenizer_path=tokenizer_path) + self.text_encoder: WanTextEncoder = None + self.image_encoder: WanImageEncoder = None + self.dit: WanModel = None + self.vae: WanVideoVAE = None + self.motion_controller: WanMotionControllerModel = None + self.vace: VaceWanModel = None + self.model_names = ['text_encoder', 'dit', 'vae', 'image_encoder', 'motion_controller', 'vace'] + self.height_division_factor = 16 + self.width_division_factor = 16 + self.use_unified_sequence_parallel = False + + + def enable_vram_management(self, num_persistent_param_in_dit=None): + dtype = next(iter(self.text_encoder.parameters())).dtype + enable_vram_management( + self.text_encoder, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Embedding: AutoWrappedModule, + T5RelativeEmbedding: AutoWrappedModule, + T5LayerNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + dtype = next(iter(self.dit.parameters())).dtype + enable_vram_management( + self.dit, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv3d: AutoWrappedModule, + torch.nn.LayerNorm: AutoWrappedModule, + RMSNorm: AutoWrappedModule, + torch.nn.Conv2d: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=self.device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + max_num_param=num_persistent_param_in_dit, + overflow_module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + dtype = next(iter(self.vae.parameters())).dtype + enable_vram_management( + self.vae, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv2d: AutoWrappedModule, + RMS_norm: AutoWrappedModule, + CausalConv3d: AutoWrappedModule, + Upsample: AutoWrappedModule, + torch.nn.SiLU: AutoWrappedModule, + torch.nn.Dropout: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=self.device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + if self.image_encoder is not None: + dtype = next(iter(self.image_encoder.parameters())).dtype + enable_vram_management( + self.image_encoder, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv2d: AutoWrappedModule, + torch.nn.LayerNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=dtype, + computation_device=self.device, + ), + ) + if self.motion_controller is not None: + dtype = next(iter(self.motion_controller.parameters())).dtype + enable_vram_management( + self.motion_controller, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=dtype, + computation_device=self.device, + ), + ) + if self.vace is not None: + enable_vram_management( + self.vace, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv3d: AutoWrappedModule, + torch.nn.LayerNorm: AutoWrappedModule, + RMSNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=self.device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + self.enable_cpu_offload() + + + def fetch_models(self, model_manager: ModelManager): + text_encoder_model_and_path = model_manager.fetch_model("wan_video_text_encoder", require_model_path=True) + if text_encoder_model_and_path is not None: + self.text_encoder, tokenizer_path = text_encoder_model_and_path + self.prompter.fetch_models(self.text_encoder) + self.prompter.fetch_tokenizer(os.path.join(os.path.dirname(tokenizer_path), "google/umt5-xxl")) + self.dit = model_manager.fetch_model("wan_video_dit") + self.vae = model_manager.fetch_model("wan_video_vae") + self.image_encoder = model_manager.fetch_model("wan_video_image_encoder") + self.motion_controller = model_manager.fetch_model("wan_video_motion_controller") + self.vace = model_manager.fetch_model("wan_video_vace") + + + @staticmethod + def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None, use_usp=False): + if device is None: device = model_manager.device + if torch_dtype is None: torch_dtype = model_manager.torch_dtype + pipe = WanVideoPipeline(device=device, torch_dtype=torch_dtype) + pipe.fetch_models(model_manager) + if use_usp: + from xfuser.core.distributed import get_sequence_parallel_world_size + from ..distributed.xdit_context_parallel import usp_attn_forward, usp_dit_forward + + for block in pipe.dit.blocks: + block.self_attn.forward = types.MethodType(usp_attn_forward, block.self_attn) + pipe.dit.forward = types.MethodType(usp_dit_forward, pipe.dit) + pipe.sp_size = get_sequence_parallel_world_size() + pipe.use_unified_sequence_parallel = True + return pipe + + + def denoising_model(self): + return self.dit + + + def encode_prompt(self, prompt, positive=True): + prompt_emb = self.prompter.encode_prompt(prompt, positive=positive, device=self.device) + return {"context": prompt_emb} + + + def encode_image(self, image, end_image, num_frames, height, width, tiled=False, tile_size=(34, 34), tile_stride=(18, 16)): + image = self.preprocess_image(image.resize((width, height))).to(self.device) + clip_context = self.image_encoder.encode_image([image]) + msk = torch.ones(1, num_frames, height//8, width//8, device=self.device) + msk[:, 1:] = 0 + if end_image is not None: + end_image = self.preprocess_image(end_image.resize((width, height))).to(self.device) + vae_input = torch.concat([image.transpose(0,1), torch.zeros(3, num_frames-2, height, width).to(image.device), end_image.transpose(0,1)],dim=1) + if self.dit.has_image_pos_emb: + clip_context = torch.concat([clip_context, self.image_encoder.encode_image([end_image])], dim=1) + msk[:, -1:] = 1 + else: + vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1) + + msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1) + msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8) + msk = msk.transpose(1, 2)[0] + + y = self.vae.encode([vae_input.to(dtype=self.torch_dtype, device=self.device)], device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0] + y = y.to(dtype=self.torch_dtype, device=self.device) + y = torch.concat([msk, y]) + y = y.unsqueeze(0) + clip_context = clip_context.to(dtype=self.torch_dtype, device=self.device) + y = y.to(dtype=self.torch_dtype, device=self.device) + return {"clip_feature": clip_context, "y": y} + + + def encode_control_video(self, control_video, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)): + control_video = self.preprocess_images(control_video) + control_video = torch.stack(control_video, dim=2).to(dtype=self.torch_dtype, device=self.device) + latents = self.encode_video(control_video, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=self.torch_dtype, device=self.device) + return latents + + + def prepare_reference_image(self, reference_image, height, width): + if reference_image is not None: + self.load_models_to_device(["vae"]) + reference_image = reference_image.resize((width, height)) + reference_image = self.preprocess_images([reference_image]) + reference_image = torch.stack(reference_image, dim=2).to(dtype=self.torch_dtype, device=self.device) + reference_latents = self.vae.encode(reference_image, device=self.device) + return {"reference_latents": reference_latents} + else: + return {} + + + def prepare_controlnet_kwargs(self, control_video, num_frames, height, width, clip_feature=None, y=None, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)): + if control_video is not None: + control_latents = self.encode_control_video(control_video, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + if clip_feature is None or y is None: + clip_feature = torch.zeros((1, 257, 1280), dtype=self.torch_dtype, device=self.device) + y = torch.zeros((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), dtype=self.torch_dtype, device=self.device) + else: + y = y[:, -16:] + y = torch.concat([control_latents, y], dim=1) + return {"clip_feature": clip_feature, "y": y} + + + def tensor2video(self, frames): + frames = rearrange(frames, "C T H W -> T H W C") + frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8) + frames = [Image.fromarray(frame) for frame in frames] + return frames + + + def prepare_extra_input(self, latents=None): + return {} + + + def encode_video(self, input_video, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)): + latents = self.vae.encode(input_video, device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + return latents + + + def decode_video(self, latents, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)): + frames = self.vae.decode(latents, device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + return frames + + + def prepare_unified_sequence_parallel(self): + return {"use_unified_sequence_parallel": self.use_unified_sequence_parallel} + + + def prepare_motion_bucket_id(self, motion_bucket_id): + motion_bucket_id = torch.Tensor((motion_bucket_id,)).to(dtype=self.torch_dtype, device=self.device) + return {"motion_bucket_id": motion_bucket_id} + + + def prepare_vace_kwargs( + self, + latents, + vace_video=None, vace_mask=None, vace_reference_image=None, vace_scale=1.0, + height=480, width=832, num_frames=81, + seed=None, rand_device="cpu", + tiled=True, tile_size=(34, 34), tile_stride=(18, 16) + ): + if vace_video is not None or vace_mask is not None or vace_reference_image is not None: + self.load_models_to_device(["vae"]) + if vace_video is None: + vace_video = torch.zeros((1, 3, num_frames, height, width), dtype=self.torch_dtype, device=self.device) + else: + vace_video = self.preprocess_images(vace_video) + vace_video = torch.stack(vace_video, dim=2).to(dtype=self.torch_dtype, device=self.device) + + if vace_mask is None: + vace_mask = torch.ones_like(vace_video) + else: + vace_mask = self.preprocess_images(vace_mask) + vace_mask = torch.stack(vace_mask, dim=2).to(dtype=self.torch_dtype, device=self.device) + + inactive = vace_video * (1 - vace_mask) + 0 * vace_mask + reactive = vace_video * vace_mask + 0 * (1 - vace_mask) + inactive = self.encode_video(inactive, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=self.torch_dtype, device=self.device) + reactive = self.encode_video(reactive, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=self.torch_dtype, device=self.device) + vace_video_latents = torch.concat((inactive, reactive), dim=1) + + vace_mask_latents = rearrange(vace_mask[0,0], "T (H P) (W Q) -> 1 (P Q) T H W", P=8, Q=8) + vace_mask_latents = torch.nn.functional.interpolate(vace_mask_latents, size=((vace_mask_latents.shape[2] + 3) // 4, vace_mask_latents.shape[3], vace_mask_latents.shape[4]), mode='nearest-exact') + + if vace_reference_image is None: + pass + else: + vace_reference_image = self.preprocess_images([vace_reference_image]) + vace_reference_image = torch.stack(vace_reference_image, dim=2).to(dtype=self.torch_dtype, device=self.device) + vace_reference_latents = self.encode_video(vace_reference_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=self.torch_dtype, device=self.device) + vace_reference_latents = torch.concat((vace_reference_latents, torch.zeros_like(vace_reference_latents)), dim=1) + vace_video_latents = torch.concat((vace_reference_latents, vace_video_latents), dim=2) + vace_mask_latents = torch.concat((torch.zeros_like(vace_mask_latents[:, :, :1]), vace_mask_latents), dim=2) + + noise = self.generate_noise((1, 16, 1, latents.shape[3], latents.shape[4]), seed=seed, device=rand_device, dtype=torch.float32) + noise = noise.to(dtype=self.torch_dtype, device=self.device) + latents = torch.concat((noise, latents), dim=2) + + vace_context = torch.concat((vace_video_latents, vace_mask_latents), dim=1) + return latents, {"vace_context": vace_context, "vace_scale": vace_scale} + else: + return latents, {"vace_context": None, "vace_scale": vace_scale} + + + @torch.no_grad() + def __call__( + self, + prompt, + negative_prompt="", + input_image=None, + end_image=None, + input_video=None, + control_video=None, + reference_image=None, + vace_video=None, + vace_video_mask=None, + vace_reference_image=None, + vace_scale=1.0, + denoising_strength=1.0, + seed=None, + rand_device="cpu", + height=480, + width=832, + num_frames=81, + cfg_scale=5.0, + num_inference_steps=50, + sigma_shift=5.0, + motion_bucket_id=None, + tiled=True, + tile_size=(30, 52), + tile_stride=(15, 26), + tea_cache_l1_thresh=None, + tea_cache_model_id="", + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + # Parameter check + height, width = self.check_resize_height_width(height, width) + if num_frames % 4 != 1: + num_frames = (num_frames + 2) // 4 * 4 + 1 + print(f"Only `num_frames % 4 == 1` is acceptable. We round it up to {num_frames}.") + + # Tiler parameters + tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} + + # Scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift) + + # Initialize noise + noise = self.generate_noise((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), seed=seed, device=rand_device, dtype=torch.float32) + noise = noise.to(dtype=self.torch_dtype, device=self.device) + if input_video is not None: + self.load_models_to_device(['vae']) + input_video = self.preprocess_images(input_video) + input_video = torch.stack(input_video, dim=2).to(dtype=self.torch_dtype, device=self.device) + latents = self.encode_video(input_video, **tiler_kwargs).to(dtype=self.torch_dtype, device=self.device) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + else: + latents = noise + + # Encode prompts + self.load_models_to_device(["text_encoder"]) + prompt_emb_posi = self.encode_prompt(prompt, positive=True) + if cfg_scale != 1.0: + prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False) + + # Encode image + if input_image is not None and self.image_encoder is not None: + self.load_models_to_device(["image_encoder", "vae"]) + image_emb = self.encode_image(input_image, end_image, num_frames, height, width, **tiler_kwargs) + else: + image_emb = {} + + # Reference image + reference_image_kwargs = self.prepare_reference_image(reference_image, height, width) + + # ControlNet + if control_video is not None: + self.load_models_to_device(["image_encoder", "vae"]) + image_emb = self.prepare_controlnet_kwargs(control_video, num_frames, height, width, **image_emb, **tiler_kwargs) + + # Motion Controller + if self.motion_controller is not None and motion_bucket_id is not None: + motion_kwargs = self.prepare_motion_bucket_id(motion_bucket_id) + else: + motion_kwargs = {} + + # Extra input + extra_input = self.prepare_extra_input(latents) + + # VACE + latents, vace_kwargs = self.prepare_vace_kwargs( + latents, vace_video, vace_video_mask, vace_reference_image, vace_scale, + height=height, width=width, num_frames=num_frames, seed=seed, rand_device=rand_device, **tiler_kwargs + ) + + # TeaCache + tea_cache_posi = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh, model_id=tea_cache_model_id) if tea_cache_l1_thresh is not None else None} + tea_cache_nega = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh, model_id=tea_cache_model_id) if tea_cache_l1_thresh is not None else None} + + # Unified Sequence Parallel + usp_kwargs = self.prepare_unified_sequence_parallel() + + # Denoise + self.load_models_to_device(["dit", "motion_controller", "vace"]) + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device) + + # Inference + noise_pred_posi = model_fn_wan_video( + self.dit, motion_controller=self.motion_controller, vace=self.vace, + x=latents, timestep=timestep, + **prompt_emb_posi, **image_emb, **extra_input, + **tea_cache_posi, **usp_kwargs, **motion_kwargs, **vace_kwargs, **reference_image_kwargs, + ) + if cfg_scale != 1.0: + noise_pred_nega = model_fn_wan_video( + self.dit, motion_controller=self.motion_controller, vace=self.vace, + x=latents, timestep=timestep, + **prompt_emb_nega, **image_emb, **extra_input, + **tea_cache_nega, **usp_kwargs, **motion_kwargs, **vace_kwargs, **reference_image_kwargs, + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + # Scheduler + latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) + + if vace_reference_image is not None: + latents = latents[:, :, 1:] + + # Decode + self.load_models_to_device(['vae']) + frames = self.decode_video(latents, **tiler_kwargs) + self.load_models_to_device([]) + frames = self.tensor2video(frames[0]) + + return frames + + + +class TeaCache: + def __init__(self, num_inference_steps, rel_l1_thresh, model_id): + self.num_inference_steps = num_inference_steps + self.step = 0 + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = None + self.rel_l1_thresh = rel_l1_thresh + self.previous_residual = None + self.previous_hidden_states = None + + self.coefficients_dict = { + "Wan2.1-T2V-1.3B": [-5.21862437e+04, 9.23041404e+03, -5.28275948e+02, 1.36987616e+01, -4.99875664e-02], + "Wan2.1-T2V-14B": [-3.03318725e+05, 4.90537029e+04, -2.65530556e+03, 5.87365115e+01, -3.15583525e-01], + "Wan2.1-I2V-14B-480P": [2.57151496e+05, -3.54229917e+04, 1.40286849e+03, -1.35890334e+01, 1.32517977e-01], + "Wan2.1-I2V-14B-720P": [ 8.10705460e+03, 2.13393892e+03, -3.72934672e+02, 1.66203073e+01, -4.17769401e-02], + } + if model_id not in self.coefficients_dict: + supported_model_ids = ", ".join([i for i in self.coefficients_dict]) + raise ValueError(f"{model_id} is not a supported TeaCache model id. Please choose a valid model id in ({supported_model_ids}).") + self.coefficients = self.coefficients_dict[model_id] + + def check(self, dit: WanModel, x, t_mod): + modulated_inp = t_mod.clone() + if self.step == 0 or self.step == self.num_inference_steps - 1: + should_calc = True + self.accumulated_rel_l1_distance = 0 + else: + coefficients = self.coefficients + rescale_func = np.poly1d(coefficients) + self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item()) + if self.accumulated_rel_l1_distance < self.rel_l1_thresh: + should_calc = False + else: + should_calc = True + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = modulated_inp + self.step += 1 + if self.step == self.num_inference_steps: + self.step = 0 + if should_calc: + self.previous_hidden_states = x.clone() + return not should_calc + + def store(self, hidden_states): + self.previous_residual = hidden_states - self.previous_hidden_states + self.previous_hidden_states = None + + def update(self, hidden_states): + hidden_states = hidden_states + self.previous_residual + return hidden_states + + + +def model_fn_wan_video( + dit: WanModel, + motion_controller: WanMotionControllerModel = None, + vace: VaceWanModel = None, + x: torch.Tensor = None, + timestep: torch.Tensor = None, + context: torch.Tensor = None, + clip_feature: Optional[torch.Tensor] = None, + y: Optional[torch.Tensor] = None, + reference_latents = None, + vace_context = None, + vace_scale = 1.0, + tea_cache: TeaCache = None, + use_unified_sequence_parallel: bool = False, + motion_bucket_id: Optional[torch.Tensor] = None, + **kwargs, +): + if use_unified_sequence_parallel: + import torch.distributed as dist + from xfuser.core.distributed import (get_sequence_parallel_rank, + get_sequence_parallel_world_size, + get_sp_group) + + t = dit.time_embedding(sinusoidal_embedding_1d(dit.freq_dim, timestep)) + t_mod = dit.time_projection(t).unflatten(1, (6, dit.dim)) + if motion_bucket_id is not None and motion_controller is not None: + t_mod = t_mod + motion_controller(motion_bucket_id).unflatten(1, (6, dit.dim)) + context = dit.text_embedding(context) + + if dit.has_image_input: + x = torch.cat([x, y], dim=1) # (b, c_x + c_y, f, h, w) + clip_embdding = dit.img_emb(clip_feature) + context = torch.cat([clip_embdding, context], dim=1) + + x, (f, h, w) = dit.patchify(x) + + # Reference image + if reference_latents is not None: + reference_latents = dit.ref_conv(reference_latents[:, :, 0]).flatten(2).transpose(1, 2) + x = torch.concat([reference_latents, x], dim=1) + f += 1 + + freqs = torch.cat([ + dit.freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1), + dit.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), + dit.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1) + ], dim=-1).reshape(f * h * w, 1, -1).to(x.device) + + # TeaCache + if tea_cache is not None: + tea_cache_update = tea_cache.check(dit, x, t_mod) + else: + tea_cache_update = False + + if vace_context is not None: + vace_hints = vace(x, vace_context, context, t_mod, freqs) + + # blocks + if use_unified_sequence_parallel: + if dist.is_initialized() and dist.get_world_size() > 1: + x = torch.chunk(x, get_sequence_parallel_world_size(), dim=1)[get_sequence_parallel_rank()] + if tea_cache_update: + x = tea_cache.update(x) + else: + for block_id, block in enumerate(dit.blocks): + x = block(x, context, t_mod, freqs) + if vace_context is not None and block_id in vace.vace_layers_mapping: + x = x + vace_hints[vace.vace_layers_mapping[block_id]] * vace_scale + if tea_cache is not None: + tea_cache.store(x) + + if reference_latents is not None: + x = x[:, reference_latents.shape[1]:] + f -= 1 + + x = dit.head(x, t) + if use_unified_sequence_parallel: + if dist.is_initialized() and dist.get_world_size() > 1: + x = get_sp_group().all_gather(x, dim=1) + x = dit.unpatchify(x, (f, h, w)) + return x diff --git a/diffsynth/pipelines/wan_video_new.py b/diffsynth/pipelines/wan_video_new.py new file mode 100644 index 0000000000000000000000000000000000000000..e5108cf292eeb939a45bde4ab93d89ad0c73320b --- /dev/null +++ b/diffsynth/pipelines/wan_video_new.py @@ -0,0 +1,2112 @@ +import torch, warnings, glob, os, types +import numpy as np +from PIL import Image +from einops import repeat, reduce +from typing import Optional, Union +from dataclasses import dataclass +from modelscope import snapshot_download +from einops import rearrange +import numpy as np +from PIL import Image +from tqdm import tqdm +from typing import Optional +from typing_extensions import Literal + +from ..models import ModelManager, load_state_dict +from ..models.wan_video_dit import WanModel, RMSNorm, sinusoidal_embedding_1d +from ..models.wan_video_text_encoder import WanTextEncoder, T5RelativeEmbedding, T5LayerNorm +from ..models.wan_video_vae import WanVideoVAE, RMS_norm, CausalConv3d, Upsample +from ..models.wan_video_image_encoder import WanImageEncoder +from ..models.wan_video_vace import VaceWanModel +from ..models.wan_video_motion_controller import WanMotionControllerModel +from ..schedulers.flow_match import FlowMatchScheduler +from ..prompters import WanPrompter +from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear, WanAutoCastLayerNorm +from ..lora import GeneralLoRALoader +from ..models.memory.framepack_length import ( + framepack_align_context_actions_to_latents, + framepack_length_compress_context_latents, +) +from ..models.memory.framepack_weight import apply_framepack_token_weights +from ..models.memory.spatial_grid_memory import ( + apply_spatial_cross_attn_readout, + inject_spatial_memory, +) + + +def pad_actions_for_condition(actions, condition_length, condition_actions=None): + """Pad actions list to account for appended condition latents (VWM-style). + + When condition latents are concatenated along the time axis, the actions + sequence must be extended by ``condition_length`` entries so that the + frame count matches the total latent length (target + condition). + """ + if actions is None or condition_length <= 0: + return actions + if isinstance(actions, torch.Tensor): + actions = actions.detach().cpu().tolist() + elif isinstance(actions, np.ndarray): + actions = actions.tolist() + else: + actions = list(actions) + if actions and isinstance(actions[0], (list, tuple)) and actions[0] and isinstance(actions[0][0], (list, tuple)): + actions = actions[0] + if len(actions) == 0: + return actions + + appended_actions = [] + if condition_actions is not None: + if isinstance(condition_actions, torch.Tensor): + condition_actions = condition_actions.detach().cpu().tolist() + elif isinstance(condition_actions, np.ndarray): + condition_actions = condition_actions.tolist() + else: + condition_actions = list(condition_actions) + if ( + condition_actions + and isinstance(condition_actions[0], (list, tuple)) + and condition_actions[0] + and isinstance(condition_actions[0][0], (list, tuple)) + ): + condition_actions = condition_actions[0] + appended_actions = [list(action) for action in condition_actions[:condition_length]] + + zero_action = [0.0] * len(actions[0]) + while len(appended_actions) < condition_length: + appended_actions.append(zero_action[:]) + return actions + appended_actions + + +class BasePipeline(torch.nn.Module): + + def __init__( + self, + device="cuda", torch_dtype=torch.float16, + height_division_factor=64, width_division_factor=64, + time_division_factor=None, time_division_remainder=None, + ): + super().__init__() + # The device and torch_dtype is used for the storage of intermediate variables, not models. + self.device = device + self.torch_dtype = torch_dtype + # The following parameters are used for shape check. + self.height_division_factor = height_division_factor + self.width_division_factor = width_division_factor + self.time_division_factor = time_division_factor + self.time_division_remainder = time_division_remainder + self.vram_management_enabled = False + + + def to(self, *args, **kwargs): + device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(*args, **kwargs) + if device is not None: + self.device = device + if dtype is not None: + self.torch_dtype = dtype + super().to(*args, **kwargs) + return self + + + def check_resize_height_width(self, height, width, num_frames=None): + # Shape check + if height % self.height_division_factor != 0: + height = (height + self.height_division_factor - 1) // self.height_division_factor * self.height_division_factor + print(f"height % {self.height_division_factor} != 0. We round it up to {height}.") + if width % self.width_division_factor != 0: + width = (width + self.width_division_factor - 1) // self.width_division_factor * self.width_division_factor + print(f"width % {self.width_division_factor} != 0. We round it up to {width}.") + if num_frames is None: + return height, width + else: + if num_frames % self.time_division_factor != self.time_division_remainder: + num_frames = (num_frames + self.time_division_factor - 1) // self.time_division_factor * self.time_division_factor + self.time_division_remainder + print(f"num_frames % {self.time_division_factor} != {self.time_division_remainder}. We round it up to {num_frames}.") + return height, width, num_frames + + + def preprocess_image(self, image, torch_dtype=None, device=None, pattern="B C H W", min_value=-1, max_value=1): + # Transform a PIL.Image to torch.Tensor + image = torch.Tensor(np.array(image, dtype=np.float32)) + image = image.to(dtype=torch_dtype or self.torch_dtype, device=device or self.device) + image = image * ((max_value - min_value) / 255) + min_value + image = repeat(image, f"H W C -> {pattern}", **({"B": 1} if "B" in pattern else {})) + return image + + + def preprocess_video(self, video, torch_dtype=None, device=None, pattern="B C T H W", min_value=-1, max_value=1): + # Transform a list of PIL.Image to torch.Tensor + video = [self.preprocess_image(image, torch_dtype=torch_dtype, device=device, min_value=min_value, max_value=max_value) for image in video] + video = torch.stack(video, dim=pattern.index("T") // 2) + return video + + + def vae_output_to_image(self, vae_output, pattern="B C H W", min_value=-1, max_value=1): + # Transform a torch.Tensor to PIL.Image + if pattern != "H W C": + vae_output = reduce(vae_output, f"{pattern} -> H W C", reduction="mean") + image = ((vae_output - min_value) * (255 / (max_value - min_value))).clip(0, 255) + image = image.to(device="cpu", dtype=torch.uint8) + image = Image.fromarray(image.numpy()) + return image + + + def vae_output_to_video(self, vae_output, pattern="B C T H W", min_value=-1, max_value=1): + # Transform a torch.Tensor to list of PIL.Image + if pattern != "T H W C": + vae_output = reduce(vae_output, f"{pattern} -> T H W C", reduction="mean") + video = [self.vae_output_to_image(image, pattern="H W C", min_value=min_value, max_value=max_value) for image in vae_output] + return video + + + def load_models_to_device(self, model_names=[]): + if self.vram_management_enabled: + # offload models + for name, model in self.named_children(): + if name not in model_names: + if hasattr(model, "vram_management_enabled") and model.vram_management_enabled: + for module in model.modules(): + if hasattr(module, "offload"): + module.offload() + else: + model.cpu() + torch.cuda.empty_cache() + # onload models + for name, model in self.named_children(): + if name in model_names: + if hasattr(model, "vram_management_enabled") and model.vram_management_enabled: + for module in model.modules(): + if hasattr(module, "onload"): + module.onload() + else: + model.to(self.device) + + + def generate_noise(self, shape, seed=None, rand_device="cpu", rand_torch_dtype=torch.float32, device=None, torch_dtype=None): + # Initialize Gaussian noise + generator = None if seed is None else torch.Generator(rand_device).manual_seed(seed) + noise = torch.randn(shape, generator=generator, device=rand_device, dtype=rand_torch_dtype) + noise = noise.to(dtype=torch_dtype or self.torch_dtype, device=device or self.device) + return noise + + + def enable_cpu_offload(self): + warnings.warn("`enable_cpu_offload` will be deprecated. Please use `enable_vram_management`.") + self.vram_management_enabled = True + + + def get_vram(self): + return torch.cuda.mem_get_info(self.device if (isinstance(self.device, torch.device) and self.device.index is not None) else 0)[1] / (1024 ** 3) + + + def freeze_except(self, model_names): + for name, model in self.named_children(): + if name in model_names: + model.train() + model.requires_grad_(True) + else: + model.eval() + model.requires_grad_(False) + + +@dataclass +class ModelConfig: + path: Union[str, list[str]] = None + model_id: str = None + origin_file_pattern: Union[str, list[str]] = None + download_resource: str = "ModelScope" + offload_device: Optional[Union[str, torch.device]] = None + offload_dtype: Optional[torch.dtype] = None + skip_download: bool = False + + def download_if_necessary(self, local_model_path="./models", skip_download=False, use_usp=False): + if self.path is None: + # Check model_id and origin_file_pattern + if self.model_id is None: + raise ValueError(f"""No valid model files. Please use `ModelConfig(path="xxx")` or `ModelConfig(model_id="xxx/yyy", origin_file_pattern="zzz")`.""") + + # Skip if not in rank 0 + if use_usp: + import torch.distributed as dist + skip_download = dist.get_rank() != 0 + + # Check whether the origin path is a folder + if self.origin_file_pattern is None or self.origin_file_pattern == "": + self.origin_file_pattern = "" + allow_file_pattern = None + is_folder = True + elif isinstance(self.origin_file_pattern, str) and self.origin_file_pattern.endswith("/"): + allow_file_pattern = self.origin_file_pattern + "*" + is_folder = True + else: + allow_file_pattern = self.origin_file_pattern + is_folder = False + + # Download + skip_download = skip_download or self.skip_download + if not skip_download: + downloaded_files = glob.glob(self.origin_file_pattern, root_dir=os.path.join(local_model_path, self.model_id)) + snapshot_download( + self.model_id, + local_dir=os.path.join(local_model_path, self.model_id), + allow_file_pattern=allow_file_pattern, + ignore_file_pattern=downloaded_files, + local_files_only=False + ) + + # Let rank 1, 2, ... wait for rank 0 + if use_usp: + import torch.distributed as dist + dist.barrier(device_ids=[dist.get_rank()]) + + # Return downloaded files + if is_folder: + self.path = os.path.join(local_model_path, self.model_id, self.origin_file_pattern) + else: + self.path = glob.glob(os.path.join(local_model_path, self.model_id, self.origin_file_pattern)) + if isinstance(self.path, list) and len(self.path) == 1: + self.path = self.path[0] + + +class WanVideoPipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.bfloat16, tokenizer_path=None): + super().__init__( + device=device, torch_dtype=torch_dtype, + height_division_factor=16, width_division_factor=16, time_division_factor=4, time_division_remainder=1 + ) + self.scheduler = FlowMatchScheduler(shift=5, sigma_min=0.0, extra_one_step=True) + self.prompter = WanPrompter(tokenizer_path=tokenizer_path) + self.text_encoder: WanTextEncoder = None + self.image_encoder: WanImageEncoder = None + self.dit: WanModel = None + self.vae: WanVideoVAE = None + self.motion_controller: WanMotionControllerModel = None + self.vace: VaceWanModel = None + self.in_iteration_models = ("dit", "motion_controller", "vace") + self.unit_runner = PipelineUnitRunner() + self.units = [ + WanVideoUnit_ShapeChecker(), + WanVideoUnit_NoiseInitializer(), + WanVideoUnit_InputVideoEmbedder(), + WanVideoUnit_PromptEmbedder(), + WanVideoUnit_ImageEmbedder(), + WanVideoUnit_FunControl(), + WanVideoUnit_FunReference(), + WanVideoUnit_FunCameraControl(), + WanVideoUnit_SpeedControl(), + WanVideoUnit_VACE(), + WanVideoUnit_UnifiedSequenceParallel(), + WanVideoUnit_TeaCache(), + WanVideoUnit_CfgMerger(), + ] + self.model_fn = model_fn_wan_video + + + def load_lora(self, module, path, alpha=1): + loader = GeneralLoRALoader(torch_dtype=self.torch_dtype, device=self.device) + lora = load_state_dict(path, torch_dtype=self.torch_dtype, device=self.device) + loader.load(module, lora, alpha=alpha) + + + def training_loss(self, **inputs): + # Use len(self.scheduler.timesteps) instead of num_train_timesteps to avoid index out of bounds + # The timesteps array may have fewer elements than num_train_timesteps + num_timesteps = len(self.scheduler.timesteps) if hasattr(self.scheduler, 'timesteps') and self.scheduler.timesteps is not None else self.scheduler.num_train_timesteps + timestep_id = torch.randint(0, num_timesteps, (1,)) + timestep = self.scheduler.timesteps[timestep_id].to(dtype=self.torch_dtype, device=self.device) + + # Context Memory mode: handle context latents separately + context_latents = inputs.get("context_latents", None) + num_context_frames = inputs.get("num_context_frames", 0) + training_mode = inputs.get("training_mode", "context") # "predict", "context", or "condition" + # Experiment 1_2: allow context to be concatenated at the END (suffix) instead of the beginning (prefix) + import os + context_position = inputs.get("context_position", os.environ.get("CONTEXT_POSITION", "prefix")) + if context_position not in ("prefix", "suffix"): + context_position = "prefix" + + # Optional: Frame-level FramePack compression on context latents (K -> K') + # Use pipeline attributes as the primary source (so inference/multichunk can align), + # but also allow per-call override via inputs. + use_framepack_len = bool( + inputs.get("use_framepack_length_compress", False) or getattr(self, "use_framepack_length_compress", False) + ) + framepack_ratio = int( + inputs.get("framepack_ratio", getattr(self, "framepack_ratio", 1)) or 1 + ) + framepack_strategy = str( + inputs.get("framepack_length_strategy", getattr(self, "framepack_length_strategy", "distance_merge")) or "distance_merge" + ).lower() + framepack_recent_keep_ratio = float( + inputs.get("framepack_recent_keep_ratio", getattr(self, "framepack_recent_keep_ratio", 0.5)) or 0.5 + ) + framepack_multiscale_w2 = float( + inputs.get("framepack_multiscale_w2", getattr(self, "framepack_multiscale_w2", 0.25)) or 0.25 + ) + framepack_multiscale_w4 = float( + inputs.get("framepack_multiscale_w4", getattr(self, "framepack_multiscale_w4", 0.15)) or 0.15 + ) + if context_latents is not None and num_context_frames > 0 and use_framepack_len and framepack_ratio > 1: + # context_latents: (B, C, K, H, W) -> (B, C, K', H, W); non-overlapping r-frames mean pool + # Pad: replicate last latent until K divisible by r; context_actions get same pad + same mean (train = infer) + r = int(framepack_ratio) + Kc0 = int(context_latents.shape[2]) + pad0 = (r - (Kc0 % r)) % r + K_after_pad = Kc0 + pad0 + if inputs.get("context_actions") is not None: + inputs["context_actions"] = framepack_align_context_actions_to_latents( + inputs["context_actions"], + K_orig_latent=Kc0, + K_after_pad=K_after_pad, + framepack_ratio=r, + device=context_latents.device, + dtype=context_latents.dtype, + strategy=framepack_strategy, + recent_keep_ratio=framepack_recent_keep_ratio, + ) + context_latents, new_k, _, _ = framepack_length_compress_context_latents( + context_latents, + r, + strategy=framepack_strategy, + recent_keep_ratio=framepack_recent_keep_ratio, + multiscale_w2=framepack_multiscale_w2, + multiscale_w4=framepack_multiscale_w4, + ) + num_context_frames = int(new_k) + inputs["context_latents"] = context_latents + inputs["num_context_frames"] = num_context_frames + + # Context Noise Augmentation: add noise to context latents during training + # Experiment 7: Align training-inference noise distribution + # Use fixed small noise (sigma=0.1) to match inference strategy (Zero-shot ICL standard trick) + context_noise_prob = inputs.get("context_noise_prob", 0.0) # Probability of adding noise (legacy, for backward compatibility) + context_noise_std = inputs.get("context_noise_std", 0.02) # Noise standard deviation (legacy) + context_fixed_noise_std = inputs.get("context_fixed_noise_std", None) # Fixed noise std (Experiment 7: sigma=0.1) + + if context_latents is not None and num_context_frames > 0: + if training_mode == "condition": + # Condition mode (Experiment 17): Full video prediction with condition frames + # Formula: (N-1)/4+1 where N is number of frames + # - Condition: First 5 frames → 2 Latent Tokens ((5-1)/4+1 = 2) + # - Target: Full 81 frames → 21 Latent Tokens ((81-1)/4+1 = 21) + # - Concatenation: [Condition (2 tokens), Target (21 tokens)] = 23 tokens total + # - Loss: Compute on Target (21 tokens), Condition (2 tokens) is used as condition only + + # input_latents shape: (B, C, 21, H, W) - 81 frames encoded as 21 latent tokens + input_latents = inputs["input_latents"] + noise = inputs["noise"] + + # Condition latents: 5 frames encoded as 2 latent tokens (clean, from context_latents parameter) + # Shape: (B, C, 2, H, W) where 2 is the number of latent tokens + condition_frames = context_latents # Already clean, no noise added + + # Get actual dimensions (in latent tokens, not frames) + condition_latent_tokens = condition_frames.shape[2] if len(condition_frames.shape) > 2 else num_context_frames + target_latent_tokens = input_latents.shape[2] if len(input_latents.shape) > 2 else 21 + + # Validate dimensions match expected values (in latent tokens) + # Formula: (N-1)/4+1 where N is number of frames + # Expected: 5 frames → 2 latent tokens ((5-1)/4+1 = 2) + expected_condition_tokens = 2 + if condition_latent_tokens != expected_condition_tokens: + import warnings + warnings.warn( + f"Condition latent tokens mismatch: expected {expected_condition_tokens} " + f"(from 5 frames), got {condition_latent_tokens}. Using actual value." + ) + num_context_frames = condition_latent_tokens + else: + num_context_frames = expected_condition_tokens + + # Target latents: 81 frames encoded as 21 latent tokens (will be noised) + # Shape: (B, C, 21, H, W) + # Formula: (81-1)/4+1 = 21 latent tokens + target_latents = input_latents # 81 frames → 21 latent tokens + target_noise = noise # Noise for 21 latent tokens + + # Ensure noise matches target_latents shape (in latent tokens) + # Formula: (N-1)/4+1 where N is number of frames + expected_target_tokens = 21 # (81-1)/4+1 = 21 tokens + if target_noise.shape[2] != target_latent_tokens: + import warnings + warnings.warn( + f"Noise latent tokens mismatch: expected {target_latent_tokens} " + f"(from 81 frames), got {target_noise.shape[2]}. Truncating or padding noise." + ) + if target_noise.shape[2] > target_latent_tokens: + target_noise = target_noise[:, :, :target_latent_tokens, :, :] + else: + # Pad noise if needed (shouldn't happen normally) + padding = target_latent_tokens - target_noise.shape[2] + target_noise = torch.cat([ + target_noise, + torch.zeros_like(target_noise[:, :, :padding, :, :]) + ], dim=2) + + # Add noise to target latents (81 frames) + noisy_target_latents = self.scheduler.add_noise(target_latents, target_noise, timestep) + training_target = self.scheduler.training_target(target_latents, target_noise, timestep) + + # Experiment 7: Fixed noise strategy (align with inference) + # If context_fixed_noise_std is set, always add fixed noise (matching inference) + # Otherwise, use legacy probabilistic noise augmentation + if context_fixed_noise_std is not None and context_fixed_noise_std > 0.0: + # Always add fixed small noise to condition (matching inference strategy) + # This ensures training-inference distribution alignment + condition_noise = torch.randn_like(condition_frames) * context_fixed_noise_std + condition_frames = condition_frames + condition_noise + elif context_noise_prob > 0.0 and torch.rand(1).item() < context_noise_prob: + # Legacy: Randomly add noise to condition latents (probabilistic augmentation) + condition_noise = torch.randn_like(condition_frames) * context_noise_std + condition_frames = condition_frames + condition_noise + + # Concatenate clean condition latents with noisy target latents + # condition_frames: (B, C, 2, H, W), noisy_target_latents: (B, C, 21, H, W) + # Result: (B, C, 2+21, H, W) = (B, C, 23, H, W) + # Ensure dimensions match before concatenation (except in dimension 2 - latent tokens) + if condition_frames.shape[:2] != noisy_target_latents.shape[:2] or \ + condition_frames.shape[3:] != noisy_target_latents.shape[3:]: + raise RuntimeError( + f"Dimension mismatch when concatenating condition and target latents:\n" + f" condition_frames shape: {condition_frames.shape}\n" + f" noisy_target_latents shape: {noisy_target_latents.shape}\n" + f" Expected same shape except in dimension 2 (latent tokens).\n" + f" Condition tokens: {condition_latent_tokens}, Target tokens: {target_latent_tokens}" + ) + + # Experiment 1_2: if suffix => [Target, Context], else [Context, Target] + if context_position == "suffix": + latents = torch.cat([noisy_target_latents, condition_frames], dim=2) + else: + latents = torch.cat([condition_frames, noisy_target_latents], dim=2) + + # Store concatenated latents and condition info for model forward + inputs["latents"] = latents + inputs["num_context_frames"] = num_context_frames + if "actions" in inputs and inputs["actions"] is not None: + inputs["actions"] = pad_actions_for_condition( + inputs["actions"], + num_context_frames, + condition_actions=inputs.get("context_actions"), + ) + + noise_pred = self.model_fn(**inputs, timestep=timestep) + + if context_position == "suffix": + target_noise_pred = noise_pred[:, :, :target_latent_tokens, :, :] + else: + target_noise_pred = noise_pred[:, :, num_context_frames:, :, :] + + loss = torch.nn.functional.mse_loss(target_noise_pred.float(), training_target.float()) + loss = loss * self.scheduler.training_weight(timestep) + else: # training_mode == "context" (default, Experiment 16) + # Context as Memory mode (Experiment 16): Inpainting mode + # - Context: First K frames (4 frames, 1 Latent Token) - clean, no noise + # - Target: Last 77 frames (20 Latent Tokens, num_frames % 4 == 1) - will be noised + # - Concatenation: [Context (K), Target (77)] = 81 frames total (21 Latent Tokens) + # - Loss: Only compute on Target (77 frames, 20 Latent Tokens), Context is masked out + + # input_latents shape: (B, C, 77, H, W) - target frames only (last 77 frames) + input_latents = inputs["input_latents"] + noise = inputs["noise"] + + # Context latents: First K frames (clean, from context_latents parameter) + # Shape: (B, C, K, H, W) where K=4 frames + context_frames = context_latents # Already clean, no noise added + + # Target latents: Last 77 frames (will be noised) + # Shape: (B, C, 77, H, W) + target_latents = input_latents # Last 77 frames + target_noise = noise # Noise for 77 frames (matches num_frames % 4 == 1 requirement) + + # Add noise to target latents (77 frames) + noisy_target_latents = self.scheduler.add_noise(target_latents, target_noise, timestep) + training_target = self.scheduler.training_target(target_latents, target_noise, timestep) + + # Experiment 7: Fixed noise strategy (align with inference) + # If context_fixed_noise_std is set, always add fixed noise (matching inference) + # Otherwise, use legacy probabilistic noise augmentation + if context_fixed_noise_std is not None and context_fixed_noise_std > 0.0: + # Always add fixed small noise to context (matching inference strategy) + # This ensures training-inference distribution alignment + context_noise = torch.randn_like(context_frames) * context_fixed_noise_std + context_frames = context_frames + context_noise + elif context_noise_prob > 0.0 and torch.rand(1).item() < context_noise_prob: + # Legacy: Randomly add noise to context latents (probabilistic augmentation) + context_noise = torch.randn_like(context_frames) * context_noise_std + context_frames = context_frames + context_noise + + # Concatenate clean context latents with noisy target latents + # context_frames: (B, C, K, H, W), noisy_target_latents: (B, C, 77, H, W) + # Result: (B, C, K+77, H, W) = (B, C, 81, H, W) + + # Debug: Check dimensions before concatenation + context_frames_dim = context_frames.shape[2] if len(context_frames.shape) > 2 else 0 + target_latents_dim = noisy_target_latents.shape[2] if len(noisy_target_latents.shape) > 2 else 0 + + # Ensure dimensions match (except in dimension 2) + if context_frames.shape[:2] != noisy_target_latents.shape[:2] or \ + context_frames.shape[3:] != noisy_target_latents.shape[3:]: + raise RuntimeError( + f"Dimension mismatch when concatenating context and target latents:\n" + f" context_frames shape: {context_frames.shape}\n" + f" noisy_target_latents shape: {noisy_target_latents.shape}\n" + f" Expected same shape except in dimension 2 (frames).\n" + f" Context frames: {context_frames_dim}, Target frames: {target_latents_dim}" + ) + + # Experiment 1_2: if suffix => [Target, Context], else [Context, Target] + if context_position == "suffix": + latents = torch.cat([noisy_target_latents, context_frames], dim=2) + else: + latents = torch.cat([context_frames, noisy_target_latents], dim=2) + + # Store concatenated latents and context info for model forward + inputs["latents"] = latents + inputs["num_context_frames"] = num_context_frames + if "actions" in inputs and inputs["actions"] is not None: + inputs["actions"] = pad_actions_for_condition( + inputs["actions"], + num_context_frames, + condition_actions=inputs.get("context_actions"), + ) + + noise_pred = self.model_fn(**inputs, timestep=timestep) + + if context_position == "suffix": + target_noise_pred = noise_pred[:, :, :target_latents.shape[2], :, :] + else: + target_noise_pred = noise_pred[:, :, num_context_frames:, :, :] + + loss = torch.nn.functional.mse_loss(target_noise_pred.float(), training_target.float()) + loss = loss * self.scheduler.training_weight(timestep) + else: + # Standard mode: add noise to all latents + inputs["latents"] = self.scheduler.add_noise(inputs["input_latents"], inputs["noise"], timestep) + training_target = self.scheduler.training_target(inputs["input_latents"], inputs["noise"], timestep) + + noise_pred = self.model_fn(**inputs, timestep=timestep) + + loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float()) + loss = loss * self.scheduler.training_weight(timestep) + + return loss + + + def enable_vram_management(self, num_persistent_param_in_dit=None, vram_limit=None, vram_buffer=0.5): + self.vram_management_enabled = True + if num_persistent_param_in_dit is not None: + vram_limit = None + else: + if vram_limit is None: + vram_limit = self.get_vram() + vram_limit = vram_limit - vram_buffer + if self.text_encoder is not None: + dtype = next(iter(self.text_encoder.parameters())).dtype + enable_vram_management( + self.text_encoder, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Embedding: AutoWrappedModule, + T5RelativeEmbedding: AutoWrappedModule, + T5LayerNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + if self.dit is not None: + dtype = next(iter(self.dit.parameters())).dtype + device = "cpu" if vram_limit is not None else self.device + enable_vram_management( + self.dit, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv3d: AutoWrappedModule, + torch.nn.LayerNorm: WanAutoCastLayerNorm, + RMSNorm: AutoWrappedModule, + torch.nn.Conv2d: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + max_num_param=num_persistent_param_in_dit, + overflow_module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + if self.vae is not None: + dtype = next(iter(self.vae.parameters())).dtype + enable_vram_management( + self.vae, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv2d: AutoWrappedModule, + RMS_norm: AutoWrappedModule, + CausalConv3d: AutoWrappedModule, + Upsample: AutoWrappedModule, + torch.nn.SiLU: AutoWrappedModule, + torch.nn.Dropout: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=self.device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + if self.image_encoder is not None: + dtype = next(iter(self.image_encoder.parameters())).dtype + enable_vram_management( + self.image_encoder, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv2d: AutoWrappedModule, + torch.nn.LayerNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=dtype, + computation_device=self.device, + ), + ) + if self.motion_controller is not None: + dtype = next(iter(self.motion_controller.parameters())).dtype + enable_vram_management( + self.motion_controller, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=dtype, + computation_device=self.device, + ), + ) + if self.vace is not None: + device = "cpu" if vram_limit is not None else self.device + enable_vram_management( + self.vace, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv3d: AutoWrappedModule, + torch.nn.LayerNorm: AutoWrappedModule, + RMSNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + + + def initialize_usp(self): + import torch.distributed as dist + from xfuser.core.distributed import initialize_model_parallel, init_distributed_environment + dist.init_process_group(backend="nccl", init_method="env://") + init_distributed_environment(rank=dist.get_rank(), world_size=dist.get_world_size()) + initialize_model_parallel( + sequence_parallel_degree=dist.get_world_size(), + ring_degree=1, + ulysses_degree=dist.get_world_size(), + ) + torch.cuda.set_device(dist.get_rank()) + + + def enable_usp(self): + from xfuser.core.distributed import get_sequence_parallel_world_size + from ..distributed.xdit_context_parallel import usp_attn_forward, usp_dit_forward + + for block in self.dit.blocks: + block.self_attn.forward = types.MethodType(usp_attn_forward, block.self_attn) + self.dit.forward = types.MethodType(usp_dit_forward, self.dit) + self.sp_size = get_sequence_parallel_world_size() + self.use_unified_sequence_parallel = True + + + @staticmethod + def from_pretrained( + torch_dtype: torch.dtype = torch.bfloat16, + device: Union[str, torch.device] = "cuda", + model_configs: list[ModelConfig] = [], + tokenizer_config: ModelConfig = ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/*"), + local_model_path: str = "./models", + skip_download: bool = False, + redirect_common_files: bool = True, + use_usp=False, + ): + # Redirect model path + if redirect_common_files: + redirect_dict = { + "models_t5_umt5-xxl-enc-bf16.pth": "Wan-AI/Wan2.1-T2V-1.3B", + "Wan2.1_VAE.pth": "Wan-AI/Wan2.1-T2V-1.3B", + "models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth": "Wan-AI/Wan2.1-I2V-14B-480P", + } + for model_config in model_configs: + if model_config.origin_file_pattern is None or model_config.model_id is None: + continue + if model_config.origin_file_pattern in redirect_dict and model_config.model_id != redirect_dict[model_config.origin_file_pattern]: + print(f"To avoid repeatedly downloading model files, ({model_config.model_id}, {model_config.origin_file_pattern}) is redirected to ({redirect_dict[model_config.origin_file_pattern]}, {model_config.origin_file_pattern}). You can use `redirect_common_files=False` to disable file redirection.") + model_config.model_id = redirect_dict[model_config.origin_file_pattern] + + # Initialize pipeline + pipe = WanVideoPipeline(device=device, torch_dtype=torch_dtype) + if use_usp: pipe.initialize_usp() + + # Download and load models + model_manager = ModelManager() + for model_config in model_configs: + model_config.download_if_necessary(local_model_path, skip_download=skip_download, use_usp=use_usp) + model_manager.load_model( + model_config.path, + device=model_config.offload_device or device, + torch_dtype=model_config.offload_dtype or torch_dtype + ) + + # Load models + pipe.text_encoder = model_manager.fetch_model("wan_video_text_encoder") + pipe.dit = model_manager.fetch_model("wan_video_dit") + pipe.vae = model_manager.fetch_model("wan_video_vae") + pipe.image_encoder = model_manager.fetch_model("wan_video_image_encoder") + pipe.motion_controller = model_manager.fetch_model("wan_video_motion_controller") + pipe.vace = model_manager.fetch_model("wan_video_vace") + + # Initialize tokenizer + if tokenizer_config is not None: + tokenizer_config.download_if_necessary(local_model_path, skip_download=skip_download) + pipe.prompter.fetch_models(pipe.text_encoder) + pipe.prompter.fetch_tokenizer(tokenizer_config.path) + else: + pipe.prompter.fetch_models(pipe.text_encoder) + + # Unified Sequence Parallel + if use_usp: pipe.enable_usp() + return pipe + + + @torch.no_grad() + def __call__( + self, + # Prompt + prompt: str, + negative_prompt: Optional[str] = "", + # Image-to-video + input_image: Optional[Image.Image] = None, + # First-last-frame-to-video + end_image: Optional[Image.Image] = None, + # Video-to-video + input_video: Optional[list[Image.Image]] = None, + denoising_strength: Optional[float] = 1.0, + # ControlNet + control_video: Optional[list[Image.Image]] = None, + reference_image: Optional[Image.Image] = None, + # Camera control + camera_control_direction: Optional[Literal["Left", "Right", "Up", "Down", "LeftUp", "LeftDown", "RightUp", "RightDown"]] = None, + camera_control_speed: Optional[float] = 1/54, + camera_control_origin: Optional[tuple] = (0, 0.532139961, 0.946026558, 0.5, 0.5, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0), + # Explicit camera trajectory control (per-frame camera params) + # Each entry should be a list/tuple like CameraCtrl format (len>=19): + # [frame_id, fx, fy, cx, cy, *, *, w2c(12 vals)] + camera_control_poses: Optional[list] = None, + # VACE + vace_video: Optional[list[Image.Image]] = None, + vace_video_mask: Optional[Image.Image] = None, + vace_reference_image: Optional[Image.Image] = None, + vace_scale: Optional[float] = 1.0, + # Randomness + seed: Optional[int] = None, + rand_device: Optional[str] = "cpu", + # Shape + height: Optional[int] = 480, + width: Optional[int] = 832, + num_frames=81, + # Classifier-free guidance + cfg_scale: Optional[float] = 5.0, + cfg_merge: Optional[bool] = False, + # Scheduler + num_inference_steps: Optional[int] = 50, + sigma_shift: Optional[float] = 5.0, + # Speed control + motion_bucket_id: Optional[int] = None, + # VAE tiling + tiled: Optional[bool] = True, + tile_size: Optional[tuple[int, int]] = (30, 52), + tile_stride: Optional[tuple[int, int]] = (15, 26), + # Sliding window + sliding_window_size: Optional[int] = None, + sliding_window_stride: Optional[int] = None, + # Teacache + tea_cache_l1_thresh: Optional[float] = None, + tea_cache_model_id: Optional[str] = "", + # progress_bar + progress_bar_cmd=tqdm, + action_path=None, + # Context Memory parameters + context_latents: Optional[torch.Tensor] = None, + num_context_frames: int = 0, + enable_context_memory: bool = False, + cfg_target_only: bool = False, # Only apply CFG to target frames (not context) + inference_noise_level: Optional[float] = None, # Experiment 19: Add small noise to context latents during inference for distribution alignment + context_actions: Optional[torch.Tensor] = None, # RT poses for context frames [N_ctx, 12] + context_position: Optional[str] = None, # "suffix" = context at end (exp1_4_2), "prefix" = context at start; default from env CONTEXT_POSITION + cam_pose_actions=None, # VWM-style: list of 21 relative RT vectors (12 floats each), already latent-frame-aligned + ): + # Scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift) + + # Inputs + inputs_posi = { + "prompt": prompt, + "tea_cache_l1_thresh": tea_cache_l1_thresh, "tea_cache_model_id": tea_cache_model_id, "num_inference_steps": num_inference_steps, + } + inputs_nega = { + "negative_prompt": negative_prompt, + "tea_cache_l1_thresh": tea_cache_l1_thresh, "tea_cache_model_id": tea_cache_model_id, "num_inference_steps": num_inference_steps, + } + inputs_shared = { + "input_image": input_image, + "end_image": end_image, + "input_video": input_video, "denoising_strength": denoising_strength, + "control_video": control_video, "reference_image": reference_image, + "camera_control_direction": camera_control_direction, "camera_control_speed": camera_control_speed, "camera_control_origin": camera_control_origin, + "camera_control_poses": camera_control_poses, + "vace_video": vace_video, "vace_video_mask": vace_video_mask, "vace_reference_image": vace_reference_image, "vace_scale": vace_scale, + "seed": seed, "rand_device": rand_device, + "height": height, "width": width, "num_frames": num_frames, + "cfg_scale": cfg_scale, "cfg_merge": cfg_merge, + "sigma_shift": sigma_shift, + "motion_bucket_id": motion_bucket_id, + "tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride, + "sliding_window_size": sliding_window_size, "sliding_window_stride": sliding_window_stride, + } + + if action_path is not None: + import json + with open(action_path, "r") as f: + data = json.load(f) + action_seq = data.get("actions", data) + if isinstance(action_seq, dict): + sorted_items = sorted(action_seq.items(), key=lambda kv: int(kv[0]) if str(kv[0]).isdigit() else float('inf')) + else: + sorted_items = list(enumerate(action_seq)) + first_val = sorted_items[0][1] if sorted_items else None + use_rt_format = isinstance(first_val, (list, tuple)) and len(first_val) == 12 + actions = [] + for _, val in sorted_items: + if use_rt_format: + actions.append(list(val) if isinstance(val, (list, tuple)) else val) + else: + action_dict = val + action = [0] * (2 + 2 + 3 + 1 + 2) + if action_dict.get('ws') == 1: + action[0] = 1 + elif action_dict.get('ws') == 2: + action[1] = 1 + if action_dict.get('ad') == 1: + action[2] = 1 + elif action_dict.get('ad') == 2: + action[3] = 1 + if action_dict.get('scs') == 1 and action_dict.get('jump_invalid', 0) == 0: + action[4] = 1 + elif action_dict.get('scs') == 2: + action[5] = 1 + elif action_dict.get('scs') == 3: + action[6] = 1 + if action_dict.get("collision") == 1: + action[7] = 1 + action[0] = action[1] = action[2] = action[3] = 0 + action[8] = action_dict.get('pre_pitch', 0) + action[9] = action_dict.get('pre_yaw', 0) + actions.append(action) + if use_rt_format: + # 12-dim RT actions: align with training (CamVideoDataset uses + # pose_indices = list(range(0, num_frames, 4)) to pick one RT per + # latent frame, since the Wan VAE compresses temporally by 4×). + # The sampling JSON written by _build_gt_action_json contains one RT + # per pixel frame (e.g. 81 entries for num_frames=81), so we must + # subsample to (num_frames - 1) // 4 + 1 latent-frame entries here. + # Otherwise DiTBlock_w_Action will try to reshape spatial tokens + # by a frame count that does not divide them, and crash with + # "shape '[1, 81, -1, 1536]' is invalid for input of size ...". + _stride = 4 + _n_pixel = int(num_frames) if num_frames else len(actions) + _n_target_latent = (_n_pixel - 1) // _stride + 1 if _n_pixel > 0 else len(actions) + if len(actions) >= _n_pixel and _n_target_latent > 0: + actions = [actions[i * _stride] for i in range(_n_target_latent)] + elif len(actions) > _n_target_latent: + # JSON is shorter than _n_pixel but still longer than _n_target_latent. + # Re-stride uniformly across what we have to land on _n_target_latent samples. + step = max(1, len(actions) // _n_target_latent) + actions = [actions[min(i * step, len(actions) - 1)] for i in range(_n_target_latent)] + elif 0 < len(actions) < _n_target_latent: + # Pad by repeating the last RT to keep the expected latent length. + actions = actions + [list(actions[-1])] * (_n_target_latent - len(actions)) + else: + # Legacy non-RT (binary action) format: keep original truncation. + actions = actions[:80] + inputs_shared["actions"] = actions + elif cam_pose_actions is not None: + inputs_shared["actions"] = cam_pose_actions + + for unit in self.units: + inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega) + + # Denoise + self.load_models_to_device(self.in_iteration_models) + models = {name: getattr(self, name) for name in self.in_iteration_models} + + # Context Memory mode: prepare context latents + # Concatenation (default): [target(noise), context(clean)] 与训练一致，衔接正确；Repainting 仅保留兼容 + use_concatenation_inference = os.environ.get("USE_CONCATENATION_INFERENCE", "true").lower() == "true" + # Experiment 1_2: optionally place context at END (suffix) instead of BEGIN (prefix); prefer explicit arg over env + _context_position = (context_position or os.environ.get("CONTEXT_POSITION", "prefix")).lower() + if _context_position not in ("prefix", "suffix"): + _context_position = "prefix" + context_position = _context_position + + if enable_context_memory and context_latents is not None and num_context_frames > 0: + # NOTE: num_context_frames is treated as *latent token count* in this codebase. + # Callers must provide context_latents already encoded by VAE (token-level). + context_latents_clean = context_latents.to(dtype=self.torch_dtype, device=self.device) # (B, C, K, H, W) + + # Experiment 19: Add small noise to context latents during inference for distribution alignment + # This aligns inference distribution with training (where context has noise) + # Prevents OOD issues and breaks "perfect match" to increase dynamicity + if inference_noise_level is None: + # Default: No noise (0.0) unless explicitly set + # For Experiment 19, training script will automatically set inference_noise_level from context_fixed_noise_std + inference_noise_level = 0.0 + + if inference_noise_level > 0.0: + # Add small Gaussian noise to context latents (only to context, not to main latents) + # This noise is added before concatenation, so it's part of the condition input + context_noise = torch.randn_like(context_latents_clean) * inference_noise_level + context_latents_clean = context_latents_clean + context_noise + + # Context Memory: runtime memory baselines (must be wired for inference/multichunk) + inputs_shared["use_framepack_memory"] = getattr(self, "use_framepack_memory", False) + inputs_shared["context_temporal_decay"] = float(getattr(self, "context_temporal_decay", 1.0) or 1.0) + inputs_shared["context_attention_weight"] = float(getattr(self, "context_attention_weight", 1.0) or 1.0) + inputs_shared["use_spatial_memory"] = getattr(self, "use_spatial_memory", False) + inputs_shared["spatial_memory_tokens"] = int(getattr(self, "spatial_memory_tokens", 64) or 64) + inputs_shared["use_spatial_memory_legacy"] = bool(getattr(self, "use_spatial_memory_legacy", False)) + inputs_shared["spatial_memory_module"] = getattr(self, "spatial_memory_module", None) + inputs_shared["spatial_memory_inject_mode"] = getattr(self, "spatial_memory_inject_mode", "concat_text") + inputs_shared["spatial_memory_readout_module"] = getattr(self, "spatial_memory_readout_module", None) + + # Frame-level FramePack length compression for inference: same as training_loss (shared helpers) + use_framepack_len = bool(getattr(self, "use_framepack_length_compress", False)) + framepack_ratio = int(getattr(self, "framepack_ratio", 1) or 1) + framepack_strategy = str(getattr(self, "framepack_length_strategy", "distance_merge") or "distance_merge").lower() + framepack_recent_keep_ratio = float(getattr(self, "framepack_recent_keep_ratio", 0.5) or 0.5) + framepack_multiscale_w2 = float(getattr(self, "framepack_multiscale_w2", 0.25) or 0.25) + framepack_multiscale_w4 = float(getattr(self, "framepack_multiscale_w4", 0.15) or 0.15) + if use_framepack_len and framepack_ratio > 1 and context_latents_clean is not None and num_context_frames > 0: + r = int(framepack_ratio) + Kc0 = int(context_latents_clean.shape[2]) + pad0 = (r - (Kc0 % r)) % r + K_after_pad = Kc0 + pad0 + if context_actions is not None: + context_actions = framepack_align_context_actions_to_latents( + context_actions, + K_orig_latent=Kc0, + K_after_pad=K_after_pad, + framepack_ratio=r, + device=context_latents_clean.device, + dtype=context_latents_clean.dtype, + strategy=framepack_strategy, + recent_keep_ratio=framepack_recent_keep_ratio, + ) + context_latents_clean, new_k, _, _ = framepack_length_compress_context_latents( + context_latents_clean, + r, + strategy=framepack_strategy, + recent_keep_ratio=framepack_recent_keep_ratio, + multiscale_w2=framepack_multiscale_w2, + multiscale_w4=framepack_multiscale_w4, + ) + num_context_frames = int(new_k) + + if use_concatenation_inference: + # Concatenation Strategy (Experiment 12/16): Direct input concatenation + # After CM training, model can handle Clean Context + Noisy Target directly + # Experiment 16: Context (4 frames) + Target (77 frames) = 81 frames total + # Concatenation: [Context (4), Target (77)] = 81 frames total + target_latents_init = inputs_shared["latents"] # (B, C, 77, H, W) - target frames noise + # Direct concatenation: [Clean Context (4), Random Noise (77)] OR [Random Noise (77), Clean Context (4)] + # Model has been trained to handle this distribution (Experiment 16: 81 frames) + # Experiment 1_2 inference: if suffix => [Target(noise), Context(clean)] (context at end) + if context_position == "suffix": + latents_concatenated = torch.cat([target_latents_init, context_latents_clean], dim=2) + else: + latents_concatenated = torch.cat([context_latents_clean, target_latents_init], dim=2) + inputs_shared["latents"] = latents_concatenated + inputs_shared["num_context_frames"] = num_context_frames + inputs_shared["context_position"] = context_position + if "actions" in inputs_shared and inputs_shared["actions"] is not None: + inputs_shared["actions"] = pad_actions_for_condition( + inputs_shared["actions"], + num_context_frames, + condition_actions=context_actions, + ) + if getattr(self, "use_moc", False) and getattr(self, "moc_module", None) is not None: + inputs_shared["moc_module"] = self.moc_module + inputs_shared["use_moc"] = True + else: + # Repainting Strategy (Experiment 11): Initialize all latents as noise, then repaint context in loop + # Store clean context latents for repainting + # Initialize all latents as random noise (including context part) + # Experiment 1_2: layout must match training — suffix => [target, context], prefix => [context, target] + target_latents_init = inputs_shared["latents"] # (B, C, T, H, W) + batch_size, channels = target_latents_init.shape[0], target_latents_init.shape[1] + height, width = target_latents_init.shape[3], target_latents_init.shape[4] + context_noise_init = torch.randn( + batch_size, channels, num_context_frames, height, width, + dtype=self.torch_dtype, device=self.device + ) + if context_position == "suffix": + # [target, context] so that last K frames are context (chunk1 last frame etc.), first T are generated + latents_full = torch.cat([target_latents_init, context_noise_init], dim=2) # (B, C, T+K, H, W) + else: + latents_full = torch.cat([context_noise_init, target_latents_init], dim=2) # (B, C, K+T, H, W) + inputs_shared["latents"] = latents_full + else: + context_latents_clean = None + use_concatenation_inference = False + if enable_context_memory and num_context_frames > 0 and getattr(self, "use_moc", False) and getattr(self, "moc_module", None) is not None: + inputs_shared["moc_module"] = self.moc_module + inputs_shared["use_moc"] = True + if enable_context_memory and num_context_frames > 0 and getattr(self, "use_unified_implicit", False) and getattr(self, "unified_implicit_encoder", None) is not None and getattr(self, "context_learning_injector", None) is not None: + inputs_shared["use_unified_implicit"] = True + inputs_shared["unified_implicit_encoder"] = self.unified_implicit_encoder + inputs_shared["context_learning_injector"] = self.context_learning_injector + + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device) + + # Context Memory: Choose inference method + if enable_context_memory and context_latents_clean is not None: + if use_concatenation_inference: + # Concatenation Strategy (Experiment 12): No special handling needed + # Context was already concatenated at input stage, just pass through + inputs_shared["num_context_frames"] = num_context_frames + else: + # Repainting Strategy (Experiment 11): Replace context part in each step + # --- DEBUG START: Latent Statistics Check (Experiment 11) --- + # Log latent statistics to file for analysis (no console print to avoid log file clutter) + import json + import time + debug_log_path = os.environ.get("CONTEXT_DEBUG_LOG", "logs/context_latent_stats.jsonl") + + # Get current latents (all noisy at this point, before repainting) + latents_current = inputs_shared["latents"] # (B, C, K+T, H, W) + if context_position == "suffix": + target_latents_current = latents_current[:, :, :-num_context_frames, :, :] # (B, C, T, H, W) + else: + target_latents_current = latents_current[:, :, num_context_frames:, :, :] # (B, C, T, H, W) + + # Calculate statistics before repainting + target_mean = target_latents_current.mean().item() + target_std = target_latents_current.std().item() + target_min = target_latents_current.min().item() + target_max = target_latents_current.max().item() + + context_mean = context_latents_clean.mean().item() + context_std = context_latents_clean.std().item() + context_min = context_latents_clean.min().item() + context_max = context_latents_clean.max().item() + + ratio = context_std / target_std if target_std > 0 else float('inf') + context_mean_abs = abs(context_mean) + + # Log to file + debug_entry = { + "timestamp": time.time(), + "timestep_id": progress_id, + "total_timesteps": len(self.scheduler.timesteps), + "target_latents": { + "mean": target_mean, + "std": target_std, + "min": target_min, + "max": target_max + }, + "context_latents_clean": { + "mean": context_mean, + "std": context_std, + "min": context_min, + "max": context_max + }, + "ratio": { + "std_ratio": ratio, + "mean_abs": context_mean_abs + }, + "warnings": [] + } + + # Add warnings + if ratio < 0.2 or ratio > 5.0: + debug_entry["warnings"].append(f"Large std ratio: {ratio:.4f} (Context/Target)") + if context_mean_abs > 2.0: + debug_entry["warnings"].append(f"Context mean far from 0: {context_mean:.4f}") + if context_std < 0.2: + debug_entry["warnings"].append(f"Very small context std: {context_std:.4f}") + if context_std > 5.0: + debug_entry["warnings"].append(f"Very large context std: {context_std:.4f}") + + # Write to log file only (no console print) + try: + os.makedirs(os.path.dirname(debug_log_path), exist_ok=True) + with open(debug_log_path, "a") as f: + f.write(json.dumps(debug_entry) + "\n") + except Exception as e: + pass # Silently fail if logging fails + # --- DEBUG END --- + + # Repainting: Add noise to clean context to match current timestep + # Generate noise for context (can be deterministic if using same seed) + noise_for_context = torch.randn_like(context_latents_clean) + + # Add noise to clean context using scheduler (forward diffusion) + # This creates noisy context at the current timestep level + context_latents_noisy = self.scheduler.add_noise( + context_latents_clean, + noise_for_context, + timestep.unsqueeze(0) + ) + + # Hard Replacement: Replace context part in latents + # latents_current: (B, C, K+T, H, W) + # Prefix: context = first K frames; Suffix: context = last K (must match concat layout) + latents_repainted = latents_current.clone() + if context_position == "suffix": + latents_repainted[:, :, -num_context_frames:, :, :] = context_latents_noisy + else: + latents_repainted[:, :, :num_context_frames, :, :] = context_latents_noisy + + inputs_shared["latents"] = latents_repainted + inputs_shared["num_context_frames"] = num_context_frames + inputs_shared["context_position"] = context_position + else: + inputs_shared.pop("num_context_frames", None) # Remove if exists + inputs_shared.pop("context_position", None) + + # Inference + try: + noise_pred_posi = self.model_fn(**models, **inputs_shared, **inputs_posi, timestep=timestep) + except Exception as e: + raise + if cfg_scale != 1.0: + if cfg_merge: + noise_pred_posi, noise_pred_nega = noise_pred_posi.chunk(2, dim=0) + else: + noise_pred_nega = self.model_fn(**models, **inputs_shared, **inputs_nega, timestep=timestep) + + # CFG only for target frames (Experiment 7) + if cfg_target_only and enable_context_memory and context_latents_clean is not None and num_context_frames > 0: + # Split context and target parts (handle suffix mode) + if context_position == "suffix": + # Experiment 1_2: target is FRONT, context is BACK + target_posi = noise_pred_posi[:, :, :-num_context_frames, :, :] + context_posi = noise_pred_posi[:, :, -num_context_frames:, :, :] + target_nega = noise_pred_nega[:, :, :-num_context_frames, :, :] + context_nega = noise_pred_nega[:, :, -num_context_frames:, :, :] + else: + # Prefix mode: context is FRONT, target is BACK + context_posi = noise_pred_posi[:, :, :num_context_frames, :, :] + target_posi = noise_pred_posi[:, :, num_context_frames:, :, :] + context_nega = noise_pred_nega[:, :, :num_context_frames, :, :] + target_nega = noise_pred_nega[:, :, num_context_frames:, :, :] + + # Apply CFG only to target part + target_cfg = target_nega + cfg_scale * (target_posi - target_nega) + + # Keep context part unchanged (use positive prediction, or average) + # Using positive prediction for context to maintain consistency + context_cfg = context_posi + + # Concatenate back (handle suffix mode) + if context_position == "suffix": + noise_pred = torch.cat([target_cfg, context_cfg], dim=2) + else: + noise_pred = torch.cat([context_cfg, target_cfg], dim=2) + else: + # Standard CFG: apply to all frames + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + # Scheduler Step + if enable_context_memory and context_latents_clean is not None: + if use_concatenation_inference: + # Concatenation Strategy: Update all latents, but lock Context to prevent corruption + # CRITICAL FIX: Model was trained with Loss only on Target frames, so noise_pred + # for Context frames is undefined/random. We must lock Context after each step + # to prevent it from being corrupted by garbage predictions. + inputs_shared["latents"] = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], inputs_shared["latents"]) + # Lock Context: Reset Context part to original Clean Context after scheduler step + # This prevents Context from being corrupted by model's undefined predictions on Context frames + # Experiment 1_2: if suffix => lock BACK, else lock FRONT + if context_position == "suffix": + inputs_shared["latents"][:, :, -num_context_frames:, :, :] = context_latents_clean + else: + inputs_shared["latents"][:, :, :num_context_frames, :, :] = context_latents_clean + else: + # Repainting Strategy: Update all latents, but context will be repainted in next step + inputs_shared["latents"] = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], inputs_shared["latents"]) + else: + # Standard mode: update all latents + inputs_shared["latents"] = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], inputs_shared["latents"]) + + # Final latents: Extract target part if context memory was used + if enable_context_memory and context_latents_clean is not None: + # After denoising, latents contain both context and target + # Extract only target part for final output (context was only for conditioning) + # Experiment 16: Extract full 81 frames (including reconstruction of first 4 frames) + # Experiment 1_2: if suffix => extract FRONT, else extract BACK + if context_position == "suffix": + inputs_shared["latents"] = inputs_shared["latents"][:, :, :-num_context_frames, :, :] # (B, C, 81, H, W) + else: + inputs_shared["latents"] = inputs_shared["latents"][:, :, num_context_frames:, :, :] # (B, C, 81, H, W) + + # VACE (TODO: remove it) + if vace_reference_image is not None: + inputs_shared["latents"] = inputs_shared["latents"][:, :, 1:] + + # Decode + self.load_models_to_device(['vae']) + video = self.vae.decode(inputs_shared["latents"], device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + video = self.vae_output_to_video(video) + self.load_models_to_device([]) + + return video + + + +class PipelineUnit: + def __init__( + self, + seperate_cfg: bool = False, + take_over: bool = False, + input_params: tuple[str] = None, + input_params_posi: dict[str, str] = None, + input_params_nega: dict[str, str] = None, + onload_model_names: tuple[str] = None + ): + self.seperate_cfg = seperate_cfg + self.take_over = take_over + self.input_params = input_params + self.input_params_posi = input_params_posi + self.input_params_nega = input_params_nega + self.onload_model_names = onload_model_names + + + def process(self, pipe: WanVideoPipeline, inputs: dict, positive=True, **kwargs) -> dict: + raise NotImplementedError("`process` is not implemented.") + + + +class PipelineUnitRunner: + def __init__(self): + pass + + def __call__(self, unit: PipelineUnit, pipe: WanVideoPipeline, inputs_shared: dict, inputs_posi: dict, inputs_nega: dict) -> tuple[dict, dict]: + if unit.take_over: + # Let the pipeline unit take over this function. + inputs_shared, inputs_posi, inputs_nega = unit.process(pipe, inputs_shared=inputs_shared, inputs_posi=inputs_posi, inputs_nega=inputs_nega) + elif unit.seperate_cfg: + # Positive side + processor_inputs = {name: inputs_posi.get(name_) for name, name_ in unit.input_params_posi.items()} + if unit.input_params is not None: + for name in unit.input_params: + processor_inputs[name] = inputs_shared.get(name) + processor_outputs = unit.process(pipe, **processor_inputs) + inputs_posi.update(processor_outputs) + # Negative side + if inputs_shared["cfg_scale"] != 1: + processor_inputs = {name: inputs_nega.get(name_) for name, name_ in unit.input_params_nega.items()} + if unit.input_params is not None: + for name in unit.input_params: + processor_inputs[name] = inputs_shared.get(name) + processor_outputs = unit.process(pipe, **processor_inputs) + inputs_nega.update(processor_outputs) + else: + inputs_nega.update(processor_outputs) + else: + processor_inputs = {name: inputs_shared.get(name) for name in unit.input_params} + processor_outputs = unit.process(pipe, **processor_inputs) + inputs_shared.update(processor_outputs) + return inputs_shared, inputs_posi, inputs_nega + + + +class WanVideoUnit_ShapeChecker(PipelineUnit): + def __init__(self): + super().__init__(input_params=("height", "width", "num_frames")) + + def process(self, pipe: WanVideoPipeline, height, width, num_frames): + height, width, num_frames = pipe.check_resize_height_width(height, width, num_frames) + return {"height": height, "width": width, "num_frames": num_frames} + + + +class WanVideoUnit_NoiseInitializer(PipelineUnit): + def __init__(self): + super().__init__(input_params=("height", "width", "num_frames", "seed", "rand_device", "vace_reference_image", "batch_size")) + + def process(self, pipe: WanVideoPipeline, height, width, num_frames, seed, rand_device, vace_reference_image, batch_size=None): + length = (num_frames - 1) // 4 + 1 + if vace_reference_image is not None: + length += 1 + batch_size = int(batch_size or 1) + noise = pipe.generate_noise((batch_size, 16, length, height//8, width//8), seed=seed, rand_device=rand_device) + if vace_reference_image is not None: + noise = torch.concat((noise[:, :, -1:], noise[:, :, :-1]), dim=2) + return {"noise": noise} + + + +class WanVideoUnit_InputVideoEmbedder(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("input_video", "noise", "tiled", "tile_size", "tile_stride", "vace_reference_image"), + onload_model_names=("vae",) + ) + + def process(self, pipe: WanVideoPipeline, input_video, noise, tiled, tile_size, tile_stride, vace_reference_image): + if input_video is None: + return {"latents": noise} + pipe.load_models_to_device(["vae"]) + # Support batch: input_video can be list of lists (batch of videos) or list of PIL (single video) + is_batch = isinstance(input_video, (list, tuple)) and len(input_video) > 0 and isinstance(input_video[0], (list, tuple)) + if is_batch: + # Batch of videos: each element is list of PIL images; preprocess_video returns (1, C, T, H, W) + videos_tensor = torch.cat([ + pipe.preprocess_video(v) for v in input_video + ], dim=0) # (B, C, T, H, W) + input_video = videos_tensor + else: + input_video = pipe.preprocess_video(input_video) + input_latents = pipe.vae.encode(input_video, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device) + if vace_reference_image is not None: + vace_reference_image = pipe.preprocess_video([vace_reference_image]) + vace_reference_latents = pipe.vae.encode(vace_reference_image, device=pipe.device).to(dtype=pipe.torch_dtype, device=pipe.device) + input_latents = torch.concat([vace_reference_latents, input_latents], dim=2) + if pipe.scheduler.training: + return {"latents": noise, "input_latents": input_latents} + else: + latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0]) + return {"latents": latents} + + + +class WanVideoUnit_PromptEmbedder(PipelineUnit): + def __init__(self): + super().__init__( + seperate_cfg=True, + input_params_posi={"prompt": "prompt", "positive": "positive"}, + input_params_nega={"prompt": "negative_prompt", "positive": "positive"}, + onload_model_names=("text_encoder",) + ) + + def process(self, pipe: WanVideoPipeline, prompt, positive) -> dict: + pipe.load_models_to_device(self.onload_model_names) + prompt_emb = pipe.prompter.encode_prompt(prompt, positive=positive, device=pipe.device) + return {"context": prompt_emb} + + + +class WanVideoUnit_ImageEmbedder(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("input_image", "end_image", "num_frames", "height", "width", "tiled", "tile_size", "tile_stride"), + onload_model_names=("image_encoder", "vae") + ) + + def process(self, pipe: WanVideoPipeline, input_image, end_image, num_frames, height, width, tiled, tile_size, tile_stride): + if input_image is None: + return {} + pipe.load_models_to_device(self.onload_model_names) + # Some base T2V checkpoints do not provide an image encoder. + # In that case, image conditioning is not supported — safely skip. + if getattr(pipe, "image_encoder", None) is None: + return {} + image = pipe.preprocess_image(input_image.resize((width, height))).to(pipe.device) + clip_context = pipe.image_encoder.encode_image([image]) + msk = torch.ones(1, num_frames, height//8, width//8, device=pipe.device) + msk[:, 1:] = 0 + if end_image is not None: + end_image = pipe.preprocess_image(end_image.resize((width, height))).to(pipe.device) + vae_input = torch.concat([image.transpose(0,1), torch.zeros(3, num_frames-2, height, width).to(image.device), end_image.transpose(0,1)],dim=1) + if pipe.dit.has_image_pos_emb: + clip_context = torch.concat([clip_context, pipe.image_encoder.encode_image([end_image])], dim=1) + msk[:, -1:] = 1 + else: + vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1) + + msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1) + msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8) + msk = msk.transpose(1, 2)[0] + + y = pipe.vae.encode([vae_input.to(dtype=pipe.torch_dtype, device=pipe.device)], device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0] + y = y.to(dtype=pipe.torch_dtype, device=pipe.device) + y = torch.concat([msk, y]) + y = y.unsqueeze(0) + clip_context = clip_context.to(dtype=pipe.torch_dtype, device=pipe.device) + y = y.to(dtype=pipe.torch_dtype, device=pipe.device) + return {"clip_feature": clip_context, "y": y} + + + +class WanVideoUnit_FunControl(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("control_video", "num_frames", "height", "width", "tiled", "tile_size", "tile_stride", "clip_feature", "y"), + onload_model_names=("vae") + ) + + def process(self, pipe: WanVideoPipeline, control_video, num_frames, height, width, tiled, tile_size, tile_stride, clip_feature, y): + if control_video is None: + return {} + pipe.load_models_to_device(self.onload_model_names) + control_video = pipe.preprocess_video(control_video) + control_latents = pipe.vae.encode(control_video, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device) + control_latents = control_latents.to(dtype=pipe.torch_dtype, device=pipe.device) + if clip_feature is None or y is None: + clip_feature = torch.zeros((1, 257, 1280), dtype=pipe.torch_dtype, device=pipe.device) + y = torch.zeros((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), dtype=pipe.torch_dtype, device=pipe.device) + else: + y = y[:, -16:] + y = torch.concat([control_latents, y], dim=1) + return {"clip_feature": clip_feature, "y": y} + + + +class WanVideoUnit_FunReference(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("reference_image", "height", "width", "reference_image"), + onload_model_names=("vae") + ) + + def process(self, pipe: WanVideoPipeline, reference_image, height, width): + if reference_image is None: + return {} + # Some base T2V checkpoints do not provide an image encoder. + # In that case, reference-image conditioning is not supported — safely skip. + if getattr(pipe, "image_encoder", None) is None: + return {} + pipe.load_models_to_device(["vae"]) + reference_image = reference_image.resize((width, height)) + reference_latents = pipe.preprocess_video([reference_image]) + reference_latents = pipe.vae.encode(reference_latents, device=pipe.device) + clip_feature = pipe.preprocess_image(reference_image) + clip_feature = pipe.image_encoder.encode_image([clip_feature]) + return {"reference_latents": reference_latents, "clip_feature": clip_feature} + + + +class WanVideoUnit_FunCameraControl(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("height", "width", "num_frames", "camera_control_direction", "camera_control_speed", "camera_control_origin", "camera_control_poses", "latents", "input_image") + ) + + def process(self, pipe: WanVideoPipeline, height, width, num_frames, camera_control_direction, camera_control_speed, camera_control_origin, camera_control_poses, latents, input_image): + if getattr(pipe.dit, "control_adapter", None) is None: + return {} + if camera_control_direction is None and camera_control_poses is None: + return {} + if camera_control_poses is not None: + # Build plucker embedding from explicit trajectory + try: + from ..models.wan_video_camera_controller import process_pose_file # type: ignore + camera_control_plucker_embedding = process_pose_file(camera_control_poses, width=width, height=height, device="cpu") + except Exception as e: + # Fallback: ignore camera control if malformed + return {} + else: + camera_control_plucker_embedding = pipe.dit.control_adapter.process_camera_coordinates( + camera_control_direction, num_frames, height, width, camera_control_speed, camera_control_origin) + + control_camera_video = camera_control_plucker_embedding[:num_frames].permute([3, 0, 1, 2]).unsqueeze(0) + control_camera_latents = torch.concat( + [ + torch.repeat_interleave(control_camera_video[:, :, 0:1], repeats=4, dim=2), + control_camera_video[:, :, 1:] + ], dim=2 + ).transpose(1, 2) + b, f, c, h, w = control_camera_latents.shape + control_camera_latents = control_camera_latents.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3) + control_camera_latents = control_camera_latents.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2) + control_camera_latents_input = control_camera_latents.to(device=pipe.device, dtype=pipe.torch_dtype) + + input_image = input_image.resize((width, height)) + input_latents = pipe.preprocess_video([input_image]) + input_latents = pipe.vae.encode(input_latents, device=pipe.device) + y = torch.zeros_like(latents).to(pipe.device) + y[:, :, :1] = input_latents + y = y.to(dtype=pipe.torch_dtype, device=pipe.device) + return {"control_camera_latents_input": control_camera_latents_input, "y": y} + + + +class WanVideoUnit_SpeedControl(PipelineUnit): + def __init__(self): + super().__init__(input_params=("motion_bucket_id",)) + + def process(self, pipe: WanVideoPipeline, motion_bucket_id): + if motion_bucket_id is None: + return {} + motion_bucket_id = torch.Tensor((motion_bucket_id,)).to(dtype=pipe.torch_dtype, device=pipe.device) + return {"motion_bucket_id": motion_bucket_id} + + + +class WanVideoUnit_VACE(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("vace_video", "vace_video_mask", "vace_reference_image", "vace_scale", "height", "width", "num_frames", "tiled", "tile_size", "tile_stride"), + onload_model_names=("vae",) + ) + + def process( + self, + pipe: WanVideoPipeline, + vace_video, vace_video_mask, vace_reference_image, vace_scale, + height, width, num_frames, + tiled, tile_size, tile_stride + ): + if vace_video is not None or vace_video_mask is not None or vace_reference_image is not None: + pipe.load_models_to_device(["vae"]) + if vace_video is None: + vace_video = torch.zeros((1, 3, num_frames, height, width), dtype=pipe.torch_dtype, device=pipe.device) + else: + vace_video = pipe.preprocess_video(vace_video) + + if vace_video_mask is None: + vace_video_mask = torch.ones_like(vace_video) + else: + vace_video_mask = pipe.preprocess_video(vace_video_mask, min_value=0, max_value=1) + + inactive = vace_video * (1 - vace_video_mask) + 0 * vace_video_mask + reactive = vace_video * vace_video_mask + 0 * (1 - vace_video_mask) + inactive = pipe.vae.encode(inactive, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device) + reactive = pipe.vae.encode(reactive, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device) + vace_video_latents = torch.concat((inactive, reactive), dim=1) + + vace_mask_latents = rearrange(vace_video_mask[0,0], "T (H P) (W Q) -> 1 (P Q) T H W", P=8, Q=8) + vace_mask_latents = torch.nn.functional.interpolate(vace_mask_latents, size=((vace_mask_latents.shape[2] + 3) // 4, vace_mask_latents.shape[3], vace_mask_latents.shape[4]), mode='nearest-exact') + + if vace_reference_image is None: + pass + else: + vace_reference_image = pipe.preprocess_video([vace_reference_image]) + vace_reference_latents = pipe.vae.encode(vace_reference_image, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device) + vace_reference_latents = torch.concat((vace_reference_latents, torch.zeros_like(vace_reference_latents)), dim=1) + vace_video_latents = torch.concat((vace_reference_latents, vace_video_latents), dim=2) + vace_mask_latents = torch.concat((torch.zeros_like(vace_mask_latents[:, :, :1]), vace_mask_latents), dim=2) + + vace_context = torch.concat((vace_video_latents, vace_mask_latents), dim=1) + return {"vace_context": vace_context, "vace_scale": vace_scale} + else: + return {"vace_context": None, "vace_scale": vace_scale} + + + +class WanVideoUnit_UnifiedSequenceParallel(PipelineUnit): + def __init__(self): + super().__init__(input_params=()) + + def process(self, pipe: WanVideoPipeline): + if hasattr(pipe, "use_unified_sequence_parallel"): + if pipe.use_unified_sequence_parallel: + return {"use_unified_sequence_parallel": True} + return {} + + + +class WanVideoUnit_TeaCache(PipelineUnit): + def __init__(self): + super().__init__( + seperate_cfg=True, + input_params_posi={"num_inference_steps": "num_inference_steps", "tea_cache_l1_thresh": "tea_cache_l1_thresh", "tea_cache_model_id": "tea_cache_model_id"}, + input_params_nega={"num_inference_steps": "num_inference_steps", "tea_cache_l1_thresh": "tea_cache_l1_thresh", "tea_cache_model_id": "tea_cache_model_id"}, + ) + + def process(self, pipe: WanVideoPipeline, num_inference_steps, tea_cache_l1_thresh, tea_cache_model_id): + if tea_cache_l1_thresh is None: + return {} + return {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh, model_id=tea_cache_model_id)} + + + +class WanVideoUnit_CfgMerger(PipelineUnit): + def __init__(self): + super().__init__(take_over=True) + self.concat_tensor_names = ["context", "clip_feature", "y", "reference_latents"] + + def process(self, pipe: WanVideoPipeline, inputs_shared, inputs_posi, inputs_nega): + if not inputs_shared["cfg_merge"]: + return inputs_shared, inputs_posi, inputs_nega + for name in self.concat_tensor_names: + tensor_posi = inputs_posi.get(name) + tensor_nega = inputs_nega.get(name) + tensor_shared = inputs_shared.get(name) + if tensor_posi is not None and tensor_nega is not None: + inputs_shared[name] = torch.concat((tensor_posi, tensor_nega), dim=0) + elif tensor_shared is not None: + inputs_shared[name] = torch.concat((tensor_shared, tensor_shared), dim=0) + inputs_posi.clear() + inputs_nega.clear() + return inputs_shared, inputs_posi, inputs_nega + + + +class TeaCache: + def __init__(self, num_inference_steps, rel_l1_thresh, model_id): + self.num_inference_steps = num_inference_steps + self.step = 0 + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = None + self.rel_l1_thresh = rel_l1_thresh + self.previous_residual = None + self.previous_hidden_states = None + + self.coefficients_dict = { + "Wan2.1-T2V-1.3B": [-5.21862437e+04, 9.23041404e+03, -5.28275948e+02, 1.36987616e+01, -4.99875664e-02], + "Wan2.1-T2V-14B": [-3.03318725e+05, 4.90537029e+04, -2.65530556e+03, 5.87365115e+01, -3.15583525e-01], + "Wan2.1-I2V-14B-480P": [2.57151496e+05, -3.54229917e+04, 1.40286849e+03, -1.35890334e+01, 1.32517977e-01], + "Wan2.1-I2V-14B-720P": [ 8.10705460e+03, 2.13393892e+03, -3.72934672e+02, 1.66203073e+01, -4.17769401e-02], + } + if model_id not in self.coefficients_dict: + supported_model_ids = ", ".join([i for i in self.coefficients_dict]) + raise ValueError(f"{model_id} is not a supported TeaCache model id. Please choose a valid model id in ({supported_model_ids}).") + self.coefficients = self.coefficients_dict[model_id] + + def check(self, dit: WanModel, x, t_mod): + modulated_inp = t_mod.clone() + if self.step == 0 or self.step == self.num_inference_steps - 1: + should_calc = True + self.accumulated_rel_l1_distance = 0 + else: + coefficients = self.coefficients + rescale_func = np.poly1d(coefficients) + self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item()) + if self.accumulated_rel_l1_distance < self.rel_l1_thresh: + should_calc = False + else: + should_calc = True + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = modulated_inp + self.step += 1 + if self.step == self.num_inference_steps: + self.step = 0 + if should_calc: + self.previous_hidden_states = x.clone() + return not should_calc + + def store(self, hidden_states): + self.previous_residual = hidden_states - self.previous_hidden_states + self.previous_hidden_states = None + + def update(self, hidden_states): + hidden_states = hidden_states + self.previous_residual + return hidden_states + + + +class TemporalTiler_BCTHW: + def __init__(self): + pass + + def build_1d_mask(self, length, left_bound, right_bound, border_width): + x = torch.ones((length,)) + if not left_bound: + x[:border_width] = (torch.arange(border_width) + 1) / border_width + if not right_bound: + x[-border_width:] = torch.flip((torch.arange(border_width) + 1) / border_width, dims=(0,)) + return x + + def build_mask(self, data, is_bound, border_width): + _, _, T, _, _ = data.shape + t = self.build_1d_mask(T, is_bound[0], is_bound[1], border_width[0]) + mask = repeat(t, "T -> 1 1 T 1 1") + return mask + + def run(self, model_fn, sliding_window_size, sliding_window_stride, computation_device, computation_dtype, model_kwargs, tensor_names, batch_size=None): + tensor_names = [tensor_name for tensor_name in tensor_names if model_kwargs.get(tensor_name) is not None] + tensor_dict = {tensor_name: model_kwargs[tensor_name] for tensor_name in tensor_names} + B, C, T, H, W = tensor_dict[tensor_names[0]].shape + if batch_size is not None: + B *= batch_size + data_device, data_dtype = tensor_dict[tensor_names[0]].device, tensor_dict[tensor_names[0]].dtype + value = torch.zeros((B, C, T, H, W), device=data_device, dtype=data_dtype) + weight = torch.zeros((1, 1, T, 1, 1), device=data_device, dtype=data_dtype) + for t in range(0, T, sliding_window_stride): + if t - sliding_window_stride >= 0 and t - sliding_window_stride + sliding_window_size >= T: + continue + t_ = min(t + sliding_window_size, T) + model_kwargs.update({ + tensor_name: tensor_dict[tensor_name][:, :, t: t_:, :].to(device=computation_device, dtype=computation_dtype) \ + for tensor_name in tensor_names + }) + model_output = model_fn(**model_kwargs).to(device=data_device, dtype=data_dtype) + mask = self.build_mask( + model_output, + is_bound=(t == 0, t_ == T), + border_width=(sliding_window_size - sliding_window_stride,) + ).to(device=data_device, dtype=data_dtype) + value[:, :, t: t_, :, :] += model_output * mask + weight[:, :, t: t_, :, :] += mask + value /= weight + model_kwargs.update(tensor_dict) + return value + + + +def model_fn_wan_video( + dit: WanModel, + motion_controller: WanMotionControllerModel = None, + vace: VaceWanModel = None, + latents: torch.Tensor = None, + timestep: torch.Tensor = None, + context: torch.Tensor = None, + clip_feature: Optional[torch.Tensor] = None, + y: Optional[torch.Tensor] = None, + reference_latents = None, + vace_context = None, + vace_scale = 1.0, + tea_cache: TeaCache = None, + use_unified_sequence_parallel: bool = False, + motion_bucket_id: Optional[torch.Tensor] = None, + sliding_window_size: Optional[int] = None, + sliding_window_stride: Optional[int] = None, + cfg_merge: bool = False, + use_gradient_checkpointing: bool = False, + use_gradient_checkpointing_offload: bool = False, + control_camera_latents_input = None, + actions = None, + **kwargs, +): + if sliding_window_size is not None and sliding_window_stride is not None: + model_kwargs = dict( + dit=dit, + motion_controller=motion_controller, + vace=vace, + latents=latents, + timestep=timestep, + context=context, + clip_feature=clip_feature, + y=y, + reference_latents=reference_latents, + vace_context=vace_context, + vace_scale=vace_scale, + tea_cache=tea_cache, + use_unified_sequence_parallel=use_unified_sequence_parallel, + motion_bucket_id=motion_bucket_id, + ) + return TemporalTiler_BCTHW().run( + model_fn_wan_video, + sliding_window_size, sliding_window_stride, + latents.device, latents.dtype, + model_kwargs=model_kwargs, + tensor_names=["latents", "y"], + batch_size=2 if cfg_merge else 1 + ) + + if use_unified_sequence_parallel: + import torch.distributed as dist + from xfuser.core.distributed import (get_sequence_parallel_rank, + get_sequence_parallel_world_size, + get_sp_group) + + t = dit.time_embedding(sinusoidal_embedding_1d(dit.freq_dim, timestep)) + t_mod = dit.time_projection(t).unflatten(1, (6, dit.dim)) + if motion_bucket_id is not None and motion_controller is not None: + t_mod = t_mod + motion_controller(motion_bucket_id).unflatten(1, (6, dit.dim)) + context = dit.text_embedding(context) + + x = latents + # Merged cfg + if x.shape[0] != context.shape[0]: + x = torch.concat([x] * context.shape[0], dim=0) + if timestep.shape[0] != context.shape[0]: + timestep = torch.concat([timestep] * context.shape[0], dim=0) + + if dit.has_image_input: + x = torch.cat([x, y], dim=1) # (b, c_x + c_y, f, h, w) + clip_embdding = dit.img_emb(clip_feature) + context = torch.cat([clip_embdding, context], dim=1) + + # Add camera control + x, (f, h, w) = dit.patchify(x, control_camera_latents_input) + + + # Reference image + if reference_latents is not None: + if len(reference_latents.shape) == 5: + reference_latents = reference_latents[:, :, 0] + reference_latents = dit.ref_conv(reference_latents).flatten(2).transpose(1, 2) + x = torch.concat([reference_latents, x], dim=1) + f += 1 + + # Build time position encoding with negative indices for context frames + # Get num_context_frames from kwargs (passed from pipeline) + num_context_frames = kwargs.get("num_context_frames", 0) + import os + context_position = kwargs.get("context_position", os.environ.get("CONTEXT_POSITION", "prefix")) + if isinstance(context_position, str): + context_position = context_position.lower() + if context_position not in ("prefix", "suffix"): + context_position = "prefix" + + # MoC [2508.21058]: reweight context chunks by query–chunk similarity before blocks + use_moc = kwargs.get("use_moc", False) + moc_module = kwargs.get("moc_module", None) + if use_moc and moc_module is not None and num_context_frames > 0 and f > num_context_frames: + x = moc_module(x, num_context_frames, int(f), int(h), int(w), context_position) + + # FramePack-Weight (FAR-style): see diffsynth.models.memory.framepack_weight + use_framepack_memory = kwargs.get("use_framepack_memory", False) + context_temporal_decay = float(kwargs.get("context_temporal_decay", 1.0) or 1.0) + context_attention_weight = float(kwargs.get("context_attention_weight", 1.0) or 1.0) + x = apply_framepack_token_weights( + x, + num_context_frames=num_context_frames, + f=int(f), + h=int(h), + w=int(w), + context_position=context_position, + use_framepack_memory=bool(use_framepack_memory), + context_temporal_decay=context_temporal_decay, + context_attention_weight=context_attention_weight, + ) + + # Spatial memory: legacy pool OR SpatialGridMemory; inject to text context (concat_text) + # or directly read memory from target tokens (cross_attn_readout). + use_spatial_memory = kwargs.get("use_spatial_memory", False) + spatial_memory_tokens = int(kwargs.get("spatial_memory_tokens", 64) or 64) + use_spatial_memory_legacy = bool(kwargs.get("use_spatial_memory_legacy", False)) + spatial_memory_module = kwargs.get("spatial_memory_module", None) + spatial_inject_mode = str(kwargs.get("spatial_memory_inject_mode", "concat_text") or "concat_text") + spatial_readout_module = kwargs.get("spatial_memory_readout_module", None) + if ( + use_spatial_memory + and spatial_inject_mode != "none" + and spatial_memory_tokens > 0 + and num_context_frames > 0 + and f > num_context_frames + ): + spatial_per_frame = int(h) * int(w) + context_tokens = num_context_frames * spatial_per_frame + if context_position == "suffix": + x_context = x[:, -context_tokens:, :] + else: + x_context = x[:, :context_tokens, :] + if spatial_memory_module is not None and not use_spatial_memory_legacy: + mem = spatial_memory_module(x_context, num_context_frames, int(h), int(w)) + else: + x_ctx = x_context.reshape(x_context.shape[0], num_context_frames, spatial_per_frame, x_context.shape[-1]) + mem = x_ctx.mean(dim=1) + if mem.shape[1] != spatial_memory_tokens: + mem_t = mem.transpose(1, 2) # (B, D, S) + mem_t = torch.nn.functional.adaptive_avg_pool1d(mem_t, spatial_memory_tokens) + mem = mem_t.transpose(1, 2) + if spatial_inject_mode == "cross_attn_readout": + target_tokens = (int(f) - int(num_context_frames)) * int(h) * int(w) + if target_tokens > 0 and target_tokens < x.shape[1]: + if context_position == "suffix": + x_target = x[:, :target_tokens, :] + x_context = x[:, target_tokens:, :] + x_target = apply_spatial_cross_attn_readout(x_target, mem, spatial_readout_module) + x = torch.cat([x_target, x_context], dim=1) + else: + x_context = x[:, :x.shape[1] - target_tokens, :] + x_target = x[:, x.shape[1] - target_tokens:, :] + x_target = apply_spatial_cross_attn_readout(x_target, mem, spatial_readout_module) + x = torch.cat([x_context, x_target], dim=1) + else: + context = inject_spatial_memory(context, mem, "concat_text") + else: + context = inject_spatial_memory(context, mem, spatial_inject_mode) + + if num_context_frames > 0 and f > num_context_frames: + # Context frames: use negative temporal indices [-k, ..., -1] to represent "past" frames (prefix mode) + # OR use positive indices [target_frames, ..., target_frames+k-1] to represent "future" frames (suffix mode) + # Target frames: use positive temporal indices [0, ..., N] to represent "current/future" frames + target_frames = f - num_context_frames + + # Construct positive position indices for target: [0, ..., target_frames-1] + target_ids = torch.arange(0, target_frames, device=x.device, dtype=torch.float64) + + # Experiment 1_2: if context is concatenated at END (suffix), use positive indices after target + if context_position == "suffix": + # Suffix mode: Context is after target, so use positive indices [target_frames, ..., target_frames+num_context_frames-1] + # Example: target_frames=21, num_context_frames=2 => context_ids = [21, 22] + context_ids = torch.arange(target_frames, target_frames + num_context_frames, device=x.device, dtype=torch.float64) + t_ids = torch.cat([target_ids, context_ids]) + else: + # Prefix mode: Context is before target, so use negative indices [-num_context_frames, ..., -1] + # Example: num_context_frames=2 => context_ids = [-2, -1] + context_ids = torch.arange(-num_context_frames, 0, device=x.device, dtype=torch.float64) + t_ids = torch.cat([context_ids, target_ids]) + + # Generate RoPE frequencies for custom position indices (including negative) + # dit.freqs[0] is precomputed RoPE frequencies (complex64), we need to generate for custom positions + # Use the same logic as precompute_freqs_cis but for arbitrary positions + # Get the frequency dimension from precomputed freqs + time_freq_dim_complex = dit.freqs[0].shape[-1] # This is the complex dimension: (head_dim - 2*(head_dim//3)) // 2 + time_freq_dim_original = time_freq_dim_complex * 2 # Original dimension: head_dim - 2*(head_dim//3) + # Compute base frequencies (same as in precompute_freqs_cis) + theta = 10000.0 + base_freqs = 1.0 / (theta ** (torch.arange(0, time_freq_dim_original, 2, device=x.device, dtype=torch.float64) + [:time_freq_dim_complex].double() / time_freq_dim_original)) + # Compute frequencies for each position: outer product of positions and base_freqs + t_freqs = torch.outer(t_ids, base_freqs) # (f, freq_dim_complex) + # Convert to complex form (RoPE format): polar(ones, freqs) + t_freqs_cis = torch.polar(torch.ones_like(t_freqs), t_freqs) # complex64, (f, freq_dim_complex) + + # Reshape and expand to match spatial dimensions: (f, 1, 1, freq_dim) -> (f, h, w, freq_dim) + freqs_t = t_freqs_cis.view(f, 1, 1, -1).expand(f, h, w, -1).to(x.device) + else: + # Standard mode: use precomputed frequencies for all frames + freqs_t = dit.freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1).to(x.device) + + # Spatial frequencies remain the same + freqs_h = dit.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1).to(x.device) + freqs_w = dit.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1).to(x.device) + + # Concatenate all frequencies (all tensors are now on the same device) + freqs = torch.cat([freqs_t, freqs_h, freqs_w], dim=-1).reshape(f * h * w, 1, -1) + + # Unified implicit (memory_design_advanced): single ctx-learning injection into t_mod + # When use_non_fov_compressed: compressor input = non_fov_context_tokens (prev chunk non-FOV frames). + # Otherwise: compressor input = x_ctx (explicit FOV context tokens). + use_unified_implicit = kwargs.get("use_unified_implicit", False) + unified_encoder = kwargs.get("unified_implicit_encoder", None) + context_learning_injector = kwargs.get("context_learning_injector", None) + use_non_fov_compressed = kwargs.get("use_non_fov_compressed", False) + non_fov_context_tokens = kwargs.get("non_fov_context_tokens", None) + if use_unified_implicit and unified_encoder is not None and context_learning_injector is not None and f > num_context_frames: + if use_non_fov_compressed and non_fov_context_tokens is not None: + z = unified_encoder(non_fov_context_tokens) + elif num_context_frames > 0: + spatial_per_frame = int(h) * int(w) + context_tokens = num_context_frames * spatial_per_frame + if context_position == "suffix": + x_ctx = x[:, -context_tokens:, :] + else: + x_ctx = x[:, :context_tokens, :] + z = unified_encoder(x_ctx) + else: + z = None + if z is not None: + delta_t_mod = context_learning_injector(z) + t_mod = t_mod + delta_t_mod + + # TeaCache + if tea_cache is not None: + tea_cache_update = tea_cache.check(dit, x, t_mod) + else: + tea_cache_update = False + + if vace_context is not None: + vace_hints = vace(x, vace_context, context, t_mod, freqs) + + # blocks + if use_unified_sequence_parallel: + if dist.is_initialized() and dist.get_world_size() > 1: + x = torch.chunk(x, get_sequence_parallel_world_size(), dim=1)[get_sequence_parallel_rank()] + if tea_cache_update: + x = tea_cache.update(x) + else: + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + return custom_forward + + if actions is not None: + actions = torch.tensor(actions, device=x.device, dtype=x.dtype) + actions = actions.unsqueeze(0) + + for block_id, block in enumerate(dit.blocks): + if use_gradient_checkpointing_offload: + with torch.autograd.graph.save_on_cpu(): + if actions is not None: + x = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + x, context, t_mod, freqs, actions, + use_reentrant=False, + ) + else: + x = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + x, context, t_mod, freqs, + use_reentrant=False, + ) + elif use_gradient_checkpointing: + if actions is not None: + x = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + x, context, t_mod, freqs, actions, + use_reentrant=False, + ) + else: + x = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + x, context, t_mod, freqs, + use_reentrant=False, + ) + else: + if actions is not None: + x = block(x, context, t_mod, freqs, actions) + else: + x = block(x, context, t_mod, freqs) + if vace_context is not None and block_id in vace.vace_layers_mapping: + current_vace_hint = vace_hints[vace.vace_layers_mapping[block_id]] + if use_unified_sequence_parallel and dist.is_initialized() and dist.get_world_size() > 1: + current_vace_hint = torch.chunk(current_vace_hint, get_sequence_parallel_world_size(), dim=1)[get_sequence_parallel_rank()] + x = x + current_vace_hint * vace_scale + if tea_cache is not None: + tea_cache.store(x) + + x = dit.head(x, t) + if use_unified_sequence_parallel: + if dist.is_initialized() and dist.get_world_size() > 1: + x = get_sp_group().all_gather(x, dim=1) + # Remove reference latents + if reference_latents is not None: + x = x[:, reference_latents.shape[1]:] + f -= 1 + x = dit.unpatchify(x, (f, h, w)) + return x diff --git a/diffsynth/processors/FastBlend.py b/diffsynth/processors/FastBlend.py new file mode 100644 index 0000000000000000000000000000000000000000..fed33f4fdd215c8c9dc46f3b07d9453a12cc6b98 --- /dev/null +++ b/diffsynth/processors/FastBlend.py @@ -0,0 +1,142 @@ +from PIL import Image +import cupy as cp +import numpy as np +from tqdm import tqdm +from ..extensions.FastBlend.patch_match import PyramidPatchMatcher +from ..extensions.FastBlend.runners.fast import TableManager +from .base import VideoProcessor + + +class FastBlendSmoother(VideoProcessor): + def __init__( + self, + inference_mode="fast", batch_size=8, window_size=60, + minimum_patch_size=5, threads_per_block=8, num_iter=5, gpu_id=0, guide_weight=10.0, initialize="identity", tracking_window_size=0 + ): + self.inference_mode = inference_mode + self.batch_size = batch_size + self.window_size = window_size + self.ebsynth_config = { + "minimum_patch_size": minimum_patch_size, + "threads_per_block": threads_per_block, + "num_iter": num_iter, + "gpu_id": gpu_id, + "guide_weight": guide_weight, + "initialize": initialize, + "tracking_window_size": tracking_window_size + } + + @staticmethod + def from_model_manager(model_manager, **kwargs): + # TODO: fetch GPU ID from model_manager + return FastBlendSmoother(**kwargs) + + def inference_fast(self, frames_guide, frames_style): + table_manager = TableManager() + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + **self.ebsynth_config + ) + # left part + table_l = table_manager.build_remapping_table(frames_guide, frames_style, patch_match_engine, self.batch_size, desc="Fast Mode Step 1/4") + table_l = table_manager.remapping_table_to_blending_table(table_l) + table_l = table_manager.process_window_sum(frames_guide, table_l, patch_match_engine, self.window_size, self.batch_size, desc="Fast Mode Step 2/4") + # right part + table_r = table_manager.build_remapping_table(frames_guide[::-1], frames_style[::-1], patch_match_engine, self.batch_size, desc="Fast Mode Step 3/4") + table_r = table_manager.remapping_table_to_blending_table(table_r) + table_r = table_manager.process_window_sum(frames_guide[::-1], table_r, patch_match_engine, self.window_size, self.batch_size, desc="Fast Mode Step 4/4")[::-1] + # merge + frames = [] + for (frame_l, weight_l), frame_m, (frame_r, weight_r) in zip(table_l, frames_style, table_r): + weight_m = -1 + weight = weight_l + weight_m + weight_r + frame = frame_l * (weight_l / weight) + frame_m * (weight_m / weight) + frame_r * (weight_r / weight) + frames.append(frame) + frames = [frame.clip(0, 255).astype("uint8") for frame in frames] + frames = [Image.fromarray(frame) for frame in frames] + return frames + + def inference_balanced(self, frames_guide, frames_style): + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + **self.ebsynth_config + ) + output_frames = [] + # tasks + n = len(frames_style) + tasks = [] + for target in range(n): + for source in range(target - self.window_size, target + self.window_size + 1): + if source >= 0 and source < n and source != target: + tasks.append((source, target)) + # run + frames = [(None, 1) for i in range(n)] + for batch_id in tqdm(range(0, len(tasks), self.batch_size), desc="Balanced Mode"): + tasks_batch = tasks[batch_id: min(batch_id+self.batch_size, len(tasks))] + source_guide = np.stack([frames_guide[source] for source, target in tasks_batch]) + target_guide = np.stack([frames_guide[target] for source, target in tasks_batch]) + source_style = np.stack([frames_style[source] for source, target in tasks_batch]) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + for (source, target), result in zip(tasks_batch, target_style): + frame, weight = frames[target] + if frame is None: + frame = frames_style[target] + frames[target] = ( + frame * (weight / (weight + 1)) + result / (weight + 1), + weight + 1 + ) + if weight + 1 == min(n, target + self.window_size + 1) - max(0, target - self.window_size): + frame = frame.clip(0, 255).astype("uint8") + output_frames.append(Image.fromarray(frame)) + frames[target] = (None, 1) + return output_frames + + def inference_accurate(self, frames_guide, frames_style): + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + use_mean_target_style=True, + **self.ebsynth_config + ) + output_frames = [] + # run + n = len(frames_style) + for target in tqdm(range(n), desc="Accurate Mode"): + l, r = max(target - self.window_size, 0), min(target + self.window_size + 1, n) + remapped_frames = [] + for i in range(l, r, self.batch_size): + j = min(i + self.batch_size, r) + source_guide = np.stack([frames_guide[source] for source in range(i, j)]) + target_guide = np.stack([frames_guide[target]] * (j - i)) + source_style = np.stack([frames_style[source] for source in range(i, j)]) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + remapped_frames.append(target_style) + frame = np.concatenate(remapped_frames, axis=0).mean(axis=0) + frame = frame.clip(0, 255).astype("uint8") + output_frames.append(Image.fromarray(frame)) + return output_frames + + def release_vram(self): + mempool = cp.get_default_memory_pool() + pinned_mempool = cp.get_default_pinned_memory_pool() + mempool.free_all_blocks() + pinned_mempool.free_all_blocks() + + def __call__(self, rendered_frames, original_frames=None, **kwargs): + rendered_frames = [np.array(frame) for frame in rendered_frames] + original_frames = [np.array(frame) for frame in original_frames] + if self.inference_mode == "fast": + output_frames = self.inference_fast(original_frames, rendered_frames) + elif self.inference_mode == "balanced": + output_frames = self.inference_balanced(original_frames, rendered_frames) + elif self.inference_mode == "accurate": + output_frames = self.inference_accurate(original_frames, rendered_frames) + else: + raise ValueError("inference_mode must be fast, balanced or accurate") + self.release_vram() + return output_frames diff --git a/diffsynth/processors/PILEditor.py b/diffsynth/processors/PILEditor.py new file mode 100644 index 0000000000000000000000000000000000000000..01011d8724f61283550d503c5c20ae6fd0375ec7 --- /dev/null +++ b/diffsynth/processors/PILEditor.py @@ -0,0 +1,28 @@ +from PIL import ImageEnhance +from .base import VideoProcessor + + +class ContrastEditor(VideoProcessor): + def __init__(self, rate=1.5): + self.rate = rate + + @staticmethod + def from_model_manager(model_manager, **kwargs): + return ContrastEditor(**kwargs) + + def __call__(self, rendered_frames, **kwargs): + rendered_frames = [ImageEnhance.Contrast(i).enhance(self.rate) for i in rendered_frames] + return rendered_frames + + +class SharpnessEditor(VideoProcessor): + def __init__(self, rate=1.5): + self.rate = rate + + @staticmethod + def from_model_manager(model_manager, **kwargs): + return SharpnessEditor(**kwargs) + + def __call__(self, rendered_frames, **kwargs): + rendered_frames = [ImageEnhance.Sharpness(i).enhance(self.rate) for i in rendered_frames] + return rendered_frames diff --git a/diffsynth/processors/RIFE.py b/diffsynth/processors/RIFE.py new file mode 100644 index 0000000000000000000000000000000000000000..4186eb31496e9a1bf38df06eb64921226f07ee09 --- /dev/null +++ b/diffsynth/processors/RIFE.py @@ -0,0 +1,77 @@ +import torch +import numpy as np +from PIL import Image +from .base import VideoProcessor + + +class RIFESmoother(VideoProcessor): + def __init__(self, model, device="cuda", scale=1.0, batch_size=4, interpolate=True): + self.model = model + self.device = device + + # IFNet only does not support float16 + self.torch_dtype = torch.float32 + + # Other parameters + self.scale = scale + self.batch_size = batch_size + self.interpolate = interpolate + + @staticmethod + def from_model_manager(model_manager, **kwargs): + return RIFESmoother(model_manager.RIFE, device=model_manager.device, **kwargs) + + def process_image(self, image): + width, height = image.size + if width % 32 != 0 or height % 32 != 0: + width = (width + 31) // 32 + height = (height + 31) // 32 + image = image.resize((width, height)) + image = torch.Tensor(np.array(image, dtype=np.float32)[:, :, [2,1,0]] / 255).permute(2, 0, 1) + return image + + def process_images(self, images): + images = [self.process_image(image) for image in images] + images = torch.stack(images) + return images + + def decode_images(self, images): + images = (images[:, [2,1,0]].permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8) + images = [Image.fromarray(image) for image in images] + return images + + def process_tensors(self, input_tensor, scale=1.0, batch_size=4): + output_tensor = [] + for batch_id in range(0, input_tensor.shape[0], batch_size): + batch_id_ = min(batch_id + batch_size, input_tensor.shape[0]) + batch_input_tensor = input_tensor[batch_id: batch_id_] + batch_input_tensor = batch_input_tensor.to(device=self.device, dtype=self.torch_dtype) + flow, mask, merged = self.model(batch_input_tensor, [4/scale, 2/scale, 1/scale]) + output_tensor.append(merged[2].cpu()) + output_tensor = torch.concat(output_tensor, dim=0) + return output_tensor + + @torch.no_grad() + def __call__(self, rendered_frames, **kwargs): + # Preprocess + processed_images = self.process_images(rendered_frames) + + # Input + input_tensor = torch.cat((processed_images[:-2], processed_images[2:]), dim=1) + + # Interpolate + output_tensor = self.process_tensors(input_tensor, scale=self.scale, batch_size=self.batch_size) + + if self.interpolate: + # Blend + input_tensor = torch.cat((processed_images[1:-1], output_tensor), dim=1) + output_tensor = self.process_tensors(input_tensor, scale=self.scale, batch_size=self.batch_size) + processed_images[1:-1] = output_tensor + else: + processed_images[1:-1] = (processed_images[1:-1] + output_tensor) / 2 + + # To images + output_images = self.decode_images(processed_images) + if output_images[0].size != rendered_frames[0].size: + output_images = [image.resize(rendered_frames[0].size) for image in output_images] + return output_images diff --git a/diffsynth/processors/__init__.py b/diffsynth/processors/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/diffsynth/processors/base.py b/diffsynth/processors/base.py new file mode 100644 index 0000000000000000000000000000000000000000..278a9c1b74044987cc116de35292a96de8b13737 --- /dev/null +++ b/diffsynth/processors/base.py @@ -0,0 +1,6 @@ +class VideoProcessor: + def __init__(self): + pass + + def __call__(self): + raise NotImplementedError diff --git a/diffsynth/processors/sequencial_processor.py b/diffsynth/processors/sequencial_processor.py new file mode 100644 index 0000000000000000000000000000000000000000..9b5bc9454f0b9d74f10bb4a6bff92db77f26325c --- /dev/null +++ b/diffsynth/processors/sequencial_processor.py @@ -0,0 +1,41 @@ +from .base import VideoProcessor + + +class AutoVideoProcessor(VideoProcessor): + def __init__(self): + pass + + @staticmethod + def from_model_manager(model_manager, processor_type, **kwargs): + if processor_type == "FastBlend": + from .FastBlend import FastBlendSmoother + return FastBlendSmoother.from_model_manager(model_manager, **kwargs) + elif processor_type == "Contrast": + from .PILEditor import ContrastEditor + return ContrastEditor.from_model_manager(model_manager, **kwargs) + elif processor_type == "Sharpness": + from .PILEditor import SharpnessEditor + return SharpnessEditor.from_model_manager(model_manager, **kwargs) + elif processor_type == "RIFE": + from .RIFE import RIFESmoother + return RIFESmoother.from_model_manager(model_manager, **kwargs) + else: + raise ValueError(f"invalid processor_type: {processor_type}") + + +class SequencialProcessor(VideoProcessor): + def __init__(self, processors=[]): + self.processors = processors + + @staticmethod + def from_model_manager(model_manager, configs): + processors = [ + AutoVideoProcessor.from_model_manager(model_manager, config["processor_type"], **config["config"]) + for config in configs + ] + return SequencialProcessor(processors) + + def __call__(self, rendered_frames, **kwargs): + for processor in self.processors: + rendered_frames = processor(rendered_frames, **kwargs) + return rendered_frames diff --git a/diffsynth/prompters/__init__.py b/diffsynth/prompters/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f27c6f153b076de484c2b650e8bf16d7142d1099 --- /dev/null +++ b/diffsynth/prompters/__init__.py @@ -0,0 +1,12 @@ +from .prompt_refiners import Translator, BeautifulPrompt, QwenPrompt +from .sd_prompter import SDPrompter +from .sdxl_prompter import SDXLPrompter +from .sd3_prompter import SD3Prompter +from .hunyuan_dit_prompter import HunyuanDiTPrompter +from .kolors_prompter import KolorsPrompter +from .flux_prompter import FluxPrompter +from .omost import OmostPromter +from .cog_prompter import CogPrompter +from .hunyuan_video_prompter import HunyuanVideoPrompter +from .stepvideo_prompter import StepVideoPrompter +from .wan_prompter import WanPrompter diff --git a/diffsynth/prompters/base_prompter.py b/diffsynth/prompters/base_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..136abd18fabdb04e618f59801420c9ce5fb94634 --- /dev/null +++ b/diffsynth/prompters/base_prompter.py @@ -0,0 +1,70 @@ +from ..models.model_manager import ModelManager +import torch + + + +def tokenize_long_prompt(tokenizer, prompt, max_length=None): + # Get model_max_length from self.tokenizer + length = tokenizer.model_max_length if max_length is None else max_length + + # To avoid the warning. set self.tokenizer.model_max_length to +oo. + tokenizer.model_max_length = 99999999 + + # Tokenize it! + input_ids = tokenizer(prompt, return_tensors="pt").input_ids + + # Determine the real length. + max_length = (input_ids.shape[1] + length - 1) // length * length + + # Restore tokenizer.model_max_length + tokenizer.model_max_length = length + + # Tokenize it again with fixed length. + input_ids = tokenizer( + prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True + ).input_ids + + # Reshape input_ids to fit the text encoder. + num_sentence = input_ids.shape[1] // length + input_ids = input_ids.reshape((num_sentence, length)) + + return input_ids + + + +class BasePrompter: + def __init__(self): + self.refiners = [] + self.extenders = [] + + + def load_prompt_refiners(self, model_manager: ModelManager, refiner_classes=[]): + for refiner_class in refiner_classes: + refiner = refiner_class.from_model_manager(model_manager) + self.refiners.append(refiner) + + def load_prompt_extenders(self,model_manager:ModelManager,extender_classes=[]): + for extender_class in extender_classes: + extender = extender_class.from_model_manager(model_manager) + self.extenders.append(extender) + + + @torch.no_grad() + def process_prompt(self, prompt, positive=True): + if isinstance(prompt, list): + prompt = [self.process_prompt(prompt_, positive=positive) for prompt_ in prompt] + else: + for refiner in self.refiners: + prompt = refiner(prompt, positive=positive) + return prompt + + @torch.no_grad() + def extend_prompt(self, prompt:str, positive=True): + extended_prompt = dict(prompt=prompt) + for extender in self.extenders: + extended_prompt = extender(extended_prompt) + return extended_prompt \ No newline at end of file diff --git a/diffsynth/prompters/cog_prompter.py b/diffsynth/prompters/cog_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..a1ab84a69c32e681e087ba7ed0642a6177fe1f7a --- /dev/null +++ b/diffsynth/prompters/cog_prompter.py @@ -0,0 +1,46 @@ +from .base_prompter import BasePrompter +from ..models.flux_text_encoder import FluxTextEncoder2 +from transformers import T5TokenizerFast +import os + + +class CogPrompter(BasePrompter): + def __init__( + self, + tokenizer_path=None + ): + if tokenizer_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_path = os.path.join(base_path, "tokenizer_configs/cog/tokenizer") + super().__init__() + self.tokenizer = T5TokenizerFast.from_pretrained(tokenizer_path) + self.text_encoder: FluxTextEncoder2 = None + + + def fetch_models(self, text_encoder: FluxTextEncoder2 = None): + self.text_encoder = text_encoder + + + def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_length, device): + input_ids = tokenizer( + prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True, + ).input_ids.to(device) + prompt_emb = text_encoder(input_ids) + prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1)) + + return prompt_emb + + + def encode_prompt( + self, + prompt, + positive=True, + device="cuda" + ): + prompt = self.process_prompt(prompt, positive=positive) + prompt_emb = self.encode_prompt_using_t5(prompt, self.text_encoder, self.tokenizer, 226, device) + return prompt_emb diff --git a/diffsynth/prompters/flux_prompter.py b/diffsynth/prompters/flux_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..a3a06ff8df29345f505873cf1b79c963229f3efb --- /dev/null +++ b/diffsynth/prompters/flux_prompter.py @@ -0,0 +1,74 @@ +from .base_prompter import BasePrompter +from ..models.flux_text_encoder import FluxTextEncoder2 +from ..models.sd3_text_encoder import SD3TextEncoder1 +from transformers import CLIPTokenizer, T5TokenizerFast +import os, torch + + +class FluxPrompter(BasePrompter): + def __init__( + self, + tokenizer_1_path=None, + tokenizer_2_path=None + ): + if tokenizer_1_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_1_path = os.path.join(base_path, "tokenizer_configs/flux/tokenizer_1") + if tokenizer_2_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/flux/tokenizer_2") + super().__init__() + self.tokenizer_1 = CLIPTokenizer.from_pretrained(tokenizer_1_path) + self.tokenizer_2 = T5TokenizerFast.from_pretrained(tokenizer_2_path) + self.text_encoder_1: SD3TextEncoder1 = None + self.text_encoder_2: FluxTextEncoder2 = None + + + def fetch_models(self, text_encoder_1: SD3TextEncoder1 = None, text_encoder_2: FluxTextEncoder2 = None): + self.text_encoder_1 = text_encoder_1 + self.text_encoder_2 = text_encoder_2 + + + def encode_prompt_using_clip(self, prompt, text_encoder, tokenizer, max_length, device): + input_ids = tokenizer( + prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True + ).input_ids.to(device) + pooled_prompt_emb, _ = text_encoder(input_ids) + return pooled_prompt_emb + + + def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_length, device): + input_ids = tokenizer( + prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True, + ).input_ids.to(device) + prompt_emb = text_encoder(input_ids) + return prompt_emb + + + def encode_prompt( + self, + prompt, + positive=True, + device="cuda", + t5_sequence_length=512, + ): + prompt = self.process_prompt(prompt, positive=positive) + + # CLIP + pooled_prompt_emb = self.encode_prompt_using_clip(prompt, self.text_encoder_1, self.tokenizer_1, 77, device) + + # T5 + prompt_emb = self.encode_prompt_using_t5(prompt, self.text_encoder_2, self.tokenizer_2, t5_sequence_length, device) + + # text_ids + text_ids = torch.zeros(prompt_emb.shape[0], prompt_emb.shape[1], 3).to(device=device, dtype=prompt_emb.dtype) + + return prompt_emb, pooled_prompt_emb, text_ids diff --git a/diffsynth/prompters/hunyuan_dit_prompter.py b/diffsynth/prompters/hunyuan_dit_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..52a22ed72ab77ef668183119fff67db3141ee561 --- /dev/null +++ b/diffsynth/prompters/hunyuan_dit_prompter.py @@ -0,0 +1,69 @@ +from .base_prompter import BasePrompter +from ..models.model_manager import ModelManager +from ..models import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder +from transformers import BertTokenizer, AutoTokenizer +import warnings, os + + +class HunyuanDiTPrompter(BasePrompter): + def __init__( + self, + tokenizer_path=None, + tokenizer_t5_path=None + ): + if tokenizer_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_path = os.path.join(base_path, "tokenizer_configs/hunyuan_dit/tokenizer") + if tokenizer_t5_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_t5_path = os.path.join(base_path, "tokenizer_configs/hunyuan_dit/tokenizer_t5") + super().__init__() + self.tokenizer = BertTokenizer.from_pretrained(tokenizer_path) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + self.tokenizer_t5 = AutoTokenizer.from_pretrained(tokenizer_t5_path) + self.text_encoder: HunyuanDiTCLIPTextEncoder = None + self.text_encoder_t5: HunyuanDiTT5TextEncoder = None + + + def fetch_models(self, text_encoder: HunyuanDiTCLIPTextEncoder = None, text_encoder_t5: HunyuanDiTT5TextEncoder = None): + self.text_encoder = text_encoder + self.text_encoder_t5 = text_encoder_t5 + + + def encode_prompt_using_signle_model(self, prompt, text_encoder, tokenizer, max_length, clip_skip, device): + text_inputs = tokenizer( + prompt, + padding="max_length", + max_length=max_length, + truncation=True, + return_attention_mask=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + attention_mask = text_inputs.attention_mask.to(device) + prompt_embeds = text_encoder( + text_input_ids.to(device), + attention_mask=attention_mask, + clip_skip=clip_skip + ) + return prompt_embeds, attention_mask + + + def encode_prompt( + self, + prompt, + clip_skip=1, + clip_skip_2=1, + positive=True, + device="cuda" + ): + prompt = self.process_prompt(prompt, positive=positive) + + # CLIP + prompt_emb, attention_mask = self.encode_prompt_using_signle_model(prompt, self.text_encoder, self.tokenizer, self.tokenizer.model_max_length, clip_skip, device) + + # T5 + prompt_emb_t5, attention_mask_t5 = self.encode_prompt_using_signle_model(prompt, self.text_encoder_t5, self.tokenizer_t5, self.tokenizer_t5.model_max_length, clip_skip_2, device) + + return prompt_emb, attention_mask, prompt_emb_t5, attention_mask_t5 diff --git a/diffsynth/prompters/hunyuan_video_prompter.py b/diffsynth/prompters/hunyuan_video_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..5b97356cacd4b9ccd9d0912b5694e1c1b4868ae9 --- /dev/null +++ b/diffsynth/prompters/hunyuan_video_prompter.py @@ -0,0 +1,275 @@ +from .base_prompter import BasePrompter +from ..models.sd3_text_encoder import SD3TextEncoder1 +from ..models.hunyuan_video_text_encoder import HunyuanVideoLLMEncoder, HunyuanVideoMLLMEncoder +from transformers import CLIPTokenizer, LlamaTokenizerFast, CLIPImageProcessor +import os, torch +from typing import Union + +PROMPT_TEMPLATE_ENCODE = ( + "<|start_header_id|>system<|end_header_id|>\n\nDescribe the image by detailing the color, shape, size, texture, " + "quantity, text, spatial relationships of the objects and background:<|eot_id|>" + "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>") + +PROMPT_TEMPLATE_ENCODE_VIDEO = ( + "<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: " + "1. The main content and theme of the video." + "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects." + "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects." + "4. background environment, light, style and atmosphere." + "5. camera angles, movements, and transitions used in the video:<|eot_id|>" + "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>") + +PROMPT_TEMPLATE_ENCODE_I2V = ( + "<|start_header_id|>system<|end_header_id|>\n\n\nDescribe the image by detailing the color, shape, size, texture, " + "quantity, text, spatial relationships of the objects and background:<|eot_id|>" + "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>" + "<|start_header_id|>assistant<|end_header_id|>\n\n" +) + +PROMPT_TEMPLATE_ENCODE_VIDEO_I2V = ( + "<|start_header_id|>system<|end_header_id|>\n\n\nDescribe the video by detailing the following aspects according to the reference image: " + "1. The main content and theme of the video." + "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects." + "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects." + "4. background environment, light, style and atmosphere." + "5. camera angles, movements, and transitions used in the video:<|eot_id|>\n\n" + "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>" + "<|start_header_id|>assistant<|end_header_id|>\n\n" +) + +PROMPT_TEMPLATE = { + "dit-llm-encode": { + "template": PROMPT_TEMPLATE_ENCODE, + "crop_start": 36, + }, + "dit-llm-encode-video": { + "template": PROMPT_TEMPLATE_ENCODE_VIDEO, + "crop_start": 95, + }, + "dit-llm-encode-i2v": { + "template": PROMPT_TEMPLATE_ENCODE_I2V, + "crop_start": 36, + "image_emb_start": 5, + "image_emb_end": 581, + "image_emb_len": 576, + "double_return_token_id": 271 + }, + "dit-llm-encode-video-i2v": { + "template": PROMPT_TEMPLATE_ENCODE_VIDEO_I2V, + "crop_start": 103, + "image_emb_start": 5, + "image_emb_end": 581, + "image_emb_len": 576, + "double_return_token_id": 271 + }, +} + +NEGATIVE_PROMPT = "Aerial view, aerial view, overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion" + + +class HunyuanVideoPrompter(BasePrompter): + + def __init__( + self, + tokenizer_1_path=None, + tokenizer_2_path=None, + ): + if tokenizer_1_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_1_path = os.path.join( + base_path, "tokenizer_configs/hunyuan_video/tokenizer_1") + if tokenizer_2_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_2_path = os.path.join( + base_path, "tokenizer_configs/hunyuan_video/tokenizer_2") + super().__init__() + self.tokenizer_1 = CLIPTokenizer.from_pretrained(tokenizer_1_path) + self.tokenizer_2 = LlamaTokenizerFast.from_pretrained(tokenizer_2_path, padding_side='right') + self.text_encoder_1: SD3TextEncoder1 = None + self.text_encoder_2: HunyuanVideoLLMEncoder = None + + self.prompt_template = PROMPT_TEMPLATE['dit-llm-encode'] + self.prompt_template_video = PROMPT_TEMPLATE['dit-llm-encode-video'] + + def fetch_models(self, + text_encoder_1: SD3TextEncoder1 = None, + text_encoder_2: Union[HunyuanVideoLLMEncoder, HunyuanVideoMLLMEncoder] = None): + self.text_encoder_1 = text_encoder_1 + self.text_encoder_2 = text_encoder_2 + if isinstance(text_encoder_2, HunyuanVideoMLLMEncoder): + # processor + # TODO: may need to replace processor with local implementation + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/hunyuan_video/tokenizer_2") + self.processor = CLIPImageProcessor.from_pretrained(tokenizer_2_path) + # template + self.prompt_template = PROMPT_TEMPLATE['dit-llm-encode-i2v'] + self.prompt_template_video = PROMPT_TEMPLATE['dit-llm-encode-video-i2v'] + + def apply_text_to_template(self, text, template): + assert isinstance(template, str) + if isinstance(text, list): + return [self.apply_text_to_template(text_) for text_ in text] + elif isinstance(text, str): + # Will send string to tokenizer. Used for llm + return template.format(text) + else: + raise TypeError(f"Unsupported prompt type: {type(text)}") + + def encode_prompt_using_clip(self, prompt, max_length, device): + tokenized_result = self.tokenizer_1( + prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True, + return_attention_mask=True + ) + input_ids = tokenized_result.input_ids.to(device) + attention_mask = tokenized_result.attention_mask.to(device) + return self.text_encoder_1(input_ids=input_ids, extra_mask=attention_mask)[0] + + def encode_prompt_using_llm(self, + prompt, + max_length, + device, + crop_start, + hidden_state_skip_layer=2, + use_attention_mask=True): + max_length += crop_start + inputs = self.tokenizer_2(prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True) + input_ids = inputs.input_ids.to(device) + attention_mask = inputs.attention_mask.to(device) + last_hidden_state = self.text_encoder_2(input_ids, attention_mask, hidden_state_skip_layer) + + # crop out + if crop_start > 0: + last_hidden_state = last_hidden_state[:, crop_start:] + attention_mask = (attention_mask[:, crop_start:] if use_attention_mask else None) + + return last_hidden_state, attention_mask + + def encode_prompt_using_mllm(self, + prompt, + images, + max_length, + device, + crop_start, + hidden_state_skip_layer=2, + use_attention_mask=True, + image_embed_interleave=4): + image_outputs = self.processor(images, return_tensors="pt")["pixel_values"].to(device) + max_length += crop_start + inputs = self.tokenizer_2(prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True) + input_ids = inputs.input_ids.to(device) + attention_mask = inputs.attention_mask.to(device) + last_hidden_state = self.text_encoder_2(input_ids=input_ids, + attention_mask=attention_mask, + hidden_state_skip_layer=hidden_state_skip_layer, + pixel_values=image_outputs) + + text_crop_start = (crop_start - 1 + self.prompt_template_video.get("image_emb_len", 576)) + image_crop_start = self.prompt_template_video.get("image_emb_start", 5) + image_crop_end = self.prompt_template_video.get("image_emb_end", 581) + batch_indices, last_double_return_token_indices = torch.where( + input_ids == self.prompt_template_video.get("double_return_token_id", 271)) + if last_double_return_token_indices.shape[0] == 3: + # in case the prompt is too long + last_double_return_token_indices = torch.cat(( + last_double_return_token_indices, + torch.tensor([input_ids.shape[-1]]), + )) + batch_indices = torch.cat((batch_indices, torch.tensor([0]))) + last_double_return_token_indices = (last_double_return_token_indices.reshape(input_ids.shape[0], -1)[:, -1]) + batch_indices = batch_indices.reshape(input_ids.shape[0], -1)[:, -1] + assistant_crop_start = (last_double_return_token_indices - 1 + self.prompt_template_video.get("image_emb_len", 576) - 4) + assistant_crop_end = (last_double_return_token_indices - 1 + self.prompt_template_video.get("image_emb_len", 576)) + attention_mask_assistant_crop_start = (last_double_return_token_indices - 4) + attention_mask_assistant_crop_end = last_double_return_token_indices + text_last_hidden_state = [] + text_attention_mask = [] + image_last_hidden_state = [] + image_attention_mask = [] + for i in range(input_ids.shape[0]): + text_last_hidden_state.append( + torch.cat([ + last_hidden_state[i, text_crop_start:assistant_crop_start[i].item()], + last_hidden_state[i, assistant_crop_end[i].item():], + ])) + text_attention_mask.append( + torch.cat([ + attention_mask[ + i, + crop_start:attention_mask_assistant_crop_start[i].item(), + ], + attention_mask[i, attention_mask_assistant_crop_end[i].item():], + ]) if use_attention_mask else None) + image_last_hidden_state.append(last_hidden_state[i, image_crop_start:image_crop_end]) + image_attention_mask.append( + torch.ones(image_last_hidden_state[-1].shape[0]).to(last_hidden_state.device). + to(attention_mask.dtype) if use_attention_mask else None) + + text_last_hidden_state = torch.stack(text_last_hidden_state) + text_attention_mask = torch.stack(text_attention_mask) + image_last_hidden_state = torch.stack(image_last_hidden_state) + image_attention_mask = torch.stack(image_attention_mask) + + image_last_hidden_state = image_last_hidden_state[:, ::image_embed_interleave, :] + image_attention_mask = image_attention_mask[:, ::image_embed_interleave] + + assert (text_last_hidden_state.shape[0] == text_attention_mask.shape[0] and + image_last_hidden_state.shape[0] == image_attention_mask.shape[0]) + + last_hidden_state = torch.cat([image_last_hidden_state, text_last_hidden_state], dim=1) + attention_mask = torch.cat([image_attention_mask, text_attention_mask], dim=1) + + return last_hidden_state, attention_mask + + def encode_prompt(self, + prompt, + images=None, + positive=True, + device="cuda", + clip_sequence_length=77, + llm_sequence_length=256, + data_type='video', + use_template=True, + hidden_state_skip_layer=2, + use_attention_mask=True, + image_embed_interleave=4): + + prompt = self.process_prompt(prompt, positive=positive) + + # apply template + if use_template: + template = self.prompt_template_video if data_type == 'video' else self.prompt_template + prompt_formated = self.apply_text_to_template(prompt, template['template']) + else: + prompt_formated = prompt + # Text encoder + if data_type == 'video': + crop_start = self.prompt_template_video.get("crop_start", 0) + else: + crop_start = self.prompt_template.get("crop_start", 0) + + # CLIP + pooled_prompt_emb = self.encode_prompt_using_clip(prompt, clip_sequence_length, device) + + # LLM + if images is None: + prompt_emb, attention_mask = self.encode_prompt_using_llm(prompt_formated, llm_sequence_length, device, crop_start, + hidden_state_skip_layer, use_attention_mask) + else: + prompt_emb, attention_mask = self.encode_prompt_using_mllm(prompt_formated, images, llm_sequence_length, device, + crop_start, hidden_state_skip_layer, use_attention_mask, + image_embed_interleave) + + return prompt_emb, pooled_prompt_emb, attention_mask diff --git a/diffsynth/prompters/kolors_prompter.py b/diffsynth/prompters/kolors_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..e3d5d58a9dbb816ea8c8e0e3b4f0433bd11d3306 --- /dev/null +++ b/diffsynth/prompters/kolors_prompter.py @@ -0,0 +1,354 @@ +from .base_prompter import BasePrompter +from ..models.model_manager import ModelManager +import json, os, re +from typing import List, Optional, Union, Dict +from sentencepiece import SentencePieceProcessor +from transformers import PreTrainedTokenizer +from transformers.utils import PaddingStrategy +from transformers.tokenization_utils_base import EncodedInput, BatchEncoding +from ..models.kolors_text_encoder import ChatGLMModel + + +class SPTokenizer: + def __init__(self, model_path: str): + # reload tokenizer + assert os.path.isfile(model_path), model_path + self.sp_model = SentencePieceProcessor(model_file=model_path) + + # BOS / EOS token IDs + self.n_words: int = self.sp_model.vocab_size() + self.bos_id: int = self.sp_model.bos_id() + self.eos_id: int = self.sp_model.eos_id() + self.pad_id: int = self.sp_model.unk_id() + assert self.sp_model.vocab_size() == self.sp_model.get_piece_size() + + role_special_tokens = ["<|system|>", "<|user|>", "<|assistant|>", "<|observation|>"] + special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens + self.special_tokens = {} + self.index_special_tokens = {} + for token in special_tokens: + self.special_tokens[token] = self.n_words + self.index_special_tokens[self.n_words] = token + self.n_words += 1 + self.role_special_token_expression = "|".join([re.escape(token) for token in role_special_tokens]) + + def tokenize(self, s: str, encode_special_tokens=False): + if encode_special_tokens: + last_index = 0 + t = [] + for match in re.finditer(self.role_special_token_expression, s): + if last_index < match.start(): + t.extend(self.sp_model.EncodeAsPieces(s[last_index:match.start()])) + t.append(s[match.start():match.end()]) + last_index = match.end() + if last_index < len(s): + t.extend(self.sp_model.EncodeAsPieces(s[last_index:])) + return t + else: + return self.sp_model.EncodeAsPieces(s) + + def encode(self, s: str, bos: bool = False, eos: bool = False) -> List[int]: + assert type(s) is str + t = self.sp_model.encode(s) + if bos: + t = [self.bos_id] + t + if eos: + t = t + [self.eos_id] + return t + + def decode(self, t: List[int]) -> str: + text, buffer = "", [] + for token in t: + if token in self.index_special_tokens: + if buffer: + text += self.sp_model.decode(buffer) + buffer = [] + text += self.index_special_tokens[token] + else: + buffer.append(token) + if buffer: + text += self.sp_model.decode(buffer) + return text + + def decode_tokens(self, tokens: List[str]) -> str: + text = self.sp_model.DecodePieces(tokens) + return text + + def convert_token_to_id(self, token): + """ Converts a token (str) in an id using the vocab. """ + if token in self.special_tokens: + return self.special_tokens[token] + return self.sp_model.PieceToId(token) + + def convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + if index in self.index_special_tokens: + return self.index_special_tokens[index] + if index in [self.eos_id, self.bos_id, self.pad_id] or index < 0: + return "" + return self.sp_model.IdToPiece(index) + + + +class ChatGLMTokenizer(PreTrainedTokenizer): + vocab_files_names = {"vocab_file": "tokenizer.model"} + + model_input_names = ["input_ids", "attention_mask", "position_ids"] + + def __init__(self, vocab_file, padding_side="left", clean_up_tokenization_spaces=False, encode_special_tokens=False, + **kwargs): + self.name = "GLMTokenizer" + + self.vocab_file = vocab_file + self.tokenizer = SPTokenizer(vocab_file) + self.special_tokens = { + "": self.tokenizer.bos_id, + "": self.tokenizer.eos_id, + "": self.tokenizer.pad_id + } + self.encode_special_tokens = encode_special_tokens + super().__init__(padding_side=padding_side, clean_up_tokenization_spaces=clean_up_tokenization_spaces, + encode_special_tokens=encode_special_tokens, + **kwargs) + + def get_command(self, token): + if token in self.special_tokens: + return self.special_tokens[token] + assert token in self.tokenizer.special_tokens, f"{token} is not a special token for {self.name}" + return self.tokenizer.special_tokens[token] + + @property + def unk_token(self) -> str: + return "" + + @property + def pad_token(self) -> str: + return "" + + @property + def pad_token_id(self): + return self.get_command("") + + @property + def eos_token(self) -> str: + return "" + + @property + def eos_token_id(self): + return self.get_command("") + + @property + def vocab_size(self): + return self.tokenizer.n_words + + def get_vocab(self): + """ Returns vocab as a dict """ + vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)} + vocab.update(self.added_tokens_encoder) + return vocab + + def _tokenize(self, text, **kwargs): + return self.tokenizer.tokenize(text, encode_special_tokens=self.encode_special_tokens) + + def _convert_token_to_id(self, token): + """ Converts a token (str) in an id using the vocab. """ + return self.tokenizer.convert_token_to_id(token) + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + return self.tokenizer.convert_id_to_token(index) + + def convert_tokens_to_string(self, tokens: List[str]) -> str: + return self.tokenizer.decode_tokens(tokens) + + def save_vocabulary(self, save_directory, filename_prefix=None): + """ + Save the vocabulary and special tokens file to a directory. + + Args: + save_directory (`str`): + The directory in which to save the vocabulary. + filename_prefix (`str`, *optional*): + An optional prefix to add to the named of the saved files. + + Returns: + `Tuple(str)`: Paths to the files saved. + """ + if os.path.isdir(save_directory): + vocab_file = os.path.join( + save_directory, self.vocab_files_names["vocab_file"] + ) + else: + vocab_file = save_directory + + with open(self.vocab_file, 'rb') as fin: + proto_str = fin.read() + + with open(vocab_file, "wb") as writer: + writer.write(proto_str) + + return (vocab_file,) + + def get_prefix_tokens(self): + prefix_tokens = [self.get_command("[gMASK]"), self.get_command("sop")] + return prefix_tokens + + def build_single_message(self, role, metadata, message): + assert role in ["system", "user", "assistant", "observation"], role + role_tokens = [self.get_command(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n") + message_tokens = self.tokenizer.encode(message) + tokens = role_tokens + message_tokens + return tokens + + def build_chat_input(self, query, history=None, role="user"): + if history is None: + history = [] + input_ids = [] + for item in history: + content = item["content"] + if item["role"] == "system" and "tools" in item: + content = content + "\n" + json.dumps(item["tools"], indent=4, ensure_ascii=False) + input_ids.extend(self.build_single_message(item["role"], item.get("metadata", ""), content)) + input_ids.extend(self.build_single_message(role, "", query)) + input_ids.extend([self.get_command("<|assistant|>")]) + return self.batch_encode_plus([input_ids], return_tensors="pt", is_split_into_words=True) + + def build_inputs_with_special_tokens( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and + adding special tokens. A BERT sequence has the following format: + + - single sequence: `[CLS] X [SEP]` + - pair of sequences: `[CLS] A [SEP] B [SEP]` + + Args: + token_ids_0 (`List[int]`): + List of IDs to which the special tokens will be added. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. + """ + prefix_tokens = self.get_prefix_tokens() + token_ids_0 = prefix_tokens + token_ids_0 + if token_ids_1 is not None: + token_ids_0 = token_ids_0 + token_ids_1 + [self.get_command("")] + return token_ids_0 + + def _pad( + self, + encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], + max_length: Optional[int] = None, + padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, + pad_to_multiple_of: Optional[int] = None, + return_attention_mask: Optional[bool] = None, + padding_side: Optional[str] = None, + ) -> dict: + """ + Pad encoded inputs (on left/right and up to predefined length or max length in the batch) + + Args: + encoded_inputs: + Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). + max_length: maximum length of the returned list and optionally padding length (see below). + Will truncate by taking into account the special tokens. + padding_strategy: PaddingStrategy to use for padding. + + - PaddingStrategy.LONGEST Pad to the longest sequence in the batch + - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) + - PaddingStrategy.DO_NOT_PAD: Do not pad + The tokenizer padding sides are defined in self.padding_side: + + - 'left': pads on the left of the sequences + - 'right': pads on the right of the sequences + pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. + This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability + `>= 7.5` (Volta). + return_attention_mask: + (optional) Set to False to avoid returning attention mask (default: set to model specifics) + """ + # Load from model defaults + assert self.padding_side == "left" + + required_input = encoded_inputs[self.model_input_names[0]] + seq_length = len(required_input) + + if padding_strategy == PaddingStrategy.LONGEST: + max_length = len(required_input) + + if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): + max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of + + needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length + + # Initialize attention mask if not present. + if "attention_mask" not in encoded_inputs: + encoded_inputs["attention_mask"] = [1] * seq_length + + if "position_ids" not in encoded_inputs: + encoded_inputs["position_ids"] = list(range(seq_length)) + + if needs_to_be_padded: + difference = max_length - len(required_input) + + if "attention_mask" in encoded_inputs: + encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"] + if "position_ids" in encoded_inputs: + encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"] + encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input + + return encoded_inputs + + + +class KolorsPrompter(BasePrompter): + def __init__( + self, + tokenizer_path=None + ): + if tokenizer_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_path = os.path.join(base_path, "tokenizer_configs/kolors/tokenizer") + super().__init__() + self.tokenizer = ChatGLMTokenizer.from_pretrained(tokenizer_path) + self.text_encoder: ChatGLMModel = None + + + def fetch_models(self, text_encoder: ChatGLMModel = None): + self.text_encoder = text_encoder + + + def encode_prompt_using_ChatGLM(self, prompt, text_encoder, tokenizer, max_length, clip_skip, device): + text_inputs = tokenizer( + prompt, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ).to(device) + output = text_encoder( + input_ids=text_inputs['input_ids'] , + attention_mask=text_inputs['attention_mask'], + position_ids=text_inputs['position_ids'], + output_hidden_states=True + ) + prompt_emb = output.hidden_states[-clip_skip].permute(1, 0, 2).clone() + pooled_prompt_emb = output.hidden_states[-1][-1, :, :].clone() + return prompt_emb, pooled_prompt_emb + + + def encode_prompt( + self, + prompt, + clip_skip=1, + clip_skip_2=2, + positive=True, + device="cuda" + ): + prompt = self.process_prompt(prompt, positive=positive) + prompt_emb, pooled_prompt_emb = self.encode_prompt_using_ChatGLM(prompt, self.text_encoder, self.tokenizer, 256, clip_skip_2, device) + + return pooled_prompt_emb, prompt_emb diff --git a/diffsynth/prompters/omnigen_prompter.py b/diffsynth/prompters/omnigen_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..616efabebb7d327ecf968165dd12341ab8f83894 --- /dev/null +++ b/diffsynth/prompters/omnigen_prompter.py @@ -0,0 +1,356 @@ +import os +import re +from typing import Dict, List + +import torch +from PIL import Image +from torchvision import transforms +from transformers import AutoTokenizer +from huggingface_hub import snapshot_download +import numpy as np + + + +def crop_arr(pil_image, max_image_size): + while min(*pil_image.size) >= 2 * max_image_size: + pil_image = pil_image.resize( + tuple(x // 2 for x in pil_image.size), resample=Image.BOX + ) + + if max(*pil_image.size) > max_image_size: + scale = max_image_size / max(*pil_image.size) + pil_image = pil_image.resize( + tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC + ) + + if min(*pil_image.size) < 16: + scale = 16 / min(*pil_image.size) + pil_image = pil_image.resize( + tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC + ) + + arr = np.array(pil_image) + crop_y1 = (arr.shape[0] % 16) // 2 + crop_y2 = arr.shape[0] % 16 - crop_y1 + + crop_x1 = (arr.shape[1] % 16) // 2 + crop_x2 = arr.shape[1] % 16 - crop_x1 + + arr = arr[crop_y1:arr.shape[0]-crop_y2, crop_x1:arr.shape[1]-crop_x2] + return Image.fromarray(arr) + + + +class OmniGenPrompter: + def __init__(self, + text_tokenizer, + max_image_size: int=1024): + self.text_tokenizer = text_tokenizer + self.max_image_size = max_image_size + + self.image_transform = transforms.Compose([ + transforms.Lambda(lambda pil_image: crop_arr(pil_image, max_image_size)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True) + ]) + + self.collator = OmniGenCollator() + self.separate_collator = OmniGenSeparateCollator() + + @classmethod + def from_pretrained(cls, model_name): + if not os.path.exists(model_name): + cache_folder = os.getenv('HF_HUB_CACHE') + model_name = snapshot_download(repo_id=model_name, + cache_dir=cache_folder, + allow_patterns="*.json") + text_tokenizer = AutoTokenizer.from_pretrained(model_name) + + return cls(text_tokenizer) + + + def process_image(self, image): + return self.image_transform(image) + + def process_multi_modal_prompt(self, text, input_images): + text = self.add_prefix_instruction(text) + if input_images is None or len(input_images) == 0: + model_inputs = self.text_tokenizer(text) + return {"input_ids": model_inputs.input_ids, "pixel_values": None, "image_sizes": None} + + pattern = r"<\|image_\d+\|>" + prompt_chunks = [self.text_tokenizer(chunk).input_ids for chunk in re.split(pattern, text)] + + for i in range(1, len(prompt_chunks)): + if prompt_chunks[i][0] == 1: + prompt_chunks[i] = prompt_chunks[i][1:] + + image_tags = re.findall(pattern, text) + image_ids = [int(s.split("|")[1].split("_")[-1]) for s in image_tags] + + unique_image_ids = sorted(list(set(image_ids))) + assert unique_image_ids == list(range(1, len(unique_image_ids)+1)), f"image_ids must start from 1, and must be continuous int, e.g. [1, 2, 3], cannot be {unique_image_ids}" + # total images must be the same as the number of image tags + assert len(unique_image_ids) == len(input_images), f"total images must be the same as the number of image tags, got {len(unique_image_ids)} image tags and {len(input_images)} images" + + input_images = [input_images[x-1] for x in image_ids] + + all_input_ids = [] + img_inx = [] + idx = 0 + for i in range(len(prompt_chunks)): + all_input_ids.extend(prompt_chunks[i]) + if i != len(prompt_chunks) -1: + start_inx = len(all_input_ids) + size = input_images[i].size(-2) * input_images[i].size(-1) // 16 // 16 + img_inx.append([start_inx, start_inx+size]) + all_input_ids.extend([0]*size) + + return {"input_ids": all_input_ids, "pixel_values": input_images, "image_sizes": img_inx} + + + def add_prefix_instruction(self, prompt): + user_prompt = '<|user|>\n' + generation_prompt = 'Generate an image according to the following instructions\n' + assistant_prompt = '<|assistant|>\n<|diffusion|>' + prompt_suffix = "<|end|>\n" + prompt = f"{user_prompt}{generation_prompt}{prompt}{prompt_suffix}{assistant_prompt}" + return prompt + + + def __call__(self, + instructions: List[str], + input_images: List[List[str]] = None, + height: int = 1024, + width: int = 1024, + negative_prompt: str = "low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers.", + use_img_cfg: bool = True, + separate_cfg_input: bool = False, + use_input_image_size_as_output: bool=False, + ) -> Dict: + + if input_images is None: + use_img_cfg = False + if isinstance(instructions, str): + instructions = [instructions] + input_images = [input_images] + + input_data = [] + for i in range(len(instructions)): + cur_instruction = instructions[i] + cur_input_images = None if input_images is None else input_images[i] + if cur_input_images is not None and len(cur_input_images) > 0: + cur_input_images = [self.process_image(x) for x in cur_input_images] + else: + cur_input_images = None + assert "<|image_1|>" not in cur_instruction + + mllm_input = self.process_multi_modal_prompt(cur_instruction, cur_input_images) + + + neg_mllm_input, img_cfg_mllm_input = None, None + neg_mllm_input = self.process_multi_modal_prompt(negative_prompt, None) + if use_img_cfg: + if cur_input_images is not None and len(cur_input_images) >= 1: + img_cfg_prompt = [f"<|image_{i+1}|>" for i in range(len(cur_input_images))] + img_cfg_mllm_input = self.process_multi_modal_prompt(" ".join(img_cfg_prompt), cur_input_images) + else: + img_cfg_mllm_input = neg_mllm_input + + if use_input_image_size_as_output: + input_data.append((mllm_input, neg_mllm_input, img_cfg_mllm_input, [mllm_input['pixel_values'][0].size(-2), mllm_input['pixel_values'][0].size(-1)])) + else: + input_data.append((mllm_input, neg_mllm_input, img_cfg_mllm_input, [height, width])) + + if separate_cfg_input: + return self.separate_collator(input_data) + return self.collator(input_data) + + + + +class OmniGenCollator: + def __init__(self, pad_token_id=2, hidden_size=3072): + self.pad_token_id = pad_token_id + self.hidden_size = hidden_size + + def create_position(self, attention_mask, num_tokens_for_output_images): + position_ids = [] + text_length = attention_mask.size(-1) + img_length = max(num_tokens_for_output_images) + for mask in attention_mask: + temp_l = torch.sum(mask) + temp_position = [0]*(text_length-temp_l) + [i for i in range(temp_l+img_length+1)] # we add a time embedding into the sequence, so add one more token + position_ids.append(temp_position) + return torch.LongTensor(position_ids) + + def create_mask(self, attention_mask, num_tokens_for_output_images): + extended_mask = [] + padding_images = [] + text_length = attention_mask.size(-1) + img_length = max(num_tokens_for_output_images) + seq_len = text_length + img_length + 1 # we add a time embedding into the sequence, so add one more token + inx = 0 + for mask in attention_mask: + temp_l = torch.sum(mask) + pad_l = text_length - temp_l + + temp_mask = torch.tril(torch.ones(size=(temp_l+1, temp_l+1))) + + image_mask = torch.zeros(size=(temp_l+1, img_length)) + temp_mask = torch.cat([temp_mask, image_mask], dim=-1) + + image_mask = torch.ones(size=(img_length, temp_l+img_length+1)) + temp_mask = torch.cat([temp_mask, image_mask], dim=0) + + if pad_l > 0: + pad_mask = torch.zeros(size=(temp_l+1+img_length, pad_l)) + temp_mask = torch.cat([pad_mask, temp_mask], dim=-1) + + pad_mask = torch.ones(size=(pad_l, seq_len)) + temp_mask = torch.cat([pad_mask, temp_mask], dim=0) + + true_img_length = num_tokens_for_output_images[inx] + pad_img_length = img_length - true_img_length + if pad_img_length > 0: + temp_mask[:, -pad_img_length:] = 0 + temp_padding_imgs = torch.zeros(size=(1, pad_img_length, self.hidden_size)) + else: + temp_padding_imgs = None + + extended_mask.append(temp_mask.unsqueeze(0)) + padding_images.append(temp_padding_imgs) + inx += 1 + return torch.cat(extended_mask, dim=0), padding_images + + def adjust_attention_for_input_images(self, attention_mask, image_sizes): + for b_inx in image_sizes.keys(): + for start_inx, end_inx in image_sizes[b_inx]: + attention_mask[b_inx][start_inx:end_inx, start_inx:end_inx] = 1 + + return attention_mask + + def pad_input_ids(self, input_ids, image_sizes): + max_l = max([len(x) for x in input_ids]) + padded_ids = [] + attention_mask = [] + new_image_sizes = [] + + for i in range(len(input_ids)): + temp_ids = input_ids[i] + temp_l = len(temp_ids) + pad_l = max_l - temp_l + if pad_l == 0: + attention_mask.append([1]*max_l) + padded_ids.append(temp_ids) + else: + attention_mask.append([0]*pad_l+[1]*temp_l) + padded_ids.append([self.pad_token_id]*pad_l+temp_ids) + + if i in image_sizes: + new_inx = [] + for old_inx in image_sizes[i]: + new_inx.append([x+pad_l for x in old_inx]) + image_sizes[i] = new_inx + + return torch.LongTensor(padded_ids), torch.LongTensor(attention_mask), image_sizes + + + def process_mllm_input(self, mllm_inputs, target_img_size): + num_tokens_for_output_images = [] + for img_size in target_img_size: + num_tokens_for_output_images.append(img_size[0]*img_size[1]//16//16) + + pixel_values, image_sizes = [], {} + b_inx = 0 + for x in mllm_inputs: + if x['pixel_values'] is not None: + pixel_values.extend(x['pixel_values']) + for size in x['image_sizes']: + if b_inx not in image_sizes: + image_sizes[b_inx] = [size] + else: + image_sizes[b_inx].append(size) + b_inx += 1 + pixel_values = [x.unsqueeze(0) for x in pixel_values] + + + input_ids = [x['input_ids'] for x in mllm_inputs] + padded_input_ids, attention_mask, image_sizes = self.pad_input_ids(input_ids, image_sizes) + position_ids = self.create_position(attention_mask, num_tokens_for_output_images) + attention_mask, padding_images = self.create_mask(attention_mask, num_tokens_for_output_images) + attention_mask = self.adjust_attention_for_input_images(attention_mask, image_sizes) + + return padded_input_ids, position_ids, attention_mask, padding_images, pixel_values, image_sizes + + + def __call__(self, features): + mllm_inputs = [f[0] for f in features] + cfg_mllm_inputs = [f[1] for f in features] + img_cfg_mllm_input = [f[2] for f in features] + target_img_size = [f[3] for f in features] + + + if img_cfg_mllm_input[0] is not None: + mllm_inputs = mllm_inputs + cfg_mllm_inputs + img_cfg_mllm_input + target_img_size = target_img_size + target_img_size + target_img_size + else: + mllm_inputs = mllm_inputs + cfg_mllm_inputs + target_img_size = target_img_size + target_img_size + + + all_padded_input_ids, all_position_ids, all_attention_mask, all_padding_images, all_pixel_values, all_image_sizes = self.process_mllm_input(mllm_inputs, target_img_size) + + data = {"input_ids": all_padded_input_ids, + "attention_mask": all_attention_mask, + "position_ids": all_position_ids, + "input_pixel_values": all_pixel_values, + "input_image_sizes": all_image_sizes, + "padding_images": all_padding_images, + } + return data + + +class OmniGenSeparateCollator(OmniGenCollator): + def __call__(self, features): + mllm_inputs = [f[0] for f in features] + cfg_mllm_inputs = [f[1] for f in features] + img_cfg_mllm_input = [f[2] for f in features] + target_img_size = [f[3] for f in features] + + all_padded_input_ids, all_attention_mask, all_position_ids, all_pixel_values, all_image_sizes, all_padding_images = [], [], [], [], [], [] + + + padded_input_ids, position_ids, attention_mask, padding_images, pixel_values, image_sizes = self.process_mllm_input(mllm_inputs, target_img_size) + all_padded_input_ids.append(padded_input_ids) + all_attention_mask.append(attention_mask) + all_position_ids.append(position_ids) + all_pixel_values.append(pixel_values) + all_image_sizes.append(image_sizes) + all_padding_images.append(padding_images) + + if cfg_mllm_inputs[0] is not None: + padded_input_ids, position_ids, attention_mask, padding_images, pixel_values, image_sizes = self.process_mllm_input(cfg_mllm_inputs, target_img_size) + all_padded_input_ids.append(padded_input_ids) + all_attention_mask.append(attention_mask) + all_position_ids.append(position_ids) + all_pixel_values.append(pixel_values) + all_image_sizes.append(image_sizes) + all_padding_images.append(padding_images) + if img_cfg_mllm_input[0] is not None: + padded_input_ids, position_ids, attention_mask, padding_images, pixel_values, image_sizes = self.process_mllm_input(img_cfg_mllm_input, target_img_size) + all_padded_input_ids.append(padded_input_ids) + all_attention_mask.append(attention_mask) + all_position_ids.append(position_ids) + all_pixel_values.append(pixel_values) + all_image_sizes.append(image_sizes) + all_padding_images.append(padding_images) + + data = {"input_ids": all_padded_input_ids, + "attention_mask": all_attention_mask, + "position_ids": all_position_ids, + "input_pixel_values": all_pixel_values, + "input_image_sizes": all_image_sizes, + "padding_images": all_padding_images, + } + return data diff --git a/diffsynth/prompters/omost.py b/diffsynth/prompters/omost.py new file mode 100644 index 0000000000000000000000000000000000000000..81828ad79978103eea42389d439847c0877cbd85 --- /dev/null +++ b/diffsynth/prompters/omost.py @@ -0,0 +1,323 @@ +from transformers import AutoTokenizer, TextIteratorStreamer +import difflib +import torch +import numpy as np +import re +from ..models.model_manager import ModelManager +from PIL import Image + +valid_colors = { # r, g, b + 'aliceblue': (240, 248, 255), 'antiquewhite': (250, 235, 215), 'aqua': (0, 255, 255), + 'aquamarine': (127, 255, 212), 'azure': (240, 255, 255), 'beige': (245, 245, 220), + 'bisque': (255, 228, 196), 'black': (0, 0, 0), 'blanchedalmond': (255, 235, 205), 'blue': (0, 0, 255), + 'blueviolet': (138, 43, 226), 'brown': (165, 42, 42), 'burlywood': (222, 184, 135), + 'cadetblue': (95, 158, 160), 'chartreuse': (127, 255, 0), 'chocolate': (210, 105, 30), + 'coral': (255, 127, 80), 'cornflowerblue': (100, 149, 237), 'cornsilk': (255, 248, 220), + 'crimson': (220, 20, 60), 'cyan': (0, 255, 255), 'darkblue': (0, 0, 139), 'darkcyan': (0, 139, 139), + 'darkgoldenrod': (184, 134, 11), 'darkgray': (169, 169, 169), 'darkgrey': (169, 169, 169), + 'darkgreen': (0, 100, 0), 'darkkhaki': (189, 183, 107), 'darkmagenta': (139, 0, 139), + 'darkolivegreen': (85, 107, 47), 'darkorange': (255, 140, 0), 'darkorchid': (153, 50, 204), + 'darkred': (139, 0, 0), 'darksalmon': (233, 150, 122), 'darkseagreen': (143, 188, 143), + 'darkslateblue': (72, 61, 139), 'darkslategray': (47, 79, 79), 'darkslategrey': (47, 79, 79), + 'darkturquoise': (0, 206, 209), 'darkviolet': (148, 0, 211), 'deeppink': (255, 20, 147), + 'deepskyblue': (0, 191, 255), 'dimgray': (105, 105, 105), 'dimgrey': (105, 105, 105), + 'dodgerblue': (30, 144, 255), 'firebrick': (178, 34, 34), 'floralwhite': (255, 250, 240), + 'forestgreen': (34, 139, 34), 'fuchsia': (255, 0, 255), 'gainsboro': (220, 220, 220), + 'ghostwhite': (248, 248, 255), 'gold': (255, 215, 0), 'goldenrod': (218, 165, 32), + 'gray': (128, 128, 128), 'grey': (128, 128, 128), 'green': (0, 128, 0), 'greenyellow': (173, 255, 47), + 'honeydew': (240, 255, 240), 'hotpink': (255, 105, 180), 'indianred': (205, 92, 92), + 'indigo': (75, 0, 130), 'ivory': (255, 255, 240), 'khaki': (240, 230, 140), 'lavender': (230, 230, 250), + 'lavenderblush': (255, 240, 245), 'lawngreen': (124, 252, 0), 'lemonchiffon': (255, 250, 205), + 'lightblue': (173, 216, 230), 'lightcoral': (240, 128, 128), 'lightcyan': (224, 255, 255), + 'lightgoldenrodyellow': (250, 250, 210), 'lightgray': (211, 211, 211), 'lightgrey': (211, 211, 211), + 'lightgreen': (144, 238, 144), 'lightpink': (255, 182, 193), 'lightsalmon': (255, 160, 122), + 'lightseagreen': (32, 178, 170), 'lightskyblue': (135, 206, 250), 'lightslategray': (119, 136, 153), + 'lightslategrey': (119, 136, 153), 'lightsteelblue': (176, 196, 222), 'lightyellow': (255, 255, 224), + 'lime': (0, 255, 0), 'limegreen': (50, 205, 50), 'linen': (250, 240, 230), 'magenta': (255, 0, 255), + 'maroon': (128, 0, 0), 'mediumaquamarine': (102, 205, 170), 'mediumblue': (0, 0, 205), + 'mediumorchid': (186, 85, 211), 'mediumpurple': (147, 112, 219), 'mediumseagreen': (60, 179, 113), + 'mediumslateblue': (123, 104, 238), 'mediumspringgreen': (0, 250, 154), + 'mediumturquoise': (72, 209, 204), 'mediumvioletred': (199, 21, 133), 'midnightblue': (25, 25, 112), + 'mintcream': (245, 255, 250), 'mistyrose': (255, 228, 225), 'moccasin': (255, 228, 181), + 'navajowhite': (255, 222, 173), 'navy': (0, 0, 128), 'navyblue': (0, 0, 128), + 'oldlace': (253, 245, 230), 'olive': (128, 128, 0), 'olivedrab': (107, 142, 35), + 'orange': (255, 165, 0), 'orangered': (255, 69, 0), 'orchid': (218, 112, 214), + 'palegoldenrod': (238, 232, 170), 'palegreen': (152, 251, 152), 'paleturquoise': (175, 238, 238), + 'palevioletred': (219, 112, 147), 'papayawhip': (255, 239, 213), 'peachpuff': (255, 218, 185), + 'peru': (205, 133, 63), 'pink': (255, 192, 203), 'plum': (221, 160, 221), 'powderblue': (176, 224, 230), + 'purple': (128, 0, 128), 'rebeccapurple': (102, 51, 153), 'red': (255, 0, 0), + 'rosybrown': (188, 143, 143), 'royalblue': (65, 105, 225), 'saddlebrown': (139, 69, 19), + 'salmon': (250, 128, 114), 'sandybrown': (244, 164, 96), 'seagreen': (46, 139, 87), + 'seashell': (255, 245, 238), 'sienna': (160, 82, 45), 'silver': (192, 192, 192), + 'skyblue': (135, 206, 235), 'slateblue': (106, 90, 205), 'slategray': (112, 128, 144), + 'slategrey': (112, 128, 144), 'snow': (255, 250, 250), 'springgreen': (0, 255, 127), + 'steelblue': (70, 130, 180), 'tan': (210, 180, 140), 'teal': (0, 128, 128), 'thistle': (216, 191, 216), + 'tomato': (255, 99, 71), 'turquoise': (64, 224, 208), 'violet': (238, 130, 238), + 'wheat': (245, 222, 179), 'white': (255, 255, 255), 'whitesmoke': (245, 245, 245), + 'yellow': (255, 255, 0), 'yellowgreen': (154, 205, 50) +} + +valid_locations = { # x, y in 90*90 + 'in the center': (45, 45), + 'on the left': (15, 45), + 'on the right': (75, 45), + 'on the top': (45, 15), + 'on the bottom': (45, 75), + 'on the top-left': (15, 15), + 'on the top-right': (75, 15), + 'on the bottom-left': (15, 75), + 'on the bottom-right': (75, 75) +} + +valid_offsets = { # x, y in 90*90 + 'no offset': (0, 0), + 'slightly to the left': (-10, 0), + 'slightly to the right': (10, 0), + 'slightly to the upper': (0, -10), + 'slightly to the lower': (0, 10), + 'slightly to the upper-left': (-10, -10), + 'slightly to the upper-right': (10, -10), + 'slightly to the lower-left': (-10, 10), + 'slightly to the lower-right': (10, 10)} + +valid_areas = { # w, h in 90*90 + "a small square area": (50, 50), + "a small vertical area": (40, 60), + "a small horizontal area": (60, 40), + "a medium-sized square area": (60, 60), + "a medium-sized vertical area": (50, 80), + "a medium-sized horizontal area": (80, 50), + "a large square area": (70, 70), + "a large vertical area": (60, 90), + "a large horizontal area": (90, 60) +} + +def safe_str(x): + return x.strip(',. ') + '.' + +def closest_name(input_str, options): + input_str = input_str.lower() + + closest_match = difflib.get_close_matches(input_str, list(options.keys()), n=1, cutoff=0.5) + assert isinstance(closest_match, list) and len(closest_match) > 0, f'The value [{input_str}] is not valid!' + result = closest_match[0] + + if result != input_str: + print(f'Automatically corrected [{input_str}] -> [{result}].') + + return result + +class Canvas: + @staticmethod + def from_bot_response(response: str): + + matched = re.search(r'```python\n(.*?)\n```', response, re.DOTALL) + assert matched, 'Response does not contain codes!' + code_content = matched.group(1) + assert 'canvas = Canvas()' in code_content, 'Code block must include valid canvas var!' + local_vars = {'Canvas': Canvas} + exec(code_content, {}, local_vars) + canvas = local_vars.get('canvas', None) + assert isinstance(canvas, Canvas), 'Code block must produce valid canvas var!' + return canvas + + def __init__(self): + self.components = [] + self.color = None + self.record_tags = True + self.prefixes = [] + self.suffixes = [] + return + + def set_global_description(self, description: str, detailed_descriptions: list, tags: str, + HTML_web_color_name: str): + assert isinstance(description, str), 'Global description is not valid!' + assert isinstance(detailed_descriptions, list) and all(isinstance(item, str) for item in detailed_descriptions), \ + 'Global detailed_descriptions is not valid!' + assert isinstance(tags, str), 'Global tags is not valid!' + + HTML_web_color_name = closest_name(HTML_web_color_name, valid_colors) + self.color = np.array([[valid_colors[HTML_web_color_name]]], dtype=np.uint8) + + self.prefixes = [description] + self.suffixes = detailed_descriptions + + if self.record_tags: + self.suffixes = self.suffixes + [tags] + + self.prefixes = [safe_str(x) for x in self.prefixes] + self.suffixes = [safe_str(x) for x in self.suffixes] + + return + + def add_local_description(self, location: str, offset: str, area: str, distance_to_viewer: float, description: str, + detailed_descriptions: list, tags: str, atmosphere: str, style: str, + quality_meta: str, HTML_web_color_name: str): + assert isinstance(description, str), 'Local description is wrong!' + assert isinstance(distance_to_viewer, (int, float)) and distance_to_viewer > 0, \ + f'The distance_to_viewer for [{description}] is not positive float number!' + assert isinstance(detailed_descriptions, list) and all(isinstance(item, str) for item in detailed_descriptions), \ + f'The detailed_descriptions for [{description}] is not valid!' + assert isinstance(tags, str), f'The tags for [{description}] is not valid!' + assert isinstance(atmosphere, str), f'The atmosphere for [{description}] is not valid!' + assert isinstance(style, str), f'The style for [{description}] is not valid!' + assert isinstance(quality_meta, str), f'The quality_meta for [{description}] is not valid!' + + location = closest_name(location, valid_locations) + offset = closest_name(offset, valid_offsets) + area = closest_name(area, valid_areas) + HTML_web_color_name = closest_name(HTML_web_color_name, valid_colors) + + xb, yb = valid_locations[location] + xo, yo = valid_offsets[offset] + w, h = valid_areas[area] + rect = (yb + yo - h // 2, yb + yo + h // 2, xb + xo - w // 2, xb + xo + w // 2) + rect = [max(0, min(90, i)) for i in rect] + color = np.array([[valid_colors[HTML_web_color_name]]], dtype=np.uint8) + + prefixes = self.prefixes + [description] + suffixes = detailed_descriptions + + if self.record_tags: + suffixes = suffixes + [tags, atmosphere, style, quality_meta] + + prefixes = [safe_str(x) for x in prefixes] + suffixes = [safe_str(x) for x in suffixes] + + self.components.append(dict( + rect=rect, + distance_to_viewer=distance_to_viewer, + color=color, + prefixes=prefixes, + suffixes=suffixes, + location=location, + )) + + return + + def process(self): + # sort components + self.components = sorted(self.components, key=lambda x: x['distance_to_viewer'], reverse=True) + + # compute initial latent + # print(self.color) + initial_latent = np.zeros(shape=(90, 90, 3), dtype=np.float32) + self.color + + for component in self.components: + a, b, c, d = component['rect'] + initial_latent[a:b, c:d] = 0.7 * component['color'] + 0.3 * initial_latent[a:b, c:d] + + initial_latent = initial_latent.clip(0, 255).astype(np.uint8) + + # compute conditions + + bag_of_conditions = [ + dict(mask=np.ones(shape=(90, 90), dtype=np.float32), prefixes=self.prefixes, suffixes=self.suffixes,location= "full") + ] + + for i, component in enumerate(self.components): + a, b, c, d = component['rect'] + m = np.zeros(shape=(90, 90), dtype=np.float32) + m[a:b, c:d] = 1.0 + bag_of_conditions.append(dict( + mask = m, + prefixes = component['prefixes'], + suffixes = component['suffixes'], + location = component['location'], + )) + + return dict( + initial_latent = initial_latent, + bag_of_conditions = bag_of_conditions, + ) + + +class OmostPromter(torch.nn.Module): + + def __init__(self,model = None,tokenizer = None, template = "",device="cpu"): + super().__init__() + self.model=model + self.tokenizer = tokenizer + self.device = device + if template == "": + template = r'''You are a helpful AI assistant to compose images using the below python class `Canvas`: + ```python + class Canvas: + def set_global_description(self, description: str, detailed_descriptions: list[str], tags: str, HTML_web_color_name: str): + pass + + def add_local_description(self, location: str, offset: str, area: str, distance_to_viewer: float, description: str, detailed_descriptions: list[str], tags: str, atmosphere: str, style: str, quality_meta: str, HTML_web_color_name: str): + assert location in ["in the center", "on the left", "on the right", "on the top", "on the bottom", "on the top-left", "on the top-right", "on the bottom-left", "on the bottom-right"] + assert offset in ["no offset", "slightly to the left", "slightly to the right", "slightly to the upper", "slightly to the lower", "slightly to the upper-left", "slightly to the upper-right", "slightly to the lower-left", "slightly to the lower-right"] + assert area in ["a small square area", "a small vertical area", "a small horizontal area", "a medium-sized square area", "a medium-sized vertical area", "a medium-sized horizontal area", "a large square area", "a large vertical area", "a large horizontal area"] + assert distance_to_viewer > 0 + pass + ```''' + self.template = template + + @staticmethod + def from_model_manager(model_manager: ModelManager): + model, model_path = model_manager.fetch_model("omost_prompt", require_model_path=True) + tokenizer = AutoTokenizer.from_pretrained(model_path) + omost = OmostPromter( + model= model, + tokenizer = tokenizer, + device = model_manager.device + ) + return omost + + + def __call__(self,prompt_dict:dict): + raw_prompt=prompt_dict["prompt"] + conversation = [{"role": "system", "content": self.template}] + conversation.append({"role": "user", "content": raw_prompt}) + + input_ids = self.tokenizer.apply_chat_template(conversation, return_tensors="pt", add_generation_prompt=True).to(self.device) + streamer = TextIteratorStreamer(self.tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) + attention_mask = torch.ones(input_ids.shape, dtype=torch.bfloat16, device=self.device) + + generate_kwargs = dict( + input_ids = input_ids, + streamer = streamer, + # stopping_criteria=stopping_criteria, + # max_new_tokens=max_new_tokens, + do_sample = True, + attention_mask = attention_mask, + pad_token_id = self.tokenizer.eos_token_id, + # temperature=temperature, + # top_p=top_p, + ) + self.model.generate(**generate_kwargs) + outputs = [] + for text in streamer: + outputs.append(text) + llm_outputs = "".join(outputs) + + canvas = Canvas.from_bot_response(llm_outputs) + canvas_output = canvas.process() + + prompts = [" ".join(_["prefixes"]+_["suffixes"][:2]) for _ in canvas_output["bag_of_conditions"]] + canvas_output["prompt"] = prompts[0] + canvas_output["prompts"] = prompts[1:] + + raw_masks = [_["mask"] for _ in canvas_output["bag_of_conditions"]] + masks=[] + for mask in raw_masks: + mask[mask>0.5]=255 + mask = np.stack([mask] * 3, axis=-1).astype("uint8") + masks.append(Image.fromarray(mask)) + + canvas_output["masks"] = masks + prompt_dict.update(canvas_output) + print(f"Your prompt is extended by Omost:\n") + cnt = 0 + for component,pmt in zip(canvas_output["bag_of_conditions"],prompts): + loc = component["location"] + cnt += 1 + print(f"Component {cnt} - Location : {loc}\nPrompt:{pmt}\n") + + return prompt_dict + + + + \ No newline at end of file diff --git a/diffsynth/prompters/prompt_refiners.py b/diffsynth/prompters/prompt_refiners.py new file mode 100644 index 0000000000000000000000000000000000000000..0ac19f565b076cccb21d9e05149b604e4bb55854 --- /dev/null +++ b/diffsynth/prompters/prompt_refiners.py @@ -0,0 +1,130 @@ +from transformers import AutoTokenizer +from ..models.model_manager import ModelManager +import torch +from .omost import OmostPromter + +class BeautifulPrompt(torch.nn.Module): + def __init__(self, tokenizer_path=None, model=None, template=""): + super().__init__() + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) + self.model = model + self.template = template + + + @staticmethod + def from_model_manager(model_manager: ModelManager): + model, model_path = model_manager.fetch_model("beautiful_prompt", require_model_path=True) + template = 'Instruction: Give a simple description of the image to generate a drawing prompt.\nInput: {raw_prompt}\nOutput:' + if model_path.endswith("v2"): + template = """Converts a simple image description into a prompt. \ +Prompts are formatted as multiple related tags separated by commas, plus you can use () to increase the weight, [] to decrease the weight, \ +or use a number to specify the weight. You should add appropriate words to make the images described in the prompt more aesthetically pleasing, \ +but make sure there is a correlation between the input and output.\n\ +### Input: {raw_prompt}\n### Output:""" + beautiful_prompt = BeautifulPrompt( + tokenizer_path=model_path, + model=model, + template=template + ) + return beautiful_prompt + + + def __call__(self, raw_prompt, positive=True, **kwargs): + if positive: + model_input = self.template.format(raw_prompt=raw_prompt) + input_ids = self.tokenizer.encode(model_input, return_tensors='pt').to(self.model.device) + outputs = self.model.generate( + input_ids, + max_new_tokens=384, + do_sample=True, + temperature=0.9, + top_k=50, + top_p=0.95, + repetition_penalty=1.1, + num_return_sequences=1 + ) + prompt = raw_prompt + ", " + self.tokenizer.batch_decode( + outputs[:, input_ids.size(1):], + skip_special_tokens=True + )[0].strip() + print(f"Your prompt is refined by BeautifulPrompt: {prompt}") + return prompt + else: + return raw_prompt + + + +class QwenPrompt(torch.nn.Module): + # This class leverages the open-source Qwen model to translate Chinese prompts into English, + # with an integrated optimization mechanism for enhanced translation quality. + def __init__(self, tokenizer_path=None, model=None, system_prompt=""): + super().__init__() + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) + self.model = model + self.system_prompt = system_prompt + + + @staticmethod + def from_model_manager(model_nameger: ModelManager): + model, model_path = model_nameger.fetch_model("qwen_prompt", require_model_path=True) + system_prompt = """You are an English image describer. Here are some example image styles:\n\n1. Extreme close-up: Clear focus on a single object with a blurred background, highlighted under natural sunlight.\n2. Vintage: A photograph of a historical scene, using techniques such as Daguerreotype or cyanotype.\n3. Anime: A stylized cartoon image, emphasizing hyper-realistic portraits and luminous brushwork.\n4. Candid: A natural, unposed shot capturing spontaneous moments, often with cinematic qualities.\n5. Landscape: A photorealistic image of natural scenery, such as a sunrise over the sea.\n6. Design: Colorful and detailed illustrations, often in the style of 2D game art or botanical illustrations.\n7. Urban: An ultrarealistic scene in a modern setting, possibly a cityscape viewed from indoors.\n\nYour task is to translate a given Chinese image description into a concise and precise English description. Ensure that the imagery is vivid and descriptive, and include stylistic elements to enrich the description.\nPlease note the following points:\n\n1. Capture the essence and mood of the Chinese description without including direct phrases or words from the examples provided.\n2. You should add appropriate words to make the images described in the prompt more aesthetically pleasing. If the Chinese description does not specify a style, you need to add some stylistic descriptions based on the essence of the Chinese text.\n3. The generated English description should not exceed 200 words.\n\n""" + qwen_prompt = QwenPrompt( + tokenizer_path=model_path, + model=model, + system_prompt=system_prompt + ) + return qwen_prompt + + + def __call__(self, raw_prompt, positive=True, **kwargs): + if positive: + messages = [{ + 'role': 'system', + 'content': self.system_prompt + }, { + 'role': 'user', + 'content': raw_prompt + }] + text = self.tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True + ) + model_inputs = self.tokenizer([text], return_tensors="pt").to(self.model.device) + + generated_ids = self.model.generate( + model_inputs.input_ids, + max_new_tokens=512 + ) + generated_ids = [ + output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) + ] + + prompt = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] + print(f"Your prompt is refined by Qwen: {prompt}") + return prompt + else: + return raw_prompt + + + +class Translator(torch.nn.Module): + def __init__(self, tokenizer_path=None, model=None): + super().__init__() + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) + self.model = model + + + @staticmethod + def from_model_manager(model_manager: ModelManager): + model, model_path = model_manager.fetch_model("translator", require_model_path=True) + translator = Translator(tokenizer_path=model_path, model=model) + return translator + + + def __call__(self, prompt, **kwargs): + input_ids = self.tokenizer.encode(prompt, return_tensors='pt').to(self.model.device) + output_ids = self.model.generate(input_ids) + prompt = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0] + print(f"Your prompt is translated: {prompt}") + return prompt diff --git a/diffsynth/prompters/sd3_prompter.py b/diffsynth/prompters/sd3_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..ecf9bca30ae53e78822d06d769a65a6c79e8b5d8 --- /dev/null +++ b/diffsynth/prompters/sd3_prompter.py @@ -0,0 +1,93 @@ +from .base_prompter import BasePrompter +from ..models.model_manager import ModelManager +from ..models import SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3 +from transformers import CLIPTokenizer, T5TokenizerFast +import os, torch + + +class SD3Prompter(BasePrompter): + def __init__( + self, + tokenizer_1_path=None, + tokenizer_2_path=None, + tokenizer_3_path=None + ): + if tokenizer_1_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_1_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_1") + if tokenizer_2_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_2") + if tokenizer_3_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_3_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_3") + super().__init__() + self.tokenizer_1 = CLIPTokenizer.from_pretrained(tokenizer_1_path) + self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path) + self.tokenizer_3 = T5TokenizerFast.from_pretrained(tokenizer_3_path) + self.text_encoder_1: SD3TextEncoder1 = None + self.text_encoder_2: SD3TextEncoder2 = None + self.text_encoder_3: SD3TextEncoder3 = None + + + def fetch_models(self, text_encoder_1: SD3TextEncoder1 = None, text_encoder_2: SD3TextEncoder2 = None, text_encoder_3: SD3TextEncoder3 = None): + self.text_encoder_1 = text_encoder_1 + self.text_encoder_2 = text_encoder_2 + self.text_encoder_3 = text_encoder_3 + + + def encode_prompt_using_clip(self, prompt, text_encoder, tokenizer, max_length, device): + input_ids = tokenizer( + prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True + ).input_ids.to(device) + pooled_prompt_emb, prompt_emb = text_encoder(input_ids) + return pooled_prompt_emb, prompt_emb + + + def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_length, device): + input_ids = tokenizer( + prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True, + add_special_tokens=True, + ).input_ids.to(device) + prompt_emb = text_encoder(input_ids) + prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1)) + + return prompt_emb + + + def encode_prompt( + self, + prompt, + positive=True, + device="cuda", + t5_sequence_length=77, + ): + prompt = self.process_prompt(prompt, positive=positive) + + # CLIP + pooled_prompt_emb_1, prompt_emb_1 = self.encode_prompt_using_clip(prompt, self.text_encoder_1, self.tokenizer_1, 77, device) + pooled_prompt_emb_2, prompt_emb_2 = self.encode_prompt_using_clip(prompt, self.text_encoder_2, self.tokenizer_2, 77, device) + + # T5 + if self.text_encoder_3 is None: + prompt_emb_3 = torch.zeros((prompt_emb_1.shape[0], t5_sequence_length, 4096), dtype=prompt_emb_1.dtype, device=device) + else: + prompt_emb_3 = self.encode_prompt_using_t5(prompt, self.text_encoder_3, self.tokenizer_3, t5_sequence_length, device) + prompt_emb_3 = prompt_emb_3.to(prompt_emb_1.dtype) # float32 -> float16 + + # Merge + prompt_emb = torch.cat([ + torch.nn.functional.pad(torch.cat([prompt_emb_1, prompt_emb_2], dim=-1), (0, 4096 - 768 - 1280)), + prompt_emb_3 + ], dim=-2) + pooled_prompt_emb = torch.cat([pooled_prompt_emb_1, pooled_prompt_emb_2], dim=-1) + + return prompt_emb, pooled_prompt_emb diff --git a/diffsynth/prompters/sd_prompter.py b/diffsynth/prompters/sd_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..e3b31ea2836b3b02edab37d7f610c13f2cf6cead --- /dev/null +++ b/diffsynth/prompters/sd_prompter.py @@ -0,0 +1,73 @@ +from .base_prompter import BasePrompter, tokenize_long_prompt +from ..models.utils import load_state_dict, search_for_embeddings +from ..models import SDTextEncoder +from transformers import CLIPTokenizer +import torch, os + + + +class SDPrompter(BasePrompter): + def __init__(self, tokenizer_path=None): + if tokenizer_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion/tokenizer") + super().__init__() + self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path) + self.text_encoder: SDTextEncoder = None + self.textual_inversion_dict = {} + self.keyword_dict = {} + + + def fetch_models(self, text_encoder: SDTextEncoder = None): + self.text_encoder = text_encoder + + + def add_textual_inversions_to_model(self, textual_inversion_dict, text_encoder): + dtype = next(iter(text_encoder.parameters())).dtype + state_dict = text_encoder.token_embedding.state_dict() + token_embeddings = [state_dict["weight"]] + for keyword in textual_inversion_dict: + _, embeddings = textual_inversion_dict[keyword] + token_embeddings.append(embeddings.to(dtype=dtype, device=token_embeddings[0].device)) + token_embeddings = torch.concat(token_embeddings, dim=0) + state_dict["weight"] = token_embeddings + text_encoder.token_embedding = torch.nn.Embedding(token_embeddings.shape[0], token_embeddings.shape[1]) + text_encoder.token_embedding = text_encoder.token_embedding.to(dtype=dtype, device=token_embeddings[0].device) + text_encoder.token_embedding.load_state_dict(state_dict) + + + def add_textual_inversions_to_tokenizer(self, textual_inversion_dict, tokenizer): + additional_tokens = [] + for keyword in textual_inversion_dict: + tokens, _ = textual_inversion_dict[keyword] + additional_tokens += tokens + self.keyword_dict[keyword] = " " + " ".join(tokens) + " " + tokenizer.add_tokens(additional_tokens) + + + def load_textual_inversions(self, model_paths): + for model_path in model_paths: + keyword = os.path.splitext(os.path.split(model_path)[-1])[0] + state_dict = load_state_dict(model_path) + + # Search for embeddings + for embeddings in search_for_embeddings(state_dict): + if len(embeddings.shape) == 2 and embeddings.shape[1] == 768: + tokens = [f"{keyword}_{i}" for i in range(embeddings.shape[0])] + self.textual_inversion_dict[keyword] = (tokens, embeddings) + + self.add_textual_inversions_to_model(self.textual_inversion_dict, self.text_encoder) + self.add_textual_inversions_to_tokenizer(self.textual_inversion_dict, self.tokenizer) + + + def encode_prompt(self, prompt, clip_skip=1, device="cuda", positive=True): + prompt = self.process_prompt(prompt, positive=positive) + for keyword in self.keyword_dict: + if keyword in prompt: + print(f"Textual inversion {keyword} is enabled.") + prompt = prompt.replace(keyword, self.keyword_dict[keyword]) + input_ids = tokenize_long_prompt(self.tokenizer, prompt).to(device) + prompt_emb = self.text_encoder(input_ids, clip_skip=clip_skip) + prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1)) + + return prompt_emb \ No newline at end of file diff --git a/diffsynth/prompters/sdxl_prompter.py b/diffsynth/prompters/sdxl_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..d84145402538b89b23d39a98271cbad64c2d9fc3 --- /dev/null +++ b/diffsynth/prompters/sdxl_prompter.py @@ -0,0 +1,61 @@ +from .base_prompter import BasePrompter, tokenize_long_prompt +from ..models.model_manager import ModelManager +from ..models import SDXLTextEncoder, SDXLTextEncoder2 +from transformers import CLIPTokenizer +import torch, os + + + +class SDXLPrompter(BasePrompter): + def __init__( + self, + tokenizer_path=None, + tokenizer_2_path=None + ): + if tokenizer_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion/tokenizer") + if tokenizer_2_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_xl/tokenizer_2") + super().__init__() + self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path) + self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path) + self.text_encoder: SDXLTextEncoder = None + self.text_encoder_2: SDXLTextEncoder2 = None + + + def fetch_models(self, text_encoder: SDXLTextEncoder = None, text_encoder_2: SDXLTextEncoder2 = None): + self.text_encoder = text_encoder + self.text_encoder_2 = text_encoder_2 + + + def encode_prompt( + self, + prompt, + clip_skip=1, + clip_skip_2=2, + positive=True, + device="cuda" + ): + prompt = self.process_prompt(prompt, positive=positive) + + # 1 + input_ids = tokenize_long_prompt(self.tokenizer, prompt).to(device) + prompt_emb_1 = self.text_encoder(input_ids, clip_skip=clip_skip) + + # 2 + input_ids_2 = tokenize_long_prompt(self.tokenizer_2, prompt).to(device) + add_text_embeds, prompt_emb_2 = self.text_encoder_2(input_ids_2, clip_skip=clip_skip_2) + + # Merge + if prompt_emb_1.shape[0] != prompt_emb_2.shape[0]: + max_batch_size = min(prompt_emb_1.shape[0], prompt_emb_2.shape[0]) + prompt_emb_1 = prompt_emb_1[: max_batch_size] + prompt_emb_2 = prompt_emb_2[: max_batch_size] + prompt_emb = torch.concatenate([prompt_emb_1, prompt_emb_2], dim=-1) + + # For very long prompt, we only use the first 77 tokens to compute `add_text_embeds`. + add_text_embeds = add_text_embeds[0:1] + prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1)) + return add_text_embeds, prompt_emb diff --git a/diffsynth/prompters/stepvideo_prompter.py b/diffsynth/prompters/stepvideo_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..79d374b1f8a4be2a2298520fcbf87800e0ca91d9 --- /dev/null +++ b/diffsynth/prompters/stepvideo_prompter.py @@ -0,0 +1,56 @@ +from .base_prompter import BasePrompter +from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder +from ..models.stepvideo_text_encoder import STEP1TextEncoder +from transformers import BertTokenizer +import os, torch + + +class StepVideoPrompter(BasePrompter): + + def __init__( + self, + tokenizer_1_path=None, + ): + if tokenizer_1_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_1_path = os.path.join( + base_path, "tokenizer_configs/hunyuan_dit/tokenizer") + super().__init__() + self.tokenizer_1 = BertTokenizer.from_pretrained(tokenizer_1_path) + + def fetch_models(self, text_encoder_1: HunyuanDiTCLIPTextEncoder = None, text_encoder_2: STEP1TextEncoder = None): + self.text_encoder_1 = text_encoder_1 + self.text_encoder_2 = text_encoder_2 + + def encode_prompt_using_clip(self, prompt, max_length, device): + text_inputs = self.tokenizer_1( + prompt, + padding="max_length", + max_length=max_length, + truncation=True, + return_attention_mask=True, + return_tensors="pt", + ) + prompt_embeds = self.text_encoder_1( + text_inputs.input_ids.to(device), + attention_mask=text_inputs.attention_mask.to(device), + ) + return prompt_embeds + + def encode_prompt_using_llm(self, prompt, max_length, device): + y, y_mask = self.text_encoder_2(prompt, max_length=max_length, device=device) + return y, y_mask + + def encode_prompt(self, + prompt, + positive=True, + device="cuda"): + + prompt = self.process_prompt(prompt, positive=positive) + + clip_embeds = self.encode_prompt_using_clip(prompt, max_length=77, device=device) + llm_embeds, llm_mask = self.encode_prompt_using_llm(prompt, max_length=320, device=device) + + llm_mask = torch.nn.functional.pad(llm_mask, (clip_embeds.shape[1], 0), value=1) + + return clip_embeds, llm_embeds, llm_mask diff --git a/diffsynth/prompters/wan_prompter.py b/diffsynth/prompters/wan_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..a6d41585238e6e29558451f8e353d8970d7c937e --- /dev/null +++ b/diffsynth/prompters/wan_prompter.py @@ -0,0 +1,130 @@ +from .base_prompter import BasePrompter +from ..models.wan_video_text_encoder import WanTextEncoder +from transformers import AutoTokenizer +import os, torch +import ftfy +import html +import string +import regex as re + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r'\s+', ' ', text) + text = text.strip() + return text + + +def canonicalize(text, keep_punctuation_exact_string=None): + text = text.replace('_', ' ') + if keep_punctuation_exact_string: + text = keep_punctuation_exact_string.join( + part.translate(str.maketrans('', '', string.punctuation)) + for part in text.split(keep_punctuation_exact_string)) + else: + text = text.translate(str.maketrans('', '', string.punctuation)) + text = text.lower() + text = re.sub(r'\s+', ' ', text) + return text.strip() + + +class HuggingfaceTokenizer: + + def __init__(self, name, seq_len=None, clean=None, **kwargs): + assert clean in (None, 'whitespace', 'lower', 'canonicalize') + self.name = name + self.seq_len = seq_len + self.clean = clean + + # init tokenizer + self.tokenizer = AutoTokenizer.from_pretrained(name, **kwargs) + self.vocab_size = self.tokenizer.vocab_size + + def __call__(self, sequence, **kwargs): + return_mask = kwargs.pop('return_mask', False) + + # arguments + _kwargs = {'return_tensors': 'pt'} + if self.seq_len is not None: + _kwargs.update({ + 'padding': 'max_length', + 'truncation': True, + 'max_length': self.seq_len + }) + _kwargs.update(**kwargs) + + # tokenization + if isinstance(sequence, str): + sequence = [sequence] + elif not isinstance(sequence, (list, tuple)): + # Handle unexpected types (e.g., float, int) - convert to string + raise TypeError(f"sequence must be a string or list/tuple of strings, but got {type(sequence).__name__}: {sequence}") + + # Ensure all items in sequence are strings + sequence = [str(s) if not isinstance(s, str) else s for s in sequence] + + if self.clean: + sequence = [self._clean(u) for u in sequence] + ids = self.tokenizer(sequence, **_kwargs) + + # output + if return_mask: + return ids.input_ids, ids.attention_mask + else: + return ids.input_ids + + def _clean(self, text): + if self.clean == 'whitespace': + text = whitespace_clean(basic_clean(text)) + elif self.clean == 'lower': + text = whitespace_clean(basic_clean(text)).lower() + elif self.clean == 'canonicalize': + text = canonicalize(basic_clean(text)) + return text + + +class WanPrompter(BasePrompter): + + def __init__(self, tokenizer_path=None, text_len=512): + super().__init__() + self.text_len = text_len + self.text_encoder = None + self.fetch_tokenizer(tokenizer_path) + + def fetch_tokenizer(self, tokenizer_path=None): + if tokenizer_path is not None: + self.tokenizer = HuggingfaceTokenizer(name=tokenizer_path, seq_len=self.text_len, clean='whitespace') + + def fetch_models(self, text_encoder: WanTextEncoder = None): + self.text_encoder = text_encoder + + def encode_prompt(self, prompt, positive=True, device="cuda"): + # Validate and convert prompt to string if needed + if prompt is None: + raise ValueError("prompt cannot be None") + if not isinstance(prompt, (str, list)): + # Convert non-string prompts to string (e.g., float, int) + if isinstance(prompt, (float, int)): + # If it's a numeric value, it's likely a data error - use empty string or raise error + raise ValueError(f"prompt must be a string or list of strings, but got {type(prompt).__name__}: {prompt}. " + f"This usually indicates a data loading issue.") + prompt = str(prompt) + elif isinstance(prompt, list): + # Validate all items in list are strings + prompt = [str(p) if not isinstance(p, str) else p for p in prompt] + + prompt = self.process_prompt(prompt, positive=positive) + + ids, mask = self.tokenizer(prompt, return_mask=True, add_special_tokens=True) + ids = ids.to(device) + mask = mask.to(device) + seq_lens = mask.gt(0).sum(dim=1).long() + prompt_emb = self.text_encoder(ids, mask) + for i, v in enumerate(seq_lens): + prompt_emb[:, v:] = 0 + return prompt_emb diff --git a/diffsynth/schedulers/__init__.py b/diffsynth/schedulers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0ec43257b687c9b5504e08e05763332755566ea5 --- /dev/null +++ b/diffsynth/schedulers/__init__.py @@ -0,0 +1,3 @@ +from .ddim import EnhancedDDIMScheduler +from .continuous_ode import ContinuousODEScheduler +from .flow_match import FlowMatchScheduler diff --git a/diffsynth/schedulers/continuous_ode.py b/diffsynth/schedulers/continuous_ode.py new file mode 100644 index 0000000000000000000000000000000000000000..c73b9e221aa54a8385322b42012c30c598550fcd --- /dev/null +++ b/diffsynth/schedulers/continuous_ode.py @@ -0,0 +1,59 @@ +import torch + + +class ContinuousODEScheduler(): + + def __init__(self, num_inference_steps=100, sigma_max=700.0, sigma_min=0.002, rho=7.0): + self.sigma_max = sigma_max + self.sigma_min = sigma_min + self.rho = rho + self.set_timesteps(num_inference_steps) + + + def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, **kwargs): + ramp = torch.linspace(1-denoising_strength, 1, num_inference_steps) + min_inv_rho = torch.pow(torch.tensor((self.sigma_min,)), (1 / self.rho)) + max_inv_rho = torch.pow(torch.tensor((self.sigma_max,)), (1 / self.rho)) + self.sigmas = torch.pow(max_inv_rho + ramp * (min_inv_rho - max_inv_rho), self.rho) + self.timesteps = torch.log(self.sigmas) * 0.25 + + + def step(self, model_output, timestep, sample, to_final=False): + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + sigma = self.sigmas[timestep_id] + sample *= (sigma*sigma + 1).sqrt() + estimated_sample = -sigma / (sigma*sigma + 1).sqrt() * model_output + 1 / (sigma*sigma + 1) * sample + if to_final or timestep_id + 1 >= len(self.timesteps): + prev_sample = estimated_sample + else: + sigma_ = self.sigmas[timestep_id + 1] + derivative = 1 / sigma * (sample - estimated_sample) + prev_sample = sample + derivative * (sigma_ - sigma) + prev_sample /= (sigma_*sigma_ + 1).sqrt() + return prev_sample + + + def return_to_timestep(self, timestep, sample, sample_stablized): + # This scheduler doesn't support this function. + pass + + + def add_noise(self, original_samples, noise, timestep): + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + sigma = self.sigmas[timestep_id] + sample = (original_samples + noise * sigma) / (sigma*sigma + 1).sqrt() + return sample + + + def training_target(self, sample, noise, timestep): + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + sigma = self.sigmas[timestep_id] + target = (-(sigma*sigma + 1).sqrt() / sigma + 1 / (sigma*sigma + 1).sqrt() / sigma) * sample + 1 / (sigma*sigma + 1).sqrt() * noise + return target + + + def training_weight(self, timestep): + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + sigma = self.sigmas[timestep_id] + weight = (1 + sigma*sigma).sqrt() / sigma + return weight diff --git a/diffsynth/schedulers/ddim.py b/diffsynth/schedulers/ddim.py new file mode 100644 index 0000000000000000000000000000000000000000..da524963c62f662016b1429d5047ebe7b5922604 --- /dev/null +++ b/diffsynth/schedulers/ddim.py @@ -0,0 +1,105 @@ +import torch, math + + +class EnhancedDDIMScheduler(): + + def __init__(self, num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", prediction_type="epsilon", rescale_zero_terminal_snr=False): + self.num_train_timesteps = num_train_timesteps + if beta_schedule == "scaled_linear": + betas = torch.square(torch.linspace(math.sqrt(beta_start), math.sqrt(beta_end), num_train_timesteps, dtype=torch.float32)) + elif beta_schedule == "linear": + betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) + else: + raise NotImplementedError(f"{beta_schedule} is not implemented") + self.alphas_cumprod = torch.cumprod(1.0 - betas, dim=0) + if rescale_zero_terminal_snr: + self.alphas_cumprod = self.rescale_zero_terminal_snr(self.alphas_cumprod) + self.alphas_cumprod = self.alphas_cumprod.tolist() + self.set_timesteps(10) + self.prediction_type = prediction_type + + + def rescale_zero_terminal_snr(self, alphas_cumprod): + alphas_bar_sqrt = alphas_cumprod.sqrt() + + # Store old values. + alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() + alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() + + # Shift so the last timestep is zero. + alphas_bar_sqrt -= alphas_bar_sqrt_T + + # Scale so the first timestep is back to the old value. + alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) + + # Convert alphas_bar_sqrt to betas + alphas_bar = alphas_bar_sqrt.square() # Revert sqrt + + return alphas_bar + + + def set_timesteps(self, num_inference_steps, denoising_strength=1.0, **kwargs): + # The timesteps are aligned to 999...0, which is different from other implementations, + # but I think this implementation is more reasonable in theory. + max_timestep = max(round(self.num_train_timesteps * denoising_strength) - 1, 0) + num_inference_steps = min(num_inference_steps, max_timestep + 1) + if num_inference_steps == 1: + self.timesteps = torch.Tensor([max_timestep]) + else: + step_length = max_timestep / (num_inference_steps - 1) + self.timesteps = torch.Tensor([round(max_timestep - i*step_length) for i in range(num_inference_steps)]) + + + def denoise(self, model_output, sample, alpha_prod_t, alpha_prod_t_prev): + if self.prediction_type == "epsilon": + weight_e = math.sqrt(1 - alpha_prod_t_prev) - math.sqrt(alpha_prod_t_prev * (1 - alpha_prod_t) / alpha_prod_t) + weight_x = math.sqrt(alpha_prod_t_prev / alpha_prod_t) + prev_sample = sample * weight_x + model_output * weight_e + elif self.prediction_type == "v_prediction": + weight_e = -math.sqrt(alpha_prod_t_prev * (1 - alpha_prod_t)) + math.sqrt(alpha_prod_t * (1 - alpha_prod_t_prev)) + weight_x = math.sqrt(alpha_prod_t * alpha_prod_t_prev) + math.sqrt((1 - alpha_prod_t) * (1 - alpha_prod_t_prev)) + prev_sample = sample * weight_x + model_output * weight_e + else: + raise NotImplementedError(f"{self.prediction_type} is not implemented") + return prev_sample + + + def step(self, model_output, timestep, sample, to_final=False): + alpha_prod_t = self.alphas_cumprod[int(timestep.flatten().tolist()[0])] + if isinstance(timestep, torch.Tensor): + timestep = timestep.cpu() + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + if to_final or timestep_id + 1 >= len(self.timesteps): + alpha_prod_t_prev = 1.0 + else: + timestep_prev = int(self.timesteps[timestep_id + 1]) + alpha_prod_t_prev = self.alphas_cumprod[timestep_prev] + + return self.denoise(model_output, sample, alpha_prod_t, alpha_prod_t_prev) + + + def return_to_timestep(self, timestep, sample, sample_stablized): + alpha_prod_t = self.alphas_cumprod[int(timestep.flatten().tolist()[0])] + noise_pred = (sample - math.sqrt(alpha_prod_t) * sample_stablized) / math.sqrt(1 - alpha_prod_t) + return noise_pred + + + def add_noise(self, original_samples, noise, timestep): + sqrt_alpha_prod = math.sqrt(self.alphas_cumprod[int(timestep.flatten().tolist()[0])]) + sqrt_one_minus_alpha_prod = math.sqrt(1 - self.alphas_cumprod[int(timestep.flatten().tolist()[0])]) + noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise + return noisy_samples + + + def training_target(self, sample, noise, timestep): + if self.prediction_type == "epsilon": + return noise + else: + sqrt_alpha_prod = math.sqrt(self.alphas_cumprod[int(timestep.flatten().tolist()[0])]) + sqrt_one_minus_alpha_prod = math.sqrt(1 - self.alphas_cumprod[int(timestep.flatten().tolist()[0])]) + target = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample + return target + + + def training_weight(self, timestep): + return 1.0 diff --git a/diffsynth/schedulers/flow_match.py b/diffsynth/schedulers/flow_match.py new file mode 100644 index 0000000000000000000000000000000000000000..9754b98f692e8afca6f1079abc0c9ce07a03ebf5 --- /dev/null +++ b/diffsynth/schedulers/flow_match.py @@ -0,0 +1,82 @@ +import torch + + + +class FlowMatchScheduler(): + + def __init__(self, num_inference_steps=100, num_train_timesteps=1000, shift=3.0, sigma_max=1.0, sigma_min=0.003/1.002, inverse_timesteps=False, extra_one_step=False, reverse_sigmas=False): + self.num_train_timesteps = num_train_timesteps + self.shift = shift + self.sigma_max = sigma_max + self.sigma_min = sigma_min + self.inverse_timesteps = inverse_timesteps + self.extra_one_step = extra_one_step + self.reverse_sigmas = reverse_sigmas + self.set_timesteps(num_inference_steps) + + + def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, training=False, shift=None): + if shift is not None: + self.shift = shift + sigma_start = self.sigma_min + (self.sigma_max - self.sigma_min) * denoising_strength + if self.extra_one_step: + self.sigmas = torch.linspace(sigma_start, self.sigma_min, num_inference_steps + 1)[:-1] + else: + self.sigmas = torch.linspace(sigma_start, self.sigma_min, num_inference_steps) + if self.inverse_timesteps: + self.sigmas = torch.flip(self.sigmas, dims=[0]) + self.sigmas = self.shift * self.sigmas / (1 + (self.shift - 1) * self.sigmas) + if self.reverse_sigmas: + self.sigmas = 1 - self.sigmas + self.timesteps = self.sigmas * self.num_train_timesteps + if training: + x = self.timesteps + y = torch.exp(-2 * ((x - num_inference_steps / 2) / num_inference_steps) ** 2) + y_shifted = y - y.min() + bsmntw_weighing = y_shifted * (num_inference_steps / y_shifted.sum()) + self.linear_timesteps_weights = bsmntw_weighing + self.training = True + else: + self.training = False + + + def step(self, model_output, timestep, sample, to_final=False, **kwargs): + if isinstance(timestep, torch.Tensor): + timestep = timestep.cpu() + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + sigma = self.sigmas[timestep_id] + if to_final or timestep_id + 1 >= len(self.timesteps): + sigma_ = 1 if (self.inverse_timesteps or self.reverse_sigmas) else 0 + else: + sigma_ = self.sigmas[timestep_id + 1] + prev_sample = sample + model_output * (sigma_ - sigma) + return prev_sample + + + def return_to_timestep(self, timestep, sample, sample_stablized): + if isinstance(timestep, torch.Tensor): + timestep = timestep.cpu() + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + sigma = self.sigmas[timestep_id] + model_output = (sample - sample_stablized) / sigma + return model_output + + + def add_noise(self, original_samples, noise, timestep): + if isinstance(timestep, torch.Tensor): + timestep = timestep.cpu() + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + sigma = self.sigmas[timestep_id] + sample = (1 - sigma) * original_samples + sigma * noise + return sample + + + def training_target(self, sample, noise, timestep): + target = noise - sample + return target + + + def training_weight(self, timestep): + timestep_id = torch.argmin((self.timesteps - timestep.to(self.timesteps.device)).abs()) + weights = self.linear_timesteps_weights[timestep_id] + return weights diff --git a/diffsynth/tokenizer_configs/__init__.py b/diffsynth/tokenizer_configs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/diffsynth/tokenizer_configs/cog/tokenizer/added_tokens.json b/diffsynth/tokenizer_configs/cog/tokenizer/added_tokens.json new file mode 100644 index 0000000000000000000000000000000000000000..3f5132007c4fcf42b75b65c8b6aa49c7098bcdf4 --- /dev/null +++ b/diffsynth/tokenizer_configs/cog/tokenizer/added_tokens.json @@ -0,0 +1,102 @@ +{ + "": 32099, + "": 32089, + "": 32088, + "": 32087, + "": 32086, + "": 32085, + "": 32084, + "": 32083, + "": 32082, + "": 32081, + "": 32080, + "": 32098, + "": 32079, + "": 32078, + "": 32077, + "": 32076, + "": 32075, + "": 32074, + "": 32073, + "": 32072, + "": 32071, + "": 32070, + "": 32097, + "": 32069, + "": 32068, + "": 32067, + "": 32066, + "": 32065, + "": 32064, + "": 32063, + "": 32062, + "": 32061, + "": 32060, + "": 32096, + "": 32059, + "": 32058, + "": 32057, + "": 32056, + "": 32055, + "": 32054, + "": 32053, + "": 32052, + "": 32051, + "": 32050, + "": 32095, + "": 32049, + "": 32048, + "": 32047, + "": 32046, + "": 32045, + "": 32044, + "": 32043, + "": 32042, + "": 32041, + "": 32040, + "": 32094, + "": 32039, + "": 32038, + "": 32037, + "": 32036, + "": 32035, + "": 32034, + "": 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+ "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "" + ], + "eos_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "unk_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + } +} diff --git a/diffsynth/tokenizer_configs/cog/tokenizer/spiece.model b/diffsynth/tokenizer_configs/cog/tokenizer/spiece.model new file mode 100644 index 0000000000000000000000000000000000000000..317a5ccbde45300f5d1d970d4d449af2108b147e --- /dev/null +++ b/diffsynth/tokenizer_configs/cog/tokenizer/spiece.model @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d60acb128cf7b7f2536e8f38a5b18a05535c9e14c7a355904270e15b0945ea86 +size 791656 diff --git a/diffsynth/tokenizer_configs/cog/tokenizer/tokenizer_config.json b/diffsynth/tokenizer_configs/cog/tokenizer/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..161715af5ee99558c9fcce7b31d3d547a72c349b --- /dev/null +++ b/diffsynth/tokenizer_configs/cog/tokenizer/tokenizer_config.json @@ -0,0 +1,940 @@ +{ + "add_prefix_space": true, + "added_tokens_decoder": { + "0": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "1": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + 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MAIBKDACA0R0AgMwEAQDHCgCAcRsAgKelAADTCgCAo40AAMwUAgCAuQAAgbkAAKeFAAAIDACAgmUAAIEbAICMNQAA8wsAgMzsHADN/AMAiRsAgK6tAADZCgCAkRsAgMzABgDN0AYAsL0BAMyQBwDfCgCAgckBAMwYHQDNIAIAhBEAAOsKAIDNuAYAzKwGAKEbAIDlCgCAgSkAALEbAICpGwCAo+0BAMxAHQDNEAIAuRsAgMEbAICBCQAAyRsAgMxAHQDN0AIAqNkBABQMAIDMkAcAzBwBAMxgBgDNZAYA8QoAgBwKAIDRGwCAkSkBAP0KAICBzR8A2RsAgPcKAIDpGwCA4RsAgMzEBgDNwAYAgTEAAIDZAAAfCgCAIgoAgIK5AQCDRQEAgLkBAIG5AQCGXQEA8R0AgIRdAQDpHQCAzcAAAMzwAACIARwAiXkBAAEeAICPVQEAjGEBAPkdAICB3R4AgRUfAJkbAICBXR8AjIEfAIdBHwDMGAMAzWgDAIBNHwCBpR8AJQoAgIOpHwCMFR8AjNEeACgKAICHtR8AgJUfAIGZHwCBEQAAg70fAICFHwCBiR8A8RsAgIQ9AACbDACAiZkfAPkbAICIBQAABgsAgAEcAICADQAAgf0AAAkcAICj2R8Ao3keAKOFAAAMCwCArTUfAKdhHgCnqR8AoQwAgIQNAACnDACAozUfACsKAICtiR8AhHEAAKchHwCxPR4AsYUfAJUMAIDhHQCAEgsAgLcLAIDMtBwAzbAcAFAMAICxQR8AVgwAgJwLAIAZHgCAER4AgCkeAIAhHgCAgLkeAIG5HgCCIQEAgzUBAIRhAQAxHgCAhokBAIe9AQCIkQEAiekBANkdAICL/QEAjOUBAIINAAAJHgCAj90BAIO5AQCRrQEAgb0BAIC9AQCAoQEAgaEBAPkLAID/CwCAhD0AABEcAICJlQEAm4EBAIHNHgCAzR4AzPwCAM3wAgCB5QAAGRwAgIHtAACjpQAAzJABAM1cAgCHHQAAGwsAgKj5AAAhHACAJwsAgFwMAIBiDACAKRwAgIQFAAAxHACAo9UAACELAIA5HACAgVEAAMz0AQDN0AEALQsAgIc9AABRHACAMwsAgEEcAIA/CwCAhwUAAFkcAIBJHACAh/EDAIHZAwCBmQMAgZEAAGEcAIB0DACAjPkDAMwkAQCHuQMAgfkDADkLAIDMZAIAgskDAIyZAwBpHACAh9EDAI+RAwCB3QYAkfUDAMwABADN7AMAh2UAABkdAIBLCwCAcRwAgHoMAIBFCwCAzBgBAIg5AACBHACAeRwAgMxcAwCMJQAALgoAgMwsAQCx/QAAozkDADEKAIA0CgCAoRwAgKdZAwDMdAMAiAkAAKNRAwCpHACAXQsAgINtDQCnnQAApq0AAKOdAACxDQMAzCgBANULAICntQAAprUAAMkLAIDMMAEAgdUHAMMLAIDMKAEAzwsAgEEeAIBjCwCArYkAAGkLAICAzQEAgd0BAMxEAQDNnB4AhPUBAL0LAIDMWAEAzUwBAIDtAQCB/QEAg7UAAGgMAICM3QEAbgwAgMwIHgCM8QYAzDgBAM08AQBRHgCAiREAAIEFBgBJHgCAYR4AgFkeAIBpHgCAgz0AAIAhAACBOQAAgDkAAIEhAAA5HgCAiRwAgMwoAQCB2QYAbwsAgIH9BgDMJAEAmRwAgJEcAICxHACAgCEBAIE1AQCjBQAAuRwAgMEcAIDJHACAzIwFAM1AAgC3HAMAdQsAgIfNBwDZHACA0RwAgB0dAIDNiAAAzJAAAIzdBQCjhQAAFgoAgMzgAgDhHACAiNUHAIFNAACATQAAUQsAgOkcAIBXCwCAkTkHADcKAICIxQcApQsAgIrJBwDxHACAmz0AAIflBwBxHgCAgYUHAICFBwA6CgCAgvkHAILVBgCDRQAAgMkGAIHdBgCG4QYAewsAgIRRAACJHgCAipUGAIuZBgCIeQAAiZ0GAK0MAICPWQcAjG0HAPkcAIDMgAMAzSQCALARBwA9CgCAgR4AgCEdAIB5HgCAhAsAgICNAACBnQAAzOwDAM3oBAABHQCAigsAgKNJBwCQCwCACR0AgKO9BwARHQCAGwAAgOcHAIALAACApKUHAOsEAICKBQCAAwAAgKhhBwDZDQCAZQAAgMgDAIAbCQCArWkHAIAtAQCBPQEAgl0BAINRAQCEYQEAuAQAgKwEAICHYQEAiK0BAIm1AQCKvQEAjykVALwFAIAdDACAzHgCAM3YBQCB3QEAgXEAAOQLAICC/QEAhBkAACMMAICH7QEAIAwAgMw0BADNMAQA5wsAgJ9pFQAmDACAjMkBAM34BADM8AIAsUkBACEHAICB1QAAoxUBAKCZFQBzCACARgcAgIT1AADMKAQAzSwEAMMIAICveQEAqH0BADENAICqaQEAUgkAgLQlAQC1KQEAowkBAAIMAIDqBgCA7gYAgLIFAQCzPQEAvPUAAL39AAC+2QAAOAgAgLgBAQC5AQEAugEBADwHAIBDBwCAhgwAALOdAwCyiQMAswgAgIC9AwBpBwCAbAcAgBIJAIDkBgCA5wYAgDUIAICJhQMAzOQHAL+hAwAFDACA1wwAgIxlAADN5AwAzCQMAIlBAACIVQAAi0UAAIpFAACFtQMAhLUDAIeVAwCGgQMAAQ0AgAQNAIAHDQCAmCwAABMAAICmyAAAzYwGAMyoBgCFaQAAFwAAgDEAAIBpAACAzPADAAcAAIA1AACA0QwAgLGVAAAlDQCAs5UAALKVAAA1DQCAOA0AgEANAIA7DQCALg0AgHUAAICmBgCAJQAAgJgJAIAdIQCAv1UDAEMNAIAZIQCAFSEAgGEgAIC4bAAAlGUNAJIAAgCcrQEAnaUBAJqJAQCbiQEAmJkBAJmJAQDMIAYAzQQGAMxABgDNXAYAzDwHAM04BwDMvAcAhXUAAIABDwCBDQ8AaSAAgLqZAQCFBQAAcSAAgFkgAIC+hQEAgSkPAIAlDwBlIACAgiEPAIUpAAC0pQEAhREAAG0gAICziQ8AsoUPALHJAQCwAQwAt4EPALbtAQC17QEAtO0BAIFlAQCAZQEAg2EBALi1DwDMPAsAhHkBAIDhDwCB3Q8AdSAAgF0gAIDMyAQAzbgEAIWtAACFFQAAISEAgDkhAIDM6BkAzbQZAKRdAQBGDQCAok0CAKPxDwCgVQEAod0PAH8IAIBuCQCAOwkAgO0eAIBsCQCA9R4AgHcJAIDxHgCAsQgAgJMNAACtHgCA+R4AgITVDACF6Q4AlGkAAIfdDgC1HgCAmbQCAL0eAIDFHgCAsR4AgD0hAIC5HgCAn3QBAMEeAICRGA0AgI0OAIGBDgCGhQ4AlYwDAISJDgCXRAIAghEAAKm4AACA0QAAge0AAMkeAIBJDQCA5R4AgIVZDwCDiQAAoTQNAIFFDgCASQ4A6R4AgKU0AQCFYQ8AzPAUAB0fAIC5xAUAzMgDAM3cAwCA3QAAgcEAACUfAIC/kAUAhREAALHsBwCA9QAAgcEAAKEgAIC1jAYALR8AgLdABgCA3Q4AgekOAMwoAgDNtAIAgM0OAIH5DgCFKQAAg4UBAIB1AQCBsQEAgPEBAIHVAQCpIACANR8AgIUFAACxIACAgJkBAIG9AQCCfQAAk9UBAJThAQCFDQAAmSAAgCEfAICACQAAgRkAACkfAICTrQEAlC0AAKUgAICFDQAAMR8AgIUFAACtIACAOR8AgIUpAACCGQAAhTUAAIDxAACB4QAAtSAAgJ0gAIBBIQCAhQUAAGEhAICDdQEAgO0BAIEpAQDM8AEAzbABAEwNAIBdIQCAWSEAgKMNAIBdHwCAZR8AgIA9AACBDQAAbR8AgHUfAICALQAAgR0AAIIVAABhHwCAzSwBAGkfAIBxHwCAeR8AgIjFAwClIQCAzJACAM28AgCE7QMATw0AgIb5AwCdHwCAgIEDAIH9AwCAPQAAgTUAAIFJAACAQQAAzdwBAIJBAAClHwCAoR8AgKkfAIDNMAEAlJ0DAI0hAIDN8AEAzAwBAIG5AwCAxQMAg6EDAJOlAwCArQAAgdUAAICdAACBqQAAiSEAgFINAICBwQAAgMkAAIC1AACBgQAAhSEAgINpBADMcAMAzbQDAIEhAIDNPAEApg0AgJMBBADNjAIAzPQCAIANAACBNQAAlNkGANEfAIDVHwCA2R8AgMwIAQDNHAEAgREAAIApAACpIQCAghkAAICRAQCBkQEAzWgFAMyUAgDMEAkAzSgWAMxYDgDNeA4AzBQNAM3YCgDMKAwAzYwNAMzgFwDM4AoAzDgLAM30CACFEQAAVQ0AgIBRBwCBUQcA4SAAgM2QDgCFBQAA6SAAgMzYDgDN7AEA8SAAgM0ADgCFGQAAzfAPAM08DgDNVA4AzGgBAM1sAQDZIACAYQgAgJSZBwDMwDsAgGEBAIHZAACFKQAAzWQOAMx4AQDNfAEAga0HAICtBwCFZQAAgp0HAIBRAQCBUQEAlOEHAM3AAACEeQEAk8UHAIZhAQDlIACAiCEBAIUNAADtIACAzRgBAMzYAADNtAAAgN0HAIHNBwCZHwCAhQkAAM0fAID1IACA/R8AgN0gAIAFIACADSAAgBUgAIAJIACAASAAgK0hAIARIACAGSAAgMy4AgDNHAMAgGUAAIF1AACCfQAAHSAAgIUJAACFQQAAASEAgKkNAICAmQYAgSEHAIUZAACDfQAACSEAgIVZAAD9IACA+SAAgIDNAACB2QAAjR4AgIURAACE6QAAlR4AgIblAABBIACAgDUAAIENAACdHgCAhR0AAEkgAIClHgCAhQUAAFEgAICAVQAAgW0AAIJ9AACTRQAAlA0AAIUNAAA5IACAkR4AgIAJAACBEQAAmR4AgIUdAABFIACAoR4AgIUFAABNIACAgOkBAIHxAQCCBQAAqR4AgIUJAACFCQAAVSAAgD0gAICAbQEAgXkBAIIZAACDpQEADSEAgIV1AACFBQAAESEAgAUhAIAhIACAzMgCAM3cAgCsDQCAzR4AgIA5AACBOQAA1R4AgN0eAIDRHgCA2R4AgIAdAACBDQAA4R4AgCUgAICAxQAAgdUAAM3AAADMJAIAgNUAAIHFAACFOQAAg8kAACUhAICvDQCAgNUAAIEJAACFBQAALSEAgP0eAICBIACAgAkAAIERAAAFHwCAk5kAAJS5AAANHwCAhWUAAIU9AACJIACAk10AABUfAICFEQAAzXAFAMx0BQCUATwAkSAAgHkgAIDNKAEAhSAAgI0gAICFGQAAlSAAgH0gAIA1IQCAKSEAgCkgAICFJQAAhTkAAMz4AgDNxAMAzTwBALINAICBlQMAgI0DAM3EAQCCpQMAhVEAAIVJAADMKAEAzSwBAM04AQDMPAEAgGk+AIFpPgBJIQCARSEAgM04PADMVDwAgdE8AJOdPgDMSAEAzcgCAM00AQBNIQCAlLk+AFgNAICAoT4AgaE+AIKhPgCIjTwAVSEAgIWtAACALQAAgSEAAIXVPwCVHwCAgO0AAIHxAACGpQAARR8AgISpAADNJAEAzSgBAE0fAICI+T4AhfE/AFUfAIBJHwCAhcU/AM0wAQDNEAEAzfQGAIDdAQCB6QEAzbwGAM1wBgDM4AYAzVwBAMxoBgDNkAYAzWQGAM14BgDMrAcAzagHAMzoBwDNyAcAgk0/AIP9AgCANQIAgekCAFEfAIBZHwCAgAU9AIV9AQBRIQCALSAAgM0UAQApDgCAge0BAIDhAQDNPAEAgs0BAM0sAQCCdQEAgW0BAIBZAQCAZQEAgcUAAIUfAIDNJAEAzTgBAILxAACB+QAAgFkBAIApAACBcQAAzBgBAM18AQDNLAEAjR8AgIEdAACAHQAAiR8AgJEfAIBxIQCAzSQBAMzkPQDNXA8AzegAAMwMAQCA1QEAgckBAIKZAACD5T8ACR8AgBEfAIAZHwCAMSEAgCMOAIB1IQCAPR8AgDEgAIBBHwCALA4AgIBNPwCBQT8AfR8AgGkhAICBHwCAZSEAgIAlPwCBKT8Ak5E/AIN9AAAmDgCAlEEAAMzYAgDNrAIAbSEAgJNVAACACQAAgR0AALUNAIB9IQCAlEEAAK0fAICAnQAAgaEAAIAdAACBEQAAhKUAALUfAICGpQAAvR8AgIjxAACC0QAAgdkAAIDNAACAJQAAgSkAAIIFAADFHwCAsR8AgLkfAIDBHwCAk7EAAJQRAADJHwCAgB0AAIEVAACAJQAAgS0AAII9AAB5IQCAgO0AAIHRAACCFQAAg4EAAIHQPQA1IACAzCACAM3cAQCFeAIAkSEAgC8OAICZIQCAiRgDAN0fAICALQAAgTUAAIAJAACBbQAA5R8AgMEgAICRsQAAkKkAAJPdOwCSAQQAlaUAAJSVOwDtHwCAlqEAAIUJAACTQQAAySAAgPUfAICFBQAA0SAAgJT1AAC5IACAgLkAAIHdAACC5QAA4R8AgOkfAICF6QAAgAkAAIE1AACFBQAAxSAAgPEfAICFHQAAzSAAgPkfAICFBQAA1SAAgLHBBQCwxQMAvSAAgLLFAwC12QUAtM0DAJ0hAICFOQAAuf0DAKEhAICVIQCAuw0AgM0NAIAXDgCAAR8AgAUOAIDTDQCAzIgCAAsOAIDN4D4AzZABAMwkAQBwDQCAjg0AgEEOAIB9DgCAgLEAAM3UPgDN5D4Agw4AgMy8PgDNuD4AgNEDAIHtAwCC/QMAhmkAAD4OAICFnQMAzTwBADgOAIDM6AIAzTw/AIjlAADNGAEAiQ4AgIhBAAA7DgCAdw4AgM0sAQCVDgCAgNUAAJsOAICG4QAAhukAAEcOAIDNJAEAoQ4AgM0QAQCI0QAAiCkAAMz4AgBNDgCAzfgCAMwkAQCnDgCAhS0DAMygPgDNbD4AgNUDAIHNAwCCAQMAg/kDAMxkAwDNzAIARA4AgM0kAQDMDAIAzQgCAIERAADMnAMAzLA+AM20PgDMxD4AzcA+AMyAPgDNuD4ArQ4AgMyEAgDMmD8AzVA+AMwgPgDNoD4AzQw/AM0wPwDNeD8AzQQ/AIhZAAC/DgCAzfgBAMzEAQBKDgCAxQ4AgMsOAIDMFAIAzAgBAM3IAQCIBQAA0Q4AgNcOAIDMKAIAuQ4AgIgNAACG0QAAgB0BAITNAACI9QAAzDwCAIQ1AQDMRAIAhikBAIAOAICIZQEAhg4AgKdEBQBiDgCAi+0AAIjtAACBDQAAiCUAAIZlAADMcAIAzXQCAMwwAgDN2AUAXA4AgIwOAICAOQAAXw4AgMzgBQB6DgCAzCgBAM0UAQCGJQAAiFUAAAgOAICGhDAAxA0AgIDVBwCG/QcAmA4AgMwkAgCIPQAAng4AgGsOAICIPQAApA4AgMxIAgDNeAIAUA4AgKoOAICXwAUAlnAFAJUYBQCAaQAAk1gFAIE5AACIZQAAkPg8AIZZAACeqAUAhEUAAGgOAIDM1AIAmrQFAIBdAACYrAUAp+wEAIgRAADM2AIAzdwCAKO8BACwDgCAzGACAMIOAIBuDgCAyA4AgK0IBADODgCAq/QEAMwsAgCIBQAA1A4AgLfoAwC2HAQAtSgEAMwAAgCzKAQAi3kAAIh9AACwdAQAhkEAAL6kAwCEdQAAiB0AANoOAIC6TAMAzNwDALj8AwCDqAIAiA0AALwOAICIFQAAh5QCAMw4AgBlDgCAzAQCAIvcAgCPDQAAcQ4AgI8ZAADMIAIAdA4AgI3wAgCIdQAAmCADAJksAwCPDgCAlA0AgMxMAgCWcAMAzCQCAIg9AACSDgCAzCwCAIgFAACzDgCAzCQCAIgNAAC2DgCAh/UAAKjUAwCpxAMA3Q4AgNlgAgDSDwCA1Q8AgNsPAICUNQAAkzEAANloAgDYDwCA2UwCAJQFAADeDwCAlSEAAJQpAABQEACAdBYAgEMXAIDSFgCA2WACADcXAIC12AMAtPADAJQ1AADZWAIAWhcAgJQFAADZVAIAlA0AADEXAIDgdAEAisgAALwVAACIyAAA4IACAIcXAICBoAAApOwCAKTIAgCoXAAAvA0AAJkXAIDghAIAvAUAAJ0XAICk+AIA4PQCALDMAwCV0AAAXRcAgLPgAwCmyAIAp2ACAJLYAABkFwCAvsEAAGsXAICXwQAAchcAgHkXAICAFwCAzXg/AMy8PwC+gA0AixcAgLx4DAC9gA0AuvQMALtUDAC49AwAkhcAgLYXAIC3uAwAuhcAgLWMDACyoAMAs6AMAKEXAICxQAMArnACAK9kAwC4BQMArUgDAKgXAICvFwCAqEQDAKnYAwDaFwCAp9gDAKRoAgCliAMAtjUDALc9AwCSyAIAtT0DAJldAQCYTQEAm2UBAJppAQCdZQEAnGUBAJ+FAQCemQEAh5wCAL6tAACWpQAAl70AAMw0BQDNjDcAzLg4AM2sOACflQEAth0AAJ2ZAQCc9QEAs7EBAK54AgDhFwCAvhcAgJk9AADFFwCAmxkAAJoJAADMFwCA0xcAgOBIAgCeCQAArFwCAK30AgD6FwCA9hcAgP4XAIDoFwCAh2ADAO8XAICvVAIAvhEAAJcFAAACGACA4KwCAAYYAICG+AMAh+wDAOC0AgAOGACAr0gCAK6QAgDgPAIAvg0AAAoYAICXGQAA4NgCAIaEAwCWEQAAvwAMAJ1tAACcYQAAEhgAgLFMAgCzUAIAlQ0AABYYAICGnAMA4MgCALMEAgCCBQAAIhgAgLNQAgCVDQAAJhgAgBoYAIAeGACA4LQCAIaMAwCH3AMAvg0AAJVpAACWeQAAKhgAgLToAgC1UAIAlwUAADIYAIDg1AIAtPQCAL4ZAADgoAIALhgAgODUAgCZjAMAt9QCAIoFAAA2GACAOhgAgIoVAAC3NAIAjx0AAD4YAIBCGACAswUAAEYYAICzBQAAWxgAgJwJAACdCQAATRgAgFQYAICMBQAAYhgAgG0YAIB0GACAexgAgJ9JAACCGACAiRgAgGYYAICQGACAlxgAgNkYAIDPGACA6hgAgOAYAICeGACAg8kBAIH5AQCsGACAsxgAgLoYAIDBGACAyBgAgKUYAICAtAIApYgDAOEIAgCuHQAA8RgAgLwJAACN9QEA9RgAgOEAAgCSlQEA45QQAJNFAACXiQEAhRQAAId4AQCGAAQARjoAgEo6AIBOOgCAUjoAgFY6AICdeQAA74xoAJyhAQBaOgCAXjoAgKKZAABiOgCAZjoAgGo6AIBuOgCAp4kAAHI6AIB2OgCAqUkBAHo6AICsqQAAfjoAgII6AICGOgCAsyUBAIo6AICOOgCAkjoAgLchAQC2OQEAtTEBAJY6AICaOgCAufkAALkRAQC4GQEAnjoAgKI6AICmOgCAqjoAgICwAQCEiAIArjoAgIPIAQCEVAMAhFwEALI6AICEXAUAgN0DAIEtAACCMQAAvjwCALo6AIC+OgCAh4gDAIacBACzLQMAwjoAgMY6AIC+AAQAvhwFALbRAwC12QMAyjoAgLv5AwC68QMAmljTAYTgBwC/xQMAvtkDAL3dAwC83QMAvgAYAKUFAwCmDQMAzjoAgIQcGADSOgCA1joAgKPxAwCsAQMArQEDAK4FAwCvGQMArKQbAq3cGgKqLQMAqyUDAL5MGQC+SBoA2joAgL6AGwC04BoCtdQdArYwHgLvCAIA3joAgOGgAQC6OBoC4/gCALoAAAC9ZBwCvvQcAr8AEAKRBNMBkOT2AeBEAQCSCD4C4joAgOY6AIDqOgCA7joAgL6sHADyOgCA9joAgPo6AID+OgCAAjsAgAY7AIAKOwCAgbBtAICAAQCDHFIAgth3AIUgmgCEkL4AhwjPAIaM5gCJbDcBiOAsAYsYfgGK2BMBjeClAYzwWgGP/OsBjliPAbDVFwCxAWgAso1rALOdawC0SWsAtZVvAA47AIDgcAEAEjsAgBY7AIAaOwCAHjsAgIAZAACBGQAAggUAACI7AIAqOwCAoaUCAKJJBwCjQQcApEEGAKXVGwCm3RsAp8EaAKgBHACp4R8AqkkfAKsBEACs9RMAra0TAK4BFACv+RcAqDEGAKkxBgCqTQYAq0UGAKxNBgCtmQYAro0GAK+FBgCGgAMAhxgDAC47AIAyOwCANjsAgDo7AIA+OwCAQjsAgLhtBwC5dQcAun0HALt1BwC8bQcAvc0HAL75BwC/+QcAsKkGALGFBgCyeQcAs3kHALRpBwC1aQcAtl0HALdVBwC2OgCAs8EGAEY7AIAmOwCAth0GAEo7AIBOOwCAtcEGALppBgC7RQYAUjsAgFY7AIC+qQcAv6kHALypBwC9qQcAo4UGAFo7AIBeOwCAYjsAgGY7AICmWQYApYUGAGo7AICrAQYAqi0GAG47AIByOwCAr+0HAK7tBwCt7QcArO0HAKjBBgCpLQEAqiUBAKs9AQCsJQEArS0BAK4lAQCvlQEAdjsAgHo7AIB+OwCAgjsAgIY7AICCvQAAgb0AAIC9AAC4nQEAua0BALqlAQC7bQAAvHUAAL19AAC+dQAAv20AALD1AQCx/QEAssEBALPBAQC0tQEAtb0BALa1AQC3rQEAijsAgI47AICSOwCAs6EBAJY7AIC1oQEAtqEBAJo7AICGgAEAh8QBALo9AQC7NQEAvBkBAL0ZAQC+fQEAv3UBAKPtAQCeOwCAojsAgKY7AICqOwCApu0BAKXtAQCuOwCAq3kBAKpxAQCyOwCAtjsAgK85AQCuMQEArVUBAKxVAQC6OwCAvjsAgMI7AIDGOwCAyjsAgOGsAQDOOwCA42AGANI7AIDWOwCA2jsAgO9UBgDeOwCA4jsAgL60GgDmOwCA6jsAgO47AICGaBwAh4wDAPI7AID2OwCA+jsAgP47AICAOQAAgTkAAIIFAAACPACACjwAgA48AIASPACAFjwAgKgdAwCpQQMAqkEDAKtBAwCsQQMArUkDAK5xAwCvcQMAhCAdABo8AIAePACAIjwAgCY8AIAqPACALjwAgDI8AIC46QAAufUAALr9AAC78QAAvJEAAL2RAAC+iQAAv4kAALDhAACx4QAAsuEAALPhAAC04QAAte0AALbZAAC32QAA4wwHAOEgBwDhMAEA4wgHADY8AIA6PACAPjwAgEI8AIBGPACASjwAgE48AIBSPACA75gHAFY8AIBaPACA74gHALOJAgBePACAYjwAgL6AGgBmPACAtokCALWJAgBqPACAu2UBALplAQBuPACAcjwAgL9pAQC+ZQEAvXUBALx1AQC3PQYAtj0GALU9BgC0IQYAszUGALI1BgCxAQYAsAkGAL9ZBgC+UQYAvVkGALxNBgC7bQYAunkGALlxBgC4eQYAgJ0AAIGtAACCpQAAejwAgH48AICCPACAhjwAgIo8AICvcQYArmkGAK1tBgCsbQYAq4EGAKqZBgCpkQYAqJkGAAY8AIB2PACAjjwAgKPFHQCSPACApcUdAKbFHQCWPACAhgADAIdkAwCqKR4AqykeAKw5HgCtOR4ArikeAK8lHgCzOR4AmjwAgJ48AICiPACApjwAgLb9HgC1/R4AqjwAgLvZHgC60R4ArjwAgLI8AIC/aR8AvmEfAL1pHwC8wR4AqPEeAKnxHgCq8R4Aq/EeAKw1HgCtPR4ArjUeAK8tHgC2PACAujwAgL48AIDCPACAxjwAgMo8AIDOPACA0jwAgLjlHwC57R8AuuUfALv5HwC86R8AvZEfAL6RHwC/jR8AsFUeALFdHgCyVR4As/0fALTlHwC17R8AtuUfALfdHwCjeR8A1jwAgNo8AIDePACA4jwAgKa9HwClvR8A5jwAgKuZHwCqkR8AhogAAIdMAQCvKR4AriEeAK0pHgCsgR8AgEkAAIFJAACCWQAAs5keAOo8AIC1iR4AtlEBAO48AIDyPACA9jwAgLotAQC7JQEAvD0BAL0lAQC+JQEAvxUBAKhNHgCpVR4Aql0eAKtVHgCsTR4ArZ0BAK6JAQCvgQEAhKwBAPo8AID+PACAAj0AgAY9AIAKPQCADj0AgBI9AIC4ZQEAuW0BALplAQC7fQEAvGUBAL1tAQC+ZQEAv9kAALClAQCxrQEAsqUBALO9AQC0rQEAtZ0BALaVAQC3XQEAo9UdABY9AIAaPQCAHj0AgCI9AICmHQIApcUdACY9AICraQIAqmECACo9AIAuPQCAr1kCAK5pAgCtaQIArHECADI9AIA2PQCAOj0AgD49AIBCPQCARj0AgEo9AIBOPQCAgDkAAIE5AACCBQAAUj0AgFo9AIBePQCAh0ADAIZcBACETAQAYj0AgGY9AICEBAUA4yABAGo9AIDhqAEAbj0AgO+UGgByPQCAdj0AgHo9AIB+PQCAgj0AgIY9AICKPQCAs6EDAI49AICSPQCAlj0AgJo9AIC2fQMAtX0DAJ49AIC7WQMAulEDAKI9AICmPQCAv/0AAL79AAC9/QAAvEEDAKhRAgCpWQIAqmkCAKtpAgCstQIArb0CAK61AgCvrQIAhKgHAKo9AICuPQCAsj0AgIKpAAC2PQCAgKkAAIGpAAC4aQEAuWkBALoJAQC7CQEAvBkBAL0ZAQC+CQEAvwkBALDVAgCx3QIAstUCALNpAQC0eQEAtXkBALZpAQC3YQEA4bgBAOHUHwDjOB8A4wwbALo9AIC+PQCAwj0AgMo9AIDOPQCA0j0AgNY9AIDaPQCAvjwJAN49AIDvhBsA74QbAKOhAgDiPQCAhugEAIe8BQDmPQCApn0CAKV9AgDqPQCAq1kCAKpRAgDuPQCA8j0AgK/9AQCu/QEArf0BAKxBAgCzhQYAxj0AgPY9AID6PQCA/j0AgLaJBgC1jQYAAj4AgLuRBgC6iQYABj4AgAo+AIC/9QYAvokGAL2BBgC8iQYADj4AgBI+AIAWPgCAGj4AgB4+AIAiPgCAJj4AgO+EHQAqPgCA4QAEAC4+AIDj/AQAgBEAAIEdAACCBQAAMj4AgKjxBgCp8QYAqg0GAKsFBgCsBQYArQkGAK49BgCvNQYANj4AgDo+AICGiAAAhxADAD4+AIBCPgCARj4AgEo+AIC4EQYAuRkGALohBgC7IQYAvPUHAL39BwC+9QcAv+kHALBNBgCxVQYAsl0GALNVBgC0TQYAtTEGALYxBgC3MQYAo4UHAE4+AIBSPgCAVj4AgFo+AICmiQcApY0HAF4+AICrkQcAqokHAGI+AIBmPgCAr/UHAK6JBwCtgQcArIkHAGo+AICz4QYAbj4AgHI+AIC25QYAdj4AgHo+AIC18QYAur0GALuNBgB+PgCAgj4AgL59AQC/ZQEAvJUGAL11AQCoHQYAqSUGAKotBgCrJQYArD0GAK0hBgCuXQYAr00GAIY+AICKPgCAjj4AgJI+AICWPgCAgrkDAIGxAwCAuQMAuO0BALmFAQC6jQEAu4UBALydAQC9hQEAvo0BAL+FAQCwPQYAsQ0GALIFBgCz5QEAtP0BALXlAQC25QEAt9UBAKOlBQCaPgCAnj4AgKI+AICqPgCApqEFAKW1BQCuPgCAq8kFAKr5BQCGCAwAhxwDAK8hAgCuOQIArTECAKzRBQCyPgCAs/ECALY+AIC6PgCAtlUDAL4+AIDCPgCAteECALpxAwC7eQMAxj4AgMo+AIC+MQMAvz0DALxRAwC9UQMAqCUCAKk1AgCqPQIAqzUCAKwtAgCtkQMArpEDAK+RAwDOPgCA0j4AgNY+AIDaPgCArAAAAN4+AIDiPgCA5j4AgLiZAwC5rQMAuqUDALttAwC8dQMAvX0DAL51AwC/bQMAsPEDALH5AwCywQMAs8EDALSxAwC1vQMAtrUDALepAwDqPgCA7j4AgPI+AID2PgCA+j4AgP4+AIACPwCA76gaAL5oDADhlAEABj8AgOMcBgCADQAAgXEAAIJxAAAKPwCAo/UDAA4/AIASPwCAhEwCABo/AICmUQIApeUDAB4/AICrfQIAqnUCAIbIDACHLA0ArzkCAK41AgCtVQIArFUCAOFQBgAiPwCA4xQHAITADAAmPwCAKj8AgC4/AIAyPwCANj8AgDo/AIA+PwCAQj8AgEY/AIBKPwCA73gbAL74DwBOPwCAUj8AgFY/AICzjQEAWj8AgLWZAQC2jQEAXj8AgFY9AIBiPwCAuoUBALtNAQC8VQEAvV0BAL5VAQC/SQEAo0EOABY/AIBmPwCAaj8AgG4/AICmQQ4ApVUOAHI/AICrgQ4AqkkOAHY/AIB6PwCAr4UOAK6ZDgCtkQ4ArJkOAIBtAACBCQAAgh0AAH4/AIDvGAkAgj8AgIY/AICKPwCA4zwNAI4/AIDhWAwAkj8AgIbQAACHvAMAlj8AgJo/AICokQ4AqZkOAKrJDgCrxQ4ArN0OAK3BDgCuwQ4Ar/UOAIToAACePwCAoj8AgKY/AICqPwCArj8AgLI/AIC2PwCAuMEPALnBDwC6wQ8Au8EPALzBDwC9wQ8AvsEPAL/1DwCwjQ4AsUUOALJNDgCzRQ4AtF0OALVBDgC2QQ4At0EOAKhRDgCpWQ4Aqo0OAKudDgCshQ4ArY0OAK6FDgCvvQ4Auj8AgL4/AIDCPwCAxj8AgMo/AIDOPwCA0j8AgNY/AIC4kQ4AuZkOALqtDgC7RQEAvF0BAL1FAQC+RQEAv3UBALDFDgCxzQ4AssUOALPdDgC0xQ4AtbUOALa9DgC3tQ4AswUOANo/AIDePwCA4j8AgOY/AIC2DQ4AtQ0OAOo/AIC7CQ4AugEOAO4/AIDyPwCAv3EOAL4BDgC9CQ4AvBEOAIJtAACjQQ4AgFUAAIFlAACmSQ4A+j8AgP4/AIClSQ4AqkUOAKtNDgCGSAAAh3gAAK5FDgCvNQ4ArFUOAK1NDgCoXQIAqWECAKplAgCrdQIArG0CAK2xAgCusQIAr7ECAITsBAACQACABkAAgApAAIAOQACAEkAAgBZAAIAaQACAuHEDALlxAwC6cQMAu3EDALzVAwC93QMAvtUDAL/NAwCw0QIAsdECALLRAgCz0QIAtFEDALVRAwC2UQMAt1EDAB5AAICz6QIAIkAAgL6ABAC2NQIAJkAAgCpAAIC14QIAuhECALsRAgAuQACAMkAAgL6RAwC/kQMAvAECAL0BAgA2QACAOkAAgKOlAgA+QACApa0CAEJAAIBGQACApnkCAEpAAIBOQACAq10CAKpdAgCtTQIArE0CAK/dAwCu3QMAqNUCAKndAgCqLQEAqyUBAKw9AQCtJQEAri0BAK8lAQBSQACAVkAAgFpAAIBeQACAYkAAgGpAAIBuQACAckAAgLiFAQC5iQEAup0BALuVAQC8sQEAvbEBAL55AAC/eQAAsF0BALHlAQCy4QEAs/kBALTpAQC13QEAttUBALe9AQDh8A4AdkAAgOMUDgB6QACAgb0AAIC9AAB+QACAgq0AAIYABACH7AUAgkAAgIZAAICKQACAjkAAgO9gDgCSQACAlkAAgJpAAICFXH0AnkAAgKJAAIDjZAEApkAAgOG0AQCqQACA76AOAK5AAICmPgCAhPgFALJAAIC2QACAukAAgLMlBgBmQACAvkAAgMJAAIDGQACAtiUGALU1BgDKQACAu6EGALoZBgDOQACA0kAAgL+ZBgC+rQYAva0GALy1BgCCbQAA7zAEAIBVAACBZQAAvlwDANZAAICG+AAAh2wDANpAAIDeQACA4kAAgOZAAIDqQACA40QEAO5AAIDhjAcAo6UGAPJAAID2QACA+kAAgP5AAICmpQYApbUGAAJBAICrIQYAqpkGAAZBAIAKQQCArxkGAK4tBgCtLQYArDUGAA5BAICz+QcAEkEAgBZBAIC2SQcAGkEAgB5BAIC1UQcAulEHALtRBwAiQQCAJkEAgL41BwC/OQcAvEUHAL09BwCoNQYAqT0GAKo1BgCriQYArJ0GAK2NBgCusQYAr7EGACpBAIAuQQCAMkEAgDZBAICADQAAgbEAAIKxAAA6QQCAuKEGALmtBgC6vQYAu7UGALytBgC9XQEAvlUBAL9NAQCw0QYAsdEGALLVBgCzrQYAtLUGALW5BgC2qQYAt6UGAKO9BgA+QQCAQkEAgISEAgC+kAEApg0GAKUVBgBKQQCAqxUGAKoVBgCGCAAAh3wBAK99BgCucQYArXkGAKwBBgBOQQCAs60BAFJBAIBWQQCAtqkBAFpBAIBeQQCAta0BALptAQC7dQEAYkEAgGZBAIC+XQEAvzUBALxlAQC9VQEAqGECAKlhAgCqYQIAq2ECAKxhAgCtbQIArp0CAK+VAgBqQQCAbkEAgHJBAIB2QQCAekEAgH5BAICCQQCAhkEAgLiVAgC5nQIAuqECALuhAgC8cQMAvXEDAL5xAwC/cQMAsO0CALH1AgCy9QIAs8UCALTdAgC1tQIAtrECALexAgCKQQCAjkEAgJJBAICj5QIAlkEAgKXlAgCm4QIAmkEAgJ5BAICiQQCAqiUCAKs9AgCsLQIArR0CAK4VAgCvfQIApkEAgKpBAICuQQCAhEB8AIAVAACBHQAAggUAALJBAIC+7HwAukEAgIZIfQCHCAMAvkEAgMJBAIDGQQCAykEAgKidAgCpxQIAqsECAKvBAgCsxQIArc0CAK7xAgCv8QIAzkEAgNJBAIDWQQCA2kEAgMkAAADeQQCA4kEAgOZBAIC4wQEAucEBALrBAQC73QEAvM0BAL31AQC+/QEAv50BALBBAQCxQQEAskEBALNBAQC0QQEAtUEBALZBAQC3QQEA4TgGAOpBAIDjaAYA7kEAgPJBAID2QQCA+kEAgISUfQC+rHwA/kEAgAJCAIAGQgCAvrh/AApCAIDvEAEADkIAgBJCAIAWQgCAGkIAgB5CAIDhkAEAIkIAgONEAAAqQgCAgS0AAIAtAADvgAAAgjkAAC5CAIAyQgCA9j8AgDZCAIDhsH8AtkEAgOPUfAA6QgCAJkIAgD5CAICGuAAAh9QCAEJCAIBGQgCASkIAgE5CAIBSQgCAVkIAgO8gfABaQgCAs4l9AF5CAIBiQgCAZkIAgGpCAIC2jX0AtY19AG5CAIC7RX4AukV+AHJCAIB2QgCAv0V+AL5FfgC9VX4AvFV+AKNJfQB6QgCAfkIAgIJCAICGQgCApk19AKVNfQCKQgCAq4V+AKqFfgCOQgCAkkIAgK+FfgCuhX4ArZV+AKyVfgCCbQAAszF+AIBVAACBZQAAtvF/AITcAwCWQgCAtSF+ALrNfwC70X8AhgAEAIfUAAC+dX8Av3l/ALzBfwC9wX8AqOV/AKn1fwCq/X8Aq/V/AKztfwCtNX4Arj1+AK81fgCaQgCAnkIAgKJCAICmQgCAqkIAgK5CAICyQgCAtkIAgLjZfgC54X4AuuF+ALvhfgC85X4Avel+AL6ZfgC/mX4AsE1+ALFRfgCyUX4As1F+ALT1fgC1+X4Atul+ALfpfgCjdX8AukIAgL5CAIDCQgCAxkIAgKa1fgClZX8AykIAgKuVfgCqiX4AzkIAgNJCAICvPX4ArjF+AK2FfgCshX4A1kIAgLMxfgDaQgCA3kIAgLbFAQDiQgCA5kIAgLXRAQC6yQEAu8kBAOpCAIDuQgCAvs0BAL+xAQC8yQEAvckBAKjdfQCp9X0Aqv19AKvxfQCsHQIArQECAK45AgCvOQIA8kIAgPZCAID6QgCA/kIAgIIFAAACQwCAgBEAAIERAAC4EQIAuRkCALohAgC7IQIAvNUCAL3dAgC+1QIAv80CALBJAgCxSQIAslkCALNZAgC0TQIAtTECALYxAgC3MQIAvgADAKNxfQCEiAIAvoAEAKaFAgAKQwCADkMAgKWRAgCqiQIAq4kCAIYoBACHDAMAro0CAK/xAgCsiQIArYkCABJDAICEyAMAhcwFALPlAwAWQwCAteUDALbtAwAaQwCAHkMAgCJDAIC6bQMAu2UDALx9AwC9ZQMAvmUDAL9VAwAmQwCAKkMAgL8ABACjJQIALkMAgKUlAgCmLQIAMkMAgDZDAIA6QwCAqq0CAKulAgCsvQIAraUCAK6lAgCvlQIAPkMAgEJDAIBGQwCASkMAgE5DAIDjzAMAUkMAgOGsAQBWQwCA7xwDAFpDAIBeQwCAYkMAgGZDAIBqQwCAbkMAgOFwfwBGQQCA4wR+AHJDAIB6QwCA4ZQBAH5DAIDjWAEAgNkAAIHZAACCJQAA7+R+AIJDAICGQwCA7+B+AIpDAICzAQEAjkMAgIboBwCHLAQAkkMAgLY1AQC1BQEAlkMAgLvxAAC64QAAmkMAgJ5DAIC/sQAAvtEAAL3ZAAC84QAABkMAgHZDAICiQwCApkMAgKEBBACgEQQAoxkAAKLFBACotQYAqb0GAKrpBgCr/QYArO0GAK3VBgCu3QYArz0HALBFBwCxVQcAslUHALNtBwC0dQcAtRUHALYdBwC3FQcAuC0HALk1BwC6MQcAuw0HALwZBwC9GQcAvgkHAL8JBwCjQQYAqkMAgK5DAICyQwCAtkMAgKZ1BgClRQYAukMAgKuxBwCqoQcAj8ltAL5DAICv8QcArpEHAK2ZBwCsoQcAld11AJTBdACXzXAAli1zAJFdaACQVWgAk9l0AJJNaQCd5XgAnB17AJ9tBwCeuXgAmR1/AJhVcACboXwAmvl8AIJhbACDhWkAwkMAgMZDAICGEXUAhxF1AISVaQCFjWgAij10AIvFcgDKQwCAzkMAgI7dfgCPMX0AjD1xAI2dcQCSGX0Ak716ANJDAIDvkAkAltUGAJdRBQCUXXkAlQl5AJpxBQCbvQUA1kMAgNpDAIDeQwCA4agFAJx5AQDjuAgAoYUBAOJDAICjqQ0AogEMAKUBCACkOQ0Ap6kJAKa9CQCppRUAqAEUAKsBFACq/RUArbkRAKyxEQCvARwArqEQALH9HACw5R0As+kZALIBGAC1ASQAtH0ZAIQUAAC+FAAAgI0AAIGVAACCbQAA6kMAgIZQDwCHZAAA7kMAgPJDAIC61QcAu90HALjBBwC5wQcAvjEEAL8xBAC88QcAvfEHALKtBwCztQcAsK0HALGlBwC2nQcAt/UHALSlBwC1lQcAqmkHAKtpBwCoaQcAqWkHAK5pBwCvaQcArGkHAK1pBwD2QwCA+kMAgP5DAIACRACABkQAgApEAIAORACAEkQAgKgRBQCpHQUAqjkFAKs5BQCsLQUArVEFAK5JBQCvQQUAFkQAgBpEAIAeRACAIkQAgCZEAIAqRACALkQAgDJEAIC4XQIAuWkCALrBAwC7wQMAvPkDAL35AwC+kQMAv7UDALAJBQCxCQUAsuECALPhAgC0dQIAtX0CALZ1AgC3bQIAs7EEAIQAAgC+BA0ANkQAgDpEAIC20QQAtaUEAD5EAIC7zQQAus0EAEJEAIBGRACAv7kDAL6xAwC9NQMAvDUDAEpEAICj9QQATkQAgFJEAICmlQQAWkQAgF5EAICl4QQAqokEAKuJBACHqA0AhswMAK71AwCv/QMArHEDAK1xAwDhUAYA4TQHAONAAADjWAcAgNEAAIHdAACC1QAAYkQAgGZEAIBqRACAbkQAgHJEAIB2RACAekQAgO+cAADvyAcAfkQAgIJEAICzNQIAhkQAgLW1AQCKRACAjkQAgLa1AQC+7AwAkkQAgLuRAQC6mQEAvVEBALyJAQC/UQEAvlkBAKjtDQCp/Q0AqvUNAKttDgCsdQ4ArX0OAK51DgCvbQ4AVkQAgJZEAICaRACAnkQAgKJEAICmRACAqkQAgK5EAIC49Q4Auf0OALr1DgC7QQ8AvEEPAL1JDwC+cQ8Av3EPALAVDgCxHQ4AshUOALPNDgC01Q4Atd0OALbVDgC3zQ4Ao30NALJEAIC2RACAukQAgL5EAICm/Q4Apf0OAMJEAICr2Q4AqtEOAISoAgDGRACArxkOAK4RDgCtGQ4ArMEOAIBNAACBVQAAglUAALNRDwDKRACAtXEPALZxDwDORACAhuAAAIcEAwC6XQ8Auy0PALw1DwC9OQ8Avi0PAL8lDwCoVQ4AqV0OAKqVDgCrrQ4ArLUOAK29DgCutQ4Ar60OANJEAIDWRACA2kQAgN5EAIDiRACA5kQAgOpEAIDuRACAuGkBALlpAQC6eQEAu3kBALxpAQC9aQEAvt0BAL/VAQCw1Q4AsaUOALKtDgCzoQ4AtKUOALWtDgC2nQ4At1kBAKMdDgDyRACA9kQAgOZDAID6RACApj0OAKU9DgD+RACAq2EOAKoRDgACRQCABkUAgK9pDgCuYQ4ArXUOAKx5DgAKRQCADkUAgBJFAIAWRQCAGkUAgB5FAIAiRQCAJkUAgIANAACBFQAAgh0AACpFAIAuRQCAMkUAgIR4AQC+FAAA4xQPADpFAIDh4A0AhAADAIawBACHFAMAPkUAgEJFAIBGRQCASkUAgE5FAIBSRQCA78APAFZFAIBaRQCAXkUAgGJFAIBmRQCAakUAgLNtAwBuRQCAtX0DALZ1AwByRQCAdkUAgHpFAIC6UQMAu1EDALz1AwC9/QMAvukDAL/hAwB+RQCAgkUAgIZFAICKRQCAjkUAgJJFAICWRQCAmkUAgKhxAgCpeQIAqokDAKuJAwCsmQMArZkDAK6JAwCviQMAsPkDALH5AwCyTQMAs0UDALRBAwC1SQMAtnEDALdxAwC4IQMAuSEDALohAwC7IQMAvCEDAL0hAwC+IQMAvyEDAICdAQCBEQAAghEAAIQEBQDvFAAAnkUAgKJFAIC+EAUA48gAAKpFAIDh0AEArkUAgLJFAIC2RQCAukUAgL5FAICqeQIAq3kCAIboBACHYAUArsECAK/JAgCs3QIArdUCAMJFAICjRQIAxkUAgMpFAICmXQIAzkUAgNJFAIClVQIA1kUAgNpFAIDeRQCA4kUAgOZFAIDqRQCA7kUAgO+EDgC+rAQA4dAOAPJFAIDjFAEA9kUAgPpFAID+RQCAAkYAgLPdAQAGRgCACkYAgA5GAIASRgCAtv0BALX9AQAaRgCAu90BALrdAQCE4AQAHkYAgL+hAQC+vQEAvb0BALy9AQCoBQYAqR0GAKoVBgCrLQYArDUGAK09BgCuNQYArykGAKZFAICC9QcAgeUHAIDlBwAWRgCAIkYAgIYcAACHsAMAuCUGALnFBgC6zQYAu8UGALzdBgC9xQYAvs0GAL/FBgCwWQYAsVkGALIpBgCzKQYAtDkGALUlBgC2JQYAtx0GAKOdBgAmRgCAKkYAgC5GAIAyRgCApr0GAKW9BgA2RgCAq50GAKqdBgA6RgCAPkYAgK/hBgCu/QYArf0GAKz9BgBCRgCAs/UHAEZGAIBKRgCAtu0HAE5GAIBSRgCAteUHALqNBwC7kQcAVkYAgFpGAIC+dQcAv30HALyBBwC9fQcAqCUGAKkpBgCqOQYAqzkGAKwpBgCtKQYArnkGAK91BgBeRgCAYkYAgGZGAIBqRgCAbkYAgHJGAIB2RgCAekYAgLjVBgC53QYAuuEGALv9BgC85QYAve0GAL7lBgC/mQYAsA0GALERBgCyEQYAs+0GALT1BgC1/QYAtvUGALftBgCjsQYAgi0AAIEVAACAsQAANkUAgKapBgCloQYAfkYAgKvVBgCqyQYAgkYAgL5oAQCvOQYArjEGAK05BgCsxQYAikYAgLPxAQCGaAAAh3wBALZdAQCORgCAkkYAgLVVAQC6SQEAu0kBAJZGAICaRgCAvj0BAL8hAQC8OQEAvTUBAJ5GAICiRgCAhAQDAL6AHACmRgCA4RwGAKpGAIDjAAYAvwguAK5GAICyRgCA78gHALZGAIC6RgCAvkYAgMJGAIDGRgCAykYAgKN9AgDORgCApdkCANJGAIDWRgCAptECANpGAIDeRgCAq8UCAKrFAgCtuQIArLUCAK+tAgCusQIAqW0FAKhZBQCrDQIAqrkCAK0dAgCsHQIArwUCAK4NAgC+aB0A4kYAgOZGAIDqRgCAgB0AAIEJAACCmQEA7kYAgLnhAwC4KQIAu+EDALrpAwC94QMAvPkDAL/hAwC+6QMAsU0CALBNAgCzIQIAsi0CALUlAgC0OQIAtxECALYlAgCowQIAqdECAKrRAgCr5QIArP0CAK0VAQCuHQEArw0BAPJGAID6RgCA/kYAgAJHAIAGRwCACkcAgA5HAIASRwCAuAUBALkJAQC6HQEAuxUBALwxAQC9MQEAvv0BAL/1AQCweQEAsUEBALJBAQCzXQEAtEUBALVNAQC2RQEAtz0BAIagHQCHxB0AFkcAgO/YAAAaRwCAHkcAgCJHAIDvxAYAhGwcAOH0BgAmRwCA47AGACpHAIDhlAEALkcAgONEBgCzGQIAMkcAgDZHAIA6RwCAhewsALbVAQC1NQIAPkcAgLvFAQC6/QEAQkcAgEZHAIC/yQEAvsEBAL3JAQC81QEAo9kdAPZGAIBKRwCATkcAgFJHAICmFR4ApfUdAFZHAICrBR4Aqj0eAFpHAIBeRwCArwkeAK4BHgCtCR4ArBUeAIBpAACBaQAAggUAAGJHAIBmRwCAakcAgIcQAwCGfAMAbkcAgHJHAIB2RwCAekcAgH5HAICCRwCAhkcAgIpHAICopR8Aqa0fAKqlHwCrvR8ArKUfAK2tHwCupR8ArxUfAI5HAICSRwCAlkcAgJpHAICeRwCAokcAgKZHAICqRwCAuA0fALkZHwC6IR8AuyEfALzZAAC92QAAvskAAL/BAACwcR8AsXEfALJxHwCzRR8AtEEfALVNHwC2PR8AtzUfALMtHgCuRwCAskcAgLZHAIC6RwCAti0eALUtHgC+RwCAu7UeALq1HgDCRwCAxkcAgL+JHgC+hR4AvZEeALylHgCCKQAAo2keAIAdAACBFQAApmkeAMpHAIDORwCApWkeAKrxHgCr8R4A0kcAgITgAQCuwR4Ar80eAKzhHgCt1R4AqNUBAKnlAQCq7QEAq+UBAKz9AQCt5QEAru0BAK/lAQC+oAEAhkYAgNZHAIDaRwCAhhAAAId0AQDeRwCA4kcAgLh9AQC5wQAAusEAALvBAAC8wQAAvckAAL7xAAC/8QAAsJ0BALFFAQCyTQEAs0UBALRdAQC1RQEAtk0BALdFAQDmRwCA6kcAgO5HAIDyRwCA9kcAgO80AgDv7B4A+kcAgOHwHQDj4AIA4zAeAOGEAQD+RwCAAkgAgAZIAIAKSACAsyUCAJQAAAAOSACAEkgAgBZIAIC2JQIAtTUCABpIAIC7wQIAuhkCAB5IAIAiSACAv8ECAL7ZAgC90QIAvNkCACZIAIAqSACALkgAgKPpAgAySACApfkCAKbpAgA2SACAOkgAgD5IAICq1QIAqw0CAKwVAgCtHQIArhUCAK8NAgCAYQAAgWEAAIIFAABCSACASkgAgIQABAC+FAQATkgAgIbABACHUAMAUkgAgFZIAIBaSACAXkgAgGJIAIBmSACAqK0CAKm9AgCqtQIAqw0BAKwVAQCtHQEArhUBAK8NAQCE7AQAakgAgG5IAIBySACAdkgAgHpIAIB+SACAgkgAgLgdAQC5LQEAuiUBALvNAQC81QEAvd0BAL7JAQC/wQEAsH0BALFVAQCyXQEAs1UBALRNAQC1PQEAtjUBALctAQDhGB4AhkgAgOM4HgCKSACAjkgAgJJIAICWSACAmkgAgJ5IAICiSACAvmAEAKZIAICBdQAAgHUAAO/gHwCCbQAAqkgAgK5IAICG6AQAh3wFALJIAIDhkAEAukgAgOOgAAC+SACAwkgAgMZIAIDvtAAAykgAgM5IAIDSSACA1kgAgLUFBgBGSACAtkgAgLYFBgDaSACA3kgAgLOlBQDiSACAvRkGALwRBgC/YQYAvhEGAOZIAIDqSACAuwkGALohBgCj/QUA7kgAgPJIAID2SACA+kgAgKZdBgClXQYA/kgAgKtRBgCqeQYAAkkAgAZJAICvOQYArkkGAK1BBgCsSQYAqFEGAKlZBgCqYQYAq2EGAKxhBgCtYQYArmEGAK9hBgAKSQCADkkAgBJJAIAWSQCAgA0AAIGxAQCCsQEAGkkAgLhNBwC5VQcAul0HALtVBwC8TQcAvXUHAL59BwC/cQcAsMUHALHNBwCyxQcAs90HALTFBwC1zQcAtsUHALd5BwCz6QcAHkkAgCJJAICEwAEAvtgBALbhBwC16QcAJkkAgLsJBgC6AQYAhogAAIesAQC/CQYAvgEGAL0JBgC8EQYAKkkAgKOtBwAuSQCAMkkAgKalBwA2SQCAOkkAgKWtBwCqRQYAq00GAD5JAIBCSQCArkUGAK9NBgCsVQYArU0GAKhZBgCpZQYAqm0GAKtlBgCsYQYArWEGAK5hBgCvYQYAhKwBAEZJAIBKSQCATkkAgFJJAIBWSQCAWkkAgF5JAIC4kQEAuZkBALqhAQC7oQEAvHEBAL1xAQC+cQEAv3EBALDxAQCx8QEAsvUBALPdAQC0xQEAtbEBALaxAQC3sQEAs+UFAGJJAIBmSQCAakkAgG5JAIC24QUAtekFAHJJAIC7NQIAujUCAHZJAIB6SQCAv3UCAL4BAgC9CQIAvCECAH5JAICjoQUAgkkAgIZJAICmpQUAikkAgI5JAIClrQUAqnECAKtxAgCSSQCAvigDAK5FAgCvMQIArGUCAK1NAgCA1QAAgd0AAILhAACaSQCA4yABAJ5JAIDhqAEAokkAgO80AgCmSQCAhggMAIdoAwCsAAAAqkkAgK5JAICySQCAs40DALZJAIC6SQCAhIAMAL5JAIC2vQMAtYEDAMJJAIC7TQMAuk0DAMZJAIDKSQCAv00DAL5NAwC9TQMAvE0DAKhBAgCpTQIAqkUCAKtZAgCsSQIArX0CAK51AgCvuQIAvmgNAM5JAIDSSQCA1kkAgIRsDADaSQCA3kkAgOJJAIC4TQEAuVUBALpVAQC7ZQEAvH0BAL0VAQC+EQEAvxEBALDJAgCxyQIAstkCALPZAgC0yQIAtckCALZ9AQC3dQEA4XgHAOOYAADjuAYA4VwGAOZJAIDqSQCA7kkAgPJJAID2SQCA+kkAgP5JAIACSgCA7AAAAO9cAADv6AYACkoAgIFpAACAYQAAo4UCAIJhAACliQIADkoAgBJKAICmtQIAhkAMAIfEDACrRQIAqkUCAK1FAgCsRQIAr0UCAK5FAgCojQ4AqZEOAKqVDgCrqQ4ArKUOAK2tDgCupQ4Ar9kOAAZKAIAWSgCAGkoAgB5KAIAiSgCAJkoAgCpKAIAuSgCAuHUPALl9DwC6dQ8Au90PALzFDwC9zQ8AvsUPAL/9DwCwqQ4AsbUOALK1DgCzhQ4AtJ0OALVRDwC2UQ8At1EPALMdDgAySgCANkoAgDpKAIA+SgCAti0OALUtDgBCSgCAu3EOALptDgBGSgCASkoAgL+VDwC+WQ4AvVEOALxhDgBOSgCAo1kOAFJKAIBWSgCApmkOAFpKAIBeSgCApWkOAKopDgCrNQ4AYkoAgGZKAICuHQ4Ar9EPAKwlDgCtFQ4AqL0OAKnRDgCq0Q4AqykBAKw5AQCtOQEArikBAK8pAQCADQAAgRUAAIIdAABqSgCAbkoAgHJKAIC+dAIAdkoAgLjtAQC5hQEAuoEBALuBAQC8hQEAvY0BAL6xAQC/sQEAsFkBALFZAQCy7QEAs+UBALT9AQC15QEAtuUBALfVAQB6SgCAtqkBALWhAQB+SgCAs0kOAIJKAICGOAAAh9wBAL8xAQC+KQEAvSEBALwpAQC7jQEAuo0BAJZJAICGSgCAoxkOAIpKAICOSgCAkkoAgJZKAICm+QEApfEBAJpKAICr3QEAqt0BAJ5KAICiSgCAr2EBAK55AQCtcQEArHkBAKZKAIDv3A8AqkoAgK5KAICySgCAtkoAgLpKAIC+SgCAwkoAgMZKAIDKSgCAzkoAgNJKAIDj6A4A1koAgOGMDgCAEQAAgREAAIIRAACEQAIA2koAgN5KAIDiSgCAvhADAIbABACHRAMA6koAgO5KAIDySgCA9koAgPpKAID+SgCA7yQCAAJLAIAGSwCACksAgA5LAIASSwCAFksAgBpLAICE7AQAHksAgCJLAIAmSwCA4+wCACpLAIDhOAEALksAgLNVAwAySwCANksAgDpLAIA+SwCAth0DALUdAwBCSwCAuwkDALo5AwBGSwCASksAgL/9AAC+/QAAvfkAALwRAwCogQIAqYkCAKqdAgCrsQIArNUCAK3dAgCu1QIAr80CAIDNAQCBCQAAghkAAE5LAIBSSwCAWksAgL5wBQBeSwCAuFkBALlZAQC6aQEAu2kBALx5AQC9eQEAvmkBAL9lAQCwvQIAsY0CALKFAgCzbQEAtHkBALV5AQC2aQEAt2kBAIYgBACHCAUAYksAgGZLAIBqSwCAbksAgHJLAIDvXAAAhOwEAOFcDgB2SwCA44wOAHpLAIB+SwCAgksAgIZLAICjVQIAiksAgI5LAICSSwCAlksAgKYdAgClHQIAmksAgKsJAgCqOQIAnksAgKJLAICv/QEArv0BAK35AQCsEQIAqGkGAKlpBgCqeQYAq3kGAKxpBgCtaQYArp0GAK+VBgBWSwCApksAgKpLAICuSwCAsksAgLZLAIC6SwCAvksAgLj1BgC5+QYAuo0GALuFBgC8nQYAvYUGAL6FBgC/tQYAsO0GALH1BgCy/QYAs/UGALTtBgC10QYAttEGALfRBgCz8QYAghUAAIG1AACAtQAAwksAgLbpBgC14QYAvtQDALsxBgC6KQYAxksAgMpLAIC/FQYAvikGAL0hBgC8KQYAzksAgKO1BgCGyAAAh8gAAKatBgDSSwCA1ksAgKWlBgCqbQYAq3UGANpLAIDeSwCArm0GAK9RBgCsbQYArWUGAKg1BgCpOQYAqoEGAKuBBgCsgQYArYEGAK6BBgCvtQYA4ksAgOZLAIDqSwCA7ksAgPJLAID2SwCA+ksAgP5LAIC4nQYAua0GALqlBgC7aQEAvHkBAL15AQC+aQEAv2kBALDRBgCx0QYAstEGALPRBgC0tQYAtb0GALa1BgC3rQYAswkGAAJMAIAGTACACkwAgA5MAIC2AQYAtQkGABJMAIC7FQYAuhUGABZMAIAaTACAv3kGAL5xBgC9BQYAvAUGAB5MAICjTQYAIkwAgOZKAICmRQYAJkwAgCpMAIClTQYAqlEGAKtRBgAuTACAMkwAgK41BgCvPQYArEEGAK1BBgCB6QMAgN0DAISIAwCC4QMAhrA8AIeIAgC+VAMAOkwAgD5MAIBCTACARkwAgEpMAIBOTACAUkwAgFZMAIBaTACA4/AGAF5MAIDhMAYAhAA8AGJMAIBmTACAakwAgG5MAIByTACAhTQ9AHZMAIB6TACA77AHAH5MAICCTACAhkwAgIpMAICOTACAkkwAgL7EPACWTACAgp0BAIGdAQCAnQEAqA0CAKllAgCqfQIAq3UCAKxZAgCtWQIArpkDAK+ZAwCw6QMAsekDALL5AwCz+QMAtOkDALXpAwC2XQMAt1UDALhtAwC5dQMAunUDALtFAwC8XQMAvTUDAL4xAwC/KQMAmkwAgJ5MAICiTACAqkwAgOFgAwDv9AMA40QCAK5MAICyTACA4zwDAO/0NwDh/AEAtkwAgLpMAIC+TACAwkwAgIZkPwCHaD0AhTQhALOZAwDGTACAtb0DALa1AwDKTACAzkwAgNJMAIC6QQIAu0ECALxBAgC9QQIAvkECAL9BAgDWTACA2kwAgN5MAIDiTACA5kwAgOpMAIDuTACA7/gBAIRoPADhPAYA8kwAgOMcBgD2TACA+kwAgP5MAIACTQCAoxUDAAZNAIAKTQCADk0AgBJNAICmOQMApTEDABpNAICrzQIAqs0CAL5kPgAeTQCAr80CAK7NAgCtzQIArM0CAKgdPgCpJT4Aqi0+AKslPgCsPT4ArSU+AK4tPgCvJT4ApkwAgIL1PwCB5T8AgOU/ABZNAIAiTQCAhgAEAIecAwC4LT4AuTE+ALoxPgC7MT4AvNE+AL3RPgC+0T4Av80+ALBdPgCxIT4Asjk+ALM5PgC0KT4AtSk+ALYZPgC3FT4As6U+ACZNAIAqTQCALk0AgDJNAIC2pT4AtbU+ADZNAIC75T4Aupk+ADpNAIA+TQCAv+0+AL7tPgC97T4AvO0+AEJNAICj4T4ARk0AgEpNAICm4T4ATk0AgFJNAICl8T4Aqt0+AKuhPgBWTQCAWk0AgK6pPgCvqT4ArKk+AK2pPgCPBSUAsyU+AF5NAIBiTQCAtik+AGZNAIBqTQCAtSk+ALp9PgC7RT4Abk0AgHJNAIC+tT4Av70+ALxdPgC9vT4An304AJ5lOQCd8TgAnFE0AJtZNQCaUTUAmfEwAJgNMQCXZTEAlsEwAJVZLQCUTS0Ak+EsAJLZKQCRWSkAkPEoALSlGQC13RgAdk0AgIQIAACwkRUAsQEVALIBGACzvRkAgA0AAIGtAwCCpQMAek0AgKNhAACiHT0AoZk9AKBxPACkxQUApUEEAKYBCACn4QkANkwAgKH1AQCi6QEAo90FAKwBEACtxREArtkRAK85EACoZQgAqQEMAKrZDQCrCQ0AijEuAIuhMwB+TQCAgk0AgI65MwCPETYAjB0yAI1NMgCCJSYAg6krAL5kAwCEYAQAhqEvAIcVLgCEGSoAhZEqAJphPgCb7T4AhsgEAIfcAwCKTQCA4Vw+AJyJAwDjAD4Akmk2AJN5NwCOTQCA7xg+AJZNOwCXuT8AlME7AJVdOgCpnT0AqIk9AKu5PQCqrT0Arak9AKyhPQCvyT0ArqE9AL7oBACSTQCAlk0AgJpNAICeTQCAok0AgKZNAICqTQCAuVk9ALhRPQC7eT0AumU9AL1pPQC8YT0Avx09AL5hPQCxgT0AsLk9ALNpPQCyiT0AtXk9ALRxPQC3aT0AtnE9AKMhPACuTQCAsk0AgLZNAIC6TQCApi08AKUtPAC+TQCAq0E8AKp5PADCTQCAxk0AgK+5PACusTwArbk8AKxZPADKTQCAzk0AgLN9AwDSTQCAtdkDANZNAIDaTQCAttEDAN5NAIDiTQCAu8UDALrFAwC9uQMAvLUDAL+tAwC+sQMA5k0AgOpNAIDuTQCA71wDAIAVAACBHQAAgjEAAO+MPgCE7AQA4fw+APJNAIDjHD4A+k0AgOGUAQD+TQCA4yAAAKP1AwACTgCAh+gEAIZsBAAGTgCAplkDAKVRAwAKTgCAq00DAKpNAwAOTgCAEk4AgK8lAwCuOQMArTEDAKw9AwCGTQCA9k0AgBZOAIAaTgCAHk4AgCJOAIAmTgCAKk4AgKhxBgCpTQYAqo0GAKuFBgCsnQYArYUGAK6NBgCvhQYAsP0GALFBBwCyQQcAs0EHALRBBwC1SQcAtnEHALdxBwC4IQcAuSEHALolBwC7OQcAvCkHAL0VBwC+HQcAv/0HALMlBgAuTgCAMk4AgDZOAIA6TgCAtiUGALU1BgA+TgCAu6UHALoZBgBCTgCARk4AgL+tBwC+pQcAvbUHALy1BwBKTgCAo2EGAE5OAIBSTgCApmEGAFZOAIBaTgCApXEGAKpdBgCr4QcAXk4AgGJOAICu4QcAr+kHAKzxBwCt8QcAqLEGAKm9BgCqzQYAq90GAKzNBgCt/QYArvUGAK8VAQCA+QEAgc0BAILFAQC+ZAIAhpAAAIcAAQBqTgCAbk4AgLjRAQC52QEAuuEBALvhAQC8kQEAvZ0BAL6VAQC/iQEAsG0BALF1AQCyfQEAs3UBALRtAQC18QEAtvEBALfxAQCzRQYAZk4AgHJOAIB2TgCAek4AgLZ9BgC1RQYAfk4AgLuxAQC6qQEAgk4AgIZOAIC/NQEAvqkBAL2hAQC8qQEAik4AgKMBBgCOTgCAkk4AgKY5BgCWTgCAmk4AgKUBBgCq7QEAq/UBAJ5OAICiTgCAru0BAK9xAQCs7QEAreUBAOEoAQCmTgCA41ACAKpOAICuTgCAsk4AgLZOAIC6TgCAvk4AgMJOAIDGTgCAyk4AgIFxAACAGQAA75wCAIJ5AADOTgCA0k4AgITIAgCzxQMA2k4AgLXFAwC2xQMAvhADAIbADACHRAwAuqkDALulAwC8vQMAvaEDAL6hAwC/lQMArhEGAK8ZBgCsAQYArQEGAKqlBgCrEQYAqEU5AKlxOQDeTgCA4k4AgOZOAIDqTgCA7k4AgPJOAID2TgCA+k4AgL7tBwC/TQcAvNEHAL3lBwC63QcAu8EHALg1BgC51QcAtjkGALcNBgC0JQYAtTkGALIxBgCzPQYAsFEGALFRBgCoOQIAqTkCAKqBAgCrgQIArIECAK2JAgCusQIAr7ECAIRsDQD+TgCAvmANAAJPAIAGTwCACk8AgA5PAIASTwCAuE0BALlVAQC6XQEAu1UBALxNAQC9dQEAvn0BAL91AQCwoQIAsa0CALKlAgCzuQIAtKkCALWdAgC2lQIAt3kBAOFUBgDh1AcA4zgGAOOwBwAWTwCAGk8AgB5PAIAiTwCAhOQMACZPAIAqTwCALk8AgDJPAIA2TwCA72wAAO/kBwCjSQIAOk8AgD5PAIBCTwCASk8AgKZJAgClSQIATk8AgKspAgCqJQIAhkgMAIfcDACvGQIAri0CAK0tAgCsMQIAqFEOAKmlDgCqrQ4Aq6UOAKy9DgCtpQ4Arq0OAK+lDgCA5Q8Age0PAILlDwBGTwCAUk8AgFZPAIBaTwCAXk8AgLjVDwC53Q8AutUPALvpDwC8+Q8AvfkPAL7pDwC/6Q8AsN0OALFBDwCyRQ8As10PALRFDwC1TQ8AtkUPALftDwCzJQ4AYk8AgGZPAIBqTwCAbk8AgLYlDgC1NQ4Ack8AgLuFDwC6GQ4Adk8AgHpPAIC/iQ8AvoEPAL2JDwC8kQ8Afk8AgKNhDgCCTwCAhk8AgKZhDgCKTwCAjk8AgKVxDgCqXQ4Aq8EPAJJPAICWTwCArsUPAK/NDwCs1Q8Arc0PAKjRDgCp2Q4AqjkBAKs5AQCsKQEArSkBAK6dAQCvlQEAmk8AgJ5PAICiTwCApk8AgIANAACBtQAAgr0AAKpPAIC4lQEAuZ0BALqhAQC7oQEAvHEAAL1xAAC+cQAAv3EAALDtAQCx9QEAsvUBALPFAQC03QEAtbUBALaxAQC3sQEArk8AgLJPAICzuQEAvsACALWpAQC2TwCAuk8AgLahAQCGgAEAh8QBALs5AQC6IQEAvRkBALwpAQC/eQEAvhEBAKPxAQC+TwCA1k4AgMJPAIDGTwCApukBAKXhAQDKTwCAq3EBAKppAQDOTwCA0k8AgK8xAQCuWQEArVEBAKxhAQDWTwCA2k8AgN5PAIDiTwCA4agBAOZPAIDjQAIA6k8AgL8oFQDuTwCA73QCAPJPAID2TwCA+k8AgP5PAIACUACABlAAgON0DwCEiAMA4TQOAApQAIAOUACAElAAgBZQAICADQAAgRUAAIIRAAAaUACAHlAAgO+kDwAiUACAKlAAgKgZAwCpQQMAqkUDAKtdAwCsTQMArX0DAK51AwCvnQAAhaQVAL58AwCGCAQAhxwDAC5QAIAyUACANlAAgDpQAIC49QAAuf0AALr1AAC7jQAAvIEAAL2BAAC+gQAAv4EAALDlAACx7QAAsuUAALP5AAC07QAAtdEAALbVAAC3zQAAPlAAgEJQAIBGUACAs8ECAEpQAIC1yQIAtvECAE5QAIBSUACAVlAAgLotAQC7JQEAvD0BAL0hAQC+JQEAvxkBAKapAgCESAIAWlAAgKWRAgBeUACAo5kCAGJQAIBmUACArn0BAK9BAQCsZQEArXkBAKp1AQCrfQEAalAAgG5QAIByUACAdlAAgHpQAIB+UACA7+QAAIJQAICGUACAilAAgOMQDgCOUACA4VgOAJJQAICALQAAgREAAIIVAAC+sAUAs3UBAJpQAICHFAUAhmwEAJ5QAIC21QAAtWUBAKJQAIC7/QAAuvUAAKZQAICqUACAv6EAAL69AAC93QAAvN0AAKh9BgCptQYAqr0GAKu1BgCsrQYArRUHAK4dBwCvFQcAllAAgK5QAICyUACAtlAAgLpQAIC+UACAwlAAgMZQAIC4OQcAuTkHALrJBwC7yQcAvNkHAL3ZBwC+zQcAv8UHALBxBwCxeQcAskkHALNJBwC0OQcAtSUHALYhBwC3IQcAozUGAMpQAIDOUACA0lAAgNZQAICmlQcApSUGANpQAICrvQcAqrUHAN5QAIDiUACAr+EHAK79BwCtnQcArJ0HAOZQAIDqUACA7lAAgPJQAID2UACAgj0AAIE9AACAPQAA+lAAgP5QAIACUQCAhKADAL6kAwAGUQCAhvgAAIfgAACoxQYAqdUGAKrVBgCr5QYArP0GAK0xAQCuMQEArzEBAApRAIAOUQCAElEAgBZRAIAaUQCAHlEAgCJRAIAmUQCAuN0BALntAQC65QEAu40BALyVAQC9nQEAvpUBAL+NAQCwUQEAsVEBALJRAQCzUQEAtPUBALX9AQC29QEAt+0BALNdBgAqUQCALlEAgDJRAIA2UQCAtrEBALV1BgA6UQCAu5UBALqVAQA+UQCAQlEAgL85AQC+MQEAvYUBALyFAQClLQYARlEAgEpRAICm6QEATlEAgFJRAICjBQYAVlEAgK3dAQCs3QEAr2EBAK5pAQBaUQCAJlAAgKvNAQCqzQEAXlEAgGJRAICExAMAvwD0AGZRAICCPQAAgT0AAIA9AABqUQCAblEAgHJRAIC+YAMAelEAgH5RAICCUQCAhlEAgIbgHACHAAMA7wwHAIpRAICOUQCAklEAgJZRAICaUQCAnlEAgKJRAICmUQCAqlEAgOHABgCuUQCA4ywHALJRAIC2UQCAulEAgL5RAIDCUQCAxlEAgMpRAIDOUQCA0lEAgKiBAwCpgQMAqoEDAKuBAwCsgQMArYEDAK6BAwCvgQMAsEUDALFNAwCyRQMAs10DALRNAwC1fQMAtnUDALcZAwC4KQMAuTUDALo9AwC7MQMAvAEDAL31AAC+/QAAv+0AALMpAgDWUQCA2lEAgN5RAIDiUQCAtiECALUpAgCEUB0Au6kCALqhAgDqUQCA7lEAgL+ZAgC+qQIAvakCALyxAgCBTQAAgE0AAO+cAwCCXQAAhvAcAId4HQC+EB0A8lEAgPZRAID6UQCA/lEAgAJSAIDhkAEABlIAgONgAwAKUgCADlIAgBJSAIAWUgCAGlIAgB5SAIAiUgCAJlIAgO+UAQCE7BwA4XAGACpSAIDjUAEALlIAgDJSAIA2UgCAOlIAgKPpAgA+UgCAQlIAgEZSAIBKUgCApuECAKXpAgBOUgCAq2kCAKphAgBSUgCAvqgcAK9ZAgCuaQIArWkCAKxxAgCoMR4AqTEeAKoxHgCrMR4ArF0eAK1FHgCuTR4Ar0UeAOZRAICCzR8AgfUfAID9HwBWUgCAWlIAgIYcAACH+AMAuMUeALnNHgC6xR4Au90eALzFHgC9zR4AvsUeAL9ZHwCwPR4AsQUeALINHgCzBR4AtB0eALUBHgC2BR4At/0eALO5HgBeUgCAYlIAgGZSAIBqUgCAtsUeALXVHgBuUgCAu8EeALr5HgByUgCAdlIAgL/FHgC+2R4AvdEeALzZHgB6UgCAo/0eAH5SAICCUgCApoEeAIZSAICKUgCApZEeAKq9HgCrhR4AjlIAgJJSAICunR4Ar4EeAKydHgCtlR4AqCkeAKkpHgCqVR4Aq20eAKx1HgCtfR4ArnUeAK9pHgCWUgCAmlIAgJ5SAICiUgCAplIAgKpSAICuUgCAslIAgLjpHgC59R4Auv0eALv1HgC87R4AvZEeAL6RHgC/kR4AsB0eALHlHgCy7R4As+UeALT9HgC15R4Atu0eALflHgCz3R4AtlIAgLpSAIC+UgCAwlIAgLb9HgC1/R4AhFgBALshHgC62R4AvigAAMpSAIC/IR4AvjkeAL0xHgC8OR4AgU0AAIBNAACjlR4Agl0AAKW1HgDGUgCAzlIAgKa1HgB2UQCA0lIAgKtpHgCqkR4ArXkeAKxxHgCvaR4ArnEeAIYABACHRAMAs4ECANZSAIC1gQIA2lIAgN5SAIC2gQIAiAAAAOJSAIC74QIAuu0CAL3lAgC8+QIAv9ECAL7lAgDmUgCA6lIAgIREAwC+jAMA4UgCAO5SAIDjAAIA7/wfAPJSAIDhPB4A79wCAONgHwD2UgCA+lIAgP5SAIACUwCAqQUCAKixAgCrBQIAqgUCAK0NAgCsBQIArzUCAK41AgCEbAUABlMAgApTAIAOUwCAElMAgBZTAIAaUwCAHlMAgLnpAwC44QMAu/kDALrhAwC96QMAvOEDAL9dAwC+4QMAsSkCALAlAgCzPQIAsiECALUZAgC0LQIAt9kDALYRAgAiUwCAJlMAgCpTAICjhQMALlMAgKWFAwCmhQMAMlMAgDpTAIA+UwCAqukDAKvlAwCs/QMAreEDAK7hAwCv1QMAgEkAAIFVAACCVQAAo6kCAL6YBAClQQEApkEBAEJTAICG4AUAh+AFAKotAQCrOQEArBEBAK0FAQCuDQEArwUBAEZTAIBKUwCATlMAgO/cAABSUwCAVlMAgFpTAIDviB4AhCwHAOHsHgBeUwCA4xweAGJTAIDhlAEAZlMAgOMwAACzJQIAhWDmAGpTAIBuUwCAclMAgLbNAQC1zQEAdlMAgLu1AQC6oQEAelMAgH5TAIC/iQEAvoEBAL2JAQC8nQEANlMAgIJTAICGUwCAilMAgI5TAICSUwCAllMAgJpTAICoAQcAqQEHAKp1BwCrrQcArLUHAK29BwCuqQcAr6kHALDZBwCx7QcAsvkHALP1BwC0mQcAtZkHALaJBwC3gQcAuIkHALmJBwC6bQAAu2UAALx9AAC9ZQAAvm0AAL9lAACBCQAAgJkAAJ5TAICCHQAAolMAgKZTAICqUwCArlMAgKgNBQCpfQUAqk0FAKuhBgCspQYAra0GAK6dBgCv/QYAsIUGALGRBgCyqQYAs70GALSlBgC1rQYAtqUGALd5BgC4SQYAuUkGALpZBgC7WQYAvEkGAL1JBgC++QcAv/kHALNdBgCyUwCAhigCAIcsAQC2UwCAtp0GALWdBgC6UwCAu4kGALq9BgC+UwCAwlMAgL/9BgC+/QYAvYEGALyNBgDGUwCAoxkGAMpTAIDOUwCAptkGANJTAIDWUwCApdkGAKr5BgCrzQYA2lMAgN5TAICuuQYAr7kGAKzJBgCtxQYAqBkBAKkZAQCqjQAAq50AAKyNAACtvQAArrUAAK/dAADiUwCA5lMAgOpTAIDuUwCA8lMAgPZTAID6UwCA/lMAgLhpAAC5aQAAunkAALt5AAC8aQAAvWkAAL7dAwC/1QMAsKkAALGpAACyvQAAs7UAALSZAAC1mQAAtlkAALdZAAC+LAIAAlQAgAZUAIAKVACADlQAgBJUAIAaVACAHlQAgIAtAACBNQAAgj0AACJUAICGkAwAh+gCACZUAIAqVACAs0UDAC5UAIAyVACANlQAgDpUAIC2fQMAtUUDAD5UAIC7LQMAui0DAEJUAIBGVACAvx0DAL4dAwC9IQMAvCkDAKvNAwCqzQMASlQAgE5UAICv/QMArv0DAK3BAwCsyQMAo6UDAFJUAIBWVACAWlQAgF5UAICmnQMApaUDAGJUAIBmVACAalQAgG5UAIByVACAdlQAgII9AACBPQAAgD0AAHpUAIB+VACAglQAgIRgAwCG0AwAhzADAIpUAICOVACAvkQCAJJUAICWVACAmlQAgOEAAACeVACA46gGAKJUAICE7AwAplQAgO/QAwCqVACArlQAgLJUAIC2VACAulQAgLNtAQC+VACAwlQAgMZUAIDKVACAthEBALVlAQDOVACAuz0BALo1AQDSVACA1lQAgL/9AQC+/QEAvRUBALwVAQDaVACA4fwGAN5UAIDjPAcA4lQAgOZUAIDqVACA7lQAgPJUAIC+bAwA+lQAgP5UAIACVQCABlUAgApVAIDvFAYAgV0AAIBdAACj5QEAgm0AAKXtAQAOVQCAElUAgKaZAQCHqAwAhuQMAKu1AQCqvQEArZ0BAKydAQCvdQEArnUBAKgZDgCpGQ4AqiUOAKs1DgCsLQ4ArVEOAK5RDgCvUQ4AhlQAgPZUAIAWVQCAGlUAgB5VAIAiVQCAJlUAgCpVAIC47Q4AufUOALr1DgC7jQ4AvJUOAL2dDgC+lQ4Av40OALAxDgCxOQ4AsgEOALMBDgC0+Q4AtfkOALbdDgC31Q4AqHkOAKl5DgCqjQ8Aq4UPAKydDwCtgQ8AroUPAK+5DwAuVQCAMlUAgDZVAIA6VQCAPlUAgEJVAIBGVQCASlUAgLiRDwC5mQ8AuqEPALuhDwC8UQ8AvV0PAL5JDwC/SQ8AsM0PALHVDwCy3Q8As9UPALTNDwC1sQ8AtrEPALexDwCzBQ4ATlUAgFJVAIBWVQCAWlUAgLYBDgC1FQ4AXlUAgLsRDgC6CQ4AYlUAgISgAQC/dQ4AvgkOAL0BDgC8CQ4AgmkAAKNBDgCAWQAAgVEAAKZFDgC+WAEAZlUAgKVRDgCqTQ4Aq1UOAIbIAACHrAEArk0OAK8xDgCsTQ4ArUUOAGpVAIBuVQCAclUAgHZVAIB6VQCAflUAgBZUAICCVQCAqAkOAKkJDgCqGQ4AqxkOAKwJDgCtYQ4ArmEOAK+VAQCw7QEAsfUBALL9AQCz9QEAtO0BALV1AQC2fQEAt3UBALhNAQC5VQEAul0BALtVAQC8TQEAvfEAAL7xAAC/8QAAhlUAgIpVAICOVQCAklUAgJZVAIDj6A4AmlUAgOE0DgC+AAQA79wPAJ5VAICiVQCAplUAgKpVAICuVQCAslUAgLPxDQC2VQCAulUAgL5VAIDCVQCAtoENALXhDQDGVQCAu1ECALpJAgDKVQCAzlUAgL/RAgC+SQIAvUECALxJAgCjMQ0A0lUAgISIAwDaVQCA3lUAgKZBDQClIQ0A4lUAgKuRAgCqiQIA5lUAgOpVAICvEQIArokCAK2BAgCsiQIAgKkAAIGpAACCTQAA7lUAgOFkEgDjTAIA4wgLAOGsAQDyVQCA7zwCAO8YFgD2VQCAhlAGAIdIAwD6VQCA/lUAgKiBAgCpgQIAqoECAKuBAgCsgQIArYECAK6FAgCvHQEAAlYAgAZWAIAKVgCADlYAgBJWAIAWVgCAGlYAgIS4BQC4dQEAuX0BALp1AQC7CQEAvBkBAL0ZAQC+CQEAvwEBALBlAQCxbQEAsmUBALN9AQC0aQEAtV0BALZVAQC3TQEAHlYAgCJWAIAmVgCAKlYAgC5WAIAyVgCA7zQAAO/ADgDhXA4A4UwPAOOUAADjnA4ANlYAgIJlAACBfQAAgH0AADpWAIA+VgCAvsQHALNFAgBCVgCAtUUCALZNAgBKVgCAhkAGAIeQBAC67QEAu+UBALz9AQC95QEAvuEBAL/VAQCflQgAngUIAJ3dDQCcPQwAmzEMAJr1DQCZ7RAAmD0QAJfVEQCWsRUAlQUUAJTlFQCTtRkAkjEYAJE5GACQDRwAj2EcANZVAICz1QYATlYAgLX9BgBGVgCAUlYAgLaRBgBWVgCAWlYAgLuVBgC6lQYAvVUHALxVBwC/VQcAvlUHAF5WAIBiVgCAqo0GAKuFBgCsnQYArYUGAK6BBgCvtQYAhKgAAGZWAIBqVgCAoyUFAG5WAIClJQUApi0FAHJWAIB2VgCAelYAgH5WAICCVgCAhlYAgIpWAICOVgCAklYAgJZWAICaVgCAnlYAgKJWAICjqQUAotEEAKHZBACgZQUAgiEdAIM1HQCmVgCAqlYAgIaVGACH3RQAhBkZAIUZGQCKDRUAi7EUAK5WAICyVgCAjsURAI/VDACMzRAAjR0RAJJhDQCTdQ0AvkwAALpWAICWxQkAl80EAJSNDACVXQkAmkEFAJtBBQCGyP8Ah0wAAIFZAACAeQAAnCEEAIJRAAChxQEAvlYAgKMB/ACi2QEApRX9AKS1/QCnufkApgH4AKkJ+AColfkAqwX1AKqt9QCtsfEArAHwAK8d8ACurfEAseHtALAB7ACzAegAsv3sALVd6QC09ekAwlYAgMZWAIDKVgCAzlYAgNJWAIDWVgCA2lYAgN5WAIDiVgCA5lYAgKiNBACplQQAqpUEAKulBACsvQQArdkEAK75BACv8QQAhGz8AOpWAIDuVgCA8lYAgPZWAID6VgCA/lYAgAJXAIC4eQUAucUFALrNBQC7xQUAvN0FAL3FBQC+zQUAv+0FALCZBACxmQQAskkFALNJBQC0WQUAtVkFALZJBQC3SQUAox0EAL7M/AAGVwCAClcAgA5XAICmWQQApTUEABJXAICrXQQAql0EABZXAIAaVwCAr50FAK6dBQCtnQUArJ0FAB5XAICznQIAIlcAgCpXAIC2UQIALlcAgDJXAIC1uQIAukkCALtVAgCGSP0Ah8D8AL41AgC/PQIAvEUCAL09AgCo3QQAqUkDAKpRAwCrbQMArHUDAK2VAwCunQMAr7kDAICNAQCB5QEAguEBADZXAIA6VwCAPlcAgEJXAIBGVwCAuJUDALmdAwC6lQMAu60DALy1AwC9vQMAvrUDAL9VAgCwyQMAsdUDALLVAwCzrQMAtLUDALW9AwC2tQMAt60DAEpXAIBOVwCAo9EDAFJXAICl9QMAVlcAgFpXAICmHQMAXlcAgGJXAICrGQMAqgUDAK1xAwCsCQMAr3EDAK55AwDhKAcAZlcAgOPkBgBqVwCA4SgGAG5XAIDjaAEAclcAgHZXAIB6VwCA71gAAH5XAICCVwCAhlcAgO/IBgCKVwCAqE39AKmB/QCq0f0Aq9H9AKzx/QCt8f0ArvH9AK/x/QAmVwCAghEAAIEZAACA0f8AjlcAgJJXAICEdAMAvnQDALh1/gC5ff4AunX+ALvF/gC83f4AvcX+AL7F/gC/9f4AsJH9ALGR/QCykf0As5H9ALRV/gC1Xf4AtlX+ALdN/gCzWf0AllcAgIasAACHRAMAmlcAgLZx/QC1ef0AnlcAgLtV/QC6Vf0AolcAgKZXAIC/mf4AvpH+AL1F/QC8Rf0AqlcAgKMd/QCuVwCAslcAgKY1/QC2VwCAulcAgKU9/QCqEf0AqxH9AL5XAIDCVwCArtX+AK/d/gCsAf0ArQH9AKjN/wCp0f8AqtH/AKsh/gCsIf4ArSH+AK4h/gCvIf4AxlcAgMpXAIDOVwCA0lcAgNZXAIDaVwCA3lcAgOJXAIC4jf4AuZH+ALqV/gC7rf4AvLX+AL25/gC+qf4Av6n+ALDh/gCx4f4AsuX+ALP5/gC06f4AtdX+ALbd/gC3uf4As1n/AOZXAIC2VgCA6lcAgO5XAIC2of4Atan+APJXAIC7Jf4AuiX+APZXAID6VwCAvxH+AL4t/gC9Lf4AvDH+AIIZAACjHf8AgGUAAIEZAACm5f4A/lcAgAJYAICl7f4AqmH+AKth/gCEZAEAviAAAK5p/gCvVf4ArHX+AK1p/gAKWACA4zT+AA5YAIDhfP0AhrAEAIcIAwASWACAFlgAgBpYAIAeWACAhCQDAIQkBAAiWACA70j+ACZYAIAqWACAs+kCAC5YAIC+RAQAvkAFADJYAIC2nQIAtZkCADZYAIC7iQIAur0CADpYAIA+WACAv1kDAL5RAwC9WQMAvJECAKkdAgCoFQIAqyUCAKolAgCtWQIArFUCAK9NAgCuUQIAvmQGAEJYAIBGWACASlgAgE5YAIBSWACAVlgAgFpYAIC5+QMAuPEDALtNAwC68QMAvUEDALxZAwC/cQMAvkEDALEJAgCwPQIAs8kDALIBAgC12QMAtNEDALfJAwC20QMA4ZABAF5YAIDj8AAAYlgAgGZYAICCPQAAgT0AAIA9AABqWACAblgAgHJYAIB6WACAflgAgIJYAIDvLAAAhlgAgKPpAwCKWACAhugEAIdgBQCOWACApp0DAKWZAwCSWACAq4kDAKq9AwCWWACAmlgAgK9ZAgCuUQIArVkCAKyRAwCeWACAolgAgKZYAICqWACArlgAgLJYAIC2WACA71gBAISgBADhVP8AulgAgOOEAQC+WACAwlgAgMZYAIDKWACAs9kBAM5YAICFzBkA0lgAgNZYAIC28QEAtfkBANpYAIC7pQEAutkBAN5YAIDiWACAv50BAL6dAQC9pQEAvK0BAKgBBgCpDQYAqhEGAKsRBgCsMQYArTEGAK4pBgCvJQYAdlgAgILJBwCBwQcAgPEHAOZYAIDqWACAhhwAAIf8AwC47QYAufUGALr9BgC79QYAvO0GAL1RBwC+VQcAv00HALBdBgCxIQYAsjkGALMxBgC0GQYAtRkGALbdBgC31QYAo5kGAO5YAIDyWACA9lgAgPpYAICmsQYApbkGAP5YAICr5QYAqpkGAAJZAIAGWQCAr90GAK7dBgCt5QYArO0GAApZAICz8QcADlkAgBJZAIC2gQcAFlkAgBpZAIC1mQcAuo0HALtlBwAeWQCAIlkAgL59BwC/ZQcAvH0HAL11BwCoLQYAqTUGAKo9BgCrMQYArFUGAK1FBgCuRQYAr3UGACZZAIAqWQCALlkAgDJZAIA2WQCAOlkAgD5ZAIBCWQCAuOkGALn1BgC6/QYAu/UGALztBgC9kQYAvpUGAL+NBgCwDQYAseUGALLtBgCz5QYAtP0GALXlBgC27QYAt+UGAKO1BgBGWQCASlkAgE5ZAIBSWQCApsUGAKXdBgAGWACAqyEGAKrJBgBWWQCAWlkAgK8hBgCuOQYArTEGAKw5BgCASQAAgUkAAIJZAACzRQEAXlkAgLVFAQC2RQEAYlkAgIZAAACHZAAAuikBALslAQC8PQEAvSEBAL4hAQC/FQEAZlkAgGpZAICEBAMAvgAMAOMoBgDv4AIA4RAGAG5ZAIDvkAYA4zwCAHJZAIDh1AEAdlkAgHpZAIB+WQCAglkAgIZZAICKWQCAo8ECAI5ZAIClwQIAklkAgJZZAICmwQIAmlkAgJ5ZAICroQIAqq0CAK2lAgCsuQIAr5ECAK6lAgCpBQIAqLECAKsFAgCqBQIArQ0CAKwFAgCvNQIArjUCAISoDACiWQCAplkAgKpZAICuWQCAslkAgLZZAIC6WQCAuekDALjhAwC7+QMAuuEDAL3pAwC84QMAv10DAL7hAwCxKQIAsCUCALM9AgCyIQIAtRkCALQtAgC32QMAthECAKitAgCp1QIAqtUCAKsNAQCsFQEArQkBAK4xAQCvLQEAvlkAgMJZAIDKWQCAzlkAgNJZAIDWWQCA2lkAgN5ZAIC4IQEAuSEBALrtAQC75QEAvP0BAL3lAQC+7QEAv+UBALBVAQCxXQEAslUBALMtAQC0NQEAtTkBALYtAQC3JQEAgD0BAIGlAACCrQAA79QHAOJZAIDmWQCA6lkAgO8oBwC+LAwA4fQGAO5ZAIDjkAcA8lkAgOGUAQD2WQCA4wwGALMdAgD6WQCAh0QNAIZMDQD+WQCAtskBALXdAQACWgCAu9kBALrRAQAGWgCACloAgL+9AQC+sQEAvbkBALzBAQDGWQCADloAgBJaAIAWWgCAGloAgB5aAIAiWgCAJloAgKgJDwCpCQ8AqhkPAKsZDwCsCQ8ArQkPAK6pDwCvqQ8AsNkPALHtDwCy+Q8As/UPALSVDwC1hQ8AtoUPALe1DwC4jQ8AuWEAALphAAC7YQAAvGEAAL1hAAC+YQAAv2EAAKNdDQCCLQAAgRUAAIAdAAAqWgCApokOAKWdDgAuWgCAq5kOAKqRDgAyWgCANloAgK/9DgCu8Q4ArfkOAKyBDgA6WgCAs/UPAIboAwCHvAMAtu0PAD5aAIBCWgCAteUPALp5DwC7TQ8ARloAgEpaAIC+NQ8AvyUPALxJDwC9RQ8AozEOAE5aAIBSWgCAVloAgFpaAICmKQ4ApSEOAF5aAICriQ4Aqr0OAGJaAIBmWgCAr+EOAK7xDgCtgQ4ArI0OAGpaAIBuWgCAcloAgHZaAIB6WgCAfloAgIJaAICGWgCAiloAgI5aAICSWgCAlloAgIANAACB1QAAgt0AAJpaAICoQQEAqVEBAKpRAQCrZQEArH0BAK2RAACukQAAr5EAAJ5aAICiWgCAhGQBAL5kAQCGkAEAh4QAAKpaAICuWgCAuJEAALmRAAC6kQAAu5EAALyxAAC9sQAAvrEAAL+xAACw8QAAsfkAALLBAACzwQAAtLEAALWxAAC2sQAAt7EAALPZAgCyWgCAvnADAL5EBAC2WgCAthEDALX1AgC6WgCAuz0DALo1AwC+WgCAwloAgL91AwC+dQMAvRUDALwVAwDGWgCAo50CAMpaAIDOWgCAplUDANJaAIDWWgCApbECAKpxAwCreQMA2loAgN5aAICuMQMArzEDAKxRAwCtUQMAqDkDAKk5AwCqjQAAq50AAKyNAACtvQAArrUAAK/dAADiWgCA5loAgOpaAIDuWgCA8loAgPZaAID6WgCA/loAgLhpAAC5aQAAunkAALt5AAC8aQAAvWkAAL7ZAQC/2QEAsKkAALGpAACyvQAAs7UAALSZAAC1mQAAtlkAALdZAAACWwCABlsAgApbAIAOWwCA70QAABJbAICGmAUAh+QCAOOYAACEqAIA4fgBABpbAICAOQAAgTkAAIItAAAeWwCAs0UBACJbAIAmWwCAKlsAgC5bAIC2fQEAtUUBADJbAIC7LQEAui0BADZbAIA6WwCAvx0BAL4dAQC9IQEAvCkBAD5bAIDhUA4AQlsAgOM8DwBGWwCASlsAgE5bAIBSWwCAVlsAgFpbAIDjAAAAXlsAgGJbAIBmWwCAhPQFAO/kDgCuqQEAr6kBAKydAQCtlQEAqpkBAKuZAQBqWwCAblsAgKbJAQByWwCAdlsAgKXxAQCC/QcAo/EBAID9BwCB9QcAFlsAgHpbAIB+WwCAglsAgIZbAICKWwCAhrgDAIeQAwCoDQcAqRkHAKptBwCrZQcArH0HAK1lBwCuZQcAr1UHALAtBwCxxQcAssEHALPdBwC0xQcAtc0HALbFBwC3/QcAuMUHALnJBwC62QcAu9kHALypBwC9qQcAvp0HAL+VBwCzxQcAjlsAgJJbAICWWwCAmlsAgLbFBwC11QcAnlsAgLshBwC6yQcAolsAgKZbAIC/KQcAviEHAL0pBwC8NQcAqlsAgKOBBwCuWwCAslsAgKaBBwC2WwCAulsAgKWRBwCqjQcAq2UHAL5bAIDCWwCArmUHAK9tBwCscQcArW0HAKgVAQCpgQEAqoEBAKuBAQCsgQEArYkBAK6xAQCvsQEAxlsAgMpbAIDOWwCA0lsAgNZbAIDaWwCA3lsAgOJbAIC4ZQAAuW0AALplAAC7fQAAvGUAAL1tAAC+ZQAAv90AALChAQCxrQEAsqUBALO5AQC0qQEAtZ0BALaVAQC3XQAA5lsAgIIdAACBHQAAgB0AAOpbAIDuWwCA8lsAgL5YAQCErAIA9lsAgIcIAQCGjAEA+lsAgKZaAID+WwCAAlwAgLNJAQAGXACAClwAgA5cAIASXACAtkkBALVJAQAWXACAuykBALolAQAaXACAHlwAgL8ZAQC+LQEAvS0BALwxAQC+2AMAIlwAgO/4BgAmXACAKlwAgC5cAIDv4AIAMlwAgOGUAQA2XACA43QCADpcAIDhmAUAPlwAgOMMBwBCXACARlwAgEpcAICjwQIAhIwDAKXBAgBOXACAUlwAgKbBAgBWXACAWlwAgKuhAgCqrQIAraUCAKy5AgCvkQIArqUCAKgxAwCpPQMAqjUDAKtJAwCsWQMArVkDAK5JAwCvQQMAgMUAAIEJAACCGQAAXlwAgGJcAIBqXACAh2wDAIYcHAC47QAAufEAALr1AAC7jQAAvJUAAL2BAAC+gQAAv70AALAJAwCxCQMAsu0AALPhAAC04QAAteEAALblAAC32QAAblwAgHJcAIB2XACAs7ECAHpcAIC13QIAttUCAH5cAICCXACAhlwAgLrBAgC7wQIAvDUBAL05AQC+KQEAvykBAKaNAgCKXACAjlwAgKWFAgCSXACAo+kCAJZcAICaXACArnEBAK9xAQCsbQEArWEBAKqZAgCrmQIAnlwAgKJcAICmXACA4YQGAKpcAIDjJAYArlwAgOGUAQCyXACA4ywAAL7oHQC2XACAulwAgO/IAACE/B0AvvAcAL5cAIDvSAcAwlwAgMZcAIDKXACAzlwAgIEdAACAHQAA0lwAgIIFAACGQBwAh8QcANpcAIDeXACA4lwAgOZcAIDqXACA7lwAgKi1HgCpBR8Aqg0fAKsFHwCsAR8ArQkfAK45HwCvOR8A1lwAgPJcAID2XACA+lwAgP5cAIACXQCABl0AgApdAIC4yR8AudUfALrRHwC76R8AvPkfAL3tHwC+mR8Av5kfALAlHwCxLR8AsjkfALM1HwC0LR8AtQ0fALYFHwC3/R8As4UfAA5dAIASXQCAFl0AgBpdAIC2iR8AtYkfAB5dAIC76R8AuuEfACJdAIAmXQCAv8kfAL7pHwC94R8AvO0fACpdAICjwR8ALl0AgDJdAICmzR8ANl0AgDpdAIClzR8AqqUfAKutHwA+XQCAQl0AgK6tHwCvjR8ArKkfAK2lHwCo6R4AqekeAKr5HgCr+R4ArOkeAK3pHgCuPQEArzUBAID5AQCBzQEAgsUBAIRgAgBGXQCASl0AgIdoAQCGnAAAuNEBALnZAQC64QEAu+EBALyRAQC9nQEAvpUBAL+JAQCwTQEAsVUBALJdAQCzVQEAtE0BALXxAQC28QEAt/EBALNxHgBOXQCAUl0AgFZdAIBaXQCAtmkeALVhHgBeXQCAu5EBALqJAQBiXQCAZl0AgL81AQC+iQEAvYEBALyJAQBqXQCAZlwAgKM5HgBuXQCApSkeAHJdAIB2XQCApiEeAHpdAIB+XQCAq9kBAKrBAQCtyQEArMEBAK99AQCuwQEAgl0AgIZdAICKXQCAjl0AgJJdAICWXQCAml0AgJ5dAICiXQCApl0AgKpdAICuXQCAsl0AgLpdAIC+XQCAvnADAOHkHgCESAIA4+gfAIQABACAeQAAgXkAAIJpAADCXQCAhsAEAIdEAwDGXQCAyl0AgM5dAIDSXQCA7yAfANZdAIDaXQCA3l0AgOJdAIDvSAIA5l0AgOpdAIDuXQCA8l0AgL7oBAD2XQCA+l0AgP5dAIACXgCA4ZABAAZeAIDj6AIAs0kDAApeAIAOXgCAEl4AgBZeAIC2SQMAtUkDABpeAIC7LQMAuiUDAB5eAIAiXgCAvxUDAL4VAwC9IQMAvCkDAKg1AgCpgQIAqoECAKuBAgCsgQIArYkCAK6xAgCvsQIAgP0BAIHNAQCCxQEAKl4AgIaQBACHBAUALl4AgIRwBAC4SQEAuUkBALpZAQC7WQEAvEkBAL1JAQC+eQEAv3kBALChAgCxqQIAsr0CALO1AgC0kQIAtZECALZ5AQC3eQEAMl4AgDZeAIA6XgCAPl4AgEJeAIBGXgCASl4AgO/QHgC+6AQA4VweAE5eAIDjkAAAUl4AgFZeAIBaXgCAXl4AgKNJAgBiXgCAZl4AgGpeAIBuXgCApkkCAKVJAgByXgCAqy0CAKolAgB2XgCAel4AgK8VAgCuFQIArSECAKwpAgCoNQYAqT0GAKpVBgCrZQYArH0GAK1lBgCubQYAr2EGACZeAIB+XgCAgl4AgIZeAICADQAAgbEAAIKxAACKXgCAuOkGALnpBgC6+QYAu/UGALyVBgC9nQYAvpUGAL+NBgCw4QYAseEGALLhBgCz/QYAtOUGALXtBgC25QYAt9kGALPdBgCOXgCAkl4AgJZeAICaXgCAtuUGALX1BgCeXgCAuyUGALolBgCGmAAAh6wAAL8pBgC+IQYAvSkGALw1BgCiXgCAo5kGAKZeAICqXgCApqEGAK5eAICyXgCApbEGAKphBgCrYQYAtl4AgLpeAICuZQYAr20GAKxxBgCtbQYAqC0GAKk9BgCqiQYAq4kGAKyZBgCtmQYArokGAK+JBgC+XgCAwl4AgMZeAIDKXgCAzl4AgNJeAIDWXgCA2l4AgLiNBgC5lQYAupUGALulBgC8vQYAvXEBAL5xAQC/cQEAsPkGALHNBgCy2QYAs9kGALTJBgC1yQYAtr0GALe1BgCzAQYA3l4AgOJeAIDmXgCA6l4AgLYZBgC1EQYA7l4AgLsJBgC6PQYA8l4AgPZeAIC/DQYAvg0GAL0NBgC8DQYA+l4AgKNFBgC2XQCA/l4AgKZdBgACXwCAhFgAAKVVBgCqeQYAq00GAL5oAQAGXwCArkkGAK9JBgCsSQYArUkGAIDBAwCByQMAgt0DAKPNAgAKXwCApdkCAKbNAgAOXwCAhoANAIeUAwCqxQIAqw0DAKwVAwCtHQMArhUDAK8NAwDhnBcA4xgGAOMUAwDhNAYA7xgCABJfAIAWXwCAGl8AgOPQAgAeXwCA4VACACJfAIAmXwCA7ywGAO/kJQAqXwCArE0CAK1RAgCuUQIAr2UCAKgBAgCpCQIAqlkCAKtVAgCE7A0ALl8AgDJfAIA2XwCAvvgNADpfAIA+XwCAQl8AgLxRAwC9WQMAvmEDAL9hAwC47QMAuVEDALpRAwC7UQMAtM0DALXVAwC23QMAt9UDALAdAgCx1QMAst0DALPVAwDjyAAARl8AgOG4AQBKXwCAhFQPAE5fAIBSXwCAVl8AgKHpAgCgFQYAo6UDAKINAwDvIAAAWl8AgF5fAIBiXwCAZl8AgGpfAICFNCYAs40DAG5fAIC1mQMAto0DAHJfAICGwA8Ah5QNALqFAwC7TQIAvFUCAL1dAgC+VQIAv00CAHpfAIB+XwCAgl8AgIZfAICKXwCAjl8AgI/d6wDvxAYAvuAPAOGMBgCSXwCA44AGAID1AACB5QAAguUAAJZfAICZbR8AmMUfAJvJGwCaeRoAnXUaAJzFGwCf+QcAnhkGAJFpFgCQsesAk20XAJLNFwCV0RMAlGkSAJdREgCWzRMAg1XkAIJB5AB2XwCAml8AgIeNHQCGkRgAhTkYAISVGQCLERwAigUcAJ5fAICiXwCAj4UVAI6ZEACNORAAjJUdAJNRFACSRRQApl8AgKpfAICXYQkAlnUIAJWdCQCU+RUAm0EMAJqtDQCuXwCAsl8AgLZfAIC6XwCAvl8AgJzxDAChbQ0Awl8AgKMBBACihQAApZkEAKSRBACnGTgApsUFAKkJOACoKTgAq4k8AKoBPACtATAArB08AK8pMACunTAAseE0ALABNACzASgAsv00ALXZKAC00SgAxl8AgMpfAIDOXwCA0l8AgNZfAIDaXwCAgB0AAIEJAACC2QEA3l8AgKgRDwCpGQ8Aql0PAKtVDwCsTQ8ArXEPAK51DwCvbQ8A4l8AgOpfAICGiAAAhxABAO5fAIDyXwCA9l8AgPpfAIC4TQ4AuVEOALpRDgC7UQ4AvGUOAL1tDgC+ZQ4Avx0OALAdDwCxwQ8AssEPALPBDwC0xQ8Atc0PALbFDwC3eQ4As9UPAP5fAIACYACABmAAgApgAIC28Q8AtcUPAA5gAIC7BQ8AutkPABJgAIAWYACAvwkPAL4BDwC9FQ8AvBUPABpgAICjkQ8AHmAAgCJgAICmtQ8AJmAAgCpgAIClgQ8Aqp0PAKtBDwAuYACAMmAAgK5FDwCvTQ8ArFEPAK1RDwCogQ0AqYENAKqBDQCrgQ0ArIENAK2BDQCusQ0Ar6ENADZgAIA6YACAPmAAgEJgAIBGYACAgrkAAIG9AACAvQAAuDUCALk9AgC6zQIAu5UCALyNAgC9tQIAvr0CAL+1AgCwbQIAsU0CALJFAgCzJQIAtD0CALUdAgC2FQIAtw0CAEpgAIBOYACAswENAFJgAIC1AQ0AWmAAgISUAwC2CQ0AviwEAF5gAIC7gQIAuqECAL35AgC8mQIAv9ECAL7xAgBiYACAZmAAgGpgAICjRQ0AbmAAgKVFDQCmTQ0AcmAAgIbgBACHpAQAquUCAKvFAgCs3QIArb0CAK61AgCvlQIAqCUCAKk1AgCqPQIAqzUCAKwtAgCtkQIArpECAK+RAgB2YACAemAAgH5gAICCYACAzAAAAIZgAICKYACAjmAAgLiZAgC5rQIAuqUCALttAQC8dQEAvX0BAL51AQC/bQEAsPECALH5AgCywQIAs8ECALSxAgC1vQIAtrUCALepAgCSYACA44QOAJZgAIDh9A4AmmAAgJ5gAICiYACApmAAgIQgBQCqYACArmAAgLJgAIC2YACA7+wOALpgAIC+YACAs/UCAMJgAICG6AQAh4wEAL5cBAC2UQIAteUCAMpgAIC7fQIAunUCAM5gAIDSYACAvzkCAL41AgC9VQIAvFUCAKM1BQBWYACAxmAAgNZgAIDaYACAppEFAKUlBQDeYACAq70FAKq1BQDiYACA5mAAgK/5BQCu9QUArZUFAKyVBQCA+QcAgfkHAIKNBwCzjQYA6mAAgLWdBgC2iQYA7mAAgPJgAID2YACAuk0HALtFBwC8XQcAvUEHAL5BBwC/QQcA+mAAgP5gAIDmXwCAAmEAgAZhAIAKYQCADmEAgBJhAICoNQYAqQEGAKppBgCraQYArHkGAK1lBgCuZQYAr50HALDlBwCx7QcAsuUHALP5BwC06QcAtekHALZZBwC3VQcAuHEHALlxBwC6cQcAu3EHALxVBwC9XQcAvlUHAL9NBwCjwQcAFmEAgBphAIAeYQCAImEAgKbFBwCl0QcAJmEAgKsJBgCqAQYAKmEAgC5hAICvDQYArg0GAK0NBgCsEQYAgGkAAIFpAACCBQAAMmEAgL6YAQCEmAEANmEAgDphAICGADwAh8QBAD5hAIBCYQCARmEAgEphAIBOYQCAUmEAgKhdBgCpbQYAqmUGAKuBAQCsgQEArYkBAK6xAQCvsQEAVmEAgFphAIBeYQCAYmEAgGZhAIBqYQCAbmEAgHJhAIC4VQEAuV0BALpVAQC7yQAAvNkAAL3ZAAC+yQAAv8EAALCxAQCxuQEAsokBALOJAQC0cQEAtXEBALZ1AQC3bQEAs+0FAHZhAIB6YQCAfmEAgIJhAIC2CQIAtQkCAIZhAIC7fQIAunUCAIphAICOYQCAv7UCAL61AgC9XQIAvF0CAL5gAgCjqQUAkmEAgJZhAICmTQIAmmEAgJ5hAIClTQIAqjECAKs5AgCiYQCAhOADAK7xAgCv8QIArBkCAK0ZAgC+iDwAqmEAgKotAwCrJQMArD0DAK0lAwCuLQMAryUDAID1AACB/QAAgsEAAKPBAwCuYQCApcEDAKbBAwCyYQCAhmA8AIdUAwC2YQCAumEAgL5hAIDjqAIAwmEAgOGkAQDGYQCA71wCAMphAIDOYQCA0mEAgNZhAIDaYQCA3mEAgOJhAIDjjAcA5mEAgOE8BADqYQCA7mEAgPJhAID2YQCAhCACAPphAID+YQCAAmIAgAZiAIDvbAcACmIAgA5iAICzLQIAhEQ9ABJiAIAaYgCAHmIAgLYtAgC1LQIAImIAgLvJAgC6wQIAJmIAgCpiAIC/yQIAvsECAL3JAgC80QIA4XgHAOPAAADjOAYA4VwGAICpAACBqQAAgtEAAC5iAIAyYgCANmIAgL6kPAA6YgCAPmIAgO8cAADvkAYAQmIAgIZgPACHBD0ARmIAgLNxAQBKYgCAtRkBALYJAQBOYgCAUmIAgFZiAIC6AQEAuwEBALwBAQC9AQEAvgEBAL8BAQCohT4AqbU+AKq1PgCrxT4ArN0+AK3FPgCuwT4Ar/0+AFpiAIBeYgCAYmIAgGZiAIBqYgCAbmIAgHJiAIB2YgCAuFE/ALlRPwC6UT8Au1E/ALx1PwC9fT8AvnU/AL9tPwCwiT4AsYk+ALKZPgCzmT4AtIk+ALWJPgC2eT8At3U/AKZhAICjOT4AemIAgBZiAICmQT4AfmIAgIJiAIClUT4Aqkk+AKtJPgCGYgCAimIAgK5JPgCvST4ArEk+AK1JPgCASQAAgVEAAIJRAACzkT8AjmIAgLW5PwC2RT8AkmIAgIZAAACHBAMAukU/ALtdPwC8TT8AvT0/AL4pPwC/IT8AqE0+AKlVPgCqVT4Aq2U+AKx9PgCtiT4Arrk+AK+5PgCWYgCAmmIAgJ5iAICiYgCApmIAgKpiAICuYgCAsmIAgLhhAQC5YQEAumEBALthAQC8YQEAvWEBAL5hAQC/YQEAsM0+ALHVPgCy1T4As6U+ALShPgC1qT4Atpk+ALeZPgCj3T4AtmIAgLpiAIC+YgCAwmIAgKYJPgCl9T4AxmIAgKsRPgCqCT4AymIAgM5iAICvbT4ArmU+AK1xPgCsAT4A0mIAgNZiAIDaYgCA3mIAgOJiAIDmYgCA6mIAgO5iAICAOQAAgTkAAIIFAADyYgCAvrgBAIS4AQD6YgCA/mIAgKitAgCp1QIAqtUCAKstAwCsNQMArT0DAK41AwCvLQMAAmMAgAZjAIAKYwCADmMAgBJjAIAWYwCAGmMAgB5jAIC46QMAuekDALqJAwC7iQMAvJkDAL2ZAwC+iQMAv4kDALBVAwCxXQMAslUDALPpAwC0+QMAtfkDALbpAwC34QMAs10CACJjAICGKAQAh8wDACZjAIC2vQMAtb0DACpjAIC7mQMAupEDAC5jAIAyYwCAvz0DAL49AwC9PQMAvIEDAIUAFACjGQIANmMAgDpjAICm+QMAPmMAgEJjAICl+QMAqtUDAKvdAwBGYwCASmMAgK55AwCveQMArMUDAK15AwDjVD4A4dw/AOHQPgDjPD4ATmMAgO8cAABSYwCAVmMAgFpjAIDjwAAAXmMAgOHUAQDvYD4AYmMAgGpjAIDvRD8AgGEAAIFtAACCfQAAhAAFAIbwBACHnAUAvhAFAG5jAIByYwCAdmMAgHpjAIB+YwCAgmMAgIZjAICKYwCAjmMAgLiJPQC5iT0Aupk9ALuRPQC8uT0Avbk9AL7RPQC/0T0AsAU+ALENPgCyBT4Asx0+ALQFPgC1DT4AtgU+ALe5PQConT4Aqa0+AKqlPgCrvT4ArKU+AK2tPgCupT4Ar30+AISsBAC+rAQAkmMAgJZjAICaYwCAnmMAgKJjAICmYwCAqPkFAKn5BQCqKQYAqykGAKw5BgCtOQYArikGAK8pBgBmYwCAqmMAgK5jAICyYwCAtmMAgLpjAIC+YwCAwmMAgLiNBgC5kQYAupEGALulBgC8vQYAvUUHAL5BBwC/QQcAsFkGALFZBgCy7QYAs/0GALTtBgC13QYAttUGALe1BgCzoQYAxmMAgMpjAIDOYwCA0mMAgLa5BgC1sQYA2mMAgLudBgC6nQYA1mMAgPZiAIC/GQYAvikGAL0pBgC8OQYAglEAAKPlBgCAQQAAgUEAAKb9BgDeYwCA4mMAgKX1BgCq2QYAq9kGAIZIAACHbAAArm0GAK9dBgCsfQYArW0GAKg5BgCpWQYAqmkGAKtpBgCseQYArXkGAK5pBgCvaQYA5mMAgOpjAIDuYwCA8mMAgPZjAID6YwCA/mMAgAJkAIC4ZQEAuW0BALplAQC7fQEAvGUBAL1tAQC+ZQEAv9kBALAZBgCxGQYAsoEGALOBBgC0gQYAtYEGALaBBgC3gQYAs+EGAAZkAIAKZACADmQAgBJkAIC2+QYAtfEGABZkAIC73QYAut0GABpkAIAeZACAv0UGAL5FBgC9VQYAvFUGACJkAICjpQYAJmQAgCpkAICmvQYALmQAgDJkAICltQYAqpkGAKuZBgA2ZACAOmQAgK4BBgCvAQYArBEGAK0RBgConQIAqdECAKrRAgCrLQMArDUDAK09AwCuNQMAry0DAD5kAIBCZACAvmQCAEpkAIBOZACAUmQAgFZkAIBaZACAuOkDALnpAwC6iQMAu4UDALydAwC9gQMAvoEDAL+1AwCwVQMAsV0DALJVAwCz6QMAtPkDALX5AwC26QMAt+EDAIBtAwCBpQAAgq0AALNVAgBeZACAtbEDALaxAwBiZACAhOACAGZkAIC6nQMAu5UDALyNAwC9MQMAvjEDAL8xAwCjGQIAamQAgIVwaQBuZACAcmQAgKb9AwCl/QMAdmQAgKvZAwCq0QMAhkgMAIe8AwCvfQMArn0DAK19AwCswQMAemQAgH5kAICCZACAhmQAgO+wBgDvxAMAimQAgI5kAIDjfAYA45QDAOG4BwDh3AEAkmQAgJZkAICaZACAnmQAgKJkAICmZACAhEQCAL5YDQCADQAAgTUAAII9AACqZACArmQAgLJkAICGyAwAh1wNALpkAIC+ZACAwmQAgMZkAIDKZACAzmQAgNJkAIDWZACA2mQAgN5kAIDiZACA74AGAISsDQDh7AYA5mQAgONcBgDqZACA7mQAgPJkAID2ZACAs/UBAPpkAID+ZACAAmUAgAZlAIC2RQEAteUBAAplAIC7LQEAuiEBAA5lAIASZQCAv/UAAL71AAC9JQEAvC0BAKgtDgCpNQ4Aqj0OAKs1DgCsLQ4ArYUOAK6FDgCvuQ4AtmQAgBZlAIAaZQCAHmUAgIAZAACBGQAAggUAACJlAIC4WQ8AuVkPALp5DwC7eQ8AvGkPAL1pDwC+GQ8AvxkPALClDgCxqQ4AsrkOALOxDgC0cQ8AtXEPALZxDwC3cQ8Apb0OAL6IAwAqZQCAph0OACZlAIAuZQCAo60OADJlAICtfQ4ArHUOAK+tDwCurQ8ARmQAgDZlAICrdQ4AqnkOALO5DwA6ZQCAhmgAAIcMAwA+ZQCAtlEPALVZDwBCZQCAu3UPALp1DwBGZQCASmUAgL9FDwC+RQ8AvVEPALxlDwCocQ4AqXEOAKpxDgCrcQ4ArJEOAK2RDgCukQ4Ar5EOAE5lAIBSZQCAVmUAgFplAIBeZQCAYmUAgGZlAIBqZQCAuIUOALmNDgC6hQ4Au50OALyNDgC9vQ4AvrUOAL95AQCw8Q4AsfEOALLxDgCzxQ4AtMEOALXBDgC2wQ4At8EOAKP5DgBuZQCAcmUAgHZlAIB6ZQCAphEOAKUZDgB+ZQCAqzUOAKo1DgCCZQCAhmUAgK8FDgCuBQ4ArREOAKwlDgCADQAAgRUAAIIdAACKZQCAjmUAgJJlAICElAEAvpQBAIZABwCH5AAAmmUAgJ5lAICiZQCApmUAgKplAICuZQCAqIkCAKmRAgCqlQIAq7kCAKzVAgCtxQIArsUCAK/1AgCyZQCAtmUAgLplAIC+ZQCAvnwDAMJlAIDGZQCAymUAgLh9AwC5wQMAusEDALvBAwC8wQMAvckDAL7xAwC/8QMAsI0CALFFAwCyTQMAs0UDALRdAwC1RQMAtk0DALdFAwCzHQIAzmUAgNJlAIDWZQCA2mUAgLZFAgC1XQIA3mUAgLuBAwC6SQIA4mUAgOZlAIC/gQMAvpkDAL2RAwC8mQMA6mUAgKNZAgDuZQCA8mUAgKYBAgD2ZQCA+mUAgKUZAgCqDQIAq8UDAP5lAIACZgCArt0DAK/FAwCs3QMArdUDAIDZAQCB7QEAguUBAO+4DgAKZgCA4cQBAISYAgDj1AAADmYAgL7sBAASZgCA7wgAABZmAIDhxA8AGmYAgONkDgCGAAUAh2gFAB5mAICzvQIAImYAgLWtAgC2pQIAJmYAgCpmAIAuZgCAukEBALtBAQC8RQEAvU0BAL5FAQC/+QEAMmYAgDZmAIA6ZgCAPmYAgEJmAIBGZgCASmYAgO/gAQCEbAQA4dQOAE5mAIDjHA4AUmYAgFZmAIBaZgCAXmYAgKMxAgBiZgCAhCQHAGZmAIBqZgCApikCAKUhAgBuZgCAq80BAKrNAQByZgCAemYAgK91AQCuyQEArcEBAKzJAQCo6QUAqekFAKr5BQCr+QUArOkFAK3pBQCuOQYArzkGAAZmAICCzQcAgfUHAID9BwB2ZgCAfmYAgIYYAwCHkAMAuNEGALnZBgC64QYAu+EGALyRBgC9nQYAvpUGAL+JBgCwSQYAsUkGALJdBgCzVQYAtE0GALXxBgC28QYAt/EGALDhBwCx4QcAsgkHALMJBwC0GQcAtRkHALYJBwC3CQcAuDkHALkNBwC6GQcAuxkHALwJBwC9CQcAvn0HAL9xBwCCZgCAlmUAgIZmAICKZgCAjmYAgJJmAICWZgCAmmYAgKjxBwCpxQcAqsEHAKvdBwCsyQcArb0HAK6pBwCvoQcAsykGAJ5mAICiZgCApmYAgKpmAIC2XQYAtSEGAK5mAIC7RQYAukUGALJmAIC2ZgCAv70GAL69BgC9vQYAvL0GALpmAICjbQYAvmYAgMJmAICmGQYAxmYAgMpmAIClZQYAqgEGAKsBBgDOZgCA0mYAgK75BgCv+QYArPkGAK35BgCobQYAqbEBAKpJAQCrRQEArF0BAK1FAQCuTQEAr0UBANZmAICCHQAAgR0AAIAdAADaZgCA3mYAgOJmAIC+VAEAuIEAALmNAAC6hQAAu5kAALyJAAC9vQAAvrUAAL99AACwPQEAseEAALLhAACz4QAAtOEAALXpAAC20QAAt9EAALsFAwC62QIAhiwCAIcsAwC/DQMAvgUDAL0VAwC8FQMAs+ECAOpmAIDuZgCAhCwDAPJmAIC25QIAtfUCAPZmAICqnQIAq0EDAPpmAID+ZgCArkEDAK9JAwCsUQMArVEDAAJnAICjpQIABmcAgApnAICmoQIADmcAgBJnAIClsQIAqakAAKihAACrtQAAqr0AAK3dAACs3QAAr/EAAK79AAC+LBwAFmcAgBpnAIAeZwCAImcAgCZnAIAqZwCALmcAgLl9AAC4fQAAu80BALrNAQC93QEAvN0BAL/NAQC+zQEAsZUAALCJAACzTQAAspUAALVdAAC0XQAAt00AALZNAAAyZwCANmcAgDpnAIA+ZwCAQmcAgEZnAIBKZwCATmcAgIA5AACBOQAAggUAAFJnAIBaZwCAXmcAgIf4AgCGfB0A4bgEAL7IHADjQAYAYmcAgGZnAIBqZwCAbmcAgHJnAIB2ZwCAemcAgH5nAICCZwCAhmcAgIpnAIDvsAcAjmcAgJJnAICWZwCAmmcAgO/IAACeZwCAomcAgKZnAIDvQAYAqmcAgOH8BgCuZwCA4xwGALJnAIDhlAEAtmcAgONkBgCAEQAAgRkAAIIpAACz/QEAumcAgLWdAQC2lQEAvmcAgMJnAICEbB0AuoUBALuZAQC8iQEAvVEBAL5RAQC/UQEAozEeAFZnAIDGZwCAymcAgM5nAICmWR4ApVEeANJnAICrVR4AqkkeAIYIAwCHbAMAr50eAK6dHgCtnR4ArEUeANZnAICzCR8A2mcAgN5nAIC2CR8A4mcAgOZnAIC1CR8AugUfALsNHwDqZwCA7mcAgL4FHwC/CR8AvBUfAL0NHwCw5R8Ase0fALLlHwCz/R8AtOUfALXpHwC2GR8AtxkfALgpHwC5NR8Auj0fALs1HwC8ER8AvR0fAL4JHwC/BR8A8mcAgPZnAIDmZgCA+mcAgP5nAIACaACABmgAgApoAICo0R8AqdEfAKqlHwCrvR8ArKUfAK2tHwCupR8Ar50fAKNNHgAOaACAEmgAgBZoAIAaaACApk0eAKVNHgAeaACAq0keAKpBHgAiaACAJmgAgK9NHgCuQR4ArUkeAKxRHgCADQAAgRUAAIIdAAAqaACALmgAgDJoAICEtAEAvrQBAL/oAQA6aACAhkgHAIc0AACEvAYAPmgAgEJoAIC+tAYAqI0BAKmVAQCqlQEAq80BAKzZAQCt2QEArs0BAK/FAQBGaACASmgAgE5oAIBSaACAVmgAgFpoAIBeaACAYmgAgLgdAQC5wQAAusEAALvBAAC8wQAAvckAAL7xAAC/8QAAsIkBALGJAQCyKQEAsykBALQ9AQC1JQEAti0BALclAQC7bQIAum0CAGZoAIBqaACAv8ECAL7ZAgC93QIAvN0CALM9AgBuaACAcmgAgHZoAICE/AYAtnkCALVxAgB6aACAqikCAKspAgB+aACAgmgAgK6dAgCvhQIArJkCAK2ZAgCGaACAo3kCAIpoAICOaACApj0CAJJoAICWaACApTUCAIJtJwCDjSoAhqgFAIdsAwCGmS4Ah80vAIQRLgCFmS4AiiESAIspEgCaaACAnmgAgI6RFgCPHRYAjBESAI0RFgCScRoAk+UaAKJoAIDvlHYAlvEeAJflHgCUSRoAlRkeAJopAgCb4QIAqmgAgK5oAICyaACA4SASAJzxAgDjIBYAnyEfAJ7BHwCdmRsAnC0bAJuhGwCavRcAmTkXAJixFwCXiRMAlqkTAJWpEwCUdS4AkzkvAJIxLwCRsS8AkDUrAI+tJgDjeB8A0gAAAOFcHwCCmQEAtmgAgIDxAQCB8QEAvqgHALpoAIC+aACAwmgAgIS8BgDvLB8AxmgAgMpoAIDhpB4A48wAAON8HgDhvAEAzmgAgNJoAIDWaACAhJwGANpoAIC+bAYA3mgAgOJoAIDmaACA7xAAAO8EHgDqaACA7mgAgPJoAID2aACA+mgAgP5oAIACaQCABmkAgAppAICAPQAAgQkAAILJBwAOaQCAo/kDAKLxAwChMQMAoM0fALBJcQCxAXwAsgl8ALMhfQC0AXgAtRV4ADZoAICmaACAEmkAgL4oDgCGDAAAh4wDABZpAIAaaQCAHmkAgCJpAIAmaQCAoV0AAKJVAACjfQAApAEMAKUVDACm9QwApwEIAKghCACpxQgAqgF0AKsJdACsAXQArR11AK55cACveXAAqOUFAKnxBQCq8QUAqy0FAKw1BQCtPQUArjUFAK8tBQAqaQCALmkAgDJpAIA2aQCAOmkAgD5pAIBCaQCARmkAgLj9BgC5jQYAuoUGALutBgC8uQYAvbkGAL6tBgC/pQYAsFUFALFdBQCyVQUAs+UGALT9BgC10QYAttEGALfRBgCzeQQASmkAgE5pAIBSaQCAVmkAgLa9BAC1vQQAWmkAgLuZBAC6kQQAXmkAgGJpAIC/FQcAvjkHAL0xBwC8gQQAZmkAgKM9BABqaQCAbmkAgKb5BAByaQCAdmkAgKX5BACq1QQAq90EAHppAIB+aQCArn0HAK9RBwCsxQQArXUHAKhpBwCpaQcAqnkHAKvZBgCs9QYArf0GAK71BgCv5QYAgMkAAIHJAACCBQAAgmkAgIZwDwCHNAAAimkAgI5pAIC4fQYAuQUGALoNBgC7BQYAvB0GAL0FBgC+DQYAvwUGALCdBgCxdQYAsn0GALN1BgC0UQYAtV0GALZVBgC3TQYAs/EEAJJpAICWaQCAmmkAgJ5pAIC2fQUAtX0FAKJpAIC7sQUAulkFAKZpAICqaQCAv5kFAL6VBQC9oQUAvKkFAK5pAICjtQQAsmkAgLZpAICmOQUAumkAgL5pAIClOQUAqh0FAKv1BQDCaQCAxmkAgK7RBQCv3QUArO0FAK3lBQCpuQIAqLECAKvJAgCqsQIArTUCAKw1AgCvNQIArjUCAMppAIDOaQCA0mkAgNZpAIDaaQCA3mkAgOJpAIDmaQCAuekDALjZAwC7iQMAuuEDAL2dAwC8nQMAv4EDAL6JAwCxVQIAsFUCALNVAgCyVQIAtfkDALTxAwC36QMAtvEDALM9AwDqaQCA7mkAgPJpAID6aQCAtrEDALW5AwD+aQCAu5UDALqVAwCGiAwAh6ANAL85AgC+MQIAvYUDALyFAwACagCAo3kDAAZqAIAKagCApvUDAA5qAIASagCApf0DAKrRAwCr0QMAFmoAgBpqAICudQIAr30CAKzBAwCtwQMAgIUAAIGNAACChQAA79AGAOOwBwDj9AQA4QgHAOHsBADvOAYA7yAEAL6kDAAeagCAImoAgOGEAQAmagCA49wGACpqAIAuagCAhMANALPJAQAyagCAtdkBALbJAQA2agCAOmoAgD5qAIC6xQEAu60BALy5AQC9uQEAvq0BAL+lAQCwLQ4AsUUOALJBDgCzQQ4AtEUOALVNDgC2cQ4At3EOALiBDgC5gQ4AuoEOALuBDgC8gQ4AvYEOAL6BDgC/gQ4A9mkAgEJqAIBGagCASmoAgIZpAIBOagCAUmoAgFZqAICo2Q0AqdkNAKptDgCrZQ4ArH0OAK1lDgCuZQ4Ar1UOAKOFDgCCLQAAgRUAAIAdAABaagCApoUOAKWVDgBeagCAq+EOAKqJDgBiagCAZmoAgK/pDgCu4Q4ArfUOAKz1DgBqagCAs4UPAIZoAACHHAMAtoUPAG5qAIByagCAtZEPALqNDwC7SQ8AdmoAgHpqAIC+MQ8AvzEPALxJDwC9RQ8AqBEOAKkZDgCqSQ4Aq0UOAKxdDgCtQQ4ArkEOAK91DgB+agCAgmoAgIZqAICKagCAjmoAgJJqAICWagCAmmoAgLihDgC5oQ4Aug0BALsFAQC8HQEAvQEBAL4BAQC/AQEAsA0OALHJDgCy2Q4As9UOALSxDgC1sQ4AtqkOALehDgCjwQ4AnmoAgKJqAICmagCAqmoAgKbBDgCl1Q4ArmoAgKsNDgCqyQ4AsmoAgLZqAICvdQ4ArnUOAK0BDgCsDQ4AumoAgL5qAIDCagCAxmoAgIANAACBNQAAgj0AAMpqAIDOagCA0moAgISEAQC+hAEAhjAHAIf4AADaagCA3moAgKjBAgCp0QIAqtECAKvlAgCs/QIArTUDAK49AwCvNQMA4moAgOZqAIDqagCA7moAgPJqAID2agCA+moAgP5qAIC40QMAudkDALrhAwC74QMAvJEDAL2RAwC+kQMAv5EDALBNAwCxVQMAsl0DALNVAwC0TQMAtfEDALbxAwC38QMAu7EDALqpAwACawCAvoQDAL8VAwC+qQMAvaEDALypAwCzeQIABmsAgAprAIAOawCAEmsAgLaVAwC1VQIAFmsAgKrtAwCr9QMAGmsAgB5rAICu7QMAr1EDAKztAwCt5QMAImsAgKM9AgAmawCAKmsAgKbRAwAuawCAMmsAgKURAgA2awCAgiEAAIEVAACAFQAA7wQAAISUAgA6awCAPmsAgOPYAABCawCA4fgBAEprAIBOawCAUmsAgFZrAIBaawCAhmAFAIcIBQBeawCAs20BAGJrAIC1fQEAtnUBAGZrAIBqawCAbmsAgLpRAQC7UQEAvPkBAL3RAQC+0QEAv9EBAHJrAICjpQEAdmsAgHprAICmvQEAfmsAgIJrAICltQEAqpkBAKuZAQCGawCAimsAgK4ZAQCvGQEArDEBAK0ZAQCOawCA4fQOAJJrAIDjFA4A9AAAAOF8DACWawCA41AKAJprAICeawCAviAEAO8wDQCiawCApmsAgIQ0BADvrA4AsDkGALE5BgCygQYAs6kGALS5BgC1uQYAtqkGALehBgC46QYAuekGALrJBgC7xQYAvN0GAL3BBgC+wQYAvz0HAEZrAICCHQAAgR0AAIAdAACqawCArmsAgLJrAIDWagCAqJkFAKmZBQCqSQYAq0kGAKxZBgCtWQYArkkGAK9JBgCorQcAqbUHAKq9BwCrtQcArK0HAK3dBwCuyQcAr8EHALZrAIC6awCAhogDAIcQAwC+awCAwmsAgMZrAIDKawCAuG0HALkFBwC6AQcAuxUHALwxBwC9MQcAvikHAL8pBwCwgQcAsYEHALJpBwCzZQcAtH0HALVhBwC2YQcAt1UHALM1BgDOawCA0msAgNZrAIDaawCAtl0GALUlBgDeawCAu0UGALpFBgDiawCA5msAgL+lBgC+uQYAvbEGALy9BgDqawCAo3EGAO5rAIDyawCAphkGAPZrAID6awCApWEGAKoBBgCrAQYA/msAgAJsAICu/QYAr+EGAKz5BgCt9QYAqCUBAKk1AQCqPQEAqzUBAKwtAQCtkQAArpEAAK+RAAAGbACACmwAgA5sAIASbACAFmwAgIK9AwCBvQMAgL0DALiZAAC5rQAAuqUAALttAAC8dQAAvX0AAL51AAC/bQAAsPEAALH5AACywQAAs8EAALSxAAC1vQAAtrUAALepAAAabACAHmwAgCJsAICEgAIAvhwCACpsAICG+HwAh8wCAISsAwAubACAMmwAgDZsAIA6bACAPmwAgEJsAIBGbACAs/UCAEpsAIBObACAkgAAAFJsAIC2UQMAteUCAFZsAIC7fQMAunUDAFpsAIBebACAvzkDAL41AwC9VQMAvFUDAKM1AgBibACAZmwAgGpsAIBubACAppEDAKUlAgBybACAq70DAKq1AwB2bACAemwAgK/5AwCu9QMArZUDAKyVAwC+wAMAfmwAgIJsAICGbACAgA0AAIE1AACCPQAAimwAgI5sAICSbACAhsh8AIcAAwCabACAnmwAgKJsAICmbACAqmwAgK5sAICybACAtmwAgLpsAIC+bACAwmwAgO/0AwCE7HwA4ZQBAMZsAIDjMAMAymwAgM5sAIDSbACA1mwAgLNpAQDabACA3mwAgOJsAIDmbACAtmEBALVpAQDqbACAuykBALohAQDubACA8mwAgL8dAQC+HQEAvSUBALwtAQD2bACA+mwAgP5sAICjpQEAAm0AgKWlAQCmrQEAvlR8AIaAfACH7HwAqu0BAKvlAQCs4QEArekBAK7RAQCv0QEACm0AgOGcBgCEBH8A4yQGAOPUBgAObQCA4TAEABJtAIDvlAcAgnUAAIFhAACAaQAAFm0AgBptAIAebQCA7+wGALiNfgC5lX4AupV+ALulfgC8vX4AvdF+AL7RfgC/0X4AsGV+ALFtfgCyeX4As3F+ALRZfgC1WX4Atr1+ALe1fgCoVX4AqWF+AKphfgCrYX4ArGF+AK1hfgCuYX4Ar2F+ACJtAICWbACAJmwAgCZtAIAGbQCAKm0AgC5tAIAybQCAqHF+AKlxfgCqcX4Aq3F+AKyRfwCtkX8ArpF/AK+RfwA2bQCAOm0AgD5tAIBCbQCARm0AgEptAIBObQCAUm0AgLiFfwC5jX8AuoV/ALudfwC8jX8Avb1/AL61fwC/XX8AsPF/ALHxfwCy8X8As8V/ALTBfwC1wX8AtsF/ALfBfwCz+X8AVm0AgFptAIBebQCAYm0AgLYRfgC1GX4AZm0AgLs1fgC6NX4Aam0AgG5tAIC/BX4AvgV+AL0RfgC8JX4AghUAAKO9fwCAYQAAgWEAAKZVfgBybQCAvpABAKVdfgCqcX4Aq3F+AHZtAIB6bQCArkF+AK9BfgCsYX4ArVV+AKhBfgCpUX4AqlV+AKt9fgCsZX4ArW1+AK75AQCv8QEAhgAAAIc0AQB+bQCAgm0AgIZtAICKbQCAjm0AgJJtAIC4dQEAuX0BALp1AQC7yQAAvNkAAL3ZAAC+yQAAv8EAALCVAQCxnQEAspUBALNNAQC0VQEAtV0BALZVAQC3TQEAs919AJZtAICabQCAnm0AgKJtAIC27X0Ate19AKZtAIC7WQIAulECAKptAICubQCAv5kCAL6RAgC9mQIAvEECALJtAICjmX0Atm0AgLptAICmqX0Avm0AgMJtAIClqX0AqhUCAKsdAgDGbQCAym0AgK7VAgCv3QIArAUCAK3dAgDObQCA0m0AgNZtAIDabQCAgB0AAIEJAACCOQAA3m0AgOJtAIC+AAQA6m0AgO5tAIDybQCA9m0AgPptAID+bQCAhIwDAAJuAICHCAMAhuwEAAZuAIDviAIACm4AgA5uAICEbAQA4zQCABJuAIDhVAEAFm4AgBpuAIAebgCAIm4AgKhtAgCprQIAqqUCAKu9AgCspQIAra0CAK6lAgCvGQEAvqwEACZuAIAqbgCALm4AgDJuAIA2bgCAOm4AgD5uAIC4DQEAuREBALoRAQC7JQEAvD0BAL3VAQC+3QEAv9UBALBpAQCxaQEAsnkBALNxAQC0WQEAtVkBALY5AQC3NQEAsy0CAEJuAIBGbgCASm4AgE5uAIC2LQIAtS0CAFJuAIC7rQEAuq0BAFpuAIBebgCAv50BAL6dAQC9pQEAvK0BAIBNAACBVQAAglUAAO9sAABibgCA7+x/AO+8fgBmbgCA4RB/AOPUfwDj2H4A4ex/AGpuAIDhTH4Abm4AgOMkfgDmbQCAVm4AgKsFBgCqBQYArQ0GAKwFBgCvNQYArjUGAIYAAwCHKAMAo4UFAHJuAIClhQUAdm4AgHpuAICmhQUAs/EGAH5uAICCbgCAhm4AgIpuAIC26QYAteEGAI5uAIC7vQYAur0GAJJuAICWbgCAv4kGAL6BBgC9iQYAvJUGAKgpBgCpKQYAqjkGAKs5BgCsKQYArSkGAK5dBgCvTQYAmm4AgJ5uAICibgCApm4AgKpuAICubgCAsm4AgLZuAIC46QcAuekHALr5BwC7+QcAvOkHAL3pBwC+XQcAv1UHALA5BgCxOQYAsgEGALMdBgC0BQYAtQ0GALYFBgC32QcAo7EHAIItAACBFQAAgB0AALpuAICmqQcApaEHAL5uAICr/QcAqv0HAMJuAICEpAIAr8kHAK7BBwCtyQcArNUHAL7MAQCzlQYAxm4AgMpuAIC2qQYAzm4AgNJuAIC1rQYAulkBALshAQCGyAAAhwwBAL4hAQC/KQEAvDEBAL0xAQCoKQYAqSkGAKpZBgCrUQYArGEGAK1tBgCutQEAr6kBAITgAQDWbgCA2m4AgN5uAIDibgCA5m4AgOpuAIDubgCAuGEBALlhAQC6YQEAu2EBALxhAQC9YQEAvmEBAL9hAQCw2QEAsaEBALKhAQCzoQEAtKEBALWpAQC2kQEAt5EBAKPRBQDybgCA9m4AgPpuAID+bgCApu0FAKXpBQACbwCAq2UCAKodAgAGbwCACm8AgK9tAgCuZQIArXUCAKx1AgAObwCAEm8AgBZvAIAabwCAHm8AgCJvAIAmbwCAKm8AgIA9AACBCQAAghkAAC5vAIAybwCAOm8AgL48AwA+bwCAhgAMAIcUAwBCbwCAs9UDAEZvAIC1PQMAtjUDAEpvAIBObwCAv4wKALoRAwC7EQMAvLUAAL29AAC+tQAAv60AAFJvAIDjdAEAVm8AgOG8AQBabwCAXm8AgGJvAIBmbwCAam8AgG5vAIBybwCAdm8AgHpvAIDvdAIAfm8AgIJvAICoTQIAqVECAKpRAgCrqQIArLkCAK25AgCuqQIAr6kCAIRsDQCGbwCAim8AgI5vAICSbwCAlm8AgJpvAIC+dA0AuG0BALkFAQC6DQEAuwUBALwdAQC9BQEAvg0BAL8FAQCw2QIAsdkCALJtAQCzZQEAtH0BALVlAQC2ZQEAt1UBAOG4AQDhUAcA47QAAON8BwCAqQAAgQkAAII5AACebwCAom8AgKpvAICubwCAsm8AgO4AAAC2bwCA7wAAAO9kBgCGYAwAh+QMAKORAgC6bwCApXkCAL5vAIDCbwCApnECAMZvAIDKbwCAq1UCAKpVAgCt+QEArPEBAK/pAQCu8QEApm8AgDZvAIDObwCA0m8AgNZvAIDabwCA3m8AgOJvAICoVQ4AqVkOAKqhDgCrvQ4ArK0OAK2VDgCu+Q4Ar/UOALCRDgCxkQ4AspEOALORDgC0sQ4AtbEOALaxDgC3sQ4AuJEOALmdDgC6lQ4Au0kPALxZDwC9WQ8AvkkPAL9JDwCzCQ4A5m8AgOpvAIDubwCA8m8AgLY1DgC1BQ4A9m8AgLt1DgC6dQ4A+m8AgP5vAIC/VQ4AvlUOAL1lDgC8ZQ4AAnAAgKNNDgAGcACACnAAgKZxDgAOcACAEnAAgKVBDgCqMQ4AqzEOAISkAwC+pAMArhEOAK8RDgCsIQ4ArSEOAKilDgCprQ4AqqUOAKu5DgCs3Q4ArcEOAK7BDgCv/Q4AgO0BAIHxAQCC8QEAFnAAgIaQAQCHtAEAGnAAgB5wAIC4yQEAuckBALrZAQC70QEAvPkBAL35AQC+mQEAv5UBALCFDgCxbQEAsmUBALN9AQC0ZQEAtW0BALZlAQC3+QEAsy0OACJwAIAmcACAKnAAgC5wAIC2QQ4AtVUOADJwAIC7qQEAukEOADZwAIA6cACAv6kBAL6hAQC9qQEAvLEBAD5wAICjaQ4AQnAAgEZwAICmBQ4ASnAAgE5wAIClEQ4AqgUOAKvtAQBScACAVnAAgK7lAQCv7QEArPUBAK3tAQCoOQMAqTkDAKqNAwCrhQMArJ0DAK2FAwCuhQMAr7UDAFpwAIBecACAYnAAgGZwAIBqcACAbnAAgHJwAIB2cACAuGEAALlhAAC6YQAAu2EAALxhAAC9YQAAvmEAAL9hAACwzQMAsaUDALKhAwCzoQMAtKUDALWtAwC2kQMAt5EDAIANAACBEQAAghEAAHpwAIDv9AIAfnAAgIJwAIC+HAMA4xQCAISIAgDhgAEAinAAgI5wAICScACAh8gDAIY8BAC7AQMAumkDAJZwAICacACAvwkDAL4BAwC9FQMAvBUDALNlAwCecACAonAAgKZwAICqcACAtmUDALV1AwCucACAsnAAgLZwAIC6cACAo4kCAL5wAIClmQIApokCAMJwAICELAIAxnAAgKqFAgCr7QIArPkCAK35AgCu7QIAr+UCAMpwAIDOcACAvkQFAIRMBQDScACA1nAAgNpwAIDecACA4nAAgOZwAIDqcACA7nAAgIAZAACBGQAAggUAAPJwAIDhGA8A4VwOAOO4DgDjdAEA+nAAgP5wAIACcQCABnEAgIYABACHZAUACnEAgA5xAIAScQCAFnEAgO98DgDvqAEAs3UBABpxAIAecQCAInEAgCZxAIC2MQEAtRUBACpxAIC7HQEAuhUBAC5xAIAycQCAv+EAAL79AAC9/QAAvP0AAPZwAIA2cQCAOnEAgD5xAICGcACAQnEAgEZxAIBKcQCAqI0GAKmVBgCqnQYAq+UGAKz9BgCt0QYArtEGAK/RBgCwsQYAsbkGALJJBwCzSQcAtFkHALVFBwC2RQcAt3kHALghBwC5IQcAujkHALs5BwC8KQcAvSkHAL4ZBwC/GQcAozUGAE5xAIBScQCAVnEAgFpxAICmcQYApVUGAF5xAICrXQYAqlUGAGJxAIC+oAMAr6EHAK69BwCtvQcArL0HAIBRAACBWQAAgmEAALNVBwCF9AAAtX0HALZ1BwBmcQCAhgAcAIfkAQC6LQcAuyUHALw9BwC9JQcAviUHAL8VBwCokQYAqZEGAKqRBgCrkQYArLkGAK25BgCuqQYAr6kGAGpxAIBucQCAcnEAgHZxAICiIQEAozUBAKA5BQChEQQAuEkBALlJAQC6XQEAu1UBALxNAQC90QEAvtEBAL/RAQCwpQYAsa0GALKlBgCzvQYAtK0GALWdBgC2lQYAt3kBAKMZBgCPnXkAenEAgH5xAICCcQCApjkGAKUxBgCGcQCAq2kGAKphBgCKcQCAjnEAgK9ZBgCuaQYArWkGAKxxBgCeiQgAn8EFAJzJCQCdyQkAmqENAJu9DACYsQ0AmbkNAJahcQCXRXEAlEV1AJWxcQCSoXUAk7V1AJDleQCRzXkAil1yAItFcgCScQCAvoAcAI51DgCPZQ4AjLlyAI11DgCCOXoAgzl6AJZxAICacQCAhnF2AIeZdgCECXoAhW12AJptBwCbVQIAnnEAgKJxAICmcQCA4ZAAAJxZAgDjCBoAkgkPAJNlCgCqcQCA7zgWAJZ1BgCXdQYAlH0KAJU1CwCpjRYAqIUWAKsBEACqMRYArXESAKy1EgCvuS4ArgEsAKF9AgCucQCAo6EeAKKpHgClsRoApPUfAKflGwCmsRoAhMwDAIRMHACycQCAtnEAgLpxAIC+cQCAwnEAgMZxAICxASgAsNkuALONKgCy6SoAtfUmALQBJACEcB0AynEAgID9AQCBFQAAgh0AAL6AHADOcQCA0nEAgIe4AgCGPB0A2nEAgN5xAIDicQCA5nEAgOpxAIDucQCA8nEAgPZxAID6cQCA/nEAgAJyAIAGcgCA44ADAApyAIDhoAEADnIAgO+UAwAScgCAFnIAgBpyAIAecgCAInIAgCZyAIAqcgCALnIAgOE8BgAycgCA49AGADZyAIDhMAcAOnIAgOOsBgCAOQAAgRUAAIIdAADvHAYAPnIAgEJyAIC+uB8A7+gBALPpAgBKcgCAh8QcAIbsHABOcgCAtlkCALVRAgBScgCAu00CALpNAgBWcgCAWnIAgL+5AQC+2QEAvdEBALz1AQCjKR0A1nEAgEZyAIBecgCAYnIAgKaZHQClkR0AZnIAgKuNHQCqjR0AanIAgG5yAICveR4ArhkeAK0RHgCsNR4AcnIAgLNtHwB2cgCAenIAgLZlHwB+cgCAgnIAgLVtHwC6IR8AuyEfAIZyAICKcgCAviUfAL8pHwC8MR8AvTEfAKihHwCpoR8AqqEfAKuhHwCsoR8AraEfAK6hHwCvoR8AjnIAgJJyAICWcgCAmnIAgJ5yAICicgCApnIAgKpyAIC4rR8AubUfALq9HwC7tR8AvK0fAL1VHwC+UR8Av00fALChHwCxoR8AsqEfALOhHwC0pR8AtakfALadHwC3lR8AoykeAIIZAACBGQAAgLEBAK5yAICmIR4ApSkeALJyAICrZR4AqmUeAIaIAACH/AEAr20eAK5hHgCtdR4ArHUeALZyAICzmR4AunIAgL5yAIC2XQEAwnIAgMZyAIC1sR4AukkBALtJAQDKcgCAznIAgL49AQC/IQEAvDkBAL01AQCoRR4AqVUeAKpVHgCrZR4ArH0eAK2ZAQCuiQEAr4EBAISsAADScgCA1nIAgNpyAIDecgCA4nIAgOZyAIDqcgCAuK0BALllAQC6bQEAu2UBALx9AQC9ZQEAvm0BAL9lAQCwyQEAsckBALKpAQCzpQEAtL0BALWhAQC2oQEAt5UBALhpHAC5oRwAusEcALvBHAC8wRwAvcEcAL7BHAC/wRwAsIkfALGJHwCyIRwAswUcALQdHAC1fRwAtnUcALdtHACoYR8AqWEfAKphHwCrYR8ArNkfAK3ZHwCuyR8Ar8EfAO5yAIDycgCA9nIAgPpyAID+cgCAAnMAgAZzAIAKcwCADnMAgBJzAIC+AAQAo1EdABZzAICleR0AppUCABpzAIAecwCAInMAgKqBAgCrgQIArPECAK39AgCu9QIAr+kCACpzAIDh9AEALnMAgON8AQCATQAAgXUAAIJ9AAAycwCAhsAEAIekBAA2cwCAOnMAgD5zAIBCcwCARnMAgO+MAgCoSQIAqUkCAKpdAgCrVQIArHkCAK15AgCuvQIAr7UCAISgBQBKcwCATnMAgFJzAIC+vAQAVnMAgFpzAIBecwCAuC0BALk1AQC6PQEAuzUBALwtAQC91QEAvt0BAL/NAQCwzQIAsdUCALLdAgCz1QIAtM0CALUVAQC2HQEAtxUBAOGEHgDjbB8A41wfAOFYHgBicwCAZnMAgGpzAIBucwCAcnMAgHZzAIB6cwCAfnMAgOkAAADv9B4A70weAIJzAICzlQIAhnMAgIpzAICOcwCAknMAgLa5AgC1sQIAmnMAgLtRAgC6SQIAhsgEAIesBAC/kQEAvkkCAL1BAgC8SQIAJnMAgKNRBQCecwCAlnMAgKZ9BQCicwCApnMAgKV1BQCqjQUAq5UFAKpzAICucwCAro0FAK9VBgCsjQUArYUFAICJBwCBiQcAgpkHALORBgCycwCAtbkGALapBgC2cwCAunMAgL5zAIC6TQcAu0UHALxdBwC9QQcAvkEHAL9BBwCoQQYAqU0GAKpVBgCrZQYArH0GAK1lBgCubQYAr2UGAMJzAIDGcwCAynMAgM5zAIDScwCA1nMAgNpzAIDecwCAuFkHALlZBwC6aQcAu2kHALx5BwC9eQcAvmUHAL8ZBwCwxQcAsc0HALLFBwCz2QcAtMkHALXJBwC2aQcAt2kHAKPdBwDicwCA5nMAgOpzAIDucwCApuUHAKX1BwDycwCAqwkGAKoBBgD2cwCA+nMAgK8NBgCuDQYArQ0GAKwRBgCAbQAAgQkAAIIZAAD+cwCAAnQAgISYAQC+kAEABnQAgIbAAACH5AEACnQAgA50AIASdACAFnQAgBp0AIAedACAqF0GAKmNAQCqnQEAq5UBAKy5AQCtuQEArskBAK/BAQCEoAAAInQAgCZ0AIAqdACALnQAgDJ0AIA2dACAOnQAgLh5AQC5eQEAus0AALvFAAC83QAAvcUAAL7FAAC/9QAAsIEBALGBAQCySQEAs0kBALRZAQC1WQEAtkkBALdJAQCzFQIAPnQAgEJ0AIBGdACASnQAgLY5AgC1MQIATnQAgLtFAgC6RQIAUnQAgFZ0AIC/nQIAvp0CAL2dAgC8nQIAhXw+AKNRAgBadACAXnQAgKZ9AgBidACAZnQAgKV1AgCqAQIAqwECAGp0AIBudACArtkCAK/ZAgCs2QIArdkCAIDpAACB6QAAggUAAHJ0AIC+AAwAenQAgIeoAwCGvAwAfnQAgIJ0AICGdACAinQAgI50AICSdACAlnQAgJp0AICedACAonQAgKZ0AICqdACA42ABAK50AIDhoAEAsnQAgO+IAgC2dACAunQAgL50AIDCdACAxnQAgMp0AIDOdACAqGkCAKlpAgCqeQIAq3kCAKxpAgCtaQIArr0CAK+1AgC+rAwA0nQAgNZ0AIDadACAgB0AAIEJAACCqQAA3nQAgLhRAQC5WQEAumEBALthAQC8GQEAvRkBAL4NAQC/BQEAsM0CALHVAgCy3QIAs9UCALTNAgC1cQEAtnEBALdxAQDjxAAA4XwHAOF4BgDjvAYA4nQAgIQYDQCGuAwAhzwNAL4sDwDqdACA7nQAgPJ0AIDvEAAA9nQAgPp0AIDvdAYA/nQAgAJ1AIAGdQCAs70CAAp1AIC1rQIAtqUCAA51AIASdQCAFnUAgLpFAgC7XQIAvEUCAL1NAgC+RQIAv/kBAHZ0AIClfQ0ApnUNAOZ0AIAadQCAHnUAgCJ1AICjbQ0ArJUNAK2dDQCulQ0ArykOACZ1AIAqdQCAqpUNAKuNDQCz5Q4ALnUAgDJ1AIA2dQCAOnUAgLblDgC19Q4APnUAgLuhDgC62Q4AQnUAgEZ1AIC/pQ4AvrkOAL2xDgC8uQ4AqBUOAKklDgCqLQ4AqyUOAKw9DgCtJQ4Ari0OAK8lDgCADQAAgRUAAIIdAABKdQCATnUAgFJ1AICEMAMAVnUAgLgpDgC5KQ4AujkOALs5DgC8KQ4AvSkOAL79DwC/9Q8AsF0OALElDgCyLQ4AsyUOALQ9DgC1IQ4AtiUOALcZDgCjpQ8AWnUAgIYoAQCHTAEAXnUAgKalDwCltQ8AYnUAgKvhDwCqmQ8AZnUAgGp1AICv5Q8ArvkPAK3xDwCs+Q8AbnUAgLPpDgBydQCAdnUAgLaRDgB6dQCAfnUAgLXlDgC6sQ4Au7kOAIJ1AICGdQCAvmEBAL9hAQC8mQ4AvZkOAKglDgCpLQ4AqiUOAKs5DgCsKQ4ArVUOAK5dDgCvVQ4AinUAgI51AICSdQCAlnUAgJp1AICedQCAonUAgKZ1AIC49QEAuYEBALqBAQC7gQEAvIEBAL2JAQC+sQEAv7EBALAxDgCxOQ4AsgkOALMJDgC04QEAteEBALbhAQC3zQEAo60NAKp1AICudQCAsnUAgLZ1AICm1Q0ApaENALp1AICr/Q0AqvUNAL51AIDCdQCAryUCAK4lAgCt3Q0ArN0NAIBdAACBbQAAgmUAALNRAwC+nAMAtXkDALYZAwDKdQCAhOACAM51AIC6PQMAuzUDALwZAwC9GQMAvtkDAL/ZAwCohQMAqZUDAKqVAwCrpQMArL0DAK3VAwCu0QMAr9EDAIYABACHNAMAv6AzANJ1AIDWdQCA2nUAgN51AIDidQCAuHEDALlxAwC6cQMAu3EDALzVAAC93QAAvtUAAL/NAACwtQMAsb0DALKBAwCzgQMAtFEDALVRAwC2UQMAt1EDAO+oAwDmdQCA6nUAgO51AICEHAIA8nUAgPZ1AID6dQCAviwFAP51AIACdgCABnYAgONAAwAKdgCA4SgAAA52AICjXQIAEnYAgBZ2AIAadgCAHnYAgKYVAgCldQIAInYAgKs5AgCqMQIAJnYAgCp2AICv1QIArtUCAK0VAgCsFQIA4ygBAOEADwDhCA4A4wgOAID9AACBCQAAgjkAAC52AIAydgCAOnYAgD52AIBCdgCA7+gOAEZ2AIBKdgCA72QOALNtAQBOdgCAhugEAIcMBQBSdgCAtm0BALVtAQBWdgCAu+0AALrtAABadgCAXnYAgL/VAAC+6QAAveEAALzpAACoXQYAqWEGAKqlBgCrvQYArKUGAK2tBgCupQYArxkHADZ2AIBidgCAZnYAgGp2AIBudgCAcnYAgHZ2AIB6dgCAuHUHALl5BwC6DQcAuwUHALwdBwC9BQcAvgUHAL81BwCwaQcAsWkHALJ9BwCzdQcAtG0HALVRBwC2UQcAt1EHAKMtBgB+dgCAgnYAgIZ2AICKdgCApi0GAKUtBgCOdgCAq60HAKqtBwCSdgCAlnYAgK+VBwCuqQcAraEHAKypBwCADQAAgRUAAIIdAACadgCAnnYAgKJ2AICEVAMAvlwAAKZ2AICqdgCAhugAAIdMAwCudgCAsnYAgLZ2AIC6dgCAvnYAgOMEBADCdgCA4bQFAMZ2AIDKdgCAznYAgNJ2AIDWdgCA2nYAgN52AIDidgCA5nYAgO/sBADqdgCA7nYAgLPtBgDydgCA9nYAgPp2AID+dgCAtpEGALXhBgACdwCAu40GALqNBgAGdwCACncAgL9BAQC+WQEAvVEBALxZAQCoJQYAqS0GAKolBgCrOQYArCkGAK1RBgCuSQYAr0EGAIDNAACBCQAAghkAAA53AIASdwCAhCwBAL40AAAadwCAuP0BALlBAQC6QQEAu0EBALxBAQC9SQEAvnEBAL9xAQCwCQYAsQkGALLNAQCzxQEAtN0BALXFAQC2zQEAt8UBAIagPACHRAMAHncAgKOhBQAidwCApa0FAKbdBQAmdwCAKncAgL4oPACqwQUAq8EFAKwVAgCtHQIArhUCAK8NAgC2QQMALncAgDJ3AIC1sQIANncAgLOhAgA6dwCAPncAgL5FAwC/TQMAvHUDAL1NAwC6ZQMAu20DAEJ3AIBGdwCASncAgE53AIDGdQCAUncAgFZ3AIBadwCAXncAgGJ3AICoRQIAqVUCAKpdAgCrVQIArE0CAK21AwCusQMAr60DALDVAwCx3QMAstUDALPtAwC09QMAtf0DALb1AwC37QMAuNkDALnZAwC6rQMAu6UDALy9AwC9pQMAvqUDAL+VAwCj9QMAZncAgGp3AIBudwCAcncAgKYVAgCl5QMAdncAgKs5AgCqMQIAencAgH53AICvGQIArhECAK0ZAgCsIQIAgGkAAIFpAACCBQAAgncAgIp3AICOdwCAkncAgO8cAACEbAIA4ZQBAJZ3AIDjyAAAmncAgJ53AICGWDwAh1A9AKJ3AICmdwCAqncAgISEPQCudwCAsncAgLZ3AIDvuAEAvmw8AOF0BgC6dwCA42QBAL53AIDCdwCAxncAgMp3AICz0QEAzncAgNJ3AIDWdwCA2ncAgLaRAQC1+QEA3ncAgLu9AQC6vQEA4ncAgOZ3AIC/dQEAvnUBAL2FAQC8hQEAqL09AKkNPgCqGT4AqxE+AKwxPgCtUT4ArlE+AK9NPgCGdwCAgh0AAIEdAACAHQAA6ncAgO53AIDydwCA9ncAgLjVPgC53T4AutU+ALtJPwC8WT8AvVk/AL5JPwC/QT8AsDk+ALE5PgCyET4AsxE+ALTxPgC18T4AtvU+ALftPgCjkT4A+ncAgIYoAACHwAMA/ncAgKbRPgCluT4AAngAgKv9PgCq/T4ABngAgAp4AICvNT4ArjU+AK3FPgCsxT4ADngAgLOdPwASeACAFngAgLalPwAaeACAHngAgLWtPwC6aT8Au3U/ACJ4AIAmeACAvlk/AL9FPwC8bT8AvWU/ACp4AIAueACAMngAgDZ4AIDjYDwAOngAgOEAPQA+eACA7/w9AEJ4AIBGeACASngAgE54AIBSeACAVngAgFp4AICjGT4AghkAAIEZAACAcQAAXngAgKYhPgClKT4AYngAgKvxPgCq7T4AhCQBAL4kAQCvwT4Art0+AK3hPgCs6T4AqNE+AKnRPgCq0T4Aq+U+AKzhPgCt4T4Arhk+AK8ZPgCGAAAAh4QAAGp4AIBueACAcngAgHZ4AIB6eACAfngAgLh9PgC5AT4AugE+ALsBPgC8AT4AvQk+AL4xPgC/MT4AsGk+ALF1PgCyfT4As3U+ALRZPgC1RT4Atk0+ALdFPgCohQIAqZUCAKqVAgCrpQIArL0CAK3VAgCu0QIAr9ECAIJ4AICGeACAingAgL8k5gGOeACAkngAgJZ4AICaeACAuFUDALlZAwC6bQMAu2UDALx9AwC9ZQMAvm0DAL9lAwCwtQIAsb0CALKBAgCzgQIAtHEDALVxAwC2cQMAt3EDALMdAgCeeACAongAgKZ4AICEiAMAtlUCALU1AgAWdwCAu3kCALpxAgCqeACArngAgL+1AwC+tQMAvVUCALxVAgCyeACAo1kCALZ4AIC6eACAphECAL54AIDCeACApXECAKo1AgCrPQIAxngAgMp4AICu8QMAr/EDAKwRAgCtEQIAqKkCAKmpAgCquQIAq7kCAKypAgCtqQIArjkBAK85AQCAzQEAgQkAAIIZAADOeACA0ngAgL64BQDaeACA3ngAgLjpAQC56QEAuokBALuFAQC8nQEAvYEBAL6BAQC/tQEAsEkBALFVAQCyXQEAs1UBALRNAQC18QEAtvEBALfxAQDvFAAA4ngAgIaoBQCH3AUA5ngAgIRYBADqeACA78Q+AO54AIDhxD4A8ngAgOMwPgDjyAAA9ngAgOEoAQD6eACAtn0CAP54AIACeQCAtXUCAAZ5AICzZQIACnkAgA55AIC+3QEAv2EBALzdAQC91QEAutkBALvFAQASeQCAFnkAgKOxBQDWeACAGnkAgB55AIAieQCApqkFAKWhBQAmeQCAqxEGAKoNBgAqeQCALnkAgK+1BgCuCQYArQEGAKwJBgAyeQCANnkAgDp5AIA+eQCAgBkAAIEZAACCBQAAQnkAgL5sAwBGeQCAhsgAAIccAwBKeQCATnkAgFJ5AIBWeQCAqLkHAKm5BwCqDQcAqx0HAKwJBwCtNQcArjEHAK8pBwCEqAMAWnkAgF55AIBieQCAZnkAgGp5AIBueQCAcnkAgLjJAAC5yQAAutkAALvRAAC8+QAAvfkAAL6ZAAC/mQAAsF0HALEhBwCyIQcAsz0HALQpBwC1KQcAtgEHALcBBwCzhQYAdnkAgHp5AIB+eQCAgnkAgLa1BgC1gQYAhnkAgLvlBgC6mQYAinkAgI55AIC/7QYAvu0GAL3pBgC89QYAknkAgJZ5AICaeQCAnnkAgKJ5AICmeQCAqnkAgO+QBACueQCA4dwGALJ5AIDj7AUAgCkAAIEVAACCEQAAvnwBAKMFBgC6eQCAhigAAIdMAQC+eQCApjUGAKUBBgDCeQCAq2UGAKoZBgDGeQCAynkAgK9tBgCubQYArWkGAKx1BgDOeQCAs70BANJ5AIDWeQCAtnkBANp5AIDeeQCAtXkBALpVAQC7XQEA4nkAgOZ5AIC++QAAv/kAALxFAQC9+QAAqHECAKlxAgCqcQIAq3ECAKy1AgCtvQIArrUCAK+tAgCE7AwA6nkAgO55AIDyeQCA9nkAgPp5AID+eQCAAnoAgLhpAwC5aQMAugkDALsJAwC8GQMAvRkDAL4JAwC/CQMAsNUCALHdAgCy1QIAs2kDALR5AwC1eQMAtmkDALdhAwAGegCACnoAgA56AICj9QIAEnoAgKUxAgCmMQIAFnoAgBp6AIAeegCAqh0CAKsVAgCsDQIArbEDAK6xAwCvsQMAgGEAAIFhAACCBQAAInoAgIbwDACHYAMAvhAMACp6AIBmeACALnoAgDJ6AIA2egCAOnoAgD56AIBCegCARnoAgKiFAgCplQIAqpUCAKulAgCsvQIArdUCAK7RAgCv0QIASnoAgE56AIBSegCAVnoAgFp6AIBeegCAYnoAgGZ6AIC4dQEAuX0BALp1AQC7zQEAvNUBAL3dAQC+yQEAv8EBALC1AgCxvQIAsoECALOBAgC0VQEAtV0BALZVAQC3TQEA4RAGAIRIDADjDAYAanoAgISYDABuegCAcnoAgHZ6AIB6egCAfnoAgIJ6AICGegCAgXUAAIB1AADvIAEAgnUAAIp6AICOegCAknoAgL7ADACFtA4A4RACAO9cAADjABYA4ZABAJp6AIDjWAEA7zwHAJ56AICiegCAhgAIAIe4DACznQ0AJnoAgKZ6AICqegCArnoAgLbVDQC1tQ0AsnoAgLv5DQC68Q0AtnoAgLp6AIC/GQ4AvhEOAL3VDQC81Q0AvnoAgKPZDQDCegCAxnoAgKaRDQDKegCAznoAgKXxDQCqtQ0Aq70NANJ6AIDWegCArlUOAK9dDgCskQ0ArZENAKhdDgCpYQ4AqmEOAKthDgCsYQ4ArWEOAK5hDgCvYQ4A2noAgN56AIDiegCA5noAgOp6AIDuegCA8noAgPZ6AIC4TQ8AuVEPALpRDwC7UQ8AvHEPAL1xDwC+cQ8Av3EPALDBDwCxwQ8AssEPALPBDwC0wQ8AtcEPALbBDwC3wQ8As+kPAPp6AIC+gAEA/noAgJZ6AIC24Q8AtekPAAJ7AIC7BQ4AugUOAAp7AIAGewCAvwUOAL4FDgC9FQ4AvBUOAIFNAACAQQAA72gNAIJRAACG8AcAh9QBAA57AIASewCAFnsAgIRwAQAaewCAHnsAgOHgDgAiewCA40gNACZ7AICjaQ8AKnsAgC57AIAyewCANnsAgKZhDwClaQ8AOnsAgKuFDgCqhQ4APnsAgEJ7AICvhQ4AroUOAK2VDgCslQ4ARnsAgLMxDgBKewCATnsAgLbBAQBSewCAVnsAgLXRAQC6zQEAu6UBAFp7AIBeewCAvqUBAL+tAQC8sQEAvbEBAI/dJgCj8Q0AYnsAgGZ7AICmAQIAansAgG57AIClEQIAqg0CAKtlAgByewCAviAEAK5lAgCvbQIArHECAK1xAgCfoQwAnnkKAJ1pCgCc0QgAm7E2AJp1NgCZ0TQAmOEyAJdtMgCWZTIAlTU/AJRhPgCTcT4AkjU7AJFxOgCQeToAgJUAAIGdAACCoQAAensAgO9EAgDhdA8AfnsAgOMcDwDj1AEAgnsAgOHgAQDvXAEAo7UCAKJBAACh3Q4AoLkOALWpAwCGewCAhMAEALahAwCG8AUAh+QEALOFAwCKewCAvXEDALxpAwC/QQMAvnEDAI57AIC2eQCAu3EDALp5AwCC3ScAgwE7AL6EBwC+wAYAhhE/AIcZPwCEETsAhV06AIp9PgCLJTMAknsAgJZ7AICOuTUAjxU3AIw1MwCNgTMAkqE3AJPZCQC+xBkAmnsAgJaxDQCXUQ8AlHkLAJVhCwCaBQ8Am5EBAJ57AICiewCApnsAgN0AAACcfQMAqnsAgOFIDwCuewCA4xwOALJ7AIC2ewCAunsAgL57AIDCewCAsUEXALChFwCzqesBsgHoAbUB7AG0EesB74wOAMZ7AICpxR8AqAEcAKsBEACqkR8ArdkTAKzREwCv2RcArgUTAKHxAgDKewCAo8kHAKLBAgClARgApGUHAKehGwCm+RsAqCkFAKldBQCqVQUAq20FAKx5BQCteQUArm0FAK9hBQB2ewCAznsAgNJ7AIDWewCAgA0AAIGxAACCsQAA2nsAgLiJBQC5iQUAup0FALuVBQC8uQUAvbkFAL5RBgC/UQYAsOUFALHtBQCy5QUAs/0FALTtBQC13QUAttUFALe9BQCj3QUA3nsAgOJ7AICEDAAA5nsAgKb5BQCl8QUA6nsAgKspBQCqIQUAhpgAAIegAACvGQUArikFAK0pBQCsMQUA7nsAgLNhBgDyewCA9nsAgLYhBgD6ewCA/nsAgLUBBgC6rQcAu40HAAJ8AIAGfACAvo0HAL9xBwC8lQcAvY0HAL65BQC/uQUAvLkFAL25BQC6uQUAu7kFALi5BQC5uQUAtkkFALdJBQC0fQUAtXUFALJ5BQCzeQUAsBUFALF9BQCuXQUAr20FAKxFBQCtXQUAqqUKAKtdBQCovQoAqa0KAAp8AIAOfACAEnwAgBZ8AIAafACAHnwAgCJ8AIAmfACAqA0HAKkdBwCqLQcAq0kHAKxNBwCtZQcArrEGAK+xBgAqfACALnwAgDJ8AIA2fACAOnwAgD58AIBCfACARnwAgLhVBgC5XQYAulUGALtxBgC8NQYAvfEBAL7xAQC/8QEAsK0GALGNBgCyhQYAs50GALSNBgC1cQYAtnUGALdtBgCjpQQAgi0AAIEVAACAHQAASnwAgKblBAClxQQATnwAgKtJBQCqaQUAUnwAgFp8AICvtQUArkkFAK1JBQCsUQUAhmAcAIcIAwBefACAs4UCAGJ8AIC1gQIAtoECAGZ8AIBqfACAbnwAgLoJAwC7CQMAvBkDAL0ZAwC+CQMAvwkDAKxVAgCtXQIArmECAK9hAgCoDQIAqVUCAKpRAgCrUQIAhKwDAHJ8AIB2fACAenwAgIT8HQB+fACAgnwAgIZ8AIC8cQMAvXEDAL5xAwC/cQMAuHEDALlxAwC6cQMAu3EDALSRAwC1kQMAtpEDALeRAwCwkQMAsZEDALKRAwCzkQMAinwAgI58AICSfACAlnwAgJp8AIDhpAEAnnwAgOOAAQC+aBwAonwAgKZ8AIDv2AYAqnwAgK58AICyfACAtnwAgKOJAwCCLQAAgRUAAIAdAAC6fACApo0DAKWNAwC+fACAqwUCAKoFAgDCfACAynwAgK8FAgCuBQIArRUCAKwVAgCGIBwAh8QdAM58AIDSfACA1nwAgNp8AIDefACA72wGAOJ8AIDhbAcA5nwAgON0BwDqfACA7nwAgPJ8AID2fACAs5EBAPp8AID+fACAAn0AgAZ9AIC2sQEAtbkBAAp9AIC7VQEAukkBAA59AIASfQCAv/UAAL71AAC9RQEAvEUBAKNRHgDGfACAFn0AgBp9AIAefQCApnEeAKV5HgAifQCAq5UeAKqJHgAmfQCAKn0AgK81HwCuNR8ArYUeAKyFHgCAbQAAgRUAAIIdAADv/BkALn0AgDJ9AIA2fQCAOn0AgIbAAACHrAMAPn0AgEJ9AIBGfQCA4SwcAEp9AIDjzBwAqK0eAKnNHgCq2R4Aq9EeAKzxHgCt8R4Arj0eAK81HgCE7AAATn0AgFJ9AIBWfQCAWn0AgF59AIBifQCAZn0AgLjRHwC53R8Auu0fALvlHwC84R8AveEfAL7hHwC/4R8AsE0eALFRHgCyUR4As1EeALTxHwC18R8AtvEfALfxHwCobR4AqY0eAKqFHgCrnR4ArIUeAK2NHgCuuR4Ar7UeAGp9AIBufQCAcn0AgHZ9AIB6fQCAfn0AgIJ9AICGfQCAuJ0eALmtHgC6pR4Au0UBALxdAQC9RQEAvkUBAL91AQCw0R4AsdEeALLRHgCz0R4AtLUeALW9HgC2tR4At60eALMNHgCKfQCAjn0AgJJ9AICWfQCAtg0eALUNHgCafQCAuxUeALoVHgCefQCAon0AgL95HgC+cR4AvQUeALwFHgCCbQAAo0keAIBVAACBZQAApkkeAL6cAQCqfQCApUkeAKpRHgCrUR4Ah3wAAIZMAACuNR4Arz0eAKxBHgCtQR4AqF0CAKltAgCqZQIAq30CAKxpAgCtsQIArrECAK+xAgCE7AQArn0AgLJ9AIC2fQCAun0AgL59AIDCfQCAxn0AgLhxAwC5cQMAunEDALtxAwC81QMAvd0DAL7VAwC/zQMAsNECALHRAgCy0QIAs9ECALRRAwC1UQMAtlEDALdRAwCz7QIAyn0AgM59AIC+gAQA0n0AgLYxAgC14QIA1n0AgLsVAgC6FQIA2n0AgN59AIC/lQMAvpUDAL0FAgC8BQIA4n0AgKOpAgDmfQCA6n0AgKZ1AgDufQCA8n0AgKWlAgCqUQIAq1ECAPZ9AID6fQCArtEDAK/RAwCsQQIArUECAKjZAgCpIQEAqiEBAKshAQCsIQEArSEBAK4hAQCvIQEA/n0AgAJ+AIAGfgCAviAEAAp+AIAOfgCAEn4AgBp+AIC4jQEAuZEBALqRAQC7pQEAvL0BAL11AAC+fQAAv3UAALDlAQCx7QEAsvkBALPxAQC02QEAtdkBALa5AQC3tQEA4RgeAB5+AIDjKB8AIn4AgIGlAACApQAAJn4AgIKlAACGAAQAh/QFACp+AIAufgCAMn4AgDZ+AIDvYB4AOn4AgD5+AIBCfgCAhfD0AUZ+AIBKfgCA42QBAE5+AIDhpAEAUn4AgO/IAABWfgCAWn4AgFZ8AICE/AUAXn4AgGJ+AICzKQYAFn4AgGZ+AIBqfgCAbn4AgLYhBgC1KQYAcn4AgLupBgC6oQYAdn4AgHp+AIC/nQYAvp0GAL2lBgC8rQYA4bQHAH5+AIDjeAQAgn4AgIB9AACBEQAAghUAAIZ+AICGwAAAh1gDAIp+AICOfgCAkn4AgJZ+AIDvDAQAmn4AgKOpBgCefgCAon4AgKZ+AICqfgCApqEGAKWpBgCufgCAqykGAKohBgCyfgCAtn4AgK8dBgCuHQYArSUGAKwtBgC6fgCAs0kHAL5+AIDCfgCAtn0HAMZ+AIDKfgCAtXUHALpdBwC7JQcAzn4AgNJ+AIC+IQcAvy0HALw9BwC9MQcAqD0GAKmBBgCqhQYAq5UGAKy5BgCtuQYArqkGAK+pBgDWfgCA2n4AgN5+AIDifgCA5n4AgIK5AACBsQAAgLkAALitBgC5vQYAurUGALtFAQC8XQEAvUUBAL5FAQC/dQEAsN0GALGlBgCyrQYAs6EGALShBgC1rQYAtpkGALeVBgCjDQYA6n4AgO5+AIDyfgCAhJgCAKY5BgClMQYAvpwBAKthBgCqGQYAhggAAId8AQCvaQYArmUGAK11BgCseQYA+n4AgLO1AQD+fgCAAn8AgLZVAQAGfwCACn8AgLWhAQC6cQEAu3kBAA5/AIASfwCAvjEBAL89AQC8UQEAvVEBAKhpAgCpaQIAqnkCAKt5AgCsbQIArZECAK6RAgCvkQIAFn8AgBp/AIAefwCAIn8AgCZ/AIAqfwCALn8AgDJ/AIC4mQIAua0CALqlAgC7bQMAvHUDAL19AwC+dQMAv20DALDxAgCx+QIAssECALPBAgC0sQIAtb0CALa1AgC3qQIANn8AgDp/AIA+fwCAo/0CAEJ/AICl6QIAph0CAEZ/AIBKfwCATn8AgKo5AgCrMQIArBkCAK0ZAgCueQIAr3UCAFJ/AIBWfwCAWn8AgIQADACAGQAAgQkAAII5AABefwCAYn8AgGp/AIBufwCAvuAMAHJ/AIB2fwCAhlgNAIcMAwCowQIAqc0CAKrFAgCr2QIArMkCAK39AgCu9QIArz0BAHp/AIB+fwCAgn8AgIZ/AICKfwCAjn8AgJJ/AIC+MAwAuMUBALnNAQC62QEAu9EBALzxAQC98QEAvpkBAL+ZAQCwRQEAsU0BALJFAQCzXQEAtEUBALVNAQC2RQEAt/0BAOE4BgCWfwCA42wGAJp/AICefwCAon8AgKZ/AICqfwCAhKgNAK5/AICyfwCAtn8AgL6wDwC6fwCA72wGAL5/AIDCfwCApn0AgMZ/AIDKfwCA41AAAM5/AIDhoAEA0n8AgO+EAADafwCAhyANAIZMDwCAPQAAgSEAAIIlAADefwCAs80NAGZ/AIDWfwCA4n8AgOZ/AIC2/Q0AtcENAOp/AIC7CQ4AugEOAO5/AIDyfwCAvwkOAL4BDgC9CQ4AvBEOAPZ/AIDjmAwA+n8AgOH8DwD+fwCAAoAAgAaAAIAKgACADoAAgBKAAIAWgACAGoAAgB6AAIDvYAwAIoAAgCaAAICjTQ0AKoAAgC6AAIAygACANoAAgKZ9DQClQQ0AOoAAgKuJDgCqgQ4APoAAgEKAAICviQ4AroEOAK2JDgCskQ4Agm0AALM1DgCAVQAAgWUAALb1DwCE3AMARoAAgLX9DwC60Q8Au9EPAIYABACH3AAAvn0PAL9lDwC8wQ8AvXkPAKjlDwCp7Q8AqvkPAKv5DwCsMQ4ArTEOAK4xDgCvMQ4ASoAAgE6AAIBSgACAVoAAgFqAAIBegACAYoAAgGaAAIC43Q4AueEOALrhDgC74Q4AvOUOAL3pDgC+mQ4Av5UOALBRDgCxUQ4AslEOALPpDgC0/Q4AteUOALbtDgC35Q4Ao3EPAGqAAIBugACAcoAAgHaAAICmsQ4ApbkOAHqAAICrlQ4AqpUOAH6AAICCgACAryEOAK45DgCtPQ4ArIUOAIaAAICzyQEAioAAgI6AAIC2+QEAkoAAgJaAAIC1wQEAuqkBALu1AQCagACAnoAAgL6tAQC/lQEAvK0BAL2lAQCo5Q0AqfkNAKoFAgCrHQIArA0CAK09AgCuNQIAr10CAKKAAICmgACAqoAAgK6AAICAGQAAgRkAAIIFAACygACAuC0CALk1AgC6MQIAuzECALzVAgC93QIAvtUCAL/NAgCwKQIAsTUCALI9AgCzNQIAtC0CALUVAgC2HQIAtxUCALqAAICEnAIAvoAAgKOBAgDCgACApYkCAKaxAgDGgACAhiAEAIfUAwCq4QIAq/0CAKzlAgCt7QIAruUCAK/dAgC29QMAvkQDAIWM/QG1/QMAyoAAgLP9AwDOgACA0oAAgL59AwC/TQMAvGUDAL19AwC6dQMAu30DANaAAIDagACA3oAAgOKAAICEBAIAoyUCAOaAAIClJQIApi0CAOqAAIDugACA8oAAgKqtAgCrpQIArL0CAK2lAgCupQIAr5UCAPaAAID6gACA/oAAgAKBAIAGgQCA48ADAAqBAIDhrAEADoEAgO9YAwASgQCAFoEAgIANAACB5QAAgu0AABqBAIDhYA8A40ABAOM4DgDheA4AHoEAgCKBAIC+lAUAKoEAgIYABACHZAUALoEAgDKBAIA2gQCA7/wOAO98DgA6gQCAs1EBAD6BAID2fgCAQoEAgEaBAIC2DQEAtQkBAEqBAIC74QAAuhkBAE6BAIBSgQCAv9EAAL7pAAC96QAAvPkAALaAAIAmgQCAVoEAgFqBAIBegQCAYoEAgGaBAIBqgQCAqKEGAKmtBgCquQYAq7EGAKzhBgCt7QYAruUGAK/FBgCwvQYAsUUHALJNBwCzXQcAtE0HALV1BwC2fQcAtx0HALglBwC5LQcAuiUHALs9BwC8KQcAvRUHAL4RBwC/EQcAoxEGAG6BAIBygQCAdoEAgHqBAICmTQYApUkGAH6BAICroQcAqlkGAIKBAICGgQCAr5EHAK6pBwCtqQcArLkHAIANAACBFQAAgh0AAIqBAICOgQCAkoEAgISUAwC+lAMAloEAgJqBAICGyAAAh4wAAJ6BAICigQCApoEAgKqBAIConQYAqa0GAKqlBgCrvQYArK0GAK3RBgCu1QYAr80GAK6BAICygQCAtoEAgLqBAIC+gQCAwoEAgMaBAIDKgQCAuF0BALnBAQC6wQEAu8EBALzBAQC9yQEAvvEBAL/xAQCwvQYAsY0GALKFBgCzZQEAtH0BALVlAQC2bQEAt2UBALMtBgDOgQCA0oEAgNaBAIDagQCAtlEGALUlBgDegQCAu0kGALp5BgDigQCA5oEAgL+hAQC+uQEAvbEBALxRBgDqgQCAo2kGAO6BAIDygQCAphUGAPaBAID6gQCApWEGAKo9BgCrDQYA/oEAgAKCAICu/QEAr+UBAKwVBgCt9QEAutUHALvdBwC4wQcAucEHAL4xBAC/MQQAvPEHAL3xBwCyrQcAs7UHALCtBwCxpQcAtp0HALf1BwC0pQcAtZUHAKppBwCraQcAqGkHAKlpBwCuaQcAr2kHAKxpBwCtaQcAgLkDAIGNAwCChQMAhKgDAIZQ/AGHCAMAvjQDAAqCAICoZQIAqXUCAKp9AgCrdQIArG0CAK21AwCuvQMAr7UDAA6CAIASggCAFoIAgBqCAIAeggCAIoIAgCaCAIAqggCAuFEDALlZAwC6YQMAu2EDALwRAwC9HQMAvhUDAL8JAwCwzQMAsdUDALLdAwCz1QMAtM0DALVxAwC2cQMAt3EDAC6CAIAyggCAs/0DADaCAIC17QMAOoIAgD6CAIC2PQIAQoIAgEaCAIC7GQIAugECAL0JAgC8AQIAv70CAL4BAgBKggCAToIAgITE/QG+wPwBUoIAgFaCAIBaggCA79wDAF6CAIDhlAEAYoIAgOMQAwBmggCAgu0AAIHtAACA7QAA4TgGAOE8BwDjQAEA45QGAGqCAIBuggCAcoIAgHqCAICGgPwBh+j9AX6CAICCggCAhoIAgIqCAIDvnAEA79wGAKM1AwCOggCAkoIAgJaCAICaggCApvUCAKUlAwCeggCAq9ECAKrJAgCiggCApoIAgK91AgCuyQIArcECAKzJAgB2ggCAqoIAgK6CAICyggCA76T9AbaCAIC6ggCAvoIAgON4/QHCggCA4UD8AcaCAIDKggCAzoIAgNKCAIDWggCAs+X+AYItAACBFQAAgB0AANqCAIC25f4BtfX+Ad6CAIC7Yf8Butn+AeKCAICE5AMAv2n/Ab5h/wG9df8BvHn/Aaj9/gGpJf4Bqi3+Aasl/gGsPf4BrSX+Aa4t/gGvJf4BviwAAOaCAICGiAAAh+wAAOqCAIDuggCA8oIAgPaCAIC4gf8BuYH/AbqZ/wG7mf8BvIn/Ab21/wG+sf8Bv63/AbBd/gGx5f8Bsu3/AbPh/wG05f8Bte3/AbbZ/wG32f8Bo6X/AfqCAID+ggCAAoMAgAaDAICmpf8BpbX/AQqDAICrIf4Bqpn/AQ6DAIASgwCAryn+Aa4h/gGtNf4BrDn+ARaDAICz6f4BGoMAgB6DAIC2lf4BIoMAgCaDAIC16f4BurH+Abu5/gEqgwCALoMAgL51AQC/fQEAvJH+Ab2R/gGoHf4BqS3+Aaol/gGrPf4BrCX+Aa1R/gGuUf4Br1H+ATKDAIA2gwCAOoMAgD6DAIBCgwCARoMAgEqDAIBOgwCAuNkBALnZAQC67QEAu+EBALzhAQC94QEAvuEBAL/hAQCwMf4BsTn+AbIB/gGzAf4BtPUBALX9AQC29QEAt+kBAKOt/QFSgwCAvkwDAFqDAIBegwCAptH9AaWt/QFigwCAq/39Aar1/QFmgwCAaoMAgK85AgCuMQIArdX9AazV/QGA+QMAgfkDAIJNAACFdCAAboMAgITYAwCE1AQAcoMAgIZABACHVAMAdoMAgHqDAIB+gwCAgoMAgIaDAIC+8AUAqDECAKkxAgCqMQIAqzECAKyVAwCtnQMArpUDAK+NAwCKgwCAjoMAgJKDAICWgwCAhHwHAJqDAICegwCAooMAgLipAwC5qQMAumkDALtpAwC8eQMAvXkDAL5pAwC/aQMAsP0DALHNAwCyxQMAs60DALS5AwC1uQMAtq0DALelAwCmgwCAqoMAgK6DAICygwCAtoMAgLqDAIDv6AMAvoMAgOGQAQDCgwCA42wDAMqDAICAJQAAgSkAAIIdAADOgwCAs/kDANKDAICGaAcAh1wFANaDAIC2XQIAtV0CANqDAIC7SQIAunkCAN6DAIDigwCAvz0CAL49AgC9OQIAvFECAOaDAIDhPP4BvkAGAOPwAQDqgwCA7oMAgPKDAID2gwCA+oMAgP6DAIAChACABoIAgAaEAIAKhACADoQAgO/kAQAShACAFoQAgKNxAwAahACApdUCAB6EAIAihACAptUCACaEAIAqhACAq8ECAKrxAgCtsQIArNkCAK+1AgCutQIA4dz8AcaDAIDjUAQA74gEAID1BwCBCQAAgj0AAC6EAICEJAEAMoQAgDaEAIA6hACAPoQAgOFMBADv5BwA43QEALNdBgBChACAhgAMAIfgAwBGhACAtgUGALV1BgBKhACAuxEGALoJBgBOhACAUoQAgL/VBgC+1QYAvQEGALwJBgCojQYAqZUGAKqVBgCrpQYArL0GAK3FBgCuxQYAr/UGAFaEAIBahACAXoQAgGKEAIBmhACAaoQAgG6EAIByhACAuHUGALl9BgC6dQYAu80HALzVBwC93QcAvtUHAL/NBwCwjQYAsZUGALKdBgCzlQYAtFEGALVRBgC2UQYAt1EGAKMdBwCPFewBdoQAgHqEAIB+hACApkUHAKU1BwCChACAq1EHAKpJBwCGhACAioQAgK+VBwCulQcArUEHAKxJBwCeRfkBn6X5AZyR/QGdTfkBmlX9AZtd/QGYBfEBmZX+AZal8gGXYfEBlG31AZU19QGS4ekBk4X2AZBV7AGRXekBsbEdALClHQCziRkAskEcALUBJAC09RkAjoQAgJKEAICWhACAgqkDAIGhAwCAaQAAohUFAKMFAgCgFQYAob0FAKHFAQCahACAo80NAKLlAQClAQgApN0NAKfRCQCm2QkAqQEUAKilCACrxRQAqs0VAK3REQCsARAArwEcAK51EQCCEe8BgynvAZ6EAICihACAhuH1AYcR9gGEOeoBhY3qAYp59gGL4fEBvqQMAKqEAICO+f0BjzH+AYw98gGNYfIBkkn+AZOd/gGHCAwAhmwMAJax+gGX+QUAlFn6AZVZ+gGaYQYAm8EGAK6EAICyhACAtoQAgLqEAICcyQEAvoQAgKitBQCpuQUAqs0FAKvdBQCszQUArf0FAK71BQCvHQUAwoQAgMaEAIDKhACAzoQAgNKEAIDWhACA2oQAgN6EAIC4dQUAuX0FALoJBQC7CQUAvB0FAL0BBQC+AQUAvz0FALBxBQCxcQUAsnEFALNxBQC0UQUAtVEFALZRBQC3TQUAs0UEAOKEAIDmhACA6oQAgO6EAIC2fQQAtUUEAPKEAIC7tQQAurUEAPaEAID6hACAv5UEAL6VBAC9pQQAvKUEAP6EAICjAQQAAoUAgAaFAICmOQQACoUAgA6FAIClAQQAqvEEAKvxBAAShQCAhOwNAK7RBACv0QQArOEEAK3hBADh0AYAhAwMAOMoBwC+AAwAGoUAgO9EAwCGuAwAhywNAB6FAIDjlAEAIoUAgOH8AQBWgwCAJoUAgO/IBgAqhQCALoUAgDKFAICzjQMANoUAgLWNAwA6hQCAPoUAgLa1AwBChQCARoUAgLtBAwC6SQMAvUEDALxZAwC/QQMAvkkDAKNFDACmhACAFoUAgEqFAIBOhQCApn0MAKVFDABShQCAq4kMAKqBDABWhQCAWoUAgK+JDACugQwArYkMAKyRDACAFQ8AgR0PAIIhDwCzIQ4AXoUAgLUhDgC2JQ4AYoUAgGaFAIBqhQCAusEOALvBDgC8wQ4AvcEOAL7BDgC/wQ4AqK0OAKntDgCq5Q4Aq/0OAKzlDgCt6Q4ArjkOAK85DgBuhQCAcoUAgHaFAIB6hQCAgB0AAIEJAACCvQEAfoUAgLjNDwC51Q8AutUPALvlDwC8/Q8AvZUPAL6RDwC/kQ8AsEkOALFJDgCyWQ4As1kOALRJDgC1SQ4Atv0PALf1DwCjbQ8AgoUAgL6EAQCKhQCAjoUAgKZpDwClbQ8AkoUAgKuNDwCqjQ8AhogAAIdsAQCvjQ8Aro0PAK2NDwCsjQ8AloUAgLPtDgCahQCAnoUAgLaRDgCihQCApoUAgLXhDgC6tQ4Au70OAKqFAICuhQCAvn0BAL9lAQC8mQ4AvZkOAKgRDgCpJQ4AqiEOAKs5DgCsLQ4ArVUOAK5dDgCvUQ4AhKgAALKFAIC2hQCAuoUAgL6FAIDChQCAxoUAgMqFAIC47QEAuZUBALqVAQC7rQEAvLUBAL11AQC+fQEAv3UBALA1DgCxPQ4AsgkOALMJDgC0/QEAteUBALblAQC31QEAo6kNAM6FAIDShQCA1oUAgNqFAICm1Q0ApaUNAN6FAICr+Q0AqvENAOKFAIDmhQCAryECAK45AgCt3Q0ArN0NAIANAACBFQAAgh0AAOqFAIDuhQCA8oUAgIeQAwCGfAQAvuwEAPqFAID+hQCAAoYAgAaGAIAKhgCADoYAgBKGAICyLQ4AszUOALAtDgCxJQ4Ati0OALedDwC0LQ4AtSUOALq9DwC7jQ8AuKUPALm9DwC+LQ8AvxUPALyVDwC9JQ8AFoYAgBqGAIAehgCAIoYAgCaGAIAqhgCALoYAgDKGAICqpQ4Aq7UOAKjFDgCp3Q4Arp0OAK9VDgCspQ4ArZUOAKgNAgCpFQIAqhUCAKtNAgCsWQIArVkCAK5NAgCvRQIAhKgFADaGAIA6hgCAPoYAgIS4BABChgCARoYAgEqGAIC4/QIAuUEBALpBAQC7QQEAvEEBAL1JAQC+cQEAv3EBALAJAgCxCQIAss0CALPFAgC03QIAtcUCALbNAgC3xQIA4dQPAOMQDgDj9A4A4QwOAE6GAIBShgCAVoYAgFqGAIBehgCAYoYAgL4kBABqhgCA7AAAAO9EAADvzA4AboYAgIJlAACz2QIAgFUAAIFtAAC2nQIAcoYAgHaGAIC1lQIAuokCALuJAgCGqAQAh+AEAL5dAgC/RQIAvF0CAL1VAgCjHQUA9oUAgGaGAIB6hgCAfoYAgKZZBQClUQUAgoYAgKtNBQCqTQUAhoYAgIqGAICvgQUArpkFAK2RBQCsmQUAjoYAgLMpBgCShgCAloYAgLYpBgCahgCAnoYAgLUpBgC6pQYAu60GAKKGAICmhgCAvqUGAL+tBgC8tQYAva0GAKjlBgCp7QYAquUGAKv9BgCs5QYAre0GAK7lBgCvXQYAqoYAgK6GAICyhgCAtoYAgLqGAIC+hgCAwoYAgMaGAIC46QcAuekHALr9BwC79QcAvO0HAL1FBwC+TQcAv0UHALAlBgCxLQYAsiUGALM9BgC0JQYAtS0GALYlBgC32QcAo20HAIItAACBFQAAgB0AAMqGAICmbQcApW0HAM6GAICr6QcAquEHANKGAIC+oAEAr+kHAK7hBwCt6QcArPEHANaGAICzkQYAhugAAIcsAQC2QQEA2oYAgN6GAIC1UQEAuk0BALslAQDihgCA5oYAgL4lAQC/LQEAvDEBAL0xAQCwrQEAscUBALLBAQCzwQEAtMUBALXNAQC28QEAt/EBALgBAQC5AQEAugEBALsBAQC8AQEAvQEBAL4BAQC/AQEA6oYAgO6GAIDyhgCA9oYAgIaFAID6hgCA/oYAgAKHAICoTQYAqVkGAKo9BgCrNQYArP0BAK3lAQCu5QEAr9UBAKPVBQAGhwCACocAgA6HAIAShwCApgUCAKUVAgAWhwCAq2ECAKoJAgAahwCAHocAgK9pAgCuYQIArXUCAKx1AgAihwCAJocAgCqHAIAuhwCAMocAgOFkBQA2hwCA4+wFAIARAACBEQAAghEAAO/0BgA6hwCAPocAgEKHAIC+MAMAhMQCAEqHAICz4QMAhMAcALVRAwBOhwCAUocAgLZZAwBWhwCAWocAgLtxAwC6eQMAvbUAALxpAwC/tQAAvrUAAF6HAIDhlAEAYocAgONcAgCGcBwAh0QDAGaHAIBqhwCAbocAgHKHAIB2hwCAeocAgH6HAICChwCAhocAgO94AgCoVQIAqV0CAKphAgCrYQIArNECAK3RAgCu0QIAr9ECAIqHAICOhwCAkocAgJaHAICahwCAnocAgKKHAICmhwCAuGkBALlpAQC6CQEAuwkBALwZAQC9GQEAvgkBAL8FAQCwtQIAsb0CALK1AgCzaQEAtHkBALV5AQC2aQEAt2EBAOHEBwDjpAYA47gGAOF8BgCADQAAgTUAAII9AACqhwCArocAgLKHAIC+4B0AuocAgL6HAIDvYAAA7+gGAMKHAICjqQIAxocAgMqHAIDOhwCA0ocAgKYRAgClGQIA1ocAgKs5AgCqMQIAhkgcAIfMHACv/QEArv0BAK39AQCsIQIAqIUeAKmRHgCqkR4Aq60eAKy1HgCt1R4ArtEeAK/FHgC2hwCA2ocAgN6HAIDihwCA5ocAgOqHAIDuhwCA8ocAgLhhHwC5YR8AumEfALthHwC8YR8AvWEfAL5hHwC/YR8AsL0eALGFHgCyjR4As4UeALSdHgC1hR4Ato0eALeFHgCzGR4A9ocAgPqHAID+hwCAAogAgLZVHgC1PR4ABogAgLtBHgC6eR4ACogAgA6IAIC/QR4AvlkeAL1RHgC8WR4AEogAgKNdHgAWiACAGogAgKYRHgAeiACAIogAgKV5HgCqPR4AqwUeAISkAwC+qAMArh0eAK8FHgCsHR4ArRUeAKitHgCptR4AqrUeAKvJHgCs2R4ArdkeAK7JHgCvwR4AgO0BAIHxAQCC8QEAJogAgIaQAACHdAEAKogAgC6IAIC4yQEAuckBALrZAQC70QEAvPkBAL35AQC+mQEAv5UBALBFAQCxTQEAskUBALNdAQC0RQEAtU0BALZFAQC3+QEAsz0eADKIAIA2iACAOogAgD6IAIC2WR4AtVEeAEKIAIC7iQEAuoEBAEaIAIBKiACAv4kBAL6BAQC9iQEAvJEBAE6IAIBSiACAo3UeAFaIAIClGR4AWogAgF6IAICmER4ARocAgGKIAICrwQEAqskBAK3BAQCs2QEAr8EBAK7JAQBmiACAaogAgG6IAIByiACAdogAgIQYAgB6iACAfogAgIKIAICGiACAiogAgI6IAICSiACAmogAgJ6IAIC+cAMAgGkAAIFpAACCeQAAhAAEAIbwBACHdAMAoogAgO8MHwCmiACA4aweAKqIAIDj8B4ArogAgLKIAIC2iACAuogAgL6IAIDCiACAxogAgMqIAIDvVAIAzogAgNKIAIDWiACA46QCANqIAIDhgAEA3ogAgOKIAIDmiACA6ogAgO6IAICzRQMA8ogAgPaIAID6iACA/ogAgLZFAwC1VQMAAokAgLshAwC6SQMAvqAEAAqJAIC/KQMAviEDAL01AwC8OQMAqDkCAKk5AgCqjQIAq4UCAKydAgCthQIAroUCAK+1AgCA7QEAgfUBAIL1AQAOiQCAhpAEAIcEBQASiQCAFokAgLhFAQC5TQEAukUBALtdAQC8SQEAvUkBAL55AQC/eQEAsM0CALGlAgCyrQIAs6ECALSlAgC1rQIAtp0CALd9AQAaiQCAHokAgCKJAIAmiQCAKokAgC6JAIAyiQCA74gBAITsBADhVB4ANokAgONUAQA6iQCAPokAgEKJAIBGiQCAo0UCAEqJAIBOiQCAUokAgFaJAICmRQIApVUCAFqJAICrIQIAqkkCAF6JAIBiiQCArykCAK4hAgCtNQIArDkCAKg1BgCpPQYAqlEGAKttBgCseQYArWUGAK5tBgCvZQYABokAgGaJAIBqiQCAbokAgIAZAACBGQAAggUAAHKJAIC45QYAuekGALr5BgC7+QYAvOkGAL3pBgC+nQYAv5UGALAdBgCx5QYAsu0GALPlBgC0/QYAteEGALbhBgC34QYAs9kGAL7QAwB2iQCAeokAgH6JAIC25QYAtfEGAIKJAIC7IQYAutkGAIaYAACHeAMAvyUGAL45BgC9MQYAvDkGAIaJAICjnQYAiokAgI6JAICmoQYAkokAgJaJAICltQYAqp0GAKtlBgCaiQCAnokAgK59BgCvYQYArH0GAK11BgCo7QcAqSkGAKoxBgCrMQYArJEGAK2RBgCukQYAr5EGAKKJAICmiQCAqokAgK6JAICyiQCAtokAgLqJAIC+iQCAuIUGALmNBgC6hQYAu50GALyNBgC9vQYAvrUGAL95AQCw8QYAsfEGALLxBgCzxQYAtMEGALXBBgC2wQYAt8EGALO5BgDCiQCAxokAgMqJAIDOiQCAthEGALUZBgDSiQCAuzUGALo1BgDWiQCA2okAgL8FBgC+BQYAvREGALwlBgClQQYA3okAgOKJAICmSQYAgRUAAIB5AACj4QYAghUAAK1JBgCsfQYAr10GAK5dBgCENAEAlogAgKttBgCqbQYAvswDAOqJAICzlQIA7okAgLXZAgDyiQCA9okAgLbRAgCGgAwAhzgDALvFAgC6xQIAvRUDALwVAwC/FQMAvhUDAPqJAID+iQCA71gGAIRAAwACigCABooAgAqKAIAOigCAEooAgBaKAIAaigCAHooAgOE4BgAiigCA4yQGAL5wDACsSQIArUkCAK5dAgCvVQIAqB0CAKkFAgCqBQIAq10CAISoDAAmigCAKooAgC6KAIC+vA0AMooAgDaKAIA6igCAvE0DAL1VAwC+VQMAv2UDALjpAwC56QMAul0DALtVAwC0yQMAtckDALbZAwC32QMAsBkCALEZAgCy2QMAs9kDAD6KAIDj5AAAQooAgOG8AQBGigCAgj0AAIE9AACAPQAASooAgE6KAIBSigCAWooAgF6KAIDvzAMAYooAgGaKAICj3QMAaooAgIboDACHYA0AbooAgKaZAwClkQMAcooAgKuNAwCqjQMAdooAgHqKAICvXQIArl0CAK1dAgCsXQIAfooAgIKKAICGigCAiooAgI6KAICSigCAlooAgO/gAQCEvAwA4YwGAJqKAIDjHAYAnooAgKKKAICmigCAqooAgLPVAQCuigCAsooAgLaKAIC6igCAtpEBALWZAQC+igCAu70BALq9AQDCigCAyooAgL+dAQC+nQEAvZ0BALydAQCoBQ4AqQkOAKodDgCrFQ4ArFEOAK1RDgCuSQ4Ar0kOAFaKAICCzQ8AgfUPAID9DwDGigCAzooAgIYcAACHsAMAuOkOALnpDgC6/Q4Au/UOALztDgC9VQ8AvlEPAL9NDwCwOQ4AsTkOALIJDgCzCQ4AtBkOALUZDgC2DQ4At9kOAKOVDgDSigCA1ooAgNqKAIDeigCAptEOAKXZDgDiigCAq/0OAKr9DgDmigCA6ooAgK/dDgCu3Q4Ard0OAKzdDgDuigCAs/0PAPKKAID2igCAtoEPAPqKAID+igCAtZkPALqNDwC7ZQ8AAosAgAaLAIC+fQ8Av2UPALx9DwC9dQ8AqC0OAKk1DgCqMQ4AqzEOAKxVDgCtRQ4ArkUOAK91DgAKiwCADosAgBKLAIAWiwCAGosAgB6LAIAiiwCAJosAgLjpDgC59Q4Auv0OALv1DgC87Q4AvZEOAL6RDgC/kQ4AsA0OALHlDgCy7Q4As+UOALT9DgC15Q4Atu0OALflDgCjuQ4Agi0AAIEVAACAHQAAKosAgKbFDgCl3Q4ALosAgKshDgCqyQ4AMosAgL4sAQCvIQ4ArjkOAK0xDgCsOQ4AOosAgLZVAQC1RQEANosAgLNVAQA+iwCAhngAAIdcAAC/OQEAvjEBAL0lAQC8JQEAuzEBALpZAQDmiQCAQosAgEaLAIBKiwCAhAQDAKOJAgBOiwCApZkCAKaJAgBSiwCAvyg5AFaLAICqhQIAq+0CAKz5AgCt+QIAru0CAK/lAgDjWAIA78AOAOGIAQBaiwCAXosAgGKLAIBmiwCAaosAgG6LAIByiwCAdosAgHqLAIDvKAIA4ygOAH6LAIDhRA4AqbUCAKhpDQCrAQIAqgkCAK0BAgCsGQIArzECAK4BAgC+AAQAgosAgIaLAICKiwCAjosAgJKLAICWiwCAmosAgLnlAwC45QMAu+UDALrlAwC95QMAvOUDAL/lAwC+5QMAsSECALBJAgCzJQIAsiUCALUpAgC0IQIAtxUCALYVAgCowQIAqdECAKr1AgCrDQEArBUBAK0FAQCuBQEArzkBAJ6LAICiiwCAqosAgK6LAICyiwCAtosAgLqLAIC+iwCAuC0BALk9AQC67QEAu+UBALz9AQC95QEAvu0BAL/lAQCwLQEAsTUBALI9AQCzNQEAtC0BALUVAQC2HQEAtxUBAIA9AQCBpQAAgq0AAO/YAACGsAUAh9gFAMKLAIDv1A8AhGwEAOH0DgDGiwCA4xwPAMqLAIDhlAEAzosAgOMMDgCzPQIA0osAgNaLAIDaiwCA3osAgLbFAQC13QEA4osAgLuxAQC6qQEA5osAgOqLAIC/kQEAvqkBAL2hAQC8qQEAposAgO6LAICqRQYAq10GAKxFBgCtTQYArkUGAK99BgDyiwCA9osAgPqLAICj0QUA/osAgKUxBgCmKQYAAowAgAaMAICCHQAAgR0AAIAdAAAKjACADowAgBKMAIC+lAMAFowAgBqMAICGSAMAh8wDAB6MAIAijACAJowAgCqMAICoqQcAqakHAKq5BwCruQcArKkHAK2pBwCuAQcArzUHAC6MAIAyjACANowAgDqMAIA+jACAQowAgEaMAIBKjACAuC0HALnBAAC66QAAu+kAALz5AAC95QAAvuUAAL+dAACwUQcAsV0HALItBwCzJQcAtD0HALUlBwC2JQcAtxUHALMxBgBOjACAUowAgFaMAIBajACAtikGALUhBgBejACAu5kGALqVBgBijACAZowAgL/hBgC++QYAvfEGALz5BgBqjACAo3UGAG6MAIByjACApm0GAHaMAIB6jACApWUGAKrRBgCr3QYAfowAgIKMAICuvQYAr6UGAKy9BgCttQYAqOUBAKn1AQCq/QEAq/UBAKztAQCtNQEArj0BAK81AQCA+QAAgc0AAILFAACEYAEAvngBAIqMAICHrAAAhpABALjRAAC52QAAuuEAALvhAAC8kQAAvZ0AAL6VAAC/iQAAsE0BALFVAQCyXQEAs1UBALRNAQC18QAAtvEAALfxAACzdQIAjowAgJKMAICWjACAmowAgLa1AgC1ZQIAnowAgLuRAgC6iQIAoowAgKaMAIC/NQMAvokCAL2BAgC8iQIAqowAgKMxAgCujACAhMADAKbxAgCyjACAtowAgKUhAgCqzQIAq9UCALqMAIC+jACArs0CAK9xAwCszQIArcUCAKuNAACqjQAAqY0AAKg5AwCvvQAArr0AAK2FAACsjQAAqgAAAKsAAADCjACAxowAgMqMAIDOjACA0owAgNaMAIC7fQAAun0AALl9AAC4fQAAv90BAL7dAQC93QEAvN0BALO5AACysQAAsaEAALCtAAC3XQAAtl0AALWVAAC0lQAA2owAgN6MAIDijACA5owAgIE1AACADQAA6owAgII1AAC+rD0A7owAgPKMAICFaD0A+owAgP6MAICGODwAh8ACALNJAQACjQCA0AAAAAaNAIAKjQCAtkkBALVJAQAOjQCAuykBALolAQASjQCAFo0AgL8dAQC+HQEAvSEBALwpAQDjNDYA4QwGAOGwAgDjPAYAGo0AgB6NAIAijQCAJo0AgIQsPwC+oD8AKo0AgC6NAIDvfDcAMo0AgDaNAIDvGAEAOo0AgD6NAICGaD4Ah8w/AEKNAIBGjQCASo0AgO+UAABOjQCA4ZQBAFKNAIDjUAAAVo0AgILpPwCB6T8AgPE/AKMJPgCPASQA9owAgFqNAIBejQCApgk+AKUJPgBijQCAq2k+AKplPgBmjQCAao0AgK9dPgCuXT4ArWE+AKxpPgCeYTgAn3U4AJzBNACdtTkAmqU1AJt1NACYeTAAmXExAJYhLQCXhTEAlG0sAJVlLACSeSgAk6UtAJBRJACReSgAsQ0UALAFFACzARgAslUUALV5GAC0tRgAbo0AgHKNAIB2jQCAeo0AgH6NAICCjQCAotE8AKMlAQCgdTkAob08AKHJAACGjQCAowEEAKLlAAClHQQApPUEAKf5CACmAQgAqQEMAKhtCACrzQwAqs0MAK3REACsARAAr9URAK7ZEACCBSUAgy0lAIqNAICOjQCAhsEsAIcRLQCEHSkAhRUpAIopLQCLZSwAko0AgJaNAICOHTAAj8E0AIzZMACNHTEAkmE1AJPNNQCajQCAno0AgJZhOQCXmTgAlKE4AJV9OQCaYT0AmwU9AKKNAICmjQCAqo0AgK6NAICc6QAAso0AgLaNAIC6jQCAvo0AgMKNAICGjACAxo0AgMqNAIDOjQCAqJE+AKmRPgCq7T4Aq+E+AKzhPgCt6T4ArtE+AK/RPgCwUT4AsVE+ALJRPgCzUT4AtHk+ALV5PgC2bT4At2U+ALghPgC5IT4Aujk+ALs5PgC8KT4AvRU+AL4RPgC/DT4AgJkDAIGZAwCCBQAA0o0AgL5UAwDhsD0A2o0AgONAPgCEOAIA3o0AgOKNAIDv9D8A5o0AgOqNAICGmAQAhxwDALMFPQCECAQA7o0AgPKNAID2jQCAtgk9ALUJPQD6jQCAu/U9ALr1PQD+jQCAAo4AgL/dPQC+3T0AveU9ALzlPQAGjgCACo4AgKPNPQC+xAQApcE9AA6OAIASjgCApsE9ABaOAIAajgCAqz09AKo9PQCtLT0ArC09AK8VPQCuFT0AtmkCAB6OAIAijgCAtWkCACaOAICzSQIAKo4AgC6OAIC+qQMAv6kDALzBAwC9wQMAuvkDALv5AwAyjgCANo4AgKgtAwCpnQMAqpUDAKutAwCstQMArb0DAK61AwCv2QMAgA0AAIEVAACCHQAAOo4AgD6OAIBCjgCAh7QFAIacBAC4MQIAuTECALo1AgC7zQIAvNUCAL3dAgC+1QIAv8kCALBpAgCxaQIAskECALNBAgC0OQIAtTkCALYRAgC3EQIASo4AgOM0PgBOjgCA4aw+AFKOAIDvfAMAVo4AgFqOAIBejgCA45QDAGKOAIDhfD4AZo4AgO/oPgBqjgCAbo4AgHKOAIB2jgCAo1UDAHqOAICldQMAfo4AgIKOAICmdQMAho4AgIqOAICr5QIAquUCAK3dAgCs3QIAr7UCAK61AgCoGQYAqSEGAKohBgCrPQYArCUGAK1dBgCuVQYAr00GAEaOAICOjgCAko4AgJaOAICajgCAno4AgKKOAICmjgCAuOUGALmBBgC6gQYAu50GALyJBgC9iQYAvqEGAL+hBgCwPQYAsQ0GALIFBgCz7QYAtPUGALXhBgC24QYAt90GALOpBgCCLQAAgRUAAIAdAACqjgCAtt0GALWtBgCujgCAu8kGALr5BgCyjgCAhOADAL8lBgC+MQYAvTkGALzRBgC+iAMAo+0GANaNAIC2jgCAppkGALqOAIC+jgCApekGAKq9BgCrjQYAhkgAAIdsAACudQYAr2EGAKyVBgCtfQYAqIEGAKmNBgCqmQYAq5UGAKyNBgCttQYArrEGAK+tBgDCjgCAxo4AgMqOAIDOjgCA0o4AgNaOAIDajgCA3o4AgLilBgC5YQEAumEBALthAQC8YQEAvWEBAL5hAQC/YQEAsNkGALHZBgCyqQYAs6kGALS9BgC1oQYAtqEGALedBgCzEQYA4o4AgOaOAIDqjgCA7o4AgLY1BgC1BQYA8o4AgLsdBgC6HQYA9o4AgPqOAIC/ZQYAvnkGAL19BgC8fQYA/o4AgKNVBgACjwCABo8AgKZxBgAKjwCADo8AgKVBBgCqWQYAq1kGABKPAIAWjwCArj0GAK8hBgCsOQYArTkGAKjVAgCp3QIAqikDAKspAwCsOQMArTkDAK4pAwCvKQMAGo8AgB6PAIAijwCAKo8AgC6PAIAyjwCAvrgDADaPAIC47QMAuYUDALqBAwC7gQMAvIUDAL2NAwC+sQMAv7EDALBZAwCxWQMAsu0DALPlAwC0/QMAteUDALblAwC31QMAgKEAAIGhAACCoQAAvoAMADqPAICEmAIAPo8AgEKPAICGAAwAh/QDAEaPAIBKjwCATo8AgFKPAIBWjwCAhLADALPhAwBajwCAXo8AgGKPAIBmjwCAtvkDALXxAwBqjwCAu90DALrdAwBujwCAco8AgL9hAwC+eQMAvXEDALx5AwB2jwCAeo8AgH6PAICjLQIAgo8AgKU9AgCmNQIAho8AgIqPAICOjwCAqhECAKsRAgCstQIArb0CAK61AgCvrQIA48QDAOMQBwDhuAEA4WwHAIBxAACBcQAAggUAAJKPAICGwAwAh1QNAJqPAICejwCA77ADAO8ABwCijwCApo8AgKqPAICujwCAso8AgLaPAIC6jwCAvo8AgMKPAIDvpAEAhKANAOGABgDGjwCA4xABAMqPAIDOjwCA0o8AgNaPAICz9QEA2o8AgN6PAIDijwCA5o8AgLZNAQC1SQEA6o8AgLtRAQC6SQEA7o8AgPKPAIC/OQEAvjEBAL1BAQC8SQEAqC0OAKk1DgCqPQ4AqzEOAKyBDgCtjQ4AroUOAK+1DgCWjwCA9o8AgPqPAID+jwCAgBkAAIEZAACCBQAAApAAgLidDgC5rQ4AuqUOALtNDwC8VQ8AvV0PAL5JDwC/QQ8AsM0OALHVDgCy3Q4As9UOALS1DgC1vQ4AtrUOALetDgCjtQ4AvogDAAaQAIAKkACADpAAgKYNDgClCQ4AEpAAgKsRDgCqCQ4AhggAAIdsAwCveQ4ArnEOAK0BDgCsCQ4AFpAAgBqQAIAekACAs7UPACKQAIC1VQ8Atl0PACaPAIAmkACAKpAAgLp5DwC7eQ8AvGkPAL1dDwC+SQ8Av0kPAKhpDgCpaQ4AqnEOAKtxDgCskQ4ArZEOAK6RDgCvkQ4ALpAAgDKQAIA2kACAOpAAgD6QAIBCkACARpAAgEqQAIC4hQ4AuY0OALqFDgC7nQ4AvI0OAL29DgC+tQ4Av3kBALDxDgCx8Q4AsvEOALPFDgC0wQ4AtcEOALbBDgC3wQ4Ao/kOAE6QAIBSkACAVpAAgFqQAICmEQ4ApRkOAF6QAICrNQ4AqjUOAGKQAIBmkACArwUOAK4FDgCtEQ4ArCUOAIANAACBFQAAgh0AAGqQAIBukACAcpAAgISUAQC+lAEAhkAHAIf0AAB6kACAfpAAgIKQAICGkACAipAAgI6QAICojQIAqZUCAKqVAgCrzQIArNUCAK3dAgCuyQIAr/0CAJKQAICWkACAmpAAgJ6QAIC/ABQAopAAgKaQAICqkACAuH0DALnBAwC6wQMAu8EDALzBAwC9yQMAvvEDAL/xAwCwhQIAsUUDALJNAwCzRQMAtF0DALVFAwC2TQMAt0UDALMdAgCukACAspAAgLaQAIC6kACAtl0CALVdAgC+kACAu4EDALpBAgDCkACAxpAAgL+BAwC+mQMAvZEDALyZAwDKkACAo1kCAM6QAIDSkACAphkCANaQAIDakACApRkCAKoFAgCrxQMA3pAAgOKQAICu3QMAr8UDAKzdAwCt1QMA6pAAgOPMAACEBAIA4bwBAIDJAQCB/QEAgvUBAL4QBQDukACAvigEAPKQAID2kACA+pAAgO8QAAD+kACAApEAgIbgBACH9AIABpEAgAqRAIDj/A8ADpEAgOHgDwASkQCA7xQPABaRAIAakQCAHpEAgCKRAIAmkQCAKpEAgC6RAIAykQCANpEAgDqRAIA+kQCAQpEAgEaRAIBKkQCA7+ABAIUEEgDh3A4ATpEAgOMcDgCAKQAAgR0AAIIFAABSkQCAszECAFqRAICEzAUAXpEAgGKRAIC2KQIAtSECAGaRAIC7zQEAus0BAGqRAIBukQCAv3UBAL7JAQC9wQEAvMkBAKjpBQCp6QUAqvkFAKv5BQCs6QUArekFAK45BgCvOQYA5pAAgFaRAICGiAAAhwADAHKRAIB2kQCAepEAgH6RAIC40QYAudkGALrhBgC74QYAvJEGAL2dBgC+lQYAv4kGALBJBgCxSQYAsl0GALNVBgC0TQYAtfEGALbxBgC38QYAo3EFAIKRAICGkQCAipEAgI6RAICmaQUApWEFAJKRAICrjQYAqo0GAJaRAICakQCArzUGAK6JBgCtgQYArIkGAJ6RAICikQCAs+EHAKaRAIC14QcAqpEAgK6RAIC25QcAdpAAgLKRAIC7vQcAuqEHAL2VBwC8qQcAv5UHAL6VBwCoAQYAqSUGAKohBgCrIQYArCEGAK0tBgCuJQYAr1UGALaRAICCHQAAgR0AAIAdAAC6kQCAvpEAgMKRAIC+MAEAuDkGALk5BgC6yQYAu8kGALzZBgC92QYAvskGAL/JBgCwLQYAsTEGALI1BgCzCQYAtBkGALUZBgC2CQYAtwkGAKOpBgCEjAIAhigfAIdEAQDKkQCApq0GAKWpBgDOkQCAq/UGAKrpBgDSkQCA1pEAgK/dBgCu3QYArd0GAKzhBgDakQCAsxUGAN6RAIDikQCAtj0GAOaRAIDqkQCAtTUGALrZAQC72QEA7pEAgPKRAIC+fQEAv2UBALx9AQC9dQEAqMUFAKnJBQCq2QUAq9EFAKz5BQCt+QUArikCAK8pAgD2kQCA+pEAgP6RAIACkgCAjAAAAAaSAIAKkgCADpIAgLjtAgC5hQIAuo0CALuBAgC8hQIAvY0CAL69AgC/fQMAsFkCALFZAgCy7QIAs+UCALT9AgC15QIAtuUCALfVAgCjUQUAEpIAgBaSAIAakgCAHpIAgKZ5BQClcQUAIpIAgKudAgCqnQIAJpIAgCqSAICvIQIArjkCAK0xAgCsOQIAghEAAC6SAICAZQAAgQkAADKSAIC+mAMAOpIAgD6SAICEJAMAQpIAgIdoAwCGjBwARpIAgEqSAIBOkgCAUpIAgFaSAIBakgCAs6ECAITAHAC10QIAXpIAgGKSAIC21QIAZpIAgGqSAIC7wQIAuvUCAL0RAQC82QIAvxEBAL4ZAQBukgCAcpIAgHaSAIB6kgCAfpIAgIKSAICGkgCA77gGAIqSAIDhnAQAjpIAgON0BgCSkgCAlpIAgJqSAICekgCAgPkAAIH5AACCBQAAopIAgL5YHACEWB8A71wAAO9ABgDhkAEA4fwGAOM8AADjdAYAqpIAgK6SAICGmBwAh/QcAKNpAgC+DB8AspIAgLaSAIC6kgCAph0CAKUZAgC+kgCAqwkCAKo9AgDCkgCAxpIAgK/ZAQCu0QEArdkBAKwRAgCokR0AqZkdAKqhHQCroR0ArNEdAK3dHQCu1R0Ar8kdADaSAICmkgCAypIAgM6SAIDSkgCA1pIAgNqSAIDekgCAuHkeALl5HgC6zR4Au8UeALzdHgC9xR4AvsUeAL/1HgCwuR0AsY0dALKFHQCzTR4AtFUeALVdHgC2VR4At0keALjNHwC51R8Aut0fALvVHwC88R8Avf0fAL7pHwC/6R8AsKUfALGxHwCysR8As40fALSVHwC19R8Atv0fALf1HwCoGR4AqRkeAKotHgCrPR4ArCUeAK0tHgCuJR4Ar90fAOKSAIDmkgCA6pIAgO6SAIDykgCAxpEAgPaSAID6kgCAs+UfAP6SAIACkwCABpMAgAqTAIC27R8Ate0fAA6TAIC7NR4AuiEeABKTAIAWkwCAv3EeAL4RHgC9GR4AvCUeAIJpAACjoR8AgFkAAIFRAACmqR8AGpMAgB6TAIClqR8AqmUeAKtxHgCGAAQAh+wBAK5VHgCvNR4ArGEeAK1dHgCoMR4AqTEeAKpBHgCrQR4ArEEeAK1JHgCucR4Ar3EeACKTAIAmkwCAKpMAgC6TAIAykwCANpMAgDqTAIA+kwCAuCkBALkpAQC6OQEAuzUBALwtAQC90QAAvtEAAL/RAACwyQEAsckBALLZAQCz2QEAtMkBALXJAQC2GQEAtxkBALPJHQBCkwCARpMAgEqTAIBOkwCAtskdALXJHQBSkwCAuw0CALoNAgBWkwCAWpMAgL8NAgC+DQIAvQ0CALwNAgBekwCAo40dAGKTAIBmkwCApo0dAGqTAIBukwCApY0dAKpJAgCrSQIAcpMAgHaTAICuSQIAr0kCAKxJAgCtSQIAgA0AAIERAACCEQAAepMAgO/MAgB+kwCAgpMAgISQAgDjLAIAvigDAOHYAQCKkwCAhhAEAIfUAwCOkwCAkpMAgLNhAwCWkwCAmpMAgJ6TAICikwCAtnkDALVxAwCmkwCAu10DALpdAwCqkwCArpMAgL/hAAC++QAAvfEAALz5AACjoQIAspMAgLaTAIC6kwCAvpMAgKa5AgClsQIAwpMAgKudAgCqnQIAxpMAgMqTAICvIQEArjkBAK0xAQCsOQEAzpMAgNKTAIDvZB8A1pMAgNqTAIDekwCA4pMAgOaTAICADQAAgREAAIIVAADqkwCA4eAcAO6TAIDjiB8A8pMAgISAAgC+jAUAh0gFAIYsBAD6kwCA/pMAgO+kHgDv9B4A4QAeAOFQHwDjLB4A47AeAAKUAIAGlACACpQAgA6UAIASlACAFpQAgISEBACzcQEAGpQAgLUdAQC2FQEAHpQAgCKUAIAmlACAugEBALsBAQC89QAAvf0AAL71AAC/7QAAqK0GAKm9BgCqtQYAq8kGAKzZBgCt2QYArskGAK/BBgAqlACALpQAgDKUAIA2lACAOpQAgD6UAIBClACARpQAgLhtBwC5BQcAug0HALsBBwC8AQcAvQEHAL4BBwC/AQcAsIkGALGJBgCybQcAs2UHALR9BwC1ZQcAtmUHALdVBwCGkwCAozkGAEqUAID2kwCApl0GAE6UAIBSlACApVUGAKpJBgCrSQYAVpQAgFqUAICuvQcAr6UHAKy9BwCttQcAgG0AAIEJAACCGQAAXpQAgGKUAIC+nAMAZpQAgGqUAICGQAAAh2AAAG6UAIBylACAdpQAgHqUAIB+lACAgpQAgKiRBgCpkQYAqrkGAKu5BgCsqQYArakGAK7ZBgCv2QYAhpQAgIqUAICOlACAkpQAgJaUAICalACAnpQAgKKUAIC4cQEAuXEBALpxAQC7cQEAvNkBAL3BAQC+wQEAv/UBALCxBgCxuQYAsokGALOJBgC0UQEAtVEBALZRAQC3UQEAszEGAKaUAICqlACArpQAgLKUAIC2KQYAtSEGALaUAIC7fQYAunUGALqUAIC+lACAv5UBAL6VAQC9XQYAvF0GAMKUAICjdQYAxpQAgMqUAICmbQYAzpQAgNKUAIClZQYAqjEGAKs5BgCErAEAvqABAK7RAQCv0QEArBkGAK0ZBgCo3QIAqe0CAKrlAgCr/QIArOUCAK3tAgCu5QIArz0DANqUAIDelACA4pQAgL5kDADmlACA6pQAgO6UAIDylACAuMkDALnJAwC62QMAu9EDALz5AwC9+QMAvpkDAL+VAwCwRQMAsU0DALJFAwCzXQMAtEUDALVNAwC2RQMAt/kDAIFVAwCASQMAs2UCAIJVAwC1ZQIA9pQAgPqUAIC2ZQIAhgAMAIfkAwC7gQMAuokDAL2BAwC8mQMAv4EDAL6JAwCjLQIA/pQAgAKVAIAGlQCACpUAgKYtAgClLQIADpUAgKvJAwCqwQMAEpUAgBaVAICvyQMArsEDAK3JAwCs0QMA49gGAOGsBwDhnAYA45wGABqVAICEWA0AHpUAgCKVAIAmlQCAKpUAgC6VAIAylQCA7xwBADaVAIA6lQCA70AGAIB5AACBFQAAghEAAIQADAA+lQCA46wAAEKVAIDhpAEASpUAgO9wAACGyAwAh6QNAE6VAIBSlQCAVpUAgFqVAIC6yQUAu8kFALilBQC5zQUAvvkFAL/5BQC8zQUAvcUFALKlBQCzrQUAsBEGALERBgC2rQUAt50FALS1BQC1rQUAqmEGAKthBgConQYAqZUGAK5hBgCvYQYArHEGAK1xBgBelQCAYpUAgGaVAIBqlQCAbpUAgHKVAIC+sAwAdpUAgKghDgCpIQ4AqiEOAKs9DgCsJQ4ArS0OAK4lDgCviQ4ARpUAgHqVAIB+lQCAgpUAgIaVAICKlQCAjpUAgJKVAIC4UQ8AuV0PALpVDwC7bQ8AvHUPAL19DwC+dQ8Av2kPALD5DgCxoQ4AsqEOALOhDgC0oQ4AtakOALaRDgC3kQ4As6kOAJaVAIDWlACAmpUAgJ6VAIC2rQ4Ata0OAKKVAIC7ZQ4Auj0OAKaVAICqlQCAv20OAL5lDgC9dQ4AvHUOAIIZAACj7Q4AgGUAAIEZAACm6Q4ArpUAgLKVAICl6Q4AqnkOAKshDgC2lQCAupUAgK4hDgCvKQ4ArDEOAK0xDgCoYQ4AqXUOAKp9DgCrdQ4ArG0OAK31DgCu/Q4Ar/UOAIaAAQCHpAEAvpUAgMKVAIDGlQCAypUAgM6VAIDSlQCAuHUBALl9AQC6dQEAu8kBALzdAQC9xQEAvsUBAL/1AQCwjQ4AsZUOALKdDgCzkQ4AtFUBALVdAQC2VQEAt00BALP1DgDWlQCA2pUAgN6VAIDilQCAtnUOALXlDgDmlQCAu1EOALpJDgDqlQCA7pUAgL+ZAQC+kQEAvUUOALxJDgDylQCAo7EOAPaVAID6lQCApjEOAP6VAIAClgCApaEOAKoNDgCrFQ4ABpYAgAqWAICu1QEAr90BAKwNDgCtAQ4AqO0CAKktAwCqJQMAqz0DAKwlAwCtLQMAriUDAK+ZAwAOlgCAEpYAgBaWAIAalgCAHpYAgCKWAIC+dAIAKpYAgLiNAwC5kQMAupEDALulAwC8vQMAvXUAAL59AAC/dQAAsOkDALHpAwCy+QMAs/EDALTZAwC12QMAtrkDALe1AwCArQAAgbUAAIK9AACzoQMALpYAgLWhAwC2oQMAMpYAgITgAgA2lgCAuiEDALshAwC8IQMAvSkDAL4RAwC/EQMAo+0DAIXABACFtG8AOpYAgD6WAICm7QMApe0DAEKWAICrbQMAqm0DAIZIBQCHbAMAr10DAK5dAwCtZQMArG0DAEaWAIDjAA4A71hsAOG0DwBKlgCATpYAgFKWAIBWlgCAoakDAKD9DwCjwQMAog0DAOHgAwDv4A8A4+QDAFqWAIBelgCAYpYAgIQEBAC+BAQAZpYAgO+UAwBqlgCAbpYAgHKWAIDj1AMAdpYAgOFUAAB6lgCAfpYAgIKWAICGlgCAgA0AAIEVAACCHQAAipYAgI6WAICSlgCAj5EbAO+cDgCE4AcA4dQOAJqWAIDj8A4AnpYAgKKWAICGGAcAh5AEAJnlFwCY5RcAm+kLAJo5CwCd/QoAnPELAJ9VDwCeXQ8AkSkfAJDNGwCTJR8Aks0fAJXREwCUKRMAlxkXAJZ1EwCM4RAAjSUQAI4tEACP+QwAJpYAgJaWAICKORQAi5UUAITpGACFBRgAhuUYAIfxFACmlgCAqpYAgIIxHACDFRwAnKkEAK6WAICylgCAtpYAgLqWAIC+lgCAmtEEAJt9BACUTQ0AleUIAJblCACXtQgAwpYAgMaWAICSWQwAk1kMAKGRAADKlgCAowF8AKKZAACluXwApJF8AKeZeACm4X0AqYF5AKiheACriXQAqgF0AK0BcACsWXQAr4VwAK6dcACx4WwAsAFsALMBaACyHWwAtfVoALT1aADOlgCA0pYAgNaWAIDalgCA3pYAgOKWAIDmlgCA6pYAgO6WAIDylgCAqD0HAKmVBwCqlQcAq6kHAKzdBwCtxQcArsUHAK8dBgD2lgCAgh0AAIEdAACAHQAA+pYAgP6WAIAClwCAvmABALgZBgC5GQYAuikGALslBgC8IQYAvSEGAL4hBgC/IQYAsHEGALFxBgCycQYAs3EGALRNBgC1NQYAtj0GALctBgCzHQcACpcAgIYoAACHqAAADpcAgLZFBwC1VQcAEpcAgLu1BgC6tQYAFpcAgBqXAIC/8QYAvokGAL2lBgC8pQYAHpcAgKNZBwAilwCAJpcAgKYBBwAqlwCALpcAgKURBwCq8QYAq/EGADKXAIA2lwCArs0GAK+1BgCs4QYAreEGAKipBQCptQUAqr0FAKs9AgCsJQIArVECAK5RAgCvUQIAOpcAgD6XAIBClwCARpcAgIQ8AwBKlwCATpcAgFKXAIC4pQIAua0CALqlAgC7vQIAvKUCAL2tAgC+pQIAv30DALAxAgCxMQIAshkCALMZAgC09QIAta0CALalAgC3nQIAVpcAgFqXAIBelwCAszkFAGKXAIC1oQIAtt0CAGaXAIBqlwCAbpcAgLr5AgC7+QIAvMECAL3BAgC+PQIAv2UCAHKXAICmgQIApf0CAHqXAICjZQUAvlh8AIbYfACHnHwArzkCAK5hAgCtnQIArJ0CAKulAgCqpQIAfpcAgIKXAICohQIAqZUCAKqVAgCrpQIArL0CAK3VAgCu0QIAr9ECAIGFAQCAhQEAhpcAgILtAQCKlwCAjpcAgJKXAICWlwCAuHUBALl9AQC6dQEAu80BALzVAQC93QEAvskBAL/BAQCwtQIAsb0CALKBAgCzgQIAtFEBALVRAQC2UQEAt1EBAJqXAICelwCAopcAgKaXAIDhMAYA4WQHAOMoBgDjxAYAhCB9AKqXAIDvbAAA7xgGAK6XAICylwCAtpcAgLqXAICzXQIAvkh8AL6XAIDClwCAxpcAgLYVAgC1dQIAypcAgLs5AgC6MQIAzpcAgNKXAIC/1QEAvtUBAL0VAgC8FQIAo519AHaXAIDWlwCA2pcAgN6XAICm1X0ApbV9AOKXAICr+X0AqvF9AOaXAIDqlwCArxV+AK4VfgCt1X0ArNV9AIBNAACBVQAAglUAALOxfgDulwCAtWV/ALZtfwDylwCAhkADAIcEAwC66X8Au+l/ALz5fwC9+X8Avt1/AL/NfwD2lwCA+pcAgAaXAID+lwCAApgAgAaYAIAKmACADpgAgKhtfgCpXX4AqlV+AKuFfwCsgX8ArYF/AK6BfwCvgX8AsEF/ALFBfwCyQX8As0F/ALR1fwC1ZX8Atm1/ALdlfwC4XX8AuS1/ALolfwC7PX8AvC1/AL0dfwC+FX8Av/UAAKP9fwASmACAFpgAgBqYAIAemACApiF+AKUpfgAimACAq6V+AKqlfgAmmACAKpgAgK+BfgCukX4ArbV+AKy1fgAumACAMpgAgDaYAIA6mACAPpgAgEKYAIBGmACASpgAgIA9AACBCQAAghkAAE6YAIBSmACAhLgBAL6wAQBWmACAqK0BAKnVAQCq1QEAqw0BAKwVAQCtGQEArgkBAK8JAQCGAAQAhwQBAFqYAIBemACAYpgAgGaYAIBqmACAbpgAgLjtAAC5hQAAuo0AALuFAAC8nQAAvYUAAL6NAAC/hQAAsHkBALF5AQCy7QAAs+UAALT9AAC15QAAtuUAALfVAACzXQIAcpgAgHaYAIB6mACAfpgAgLaZAgC1nQIAgpgAgLu9AgC6vQIAhpgAgIqYAIC/IQMAvjkDAL0xAwC8OQMAvigDAKMZAgCOmACAkpgAgKbdAgCWmACAmpgAgKXZAgCq+QIAq/kCAJ6YAICimACArn0DAK9lAwCsfQMArXUDAL7IBACmmACAqpgAgL7EBQCumACAspgAgLaYAIC6mACAgD0AAIEJAACCGQAAvpgAgMKYAICEOAMAypgAgM6YAIDveAIA0pgAgIZIBACHVAMA1pgAgNqYAIDemACA4pgAgOaYAIDqmACA7pgAgPKYAIDjVAIA9pgAgOFAAQD6mACA/pgAgOMkfwACmQCA4Zx8AAaZAIAKmQCADpkAgBKZAICEbAUAFpkAgBqZAIAemQCAIpkAgO8YfwAmmQCAKpkAgLPxAgAumQCAMpkAgDqZAIA+mQCAtukCALXhAgBCmQCAu3EBALppAQCHoAUAhswEAL85AQC+WQEAvVEBALxhAQDhQH8ARpkAgOM4fgCEwAQAgtkAAO8UAACApQAAgdkAAEqZAIDjwAAATpkAgOHUAQBSmQCAVpkAgO+EfgBamQCAqs0BAKvVAQBemQCAYpkAgK79AQCvnQEArMUBAK31AQBmmQCAo1UCAGqZAIBumQCApk0CAHKZAIB2mQCApUUCAMaYAIA2mQCAepkAgH6ZAICCmQCAhpkAgIqZAICOmQCAqJkGAKmZBgCq7QYAq/0GAKzlBgCt7QYAruUGAK/dBgCwpQYAsa0GALKlBgCzuQYAtK0GALVVBwC2UQcAt00HALh1BwC5fQcAunUHALtJBwC8WQcAvVkHAL5JBwC/RQcAs0UGAJKZAICWmQCAmpkAgJ6ZAIC2TQYAtU0GAKKZAIC7SQYAukEGAIYIAACHjAAAv7EHAL5JBgC9TQYAvFEGAIJdAACjAQYAgEUAAIFdAACmCQYAqpkAgK6ZAIClCQYAqgUGAKsNBgCymQCAtpkAgK4NBgCv9QcArBUGAK0JBgCoTQYAqVUGAKpVBgCriQYArLEGAK29BgCuqQYAr6kGAKaZAIC6mQCAvpkAgMKZAIDGmQCAypkAgM6ZAIDSmQCAuEkBALlJAQC6WQEAu1kBALxJAQC9SQEAvt0BAL/VAQCw3QYAsa0GALKlBgCzjQYAtJkGALWZBgC2jQYAt4UGALPdBgDWmQCA2pkAgN6ZAIDimQCAtj0GALU5BgDmmQCAu2kGALoZBgDqmQCA7pkAgL9dBgC+XQYAvVkGALxxBgDymQCAo5kGAPaZAID6mQCApnkGAP6ZAIACmgCApX0GAKpdBgCrLQYABpoAgAqaAICuGQYArxkGAKw1BgCtHQYAqNUCAKndAgCq4QIAq+ECAKw1AwCtPQMArjUDAK8tAwCAzQMAgQkAAIIZAAAOmgCAEpoAgIQYAgC+dAMAGpoAgLjpAwC56QMAuokDALuFAwC8nQMAvYEDAL6BAwC/tQMAsFUDALFdAwCyVQMAs+kDALT5AwC1+QMAtukDALfhAwCGIAwAhxADAB6aAIAimgCAJpoAgCqaAIAumgCA71wCADKaAIDhFAAANpoAgOOIAgC++AwAOpoAgD6aAIBCmgCAu/kDALrxAwC+gA0ARpoAgL9dAwC+XQMAvV0DALzhAwCzCQIASpoAgE6aAIBSmgCAVpoAgLbdAwC13QMAWpoAgKipBgCpqQYAqrkGAKu5BgCsqQYArakGAK4dBQCvFQUAXpoAgGKaAIBmmgCAapoAgG6aAIBymgCAdpoAgHqaAIC4GQUAuS0FALolBQC7yQUAvNkFAL3FBQC+zQUAv8UFALBtBQCxdQUAsnUFALNFBQC0XQUAtT0FALY1BQC3KQUA4fQGAOFUBwDjFAYA47wGAIEJAACAqQAAfpoAgII5AACE7A0AgpoAgIeIDACGDAwAipoAgI6aAIDvzAcA78QHAKMpAwCSmgCAlpoAgJqaAICemgCApv0CAKX9AgCimgCAq9kCAKrRAgCmmgCAqpoAgK99AgCufQIArX0CAKzBAgCoPQ4AqY0OAKqFDgCrnQ4ArIUOAK2NDgCuuQ4Ar7UOAIaaAICumgCAspoAgLaaAIC6mgCAvpoAgMKaAIDGmgCAuL0OALllDwC6bQ8Au2UPALx9DwC9ZQ8Avm0PAL9lDwCw1Q4Asd0OALLVDgCzoQ4AtJUOALWdDgC2lQ4At40OALMNDgDKmgCAzpoAgNKaAIDWmgCAtg0OALUNDgDamgCAuxkOALoRDgDemgCAFpoAgL9ZDgC+UQ4AvXUOALwBDgDimgCAo0kOAOaaAIDqmgCApkkOAO6aAIDymgCApUkOAKpVDgCrXQ4AhKQDAPaaAICuFQ4Arx0OAKxFDgCtMQ4AqLEOAKmxDgCqzQ4Aq8UOAKzdDgCtxQ4ArsUOAK/1DgCA7QEAgfEBAILxAQD6mgCAhpABAIe0AQD+mgCAApsAgLjFAQC5zQEAusUBALvdAQC8zQEAvf0BAL6ZAQC/lQEAsI0OALFBAQCyQQEAs0EBALRBAQC1QQEAtkEBALdBAQCzRQ4ABpsAgAqbAIAOmwCAEpsAgLZFDgC1VQ4AFpsAgLuFAQC6SQ4AGpsAgB6bAIC/hQEAvoUBAL2VAQC8lQEAIpsAgKMBDgAmmwCAKpsAgKYBDgAumwCAMpsAgKURDgCqDQ4Aq8EBADabAIA6mwCArsEBAK/BAQCs0QEArdEBAKgtAwCpPQMAqjUDAKuJAwCsmQMArZkDAK6JAwCvgQMAPpsAgEKbAIBGmwCASpsAgE6bAIBSmwCAVpsAgFqbAIC4rQMAuWUAALptAAC7ZQAAvH0AAL1lAAC+bQAAv2UAALDJAwCxyQMAsqkDALOlAwC0vQMAtaEDALahAwC3lQMAgL0AAIEJAACCGQAAXpsAgGKbAIC+2AMAapsAgG6bAICErAIAcpsAgIfoAwCGDAQAdpsAgHqbAIB+mwCAgpsAgLP9AwCGmwCAipsAgI6bAICSmwCAtlkDALVRAwCWmwCAu00DALpNAwCamwCAnpsAgL8lAwC+OQMAvTEDALw9AwCimwCAppsAgKqbAICumwCA71gPALKbAIC2mwCAupsAgOOQDgC+mwCA4bAPAMKbAIDGmwCAypsAgM6bAIDSmwCAgHUAAIF9AACCdQAAhBgFAO88AwDamwCAvhQFAN6bAIDj0AMA4psAgOFAAADmmwCAhtAEAIdYBQDqmwCA7psAgPKbAID2mwCA+psAgP6bAIACnACABpwAgAqcAIDvrA8AhOwEAOEQDgAOnACA41QBABKcAIAWnACAGpwAgB6cAICj/QIAIpwAgCacAIAqnACALpwAgKZZAgClUQIAMpwAgKtNAgCqTQIANpwAgDqcAICvJQIArjkCAK0xAgCsPQIAqJkGAKmZBgCqrQYAq70GAKylBgCtrQYArqUGAK/ZBgDWmwCAghEAAIEZAACAwQcAPpwAgEKcAIC+cAMARpwAgLhJBwC5SQcAul0HALtVBwC8TQcAvXEHAL51BwC/bQcAsKkGALGpBgCyuQYAs7EGALSZBgC1mQYAtnkHALd5BwC1NQYASpwAgE6cAIC2NQYAhjAAAIdcAwCzPQYAUpwAgL19BgC8dQYAv0UGAL5FBgBmmwCAVpwAgLt1BgC6dQYAo2UGAFqcAIBenACAYpwAgGacAICmbQYApW0GAGqcAICrLQYAqi0GAG6cAIBynACArx0GAK4dBgCtJQYArC0GAKhVBgCpWQYAqm0GAKthBgCsaQYArWkGAK6ZBgCvmQYAdpwAgHqcAIB+nACAgpwAgIacAICKnACAjpwAgJKcAIC4+QYAufkGALqNBgC7hQYAvJ0GAL2FBgC+hQYAv7UGALDpBgCx6QYAsvkGALP5BgC06QYAtd0GALbJBgC3yQYAs+UGAJacAICanACAnpwAgKKcAIC26QYAteEGAKacAIC7LQYAui0GAKqcAICunACAvxkGAL4tBgC9LQYAvC0GAIIVAACjoQYAgGEAAIFhAACmrQYAspwAgL6QAQClpQYAqmkGAKtpBgCEpAEAupwAgK5pBgCvXQYArGkGAK1pBgCohQIAqY0CAKqVAgCruQIArNUCAK3dAgCu1QIAr80CAIaAHACHZAMAvpwAgL5gAwDCnACAxpwAgMqcAIDOnACAuHUDALl9AwC6dQMAu8kDALzZAwC92QMAvskDAL/BAwCwvQIAsY0CALKFAgCzTQMAtFUDALVdAwC2VQMAt00DALMdAgDSnACAhAgDANacAIDanACAtl0CALVdAgDenACAu0kCALp5AgDinACA5pwAgL+ZAwC+kQMAvZkDALxRAgCwAAAAo1kCAOqcAIDunACAphkCAPKcAID2nACApRkCAKo9AgCrDQIA+pwAgP6cAICu1QMAr90DAKwVAgCt3QMAAp0AgAadAIAKnQCA76wGAA6dAIASnQCAFp0AgBqdAIC+6BwAHp0AgCKdAIAqnQCALp0AgOGABwAynQCA42AGAIBdAACBYQAAgmEAALN9AQA2nQCAtW0BALZlAQA6nQCAhiAdAIdYHQC6+QEAu/EBALzZAQC92QEAvrEBAL+xAQDvoAAAPp0AgEKdAIBGnQCASp0AgE6dAIBSnQCA71wBAIRsHADhzAYAVp0AgOMcBgDjSAAAWp0AgOEwAQBenQCAo/EBAGKdAICFABQAZp0AgGqdAICm6QEApeEBAG6dAICrfQEAqnUBAHKdAIB2nQCArz0BAK49AQCtVQEArFUBAKjtHQCpLR4AqjkeAKs5HgCsKR4ArSkeAK6dHgCvkR4AJp0AgHqdAIB+nQCAgp0AgIadAICC+QAAgfEAAID9AAC4qR4AuakeALpJHwC7SR8AvFkfAL1FHwC+TR8Av0UfALDxHgCx+R4AssEeALPBHgC0uR4AtbkeALatHgC3pR4AsBEfALERHwCyER8AsyUfALQlHwC1KR8Atl0fALdRHwC4cR8AuXkfALpBHwC7QR8AvJUAAL2dAAC+lQAAv40AAIqdAIC2nACAjp0AgJKdAICWnQCAmp0AgIb4AwCH0AAAqM0fAKnVHwCq0R8Aq70fAKytHwCtcR8ArnEfAK9xHwCzOR4Anp0AgKKdAICmnQCAqp0AgLaRHgC1RR4Arp0AgLu1HgC6tR4Asp0AgLadAIC/jR4AvoEeAL2RHgC8pR4Aup0AgKN9HgC+nQCAwp0AgKbVHgDGnQCAyp0AgKUBHgCq8R4Aq/EeAM6dAIDSnQCArsUeAK/JHgCs4R4ArdUeAKhVAQCpgQAAqoEAAKuBAACsgQAArYkAAK6xAACvsQAA1p0AgNqdAIDenQCA4p0AgOadAIDqnQCA7p0AgPKdAIC4ZQAAuW0AALplAAC7fQAAvGUAAL1tAAC+ZQAAv90DALChAACxrQAAsqUAALO5AAC0qQAAtZ0AALaVAAC3XQAA9p0AgIIdAACBHQAAgB0AAPqdAID+nQCAAp4AgL4UAgAKngCAhKgCAA6eAIASngCAFp4AgBqeAIAengCAjwAAALNJAwAingCAhugEAIesAgAmngCAtkkDALVJAwAqngCAuykDALolAwAungCAMp4AgL8ZAwC+LQMAvS0DALwxAwA2ngCAo40DADqeAIA+ngCApo0DAEKeAIBGngCApY0DAKrhAwCr7QMASp4AgE6eAICu6QMAr90DAKz1AwCt6QMAvoQDAFKeAIBWngCAWp4AgF6eAIBingCAZp4AgGqeAICAPQAAgQkAAIIZAABungCAcp4AgHqeAICENAMAfp4AgLMtAQCCngCAh8wCAIZMBQCGngCAti0BALUtAQCKngCAu0kBALp5AQCOngCAkp4AgL+9AQC+vQEAvbkBALxRAQDheB8Alp4AgOPQHwCangCAnp4AgOGUAQCingCA42gDAKaeAICqngCArp4AgO+IAwCyngCAtp4AgO+sHwC6ngCAvp4AgMKeAIDGngCAyp4AgM6eAIDSngCA1p4AgO9EHgDangCA4dweAN6eAIDjHB4A4p4AgOqeAIDungCA8p4AgIFpAACAZQAAo+UBAIJ9AACl5QEA9p4AgIQUBACm5QEAvigEAPqeAICrgQEAqrEBAK1xAQCsmQEAr3UBAK51AQCoIQYAqS0GAKolBgCrPQYArCUGAK0tBgCuXQYAr00GAHaeAIDmngCAhggDAIeMAwD+ngCAAp8AgAafAIAKnwCAuOkGALnpBgC6jQYAu4UGALydBgC9hQYAvo0GAL+FBgCwPQYAsQ0GALIFBgCz7QYAtPkGALX5BgC27QYAt+UGALDNBwCx1QcAstEHALPtBwC09QcAtf0HALbpBwC36QcAuN0HALklBwC6LQcAuyUHALw9BwC9JQcAvi0HAL8lBwAOnwCAEp8AgAaeAIAWnwCAGp8AgB6fAIAinwCAJp8AgKgVBgCpGQYAqu0HAKv9BwCs7QcArd0HAK7VBwCvuQcAswUGACqfAIAunwCAMp8AgDafAIC2PQYAtQUGADqfAIC7cQYAumkGAD6fAIBCnwCAv1kGAL5RBgC9WQYAvGUGAEafAICjQQYASp8AgE6fAICmeQYAUp8AgIS0AQClQQYAqi0GAKs1BgC+gAEAWp8AgK4VBgCvHQYArCEGAK0dBgCoNQYAqT0GAKo1BgCrWQYArHUGAK2lAQCurQEAr6UBAIDpAACB6QAAgv0AAL8kAQCGMA8Ah+QAAF6fAIBinwCAuMUAALnNAAC6xQAAu90AALzNAAC9/QAAvvUAAL+dAACw3QEAsSUBALItAQCzIQEAtCEBALUhAQC2IQEAtyEBALvBAgC6OQIAZp8AgGqfAIC/xQIAvsUCAL3VAgC82QIAs50FAG6fAIBynwCAdp8AgIwAAAC2BQIAtd0FAHqfAICqfQIAq4UCAH6fAICCnwCAroECAK+BAgCsnQIArZECAIafAICj2QUAip8AgI6fAICmQQIAkp8AgJafAIClmQUAgpFqAIORagCanwCAnp8AgIa5FgCH6RcAhBEWAIWZFgCKoRIAi6ESAKKfAICmnwCAjpEeAI9ZHgCMmRMAjREeAJJxGgCT5RoAqp8AgO/oJACW8QYAlwUGAJTlGgCVGQYAmikCAJvFAgCunwCAsp8AgLafAIDhKBsAnN0CAOMgDwCfIQcAnsEHAJ01GwCcLRsAm6EbAJr5HwCZOR8AmLEfAJcBEgCWIRMAlSkTAJRRFgCTGRcAkjEXAJGxFwCQKWsAj1FrAOOsBwCEBA0A4RwHAIANAACBNQAAgj0AALqfAIC+nwCAwp8AgL4gDQDKnwCAzp8AgO9MBwCGWAwAh2ANANKfAIDWnwCA2p8AgN6fAICEXA8A4p8AgO8IAADvhAYA4ZABAOGwBgDj4AAA42QGAOafAIDqnwCA7p8AgPKfAID2nwCA+p8AgL4ADwCEQA4A/p8AgAKgAIAGoACACqAAgA6gAIASoACAFqAAgBqgAICj1QMAotUDAKExAwCgLQcAVp8AgMafAIAeoACAIqAAgCagAICCmQAAgZEAAICZAACoTQ0AqZ0NAKqVDQCrJQ4ArD0OAK0RDgCuEQ4ArxEOALB9DgCxDQ4AsgUOALMtDgC0OQ4AtTkOALYtDgC3JQ4AuOkOALnpDgC6wQ4Au8EOALy5DgC9nQ4AvpUOAL+NDgCzPQ0AKqAAgC6gAIAyoACANqAAgLaxDgC1lQ4AOqAAgLvpDgC6mQ4AhogAAIfkAAC/3Q4Avt0OAL3ZDgC88Q4APqAAgKN5DQC+hAEAhIAGAKb1DgBCoACARqAAgKXRDgCq3Q4Aq60OAEqgAIBOoACArpkOAK+ZDgCstQ4ArZ0OALIFNQCzGTQAsG0wALENNQBSoACAVqAAgLQBKAC1PSkAWqAAgF6gAIBioACAZqAAgGqgAIBuoACAcqAAgHagAICiRQEAo9UBAHqgAIChTQEAps0FAKcBOACkAQQApX0FAKoBPACrRT0AqEk5AKnlOQCudTEAr30xAKxdPQCtATAAqO0OAKn1DgCqCQ4AqwkOAKwZDgCtGQ4Arg0OAK8tDgB+oACAgqAAgIagAICKoACAjqAAgJKgAICWoACAmqAAgLgdDgC5JQ4Aui0OALslDgC8PQ4Avd0BAL7VAQC/zQEAsFUOALFdDgCyVQ4Asy0OALQ1DgC1JQ4Ati0OALclDgCzgQ0AnqAAgKKgAICqoACArqAAgLaZDQC1kQ0AvlQEALuZDQC6kQ0AhogEAIe8AwC/4Q0AvvENAL35DQC8gQ0AgkkAAKPFDQCA9QMAgUkAAKbdDQCyoACAtqAAgKXVDQCq1Q0Aq90NALqgAIC+oACArrUNAK+lDQCsxQ0Arb0NAKgdAgCpRQIAql0CAKtVAgCseQIArXkCAK6JAwCviQMAwqAAgMagAIDKoACAzqAAgIT8BQDSoACA1qAAgNqgAIC4iQMAuWUDALptAwC7ZQMAvH0DAL1lAwC+bQMAv2UDALDBAwCxwQMAssEDALPBAwC0wQMAtcEDALbBAwC3wQMA3qAAgOKgAIDmoACA6qAAgO6gAIDhpAEA8qAAgOPADgC+aAQA9qAAgPqgAIDvHAEA/qAAgAKhAIAGoQCACqEAgLOVAwAOoQCAEqEAgBqhAIAeoQCAtrkDALWxAwAioQCAu0UCALpFAgCGqAQAh6QFAL9FAgC+RQIAvVUCALxVAgDh4A4A4SwMAOMIDgDj1A4AgK0AAIHRAACC0QAAJqEAgCqhAIAuoQCAMqEAgDahAIA6oQCAPqEAgO+IDgDvLA4AoxUDAEKhAICFxCsARqEAgEqhAICmOQMApTEDAE6hAICrxQIAqsUCAFKhAIBWoQCAr8UCAK7FAgCt1QIArNUCAKgNBgCpFQYAql0GAKtVBgCseQYArXkGAK65BgCvuQYAFqEAgFqhAIBeoQCAYqEAgGahAIBqoQCAbqEAgHKhAIC4TQcAuVUHALpRBwC7aQcAvHkHAL1lBwC+bQcAv2UHALDJBgCxyQYAst0GALPVBgC0zQYAtXUHALZ9BwC3dQcAs9UGAHahAIB6oQCAfqEAgIKhAIC2+QYAtfEGAIahAIC7DQYAug0GAIYIAACHLAAAv7EHAL4JBgC9AQYAvAkGAIJRAACjkQYAgEEAAIFBAACmvQYAiqEAgI6hAICltQYAqkkGAKtJBgCSoQCAlqEAgK5NBgCv9QcArE0GAK1FBgCwsQYAsbEGALLNBgCzwQYAtMEGALXJBgC28QYAt/EGALgFAQC5DQEAugUBALsdAQC8BQEAvQ0BAL4FAQC/uQEAmqEAgJ6hAICioQCApqEAgKqhAICuoQCApqAAgLKhAICoLQYAqTUGAKo1BgCr8QYArNEGAK3RBgCu0QYAr9EGALPdBgC2oQCAuqEAgL6hAIDCoQCAtjEGALU5BgDGoQCAuxUGALoVBgDKoQCAzqEAgL9tBgC+ZQYAvXUGALx5BgDSoQCAo5kGANahAIDaoQCApnUGAN6hAIDioQCApX0GAKpRBgCrUQYA5qEAgOqhAICuIQYArykGAKw9BgCtMQYAqNUCAKndAgCq4QIAq+ECAKxRAwCtUQMArlEDAK9RAwDuoQCA8qEAgL7sAwD6oQCA/qEAgAKiAIAGogCACqIAgLjpAwC56QMAuokDALuFAwC8nQMAvYEDAL6BAwC/tQMAsDEDALExAwCyNQMAs+kDALT5AwC1+QMAtukDALfhAwCAbQMAgaUAAIKtAACzZQIADqIAgLXVAwC23QMAEqIAgITgAgAWogCAuvkDALv5AwC87QMAvTEDAL4xAwC/MQMAh+wDAIZkPACyAAAAGqIAgB6iAIDjCAQAIqIAgOHsBgAmogCA7wAGACqiAIAuogCAMqIAgDaiAIA6ogCAPqIAgEKiAIBGogCASqIAgE6iAIDjoAMAUqIAgOGoAQBWogCA7/ADAIIdAACBHQAAgB0AAFqiAIBeogCAYqIAgGqiAIC+TD0AbqIAgKOhAwC+QDwApRECAHKiAIB2ogCAphkCAIRsAgB6ogCAqz0CAKo9AgCt9QIArCkCAK/1AgCu9QIAhkA8AIe0PQB+ogCAgqIAgIaiAICKogCAjqIAgO9EBgCSogCA4dQGAJaiAIDjDAcAmqIAgJ6iAICiogCApqIAgLP1AQCqogCArqIAgLKiAIC2ogCAtkUBALXlAQC6ogCAuzEBALopAQC+ogCAwqIAgL8dAQC+HQEAvRkBALwlAQCoLT4AqTU+AKo9PgCrNT4ArC0+AK2FPgCuhT4Ar7k+AGaiAIDGogCAyqIAgM6iAICAGQAAgRkAAIIFAADSogCAuLk+ALm5PgC6ST8Au0k/ALxZPwC9WT8Avk0/AL9BPwCwrT4AsbU+ALKxPgCzjT4AtJk+ALWZPgC2iT4At4k+AKO1PgCEjAIA1qIAgNqiAIDeogCApgU+AKWlPgDiogCAq3E+AKppPgCGCAAAh2gDAK9dPgCuXT4ArVk+AKxlPgDmogCAs5E/AOqiAIDuogCAtlk/APKiAID2ogCAtbk/ALp1PwC7fT8A+qIAgP6iAIC+QT8Av0E/ALxZPwC9VT8AsJU+ALGdPgCyqT4As6U+ALShPgC1oT4AtqE+ALehPgC45T4Aue0+ALrlPgC7/T4AvO0+AL3dPgC+1T4AvxkBAAKjAIAGowCACqMAgA6jAIASowCA9qEAgBajAIAaowCAqF0+AKkhPgCqPT4AqzU+AKwVPgCt/T4ArvU+AK/tPgCj1T4AHqMAgCKjAIAmowCAKqMAgKYdPgCl/T4ALqMAgKs5PgCqMT4AMqMAgDajAICvBT4ArgU+AK0RPgCsHT4AgREAAIANAAA6owCAghkAAD6jAIBCowCAhJQBAL4QAACGQAcAhwABAEqjAIBOowCAUqMAgFajAIBaowCAXqMAgKiNAgCplQIAqpUCAKvNAgCs2QIArdkCAK7NAgCvxQIAYqMAgGajAIBqowCAbqMAgIwAAAByowCAdqMAgHqjAIC4HQMAucEDALrBAwC7wQMAvMEDAL3JAwC+8QMAv/EDALCJAgCxiQIAsikDALMpAwC0OQMAtTkDALYpAwC3JQMAsx0CAH6jAICCowCAhqMAgIqjAIC2WQIAtVECAI6jAIC7TQIAuk0CAJKjAICWowCAv/0DAL79AwC9/QMAvP0DAJqjAICeowCAoqMAgKajAIDhDD4AqqMAgOOoPwCuowCAgT0AAIAxAADvUD8Agh0AALKjAIC++AQAhhgFAIdMAwCEDAIA48wAALqjAIDhvAEAvqMAgMKjAIDGowCAyqMAgM6jAICELAUA0qMAgNajAIDaowCA7xAAAN6jAIDiowCAo90DAOajAIDqowCA7qMAgPKjAICmmQMApZEDAPajAICrjQMAqo0DAPqjAID+owCArz0CAK49AgCtPQIArD0CAAKkAIAGpACACqQAgA6kAIASpACAFqQAgBqkAIDvKD4AHqQAgOE8PgAipACA4zgBAIApAACBFQAAghEAACqkAICzMQIAvsgEAITABAAupACAMqQAgLYpAgC1IQIANqQAgLvNAQC6zQEAOqQAgD6kAIC/dQEAvskBAL3BAQC8yQEAqOkFAKnpBQCq+QUAq/kFAKzpBQCt6QUArjkGAK85BgC2owCAJqQAgIaIAACHQAMAQqQAgEakAIBKpACATqQAgLjRBgC52QYAuuEGALvhBgC8kQYAvZEGAL6RBgC/kQYAsEkGALFJBgCyXQYAs1UGALRNBgC18QYAtvEGALfxBgCjcQUAUqQAgFakAIBapACAXqQAgKZpBQClYQUAYqQAgKuNBgCqjQYAZqQAgGqkAICvNQYArokGAK2BBgCsiQYAbqQAgLPRBwBypACAdqQAgLbxBwB6pACAfqQAgLXBBwC60QcAu90HAIKkAICGpACAvrkHAL+5BwC8xQcAvbkHALhpBgC5aQYAuokGALuJBgC8mQYAvZkGAL6JBgC/iQYAsBEGALEdBgCyFQYAs2kGALR5BgC1eQYAtmkGALdhBgCoSQYAqVUGAKpdBgCrVQYArE0GAK11BgCucQYAr3EGAEajAICCHQAAgR0AAIAdAACKpACAjqQAgJKkAIC+cAEAo5UGAJqkAICGKAAAh0gBAJ6kAICmtQYApYUGAKKkAICrmQYAqpUGAKakAICqpACAr/0GAK79BgCt/QYArIEGAK6kAICzFQYAsqQAgLakAIC2PQYAuqQAgL6kAIC1NQYAutkBALvZAQDCpACAxqQAgL59AQC/ZQEAvH0BAL11AQCovQUAqckFAKrZBQCr0QUArPkFAK35BQCuKQIArykCAMqkAIDOpACA0qQAgNakAICMAAAA2qQAgN6kAIDipACAuO0CALmFAgC6gQIAu4ECALyFAgC9jQIAvrECAL+xAgCwWQIAsVkCALLtAgCz5QIAtP0CALXlAgC25QIAt9UCAKNRBQDmpACA6qQAgO6kAIDypACApnkFAKVxBQD2pACAq50CAKqdAgD6pACA/qQAgK8hAgCuOQIArTECAKw5AgCBbQAAgG0AAAKlAICCBQAAvlwMAAqlAIAOpQCA79AGAITsAwDhHAUAEqUAgOP8BwAWpQCAGqUAgIbYDACHvAwAqIUCAKmVAgCqlQIAq6UCAKy9AgCt1QIArtECAK/RAgAepQCAIqUAgCalAIAqpQCALqUAgDKlAIA2pQCAOqUAgLh1AQC5fQEAunUBALvJAQC82QEAvdkBAL7JAQC/wQEAsLUCALG9AgCygQIAs4ECALRRAQC1UQEAtlEBALdRAQA+pQCAhAQNAEKlAIBGpQCAvhwMAEqlAIDvHAAA76AGAOGQAQDhRAcA43AGAOOYBgBOpQCAUqUAgFalAIBapQCAs10CAF6lAIBipQCAZqUAgGqlAIC2FQIAtXUCAG6lAIC7OQIAujECAHKlAIB6pQCAv9UBAL7VAQC9FQIAvBUCAKOdDQAGpQCAdqUAgH6lAICCpQCAptUNAKW1DQCGpQCAq/kNAKrxDQCGCAMAh2ADAK8VDgCuFQ4ArdUNAKzVDQCAkQ8AgZkPAIKhDwCzpQ4AiqUAgLWhDgC2eQ8AjqUAgJKlAICWpQCAukUPALtdDwC8RQ8AvU0PAL5FDwC//Q8AqFUOAKldDgCqYQ4Aq30OAKxlDgCttQ8Arr0PAK+1DwCapQCAnqUAgKKlAICmpQCAqqUAgK6lAICypQCAtqUAgLhVDwC5dQ8Aun0PALt1DwC8bQ8AvREPAL4RDwC/EQ8AsM0PALHVDwCy3Q8As9UPALTNDwC1dQ8AtnEPALdxDwCj6Q8AuqUAgL6lAIDCpQCAxqUAgKY1DgCl7Q8AyqUAgKsRDgCqCQ4AzqUAgNKlAICvsQ4ArgkOAK0BDgCsCQ4A1qUAgIIdAACBHQAAgB0AANqlAIDepQCA4qUAgL6UAQCErAEA5qUAgIfgAQCGzAAA6qUAgO6lAIDypQCAlqQAgKhtDgCpiQEAqpkBAKuRAQCswQEArckBAK75AQCv+QEAhKAAAPalAID6pQCA/qUAgAKmAIAGpgCACqYAgA6mAIC4xQAAuc0AALrFAAC73QAAvM0AAL39AAC+9QAAv50AALBBAQCxQQEAskEBALNBAQC0QQEAtUEBALZBAQC3QQEAsxECABKmAIAWpgCAGqYAgB6mAIC2SQIAtUkCACKmAIC7hQIAuoUCACamAIAqpgCAv4UCAL6FAgC9lQIAvJUCAIU8GgCjVQIALqYAgDKmAICmDQIANqYAgDqmAIClDQIAqsECAKvBAgA+pgCAQqYAgK7BAgCvwQIArNECAK3RAgCCGQAARqYAgIAZAACBGQAASqYAgE6mAIBSpgCAWqYAgL4ABABepgCAYqYAgGamAIBqpgCAbqYAgHKmAIB2pgCA7+gOAHqmAICG6AQAh1ADAH6mAICCpgCA74ACAIamAIDhlAEAiqYAgONYAQCOpgCA4wAOAJKmAIDhaA0AlqYAgKhxAgCpcQIAqnECAKupAgCsuQIArbkCAK6pAgCvqQIAhKwFAJqmAICepgCAoqYAgKamAICqpgCArqYAgLKmAIC4bQEAuQ0BALoFAQC7GQEAvAkBAL09AQC+NQEAv9kBALDZAgCx2QIAsm0BALNlAQC0fQEAtWUBALZlAQC3VQEA4WAPAOP0AADjHA4A4bwBALamAICCOQAAgTEAAIA9AAC6pgCAvigEAL6mAIDCpgCAvjwHAO8QAADv0A4AyqYAgIbgBACHyAQAzqYAgLO1AgDSpgCAtX0CALZ1AgDWpgCA2qYAgN6mAIC6UQIAu1ECALz1AQC9/QEAvvUBAL/tAQBWpgCAxqYAgKqxBQCrsQUArBUGAK0dBgCuFQYArw0GAOKmAIDmpgCA6qYAgKNVBQDupgCApZ0FAKaVBQDypgCAs+kGAPamAID6pgCA/qYAgAKnAIC24QYAtekGAAanAIC7sQYAuqEGAAqnAIAOpwCAv50GAL6RBgC9pQYAvKkGAKgdBgCpIQYAqiEGAKshBgCsIQYArSEGAK4hBgCvIQYAEqcAgBanAIAapwCAHqcAgCKnAIAmpwCAKqcAgC6nAIC45QcAue0HALrlBwC7/QcAvOUHAL3tBwC+5QcAv00HALAlBgCxNQYAsj0GALMxBgC0FQYAtRkGALYNBgC3AQYAo6kHAIIVAACBtQEAgLUBADKnAICmoQcApakHADanAICr8QcAquEHAISgAgA6pwCAr90HAK7RBwCt5QcArOkHAD6nAICzlQYAhugAAIcYAQC2tQYAQqcAgEanAIC1vQYAukkBALtVAQBKpwCATqcAgL45AQC/OQEAvEUBAL05AQCoPQYAqU0GAKpZBgCrUQYArHEGAK1xBgCuuQEAr7kBAISsAQBSpwCAVqcAgFqnAIBepwCAYqcAgGanAIBqpwCAuKkBALmpAQC6aQEAu2kBALx5AQC9eQEAvmkBAL9pAQCwyQEAsdUBALLVAQCzqQEAtLkBALW5AQC2qQEAt6EBAKPRBQBupwCAcqcAgHanAIB6pwCApvEFAKX5BQB+pwCAqxECAKoNAgCCpwCAhqcAgK99AgCufQIArX0CAKwBAgCKpwCAjqcAgJKnAICWpwCAgTEAAIANAACapwCAgjkAAJ6nAICipwCAviQDAKqnAICupwCAsqcAgIbYHACHTAMAtqcAgLqnAIC+pwCAhMAcAOMgAQDCpwCA4cgBAManAIDvMAIAyqcAgM6nAIDSpwCA1qcAgNqnAIDepwCA4qcAgLOVAwDmpwCA6qcAgO6nAIDypwCAtrkDALWxAwD2pwCAu1EDALpJAwD6pwCA/qcAgL/1AAC+SQMAvUEDALxJAwCoLQIAqUUCAKpdAgCrVQIArHkCAK15AgCuvQIAr7UCAL5oHQACqACABqgAgAqoAICAHQAAgQkAAIKpAAAOqACAuFEBALlZAQC6YQEAu2EBALwRAQC9EQEAvhEBAL8RAQCwzQIAsdUCALLdAgCz1QIAtM0CALVxAQC2cQEAt3EBAOFYBgDhVAcA47AAAOO8BgASqACAGqgAgIYYHACHVB0AHqgAgCKoAIAmqACAKqgAgL74HAAuqACA7/AGAO/gBgCjlQIAMqgAgDaoAIA6qACAPqgAgKa5AgClsQIAQqgAgKtRAgCqSQIARqgAgEqoAICv9QEArkkCAK1BAgCsSQIAqG0eAKl1HgCqfR4Aq40eAKyVHgCtnR4Aro0eAK+BHgAWqACATqgAgFKoAIBWqACAWqgAgF6oAIBiqACAZqgAgLiJHgC5iR4AupkeALuRHgC8uR4AvbkeAL59HwC/dR8AsMUeALHNHgCyxR4As90eALTFHgC1zR4AtsUeALe5HgCz9R4AaqgAgG6oAIByqACAdqgAgLYdHgC1HR4AeqgAgLsJHgC6AR4AfqgAgIKoAIC/CR4AvgEeAL0JHgC8ER4Agm0AAKOxHgCAVQAAgWUAAKZZHgCEmAMAv9ABAKVZHgCqRR4Aq00eAIYABACHmAEArkUeAK9NHgCsVR4ArU0eAIqoAICOqACAhCQAAJKoAICWqACAmqgAgKanAICGqACAqLUeAKmFHgCqjR4Aq4UeAKydHgCtgR4Arv0eAK/1HgCwjR4AsZUeALKVHgCzpR4AtL0eALVxAQC2cQEAt3EBALhRAQC5UQEAulEBALtRAQC89QEAvf0BAL71AQC/7QEAsyUeAL4IBwCeqACAoqgAgKaoAIC2IR4AtTUeAKqoAIC7cR4AumkeAK6oAICyqACAv5UBAL5ZHgC9UR4AvGEeALaoAICjYR4AuqgAgL6oAICmZR4AwqgAgMaoAIClcR4Aqi0eAKs1HgDKqACAzqgAgK4dHgCv0QEArCUeAK0VHgDhVBoA0qgAgONcCgDWqACA2qgAgN6oAIDiqACA5qgAgOqoAIC+qAUA7qgAgPKoAICPMSoA+qgAgO/E+wD+qACAk2EuAJIdLwCR2SoAkEkqAJfZEgCWdRIAlQ0TAJTBLgCbHRsAmkEWAJlJFgCYDRcAn3EeAJ4RGwCdcRoAnHkaAKOhAgCinQMAoZUfAKCJHgDjiAEA4wgeAOFoAADh/B4A79wBAO98HwC1if4AtAH8ALMB+gCylfoAsQH4ALAR9gCv4fYArgH0AK0l8gCs7fIAqwHwAKrpDwCp1Q4AqN0OAKcBDACmyQoApe0KAKQBCACj4QYAovEGAKHlAwACqQCAggErAIMBKwAGqQCACqkAgIYxLwCHiS8AhIkrAIVFLgCKdRIAiwUTAIYIBQCHbAUAjhEXAI8RFwCMsRMAjV0WAJI9GgCTQRsAhMgFAIQABwCWUR8Al1EfAJRRGwCVORoAmn0eAJt9AgAOqQCAEqkAgIFZAQCAVQEAnFkDAIJRAQC+yAcAFqkAgBqpAIAeqQCAIqkAgCapAIAqqQCA79QeAC6pAIDhJB4AMqkAgONoAQA2qQCAOqkAgD6pAIBCqQCAu2kCALpZAgBGqQCASqkAgL8dAgC+HQIAvRkCALxxAgCz7QIATqkAgFKpAIBWqQCAWqkAgLZ9AgC17QIAXqkAgKMNBQD2qACAYqkAgGqpAIBmqQCApp0FAKUNBQBuqQCAq4kFAKq5BQCGCAMAh3wDAK/9BQCu/QUArfkFAKyRBQCAsQcAgbkHAIJBAACzsQYAcqkAgLVZBwC2MQcAdqkAgHqpAIB+qQCAuuEHALvhBwC84QcAveEHAL7hBwC/3QcAqLUGAKm5BgCqdQYAq4UHAKydBwCt/QcArvUHAK8ZBwCCqQCAhqkAgIqpAICOqQCAkqkAgJapAICaqQCAnqkAgLh1BwC5fQcAunUHALsFBwC8HQcAvTEHAL4xBwC/MQcAsGkHALFpBwCyeQcAs3kHALRpBwC1VQcAtlEHALdNBwCj/QcAoqkAgKapAICqqQCArqkAgKZ9BgClFQYAsqkAgKutBgCqrQYAtqkAgLqpAICvkQYArq0GAK2tBgCsrQYAvqkAgMKpAIDGqQCAyqkAgIAdAACBCQAAgjkAAM6pAIDSqQCA2qkAgIbIAACHpAEA3qkAgOKpAIDmqQCA6qkAgKiNAQCpmQEAqtkBAKvRAQCs8QEArfEBAK45AQCvOQEAhKAAAO6pAIDyqQCA9qkAgPqpAID+qQCAAqoAgAaqAIC4zQAAudUAALrVAAC75QAAvP0AAL2VAAC+nQAAv5UAALBJAQCxSQEAslkBALNZAQC0SQEAtUkBALb9AAC39QAAugUEALsJBAC44QcAueEHAL4JBAC/CQQAvAkEAL0JBACyjQcAs+UHALC1BwCxhQcAtuUHALftBwC08QcAtfEHAKpNBwCrVQcAqEkHAKlJBwCu3QcAr8UHAKxNBwCt1QcACqoAgA6qAIASqgCAFqoAgBqqAIAeqgCAIqoAgCaqAICz0QIAKqoAgC6qAIC+AAwAMqoAgLbxAgC1+QIANqoAgLsNAgC6DQIAOqoAgD6qAIC/DQIAvg0CAL0NAgC8DQIAghUAAKOVAgCAYQAAgWEAAKa1AgBCqgCASqoAgKW9AgCqSQIAq0kCAIbIDACHrAwArkkCAK9JAgCsSQIArUkCAKhlAgCpdQIAqn0CAKt1AgCsbQIArbECAK6xAgCvsQIAhKANAE6qAIBSqgCAVqoAgFqqAIBeqgCAYqoAgGaqAIC4MQEAuTEBALoxAQC7MQEAvNUBAL3dAQC+yQEAv8EBALDRAgCx0QIAstECALPRAgC0EQEAtREBALYRAQC3EQEA4bAGAGqqAIDj0AYAhEAPAG6qAIDhpAEAcqoAgOPABgB2qgCAeqoAgH6qAIDv1AYA7AAAAIKqAIDvZAcAhqoAgIqqAICOqgCAkqoAgLO5AgCWqgCAtakCALZ9AgCaqgCAnqoAgKKqAIC6WQIAu1kCALxJAgC9SQIAvpkBAL+ZAQCjdQ0ARqoAgKaqAICqqgCArqoAgKaxDQClZQ0AsqoAgKuVDQCqlQ0AvqQDALaqAICvVQ4ArlUOAK2FDQCshQ0AgE0AAIFVAACCVQAAs2UPALqqAIC1ZQ8Atm0PAL6qAICGQAMAhxQDALrtDwC7/Q8AvOkPAL3VDwC+3Q8Av9UPAKhZDgCpoQ8AqqEPAKuhDwCsoQ8AraEPAK6hDwCvoQ8AwqoAgMaqAIDKqgCAzqoAgNKqAIDWqgCA2qoAgN6qAIC4AQ8AuQEPALoBDwC7HQ8AvA0PAL01DwC+PQ8Av9UAALBlDwCxdQ8AsnEPALNNDwC0VQ8AtV0PALZNDwC3QQ8AoykOAOKqAIDmqgCA6qoAgO6qAICmIQ4ApSkOAPKqAICrsQ4AqqEOAPaqAID6qgCAr5kOAK6RDgCtmQ4ArKUOAP6qAIACqwCABqsAgAqrAIDvJA0ADqsAgBKrAIAWqwCA49AOABqrAIDhGA4AHqsAgIAVAACBGQAAggUAACKrAICo0QEAqdkBAKopAQCrKQEArDkBAK05AQCuKQEArykBAL5oAQAqqwCAhsgBAIesAAAuqwCAMqsAgDarAIA6qwCAuO0AALmFAAC6jQAAu4UAALydAAC9gQAAvoEAAL+BAACwWQEAsVkBALLtAACz5QAAtP0AALXlAAC25QAAt9UAALOhAgA+qwCAQqsAgEarAIBKqwCAtrkCALWxAgBOqwCAu50CALqdAgBSqwCAVqsAgL8hAwC+OQMAvTEDALw5AwCF+PUAo+UCAFqrAIBeqwCApv0CAGKrAIBmqwCApfUCAKrZAgCr2QIAaqsAgG6rAICufQMAr2UDAKx9AwCtdQMAuOkAALnpAAC6aQAAu2kAALx5AAC9ZQAAvm0AAL9lAACwsQAAsbkAALKBAACzgQAAtPkAALX5AAC27QAAt+UAAKhlAwCpdQMAqn0DAKt1AwCsbQMArdEAAK7RAACv0QAAcqsAgHarAIB6qwCA1qkAgH6rAICCqwCAhqsAgIqrAICA/QEAgQkAAIIZAACOqwCAkqsAgL5EAgCaqwCAnqsAgISsAgCiqwCAh/gCAIasBQCmqwCAqqsAgK6rAICyqwCAs/UCALarAIC6qwCAvqsAgMKrAIC2UQEAteUCAMarAIC7fQEAunUBAMqrAIDOqwCAvz0BAL49AQC9VQEAvFUBAOFwDwDSqwCA47gOAITABQDvyAAA1qsAgNqrAIDeqwCA4zwOAOKrAIDh0AEA5qsAgIR0BwDqqwCA72gBAO6rAIDyqwCApXkCAKbNAQD2qwCAgCEAAIEhAACC3QcAo2kCAKzJAQCtyQEArqEBAK+hAQD6qwCA/qsAgKrpAQCr4QEAlqsAgAKsAIC+QAIABqwAgIYwAwCHMAMACqwAgA6sAICoOQcAqTkHAKoNBwCrHQcArAUHAK0NBwCuBQcAr3kHALAJBwCxCQcAshkHALMRBwC0OQcAtTkHALbdBwC3yQcAuPkHALn5BwC6zQcAu8EHALzFBwC9yQcAvrkHAL+xBwCzpQcAEqwAgBasAIAarACAHqwAgLatBwC1rQcAIqwAgLvtBwC67QcAJqwAgCqsAIC/3QcAvt0HAL3lBwC87QcALqwAgKPhBwAyrACANqwAgKbpBwA6rACAPqwAgKXpBwCqqQcAq6kHAEKsAIBGrACArpkHAK+ZBwCsqQcAraEHAEqsAIBOrACAUqwAgFasAIBarACAXqwAgGKsAIBmrACAgREAAIANAABqrACAghkAAG6sAIByrACAvuQBAHasAICG4AAAhxgBAHqsAIB+rACAgqwAgIasAICKrACA77AEAI6sAIDh1AYAkqwAgONcBACWrACAmqwAgJ6sAICirACAqJkBAKmZAQCqDQEAqwUBAKwdAQCtBQEArgUBAK81AQCEiAEApqwAgKqsAICurACAsqwAgLasAIC6rACAvqwAgLjBAAC5wQAAusEAALvBAAC8wQAAvcEAAL7BAAC/wQAAsE0BALElAQCyIQEAsyEBALQlAQC1LQEAthEBALcRAQDCrACAxqwAgLONAgDKrACAtZ0CAM6sAIDSrACAto0CANasAIDarACAu+kCALqBAgC9/QIAvP0CAL/hAgC+6QIA3qwAgKbVAgClxQIAvggDAKPVAgCCLQAAgRkAAIB5AACvuQIArrECAK2lAgCspQIAq7ECAKrZAgDirACA6qwAgO80AgDurACAhxgDAIYs/ADyrACA9qwAgPqsAID+rACAAq0AgAatAIAKrQCADq0AgOMAAQASrQCA4eABABatAIC6tQMAu70DABqtAIAerQCAvnkDAL95AwC8pQMAvXkDACarAICztQMAIq0AgCatAIC2kQMAKq0AgC6tAIC1pQMAqEkCAKlJAgCqWQIAq1kCAKxJAgCtdQIArnECAK9tAgC+aP0AvqT/ADKtAIA2rQCAOq0AgD6tAIBCrQCARq0AgLj5AgC5+QIAukkBALtJAQC8XQEAvUEBAL5BAQC/fQEAsBUCALEdAgCyFQIAs8kCALTZAgC12QIAtskCALfJAgDjIAYA4bAGAOGAAQDjEAYAgA0AAIE1AACCPQAASq0AgE6tAIBSrQCAWq0AgF6tAIDvcAAAYq0AgGatAIDvTAEAhIz9AGqtAICjmQIAbq0AgKWJAgByrQCAdq0AgKa9AgCGwPwAh+T8AKuRAgCqmQIArVUCAKyJAgCvVQIArlUCAKh9/gCpgf4Aqpn+AKuZ/gCsif4ArYn+AK65/gCvuf4AVq0AgHqtAIB+rQCAgq0AgIatAICKrQCAjq0AgJKtAIC4tf4Aub3+ALph/wC7Yf8AvGH/AL1h/wC+Yf8Av2H/ALDJ/gCxyf4Ast3+ALPR/gC0uf4Atbn+ALaR/gC3kf4AsxH+AJatAICarQCAnq0AgKKtAIC2Cf4AtQH+AKatAIC7Df4Aug3+AKqtAICurQCAv33+AL59/gC9Bf4AvAn+ALKtAICjVf4Atq0AgLqtAICmTf4Avq0AgMKtAIClRf4Aqkn+AKtJ/gCEKAMAxq0AgK45/gCvOf4ArE3+AK1B/gCAzQEAgdEBAILRAQCzuf4Ayq0AgLXR/gC21f4Azq0AgIZgAQCHYAEAug0BALsFAQC8HQEAvQUBAL4NAQC/BQEA0q0AgNatAIDarQCA3q0AgOKtAIDhwP0A5q0AgOOM/ADqrQCA7q0AgPKtAIDvtPwA9q0AgPqtAID+rQCAAq4AgKgp/gCpKf4Aqj3+AKs1/gCsVf4ArVn+AK5N/gCvRf4ABq4AgAquAIAOrgCAEq4AgBauAIAargCAHq4AgCKuAIC4SQEAuUkBALpZAQC7UQEAvHkBAL15AQC+GQEAvxUBALDFAQCxzQEAssUBALPdAQC0xQEAtc0BALbFAQC3eQEAJq4AgCquAIAurgCAo7n9ADKuAICl0f0AptX9AITQAwBBrgCAvuACAKoNAgCrBQIArB0CAK0FAgCuDQIArwUCAIFJAACAQQAAowkDAIJdAAClGQMARa4AgEmuAICmEQMAhsAEAIfkAwCrDQMAqg0DAK0BAwCsHQMArwEDAK4JAwCw4QMAseEDALLhAwCz/QMAtOUDALXtAwC25QMAtz0DALgFAwC5DQMAugUDALsdAwC8BQMAvQ0DAL4FAwC/vQAATa4AgFGuAIBVrgCAWa4AgOasAIBdrgCAYa4AgGWuAICo8QMAqfkDAKqpAwCrqQMArLkDAK25AwCuqQMAr6UDALNBAgBprgCAba4AgHGuAIB1rgCAtlkCALVRAgB5rgCAu0UCALpFAgB9rgCAga4AgL9JAgC+QQIAvUkCALxVAgCFrgCAia4AgI2uAICRrgCA74wDAJWuAICZrgCAna4AgONsAwChrgCA4VAAAKWuAICprgCAvngFALGuAICEcAIAgOUAAIHpAACC+QAAta4AgIawBACHVAUAua4AgO9A/gC9rgCA4Vz+AMGuAIDjVAEAxa4AgMmuAIDNrgCA0a4AgLOZAQDVrgCA2a4AgN2uAIDhrgCAth0BALUdAQDlrgCAuz0BALo9AQDprgCA7a4AgL/hAAC++QAAvfEAALz5AACoIQYAqVEGAKpRBgCrzQYArNUGAK3dBgCu1QYAr8kGAK2uAIDxrgCA9a4AgPmuAID9rgCAAa8AgAWvAIAJrwCAuG0HALkFBwC6DQcAuwUHALwdBwC9AQcAvgEHAL8BBwCwuQYAsbkGALJtBwCzZQcAtH0HALVlBwC2ZQcAt1UHAKPZBgANrwCAEa8AgBWvAIAZrwCApl0GAKVdBgCEnAIAq30GAKp9BgC+JAMAHa8AgK+hBwCuuQcArbEHAKy5BwCASQAAgUkAAIJZAACzVQcAIa8AgLV9BwC2aQcAJa8AgIZAAACHVAMAulUHALspBwC8OQcAvTkHAL4pBwC/IQcAo5kGACmvAIAtrwCAMa8AgDWvAICmpQYApbEGADmvAICr5QYAqpkGAD2vAIBBrwCAr+0GAK7lBgCt9QYArPUGAOE4BQBFrwCA4yQEAEmvAIBNrwCAUa8AgFWvAIBZrwCAXa8AgGGvAIBlrwCAaa8AgG2vAIBxrwCA7/QEAHWvAICo+QYAqQkGAKoRBgCrLQYArDkGAK0lBgCuLQYAryUGAHmvAIB9rwCAga8AgIWvAICAGQAAgRkAAIIFAACJrwCAuOUBALntAQC65QEAu/0BALzlAQC97QEAvuUBAL9ZAQCwXQYAsSEGALIhBgCzIQYAtCEGALUpBgC2EQYAtxEGAKjRAgCp2QIAqg0DAKsFAwCsHQMArQUDAK4FAwCvNQMAvmQCAJGvAICVrwCAma8AgJ2vAIChrwCApa8AgKmvAIC4JQMAuS0DALolAwC7PQMAvCUDAL0pAwC++QMAv/kDALBNAwCxIQMAsiUDALM9AwC0JQMAtS0DALYlAwC3HQMAs4UDAITIAgCtrwCAhAgDALGvAIC2hQMAtZUDALWvAIC75QMAuokDAIYIDACHnAMAv+kDAL7hAwC96QMAvPEDAIXsCgA2rgCAo80DALmvAICl3QMAva8AgMGvAICmzQMAxa8AgMmvAICrrQMAqsEDAK2hAwCsuQMAr6EDAK6pAwDNrwCA0a8AgNWvAIDZrwCA78gDAN2vAIDhrwCA5a8AgOO0AwDprwCA4dABAO2vAICADQAAgXUAAIJ9AADxrwCA9a8AgPmvAICzZQEAvgQCALVlAQABsACABbAAgLZlAQCGQA0Ah1gNALv1AQC6/QEAvaUBALy5AQC/mQEAvqUBAAmwAIANsACAEbAAgIQADAAVsACAGbAAgB2wAIDvzAEAIbAAgOEsBgAlsACA4yABAOwAAAApsACALbAAgDGwAIA1sACAo+kBADmwAIA9sACApukBAEGwAIBFsACApekBAKpxAQCreQEASbAAgE2wAICuKQEArxUBAKw1AQCtKQEAqCUOAKktDgCqJQ4Aqz0OAKwlDgCtLQ4AriUOAK+VDgD9rwCAUbAAgFWwAIBZsACAXbAAgIKdAACBnQAAgJ0AALhFDwC5TQ8AukUPALtZDwC8SQ8AvUkPAL59DwC/cQ8AsPEOALH5DgCypQ4As7kOALSpDgC1lQ4Atp0OALd9DwCo1Q8Aqd0PAKoJDwCrCQ8ArBkPAK0FDwCuDQ8ArwUPAGGwAIBlsACAabAAgL6gAwBtsACAcbAAgId4AwCGEAAAuBUPALkdDwC6IQ8AuyEPALz1AAC9/QAAvvUAAL/tAACwQQ8AsU0PALJdDwCzVQ8AtE0PALU1DwC2MQ8AtzEPAHWwAIDvsAwAebAAgH2wAICBsACAhbAAgImwAICNsACAkbAAgJWwAICZsACAnbAAgKGwAIDjqA0ApbAAgOGMDQCzwQ4AqbAAgK2wAICxsACAtbAAgLbFDgC10Q4AubAAgLvJDgC6xQ4AvbAAgMGwAIC/sQ4AvskOAL3BDgC8yQ4AowEOAMWwAIDJsACAzbAAgNGwAICmBQ4ApREOANWwAICrCQ4AqgUOANmwAICErAIAr3EOAK4JDgCtAQ4ArAkOAIBRAACBWQAAgmEAALPFAAC+zAEAtcUAALbNAADhsACAhkAHAIcUAQC6yQAAu8kAALzZAAC92QAAvskAAL/FAACrDQMAqg0DAKkJAwCouQIArw0DAK4NAwCtDQMArA0DAL5gAwDlsACA6bAAgO2wAIDxsACA9bAAgPmwAIC+MAUAuykDALoZAwC5GQMAuAEDAL/dAwC+3QMAvd0DALwxAwCzTQMAsk0DALFNAwCwTQMAtzkDALYxAwC1QQMAtE0DAP2wAICmkQMApZkDAAGxAICjmQMABbEAgAmxAIANsQCAr5kDAK6VAwCthQMArIUDAKuVAwCqlQMAja8AgBGxAIAVsQCAGbEAgB2xAIAhsQCAJbEAgCmxAIAtsQCAMbEAgDWxAIA5sQCAPbEAgEGxAICAHQAAgQkAAIL9AQBFsQCAvwgHAEmxAIBRsQCA7yQAAFWxAICElAIAWbEAgF2xAICH4AIAhgQFAL4AGABhsQCAZbEAgOGQAQBpsQCA44AAAG2xAIBxsQCAdbEAgLNlAQB5sQCAtWUBALZtAQB9sQCAgbEAgIWxAIC65QEAu/kBALzpAQC96QEAvsUBAL+9AQCJsQCAjbEAgJGxAIC+xBkAlbEAgJmxAICdsQCA78gBAKGxAIDh3A4ApbEAgOMwDgCpsQCArbEAgLGxAICEMAQAgHkAAIEVAACCFQAAo+UBALWxAICl5QEApu0BALmxAICGQAYAh5AHAKplAQCreQEArGkBAK1pAQCuRQEArz0BAKjdBQCpIQYAqiEGAKshBgCsIQYArSEGAK4hBgCvnQYATbEAgL2xAIDBsQCAhDABAMWxAIDJsQCAzbEAgNGxAIC4jQYAuZUGALqdBgC7lQYAvI0GAL21BgC+vQYAv7UGALDtBgCx8QYAsvEGALPxBgC0zQYAtbUGALa9BgC3tQYAqIkHAKmVBwCqkQcAq5EHAKy9BwCtpQcArqEHAK/dBwDVsQCA2bEAgN2xAIDhsQCA5bEAgOmxAIDtsQCA8bEAgLhJBwC5VQcAul0HALtVBwC8cQcAvX0HAL5pBwC/aQcAsKUHALGtBwCyuQcAs7EHALSRBwC1kQcAtnkHALd5BwD1sQCA+bEAgP2xAIABsgCA78gFAOHACQAFsgCA48AZAOMkBAAJsgCA4dAGAO/cKACinQMAoxUBAKAZBQChjQUAs1kGAA2yAIARsgCAFbIAgBmyAIC2ZQYAtXUGAB2yAIC7KQYAuiEGACGyAIAlsgCAvxUGAL4VBgC9JQYAvC0GAKOZBgCPmfwAKbIAgDGyAIA1sgCApqUGAKW1BgA5sgCAq+kGAKrhBgCGKB8Ah5wAAK/VBgCu1QYAreUGAKztBgCebQkAn30HAJwNCwCd7QkAmvENAJs5DQCY5fAAmQ0PAJbh8QCX6fEAlMX1AJUN8wCSHfcAk/H1AJD9+QCR7fkAgh3/AIMB+gA9sgCAQbIAgIYV9gCHOfYAhAn6AIXx9ACKwfAAiyXyAEWyAIBJsgCAjuEMAI8VDgCMNfIAjQHzAJKtDgCTgQgATbIAgFGyAICW6QQAl3UGAJR5CgCV8QoAmtEGAJvJAABVsgCAWbIAgIEdAwCAHQMAnFkCAIL1AwCrARAAqpUWAKmNFgCojRYAr5UuAK4BLACt/RIArJkSAKOlHgCipR4AoY0CAN2wAICnGRoAppUaAKUBGACknR8AXbIAgGGyAIBlsgCAabIAgG2yAIBxsgCAdbIAgHmyAICz5SoAsuUqALGtLwCw5S4AfbIAgIGyAIC1ASQAtBEqAKgpAwCpNQMAqj0DAKs1AwCsLQMArbUDAK69AwCvtQMAhbIAgImyAICNsgCAkbIAgIAdAACBCQAAgrkAAJWyAIC4TQIAuV0CALptAgC7CQIAvBkCAL0ZAgC+CQIAvwECALDNAwCx1QMAst0DALPVAwC0zQMAtXUCALZ9AgC3dQIAmbIAgITIHQChsgCAvgwfAKWyAICpsgCA70gGAO9YBwDhWAYA4ZgGAOOUAQDjAAYAhhAcAId8HQC+9B4ArbIAgLGyAIC2ZQMAtfUDALWyAICz5QMAubIAgL2yAIDBsgCAv+ECAL5ZAwC9UQMAvFkDALtBAwC6WQMAxbIAgMmyAIAtsgCAnbIAgM2yAIDRsgCA1bIAgNmyAIDdsgCA4bIAgKitHQCptR0AqrUdAKslHgCsPR4ArR0eAK4VHgCvdR4AsA0eALEtHgCyJR4As40eALSVHgC1nR4AtpUeALeNHgC4tR4Aub0eALq1HgC7nR4AvIUeAL1VHwC+XR8Av1UfALMdHQDlsgCA6bIAgO2yAIDxsgCAtr0eALWVHgD1sgCAu8keALrpHgD5sgCA/bIAgL95HgC+cR4AvXkeALzRHgCCKQAAo1kdAIAdAACBFQAApvkeAAGzAIAFswCApdEeAKqtHgCrjR4ACbMAgITgAwCuNR4Arz0eAKyVHgCtPR4AqIkeAKmVHgCqnR4Aq7EeAKzRHgCt2R4Ars0eAK/FHgANswCAEbMAgIaIAACHbAEAFbMAgBmzAIAdswCAIbMAgLhdAQC5wQEAusEBALvBAQC8wQEAvckBAL7xAQC/8QEAsL0eALGdHgCylR4As2UBALR9AQC1ZQEAtm0BALdlAQCqLR0AqzUdACWzAIApswCAri0dAK+VHACsLR0ArSUdAISMAQCjkR0ALbMAgDGzAICmER0ANbMAgDmzAIClgR0As1UeAD2zAIBBswCARbMAgEmzAIC2GR4AtRkeAE2zAIC7GR4AujkeAFGzAIBVswCAv+EBAL75AQC98QEAvAEeAFmzAIBdswCAYbMAgKOZHQBlswCApdUdAKbVHQBpswCAbbMAgHGzAICq9R0Aq9UdAKzNHQCtPQIArjUCAK8tAgCAZQAAgRUAAIIdAACEAAQAdbMAgHmzAICHcAMAhvwEAIGzAICFswCAibMAgI2zAICRswCAlbMAgJmzAICdswCAvsgEAKGzAIClswCAqbMAgK2zAICxswCAtbMAgO/cHwC5swCA4ZQBAL2zAIDjHAEAwbMAgMWzAIDJswCAzbMAgLt1AwC6aQMAvkgGANGzAIC/HQMAvh0DAL0dAwC8ZQMAs9UDANWzAIDZswCA3bMAgOGzAIC2fQMAtcUDAIRwBQCoJQIAqTUCAKo9AgCrNQIArC0CAK2dAgCulQIAr7UCAIIVAADlswCAgNkBAIEJAADEAAAA6bMAgPGzAID1swCAuKkCALmpAgC6SQEAu0kBALxZAQC9RQEAvkUBAL99AQCwzQIAsdECALLRAgCzqQIAtLkCALW5AgC2qQIAt6ECAOEoHgDhNBwA43QBAOMYHgD5swCA/bMAgIa4BACHVAUAhDgHAAG0AIAFtACACbQAgL6sBwANtACA78weAO/IGgCj9QIAEbQAgBW0AIAZtACAHbQAgKZdAgCl5QIAIbQAgKtVAgCqSQIAJbQAgCm0AICvPQIArj0CAK09AgCsRQIAqGEGAKlhBgCqYQYAq2EGAKxhBgCtYQYArmEGAK9hBgDtswCALbQAgDG0AIA1tACAObQAgD20AIBBtACARbQAgLjxBgC58QYAuvEGALvxBgC8nQYAvbEGAL6xBgC/sQYAsOUGALHtBgCy5QYAs/0GALTlBgC17QYAttkGALfVBgCz6QYASbQAgE20AIBRtACAVbQAgLbhBgC16QYAWbQAgLspBgC6IQYAXbQAgGG0AIC/KQYAviEGAL0pBgC8MQYAgl0AAKOtBgCARQAAgV0AAKalBgBltACAabQAgKWtBgCqZQYAq20GAIYADACHQAMArmUGAK9tBgCsdQYArW0GAG20AIDvfAUAcbQAgHW0AIB5tACAfbQAgIG0AICFtACAibQAgI20AICRtACAlbQAgJm0AIDjaAUAnbQAgOF4BQCz0QYAobQAgKW0AICptACArbQAgLb9BgC1/QYAsbQAgLupBgC6oQYAtbQAgLm0AIC/mQYAvqkGAL2pBgC8sQYAqLkGAKm5BgCqGQYAqxkGAKw1BgCtPQYArjUGAK8pBgC9tACAgh0AAIEdAACAHQAAwbQAgMW0AIDJtACA0bQAgLjpAQC56QEAuvkBALv5AQC86QEAvekBAL5dAQC/VQEAsCUGALEtBgCyJQYAsz0GALQtBgC1HQYAthUGALfZAQCGgAwAh+QCANW0AICjnQUA2bQAgKWxBQCmsQUA3bQAgOG0AIDltACAqu0FAKvlBQCs/QUAreUFAK7lBQCv1QUAtk0DAOm0AICExAMAtUUDAO20AICzjQIA8bQAgPW0AIC+SQMAv0kDALxJAwC9SQMAumkDALtpAwD5tACA/bQAgAG1AICmiQMApYEDAAW1AICjSQIACbUAgA21AIARtQCAr40DAK6NAwCtjQMArI0DAKutAwCqrQMAfbMAgBW1AIAZtQCAHbUAgIW0PQAhtQCAJbUAgCm1AIAttQCAMbUAgIA9AACBCQAAgh0AADW1AIC+sAMAObUAgIc4AwCG3AwAQbUAgEW1AIBJtQCATbUAgFG1AIDvXAYAVbUAgFm1AIC+6AwA45QGAF21AIDh3AEAYbUAgGW1AIBptQCAbbUAgLNRAQBxtQCAdbUAgHm1AIB9tQCAtnEBALV5AQCBtQCAuz0BALo9AQCFtQCAibUAgL/9AQC+9QEAvQUBALwFAQCNtQCAkbUAgJW1AICEQAwAmbUAgJ21AIChtQCA76wHAKW1AIDhJAYAqbUAgONABwCGkAwAh/wMALG1AIC1tQCAgFkAAIFlAACCYQAAo90BALm1AICl9QEApv0BAL21AIDBtQCAxbUAgKqxAQCrsQEArIkBAK2JAQCueQEAr3EBAM20AIA9tQCAybUAgM21AICttQCA0bUAgNW1AIDZtQCAqJ0NAKktDgCqOQ4AqzEOAKwRDgCtEQ4Arn0OAK9tDgCwGQ4AsRkOALIxDgCzMQ4AtNEOALXZDgC2zQ4At8UOALj9DgC52Q4AuqkOALupDgC8vQ4AvaUOAL6tDgC/pQ4AqIEPAKmBDwCqgQ8Aq4EPAKyBDwCtjQ8AroUPAK+1DwDdtQCA4bUAgOW1AIDptQCA7bUAgPG1AID1tQCA+bUAgLidDwC5rQ8AuqUPALtNDwC8VQ8AvV0PAL5JDwC/SQ8AsNEPALHRDwCy0Q8As9EPALS1DwC1vQ8AtrUPALetDwCzCQ4A/bUAgAG2AIAFtgCACbYAgLYNDgC1CQ4ADbYAgLsVDgC6FQ4AEbYAgBW2AIC/eQ4AvnEOAL0FDgC8BQ4AghUAAKNNDgCAYQAAgWEAAKZJDgAZtgCAvhABAKVNDgCqUQ4Aq1EOAIQkAQAhtgCArjUOAK89DgCsQQ4ArUEOAKg5DgCpOQ4AqlkOAKtRDgCscQ4ArXEOAK6RAQCvkQEAhgAAAIeEAAAltgCAKbYAgC22AIAxtgCANbYAgDm2AIC4dQEAuX0BALp1AQC7yQAAvNkAAL3ZAAC+yQAAv8EAALD1AQCx/QEAsvUBALNNAQC0VQEAtV0BALZVAQC3TQEAuk0PALtVDwC4TQ8AuUUPAL59DwC/tQ8AvEUPAL11DwCyAQ8AswEPALAxDwCxMQ8AtgEPALcNDwC0EQ8AtREPAKqZDgCrRQ8AqOUOAKmZDgCuQQ8Ar0EPAKxRDwCtUQ8APbYAgEG2AIBFtgCASbYAgE22AIBRtgCAVbYAgFm2AICzUQ0AXbYAgGG2AIBltgCAabYAgLZxDQC1eQ0AbbYAgLu5AgC6sQIAcbYAgHW2AIC/GQIAvhECAL0ZAgC8oQIAebYAgKMVDQB9tgCAgbYAgKY1DQCFtgCAibYAgKU9DQCq9QIAq/0CAIToAwCRtgCArlUCAK9dAgCs5QIArV0CAKhtAgCprQIAqqUCAKu9AgCspQIAra0CAK6lAgCvfQEAgO0BAIHxAQCC8QEAvqAFAJW2AICZtgCAh2gFAIYcBQC4yQEAuckBALrZAQC70QEAvPkBAL35AQC+mQEAv5UBALAFAQCxDQEAsgUBALMdAQC0BQEAtQ0BALYFAQC3+QEA4WQPAOGcDwDjFA4A49QPAJ22AIDhPA4AobYAgOPkAAC+rAQApbYAgKm2AIDvDAAArbYAgLG2AIDvYA4A77QPALW2AIC5tgCAhEQEALNhAgC9tgCAtWECALZhAgDBtgCAxbYAgMm2AIC6jQEAu4UBALydAQC9hQEAvo0BAL+FAQCjrQUAjbYAgM22AIDRtgCA1bYAgKatBQClrQUA2bYAgKtJBgCqQQYA3bYAgOG2AICvSQYArkEGAK1JBgCsUQYA5bYAgOm2AIDttgCA8bYAgIAdAACBCQAAgjkAAPW2AID5tgCA/bYAgIbIAACHIAMAAbcAgAW3AIAJtwCADbcAgKhtBgCptQcAqr0HAKsdBwCsCQcArTEHAK4xBwCvLQcAhKgDABG3AIAVtwCAGbcAgB23AIAhtwCAJbcAgCm3AIC4zQAAudUAALrVAAC75QAAvP0AAL2VAAC+nQAAv5UAALBVBwCxJQcAsi0HALM9BwC0LQcAtRUHALYdBwC39QAALbcAgOG8BgAxtwCA4/QFADW3AIA5twCAPbcAgEG3AIBFtwCASbcAgE23AIBRtwCAVbcAgFm3AIBdtwCA7+gEALN1BgCCLQAAgRUAAIAdAABhtwCAtvEGALXBBgBltwCAu6EGALrRBgBptwCAvmwBAL+RBgC+qQYAvakGALy5BgCjtQYAcbcAgIYoAACHTAEAdbcAgKYxBgClAQYAebcAgKthBgCqEQYAfbcAgIG3AICvUQYArmkGAK1pBgCseQYAhbcAgLO9AQCJtwCAjbcAgLZ5AQCRtwCAlbcAgLV5AQC6VQEAu10BAJm3AICdtwCAvvkAAL/lAAC8RQEAvf0AAKhxAgCpcQIAqnECAKtxAgCstQIArb0CAK61AgCvrQIAhOw8AKG3AICltwCAqbcAgK23AICxtwCAtbcAgLm3AIC4XQMAuWUDALptAwC7ZQMAvH0DAL1lAwC+bQMAv2UDALDVAgCx3QIAstUCALNtAwC0eQMAtWUDALZtAwC3ZQMAHbYAgL23AIDBtwCAo/UCAMW3AIClMQIApjECAMm3AIDNtwCA0bcAgKodAgCrFQIArA0CAK21AwCusQMAr60DAIBlAACBCQAAghkAANW3AIDZtwCA4bcAgL4QPADltwCAhsA8AIcgAwDptwCA7bcAgPG3AID1twCA+bcAgP23AICohQIAqZUCAKqVAgCrpQIArL0CAK3VAgCu0QIAr9ECAAG4AIAFuACACbgAgA24AIARuACAFbgAgBm4AIAduACAuHUBALl9AQC6dQEAu8kBALzZAQC9xQEAvsUBAL/9AQCwtQIAsb0CALKBAgCzgQIAtFUBALVdAQC2VQEAt00BAOGkBgAhuACA41AGAL6APACEHDwAvoA/ACW4AIApuACALbgAgDG4AIA1uACAObgAgD24AIBBuACA7+AGAEW4AICBfQAAgHEAAEm4AICCBQAAUbgAgFW4AIDvTAAAWbgAgOGQAQBduACA41gBAGG4AIBluACAabgAgIZYPwCH/DwAs509AN23AIBNuACAbbgAgHG4AIC21T0AtbU9AHW4AIC7+T0AuvE9AHm4AIB9uACAvxk+AL4RPgC91T0AvNU9AIG4AICj2T0AhbgAgIm4AICmkT0AjbgAgJG4AICl8T0AqrU9AKu9PQCVuACAmbgAgK5VPgCvXT4ArJE9AK2RPQCoVT4AqVk+AKphPgCrYT4ArGE+AK1hPgCuYT4Ar2E+AISoAwCduACAobgAgKW4AICpuACArbgAgLG4AIC1uACAuEU/ALldPwC6VT8Au20/ALx1PwC9fT8AvnU/AL9tPwCwwT8AscE/ALLBPwCzwT8AtME/ALXBPwC2wT8At8E/AIC5AQCBuQEAggUAALm4AIDhgD4AwbgAgOMoPQDFuACAhoAAAIcEAQDvCD0AybgAgM24AIDRuACA1bgAgNm4AICzqT8AvbgAgN24AIDhuACA5bgAgLahPwC1qT8A6bgAgLtFPgC6RT4A7bgAgPG4AIC/RT4AvkU+AL1VPgC8VT4Ao2k/APW4AID5uACA/bgAgAG5AICmYT8ApWk/AAW5AICrhT4AqoU+AAm5AIANuQCAr4U+AK6FPgCtlT4ArJU+ABG5AICzGT4AFbkAgBm5AIC2IT4AHbkAgCG5AIC1MT4AuvEBALv5AQAluQCAKbkAgL6xAQC/vQEAvNEBAL3RAQCo0T0AqdE9AKrVPQCr6T0ArP09AK3lPQCu7T0ArxECAID5AwCBzQMAgsUDAIQkAwC+AAQAMbkAgIesAwCGvAQAuBkCALktAgC6JQIAu+kCALz5AgC9+QIAvukCAL/pAgCwcQIAsXkCALJBAgCzQQIAtDECALU9AgC2NQIAtykCAKVtPQA1uQCAObkAgKZ9PQA9uQCAbbcAgKNFPQBBuQCArY0CAKyNAgCv4QIAru0CAKwAAABFuQCAq6UCAKqtAgDh+AEASbkAgOP0AgCEwAQATbkAgFG5AIBVuQCAWbkAgF25AIBhuQCAZbkAgGm5AIBtuQCAcbkAgO8wAgB1uQCAqBUCAKkZAgCqJQIAqz0CAKwlAgCtLQIAriUCAK9VAgB5uQCAfbkAgIG5AICFuQCAibkAgI25AICEsAQAkbkAgLjRAgC52QIAuuECALvhAgC8kQIAvZ0CAL6VAgC/iQIAsC0CALE1AgCyNQIAswUCALQdAgC18QIAtvECALfxAgDheD8A4zQBAOMIPgDhbD4AgQkAAICpAACVuQCAgj0AAJm5AIChuQCApbkAgL4gBACpuQCA79g+AO/MPgCtuQCAsbkAgLPpAgCG6AQAh8AEALbpAgC1uQCAubkAgLXpAgC6rQIAu7UCAL25AIDBuQCAvp0CAL9xAgC8pQIAvZUCAC25AICduQCAxbkAgMm5AIDNuQCA0bkAgNW5AIDZuQCAqBUGAKmhBgCqoQYAq70GAKytBgCtgQYArv0GAK/tBgCwlQYAsZ0GALKVBgCzrQYAtLUGALW9BgC2tQYAt60GALiVBgC5mQYAukkHALtJBwC8WQcAvVkHAL5JBwC/SQcArN0FAK3tBQCu5QUArwkFAN25AIDhuQCAqtUFAKvNBQDluQCApZEFAKaRBQDpuQCA7bkAgPG5AID1uQCAo5EFALNJBgD5uQCA/bkAgAG6AIAFugCAtmEGALVFBgAJugCAuzkGALoxBgC+ZAAADboAgL8ZBgC+EQYAvRkGALwhBgCjiQcAgtkBAIHZAQCAwQEAEboAgKahBwClhQcAFboAgKv5BwCq8QcAhggBAId8AQCv2QcArtEHAK3ZBwCs4QcAGboAgLP1BgAdugCAIboAgLaFBgAlugCAKboAgLWdBgC6jQYAu20BAC26AIAxugCAvmUBAL9tAQC8dQEAvW0BAKglBgCpLQYAqjkGAKsxBgCsUQYArUEGAK5BBgCvdQYANboAgDm6AIA9ugCAQboAgEW6AIBJugCATboAgFG6AIC4VQEAuWUBALplAQC7fQEAvGUBAL1tAQC+HQEAvxUBALANBgCx7QEAsuUBALP9AQC05QEAte0BALblAQC3bQEAo7EFAFW6AIBZugCAvkgDAL5YDACmwQUApdkFAF26AICrKQIAqskFAGG6AIBlugCArykCAK4hAgCtKQIArDECAGm6AIBtugCAcboAgHW6AICAGQAAgRkAAIIFAAB5ugCAhKwDAIG6AICHGAMAhswMAIW6AICJugCAjboAgJG6AICokQMAqZkDAKrJAwCrxQMArN0DAK3BAwCuwQMAr/UDAJW6AICZugCAnboAgKG6AIClugCAqboAgK26AICxugCAuH0DALnBAAC6wQAAu9EAALz5AAC9+QAAvpkAAL+ZAACwjQMAsUUDALJNAwCzRQMAtF0DALVFAwC2TQMAt0UDALNBAgC1ugCAuboAgL8EDwC9ugCAtkECALVVAgDBugCAu4ECALpJAgDFugCAyboAgL+BAgC+mQIAvZECALyZAgDNugCA0boAgNW6AIDZugCA76QDAN26AIDhugCA5boAgOMQAwDpugCA4VgAAIQgDQCAKQAAgSkAAIIdAADxugCA4VAGAOGgBwDjoAYA41AHAIWUDAD1ugCA70gbAPm6AIDhJAIA/boAgONwGgABuwCABbsAgAm7AIDvqAEA7+gGAIagDwCHDA0Ao4kCAA27AIClnQIAEbsAgBW7AICmiQIAGbsAgB27AICrSQIAqoECAK1ZAgCsUQIAr0kCAK5RAgCoZQ4AqXUOAKp9DgCrdQ4ArG0OAK21DgCuvQ4Ar7UOAO26AIAhuwCAJbsAgCm7AIAtuwCAOLsAgDy7AIBAuwCAuF0PALltDwC6ZQ8Auw0PALwVDwC9HQ8AvhUPAL8JDwCwzQ4AsdUOALLdDgCz1Q4AtM0OALVxDwC2cQ8At20PALP1DgBEuwCASLsAgEy7AIBQuwCAtjUOALXlDgBUuwCAuxEOALoJDgBYuwCAXLsAgL+1DwC+CQ4AvQEOALwJDgCCFQAAo7EOAIBhAACBYQAApnEOAGC7AIC+EAEApaEOAKpNDgCrVQ4AaLsAgIQgAQCuTQ4Ar/EPAKxNDgCtRQ4An0UIAJ4NCQCdDQkAnJkLAJt1NQCaETUAmZk3AJgNMQCXJTEAliUxAJWBPQCUDT0Ak4k/AJIVOACRPTkAkD05AI9lJQDvrA0AhgAEAIegAQBsuwCAcLsAgHS7AIDv6AEAeLsAgOE0AgB8uwCA4zQBAIC7AIDjCAwAhLsAgOEIDQChoQEAiLsAgKMJBQCibQMApc0EAKQRBQCnHRkAph0ZAKmhHQCoORkAq+kcAKqpHQCtkREArAEQAK8BFACuUREAsfkVALDlFQCz6WkAsgFoALUBbAC0eWkAjLsAgJC7AICUuwCAmLsAgJy7AICguwCAowkDAKIZDQCh/Q0AoP0NAIIlJgCDBToApLsAgKi7AICGqTwAhzU+AIQdOgCFPTsAiok+AIslMgCsuwCAsLsAgI6xNACPMTYAjD0yAI0tMgCSJTYAk9EIAIREAwC+wAQAlhULAJdVDgCUXQoAlVUKAJplDgCbiQ4AtLsAgLi7AIC8uwCAwLsAgJyBAADEuwCAuLUCALm9AgC6tQIAuwkCALwZAgC9GQIAvgkCAL8BAgCwdQ0AsX0NALJJDQCzSQ0AtJUCALWdAgC2lQIAt40CAKi9DQCpUQ0AqlUNAKtpDQCsfQ0ArWUNAK5tDQCvEQ0AZLsAgILtAQCBHQAAgB0AAMi7AIDMuwCAfboAgL5wBQCznQwAhIwFANC7AIDYuwCA3LsAgLalDAC1tQwA4LsAgLv5DAC68QwAhigFAIcgBQC/GQMAvhEDAL3dDAC83QwA5LsAgKPZDADouwCA7LsAgKbhDADwuwCA9LsAgKXxDACqtQwAq70MAPi7AID8uwCArlUDAK9dAwCsmQwArZkMAAC8AIAEvACACLwAgAy8AIAQvACAFLwAgBi8AIDvvAEAHLwAgOF8DgAgvACA41ABACS8AIAovACALLwAgDC8AICzlQIANLwAgDi8AIA8vACAQLwAgLa9AgC1uQIASLwAgLs5AgC6YQIAhsgEAIesBAC/GQIAvhECAL0ZAgC8IQIAo1UFAILVBwCBxQcAgMUHAEy8AICmfQUApXkFAFC8AICr+QUAqqEFAFS8AIBYvACAr9kFAK7RBQCt2QUArOEFAFy8AICzWQcAYLwAgGS8AIC2HQcAaLwAgGy8AIC1FQcAugkHALsJBwBwvACAdLwAgL75BwC/+QcAvPkHAL35BwDUuwCARLwAgHi8AIB8vACAgLwAgIS8AICIvACAjLwAgKitBwCptQcAqrUHAKvtBwCs+QcArfkHAK7tBwCv5QcAsKkHALGpBwCySQcAs0kHALRZBwC1WQcAtkkHALdJBwC4eQcAuUUHALpBBwC7XQcAvEUHAL1NBwC+RQcAvzkHAKMdBgCQvACAlLwAgJi8AICcvACAplkGAKVRBgCgvACAq00GAKpNBgCkvACAqLwAgK+9BgCuvQYArb0GAKy9BgCAbQAAgQkAAIIZAACsvACAsLwAgISYAQC+kAEAtLwAgIYAHACHxAEAuLwAgLy8AIDAvACAxLwAgMi8AIDMvACAqF0GAKmVAQCqlQEAq6UBAKy9AQCt1QEArtEBAK/RAQDQvACA1LwAgNi8AIDcvACA4LwAgOS8AIDovACA7LwAgLhZAQC5WQEAus0AALvFAAC83QAAvcUAAL7FAAC/9QAAsLUBALG9AQCygQEAs4EBALR5AQC1eQEAtmkBALdpAQCzHQIA8LwAgPS8AIC+gBwA+LwAgLZVAgC1NQIA/LwAgLt5AgC6cQIAAL0AgAS9AIC/vQIAvr0CAL1VAgC8VQIACL0AgKNZAgAMvQCAEL0AgKYRAgAUvQCAGL0AgKVxAgCqNQIAqz0CABy9AIAgvQCArvkCAK/5AgCsEQIArRECACi9AIAsvQCAvgQdAL4AHgAwvQCANL0AgDi9AIA8vQCAgPkAAIHNAACCxQAAhCADAIawHACHlAMAQL0AgES9AIBIvQCATL0AgFC9AIBUvQCA42wCAFi9AIDhoAEAXL0AgO8UAgBgvQCAZL0AgGi9AIBsvQCAcL0AgHS9AIB4vQCA4fAGAOE0BgDjTAAA4xgGAHy9AICAvQCAhL0AgIi9AICAPQAAgQkAAIIZAACMvQCAkL0AgIS8HQDvmAAA7zgHALMxAgDRAAAAh9gdAIZsHACYvQCAtikCALUhAgCcvQCAu80CALrNAgCgvQCApL0AgL/NAgC+zQIAvc0CALzNAgCyXQYAs2UGALANBgCxVQYAtn0GALedBQC0fQYAtXUGALqNBQC7zQUAuKUFALmFBQC+xQUAv8kFALzVBQC9zQUAqL0AgKy9AICwvQCAtL0AgLi9AIC8vQCAwL0AgMS9AICqtQYAq70GAKgBBwCpvQYAroEGAK+NBgCsmQYArZUGAKNxHQDIvQCAzL0AgNC9AIDUvQCApmkdAKVhHQDYvQCAq40dAKqNHQDcvQCA4L0AgK+NHQCujR0ArY0dAKyNHQDkvQCAs9UeAOi9AIDsvQCAts0eAPC9AID0vQCAtcUeALqhHgC7oR4A+L0AgPy9AIC+pR4Av6keALyxHgC9sR4AJL0AgJS9AIAAvgCAhAQDAID5AACB+QAAghEAAAS+AICoIR4AqSEeAKo5HgCrOR4ArCkeAK0pHgCuAR4ArwEeALABHgCxAR4AsgEeALMBHgC0BR4AtQkeALY9HgC3NR4AuA0eALkVHgC6HR4AuxUeALwNHgC95R8Avu0fAL/lHwCjkR8ACL4AgIYoAQCHSAEADL4AgKaJHwClgR8AEL4AgKvlHwCq5R8AFL4AgBi+AICv7R8AruEfAK31HwCs9R8AHL4AgLMtHgAgvgCAJL4AgLaVHgAovgCALL4AgLWdHgC6sR4Au7EeADC+AIA0vgCAvnUBAL99AQC8oR4AvaEeAKjRHgCp2R4AquEeAKvhHgCsUR4ArVEeAK5RHgCvUR4AOL4AgDy+AIBAvgCARL4AgEi+AIBMvgCAUL4AgFS+AIC43QEAue0BALrlAQC7jQEAvJkBAL2ZAQC+jQEAv4UBALAxHgCxMR4AsjEeALMxHgC09QEAtf0BALb1AQC37QEAo2kdAFi+AIBcvgCAYL4AgGS+AICm0R0ApdkdAGi+AICr9R0AqvUdAGy+AIBwvgCArzkCAK4xAgCt5R0ArOUdAIFpAACAWQAAvgAEAIJhAAB4vgCAfL4AgIC+AICEvgCAhOwDAIi+AICHiAMAhuwEAIy+AICQvgCAlL4AgJi+AICohQMAqZUDAKqVAwCrpQMArL0DAK3VAwCu0QMAr9EDAJy+AICgvgCApL4AgKi+AICsvgCAsL4AgLS+AIC4vgCAuHEDALlxAwC6cQMAu3EDALzVAAC93QAAvtUAAL/NAACwtQMAsb0DALKBAwCzgQMAtFEDALVRAwC2UQMAt1EDAOFUHgDhrB8A45QBAOMoHgDjYAMAvL4AgOEIAADAvgCA75ADAMS+AIDIvgCAzL4AgNC+AIDUvgCA70wfAO9MHwCzXQIA2L4AgNy+AIDgvgCA6L4AgLYVAgC1dQIA7L4AgLs5AgC6MQIAhCQFAL7gBAC/1QIAvtUCAL0VAgC8FQIAuJEdALmZHQC6oR0Au6EdALzRHQC93R0AvtUdAL/JHQCwCR4AsQkeALIZHgCzGR4AtAkeALUJHgC2vR0At7UdAKipHgCpqR4AqrkeAKu5HgCsqR4ArakeAK55HgCveR4AgKUAAIGtAACCpQAA8L4AgIbQBACH+AQA9L4AgPi+AIB0vgCA5L4AgPy+AIAAvwCABL8AgAi/AIAMvwCAEL8AgKhxBgCpcQYAqnEGAKtxBgCsVQYArUUGAK5NBgCvRQYAsD0GALHlBgCy7QYAs+UGALT9BgC15QYAtu0GALflBgC43QYAuXEHALp1BwC7SQcAvFkHAL1ZBwC+SQcAv0kHALPZBgAUvwCAGL8AgBy/AIAgvwCAtuUGALX9BgAkvwCAuwEGALrZBgAovwCALL8AgL8BBgC+GQYAvREGALwZBgAwvwCAo9kFADS/AIA4vwCAppEFADy/AIBAvwCApfEFAKq1BQCrvQUARL8AgEi/AICuUQUAr1EFAKyRBQCtkQUAo1kHAIIZAACBGQAAgOEBAEy/AICmZQcApX0HAFC/AICrgQcAqlkHAISgAgC+rAEAr4EHAK6ZBwCtkQcArJkHAFS/AICzqQYAhugAAIcsAQC2WQEAWL8AgFy/AIC1oQYAunUBALt9AQBgvwCAZL8AgL75AQC/+QEAvGUBAL35AQCo0QYAqdkGAKplBgCrdQYArG0GAK2dAQCulQEAr40BAITsAQBovwCAbL8AgHC/AIB0vwCAeL8AgHy/AICAvwCAuGkBALlpAQC6CQEAuwUBALwdAQC9AQEAvgEBAL81AQCw9QEAsf0BALL1AQCzaQEAtHkBALV5AQC2aQEAt2EBAIS/AICIvwCAjL8AgKPhBQCQvwCApekFAKYRAgCUvwCAmL8AgJy/AICqPQIAqzUCAKwtAgCtsQIArrECAK+xAgCgvwCApL8AgL4EAwCEAAwAqL8AgKy/AICwvwCAtL8AgIANAACBFQAAgh0AALi/AIC8vwCAwL8AgIdEAwCG3AwAs+kDAMi/AIDMvwCA0L8AgNS/AIC2PQMAtT0DANi/AIC7GQMAuhEDANy/AIDgvwCAv7kAAL6xAAC9uQAAvAEDAOS/AIDhlAEA6L8AgON8AQDsvwCA8L8AgPS/AID4vwCA/L8AgADAAIAEwACACMAAgAzAAIAQwACAFMAAgO9MAgCoVQIAqV0CAKphAgCrYQIArLUCAK29AgCutQIAr60CAL5oDQAYwACAHMAAgCDAAIAkwACAgq0AAIGtAACArQAAuGEBALlhAQC6CQEAuwkBALwBAQC9AQEAvgEBAL8BAQCw1QIAsd0CALLVAgCzbQEAtHUBALV9AQC2aQEAt2EBAOFoBgDh8AcA47AAAOP0BgAowACALMAAgDDAAIA4wACAPMAAgEDAAIBEwACASMAAgL78DABMwACA72wAAO8oBgCjqQIAUMAAgIZoDACHBA0AVMAAgKZ9AgClfQIAWMAAgKtZAgCqUQIAXMAAgGDAAICv+QEArvEBAK35AQCsQQIAqIUOAKmNDgCqhQ4Aq50OAKyNDgCtvQ4ArrUOAK/dDgA0wACAZMAAgGjAAIBswACAcMAAgHTAAIB4wACAfMAAgLitDgC5tQ4Aur0OALu1DgC8dQ8AvX0PAL51DwC/bQ8AsKkOALG1DgCyvQ4As7UOALStDgC1lQ4Atp0OALeVDgCzDQ4AgMAAgITAAICIwACAjMAAgLY9DgC1BQ4AkMAAgLtxDgC6bQ4AlMAAgJjAAIC/UQ4AvmkOAL1hDgC8aQ4AghkAAKNJDgCAZQAAgRkAAKZ5DgCcwACAoMAAgKVBDgCqKQ4AqzUOAIS8AwCkwACAri0OAK8VDgCsLQ4ArSUOAKidDgCppQ4Aqq0OAKulDgCsvQ4AraEOAK7dDgCvzQ4AhiABAIdkAQCowACArMAAgLDAAIC0wACAuMAAgLzAAIC4eQEAuXkBALrNAQC7xQEAvN0BAL3FAQC+xQEAv/UBALC9DgCxjQ4AsoUOALNJAQC0WQEAtVkBALZJAQC3SQEAtS0OAMDAAIDEwACAtjkOAMjAAIDMwACAsz0OANDAAIC9hQEAvEkOAL+FAQC+hQEA1MAAgMS/AIC7UQ4AumEOAKNlDgDYwACA3MAAgODAAIDkwACApmEOAKV1DgDowACAqwkOAKo5DgDswACA8MAAgK/dAQCu3QEArd0BAKwRDgD0wACA+MAAgO/QDwD8wACAAMEAgATBAIAIwQCADMEAgBDBAIC+aAMAGMEAgBzBAIDhVA4AIMEAgONkDgAkwQCAgFkAAIFZAACCaQAAhIwDAIbwBACHFAMAKMEAgCzBAIAwwQCANMEAgDjBAIA8wQCAQMEAgETBAIBIwQCATMEAgFDBAIBUwQCAWMEAgFzBAIBgwQCAZMEAgGjBAIBswQCAqIkDAKmJAwCqmQMAq5kDAKyJAwCtiQMArj0DAK81AwCwUQMAsVEDALJVAwCzfQMAtBUDALUdAwC2FQMAtw0DALg9AwC5DQMAugUDALvtAAC89QAAvfkAAL7pAAC/6QAAcMEAgHTBAIB4wQCAsz0CAHzBAIC1LQIAtiUCAIDBAIC+aAUAiMEAgLq5AgC7uQIAvK0CAL2FAgC+/QIAv/UCAIBJAACBVQAAglUAAIQABQDvjAMAvhgEAId0BQCG/AQA4zwDAIzBAIDhUAAAkMEAgJTBAICYwQCAnMEAgKDBAICkwQCAqMEAgKzBAICwwQCAtMEAgLjBAIC8wQCA79QOAL4oBgDhdA4AwMEAgONUAQDEwQCAyMEAgMzBAIDQwQCAo/ECANTBAIDYwQCA3MEAgODBAICm6QIApeECAOTBAICrdQIAqnUCAOjBAIDswQCArzkCAK4xAgCtSQIArGECAKgpBgCpKQYAqj0GAKsxBgCsSQYArUkGAK55BgCveQYAhMEAgIIVAACBxQcAgMUHAPDBAICEaAMA9MEAgPjBAIC4yQYAuckGALrZBgC72QYAvMkGAL3JBgC+WQcAv1kHALAJBgCxCQYAshkGALMZBgC0CQYAtQkGALb5BgC3+QYAs7UGAPzBAICGrAAAh0ADAADCAIC2yQYAtcEGAATCAIC7zQYAus0GAAjCAIAMwgCAv80GAL7NBgC9zQYAvM0GABDCAICj8QYAFMIAgBjCAICmjQYAHMIAgCDCAIClhQYAqokGAKuJBgAkwgCAKMIAgK6JBgCviQYArIkGAK2JBgCoJQYAqWEGAKplBgCrfQYArGUGAK1tBgCuZQYAr50GACzCAIAwwgCANMIAgDjCAIA8wgCAQMIAgETCAIBIwgCAuPUGALn9BgC69QYAu4kGALyZBgC9mQYAvokGAL+BBgCw5QYAse0GALLlBgCz/QYAtOUGALXtBgC20QYAt80GAEzCAIC2/QYAtf0GAFDCAICz/QYAVMIAgFjCAIBcwgCAvzkGAL4xBgC9OQYAvCEGALs5BgC6MQYAFMEAgGDCAICjrQYAgnkAAIFVAACAVQAAhFwBAKatBgClrQYAaMIAgKtpBgCqYQYAhkh/AIfkAACvaQYArmEGAK1pBgCscQYAbMIAgO/cBwBwwgCAdMIAgHjCAIB8wgCAgMIAgITCAICIwgCAhKADAIzCAIC/JHkAkMIAgONoBwCUwgCA4XQGALPRAgCYwgCAvgQDAISAfQCcwgCAtvkCALXxAgCgwgCAu7UCALqpAgCkwgCAqMIAgL9RAwC+mQIAvZECALylAgCpBQIAqLkCAKsVAgCqHQIArT0CAKw9AgCvUQIArl0CAL5ofQCswgCAsMIAgLTCAIC4wgCAvMIAgMDCAIDEwgCAufEDALjpAwC78QMAuvkDAL1RAwC86QMAv00DAL5RAwCxNQIAsCkCALMBAgCyNQIAtdEDALQZAgC30QMAttkDAIIpAACjlQMAgB0AAIEVAACmvQMAyMIAgMzCAICltQMAqu0DAKvxAwDQwgCA2MIAgK7dAwCvFQIArOEDAK3VAwCGYH0Ah3h9ALNBAQCEAH8AtUEBANzCAIDgwgCAtkkBAOTCAIDowgCAu0EBALpNAQC9SQEAvEUBAL8pAQC+OQEA7MIAgO/cBgDwwgCA9MIAgPjCAID8wgCAAMMAgO8wBgCELH4A4eAGAATDAIDjiAEACMMAgON0AAAMwwCA4SwBAKPJAQAQwwCAFMMAgIVweQAYwwCApsEBAKXJAQAcwwCAq8kBAKrFAQAgwwCAJMMAgK+hAQCusQEArcEBAKzNAQCo3X0AqQV+AKoBfgCrAX4ArAF+AK0BfgCuAX4ArwF+ANTCAIAowwCALMMAgDDDAIA0wwCAgp0AAIGdAACAnQAAuC1+ALnhfgC64X4Au+F+ALzhfgC94X4AvuF+AL/hfgCwQX4AsU1+ALJZfgCzVX4AtDV+ALUlfgC2JX4AtxV+AKitfwCp0X8AqtF/AKvtfwCs9X8ArRV/AK4RfwCvEX8AOMMAgDzDAIBAwwCARMMAgIbwAwCHuAAASMMAgEzDAIC4EX8AuRl/ALohfwC7IX8AvPUAAL39AAC+9QAAv+0AALBxfwCxcX8AsnF/ALNFfwC0QX8AtU1/ALY9fwC3NX8As1l+AFDDAIBUwwCAWMMAgFzDAIC2lX4AtX1+AGDDAIC7tX4AurV+AGTDAIBowwCAv4l+AL6FfgC9kX4AvKV+AGzDAICjHX4AcMMAgHTDAICm0X4AeMMAgHzDAIClOX4AqvF+AKvxfgCAwwCAhMMAgK7BfgCvzX4ArOF+AK3VfgCwrQAAscUAALLBAACzwQAAtMUAALXNAAC28QAAt/EAALhhAAC5YQAAumEAALt9AAC8ZQAAvW0AAL5lAAC/vQMAiMMAgIzDAICQwwCAZMIAgJTDAICYwwCAnMMAgKDDAICoWQEAqVkBAKrtAACr5QAArP0AAK3lAACu5QAAr9UAAKTDAICCHQAAgR0AAIAdAACowwCArMMAgLDDAIC+VAIAhoAEAIfsAgC4wwCAvMMAgMDDAIDEwwCAyMMAgL54AwDjdH4AzMMAgOG4fQDQwwCA1MMAgNjDAIDcwwCA4MMAgOTDAIDowwCA7MMAgPDDAIDvwH4A9MMAgPjDAID8wwCAs4UDAADEAIAExACACMQAgAzEAIC2hQMAtZUDABDEAIC74QMAuokDAL4kBgAUxACAv+kDAL7hAwC99QMAvPUDAIIpAACjwQMAgB0AAIEVAACmwQMAGMQAgBzEAICl0QMAqs0DAKulAwAgxACAheAFAK6lAwCvrQMArLEDAK2xAwDh+AMAKMQAgONcHwAsxACA7/QDADDEAICGPAcAh6wCAON8fgA0xACA4YABADjEAIA8xACAQMQAgO/kEwBExACAs3EBAEjEAIBMxACAUMQAgFTEAIC2EQEAtWEBAFjEAIC7OQEAujEBAFzEAIBgxACAvxkBAL4RAQC9GQEAvCEBAGTEAIBoxACAbMQAgHDEAIB0xACAeMQAgHzEAIDvxH8AgMQAgOH8fgCExACA4/B/AIANAACBdQAAgn0AAIjEAICMxACAkMQAgKP5AQC+AAgApekBAJjEAICcxACAppkBAISoBQCgxACAq7EBAKq5AQCtkQEArKkBAK+RAQCumQEAqCkGAKkpBgCqOQYAqzkGAKwpBgCtUQYArlUGAK9NBgAkxACAhCABAKTEAICUxACAo+EBAKKZBAChGQQAoPEFALg5BgC5OQYAus0GALvFBgC83QYAvcUGAL7FBgC/8QYAsDUGALE9BgCyNQYAsw0GALQVBgC1HQYAthUGALcJBgCPoWwAs5EHAIYoAQCHfAMAtqEHAKjEAICsxACAtbEHALrlBwC77QcAsMQAgLTEAIC+7QcAv90HALz1BwC97QcAn/l4AJ7leACdcXkAnCF8AJvxfACaYX0AmZlxAJjZcACX4XAAlnl0AJVtdACUbXQAk61pAJJxaACReWgAkB1uAIIhbQCD5W8AuMQAgLzEAICGTWgAh5V1AISZaQCFmWkAiqV1AIu5dQDAxACAxMQAgI5xcACPgXwAjDlxAI05cQCSYX0Ak6l9AMjEAIDMxACAlml5AJeZBACU4XgAlX15AJpBBQCbyQUA0MQAgNTEAIDYxACA3MQAgJypAADgxACAo4ENAKKpAQChqQEA5MQAgKexCQCmAQgApU0NAKSZDQCrkRUAqoUVAKkBFACocQkArx0QAK7pEQCtvREArAEQALMBGACy8RwAscEdALDJHQC0wwCA6MQAgLXhGAC0/RkA7MQAgPDEAID0xACA+MQAgIAdAACBCQAAgv0DAPzEAICjFQUAAMUAgIaIDACHPAMACMUAgKYlBQClNQUADMUAgKtpBQCqYQUAEMUAgBTFAICvWQUArmkFAK1pBQCscQUAGMUAgBzFAICEBAwAIMUAgCTFAIDhbAYAKMUAgOPsewAsxQCAMMUAgDTFAIDvqAYAOMUAgDzFAIBAxQCARMUAgKmNBQCogQUAq60FAKqZBQCtoQUArLkFAK+lBQCuqQUAhGgNAEjFAIBMxQCAUMUAgFTFAIBYxQCAXMUAgL70DAC5SQUAuEEFALtZBQC6QQUAvUkFALxBBQC/cQUAvn0FALGpBQCwoQUAs7kFALKhBQC1mQUAtKkFALd5BQC2kQUAqNUEAKndBACq7QQAqyUDAKyFAwCtjQMArrEDAK+xAwBgxQCAZMUAgGjFAIBsxQCAgBkAAIEZAACCBQAAcMUAgLgxAgC5MQIAujUCALvBAgC8hQIAvbUCAL69AgC/tQIAsGkCALFpAgCyQQIAs0ECALQ5AgC1OQIAthECALcRAgCGoAwAh0wNAHjFAIB8xQCA76QGAIDFAICExQCA78wHAOOUAQDhpAYA4TgBAONcBgCIxQCAjMUAgJDFAICUxQCAmMUAgJzFAICzLQQAoMUAgLVFAwCkxQCAqMUAgLZFAwCsxQCAsMUAgLvlAgC65QIAvd0CALzdAgC/tQIAvrUCAATFAIB0xQCAtMUAgLjFAIC8xQCAwMUAgMTFAIDIxQCAqDEOAKk5DgCqAQ4AqwEOAKxxDgCtcQ4ArnUOAK9tDgCwGQ4AsSUOALItDgCzJQ4AtCEOALUhDgC2IQ4AtyEOALjFDgC5zQ4AusUOALvdDgC8xQ4Avc0OAL5ZDwC/WQ8As6kOAMzFAIDQxQCA1MUAgNjFAIC20Q4AtdkOANzFAIC7wQ4Auv0OAODFAIC+LAAAv8UOAL7FDgC90Q4AvNkOAIJpAACj7Q4AgFkAAIFRAACmlQ4A5MUAgOjFAIClnQ4AqrkOAKuFDgCGyAAAh6wAAK6BDgCvgQ4ArJ0OAK2VDgDsxQCAs5EOAPDFAID0xQCAtqUOAPjFAID8xQCAta0OALrhDgC74Q4AAMYAgATGAIC+6Q4Av9UOALz1DgC96Q4Ao6UKAAjGAIAMxgCAEMYAgBTGAICmzQ0Apc0NABjGAICrbQwAqm0MABzGAIAgxgCArz0MAK49DACtVQwArFUMAKgJDgCpCQ4Aqh0OAKsVDgCsIQ4ArSEOAK4hDgCvIQ4AJMYAgCjGAIAsxgCAMMYAgDTGAIA4xgCAPMYAgEDGAIC4zQEAudUBALrdAQC71QEAvM0BAL1RAQC+UQEAv1EBALAhDgCxIQ4AsiUOALM5DgC0KQ4AtRUOALYdDgC39QEARMYAgEjGAIBMxgCAo5kNAFDGAIClpQ0Apq0NAL7cAgCE7AMAWMYAgKrpDQCr6Q0ArP0NAK3hDQCu4Q0Ar90NAIBFAACBTQAAglkAAKNFAwBcxgCApUEDAKZBAwBgxgCAhsAEAIcAAwCqLQMAqyUDAKw9AwCtJQMAriUDAK8VAwCoWQIAqYUDAKqBAwCrgQMArIUDAK2NAwCusQMAr7EDAGTGAIBoxgCAbMYAgHDGAIB0xgCAeMYAgHzGAICAxgCAuGUDALltAwC6ZQMAu30DALxlAwC9bQMAvmUDAL/dAACwpQMAsa0DALKlAwCzvQMAtK0DALWdAwC2lQMAt10DALMJAgCExgCAiMYAgIzGAICQxgCAtg0CALUNAgCUxgCAu2kCALphAgCYxgCAnMYAgL9ZAgC+aQIAvWkCALxxAgCgxgCApMYAgKjGAICsxgCA4aABALDGAIDjaAMAtMYAgIEVAACAFQAA74wDAIIVAAC4xgCAvMYAgMDGAIC+cAUA4RgOAOGUDwDjOA8A49QPAISUAgDIxgCAzMYAgNDGAIDUxgCA2MYAgNzGAIDgxgCA5MYAgOjGAIDv7AEA7/gPAIZgBACHBAUAs5UBAITMBQC1dQEA7MYAgPDGAIC2dQEA9MYAgPjGAIC7UQEAulkBAL31AAC8SQEAv/UAAL71AACoJQYAqVUGAKpVBgCrrQYArLUGAK29BgCutQYAr60GAMTGAID8xgCAAMcAgATHAIAIxwCADMcAgBDHAIAUxwCAuGkHALlpBwC6CQcAuwkHALwZBwC9GQcAvg0HAL8BBwCw1QYAsd0GALLVBgCzaQcAtHkHALV5BwC2aQcAt2EHAKPdBgAYxwCAHMcAgCDHAIAkxwCApj0GAKU9BgAoxwCAqxkGAKoRBgAsxwCAMMcAgK+9BwCuvQcArb0HAKwBBgCAXQAAgW0AAIJlAACzUQcAvtgDALVxBwC2cQcANMcAgIbgAACHFAMAul0HALs5BwC8KQcAvRUHAL4dBwC/2QAAqJUGAKmdBgCqlQYAq60GAKy1BgCtvQYArrUGAK+tBgA4xwCAPMcAgEDHAIBExwCASMcAgEzHAIBQxwCAVMcAgLhxAQC5cQEAunEBALtxAQC81QEAvd0BAL7VAQC/zQEAsNUGALGxBgCysQYAs40GALSVBgC1UQEAtlEBALdRAQBYxwCAoxkGAFzHAIBgxwCApjkGAFTGAIBkxwCApTkGAKoVBgCrcQYAaMcAgGzHAICuVQYAr5EBAKxhBgCtXQYAcMcAgHTHAIB4xwCAfMcAgIDHAICExwCAiMcAgIzHAICQxwCAlMcAgJjHAICcxwCAgBkAAIEZAACCBQAAoMcAgISAAgC+gAMAhwwDAIasHADhaAYAqMcAgOOYBwCsxwCAsMcAgLTHAIDvrAcAuMcAgLzHAIDAxwCAxMcAgMjHAIDMxwCA0McAgNTHAICzZQMA2McAgLVlAwC2bQMA3McAgODHAIDkxwCAuukDALvlAwC8/QMAve0DAL7RAwC/0QMA6McAgOzHAIDwxwCA9McAgPjHAID8xwCAAMgAgATIAICogQMAqYEDAKqBAwCrgQMArIEDAK2BAwCugQMAr4EDALBBAwCxTQMAskUDALNVAwC0eQMAtXkDALYZAwC3GQMAuCkDALkpAwC6OQMAuzkDALwpAwC9KQMAvhkDAL8ZAwCBGQAAgBEAAKMhAgCCLQAApSECAAjIAIAMyACApikCABDIAIAYyACAq6ECAKqtAgCtqQIArLkCAK+VAgCulQIAhEwCAL5IHQCHZB0AhuwcAONAAwAcyACA4aABACDIAIDvnAMAJMgAgCjIAIAsyACAMMgAgDTIAIA4yACAPMgAgEDIAIBEyACASMgAgEzIAIBQyACAVMgAgFjIAIDvtAEAhKgdAOF8BgBcyACA43AGAGDIAIBkyACAaMgAgGzIAICz4QEAcMgAgHTIAIB4yACAfMgAgLblAQC19QEAgMgAgLuhAQC62QEAvuQcAIjIAIC/rQEAvqUBAL2xAQC8uQEAqBUeAKkZHgCqKR4AqykeAKw9HgCtJR4Ari0eAK8lHgAUyACAgvkfAIH5HwCA4R8AhMgAgIzIAICGHAAAh7ADALjBHgC5wR4AusEeALvBHgC8wR4AvcEeAL7BHgC/wR4AsF0eALElHgCyLR4AsyUeALQhHgC1KR4AthkeALcZHgCjoR4AkMgAgJTIAICYyACAnMgAgKalHgCltR4AoMgAgKvhHgCqmR4ApMgAgKjIAICv7R4AruUeAK3xHgCs+R4ArMgAgLOZHwCwyACAtMgAgLa9HwC4yACAvMgAgLW1HwC6mR8Au5kfAMDIAIDEyACAvnkfAL95HwC8eR8AvXkfAKglHgCpUR4AqlUeAKtpHgCseR4ArXkeAK5pHgCvaR4AyMgAgMzIAIDQyACA1MgAgNjIAIDcyACA4MgAgOTIAIC42R4Aue0eALr5HgC7+R4AvOkeAL3pHgC+nR4Av5UeALAZHgCxGR4AsukeALPpHgC0+R4AtfkeALbpHgC36R4Ao90eAIIpAACBFQAAgB0AAOjIAICm+R4ApfEeAOzIAICr3R4Aqt0eAKTHAIDwyACArz0eAK49HgCtPR4ArD0eAITIAgCzQQEAvgwBAPjIAIC2QQEA/MgAgADJAIC1UQEAuk0BALslAQCGSAAAh1ABAL4lAQC/LQEAvDEBAL0xAQAEyQCACMkAgIQEAwC+gAQADMkAgO+oHwAQyQCAFMkAgL8oMQDjdB8AGMkAgOE4HgAcyQCAIMkAgCTJAIAoyQCALMkAgDDJAICjzQIANMkAgKXdAgA4yQCAPMkAgKbNAgBAyQCARMkAgKupAgCqwQIArb0CAKy9AgCvoQIArqkCAKm1AgCoaR0AqwECAKoJAgCtAQIArBkCAK8xAgCuAQIAhGwFAEjJAIBMyQCAUMkAgFTJAICCnQEAgZ0BAICdAQC55QMAuOUDALvlAwC65QMAveUDALzlAwC/5QMAvuUDALEhAgCwSQIAsyUCALIlAgC1KQIAtCECALcVAgC2FQIAqM0CAKnRAgCq0QIAqw0BAKwVAQCtBQEArgEBAK8BAQBYyQCAXMkAgGDJAIBoyQCAvvgEAGzJAIBwyQCAdMkAgLgVAQC5HQEAuikBALspAQC89QEAvf0BAL71AQC/7QEAsEkBALFVAQCyXQEAs1UBALRNAQC1NQEAtj0BALcxAQCGoAUAh8gFAHjJAIDvvAAAfMkAgIDJAICEyQCA74weAIQsBwDh8B4AiMkAgOMcHgCMyQCA4ZQBAJDJAIDjbAAAsxkCAJTJAICYyQCAnMkAgIQACAC2xQEAtd0BAKDJAIC70QEAus0BAKTJAICoyQCAv7EBAL7JAQC9wQEAvMkBAKPZBQBkyQCArMkAgLDJAIC0yQCApgUGAKUdBgC4yQCAqxEGAKoNBgC8yQCAwMkAgK9xBgCuCQYArQEGAKwJBgDEyQCAgh0AAIEdAACAHQAAyMkAgMzJAIDQyQCA1MkAgIZAAwCHxAMA2MkAgNzJAIDgyQCA5MkAgOjJAIDsyQCAqK0HAKmxBwCqsQcAq7EHAKwZBwCtBQcArg0HAK8FBwDwyQCA9MkAgPjJAID8yQCAAMoAgATKAIAIygCADMoAgLgtBwC5zQAAusUAALvdAAC8zQAAvf0AAL71AAC/nQAAsEkHALFVBwCyUQcAsykHALQ5BwC1OQcAtiUHALcVBwCzOQYAEMoAgBTKAIAYygCAHMoAgLaFBgC1kQYAIMoAgLuRBgC6jQYAJMoAgCjKAIC//QYAvv0GAL39BgC8hQYALMoAgKN9BgAwygCANMoAgKbBBgA4ygCAPMoAgKXVBgCqyQYAq9UGAEDKAIC+bAEArrkGAK+5BgCswQYArbkGAKjpAQCp6QEAqvkBAKv5AQCs6QEArekBAK45AQCvOQEAgPUAAIH9AACCwQAARMoAgIYQAACHdAEASMoAgPTIAIC4zQAAudUAALrVAAC75QAAvP0AAL2VAAC+kQAAv5EAALBJAQCxSQEAslkBALNZAQC0SQEAtUkBALb9AAC39QAA7/QGAEzKAIBQygCAVMoAgO8wAgBYygCAXMoAgGDKAIDj4AcAZMoAgOGAAQBoygCA4ygGAGzKAIDhyAUAcMoAgLMxAgB0ygCAeMoAgJYAAAB8ygCAtikCALUhAgCAygCAu80CALrNAgCEygCAiMoAgL/NAgC+zQIAvc0CALzNAgCMygCAkMoAgJTKAICj/QIAmMoAgKXtAgCm5QIAnMoAgKDKAICkygCAqgECAKsBAgCsAQIArQECAK4BAgCvAQIAgA0AAIEVAACCHQAAqMoAgKzKAICwygCAvlQMALjKAICGwAwAhyQDALzKAIDAygCAxMoAgMjKAIDMygCA0MoAgKi5AgCpAQEAqgEBAKsBAQCsBQEArQ0BAK4FAQCvOQEAhKgNANTKAIDYygCA3MoAgODKAIDkygCA6MoAgOzKAIC4LQEAucUBALrNAQC7xQEAvMEBAL3JAQC++QEAv/kBALBNAQCxUQEAslUBALMpAQC0OQEAtSUBALYlAQC3FQEA4RgGAPDKAIDjOAcA9MoAgPjKAIC+WAwA/MoAgADLAICEbA8ABMsAgL5gDwAIywCADMsAgBDLAIDvcAYAFMsAgIAVAACBGQAAgi0AAITMDwDjYAYAGMsAgOGgAQAcywCA73QAACDLAICGyAwAh/wMACjLAIAsywCAMMsAgDTLAICjCQ4AtMoAgCTLAIA4ywCAPMsAgKYNDgClDQ4AQMsAgKsVDgCqCQ4ARMsAgEjLAICvYQ4Arn0OAK19DgCsAQ4ATMsAgLOpDgBQywCAVMsAgLapDgBYywCAXMsAgLWpDgC6SQ8Au0kPAGDLAIBkywCAvkkPAL9JDwC8SQ8AvUkPAKhdDgCpbQ4AqmUOAKt9DgCsZQ4ArW0OAK5lDgCvuQ8AaMsAgGzLAIBwywCAdMsAgHjLAIB8ywCAgMsAgITLAIC4UQ8AuV0PALpVDwC7aQ8AvH0PAL1lDwC+bQ8Av2EPALDJDwCxyQ8AstkPALPZDwC0yQ8AtckPALZ9DwC3cQ8AiMsAgLURDwC2EQ8AjMsAgIARAACBGQAAgikAALMVDwC8HQ8AvWEPAL5hDwC/fQ8AkMsAgJTLAIC6FQ8AuwkPAKOtDwCYywCAhugAAIfIAQCcywCApq0PAKWtDwCgywCAq00OAKpNDgCkywCAqMsAgK9NDgCuTQ4ArU0OAKxNDgCocQ4AqXEOAKpxDgCrcQ4ArJ0BAK2FAQCuhQEAr7UBAL7sAACsywCAsMsAgLTLAIC4ywCAvMsAgMDLAIDEywCAuGEBALlhAQC6YQEAu2EBALxhAQC9YQEAvmEBAL9hAQCwzQEAsaUBALKhAQCzoQEAtKUBALWtAQC2kQEAt5EBALP5DQDIywCAzMsAgNDLAIDUywCAtgUCALUVAgDYywCAu2ECALoJAgDcywCA4MsAgL9pAgC+YQIAvXUCALx1AgDkywCAo70NAOjLAIDsywCApkECAPDLAID0ywCApVECAKpNAgCrJQIA+MsAgPzLAICuJQIAry0CAKwxAgCtMQIAge0AAIDtAADv0AEAgh0AAADMAIAIzACAhjgEAIdQAwAMzACAEMwAgBTMAIAYzACA4eABABzMAIDjZA8AIMwAgCTMAIAozACALMwAgLORAwAwzACAtbkDALZ9AwA0zACAOMwAgDzMAIC6WQMAu1kDALxJAwC9SQMAvv0AAL/1AACoRQIAqVUCAKpVAgCrZQIArH0CAK2xAgCusQIAr7ECAL5oBQBAzACARMwAgEjMAIBMzACAUMwAgFTMAIBYzACAuF0BALltAQC6ZQEAuw0BALwZAQC9GQEAvg0BAL8FAQCw0QIAsdECALLRAgCz0QIAtHUBALV9AQC2dQEAt20BAOF4DwDjNA4A47gOAOF8DgBczACAYMwAgGTMAIBozACAbMwAgHDMAIB4zACAfMwAgIDMAIDv5A4A79QOAITMAICjnQIAgmEAAIFpAACAUQAAhJwFAKZxAgCltQIAiMwAgKtVAgCqVQIAhkgEAIfMBACv+QEArvEBAK1FAgCsRQIAqJUGAKmlBgCqrQYAq6UGAKy9BgCtoQYArqUGAK/dBgB0zACAjMwAgJDMAICUzACAmMwAgJzMAICgzACApMwAgLhtBwC5dQcAun0HALt1BwC8bQcAvcUHAL7NBwC/xQcAsKUGALGtBgCyuQYAs7EGALSRBgC1kQYAtl0HALdVBwCzJQYAqMwAgKzMAICwzACAtMwAgLYhBgC1NQYAuMwAgLtpBgC6YQYAvMwAgMDMAIC/VQYAvlUGAL1lBgC8bQYAxMwAgKNhBgDIzACAzMwAgKZlBgDQzACA1MwAgKVxBgCqJQYAqy0GANjMAIDczACArhEGAK8RBgCsKQYArSEGAKipBgCpqQYAqrkGAKuxBgCszQYArTEBAK4xAQCvMQEAgMkBAIHJAQCCBQAA4MwAgL54AgCEeAIA5MwAgOjMAIC43QEAue0BALrlAQC7jQEAvJkBAL2ZAQC+jQEAv4UBALBRAQCxUQEAslEBALNRAQC09QEAtf0BALb1AQC37QEAszEGAOzMAICGKAAAh9wBAPDMAIC2sQEAtUUGAPTMAIC7lQEAupUBAPjMAID8zACAvzkBAL4xAQC9hQEAvIUBAATMAICjdQYAAM0AgATNAICm9QEACM0AgAzNAIClAQYAqtEBAKvRAQAQzQCAFM0AgK51AQCvfQEArMEBAK3BAQAYzQCAHM0AgCDNAIAkzQCAKM0AgCzNAIAwzQCANM0AgDjNAIA8zQCAQM0AgETNAIBIzQCATM0AgFDNAIC+cAMAhQA8AOHEBgCERAIA44wHAIBhAACBYQAAgmEAAO9oAwCFRDwA4RACAFjNAIDj2CsAhlA9AIf0AwBczQCA76QHAGDNAIDvQAIAZM0AgGjNAIBszQCAcM0AgHTNAIB4zQCAhDw8AHzNAICAzQCAhM0AgIjNAIDj7AIAjM0AgOEsAQCzUQMAkM0AgJTNAICYzQCAnM0AgLZ5AwC1cQMAoM0AgLs5AwC6MQMApM0AgKjNAIC/9QAAvvUAAL0VAwC8FQMAqD0CAKmBAgCqmQIAq5ECAKy5AgCtuQIArtECAK/RAgCEqD8Avqg/AKzNAICwzQCAtM0AgLjNAIC8zQCAwM0AgLhRAQC5UQEAulEBALtRAQC8cQEAvXEBAL5xAQC/cQEAsLUCALG9AgCygQIAs4ECALRxAQC1cQEAtnEBALdxAQCAtQAAgb0AAIK1AADIzQCAhrA/AIfgPADMzQCA71QAAL4sPgDhVAYA0M0AgOOIAADUzQCA2M0AgNzNAIDgzQCAo1ECAOTNAIC/2CYA6M0AgOzNAICmeQIApXECAPDNAICrOQIAqjECAPTNAID4zQCAr/UBAK71AQCtFQIArBUCAJAtJACRBSgAkg0oAJPZKACUhS0AlTUsAJbFLACXtTEAmAEwAJkVMACalTUAmyk0AJxtNACdmTUAnj04AJ81OABUzQCAttU+ALXFPgDEzQCAs9E+APzNAIAAzgCABM4AgL/ZPgC+1T4AvcU+ALzFPgC71T4Auuk+AAjOAICPXSQAqeUJAKgVCACrBQwAqg0MAK0BEACsAQwAr0EQAK69EACh4QAADM4AgKMBBACi4QAApZ0EAKSVBACnuQgApgEIAKD1OQChBT0Aouk8AKP1PQAQzgCAFM4AgBjOAIAczgCAscEUALABFACzARgAsn0UALXVGAC01RgAIM4AgCTOAICCISUAgyklACjOAIAszgCAhsUpAIeBLACEGSkAhRkpAIoBLQCL+S0AMM4AgDjOAICOATEAj4k0AIyRMACNHTEAkkU1AJMZNQCG6AcAh+wBAJZZOQCXYTgAlPU0AJVZOQCaoTwAm0U9ADzOAIBAzgCAgX0AAIB9AACcQTwAglUAAKjpPwCp/T8Aqgk/AKsFPwCsHT8ArQU/AK4NPwCvBT8ARM4AgEjOAIBMzgCAUM4AgFTOAIBYzgCAXM4AgGDOAIC4DT8AuRU/ALoVPwC7JT8AvD0/AL39PgC+9T4Av+0+ALB9PwCxQT8AskE/ALNBPwC0QT8AtU0/ALY9PwC3NT8Ao4E8AGTOAIBozgCAbM4AgHDOAICmhTwApZU8AHTOAICrhTwAqrk8AHjOAIB8zgCAr4k8AK6FPACtlTwArJU8AITIAwCz7T0AgM4AgITOAIC26T0AiM4AgIzOAIC16T0Auq09ALu1PQCQzgCAlM4AgL6dPQC/IQIAvKU9AL2VPQCoDT0AqR09AKohPQCrPT0ArCU9AK0tPQCuJT0Ar1k9AIANAACBFQAAgh0AAJjOAICczgCAoM4AgKjOAIC+uAMAuLkCALlhAgC6GQIAuxkCALwJAgC9CQIAviECAL8hAgCwLT0AsTU9ALI1PQCzBT0AtB09ALWhAgC2oQIAt6ECAKOpPACszgCAhigFAIfsAgCwzgCApq08AKWtPAC0zgCAq/E8AKrpPAC4zgCAvM4AgK9lAwCu2TwArdE8AKzhPADAzgCAsykCAMTOAIDIzgCAtvkCAMzOAIDQzgCAtfkCALrVAgC73QIA1M4AgNjOAIC+eQEAv3kBALzFAgC9eQEA3M4AgODOAICj5QIA5M4AgKU1AgDozgCA7M4AgKY1AgDwzgCA9M4AgKsRAgCqGQIArbUBAKwJAgCvtQEArrUBAOPwPgDhrD8A4UA+AON8PwD4zgCA/M4AgADPAIAEzwCAgA0AAIERAACCEQAACM8AgO+oPgAMzwCAEM8AgO8gPgCoLQUAqW0FAKplBQCrrQUArLUFAK29BQCutQUAr60FAKTOAICE6AMAvuADABTPAICGEAMAh5gDABjPAIAczwCAuGkGALlpBgC6AQYAuwEGALwFBgC9DQYAvjEGAL8xBgCw1QUAsd0FALLVBQCzaQYAtHkGALV5BgC2aQYAt2EGAKg5BgCpgQcAqpkHAKuRBwCsuQcArbkHAK7ZBwCv1QcAIM8AgCTPAIA0zgCAKM8AgCzPAIAwzwCANM8AgDjPAIC4VQcAuV0HALppBwC7aQcAvAEHAL0BBwC+AQcAvwEHALCtBwCxsQcAsrEHALOFBwC0nQcAtXUHALZ9BwC3cQcAsxEGADzPAIBAzwCARM8AgEjPAIC2OQYAtTEGAEzPAIC7dQYAumkGAFDPAIBUzwCAv7EGAL5ZBgC9UQYAvGUGAFjPAICjVQYAXM8AgGDPAICmfQYAZM8AgGjPAICldQYAqi0GAKsxBgBszwCAcM8AgK4dBgCv9QYArCEGAK0VBgCouQEAqbkBAKopAQCrKQEArD0BAK0lAQCuLQEAryUBAHTPAICCHQAAgR0AAIAdAAB4zwCAfM8AgIDPAIC+cAEAuIEAALmNAAC6hQAAu5kAALyJAAC9vQAAvrUAAL99AACwXQEAseEAALLhAACz4QAAtOEAALXpAAC20QAAt9EAAITIAgCzpQIAhzgDAIYoAgC2oQIAiM8AgIzPAIC1sQIAup0CALshAwC+bAMAkM8AgL4hAwC/KQMAvDEDAL0xAwCj4QIAlM8AgJjPAICczwCAoM8AgKblAgCl9QIApM8AgKtlAwCq2QIAqM8AgKzPAICvbQMArmUDAK11AwCsdQMAqZkAAKiRAACrzQAAqqEAAK3dAACs3QAAr8UAAK7NAAC+LA0AsM8AgLTPAIC4zwCAvM8AgMDPAIDEzwCAyM8AgLnBAQC4eQAAu8EBALrJAQC9wQEAvNkBAL/FAQC+xQEAsY0AALCNAACzQQAAskkAALVBAAC0WQAAt0EAALZJAADMzwCA0M8AgNTPAIDYzwCA3M8AgO9QBwDgzwCA5M8AgL74DwDjdAcA6M8AgOF8BACAGQAAgQkAAIJ5AADszwCA8M8AgLNpAQD4zwCAhMQCALYdAQD8zwCAANAAgLUVAQC6CQEAuwkBAIboDQCH6A0Avt0BAL/FAQC83QEAvdUBAATQAIAI0ACADNAAgBDQAIDv1AAAFNAAgBjQAIDvTAEA47ADAOG0BgDhgAEA45gBABzQAIAg0ACAJNAAgCjQAIAs0ACAMNAAgKPlAQCEwA0ApZkBADTQAIA40ACAppEBADzQAIBA0ACAq4UBAKqFAQCtWQEArFEBAK9JAQCuUQEA9M8AgETQAIBI0ACATNAAgFDQAIBU0ACAWNAAgFzQAICoaQ8AqXEPAKpxDwCrrQ8ArLUPAK29DwCutQ8Ar6kPALDZDwCx9Q8Asv0PALP1DwC07Q8AtZUPALadDwC3iQ8AuLkPALmFDwC6jQ8Au2kAALx5AAC9eQAAvmkAAL9pAACBnQAAgJ0AAGDQAICCBQAAZNAAgGjQAIBs0ACAcNAAgIaAAwCH9AMAdNAAgHjQAIB80ACAgNAAgITQAICEzwCAs5kPAIjQAICM0ACAkNAAgJTQAIC2XQ8AtV0PAJjQAIC7UQ8Aun0PAJzQAICg0ACAvzEPAL5JDwC9QQ8AvEkPAKNZDgCk0ACAqNAAgKzQAICw0ACApp0OAKWdDgC00ACAq5EOAKq9DgC40ACAvNAAgK/xDgCuiQ4ArYEOAKyJDgDA0ACAxNAAgMjQAIDM0ACAgBkAAIEZAACCBQAA0NAAgISgAQDU0ACAh+gBAIYABADY0ACA3NAAgODQAIDk0ACAqBUBAKkdAQCqFQEAqyUBAKw9AQCtJQEAri0BAK8lAQDo0ACA7NAAgPDQAID00ACA+NAAgPzQAIAA0QCABNEAgLjJAAC5yQAAutkAALvRAAC8+QAAvfkAAL6ZAAC/mQAAsCUBALEtAQCyJQEAsz0BALQtAQC1HQEAthUBALf5AAAI0QCADNEAgBDRAICzkQIAFNEAgLW5AgC2qQIAGNEAgBzRAIAg0QCAuu0CALvlAgC8/QIAveUCAL7lAgC/1QIApvECACTRAIAo0QCApeECACzRAICjyQIAMNEAgDTRAICuvQIAr40CAKylAgCtvQIAqrUCAKu9AgA40QCAPNEAgID5AACB+QAAggUAAEDRAIC+yAMAhBgDAEjRAIBM0QCAUNEAgFTRAIBY0QCAXNEAgGDRAIBk0QCAhhgEAIecAwBo0QCAbNEAgHDRAIB00QCAeNEAgHzRAIDvsAIAgNEAgOGUAQCE0QCA42wCAIjRAICM0QCAkNEAgJTRAICY0QCA79APAJzRAICg0QCApNEAgKjRAIDhrAEArNEAgONsAACAMQAAgT0AAIIdAADv9A4A42wOALDRAIDhLA8AvnAFALM5AgCEDAUAhugEAIdgBQDcAAAAtvECALX5AgC40QCAu9UCALrVAgC80QCAwNEAgL91AQC+dQEAvcUCALzFAgDE0QCA4fQOAMjRAIDjUA4AzNEAgNDRAIDU0QCA2NEAgNzRAIDg0QCA5NEAgOjRAIDs0QCA8NEAgPTRAIDv5A8ApmUCAPjRAID80QCApW0CAADSAICjrQIABNIAgAjSAICu4QEAr+EBAKxRAgCtUQIAqkECAKtBAgAM0gCAENIAgKiZBgCpmQYAqqkGAKupBgCsuQYArbkGAK6pBgCvqQYAFNIAgIIdAACBHQAAgB0AABjSAIAc0gCAINIAgL50AwC4rQYAubUGALq9BgC7tQYAvK0GAL1RBwC+UQcAv1EHALChBgCxoQYAsqEGALOhBgC0oQYAtaEGALalBgC3mQYARNEAgLMlBgCExAMAtNEAgLY9BgAk0gCAKNIAgLU1BgC6YQYAu2EGAIYIAACHiAAAvmEGAL9hBgC8cQYAvXEGAKNhBgAs0gCAMNIAgDTSAIA40gCApnkGAKVxBgA80gCAqyUGAKolBgBA0gCARNIAgK8lBgCuJQYArTUGAKw1BgCoXQYAqW0GAKplBgCrjQYArJkGAK2FBgCujQYAr4UGAEjSAIBM0gCAUNIAgFTSAIBY0gCAXNIAgGDSAIBk0gCAuIUGALmNBgC6mQYAu5UGALyNBgC9rQYAvqUGAL99AQCw/QYAscUGALLNBgCzxQYAtN0GALXFBgC2zQYAt8UGALPtBgBo0gCAbNIAgHDSAIB00gCAtgUGALURBgB40gCAuwEGALo5BgB80gCAgNIAgL8BBgC+GQYAvREGALwZBgCE0gCAo6kGAIjSAICM0gCApkEGAJDSAICElAEApVUGAKp9BgCrRQYAvqABAJjSAICuXQYAr0UGAKxdBgCtVQYAqJkCAKnBAgCqwQIAq8ECAKzBAgCtyQIArvECAK/xAgCB7QMAgO0DAJzSAICC+QMAhpAcAId0AwCg0gCApNIAgLjFAwC5zQMAusUDALvdAwC8zQMAvf0DAL71AwC/nQMAsEEDALFBAwCyQQMAs0EDALRBAwC1QQMAtkEDALdBAwCzSQIAqNIAgKzSAICw0gCAtNIAgLZJAgC1SQIAuNIAgLuFAwC6hQMAvNIAgMDSAIC/hQMAvoUDAL2VAwC8lQMAxNIAgKMNAgDI0gCAzNIAgKYNAgDQ0gCA1NIAgKUNAgCqwQMAq8EDANjSAIDc0gCArsEDAK/BAwCs0QMArdEDAOOYAQDhpAcA4VgGAONYBgDhoAEA4NIAgOPQAADk0gCA6NIAgOzSAIDvOAAA8NIAgO/0AQD00gCA+NIAgO/4BgCAeQAAgRUAAIIdAACEAB0A/NIAgADTAIC+EB0ACNMAgIbAHACHrB0ADNMAgBDTAIAU0wCAGNMAgBzTAIAg0wCAu8UFALqhBQC5qQUAuJEFAL/NBQC+zQUAvckFALzVBQCzHQYAsh0GALEdBgCwHQYAt6EFALa9BQC1vQUAtL0FAKu9BgCqvQYAqb0GAKi9BgCvfQYArn0GAK19BgCsfQYAJNMAgCjTAIAs0wCAMNMAgDTTAIA40wCAPNMAgEDTAICo7R0AqS0eAKoxHgCrMR4ArJUeAK2dHgCulR4Ar40eAATTAIBE0wCASNMAgEzTAIBQ0wCAVNMAgFjTAIBc0wCAuKkeALmpHgC6XR8Au1EfALxxHwC9cR8AvnUfAL9pHwCw/R4Asc0eALLFHgCzrR4AtLkeALW5HgC2rR4At6UeALO5HgBg0wCAZNMAgGjTAICU0gCAth0eALUdHgBs0wCAuwkeALo5HgBw0wCAhOADAL99HgC+fR4AvXkeALwRHgCCaQAAo/0eAIBFAACBUQAAplkeAL6cAwB00wCApVkeAKp9HgCrTR4AhkgAAIdsAACuOR4ArzkeAKxVHgCtPR4AqF0eAKltHgCqZR4Aq30eAKxlHgCtbR4ArmUeAK/9HgB40wCAfNMAgIDTAICE0wCAiNMAgIzTAICQ0wCAlNMAgLhpAQC5aQEAunkBALt5AQC8aQEAvWkBAL7dAQC/1QEAsIUeALGNHgCyhR4As50eALSFHgC1jR4AtoUeALdZAQCz7R4AmNMAgJzTAICg0wCApNMAgLbtHgC17R4AqNMAgLtJHgC6QR4ArNMAgLDTAIC/SR4AvkEeAL1JHgC8UR4AtNMAgKOpHgC40wCAvNMAgKapHgDA0wCAxNMAgKWpHgCqBR4Aqw0eAMjTAIDM0wCArgUeAK8NHgCsFR4ArQ0eAKghAwCpIQMAqiEDAKshAwCsIQMArSEDAK4hAwCvIQMA0NMAgNTTAIDY0wCAvmACANzTAIDg0wCA6NMAgOzTAIC4iQMAuYkDALqdAwC7lQMAvLkDAL25AwC+eQAAv3kAALDlAwCx7QMAsuUDALP9AwC07QMAtd0DALbVAwC3vQMAgKkAAIG1AACCvQAAs6UDAPDTAIC1pQMAtq0DAPTTAICE4AIA+NMAgLotAwC7JQMAvD0DAL0lAwC+JQMAvxUDAKPpAwD80wCAhmgEAIeAAwAA1ACApuEDAKXpAwAE1ACAq2kDAKphAwAI1ACADNQAgK9ZAwCuaQMArWkDAKxxAwAQ1ACAFNQAgBjUAIAc1ACAINQAgOE8HwAk1ACA40AeACjUAIAs1ACAMNQAgO+MHgA01ACAONQAgDzUAIBA1ACARNQAgIIlAACBEQAAgB0AAEjUAIDj5AMATNQAgOGsAQBQ1ACA77ADAIRkAgC+YAUAhtAEAIdEBQBY1ACAXNQAgGDUAIBk1ACAaNQAgGzUAIBw1ACAdNQAgHjUAIDvsAEAhKQFAOHcHgB81ACA4xABAIDUAICE1ACAiNQAgIzUAICzUQEAkNQAgJTUAICY1ACAnNQAgLYRAQC1fQEAoNQAgLsNAQC6DQEApNQAgKjUAIC//QAAvv0AAL39AAC8/QAAqDkGAKk5BgCqmQYAq5EGAKy1BgCt0QYArskGAK/BBgBU1ACArNQAgLDUAIC01ACAgA0AAIGxAACCsQAAuNQAgLhhBwC5YQcAumEHALt9BwC8ZQcAvW0HAL5lBwC/HQcAsIkGALGJBgCyaQcAs2kHALR5BwC1eQcAtmkHALdlBwCjEQYAvNQAgMDUAIC+gAMAxNQAgKZRBgClPQYAyNQAgKtNBgCqTQYAhggAAId8AwCvvQcArr0HAK29BwCsvQcAzNQAgNDUAICzSQcA1NQAgLVZBwDY1ACA3NQAgLZRBwDg1ACA5NMAgLtBBwC6dQcAvUUHALxFBwC/RQcAvkUHAKh5BgCpeQYAqokGAKuJBgCsmQYArZkGAK6JBgCviQYA5NQAgOjUAIDs1ACA8NQAgPTUAID41ACA/NQAgADVAIC4jQYAuZUGALqVBgC7pQYAvL0GAL1xAQC+cQEAv3EBALD5BgCxzQYAstkGALPZBgC0yQYAtckGALa9BgC3tQYAowEGAATVAIAI1QCADNUAgBDVAICmGQYApREGABTVAICrCQYAqj0GABjVAIAc1QCArw0GAK4NBgCtDQYArA0GACDVAIAk1QCAKNUAgCzVAICAGQAAgRkAAIIFAAAw1QCAhKwBAL6sAQCH6AAAhkwPADjVAIA81QCAQNUAgETVAIConQIAqcUCAKrNAgCrwQIArMUCAK3NAgCu+QIArz0DAEjVAIBM1QCAUNUAgFTVAIC+PAwAWNUAgFzVAIBg1QCAuMkDALnJAwC62QMAu9EDALz5AwC9+QMAvpkDAL+ZAwCwRQMAsU0DALJFAwCzXQMAtEUDALVNAwC2RQMAt/kDALNFAgBk1QCAaNUAgGzVAIBw1QCAtk0CALVNAgB01QCAu4kDALqBAwB41QCAfNUAgL+JAwC+gQMAvYkDALyRAwCA1QCAowECAITVAICI1QCApgkCAIzVAICQ1QCApQkCAKrFAwCrzQMAlNUAgJjVAICuxQMAr80DAKzVAwCtzQMAgO0BAIEVAACCEQAAhAACAJzVAIDhpAEAoNUAgOPsAACo1QCArNUAgLDVAIDvMAAAtNUAgLjVAIC81QCAwNUAgIbgDACH9AIAxNUAgMjVAIDM1QCA0NUAgO/MBgDU1QCA4bAHANjVAIDjEAYA3NUAgODVAIDk1QCA6NUAgOzVAIDw1QCA9NUAgPjVAID81QCAANYAgATWAIAI1gCA7+gBAIUYDwDhzAYADNYAgOMcBgCAKQAAgR0AAIIFAAAQ1gCAszkCAITMDQCGaA8Ah/wMAOHQ0gO28QEAtfkBABjWAIC72QEAutEBAL7kDAAc1gCAv30BAL59AQC9fQEAvMEBAKjxDQCp8Q0AqvENAKvxDQCsMQ4ArTEOAK4xDgCvMQ4ApNUAgBTWAIAg1gCAJNYAgCjWAIAs1gCAMNYAgDTWAIC46Q4AuekOALqJDgC7hQ4AvJ0OAL2BDgC+gQ4Av7UOALBVDgCxXQ4AslUOALPpDgC0+Q4AtfkOALbpDgC34Q4Ao3kNADjWAIA81gCAQNYAgETWAICmsQ4ApbkOAEjWAICrmQ4AqpEOAEzWAIBQ1gCArz0OAK49DgCtPQ4ArIEOAFTWAICz7Q8AWNYAgFzWAIC26Q8AYNYAgGTWAIC16Q8Auq0PALu1DwA01QCAaNYAgL6VDwC/mQ8AvK0PAL2hDwCoIQ4AqSEOAKohDgCrPQ4ArCUOAK0tDgCuJQ4Ar1UOAGzWAIBw1gCAdNYAgHjWAICAHQAAgQkAAIK9AAB81gCAuDkOALk5DgC6yQ4Au8kOALzZDgC92Q4AvskOAL/JDgCwLQ4AsTUOALI9DgCzMQ4AtBUOALUZDgC2CQ4AtwkOAKOpDgCA1gCAhIACAL6AAQCFAAQApq0OAKWtDgCI1gCAq/EOAKrpDgCGKAcAhxgAAK/dDgCu0Q4AreUOAKzpDgCM1gCAs+0BAJDWAICU1gCAtuUBAJjWAICc1gCAte0BALplAQC7bQEAoNYAgKTWAIC+bQEAv10BALx1AQC9bQEAqN0NAKnpDQCqIQIAqyECAKwhAgCtIQIAriECAK8hAgCo1gCArNYAgLDWAIC01gCAohECAKMRAgCgqQ4AodUCALiJAgC5iQIAup0CALuVAgC8vQIAvXUDAL59AwC/dQMAsOUCALHtAgCy5QIAs/0CALTtAgC13QIAttUCALe9AgCjqQIAj8UaALjWAIC81gCAwNYAgKahAgClqQIAxNYAgKspAgCqIQIAyNYAgMzWAICvGQIArikCAK0pAgCsMQIAniUOAJ/lDgCc6QoAnRUKAJpFFgCbRQoAmFkWAJlRFgCWcRIAl4ETAJRVEgCV7RIAktEeAJPZHgCQtRoAkVUeAISpHwCFJR8AhiUfAIexEwDQ1gCA1NYAgIJZGwCDURsAjEUSAI2lFwCOpRcAj7kXAIA5+wHY1gCAijkTAIutEwCUmQsAlaEPAJZpDwCX3Q8A3NYAgO+cDwCSyQsAk30LAJxFAwDjeA4A4NYAgOGYDADk1gCAhHgCAJqRAwCbXQMA4QQAAL6IBQDj3OoD6NYAgOzWAIDw1gCA7+wAAO+MDgDhcA4A4fwOAOMwAADjeA4AgSEAAIA5AADvtO0DgikAALMJAgD41gCAhmgEAIcsBQD81gCAtg0CALUNAgAA1wCAu8UBALrFAQAE1wCACNcAgL99AQC+fQEAvdUBALzVAQCE1gCA9NYAgAzXAIAQ1wCAFNcAgBjXAIAc1wCAINcAgKi9BQCp5QUAquEFAKvhBQCs5QUAre0FAK7RBQCv0QUAsGEGALFhBgCyYQYAs2EGALTZBgC12QYAtskGALfBBgC4yQYAuckGALp5BwC7eQcAvEUHAL0lBwC+EQcAvw0HAKNJBQAk1wCAKNcAgCzXAIAw1wCApk0FAKVNBQA01wCAq4UGAKqFBgA41wCAPNcAgK89BgCuPQYArZUGAKyVBgBA1wCARNcAgEjXAIBM1wCAUNcAgFTXAIBY1wCAXNcAgIA5AACBOQAAggUAAGDXAIC+uAMAhLgDAGjXAIBs1wCAqMUGAKnVBgCq1QYAq+UGAKz9BgCtHQEArhUBAK8NAQBk1wCAcNcAgIaIAQCHHAEAdNcAgHjXAIB81wCAgNcAgLjpAQC56QEAuokBALuJAQC8mQEAvZkBAL6JAQC/iQEAsHUBALF9AQCydQEAs+kBALT5AQC1+QEAtukBALfhAQCzXQYAhNcAgIjXAICM1wCAhLwBALadAQC1dQYAkNcAgLu5AQC6sQEAlNcAgJjXAIC/PQEAvj0BAL09AQC8oQEAnNcAgKMZBgCg1wCApNcAgKbZAQCo1wCArNcAgKUxBgCq9QEAq/0BALDXAIC01wCArnkBAK95AQCs5QEArXkBAKj5AgCp+QIAqi0DAKs9AwCsJQMArS0DAK4lAwCvmQMAuNcAgLzXAIDA1wCAxNcAgIANAACBsQAAgrEAAMjXAIC4lQMAuZ0DALqhAwC7oQMAvHEAAL1xAAC+cQAAv3EAALDpAwCx6QMAsvUDALPFAwC03QMAtbUDALaxAwC3sQMAvswDAMzXAIDQ1wCA2NcAgNzXAIDg1wCA5NcAgO/kAgDo1wCA4ZQBAOzXAIDjLAEA8NcAgPTXAICHGAMAhhz8A7tNAwC6TQMA+NcAgPzXAIC/EQMAvnkDAL1xAwC8QQMAs8UDAITo/AMA2ACABNgAgAjYAIC2zQMAtc0DAAzYAICkAfwDpSX/A6bZ/wOnAfgDENgAgKEVAwCiHQMAoz0CAKwR9wOtAfADri3zA68B8wOoEfsDqZn7A6oB9AOrHfcDtAHoA7Vl6wO+xPwDhMT8A7AB7AOxVe8Dsk3vA7Nx7gMU2ACAGNgAgBzYAIAg2ACAJNgAgCjYAIAs2ACAMNgAgOFQBgDhNAQA42wBAOPoBgA02ACAONgAgDzYAIBA2ACAgDUAAIE9AACCNQAASNgAgEzYAIBQ2ACA77ABAO/ABgCj5QIAVNgAgIbo/AOHfP0DWNgAgKbtAgCl7QIAXNgAgKttAgCqbQIAYNgAgGTYAICvMQIArlkCAK1RAgCsYQIAqI3+A6mV/gOqnf4Dq5X+A6yx/gOtvf4Drqn+A6+p/gNE2ACAaNgAgGzYAIBw2ACAdNgAgHjYAIB82ACAgNgAgLgl/wO5Lf8DuiX/A7s9/wO8Jf8DvS3/A74l/wO/zf8DsKn+A7Gp/gOygf4Ds4H+A7SB/gO1if4Dtmn/A7cd/wOE2ACA4SD8A4jYAIDjePwDjNgAgJDYAICU2ACAmNgAgJzYAICg2ACApNgAgKjYAICAHQAAgXEAAIJxAADvDP0Ds1X+A6zYAICw2ACAvkAAALTYAIC2ff4DtXn+A7jYAIC7Lf4Dui3+A4boAACHrAAAvw3+A74F/gO9Ff4DvBX+A6OV/wO82ACAwNgAgMTYAIDI2ACApr3/A6W5/wPM2ACAq+3/A6rt/wPQ2ACA1NgAgK/N/wOuxf8DrdX/A6zV/wPY2ACAs/H+A9zYAIDg2ACAto3+A+TYAIDo2ACAtY3+A7pFAQC7TQEA7NgAgPDYAIC+RQEAv00BALxVAQC9TQEAqC3+A6k1/gOqPf4Dq0n+A6xB/gOtSf4DrnH+A69x/gP02ACA+NgAgPzYAIAA2QCABNkAgAjZAIAM2QCAENkAgLhJAQC5VQEAul0BALtVAQC8TQEAvXUBAL59AQC/dQEAsMUBALHNAQCyxQEAs90BALTFAQC1zQEAtsUBALd9AQCjtf0DFNkAgBjZAICExAMAHNkAgKbJ/QOlyf0DINkAgKsJAgCqAQIAKNkAgL7sAgCvCQIArgECAK0JAgCsEQIAgEkAAIFVAACCVQAAo0UDACzZAIClRQMApkUDADDZAICGwAQAhxQDAKopAwCrJQMArD0DAK0hAwCuIQMArxUDADTZAIA42QCAPNkAgEDZAIBE2QCASNkAgEzZAIBQ2QCAqH0CAKmhAwCqoQMAq6EDAKyhAwCtqQMArpEDAK+RAwCwgQMAsY0DALKFAwCzmQMAtIkDALW9AwC2tQMAt30DALhFAwC5TQMAukUDALtdAwC8RQMAvU0DAL5FAwC/+QAA1NcAgLMNAgBU2QCAWNkAgLYNAgBc2QCAYNkAgLUNAgC6YQIAu20CAGTZAIBo2QCAvmkCAL9dAgC8dQIAvWkCAGzZAIBw2QCAdNkAgHjZAIB82QCA4aQBAIDZAIDjQAMAhNkAgIjZAICM2QCA77gDAIAVAACBHQAAggUAAJDZAICEgAIAvsgFAIcYBQCGLAQAmNkAgJzZAICg2QCA76gBAKTZAIDhdP4DqNkAgOPw/gOs2QCAsNkAgLTZAIC42QCAvNkAgMDZAIDE2QCAs5EBAMjZAIC1UQEAtlEBAMzZAIDQ2QCA1NkAgLp9AQC7dQEAvG0BAL39AAC+9QAAv+kAAKgpBgCpVQYAqlUGAKuNBgCslQYArZ0GAK6VBgCvjQYAlNkAgNjZAIDc2QCA4NkAgOTZAIDo2QCA7NkAgPDZAIC4bQcAuQUHALoNBwC7BQcAvB0HAL0FBwC+AQcAvz0HALD1BgCx/QYAsvUGALNlBwC0fQcAtWEHALZhBwC3VQcA4xAFAPTZAIDh8AQA+NkAgIAdAACBCQAAgjkAAPzZAIAA2gCAhOgDAL7gAwAE2gCA78wFAAjaAICHOAAAhhgAAKOdBgAM2gCAENoAgBTaAIAY2gCApl0GAKVdBgAc2gCAq3kGAKpxBgAg2gCAJNoAgK/lBwCu+QcArfEHAKxhBgCokQYAqZEGAKqRBgCrrQYArLkGAK2lBgCurQYAr6UGACjaAIAs2gCAMNoAgDTaAIA42gCAPNoAgEDaAIBE2gCAuGUBALltAQC6ZQEAu30BALxlAQC9bQEAvmUBAL/ZAQCw3QYAsaUGALKtBgCzpQYAtKEGALWpBgC2mQYAt5kGALMZBgBI2gCATNoAgFDaAIBU2gCAtiUGALUxBgBY2gCAu2EGALoZBgBc2gCAYNoAgL9tBgC+ZQYAvXEGALx5BgBk2gCAo10GAGjaAIBs2gCApmEGAHDaAICEmAEApXUGAKpdBgCrJQYAvqQBAHjaAICuIQYArykGAKw9BgCtNQYAqcUCAKixAgCrxQIAqsUCAK3NAgCsxQIAr/UCAK71AgB82gCAgNoAgITaAICI2gCAjNoAgJDaAICU2gCAmNoAgLnJAwC4wQMAu9kDALrBAwC9+QMAvMkDAL+ZAwC+8QMAsUUDALBFAwCzRQMAskUDALVFAwC0RQMAt0UDALZFAwCASQMAgUkDAIJdAwCzRQIAvtwMALVFAgC2RQIAnNoAgIYADACH5AMAuokDALuJAwC8mQMAvZkDAL6JAwC/iQMAowkCAKDaAICk2gCAqNoAgKzaAICmCQIApQkCALDaAICrxQMAqsUDALTaAIC42gCAr8UDAK7FAwCt1QMArNUDALzaAIDA2gCAxNoAgCTZAIDvAAAAyNoAgMzaAIDQ2gCA4+gAANTaAIDhjAEA2NoAgNzaAIDg2gCA6NoAgOzaAICAbQAAgXUAAIJ9AACEQAIAhvAMAId4DQDw2gCA9NoAgPjaAID82gCAANsAgATbAIAI2wCADNsAgBDbAIAU2wCAGNsAgBzbAIAg2wCAJNsAgCjbAIAs2wCAMNsAgO/MAQCE7AwA4TAGADTbAIDjGAEAONsAgDzbAIBA2wCARNsAgLPlAQBI2wCAhIQPAEzbAIBQ2wCAtuUBALX1AQBY2wCAu30BALrZAQC+oAwAXNsAgL8hAQC+OQEAvTEBALw5AQCo7Q0AqSUOAKotDgCrJQ4ArD0OAK0lDgCuLQ4AryUOAOTaAICC9Q8AgeUPAIDpDwBU2wCAYNsAgIaYAACHDAMAuK0OALlFDwC6TQ8Au0UPALxFDwC9TQ8AvkUPAL95DwCwXQ4AsfkOALKtDgCzpQ4AtL0OALWlDgC2pQ4At5UOAGTbAIDv7AwAaNsAgGzbAIBw2wCAdNsAgHjbAIB82wCAvugAAIDbAICE2wCAiNsAgIzbAIDj6A0AkNsAgOEEDACj5Q4AlNsAgJjbAICc2wCAoNsAgKblDgCl9Q4ApNsAgKt9DgCq2Q4AqNsAgKzbAICvIQ4ArjkOAK0xDgCsOQ4AqDkOAKk5DgCqUQ4Aq1EOAKxxDgCtcQ4ArnEOAK9xDgCw2wCAtNsAgLjbAIC82wCAgBkAAIEZAACCBQAAwNsAgLjRDgC50Q4AutEOALvlDgC84Q4AveEOAL7hDgC/4Q4AsBEOALERDgCyEQ4AsxEOALTxDgC18Q4AtvEOALfxDgCz2Q4AyNsAgIYoAACHuAAAzNsAgLbxDgC1+Q4A0NsAgLvVDgC61Q4A1NsAgNjbAIC/NQ4AvjUOAL3FDgC8xQ4A3NsAgKOdDgDg2wCA5NsAgKa1DgDo2wCA7NsAgKW9DgCqkQ4Aq5EOAPDbAID02wCArnEOAK9xDgCsgQ4ArYEOAKjdDQCp6Q0Aqj0CAKuNAgCsmQIArZkCAK6JAgCviQIAvqwEAPjbAID82wCAhCADAADcAIAE3ACACNwAgAzcAIC4iQIAuYkCALqZAgC7kQIAvLkCAL25AgC+eQMAv3kDALD5AgCx+QIAss0CALPFAgC03QIAtcUCALbBAgC3uQIAs7UCABDcAIAU3ACAGNwAgBzcAIC2GQIAtRECACDcAIC7PQIAuj0CACTcAIAo3ACAvwECAL4ZAgC9EQIAvBkCACzcAICj8QIAMNwAgDjcAICmXQIAPNwAgEDcAIClVQIAqnkCAKt5AgCGSAUAh6wEAK5dAgCvRQIArF0CAK1VAgCohQIAqZUCAKqVAgCrpQIArL0CAK3VAgCu0QIAr9ECAETcAIBI3ACATNwAgFDcAICB8QEAgJkBAHTaAICC9QEAuHkBALl5AQC6zQEAu8UBALzdAQC9xQEAvsUBAL/1AQCwtQIAsb0CALKBAgCzgQIAtFUBALVdAQC2SQEAt0kBAFTcAIBY3ACAXNwAgO/UAQCEEAUAYNwAgGTcAIDvjA4AvuwFAOHsDgBo3ACA4xwOAGzcAIDhlAEAcNwAgONkDgCzXQIAdNwAgHjcAIB83ACAgNwAgLYVAgC1dQIAhNwAgLs5AgC6MQIAiNwAgIzcAIC/2QEAvtEBAL0VAgC8FQIAo50FADTcAICQ3ACAlNwAgJjcAICm1QUApbUFAJzcAICr+QUAqvEFAKDcAICk3ACArxkGAK4RBgCt1QUArNUFAIBRAACBWQAAgmEAALOVBgCo3ACAtXEHALZxBwCs3ACAhkADAIdUAwC67QcAu+UHALzlBwC97QcAvtEHAL/NBwCw3ACAtNwAgLjcAIC83ACAwNwAgMTcAIDvQAQAyNwAgOEwBwDM3ACA45QEANDcAIDU3ACA2NwAgNzcAIDg3ACAoxkGAOTcAIDo3ACA7NwAgPDcAICm/QcApf0HAPTcAICraQcAqmEHAPjcAID83ACAr0EHAK5dBwCtYQcArGkHAKjNBwCp0QcAqtEHAKstBgCsNQYArT0GAK41BgCvnQYAAN0AgATdAIAI3QCADN0AgIAZAACBGQAAggUAABDdAIC4iQYAuYkGALqZBgC7kQYAvLkGAL25BgC+UQEAv1EBALDlBgCx7QYAsv0GALP1BgC02QYAtcUGALbBBgC3uQYAqNEBAKnZAQCqCQEAqwkBAKwZAQCtGQEArgkBAK8JAQCEYAEAvnwBAIeoAACGjAEAGN0AgBzdAIAg3QCAJN0AgLgJAQC5CQEAuhkBALsRAQC8OQEAvTkBAL75AAC/+QAAsH0BALFBAQCyRQEAs10BALRFAQC1TQEAtkUBALc5AQAo3QCALN0AgDDdAICzjQIANN0AgLWdAgC2lQIAON0AgDzdAIBA3QCAurUCALuJAgC8nQIAvYUCAL6NAgC/hQIAps0CAETdAIBI3QCApcUCAEzdAICj1QIAUN0AgFTdAICu1QIAr90CAKzFAgCt3QIAqu0CAKvRAgCE9AMAWN0AgKgxAwCpMQMAqjEDAKsxAwCskQAArZEAAK6RAACvjQAAXN0AgGDdAIBk3QCAaN0AgGzdAIBw3QCAdN0AgHjdAIC4vQAAuWUAALptAAC7ZQAAvH0AAL1lAAC+bQAAv2UAALD9AACxxQAAss0AALOpAAC0uQAAtaUAALahAAC3oQAAgL0BAIEJAACCGQAAfN0AgIDdAIC+WAIAhxQdAIacHQCEbB0AxNsAgIjdAICM3QCAvrwcAJDdAICU3QCAmN0AgLP5AgCc3QCAoN0AgKTdAICo3QCAtlEBALVZAQC+3B8Au0EBALp5AQCs3QCAsN0AgL8hAQC+PQEAvT0BALxZAQDhcAcAtN0AgOMIBgC43QCA78wAALzdAIDA3QCAxN0AgOMQAADI3QCA4dABAMzdAICGkBwAh/QcAO/gBgDQ3QCAo3kCANTdAIDY3QCA3N0AgODdAICm0QEApdkBAOTdAICrwQEAqvkBAOjdAIDs3QCAr6EBAK69AQCtvQEArNkBAITdAICCFQAAgeUfAIDlHwDw3QCA9N0AgPjdAID83QCAqAkfAKkJHwCqHR8AqxUfAKwNHwCtcR8ArnEfAK9xHwCwER8AsS0fALIlHwCzyR8AtN0fALXBHwC2wR8At8EfALjFHwC5yR8AutUfALupHwC8uR8AvbkfAL6pHwC/oR8As7UfAADeAIAE3gCACN4AgAzeAIC20R8AtaUfABDeAIC7yR8AuvUfABTeAIAY3gCAvyUfAL45HwC9PR8AvNEfABzeAIAg3gCAJN4AgCjeAIAs3gCA4WAfADDeAIDjtBwANN4AgDjeAIA83gCA7wAdAEDeAIBE3gCASN4AgEzeAICjNR4AUN4AgFTeAIBY3gCAXN4AgKZRHgClJR4AYN4AgKtJHgCqdR4AhKgCAGTeAICvpR4ArrkeAK29HgCsUR4AgE0AAIFVAACCVQAAs8kBAGjeAIC12QEAtskBAGzeAICGoAAAhwQBALrFAQC7rQEAvLUBAL29AQC+tQEAv60BAKiZAQCpmQEAqg0BAKsFAQCsHQEArQUBAK4FAQCvNQEAcN4AgHTeAIB43gCAfN4AgIDeAICE3gCAiN4AgIzeAIC4JQEAuS0BALo5AQC7OQEAvCkBAL0pAQC+3QAAv9UAALBNAQCxJQEAsi0BALMlAQC0PQEAtSUBALYhAQC3HQEAkN4AgJTeAICY3gCAo4kCAJzeAIClmQIApokCAKDeAICk3gCAqN4AgKqFAgCr7QIArPUCAK39AgCu9QIAr+0CAKzeAICw3gCAtN4AgIRAAgC43gCAvN4AgMDeAIDE3gCAgA0AAIEVAACCHQAAyN4AgMzeAIDQ3gCAh7QDAIbcBAC+zAMA2N4AgNzeAIDg3gCA7+gCAOTeAIDo3gCA7N4AgOP8AgDw3gCA4dABAPTeAID43gCA/N4AgADfAIAE3wCAs2EDAAjfAIAM3wCAEN8AgBTfAIC2eQMAtXEDABjfAIC7XQMAul0DABzfAIAg3wCAv+EAAL79AAC9/QAAvP0AALC5AgCxuQIAsgkBALMJAQC0GQEAtQUBALYFAQC3PQEAuAUBALllAQC6bQEAu2UBALxhAQC9YQEAvmEBAL9hAQCFXAcAJN8AgCjfAIAs3wCAFN0AgDDfAIA03wCAON8AgKgxAgCpOQIAqskCAKvJAgCs2QIArdkCAK7JAgCvyQIAhMwFAOGAHgA83wCA47weAOE4HgBA3wCA46AAAL4QBABI3wCATN8AgO8MHgBQ3wCAVN8AgFjfAIBc3wCA73QeAKNhAgCCUQAAgUEAAICRAABg3wCApnkCAKVxAgBk3wCAq10CAKpdAgCGyAQAhzwFAK/hAQCu/QEArf0BAKz9AQCohQYAqY0GAKqFBgCrmQYArIkGAK2JBgCuvQYAr7EGAETfAIBo3wCAbN8AgHDfAIB03wCAeN8AgHzfAICA3wCAuJ0GALmtBgC6pQYAuwkHALwZBwC9GQcAvg0HAL8FBwCw0QYAsdEGALLRBgCz0QYAtLUGALW9BgC2tQYAt60GALMNBgCE3wCAiN8AgIzfAICQ3wCAtgkGALUBBgCU3wCAuxUGALoVBgCY3wCAnN8AgL95BgC+cQYAvQUGALwFBgCg3wCA4aAEAKTfAIDjXAUAgA0AAIE1AACCPQAAqN8AgKzfAICw3wCAhGADAL5sAAC/8AEAhZAAALTfAIDvmAUAo40HAIQIAACGAAwAh4wAALjfAICmiQcApYEHALzfAICrlQcAqpUHAMDfAIDE3wCAr/kHAK7xBwCthQcArIUHAMjfAICz6QYAzN8AgNDfAIC26QYA1N8AgNjfAIC16QYAukUBALtNAQDc3wCA4N8AgL5FAQC/TQEAvFUBAL1NAQCoIQYAqSEGAKolBgCrPQYArCUGAK0tBgCuSQYAr0EGAOTfAIDo3wCA7N8AgPDfAID03wCA+N8AgPzfAIAA4ACAuEkBALlJAQC6WQEAu1EBALx5AQC9eQEAvhkBAL8VAQCwxQEAsc0BALLFAQCz3QEAtMUBALXNAQC2xQEAt3kBAATgAIAI4ACADOAAgKOhBQAQ4ACApaEFAKahBQAU4ACAjyHqAxjgAICqDQIAqwUCAKwdAgCtBQIArg0CAK8FAgCX7RIAlmUSAJVFEQCUnRYAk3EWAJJVFQCReesDkFnqA59hBgCeNQUAnUUaAJxpGgCbVRkAmkUeAJlZHgCYRR0A4WAAABzgAIDjTD4AIOAAgKOxAgCi1QEAobUHAKCJBgCxATgAsAk+ALOVOgCyjToAtbUmALQBJADvaDoAvjAMAKnJNgCowTYAqwEwAKrhNwCtzTMArPUyAK/5PgCuATwAoRkCACjgAICjbQ4Aom0OAKX1CgCkAQgAp4ULAKaZCgCGAA0Ah0QNAIIJ6wODCesDhDHqA4UVFACGORcAh80XAISgDQAs4ACAiiUQAIsNEwCMnRMAjQ0cAI4ZHwCPDR8A1N4AgO8AAwCSbRgAk0kbAJR9GwCVBQQAllkHAJdJBwAw4ACANOAAgJpFBgCbLQAAnFEDAONgAAA44ACA4WwAAIClAQCBAQEAggUBAL4ADAA84ACAQOAAgETgAIDviAEASOAAgOFUBgBM4ACA41QBAFDgAIBU4ACAWOAAgFzgAICz6QIAYOAAgGTgAIBo4ACAbOAAgLadAgC1mQIAcOAAgLuJAgC6vQIAdOAAgHjgAIC/WQIAvlECAL1ZAgC8kQIAoykNAHzgAICA4ACAhOAAgIjgAICmXQ0ApVkNAIzgAICrSQ0Aqn0NAJDgAICY4ACAr5kNAK6RDQCtmQ0ArFENAIBRAACBWQAAgmEAALMtDwCc4ACAtS0PALbJDwCg4ACAhkADAIcIAwC6yQ8Au8UPALzBDwC9wQ8AvsEPAL/BDwAk4ACAlOAAgKTgAICo4ACArOAAgLDgAIC04ACAuOAAgKhFDgCpgQ8AqskPAKvJDwCsyQ8ArSUPAK4tDwCvJQ8AsGEPALFtDwCyeQ8As3kPALRpDwC1aQ8Ath0PALcVDwC4LQ8AuTUPALo1DwC7BQ8AvB0PAL3xAAC+8QAAv/EAAKNhDgC84ACAhMQBAMDgAIDE4ACApoUOAKVhDgDI4ACAq4kOAKqFDgDM4ACA0OAAgK+NDgCujQ4ArY0OAKyNDgDU4ACA2OAAgNzgAIDg4ACA5OAAgOjgAIDs4ACA8OAAgPTgAICCHQAAgR0AAIAdAAD44ACA/OAAgADhAIC+tAEAqK0BAKnVAQCq1QEAqwUBAKwdAQCtBQEArg0BAK8FAQCGgAEAhxgBAAjhAIAM4QCAEOEAgBThAIAY4QCAHOEAgLiFAAC5jQAAuoUAALudAAC8hQAAvY0AAL6FAAC/vQAAsH0BALHhAACy5QAAs/0AALTtAAC13QAAttUAALe9AACzXQIAIOEAgCThAIAo4QCALOEAgLaFAgC1lQIAMOEAgLslAwC6uQIANOEAgDjhAIC/GQMAvikDAL0pAwC8MQMAvswEAKMZAgA84QCAQOEAgKbBAgBE4QCASOEAgKXRAgCq/QIAq2EDAEzhAIBQ4QCArm0DAK9dAwCsdQMArW0DAKgpAwCpKQMAqjkDAKs5AwCsKQMArSkDAK6dAACvlQAAVOEAgFjhAIBc4QCAYOEAgGThAICCqQEAga0BAICtAQC4mQAAua0AALqlAAC7bQAAvHUAAL19AAC+dQAAv20AALDtAACx9QAAsvUAALPFAAC03QAAtb0AALa1AAC3qQAA4XgBAOEcDgDjEAAA4zwOAGjhAIBs4QCAvhQEAHDhAICErAIAeOEAgId4BQCGDAUAfOEAgIDhAIDvvAAA70gOALPxAgCE4QCAiOEAgIzhAICQ4QCAtukCALXhAgCU4QCAu3EBALppAQCY4QCAhKAEAL85AQC+WQEAvVEBALxhAQCc4QCAhIwEAKDhAICEADgApOEAgKjhAICs4QCAsOEAgKqJDgCriQ4AqLkOAKmxDgCu/Q4Ar+EOAKz5DgCt9Q4Asq0OALNlDgCwkQ4AsaUOALZ9DgC3ZQ4AtH0OALV1DgC6XQ4Au+UNALhdDgC5VQ4AvuENAL/pDQC8/Q0AvfUNAKOxBQB04QCAtOEAgLjhAIC84QCApqkFAKWhBQDA4QCAqzEGAKopBgDE4QCAyOEAgK95BgCuGQYArREGAKwhBgDM4QCA0OEAgNThAIDY4QCAgB0AAIEJAACCOQAA3OEAgODhAIDk4QCAhsgAAIcMAwDo4QCA7OEAgPDhAID04QCAqKUHAKm1BwCqvQcAq8kHAKzZBwCt2QcArskHAK/BBwC+oAAA+OEAgPzhAIAA4gCABOIAgAjiAIAM4gCAEOIAgLjNAAC51QAAutUAALvlAAC8/QAAvZUAAL6dAAC/lQAAsIkHALFlBwCyYQcAs30HALRlBwC1bQcAtmUHALf1AACzNQYAFOIAgBjiAIAc4gCAIOIAgLZZBgC1UQYAJOIAgLuhBgC6TQYAKOIAgCziAIC/qQYAvqEGAL2pBgC8tQYAMOIAgDTiAIDv8AUAOOIAgDziAIBA4gCAROIAgEjiAICAPQAAgQkAAIIdAABM4gCA4cgGAFDiAIDjSAQAVOIAgKO1BgBY4gCAhigAAIdAAQBc4gCAptkGAKXRBgBg4gCAqyEGAKrNBgBk4gCAaOIAgK8pBgCuIQYArSkGAKw1BgBs4gCAs70BAHDiAIB04gCAtnkBAHjiAIB84gCAtXkBALpVAQC7XQEAgOIAgITiAIC++QAAv/kAALxFAQC9+QAAqHECAKlxAgCqcQIAq3ECAKy1AgCtvQIArrUCAK+tAgC+rDwAiOIAgIziAICQ4gCAlOIAgJjiAICc4gCAoOIAgLhpAwC5aQMAugkDALsJAwC8HQMAvQUDAL4NAwC/BQMAsNUCALHdAgCy1QIAs2kDALR5AwC1eQMAtmkDALdhAwCk4gCAqOIAgKziAICj9QIAsOIAgKUxAgCmMQIAtOIAgLjiAIC84gCAqh0CAKsVAgCsDQIArbEDAK6xAwCvsQMA7xgCAIIVAACBbQAAgG0AAMDiAIDI4gCAhvg8AIcYAwDM4gCA0OIAgNTiAIDY4gCA42wHAAThAIDhaAEA3OIAgKiFAgCplQIAqpUCAKulAgCsvQIArdUCAK7RAgCv0QIA4OIAgOTiAIDo4gCA7OIAgPDiAID04gCA+OIAgPziAIC4dQEAuX0BALp1AQC7zQEAvNUBAL3dAQC+yQEAv8EBALC1AgCxvQIAsoECALOBAgC0VQEAtV0BALZVAQC3TQEA4bQGAADjAIDj9AYABOMAgIQYPQAI4wCADOMAgBDjAIAU4wCAGOMAgBzjAIAg4wCAJOMAgCjjAIDvWAYALOMAgIF9AACAcQAAMOMAgIIFAAA44wCAPOMAgO+AAQC+VDwA4ZABAEDjAIDjfAYAROMAgEjjAIBM4wCAhtg8AIf0PACjnT0AxOIAgDTjAIBQ4wCAVOMAgKbVPQCltT0AWOMAgKv5PQCq8T0AXOMAgGDjAICvGT4ArhE+AK3VPQCs1T0AZOMAgLOhPgBo4wCAbOMAgLatPgBw4wCAdOMAgLWxPgC6ST8Au0k/AHjjAIB84wCAvkk/AL9JPwC8ST8AvUk/AKhVPgCpZT4Aqm0+AKtlPgCsfT4ArWk+AK65PwCvuT8AgOMAgITjAICI4wCAjOMAgJDjAICU4wCAmOMAgJzjAIC4VT8AuV0/ALpVPwC7bT8AvHU/AL19PwC+dT8Av20/ALDJPwCxyT8Astk/ALPZPwC0yT8Atck/ALZ9PwC3cT8AghUAAKPhPwCAsQEAgbEBAKbtPwCg4wCAvtABAKXxPwCqCT4Aqwk+AITkAQCk4wCArgk+AK8JPgCsCT4ArQk+ALPdPACo4wCAhugAAIfMAQCs4wCAtpU8ALX1PACw4wCAu7k8ALqxPAC04wCAuOMAgL9ZPwC+UT8AvZU8ALyVPACoUT4AqVE+AKptPgCrYT4ArGE+AK1hPgCulQEAr40BAISgAQC84wCAwOMAgMTjAIDI4wCAzOMAgNDjAIDU4wCAuKkBALmpAQC6aQEAu2kBALx5AQC9eQEAvmkBAL9pAQCw/QEAsc0BALLFAQCzrQEAtLkBALW5AQC2rQEAt6UBALPlPQDY4wCA3OMAgODjAIDk4wCAtuE9ALXpPQDo4wCAuwkCALo5AgDs4wCA8OMAgL99AgC+fQIAvXkCALwRAgD04wCAo6E9APjjAID84wCApqU9AADkAIAE5ACApa09AKp9AgCrTQIACOQAgAzkAICuOQIArzkCAKxVAgCtPQIAgOkAAIHpAACCHQAAvsADAO/kAgAQ5ACAh1QDAIY8BADjEAEAGOQAgOH4AQAc5ACAIOQAgCTkAIAo5ACALOQAgDDkAIA05ACAOOQAgLORAwA85ACAtbkDALZ9AwBA5ACAROQAgEjkAIC6WQMAu1kDALxJAwC9SQMAvv0AAL/1AACoRQIAqVUCAKpVAgCrZQIArH0CAK2xAgCusQIAr7ECAIRsBQBM5ACAUOQAgFTkAIBY5ACAXOQAgL5wBQBg5ACAuF0BALltAQC6ZQEAuw0BALwZAQC9GQEAvg0BAL8FAQCw0QIAsdECALLRAgCz0QIAtHUBALV9AQC2dQEAt20BAOFAPwDjvAAA4wg+AOFsPgBk5ACAaOQAgGzkAIBw5ACAdOQAgHjkAIB85ACAgOQAgL5sBwDvVAAA75w+AIjkAICjnQIAgmkAAIFhAACAaQAAjOQAgKZxAgCltQIAkOQAgKtVAgCqVQIAhsgEAIfsBACv+QEArvEBAK1FAgCsRQIAqKUGAKmpBgCquQYAq7kGAKypBgCtqQYArtkGAK/ZBgCE5ACAlOQAgJjkAICc5ACAoOQAgKTkAICo5ACArOQAgLhxBwC5cQcAunUHALvdBwC8xQcAvc0HAL7FBwC//QcAsKkGALG1BgCytQYAs40GALSVBgC1UQcAtlEHALdRBwCzMQYAsOQAgLTkAIC45ACAvOQAgLYpBgC1IQYAwOQAgLtxBgC6bQYAxOQAgMjkAIC/lQcAvlEGAL1ZBgC8YQYAzOQAgKN1BgDQ5ACA1OQAgKZtBgDY5ACA3OQAgKVlBgCqKQYAqzUGAODkAIDk5ACArhUGAK/RBwCsJQYArR0GAIANAACBFQAAgh0AAOjkAIDs5ACA8OQAgITcAQD05ACAhoAAAIcgAQD45ACA/OQAgADlAIAE5QCACOUAgAzlAIAQ5QCA43QEABTlAIDhyAUAGOUAgBzlAIAg5QCAJOUAgCjlAIAs5QCAMOUAgDTlAIA45QCA77QEADzlAIBA5QCAqD0GAKlVBgCqVQYAq6kBAKy5AQCtuQEArqkBAK+pAQCErAEAROUAgEjlAIBM5QCAUOUAgFTlAIBY5QCAXOUAgLhtAQC5BQEAugEBALsBAQC8BQEAvQ0BAL4xAQC/MQEAsNkBALHZAQCybQEAs2UBALR9AQC1ZQEAtmUBALdVAQCBvQMAgL0DALPVBQCCGQAAtTkCAGDlAIC+VAMAtjECAGjlAIBs5QCAuxUCALoVAgC9uQIAvLECAL+pAgC+sQIAcOUAgKZpAgClYQIAhAAMAKONBQB05QCAhvgMAId8AwCv8QIArukCAK3hAgCs6QIAq00CAKpNAgB45QCAfOUAgIDlAICE5QCAiOUAgIzlAIDjIAEAkOUAgOGgAQCU5QCA70ACAJjlAICc5QCAoOUAgKTlAICo5QCArOUAgLDlAICz8QMAtOUAgBTkAIC45QCAvOUAgLbpAwC14QMAwOUAgLu1AwC6tQMAxOUAgMjlAIC/lQMAvpUDAL2lAwC8pQMAqCkCAKkpAgCqOQIAqzkCAKwpAgCtKQIArlkCAK9VAgCAzQEAgQkAAIIZAADM5QCA0OUAgL58DQCHtA0AhhwMALgxAgC5PQIAujUCALvpAgC8+QIAvfkCAL7pAgC/6QIAsDECALExAgCyMQIAszECALQRAgC1EQIAthECALcRAgDY5QCA3OUAgODlAIDk5QCA6OUAgOzlAIDw5QCA79QGAPTlAIDhVAYA+OUAgOOkAACsDBUA/OUAgADmAIAE5gCAo/ECAAjmAIAM5gCAEOYAgBTmAICm6QIApeECABjmAICrtQIAqrUCABzmAIAg5gCAr5UCAK6VAgCtpQIArKUCAKghDgCpIQ4AqkkOAKtZDgCsaQ4ArWkOAK6ZDgCvmQ4A1OUAgCTmAIAo5gCALOYAgDDmAIA05gCAOOYAgDzmAIC49Q4Auf0OALr1DgC7iQ4AvJ0OAL2FDgC+hQ4Av7UOALDpDgCx6Q4Asv0OALPxDgC01Q4Atd0OALbVDgC3zQ4As8EOAIIVAACBtQAAgLUAAEDmAIC26Q4AteEOAL4QAAC7LQ4Aui0OAIRkAwBE5gCAvxkOAL4RDgC9JQ4AvCkOAEjmAICjhQ4AhogAAIdsAwCmrQ4ATOYAgFDmAIClpQ4AqmkOAKtpDgBU5gCAWOYAgK5VDgCvXQ4ArG0OAK1hDgCziQ4AXOYAgGDmAIBk5gCAaOYAgLaBDgC1iQ4AbOYAgLuVDgC6jQ4AcOYAgHTmAIC/+Q4AvvEOAL2FDgC8hQ4AeOYAgHzmAICA5gCAhOYAgOMMDQCI5gCA4RgNAIzmAIDvrAwAkOYAgJTmAICY5gCAnOYAgKDmAICk5gCAqOYAgKgBDgCpAQ4AqgEOAKsBDgCsAQ4ArQEOAK4BDgCvPQ4AgN0AAIEJAACCGQAArOYAgLDmAICEPAEAvnQAALjmAIC4HQ4AuS0OALolDgC76QEAvPkBAL35AQC+6QEAv+kBALBJDgCxUQ4AslEOALNRDgC0NQ4AtT0OALY1DgC3LQ4Ao4kNALzmAICGrAQAhzwDAMDmAICmgQ0ApYkNAMTmAICrlQ0Aqo0NAMjmAIDM5gCAr/kNAK7xDQCthQ0ArIUNANDmAICznQIAhEgDAL5ABAC2VQMA1OYAgNjmAIC1sQIAunEDALt5AwDc5gCA4OYAgL4xAwC/MQMAvFEDAL1RAwCwkQMAsZkDALKhAwCzoQMAtNEDALXRAwC20QMAt9EDALj1AwC5+QMAus0DALvFAwC83QMAvcUDAL7NAwC/xQMA5OYAgOjmAIDs5gCA8OYAgIV8GQD05gCA+OYAgGTlAICoIQIAqTECAKoxAgCrBQIArB0CAK3xAwCu8QMAr/EDAPzmAIAA5wCABOcAgAjnAIDvUAAADOcAgBDnAIAU5wCA44QAABjnAIDh+AEAHOcAgIAVAACBGQAAggUAACDnAICjmQMAKOcAgIZoBACHYAUALOcAgKZRAgCltQMAMOcAgKt9AgCqdQIANOcAgDjnAICvNQIArjUCAK1VAgCsVQIAPOcAgEDnAIBE5wCASOcAgEznAIBQ5wCAVOcAgO/4AQC+bAQA4YAOAFjnAIDjFAEAXOcAgGDnAIBk5wCAaOcAgGznAIBw5wCAdOcAgLPdAQB45wCAtf0BALb1AQB85wCAgOcAgITnAIC6sQEAu4UBALydAQC9NQEAvj0BAL81AQCpBQYAqLkFAKsVBgCqHQYArT0GAKw9BgCvTQYArl0GACTnAICCHQAAgR0AAIAdAACI5wCAjOcAgJDnAICU5wCAuUEHALidBgC7QQcAukkHAL1FBwC8WQcAv0UHAL5FBwCxCQYAsD0GALOpBgCyAQYAtbkGALSxBgC3rQYAtrEGAKORBgCEjAIAhigAAIfAAwCY5wCAprkGAKWxBgCc5wCAq8kGAKr9BgCg5wCApOcAgK95BgCucQYArXkGAKzRBgCo5wCAs5kHAKznAICw5wCAtlEHALTnAIC45wCAtbEHALptBwC7dQcAvOcAgMDnAIC+WQcAv0UHALxtBwC9ZQcAxOcAgMjnAIDM5wCA0OcAgNTnAIDY5wCA3OcAgO+oBQDg5wCA4TQFAOTnAIDjdAUA6OcAgOznAIDw5wCA9OcAgKMdBgCCLQAAgRUAAIAdAAD45wCAptUGAKU1BgD85wCAq/EGAKrpBgAA6ACAhCgBAK/BBgCu3QYAreEGAKzpBgCoxQYAqdUGAKrVBgCr5QYArP0GAK0VBgCuHQYArxUGAL7sAQAI6ACAhggAAIcgAAAM6ACAEOgAgBToAIAY6ACAuH0GALkFBgC6DQYAuwUGALwBBgC9CQYAvjkGAL85BgCwbQYAsXUGALJ9BgCzdQYAtFkGALVFBgC2TQYAt0UGAKiRAgCpmQIAqqECAKuhAgCs0QIArd0CAK7VAgCvyQIAHOgAgCDoAIAk6ACAvyweACjoAIAs6ACAMOgAgDToAIC4VQMAuV0DALppAwC7ZQMAvGEDAL1hAwC+YQMAv2EDALC5AgCxjQIAsoUCALNtAwC0dQMAtX0DALZ1AwC3bQMAOOgAgDzoAICzIQIAQOgAgLVRAgCEiAMAROgAgLZVAgC05gCAvigcALtBAgC6dQIAvbEDALxZAgC/sQMAvrkDAKNpAgBI6ACATOgAgFDoAIBU6ACAph0CAKUZAgBY6ACAqwkCAKo9AgBc6ACAYOgAgK/5AwCu8QMArfkDAKwRAgCopQIAqbUCAKq9AgCrtQIArK0CAK01AQCuPQEArzUBAL4sHABk6ACAaOgAgGzoAIBw6ACAeOgAgIdoHQCGHB0AuIUBALmNAQC6hQEAu50BALyNAQC9vQEAvrUBAL95AACwUQEAsVEBALJRAQCzUQEAtPEBALXxAQC29QEAt+UBAO/YAACCtQAAgaUAAIClAAB86ACAgOgAgIToAIDvxAYAiOgAgOH0BgCM6ACA4zgBAOPMAACQ6ACA4SgBAJToAICY6ACAtuUBALV1AgCEQBwAs2UCAJzoAICg6ACApOgAgL9lAQC+ZQEAvdUBALzVAQC7xQEAusUBAKjoAICs6ACAo7UdAHToAICw6ACAtOgAgLjoAICmNR4ApaUdALzoAICrFR4AqhUeAMDoAIDE6ACAr7UeAK61HgCtBR4ArAUeAMjoAIDM6ACA0OgAgNToAICADQAAgTUAAII9AADY6ACA3OgAgODoAIC1BQAAcRoAgOG0AgCs2AIAtQUAAHUaAICotR8AqRUfAKodHwCrFR8ArDEfAK09HwCuLR8AryEfAOG0AgCs2AIAtQUAAHkaAIDhtAIArNgCALUFAAB9GgCAuNEAALnZAAC64QAAu+EAALyRAAC9kQAAvpEAAL+RAACwIR8AsTEfALIxHwCzMR8AtAkfALUJHwC28QAAt/EAAOG0AgCs3AIA71QdALUdAACBGgCA4bwCAKzQAgC1KQAAoyUBAKKRAwChFR0AoA0dAOGAHgCFGgCA47wdAOHEAgCz1R4AtQkAAKzYAgCJGgCA4bwCALb9HgC1+R4ArOACALu1HgC6pR4AtQUAAI0aAIC/jR4Avo0eAL2lHgC8pR4AoxUeAOG8AgCs0AIAtREAAI9pJQCmPR4ApTkeAJEaAICrdR4AqmUeAOG0AgCseAEAr00eAK5NHgCtZR4ArGUeAJvdFACa5RUAmQEXAJjhEACfcR8AnnkZAJ35GQCcARsAk+UtAJIRLwCRbSkAkG0pAJf5EQCW8REAlYUsAJSZLQC1JQAA4ZQCAILxJgCDjSoAhJUqAIXhLACGHS4Ah3kuAKy0AgCVGgCAilUvAIspEgCMORIAjRkTAI7xFACPHRYAtQUAAJkaAICSVRcAk5EYAJRxGgCV+RoAlvkcAJd9HgCC4AMAkwsAgJpVHgCb2QAAnHUCAIMMAICzDACAuIkKAKwBBACthQYAroEGAMwQAgDMfAMAtgwAgJ0aAIDCDACAxQwAgMgMAIAACwCAgaUyArwMAIAE6ACAmpUGAJtVIwK8kQYAvbEAAL6RBgC/rQYAuOkGALmVBgC6kQYAoRoAgLTBBgC1zQYAts0GALfdBgCw/QYAseUGALKdAACz5QYAhVTHA6UaAICH/AAAuAEKAK0aAIDpDACAsRoAgIyRcwCNpAEAzPACAL4NAIDBDQCAiRQAALgZCgCLDAAAGg4AgFMOAIC5DACAvwwAgBkKAICRwAEAywwAgLhtCgDODACA1AwAgNoMAIDdDACA4AwAgLUaAIAoDQCA5gwAgLkaAIDhpB4AKw0AgONUHgCvIXMAzCgCAO8MAIDsDACA8gwAgPUMAID4DACAzIACAJS4AwD7DACAkhQCAO9gHgCQAAIA/gwAgAoNAIC48QoADQ0AgJ8LAIAQDQCAiSkLABMNAICpGgCAvDABAL/EAQC+7AEAFg0AgMzsAgC4xQoAukQBAK0JAIAZDQCAygYAgN8GAIDyBgCAHA0AgPoGAIAfDQCACgcAgC0HAIAYBwCA9gcAgC8HAICpDQCAOgcAgK8NAIBKBwCAtXkAAGcHAIC3cSoCcgcAgLFhAAB0BwCAsw0pAo0HAIC96QAAoAcAgPoHAICtBwCAuRkrAsMHAIC7WRQCHwgAgFoJAIA8CACALw4AgFsIAIA5AACAgQgAgHEAAIDHCACAKwAAgCAJAIA9AACAXAkAgEMAAIBeCQCARQgAgGoIAIBJAACAAAgAgFMAAIB5CQCAWQAAgCINAIBfAACAuw0iAtANAIDMFDYCHwAAgL9lAAC+EQAAvW0AAOUHAICAaQEAgXUBAIJxAQCD3SEChGkHAIWBBwCGgQcAh3EBAIihAQCJrQEAirUHAIuNBwCMlQcAjaUBAE8AAICPpQEAkOEBAJHtBwCSsSECk/0HAJSNBwCVUQYAlvEBAJfZAQCY0QEAmXUGAJp9BgCb1QEAnGkGAJ2ZFAKeUQYAn1EGAKB1FAKhuQYAokkBAKOFLQKkIQEApS0BAKZ1FAKntQYAqKERAqlRFAKqlQYAsSEAgMy8NQLNPDUCbQAAgKoDAICsAwCArwMAgL0hAIDEIQCA2yEAgOIhAIDJAACADwAAgLihBgC6BgCAtwYAgMwAAIDOIQCAtQMAgN0FAIAYBgCAugUCALvVAgC46QUAuf0FAL7JAgC/5RcCvA0CAL0BAgCy4QUAs+EFALCNBQCxnQUAtuUFALfpBQC09QUAte0FAKo9BQCrwQUAqD0FAKk1BQCuzQUAr/UFAKzNBQCtxQUAoj0FAKMFBQCg1QIAoTkFAKYdBQCnBQUApB0FAKUVBQC/BgCAm8EFAD4GAIBVBgCAnt0FAJ8xBACcUQIAndUFAHIGAICJBgCApAMAgDAiAIDbAACAoAMAgI8HAIDuBwCA8gcAgJAJAIACCACABggAgJYLAICUCQCArwoAgG8HAICLBwCAlwcAgKIHAICqBwCAqgkAgPsOAIASDwCAHw8AgMwEMwLNsDACzCAzAs3gMALMEDACzGgwAsxYMALNjDACzGgxAs0UMQLM1DECzRQ2AsxwIALN0CcCzDA2AswkMQLMDDwCzWg/AswYPwLNND8CzBg9As3AMgLMRDwCzBg5Asw4MgLNqDICzIgyAs34MwLMfDMCzUAzAswoMwLNCDMCzMghAs0kJgLMrCYCzEA4AsyYJQLNyDoCzBwkAs0QJALMhDsCzag7AsysJQLNvDoCzKw4Asz4JwLM4DgCzXQ4AicPAID2BgCAYQ0AgIgNAIDNICoCzBwrAqoGAIAsIgCAzKQgAs2gJwLMOCYCygQAgMw4OgLNPDsCzBA5As1gPgLMoAMAvj0NAL3tLALWBACAu1UjAgQJAIC5PSICzwYAgNkHAIClBACAoA0AgLIEAIBvBQCA9AYAgL4EAIB1BQCAr70MAK6ZLgKtpQwAwgUAgKvFIgIDBgCAxAQAgCMGAIDQBACAyAUAgCkGAIBdBgCAowEYAqAEAIAaBwCAHQcAgJ9dDACeUQwAnUUMACcHAICbWSECrwcAgLEHAIC0BwCAuAcAgCoHAIDOBwCA0AcAgJMtJgLTBwCAbAgAgG8IAICPBQwAjnEMAI1lDAB5CACAi0UgAmAJAICJNS8CYwkAgGcJAIB8CACAcAkAgHMJAIC9AwCAACIAgIFdDACAYQwAgAABAIEYAACCAAQABCIAgIQQBwCFFAYAhuQIAIc8AgCILAUAiaQFAIoAeAAIIgCAjCQAAAwiAIAUIgCAECIAgLgRAACRxHsAkkh6AJNMeQAcIgCAzOgCAJbwCQC4OQAAkMAJACQiAICS8AkAzPgCAJS0CQC4DQAAKCIAgMwcAgC4BQAANCIAgMzkAgC4HQAAOCIAgDwiAIBDIgCAWiIAgKiMCACp5HsAYSIAgKvUBgDM5AIAuA0AAGsiAIDMlAIAbyIAgLGAewC4CQAAuBUAAMz8AgC15AgAcyIAgMzYAgB3IgCAuAUAALqcBQC7XAUAvAB8AL30fwC++H0Av/xyAIAJOgKBDToCggE6AoMFOgKEGToChR06AoYROgKHFToCiCk6AoktOgKKIToCiyU6Aow5OgKNPToCjjE6Ao81OgLM8AIAkekPAIMiAIDMzAIAuBkAAH8iAIDM3AIAl+UPALg1AAC4DQAAjyIAgMz8AgC4BQAAkyIAgMwwAgCXIgCAzNACAJsiAICfIgCAzIgCAKQtDwClVQ8Apl0PAMyUAgCoqToCqa06ArjVAACjIgCAuDUAAKciAIDMUAMAr7U6AswsAwCrIgCAzBgDALMFDwC0HQ8AzyIAgLYJDwC3CQ8Avmh9ALhtAAC4RQAAzDgDALwpDwDTIgCAviUPAMxYAwCH5Q4AzOg6Ari9AQC4yQEAzPA1As2kMwLMgCICzXwlAs2UNgLMBCkCzew7AsxkOgK45QEAuMEBAInVDgCI1Q4Al7EOALgNAACvIgCAsyIAgLciAIC4GQAAuyIAgNciAICfaTsC2yIAgL8iAIC4PQAAzMQCAMz4AgDDIgCAxyIAgLjZAADLIgCA3yIAgLjRAADjIgCAuPEAAMzMMwLnIgCAuMkAAMzoMwLrIgCAuNUAAKllAAC4yQAAzNgCAKq5BgC3TQ0Atk0NALU1DgC0NQ4AuFUAABUjAICxGQ8AsCkOAL/1AwC+UQ0AvVkNALw1DAC7XQ0Aul0NALldDQC4XQ0AgL0KAIHFCgCCFQQAg8kKAMx8BQCF3QoAhtUKAIfNCgDMVAUAifEKAIq5CACLDQgAjBEIAI0VCACOtScCj+UKAJBpCACRbQgAknEIAJNtJALMEAUAlR0IAJaFCgDMEAUAzDQFAJk9CACaiQoAmw0IAJwRCACdFQgAzEgFAMwQAgCgZQoAoW0KAKJlCgC4BQcApLEEAMzoAgCmsQQAuA0HAKiBBADM/AIAqpkIAKtdCgCsuQgArakEALglBwCvNQgAsNEIALHxBADMwAIAs40IALQpKAK1IQoAtiEKALchCgC4IQsAuSUIALhBBwC7KQsAvA0dAr3dDwC+MQsAvzELAIDdCgAZIwCAnKF9ANADAIDpAwCAhRkJAIaZCQCHlQkAiOEJAIklJQICBACAGwQAgC4EAIBBBACAVAQAgGcEAICQrQoAkUkFAJJtBQCTYQUAlGEFAJVtBQCWZQUAlxEFAJg1BQCZPQUAmjUFAJsNBQCcFQUAnR0FAJ4VBQCfCQUAoKkJAKH9BQCi9QUAowEFAKQFBQClDQUApgUFAKc9BQCoBQUAqQ0FAKoFBQCrGQUArIkJAK2pBQCutQkAr/0JALABCQCxfQUAsnUFALMBBQC0aQkAtQEFALYFBQC3PQUAuAUFALnhJQK6AQUAuwEFALzRJQK9PQkAvnkJAL9dCQCDMAUAoXgHAJ+xfgB6BACApHgHAKVIBwCNBACA8wQAgIt8BADdAACAEwEAgIhIBAAcAQCAIAEAgCQBAIAoAQCALAEAgDABAICyAAcAs/wHADQBAIDhAACAtuQHALfwBwDmAACA6wAAgLrgBwC7nAcAvIgHAL2oBwDwAACAs8F+AKPMBAD1AACA+gAAgIMABAD/AACAhXQEAKUgBAAEAQCAiEwEAAkBAIAOAQCAFwEAgK8tBwCNxAcArSEHAKwpBwDNAwCA8AQAgI8FAICwZQcA4gUAgB0GAIBDBgCAWgYAgHcGAICOBgCA0wMAgOwDAIAFBACAHgQAgDEEAIC8fAQAgt0rAoPlKwKA/QoAgfkrAoaZCQCHmQkAhOEKAIXhCgCKiQkAi4kJAIiJCQCJiQkAjoUJAEQEAICM4QgAjY0JAJK5KwKTQScCkJkrApHFCwCWyQsAl3UnApTFDQCV0SQCmskLAJvZKgKYyQsAmXkHAFcEAIBqBACAnP0LAH0EAICQBACA9gQAgKABAICkAQCAqAEAgONkAgCsAQCAsAEAgLQBAIDvvAcAqBEJALgBAIC8AQCAwAEAgMQBAIDIAQCAzAEAgNABAIDUAQCA2AEAgNwBAIDgAQCA5AEAgOgBAIDsAQCA8AEAgPQBAID4AQCA/AEAgAACAICCnH4ABAIAgKD1VAKh2VQCoulUAqP1dQCk7XUApZ12AKaVdgCnvXYAqIV2AKkpfQCqOX0AqwV9AKwdfQCtBX0Arg19AK8FfQCwfX0AsUl+ALJRfgCzUX4AtHV+ALV9fgC2aX4At2l+ALhZfgC5WX4Auil+ALspfgC8IX4AvSF+AL4ZfgC/GX4AkgcAgDkJAIDXBwCATSIAgLQNAAC1NQAAtj0AAKIGAICsBgCArwYAgAMjAIAJIwCAvSV4ALy1WALGMQCALjoAgJkqAIC9KgCAySoAgNkqAIDhKgCA7SoAgPUqAID9KgCACSsAgF0rAIB1KwCAhSsAgJUrAIClKwCAtSsAgNUrAICAeX8AgYF/AIKBfwCDnX8AhI1/AIWxfwCGsX8Ah7F/AIjhfwCJ4X8AiuF/AIv9fwCM5X8Aje1/AI7lfwCP3X8AkKV/AJGtfwCSpX8Ak71/AJSlfwCVrX8Alm1+AJctfgCYFX4AmRl+AJrpfgCb6X4AnPl+AJ35fgCe6X4An+V+AKAdfgChJX4AoiV+AKM9fgCkJX4ApS1+AKYlfgCnXX4AqGV+AKltfgCqZX4Aq31+AKxlfgCtbX4ArmV+AK9dfgCwJX4AsS1+ALIlfgCzPX4AtCV+ALUpfgC2WXcAt9V1ALj9eQC56XUAuvl1ALvZeQC86XUAvdV1AL7RdQC/2XUAgDF2AIE9dgCCSXYAg0V2AIRBdgCFTXYAhvl0AId9dgCIoQIAiU12AIpZdgCLuXoAjEl2AI2degCOsQIAjx16AJCRVgKRKXYAkoF2AJPNdgCU2XYAlel2AJbJdgCX0VkCmKF2AJllWgKa8XYAm01aApzRdgCdYXoAnoFWAp/VdgCgBQIAoY1aAqI1VwKjCXYApCF2AKUtdgCmiVoCp5laAqi5WgKpdXYAql13ANkrAIDdKwCAESwAgDksAIBJLACAUSwAgFUsAIBhLACAfSwAgIEsAICZLACAnSwAgKUsAIC1LACAUS0AgGUtAIClLQCAuS0AgMEtAIDFLQCA1S0AgJl1CgD4LQCAJC4AgDAuAIBQLgCAXC4AgGAuAIBkLgCAgux6AINkewB8LgCAgC4AgIZ0ewCHvHsArC4AgLguAIDALgCAyC4AgNguAIDnLgCA7y4AgBsvAIAfLwCAJy8AgJJwfAArLwCAMy8AgJFMfAA7LwCASy8AgGcvAIDfLwCA8y8AgKvMfACo5HwAqdx8APcvAIB3MACAezAAgI8wAICiwHwAkzAAgJswAICjMACAzEBJAs0ASQLM/EoCzWhLAqswAIC3MACA7TAAgP0wAIARMQCAjjEAgJoxAICqMQCAsqx8ALNAfAC2MQCAwjEAgMoxAIDOMQCAtGx8ALUEfACAlQcAgZ0HAIKVBwCDqQcAhLkHAIW5BwCG2QcAh9kHAIjpBwCJ6QcAivkHAIv5BwCM6QcAjekHAI7RBwCP0QcAkLEHAJGxBwCSSQEAk0kBAJRZAQCVWQEAlkkBAJdJAQCYeQEAmXkBAJpJAQCbSQEAnFkBAJ1ZAQCeSQEAn0kBAKC5AQChuQEAoskBAKPJAQCk2QEApdkBAKbJAQCnyQEAqPkBAKn5AQCqyQEAq8kBAKzZAQCt2QEArskBAK/JAQCwuQEAsbkBALJJAQCzSQEAtFkBALVZAQC2SQEAt0kBALh5AQC5eQEAukkBALtJAQC8WQEAvVkBAL5JAQC/SQEA0jEAgNYxAIDaMQCAkjIAgNoyAIDmMgCA6jIAgO4yAIDyMgCA+jIAgP4yAIASMwCALjMAgDYzAIB2MwCAejMAgIIzAICGMwCAjjMAgJIzAIC2MwCAujMAgNYzAIDaMwCA3jMAgOIzAID2MwCAGjQAgB40AIAiNACARjQAgIY0AICKNACAqjQAgLo0AIDCNACA4jQAgAY1AIBKNQCAUjUAgGY1AIByNQCAejUAgII1AICGNQCAijUAgKI1AICmNQCAwjUAgMo1AIDSNQCA1jUAgOI1AIDqNQCA7jUAgPI1AID6NQCA/jUAgJ42AICyNgCAnoUMAOY2AIDqNgCA8jYAgIC5AwCBuQMAgskDAIPJAwCE2QMAhdkDAIbJAwCHyQMAiPkDAIn5AwCKyQMAi8kDAIzZAwCN2QMAjs0DAI/FAwCQvQMAkQEMAJJJDgCTSQ4AlFkOAJVZDgCWSQ4Al0kOAJh5DgCZeQ4AmkkOAJtJDgCcWQ4AnVkOAJ5JDgCfSQ4AoLkOAKG5DgCiyQ4Ao8kOAKTZDgCl2Q4ApskOAKfJDgCo+Q4AqfkOAKrJDgCryQ4ArNkOAK3ZDgCuyQ4Ar8kOALC5DgCxuQ4AskkOALNJDgC0WQ4AtVkOALZJDgC3SQ4AuHkOALl5DgC6SQ4Au0kOALxZDgC9WQ4AvkkOAL9JDgC8eQQAvXkEAL6JBAC/nQQAuHUEALl9BAC6aQQAu2kEALRxBAC1cQQAtnEEALdxBACwcQQAsXEEALJxBACzcQQArGkEAK1pBACucQQAr3EEAKhBBACpQQQAqkEEAKtBBACknQUApWEEAKZhBACnYQQAoJ0FAKGFBQCijQUAo4UFAJxdBQCdZQUAnm0FAJ9lBQCYXQUAmUUFAJpNBQCbRQUAlB0FAJVlBQCWbQUAl2UFAJAdBQCRBQUAkg0FAJMFBQCMMQcAjTEHAI4xBwCPMQcAiDEHAIkxBwCKMQcAizEHAIQxBwCFMQcAhjEHAIcxBwCAMQcAgTEHAIIxBwCDMQcAJjcAgC43AIA2NwCAcjcAgHY3AIB+NwCAgjcAgIY3AICyNwCAtjcAgL43AIDSNwCA1jcAgPI3AID6NwCA/jcAgCI4AIBCOACAUjgAgFY4AIBeOACAijgAgI44AICeOACAwjgAgM44AIDeOACA9jgAgP44AIACOQCABjkAgAo5AIAWOQCAGjkAgCI5AIA+OQCAQjkAgEY5AIBeOQCAYjkAgGo5AIB+OQCAgjkAgIY5AICOOQCAkjkAgJY5AICaOQCAnjkAgK45AIDGOQCAyjkAgNY5AIDaOQCA3jkAgOI5AIDqOQCA7jkAgPI5AID+OQCABjoAgA46AIASOgCAGjoAgIC5AQCBuQEAgskBAIPJAQCE2QEAhdkBAIbJAQCHyQEAiPkBAIn5AQCKyQEAi8kBAIzZAQCN2QEAjskBAI/JAQCQuQEAkbkBAJIRAACTEQAAlDEAAJUxAAAeOgCAIjoAgCo6AIAyOgCAPSMAgGUsAIBpLACAJSQAgIJgAgCZ4QAAgIAAAIGYAACC5AYAg4gEAITUGwCFlBoAhhgfALMjAICIxB4AiQAQAIqoEwCLrBEAjAAoAI20KwCOuCoAj7wpAOOwAgC+dAIAnlUAAOMUAgCCbAIAtyMAgJkNAAC+RAIAnjUAAIJoAgCZBQAAuyMAgO/MAgC+oAAAgoQAAO/YAgDj7AEA4/QBAL8jAIDjCAMAwyMAgOM4AwDHIwCA44gDAMsjAIDv4AMAzyMAgO+IAwDvPAEA78QDANMjAIDv1AMA4+wDAB43AIDXIwCA4+wDAOPsAwDj5AMA2yMAgOO4AwDvXAMA70wDAN8jAIDvSAMA7/QDAOMjAIDnIwCA7zQDAON8AwDjlAQA6yMAgO8jAIDzIwCA47QEAPcjAID7IwCA/yMAgO9sBAADJACAByQAgO9YBADvUAQACyQAgBYkAIAaJACAvQAAgOP4BADCAACAMSQAgB4kAIBtKQCA45wEAAglAIBrJQCAriUAgO9QBADaJQCABCYAgO88BAApJgCAgAlLAoYcdwC+RAIAgnQCAL5QAgA+JgCAmREBAJkNAQCPrAIAggQCAI1oAQCewQIAi3wBAJ49AQCeKQEAvggCAJfQAgCZXQEAldACAJ5VAQCT0AIAmXUBAJHQAgC+SAIAn7gCAEYmAICdtAIAnk0BAJuwAgCZXQEAmbQCAL6EAgCeqQEApowCAGImAICkgAIAmakBAGomAIChSAIAgqwCAK/kAgCCtAIAglwCAJnlAQC+CAIAgnwCAIIABACopAIAnvkBAL5wAgC1HAQAnoUBAL6oBQCyhAIAtrECAL6sBQC4KQkAuYkCALqZAgCCjAUAu+gEAIKcBQByJgCAuPAEAJ5ZBgCZbQYAnmEGAJl5BgC+fAIAnmEGAIJcAgC+QAIAmVkGAJ5dBgCCYAIAmaUGAL58AgCevQYAghwCAL4UAgCZzQYAvkwCAIJMAgCa3QYAnt0GAJ/FBgDjDAIAgrwCAJn5BgC+ZAIA7/QCAJrxBgCe6QYAn+kGAJ7ZBgCf1QYA4wQCAJklBgCaIQYAgngCAJk9BgDjBAIAgkQCAJolBgC+cAIA75wCAJ4FBgCfFQYA7+gCAJp1BgCZBQYAggQCAL5wAgDjcAIAnnUGAJ8NBgCeAQYAvnwCAOM0AgCZDQYAvmACAIJsAgDv8AIAmTUGAIKQAwDv2AIAniEGAIQmAICbxQcAmeUHAL58AgCe7QcAn8UHAOPsAwCdUAIAnNEHAIJsAgDv1AIAmc0HAIJ8AgC+cAIAmd0HAJ7dBwC+AAIA42gCAJ6tBwCZuQcA42gCAIJ8AgDjDAIAvkgCAJmpBwCCWAIA78QCAJ6ZBwC+bAIA77gCAIKUAgCejQcA77gCALsAAACZeQcAuQwAAJ5xBwC/AAAAglQCAL0EAAC+aAIAs9QDAJmxBgCxcAMAggQCALc4AACeoQYAtTQAAL5wAgCrWAMAnqEGAO9cAgCZqQYArxADAIJQAgCtFAMAmYUHAJlpBgC+WAIAnmEGAL58AgCCaAIApqACAOOQAgCZaQYA43wBAOOYAQDjrAEA49ABAOPoAQC+dAIAno0FAOMwAgDvzAIAgmgCAJnRBQDvlAIA71QBAO9wAQDvJAEA7ygBAL58AgCevQUA4wwCAIJ4AgCZrQIAvnQCAJ6lAgDjNAIAgmACAJkZAAC+YAIA7/wCAJ4NAACClAIA79QCAJAmAIDj/AIAmQkAAL5gAgCYJgCAnh0AAOMAAgCwJSoAglgCAJkNAADv9AIAvmQCAK4mAIDvwAIAnhkAAIIYAgCCOAIA43ACAJkRAACaNQAAmSkBAL50AgDsJgCAnyUAAJ4JAACZ6QEAvrQDAL7gAwCazQEA79gCAJ4RAQCC2AMA/SYAgIHEAgDjsAMAHycAgOP8AwC+/AIAhMQCAIIoAgCGEAIAKicAgIg8AgCeIQAAnw0AAHonAIDvKAMAj3QCAO8sAwCCiAIAmXUAAJoVAACSxAMAldADAJktAACa0QAAjicAgL7IAgCYaAMAm3wDAILEAwCeQQAAnykAALAnAICChAIA45ACAL4IAwC+JwCABigAgJ8ZAACe7QAA49ACAJlxAACaFQAAvhQCAO8wAgCZIQAA71gCABQoAICv7AMAggQCALFMHACwABwAniUAALJMHACeXQAAn2EAAOO8AgCZIQAA+QAAAHEpAIDvlAIAdSkAgL08HACCgB0Av8EfAHkpAIDjtB0AvnQCAJ71HwDj8B0AmQUAAH0pAIC+fAIAngkAAIJgAgCZDQAAiSkAgL5gAgDvzAIAnh0AAOklAIDv3AIA42gCAPkYAIDjPB0AIRoAgP0YAIABGQCAJRoAgCkaAIAtGgCAMRoAgDUaAIA5GgCA76QCAD0aAIDvJB0AQRoAgLHFAAAFGQCAs8UAALLdAAC1yQAAtMEAALcdAAC2wQAAuWUAALhlAAC7zQAAus0AAL3dAAC83QAAv8UAAL7JAAAJGQCADRkAgE0ZAIBhGQCAERkAgBUZAIDvFHgD7wBIA+HYTQPhOKgC41x5A+O0UAOtGQCAsRkAgLUZAIC5GQCAgMkBAIHVAQCC3QEAg20CAITdAQCFcQIAhgEEAIcdBQCIJQUAiTUFAIo9BQCLbQUAjHUFAI1lBQCObQUAj80BAJC1AQCRvQEAkrUBAJNNAwCUVQMAlV0DAJZVAwCXTQMAmHUDAJl9AwCadQMAm00DAJxVAwCdWQMAnkkDAJ9JAwCguQMAobkDAKLBAwCj3QMApMUDAKXNAwCmxQMAp/0DAKjJAwCpyQMAqtEDAKvRAwCsMQMArTEDAK4xAwCvMQMAsFEDALFRAwCyUQMAs1EDALRxAwC1cQMAtnEDALdxAwC4UQMAuVEDALpRAwC7UQMAvDEDAL0xAwC+MQMAvzEDAL0ZAIDBGQCAxRkAgMkZAIDNGQCA0RkAgNUZAIDZGQCA3RkAgOEZAIDwIAIA5RkAgOkZAIDtGQCA8RkAgPUZAICc9TYAnf02APkZAICRkAIA/RkAgKkZAIBFGQCASRkAgEUaAIC6adgASRoAgE0aAIC4sTYAubE2AFEaAIBVGgCAWRoAgF0aAIBRGQCAYRoAgGUaAIBVGQCAWRkAgF0ZAIBlGQCAaRkAgG0ZAIBxGQCAdRkAgHkZAIB9GQCAgRkAgIUZAICJGQCAjRkAgJEZAICVGQCAglgCAJkZAIBpGgCA8FgCAG0aAICdGQCAoRkAgKUZAIABGgCABRoAgJF0AwDhtDsCCRoAgOPYIgINGgCAERoAgBUaAIAZGgCAHRoAgKUqAIBVLQCAqSoAgMEqAICtKgCAljMAgO/IPwK1KgCA4ZTzAuGY0gLjlPcC4xDGAuGUtgLhkJ0C44SiAuMIhwIZGQCAHRkAgO+4swLvOIsCnSoAgOAtAIDvIJcC7+DgAoLkAgBpLQCACAIAgLrF2QAOAgCAFAIAgBoCAIAgAgCAJgIAgCwCAIAyAgCAOAIAgD4CAIBEAgCASgIAgFACAIDhgHgC8OQGAOMUagKCgAgA4aAPAuEIEwLjhA4C4xgeAlYCAIA0AwCA7zQ7Au8wHwI6AwCAQAMAgO8MEgJGAwCAJRkAgCkZAIBMAwCAUgMAgC0ZAIAxGQCAWAMAgF4DAIB2AwCAggMAgIgDAICOAwCAlAMAgJoDAIB8AwCAZAMAgDUZAIA5GQCAbQMAgFwCAIA9GQCAQRkAgHQCAIBoAgCAvAIAgHoCAICYAgCAYgIAgJICAIBuAgCApAIAgNQCAICAUQYAgV0GAIJVBgCDaQYAhHkGAIV5BgCGaQYAh2kGAIhZBgCJoQcAiqUHAIu9BwCMpQcAja0HAI6lBwDyAgCA7AIAgOACAICSCRQAkxUUAJTxBwCV8QcAlvEHAJfxBwCY0QcAmdEHAJo5FACb0QcAnIEHAJ2BBwCefQcAnx0UAJktAQCYLQEAmz0BAJo9AQCdLQEAnC0BACEZAICeVQEAkd0GAJDRBgCTJQEAkiUBAJUtAQCULQEAlx0BAJYdAQCJ8QYAiOkGAIvxBgCK+QYAjbEGAIzpBgCPqQYAjrkGAIHxBgCA7QYAg/EGAIL5BgCF0QYAhOkGAIfRBgCG2QYAua0DALitAwC7vQMAur0DAL2tAwC8rQMAv90DAL7dAwCxrQMAsK0DALO9AwCyvQMAta0DALStAwC3nQMAtp0DAKm5AQCosQEAq3UBAKqxAQCtFQEArBUBAK/dAwCu3QMAobkBAKCpAQCjiQEAorEBAKWZAQCkkQEAp4kBAKaRAQAuAwCAwgIAgM4CAIDmAgCA2gIAgAQDAICwAgCA+AIAgCIDAIAKAwCAngIAgIACAIC2AgCAyAIAgP4CAICGAgCAKAMAgKoCAIAQAwCAjAIAgBYDAIAcAwCACS0AgOsuAIDKNACAhAcAgAYFAIAVBQCAJAUAgDMFAIBCBQCASwUAgPAsOABUBQCAXQUAgGYFAICSBQCA40huA5sFAIDhTG4DpAUAgO/0AQOnBQCAqgUAgK0FAIBGOgCApkwAgNZVAIA2aACAZnEAgJZ6AID2jACAVp8AgIaoAIDtugCAJMQAgFTNAICE1gCAtN8AgDG7AIA6rgCABqUAgPkqAICJKwCAoSoAgOUqAIBBMQCAATEAgE40AIDVLACABjMAgIo3AIBiNACAHSwAgJI0AICeMwCAEjgAgFkrAICFLACA+jEAgCY5AIAdKwCArSsAgJ4xAIC8LgCAySwAgFksAIA4LgCALC4AgJGgBgDuMwCAGSsAgJ43AIB1LACAzS0AgLAFAIDh1D8D4VgaA+PcLwPjUA4D4RTyA+FA0wPjQOoD40DDA7MFAIC2BQCA73jrA+9c8gO5BQCA5QUAgO9E3gPvmCUD4bSLA+E8lwPjfKID45iLA+EwQQDhUKwD4xx/AOOIRgDoBQCA6wUAgO84ewDv4EEA7gUAgPEFAIDvzIoD7yCHA4DBGACB3RgAgikLAIMpCwCE6Q4AhekOAIYZDwCH8RgAiCUPAIntGgCK5RsAiyEdAIw5HQCN5RsAjmkQAI/VGgCQhRsAkU0PAJJFDwCTXQ8AlEUPAJVNDwCWRQ8Al30PAJhFDwCZTQ8AmkUPAJtpGwCcQQ8AnUEPAJ5BDwCfQQ8AoMEPAKHBDwCiwQ8Ao8EPAKS5CwCluQsApqkLAKfNDwCo9Q8Aqf0PAKr1DwCrzQ8ArNkPAK3ZDwCuyQ8Ar8kPALC5DwCxuQ8AsmkPALNpDwC0YQ8AtWEPALY5DwC3OQ8AuBEPALkRDwC66QEAu+kBALz5AQC9+QEAvukBAL/pAQD0BQCA9wUAgPoFAID9BQCAAAYAgCAGAIDhBACAgAUAgNMFAIAOBgCANAYAgEsGAIBoBgCAfwYAgJYGAIDdAwCA9gMAgA8EAIASBwCAQQgAgD4IAIA/BwCAOSQAgHIkAICjJACAyCQAgLkmAIDEJgCAyCYAgMwmAIDQJgCALygAgG4oAICWKACAmigAgL8oAIDHKACA4ygAgPUoAID5KACA/SgAgLrp0wAVKQCAMCkAgEspAIA9JACASiQAgFckAIBkJACAdiQAgIMkAICVJACApyQAgLckAIDMJACA1iQAgOQkAIDuJACA+yQAgAwlAIAWJQCAbyUAgHYlAIAkJQCAgBkDAIEZAwCCKQMAgykDAIQ5AwCFOQMAhikDAIcpAwCIGQMAiRkDAIppAwCLaQMAjHkDAI15AwCOaQMAj2kDAJAZAwCRGQMAkgEEAJMtAwCUNQMAlVUGAJZdBgCXVQYAmG0GAJl1BgCafQYAm3UGAJxtBgCdNQYAnj0GAJ81BgCgzQYAodUGAKLdBgCj1QYApPkDAKX5AwCm6QMAp+kDAKjZAwCp+QYAqikGAKspBgCsOQYArTkGAK7FAwCvPQMAsEUDALFNAwCyRQMAs10DALRFAwC1TQMAtkUDALd9AwC4SQMAuUkDALpZAwC7fQYAvGUGAL1tBgC+ZQYAgCUAgKkVDwCoAQ8Aq00PAKpNDwCtRQ8ArEUPAK+hDQCuqQ0AoXULAKBhCwCj7QsAoqkLAKXlCwCk5QsApzkPAKZZCAC5oQ0AuJkNALuhDQC6qQ0AvaENALy5DQAxJQCAvqkNALGhDQCw2Q0As6ENALKpDQC1oQ0AtLkNALehDQC2qQ0AOCUAgEglAIBbJQCAsiUAgLwlAICRJQCAoSUAgNAlAICB7Q0AgO0NAIP9DQCC/Q0Ahe0NAITtDQCH2Q0AhiEYAJlNDQCYTQ0Am1ENAJpdDQCdeQ0AnHUNAJ9pDQCecQ0AkYkNAJCBDQCTmQ0AkoENAJWJDQCUgQ0Al30NAJaBDQDgJACAICUAgI0lAIDMJQCA3iUAgAgmAIAtJgCAQiYAgPAlAID6JQCADCYAgBkmAIAxJgCATiYAgFgmAIB2JgCASiYAgGYmAIBuJgCAgCYAgIwmAICUJgCAoyYAgN4mAICcJgCAsiYAgKcmAIC9JgCA1CYAgOImAIABJwCAEScAgBsnAIBPJwCAkicAgOcnAIBPKQCAXSkAgGEpAIBlKQCA8CYAgC4nAIA+JwCASCcAgCMnAIBTJwCAYycAgH4nAIBwJwCAlicAgMInAIDJJwCApicAgNMnAIDdJwCAtCcAgBgoAIAKKACA6ycAgCUoAIDyJwCA/CcAgDMoAIBAKACASigAgFQoAIBeKACAcigAgH8oAICGKACAnigAgKUoAICyKACAyygAgNUoAIDnKACAASkAgA4pAIAZKQCAIykAgDQpAIA7KQCAUykAgMMDAIDmBACAhQUAgNgFAIATBgCAOQYAgFAGAIBtBgCAhAYAgJsGAIDjAwCA/AMAgBUEAIAoBACAOwQAgE4EAIBhBACAdAQAgIcEAICaBACAAAUAgA8FAIAeBQCALQUAgDwFAIBjCACAJAgAgMEGAID8BwCAHQkAgOMoEwAzCQCAKggAgC0IAIAxCACAJAcAgNwuAIDKMACA2S0AgLswAIBFMQCAJwkAgO/sEwAGCQCA3A0AgM8IAICDCACAMQcAgEwHAID8BgCACggAgJQIAIAqCQCACQkAgOANAIDsDQCA2wgAgJkIAIAVBwCAhggAgFUHAID/BgCApgcAgJEkAIDwDQCA4ggAgCcIAICcCACAWAgAgBUJAID0DQCA5QgAgBQIAICfCACA6AgAgBcIAIDJCACAoggAgOwIAIAbCACAzAgAgKYIAID3CACA/QgAgIgHAICKCACAWQcAgAMHAIA9CQCAQQkAgEkJAIA2CQCAGAkAgPgNAID0CACALQkAgAwJAIDkDQCA0ggAgI4IAIBdBwCAMAkAgA8JAIDoDQCA1QgAgJEIAIBgBwCArQgAgGMHAIDjSBIA4xQSAOP4EwDjuBMA4+wSAOOgEgDjbBIA43gSAO/ADQDv2A0A73QSAO9QEgDvqBIA79wSAO8oEwDvIBMA6QcAgMwGAIAOCACAEQgAgNgGAIDUBgCAIQgAgAcHAIBnCACADAcAgHYIAIA0BwCANwcAgKoIAIC2CACAuQgAgOPYEADjoBAA46AQAON0EQDjNBAA4wgQAOPkEADj9BAA77wQAO/gEADvzBAA7zgQAO8QEADvcBAA73AQAO9MEADjhBMA4+gTAOMwEADjEBAA42ATAONAEwDjpBMA47QTAO/IEwDvtBMA75gTAO98EwDvXBMA70wTAO8UEwDv6BAAgO08AIH1PACC/TwAg/U8AITtPACFFT0Ahh09AIcVPQCILT0AiTU9AIo9PQCLNT0AjC09AI0VPQCOHT0AjxU9AJBtPQCRdT0Akn09AJN1PQCUbT0AlRU9AJYdPQCXFT0AmC09AJk1PQCaPT0AmzU9AJwtPQCdFT0Anh09AJ8VPQCg7T0AofU9AKL9PQCj9T0ApO09AKUVPQCmHT0ApxU9AKgtPQCpNT0Aqj09AKs1PQCsLT0ArRU9AK4dPQCvFT0AsG09ALF1PQCyfT0As3U9ALRtPQC1FT0AthE9ALcRPQC4MT0AuTE9ALoxPQC7MT0AvBE9AL0RPQC+ET0AvxE9AIDxPACB/TwAgvU8AIMNPwCEFT8AhR0/AIYVPwCHDT8AiDU/AIk9PwCKNT8Aiw0/AIwVPwCNHT8AjhU/AI8NPwCQdT8AkX0/AJJ1PwCTDT8AlBU/AJUZPwCWCT8Alwk/AJg5PwCZOT8Amgk/AJsJPwCcGT8AnRk/AJ4JPwCfCT8AoPk/AKH5PwCiCT8Aowk/AKQZPwClGT8Apgk/AKcJPwCoOT8AqTk/AKoJPwCrCT8ArBk/AK0ZPwCuCT8Arwk/ALB5PwCxeT8Asgk/ALMJPwC0GT8AtRk/ALYJPwC3CT8AuDk/ALk5PwC6CT8Auwk/ALwZPwC9GT8Avgk/AL8JPwCA+TwAgfk8AIJJPQCDST0AhFk9AIVZPQCGST0Ah0k9AIh5PQCJeT0Aikk9AItJPQCMWT0AjVk9AI5JPQCPST0AkDk9AJE5PQCSAQQAk00GAJRVBgCVXQYAllUGAJdNBgCYdQYAmX0GAJp1BgCbTQYAnFUGAJ1dBgCeVQYAn00GAKC1BgChvQYAorUGAKPNBgCk1QYApd0GAKbVBgCnzQYAqPUGAKn9BgCq9QYAq80GAKzVBgCt3QYArtUGAK/NBgCwtQYAsb0GALK1BgCzTQYAtFUGALVdBgC2VQYAt00GALh1BgC5fQYAunUGALtNBgC8VQYAvV0GAL5VBgC/TQYArH0/AK2lPwCurT8Ar6U/AKh9PwCpZT8Aqm0/AKtlPwCkHT8ApUU/AKZNPwCnRT8AoB0/AKEFPwCiDT8AowU/ALydPwC9pT8Avq0/AL+lPwC4nT8AuYU/ALqNPwC7hT8AtN0/ALWlPwC2rT8At6U/ALDdPwCxxT8Ass0/ALPFPwCMZToAjW06AI5lOgCPfToAiEU6AIlNOgCKRToAi306AIRlOgCFbToAhmU6AId9OgCABToAgQ06AIIFOgCDfToAnF04AJ3lPwCe7T8An+U/AJhdOACZRTgAmk04AJtFOACUuTgAlWU4AJZtOACXZTgAkAU6AJENOgCSBToAkwE5AMAIAIDYCACA3ggAgPAIAIB2BwCAIgkAgHkHAICBBwCAVAkAgJ0HAIDLBwCAvQcAgMQGAIDcBACAewUAgM4FAIAJBgCALwYAgEYGAIBjBgCAegYAgJEGAIDXAwCA8AMAgAkEAIAiBACANQQAgEgEAIBbBACAbgQAgIEEAICUBACA+gQAgAkFAIAYBQCAJwUAgDYFAIBFBQCATgUAgFcFAIBgBQCAaQUAgJUFAICeBQCAXQgAgFYOAIBZDgCAOjoAgKwKAIAVCwCANjoAgD46AICcGQAAnRkAAJ45AACfOQAA4wwAgEI6AIB6NwCA8TAAgKI3AIBaMgCAxSoAgLksAICaMDUA7C0AgB0tAIDoLQCA1y8AgJ+ENQDSMwCAnUQpAGI1AICaNgCA1jYAgAo3AIAeOACAdjEAgAIyAICuMgCARjMAgGI2AIBGOACAcjkAgOkqAICNLACAijEAgNIyAICWNgCAwjkAgJQuAIB6MgCAhjYAgBo3AIALMACAvjUAgLSAGgC1hBkAtojmALeM5ACwABwAsZQeALIAGACznBsAvADsAL2k7wC+qO4Av6TtALgA4AC5tOMAurjiALu84QCkwAAApQAMAKbIDgCnAAgA4jYAgAcvAIAFMQCArXwDAKwAEACt5BMArugSAK9gEQCo8AoAqRwJAKr4FgCr/BQAGjIAgB4zAIAqOACAKSsAgMErAIAtLACAczAAgIIxAIDOMgCA8jMAgI42AICmNgCAyjcAgO44AICiOQCAvjkAgC40AIBuNACAvAgAgCY1AIBGNgCAejgAgE43AIChLQCAIy8AgN40AICeNQCAAjMAgDY0AICaNwCA5jgAgJ0tAIBwLgCAejEAgC4yAIBiMgCAFjUAgD41AICmOACAKSwAgJwAAACqNQCAzSsAgMkrAICaNACAKjUAgF42AICuOACAajcAgA8wAIBaNwCA0SoAgEQuAIB7LwCAMjMAgLIzAIBNLACAPjQAgDkrAIBfLwCAsSoAgO4xAICLMACAEjUAgIDpAwCB6QMAgjkvAIP9AwCE5QMAhe0DAIblAwCHfS4AiEEuAIkhAgCKeS8AiyUCAIw9AgCNJQIAjiECAI8dAgCQZQIAkW0CAJJlAgCTfQIAlGUCAJVtAgCWZQIAlx0CAJglAgCZLQIAmiUCAJs9AgCcJQIAnS0CAJ4lAgCfHQIAoOUCAKHtAgCi5QIAo/0CAKTlAgCl7QIApuUCAKdNAgCodQIAqX0CAKqpAQCrqQEArLkBAK25AQCuqQEAr6kBALDZAQCx2QEAsukBALPpAQC0eSIAtf0BALb1AQC37QEAuNUBALndAQC61QEAu60BALy1AQC9uQEAvqkBAL+pAQChLACAjS0AgP4zAIBmNgCAPjcAgLoxAIDmMQCAHzAAgB42AIA/MACArjMAgAUrAICBKwCAxSsAgFYxAID+NACA9jUAgEo3AIBaOACANSwAgOksAIAXLwCApzAAgH4yAIBCNACAljgAgHo5AIDOOQCA5jkAgOkwAICmMQCA7jcAgOMuAIC/LwCA2y8AgGswAIBuMgCAujIAgGozAICONACAMjUAgJY1AIDeNwCAbjYAgAY4AIB+OACA6SsAgBUsAID9LACAqjIAgPY2AIADLwCAcy8AgDcwAICyMQCA2jQAgCYzAIAVKwCAWS0AgKguAIB/LwCAQjMAgF4zAIBuNQCAgFEBAIEBKgCCXQEAg1UBAIRNAQCFdQEAhn0BAId1AQCITQEAiVUBAIqdKwCLWQEAjEkBAI1JAQCOuQEAj7kBAJDJAQCRyQEAktkBAJPZAQCUyQEAlckBAJb5AQCX+QEAmMkBAJnJAQCa2QEAm9kBAJzJAQCdyQEAnrkBAJ+5AQCgSQEAoZUBAKJFAQCjXQEApEUBAKVNAQCmRQEAp30BAKhFAQCpTQEAqnkPAKtBAQCsQQEArUEBAK5BAQCvQQEAsMEDALHBAwCywQMAs8EDALTBAwC1wQMAtsEDALfBAwC4wQMAucEDALrBAwC7wQMAvMEDAL3BAwC+wQMAv8kMAI41AIBiOACA4jgAgPI4AIAuOQCALSsAgII0AIBOOACAyjgAgJcvAIDxKgCAUSsAgEguAIBoLgCAlzAAgMYyAIDOMwCAejYAgBo4AIDZMACAojgAgA0sAIAlMQCAMTEAgBIyAIBKMgCATjMAgKozAIAqNACADjUAgDo5AIDrLwCAsjgAgEErAICMLgCAMjIAgOI3AIBPLwCAny8AgDkxAIC6OACA8SsAgNksAIB4LgCAwjAAgBUxAIBiMQCA9jEAgEozAIC+MwCAWjUAgPo2AIAGNwCA1jgAgF0sAIBOMgCA3SwAgMoyAIBuMwCAijYAgL44AICqOQCA0jkAgC0xAICxOSMAsBEDALMVAwCyFQMAtTUDALQ1AwC3NQMAtjUDALkVAwC4FQMAuxUDALoVAwC9dQMAvHUDAL91AwC+dQMAoZkNAKCRDQCjqQ0AopENAKW5DQCksQ0Ap6kNAKaxDQCpmQ0AqJENAKtpAwCqkQ0ArXkDAKxxAwCvaQMArnEDAJEZDQCQEQ0Aky0NAJIRDQCVPQ0AlD0NAJctDQCWLQ0AmR0NAJgdDQCbbQ0Amm0NAJ15DQCcgQ4An2kNAJ5xDQCBmQ0AgAkjAIOpDQCCkQ0AhbkNAISxDQCHqQ0AhrENAImZDQCIkQ0Ai2kNAIqRDQCNeQ0AjHENAI9pDQCOcQ0AKjIAgMY1AIDGNACA6jQAgBozAICiMgCAZjcAgA0rAIAuNgCA9SsAgOUrAIDzLgCAEzAAgPY0AIA0LgCABjIAgOUwAIDqNwCAqjgAgA8vAIBhKwCANS0AgIktAIDVMACA0SsAgCIzAIDmMwCASjQAgGY0AIBqNACAfjQAgPo4AIDuNACAkjYAgFY3AIAKOACANjgAgE45AIBSOQCAVjkAgLo5AIAuOACAxjgAgDErAIBVKwCAaSsAgCUsAIAxLACAcSwAgCUtAIBBLQCASS0AgIUtAICRLQCAdC4AgIsvAICzLwCAuy8AgJH4EADTLwCAfzAAgK8wAIDdMACAWjEAgIApAQCBKQEAgjkBAIM5AQCEKQEAhSkBAIZZAQCHWQEAiNkoAIltAQCKKSUAi2EBAIxhAQCNYQEAHjIAgDoyAICQGQEAajIAgJIVAQC+MgCA3jIAgJU1AQCWPQEAlzUBAJgNAQCZFQEAmh0BAJsVAQCcDQEAnfUBAJ7dKABSMwCAoAUBADI0AICiAQEAVjQAgFI0AIClGQEApgkBAFo0AIBeNACAdjQAgKo9AQCrNQEArC0BAK0VAQCuHQEArxUBALBtAQCxdQEAsn0BALN1AQC0bQEAtRUBALYdAQC3FQEAuC0BALk1AQC6PQEAuzUBALzZLgC9KQEAvhkBAL8ZAQC6eR4Au3keALjNAgC5eR4AvpUeAL+dHgC8QQIAvZ0eALJ9HgCzRR4AsH0eALF1HgC2XR4At0UeALRdHgC1VR4AqgUeAKsNHgCodR4AqQ0eAHo0AICeNACArBUeAK0NHgCiSR4Ao0keAKBJHgChSR4ApkkeAKf5AgCkSR4ApUkeAJqNHgCblR4AmI0eAJmFHgCeiR4An4keAJyNHgCdhR4AkgUDAJP1AACQCQMAkY05AJaxHgCXFQYAlO0AAJUBHACKvQMAi0EDAIiFAwCJnQMAjkEDAI9JAwCMyTkAjVEDAIIVAgCDHQIAgAUCAIEdAgCGzQMAh7EDAIQFAgCFxQMAs/kFALLxBQCx+QUAsOEFALeZKgC2EQMAtRkDALThBQC7NQMAujUDALklAwC4JQMAvxUDAL4VAwC9JQMAvCUDAKP9BQCi/QUAof0FAKD9BQCnnQUApp0FAKWdBQCknQUAq7kFAKqxBQCpJScAqL0FAK+ZBQCukQUArZkFAKyhBQCTAQUAkvkFAJF1OQCQ9QUAlwEFAJYZBQCVEQUAlBkFAJt5CQCaOQUAmTEFAJg5BQCfHQUAnh0FAJ0dBQCcHQUAg4kFAIKBBQCBiQUAgPEFAIeFBQCGhQUAhZUFAISBJgCLhQUAioUFAIm1BQCItQUAj4UFAI6FBQCNlQUAjJUFAM40AIA6NQCAQjUAgFY1AIB+NQCAzjUAgAI2AIBqNgCAEjcAgCo3AIBeNwCAYjcAgKY3AICqNwCAAjgAgNo4AIAeOQCANjkAgIMvAICQ6gCA5jUAgLkqAIC9KwCAfSsAgCUrAIBlKwCAkSsAgCEsAIA9LACAES0AgCEtAIA9LQCAmS0AgOQtAIDwLQCADC4AgBwuAIALLwCAEy8AgEMvAIBjLwCAky8AgKsvAICbLwCAry8AgO8vAIBHMACAUzAAgFswAICDMACACTEAgB0xAIBeMgCAVjIAgIYyAIAWNACA4jIAgBYzAIBiMwCAfjMAgKIzAIDGMwCAyjMAgOozAICAjQEAgZUBAIKdAQCDlQEAhI0BAIW1AQCGvQEAh7UBAIiNAQCJwR0AipkBAIvBHQCMhQEAjY0BAI6FAQCP/QEAkIUBAJEZHQCSkRQAk4UBAJSdAQCViTIAlk0ZAJc9GwCYsQEAmbEBAJotHACbtQEAnD0cAJ2pAQCemQEAn5kBAKDlHQChbQEAomUBAKN9AQCkZQEApW0BAKbxHQCnYQEAqKEDAKmhAwCqoQMAq6EDAKyhAwCttQEArq0DAK+lAwCwYRkAsdkDALLZAQCz7QMAtPUDALX9AwC29QMAt+0DALjFAQC50QMAumEdALvVAwC82QEAvT0XAL7FAwC/0QEA+jMAgA40AIAKNACAOjQAgLY0AIDmNACAHjUAgE41AIAyNgCAWjYAgM42AIAWNwCAIjcAgEI3AIBGNwCAUjcAgG43AIDmNwCAFjgAgEo4AIBqOACAtjgAgA45AIAqOQCAijkAgCfqAIAi6gCAVOoAgOEpAIAJKgCADSoAgNbqAIAD6wCAe+sAgBY6AIAmOgCARwgAgFIIAIBVCACASggAgE4IAIBXCQCA8Q4AgOIOAIDnDgCA9g4AgOwOAICyNACASw8AgMoPAICBDwCALw8AgFoPAIBnDwCAbw8AgJ0PAIDCDwCAuA8AgL0PAICqDwCAsQ8AgP4OAIADDwCACA8AgIBBAQCBMQMAgk0BAINFAQCEXQEAhUUBAIZNAQCHIQMAiF0fAIl9AQCKaQMAi3EBAIx1AwCNVQEAjlk6AI9ZAQCQKQEAkSkBAJI5AQCTOQEAlCkBAJUpAQCW2QEAl9kBAJjpAQCZ6QEAFQ8AgCIPAIAqDwCAMg8AgDwPAIBBDwCARg8AgFAPAIBVDwCAXQ8AgGoPAIByDwCAdw8AgHwPAICEDwCAiQ8AgJMPAICYDwCAoA8AgKUPAIDFDwCANw8AgBoPAIBiDwCAjg8AgA0PAIDdFgCA5hYAgOkWAIDvFgCA4xYAgOwWAIDgFgCAExcAgBYXAID1FgCA8hYAgPgWAICAmQcAgZkHAPsWAICDrQcAhLUHAAQXAICGsQcAh7EHAIiRBwCJkQcAipEHAIuRBwCM8QcAjfEHAI7xBwCP8QcAkJEHAJGVBwCSnQcAk5kHAJSFBwCVgQcAloEHAJeFBwCYuQcAmb0HAJq1BwCbsQcAnK0HAJ2pBwCemQcAn50HAKBhBwChZQcAom0HAKNpBwCkdQcApXEHAKZxBwCndQcAqEkHAKlNBwCqRQcAq0EHAKxdBwCtWQcArkkHAK9NBwCwMQcAsTUHALI9BwCzOQcAtCUHALUhBwC2IQcAtyUHALgZBwC5HQcAuhUHALsRBwC8DQcAvQkHAL7xAAC/9QAAgAkBAIENAQCCHQEAgxkBAITZAACF3QAAhtUAAIfRAACI8QAAifUAAIr9AACL+QAAjOkAAI3tAACO5QAAj+EAAJCdAACRmQAAkq0AAJOpAACUtQAAlbEAAJaxAACXtQAAmIkAAJmNAACahQAAm4EAAJydAACdmQAAnokAAJ+NAACgdQAAoXEAAKJ9AACjeQAApGlQAqVtUAKmYQAAp2UAAKhZAACpXQAAqlUAAKtRAACsTQAArUkAAK49AwCvOQMAsClQArEtUAIBFwCABxcAgP4WAIANFwCAChcAgBkXAIDZXFICHxcAgCUXAIAiFwCAKBcAgCsXAIA0FwCALhcAgKOhAACipQAAoZEAAKCVAACntQAAprEAAKW9AACkuQAAq40AAKqJAACpgQAAqIUAAK+FAACugQAArYkAAKyNAACz/QAAsvkAALHxAACw9QAAt5kAALadAAC1nQAAtJkAALutAAC6qQAAuaUAALilAAC/ZQEAvmEBAL1tAQC8aQEAHBcAgFcXAIBAFwCAPRcAgEgXAIBOFwCAOhcAgNksUQJLFwCAVBcAgHkWAIDhDwCAMRAAgA4QAIAiEACAHRAAgJNBAAAnEACALBAAgBMQAICXWQAAllUAAJVZAACUXQAAm3EAAJppAACZZQAAmGUAAJ9lAACeYQAAnTFTApxtAAC4gQQAuYEEALqBBAC7gQQAvIEEAFEXAIC+jQQA5g8AgLDdBQCxTQQAskUEALNdBAC0RQQAtU0EALZFBADrDwCAqKEFAKntQQCqrQUAq6UFAKy9BQCtpQUArq0FAK+lBQCgqQUAoZFBAKKpQACjoQUApKEFAKWhBQCmoQUAp6EFAP8PAIAYEACAWBAAgF0QAIBpEACAnVUFAH8QAICfWQUAjhAAgJMQAICeEACAkwUFAJQdBQCVBQUAlg0FAJcFBQC4EACAyxAAgO8QAIAhEQCAJhEAgC4RAIA9EQCATBEAgIBxBQCBcQUAgnEFAINxBQCEUQUAhVEFAIZdBQBREQCAWREAgHwRAICjEQCArxEAgM8RAIDUEQCA2REAgBMSAIAmEgCAMhIAgEoSAIDEEgCAGhMAgDMTAIA4EwCASxMAgFwTAIBuEwCAcxMAgJoTAICiEwCAtxMAgN4TAIDjEwCAPRQAgEIUAIBHFACAUxQAgF8UAIBkFACAbBQAgHgUAICSFACAlxQAgJ8UAICkFACAqRQAgK4UAICzFACAuBQAgMsUAIDQFACA7BQAgAYVAIAgFQCALBUAgEQVAIBJFQCAVhUAgHcVAICaFQCAtBUAgMAVAIDFFQCAzRUAgO4VAIAIFgCAFxYAgDQWAIA5FgCAQRYAgEYWAIBZFgCAXhYAgICtAQCBtQEAgr0BAIO1AQCErQEAhdUBAIbdAQCH1QEAiO0BAIn1AQCK/QEAi/UBAIztAQCN1QEAjt0BAI/VAQCQrQEAkbUBAJK9AQCTtQEAlK0BAJVVAwCWXQMAl1UDAJhtAwCZdQMAmn0DAJt1AwCcbQMAnVUDAJ5dAwCfVQMAoK0DAKG1AwCivQMAo7UDAKStAwCl1QMAphkOAKfZAwCobQ8AqSEOAKrhAwCr4QMArCkOAK3lAwCuGQ4ArxkOALCVAwCxnQMAsgEOALORAwC0HQ4AtQUOALa5AwC3uQMAuDkOALmNAwC6NQ4AuxEOALyBAQC9gQEAvnkBAL95AQCEFgCAkBYAgJwWAICrFgCAyBYAgM0WAIDuEQCA/xEAgHwWAICBAACAiwAAgJUAAICfAACAqQAAgLMAAID1DwCA+g8AgAQQAIB1EACAehAAgIQQAIDlEACA6hAAgBcRAIAzEQCAOBEAgEIRAIBRFQCADRYAgBIWAIAqFgCAoRYAgKYWAIC+FgCA8A8AgAkQAICJEACAHBEAgNcSAIA/FQCALxYAgGMWAIDDFgCARxEAgGQSAICfEgCAshIAgBEUAIAdFACAKRQAgI0TAICSEwCA0RMAgNYTAID9EwCAAhQAgGkSAIBuEgCAtxIAgLwSAIDCEQCAxxEAgJYRAICbEQCApD0DAKVFAwCmTQMAp0UDAKA9AwChJQMAoi0DAKMlAwCsfQMArUUDAK5NAwCvRQMAqH0DAKllAwCqbQMAq2UDALQ9AwC1xQMAts0DALfFAwCwPQMAsSUDALItAwCzJQMAvP0DAL3FAwC+zQMAv8UDALj9AwC55QMAuu0DALvlAwCEBQwAhQ0MAIYFDACHHQwAgI0MAIGpDACCGQwAg1ENAIxhDACNYQwAjmEMAI9hDACIKQwAiRUMAIodDACLFQwAlD0MAJXFAwCWzQMAl8UDAJABDACRAQwAkgEMAJMBDACc/QMAncUDAJ7NAwCfxQMAmP0DAJnlAwCa7QMAm+UDAIBpBACBaQQAgnEEAINxBACEnQQAhYUEAIaNBACHhQQAiL0EAImNBACKhQQAi50EAIyFBACNqQYAjvkEAI/5BACQiQQAkYkEAJKRBACTkQQAlLEEAJWxBACW+QYAl60EAJiVBACZwQYAmmkGAJtpBgCceQYAnXkGAJ7RBgCf/QsAoA0GAKEdCwCiGQYAo0ULAKQFBgClTQsApjUGAKe1BACoEQYAqREGAKoRBgCrNQQArC0EAK0BBACuXQQArx0GALDNBgCxbQYAsnUGALMNBgC0FQYAtR0GALYVBgC3DQYAuDUGALk9BgC6NQYAuw0GALwVBgC9HQYAvhUGAL8NBgCA9QcAgf0HAIL1BwCD9QAAhO0AAIURAwCGEQMAhxEDAIgxAwCJMQMAijEDAIsxAwCMhQcAjRUDAI4dAwCPFQMAkG0DAJGNBwCShQcAk50HAJSFBwCVjQcAloUHAJe9BwCYhQcAmY0HAJqFBwCbnQcAnIUHAJ2NBwCehQcAn4UAAKB9AAChgQMAooEDAKOBAwCkgQMApYEDAKaBAwCngQMAqBUHAKmFAwCqjQMAq4UDAKydAwCtoQMArqEDAK+hAwCwdQcAsXUHALJxBwCzhQUAtM0FALX1BQC2/QUAt8kDALj5AwC5+QMAuqEFALuhBQC8wQMAvcUDAN4RAIDjEQCAhJz7ACYTAIArEwCAYRMAgGYTAIB2EgCAghIAgJUSAICaEgCARRIAgNwSAIBXEwCASxAAgKMQAIC9EACAxBAAgJB1AACRfQAAknEAAJNxAACUAfwAlVX+AJZd/gCXVf4AmG3+AJlp/gCaef4Am3n+AJxp/gCdaf4Anln+AJ9Z/gCgpf4Aoa3+AKKl/gCjof4ApKH+AKWl/gCmrf4Ap6X+AKiZ/gCpmf4Aqun+AKvt/gCs9f4ArfH+AK7x/gCv8f4AsI3+ALGV/gCymf4As5n+ALSJ/gC1if4Atrn+ALe9/gC4hf4AuY3+ALqF/gC7nf4AvIX+AL2B/gC+gf4Av4H+AKbZCACnBQcApMEIAKWZBQCi0QgAo9EIAKCJBQChtQgArgEHAK8BBwCsMQcArTEHAKo9BwCrJQcAqD0HAKk1BwC2fQcAtwUHALR9BwC1dQcAsskFALNlBwCwcQcAsXEHAL4BBwC/AQcAvDEHAL0xBwC6IQcAuyEHALg9BwC5MQcAhjkHAIc5BwCELQcAhTkHAIINBwCDNQcAgBEHAIEFBwCOSQcAj0kHAIxNBwCN1QUAisEFAIvBBQCI1QUAiXEHAJbVBQCX2QgAlE0FAJXdBQCSUQUAk9kFAJD5BQCRoQUAnnEIAJ99CACcYQgAnWEIAJpxCACbeQUAmMUIAJl1BQD0EACA+xAAgAIRAICBEQCAuxEAgLQRAIArEgCAGBIAgB8SAIBWEgCATxIAgF0SAIDJEgCAHxMAgIcSAIB7EgCApBIAgKsSAIA9EwCAUBMAgHgTAIB/EwCAhhMAgKcTAIC8EwCAwxMAgOgTAID2EwCA7xMAgEwUAIB9FACAhBQAgAsVAIAZFQCAEhUAgPEUAIAlFQCAMRUAgHwVAICDFQCAkxUAgFsVAIBpFQCAnxUAgKYVAIBiFQCASxYAgFIWAIDzFQCA+hUAgNkVAIDgFQCAIxYAgBwWAICwFgCAbhAAgLEQAICqEACA3hAAgNcQAIAQEQCACREAgI8RAIBeEQCAgIEBAIGBAQCCgQEAg4EBAISdAQCFhQEAhokBAIeJAQCItQEAib0BAIq1AQCLjQEAjJUBAI2dAQCOlQEAj40BAIgRAIA3EgCAkv0BAJP1AQCU7QEAlZUBAJadAQCXlQEAmKkBAJmpAQCauQEAm7kBAJypAQCdrQEAnqUBAJ+dAQCgZQEAoW0BAKJlAQCjfQEApGUBAKVtAQCmZQEAp90AAKjlAACppQMAqq0DAKulAwCsvQMAraUDAK6tAwCvpQMAsN0DALHlAwCy7QMAs+UDALSpAQC1VQEAtvUDALftAwC41QMAud0DALrVAwC7rQMAvM0DAL3BAwC+vQMAv7UDANASAICOEgCARBMAgP8UAIA4FQCAlRYAgIkWAIC3FgCAuRUAgIsUAIABFgCAyhMAgMQUAIDSFQCArRUAgPgUAIC9FACAZREAgKgRAIBwFQCA0BAAgFgUAIBiEACAPhIAgOcVAIATEwCAcRQAgEIQAIA5EACAihUAgOESAID2EQCArhMAgGsWAIDqEgCA8RIAgGwRAIAEEgCApgMAgA0jAIARIwCAoAYAgMcAAIC1BgCAqyMAgK8jAIC5IQCAtSEAgOMHAIB7CQCAfwkAgEEjAICnIwCANSMAgDkjAIAdIwCAISMAgCUjAIApIwCALSMAgDEjAIDbBwCA3wcAgNEAAICATQEAgVEBAIJRAQCDTQEAhE0DAIUhAwCGRQEAh30BANcAAICiAwCAqAMAgN0HAIDTAACA1QAAgL0GAIB5AACABxQAgH0AAICHAACAkQAAgAwUAICbAACAGBQAgKUAAIAkFACArwAAgDAUAIC5AACANRQAgM8PAIBVEACAmBAAgJsQAIArEQCAVhEAgKARAIDMEQCA6BEAgOsRAIDzEQCADRIAgBASAIBzEgCAwRIAgDATAIBrEwCAlxMAgJ8TAICwpQEAsa0BALKlAQCzvQEAtKUBALWtAQC2pQEAt10BALhlAQC5bQEAumUBALt9AQC8ZQEA2xMAgDoUAIBpFACAgAW5AIHhBgCC4QYAg+EGAIThBgCoBgCAswYAgIfpBgCI2QYAifmxAIr1sQCL8bEAjO2xAI31BgCO+QYAj/0GAJDZBgCR2QYAkvWxAJwUAICUiZIClfEGAJb1BgCX9QYAmNkGAJnVsgCa3bIAm6kGAJy5BgCduQYAnqkGAJ+BBgCgoQcAoaEHAKIhsgCjpQcApIUAAKWNAACmQbMA1RQAgKiNBwCplQcAqp0HAKuVBwBOFQCAyhUAgDYQAIA+FgCAsP0HALGFBwCyjQcAaBYAgLSZBwCBFgCAtpUHALeNBwC4tQcAub0HALq1BwC7jQcAvJUHAL2dBwC+lQcAv40HAIB1BgCBlaACgpmgAoOZoAKEhaAChb2gAoaxoAKHhaACiLmgAomRoAKKnaACi5mgAoyFoAKNjQEAjoEBAI9FBgCQOQYAkT0GAJIxBgCTMQYAlC0GAJXVBgCW2QYAl90GAJjhBgCZ4QYAmu0GAJvpBgCc9QYAnf0GAJ7xBgCf9QYAoAkGAKEJBgCiBQYAowEGAKQdBgClBQYApgkGAKcNBgCoMQYAqTEGAKo9BgCrNQYArCkGAK0pBgCuJQYArx0GALBhBgCxYQYAsm0GALNpBgC0dQYAtX0GALZxBgC3dQYAuEkGALlJBgC6RQYAu0EGALxdBgC9RQYAvkkGAL9NBgCAsQUAgbEFAIK9BQCDuQUAhKUFAIWtBQCGoQUAh6UFAIiZBQCJmQUAipUFAIuRBQCMjQUAjcEFAI7NBQCPyQUAkLUFAJG9BQCSsQUAk7UFAJSpBQCVqQUAlqUFAJehBQCYnQUAmSkCAJolAgCbIQIAnD0CAJ3pAgCe5QIAn+ECAKAdAgChNQIAojkCAKM9AgCkIQIApSECAKYtAgCnKQIAqBUCAKkZAgCqFQIAqxECAKwNAgCteQIArnUCAK8V8ACwafAAsRECALIdAgCzGQIAtAUCALUhAAC2LQAAtyUAALgZAAC54QEAuu0BALvlAQC8+QEA2BQAgN0UAIC/9YYCp2kNAOIUAIDnFACAzwAAgNkAAICzAwCA4QcAgH0JAID7IgCAzNSFAszghQL/IgCAgSkAgDUkAIBuJACAjSQAgLyZBQC9mQUAvqkFAL+ZvAC4mQUAuZkFALqJBQC7iQUAtKEFALXVsQC23bEAt6kFALCxsgCxzQUAssUFALO9BQCfJACAxCQAgMMoAIDfKACA8SgAgIgmAICFKQCAaSkAgCkkAIAtJACA2WSgAoEJAIDZUKAChAkAgI0JAICKCQCAhwkAgOwhAIDvIgCA9CEAgJhlBQCZEbIA/CEAgNkwoAKUOZEClU0FAJZFBQCXXQUAkGkFAJFpBQCSWQUAk1kFAID9vACB1ZwCgmW8AIPFvACEkbwAhZ28AIalvACHjbwAiK2TAonlvACKKZACi7W8AIwRkAKNlbwAji2wAI/FnAKQ6bwAkcHIAJJBkAKT8Z0ClNW8AJXlvACW4bwAl02QAphlkAKZfZACmrm8AJupCgCcbQ8Anb0KAPMiAICfXQ8AoK0PAKElCgCibQoAo2UKAKQNCgClpQ8ApgXUAKepDwComQ8AqZkPAKopDwCrKQ8ArDkPAK05DwCuKQ8ArykPALBZDwCxndEAspXRALOF1gC0sdEAtbHRALbZ1AC32dQAuOnUALnp1AC6+dQAu/nUALzp1AC96dQAvrnUAL+51ACASdUAgUnVAIJZ1QCDWdUAhEnVAIV90ACGddAAh23QAIhV0ACJXdAAinXVAIut1QCMtdUAjb3VAI611QCPQdAAkMHQAJHB0ACSwdAAk8HQAJTB0ACVwdAAlsHQAJfB0ACYwdAAmc3QAJrF0ACb3dAAnOHVAJ3pDgCe2Q4An9kOAKDV2wChwdkAotnZAKPB2QCkxdkApc3ZAKbF2QCnGdkAqGHZAKlh2QCqydkAq8nZAKzZ2QCt2dkArs3ZAK/B2QCwCdkAsRXZALId2QCzrdoAtB3ZALWx2gC2wdwAt93dALjl3QC59d0Auv3dALut3QC8td0AvaXdAL6t3QDwIQCAgvHaAIPx2gD3IgCA5OgAgIYR2ACHEdgAhOHaAIXh2gCKKdgAiynYAK9AEwClKNoAjinYAI8p2ACMKdgAjSnYAJJh2ACTYdgA6egAgO7oAICWZdgAl23YAJR12ACVbdgAml3YAJst2ADz6ACA8FwCALEw3wCR8AIAnCnYALLQAwCiOQ0Ao1GeAqAlDQChOQ0AplUNAIS8AgCkJQ0ApV0NAKptDQCrAQQAqGENAKlRAwCuuQAAp3UAAKxhDQCtxQIA+OgAgIfMAwDwVAIAzFC6AJHYBACb9NsAkRgCAJk02wCddAQAvh0AAJ9gBQCejAUAjOwCAI2sBAD96ACAvfWKAqghvwCpLb8Aqi2/AKs9vwCsKb8ArVW/AK5RvwCvTb8AoBkIAKGlvQCiIb8AozGzAKQ9vwClJb8Apg2zAKclvwC46bMAuc3LALppswC7uQkAvH0IAL2tCQC+QQwAv50JALA5vwCxhb0Asgm/ALPtywC0Gb8AtQW/ALbtswC3Bb8AiDG9AIkxvQCKrQgAiyW9AIwJCQCNvQgAjiW+AI+JDAAC6QCAgQ0JAIKlDACDUQkAhIEIAIWBCACGmQgAh60MAJhhvQCZYb0Amm0JAJsVnQKcxQ8AnQ28AJ7BDwCfcQkAkBW+AJERnwKSNZ8Ckw2fApQJvgCVCb4AlnG9AJdxvQCCuAQAl6UHALnEAwDwWAIAkUwCAJLIAgCErAQAsD0AAAzpAIAH6QCAvQUAABHpAIDwTAIAuhEAAJEkAgCN5AQAkqwCAJasAgC4uAMAudADAJb4AgCvDQAAFukAgPB4AgCRXAIAlrACAK8FAAAb6QCAIOkAgCnpAIAy6QCAP+kAgIX4AwBM6QCAh4ADAIbAAgBZ6QCAZukAgHPpAICW6QCAuzkAAHzpAICf6QCAiekAgL8dAAC+HQAAvR0AALwhAACVwB0AlMQfAJfIGgCWABgAkSAAAJDUAQCT2B4AkgAcAJ3gEgCcABAAn+gRAJ7sEwCZ8BkAmPQbAJv4FwCaABQAnnEBAJ9xAQCABQAArOkAgM0KAICwDACAXg0AgGQNAIBqDQCAdg0AgHkNAIB8DQCAfw0AgIINAICRDQCAlw0AgJoNAICdDQCAICIAgMcNAIDWDQCA/A0AgP8NAIAODgCAEQ4AgB0OAIAYIgCAMg4AgDUOAIDXFgCAEBcAgNoWAIC4ACwAuYwvALqILgC6AwCAhpwXAMx4vACEmC0AhVwXALcDAIDKAwCAiAAoAIksFADtBACAjAUAgN8FAIAaBgCAQAYAgFcGAIB0BgCAiwYAgDgBAIA8AQCAQAEAgEQBAIBIAQCATAEAgKR9AQBQAQCAonUBAKNlAQCggQEAoYEBALxxugC9kbYAvnG6AL+ltgC48bgAuXW6ALqZzgC7dboAtGG6ALVtugC2eboAt3W6ALAZugCxEboAsgm6ALMFugCsUboArXG2AK5RugCvbboAqNG4AKldugCqRbYAq1G6AKRxlgKlYZYCpnGWAqe9ugCgzZsCofG6AKLJugCjxboAnHmaAp0tugCeDc4An4WWApgJugCZtZYCmjm6AJuJtgCUMboA+CEAgJZpugCXrZYCkHm6AJE1ugCSMboAkwG6AIxJzgCN5bYAjhmaAo+hugCIoboAiUG2AIqhugCLdbYAhAG4AIWFugCGac4Ah4W6AICxugCBvboAgqm6AIOlugCAgbkAgQ27AIIVtwCDAbsAhAG7AIUhtwCGAbsAhz27AIgJuwCJAbsAihm7AIsVuwCMcbsAjX27AI5puwCPZbsAkKG5AJEluwCSyc8AkyW7AJQhuwCVwbcAliG7AJf1twCY6c8AmUW3AJq5mwKbAbsAnLm7AJ31uwCe8bsAn8G7AKARuwChCZQCokm7AKONlwKkCbsApbWXAqY5uwCnibcAqFmbAqkNuwCqLc8Aq6WXAqwNmgKtMbsArgm7AK8FuwCw0ZcCscGXArLRlwKzHbsAtFG5ALXduwC2xbcAt9G7ALjxuwC50bcAuvG7ALvNuwC82bsAvdG7AL7JuwC/xbsAgJmkAIEliAKCqaQAgxmoAFsNAICFvaQAhp3QAIcViAKInYUCiaGkAIqZpACLlaQAjCGIAo0xiAKOIYgCj+2kAJDBpgCRTaQAklWoAJNBpACUQaQAlWGoAJZBpACXfaQAmEmkAJlBpACaWaQAm1WkAJwxpACdPaQAnimkAJ8lpACgYaYAoeWkAKIJ0ACj5aQApOGkAKUBqACm4aQApzWoAKgp0ACphagAqnmEAqvBpACseaQArTWkAK4xpACvAaQAsFGkALFJiwKyCaQAs82IArRJpAC19YgCtnmkALfJqAC4GYQCuU2kALpt0AC75YgCvE2FAr1xpAC+SaQAv0WkAIARiQKBAYkCghGJAoPdpQCEkacAhR2lAFQBAICHEaUAiDGlAIkRqQCKMaUAWAEAgFwBAICNEaUAjgmlAI8FpQCQAaUAkQ2lAJIZpQCTFaUAlLGnAGABAICW2dEAlzWlAJgRpQCZ8akAmhGlAJvFqQCc+dEAZAEAgJ6phQKfEaUAoEmlAKEFpQCiAaUAozGlAKQBpQClGYoCplmlAKediQKoOaUAqYWJAqoJpQCruakArEmFAq0dpQCuPdEAr7WJArB9hAKxQaUAsnmlALN1pQC0wYkCtdGJArbBiQK3DaUAuGGnALntpQBoAQCAu+GlALzhpQC9wakAvuGlAGwBAIC3baYAttWGArUpqgC0hdIAs7mqALJtpgCxjaoAsG2mAL8higK+5aYAvaWJAnABAIC7jaYAdAEAgLm5pgC49aYAeAEAgKZ1pgClbaYAfAEAgIABAICiTaYAhAEAgIgBAICvCaYAruXSAIwBAICsjaQAqymmAKolpgCpMaYAkAEAgJc5pgCWNaYAlQ2mAJQxhwKTmYoCkhHSAJExpgCQZYYCn62mAJ65qgCUAQCAnC2kAJthpgCarYoCmb2KApitigKHfaYAhk2mAIVJpgCEBaYAg72mAIIFhgKB+aoAgFXSAI/1qgCORaYAjcmKAox1pgCL8YoCijWmAIl1iQKIbaYAgCmnAIEhpwCCOacAgzWnAIRRpwCYAQCAhkmnAJwBAIDMSIkCzYiJAoqp0wCLRacAjEGnAI2hqwCOQacAj5WrAJDJ0wBFIwCAkpmHApMhpwCUmacAldWnAJbRpwCX4acAmPGnAJnpiAKaqacAm22LApzppwCdVYsCntmnAJ9pqwCgeYcCoS2nAKIN0wCjhYsCpC2GAqURpwCmKacApyWnAKixiwKpoYsCqrGLAqt9pwCsMaUArb2nAK6lqwCvsacAsNGnALHxqwCy0acAs+2nALT5pwC18acAtumnALflpwC4oacAua2nALq5pwC7tacAvBGlAL2VpwC+edMAv5WnAICRoACBiY8CgsmgAIMNjAKEiaAAhTWMAoa5oACHCawAiNmAAomNoACKrdQAiyWMAoyNgQKNsaAAjomgAI+FoACQUYwCkUGMApJRjAKTnaAAlNGiAJVdoACWRawAl1GgAJhxoACZUawAmnGgAJtNoACcWaAAnVGgAJ5JoACfRaAAoMGgAKHNoACi2aAAo9WgAKRxogCl9aAAphnUAKf1oACo0aAAqTGsAKrRoACrBawArDnUAK2VrACuaYACr9GgALAJoACxRaAAskGgALNxoAC0QaAAtVmPArYZoAC33YwCuHmgALnFjAK6SaAAu/msALwJgAK9XaAAvn3UAL/1jAKAvYACgYGhAIK5oQCDtaEAhAGNAoURjQKGAY0Ch82hAIihowCJLaEAijWtAIshoQCMIaEAjQGtAI4hoQCPHaEAkGmhAJFhoQCSeaEAk3WhAJQRoQCVHaEAlgmhAJcFoQCYgaMAmQWhAJrp1QCbBaEAnAGhAJ3hrQCeAaEAn9WtAKAJ1QChpa0AolmBAqPhoQCkWaEApRWhAKYRoQCnIaEAqDGhAKkpjgKqaaEAq62NAqwpoQCtlY0CrhmhAK+prQCwOYECsW2hALJN1QCzxY0CtG2AArVRoQC2aaEAt2WhALjxjQK54Y0CuvGNArs9oQC8caMAvf2hAL7lrQC/8aEAs2miALKF1gCxaaIAsO2gALe5rgC2baIAtY2uALRtogC7TaIAuvWCArkJrgC4pdYAv42iAL69ogC9uaIAvPWiAKNNogCiWa4AoUGiAKDNoACncaIApk2iAKVtrgCkTaIAq1miAKpVogCpTaIAqEWiAK8pogCuJaIArTGiAKw9ogCTla4AkiWiAJGpjgKQFaIAl5mOApYR1gCVMaIAlGWCApsZogCaFaIAmS2iAJgRgwKfYaIAnq2OAp29jgKcrY4Cg2muAIK9ogCBXa4AgL2iAIe9ogCGBYIChfmuAIRV1gCLXaIAim2iAIlpogCIJaIAj/GOAo41ogCNdY0CjG2iAIARowCBMa8AghGjAIMtowCEOaMAhTGjAIYpowCHJaMAiGGjAIltowCKeaMAi3WjAIzRoQCNVaMAjrnXAI9VowCQMaMAkdGvAJIxowCT5a8AlNnXAJV1rwCWiYMClzGjAJipowCZ5aMAmuGjAJvRowCc4aMAnfmMAp65owCffY8CoBmjAKGljwKiKaMAo5mvAKRpgwKlPaMAph3XAKeVjwKoHYICqSGjAKoZowCrFaMArKGPAq2xjwKuoY8Cr22jALBBoQCxzaMAstWvALPBowC0waMAteGvALbBowC3/aMAuMmjALnBowC62aMAu9WjALyxowC9vaMAvqmjAL+lowBnDQCA0QYAgG0NAIDIBwCAcw0AgA8HAICFDQCAlAcAgIsNAICaBwCAuA0AgH0HAIDKDQCAxQcAgAIOAIBPBwCAFA4AgFIHAIAgDgCAkB0AAOEGAIAPJACA4iUAgCguAICtLACAyS0AgKpVAACrKQAAMjcAgAErAIDGMACAsjIAgAEsAIBTLwCAmSsAgJ8wAIDtKwCAGjUAgI43AICtLQCA5SwAgGYyAIADMACALzAAgA44AIAjMACA+y8AgHI0AICAIa4AgaWsAIJJ2ACDpawAhKGsAIVBoACGoawAh3WgAIhp2ACJxaAAiv0AAIsxxgCM7QAAjdEAAI7VAACPyQAAgCmhAIFNFACCIQEAg+G4AoQ5qgCFOaoAhhG9AodRFACIEQEAidW4AorNrQCLLbsCjGEUAI3ZjQKObRQAj2UUAJB5AQCRubgCkkm9ApNFuwKUDRQAlTUUAJYZAQCXqbgCmF2qAJkBFACaIQEAmwUUAJx5vQKdhbgCnnm7Ap+JuAKggb0CoXm4AqKZCQCjlRQApFmuAKWJFACmmQEAp70UAKipAQCpvbsCqrkBAKuJFACsmRQArZkUAK6JFACviRQAsNkBALEJrgCy6QEAs9W7ArTNuwK17RQAtpW8ArfhFAC4oRQAuaEUALrBoQC7pRQAvNkBAL0ZuAK+0aoAv9GqAL9FFwC+RRcAvTUXALxBvwK7KRcAugm4ArkBuAK4PQIAt+2tALY9AgC1HRcAtB0XALMdFwCyHRcAsR0XALAtAgCvWbgCrk0CAK1pFwCsTQIAq00XAKqdrQCpQRcAqE0KAK40AIDRLACApX0XAKR9FwCjoa4Aom2CAqF9ggKgbYICnzmuAJ41rgCdDa4AnDGPApuZggKaEdoAmTGuAJhljgKXtaIAlgWuAJWJggKUNa4Ak7GCApJ1rgCRNYECkC2uAI99rgCOTa4AjUmuAIwFrgCLva4AigWOAon5ogCIVdoAh0miAIadrgCFfaIAhJ2uAIOZrgCCddoAgZmuAIAdrADMqIQCzUyGAswguQLNTLkCzECOAkYyAIDMmIUCzTyEAswQgwLNUIMCzKCDAs2MgwLMMIACzSSAAswYgALNhIACmjMAgAUsAIAxLQCAiSMAgE0jAIBXIwCAayMAgJMjAIB1IwCAnSMAgGEjAIB/IwCAzPC5As2EuQLMULgCzay7AoDNAACB1QAAgt0AAIPVAACEzQAAhfUAAIb9AACH9QAAiM0AAFcvAIDBLACA1SoAgM0qAIDdKgCAuekAgCErAICQZQAAkW0AAKiIKgA1KwCAPSsAgEUrAIBJKwCATSsAgKIAMACjzDMAoOg9AKHsPACm8DYAp/QoAKQANACl/DUAgFERAIHpiAKCXREAg1URAIQpBACF6b0Chhm4AocVvgKIfREAiUURAIppBACL2b0CjA2vAI1REQCOcQQAj1URAJBJuAKRtb0Ckkm+ApO5vQKUUbgClam9ApZJDACXRREAmKmrAJl5EQCaaQQAm00RAJx5BACdbb4CnmkEAJ9ZEQCgqREAoakRAKK5EQCjuREApIkEAKVZqwCmuQQAp4W+Aqi9vgKpnREAquW5AquREQCs8REArfERAK6RpACv9REAsOkEALEpvQKy4a8As+GvALTZuAK1mREAtukEALctvQK4BagAueW+Arq5EQC7AYgCvKURAL2tEQC+wQQAvwG9AoABuQKBDb8CglUQAINtEACEUQUAheG8AoYlrgCHeRAAiGkFAIlNEACKIbkCi928AowxvwKNwbwCjjm5Ao/BvAKQUQ0AkV0QAJKBqgCTURAAlFEFAJV1EACWUQUAl0W/AphxBQCZQRAAmkEQAJtBEACcQRAAnUEQAJ5hBQCfsaoAoKEFAKGdvwKilb8Co7UQAKTduAKlqRAAptkQAKfZEACoiaUAqe0QAKqBBQCrQbwCrJmuAK2ZrgCusbkCr/EQALDxBQCxNbwCsi2pALPNvwK0gRAAtTmJAraNEAC3hRAAuNkFALkZvAK66bkCu+W/ArytEAC9lRAAvrkFAL8JvAK5La0AuC2tALtFEwC6BboCveG/ArwlBgC/GbwCvvmqALEdEwCwabsCs20TALJtEwC1eRMAtB2mALfVvwK2FQYAqXUTAKh1EwCrhakAqlUGAK1JvAKsdQYAr2ETAK5BvAKhQRMAoGUGAKNxvAKiZQYApVUTAKRlBgCnVRMAplUTAJl1vwKYhbwCm3W/ApqNugKdiRMAnIUOAJ+FEwCeVakAkVW/ApDlBgCTzRMAkpGtAJXZEwCU/QYAl0m/Apa1ugKJmRMAiJETAIs1vwKK9QYAjdm8AozVugKPuRMAjoETAIGtEwCA7boCgxm/AoLdBgCF8bwChBGqAIcVigKGrRMAgD2sAIFhEgCCQQcAg2USAIQZuwKF5b4Chhm9AofpvgKIIbsCidm+AopFEgCLXRIAjSkAgM3pAICOzaoAj8mLApCdiwKRpYsCkrGqAJOxqgCU2akAldmpAJb5qQCX+akAmJWqAJmRiwKatYsCm42LApyJqgCdiaoAnvGpAJ/xqQCgIakAoSGpAKJ9qgCjeYsCpE2LAqV1iwKmYaoAp2GqAKgpqQCpKakAqgmpAKsJqQCsRaoArUGLAq5liwKvXYsCsDmqALE5qgCyQakAs0GpALRxqQC1cakAti2qALcpiwK4PYsCuQWLAroRqgC7EaoAvHmpAL15qQC+WakAv1mpAIKJIwBtKwCAcSsAgI0rAIC+6QCAh5kjAJEpAIB5KwCAyOkAgIu5JACpKwCAifkkAI6VIwCPiSMAsSsAgI2JJACSvSMAESsAgLkrAICR4SMAo+sAgJfFIwCU8SMA4SsAgJkpAICbkSMA+SsAgJndIwD9KwCAnwktAAksAICdjdUAogkjAJ0pAIBBLACAofUjAEUsAICnGSMApCUkAG0sAICq7SQAeSwAgKgdIwCpeSQArhUjAK8JIwCsCSQArQkkALI9IwCJLACAsDEjALFhIwC2VSMAt0UjALRxIwC1XSMAulkjALsRIwCRLACAuV0jAL6JLQCVLACAvI0tANzpAICAuSUAgX0iAIKBIgCDmSIAhK0lAIXZJQCGuSIAh5EiAIiVIgCJ8SUAljIAgIuxJQCMgSUAjYElAI6dIgCPgSIAkLkiAJHpIgCStSIAk9EiAJT5IgCV1SIAlt0iAJfNIgCY+SIAmdUiAJrRIgCbmSIAqSwAgLEsAIDh6QCAvSwAgGUAAACh/SIAogEiAKMZIgDFLACApVklAKY5IgCnESIAqBUiAKlxJQDNLACAqzElAKwBJQCtASUArh0iAK8BIgCwOSIAsWkiALI1IgCzUSIAtHkiALVVIgC2XSIAt00iALh5IgC5VSIAulEiALsZIgD1LACA4SwAgO0sAIDxLACAgI0vAIGlLwCCrS8Ag70vAISlLwCFrS8AhqUvAIfdLwCI5S8Aie0vAIrlLwD5LACAAS0AgAUtAIANLQCAFS0AgJCRLwCRkS8AkpEvAJORLwCUsS8AlbEvAJa1LwCXRTMAmE0zAJlVMwCaPTMAmxkzAJyZMwCdiTMAnlUwAJ9JMACgwTAAockwAKLZMACj1TAApM0wAKX9MACm5TAApzUwAKi1MQCpuTEAqu0xAKuxmgCs0ZYArbE6AK61OgAZLQCAsEGUALHNlgCy1ZoAs8GWALTBlgC14ZoAtsGWALf9lgC4yZYAucGWALrZlgC71ZYAvLGWAL29lgC+qZYAv6WWAMUAAAChfSAAooEgACktAICkrScALS0AgDktAICnkSAAXS0AgKnxJwCqZScAq7EnAKyBJwCtgScArp0gAK+BIACwuSAAsekgALK1IABhLQCAtPkgALXVIAC23SAAt80gAEUtAIC51SAATS0AgLuZIACpLQCAcS0AgHUtAIB5LQCAgDknAIH9IACCASAAgxkgAG0tAICFWScAhjkgAIcRIACIFSAAiXEnAIrlJwCLMScAjAEnAI0BJwCOHSAAjwEgAJA5IACRaSAAkjUgAJNRIACUeSAAlVUgAJZdIACXTSAAmHkgAJlVIACaUSAAmxkgAJyFLgCdBdYAnoEuAJ+BLgCArT8AgbU/AIK9PwCDtT8AhK0/AIW5yACG1T8Ah80/AIj1PwCJ/T8AipnIAIvxPwCMATsAjQE7AI6NyACPOQQAkEkEAJFJBACSWQQAk1UEAJRNBACV3TwAlnkEAJd1BACYWQQAmSEEAJohBACbNdQAnCEEAJ3Z5gCeJQQAnx0EAKDpBACh9QQAos0/AKP1BACkFQQApfnUAKYhyACnIcgAqNHUAKktBACqOQQAq03CAKwtBACtdcgArh0EAK95BACwKQQAsTEEALI9BACzOQQAtC0EALX9BQC2qQUAt6kFALiZBQC5mQUAunkFALtFBQC8AQUAvQEFAL4BBQC/AQUAgC0HAIE1BwCCPQcAgzUHAIQtBwCFqQcAhqUHAIdl1QCILQYAiTEGAIoxBgCLDQYAjPnJAI15BgCOWQYAj1UGAJBpyQCRNQYAkj0GAJM1BgCULQYAlcUGAJZdAwCXVQMAmG0DAJl1AwCafQMAm3UDAJxtAwCdET0AnlkDAJ9ZAwCgqQMAoakDAKK5AwCjuQMApKkDAKWpAwCm2QMAp9kDAKjpAwCp6QMAqvkDAKv9AwCs5QMAre0DAK7lAwCvbcMAsKEDALGhAwCyoQMAs6EDALShAwC1zeYAtq0DALelAwC4yeYAuZkDALppAwC7aQMAvHkDAL15AwC+aQMAv2kDAIAAAACBLQCAfS0AgJUtAIDm6QCAsS0AgLUtAIC9LQCA0S0AgPQtAIDr6QCA8OkAgAAuAIAELgCACC4AgPwtAIAQLgCAoSkAgKUpAIAYLgCAIC4AgPXpAIA8LgCAQC4AgEwuAID66QCAVC4AgFguAIA3LwCAqSkAgGwuAICILgCAhC4AgATqAICQLgCACeoAgJwuAICYLgCAoC4AgLAuAIC0LgCArSkAgMQuAIDMLgCA0C4AgNQuAICxKQCADuoAgLUpAID3LgCA+y4AgP8uAIDV6wCAGOoAgNo1AIAvLwCAuSkAgDvqAIAN6wCAPy8AgEcvAIC9KQCAWy8AgGsvAICqIfQAq7U/AKilPwCpzecArkXwAK+hPwCsSfAArTH0AKJl4gCjvT8AoLk/AKG5PwCmlT8Ap50/AKSlPwClnT8Augk8AG8vAIC4CTwAuQk8AHcvAICHLwCAxSkAgMEpAICy3T8AswU9ALBN7wCx1T8Atn3wALe55AC0HT0AtWk8AB3qAICPLwCAoy8AgKcvAIC3LwCAyy8AgMMvAIDHLwCAgrX7AM8vAICA/T8AgfU/AOMvAIDnLwCA/y8AgAcwAICavT8Am/3NAJi9PwCZtT8Anlk/AJ9ZPwCcWT8AnVk/AJKBPwCTaekAkHnkAJGxPwCWgT8Al4H0AJQh5wCVmT8AFzAAgCswAIAs6gCAJzAAgBswAIAzMACAOzAAgE8wAIAx6gCAVzAAgEoAAABLMACAQzAAgMkpAIBfMACAZzAAgG8wAIBjMACAzSkAgIcwAIA26gCAszAAgPUwAIDRMACA2SkAgNUpAIDRKQCAnSsAgKErAID5MACA4TAAgK41AIA9KgCADTEAgCExAIAZMQCAT+oAgN0pAIA1MQCAKTEAgFIxAIBZ6gCAXjEAgD0xAIBmMQCAajEAgG4xAIByMQCAfjEAgF7qAICGMQCA5SkAgJIxAIBj6gCAljEAgOkpAICiMQCArjEAgL4xAIBo6gCA/+kAgG3qAIDeMQCAcuoAgLgJAQC5CQEAuhkBALsZAQC8CQEAvQkBAL45AQC/OQEAsM3FALE1zACymQ4As5kOALSJDgC1iQ4AtjkBALc5AQCo6dkAqckOAKrZDgCrqcUArMUOAK3NDgCuxQ4Ar/kOAKA1DgChPQ4AojUOAKOxxQCk8Q4ApfEOAKbxDgCn8Q4AmGkPAJlpDwCaeQ8Am3kPAJxpDwCdaQ8Ant0OAJ/NDgCQ+eoAkXEPAJJ9DwCTdQ8AlG0PAJVpDwCWWQ8Al1kPAIh5DwCJeQ8AigkPAIsJDwCMGQ8AjRkPAI4NzACPDQ8AgHkPAIF5DwCCSQ8Ag0kPAIRZDwCFWQ8AhkkPAIdJDwCKUQIAi1ECAIj5xgCJQQIAjnECAI/txgCMQQIAjUECAIIVAgCDHQIAgAUCAIEdAgCGdQIAh30CAIQFAgCFfQIAmsUCAJvNAgCYkc8AmYXaAJ7FAgCfzQIAnNUCAJ3NAgCSDQIAkxUCAJANAgCRBQIAlg0CAJf1AgCUDQIAlQUCAKo9AgCrRQIAqD0CAKk1AgCuXQIAr0UCAKxdAgCtVQIAol3GAKMBAgCgNQIAoQ0CAKYBAgCnxdgApBECAKURAgC6OQIAuzkCALg5AgC5OQIAvtkBAL/ZAQC82QEAvdkBALI9AgCzBQIAsD0CALE1AgC2GQIAtxkCALQdAgC16cIA6jEAgPIxAIDiMQCA/jEAgA4yAIAWMgCAIjIAgCYyAIB36gCACjIAgD4yAIBCMgCA7SkAgFIyAIB86gCANjIAgHIyAICB6gCAhuoAgHYyAICKMgCAgjIAgPEpAICOMgCAnjIAgJoyAICmMgCAw+kAgLYyAICL6gCAwjIAgJXqAIDWMgCA9jIAgJrqAIAKMwCADjMAgJ/qAICk6gCAKjMAgDozAID1KQCAPjMAgPkpAIBWMwCAWjMAgGYzAIByMwCA/SkAgIozAICp6gCApjMAgK7qAIAT6gCAwjMAgLPqAIC4AAAAuOoAgL3qAIABKgCABSoAgMfqAIDC6gCAzOoAgIAB3gCB8QcAgvEHAIPxBwCEFQIAhR0CAIYVAgCHEQIAiCXeAIld3gCKOQIAizkCAIwpAgCNKQIAjhkCAI99ygCQTd4AkWECAJJhAgCT7cEAlH0CAJVlAgCWIcAAl2kCAJhZAgCZMcIAmlUCAJstAgCcNQIAnT0CAJ4xAgCfMQIAoNECAKHRAgCi0QIAo9ECAKTxAgCl8QIApvECAKfxAgCo0QIAqdECAKrRAgCr0QIArDECAK0xAgCuMQIArzECALBRAgCxUQIAslECALNRAgC0cQIAtXECALZxAgC3cQIAuFECALlRAgC6+dwAu1UCALxNAgC9NQIAvj0CAL81AgC+7QYAv/UGALztBgC95QYAuskGALvJBgC4xcsAuckGALbtBgC39QYAtO0GALXlBgCyjQYAs/UGALDR3QCxhQYArvEGAK/xBgCs5QYAreEGAKr1BgCr/QYAqMUGAKn9BgCm9QYAp/0GAKTlBgCl/QYAovUGAKP9BgCg+QYAoZ3dAJ75BgCf+QYAnPkGAJ35BgCa+QYAm/kGAJj5BgCZ+QYAlvkGAJf5BgCUcd0AlfkGAJL9BgCT5QYAkP0GAJH1BgCO/QYAj4UGAIz9BgCN9QYAiuEGAIsB3QCI8QYAifEGAIbBBgCHwQYAhPEGAIXxBgCCkccAg+EGAIDpBgCBxcAAgAAAANHqAIACNACABjQAgBI0AIARKgCAFSoAgNvqAIAmNACAGSoAgODqAIDl6gCA6uoAgJY0AIAdKgCAojQAgKY0AIDv6gCA9OoAgL40AIAhKgCA+eoAgNI0AIDWNACAJSoAgP7qAIDyNACAKSoAgAI1AID6NACACjUAgAjrAIAiNQCALSoAgC41AIA2NQCARjUAgDEqAIAS6wCAF+sAgDUqAIAc6wCAXjUAgCHrAIBqNQCAdjUAgCbrAIAr6wCAkjUAgDDrAICaNQCAQOoAgDkqAICyNQCAtjUAgEEqAIC6NQCAFC4AgDXrAIA66wCAReoAgErqAIDeNQCA9jcAgIDNAQCB1QEAgt0BAIPVAQCEzQEAhfUBAIb9AQCH9QEAiM0BAInVAQCK3QEAi/UJAIzJAQCNyQEAjgEcAI89HwCQRR8AkU0fAJJFHwCTXR8AlEUfAJVNHwCWRR8Al30fAJhBxwCZQR8AmkEfAJtBHwCcQR8AnUEfAJ5BHwCfYd8AoL0fAKHFHwCizR8Ao8UfAKTdHwClxR8Aps0fAKfFHwCo/R8AqcUfAKrNHwCrxR8ArN0fAK3FHwCuzR8Ar8UfALC9HwCxRR8Ask0fALNFHwC0/ckAtVkfALZJHwC3SR8AuHkfALl5HwC6SR8Au8XdALxVHwC9XR8AvlUfAL9NHwAKNgCABjYAgA42AIAZLACAEjYAgBY2AIAaNgCAIjYAgD/rAIAmNgCAOjYAgD42AIAqNgCAQjYAgFY2AIA2NgCASjYAgE42AIBSNgCAROsAgE7rAIBJ6wCASSoAgHI2AIB2NgCAfjYAgGLrAICCNgCAU+sAgE0qAIBRKgCAWOsAgF3rAIBVKgCAojYAgKo2AICuNgCAujYAgLY2AIDCNgCAvjYAgMY2AIDKNgCA0jYAgFkqAIDaNgCA3jYAgF0qAIDuNgCAZ+sAgP42AIACNwCAYSoAgA43AICVKQCAbOsAgHHrAIBlKgCAaSoAgDo3AIB26wCAkjcAgJY3AICuNwCAgLUBAIG9AQCCtQEAg80BAITt9ACF0QEAhtEBAIfRAQCI8QEAifEBAIrxAQCL8QEAjNEBAI3RAQCO0QEAj9EBAJB9wwCRBcMAkl35AJO9AQCUpQEAla0BAJalAQCXXQMAmGUDAJltAwCaZQMAm30DAJxlAwCdbQMAnmUDAJ85wwCgoQMAoaEDAKKhAwCjoQMApKEDAKWhAwCmoQMAp6EDAKjhAwCp4QMAquEDAKvhAwCs4QMAreEDAK7hAwCv4QMAsKEDALGhAwCyoQMAs6EDALShAwC1oQMAtqEDALehAwC4YQMAuWEDALphAwC7YQMAvGEDAL1hAwC+pcMAv6HDALo3AICA6wCA0ukAgMY3AIDCNwCAzjcAgNfpAIDaNwCAhesAgIrrAIAmOACAMjgAgDo4AICP6wCAPjgAgGY4AIByOACAdjgAgG44AICCOACAhjgAgJTrAICSOACAbSoAgJo4AICZ6wCAcSoAgNI4AICkLgCA6jgAgJ7rAICo6wCAdSoAgHkqAIASOQCAresAgH0qAICy6wCAMjkAgLfrAIBKOQCAgSoAgFo5AIBmOQCAbjkAgHY5AICFKgCAvOsAgKY5AICyOQCAiSoAgI0qAIC2OQCAwesAgJEqAIDG6wCAy+sAgNDrAICVKgCA9jkAgPo5AIACOgCACjoAgNrrAICQ1QEAkd0BAJLVAQCT7QEAlPUBAJXB+wCW8QEAl/n7AJjNAQCZ1QEAmt0BAJvVAQCcyfsAnckBAEUqAICPAAAAgNkBAIHZAQCC6QEAg+kBAIT5AQCF+QEAhukBAIfpAQCI2QEAidkBAIoJwQCLrQEAjLUBAI29AQCOtQEAj60BAKAAAAChAAAAogAAAKMAAACkAAAApQAAAKYAAACnAAAAqAAAAKkAAACqAAAAqwAAAKwAAACtAAAArgAAAK8AAACwAAAAsQAAALIAAACzAAAAtAAAALUAAAC2AAAAtwAAALgAAAC5AAAAugAAALsAAAC8AAAAvQAAAL4AAAC/AAAAACAAIMyBACDMgwAgzIQAIMyFACDMhgAgzIcAIMyIACDMiMyAACDMiMyBACDMiM2CACDMigAgzIsAIMyTACDMk8yAACDMk8yBACDMk82CACDMlAAgzJTMgAAgzJTMgQAgzJTNggAgzKcAIMyoACDMswAgzYIAIM2FACDZiwAg2YwAINmM2ZEAINmNACDZjdmRACDZjgAg2Y7ZkQAg2Y8AINmP2ZEAINmQACDZkNmRACDZkQAg2ZHZsAAg2ZIAIOOCmQAg44KaACEAISEAIT8AIgAjACQAJQAmACcAKAAoMSkAKDEwKQAoMTEpACgxMikAKDEzKQAoMTQpACgxNSkAKDE2KQAoMTcpACgxOCkAKDE5KQAoMikAKDIwKQAoMykAKDQpACg1KQAoNikAKDcpACg4KQAoOSkAKEEpAChCKQAoQykAKEQpAChFKQAoRikAKEcpAChIKQAoSSkAKEopAChLKQAoTCkAKE0pAChOKQAoTykAKFApAChRKQAoUikAKFMpAChUKQAoVSkAKFYpAChXKQAoWCkAKFkpAChaKQAoYSkAKGIpAChjKQAoZCkAKGUpAChmKQAoZykAKGgpAChpKQAoaikAKGspAChsKQAobSkAKG4pAChvKQAocCkAKHEpAChyKQAocykAKHQpACh1KQAodikAKHcpACh4KQAoeSkAKHopACjhhIApACjhhIIpACjhhIMpACjhhIUpACjhhIYpACjhhIcpACjhhIkpACjhhIspACjhhIwpACjhhI4pACjhhI8pACjhhJApACjhhJEpACjhhJIpACjkuIApACjkuIMpACjkuIkpACjkuZ0pACjkuowpACjkupQpACjku6MpACjkvIEpACjkvJEpACjlhaspACjlha0pACjlirQpACjljYEpACjljZQpACjlkI0pACjlkbwpACjlm5spACjlnJ8pACjlraYpACjml6UpACjmnIgpACjmnIkpACjmnKgpACjmoKopACjmsLQpACjngaspACjnibkpACjnm6MpACjnpL4pACjnpZ0pACjnpa0pACjoh6opACjoh7MpACjosqEpACjos4cpACjph5EpACjqsIApACjrgpgpACjri6QpACjrnbwpACjrp4gpACjrsJQpACjsgqwpACjslYQpACjsmKTsoIQpACjsmKTtm4QpACjsnpApACjso7wpACjssKgpACjsubQpACjtg4ApACjtjIwpACjtlZgpACkAKgArACwALQAuAC4uAC4uLgAvADAAMCwAMC4AMOKBhDMAMOeCuQAxADEsADEuADEwADEwLgAxMOaXpQAxMOaciAAxMOeCuQAxMQAxMS4AMTHml6UAMTHmnIgAMTHngrkAMTIAMTIuADEy5pelADEy5pyIADEy54K5ADEzADEzLgAxM+aXpQAxM+eCuQAxNAAxNC4AMTTml6UAMTTngrkAMTUAMTUuADE15pelADE154K5ADE2ADE2LgAxNuaXpQAxNueCuQAxNwAxNy4AMTfml6UAMTfngrkAMTgAMTguADE45pelADE454K5ADE5ADE5LgAxOeaXpQAxOeeCuQAx4oGEADHigYQxMAAx4oGEMgAx4oGEMwAx4oGENAAx4oGENQAx4oGENgAx4oGENwAx4oGEOAAx4oGEOQAx5pelADHmnIgAMeeCuQAyADIsADIuADIwADIwLgAyMOaXpQAyMOeCuQAyMQAyMeaXpQAyMeeCuQAyMgAyMuaXpQAyMueCuQAyMwAyM+aXpQAyM+eCuQAyNAAyNOaXpQAyNOeCuQAyNQAyNeaXpQAyNgAyNuaXpQAyNwAyN+aXpQAyOAAyOOaXpQAyOQAyOeaXpQAy4oGEMwAy4oGENQAy5pelADLmnIgAMueCuQAzADMsADMuADMwADMw5pelADMxADMx5pelADMyADMzADM0ADM1ADM2ADM3ADM4ADM5ADPigYQ0ADPigYQ1ADPigYQ4ADPml6UAM+aciAAz54K5ADQANCwANC4ANDAANDEANDIANDMANDQANDUANDYANDcANDgANDkANOKBhDUANOaXpQA05pyIADTngrkANQA1LAA1LgA1MAA14oGENgA14oGEOAA15pelADXmnIgANeeCuQA2ADYsADYuADbml6UANuaciAA254K5ADcANywANy4AN+KBhDgAN+aXpQA35pyIADfngrkAOAA4LAA4LgA45pelADjmnIgAOOeCuQA5ADksADkuADnml6UAOeaciAA554K5ADoAOjo9ADsAPAA9AD09AD09PQA+AD8APyEAPz8AQABBAEFVAEHiiJVtAEIAQnEAQwBDRABDby4AQ+KIlWtnAEQAREoARFoARHoARMW9AETFvgBFAEYARkFYAEcAR0IAR0h6AEdQYQBHeQBIAEhQAEhWAEhnAEh6AEkASUkASUlJAElKAElVAElWAElYAEoASwBLQgBLSwBLTQBMAExKAExURABMagBMwrcATQBNQgBNQwBNRABNSHoATVBhAE1WAE1XAE3OqQBOAE5KAE5qAE5vAE8AUABQSABQUE0AUFBWAFBSAFBURQBQYQBRAFIAUnMAUwBTRABTTQBTUwBTdgBUAFRFTABUSHoAVE0AVQBWAFZJAFZJSQBWSUlJAFbiiJVtAFcAV0MAV1oAV2IAWABYSQBYSUkAWQBaAFsAXABdAF4AXwBgAGEAYS5tLgBhL2MAYS9zAGHKvgBiAGJhcgBjAGMvbwBjL3UAY2FsAGNjAGNkAGNtAGNtMgBjbTMAZABkQgBkYQBkbABkbQBkbTIAZG0zAGR6AGTFvgBlAGVWAGVyZwBmAGZmAGZmaQBmZmwAZmkAZmwAZm0AZwBnYWwAaABoUGEAaGEAaQBpaQBpaWkAaWoAaW4AaXYAaXgAagBrAGtBAGtIegBrUGEAa1YAa1cAa2NhbABrZwBrbABrbQBrbTIAa20zAGt0AGvOqQBsAGxqAGxtAGxuAGxvZwBseABswrcAbQBtMgBtMwBtQQBtVgBtVwBtYgBtZwBtaWwAbWwAbW0AbW0yAG1tMwBtb2wAbXMAbeKIlXMAbeKIlXMyAG4AbkEAbkYAblYAblcAbmoAbm0AbnMAbwBvVgBwAHAubS4AcEEAcEYAcFYAcFcAcGMAcHMAcQByAHJhZAByYWTiiJVzAHJhZOKIlXMyAHMAc3IAc3QAdAB1AHYAdmkAdmlpAHZpaWkAdwB4AHhpAHhpaQB5AHoAewB8AH0AwqIAwqMAwqUAwqYAwqwAwrBDAMKwRgDCtwDDgADDgQDDggDDgwDDhADDhQDDhgDDhwDDiADDiQDDigDDiwDDjADDjQDDjgDDjwDDkQDDkgDDkwDDlADDlQDDlgDDmQDDmgDDmwDDnADDnQDDoADDoQDDogDDowDDpADDpQDDpwDDqADDqQDDqgDDqwDDrADDrQDDrgDDrwDDsADDsQDDsgDDswDDtADDtQDDtgDDuQDDugDDuwDDvADDvQDDvwDEgADEgQDEggDEgwDEhADEhQDEhgDEhwDEiADEiQDEigDEiwDEjADEjQDEjgDEjwDEkgDEkwDElADElQDElgDElwDEmADEmQDEmgDEmwDEnADEnQDEngDEnwDEoADEoQDEogDEowDEpADEpQDEpgDEpwDEqADEqQDEqgDEqwDErADErQDErgDErwDEsADEsQDEtADEtQDEtgDEtwDEuQDEugDEuwDEvADEvQDEvgDFgwDFhADFhQDFhgDFhwDFiADFiwDFjADFjQDFjgDFjwDFkADFkQDFkwDFlADFlQDFlgDFlwDFmADFmQDFmgDFmwDFnADFnQDFngDFnwDFoADFoQDFogDFowDFpADFpQDFqADFqQDFqgDFqwDFrADFrQDFrgDFrwDFsADFsQDFsgDFswDFtADFtQDFtgDFtwDFuADFuQDFugDFuwDFvADFvQDFvgDGjgDGkADGoADGoQDGqwDGrwDGsADHjQDHjgDHjwDHkADHkQDHkgDHkwDHlADHlQDHlgDHlwDHmADHmQDHmgDHmwDHnADHngDHnwDHoADHoQDHogDHowDHpgDHpwDHqADHqQDHqgDHqwDHrADHrQDHrgDHrwDHsADHtADHtQDHuADHuQDHugDHuwDHvADHvQDHvgDHvwDIgADIgQDIggDIgwDIhADIhQDIhgDIhwDIiADIiQDIigDIiwDIjADIjQDIjgDIjwDIkADIkQDIkgDIkwDIlADIlQDIlgDIlwDImADImQDImgDImwDIngDInwDIogDIpgDIpwDIqADIqQDIqgDIqwDIrADIrQDIrgDIrwDIsADIsQDIsgDIswDItwDJkADJkQDJkgDJlADJlQDJmQDJmwDJnADJnwDJoQDJowDJpQDJpgDJqADJqQDJqgDJqwDJrQDJrwDJsADJsQDJsgDJswDJtADJtQDJuADJuQDJuwDKgQDKggDKgwDKiQDKigDKiwDKjADKkADKkQDKkgDKlQDKnQDKnwDKuQDKvG4AzIAAzIEAzIjMgQDMkwDOhgDOiADOiQDOigDOjADOjgDOjwDOkADOkQDOkgDOkwDOlADOlQDOlgDOlwDOmADOmQDOmgDOmwDOnADOnQDOngDOnwDOoADOoQDOowDOpADOpQDOpgDOpwDOqADOqQDOqgDOqwDOrADOrQDOrgDOrwDOsADOsQDOsgDOswDOtADOtQDOtgDOtwDOuADOuQDOugDOuwDOvADOvEEAzrxGAM68VgDOvFcAzrxnAM68bADOvG0AzrxzAM69AM6+AM6/AM+AAM+BAM+CAM+DAM+EAM+FAM+GAM+HAM+IAM+JAM+KAM+LAM+MAM+NAM+OAM+cAM+dANCAANCBANCDANCHANCMANCNANCOANCZANC5ANC9ANGKANGMANGQANGRANGTANGXANGcANGdANGeANG2ANG3ANOBANOCANOQANORANOSANOTANOWANOXANOaANObANOcANOdANOeANOfANOiANOjANOkANOlANOmANOnANOqANOrANOsANOtANOuANOvANOwANOxANOyANOzANO0ANO1ANO4ANO5ANWl1oIA1bTVpQDVtNWrANW01a0A1bTVtgDVvtW2ANeQANeQ1rcA15DWuADXkNa8ANeQ15wA15EA15HWvADXkda/ANeSANeS1rwA15MA15PWvADXlADXlNa8ANeV1rkA15XWvADXlta8ANeY1rwA15nWtADXmda8ANea1rwA15sA15vWvADXm9a/ANecANec1rwA150A157WvADXoNa8ANeh1rwA16IA16PWvADXpNa8ANek1r8A16bWvADXp9a8ANeoANeo1rwA16nWvADXqda814EA16nWvNeCANep14EA16nXggDXqgDXqta8ANey1rcA2KEA2KIA2KMA2KQA2KUA2KYA2KbYpwDYptisANim2K0A2KbYrgDYptixANim2LIA2KbZhQDYptmGANim2YcA2KbZiADYptmJANim2YoA2KbbhgDYptuHANim24gA2KbbkADYptuVANinANin2YPYqNixANin2YTZhNmHANin2YsA2KfZtADYqADYqNisANio2K0A2KjYrdmKANio2K4A2KjYrtmKANio2LEA2KjYsgDYqNmFANio2YYA2KjZhwDYqNmJANio2YoA2KkA2KoA2KrYrADYqtis2YUA2KrYrNmJANiq2KzZigDYqtitANiq2K3YrADYqtit2YUA2KrYrgDYqtiu2YUA2KrYrtmJANiq2K7ZigDYqtixANiq2LIA2KrZhQDYqtmF2KwA2KrZhditANiq2YXYrgDYqtmF2YkA2KrZhdmKANiq2YYA2KrZhwDYqtmJANiq2YoA2KsA2KvYrADYq9ixANir2LIA2KvZhQDYq9mGANir2YcA2KvZiQDYq9mKANisANis2K0A2KzYrdmJANis2K3ZigDYrNmEINis2YTYp9mE2YcA2KzZhQDYrNmF2K0A2KzZhdmJANis2YXZigDYrNmJANis2YoA2K0A2K3YrADYrdis2YoA2K3ZhQDYrdmF2YkA2K3ZhdmKANit2YkA2K3ZigDYrgDYrtisANiu2K0A2K7ZhQDYrtmJANiu2YoA2K8A2LAA2LDZsADYsQDYsdiz2YjZhADYsdmwANix24zYp9mEANiyANizANiz2KwA2LPYrNitANiz2KzZiQDYs9itANiz2K3YrADYs9iuANiz2K7ZiQDYs9iu2YoA2LPYsQDYs9mFANiz2YXYrADYs9mF2K0A2LPZhdmFANiz2YcA2LPZiQDYs9mKANi0ANi02KwA2LTYrNmKANi02K0A2LTYrdmFANi02K3ZigDYtNiuANi02LEA2LTZhQDYtNmF2K4A2LTZhdmFANi02YcA2LTZiQDYtNmKANi1ANi12K0A2LXYrditANi12K3ZigDYtdiuANi12LEA2LXZhNi52YUA2LXZhNmJANi12YTZiSDYp9mE2YTZhyDYudmE2YrZhyDZiNiz2YTZhQDYtdmE25IA2LXZhQDYtdmF2YUA2LXZiQDYtdmKANi2ANi22KwA2LbYrQDYttit2YkA2LbYrdmKANi22K4A2LbYrtmFANi22LEA2LbZhQDYttmJANi22YoA2LcA2LfYrQDYt9mFANi32YXYrQDYt9mF2YUA2LfZhdmKANi32YkA2LfZigDYuADYuNmFANi5ANi52KwA2LnYrNmFANi52YTZitmHANi52YUA2LnZhdmFANi52YXZiQDYudmF2YoA2LnZiQDYudmKANi6ANi62KwA2LrZhQDYutmF2YUA2LrZhdmJANi62YXZigDYutmJANi62YoA2YDZiwDZgNmOANmA2Y7ZkQDZgNmPANmA2Y/ZkQDZgNmQANmA2ZDZkQDZgNmRANmA2ZIA2YEA2YHYrADZgditANmB2K4A2YHYrtmFANmB2YUA2YHZhdmKANmB2YkA2YHZigDZggDZgtitANmC2YTbkgDZgtmFANmC2YXYrQDZgtmF2YUA2YLZhdmKANmC2YkA2YLZigDZgwDZg9inANmD2KwA2YPYrQDZg9iuANmD2YQA2YPZhQDZg9mF2YUA2YPZhdmKANmD2YkA2YPZigDZhADZhNiiANmE2KMA2YTYpQDZhNinANmE2KwA2YTYrNisANmE2KzZhQDZhNis2YoA2YTYrQDZhNit2YUA2YTYrdmJANmE2K3ZigDZhNiuANmE2K7ZhQDZhNmFANmE2YXYrQDZhNmF2YoA2YTZhwDZhNmJANmE2YoA2YUA2YXYpwDZhdisANmF2KzYrQDZhdis2K4A2YXYrNmFANmF2KzZigDZhditANmF2K3YrADZhdit2YUA2YXYrdmF2K8A2YXYrdmKANmF2K4A2YXYrtisANmF2K7ZhQDZhdiu2YoA2YXZhQDZhdmF2YoA2YXZiQDZhdmKANmGANmG2KwA2YbYrNitANmG2KzZhQDZhtis2YkA2YbYrNmKANmG2K0A2YbYrdmFANmG2K3ZiQDZhtit2YoA2YbYrgDZhtixANmG2LIA2YbZhQDZhtmF2YkA2YbZhdmKANmG2YYA2YbZhwDZhtmJANmG2YoA2YcA2YfYrADZh9mFANmH2YXYrADZh9mF2YUA2YfZiQDZh9mKANmH2bAA2YgA2YjYs9mE2YUA2YjZtADZiQDZidmwANmKANmK2KwA2YrYrNmKANmK2K0A2YrYrdmKANmK2K4A2YrYsQDZitiyANmK2YUA2YrZhdmFANmK2YXZigDZitmGANmK2YcA2YrZiQDZitmKANmK2bQA2a4A2a8A2bEA2bkA2boA2bsA2b4A2b8A2oAA2oMA2oQA2oYA2ocA2ogA2owA2o0A2o4A2pEA2pgA2qEA2qQA2qYA2qkA2q0A2q8A2rEA2rMA2roA2rsA2r4A24AA24EA24IA24UA24YA24cA24fZtADbiADbiQDbiwDbjADbkADbkgDbkwDgpJXgpLwA4KSW4KS8AOCkl+CkvADgpJzgpLwA4KSh4KS8AOCkouCkvADgpKkA4KSr4KS8AOCkr+CkvADgpLEA4KS0AOCmoeCmvADgpqLgprwA4Kav4Ka8AOCniwDgp4wA4KiW4Ki8AOCol+CovADgqJzgqLwA4Kir4Ki8AOCosuCovADgqLjgqLwA4Kyh4Ky8AOCsouCsvADgrYgA4K2LAOCtjADgrpQA4K+KAOCviwDgr4wA4LGIAOCzgADgs4cA4LOIAOCzigDgs4sA4LWKAOC1iwDgtYwA4LeaAOC3nADgt50A4LeeAOC5jeC4sgDguqvgupkA4Lqr4LqhAOC7jeC6sgDgvIsA4L2A4L61AOC9guC+twDgvYzgvrcA4L2R4L63AOC9luC+twDgvZvgvrcA4L2x4L2yAOC9seC9tADgvbHgvoAA4L6Q4L61AOC+kuC+twDgvpzgvrcA4L6h4L63AOC+puC+twDgvqvgvrcA4L6y4L2x4L6AAOC+suC+gADgvrPgvbHgvoAA4L6z4L6AAOGApgDhg5wA4YSAAOGEgQDhhIIA4YSDAOGEhADhhIUA4YSGAOGEhwDhhIgA4YSJAOGEigDhhIsA4YSMAOGEjQDhhI4A4YSPAOGEkADhhJEA4YSSAOGElADhhJUA4YSaAOGEnADhhJ0A4YSeAOGEoADhhKEA4YSiAOGEowDhhKcA4YSpAOGEqwDhhKwA4YStAOGErgDhhK8A4YSyAOGEtgDhhYAA4YWHAOGFjADhhZcA4YWYAOGFmQDhhaAA4YWhAOGFogDhhaMA4YWkAOGFpQDhhaYA4YWnAOGFqADhhakA4YWqAOGFqwDhhawA4YWtAOGFrgDhha8A4YWwAOGFsQDhhbIA4YWzAOGFtADhhbUA4YaEAOGGhQDhhogA4YaRAOGGkgDhhpQA4YaeAOGGoQDhhqoA4YasAOGGrQDhhrAA4YaxAOGGsgDhhrMA4Ya0AOGGtQDhh4cA4YeIAOGHjADhh44A4YeTAOGHlwDhh5kA4YedAOGHnwDhh7EA4YeyAOGshgDhrIgA4ayKAOGsjADhrI4A4aySAOGsuwDhrL0A4a2AAOGtgQDhrYMA4bSCAOG0lgDhtJcA4bScAOG0nQDhtKUA4bW7AOG2hQDhuIAA4biBAOG4ggDhuIMA4biEAOG4hQDhuIYA4biHAOG4iADhuIkA4biKAOG4iwDhuIwA4biNAOG4jgDhuI8A4biQAOG4kQDhuJIA4biTAOG4lADhuJUA4biWAOG4lwDhuJgA4biZAOG4mgDhuJsA4bicAOG4nQDhuJ4A4bifAOG4oADhuKEA4biiAOG4owDhuKQA4bilAOG4pgDhuKcA4bioAOG4qQDhuKoA4birAOG4rADhuK0A4biuAOG4rwDhuLAA4bixAOG4sgDhuLMA4bi0AOG4tQDhuLYA4bi3AOG4uADhuLkA4bi6AOG4uwDhuLwA4bi9AOG4vgDhuL8A4bmAAOG5gQDhuYIA4bmDAOG5hADhuYUA4bmGAOG5hwDhuYgA4bmJAOG5igDhuYsA4bmMAOG5jQDhuY4A4bmPAOG5kADhuZEA4bmSAOG5kwDhuZQA4bmVAOG5lgDhuZcA4bmYAOG5mQDhuZoA4bmbAOG5nADhuZ0A4bmeAOG5nwDhuaAA4bmhAOG5ogDhuaMA4bmkAOG5pQDhuaYA4bmnAOG5qADhuakA4bmqAOG5qwDhuawA4bmtAOG5rgDhua8A4bmwAOG5sQDhubIA4bmzAOG5tADhubUA4bm2AOG5twDhubgA4bm5AOG5ugDhubsA4bm8AOG5vQDhub4A4bm/AOG6gADhuoEA4bqCAOG6gwDhuoQA4bqFAOG6hgDhuocA4bqIAOG6iQDhuooA4bqLAOG6jADhuo0A4bqOAOG6jwDhupAA4bqRAOG6kgDhupMA4bqUAOG6lQDhupYA4bqXAOG6mADhupkA4bqgAOG6oQDhuqIA4bqjAOG6pADhuqUA4bqmAOG6pwDhuqgA4bqpAOG6qgDhuqsA4bqsAOG6rQDhuq4A4bqvAOG6sADhurEA4bqyAOG6swDhurQA4bq1AOG6tgDhurcA4bq4AOG6uQDhuroA4bq7AOG6vADhur0A4bq+AOG6vwDhu4AA4buBAOG7ggDhu4MA4buEAOG7hQDhu4YA4buHAOG7iADhu4kA4buKAOG7iwDhu4wA4buNAOG7jgDhu48A4buQAOG7kQDhu5IA4buTAOG7lADhu5UA4buWAOG7lwDhu5gA4buZAOG7mgDhu5sA4bucAOG7nQDhu54A4bufAOG7oADhu6EA4buiAOG7owDhu6QA4bulAOG7pgDhu6cA4buoAOG7qQDhu6oA4burAOG7rADhu60A4buuAOG7rwDhu7AA4buxAOG7sgDhu7MA4bu0AOG7tQDhu7YA4bu3AOG7uADhu7kA4byAAOG8gQDhvIIA4byDAOG8hADhvIUA4byGAOG8hwDhvIgA4byJAOG8igDhvIsA4byMAOG8jQDhvI4A4byPAOG8kADhvJEA4bySAOG8kwDhvJQA4byVAOG8mADhvJkA4byaAOG8mwDhvJwA4bydAOG8oADhvKEA4byiAOG8owDhvKQA4bylAOG8pgDhvKcA4byoAOG8qQDhvKoA4byrAOG8rADhvK0A4byuAOG8rwDhvLAA4byxAOG8sgDhvLMA4by0AOG8tQDhvLYA4by3AOG8uADhvLkA4by6AOG8uwDhvLwA4by9AOG8vgDhvL8A4b2AAOG9gQDhvYIA4b2DAOG9hADhvYUA4b2IAOG9iQDhvYoA4b2LAOG9jADhvY0A4b2QAOG9kQDhvZIA4b2TAOG9lADhvZUA4b2WAOG9lwDhvZkA4b2bAOG9nQDhvZ8A4b2gAOG9oQDhvaIA4b2jAOG9pADhvaUA4b2mAOG9pwDhvagA4b2pAOG9qgDhvasA4b2sAOG9rQDhva4A4b2vAOG9sADhvbIA4b20AOG9tgDhvbgA4b26AOG9vADhvoAA4b6BAOG+ggDhvoMA4b6EAOG+hQDhvoYA4b6HAOG+iADhvokA4b6KAOG+iwDhvowA4b6NAOG+jgDhvo8A4b6QAOG+kQDhvpIA4b6TAOG+lADhvpUA4b6WAOG+lwDhvpgA4b6ZAOG+mgDhvpsA4b6cAOG+nQDhvp4A4b6fAOG+oADhvqEA4b6iAOG+owDhvqQA4b6lAOG+pgDhvqcA4b6oAOG+qQDhvqoA4b6rAOG+rADhvq0A4b6uAOG+rwDhvrAA4b6xAOG+sgDhvrMA4b60AOG+tgDhvrcA4b64AOG+uQDhvroA4b68AOG/ggDhv4MA4b+EAOG/hgDhv4cA4b+IAOG/igDhv4wA4b+QAOG/kQDhv5IA4b+WAOG/lwDhv5gA4b+ZAOG/mgDhv6AA4b+hAOG/ogDhv6QA4b+lAOG/pgDhv6cA4b+oAOG/qQDhv6oA4b+sAOG/sgDhv7MA4b+0AOG/tgDhv7cA4b+4AOG/ugDhv7wA4oCQAOKAkwDigJQA4oCy4oCyAOKAsuKAsuKAsgDigLLigLLigLLigLIA4oC14oC1AOKAteKAteKAtQDigqkA4oaQAOKGkQDihpIA4oaTAOKGmgDihpsA4oauAOKHjQDih44A4oePAOKIggDiiIQA4oiHAOKIiQDiiIwA4oiRAOKIkgDiiKQA4oimAOKIq+KIqwDiiKviiKviiKsA4oir4oir4oir4oirAOKIruKIrgDiiK7iiK7iiK4A4omBAOKJhADiiYcA4omJAOKJoADiiaIA4omtAOKJrgDiia8A4omwAOKJsQDiibQA4om1AOKJuADiibkA4oqAAOKKgQDiioQA4oqFAOKKiADiiokA4oqsAOKKrQDiiq4A4oqvAOKLoADii6EA4ouiAOKLowDii6oA4ourAOKLrADii60A4pSCAOKWoADil4sA4qaFAOKmhgDiq53MuADitaEA44CBAOOAggDjgIgA44CJAOOAigDjgIsA44CMAOOAjQDjgI4A44CPAOOAkADjgJEA44CSAOOAlADjgJRT44CVAOOAlOS4ieOAlQDjgJTkuozjgJUA44CU5Yud44CVAOOAlOWuieOAlQDjgJTmiZPjgJUA44CU5pWX44CVAOOAlOacrOOAlQDjgJTngrnjgJUA44CU55uX44CVAOOAlQDjgJYA44CXAOOBjADjgY4A44GQAOOBkgDjgZQA44GWAOOBmADjgZoA44GcAOOBngDjgaAA44GiAOOBpQDjgacA44GpAOOBsADjgbEA44GzAOOBtADjgbYA44G3AOOBuQDjgboA44G744GLAOOBvADjgb0A44KI44KKAOOClADjgpkA44KaAOOCngDjgqEA44KiAOOCouODkeODvOODiADjgqLjg6vjg5XjgqEA44Ki44Oz44Oa44KiAOOCouODvOODqwDjgqMA44KkAOOCpOODi+ODs+OCsADjgqTjg7Pjg4EA44KlAOOCpgDjgqbjgqnjg7MA44KnAOOCqADjgqjjgrnjgq/jg7zjg4kA44Ko44O844Kr44O8AOOCqQDjgqoA44Kq44Oz44K5AOOCquODvOODoADjgqsA44Kr44Kk44OqAOOCq+ODqeODg+ODiADjgqvjg63jg6rjg7wA44KsAOOCrOODreODswDjgqzjg7Pjg54A44KtAOOCreODpeODquODvADjgq3jg60A44Kt44Ot44Kw44Op44OgAOOCreODreODoeODvOODiOODqwDjgq3jg63jg6/jg4Pjg4gA44KuAOOCruOCrADjgq7jg4vjg7wA44Ku44Or44OA44O8AOOCrwDjgq/jg6vjgrzjgqTjg60A44Kv44Ot44O844ONAOOCsADjgrDjg6njg6AA44Kw44Op44Og44OI44OzAOOCsQDjgrHjg7zjgrkA44KyAOOCswDjgrPjgrMA44Kz44OIAOOCs+ODq+ODigDjgrPjg7zjg50A44K0AOOCtQDjgrXjgqTjgq/jg6sA44K144Oz44OB44O844OgAOOCtgDjgrcA44K344Oq44Oz44KwAOOCuADjgrkA44K6AOOCuwDjgrvjg7Pjg4EA44K744Oz44OIAOOCvADjgr0A44K+AOOCvwDjg4AA44OA44O844K5AOODgQDjg4IA44ODAOODhADjg4UA44OGAOODhwDjg4fjgrcA44OIAOODiOODswDjg4kA44OJ44OrAOODigDjg4rjg44A44OLAOODjADjg40A44OOAOODjuODg+ODiADjg48A44OP44Kk44OEAOODkADjg5Djg7zjg6zjg6sA44ORAOODkeODvOOCu+ODs+ODiADjg5Hjg7zjg4QA44OSAOODkwDjg5Pjg6sA44OUAOODlOOCouOCueODiOODqwDjg5Tjgq/jg6sA44OU44KzAOODlQDjg5XjgqHjg6njg4Pjg4kA44OV44Kj44O844OIAOODleODqeODswDjg5YA44OW44OD44K344Kn44OrAOODlwDjg5gA44OY44Kv44K/44O844OrAOODmOODq+ODhADjg5kA44OZ44O844K/AOODmgDjg5rjgr0A44Oa44OL44OSAOODmuODs+OCuQDjg5rjg7zjgrgA44ObAOODm+ODswDjg5vjg7zjg6sA44Ob44O844OzAOODnADjg5zjg6vjg4gA44OdAOODneOCpOODs+ODiADjg53jg7Pjg4kA44OeAOODnuOCpOOCr+ODrQDjg57jgqTjg6sA44Oe44OD44OPAOODnuODq+OCrwDjg57jg7Pjgrfjg6fjg7MA44OfAOODn+OCr+ODreODswDjg5/jg6oA44Of44Oq44OQ44O844OrAOODoADjg6EA44Oh44KsAOODoeOCrOODiOODswDjg6Hjg7zjg4jjg6sA44OiAOODowDjg6QA44Ok44O844OJAOODpOODvOODqwDjg6UA44OmAOODpuOCouODswDjg6cA44OoAOODqQDjg6oA44Oq44OD44OI44OrAOODquODqQDjg6sA44Or44OU44O8AOODq+ODvOODluODqwDjg6wA44Os44OgAOODrOODs+ODiOOCsuODswDjg60A44OvAOODr+ODg+ODiADjg7AA44OxAOODsgDjg7MA44O0AOODtwDjg7gA44O5AOODugDjg7sA44O8AOODvgDjkp4A45K5AOOSuwDjk58A45SVAOObrgDjm7wA456BAOOgrwDjoaIA46G8AOOjhwDjo6MA46ScAOOkugDjqK4A46msAOOrpADjrIgA46yZAOOtiQDjrp0A47CYAOOxjgDjtLMA47aWAOO6rADjurgA47ybAOO/vADkgIgA5ICYAOSAuQDkgYYA5IKWAOSDowDkhK8A5IiCAOSIpwDkiqAA5IyBAOSMtADkjZkA5I+VAOSPmQDkkIsA5JGrAOSUqwDklZ0A5JWhAOSVqwDkl5cA5Je5AOSYtQDkmr4A5JuHAOSmlQDkp6YA5KmuAOSptgDkqrIA5KyzAOSvjgDks44A5LOtAOSzuADktZYA5LiAAOS4gQDkuIMA5LiJAOS4igDkuIsA5LiNAOS4mQDkuKYA5LioAOS4rQDkuLIA5Li2AOS4uADkuLkA5Li9AOS4vwDkuYEA5LmZAOS5nQDkuoIA5LqFAOS6hgDkuowA5LqUAOS6oADkuqQA5LquAOS6ugDku4AA5LuMAOS7pADkvIEA5LyRAOS9oADkvoAA5L6GAOS+iwDkvq4A5L67AOS+vwDlgIIA5YCrAOWBugDlgpkA5YOPAOWDmgDlg6cA5YSqAOWEvwDlhYAA5YWFAOWFjQDlhZQA5YWkAOWFpQDlhacA5YWoAOWFqQDlhasA5YWtAOWFtwDlhoAA5YaCAOWGjQDlhpIA5YaVAOWGlgDlhpcA5YaZAOWGpADlhqsA5YasAOWGtQDlhrcA5YeJAOWHjADlh5wA5YeeAOWHoADlh7UA5YiAAOWIgwDliIcA5YiXAOWInQDliKkA5Yi6AOWIuwDliYYA5YmNAOWJsgDlibcA5YqJAOWKmwDliqMA5YqzAOWKtADli4cA5YuJAOWLkgDli54A5YukAOWLtQDli7kA5Yu6AOWMhQDljIYA5YyVAOWMlwDljJoA5Yy4AOWMuwDljL8A5Y2BAOWNhADljYUA5Y2JAOWNkQDljZQA5Y2aAOWNnADljakA5Y2wAOWNswDljbUA5Y29AOWNvwDljoIA5Y62AOWPgwDlj4gA5Y+KAOWPjADlj58A5Y+jAOWPpQDlj6sA5Y+vAOWPsQDlj7MA5ZCGAOWQiADlkI0A5ZCPAOWQnQDlkLgA5ZC5AOWRggDlkYgA5ZGoAOWSngDlkqIA5ZK9AOWTtgDllJAA5ZWPAOWVkwDllZUA5ZWjAOWWhADllocA5ZaZAOWWnQDllqsA5ZazAOWWtgDll4AA5ZeCAOWXogDlmIYA5ZmRAOWZqADlmbQA5ZuXAOWbmwDlm7kA5ZyWAOWclwDlnJ8A5ZywAOWeiwDln44A5Z+0AOWgjQDloLEA5aCyAOWhgADloZoA5aGeAOWiqADloqwA5aKzAOWjmADlo58A5aOrAOWjrgDlo7AA5aOyAOWjtwDlpIIA5aSGAOWkigDlpJUA5aSaAOWknADlpKIA5aSnAOWkp+atowDlpKkA5aWEAOWliADlpZEA5aWUAOWlogDlpbMA5aeYAOWnrADlqJsA5ainAOWpogDlqaYA5aq1AOWsiADlrKgA5ay+AOWtkADlrZcA5a2mAOWugADlroUA5a6XAOWvgwDlr5gA5a+nAOWvrgDlr7MA5a+4AOWvvwDlsIYA5bCPAOWwogDlsLgA5bC/AOWxoADlsaIA5bGkAOWxpQDlsa4A5bGxAOWyjQDls4AA5bSZAOW1gwDltZAA5bWrAOW1rgDltbwA5bayAOW2ugDlt5sA5behAOW3ogDlt6UA5bemAOW3sQDlt70A5be+AOW4qADluL0A5bmpAOW5sgDlubPmiJAA5bm0AOW5ugDlubwA5bm/AOW6pgDlurAA5bqzAOW6tgDlu4kA5buKAOW7kgDlu5MA5buZAOW7rADlu7QA5bu+AOW8hADlvIsA5byTAOW8ogDlvZAA5b2TAOW9oQDlvaIA5b2pAOW9qwDlvbMA5b6LAOW+jADlvpcA5b6aAOW+qQDlvq0A5b+DAOW/jQDlv5cA5b+1AOW/uQDmgJIA5oCcAOaBtQDmgoEA5oKUAOaDhwDmg5gA5oOhAOaEiADmhYQA5oWIAOaFjADmhY4A5oWgAOaFqADmhboA5oaOAOaGkADmhqQA5oavAOaGsgDmh54A5oeyAOaHtgDmiIAA5oiIAOaIkADmiJsA5oiuAOaItADmiLYA5omLAOaJkwDmiZ0A5oqVAOaKsQDmi4kA5ouPAOaLkwDmi5QA5ou8AOaLvgDmjIcA5oy9AOaNkADmjZUA5o2oAOaNuwDmjoMA5o6gAOaOqQDmj4QA5o+FAOaPpADmkJwA5pCiAOaRkgDmkakA5pG3AOaRvgDmkpoA5pKdAOaThADmlK8A5pS0AOaVjwDmlZYA5pWsAOaVuADmlocA5paXAOaWmQDmlqQA5pawAOaWuQDml4UA5pegAOaXogDml6MA5pelAOaYjuayuwDmmJMA5pigAOaYreWSjADmmYkA5pm0AOaaiADmmpEA5pqcAOaatADmm4YA5puwAOabtADmm7gA5pyAAOaciADmnIkA5pyXAOacmwDmnKEA5pyoAOadjgDmnZMA5p2WAOadngDmnbsA5p6FAOaelwDmn7MA5p+6AOaglwDmoJ8A5qCqAOagquW8j+S8muekvgDmoZIA5qKBAOaihQDmoo4A5qKoAOaklADmpYIA5qajAOanqgDmqIIA5qiTAOaqqADmq5MA5qubAOashADmrKAA5qyhAOatlADmraIA5q2jAOatsgDmrbcA5q25AOaunwDmrq4A5q6zAOauugDmrrsA5q+LAOavjQDmr5QA5q+bAOawjwDmsJQA5rC0AOaxjgDmsacA5rKIAOayvwDms4wA5rONAOazpQDms6gA5rSWAOa0mwDmtJ4A5rS0AOa0vgDmtYEA5rWpAOa1qgDmtbcA5rW4AOa2hQDmt4sA5reaAOa3qgDmt7kA5riaAOa4rwDmua4A5rqAAOa6nADmuroA5ruHAOa7iwDmu5EA5rubAOa8jwDmvJQA5ryiAOa8owDmva4A5r+GAOa/qwDmv74A54CbAOeAngDngLkA54GKAOeBqwDngbAA54G3AOeBvQDngpkA54KtAOeDiADng5kA54ShAOeFhQDnhYkA54WuAOeGnADnh44A54eQAOeIkADniJsA54ioAOeIqgDniKsA54i1AOeItgDniLsA54i/AOeJhwDniZAA54mZAOeJmwDniaIA54m5AOeKgADnipUA54qsAOeKrwDni4AA54u8AOeMqgDnjbUA5426AOeOhADnjocA546JAOeOiwDnjqUA546yAOePngDnkIYA55CJAOeQogDnkYcA55GcAOeRqQDnkbEA55KFAOeSiQDnkpgA55OKAOeTnADnk6YA55SGAOeUmADnlJ8A55SkAOeUqADnlLAA55SyAOeUswDnlLcA55S7AOeUvgDnlZkA55WlAOeVsADnlosA55aSAOeXogDnmJAA55idAOeYnwDnmYIA55mpAOeZtgDnmb0A55quAOeavwDnm4oA55ubAOebowDnm6cA55uuAOebtADnnIEA55yeAOecnwDnnYAA552KAOeeiwDnnqcA55+bAOefogDnn7MA56GOAOehqwDnoowA56KRAOejigDno4wA56O7AOekqgDnpLoA56S8AOekvgDnpYgA56WJAOelkADnpZYA56WdAOelngDnpaUA56W/AOemgQDnpo0A56aOAOemjwDnpq4A56a4AOemvgDnp4oA56eYAOenqwDnqJwA56mAAOepigDnqY8A56m0AOepugDnqoEA56qxAOeriwDnq64A56u5AOesoADnro8A56+AAOevhgDnr4kA57C+AOexoADnsbMA57G7AOeykgDnsr4A57OSAOezlgDns6MA57OnAOezqADns7gA57SAAOe0kADntKIA57SvAOe1ggDntZsA57WjAOe2oADntr4A57eHAOe3tADnuIIA57iJAOe4twDnuYEA57mFAOe8tgDnvL4A572RAOe9sgDnvbkA5726AOe+hQDnvooA576VAOe+mgDnvr0A57+6AOiAgQDogIUA6ICMAOiAkgDogLMA6IGGAOiBoADoga8A6IGwAOiBvgDogb8A6IKJAOiCiwDogq0A6IKyAOiEgwDohL4A6IeYAOiHowDoh6gA6IeqAOiHrQDoh7MA6Ie8AOiIgQDoiIQA6IiMAOiImADoiJsA6IifAOiJrgDoia8A6ImyAOiJuADoibkA6IqLAOiKkQDoip0A6IqxAOiKswDoir0A6IulAOiLpgDojJ0A6IyjAOiMtgDojZIA6I2TAOiNowDojq0A6I69AOiPiQDoj4oA6I+MAOiPnADoj6cA6I+vAOiPsQDokL0A6JGJAOiRlwDok64A6JOxAOiTswDok7wA6JSWAOiVpADol40A6Je6AOiYhgDomJIA6JitAOiYvwDomY0A6JmQAOiZnADomacA6JmpAOiZqwDomogA6JqpAOibogDonI4A6JyoAOidqwDonbkA6J6GAOieugDon6EA6KCBAOignwDooYAA6KGMAOihoADooaMA6KOCAOijjwDoo5cA6KOeAOijoQDoo7gA6KO6AOikkADopYEA6KWkAOilvgDopoYA6KaLAOimlgDop5IA6KejAOiogADoqqAA6KqqAOiqvwDoq4sA6KuSAOirlgDoq60A6Ku4AOirvgDorIEA6Ky5AOitmADoroAA6K6KAOiwtwDosYYA6LGIAOixlQDosbgA6LKdAOiyoQDosqkA6LKrAOizgQDos4IA6LOHAOiziADos5MA6LSIAOi0mwDotaQA6LWwAOi1twDotrMA6La8AOi3iwDot68A6LewAOi6qwDou4oA6LuUAOi8pgDovKoA6Ly4AOi8uwDovaIA6L6bAOi+ngDovrAA6L61AOi+tgDpgKMA6YC4AOmBigDpgakA6YGyAOmBvADpgo8A6YKRAOmClADpg44A6YOeAOmDsQDpg70A6YSRAOmEmwDphYkA6YWqAOmGmQDphrQA6YeGAOmHjADph48A6YeRAOmItADpiLgA6Ym2AOmJvADpi5cA6YuYAOmMhADpjYoA6Y+5AOmQlQDplbcA6ZaAAOmWiwDplq0A6Za3AOmYnADpmK4A6ZmLAOmZjQDpmbUA6Zm4AOmZvADpmoYA6ZqjAOmatgDpmrcA6Zq4AOmauQDpm4MA6ZuiAOmbowDpm6gA6Zu2AOmbtwDpnKMA6ZyyAOmdiADpnZEA6Z2WAOmdngDpnaIA6Z2pAOmfiwDpn5sA6Z+gAOmfrQDpn7MA6Z+/AOmggQDpoIUA6aCLAOmgmADpoKkA6aC7AOmhngDpoqgA6aObAOmjnwDpo6IA6aOvAOmjvADppKgA6aSpAOmmlgDpppkA6aanAOmmrADpp4IA6aexAOmnvgDpqaoA6aqoAOmrmADpq58A6aySAOmspQDprK8A6ayyAOmsvADprZoA6a2vAOmxgADpsZcA6bOlAOmzvQDptacA6ba0AOm3ugDpuJ4A6bm1AOm5vwDpupcA6bqfAOm6pQDpursA6buDAOm7jQDpu44A6buRAOm7uQDpu70A6bu+AOm8hQDpvI4A6byPAOm8kwDpvJYA6bygAOm8uwDpvYMA6b2KAOm9kgDpvo0A6b6OAOm+nADpvp8A6b6gAOqcpwDqna8A6qy3AOqtkgDqsIAA6rCBAOqwggDqsIMA6rCEAOqwhQDqsIYA6rCHAOqwiADqsIkA6rCKAOqwiwDqsIwA6rCNAOqwjgDqsI8A6rCQAOqwkQDqsJIA6rCTAOqwlADqsJUA6rCWAOqwlwDqsJgA6rCZAOqwmgDqsJsA6rCcAOqwnQDqsJ4A6rCfAOqwoADqsKEA6rCiAOqwowDqsKQA6rClAOqwpgDqsKcA6rCoAOqwqQDqsKoA6rCrAOqwrADqsK0A6rCuAOqwrwDqsLAA6rCxAOqwsgDqsLMA6rC0AOqwtQDqsLYA6rC3AOqwuADqsLkA6rC6AOqwuwDqsLwA6rC9AOqwvgDqsL8A6rGAAOqxgQDqsYIA6rGDAOqxhADqsYUA6rGGAOqxhwDqsYgA6rGJAOqxigDqsYsA6rGMAOqxjQDqsY4A6rGPAOqxkADqsZEA6rGSAOqxkwDqsZQA6rGVAOqxlgDqsZcA6rGYAOqxmQDqsZoA6rGbAOqxnADqsZ0A6rGeAOqxnwDqsaAA6rGhAOqxogDqsaMA6rGkAOqxpQDqsaYA6rGnAOqxqADqsakA6rGqAOqxqwDqsawA6rGtAOqxrgDqsa8A6rGwAOqxsQDqsbIA6rGzAOqxtADqsbUA6rG2AOqxtwDqsbgA6rG5AOqxugDqsbsA6rG8AOqxvQDqsb4A6rG/AOqygADqsoEA6rKCAOqygwDqsoQA6rKFAOqyhgDqsocA6rKIAOqyiQDqsooA6rKLAOqyjADqso0A6rKOAOqyjwDqspAA6rKRAOqykgDqspMA6rKUAOqylQDqspYA6rKXAOqymADqspkA6rKaAOqymwDqspwA6rKdAOqyngDqsp8A6rKgAOqyoQDqsqIA6rKjAOqypADqsqUA6rKmAOqypwDqsqgA6rKpAOqyqgDqsqsA6rKsAOqyrQDqsq4A6rKvAOqysADqsrEA6rKyAOqyswDqsrQA6rK1AOqytgDqsrcA6rK4AOqyuQDqsroA6rK7AOqyvADqsr0A6rK+AOqyvwDqs4AA6rOBAOqzggDqs4MA6rOEAOqzhQDqs4YA6rOHAOqziADqs4kA6rOKAOqziwDqs4wA6rONAOqzjgDqs48A6rOQAOqzkQDqs5IA6rOTAOqzlADqs5UA6rOWAOqzlwDqs5gA6rOZAOqzmgDqs5sA6rOcAOqznQDqs54A6rOfAOqzoADqs6EA6rOiAOqzowDqs6QA6rOlAOqzpgDqs6cA6rOoAOqzqQDqs6oA6rOrAOqzrADqs60A6rOuAOqzrwDqs7AA6rOxAOqzsgDqs7MA6rO0AOqztQDqs7YA6rO3AOqzuADqs7kA6rO6AOqzuwDqs7wA6rO9AOqzvgDqs78A6rSAAOq0gQDqtIIA6rSDAOq0hADqtIUA6rSGAOq0hwDqtIgA6rSJAOq0igDqtIsA6rSMAOq0jQDqtI4A6rSPAOq0kADqtJEA6rSSAOq0kwDqtJQA6rSVAOq0lgDqtJcA6rSYAOq0mQDqtJoA6rSbAOq0nADqtJ0A6rSeAOq0nwDqtKAA6rShAOq0ogDqtKMA6rSkAOq0pQDqtKYA6rSnAOq0qADqtKkA6rSqAOq0qwDqtKwA6rStAOq0rgDqtK8A6rSwAOq0sQDqtLIA6rSzAOq0tADqtLUA6rS2AOq0twDqtLgA6rS5AOq0ugDqtLsA6rS8AOq0vQDqtL4A6rS/AOq1gADqtYEA6rWCAOq1gwDqtYQA6rWFAOq1hgDqtYcA6rWIAOq1iQDqtYoA6rWLAOq1jADqtY0A6rWOAOq1jwDqtZAA6rWRAOq1kgDqtZMA6rWUAOq1lQDqtZYA6rWXAOq1mADqtZkA6rWaAOq1mwDqtZwA6rWdAOq1ngDqtZ8A6rWgAOq1oQDqtaIA6rWjAOq1pADqtaUA6rWmAOq1pwDqtagA6rWpAOq1qgDqtasA6rWsAOq1rQDqta4A6rWvAOq1sADqtbEA6rWyAOq1swDqtbQA6rW1AOq1tgDqtbcA6rW4AOq1uQDqtboA6rW7AOq1vADqtb0A6rW+AOq1vwDqtoAA6raBAOq2ggDqtoMA6raEAOq2hQDqtoYA6raHAOq2iADqtokA6raKAOq2iwDqtowA6raNAOq2jgDqto8A6raQAOq2kQDqtpIA6raTAOq2lADqtpUA6raWAOq2lwDqtpgA6raZAOq2mgDqtpsA6racAOq2nQDqtp4A6rafAOq2oADqtqEA6raiAOq2owDqtqQA6ralAOq2pgDqtqcA6raoAOq2qQDqtqoA6rarAOq2rADqtq0A6rauAOq2rwDqtrAA6raxAOq2sgDqtrMA6ra0AOq2tQDqtrYA6ra3AOq2uADqtrkA6ra6AOq2uwDqtrwA6ra9AOq2vgDqtr8A6reAAOq3gQDqt4IA6reDAOq3hADqt4UA6reGAOq3hwDqt4gA6reJAOq3igDqt4sA6reMAOq3jQDqt44A6rePAOq3kADqt5EA6reSAOq3kwDqt5QA6reVAOq3lgDqt5cA6reYAOq3mQDqt5oA6rebAOq3nADqt50A6reeAOq3nwDqt6AA6rehAOq3ogDqt6MA6rekAOq3pQDqt6YA6renAOq3qADqt6kA6reqAOq3qwDqt6wA6retAOq3rgDqt68A6rewAOq3sQDqt7IA6rezAOq3tADqt7UA6re2AOq3twDqt7gA6re5AOq3ugDqt7sA6re8AOq3vQDqt74A6re/AOq4gADquIEA6riCAOq4gwDquIQA6riFAOq4hgDquIcA6riIAOq4iQDquIoA6riLAOq4jADquI0A6riOAOq4jwDquJAA6riRAOq4kgDquJMA6riUAOq4lQDquJYA6riXAOq4mADquJkA6riaAOq4mwDquJwA6ridAOq4ngDquJ8A6rigAOq4oQDquKIA6rijAOq4pADquKUA6rimAOq4pwDquKgA6ripAOq4qgDquKsA6risAOq4rQDquK4A6rivAOq4sADquLEA6riyAOq4swDquLQA6ri1AOq4tgDquLcA6ri4AOq4uQDquLoA6ri7AOq4vADquL0A6ri+AOq4vwDquYAA6rmBAOq5ggDquYMA6rmEAOq5hQDquYYA6rmHAOq5iADquYkA6rmKAOq5iwDquYwA6rmNAOq5jgDquY8A6rmQAOq5kQDquZIA6rmTAOq5lADquZUA6rmWAOq5lwDquZgA6rmZAOq5mgDquZsA6rmcAOq5nQDquZ4A6rmfAOq5oADquaEA6rmiAOq5owDquaQA6rmlAOq5pgDquacA6rmoAOq5qQDquaoA6rmrAOq5rADqua0A6rmuAOq5rwDqubAA6rmxAOq5sgDqubMA6rm0AOq5tQDqubYA6rm3AOq5uADqubkA6rm6AOq5uwDqubwA6rm9AOq5vgDqub8A6rqAAOq6gQDquoIA6rqDAOq6hADquoUA6rqGAOq6hwDquogA6rqJAOq6igDquosA6rqMAOq6jQDquo4A6rqPAOq6kADqupEA6rqSAOq6kwDqupQA6rqVAOq6lgDqupcA6rqYAOq6mQDqupoA6rqbAOq6nADqup0A6rqeAOq6nwDquqAA6rqhAOq6ogDquqMA6rqkAOq6pQDquqYA6rqnAOq6qADquqkA6rqqAOq6qwDquqwA6rqtAOq6rgDquq8A6rqwAOq6sQDqurIA6rqzAOq6tADqurUA6rq2AOq6twDqurgA6rq5AOq6ugDqursA6rq8AOq6vQDqur4A6rq/AOq7gADqu4EA6ruCAOq7gwDqu4QA6ruFAOq7hgDqu4cA6ruIAOq7iQDqu4oA6ruLAOq7jADqu40A6ruOAOq7jwDqu5AA6ruRAOq7kgDqu5MA6ruUAOq7lQDqu5YA6ruXAOq7mADqu5kA6ruaAOq7mwDqu5wA6rudAOq7ngDqu58A6rugAOq7oQDqu6IA6rujAOq7pADqu6UA6rumAOq7pwDqu6gA6rupAOq7qgDqu6sA6rusAOq7rQDqu64A6ruvAOq7sADqu7EA6ruyAOq7swDqu7QA6ru1AOq7tgDqu7cA6ru4AOq7uQDqu7oA6ru7AOq7vADqu70A6ru+AOq7vwDqvIAA6ryBAOq8ggDqvIMA6ryEAOq8hQDqvIYA6ryHAOq8iADqvIkA6ryKAOq8iwDqvIwA6ryNAOq8jgDqvI8A6ryQAOq8kQDqvJIA6ryTAOq8lADqvJUA6ryWAOq8lwDqvJgA6ryZAOq8mgDqvJsA6rycAOq8nQDqvJ4A6ryfAOq8oADqvKEA6ryiAOq8owDqvKQA6rylAOq8pgDqvKcA6ryoAOq8qQDqvKoA6ryrAOq8rADqvK0A6ryuAOq8rwDqvLAA6ryxAOq8sgDqvLMA6ry0AOq8tQDqvLYA6ry3AOq8uADqvLkA6ry6AOq8uwDqvLwA6ry9AOq8vgDqvL8A6r2AAOq9gQDqvYIA6r2DAOq9hADqvYUA6r2GAOq9hwDqvYgA6r2JAOq9igDqvYsA6r2MAOq9jQDqvY4A6r2PAOq9kADqvZEA6r2SAOq9kwDqvZQA6r2VAOq9lgDqvZcA6r2YAOq9mQDqvZoA6r2bAOq9nADqvZ0A6r2eAOq9nwDqvaAA6r2hAOq9ogDqvaMA6r2kAOq9pQDqvaYA6r2nAOq9qADqvakA6r2qAOq9qwDqvawA6r2tAOq9rgDqva8A6r2wAOq9sQDqvbIA6r2zAOq9tADqvbUA6r22AOq9twDqvbgA6r25AOq9ugDqvbsA6r28AOq9vQDqvb4A6r2/AOq+gADqvoEA6r6CAOq+gwDqvoQA6r6FAOq+hgDqvocA6r6IAOq+iQDqvooA6r6LAOq+jADqvo0A6r6OAOq+jwDqvpAA6r6RAOq+kgDqvpMA6r6UAOq+lQDqvpYA6r6XAOq+mADqvpkA6r6aAOq+mwDqvpwA6r6dAOq+ngDqvp8A6r6gAOq+oQDqvqIA6r6jAOq+pADqvqUA6r6mAOq+pwDqvqgA6r6pAOq+qgDqvqsA6r6sAOq+rQDqvq4A6r6vAOq+sADqvrEA6r6yAOq+swDqvrQA6r61AOq+tgDqvrcA6r64AOq+uQDqvroA6r67AOq+vADqvr0A6r6+AOq+vwDqv4AA6r+BAOq/ggDqv4MA6r+EAOq/hQDqv4YA6r+HAOq/iADqv4kA6r+KAOq/iwDqv4wA6r+NAOq/jgDqv48A6r+QAOq/kQDqv5IA6r+TAOq/lADqv5UA6r+WAOq/lwDqv5gA6r+ZAOq/mgDqv5sA6r+cAOq/nQDqv54A6r+fAOq/oADqv6EA6r+iAOq/owDqv6QA6r+lAOq/pgDqv6cA6r+oAOq/qQDqv6oA6r+rAOq/rADqv60A6r+uAOq/rwDqv7AA6r+xAOq/sgDqv7MA6r+0AOq/tQDqv7YA6r+3AOq/uADqv7kA6r+6AOq/uwDqv7wA6r+9AOq/vgDqv78A64CAAOuAgQDrgIIA64CDAOuAhADrgIUA64CGAOuAhwDrgIgA64CJAOuAigDrgIsA64CMAOuAjQDrgI4A64CPAOuAkADrgJEA64CSAOuAkwDrgJQA64CVAOuAlgDrgJcA64CYAOuAmQDrgJoA64CbAOuAnADrgJ0A64CeAOuAnwDrgKAA64ChAOuAogDrgKMA64CkAOuApQDrgKYA64CnAOuAqADrgKkA64CqAOuAqwDrgKwA64CtAOuArgDrgK8A64CwAOuAsQDrgLIA64CzAOuAtADrgLUA64C2AOuAtwDrgLgA64C5AOuAugDrgLsA64C8AOuAvQDrgL4A64C/AOuBgADrgYEA64GCAOuBgwDrgYQA64GFAOuBhgDrgYcA64GIAOuBiQDrgYoA64GLAOuBjADrgY0A64GOAOuBjwDrgZAA64GRAOuBkgDrgZMA64GUAOuBlQDrgZYA64GXAOuBmADrgZkA64GaAOuBmwDrgZwA64GdAOuBngDrgZ8A64GgAOuBoQDrgaIA64GjAOuBpADrgaUA64GmAOuBpwDrgagA64GpAOuBqgDrgasA64GsAOuBrQDrga4A64GvAOuBsADrgbEA64GyAOuBswDrgbQA64G1AOuBtgDrgbcA64G4AOuBuQDrgboA64G7AOuBvADrgb0A64G+AOuBvwDrgoAA64KBAOuCggDrgoMA64KEAOuChQDrgoYA64KHAOuCiADrgokA64KKAOuCiwDrgowA64KNAOuCjgDrgo8A64KQAOuCkQDrgpIA64KTAOuClADrgpUA64KWAOuClwDrgpgA64KZAOuCmgDrgpsA64KcAOuCnQDrgp4A64KfAOuCoADrgqEA64KiAOuCowDrgqQA64KlAOuCpgDrgqcA64KoAOuCqQDrgqoA64KrAOuCrADrgq0A64KuAOuCrwDrgrAA64KxAOuCsgDrgrMA64K0AOuCtQDrgrYA64K3AOuCuADrgrkA64K6AOuCuwDrgrwA64K9AOuCvgDrgr8A64OAAOuDgQDrg4IA64ODAOuDhADrg4UA64OGAOuDhwDrg4gA64OJAOuDigDrg4sA64OMAOuDjQDrg44A64OPAOuDkADrg5EA64OSAOuDkwDrg5QA64OVAOuDlgDrg5cA64OYAOuDmQDrg5oA64ObAOuDnADrg50A64OeAOuDnwDrg6AA64OhAOuDogDrg6MA64OkAOuDpQDrg6YA64OnAOuDqADrg6kA64OqAOuDqwDrg6wA64OtAOuDrgDrg68A64OwAOuDsQDrg7IA64OzAOuDtADrg7UA64O2AOuDtwDrg7gA64O5AOuDugDrg7sA64O8AOuDvQDrg74A64O/AOuEgADrhIEA64SCAOuEgwDrhIQA64SFAOuEhgDrhIcA64SIAOuEiQDrhIoA64SLAOuEjADrhI0A64SOAOuEjwDrhJAA64SRAOuEkgDrhJMA64SUAOuElQDrhJYA64SXAOuEmADrhJkA64SaAOuEmwDrhJwA64SdAOuEngDrhJ8A64SgAOuEoQDrhKIA64SjAOuEpADrhKUA64SmAOuEpwDrhKgA64SpAOuEqgDrhKsA64SsAOuErQDrhK4A64SvAOuEsADrhLEA64SyAOuEswDrhLQA64S1AOuEtgDrhLcA64S4AOuEuQDrhLoA64S7AOuEvADrhL0A64S+AOuEvwDrhYAA64WBAOuFggDrhYMA64WEAOuFhQDrhYYA64WHAOuFiADrhYkA64WKAOuFiwDrhYwA64WNAOuFjgDrhY8A64WQAOuFkQDrhZIA64WTAOuFlADrhZUA64WWAOuFlwDrhZgA64WZAOuFmgDrhZsA64WcAOuFnQDrhZ4A64WfAOuFoADrhaEA64WiAOuFowDrhaQA64WlAOuFpgDrhacA64WoAOuFqQDrhaoA64WrAOuFrADrha0A64WuAOuFrwDrhbAA64WxAOuFsgDrhbMA64W0AOuFtQDrhbYA64W3AOuFuADrhbkA64W6AOuFuwDrhbwA64W9AOuFvgDrhb8A64aAAOuGgQDrhoIA64aDAOuGhADrhoUA64aGAOuGhwDrhogA64aJAOuGigDrhosA64aMAOuGjQDrho4A64aPAOuGkADrhpEA64aSAOuGkwDrhpQA64aVAOuGlgDrhpcA64aYAOuGmQDrhpoA64abAOuGnADrhp0A64aeAOuGnwDrhqAA64ahAOuGogDrhqMA64akAOuGpQDrhqYA64anAOuGqADrhqkA64aqAOuGqwDrhqwA64atAOuGrgDrhq8A64awAOuGsQDrhrIA64azAOuGtADrhrUA64a2AOuGtwDrhrgA64a5AOuGugDrhrsA64a8AOuGvQDrhr4A64a/AOuHgADrh4EA64eCAOuHgwDrh4QA64eFAOuHhgDrh4cA64eIAOuHiQDrh4oA64eLAOuHjADrh40A64eOAOuHjwDrh5AA64eRAOuHkgDrh5MA64eUAOuHlQDrh5YA64eXAOuHmADrh5kA64eaAOuHmwDrh5wA64edAOuHngDrh58A64egAOuHoQDrh6IA64ejAOuHpADrh6UA64emAOuHpwDrh6gA64epAOuHqgDrh6sA64esAOuHrQDrh64A64evAOuHsADrh7EA64eyAOuHswDrh7QA64e1AOuHtgDrh7cA64e4AOuHuQDrh7oA64e7AOuHvADrh70A64e+AOuHvwDriIAA64iBAOuIggDriIMA64iEAOuIhQDriIYA64iHAOuIiADriIkA64iKAOuIiwDriIwA64iNAOuIjgDriI8A64iQAOuIkQDriJIA64iTAOuIlADriJUA64iWAOuIlwDriJgA64iZAOuImgDriJsA64icAOuInQDriJ4A64ifAOuIoADriKEA64iiAOuIowDriKQA64ilAOuIpgDriKcA64ioAOuIqQDriKoA64irAOuIrADriK0A64iuAOuIrwDriLAA64ixAOuIsgDriLMA64i0AOuItQDriLYA64i3AOuIuADriLkA64i6AOuIuwDriLwA64i9AOuIvgDriL8A64mAAOuJgQDriYIA64mDAOuJhADriYUA64mGAOuJhwDriYgA64mJAOuJigDriYsA64mMAOuJjQDriY4A64mPAOuJkADriZEA64mSAOuJkwDriZQA64mVAOuJlgDriZcA64mYAOuJmQDriZoA64mbAOuJnADriZ0A64meAOuJnwDriaAA64mhAOuJogDriaMA64mkAOuJpQDriaYA64mnAOuJqADriakA64mqAOuJqwDriawA64mtAOuJrgDria8A64mwAOuJsQDribIA64mzAOuJtADribUA64m2AOuJtwDribgA64m5AOuJugDribsA64m8AOuJvQDrib4A64m/AOuKgADrioEA64qCAOuKgwDrioQA64qFAOuKhgDriocA64qIAOuKiQDriooA64qLAOuKjADrio0A64qOAOuKjwDripAA64qRAOuKkgDripMA64qUAOuKlQDripYA64qXAOuKmADripkA64qaAOuKmwDripwA64qdAOuKngDrip8A64qgAOuKoQDriqIA64qjAOuKpADriqUA64qmAOuKpwDriqgA64qpAOuKqgDriqsA64qsAOuKrQDriq4A64qvAOuKsADrirEA64qyAOuKswDrirQA64q1AOuKtgDrircA64q4AOuKuQDriroA64q7AOuKvADrir0A64q+AOuKvwDri4AA64uBAOuLggDri4MA64uEAOuLhQDri4YA64uHAOuLiADri4kA64uKAOuLiwDri4wA64uNAOuLjgDri48A64uQAOuLkQDri5IA64uTAOuLlADri5UA64uWAOuLlwDri5gA64uZAOuLmgDri5sA64ucAOuLnQDri54A64ufAOuLoADri6EA64uiAOuLowDri6QA64ulAOuLpgDri6cA64uoAOuLqQDri6oA64urAOuLrADri60A64uuAOuLrwDri7AA64uxAOuLsgDri7MA64u0AOuLtQDri7YA64u3AOuLuADri7kA64u6AOuLuwDri7wA64u9AOuLvgDri78A64yAAOuMgQDrjIIA64yDAOuMhADrjIUA64yGAOuMhwDrjIgA64yJAOuMigDrjIsA64yMAOuMjQDrjI4A64yPAOuMkADrjJEA64ySAOuMkwDrjJQA64yVAOuMlgDrjJcA64yYAOuMmQDrjJoA64ybAOuMnADrjJ0A64yeAOuMnwDrjKAA64yhAOuMogDrjKMA64ykAOuMpQDrjKYA64ynAOuMqADrjKkA64yqAOuMqwDrjKwA64ytAOuMrgDrjK8A64ywAOuMsQDrjLIA64yzAOuMtADrjLUA64y2AOuMtwDrjLgA64y5AOuMugDrjLsA64y8AOuMvQDrjL4A64y/AOuNgADrjYEA642CAOuNgwDrjYQA642FAOuNhgDrjYcA642IAOuNiQDrjYoA642LAOuNjADrjY0A642OAOuNjwDrjZAA642RAOuNkgDrjZMA642UAOuNlQDrjZYA642XAOuNmADrjZkA642aAOuNmwDrjZwA642dAOuNngDrjZ8A642gAOuNoQDrjaIA642jAOuNpADrjaUA642mAOuNpwDrjagA642pAOuNqgDrjasA642sAOuNrQDrja4A642vAOuNsADrjbEA642yAOuNswDrjbQA6421AOuNtgDrjbcA6424AOuNuQDrjboA6427AOuNvADrjb0A642+AOuNvwDrjoAA646BAOuOggDrjoMA646EAOuOhQDrjoYA646HAOuOiADrjokA646KAOuOiwDrjowA646NAOuOjgDrjo8A646QAOuOkQDrjpIA646TAOuOlADrjpUA646WAOuOlwDrjpgA646ZAOuOmgDrjpsA646cAOuOnQDrjp4A646fAOuOoADrjqEA646iAOuOowDrjqQA646lAOuOpgDrjqcA646oAOuOqQDrjqoA646rAOuOrADrjq0A646uAOuOrwDrjrAA646xAOuOsgDrjrMA6460AOuOtQDrjrYA6463AOuOuADrjrkA6466AOuOuwDrjrwA6469AOuOvgDrjr8A64+AAOuPgQDrj4IA64+DAOuPhADrj4UA64+GAOuPhwDrj4gA64+JAOuPigDrj4sA64+MAOuPjQDrj44A64+PAOuPkADrj5EA64+SAOuPkwDrj5QA64+VAOuPlgDrj5cA64+YAOuPmQDrj5oA64+bAOuPnADrj50A64+eAOuPnwDrj6AA64+hAOuPogDrj6MA64+kAOuPpQDrj6YA64+nAOuPqADrj6kA64+qAOuPqwDrj6wA64+tAOuPrgDrj68A64+wAOuPsQDrj7IA64+zAOuPtADrj7UA64+2AOuPtwDrj7gA64+5AOuPugDrj7sA64+8AOuPvQDrj74A64+/AOuQgADrkIEA65CCAOuQgwDrkIQA65CFAOuQhgDrkIcA65CIAOuQiQDrkIoA65CLAOuQjADrkI0A65COAOuQjwDrkJAA65CRAOuQkgDrkJMA65CUAOuQlQDrkJYA65CXAOuQmADrkJkA65CaAOuQmwDrkJwA65CdAOuQngDrkJ8A65CgAOuQoQDrkKIA65CjAOuQpADrkKUA65CmAOuQpwDrkKgA65CpAOuQqgDrkKsA65CsAOuQrQDrkK4A65CvAOuQsADrkLEA65CyAOuQswDrkLQA65C1AOuQtgDrkLcA65C4AOuQuQDrkLoA65C7AOuQvADrkL0A65C+AOuQvwDrkYAA65GBAOuRggDrkYMA65GEAOuRhQDrkYYA65GHAOuRiADrkYkA65GKAOuRiwDrkYwA65GNAOuRjgDrkY8A65GQAOuRkQDrkZIA65GTAOuRlADrkZUA65GWAOuRlwDrkZgA65GZAOuRmgDrkZsA65GcAOuRnQDrkZ4A65GfAOuRoADrkaEA65GiAOuRowDrkaQA65GlAOuRpgDrkacA65GoAOuRqQDrkaoA65GrAOuRrADrka0A65GuAOuRrwDrkbAA65GxAOuRsgDrkbMA65G0AOuRtQDrkbYA65G3AOuRuADrkbkA65G6AOuRuwDrkbwA65G9AOuRvgDrkb8A65KAAOuSgQDrkoIA65KDAOuShADrkoUA65KGAOuShwDrkogA65KJAOuSigDrkosA65KMAOuSjQDrko4A65KPAOuSkADrkpEA65KSAOuSkwDrkpQA65KVAOuSlgDrkpcA65KYAOuSmQDrkpoA65KbAOuSnADrkp0A65KeAOuSnwDrkqAA65KhAOuSogDrkqMA65KkAOuSpQDrkqYA65KnAOuSqADrkqkA65KqAOuSqwDrkqwA65KtAOuSrgDrkq8A65KwAOuSsQDrkrIA65KzAOuStADrkrUA65K2AOuStwDrkrgA65K5AOuSugDrkrsA65K8AOuSvQDrkr4A65K/AOuTgADrk4EA65OCAOuTgwDrk4QA65OFAOuThgDrk4cA65OIAOuTiQDrk4oA65OLAOuTjADrk40A65OOAOuTjwDrk5AA65ORAOuTkgDrk5MA65OUAOuTlQDrk5YA65OXAOuTmADrk5kA65OaAOuTmwDrk5wA65OdAOuTngDrk58A65OgAOuToQDrk6IA65OjAOuTpADrk6UA65OmAOuTpwDrk6gA65OpAOuTqgDrk6sA65OsAOuTrQDrk64A65OvAOuTsADrk7EA65OyAOuTswDrk7QA65O1AOuTtgDrk7cA65O4AOuTuQDrk7oA65O7AOuTvADrk70A65O+AOuTvwDrlIAA65SBAOuUggDrlIMA65SEAOuUhQDrlIYA65SHAOuUiADrlIkA65SKAOuUiwDrlIwA65SNAOuUjgDrlI8A65SQAOuUkQDrlJIA65STAOuUlADrlJUA65SWAOuUlwDrlJgA65SZAOuUmgDrlJsA65ScAOuUnQDrlJ4A65SfAOuUoADrlKEA65SiAOuUowDrlKQA65SlAOuUpgDrlKcA65SoAOuUqQDrlKoA65SrAOuUrADrlK0A65SuAOuUrwDrlLAA65SxAOuUsgDrlLMA65S0AOuUtQDrlLYA65S3AOuUuADrlLkA65S6AOuUuwDrlLwA65S9AOuUvgDrlL8A65WAAOuVgQDrlYIA65WDAOuVhADrlYUA65WGAOuVhwDrlYgA65WJAOuVigDrlYsA65WMAOuVjQDrlY4A65WPAOuVkADrlZEA65WSAOuVkwDrlZQA65WVAOuVlgDrlZcA65WYAOuVmQDrlZoA65WbAOuVnADrlZ0A65WeAOuVnwDrlaAA65WhAOuVogDrlaMA65WkAOuVpQDrlaYA65WnAOuVqADrlakA65WqAOuVqwDrlawA65WtAOuVrgDrla8A65WwAOuVsQDrlbIA65WzAOuVtADrlbUA65W2AOuVtwDrlbgA65W5AOuVugDrlbsA65W8AOuVvQDrlb4A65W/AOuWgADrloEA65aCAOuWgwDrloQA65aFAOuWhgDrlocA65aIAOuWiQDrlooA65aLAOuWjADrlo0A65aOAOuWjwDrlpAA65aRAOuWkgDrlpMA65aUAOuWlQDrlpYA65aXAOuWmADrlpkA65aaAOuWmwDrlpwA65adAOuWngDrlp8A65agAOuWoQDrlqIA65ajAOuWpADrlqUA65amAOuWpwDrlqgA65apAOuWqgDrlqsA65asAOuWrQDrlq4A65avAOuWsADrlrEA65ayAOuWswDrlrQA65a1AOuWtgDrlrcA65a4AOuWuQDrlroA65a7AOuWvADrlr0A65a+AOuWvwDrl4AA65eBAOuXggDrl4MA65eEAOuXhQDrl4YA65eHAOuXiADrl4kA65eKAOuXiwDrl4wA65eNAOuXjgDrl48A65eQAOuXkQDrl5IA65eTAOuXlADrl5UA65eWAOuXlwDrl5gA65eZAOuXmgDrl5sA65ecAOuXnQDrl54A65efAOuXoADrl6EA65eiAOuXowDrl6QA65elAOuXpgDrl6cA65eoAOuXqQDrl6oA65erAOuXrADrl60A65euAOuXrwDrl7AA65exAOuXsgDrl7MA65e0AOuXtQDrl7YA65e3AOuXuADrl7kA65e6AOuXuwDrl7wA65e9AOuXvgDrl78A65iAAOuYgQDrmIIA65iDAOuYhADrmIUA65iGAOuYhwDrmIgA65iJAOuYigDrmIsA65iMAOuYjQDrmI4A65iPAOuYkADrmJEA65iSAOuYkwDrmJQA65iVAOuYlgDrmJcA65iYAOuYmQDrmJoA65ibAOuYnADrmJ0A65ieAOuYnwDrmKAA65ihAOuYogDrmKMA65ikAOuYpQDrmKYA65inAOuYqADrmKkA65iqAOuYqwDrmKwA65itAOuYrgDrmK8A65iwAOuYsQDrmLIA65izAOuYtADrmLUA65i2AOuYtwDrmLgA65i5AOuYugDrmLsA65i8AOuYvQDrmL4A65i/AOuZgADrmYEA65mCAOuZgwDrmYQA65mFAOuZhgDrmYcA65mIAOuZiQDrmYoA65mLAOuZjADrmY0A65mOAOuZjwDrmZAA65mRAOuZkgDrmZMA65mUAOuZlQDrmZYA65mXAOuZmADrmZkA65maAOuZmwDrmZwA65mdAOuZngDrmZ8A65mgAOuZoQDrmaIA65mjAOuZpADrmaUA65mmAOuZpwDrmagA65mpAOuZqgDrmasA65msAOuZrQDrma4A65mvAOuZsADrmbEA65myAOuZswDrmbQA65m1AOuZtgDrmbcA65m4AOuZuQDrmboA65m7AOuZvADrmb0A65m+AOuZvwDrmoAA65qBAOuaggDrmoMA65qEAOuahQDrmoYA65qHAOuaiADrmokA65qKAOuaiwDrmowA65qNAOuajgDrmo8A65qQAOuakQDrmpIA65qTAOualADrmpUA65qWAOualwDrmpgA65qZAOuamgDrmpsA65qcAOuanQDrmp4A65qfAOuaoADrmqEA65qiAOuaowDrmqQA65qlAOuapgDrmqcA65qoAOuaqQDrmqoA65qrAOuarADrmq0A65quAOuarwDrmrAA65qxAOuasgDrmrMA65q0AOuatQDrmrYA65q3AOuauADrmrkA65q6AOuauwDrmrwA65q9AOuavgDrmr8A65uAAOubgQDrm4IA65uDAOubhADrm4UA65uGAOubhwDrm4gA65uJAOubigDrm4sA65uMAOubjQDrm44A65uPAOubkADrm5EA65uSAOubkwDrm5QA65uVAOublgDrm5cA65uYAOubmQDrm5oA65ubAOubnADrm50A65ueAOubnwDrm6AA65uhAOubogDrm6MA65ukAOubpQDrm6YA65unAOubqADrm6kA65uqAOubqwDrm6wA65utAOubrgDrm68A65uwAOubsQDrm7IA65uzAOubtADrm7UA65u2AOubtwDrm7gA65u5AOubugDrm7sA65u8AOubvQDrm74A65u/AOucgADrnIEA65yCAOucgwDrnIQA65yFAOuchgDrnIcA65yIAOuciQDrnIoA65yLAOucjADrnI0A65yOAOucjwDrnJAA65yRAOuckgDrnJMA65yUAOuclQDrnJYA65yXAOucmADrnJkA65yaAOucmwDrnJwA65ydAOucngDrnJ8A65ygAOucoQDrnKIA65yjAOucpADrnKUA65ymAOucpwDrnKgA65ypAOucqgDrnKsA65ysAOucrQDrnK4A65yvAOucsADrnLEA65yyAOucswDrnLQA65y1AOuctgDrnLcA65y4AOucuQDrnLoA65y7AOucvADrnL0A65y+AOucvwDrnYAA652BAOudggDrnYMA652EAOudhQDrnYYA652HAOudiADrnYkA652KAOudiwDrnYwA652NAOudjgDrnY8A652QAOudkQDrnZIA652TAOudlADrnZUA652WAOudlwDrnZgA652ZAOudmgDrnZsA652cAOudnQDrnZ4A652fAOudoADrnaEA652iAOudowDrnaQA652lAOudpgDrnacA652oAOudqQDrnaoA652rAOudrADrna0A652uAOudrwDrnbAA652xAOudsgDrnbMA6520AOudtQDrnbYA6523AOuduADrnbkA6526AOuduwDrnbwA6529AOudvgDrnb8A656AAOuegQDrnoIA656DAOuehADrnoUA656GAOuehwDrnogA656JAOueigDrnosA656MAOuejQDrno4A656PAOuekADrnpEA656SAOuekwDrnpQA656VAOuelgDrnpcA656YAOuemQDrnpoA656bAOuenADrnp0A656eAOuenwDrnqAA656hAOueogDrnqMA656kAOuepQDrnqYA656nAOueqADrnqkA656qAOueqwDrnqwA656tAOuergDrnq8A656wAOuesQDrnrIA656zAOuetADrnrUA6562AOuetwDrnrgA6565AOueugDrnrsA6568AOuevQDrnr4A656/AOufgADrn4EA65+CAOufgwDrn4QA65+FAOufhgDrn4cA65+IAOufiQDrn4oA65+LAOufjADrn40A65+OAOufjwDrn5AA65+RAOufkgDrn5MA65+UAOuflQDrn5YA65+XAOufmADrn5kA65+aAOufmwDrn5wA65+dAOufngDrn58A65+gAOufoQDrn6IA65+jAOufpADrn6UA65+mAOufpwDrn6gA65+pAOufqgDrn6sA65+sAOufrQDrn64A65+vAOufsADrn7EA65+yAOufswDrn7QA65+1AOuftgDrn7cA65+4AOufuQDrn7oA65+7AOufvADrn70A65++AOufvwDroIAA66CBAOugggDroIMA66CEAOughQDroIYA66CHAOugiADroIkA66CKAOugiwDroIwA66CNAOugjgDroI8A66CQAOugkQDroJIA66CTAOuglADroJUA66CWAOuglwDroJgA66CZAOugmgDroJsA66CcAOugnQDroJ4A66CfAOugoADroKEA66CiAOugowDroKQA66ClAOugpgDroKcA66CoAOugqQDroKoA66CrAOugrADroK0A66CuAOugrwDroLAA66CxAOugsgDroLMA66C0AOugtQDroLYA66C3AOuguADroLkA66C6AOuguwDroLwA66C9AOugvgDroL8A66GAAOuhgQDroYIA66GDAOuhhADroYUA66GGAOuhhwDroYgA66GJAOuhigDroYsA66GMAOuhjQDroY4A66GPAOuhkADroZEA66GSAOuhkwDroZQA66GVAOuhlgDroZcA66GYAOuhmQDroZoA66GbAOuhnADroZ0A66GeAOuhnwDroaAA66GhAOuhogDroaMA66GkAOuhpQDroaYA66GnAOuhqADroakA66GqAOuhqwDroawA66GtAOuhrgDroa8A66GwAOuhsQDrobIA66GzAOuhtADrobUA66G2AOuhtwDrobgA66G5AOuhugDrobsA66G8AOuhvQDrob4A66G/AOuigADrooEA66KCAOuigwDrooQA66KFAOuihgDroocA66KIAOuiiQDroooA66KLAOuijADroo0A66KOAOuijwDropAA66KRAOuikgDropMA66KUAOuilQDropYA66KXAOuimADropkA66KaAOuimwDropwA66KdAOuingDrop8A66KgAOuioQDroqIA66KjAOuipADroqUA66KmAOuipwDroqgA66KpAOuiqgDroqsA66KsAOuirQDroq4A66KvAOuisADrorEA66KyAOuiswDrorQA66K1AOuitgDrorcA66K4AOuiuQDroroA66K7AOuivADror0A66K+AOuivwDro4AA66OBAOujggDro4MA66OEAOujhQDro4YA66OHAOujiADro4kA66OKAOujiwDro4wA66ONAOujjgDro48A66OQAOujkQDro5IA66OTAOujlADro5UA66OWAOujlwDro5gA66OZAOujmgDro5sA66OcAOujnQDro54A66OfAOujoADro6EA66OiAOujowDro6QA66OlAOujpgDro6cA66OoAOujqQDro6oA66OrAOujrADro60A66OuAOujrwDro7AA66OxAOujsgDro7MA66O0AOujtQDro7YA66O3AOujuADro7kA66O6AOujuwDro7wA66O9AOujvgDro78A66SAAOukgQDrpIIA66SDAOukhADrpIUA66SGAOukhwDrpIgA66SJAOukigDrpIsA66SMAOukjQDrpI4A66SPAOukkADrpJEA66SSAOukkwDrpJQA66SVAOuklgDrpJcA66SYAOukmQDrpJoA66SbAOuknADrpJ0A66SeAOuknwDrpKAA66ShAOukogDrpKMA66SkAOukpQDrpKYA66SnAOukqADrpKkA66SqAOukqwDrpKwA66StAOukrgDrpK8A66SwAOuksQDrpLIA66SzAOuktADrpLUA66S2AOuktwDrpLgA66S5AOukugDrpLsA66S8AOukvQDrpL4A66S/AOulgADrpYEA66WCAOulgwDrpYQA66WFAOulhgDrpYcA66WIAOuliQDrpYoA66WLAOuljADrpY0A66WOAOuljwDrpZAA66WRAOulkgDrpZMA66WUAOullQDrpZYA66WXAOulmADrpZkA66WaAOulmwDrpZwA66WdAOulngDrpZ8A66WgAOuloQDrpaIA66WjAOulpADrpaUA66WmAOulpwDrpagA66WpAOulqgDrpasA66WsAOulrQDrpa4A66WvAOulsADrpbEA66WyAOulswDrpbQA66W1AOultgDrpbcA66W4AOuluQDrpboA66W7AOulvADrpb0A66W+AOulvwDrpoAA66aBAOumggDrpoMA66aEAOumhQDrpoYA66aHAOumiADrpokA66aKAOumiwDrpowA66aNAOumjgDrpo8A66aQAOumkQDrppIA66aTAOumlADrppUA66aWAOumlwDrppgA66aZAOummgDrppsA66acAOumnQDrpp4A66afAOumoADrpqEA66aiAOumowDrpqQA66alAOumpgDrpqcA66aoAOumqQDrpqoA66arAOumrADrpq0A66auAOumrwDrprAA66axAOumsgDrprMA66a0AOumtQDrprYA66a3AOumuADrprkA66a6AOumuwDrprwA66a9AOumvgDrpr8A66eAAOungQDrp4IA66eDAOunhADrp4UA66eGAOunhwDrp4gA66eJAOunigDrp4sA66eMAOunjQDrp44A66ePAOunkADrp5EA66eSAOunkwDrp5QA66eVAOunlgDrp5cA66eYAOunmQDrp5oA66ebAOunnADrp50A66eeAOunnwDrp6AA66ehAOunogDrp6MA66ekAOunpQDrp6YA66enAOunqADrp6kA66eqAOunqwDrp6wA66etAOunrgDrp68A66ewAOunsQDrp7IA66ezAOuntADrp7UA66e2AOuntwDrp7gA66e5AOunugDrp7sA66e8AOunvQDrp74A66e/AOuogADrqIEA66iCAOuogwDrqIQA66iFAOuohgDrqIcA66iIAOuoiQDrqIoA66iLAOuojADrqI0A66iOAOuojwDrqJAA66iRAOuokgDrqJMA66iUAOuolQDrqJYA66iXAOuomADrqJkA66iaAOuomwDrqJwA66idAOuongDrqJ8A66igAOuooQDrqKIA66ijAOuopADrqKUA66imAOuopwDrqKgA66ipAOuoqgDrqKsA66isAOuorQDrqK4A66ivAOuosADrqLEA66iyAOuoswDrqLQA66i1AOuotgDrqLcA66i4AOuouQDrqLoA66i7AOuovADrqL0A66i+AOuovwDrqYAA66mBAOupggDrqYMA66mEAOuphQDrqYYA66mHAOupiADrqYkA66mKAOupiwDrqYwA66mNAOupjgDrqY8A66mQAOupkQDrqZIA66mTAOuplADrqZUA66mWAOuplwDrqZgA66mZAOupmgDrqZsA66mcAOupnQDrqZ4A66mfAOupoADrqaEA66miAOupowDrqaQA66mlAOuppgDrqacA66moAOupqQDrqaoA66mrAOuprADrqa0A66muAOuprwDrqbAA66mxAOupsgDrqbMA66m0AOuptQDrqbYA66m3AOupuADrqbkA66m6AOupuwDrqbwA66m9AOupvgDrqb8A66qAAOuqgQDrqoIA66qDAOuqhADrqoUA66qGAOuqhwDrqogA66qJAOuqigDrqosA66qMAOuqjQDrqo4A66qPAOuqkADrqpEA66qSAOuqkwDrqpQA66qVAOuqlgDrqpcA66qYAOuqmQDrqpoA66qbAOuqnADrqp0A66qeAOuqnwDrqqAA66qhAOuqogDrqqMA66qkAOuqpQDrqqYA66qnAOuqqADrqqkA66qqAOuqqwDrqqwA66qtAOuqrgDrqq8A66qwAOuqsQDrqrIA66qzAOuqtADrqrUA66q2AOuqtwDrqrgA66q5AOuqugDrqrsA66q8AOuqvQDrqr4A66q/AOurgADrq4EA66uCAOurgwDrq4QA66uFAOurhgDrq4cA66uIAOuriQDrq4oA66uLAOurjADrq40A66uOAOurjwDrq5AA66uRAOurkgDrq5MA66uUAOurlQDrq5YA66uXAOurmADrq5kA66uaAOurmwDrq5wA66udAOurngDrq58A66ugAOuroQDrq6IA66ujAOurpADrq6UA66umAOurpwDrq6gA66upAOurqgDrq6sA66usAOurrQDrq64A66uvAOursADrq7EA66uyAOurswDrq7QA66u1AOurtgDrq7cA66u4AOuruQDrq7oA66u7AOurvADrq70A66u+AOurvwDrrIAA66yBAOusggDrrIMA66yEAOushQDrrIYA66yHAOusiADrrIkA66yKAOusiwDrrIwA66yNAOusjgDrrI8A66yQAOuskQDrrJIA66yTAOuslADrrJUA66yWAOuslwDrrJgA66yZAOusmgDrrJsA66ycAOusnQDrrJ4A66yfAOusoADrrKEA66yiAOusowDrrKQA66ylAOuspgDrrKcA66yoAOusqQDrrKoA66yrAOusrADrrK0A66yuAOusrwDrrLAA66yxAOussgDrrLMA66y0AOustQDrrLYA66y3AOusuADrrLkA66y6AOusuwDrrLwA66y9AOusvgDrrL8A662AAOutgQDrrYIA662DAOuthADrrYUA662GAOuthwDrrYgA662JAOutigDrrYsA662MAOutjQDrrY4A662PAOutkADrrZEA662SAOutkwDrrZQA662VAOutlgDrrZcA662YAOutmQDrrZoA662bAOutnADrrZ0A662eAOutnwDrraAA662hAOutogDrraMA662kAOutpQDrraYA662nAOutqADrrakA662qAOutqwDrrawA662tAOutrgDrra8A662wAOutsQDrrbIA662zAOuttADrrbUA6622AOuttwDrrbgA6625AOutugDrrbsA6628AOutvQDrrb4A662/AOuugADrroEA666CAOuugwDrroQA666FAOuuhgDrrocA666IAOuuiQDrrooA666LAOuujADrro0A666OAOuujwDrrpAA666RAOuukgDrrpMA666UAOuulQDrrpYA666XAOuumADrrpkA666aAOuumwDrrpwA666dAOuungDrrp8A666gAOuuoQDrrqIA666jAOuupADrrqUA666mAOuupwDrrqgA666pAOuuqgDrrqsA666sAOuurQDrrq4A666vAOuusADrrrEA666yAOuuswDrrrQA6661AOuutgDrrrcA6664AOuuuQDrrroA6667AOuuvADrrr0A666+AOuuvwDrr4AA66+BAOuvggDrr4MA66+EAOuvhQDrr4YA66+HAOuviADrr4kA66+KAOuviwDrr4wA66+NAOuvjgDrr48A66+QAOuvkQDrr5IA66+TAOuvlADrr5UA66+WAOuvlwDrr5gA66+ZAOuvmgDrr5sA66+cAOuvnQDrr54A66+fAOuvoADrr6EA66+iAOuvowDrr6QA66+lAOuvpgDrr6cA66+oAOuvqQDrr6oA66+rAOuvrADrr60A66+uAOuvrwDrr7AA66+xAOuvsgDrr7MA66+0AOuvtQDrr7YA66+3AOuvuADrr7kA66+6AOuvuwDrr7wA66+9AOuvvgDrr78A67CAAOuwgQDrsIIA67CDAOuwhADrsIUA67CGAOuwhwDrsIgA67CJAOuwigDrsIsA67CMAOuwjQDrsI4A67CPAOuwkADrsJEA67CSAOuwkwDrsJQA67CVAOuwlgDrsJcA67CYAOuwmQDrsJoA67CbAOuwnADrsJ0A67CeAOuwnwDrsKAA67ChAOuwogDrsKMA67CkAOuwpQDrsKYA67CnAOuwqADrsKkA67CqAOuwqwDrsKwA67CtAOuwrgDrsK8A67CwAOuwsQDrsLIA67CzAOuwtADrsLUA67C2AOuwtwDrsLgA67C5AOuwugDrsLsA67C8AOuwvQDrsL4A67C/AOuxgADrsYEA67GCAOuxgwDrsYQA67GFAOuxhgDrsYcA67GIAOuxiQDrsYoA67GLAOuxjADrsY0A67GOAOuxjwDrsZAA67GRAOuxkgDrsZMA67GUAOuxlQDrsZYA67GXAOuxmADrsZkA67GaAOuxmwDrsZwA67GdAOuxngDrsZ8A67GgAOuxoQDrsaIA67GjAOuxpADrsaUA67GmAOuxpwDrsagA67GpAOuxqgDrsasA67GsAOuxrQDrsa4A67GvAOuxsADrsbEA67GyAOuxswDrsbQA67G1AOuxtgDrsbcA67G4AOuxuQDrsboA67G7AOuxvADrsb0A67G+AOuxvwDrsoAA67KBAOuyggDrsoMA67KEAOuyhQDrsoYA67KHAOuyiADrsokA67KKAOuyiwDrsowA67KNAOuyjgDrso8A67KQAOuykQDrspIA67KTAOuylADrspUA67KWAOuylwDrspgA67KZAOuymgDrspsA67KcAOuynQDrsp4A67KfAOuyoADrsqEA67KiAOuyowDrsqQA67KlAOuypgDrsqcA67KoAOuyqQDrsqoA67KrAOuyrADrsq0A67KuAOuyrwDrsrAA67KxAOuysgDrsrMA67K0AOuytQDrsrYA67K3AOuyuADrsrkA67K6AOuyuwDrsrwA67K9AOuyvgDrsr8A67OAAOuzgQDrs4IA67ODAOuzhADrs4UA67OGAOuzhwDrs4gA67OJAOuzigDrs4sA67OMAOuzjQDrs44A67OPAOuzkADrs5EA67OSAOuzkwDrs5QA67OVAOuzlgDrs5cA67OYAOuzmQDrs5oA67ObAOuznADrs50A67OeAOuznwDrs6AA67OhAOuzogDrs6MA67OkAOuzpQDrs6YA67OnAOuzqADrs6kA67OqAOuzqwDrs6wA67OtAOuzrgDrs68A67OwAOuzsQDrs7IA67OzAOuztADrs7UA67O2AOuztwDrs7gA67O5AOuzugDrs7sA67O8AOuzvQDrs74A67O/AOu0gADrtIEA67SCAOu0gwDrtIQA67SFAOu0hgDrtIcA67SIAOu0iQDrtIoA67SLAOu0jADrtI0A67SOAOu0jwDrtJAA67SRAOu0kgDrtJMA67SUAOu0lQDrtJYA67SXAOu0mADrtJkA67SaAOu0mwDrtJwA67SdAOu0ngDrtJ8A67SgAOu0oQDrtKIA67SjAOu0pADrtKUA67SmAOu0pwDrtKgA67SpAOu0qgDrtKsA67SsAOu0rQDrtK4A67SvAOu0sADrtLEA67SyAOu0swDrtLQA67S1AOu0tgDrtLcA67S4AOu0uQDrtLoA67S7AOu0vADrtL0A67S+AOu0vwDrtYAA67WBAOu1ggDrtYMA67WEAOu1hQDrtYYA67WHAOu1iADrtYkA67WKAOu1iwDrtYwA67WNAOu1jgDrtY8A67WQAOu1kQDrtZIA67WTAOu1lADrtZUA67WWAOu1lwDrtZgA67WZAOu1mgDrtZsA67WcAOu1nQDrtZ4A67WfAOu1oADrtaEA67WiAOu1owDrtaQA67WlAOu1pgDrtacA67WoAOu1qQDrtaoA67WrAOu1rADrta0A67WuAOu1rwDrtbAA67WxAOu1sgDrtbMA67W0AOu1tQDrtbYA67W3AOu1uADrtbkA67W6AOu1uwDrtbwA67W9AOu1vgDrtb8A67aAAOu2gQDrtoIA67aDAOu2hADrtoUA67aGAOu2hwDrtogA67aJAOu2igDrtosA67aMAOu2jQDrto4A67aPAOu2kADrtpEA67aSAOu2kwDrtpQA67aVAOu2lgDrtpcA67aYAOu2mQDrtpoA67abAOu2nADrtp0A67aeAOu2nwDrtqAA67ahAOu2ogDrtqMA67akAOu2pQDrtqYA67anAOu2qADrtqkA67aqAOu2qwDrtqwA67atAOu2rgDrtq8A67awAOu2sQDrtrIA67azAOu2tADrtrUA67a2AOu2twDrtrgA67a5AOu2ugDrtrsA67a8AOu2vQDrtr4A67a/AOu3gADrt4EA67eCAOu3gwDrt4QA67eFAOu3hgDrt4cA67eIAOu3iQDrt4oA67eLAOu3jADrt40A67eOAOu3jwDrt5AA67eRAOu3kgDrt5MA67eUAOu3lQDrt5YA67eXAOu3mADrt5kA67eaAOu3mwDrt5wA67edAOu3ngDrt58A67egAOu3oQDrt6IA67ejAOu3pADrt6UA67emAOu3pwDrt6gA67epAOu3qgDrt6sA67esAOu3rQDrt64A67evAOu3sADrt7EA67eyAOu3swDrt7QA67e1AOu3tgDrt7cA67e4AOu3uQDrt7oA67e7AOu3vADrt70A67e+AOu3vwDruIAA67iBAOu4ggDruIMA67iEAOu4hQDruIYA67iHAOu4iADruIkA67iKAOu4iwDruIwA67iNAOu4jgDruI8A67iQAOu4kQDruJIA67iTAOu4lADruJUA67iWAOu4lwDruJgA67iZAOu4mgDruJsA67icAOu4nQDruJ4A67ifAOu4oADruKEA67iiAOu4owDruKQA67ilAOu4pgDruKcA67ioAOu4qQDruKoA67irAOu4rADruK0A67iuAOu4rwDruLAA67ixAOu4sgDruLMA67i0AOu4tQDruLYA67i3AOu4uADruLkA67i6AOu4uwDruLwA67i9AOu4vgDruL8A67mAAOu5gQDruYIA67mDAOu5hADruYUA67mGAOu5hwDruYgA67mJAOu5igDruYsA67mMAOu5jQDruY4A67mPAOu5kADruZEA67mSAOu5kwDruZQA67mVAOu5lgDruZcA67mYAOu5mQDruZoA67mbAOu5nADruZ0A67meAOu5nwDruaAA67mhAOu5ogDruaMA67mkAOu5pQDruaYA67mnAOu5qADruakA67mqAOu5qwDruawA67mtAOu5rgDrua8A67mwAOu5sQDrubIA67mzAOu5tADrubUA67m2AOu5twDrubgA67m5AOu5ugDrubsA67m8AOu5vQDrub4A67m/AOu6gADruoEA67qCAOu6gwDruoQA67qFAOu6hgDruocA67qIAOu6iQDruooA67qLAOu6jADruo0A67qOAOu6jwDrupAA67qRAOu6kgDrupMA67qUAOu6lQDrupYA67qXAOu6mADrupkA67qaAOu6mwDrupwA67qdAOu6ngDrup8A67qgAOu6oQDruqIA67qjAOu6pADruqUA67qmAOu6pwDruqgA67qpAOu6qgDruqsA67qsAOu6rQDruq4A67qvAOu6sADrurEA67qyAOu6swDrurQA67q1AOu6tgDrurcA67q4AOu6uQDruroA67q7AOu6vADrur0A67q+AOu6vwDru4AA67uBAOu7ggDru4MA67uEAOu7hQDru4YA67uHAOu7iADru4kA67uKAOu7iwDru4wA67uNAOu7jgDru48A67uQAOu7kQDru5IA67uTAOu7lADru5UA67uWAOu7lwDru5gA67uZAOu7mgDru5sA67ucAOu7nQDru54A67ufAOu7oADru6EA67uiAOu7owDru6QA67ulAOu7pgDru6cA67uoAOu7qQDru6oA67urAOu7rADru60A67uuAOu7rwDru7AA67uxAOu7sgDru7MA67u0AOu7tQDru7YA67u3AOu7uADru7kA67u6AOu7uwDru7wA67u9AOu7vgDru78A67yAAOu8gQDrvIIA67yDAOu8hADrvIUA67yGAOu8hwDrvIgA67yJAOu8igDrvIsA67yMAOu8jQDrvI4A67yPAOu8kADrvJEA67ySAOu8kwDrvJQA67yVAOu8lgDrvJcA67yYAOu8mQDrvJoA67ybAOu8nADrvJ0A67yeAOu8nwDrvKAA67yhAOu8ogDrvKMA67ykAOu8pQDrvKYA67ynAOu8qADrvKkA67yqAOu8qwDrvKwA67ytAOu8rgDrvK8A67ywAOu8sQDrvLIA67yzAOu8tADrvLUA67y2AOu8twDrvLgA67y5AOu8ugDrvLsA67y8AOu8vQDrvL4A67y/AOu9gADrvYEA672CAOu9gwDrvYQA672FAOu9hgDrvYcA672IAOu9iQDrvYoA672LAOu9jADrvY0A672OAOu9jwDrvZAA672RAOu9kgDrvZMA672UAOu9lQDrvZYA672XAOu9mADrvZkA672aAOu9mwDrvZwA672dAOu9ngDrvZ8A672gAOu9oQDrvaIA672jAOu9pADrvaUA672mAOu9pwDrvagA672pAOu9qgDrvasA672sAOu9rQDrva4A672vAOu9sADrvbEA672yAOu9swDrvbQA6721AOu9tgDrvbcA6724AOu9uQDrvboA6727AOu9vADrvb0A672+AOu9vwDrvoAA676BAOu+ggDrvoMA676EAOu+hQDrvoYA676HAOu+iADrvokA676KAOu+iwDrvowA676NAOu+jgDrvo8A676QAOu+kQDrvpIA676TAOu+lADrvpUA676WAOu+lwDrvpgA676ZAOu+mgDrvpsA676cAOu+nQDrvp4A676fAOu+oADrvqEA676iAOu+owDrvqQA676lAOu+pgDrvqcA676oAOu+qQDrvqoA676rAOu+rADrvq0A676uAOu+rwDrvrAA676xAOu+sgDrvrMA6760AOu+tQDrvrYA6763AOu+uADrvrkA6766AOu+uwDrvrwA6769AOu+vgDrvr8A67+AAOu/gQDrv4IA67+DAOu/hADrv4UA67+GAOu/hwDrv4gA67+JAOu/igDrv4sA67+MAOu/jQDrv44A67+PAOu/kADrv5EA67+SAOu/kwDrv5QA67+VAOu/lgDrv5cA67+YAOu/mQDrv5oA67+bAOu/nADrv50A67+eAOu/nwDrv6AA67+hAOu/ogDrv6MA67+kAOu/pQDrv6YA67+nAOu/qADrv6kA67+qAOu/qwDrv6wA67+tAOu/rgDrv68A67+wAOu/sQDrv7IA67+zAOu/tADrv7UA67+2AOu/twDrv7gA67+5AOu/ugDrv7sA67+8AOu/vQDrv74A67+/AOyAgADsgIEA7ICCAOyAgwDsgIQA7ICFAOyAhgDsgIcA7ICIAOyAiQDsgIoA7ICLAOyAjADsgI0A7ICOAOyAjwDsgJAA7ICRAOyAkgDsgJMA7ICUAOyAlQDsgJYA7ICXAOyAmADsgJkA7ICaAOyAmwDsgJwA7ICdAOyAngDsgJ8A7ICgAOyAoQDsgKIA7ICjAOyApADsgKUA7ICmAOyApwDsgKgA7ICpAOyAqgDsgKsA7ICsAOyArQDsgK4A7ICvAOyAsADsgLEA7ICyAOyAswDsgLQA7IC1AOyAtgDsgLcA7IC4AOyAuQDsgLoA7IC7AOyAvADsgL0A7IC+AOyAvwDsgYAA7IGBAOyBggDsgYMA7IGEAOyBhQDsgYYA7IGHAOyBiADsgYkA7IGKAOyBiwDsgYwA7IGNAOyBjgDsgY8A7IGQAOyBkQDsgZIA7IGTAOyBlADsgZUA7IGWAOyBlwDsgZgA7IGZAOyBmgDsgZsA7IGcAOyBnQDsgZ4A7IGfAOyBoADsgaEA7IGiAOyBowDsgaQA7IGlAOyBpgDsgacA7IGoAOyBqQDsgaoA7IGrAOyBrADsga0A7IGuAOyBrwDsgbAA7IGxAOyBsgDsgbMA7IG0AOyBtQDsgbYA7IG3AOyBuADsgbkA7IG6AOyBuwDsgbwA7IG9AOyBvgDsgb8A7IKAAOyCgQDsgoIA7IKDAOyChADsgoUA7IKGAOyChwDsgogA7IKJAOyCigDsgosA7IKMAOyCjQDsgo4A7IKPAOyCkADsgpEA7IKSAOyCkwDsgpQA7IKVAOyClgDsgpcA7IKYAOyCmQDsgpoA7IKbAOyCnADsgp0A7IKeAOyCnwDsgqAA7IKhAOyCogDsgqMA7IKkAOyCpQDsgqYA7IKnAOyCqADsgqkA7IKqAOyCqwDsgqwA7IKtAOyCrgDsgq8A7IKwAOyCsQDsgrIA7IKzAOyCtADsgrUA7IK2AOyCtwDsgrgA7IK5AOyCugDsgrsA7IK8AOyCvQDsgr4A7IK/AOyDgADsg4EA7IOCAOyDgwDsg4QA7IOFAOyDhgDsg4cA7IOIAOyDiQDsg4oA7IOLAOyDjADsg40A7IOOAOyDjwDsg5AA7IORAOyDkgDsg5MA7IOUAOyDlQDsg5YA7IOXAOyDmADsg5kA7IOaAOyDmwDsg5wA7IOdAOyDngDsg58A7IOgAOyDoQDsg6IA7IOjAOyDpADsg6UA7IOmAOyDpwDsg6gA7IOpAOyDqgDsg6sA7IOsAOyDrQDsg64A7IOvAOyDsADsg7EA7IOyAOyDswDsg7QA7IO1AOyDtgDsg7cA7IO4AOyDuQDsg7oA7IO7AOyDvADsg70A7IO+AOyDvwDshIAA7ISBAOyEggDshIMA7ISEAOyEhQDshIYA7ISHAOyEiADshIkA7ISKAOyEiwDshIwA7ISNAOyEjgDshI8A7ISQAOyEkQDshJIA7ISTAOyElADshJUA7ISWAOyElwDshJgA7ISZAOyEmgDshJsA7IScAOyEnQDshJ4A7ISfAOyEoADshKEA7ISiAOyEowDshKQA7ISlAOyEpgDshKcA7ISoAOyEqQDshKoA7ISrAOyErADshK0A7ISuAOyErwDshLAA7ISxAOyEsgDshLMA7IS0AOyEtQDshLYA7IS3AOyEuADshLkA7IS6AOyEuwDshLwA7IS9AOyEvgDshL8A7IWAAOyFgQDshYIA7IWDAOyFhADshYUA7IWGAOyFhwDshYgA7IWJAOyFigDshYsA7IWMAOyFjQDshY4A7IWPAOyFkADshZEA7IWSAOyFkwDshZQA7IWVAOyFlgDshZcA7IWYAOyFmQDshZoA7IWbAOyFnADshZ0A7IWeAOyFnwDshaAA7IWhAOyFogDshaMA7IWkAOyFpQDshaYA7IWnAOyFqADshakA7IWqAOyFqwDshawA7IWtAOyFrgDsha8A7IWwAOyFsQDshbIA7IWzAOyFtADshbUA7IW2AOyFtwDshbgA7IW5AOyFugDshbsA7IW8AOyFvQDshb4A7IW/AOyGgADshoEA7IaCAOyGgwDshoQA7IaFAOyGhgDshocA7IaIAOyGiQDshooA7IaLAOyGjADsho0A7IaOAOyGjwDshpAA7IaRAOyGkgDshpMA7IaUAOyGlQDshpYA7IaXAOyGmADshpkA7IaaAOyGmwDshpwA7IadAOyGngDshp8A7IagAOyGoQDshqIA7IajAOyGpADshqUA7IamAOyGpwDshqgA7IapAOyGqgDshqsA7IasAOyGrQDshq4A7IavAOyGsADshrEA7IayAOyGswDshrQA7Ia1AOyGtgDshrcA7Ia4AOyGuQDshroA7Ia7AOyGvADshr0A7Ia+AOyGvwDsh4AA7IeBAOyHggDsh4MA7IeEAOyHhQDsh4YA7IeHAOyHiADsh4kA7IeKAOyHiwDsh4wA7IeNAOyHjgDsh48A7IeQAOyHkQDsh5IA7IeTAOyHlADsh5UA7IeWAOyHlwDsh5gA7IeZAOyHmgDsh5sA7IecAOyHnQDsh54A7IefAOyHoADsh6EA7IeiAOyHowDsh6QA7IelAOyHpgDsh6cA7IeoAOyHqQDsh6oA7IerAOyHrADsh60A7IeuAOyHrwDsh7AA7IexAOyHsgDsh7MA7Ie0AOyHtQDsh7YA7Ie3AOyHuADsh7kA7Ie6AOyHuwDsh7wA7Ie9AOyHvgDsh78A7IiAAOyIgQDsiIIA7IiDAOyIhADsiIUA7IiGAOyIhwDsiIgA7IiJAOyIigDsiIsA7IiMAOyIjQDsiI4A7IiPAOyIkADsiJEA7IiSAOyIkwDsiJQA7IiVAOyIlgDsiJcA7IiYAOyImQDsiJoA7IibAOyInADsiJ0A7IieAOyInwDsiKAA7IihAOyIogDsiKMA7IikAOyIpQDsiKYA7IinAOyIqADsiKkA7IiqAOyIqwDsiKwA7IitAOyIrgDsiK8A7IiwAOyIsQDsiLIA7IizAOyItADsiLUA7Ii2AOyItwDsiLgA7Ii5AOyIugDsiLsA7Ii8AOyIvQDsiL4A7Ii/AOyJgADsiYEA7ImCAOyJgwDsiYQA7ImFAOyJhgDsiYcA7ImIAOyJiQDsiYoA7ImLAOyJjADsiY0A7ImOAOyJjwDsiZAA7ImRAOyJkgDsiZMA7ImUAOyJlQDsiZYA7ImXAOyJmADsiZkA7ImaAOyJmwDsiZwA7ImdAOyJngDsiZ8A7ImgAOyJoQDsiaIA7ImjAOyJpADsiaUA7ImmAOyJpwDsiagA7ImpAOyJqgDsiasA7ImsAOyJrQDsia4A7ImvAOyJsADsibEA7ImyAOyJswDsibQA7Im1AOyJtgDsibcA7Im4AOyJuQDsiboA7Im7AOyJvADsib0A7Im+AOyJvwDsioAA7IqBAOyKggDsioMA7IqEAOyKhQDsioYA7IqHAOyKiADsiokA7IqKAOyKiwDsiowA7IqNAOyKjgDsio8A7IqQAOyKkQDsipIA7IqTAOyKlADsipUA7IqWAOyKlwDsipgA7IqZAOyKmgDsipsA7IqcAOyKnQDsip4A7IqfAOyKoADsiqEA7IqiAOyKowDsiqQA7IqlAOyKpgDsiqcA7IqoAOyKqQDsiqoA7IqrAOyKrADsiq0A7IquAOyKrwDsirAA7IqxAOyKsgDsirMA7Iq0AOyKtQDsirYA7Iq3AOyKuADsirkA7Iq6AOyKuwDsirwA7Iq9AOyKvgDsir8A7IuAAOyLgQDsi4IA7IuDAOyLhADsi4UA7IuGAOyLhwDsi4gA7IuJAOyLigDsi4sA7IuMAOyLjQDsi44A7IuPAOyLkADsi5EA7IuSAOyLkwDsi5QA7IuVAOyLlgDsi5cA7IuYAOyLmQDsi5oA7IubAOyLnADsi50A7IueAOyLnwDsi6AA7IuhAOyLogDsi6MA7IukAOyLpQDsi6YA7IunAOyLqADsi6kA7IuqAOyLqwDsi6wA7IutAOyLrgDsi68A7IuwAOyLsQDsi7IA7IuzAOyLtADsi7UA7Iu2AOyLtwDsi7gA7Iu5AOyLugDsi7sA7Iu8AOyLvQDsi74A7Iu/AOyMgADsjIEA7IyCAOyMgwDsjIQA7IyFAOyMhgDsjIcA7IyIAOyMiQDsjIoA7IyLAOyMjADsjI0A7IyOAOyMjwDsjJAA7IyRAOyMkgDsjJMA7IyUAOyMlQDsjJYA7IyXAOyMmADsjJkA7IyaAOyMmwDsjJwA7IydAOyMngDsjJ8A7IygAOyMoQDsjKIA7IyjAOyMpADsjKUA7IymAOyMpwDsjKgA7IypAOyMqgDsjKsA7IysAOyMrQDsjK4A7IyvAOyMsADsjLEA7IyyAOyMswDsjLQA7Iy1AOyMtgDsjLcA7Iy4AOyMuQDsjLoA7Iy7AOyMvADsjL0A7Iy+AOyMvwDsjYAA7I2BAOyNggDsjYMA7I2EAOyNhQDsjYYA7I2HAOyNiADsjYkA7I2KAOyNiwDsjYwA7I2NAOyNjgDsjY8A7I2QAOyNkQDsjZIA7I2TAOyNlADsjZUA7I2WAOyNlwDsjZgA7I2ZAOyNmgDsjZsA7I2cAOyNnQDsjZ4A7I2fAOyNoADsjaEA7I2iAOyNowDsjaQA7I2lAOyNpgDsjacA7I2oAOyNqQDsjaoA7I2rAOyNrADsja0A7I2uAOyNrwDsjbAA7I2xAOyNsgDsjbMA7I20AOyNtQDsjbYA7I23AOyNuADsjbkA7I26AOyNuwDsjbwA7I29AOyNvgDsjb8A7I6AAOyOgQDsjoIA7I6DAOyOhADsjoUA7I6GAOyOhwDsjogA7I6JAOyOigDsjosA7I6MAOyOjQDsjo4A7I6PAOyOkADsjpEA7I6SAOyOkwDsjpQA7I6VAOyOlgDsjpcA7I6YAOyOmQDsjpoA7I6bAOyOnADsjp0A7I6eAOyOnwDsjqAA7I6hAOyOogDsjqMA7I6kAOyOpQDsjqYA7I6nAOyOqADsjqkA7I6qAOyOqwDsjqwA7I6tAOyOrgDsjq8A7I6wAOyOsQDsjrIA7I6zAOyOtADsjrUA7I62AOyOtwDsjrgA7I65AOyOugDsjrsA7I68AOyOvQDsjr4A7I6/AOyPgADsj4EA7I+CAOyPgwDsj4QA7I+FAOyPhgDsj4cA7I+IAOyPiQDsj4oA7I+LAOyPjADsj40A7I+OAOyPjwDsj5AA7I+RAOyPkgDsj5MA7I+UAOyPlQDsj5YA7I+XAOyPmADsj5kA7I+aAOyPmwDsj5wA7I+dAOyPngDsj58A7I+gAOyPoQDsj6IA7I+jAOyPpADsj6UA7I+mAOyPpwDsj6gA7I+pAOyPqgDsj6sA7I+sAOyPrQDsj64A7I+vAOyPsADsj7EA7I+yAOyPswDsj7QA7I+1AOyPtgDsj7cA7I+4AOyPuQDsj7oA7I+7AOyPvADsj70A7I++AOyPvwDskIAA7JCBAOyQggDskIMA7JCEAOyQhQDskIYA7JCHAOyQiADskIkA7JCKAOyQiwDskIwA7JCNAOyQjgDskI8A7JCQAOyQkQDskJIA7JCTAOyQlADskJUA7JCWAOyQlwDskJgA7JCZAOyQmgDskJsA7JCcAOyQnQDskJ4A7JCfAOyQoADskKEA7JCiAOyQowDskKQA7JClAOyQpgDskKcA7JCoAOyQqQDskKoA7JCrAOyQrADskK0A7JCuAOyQrwDskLAA7JCxAOyQsgDskLMA7JC0AOyQtQDskLYA7JC3AOyQuADskLkA7JC6AOyQuwDskLwA7JC9AOyQvgDskL8A7JGAAOyRgQDskYIA7JGDAOyRhADskYUA7JGGAOyRhwDskYgA7JGJAOyRigDskYsA7JGMAOyRjQDskY4A7JGPAOyRkADskZEA7JGSAOyRkwDskZQA7JGVAOyRlgDskZcA7JGYAOyRmQDskZoA7JGbAOyRnADskZ0A7JGeAOyRnwDskaAA7JGhAOyRogDskaMA7JGkAOyRpQDskaYA7JGnAOyRqADskakA7JGqAOyRqwDskawA7JGtAOyRrgDska8A7JGwAOyRsQDskbIA7JGzAOyRtADskbUA7JG2AOyRtwDskbgA7JG5AOyRugDskbsA7JG8AOyRvQDskb4A7JG/AOySgADskoEA7JKCAOySgwDskoQA7JKFAOyShgDskocA7JKIAOySiQDskooA7JKLAOySjADsko0A7JKOAOySjwDskpAA7JKRAOySkgDskpMA7JKUAOySlQDskpYA7JKXAOySmADskpkA7JKaAOySmwDskpwA7JKdAOySngDskp8A7JKgAOySoQDskqIA7JKjAOySpADskqUA7JKmAOySpwDskqgA7JKpAOySqgDskqsA7JKsAOySrQDskq4A7JKvAOySsADskrEA7JKyAOySswDskrQA7JK1AOyStgDskrcA7JK4AOySuQDskroA7JK7AOySvADskr0A7JK+AOySvwDsk4AA7JOBAOyTggDsk4MA7JOEAOyThQDsk4YA7JOHAOyTiADsk4kA7JOKAOyTiwDsk4wA7JONAOyTjgDsk48A7JOQAOyTkQDsk5IA7JOTAOyTlADsk5UA7JOWAOyTlwDsk5gA7JOZAOyTmgDsk5sA7JOcAOyTnQDsk54A7JOfAOyToADsk6EA7JOiAOyTowDsk6QA7JOlAOyTpgDsk6cA7JOoAOyTqQDsk6oA7JOrAOyTrADsk60A7JOuAOyTrwDsk7AA7JOxAOyTsgDsk7MA7JO0AOyTtQDsk7YA7JO3AOyTuADsk7kA7JO6AOyTuwDsk7wA7JO9AOyTvgDsk78A7JSAAOyUgQDslIIA7JSDAOyUhADslIUA7JSGAOyUhwDslIgA7JSJAOyUigDslIsA7JSMAOyUjQDslI4A7JSPAOyUkADslJEA7JSSAOyUkwDslJQA7JSVAOyUlgDslJcA7JSYAOyUmQDslJoA7JSbAOyUnADslJ0A7JSeAOyUnwDslKAA7JShAOyUogDslKMA7JSkAOyUpQDslKYA7JSnAOyUqADslKkA7JSqAOyUqwDslKwA7JStAOyUrgDslK8A7JSwAOyUsQDslLIA7JSzAOyUtADslLUA7JS2AOyUtwDslLgA7JS5AOyUugDslLsA7JS8AOyUvQDslL4A7JS/AOyVgADslYEA7JWCAOyVgwDslYQA7JWFAOyVhgDslYcA7JWIAOyViQDslYoA7JWLAOyVjADslY0A7JWOAOyVjwDslZAA7JWRAOyVkgDslZMA7JWUAOyVlQDslZYA7JWXAOyVmADslZkA7JWaAOyVmwDslZwA7JWdAOyVngDslZ8A7JWgAOyVoQDslaIA7JWjAOyVpADslaUA7JWmAOyVpwDslagA7JWpAOyVqgDslasA7JWsAOyVrQDsla4A7JWvAOyVsADslbEA7JWyAOyVswDslbQA7JW1AOyVtgDslbcA7JW4AOyVuQDslboA7JW7AOyVvADslb0A7JW+AOyVvwDsloAA7JaBAOyWggDsloMA7JaEAOyWhQDsloYA7JaHAOyWiADslokA7JaKAOyWiwDslowA7JaNAOyWjgDslo8A7JaQAOyWkQDslpIA7JaTAOyWlADslpUA7JaWAOyWlwDslpgA7JaZAOyWmgDslpsA7JacAOyWnQDslp4A7JafAOyWoADslqEA7JaiAOyWowDslqQA7JalAOyWpgDslqcA7JaoAOyWqQDslqoA7JarAOyWrADslq0A7JauAOyWrwDslrAA7JaxAOyWsgDslrMA7Ja0AOyWtQDslrYA7Ja3AOyWuADslrkA7Ja6AOyWuwDslrwA7Ja9AOyWvgDslr8A7JeAAOyXgQDsl4IA7JeDAOyXhADsl4UA7JeGAOyXhwDsl4gA7JeJAOyXigDsl4sA7JeMAOyXjQDsl44A7JePAOyXkADsl5EA7JeSAOyXkwDsl5QA7JeVAOyXlgDsl5cA7JeYAOyXmQDsl5oA7JebAOyXnADsl50A7JeeAOyXnwDsl6AA7JehAOyXogDsl6MA7JekAOyXpQDsl6YA7JenAOyXqADsl6kA7JeqAOyXqwDsl6wA7JetAOyXrgDsl68A7JewAOyXsQDsl7IA7JezAOyXtADsl7UA7Je2AOyXtwDsl7gA7Je5AOyXugDsl7sA7Je8AOyXvQDsl74A7Je/AOyYgADsmIEA7JiCAOyYgwDsmIQA7JiFAOyYhgDsmIcA7JiIAOyYiQDsmIoA7JiLAOyYjADsmI0A7JiOAOyYjwDsmJAA7JiRAOyYkgDsmJMA7JiUAOyYlQDsmJYA7JiXAOyYmADsmJkA7JiaAOyYmwDsmJwA7JidAOyYngDsmJ8A7JigAOyYoQDsmKIA7JijAOyYpADsmKUA7JimAOyYpwDsmKgA7JipAOyYqgDsmKsA7JisAOyYrQDsmK4A7JivAOyYsADsmLEA7JiyAOyYswDsmLQA7Ji1AOyYtgDsmLcA7Ji4AOyYuQDsmLoA7Ji7AOyYvADsmL0A7Ji+AOyYvwDsmYAA7JmBAOyZggDsmYMA7JmEAOyZhQDsmYYA7JmHAOyZiADsmYkA7JmKAOyZiwDsmYwA7JmNAOyZjgDsmY8A7JmQAOyZkQDsmZIA7JmTAOyZlADsmZUA7JmWAOyZlwDsmZgA7JmZAOyZmgDsmZsA7JmcAOyZnQDsmZ4A7JmfAOyZoADsmaEA7JmiAOyZowDsmaQA7JmlAOyZpgDsmacA7JmoAOyZqQDsmaoA7JmrAOyZrADsma0A7JmuAOyZrwDsmbAA7JmxAOyZsgDsmbMA7Jm0AOyZtQDsmbYA7Jm3AOyZuADsmbkA7Jm6AOyZuwDsmbwA7Jm9AOyZvgDsmb8A7JqAAOyagQDsmoIA7JqDAOyahADsmoUA7JqGAOyahwDsmogA7JqJAOyaigDsmosA7JqMAOyajQDsmo4A7JqPAOyakADsmpEA7JqSAOyakwDsmpQA7JqVAOyalgDsmpcA7JqYAOyamQDsmpoA7JqbAOyanADsmp0A7JqeAOyanwDsmqAA7JqhAOyaogDsmqMA7JqkAOyapQDsmqYA7JqnAOyaqADsmqkA7JqqAOyaqwDsmqwA7JqtAOyargDsmq8A7JqwAOyasQDsmrIA7JqzAOyatADsmrUA7Jq2AOyatwDsmrgA7Jq5AOyaugDsmrsA7Jq8AOyavQDsmr4A7Jq/AOybgADsm4EA7JuCAOybgwDsm4QA7JuFAOybhgDsm4cA7JuIAOybiQDsm4oA7JuLAOybjADsm40A7JuOAOybjwDsm5AA7JuRAOybkgDsm5MA7JuUAOyblQDsm5YA7JuXAOybmADsm5kA7JuaAOybmwDsm5wA7JudAOybngDsm58A7JugAOyboQDsm6IA7JujAOybpADsm6UA7JumAOybpwDsm6gA7JupAOybqgDsm6sA7JusAOybrQDsm64A7JuvAOybsADsm7EA7JuyAOybswDsm7QA7Ju1AOybtgDsm7cA7Ju4AOybuQDsm7oA7Ju7AOybvADsm70A7Ju+AOybvwDsnIAA7JyBAOycggDsnIMA7JyEAOychQDsnIYA7JyHAOyciADsnIkA7JyKAOyciwDsnIwA7JyNAOycjgDsnI8A7JyQAOyckQDsnJIA7JyTAOyclADsnJUA7JyWAOyclwDsnJgA7JyZAOycmgDsnJsA7JycAOycnQDsnJ4A7JyfAOycoADsnKEA7JyiAOycowDsnKQA7JylAOycpgDsnKcA7JyoAOycqQDsnKoA7JyrAOycrADsnK0A7JyuAOycrwDsnLAA7JyxAOycsgDsnLMA7Jy0AOyctQDsnLYA7Jy3AOycuADsnLkA7Jy6AOycuwDsnLwA7Jy9AOycvgDsnL8A7J2AAOydgQDsnYIA7J2DAOydhADsnYUA7J2GAOydhwDsnYgA7J2JAOydigDsnYsA7J2MAOydjQDsnY4A7J2PAOydkADsnZEA7J2SAOydkwDsnZQA7J2VAOydlgDsnZcA7J2YAOydmQDsnZoA7J2bAOydnADsnZ0A7J2eAOydnwDsnaAA7J2hAOydogDsnaMA7J2kAOydpQDsnaYA7J2nAOydqADsnakA7J2qAOydqwDsnawA7J2tAOydrgDsna8A7J2wAOydsQDsnbIA7J2zAOydtADsnbUA7J22AOydtwDsnbgA7J25AOydugDsnbsA7J28AOydvQDsnb4A7J2/AOyegADsnoEA7J6CAOyegwDsnoQA7J6FAOyehgDsnocA7J6IAOyeiQDsnooA7J6LAOyejADsno0A7J6OAOyejwDsnpAA7J6RAOyekgDsnpMA7J6UAOyelQDsnpYA7J6XAOyemADsnpkA7J6aAOyemwDsnpwA7J6dAOyengDsnp8A7J6gAOyeoQDsnqIA7J6jAOyepADsnqUA7J6mAOyepwDsnqgA7J6pAOyeqgDsnqsA7J6sAOyerQDsnq4A7J6vAOyesADsnrEA7J6yAOyeswDsnrQA7J61AOyetgDsnrcA7J64AOyeuQDsnroA7J67AOyevADsnr0A7J6+AOyevwDsn4AA7J+BAOyfggDsn4MA7J+EAOyfhQDsn4YA7J+HAOyfiADsn4kA7J+KAOyfiwDsn4wA7J+NAOyfjgDsn48A7J+QAOyfkQDsn5IA7J+TAOyflADsn5UA7J+WAOyflwDsn5gA7J+ZAOyfmgDsn5sA7J+cAOyfnQDsn54A7J+fAOyfoADsn6EA7J+iAOyfowDsn6QA7J+lAOyfpgDsn6cA7J+oAOyfqQDsn6oA7J+rAOyfrADsn60A7J+uAOyfrwDsn7AA7J+xAOyfsgDsn7MA7J+0AOyftQDsn7YA7J+3AOyfuADsn7kA7J+6AOyfuwDsn7wA7J+9AOyfvgDsn78A7KCAAOyggQDsoIIA7KCDAOyghADsoIUA7KCGAOyghwDsoIgA7KCJAOygigDsoIsA7KCMAOygjQDsoI4A7KCPAOygkADsoJEA7KCSAOygkwDsoJQA7KCVAOyglgDsoJcA7KCYAOygmQDsoJoA7KCbAOygnADsoJ0A7KCeAOygnwDsoKAA7KChAOygogDsoKMA7KCkAOygpQDsoKYA7KCnAOygqADsoKkA7KCqAOygqwDsoKwA7KCtAOygrgDsoK8A7KCwAOygsQDsoLIA7KCzAOygtADsoLUA7KC2AOygtwDsoLgA7KC5AOygugDsoLsA7KC8AOygvQDsoL4A7KC/AOyhgADsoYEA7KGCAOyhgwDsoYQA7KGFAOyhhgDsoYcA7KGIAOyhiQDsoYoA7KGLAOyhjADsoY0A7KGOAOyhjwDsoZAA7KGRAOyhkgDsoZMA7KGUAOyhlQDsoZYA7KGXAOyhmADsoZkA7KGaAOyhmwDsoZwA7KGdAOyhngDsoZ8A7KGgAOyhoQDsoaIA7KGjAOyhpADsoaUA7KGmAOyhpwDsoagA7KGpAOyhqgDsoasA7KGsAOyhrQDsoa4A7KGvAOyhsADsobEA7KGyAOyhswDsobQA7KG1AOyhtgDsobcA7KG4AOyhuQDsoboA7KG7AOyhvADsob0A7KG+AOyhvwDsooAA7KKBAOyiggDsooMA7KKEAOyihQDsooYA7KKHAOyiiADsookA7KKKAOyiiwDsoowA7KKNAOyijgDsoo8A7KKQAOyikQDsopIA7KKTAOyilADsopUA7KKWAOyilwDsopgA7KKZAOyimgDsopsA7KKcAOyinQDsop4A7KKfAOyioADsoqEA7KKiAOyiowDsoqQA7KKlAOyipgDsoqcA7KKoAOyiqQDsoqoA7KKrAOyirADsoq0A7KKuAOyirwDsorAA7KKxAOyisgDsorMA7KK0AOyitQDsorYA7KK3AOyiuADsorkA7KK6AOyiuwDsorwA7KK9AOyivgDsor8A7KOAAOyjgQDso4IA7KODAOyjhADso4UA7KOGAOyjhwDso4gA7KOJAOyjigDso4sA7KOMAOyjjQDso44A7KOPAOyjkADso5EA7KOSAOyjkwDso5QA7KOVAOyjlgDso5cA7KOYAOyjmQDso5oA7KObAOyjnADso50A7KOeAOyjnwDso6AA7KOhAOyjogDso6MA7KOkAOyjpQDso6YA7KOnAOyjqADso6kA7KOqAOyjqwDso6wA7KOtAOyjrgDso68A7KOwAOyjsQDso7IA7KOzAOyjtADso7UA7KO2AOyjtwDso7gA7KO5AOyjugDso7sA7KO8AOyjvOydmADso70A7KO+AOyjvwDspIAA7KSBAOykggDspIMA7KSEAOykhQDspIYA7KSHAOykiADspIkA7KSKAOykiwDspIwA7KSNAOykjgDspI8A7KSQAOykkQDspJIA7KSTAOyklADspJUA7KSWAOyklwDspJgA7KSZAOykmgDspJsA7KScAOyknQDspJ4A7KSfAOykoADspKEA7KSiAOykowDspKQA7KSlAOykpgDspKcA7KSoAOykqQDspKoA7KSrAOykrADspK0A7KSuAOykrwDspLAA7KSxAOyksgDspLMA7KS0AOyktQDspLYA7KS3AOykuADspLkA7KS6AOykuwDspLwA7KS9AOykvgDspL8A7KWAAOylgQDspYIA7KWDAOylhADspYUA7KWGAOylhwDspYgA7KWJAOyligDspYsA7KWMAOyljQDspY4A7KWPAOylkADspZEA7KWSAOylkwDspZQA7KWVAOyllgDspZcA7KWYAOylmQDspZoA7KWbAOylnADspZ0A7KWeAOylnwDspaAA7KWhAOylogDspaMA7KWkAOylpQDspaYA7KWnAOylqADspakA7KWqAOylqwDspawA7KWtAOylrgDspa8A7KWwAOylsQDspbIA7KWzAOyltADspbUA7KW2AOyltwDspbgA7KW5AOylugDspbsA7KW8AOylvQDspb4A7KW/AOymgADspoEA7KaCAOymgwDspoQA7KaFAOymhgDspocA7KaIAOymiQDspooA7KaLAOymjADspo0A7KaOAOymjwDsppAA7KaRAOymkgDsppMA7KaUAOymlQDsppYA7KaXAOymmADsppkA7KaaAOymmwDsppwA7KadAOymngDspp8A7KagAOymoQDspqIA7KajAOympADspqUA7KamAOympwDspqgA7KapAOymqgDspqsA7KasAOymrQDspq4A7KavAOymsADsprEA7KayAOymswDsprQA7Ka1AOymtgDsprcA7Ka4AOymuQDsproA7Ka7AOymvADspr0A7Ka+AOymvwDsp4AA7KeBAOynggDsp4MA7KeEAOynhQDsp4YA7KeHAOyniADsp4kA7KeKAOyniwDsp4wA7KeNAOynjgDsp48A7KeQAOynkQDsp5IA7KeTAOynlADsp5UA7KeWAOynlwDsp5gA7KeZAOynmgDsp5sA7KecAOynnQDsp54A7KefAOynoADsp6EA7KeiAOynowDsp6QA7KelAOynpgDsp6cA7KeoAOynqQDsp6oA7KerAOynrADsp60A7KeuAOynrwDsp7AA7KexAOynsgDsp7MA7Ke0AOyntQDsp7YA7Ke3AOynuADsp7kA7Ke6AOynuwDsp7wA7Ke9AOynvgDsp78A7KiAAOyogQDsqIIA7KiDAOyohADsqIUA7KiGAOyohwDsqIgA7KiJAOyoigDsqIsA7KiMAOyojQDsqI4A7KiPAOyokADsqJEA7KiSAOyokwDsqJQA7KiVAOyolgDsqJcA7KiYAOyomQDsqJoA7KibAOyonADsqJ0A7KieAOyonwDsqKAA7KihAOyoogDsqKMA7KikAOyopQDsqKYA7KinAOyoqADsqKkA7KiqAOyoqwDsqKwA7KitAOyorgDsqK8A7KiwAOyosQDsqLIA7KizAOyotADsqLUA7Ki2AOyotwDsqLgA7Ki5AOyougDsqLsA7Ki8AOyovQDsqL4A7Ki/AOypgADsqYEA7KmCAOypgwDsqYQA7KmFAOyphgDsqYcA7KmIAOypiQDsqYoA7KmLAOypjADsqY0A7KmOAOypjwDsqZAA7KmRAOypkgDsqZMA7KmUAOyplQDsqZYA7KmXAOypmADsqZkA7KmaAOypmwDsqZwA7KmdAOypngDsqZ8A7KmgAOypoQDsqaIA7KmjAOyppADsqaUA7KmmAOyppwDsqagA7KmpAOypqgDsqasA7KmsAOyprQDsqa4A7KmvAOypsADsqbEA7KmyAOypswDsqbQA7Km1AOyptgDsqbcA7Km4AOypuQDsqboA7Km7AOypvADsqb0A7Km+AOypvwDsqoAA7KqBAOyqggDsqoMA7KqEAOyqhQDsqoYA7KqHAOyqiADsqokA7KqKAOyqiwDsqowA7KqNAOyqjgDsqo8A7KqQAOyqkQDsqpIA7KqTAOyqlADsqpUA7KqWAOyqlwDsqpgA7KqZAOyqmgDsqpsA7KqcAOyqnQDsqp4A7KqfAOyqoADsqqEA7KqiAOyqowDsqqQA7KqlAOyqpgDsqqcA7KqoAOyqqQDsqqoA7KqrAOyqrADsqq0A7KquAOyqrwDsqrAA7KqxAOyqsgDsqrMA7Kq0AOyqtQDsqrYA7Kq3AOyquADsqrkA7Kq6AOyquwDsqrwA7Kq9AOyqvgDsqr8A7KuAAOyrgQDsq4IA7KuDAOyrhADsq4UA7KuGAOyrhwDsq4gA7KuJAOyrigDsq4sA7KuMAOyrjQDsq44A7KuPAOyrkADsq5EA7KuSAOyrkwDsq5QA7KuVAOyrlgDsq5cA7KuYAOyrmQDsq5oA7KubAOyrnADsq50A7KueAOyrnwDsq6AA7KuhAOyrogDsq6MA7KukAOyrpQDsq6YA7KunAOyrqADsq6kA7KuqAOyrqwDsq6wA7KutAOyrrgDsq68A7KuwAOyrsQDsq7IA7KuzAOyrtADsq7UA7Ku2AOyrtwDsq7gA7Ku5AOyrugDsq7sA7Ku8AOyrvQDsq74A7Ku/AOysgADsrIEA7KyCAOysgwDsrIQA7KyFAOyshgDsrIcA7KyIAOysiQDsrIoA7KyLAOysjADsrI0A7KyOAOysjwDsrJAA7KyRAOyskgDsrJMA7KyUAOyslQDsrJYA7KyXAOysmADsrJkA7KyaAOysmwDsrJwA7KydAOysngDsrJ8A7KygAOysoQDsrKIA7KyjAOyspADsrKUA7KymAOyspwDsrKgA7KypAOysqgDsrKsA7KysAOysrQDsrK4A7KyvAOyssADsrLEA7KyyAOysswDsrLQA7Ky1AOystgDsrLcA7Ky4AOysuQDsrLoA7Ky7AOysvADsrL0A7Ky+AOysvwDsrYAA7K2BAOytggDsrYMA7K2EAOythQDsrYYA7K2HAOytiADsrYkA7K2KAOytiwDsrYwA7K2NAOytjgDsrY8A7K2QAOytkQDsrZIA7K2TAOytlADsrZUA7K2WAOytlwDsrZgA7K2ZAOytmgDsrZsA7K2cAOytnQDsrZ4A7K2fAOytoADsraEA7K2iAOytowDsraQA7K2lAOytpgDsracA7K2oAOytqQDsraoA7K2rAOytrADsra0A7K2uAOytrwDsrbAA7K2xAOytsgDsrbMA7K20AOyttQDsrbYA7K23AOytuADsrbkA7K26AOytuwDsrbwA7K29AOytvgDsrb8A7K6AAOyugQDsroIA7K6DAOyuhADsroUA7K6GAOyuhwDsrogA7K6JAOyuigDsrosA7K6MAOyujQDsro4A7K6PAOyukADsrpEA7K6SAOyukwDsrpQA7K6VAOyulgDsrpcA7K6YAOyumQDsrpoA7K6bAOyunADsrp0A7K6eAOyunwDsrqAA7K6hAOyuogDsrqMA7K6kAOyupQDsrqYA7K6nAOyuqADsrqkA7K6qAOyuqwDsrqwA7K6tAOyurgDsrq8A7K6wAOyusQDsrrIA7K6zAOyutADsrrUA7K62AOyutwDsrrgA7K65AOyuugDsrrsA7K68AOyuvQDsrr4A7K6/AOyvgADsr4EA7K+CAOyvgwDsr4QA7K+FAOyvhgDsr4cA7K+IAOyviQDsr4oA7K+LAOyvjADsr40A7K+OAOyvjwDsr5AA7K+RAOyvkgDsr5MA7K+UAOyvlQDsr5YA7K+XAOyvmADsr5kA7K+aAOyvmwDsr5wA7K+dAOyvngDsr58A7K+gAOyvoQDsr6IA7K+jAOyvpADsr6UA7K+mAOyvpwDsr6gA7K+pAOyvqgDsr6sA7K+sAOyvrQDsr64A7K+vAOyvsADsr7EA7K+yAOyvswDsr7QA7K+1AOyvtgDsr7cA7K+4AOyvuQDsr7oA7K+7AOyvvADsr70A7K++AOyvvwDssIAA7LCBAOywggDssIMA7LCEAOywhQDssIYA7LCHAOywiADssIkA7LCKAOywiwDssIwA7LCNAOywjgDssI8A7LCQAOywkQDssJIA7LCTAOywlADssJUA7LCWAOywlwDssJgA7LCZAOywmgDssJsA7LCcAOywnQDssJ4A7LCfAOywoADssKEA7LCiAOywowDssKQA7LClAOywpgDssKcA7LCoAOywqQDssKoA7LCrAOywrADssK0A7LCuAOywrwDssLAA7LCxAOywsgDssLMA7LC0AOywtQDssLYA7LC3AOywuADssLjqs6AA7LC5AOywugDssLsA7LC8AOywvQDssL4A7LC/AOyxgADssYEA7LGCAOyxgwDssYQA7LGFAOyxhgDssYcA7LGIAOyxiQDssYoA7LGLAOyxjADssY0A7LGOAOyxjwDssZAA7LGRAOyxkgDssZMA7LGUAOyxlQDssZYA7LGXAOyxmADssZkA7LGaAOyxmwDssZwA7LGdAOyxngDssZ8A7LGgAOyxoQDssaIA7LGjAOyxpADssaUA7LGmAOyxpwDssagA7LGpAOyxqgDssasA7LGsAOyxrQDssa4A7LGvAOyxsADssbEA7LGyAOyxswDssbQA7LG1AOyxtgDssbcA7LG4AOyxuQDssboA7LG7AOyxvADssb0A7LG+AOyxvwDssoAA7LKBAOyyggDssoMA7LKEAOyyhQDssoYA7LKHAOyyiADssokA7LKKAOyyiwDssowA7LKNAOyyjgDsso8A7LKQAOyykQDsspIA7LKTAOyylADsspUA7LKWAOyylwDsspgA7LKZAOyymgDsspsA7LKcAOyynQDssp4A7LKfAOyyoADssqEA7LKiAOyyowDssqQA7LKlAOyypgDssqcA7LKoAOyyqQDssqoA7LKrAOyyrADssq0A7LKuAOyyrwDssrAA7LKxAOyysgDssrMA7LK0AOyytQDssrYA7LK3AOyyuADssrkA7LK6AOyyuwDssrwA7LK9AOyyvgDssr8A7LOAAOyzgQDss4IA7LODAOyzhADss4UA7LOGAOyzhwDss4gA7LOJAOyzigDss4sA7LOMAOyzjQDss44A7LOPAOyzkADss5EA7LOSAOyzkwDss5QA7LOVAOyzlgDss5cA7LOYAOyzmQDss5oA7LObAOyznADss50A7LOeAOyznwDss6AA7LOhAOyzogDss6MA7LOkAOyzpQDss6YA7LOnAOyzqADss6kA7LOqAOyzqwDss6wA7LOtAOyzrgDss68A7LOwAOyzsQDss7IA7LOzAOyztADss7UA7LO2AOyztwDss7gA7LO5AOyzugDss7sA7LO8AOyzvQDss74A7LO/AOy0gADstIEA7LSCAOy0gwDstIQA7LSFAOy0hgDstIcA7LSIAOy0iQDstIoA7LSLAOy0jADstI0A7LSOAOy0jwDstJAA7LSRAOy0kgDstJMA7LSUAOy0lQDstJYA7LSXAOy0mADstJkA7LSaAOy0mwDstJwA7LSdAOy0ngDstJ8A7LSgAOy0oQDstKIA7LSjAOy0pADstKUA7LSmAOy0pwDstKgA7LSpAOy0qgDstKsA7LSsAOy0rQDstK4A7LSvAOy0sADstLEA7LSyAOy0swDstLQA7LS1AOy0tgDstLcA7LS4AOy0uQDstLoA7LS7AOy0vADstL0A7LS+AOy0vwDstYAA7LWBAOy1ggDstYMA7LWEAOy1hQDstYYA7LWHAOy1iADstYkA7LWKAOy1iwDstYwA7LWNAOy1jgDstY8A7LWQAOy1kQDstZIA7LWTAOy1lADstZUA7LWWAOy1lwDstZgA7LWZAOy1mgDstZsA7LWcAOy1nQDstZ4A7LWfAOy1oADstaEA7LWiAOy1owDstaQA7LWlAOy1pgDstacA7LWoAOy1qQDstaoA7LWrAOy1rADsta0A7LWuAOy1rwDstbAA7LWxAOy1sgDstbMA7LW0AOy1tQDstbYA7LW3AOy1uADstbkA7LW6AOy1uwDstbwA7LW9AOy1vgDstb8A7LaAAOy2gQDstoIA7LaDAOy2hADstoUA7LaGAOy2hwDstogA7LaJAOy2igDstosA7LaMAOy2jQDsto4A7LaPAOy2kADstpEA7LaSAOy2kwDstpQA7LaVAOy2lgDstpcA7LaYAOy2mQDstpoA7LabAOy2nADstp0A7LaeAOy2nwDstqAA7LahAOy2ogDstqMA7LakAOy2pQDstqYA7LanAOy2qADstqkA7LaqAOy2qwDstqwA7LatAOy2rgDstq8A7LawAOy2sQDstrIA7LazAOy2tADstrUA7La2AOy2twDstrgA7La5AOy2ugDstrsA7La8AOy2vQDstr4A7La/AOy3gADst4EA7LeCAOy3gwDst4QA7LeFAOy3hgDst4cA7LeIAOy3iQDst4oA7LeLAOy3jADst40A7LeOAOy3jwDst5AA7LeRAOy3kgDst5MA7LeUAOy3lQDst5YA7LeXAOy3mADst5kA7LeaAOy3mwDst5wA7LedAOy3ngDst58A7LegAOy3oQDst6IA7LejAOy3pADst6UA7LemAOy3pwDst6gA7LepAOy3qgDst6sA7LesAOy3rQDst64A7LevAOy3sADst7EA7LeyAOy3swDst7QA7Le1AOy3tgDst7cA7Le4AOy3uQDst7oA7Le7AOy3vADst70A7Le+AOy3vwDsuIAA7LiBAOy4ggDsuIMA7LiEAOy4hQDsuIYA7LiHAOy4iADsuIkA7LiKAOy4iwDsuIwA7LiNAOy4jgDsuI8A7LiQAOy4kQDsuJIA7LiTAOy4lADsuJUA7LiWAOy4lwDsuJgA7LiZAOy4mgDsuJsA7LicAOy4nQDsuJ4A7LifAOy4oADsuKEA7LiiAOy4owDsuKQA7LilAOy4pgDsuKcA7LioAOy4qQDsuKoA7LirAOy4rADsuK0A7LiuAOy4rwDsuLAA7LixAOy4sgDsuLMA7Li0AOy4tQDsuLYA7Li3AOy4uADsuLkA7Li6AOy4uwDsuLwA7Li9AOy4vgDsuL8A7LmAAOy5gQDsuYIA7LmDAOy5hADsuYUA7LmGAOy5hwDsuYgA7LmJAOy5igDsuYsA7LmMAOy5jQDsuY4A7LmPAOy5kADsuZEA7LmSAOy5kwDsuZQA7LmVAOy5lgDsuZcA7LmYAOy5mQDsuZoA7LmbAOy5nADsuZ0A7LmeAOy5nwDsuaAA7LmhAOy5ogDsuaMA7LmkAOy5pQDsuaYA7LmnAOy5qADsuakA7LmqAOy5qwDsuawA7LmtAOy5rgDsua8A7LmwAOy5sQDsubIA7LmzAOy5tADsubUA7Lm2AOy5twDsubgA7Lm5AOy5ugDsubsA7Lm8AOy5vQDsub4A7Lm/AOy6gADsuoEA7LqCAOy6gwDsuoQA7LqFAOy6hgDsuocA7LqIAOy6iQDsuooA7LqLAOy6jADsuo0A7LqOAOy6jwDsupAA7LqRAOy6kgDsupMA7LqUAOy6lQDsupYA7LqXAOy6mADsupkA7LqaAOy6mwDsupwA7LqdAOy6ngDsup8A7LqgAOy6oQDsuqIA7LqjAOy6pADsuqUA7LqmAOy6pwDsuqgA7LqpAOy6qgDsuqsA7LqsAOy6rQDsuq4A7LqvAOy6sADsurEA7LqyAOy6swDsurQA7Lq1AOy6tgDsurcA7Lq4AOy6uQDsuroA7Lq7AOy6vADsur0A7Lq+AOy6vwDsu4AA7LuBAOy7ggDsu4MA7LuEAOy7hQDsu4YA7LuHAOy7iADsu4kA7LuKAOy7iwDsu4wA7LuNAOy7jgDsu48A7LuQAOy7kQDsu5IA7LuTAOy7lADsu5UA7LuWAOy7lwDsu5gA7LuZAOy7mgDsu5sA7LucAOy7nQDsu54A7LufAOy7oADsu6EA7LuiAOy7owDsu6QA7LulAOy7pgDsu6cA7LuoAOy7qQDsu6oA7LurAOy7rADsu60A7LuuAOy7rwDsu7AA7LuxAOy7sgDsu7MA7Lu0AOy7tQDsu7YA7Lu3AOy7uADsu7kA7Lu6AOy7uwDsu7wA7Lu9AOy7vgDsu78A7LyAAOy8gQDsvIIA7LyDAOy8hADsvIUA7LyGAOy8hwDsvIgA7LyJAOy8igDsvIsA7LyMAOy8jQDsvI4A7LyPAOy8kADsvJEA7LySAOy8kwDsvJQA7LyVAOy8lgDsvJcA7LyYAOy8mQDsvJoA7LybAOy8nADsvJ0A7LyeAOy8nwDsvKAA7LyhAOy8ogDsvKMA7LykAOy8pQDsvKYA7LynAOy8qADsvKkA7LyqAOy8qwDsvKwA7LytAOy8rgDsvK8A7LywAOy8sQDsvLIA7LyzAOy8tADsvLUA7Ly2AOy8twDsvLgA7Ly5AOy8ugDsvLsA7Ly8AOy8vQDsvL4A7Ly/AOy9gADsvYEA7L2CAOy9gwDsvYQA7L2FAOy9hgDsvYcA7L2IAOy9iQDsvYoA7L2LAOy9jADsvY0A7L2OAOy9jwDsvZAA7L2RAOy9kgDsvZMA7L2UAOy9lQDsvZYA7L2XAOy9mADsvZkA7L2aAOy9mwDsvZwA7L2dAOy9ngDsvZ8A7L2gAOy9oQDsvaIA7L2jAOy9pADsvaUA7L2mAOy9pwDsvagA7L2pAOy9qgDsvasA7L2sAOy9rQDsva4A7L2vAOy9sADsvbEA7L2yAOy9swDsvbQA7L21AOy9tgDsvbcA7L24AOy9uQDsvboA7L27AOy9vADsvb0A7L2+AOy9vwDsvoAA7L6BAOy+ggDsvoMA7L6EAOy+hQDsvoYA7L6HAOy+iADsvokA7L6KAOy+iwDsvowA7L6NAOy+jgDsvo8A7L6QAOy+kQDsvpIA7L6TAOy+lADsvpUA7L6WAOy+lwDsvpgA7L6ZAOy+mgDsvpsA7L6cAOy+nQDsvp4A7L6fAOy+oADsvqEA7L6iAOy+owDsvqQA7L6lAOy+pgDsvqcA7L6oAOy+qQDsvqoA7L6rAOy+rADsvq0A7L6uAOy+rwDsvrAA7L6xAOy+sgDsvrMA7L60AOy+tQDsvrYA7L63AOy+uADsvrkA7L66AOy+uwDsvrwA7L69AOy+vgDsvr8A7L+AAOy/gQDsv4IA7L+DAOy/hADsv4UA7L+GAOy/hwDsv4gA7L+JAOy/igDsv4sA7L+MAOy/jQDsv44A7L+PAOy/kADsv5EA7L+SAOy/kwDsv5QA7L+VAOy/lgDsv5cA7L+YAOy/mQDsv5oA7L+bAOy/nADsv50A7L+eAOy/nwDsv6AA7L+hAOy/ogDsv6MA7L+kAOy/pQDsv6YA7L+nAOy/qADsv6kA7L+qAOy/qwDsv6wA7L+tAOy/rgDsv68A7L+wAOy/sQDsv7IA7L+zAOy/tADsv7UA7L+2AOy/twDsv7gA7L+5AOy/ugDsv7sA7L+8AOy/vQDsv74A7L+/AO2AgADtgIEA7YCCAO2AgwDtgIQA7YCFAO2AhgDtgIcA7YCIAO2AiQDtgIoA7YCLAO2AjADtgI0A7YCOAO2AjwDtgJAA7YCRAO2AkgDtgJMA7YCUAO2AlQDtgJYA7YCXAO2AmADtgJkA7YCaAO2AmwDtgJwA7YCdAO2AngDtgJ8A7YCgAO2AoQDtgKIA7YCjAO2ApADtgKUA7YCmAO2ApwDtgKgA7YCpAO2AqgDtgKsA7YCsAO2ArQDtgK4A7YCvAO2AsADtgLEA7YCyAO2AswDtgLQA7YC1AO2AtgDtgLcA7YC4AO2AuQDtgLoA7YC7AO2AvADtgL0A7YC+AO2AvwDtgYAA7YGBAO2BggDtgYMA7YGEAO2BhQDtgYYA7YGHAO2BiADtgYkA7YGKAO2BiwDtgYwA7YGNAO2BjgDtgY8A7YGQAO2BkQDtgZIA7YGTAO2BlADtgZUA7YGWAO2BlwDtgZgA7YGZAO2BmgDtgZsA7YGcAO2BnQDtgZ4A7YGfAO2BoADtgaEA7YGiAO2BowDtgaQA7YGlAO2BpgDtgacA7YGoAO2BqQDtgaoA7YGrAO2BrADtga0A7YGuAO2BrwDtgbAA7YGxAO2BsgDtgbMA7YG0AO2BtQDtgbYA7YG3AO2BuADtgbkA7YG6AO2BuwDtgbwA7YG9AO2BvgDtgb8A7YKAAO2CgQDtgoIA7YKDAO2ChADtgoUA7YKGAO2ChwDtgogA7YKJAO2CigDtgosA7YKMAO2CjQDtgo4A7YKPAO2CkADtgpEA7YKSAO2CkwDtgpQA7YKVAO2ClgDtgpcA7YKYAO2CmQDtgpoA7YKbAO2CnADtgp0A7YKeAO2CnwDtgqAA7YKhAO2CogDtgqMA7YKkAO2CpQDtgqYA7YKnAO2CqADtgqkA7YKqAO2CqwDtgqwA7YKtAO2CrgDtgq8A7YKwAO2CsQDtgrIA7YKzAO2CtADtgrUA7YK2AO2CtwDtgrgA7YK5AO2CugDtgrsA7YK8AO2CvQDtgr4A7YK/AO2DgADtg4EA7YOCAO2DgwDtg4QA7YOFAO2DhgDtg4cA7YOIAO2DiQDtg4oA7YOLAO2DjADtg40A7YOOAO2DjwDtg5AA7YORAO2DkgDtg5MA7YOUAO2DlQDtg5YA7YOXAO2DmADtg5kA7YOaAO2DmwDtg5wA7YOdAO2DngDtg58A7YOgAO2DoQDtg6IA7YOjAO2DpADtg6UA7YOmAO2DpwDtg6gA7YOpAO2DqgDtg6sA7YOsAO2DrQDtg64A7YOvAO2DsADtg7EA7YOyAO2DswDtg7QA7YO1AO2DtgDtg7cA7YO4AO2DuQDtg7oA7YO7AO2DvADtg70A7YO+AO2DvwDthIAA7YSBAO2EggDthIMA7YSEAO2EhQDthIYA7YSHAO2EiADthIkA7YSKAO2EiwDthIwA7YSNAO2EjgDthI8A7YSQAO2EkQDthJIA7YSTAO2ElADthJUA7YSWAO2ElwDthJgA7YSZAO2EmgDthJsA7YScAO2EnQDthJ4A7YSfAO2EoADthKEA7YSiAO2EowDthKQA7YSlAO2EpgDthKcA7YSoAO2EqQDthKoA7YSrAO2ErADthK0A7YSuAO2ErwDthLAA7YSxAO2EsgDthLMA7YS0AO2EtQDthLYA7YS3AO2EuADthLkA7YS6AO2EuwDthLwA7YS9AO2EvgDthL8A7YWAAO2FgQDthYIA7YWDAO2FhADthYUA7YWGAO2FhwDthYgA7YWJAO2FigDthYsA7YWMAO2FjQDthY4A7YWPAO2FkADthZEA7YWSAO2FkwDthZQA7YWVAO2FlgDthZcA7YWYAO2FmQDthZoA7YWbAO2FnADthZ0A7YWeAO2FnwDthaAA7YWhAO2FogDthaMA7YWkAO2FpQDthaYA7YWnAO2FqADthakA7YWqAO2FqwDthawA7YWtAO2FrgDtha8A7YWwAO2FsQDthbIA7YWzAO2FtADthbUA7YW2AO2FtwDthbgA7YW5AO2FugDthbsA7YW8AO2FvQDthb4A7YW/AO2GgADthoEA7YaCAO2GgwDthoQA7YaFAO2GhgDthocA7YaIAO2GiQDthooA7YaLAO2GjADtho0A7YaOAO2GjwDthpAA7YaRAO2GkgDthpMA7YaUAO2GlQDthpYA7YaXAO2GmADthpkA7YaaAO2GmwDthpwA7YadAO2GngDthp8A7YagAO2GoQDthqIA7YajAO2GpADthqUA7YamAO2GpwDthqgA7YapAO2GqgDthqsA7YasAO2GrQDthq4A7YavAO2GsADthrEA7YayAO2GswDthrQA7Ya1AO2GtgDthrcA7Ya4AO2GuQDthroA7Ya7AO2GvADthr0A7Ya+AO2GvwDth4AA7YeBAO2HggDth4MA7YeEAO2HhQDth4YA7YeHAO2HiADth4kA7YeKAO2HiwDth4wA7YeNAO2HjgDth48A7YeQAO2HkQDth5IA7YeTAO2HlADth5UA7YeWAO2HlwDth5gA7YeZAO2HmgDth5sA7YecAO2HnQDth54A7YefAO2HoADth6EA7YeiAO2HowDth6QA7YelAO2HpgDth6cA7YeoAO2HqQDth6oA7YerAO2HrADth60A7YeuAO2HrwDth7AA7YexAO2HsgDth7MA7Ye0AO2HtQDth7YA7Ye3AO2HuADth7kA7Ye6AO2HuwDth7wA7Ye9AO2HvgDth78A7YiAAO2IgQDtiIIA7YiDAO2IhADtiIUA7YiGAO2IhwDtiIgA7YiJAO2IigDtiIsA7YiMAO2IjQDtiI4A7YiPAO2IkADtiJEA7YiSAO2IkwDtiJQA7YiVAO2IlgDtiJcA7YiYAO2ImQDtiJoA7YibAO2InADtiJ0A7YieAO2InwDtiKAA7YihAO2IogDtiKMA7YikAO2IpQDtiKYA7YinAO2IqADtiKkA7YiqAO2IqwDtiKwA7YitAO2IrgDtiK8A7YiwAO2IsQDtiLIA7YizAO2ItADtiLUA7Yi2AO2ItwDtiLgA7Yi5AO2IugDtiLsA7Yi8AO2IvQDtiL4A7Yi/AO2JgADtiYEA7YmCAO2JgwDtiYQA7YmFAO2JhgDtiYcA7YmIAO2JiQDtiYoA7YmLAO2JjADtiY0A7YmOAO2JjwDtiZAA7YmRAO2JkgDtiZMA7YmUAO2JlQDtiZYA7YmXAO2JmADtiZkA7YmaAO2JmwDtiZwA7YmdAO2JngDtiZ8A7YmgAO2JoQDtiaIA7YmjAO2JpADtiaUA7YmmAO2JpwDtiagA7YmpAO2JqgDtiasA7YmsAO2JrQDtia4A7YmvAO2JsADtibEA7YmyAO2JswDtibQA7Ym1AO2JtgDtibcA7Ym4AO2JuQDtiboA7Ym7AO2JvADtib0A7Ym+AO2JvwDtioAA7YqBAO2KggDtioMA7YqEAO2KhQDtioYA7YqHAO2KiADtiokA7YqKAO2KiwDtiowA7YqNAO2KjgDtio8A7YqQAO2KkQDtipIA7YqTAO2KlADtipUA7YqWAO2KlwDtipgA7YqZAO2KmgDtipsA7YqcAO2KnQDtip4A7YqfAO2KoADtiqEA7YqiAO2KowDtiqQA7YqlAO2KpgDtiqcA7YqoAO2KqQDtiqoA7YqrAO2KrADtiq0A7YquAO2KrwDtirAA7YqxAO2KsgDtirMA7Yq0AO2KtQDtirYA7Yq3AO2KuADtirkA7Yq6AO2KuwDtirwA7Yq9AO2KvgDtir8A7YuAAO2LgQDti4IA7YuDAO2LhADti4UA7YuGAO2LhwDti4gA7YuJAO2LigDti4sA7YuMAO2LjQDti44A7YuPAO2LkADti5EA7YuSAO2LkwDti5QA7YuVAO2LlgDti5cA7YuYAO2LmQDti5oA7YubAO2LnADti50A7YueAO2LnwDti6AA7YuhAO2LogDti6MA7YukAO2LpQDti6YA7YunAO2LqADti6kA7YuqAO2LqwDti6wA7YutAO2LrgDti68A7YuwAO2LsQDti7IA7YuzAO2LtADti7UA7Yu2AO2LtwDti7gA7Yu5AO2LugDti7sA7Yu8AO2LvQDti74A7Yu/AO2MgADtjIEA7YyCAO2MgwDtjIQA7YyFAO2MhgDtjIcA7YyIAO2MiQDtjIoA7YyLAO2MjADtjI0A7YyOAO2MjwDtjJAA7YyRAO2MkgDtjJMA7YyUAO2MlQDtjJYA7YyXAO2MmADtjJkA7YyaAO2MmwDtjJwA7YydAO2MngDtjJ8A7YygAO2MoQDtjKIA7YyjAO2MpADtjKUA7YymAO2MpwDtjKgA7YypAO2MqgDtjKsA7YysAO2MrQDtjK4A7YyvAO2MsADtjLEA7YyyAO2MswDtjLQA7Yy1AO2MtgDtjLcA7Yy4AO2MuQDtjLoA7Yy7AO2MvADtjL0A7Yy+AO2MvwDtjYAA7Y2BAO2NggDtjYMA7Y2EAO2NhQDtjYYA7Y2HAO2NiADtjYkA7Y2KAO2NiwDtjYwA7Y2NAO2NjgDtjY8A7Y2QAO2NkQDtjZIA7Y2TAO2NlADtjZUA7Y2WAO2NlwDtjZgA7Y2ZAO2NmgDtjZsA7Y2cAO2NnQDtjZ4A7Y2fAO2NoADtjaEA7Y2iAO2NowDtjaQA7Y2lAO2NpgDtjacA7Y2oAO2NqQDtjaoA7Y2rAO2NrADtja0A7Y2uAO2NrwDtjbAA7Y2xAO2NsgDtjbMA7Y20AO2NtQDtjbY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"chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}", + "clean_up_tokenization_spaces": true, + "eos_token": "<|end_of_text|>", + "legacy": true, + "model_input_names": [ + "input_ids", + "attention_mask" + ], + "model_max_length": 1000000000000000019884624838656, + "pad_token": "", + "padding_side": "right", + "processor_class": "LlavaProcessor", + "tokenizer_class": "LlamaTokenizer", + "unk_token": "", + "use_default_system_prompt": false +} diff --git a/diffsynth/tokenizer_configs/kolors/tokenizer/tokenizer.model b/diffsynth/tokenizer_configs/kolors/tokenizer/tokenizer.model new file mode 100644 index 0000000000000000000000000000000000000000..8a8007697b7cc3d3868dcffbbebf8c1f2bd690ba --- /dev/null +++ b/diffsynth/tokenizer_configs/kolors/tokenizer/tokenizer.model @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e7dc4c393423b76e4373e5157ddc34803a0189ba96b21ddbb40269d31468a6f2 +size 1018370 diff --git a/diffsynth/tokenizer_configs/kolors/tokenizer/tokenizer_config.json b/diffsynth/tokenizer_configs/kolors/tokenizer/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..f6f13c88707490cebd8023da86e8bf7a56fa21e3 --- /dev/null +++ b/diffsynth/tokenizer_configs/kolors/tokenizer/tokenizer_config.json @@ -0,0 +1,12 @@ +{ + "name_or_path": "THUDM/chatglm3-6b-base", + "remove_space": false, + "do_lower_case": false, + "tokenizer_class": "ChatGLMTokenizer", + "auto_map": { + "AutoTokenizer": [ + "tokenization_chatglm.ChatGLMTokenizer", + null + ] + } +} diff --git a/diffsynth/tokenizer_configs/kolors/tokenizer/vocab.txt b/diffsynth/tokenizer_configs/kolors/tokenizer/vocab.txt new file mode 100644 index 0000000000000000000000000000000000000000..8a8007697b7cc3d3868dcffbbebf8c1f2bd690ba --- /dev/null +++ b/diffsynth/tokenizer_configs/kolors/tokenizer/vocab.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e7dc4c393423b76e4373e5157ddc34803a0189ba96b21ddbb40269d31468a6f2 +size 1018370 diff --git a/diffsynth/tokenizer_configs/stable_diffusion/tokenizer/merges.txt b/diffsynth/tokenizer_configs/stable_diffusion/tokenizer/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..76e821f1b6f0a9709293c3b6b51ed90980b3166b --- /dev/null +++ b/diffsynth/tokenizer_configs/stable_diffusion/tokenizer/merges.txt @@ -0,0 +1,48895 @@ +#version: 0.2 +i n +t h +a n +r e +a r +e r +th e +in g +o u +o n +s t +o r +e n +o n +a l +a t +e r +i t +i n +t o +r o +i s +l e +i c +a t +an d +e d +o f +c h +o r +e s +i l +e l +s t +a c +o m +a m +l o +a n +a y +s h +r i +l i +t i +f or 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Returns samples with: +- context_latents: (1, C, K, H//8, W//8) - 1 latent per context frame +- target_latents: (1, C, T, H//8, W//8) - 1 latent per 4 target frames +- prompt, video_name, start_frame, end_frame, actions + +Compatible with training that uses precomputed latents instead of encoding on the fly. +""" + +import json +import os +import warnings + +import torch + + +class LatentDataset(torch.utils.data.Dataset): + """ + Dataset that loads precomputed ctx and target latents. + """ + + def __init__( + self, + latent_dir, + metadata_path=None, + action_base_path=None, + repeat=1, + num_frames=81, + context_frames=5, + target_frames_per_latent=4, + ): + """ + Args: + latent_dir: Directory containing ctx_latents/ and target_latents/ subdirs. + metadata_path: Optional. If provided, used to get total_samples and validate. + action_base_path: Base path for action JSON files (for loading actions if not in .pt). + repeat: Dataset repeat factor. + num_frames: Expected num_frames per segment. + context_frames: Number of context frames (K). + target_frames_per_latent: Target: 1 latent per N frames. + """ + self.latent_dir = latent_dir + self.ctx_dir = os.path.join(latent_dir, "ctx_latents") + self.target_dir = os.path.join(latent_dir, "target_latents") + self.action_base_path = action_base_path or latent_dir + self.repeat = repeat + self.num_frames = num_frames + self.context_frames = context_frames + self.target_frames_per_latent = target_frames_per_latent + + # Infer valid indices from existing files (both ctx and target must exist) + self._indices = [] + if os.path.isdir(self.ctx_dir) and os.path.isdir(self.target_dir): + ctx_files = {f.replace(".pt", "") for f in os.listdir(self.ctx_dir) if f.endswith(".pt")} + target_files = {f.replace(".pt", "") for f in os.listdir(self.target_dir) if f.endswith(".pt")} + common = sorted([int(x) for x in ctx_files & target_files]) + self._indices = common + if not self._indices: + meta_path = os.path.join(latent_dir, "metadata_precompute.json") + if os.path.isfile(meta_path): + with open(meta_path) as f: + meta = json.load(f) + self._total = meta.get("total_samples", 0) + self._indices = list(range(self._total)) + else: + self._total = 0 + else: + self._total = len(self._indices) + + def __len__(self): + return self._total * self.repeat + + def __getitem__(self, idx): + real_idx = idx % self._total + if self._indices is not None: + real_idx = self._indices[real_idx] + + ctx_path = os.path.join(self.ctx_dir, f"{real_idx:08d}.pt") + target_path = os.path.join(self.target_dir, f"{real_idx:08d}.pt") + + if not os.path.isfile(ctx_path) or not os.path.isfile(target_path): + warnings.warn(f"Latent files not found for idx {real_idx}. Returning None.") + return None + + ctx_data = torch.load(ctx_path, map_location="cpu", weights_only=True) + target_data = torch.load(target_path, map_location="cpu", weights_only=True) + + ctx_latent = ctx_data["latent"] + target_latent = target_data["latent"] + + # Ensure batch dimension: (C, K, H, W) -> (1, C, K, H, W) + if ctx_latent.dim() == 4: + ctx_latent = ctx_latent.unsqueeze(0) + if target_latent.dim() == 4: + target_latent = target_latent.unsqueeze(0) + + out = { + "context_latents": ctx_latent, + "target_latents": target_latent, + "prompt": ctx_data.get("prompt", ""), + "video_name": ctx_data.get("video_name"), + "start_frame": ctx_data.get("start_frame"), + "end_frame": ctx_data.get("end_frame"), + } + if "actions" in ctx_data and ctx_data["actions"] is not None: + out["actions"] = ctx_data["actions"] + elif "actions" in target_data and target_data["actions"] is not None: + out["actions"] = target_data["actions"] + + return out + + +def get_latent_dataset_args(latent_dir, action_base_path=None, **kwargs): + """Build argparse.Namespace for LatentDataset from precompute metadata.""" + meta_path = os.path.join(latent_dir, "metadata_precompute.json") + if not os.path.isfile(meta_path): + return None + with open(meta_path) as f: + meta = json.load(f) + from argparse import Namespace + return Namespace( + latent_dir=latent_dir, + action_base_path=action_base_path or meta.get("dataset_base_path", latent_dir), + num_frames=meta.get("num_frames", 81), + context_frames=meta.get("context_frames", 5), + target_frames_per_latent=meta.get("target_frames_per_latent", 4), + **kwargs, + ) diff --git a/diffsynth/trainers/text_to_image.py b/diffsynth/trainers/text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..a76a7912da12f1ca665758542d92e50fea02713c --- /dev/null +++ b/diffsynth/trainers/text_to_image.py @@ -0,0 +1,318 @@ +import lightning as pl +from peft import LoraConfig, inject_adapter_in_model +import torch, os +from ..data.simple_text_image import TextImageDataset +from modelscope.hub.api import HubApi +from ..models.utils import load_state_dict + + + +class LightningModelForT2ILoRA(pl.LightningModule): + def __init__( + self, + learning_rate=1e-4, + use_gradient_checkpointing=True, + state_dict_converter=None, + ): + super().__init__() + # Set parameters + self.learning_rate = learning_rate + self.use_gradient_checkpointing = use_gradient_checkpointing + self.state_dict_converter = state_dict_converter + self.lora_alpha = None + + + def load_models(self): + # This function is implemented in other modules + self.pipe = None + + + def freeze_parameters(self): + # Freeze parameters + self.pipe.requires_grad_(False) + self.pipe.eval() + self.pipe.denoising_model().train() + + + def add_lora_to_model(self, model, lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian", pretrained_lora_path=None, state_dict_converter=None): + # Add LoRA to UNet + self.lora_alpha = lora_alpha + if init_lora_weights == "kaiming": + init_lora_weights = True + + lora_config = LoraConfig( + r=lora_rank, + lora_alpha=lora_alpha, + init_lora_weights=init_lora_weights, + target_modules=lora_target_modules.split(","), + ) + model = inject_adapter_in_model(lora_config, model) + for param in model.parameters(): + # Upcast LoRA parameters into fp32 + if param.requires_grad: + param.data = param.to(torch.float32) + + # Lora pretrained lora weights + if pretrained_lora_path is not None: + state_dict = load_state_dict(pretrained_lora_path) + if state_dict_converter is not None: + state_dict = state_dict_converter(state_dict) + missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False) + all_keys = [i for i, _ in model.named_parameters()] + num_updated_keys = len(all_keys) - len(missing_keys) + num_unexpected_keys = len(unexpected_keys) + print(f"{num_updated_keys} parameters are loaded from {pretrained_lora_path}. {num_unexpected_keys} parameters are unexpected.") + + + def training_step(self, batch, batch_idx): + # Data + text, image = batch["text"], batch["image"] + + # Prepare input parameters + self.pipe.device = self.device + prompt_emb = self.pipe.encode_prompt(text, positive=True) + if "latents" in batch: + latents = batch["latents"].to(dtype=self.pipe.torch_dtype, device=self.device) + else: + latents = self.pipe.vae_encoder(image.to(dtype=self.pipe.torch_dtype, device=self.device)) + noise = torch.randn_like(latents) + timestep_id = torch.randint(0, self.pipe.scheduler.num_train_timesteps, (1,)) + timestep = self.pipe.scheduler.timesteps[timestep_id].to(self.device) + extra_input = self.pipe.prepare_extra_input(latents) + noisy_latents = self.pipe.scheduler.add_noise(latents, noise, timestep) + training_target = self.pipe.scheduler.training_target(latents, noise, timestep) + + # Compute loss + noise_pred = self.pipe.denoising_model()( + noisy_latents, timestep=timestep, **prompt_emb, **extra_input, + use_gradient_checkpointing=self.use_gradient_checkpointing + ) + loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float()) + loss = loss * self.pipe.scheduler.training_weight(timestep) + + # Record log + self.log("train_loss", loss, prog_bar=True) + return loss + + + def configure_optimizers(self): + trainable_modules = filter(lambda p: p.requires_grad, self.pipe.denoising_model().parameters()) + optimizer = torch.optim.AdamW(trainable_modules, lr=self.learning_rate) + return optimizer + + + def on_save_checkpoint(self, checkpoint): + checkpoint.clear() + trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.pipe.denoising_model().named_parameters())) + trainable_param_names = set([named_param[0] for named_param in trainable_param_names]) + state_dict = self.pipe.denoising_model().state_dict() + lora_state_dict = {} + for name, param in state_dict.items(): + if name in trainable_param_names: + lora_state_dict[name] = param + if self.state_dict_converter is not None: + lora_state_dict = self.state_dict_converter(lora_state_dict, alpha=self.lora_alpha) + checkpoint.update(lora_state_dict) + + + +def add_general_parsers(parser): + parser.add_argument( + "--dataset_path", + type=str, + default=None, + required=True, + help="The path of the Dataset.", + ) + parser.add_argument( + "--output_path", + type=str, + default="./", + help="Path to save the model.", + ) + parser.add_argument( + "--steps_per_epoch", + type=int, + default=500, + help="Number of steps per epoch.", + ) + parser.add_argument( + "--height", + type=int, + default=1024, + help="Image height.", + ) + parser.add_argument( + "--width", + type=int, + default=1024, + help="Image width.", + ) + parser.add_argument( + "--center_crop", + default=False, + action="store_true", + help=( + "Whether to center crop the input images to the resolution. If not set, the images will be randomly" + " cropped. The images will be resized to the resolution first before cropping." + ), + ) + parser.add_argument( + "--random_flip", + default=False, + action="store_true", + help="Whether to randomly flip images horizontally", + ) + parser.add_argument( + "--batch_size", + type=int, + default=1, + help="Batch size (per device) for the training dataloader.", + ) + parser.add_argument( + "--dataloader_num_workers", + type=int, + default=0, + help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", + ) + parser.add_argument( + "--precision", + type=str, + default="16-mixed", + choices=["32", "16", "16-mixed", "bf16"], + help="Training precision", + ) + parser.add_argument( + "--learning_rate", + type=float, + default=1e-4, + help="Learning rate.", + ) + parser.add_argument( + "--lora_rank", + type=int, + default=4, + help="The dimension of the LoRA update matrices.", + ) + parser.add_argument( + "--lora_alpha", + type=float, + default=4.0, + help="The weight of the LoRA update matrices.", + ) + parser.add_argument( + "--init_lora_weights", + type=str, + default="kaiming", + choices=["gaussian", "kaiming"], + help="The initializing method of LoRA weight.", + ) + parser.add_argument( + "--use_gradient_checkpointing", + default=False, + action="store_true", + help="Whether to use gradient checkpointing.", + ) + parser.add_argument( + "--accumulate_grad_batches", + type=int, + default=1, + help="The number of batches in gradient accumulation.", + ) + parser.add_argument( + "--training_strategy", + type=str, + default="auto", + choices=["auto", "deepspeed_stage_1", "deepspeed_stage_2", "deepspeed_stage_3"], + help="Training strategy", + ) + parser.add_argument( + "--max_epochs", + type=int, + default=1, + help="Number of epochs.", + ) + parser.add_argument( + "--modelscope_model_id", + type=str, + default=None, + help="Model ID on ModelScope (https://www.modelscope.cn/). The model will be uploaded to ModelScope automatically if you provide a Model ID.", + ) + parser.add_argument( + "--modelscope_access_token", + type=str, + default=None, + help="Access key on ModelScope (https://www.modelscope.cn/). Required if you want to upload the model to ModelScope.", + ) + parser.add_argument( + "--pretrained_lora_path", + type=str, + default=None, + help="Pretrained LoRA path. Required if the training is resumed.", + ) + parser.add_argument( + "--use_swanlab", + default=False, + action="store_true", + help="Whether to use SwanLab logger.", + ) + parser.add_argument( + "--swanlab_mode", + default=None, + help="SwanLab mode (cloud or local).", + ) + return parser + + +def launch_training_task(model, args): + # dataset and data loader + dataset = TextImageDataset( + args.dataset_path, + steps_per_epoch=args.steps_per_epoch * args.batch_size, + height=args.height, + width=args.width, + center_crop=args.center_crop, + random_flip=args.random_flip + ) + train_loader = torch.utils.data.DataLoader( + dataset, + shuffle=True, + batch_size=args.batch_size, + num_workers=args.dataloader_num_workers + ) + # train + if args.use_swanlab: + from swanlab.integration.pytorch_lightning import SwanLabLogger + swanlab_config = {"UPPERFRAMEWORK": "DiffSynth-Studio"} + swanlab_config.update(vars(args)) + swanlab_logger = SwanLabLogger( + project="diffsynth_studio", + name="diffsynth_studio", + config=swanlab_config, + mode=args.swanlab_mode, + logdir=os.path.join(args.output_path, "swanlog"), + ) + logger = [swanlab_logger] + else: + logger = None + trainer = pl.Trainer( + max_epochs=args.max_epochs, + accelerator="gpu", + devices="auto", + precision=args.precision, + strategy=args.training_strategy, + default_root_dir=args.output_path, + accumulate_grad_batches=args.accumulate_grad_batches, + callbacks=[pl.pytorch.callbacks.ModelCheckpoint(save_top_k=-1)], + logger=logger, + ) + trainer.fit(model=model, train_dataloaders=train_loader) + + # Upload models + if args.modelscope_model_id is not None and args.modelscope_access_token is not None: + print(f"Uploading models to modelscope. model_id: {args.modelscope_model_id} local_path: {trainer.log_dir}") + with open(os.path.join(trainer.log_dir, "configuration.json"), "w", encoding="utf-8") as f: + f.write('{"framework":"Pytorch","task":"text-to-image-synthesis"}\n') + api = HubApi() + api.login(args.modelscope_access_token) + api.push_model(model_id=args.modelscope_model_id, model_dir=trainer.log_dir) diff --git a/diffsynth/trainers/utils.py b/diffsynth/trainers/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7980db339f5f5657f52ba7b89cc717fcdb3ebb30 --- /dev/null +++ b/diffsynth/trainers/utils.py @@ -0,0 +1,1169 @@ +import imageio, os, torch, warnings, torchvision, argparse, json, random +from peft import LoraConfig, inject_adapter_in_model +from PIL import Image +import pandas as pd +from tqdm import tqdm +from accelerate import Accelerator + + + +class ImageDataset(torch.utils.data.Dataset): + def __init__( + self, + base_path=None, metadata_path=None, + max_pixels=1920*1080, height=None, width=None, + height_division_factor=16, width_division_factor=16, + data_file_keys=("image",), + image_file_extension=("jpg", "jpeg", "png", "webp"), + repeat=1, + args=None, + ): + if args is not None: + base_path = args.dataset_base_path + metadata_path = args.dataset_metadata_path + height = args.height + width = args.width + max_pixels = args.max_pixels + data_file_keys = args.data_file_keys.split(",") + repeat = args.dataset_repeat + + self.base_path = base_path + self.max_pixels = max_pixels + self.height = height + self.width = width + self.height_division_factor = height_division_factor + self.width_division_factor = width_division_factor + self.data_file_keys = data_file_keys + self.image_file_extension = image_file_extension + self.repeat = repeat + + if height is not None and width is not None: + print("Height and width are fixed. Setting `dynamic_resolution` to False.") + self.dynamic_resolution = False + elif height is None and width is None: + print("Height and width are none. Setting `dynamic_resolution` to True.") + self.dynamic_resolution = True + + if metadata_path is None: + print("No metadata. Trying to generate it.") + metadata = self.generate_metadata(base_path) + print(f"{len(metadata)} lines in metadata.") + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + elif metadata_path.endswith(".json"): + with open(metadata_path, "r") as f: + metadata = json.load(f) + self.data = metadata + else: + metadata = pd.read_csv(metadata_path) + # Ensure prompt column is string type to avoid float conversion for NaN values + if 'prompt' in metadata.columns: + metadata['prompt'] = metadata['prompt'].astype(str) + # Replace 'nan' string (from NaN) with empty string + metadata['prompt'] = metadata['prompt'].replace('nan', '') + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + + + def generate_metadata(self, folder): + image_list, prompt_list = [], [] + file_set = set(os.listdir(folder)) + for file_name in file_set: + if "." not in file_name: + continue + file_ext_name = file_name.split(".")[-1].lower() + file_base_name = file_name[:-len(file_ext_name)-1] + if file_ext_name not in self.image_file_extension: + continue + prompt_file_name = file_base_name + ".txt" + if prompt_file_name not in file_set: + continue + with open(os.path.join(folder, prompt_file_name), "r", encoding="utf-8") as f: + prompt = f.read().strip() + image_list.append(file_name) + prompt_list.append(prompt) + metadata = pd.DataFrame() + metadata["image"] = image_list + metadata["prompt"] = prompt_list + return metadata + + + def crop_and_resize(self, image, target_height, target_width): + width, height = image.size + scale = max(target_width / width, target_height / height) + image = torchvision.transforms.functional.resize( + image, + (round(height*scale), round(width*scale)), + interpolation=torchvision.transforms.InterpolationMode.BILINEAR + ) + image = torchvision.transforms.functional.center_crop(image, (target_height, target_width)) + return image + + + def get_height_width(self, image): + if self.dynamic_resolution: + width, height = image.size + if width * height > self.max_pixels: + scale = (width * height / self.max_pixels) ** 0.5 + height, width = int(height / scale), int(width / scale) + height = height // self.height_division_factor * self.height_division_factor + width = width // self.width_division_factor * self.width_division_factor + else: + height, width = self.height, self.width + return height, width + + + def load_image(self, file_path): + image = Image.open(file_path).convert("RGB") + image = self.crop_and_resize(image, *self.get_height_width(image)) + return image + + + def load_data(self, file_path): + return self.load_image(file_path) + + + def __getitem__(self, data_id): + data = self.data[data_id % len(self.data)].copy() + for key in self.data_file_keys: + if key in data: + path = os.path.join(self.base_path, data[key]) + data[key] = self.load_data(path) + if data[key] is None: + warnings.warn(f"cannot load file {data[key]}.") + return None + return data + + + def __len__(self): + return len(self.data) * self.repeat + + + +class VideoDataset(torch.utils.data.Dataset): + def __init__( + self, + base_path=None, metadata_path=None, + num_frames=81, + time_division_factor=4, time_division_remainder=1, + max_pixels=1920*1080, height=None, width=None, + height_division_factor=16, width_division_factor=16, + data_file_keys=("video",), + image_file_extension=("jpg", "jpeg", "png", "webp"), + video_file_extension=("mp4", "avi", "mov", "wmv", "mkv", "flv", "webm"), + repeat=1, + args=None, + action_base_path=None, + enable_icl=False, + icl_num_examples=2, + icl_context_frames=8, + ): + if args is not None: + base_path = args.dataset_base_path + metadata_path = args.dataset_metadata_path + height = args.height + width = args.width + max_pixels = args.max_pixels + num_frames = args.num_frames + data_file_keys = args.data_file_keys.split(",") + repeat = args.dataset_repeat + # In-context learning parameters + if hasattr(args, 'enable_icl'): + enable_icl = args.enable_icl + if hasattr(args, 'icl_num_examples'): + icl_num_examples = args.icl_num_examples + if hasattr(args, 'icl_context_frames'): + icl_context_frames = args.icl_context_frames + + self.base_path = base_path + self.num_frames = num_frames + self.time_division_factor = time_division_factor + self.time_division_remainder = time_division_remainder + self.max_pixels = max_pixels + self.height = height + self.width = width + self.height_division_factor = height_division_factor + self.width_division_factor = width_division_factor + self.data_file_keys = data_file_keys + self.image_file_extension = image_file_extension + self.video_file_extension = video_file_extension + self.repeat = repeat + + # In-context learning parameters + self.enable_icl = enable_icl + self.icl_num_examples = icl_num_examples + self.icl_context_frames = icl_context_frames + + if height is not None and width is not None: + print("Height and width are fixed. Setting `dynamic_resolution` to False.") + self.dynamic_resolution = False + elif height is None and width is None: + print("Height and width are none. Setting `dynamic_resolution` to True.") + self.dynamic_resolution = True + + if metadata_path is None: + print("No metadata. Trying to generate it.") + metadata = self.generate_metadata(base_path) + print(f"{len(metadata)} lines in metadata.") + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + elif metadata_path.endswith(".json"): + with open(metadata_path, "r") as f: + metadata = json.load(f) + self.data = metadata + else: + metadata = pd.read_csv(metadata_path) + # Ensure prompt column is string type to avoid float conversion for NaN values + if 'prompt' in metadata.columns: + metadata['prompt'] = metadata['prompt'].astype(str) + # Replace 'nan' string (from NaN) with empty string + metadata['prompt'] = metadata['prompt'].replace('nan', '') + + # CRITICAL FIX: Clean prompt - remove video path prefix if present + # Some CSV prompts start with "video_name.mp4 " prefix, which should be removed + def clean_prompt(prompt_str): + if not isinstance(prompt_str, str) or not prompt_str: + return prompt_str + # Check if prompt starts with a video path (contains .mp4 or /) + # Pattern: "VideoName/1234_5678.mp4 " or "VideoName.mp4 " + import re + # Match pattern: word/word.mp4 or word.mp4 at the start, followed by space + pattern = r'^[A-Za-z0-9_]+(/[A-Za-z0-9_]+)?\.mp4\s+' + cleaned = re.sub(pattern, '', prompt_str) + # Also handle truncated prompts ending with "..." + if cleaned.endswith('...'): + cleaned = cleaned[:-3].rstrip() + return cleaned.strip() + + metadata['prompt'] = metadata['prompt'].apply(clean_prompt) + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + + self.action_base_path = action_base_path + + if self.enable_icl: + print(f"In-context learning enabled: {icl_num_examples} examples, {icl_context_frames} context frames each") + + + def generate_metadata(self, folder): + video_list, prompt_list = [], [] + file_set = set(os.listdir(folder)) + for file_name in file_set: + if "." not in file_name: + continue + file_ext_name = file_name.split(".")[-1].lower() + file_base_name = file_name[:-len(file_ext_name)-1] + if file_ext_name not in self.image_file_extension and file_ext_name not in self.video_file_extension: + continue + prompt_file_name = file_base_name + ".txt" + if prompt_file_name not in file_set: + continue + with open(os.path.join(folder, prompt_file_name), "r", encoding="utf-8") as f: + prompt = f.read().strip() + video_list.append(file_name) + prompt_list.append(prompt) + metadata = pd.DataFrame() + metadata["video"] = video_list + metadata["prompt"] = prompt_list + return metadata + + + def crop_and_resize(self, image, target_height, target_width): + width, height = image.size + scale = max(target_width / width, target_height / height) + image = torchvision.transforms.functional.resize( + image, + (round(height*scale), round(width*scale)), + interpolation=torchvision.transforms.InterpolationMode.BILINEAR + ) + image = torchvision.transforms.functional.center_crop(image, (target_height, target_width)) + return image + + + def get_height_width(self, image): + if self.dynamic_resolution: + width, height = image.size + if width * height > self.max_pixels: + scale = (width * height / self.max_pixels) ** 0.5 + height, width = int(height / scale), int(width / scale) + height = height // self.height_division_factor * self.height_division_factor + width = width // self.width_division_factor * self.width_division_factor + else: + height, width = self.height, self.width + return height, width + + + def get_num_frames(self, reader): + num_frames = self.num_frames + if int(reader.count_frames()) < num_frames: + num_frames = int(reader.count_frames()) + while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder: + num_frames -= 1 + return num_frames + + + def load_video(self, file_path): + reader = imageio.get_reader(file_path) + num_frames = self.get_num_frames(reader) + frames = [] + for frame_id in range(num_frames): + frame = reader.get_data(frame_id) + frame = Image.fromarray(frame) + frame = self.crop_and_resize(frame, *self.get_height_width(frame)) + frames.append(frame) + reader.close() + return frames + + + def load_image(self, file_path): + image = Image.open(file_path).convert("RGB") + image = self.crop_and_resize(image, *self.get_height_width(image)) + frames = [image] + return frames + + + def is_image(self, file_path): + file_ext_name = file_path.split(".")[-1] + return file_ext_name.lower() in self.image_file_extension + + + def is_video(self, file_path): + file_ext_name = file_path.split(".")[-1] + return file_ext_name.lower() in self.video_file_extension + + + def load_data(self, file_path): + # Handle multiple frame paths separated by '|' (for frame sequences) + if '|' in str(file_path): + # Split the path by '|' to get individual frame paths + frame_paths = str(file_path).split('|') + frames = [] + + # Get base_path (dataset root) + if not hasattr(self, 'base_path') or not self.base_path: + warnings.warn(f"Cannot determine base directory for frame sequence: {file_path}") + return None + + base_dir = self.base_path # This is the dataset root + + # Check the first path to determine the format + first_frame = frame_paths[0].strip() if frame_paths else "" + + # If first frame is already an absolute path (from __getitem__ joining), + # extract the base directory from it + if os.path.isabs(first_frame): + # Extract base directory from first frame path + # First frame format: /path/to/dataset/frames/video_name/frame.png + # We need to get /path/to/dataset + parts = first_frame.split(os.sep) + # Find 'frames' in the path and get everything before it + if 'frames' in parts: + frames_idx = parts.index('frames') + base_dir = os.sep.join(parts[:frames_idx]) + else: + # Fallback: use self.base_path + base_dir = self.base_path + + for frame_path in frame_paths: + frame_path = frame_path.strip() + if not frame_path: + continue + + # Construct full path + if os.path.isabs(frame_path): + # Already absolute path (from __getitem__) + full_frame_path = frame_path + else: + # Relative path - need to construct full path + # Remove 'frames/' prefix if present (we'll add it consistently) + if frame_path.startswith('frames/'): + frame_path = frame_path[7:] # Remove 'frames/' prefix + + # Always join with base_dir + 'frames/' since base_dir is dataset root + full_frame_path = os.path.join(base_dir, 'frames', frame_path) + + # Load individual frame + if os.path.exists(full_frame_path): + if self.is_image(full_frame_path): + frame_data = self.load_image(full_frame_path) + if frame_data: + frames.extend(frame_data) + else: + warnings.warn(f"Frame is not an image: {full_frame_path}") + else: + warnings.warn(f"Frame not found: {full_frame_path}") + + if frames: + return frames + else: + warnings.warn(f"No frames loaded from sequence: {file_path}") + return None + + # Handle single file (image or video) + if self.is_image(file_path): + return self.load_image(file_path) + elif self.is_video(file_path): + return self.load_video(file_path) + else: + return None + + + def __getitem__(self, data_id): + data = self.data[data_id % len(self.data)].copy() + for key in self.data_file_keys: + if key in ["video_name", "start_frame", "end_frame"]: + if "actions" in data: + continue + try: + video_name = data.get("video_name") + if video_name is None: + warnings.warn(f"video_name is missing in metadata for data_id {data_id}. Skipping action loading.") + continue + + if video_name.endswith(".mp4"): + video_name = ".".join(video_name.split(".")[:-1]) + if "_" in video_name: + video_name = "_".join(video_name.split("_")[:4]) + + import json + json_path = os.path.join(self.action_base_path, video_name + ".json") + + # Check if action file exists + if not os.path.exists(json_path): + warnings.warn(f"Action file does not exist: {json_path}. Skipping action loading for data_id {data_id}.") + continue + + start_frame = data.get("start_frame") + end_frame = data.get("end_frame") + if start_frame is None or end_frame is None: + warnings.warn(f"start_frame or end_frame is missing in metadata for data_id {data_id}. Skipping action loading.") + continue + + json_data = json.load(open(json_path, "r"))['actions'] + actions = [] + current_yaw = 0.0 + for frame_id in range(start_frame+1, end_frame+1): + frame_str = str(frame_id) + if frame_str not in json_data: + warnings.warn(f"Frame {frame_id} not found in action file {json_path}. Skipping this frame.") + continue + + action = json_data[frame_str] + new_action = [0.0] * (2 + 2 + 3 + 1 + 2) + if action['ws'] == 1: + new_action[0] = 1 + elif action['ws'] == 2: + new_action[1] = 1 + + if action['ad'] == 1: + new_action[2] = 1 + elif action['ad'] == 2: + new_action[3] = 1 + + if action['scs'] == 1 and action.get("jump_invalid", 0) == 0: + new_action[4] = 1 + elif action['scs'] == 2: + new_action[5] = 1 + elif action['scs'] == 3: + new_action[6] = 1 + + if action.get('collision', 0) == 1: + new_action[7] = 1 + new_action[0] = 0 + new_action[1] = 0 + new_action[2] = 0 + new_action[3] = 0 + + pre_pitch = action.get('pre_pitch', 0.0) + current_pitch = pre_pitch + action.get('pitch_delta', 0.0) * 15.0 + current_yaw += action.get('yaw_delta', 0.0) * 15.0 + new_action[8] = current_pitch + new_action[9] = current_yaw + + actions.append(new_action) + data["actions"] = actions + except Exception as e: + warnings.warn(f"Exception while loading actions for data_id {data_id}: {e}. Continuing without actions.") + # Don't return None, just continue without actions + continue + elif key == "video": + # Check if data[key] exists and is not None + if key not in data or data[key] is None: + warnings.warn(f"Video key '{key}' is missing or None in metadata for data_id {data_id}. Skipping this sample.") + return None + + # Handle frame sequences (paths with '|' separator) + video_path_str = str(data[key]) + if '|' in video_path_str: + # For frame sequences, pass the full path string to load_data + # load_data will handle splitting and loading individual frames + path = os.path.join(self.base_path, video_path_str) + # Don't check path existence here for frame sequences + # load_data will handle individual frame loading + else: + path = os.path.join(self.base_path, data[key]) + # Check if path exists (only for single files) + if not os.path.exists(path): + warnings.warn(f"Video file does not exist: {path}. Skipping this sample.") + return None + try: + data[key] = self.load_data(path) + if data[key] is None: + warnings.warn(f"Failed to load video file: {path}. load_data returned None.") + return None + except Exception as e: + warnings.warn(f"Exception while loading video file {path}: {e}. Skipping this sample.") + return None + + # In-context learning: sample context examples from dataset + if self.enable_icl and len(self.data) > 1: + context_frames_list = [] + context_actions_list = [] + + # Sample random examples from dataset (excluding current one) + current_idx = data_id % len(self.data) + candidate_indices = [i for i in range(len(self.data)) if i != current_idx] + if len(candidate_indices) > 0: + num_samples = min(self.icl_num_examples, len(candidate_indices)) + sampled_indices = random.sample(candidate_indices, num_samples) + + for sample_idx in sampled_indices: + sample_data = self.data[sample_idx].copy() + # Load video for context + if "video" in self.data_file_keys and "video" in sample_data: + video_path = os.path.join(self.base_path, sample_data["video"]) + sample_video = self.load_data(video_path) + if sample_video is not None and len(sample_video) >= self.icl_context_frames: + # Sample context_frames from the video + start_idx = random.randint(0, max(0, len(sample_video) - self.icl_context_frames)) + context_frames = sample_video[start_idx:start_idx + self.icl_context_frames] + context_frames_list.extend(context_frames) + + # Load corresponding actions if available + if self.action_base_path is not None and "video_name" in sample_data: + try: + sample_video_name = sample_data["video_name"] + if sample_video_name.endswith(".mp4"): + sample_video_name = ".".join(sample_video_name.split(".")[:-1]) + if "_" in sample_video_name: + sample_video_name = "_".join(sample_video_name.split("_")[:4]) + sample_json_path = os.path.join(self.action_base_path, sample_video_name + ".json") + if os.path.exists(sample_json_path): + sample_json_data = json.load(open(sample_json_path, "r"))['actions'] + sample_start_frame = sample_data.get("start_frame", 0) + sample_end_frame = sample_data.get("end_frame", len(sample_video)) + + # Get actions for the context frames + context_actions = [] + context_yaw = 0.0 + for frame_idx in range(sample_start_frame + start_idx + 1, + min(sample_start_frame + start_idx + self.icl_context_frames + 1, sample_end_frame + 1)): + if str(frame_idx) in sample_json_data: + action = sample_json_data[str(frame_idx)] + new_action = [0.0] * (2 + 2 + 3 + 1 + 2) + if action['ws'] == 1: + new_action[0] = 1 + elif action['ws'] == 2: + new_action[1] = 1 + if action['ad'] == 1: + new_action[2] = 1 + elif action['ad'] == 2: + new_action[3] = 1 + if action['scs'] == 1 and action.get("jump_invalid", 0) == 0: + new_action[4] = 1 + elif action['scs'] == 2: + new_action[5] = 1 + elif action['scs'] == 3: + new_action[6] = 1 + if action.get('collision', 0) == 1: + new_action[7] = 1 + new_action[0] = 0 + new_action[1] = 0 + new_action[2] = 0 + new_action[3] = 0 + pre_pitch = action.get('pre_pitch', 0.0) + current_pitch = pre_pitch + action.get('pitch_delta', 0.0) * 15.0 + context_yaw += action.get('yaw_delta', 0.0) * 15.0 + new_action[8] = current_pitch + new_action[9] = context_yaw + context_actions.append(new_action) + context_actions_list.extend(context_actions[:len(context_frames)]) + except Exception as e: + # If loading actions fails, just skip + pass + + if context_frames_list: + data["context_frames"] = context_frames_list + if context_actions_list and len(context_actions_list) == len(context_frames_list): + data["context_actions"] = context_actions_list + + return data + + + def __len__(self): + return len(self.data) * self.repeat + + @staticmethod + def get_one_hot(action, range=2): + one_hot = [0] * (range + 1) + one_hot[action] = 1 + return one_hot + + + +import numpy as np + + +class CamVideoDataset(torch.utils.data.Dataset): + """Dataset for Context-as-Memory camera pose conditioned training (ported from VWM). + + Loads 81 PNG frames from UE scenes with random temporal cropping and extracts + corresponding camera poses as 12-dim relative RT vectors subsampled to match + the 21 latent frames. + """ + def __init__( + self, + base_path=None, + num_frames=81, + height=None, width=None, + max_pixels=1920*1080, + height_division_factor=16, width_division_factor=16, + repeat=1, + args=None, + cam_position_scale=None, + ): + if args is not None: + base_path = args.dataset_base_path + height = args.height + width = args.width + max_pixels = args.max_pixels + num_frames = args.num_frames + repeat = args.dataset_repeat + cam_position_scale = getattr(args, "cam_position_scale", 0.01) + self.use_condition_context_frames = getattr(args, "use_condition_context_frames", False) + self.condition_first_frame = getattr(args, "condition_first_frame", False) + self.condition_history_keyframes = getattr(args, "condition_history_keyframes", False) + self.condition_use_camera_pose = getattr(args, "condition_use_camera_pose", True) + self.num_condition_frames = getattr(args, "num_condition_frames", 1) + self.condition_frame_mode = getattr(args, "condition_frame_mode", "first_frame_only") + self.overlap_labels_root = getattr(args, "overlap_labels_root", None) + self.condition_t2v_ratio = getattr(args, "condition_t2v_ratio", 0.10) + self.condition_i2v_ratio = getattr(args, "condition_i2v_ratio", 0.10) + else: + self.use_condition_context_frames = False + self.condition_first_frame = False + self.condition_history_keyframes = False + self.condition_use_camera_pose = True + self.num_condition_frames = 1 + self.condition_frame_mode = "first_frame_only" + self.overlap_labels_root = None + self.condition_t2v_ratio = 0.10 + self.condition_i2v_ratio = 0.10 + + if cam_position_scale is None: + cam_position_scale = 0.01 + self.cam_position_scale = float(cam_position_scale) + + self.base_path = base_path + self.frames_dir = os.path.join(base_path, "frames") + self.jsons_dir = os.path.join(base_path, "jsons") + self.num_frames = num_frames + self.max_pixels = max_pixels + self.height = height + self.width = width + self.height_division_factor = height_division_factor + self.width_division_factor = width_division_factor + self.repeat = repeat + + if height is not None and width is not None: + self.dynamic_resolution = False + else: + self.dynamic_resolution = True + + captions_path = os.path.join(base_path, "captions.txt") + self.scene_captions = {} + with open(captions_path, "r") as f: + for line in f: + parts = line.strip().split("\t", 1) + if len(parts) < 2: + continue + clip_path, caption = parts + scene_name = "/".join(clip_path.split("/")[:-1]) + fname = clip_path.split("/")[-1].replace(".mp4", "") + clip_start = int(fname.split("_")[0]) + if scene_name not in self.scene_captions: + self.scene_captions[scene_name] = [] + self.scene_captions[scene_name].append((clip_start, caption)) + + for scene_name in self.scene_captions: + self.scene_captions[scene_name].sort(key=lambda x: x[0]) + + self.scene_names = sorted(self.scene_captions.keys()) + self.pose_cache = {} + self.overlap_cache = {} + self.invalid_scenes = set() + self.overlap_labels_root = self._resolve_overlap_labels_root(base_path, self.overlap_labels_root) + self._validate_condition_config() + + total_scenes = len(self.scene_names) + total_captions = sum(len(v) for v in self.scene_captions.values()) + print(f"CamVideoDataset: {total_scenes} scenes, {total_captions} captions, " + f"repeat={repeat}, cam_position_scale={self.cam_position_scale}, " + f"effective length={total_scenes * repeat}") + + def _resolve_overlap_labels_root(self, base_path, overlap_labels_root): + candidate_roots = [] + if overlap_labels_root is not None: + candidate_roots.append(overlap_labels_root) + if base_path is not None: + candidate_roots.append(os.path.join(base_path, "overlap_labels")) + for root in candidate_roots: + if root is not None and os.path.isdir(root): + return root + return overlap_labels_root + + def _validate_condition_config(self): + if self.condition_t2v_ratio < 0 or self.condition_i2v_ratio < 0: + raise ValueError("Condition sampling ratios must be non-negative.") + if self.condition_t2v_ratio + self.condition_i2v_ratio >= 1.0: + raise ValueError("condition_t2v_ratio + condition_i2v_ratio must be < 1.0.") + needs_overlap = ( + self.use_condition_context_frames + and self.condition_frame_mode == "first_plus_overlap" + and self.condition_history_keyframes + and self.num_condition_frames > 1 + ) + if needs_overlap and (self.overlap_labels_root is None or not os.path.isdir(self.overlap_labels_root)): + raise FileNotFoundError( + "K-frame condition mode requires overlap_labels_root. " + "Pass --overlap_labels_root or keep overlap_labels under dataset_base_path/overlap_labels." + ) + + def _load_scene_poses(self, scene_name): + if scene_name not in self.pose_cache: + json_path = os.path.join(self.jsons_dir, scene_name + ".json") + try: + with open(json_path, "r") as f: + data = json.load(f) + except (FileNotFoundError, json.JSONDecodeError) as e: + raise ValueError(f"Pose JSON for scene '{scene_name}' is missing or corrupt: {e}") + if not isinstance(data, dict) or "CineCameraActor" not in data: + raise ValueError( + f"Pose JSON for scene '{scene_name}' lacks 'CineCameraActor' key " + f"(found keys: {list(data.keys()) if isinstance(data, dict) else type(data).__name__})." + ) + cine = data["CineCameraActor"] + if not isinstance(cine, dict) or len(cine) == 0: + raise ValueError(f"Pose JSON for scene '{scene_name}' has empty 'CineCameraActor' entries.") + self.pose_cache[scene_name] = cine + return self.pose_cache[scene_name] + + def _find_nearest_caption(self, scene_name, start_frame): + captions = self.scene_captions[scene_name] + best_idx = 0 + best_dist = abs(captions[0][0] - start_frame) + for i, (clip_start, _) in enumerate(captions): + dist = abs(clip_start - start_frame) + if dist < best_dist: + best_dist = dist + best_idx = i + return captions[best_idx][1] + + @staticmethod + def _compute_rt(position, rotation): + x, y, z = position + yaw_rad = np.radians(rotation[2]) + cos_y, sin_y = np.cos(yaw_rad), np.sin(yaw_rad) + R = np.array([[cos_y, -sin_y, 0], [sin_y, cos_y, 0], [0, 0, 1]]) + return [x, y, z] + R.flatten().tolist() + + @staticmethod + def _to_relative_rt(rt_list, ref_rt): + R_ref = np.array(ref_rt[3:]).reshape(3, 3) + T_ref = np.array(ref_rt[:3]).reshape(3, 1) + R_ref_inv = R_ref.T + T_ref_inv = -R_ref_inv @ T_ref + result = [] + for rt in rt_list: + R_i = np.array(rt[3:]).reshape(3, 3) + T_i = np.array(rt[:3]).reshape(3, 1) + R_new = R_ref_inv @ R_i + T_new = R_ref_inv @ T_i + T_ref_inv + result.append(T_new.flatten().tolist() + R_new.flatten().tolist()) + return result + + def crop_and_resize(self, image, target_height, target_width): + width, height = image.size + scale = max(target_width / width, target_height / height) + image = torchvision.transforms.functional.resize( + image, + (round(height * scale), round(width * scale)), + interpolation=torchvision.transforms.InterpolationMode.BILINEAR + ) + image = torchvision.transforms.functional.center_crop(image, (target_height, target_width)) + return image + + def get_height_width(self, image): + if self.dynamic_resolution: + width, height = image.size + if width * height > self.max_pixels: + scale = (width * height / self.max_pixels) ** 0.5 + height, width = int(height / scale), int(width / scale) + height = height // self.height_division_factor * self.height_division_factor + width = width // self.width_division_factor * self.width_division_factor + else: + height, width = self.height, self.width + return height, width + + def _load_resized_frame(self, scene_name, frame_index, target_height, target_width): + frame_path = os.path.join(self.frames_dir, scene_name, f"{frame_index:04d}.png") + img = Image.open(frame_path).convert("RGB") + return self.crop_and_resize(img, target_height, target_width) + + def _load_overlap_frames(self, scene_name, frame_index): + if self.overlap_labels_root is None: + return [] + cache_key = (scene_name, int(frame_index)) + if cache_key not in self.overlap_cache: + overlap_path = os.path.join(self.overlap_labels_root, scene_name, f"{int(frame_index)}.json") + if not os.path.exists(overlap_path): + self.overlap_cache[cache_key] = [] + else: + with open(overlap_path, "r") as f: + overlap_data = json.load(f) + overlaps = overlap_data.get("overlapping_frames", []) + self.overlap_cache[cache_key] = [int(idx) for idx in overlaps] + return self.overlap_cache[cache_key] + + def _compute_scene_rt(self, scene_name, frame_index): + frame_data = self._load_scene_poses(scene_name)[str(int(frame_index))] + raw_pos = frame_data["position"] + pos = [float(p) * self.cam_position_scale for p in raw_pos] + return self._compute_rt(pos, frame_data["rotation"]) + + def _sample_condition_mode(self): + if not self.use_condition_context_frames: + return "disabled" + if ( + self.condition_frame_mode != "first_plus_overlap" + or not self.condition_history_keyframes + or self.num_condition_frames <= 1 + ): + return "first_frame_only" + sample = random.random() + if sample < self.condition_t2v_ratio: + return "text_only" + if sample < self.condition_t2v_ratio + self.condition_i2v_ratio: + return "first_frame_only" + return "first_plus_overlap" + + def _sample_overlap_conditions(self, scene_name, start_frame, ref_rt, target_height, target_width, num_extra_conditions): + if num_extra_conditions <= 0: + return [], [], [] + window_indices = set(range(start_frame, start_frame + self.num_frames)) + target_candidates = list(range(start_frame + 1, start_frame + self.num_frames)) + sampled_target_frames = random.sample(target_candidates, k=min(num_extra_conditions, len(target_candidates))) + overlap_frames = [] + overlap_indices = [] + overlap_actions = [] + used_condition_indices = set() + for target_frame_idx in sampled_target_frames: + candidate_indices = [ + idx for idx in self._load_overlap_frames(scene_name, target_frame_idx) + if idx not in window_indices and idx != target_frame_idx and idx not in used_condition_indices + ] + if len(candidate_indices) == 0: + return None + chosen_idx = random.choice(candidate_indices) + used_condition_indices.add(chosen_idx) + overlap_indices.append(chosen_idx) + overlap_frames.append(self._load_resized_frame(scene_name, chosen_idx, target_height, target_width)) + if self.condition_use_camera_pose: + overlap_rt = self._compute_scene_rt(scene_name, chosen_idx) + overlap_actions.append(self._to_relative_rt([overlap_rt], ref_rt)[0]) + if len(overlap_frames) != num_extra_conditions: + return None + return overlap_frames, overlap_indices, overlap_actions + + def _try_get_sample(self, scene_name): + cam_data = self._load_scene_poses(scene_name) + max_start = len(cam_data) - self.num_frames + if max_start < 0: + raise ValueError(f"Scene {scene_name} has fewer than {self.num_frames} frames.") + start_frame = random.randint(0, max_start) + end_frame = start_frame + self.num_frames - 1 + + frames = [] + for i in range(start_frame, end_frame + 1): + frame_path = os.path.join(self.frames_dir, scene_name, f"{i:04d}.png") + img = Image.open(frame_path).convert("RGB") + img = self.crop_and_resize(img, *self.get_height_width(img)) + frames.append(img) + + prompt = self._find_nearest_caption(scene_name, start_frame) + + rt_list_abs = [] + for i in range(start_frame, end_frame + 1): + key = str(i) + if key not in cam_data: + raise ValueError(f"Scene {scene_name} missing pose for frame {i}.") + frame_data = cam_data[key] + raw_pos = frame_data["position"] + pos = [float(p) * self.cam_position_scale for p in raw_pos] + rt = self._compute_rt(pos, frame_data["rotation"]) + rt_list_abs.append(rt) + + rt_list = self._to_relative_rt(rt_list_abs, rt_list_abs[0]) + pose_indices = list(range(0, self.num_frames, 4)) + actions = [rt_list[i] for i in pose_indices] + + return { + "video": frames, + "prompt": prompt, + "actions": actions, + **self._build_condition_context_payload( + frames=frames, + scene_name=scene_name, + start_frame=start_frame, + ref_rt=rt_list_abs[0], + actions=actions, + ), + } + + def __getitem__(self, data_id): + n = len(self.scene_names) + if n == 0: + raise RuntimeError("CamVideoDataset has no scenes.") + max_attempts = min(64, n) + last_error = None + for attempt in range(max_attempts): + idx = (data_id + attempt) % n + scene_name = self.scene_names[idx] + if scene_name in self.invalid_scenes: + continue + try: + return self._try_get_sample(scene_name) + except (ValueError, FileNotFoundError, KeyError, OSError) as e: + self.invalid_scenes.add(scene_name) + last_error = e + if attempt < 3 or attempt % 8 == 0: + print( + f"[CamVideoDataset] Skipping invalid scene '{scene_name}' " + f"({type(e).__name__}: {e}); attempt {attempt + 1}/{max_attempts}" + ) + continue + raise RuntimeError( + f"CamVideoDataset: exhausted {max_attempts} attempts starting from index {data_id}; " + f"last error: {type(last_error).__name__}: {last_error}" + ) + + def _build_condition_context_payload(self, frames, scene_name, start_frame, ref_rt, actions): + if not self.use_condition_context_frames: + return {} + payload = { + "use_condition_context_frames": False, + "condition_frames": [], + "condition_frame_indices": [], + "condition_source": None, + "condition_actions": [], + } + condition_mode = self._sample_condition_mode() + payload["condition_source"] = condition_mode + if condition_mode == "text_only": + return payload + payload["use_condition_context_frames"] = True + if self.condition_first_frame: + payload["condition_frames"].append(frames[0]) + payload["condition_frame_indices"].append(start_frame) + payload["condition_source"] = "first_frame_only" + if self.condition_use_camera_pose and actions: + payload["condition_actions"].append(list(actions[0])) + if ( + condition_mode == "first_plus_overlap" + and self.condition_history_keyframes + and self.num_condition_frames > len(payload["condition_frames"]) + ): + num_extra_conditions = self.num_condition_frames - len(payload["condition_frames"]) + overlap_payload = self._sample_overlap_conditions( + scene_name=scene_name, + start_frame=start_frame, + ref_rt=ref_rt, + target_height=frames[0].size[1], + target_width=frames[0].size[0], + num_extra_conditions=num_extra_conditions, + ) + if overlap_payload is None: + return payload + overlap_frames, overlap_indices, overlap_actions = overlap_payload + payload["condition_frames"].extend(overlap_frames) + payload["condition_frame_indices"].extend(overlap_indices) + if self.condition_use_camera_pose: + payload["condition_actions"].extend(overlap_actions) + payload["condition_source"] = "first_plus_overlap" + return payload + + def __len__(self): + return len(self.scene_names) * self.repeat + + +class DiffusionTrainingModule(torch.nn.Module): + def __init__(self): + super().__init__() + + + def to(self, *args, **kwargs): + for name, model in self.named_children(): + model.to(*args, **kwargs) + return self + + + def trainable_modules(self): + trainable_modules = filter(lambda p: p.requires_grad, self.parameters()) + return trainable_modules + + + def trainable_param_names(self): + trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.named_parameters())) + trainable_param_names = set([named_param[0] for named_param in trainable_param_names]) + return trainable_param_names + + + def add_lora_to_model(self, model, target_modules, lora_rank, lora_alpha=None): + if lora_alpha is None: + lora_alpha = lora_rank + lora_config = LoraConfig(r=lora_rank, lora_alpha=lora_alpha, target_modules=target_modules) + model = inject_adapter_in_model(lora_config, model) + return model + + + def export_trainable_state_dict(self, state_dict, remove_prefix=None): + trainable_param_names = self.trainable_param_names() + state_dict = {name: param for name, param in state_dict.items() if name in trainable_param_names} + if remove_prefix is not None: + state_dict_ = {} + for name, param in state_dict.items(): + if name.startswith(remove_prefix): + name = name[len(remove_prefix):] + state_dict_[name] = param + state_dict = state_dict_ + return state_dict + + + +class ModelLogger: + def __init__(self, output_path, remove_prefix_in_ckpt=None, state_dict_converter=lambda x:x): + self.output_path = output_path + self.remove_prefix_in_ckpt = remove_prefix_in_ckpt + self.state_dict_converter = state_dict_converter + + + def on_step_end(self, loss): + pass + + + def on_epoch_end(self, accelerator, model, epoch_id): + accelerator.wait_for_everyone() + if accelerator.is_main_process: + state_dict = accelerator.get_state_dict(model) + state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt) + state_dict = self.state_dict_converter(state_dict) + os.makedirs(self.output_path, exist_ok=True) + path = os.path.join(self.output_path, f"epoch-{epoch_id}.safetensors") + accelerator.save(state_dict, path, safe_serialization=True) + + + +def launch_training_task( + dataset: torch.utils.data.Dataset, + model: DiffusionTrainingModule, + model_logger: ModelLogger, + optimizer: torch.optim.Optimizer, + scheduler: torch.optim.lr_scheduler.LRScheduler, + num_epochs: int = 1, + gradient_accumulation_steps: int = 1, +): + dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0], drop_last=True) + accelerator = Accelerator(gradient_accumulation_steps=gradient_accumulation_steps) + model, optimizer, dataloader, scheduler = accelerator.prepare(model, optimizer, dataloader, scheduler) + + for epoch_id in range(num_epochs): + for data in tqdm(dataloader): + with accelerator.accumulate(model): + optimizer.zero_grad() + loss = model(data) + accelerator.backward(loss) + optimizer.step() + model_logger.on_step_end(loss) + scheduler.step() + model_logger.on_epoch_end(accelerator, model, epoch_id) + +def launch_data_process_task(model: DiffusionTrainingModule, dataset, output_path="./models"): + dataloader = torch.utils.data.DataLoader(dataset, shuffle=False, collate_fn=lambda x: x[0], drop_last=True) + accelerator = Accelerator() + model, dataloader = accelerator.prepare(model, dataloader) + os.makedirs(os.path.join(output_path, "data_cache"), exist_ok=True) + for data_id, data in enumerate(tqdm(dataloader)): + with torch.no_grad(): + inputs = model.forward_preprocess(data) + inputs = {key: inputs[key] for key in model.model_input_keys if key in inputs} + torch.save(inputs, os.path.join(output_path, "data_cache", f"{data_id}.pth")) + + + +def wan_parser(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument("--dataset_base_path", type=str, default="", required=True, help="Base path of the dataset.") + parser.add_argument("--dataset_metadata_path", type=str, default=None, help="Path to the metadata file of the dataset.") + parser.add_argument("--max_pixels", type=int, default=1280*720, help="Maximum number of pixels per frame, used for dynamic resolution..") + parser.add_argument("--height", type=int, default=None, help="Height of images or videos. Leave `height` and `width` empty to enable dynamic resolution.") + parser.add_argument("--width", type=int, default=None, help="Width of images or videos. Leave `height` and `width` empty to enable dynamic resolution.") + parser.add_argument("--num_frames", type=int, default=81, help="Number of frames per video. Frames are sampled from the video prefix.") + parser.add_argument("--data_file_keys", type=str, default="image,video", help="Data file keys in the metadata. Comma-separated.") + parser.add_argument("--dataset_repeat", type=int, default=1, help="Number of times to repeat the dataset per epoch.") + parser.add_argument("--model_paths", type=str, default=None, help="Paths to load models. In JSON format.") + parser.add_argument("--model_id_with_origin_paths", type=str, default=None, help="Model ID with origin paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated.") + parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate.") + parser.add_argument("--num_epochs", type=int, default=1, help="Number of epochs.") + parser.add_argument("--output_path", type=str, default="./models", help="Output save path.") + parser.add_argument("--remove_prefix_in_ckpt", type=str, default="pipe.dit.", help="Remove prefix in ckpt.") + parser.add_argument("--trainable_models", type=str, default=None, help="Models to train, e.g., dit, vae, text_encoder.") + parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.") + parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.") + parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.") + parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.") + parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.") + parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.") + parser.add_argument("--use_condition_context_frames", default=False, action="store_true", help="Enable appended clean condition latents.") + parser.add_argument("--condition_first_frame", default=False, action="store_true", help="Use the current clip first frame as a clean condition frame.") + parser.add_argument("--condition_history_keyframes", default=False, action="store_true", help="Use overlap-based keyframes as conditions.") + parser.add_argument("--condition_use_camera_pose", default=True, action="store_true", help="Inject camera pose for condition frames.") + parser.add_argument("--num_condition_frames", type=int, default=1, help="Number of condition frames.") + parser.add_argument("--condition_frame_mode", type=str, default="first_frame_only", help="Condition frame selection mode.") + parser.add_argument("--overlap_labels_root", type=str, default=None, help="Root dir for overlap label JSONs.") + parser.add_argument("--condition_t2v_ratio", type=float, default=0.10, help="Ratio of text-only condition samples.") + parser.add_argument("--condition_i2v_ratio", type=float, default=0.10, help="Ratio of first-frame-only condition samples.") + return parser + + + +def flux_parser(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument("--dataset_base_path", type=str, default="", required=True, help="Base path of the dataset.") + parser.add_argument("--dataset_metadata_path", type=str, default=None, help="Path to the metadata file of the dataset.") + parser.add_argument("--max_pixels", type=int, default=1024*1024, help="Maximum number of pixels per frame, used for dynamic resolution..") + parser.add_argument("--height", type=int, default=None, help="Height of images. Leave `height` and `width` empty to enable dynamic resolution.") + parser.add_argument("--width", type=int, default=None, help="Width of images. Leave `height` and `width` empty to enable dynamic resolution.") + parser.add_argument("--data_file_keys", type=str, default="image", help="Data file keys in the metadata. Comma-separated.") + parser.add_argument("--dataset_repeat", type=int, default=1, help="Number of times to repeat the dataset per epoch.") + parser.add_argument("--model_paths", type=str, default=None, help="Paths to load models. In JSON format.") + parser.add_argument("--model_id_with_origin_paths", type=str, default=None, help="Model ID with origin paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated.") + parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate.") + parser.add_argument("--num_epochs", type=int, default=1, help="Number of epochs.") + parser.add_argument("--output_path", type=str, default="./models", help="Output save path.") + parser.add_argument("--remove_prefix_in_ckpt", type=str, default="pipe.dit.", help="Remove prefix in ckpt.") + parser.add_argument("--trainable_models", type=str, default=None, help="Models to train, e.g., dit, vae, text_encoder.") + parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.") + parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.") + parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.") + parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.") + parser.add_argument("--align_to_opensource_format", default=False, action="store_true", help="Whether to align the lora format to opensource format. Only for DiT's LoRA.") + parser.add_argument("--use_gradient_checkpointing", default=False, action="store_true", help="Whether to use gradient checkpointing.") + parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.") + parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.") + return parser diff --git a/diffsynth/vram_management/__init__.py b/diffsynth/vram_management/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5b075800b6fd5b1702eeb7ff9c619aef1093ed46 --- /dev/null +++ b/diffsynth/vram_management/__init__.py @@ -0,0 +1,2 @@ +from .layers import * +from .gradient_checkpointing import * diff --git a/diffsynth/vram_management/gradient_checkpointing.py b/diffsynth/vram_management/gradient_checkpointing.py new file mode 100644 index 0000000000000000000000000000000000000000..b356415a004f3d74afdd45840f1fc4caf6659e16 --- /dev/null +++ b/diffsynth/vram_management/gradient_checkpointing.py @@ -0,0 +1,34 @@ +import torch + + +def create_custom_forward(module): + def custom_forward(*inputs, **kwargs): + return module(*inputs, **kwargs) + return custom_forward + + +def gradient_checkpoint_forward( + model, + use_gradient_checkpointing, + use_gradient_checkpointing_offload, + *args, + **kwargs, +): + if use_gradient_checkpointing_offload: + with torch.autograd.graph.save_on_cpu(): + model_output = torch.utils.checkpoint.checkpoint( + create_custom_forward(model), + *args, + **kwargs, + use_reentrant=False, + ) + elif use_gradient_checkpointing: + model_output = torch.utils.checkpoint.checkpoint( + create_custom_forward(model), + *args, + **kwargs, + use_reentrant=False, + ) + else: + model_output = model(*args, **kwargs) + return model_output diff --git a/diffsynth/vram_management/layers.py b/diffsynth/vram_management/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..cca7c50b8b7fcb068e39e3e82eef8f9d7f3c118f --- /dev/null +++ b/diffsynth/vram_management/layers.py @@ -0,0 +1,170 @@ +import torch, copy +from ..models.utils import init_weights_on_device + + +def cast_to(weight, dtype, device): + r = torch.empty_like(weight, dtype=dtype, device=device) + r.copy_(weight) + return r + + +class AutoTorchModule(torch.nn.Module): + def __init__(self): + super().__init__() + + def check_free_vram(self): + _dev = self.computation_device + if not (isinstance(_dev, torch.device) and _dev.index is not None): + _dev = 0 + gpu_mem_state = torch.cuda.mem_get_info(_dev) + used_memory = (gpu_mem_state[1] - gpu_mem_state[0]) / (1024 ** 3) + return used_memory < self.vram_limit + + def offload(self): + if self.state != 0: + self.to(dtype=self.offload_dtype, device=self.offload_device) + self.state = 0 + + def onload(self): + if self.state != 1: + self.to(dtype=self.onload_dtype, device=self.onload_device) + self.state = 1 + + def keep(self): + if self.state != 2: + self.to(dtype=self.computation_dtype, device=self.computation_device) + self.state = 2 + + +class AutoWrappedModule(AutoTorchModule): + def __init__(self, module: torch.nn.Module, offload_dtype, offload_device, onload_dtype, onload_device, computation_dtype, computation_device, vram_limit, **kwargs): + super().__init__() + self.module = module.to(dtype=offload_dtype, device=offload_device) + self.offload_dtype = offload_dtype + self.offload_device = offload_device + self.onload_dtype = onload_dtype + self.onload_device = onload_device + self.computation_dtype = computation_dtype + self.computation_device = computation_device + self.vram_limit = vram_limit + self.state = 0 + + def forward(self, *args, **kwargs): + if self.state == 2: + module = self.module + else: + if self.onload_dtype == self.computation_dtype and self.onload_device == self.computation_device: + module = self.module + elif self.vram_limit is not None and self.check_free_vram(): + self.keep() + module = self.module + else: + module = copy.deepcopy(self.module).to(dtype=self.computation_dtype, device=self.computation_device) + return module(*args, **kwargs) + + +class WanAutoCastLayerNorm(torch.nn.LayerNorm, AutoTorchModule): + def __init__(self, module: torch.nn.LayerNorm, offload_dtype, offload_device, onload_dtype, onload_device, computation_dtype, computation_device, vram_limit, **kwargs): + with init_weights_on_device(device=torch.device("meta")): + super().__init__(module.normalized_shape, eps=module.eps, elementwise_affine=module.elementwise_affine, bias=module.bias is not None, dtype=offload_dtype, device=offload_device) + self.weight = module.weight + self.bias = module.bias + self.offload_dtype = offload_dtype + self.offload_device = offload_device + self.onload_dtype = onload_dtype + self.onload_device = onload_device + self.computation_dtype = computation_dtype + self.computation_device = computation_device + self.vram_limit = vram_limit + self.state = 0 + + def forward(self, x, *args, **kwargs): + if self.state == 2: + weight, bias = self.weight, self.bias + else: + if self.onload_dtype == self.computation_dtype and self.onload_device == self.computation_device: + weight, bias = self.weight, self.bias + elif self.vram_limit is not None and self.check_free_vram(): + self.keep() + weight, bias = self.weight, self.bias + else: + weight = None if self.weight is None else cast_to(self.weight, self.computation_dtype, self.computation_device) + bias = None if self.bias is None else cast_to(self.bias, self.computation_dtype, self.computation_device) + with torch.amp.autocast(device_type=x.device.type): + x = torch.nn.functional.layer_norm(x.float(), self.normalized_shape, weight, bias, self.eps).type_as(x) + return x + + +class AutoWrappedLinear(torch.nn.Linear, AutoTorchModule): + def __init__(self, module: torch.nn.Linear, offload_dtype, offload_device, onload_dtype, onload_device, computation_dtype, computation_device, vram_limit, name="", **kwargs): + with init_weights_on_device(device=torch.device("meta")): + super().__init__(in_features=module.in_features, out_features=module.out_features, bias=module.bias is not None, dtype=offload_dtype, device=offload_device) + self.weight = module.weight + self.bias = module.bias + self.offload_dtype = offload_dtype + self.offload_device = offload_device + self.onload_dtype = onload_dtype + self.onload_device = onload_device + self.computation_dtype = computation_dtype + self.computation_device = computation_device + self.vram_limit = vram_limit + self.state = 0 + self.name = name + self.lora_A_weights = [] + self.lora_B_weights = [] + self.lora_merger = None + + def forward(self, x, *args, **kwargs): + if self.state == 2: + weight, bias = self.weight, self.bias + else: + if self.onload_dtype == self.computation_dtype and self.onload_device == self.computation_device: + weight, bias = self.weight, self.bias + elif self.vram_limit is not None and self.check_free_vram(): + self.keep() + weight, bias = self.weight, self.bias + else: + weight = cast_to(self.weight, self.computation_dtype, self.computation_device) + bias = None if self.bias is None else cast_to(self.bias, self.computation_dtype, self.computation_device) + out = torch.nn.functional.linear(x, weight, bias) + + if len(self.lora_A_weights) == 0: + # No LoRA + return out + elif self.lora_merger is None: + # Native LoRA inference + for lora_A, lora_B in zip(self.lora_A_weights, self.lora_B_weights): + out = out + x @ lora_A.T @ lora_B.T + else: + # LoRA fusion + lora_output = [] + for lora_A, lora_B in zip(self.lora_A_weights, self.lora_B_weights): + lora_output.append(x @ lora_A.T @ lora_B.T) + lora_output = torch.stack(lora_output) + out = self.lora_merger(out, lora_output) + return out + + +def enable_vram_management_recursively(model: torch.nn.Module, module_map: dict, module_config: dict, max_num_param=None, overflow_module_config: dict = None, total_num_param=0, vram_limit=None, name_prefix=""): + for name, module in model.named_children(): + layer_name = name if name_prefix == "" else name_prefix + "." + name + for source_module, target_module in module_map.items(): + if isinstance(module, source_module): + num_param = sum(p.numel() for p in module.parameters()) + if max_num_param is not None and total_num_param + num_param > max_num_param: + module_config_ = overflow_module_config + else: + module_config_ = module_config + module_ = target_module(module, **module_config_, vram_limit=vram_limit, name=layer_name) + setattr(model, name, module_) + total_num_param += num_param + break + else: + total_num_param = enable_vram_management_recursively(module, module_map, module_config, max_num_param, overflow_module_config, total_num_param, vram_limit=vram_limit, name_prefix=layer_name) + return total_num_param + + +def enable_vram_management(model: torch.nn.Module, module_map: dict, module_config: dict, max_num_param=None, overflow_module_config: dict = None, vram_limit=None): + enable_vram_management_recursively(model, module_map, module_config, max_num_param, overflow_module_config, total_num_param=0, vram_limit=vram_limit) + model.vram_management_enabled = True + diff --git a/env/action_rotation_left_45.json b/env/action_rotation_left_45.json new file mode 100644 index 0000000000000000000000000000000000000000..e278fee8e2d8263bb13719bc9669b43e706ca718 --- /dev/null +++ b/env/action_rotation_left_45.json @@ -0,0 +1,1136 @@ +{ + "0": [ + 0.0, + 0.0, + 0.0, + 1.0, + -0.0, + 0.0, + 0.0, + 1.0, + 0.0, + 0.0, + 0.0, + 1.0 + ], + "1": [ + 0.0, + 0.0, + 0.0, + 0.999951808959328, + -0.009817319337149617, + 0.0, + 0.009817319337149617, + 0.999951808959328, + 0.0, + 0.0, + 0.0, + 1.0 + ], + "2": [ + 0.0, + 0.0, + 0.0, + 0.9998072404820648, + -0.0196336924606283, + 0.0, + 0.0196336924606283, + 0.9998072404820648, + 0.0, + 0.0, + 0.0, + 1.0 + ], + "3": [ + 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============================================================================= +# 推理环境与训练对齐（务必在跑 ep0 / eval_v2 / replay_gt 前 source） +# +# 对应训练侧： +# - train/memory_baselines_basic/common_env.sh +# - train/ctx_5_20_per_frame_vae/common_env.sh 等 +# +# 说明： +# - CONTEXT_POSITION=suffix：与训练 CONTEXT_POSITION 一致；run_one_chunk 内也会显式传 context_position=suffix +# - USE_CONCATENATION_INFERENCE：WanVideoPipeline 默认即为 true，此处显式导出避免子进程/调度环境缺失 +# - USE_RT_RELATIVE：训练 common_env 开启，评测脚本保持一致（供后续扩展或外部工具读取） +# +# 覆盖示例：CONTEXT_POSITION=prefix bash run_eval_... +# CAMERA_INJECT_MODE：默认 pre_qkv；若你的 ckpt 为 pre_norm/post 训练请显式覆盖。 +# ============================================================================= +: "${CONTEXT_POSITION:=suffix}" +: "${USE_CONCATENATION_INFERENCE:=true}" +: "${USE_RT_RELATIVE:=true}" +# 与 train/memory_baselines_basic/*.sh 一致：默认 pre_qkv（避免 replay_gt / combo 仅因 ckpt 路径无子串而落到 pre_norm 导致 action 注入错位） +: "${CAMERA_INJECT_MODE:=pre_qkv}" +export CONTEXT_POSITION USE_CONCATENATION_INFERENCE USE_RT_RELATIVE CAMERA_INJECT_MODE + +if [ "${PRINT_EVAL_ALIGN_SUMMARY:-1}" = "1" ]; then + echo "[eval_align_env] CONTEXT_POSITION=${CONTEXT_POSITION} USE_CONCATENATION_INFERENCE=${USE_CONCATENATION_INFERENCE} USE_RT_RELATIVE=${USE_RT_RELATIVE} CAMERA_INJECT_MODE=${CAMERA_INJECT_MODE}" +fi diff --git a/env/generate_rotation_actions.py b/env/generate_rotation_actions.py new file mode 100644 index 0000000000000000000000000000000000000000..4a03e2a79bc3a9008a921b327106aba43a76507a --- /dev/null +++ b/env/generate_rotation_actions.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python3 +"""Generate the action JSON files used by training and inference recipes.""" + +import json +import math +import os + +def degrees_to_radians(degrees): + return degrees * math.pi / 180 + +def generate_rotation_matrix(yaw_degrees): + """Z-axis (yaw) rotation matrix, paper-aligned; row-major flatten: R_11..R_33.""" + yaw_rad = degrees_to_radians(yaw_degrees) + c, s = math.cos(yaw_rad), math.sin(yaw_rad) + # R_z = [[c, -s, 0], [s, c, 0], [0, 0, 1]] + return [c, -s, 0.0, s, c, 0.0, 0.0, 0.0, 1.0] + +def generate_action_json(total_frames=81, total_rotation_degrees=180, output_path=None): + actions = {} + for frame_idx in range(total_frames): + rotation_degrees = (frame_idx / (total_frames - 1)) * total_rotation_degrees + R = generate_rotation_matrix(rotation_degrees) + action = [0.0, 0.0, 0.0] + R + actions[str(frame_idx)] = action + if output_path: + d = os.path.dirname(output_path) + if d: + os.makedirs(d, exist_ok=True) + with open(output_path, "w") as f: + json.dump(actions, f, indent=2) + print(f"Generated {output_path} with {total_rotation_degrees}° rotation over {total_frames} frames") + return actions + +if __name__ == "__main__": + script_dir = os.path.dirname(os.path.abspath(__file__)) + out_left_45 = os.path.join(script_dir, "action_rotation_left_45.json") + out_right_45 = os.path.join(script_dir, "action_rotation_right_45.json") + generate_action_json(total_frames=81, total_rotation_degrees=45, output_path=out_left_45) + generate_action_json(total_frames=81, total_rotation_degrees=-45, output_path=out_right_45) + print("\nGenerated action JSON files: left_45, right_45") diff --git a/env/loop_utils.py b/env/loop_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ad3f94fc357d72d08df8602f8a4c1667cf3dd06c --- /dev/null +++ b/env/loop_utils.py @@ -0,0 +1,378 @@ +""" +推理工具：供 run_replay_loop_two_chunk 及评估脚本使用。 +提供：load_pipeline_and_ckpt、load_prompt_for_video、sample_trajectory_samples_from_dataset。 + +VWM-style 简化版：使用 DiTBlock_w_Action + MLP_CamPose（block 内 action_mlp）， +去除 CameraEncoder / camera_encoder_shallow 等冗余路径。 +""" + +import os +import re +import sys +import csv +import random + +_script_dir = os.path.dirname(os.path.abspath(__file__)) +_repo_root = os.path.dirname(os.path.dirname(_script_dir)) +if _repo_root not in sys.path: + sys.path.insert(0, _repo_root) + +import torch +import torch.nn as nn +from safetensors.torch import load_file as safe_load_file +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from diffsynth.models.wan_video_dit import SelfAttention, CrossAttention, GateModule, modulate +from diffsynth.models.memory.block_wise_ssm import BlockWiseStateSpaceMemory +from diffsynth.models.memory.videossm_hybrid import HybridStateSpaceMemory + +DEFAULT_NEGATIVE_PROMPT = "oversaturated colors, overexposed, static, blurry details" + + +# ── MLP_CamPose + DiTBlock_w_Action（与训练侧 train.py 完全一致）────────── +class MLP_CamPose(nn.Module): + def __init__(self, out_dim, pose_dim=12): + super().__init__() + self.proj = nn.Linear(pose_dim, out_dim) + nn.init.zeros_(self.proj.weight) + nn.init.zeros_(self.proj.bias) + + def forward(self, x): + return self.proj(x) + + +class DiTBlock_w_Action(nn.Module): + def __init__(self, has_image_input, dim, num_heads, ffn_dim, eps=1e-6, + add_action_attn=False, action_use_temporal_attention=True, + use_cam_pose=False, use_block_wise_ssm=False, use_videossm_hybrid=False, + videossm_kernel_size=3, videossm_expand=2): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.ffn_dim = ffn_dim + if add_action_attn: + self.self_attn_with_action = SelfAttention(dim, num_heads, eps) + nn.init.zeros_(self.self_attn_with_action.o.weight) + nn.init.zeros_(self.self_attn_with_action.o.bias) + if use_cam_pose: + self.action_mlp = MLP_CamPose(dim) + else: + self.action_mlp = MLP_CamPose(dim) + self.self_attn = SelfAttention(dim, num_heads, eps) + self.cross_attn = CrossAttention(dim, num_heads, eps, has_image_input=has_image_input) + self.norm1 = nn.LayerNorm(dim, eps=eps, elementwise_affine=False) + self.norm2 = nn.LayerNorm(dim, eps=eps, elementwise_affine=False) + self.norm3 = nn.LayerNorm(dim, eps=eps) + self.ffn = nn.Sequential(nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'), nn.Linear(ffn_dim, dim)) + self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5) + self.gate = GateModule() + self.action_use_temporal_attention = action_use_temporal_attention + self.use_block_wise_ssm = bool(use_block_wise_ssm) + self.use_videossm_hybrid = bool(use_videossm_hybrid) + if use_block_wise_ssm: + self.block_wise_ssm = BlockWiseStateSpaceMemory(dim) + if use_videossm_hybrid: + self.videossm_hybrid = HybridStateSpaceMemory( + dim, kernel_size=videossm_kernel_size, expand=videossm_expand + ) + + def forward(self, x, context, t_mod, freqs, actions=None): + has_seq = len(t_mod.shape) == 4 + chunk_dim = 2 if has_seq else 1 + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( + self.modulation.to(dtype=t_mod.dtype, device=t_mod.device) + t_mod).chunk(6, dim=chunk_dim) + if has_seq: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( + shift_msa.squeeze(2), scale_msa.squeeze(2), gate_msa.squeeze(2), + shift_mlp.squeeze(2), scale_mlp.squeeze(2), gate_mlp.squeeze(2), + ) + num_frames = None + if actions is not None: + original_x = x + actions = self.action_mlp(actions.to(x.dtype)).to(x.dtype) + bs, num_frames, dim = actions.shape + actions = actions.reshape(bs, num_frames, 1, dim) + x = x.reshape(bs, num_frames, -1, dim) + x = x + actions + if hasattr(self, "self_attn_with_action"): + if not self.action_use_temporal_attention: + x = x.reshape(bs, -1, dim) + x = original_x + self.self_attn_with_action(x, freqs) + else: + from einops import rearrange + x = rearrange(x, "b f p d -> (b p) f d") + attn_out = self.self_attn_with_action(x) + attn_out = rearrange(attn_out, "(b p) f d -> b f p d", b=bs) + x = original_x + attn_out.reshape(bs, -1, dim) + else: + x = x.reshape(bs, -1, dim) + input_x = modulate(self.norm1(x), shift_msa, scale_msa) + x = self.gate(x, gate_msa, self.self_attn(input_x, freqs)) + if num_frames is not None: + if hasattr(self, "block_wise_ssm"): + x = self.block_wise_ssm(x, f=num_frames) + if hasattr(self, "videossm_hybrid"): + spatial = x.shape[1] // int(num_frames) if int(num_frames) > 0 else 0 + x = self.videossm_hybrid(x, f=num_frames, h=1, w=spatial) + x = x + self.cross_attn(self.norm3(x), context) + input_x = modulate(self.norm2(x), shift_mlp, scale_mlp) + x = self.gate(x, gate_mlp, self.ffn(input_x)) + return x + + +# ── Utility functions ───────────────────────────────────────────────────── + +def load_pose_rt(json_file, frame_idx): + """从数据集 camera json 读取单帧 12 维 RT。""" + from src.model_training.fov_retrieval import load_camera_pose, pose_to_rt + pose = load_camera_pose(json_file, int(frame_idx)) + if pose is None: + return None + return pose_to_rt(pose, constrain_to_xy=True) + + +def get_relative_rt(rt, ref_rt): + """单帧相对位姿。""" + from src.model_training.fov_retrieval import convert_rt_to_relative + if rt is None or ref_rt is None or len(rt) < 12 or len(ref_rt) < 12: + return None + out = convert_rt_to_relative([rt], ref_rt) + return out[0] if out else None + + +def load_prompt_for_video(dataset_base, video_name): + """从 dataset 目录下的 metadata CSV 读取该视频的 prompt。""" + if not dataset_base or not video_name: + return None + vn = str(video_name).replace(".mp4", "").replace(".avi", "").strip() + for name in ("metadata_full.csv", "metadata.csv", "prompts.csv"): + path = os.path.join(dataset_base, name) + if not os.path.isfile(path): + continue + try: + with open(path, "r", encoding="utf-8") as f: + for row in csv.DictReader(f): + if row.get("video_name", "").strip() == vn: + p = row.get("prompt", "").strip() + if p: + return p + except Exception: + pass + return None + + +def sample_trajectory_samples_from_dataset(dataset_base, num_samples=4, num_frames=81, seed=42): + """从 dataset 枚举 (video_name, start_frame)。""" + frames_dir = os.path.join(dataset_base, "frames") + if not os.path.isdir(frames_dir): + return [] + candidates = [] + for vn in sorted(os.listdir(frames_dir)): + vd = os.path.join(frames_dir, vn) + if not os.path.isdir(vd): + continue + try: + names = [f for f in os.listdir(vd) if f.endswith(".png")] + indices = sorted({int(os.path.splitext(n)[0]) for n in names if n[:-4].isdigit()}) + if not indices: + continue + max_idx = max(indices) + for start in indices: + if start + num_frames - 1 <= max_idx: + candidates.append((vn, start)) + except Exception: + continue + if not candidates: + return [] + rng = random.Random(seed) + if len(candidates) <= num_samples: + return candidates + return [candidates[i] for i in rng.sample(range(len(candidates)), num_samples)] + + +# ── Pipeline loading (VWM-style) ────────────────────────────────────────── + +def _build_action_blocks( + pipe, + add_action_attn=False, + action_use_temporal_attention=True, + block_wise_block_ids=None, + videossm_block_ids=None, +): + """Replace DiT blocks with DiTBlock_w_Action (VWM cam_infer.py style).""" + dit = pipe.dit + old_blocks = dit.blocks + has_image_input = getattr(dit, "has_image_input", False) + dim = dit.dim + num_heads = getattr(dit, "num_heads", None) or getattr(old_blocks[0], "num_heads", None) + ffn_dim = getattr(dit, "ffn_dim", None) or getattr(old_blocks[0], "ffn_dim", None) + eps = getattr(dit, "eps", 1e-6) + + block_dtype = next(old_blocks[0].parameters()).dtype + block_device = next(old_blocks[0].parameters()).device + + block_wise_block_ids = set(block_wise_block_ids or []) + videossm_block_ids = set(videossm_block_ids or []) + + new_blocks = torch.nn.ModuleList() + for block_id, old_block in enumerate(old_blocks): + new_block = DiTBlock_w_Action( + has_image_input=has_image_input, + dim=dim, num_heads=num_heads, ffn_dim=ffn_dim, eps=eps, + add_action_attn=add_action_attn, + action_use_temporal_attention=action_use_temporal_attention, + use_cam_pose=True, + use_block_wise_ssm=block_id in block_wise_block_ids, + use_videossm_hybrid=block_id in videossm_block_ids, + ) + new_block = new_block.to(dtype=block_dtype, device=block_device) + for attr in ("self_attn", "cross_attn", "norm1", "norm2", "norm3", "ffn"): + if hasattr(old_block, attr) and hasattr(new_block, attr): + getattr(new_block, attr).load_state_dict(getattr(old_block, attr).state_dict()) + if hasattr(old_block, "modulation") and hasattr(new_block, "modulation"): + with torch.no_grad(): + new_block.modulation.copy_(old_block.modulation.to(dtype=block_dtype)) + new_blocks.append(new_block) + + dit.blocks = new_blocks + print(f"[loop_utils] Replaced {len(new_blocks)} blocks with DiTBlock_w_Action (MLP_CamPose)") + if block_wise_block_ids: + print(f"[loop_utils] Loaded Block-wise SSM slots on blocks: {sorted(block_wise_block_ids)[:8]}{'...' if len(block_wise_block_ids) > 8 else ''}") + if videossm_block_ids: + print(f"[loop_utils] Loaded VideoSSM hybrid slots on blocks: {sorted(videossm_block_ids)[:8]}{'...' if len(videossm_block_ids) > 8 else ''}") + + +def load_pipeline_and_ckpt( + ckpt_path, + dit_path, + text_encoder_path, + vae_path, + device="cuda", + add_action_attn=False, + action_use_temporal_attention=True, + tokenizer_path=None, + # Legacy kwargs accepted but ignored (CameraEncoder removed) + **kwargs, +): + """Load WanVideoPipeline, replace blocks with DiTBlock_w_Action, load ckpt (strict=False). + + VWM-style: no CameraEncoder, no complex inference logic. Action is injected + via MLP_CamPose (nn.Linear(12, dim), zero-init) inside each DiTBlock_w_Action. + """ + print(f"[loop_utils] Loading pipeline (DiT -> {device})") + if not tokenizer_path: + import os as _os + _base = _os.path.dirname(dit_path) + _cand = _os.path.join(_base, "google", "umt5-xxl") + if _os.path.isdir(_cand): + tokenizer_path = _cand + print(f"[loop_utils] Auto-detected tokenizer at {tokenizer_path}") + model_configs = [ + ModelConfig(path=dit_path, offload_device=device), + ModelConfig(path=text_encoder_path, offload_device="cpu"), + ModelConfig(path=vae_path, offload_device="cpu"), + ] + pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device=device, + model_configs=model_configs, + tokenizer_config=ModelConfig(path=tokenizer_path) if tokenizer_path else None, + ) + + ckpt = None + block_wise_block_ids = set() + videossm_block_ids = set() + action_attn_block_ids = set() + if ckpt_path and os.path.isfile(ckpt_path): + ckpt = safe_load_file(ckpt_path) + for key in ckpt.keys(): + m = re.match(r"blocks\.(\d+)\.block_wise_ssm\.", key) + if m: + block_wise_block_ids.add(int(m.group(1))) + m = re.match(r"blocks\.(\d+)\.videossm_hybrid\.", key) + if m: + videossm_block_ids.add(int(m.group(1))) + m = re.match(r"blocks\.(\d+)\.self_attn_with_action\.", key) + if m: + action_attn_block_ids.add(int(m.group(1))) + + if action_attn_block_ids and not add_action_attn: + add_action_attn = True + print("[loop_utils] Detected action-attention weights in checkpoint; enabling self_attn_with_action") + + # Replace blocks with DiTBlock_w_Action, including memory slots implied by ckpt keys. + _build_action_blocks( + pipe, + add_action_attn=add_action_attn, + action_use_temporal_attention=action_use_temporal_attention, + block_wise_block_ids=block_wise_block_ids, + videossm_block_ids=videossm_block_ids, + ) + + # Load ckpt (strict=False: base model keys match, action_mlp keys are extra) + if ckpt_path and not os.path.isfile(ckpt_path): + print(f"[loop_utils] WARNING: ckpt not found: {ckpt_path} — running with base model weights only!") + if ckpt_path and os.path.isfile(ckpt_path): + if ckpt is None: + ckpt = safe_load_file(ckpt_path) + missing, unexpected = pipe.dit.load_state_dict(ckpt, strict=False) + action_keys = [k for k in ckpt if "action_mlp" in k] + if not missing and not unexpected: + print(f"[loop_utils] Ckpt loaded: {len(ckpt)} keys, perfect match") + else: + print(f"[loop_utils] Ckpt loaded: {len(ckpt)} keys, " + f"missing={len(missing)}, unexpected={len(unexpected)}, " + f"action_mlp_keys={len(action_keys)}") + if missing: + for k in sorted(missing)[:5]: + print(f" missing: {k}") + if unexpected: + for k in sorted(unexpected)[:5]: + print(f" unexpected: {k}") + + # Optional: load SpatialGridMemory if present in ckpt + _smsd = { + k.replace("spatial_memory_module.", "", 1): v + for k, v in ckpt.items() + if k.startswith("spatial_memory_module.") + } + if _smsd: + try: + from diffsynth.models.memory.spatial_grid_memory import SpatialGridMemory + except ImportError: + SpatialGridMemory = None + if SpatialGridMemory is not None: + dim = pipe.dit.dim + w = _smsd.get("spatial_to_tokens") + if w is not None: + g2, num_tok = int(w.shape[0]), int(w.shape[1]) + gsz = int(round(g2 ** 0.5)) + if gsz * gsz != g2: + gsz = 8 + sm = SpatialGridMemory(dim, grid_size=gsz, num_tokens=num_tok) + sm.load_state_dict(_smsd, strict=False) + sm = sm.to(dtype=next(pipe.dit.parameters()).dtype, device=next(pipe.dit.parameters()).device) + pipe.spatial_memory_module = sm + pipe.use_spatial_memory_legacy = False + print(f"[loop_utils] Loaded spatial_memory_module (grid={gsz}, tokens={num_tok})") + + _srmsd = { + k.replace("spatial_memory_readout_module.", "", 1): v + for k, v in ckpt.items() + if k.startswith("spatial_memory_readout_module.") + } + if _srmsd: + try: + from diffsynth.models.memory.spatial_grid_memory import SpatialCrossAttnReadout + except ImportError: + SpatialCrossAttnReadout = None + if SpatialCrossAttnReadout is not None: + dim = pipe.dit.dim + readout = SpatialCrossAttnReadout(dim=dim, num_heads=8) + readout.load_state_dict(_srmsd, strict=False) + readout = readout.to(dtype=next(pipe.dit.parameters()).dtype, device=next(pipe.dit.parameters()).device) + pipe.spatial_memory_readout_module = readout + print("[loop_utils] Loaded spatial_memory_readout_module") + + if getattr(pipe, "enable_vram_management", None): + pipe.enable_vram_management() + return pipe diff --git a/env/memory_baseline_runtime.py b/env/memory_baseline_runtime.py new file mode 100644 index 0000000000000000000000000000000000000000..eb4fe46e8b68c80a02caddb2518f5aae32eff114 --- /dev/null +++ b/env/memory_baseline_runtime.py @@ -0,0 +1,514 @@ +#!/usr/bin/env python3 +""" +Infer memory-baseline runtime flags from CKPT path (aligned with train/memory_baselines_basic). + +与训练脚本逐项对应（exp1_4_4_cam_rt_paper_style/train/memory_baselines_basic/）： + +| 路径子串 | 训练脚本 | 推理 pipe / MEM_ARGS | +|----------|----------|----------------------| +| *_framepack_weight_only | run_framepack_baseline.sh | use_framepack_memory, decay=0.9, weight=1.0；**无** length compress | +| *_framepack_lencompress_r2 | run_framepack_lencompress_r2.sh | use_framepack_length_compress, framepack_ratio=2 | +| *_framepack_lencompress_r4 | run_framepack_lencompress_r4.sh | use_framepack_length_compress, framepack_ratio=4 | +| *_spatial_mem | run_spatial_memory_baseline.sh | use_spatial_memory, tokens=64, **legacy=False**（SpatialGridMemory，与 train 默认一致；grid=8 由 ckpt 权重形状在 loop_utils 中还原） | +| *_videossm_hybrid | run_videossm_hybrid_baseline.sh | 仅 context_frames=5；VideoSSM 结构由 loop_utils.load_pipeline_and_ckpt 按 ckpt key 推断 | + +共同：训练均为 context_memory_frames=5 → CONTEXT_FRAMES_MEM_OVERRIDE=5。 + +压缩记忆：训练侧 K→K' 与 RT 对齐在 wan_video_new.framepack_*；推理传 pipe.use_framepack_length_compress + framepack_ratio 即可，与 train.py 写入 pipe 一致。 + +空间记忆：若 ckpt 中无 spatial_memory_module.*，则无法与训练 SpatialGridMemory 对齐；apply_memory_baseline_pipe 会降级 legacy 并打印警告（与 run_replay_loop_two_chunk 逻辑一致）。 + +Used by: +- bash: `eval "$(python3 memory_baseline_runtime.py bash-export "$CKPT")"` → MEM_ARGS array + CONTEXT_FRAMES_MEM_OVERRIDE +- Python: apply_memory_baseline_pipe(pipe, ckpt) after load_pipeline_and_ckpt +""" +from __future__ import annotations + +import argparse +import json +import shlex +import sys +from dataclasses import dataclass +from typing import Dict, List, Optional, Sequence + + +@dataclass +class MemoryProfile: + use_framepack_memory: bool = False + context_temporal_decay: float = 1.0 + context_attention_weight: float = 1.0 + use_framepack_length_compress: bool = False + framepack_ratio: int = 2 + use_spatial_memory: bool = False + use_spatial_memory_legacy: bool = False # True = old adaptive pool only (no SpatialGridMemory in ckpt) + spatial_memory_tokens: int = 64 + spatial_memory_inject_mode: Optional[str] = None + use_block_wise_ssm: bool = False + use_videossm_hybrid: bool = False + # Training uses context_memory_frames=5 for memory_baselines_basic + context_override: Optional[int] = None + + +@dataclass(frozen=True) +class MemoryProfileSpec: + profile_id: str + ckpt_substrings: Sequence[str] + paper_tag: str + train_flags: Sequence[str] + infer_flags: Sequence[str] + eval_flags: Sequence[str] + profile: MemoryProfile + + +MEMORY_PROFILE_REGISTRY: List[MemoryProfileSpec] = [ + MemoryProfileSpec( + profile_id="context_k1", + ckpt_substrings=("context_k1", "ctx_1", "ctx1"), + paper_tag="raw_context", + train_flags=("--context_memory_frames 1",), + infer_flags=(), + eval_flags=("--context_frames 1",), + profile=MemoryProfile(context_override=1), + ), + MemoryProfileSpec( + profile_id="context_k5", + ckpt_substrings=("context_k5", "ctx_5", "ctx5"), + paper_tag="raw_context", + train_flags=("--context_memory_frames 5",), + infer_flags=(), + eval_flags=("--context_frames 5",), + profile=MemoryProfile(context_override=5), + ), + MemoryProfileSpec( + profile_id="context_k20", + ckpt_substrings=("context_k20", "ctx_20", "ctx20"), + paper_tag="raw_context", + train_flags=("--context_memory_frames 20",), + infer_flags=(), + eval_flags=("--context_frames 20",), + profile=MemoryProfile(context_override=20), + ), + MemoryProfileSpec( + profile_id="framepack_weight_only", + ckpt_substrings=( + "memory_baselines_basic_framepack_weight_only", + "memory_baselines_basic_abl_framepack_weight_two_chunk", + ), + paper_tag="arXiv:2504.12626", + train_flags=( + "--use_framepack_memory", + "--context_temporal_decay 0.9", + "--context_attention_weight 1.0", + "--context_memory_frames 81", + ), + infer_flags=("--use_framepack_memory", "--context_temporal_decay 0.9", "--context_attention_weight 1.0"), + eval_flags=("--context_frames 81",), + profile=MemoryProfile( + use_framepack_memory=True, + context_temporal_decay=0.9, + context_attention_weight=1.0, + context_override=81, + ), + ), + MemoryProfileSpec( + profile_id="framepack_lencompress_r2", + ckpt_substrings=( + "memory_baselines_basic_framepack_lencompress_r2", + "memory_baselines_basic_abl_framepack_len_r2_two_chunk", + ), + paper_tag="arXiv:2504.12626", + train_flags=("--use_framepack_length_compress", "--framepack_ratio 2", "--context_memory_frames 81"), + infer_flags=("--use_framepack_length_compress", "--framepack_ratio 2"), + eval_flags=("--context_frames 81",), + profile=MemoryProfile( + use_framepack_length_compress=True, + framepack_ratio=2, + context_override=81, + ), + ), + MemoryProfileSpec( + profile_id="framepack_lencompress_r4", + ckpt_substrings=( + "memory_baselines_basic_framepack_lencompress_r4", + "memory_baselines_basic_abl_framepack_len_r4_two_chunk", + ), + paper_tag="arXiv:2504.12626", + train_flags=("--use_framepack_length_compress", "--framepack_ratio 4", "--context_memory_frames 81"), + infer_flags=("--use_framepack_length_compress", "--framepack_ratio 4"), + eval_flags=("--context_frames 81",), + profile=MemoryProfile( + use_framepack_length_compress=True, + framepack_ratio=4, + context_override=81, + ), + ), + MemoryProfileSpec( + profile_id="spatial_mem", + ckpt_substrings=("memory_baselines_basic_spatial_mem", "spatial_mem"), + paper_tag="arXiv:2506.05284", + train_flags=("--use_spatial_memory", "--spatial_memory_tokens 64", "--context_memory_frames 5"), + infer_flags=("--use_spatial_memory", "--spatial_memory_tokens 64"), + eval_flags=("--context_frames 5",), + profile=MemoryProfile( + use_spatial_memory=True, + use_spatial_memory_legacy=False, + spatial_memory_tokens=64, + context_override=5, + ), + ), + MemoryProfileSpec( + profile_id="framepack_hybrid_r2_weight_two_chunk", + ckpt_substrings=("memory_baselines_basic_abl_framepack_hybrid_r2_weight_two_chunk",), + paper_tag="hybrid_compression", + train_flags=( + "--use_framepack_memory", + "--context_temporal_decay 0.95", + "--context_attention_weight 1.1", + "--use_framepack_length_compress", + "--framepack_ratio 2", + "--context_memory_frames 81", + ), + infer_flags=( + "--use_framepack_memory", + "--context_temporal_decay 0.95", + "--context_attention_weight 1.1", + "--use_framepack_length_compress", + "--framepack_ratio 2", + ), + eval_flags=("--context_frames 81",), + profile=MemoryProfile( + use_framepack_memory=True, + context_temporal_decay=0.95, + context_attention_weight=1.1, + use_framepack_length_compress=True, + framepack_ratio=2, + context_override=81, + ), + ), + MemoryProfileSpec( + profile_id="framepack_hybrid_r4_weight_two_chunk", + ckpt_substrings=("memory_baselines_basic_abl_framepack_hybrid_r4_weight_two_chunk",), + paper_tag="hybrid_compression", + train_flags=( + "--use_framepack_memory", + "--context_temporal_decay 0.95", + "--context_attention_weight 1.1", + "--use_framepack_length_compress", + "--framepack_ratio 4", + "--context_memory_frames 81", + ), + infer_flags=( + "--use_framepack_memory", + "--context_temporal_decay 0.95", + "--context_attention_weight 1.1", + "--use_framepack_length_compress", + "--framepack_ratio 4", + ), + eval_flags=("--context_frames 81",), + profile=MemoryProfile( + use_framepack_memory=True, + context_temporal_decay=0.95, + context_attention_weight=1.1, + use_framepack_length_compress=True, + framepack_ratio=4, + context_override=81, + ), + ), + MemoryProfileSpec( + profile_id="spatial_concat_text_two_chunk", + ckpt_substrings=("memory_baselines_basic_abl_spatial_concat_text_two_chunk", "spatial_concat_text_two_chunk"), + paper_tag="arXiv:2506.05284", + train_flags=( + "--use_spatial_memory", + "--spatial_memory_tokens 64", + "--spatial_memory_inject_mode concat_text", + "--context_memory_frames 1", + ), + infer_flags=("--use_spatial_memory", "--spatial_memory_tokens 64", "--spatial_memory_inject_mode concat_text"), + eval_flags=("--context_frames 1",), + profile=MemoryProfile( + use_spatial_memory=True, + use_spatial_memory_legacy=False, + spatial_memory_tokens=64, + spatial_memory_inject_mode="concat_text", + context_override=1, + ), + ), + MemoryProfileSpec( + profile_id="spatial_inject_none_two_chunk", + ckpt_substrings=("memory_baselines_basic_abl_spatial_inject_none_two_chunk", "spatial_inject_none_two_chunk"), + paper_tag="arXiv:2506.05284", + train_flags=( + "--use_spatial_memory", + "--spatial_memory_tokens 64", + "--spatial_memory_inject_mode none", + "--context_memory_frames 1", + ), + infer_flags=("--use_spatial_memory", "--spatial_memory_tokens 64", "--spatial_memory_inject_mode none"), + eval_flags=("--context_frames 1",), + profile=MemoryProfile( + use_spatial_memory=True, + use_spatial_memory_legacy=False, + spatial_memory_tokens=64, + spatial_memory_inject_mode="none", + context_override=1, + ), + ), + MemoryProfileSpec( + profile_id="spatial_cross_attn_readout_two_chunk", + ckpt_substrings=("memory_baselines_basic_abl_spatial_cross_attn_readout_two_chunk", "spatial_cross_attn_readout_two_chunk"), + paper_tag="arXiv:2506.05284", + train_flags=( + "--use_spatial_memory", + "--spatial_memory_tokens 64", + "--spatial_memory_inject_mode cross_attn_readout", + "--context_memory_frames 1", + ), + infer_flags=("--use_spatial_memory", "--spatial_memory_tokens 64", "--spatial_memory_inject_mode cross_attn_readout"), + eval_flags=("--context_frames 1",), + profile=MemoryProfile( + use_spatial_memory=True, + use_spatial_memory_legacy=False, + spatial_memory_tokens=64, + spatial_memory_inject_mode="cross_attn_readout", + context_override=1, + ), + ), + MemoryProfileSpec( + profile_id="spatial_cross_attn_readout_t32_g4_two_chunk", + ckpt_substrings=( + "memory_baselines_basic_abl_spatial_cross_attn_readout_t32_g4_two_chunk", + "spatial_cross_attn_readout_t32_g4_two_chunk", + ), + paper_tag="arXiv:2506.05284", + train_flags=( + "--use_spatial_memory", + "--spatial_memory_tokens 32", + "--spatial_memory_inject_mode cross_attn_readout", + "--context_memory_frames 1", + ), + infer_flags=("--use_spatial_memory", "--spatial_memory_tokens 32", "--spatial_memory_inject_mode cross_attn_readout"), + eval_flags=("--context_frames 1",), + profile=MemoryProfile( + use_spatial_memory=True, + use_spatial_memory_legacy=False, + spatial_memory_tokens=32, + spatial_memory_inject_mode="cross_attn_readout", + context_override=1, + ), + ), + MemoryProfileSpec( + profile_id="spatial_legacy_concat_t32_two_chunk", + ckpt_substrings=("memory_baselines_basic_abl_spatial_legacy_concat_t32_two_chunk",), + paper_tag="arXiv:2506.05284", + train_flags=( + "--use_spatial_memory", + "--use_spatial_memory_legacy", + "--spatial_memory_tokens 32", + "--spatial_memory_inject_mode concat_text", + "--context_memory_frames 1", + ), + infer_flags=( + "--use_spatial_memory", + "--use_spatial_memory_legacy", + "--spatial_memory_tokens 32", + "--spatial_memory_inject_mode concat_text", + ), + eval_flags=("--context_frames 1",), + profile=MemoryProfile( + use_spatial_memory=True, + use_spatial_memory_legacy=True, + spatial_memory_tokens=32, + spatial_memory_inject_mode="concat_text", + context_override=1, + ), + ), + MemoryProfileSpec( + profile_id="videossm_hybrid_legacy", + ckpt_substrings=( + "memory_baselines_basic_videossm_hybrid", + "memory_baselines_basic_abl_videossm_hybrid_two_chunk", + ), + paper_tag="legacy_hybrid", + train_flags=("--use_videossm_hybrid", "--context_memory_frames 5"), + infer_flags=("load_pipeline_and_ckpt auto-infers VideoSSM hybrid from videossm_hybrid.* ckpt keys",), + eval_flags=("--context_frames 5",), + profile=MemoryProfile(use_videossm_hybrid=True, context_override=5), + ), + MemoryProfileSpec( + profile_id="block_wise_ssm_two_chunk", + ckpt_substrings=("memory_baselines_basic_abl_block_wise_ssm_two_chunk",), + paper_tag="block_wise_recurrence", + train_flags=("--use_block_wise_ssm", "--context_memory_frames 5"), + infer_flags=("load_pipeline_and_ckpt auto-infers Block-wise SSM from block_wise_ssm.* ckpt keys",), + eval_flags=("--context_frames 5",), + profile=MemoryProfile(use_block_wise_ssm=True, context_override=5), + ), + MemoryProfileSpec( + profile_id="no_memory_extra_two_chunk", + ckpt_substrings=("memory_baselines_basic_abl_no_memory_extra_two_chunk",), + paper_tag="anchor_only_reference", + train_flags=("--context_memory_frames 5",), + infer_flags=(), + eval_flags=("--context_frames 5",), + profile=MemoryProfile(context_override=5), + ), +] + + +def profile_registry_payload() -> List[Dict[str, object]]: + payload: List[Dict[str, object]] = [] + for spec in MEMORY_PROFILE_REGISTRY: + payload.append( + { + "profile_id": spec.profile_id, + "paper_tag": spec.paper_tag, + "ckpt_substrings": list(spec.ckpt_substrings), + "train_flags": list(spec.train_flags), + "infer_flags": list(spec.infer_flags), + "eval_flags": list(spec.eval_flags), + "context_override": spec.profile.context_override, + } + ) + return payload + + +def infer_memory_profile_spec(ckpt: str) -> Optional[MemoryProfileSpec]: + c = (ckpt or "").lower() + for spec in MEMORY_PROFILE_REGISTRY: + if any(token.lower() in c for token in spec.ckpt_substrings): + return spec + return None + + +def infer_memory_profile(ckpt: str) -> MemoryProfile: + spec = infer_memory_profile_spec(ckpt) + if spec is not None: + return spec.profile + return MemoryProfile() + + +def profile_to_argv(p: MemoryProfile) -> List[str]: + out: List[str] = [] + if p.use_framepack_memory: + out.extend( + [ + "--use_framepack_memory", + "--context_temporal_decay", + str(p.context_temporal_decay), + "--context_attention_weight", + str(p.context_attention_weight), + ] + ) + if p.use_framepack_length_compress: + out.extend( + [ + "--use_framepack_length_compress", + "--framepack_ratio", + str(int(p.framepack_ratio)), + ] + ) + if p.use_spatial_memory: + out.extend( + [ + "--use_spatial_memory", + "--spatial_memory_tokens", + str(int(p.spatial_memory_tokens)), + ] + ) + if p.spatial_memory_inject_mode: + out.extend(["--spatial_memory_inject_mode", str(p.spatial_memory_inject_mode)]) + if p.use_spatial_memory_legacy: + out.append("--use_spatial_memory_legacy") + if p.use_block_wise_ssm: + out.append("--use_block_wise_ssm") + if p.use_videossm_hybrid: + out.append("--use_videossm_hybrid") + return out + + +def bash_export(ckpt: str) -> str: + """Print bash snippet: unset override, MEM_ARGS=(), optional CONTEXT_FRAMES_MEM_OVERRIDE, MEM_ARGS+=.""" + prof = infer_memory_profile(ckpt) + lines = [ + "unset CONTEXT_FRAMES_MEM_OVERRIDE 2>/dev/null || true", + "MEM_ARGS=()", + ] + if prof.context_override is not None: + lines.append(f"CONTEXT_FRAMES_MEM_OVERRIDE={int(prof.context_override)}") + for arg in profile_to_argv(prof): + lines.append(f"MEM_ARGS+=({shlex.quote(arg)})") + return "\n".join(lines) + "\n" + + +def apply_memory_baseline_pipe(pipe, ckpt: str) -> None: + """Set pipe.* memory flags from ckpt path (no-op for non–memory-baseline paths).""" + p = infer_memory_profile(ckpt) + pipe.use_framepack_memory = bool(p.use_framepack_memory) + pipe.context_temporal_decay = float(p.context_temporal_decay or 1.0) + pipe.context_attention_weight = float(p.context_attention_weight or 1.0) + pipe.use_framepack_length_compress = bool(p.use_framepack_length_compress) + pipe.framepack_ratio = int(p.framepack_ratio or 2) + pipe.use_spatial_memory = bool(p.use_spatial_memory) + pipe.spatial_memory_tokens = int(p.spatial_memory_tokens or 64) + if getattr(p, "spatial_memory_inject_mode", None): + pipe.spatial_memory_inject_mode = str(p.spatial_memory_inject_mode) + pipe.use_spatial_memory_legacy = bool(p.use_spatial_memory_legacy) + pipe.use_block_wise_ssm = bool(p.use_block_wise_ssm) + pipe.use_videossm_hybrid = bool(p.use_videossm_hybrid) + # 与 run_replay_loop_two_chunk 一致：宣称 SpatialGridMemory 但 ckpt 未载入模块时，避免 silent 与训练不一致 + if ( + pipe.use_spatial_memory + and not pipe.use_spatial_memory_legacy + and getattr(pipe, "spatial_memory_module", None) is None + ): + pipe.use_spatial_memory_legacy = True + print( + "[memory_baseline_runtime] WARN: spatial_mem profile but spatial_memory_module missing on pipe " + "(ckpt 缺少 spatial_memory_module.* 时与 run_spatial_memory_baseline.sh 的 SpatialGridMemory 不对齐；" + "已降级为 adaptive pool，数值仅作参考)。", + file=sys.stderr, + flush=True, + ) + + +def effective_context_frames(ckpt: str, env_context_frames: Optional[int]) -> int: + """For shell helpers: default CONTEXT_FRAMES from env/int, else profile override, else 1.""" + prof = infer_memory_profile(ckpt) + if env_context_frames is not None: + return int(env_context_frames) + if prof.context_override is not None: + return int(prof.context_override) + return 1 + + +def main() -> None: + ap = argparse.ArgumentParser(description="Memory baseline CKPT path → MEM_ARGS / pipe flags") + sub = ap.add_subparsers(dest="cmd", required=True) + + p_export = sub.add_parser("bash-export", help="Print eval-able bash for MEM_ARGS + CONTEXT_FRAMES_MEM_OVERRIDE") + p_export.add_argument("ckpt") + + p_ctx = sub.add_parser("print-context", help="Print effective context_frames (int) for replay_gt-style scripts") + p_ctx.add_argument("ckpt") + p_ctx.add_argument( + "--fallback", + type=int, + default=None, + help="If no profile override, use this (else 1)", + ) + sub.add_parser("list-profiles", help="Print memory profile registry JSON") + + args = ap.parse_args() + if args.cmd == "bash-export": + sys.stdout.write(bash_export(args.ckpt)) + elif args.cmd == "print-context": + prof = infer_memory_profile(args.ckpt) + v = prof.context_override if prof.context_override is not None else (args.fallback if args.fallback is not None else 1) + print(int(v)) + elif args.cmd == "list-profiles": + print(json.dumps(profile_registry_payload(), ensure_ascii=False, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/env/run_replay_loop_two_chunk.py b/env/run_replay_loop_two_chunk.py new file mode 100644 index 0000000000000000000000000000000000000000..571fe668d88f1d4ccfbef2ada327268f2e724cd1 --- /dev/null +++ b/env/run_replay_loop_two_chunk.py @@ -0,0 +1,1945 @@ +#!/usr/bin/env python3 +""" +回环：两栏输出 [输入轨迹 | 生成视频]。context 一律放在右侧（suffix），I2V 首帧也放在最右作为 clean 去噪 target。 + +- 第一帧：从训练数据随机采样一帧作为首 chunk 的 context（I2V，1 帧放右侧） +- 之后每 chunk 的第一帧（context）= 上一 chunk 的 last n 帧，同样放在该段生成视频的右侧 +- 右栏视频 = 每段 [生成 81 帧 | context 帧]，context 始终在右 + +默认回环两场景（每 chunk 45°）：1) 1左1右 2chunk 2) 2左2右 4chunk。--run_legacy_loop 时跑旧三组（roundtrip/onedir/replay）。多卡按 rank 划分样本。 + +多 chunk 时 RT（相对位姿）与训练一致： +- 训练：ref_rt = rt_list[0]，target_actions = convert_rt_to_relative(rt_list, ref_rt)，即每段内 action 相对本段首帧。 +- 推理：每个 chunk 的 action 也是相对该 chunk 的首帧。chunk1 注入 0°→45° 表示本段内转 45°； + chunk2 的首帧 = chunk1 的末帧（已 45°），chunk2 再注入 0°→45° 表示在 chunk2 局部再转 45°，世界系共 90°。 + 因此是 45+45，不是第二个 chunk 直接 90°（直接 90° 表示相对 chunk2 首帧转 90°，世界系会变成 45+90=135°）。 + +注意：条件以 action + context 为主；prompt 使用 GT 帧对应视频的文案以补充场景描述，利于生成清晰度。 +""" + +import os +import sys +import json +import argparse +import random +import math +import io +from datetime import datetime +import torch +import numpy as np +from PIL import Image + +_script_dir = os.path.dirname(os.path.abspath(__file__)) +_repo_root = os.path.dirname(os.path.dirname(_script_dir)) +_exp1_4_2_dir = os.path.join(_repo_root, "ab_study", "exp1_4_2_context_suffix_cam_rt_relative") +if _repo_root not in sys.path: + sys.path.insert(0, _repo_root) +if _exp1_4_2_dir not in sys.path: + sys.path.insert(0, _exp1_4_2_dir) +if _script_dir not in sys.path: + sys.path.insert(0, _script_dir) + +import loop_utils as irc +from diffsynth import save_video + +from src.model_training.fov_retrieval import compute_rotation_list +from src.model_training.multichunk_sample_utils import ( + context_frames_for_next_chunk, + replay_context_from_generated_frames, +) + + +def encode_context_frames_per_frame(pipe, pil_list, device, dtype=torch.bfloat16): + """与训练 context_per_frame_vae 一致：每帧单独 VAE 编码再在时间维 concat，不做时序降采样。ctx=K → K 个 latent tokens（5/20 等均一帧一过 VAE）。""" + if not pil_list: + return None + encoded = [] + for pil in pil_list: + frame_video = pipe.preprocess_video([pil]).to(device=device) + frame_sq = frame_video.squeeze(0) if frame_video.dim() == 5 else frame_video + if frame_sq.dim() == 3: + frame_sq = frame_sq.unsqueeze(0) + lat_one = pipe.vae.encode([frame_sq], device=pipe.device, tiled=False, tile_size=None, tile_stride=None) + encoded.append(lat_one) + context_latents = torch.cat(encoded, dim=2).to(dtype=dtype, device=device) + return context_latents + + +def _yaw_deg_from_rt(rt_list): + """从 12 维 RT [t_x,t_y,t_z, R11,R12,...,R33] 提取 yaw（度）。R_z 时 yaw = atan2(R21, R11).""" + if not rt_list or len(rt_list) < 12: + return 0.0 + R11, R21 = float(rt_list[3]), float(rt_list[6]) + return math.degrees(math.atan2(R21, R11)) + + +def build_action_ccw_cw(deg: float, chunk_frames: int = 81): + """R_z(yaw)，相对本段首帧。CCW=0→+deg，CW=0→-deg。匀速。""" + denom = max(1, chunk_frames - 1) + actions_ccw = {} + for i in range(chunk_frames): + yaw = (i / denom) * deg + actions_ccw[str(i)] = compute_rotation_list([0.0, 0.0, 0.0, yaw]) + actions_cw = {} + for i in range(chunk_frames): + yaw = -(i / denom) * deg + actions_cw[str(i)] = compute_rotation_list([0.0, 0.0, 0.0, yaw]) + return actions_ccw, actions_cw + + +def build_action_cw_then_ccw(deg: float, chunk_frames: int = 81): + """匀速：chunk1 顺时针 0→-deg，chunk2 逆时针 0→+deg（本段局部）。折返：先 CW 30° 再 CCW 30° 回起点。""" + denom = max(1, chunk_frames - 1) + ch1 = {} + for i in range(chunk_frames): + ch1[str(i)] = compute_rotation_list([0.0, 0.0, 0.0, -(i / denom) * deg]) + ch2 = {} + for i in range(chunk_frames): + ch2[str(i)] = compute_rotation_list([0.0, 0.0, 0.0, (i / denom) * deg]) + return ch1, ch2 + + +def build_gt_trajectory_actions(dataset_base, video_name, start_frame, chunk_frames, json_file=None): + """与 Replay 完全一致：从数据集 json 取同段 GT pose，转成 relative RT 作为 action。若帧不足则返回 None。""" + if json_file is None: + json_file = os.path.join(dataset_base, "jsons", f"{video_name}.json") + if not os.path.isfile(json_file): + return None + try: + rt_list = [irc.load_pose_rt(json_file, start_frame + i) for i in range(chunk_frames)] + if not rt_list or any(r is None or len(r) < 12 for r in rt_list): + return None + ref_rt = rt_list[0] + rel_actions = {str(i): irc.get_relative_rt(rt_list[i], ref_rt) for i in range(chunk_frames)} + return rel_actions + except Exception: + return None + + +def build_action_from_gt_yaw_profile(dataset_base, video_name, start_frame, chunk_frames, target_total_deg, clockwise=False, json_file=None): + """参考训练集方式：用同段 GT 轨迹的 yaw 曲线形状（时间分布）缩放为目标角度，使旋转节奏与训练集一致，而非简单线性。 + 若该段无 GT 或总转角过小则返回 None，调用方回退线性合成。""" + if json_file is None: + json_file = os.path.join(dataset_base, "jsons", f"{video_name}.json") + if not os.path.isfile(json_file): + return None + try: + rt_list = [irc.load_pose_rt(json_file, start_frame + i) for i in range(chunk_frames)] + if not rt_list or any(r is None or len(r) < 12 for r in rt_list): + return None + ref_rt = rt_list[0] + # GT 相对首帧的 yaw 曲线（与训练一致） + yaw_deg = [_yaw_deg_from_rt(irc.get_relative_rt(rt_list[i], ref_rt)) for i in range(chunk_frames)] + total_gt_deg = yaw_deg[-1] - yaw_deg[0] if len(yaw_deg) > 1 else 0.0 + if abs(total_gt_deg) < 1e-4: + return None # 几乎无旋转，用线性更稳 + # 归一化曲线 t[i] in [0,1]，再缩放到 target_total_deg，保持 GT 的时间节奏 + scale = target_total_deg / total_gt_deg + sign = -1 if clockwise else 1 + actions = {} + for i in range(chunk_frames): + t = (yaw_deg[i] - yaw_deg[0]) / total_gt_deg # 0 -> 1 + yaw = sign * t * target_total_deg + actions[str(i)] = compute_rotation_list([0.0, 0.0, 0.0, yaw]) + return actions + except Exception: + return None + + +def build_action_chunk(deg: float, clockwise: bool, chunk_frames: int = 81): + """单 chunk：0→deg（逆时针）或 0→-deg（顺时针）。相对本 chunk 首帧，与训练一致。 + 多 chunk 时：chunk2 的首帧=chunk1 的末帧，所以 chunk2 注入 0→deg 表示在 chunk2 局部再转 deg； + 世界系转角 = chunk1 末帧 yaw + deg。例如 chunk1=45°，chunk2 再 45° 则注入 0→45°（不是 0→90°）。 + 返回 (actions_dict, yaw_list_deg).""" + denom = max(1, chunk_frames - 1) + actions = {} + yaw_list = [] + for i in range(chunk_frames): + yaw = (i / denom) * (-deg if clockwise else deg) + yaw_list.append(yaw) + actions[str(i)] = compute_rotation_list([0.0, 0.0, 0.0, yaw]) + return actions, yaw_list + + +def build_action_translation_only(direction: str, translation_delta: float, chunk_frames: int = 81): + """纯平移单 chunk：R=I，沿单轴线性位移。与训练一致：fov_retrieval.pose_to_rt(..., constrain_to_xy=True) 仅用 XY，tz=0。 + direction in ('forward','backward','left','right')：forward=+Y, backward=-Y, left=-X, right=+X（XY 平面，无 Z）。 + RT 格式：前 3 维 [tx,ty,tz]，后 9 维 3x3 行优先单位阵；相对本段首帧（与 convert_rt_to_relative(ref=首帧) 一致）。""" + # 与训练一致：2D 平面位移，z 恒为 0（见 fov_retrieval.pose_to_rt constrain_to_xy=True） + identity_rot = [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0] + denom = max(1, chunk_frames - 1) + actions = {} + yaw_list = [0.0] * chunk_frames + for i in range(chunk_frames): + t = (i / denom) * translation_delta + if direction == "forward": + tx, ty, tz = 0.0, t, 0.0 + elif direction == "backward": + tx, ty, tz = 0.0, -t, 0.0 + elif direction == "left": + tx, ty, tz = -t, 0.0, 0.0 + elif direction == "right": + tx, ty, tz = t, 0.0, 0.0 + else: + tx, ty, tz = 0.0, 0.0, 0.0 + actions[str(i)] = [tx, ty, tz] + identity_rot + return actions, yaw_list + + +def _draw_trajectory_pil(yaw_list, plot_width, plot_height, frame_index): + """PIL-only fallback: draw trajectory 0..frame_index. Returns one PIL Image.""" + pad = 40 + w, h = plot_width - 2 * pad, plot_height - 2 * pad + if w <= 0 or h <= 0: + return Image.new("RGB", (plot_width, plot_height), (50, 50, 50)) + img = Image.new("RGB", (plot_width, plot_height), (45, 45, 48)) + end = min(frame_index + 1, len(yaw_list)) + if end == 0: + return img + ys = [float(yaw_list[i]) for i in range(end)] + y_min, y_max = min(ys), max(ys) + if y_max <= y_min: + y_min, y_max = y_min - 10.0, y_max + 10.0 + from PIL import ImageDraw + draw = ImageDraw.Draw(img) + n_x = max(1, len(yaw_list)) + pts = [] + for i in range(end): + x = pad + int((i / max(1, n_x - 1)) * w) if n_x > 1 else pad + yy = (ys[i] - y_min) / max(1e-6, y_max - y_min) + y = pad + int((1 - yy) * h) + pts.append((x, y)) + if len(pts) >= 2: + draw.line(pts, fill=(0, 200, 255), width=2) + if pts: + draw.ellipse([pts[-1][0] - 4, pts[-1][1] - 4, pts[-1][0] + 4, pts[-1][1] + 4], fill=(255, 220, 0), outline=(255, 255, 255)) + return img + + +def draw_trajectory_frames(yaw_history_deg, plot_width, plot_height, total_frames=None): + """ + yaw_history_deg: list of cumulative yaw (one per frame). + Returns list of PIL images (one per frame), each showing trajectory from 0 to current frame. + Uses matplotlib; if output is too dark, falls back to PIL-only drawing. + """ + yaw_list = [float(y) for y in (yaw_history_deg or [])] + n = len(yaw_list) + if total_frames is not None and total_frames > n: + n = total_frames + if n == 0: + return [Image.new("RGB", (plot_width, plot_height), (45, 45, 48))] + + use_pil_fallback = False + try: + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + except ImportError: + use_pil_fallback = True + + if not use_pil_fallback: + y_min = min(yaw_list) if yaw_list else 0.0 + y_max = max(yaw_list) if yaw_list else 0.0 + if y_max <= y_min: + y_min, y_max = y_min - 15.0, y_max + 15.0 + margin = max(5, (y_max - y_min) * 0.15) + y_lo, y_hi = y_min - margin, y_max + margin + out = [] + for t in range(n): + fig, ax = plt.subplots(1, 1, figsize=(plot_width / 100.0, plot_height / 100.0), dpi=100) + fig.patch.set_facecolor("#252525") + ax.set_facecolor("#252525") + ax.set_xlim(0, max(n, 1)) + ax.set_ylim(y_lo, y_hi) + ax.tick_params(colors="0.9", labelsize=7) + for spine in ax.spines.values(): + spine.set_color("0.6") + end = min(t + 1, len(yaw_list)) + if end > 0: + x = list(range(end)) + y = [yaw_list[i] for i in range(end)] + ax.plot(x, y, color="cyan", linewidth=2.0, zorder=2) + cur_y = yaw_list[min(t, len(yaw_list) - 1)] if t < len(yaw_list) else (yaw_list[-1] if yaw_list else 0) + ax.scatter([t], [cur_y], color="yellow", s=35, zorder=3) + ax.set_xlabel("Frame", color="0.9", fontsize=8) + ax.set_ylabel("Yaw (deg)", color="0.9", fontsize=8) + ax.set_title("Trajectory (yaw)", color="0.95", fontsize=9) + fig.tight_layout(pad=0.5) + buf = io.BytesIO() + plt.savefig(buf, format="png", dpi=100, facecolor="#252525", edgecolor="none", bbox_inches="tight", pad_inches=0.2) + plt.close(fig) + buf.seek(0) + img = Image.open(buf).convert("RGB") + if img.size != (plot_width, plot_height): + img = img.resize((plot_width, plot_height), Image.Resampling.LANCZOS) + # If image is too dark (matplotlib failed in headless?), use PIL fallback for rest + arr = np.array(img) + if arr.mean() < 50: + use_pil_fallback = True + out = [_draw_trajectory_pil(yaw_list, plot_width, plot_height, t) for t in range(n)] + break + out.append(img) + if not use_pil_fallback: + return out + if use_pil_fallback: + return [_draw_trajectory_pil(yaw_list, plot_width, plot_height, t) for t in range(n)] + return out + + +def composite_two_panel(trajectory_frames, right_panel_frames, w_traj=320, h_traj=352, w_gen=640, h_gen=352): + """左：输入轨迹可视化；右：生成视频（每段后接 context，context 在右）。""" + n = len(right_panel_frames) + if not trajectory_frames or len(trajectory_frames) != n: + traj_frames = draw_trajectory_frames([], w_traj, h_traj, total_frames=n) + else: + traj_frames = trajectory_frames + out = [] + for i in range(n): + traj = traj_frames[i] if i < len(traj_frames) else traj_frames[-1] + if traj.size != (w_traj, h_traj): + traj = traj.resize((w_traj, h_traj)) + gen = _frame_to_pil(right_panel_frames[i], w_gen, h_gen) + canvas_w = w_traj + w_gen + canvas_h = max(h_traj, h_gen) + canvas = Image.new("RGB", (canvas_w, canvas_h), (40, 40, 40)) + canvas.paste(traj, (0, 0)) + canvas.paste(gen, (w_traj, 0)) + out.append(canvas) + return out + + +def load_sample_first_frame(dataset_base, video_name, start_frame, w, h): + """加载当前样本的首帧（与 Replay 一致：context = 同条轨迹同视频的首帧），避免 loop 用随机帧导致 prompt 与 context 场景不一致而糊。返回 PIL 或 None。""" + if not video_name or start_frame is None: + return None + for fmt in (f"{int(start_frame):04d}.png", f"{int(start_frame)}.png"): + img_p = os.path.join(dataset_base, "frames", str(video_name), fmt) + if os.path.isfile(img_p): + return Image.open(img_p).convert("RGB").resize((w, h)) + return None + + +def load_gt_frames_at_indices(dataset_base, video_name, frame_indices, w, h): + """从数据集加载指定帧序号的 GT 图像，用于 chunk 间衔接的 context（与训练一致：context=真实帧，避免用生成帧再编码导致糊）。 + frame_indices: 从左到右使用（如 [80, 79] 表示最后一帧、倒数第二帧）。返回 list[PIL] 若全部存在，否则 None。""" + if not video_name or not frame_indices: + return None + out = [] + base = os.path.join(dataset_base, "frames", str(video_name)) + for idx in frame_indices: + for fmt in (f"{int(idx):04d}.png", f"{int(idx)}.png"): + p = os.path.join(base, fmt) + if os.path.isfile(p): + out.append(Image.open(p).convert("RGB").resize((w, h))) + break + else: + return None + return out + + +def seed_for_sample(base_seed: int, video_name, start_frame) -> int: + """与 (video_name, start_frame) 一一对应的确定性 seed，使同一样本在 loop_traj_fov_gen 与 evals_ep0 等不同调用下结果一致（避免因 idx/rank 不同导致偏移）。""" + try: + h = hash((str(video_name), int(start_frame))) + return base_seed + (h & 0x7FFFFFFF) % 100000 + except Exception: + return base_seed + + +def sample_random_frame_from_dataset(dataset_base, w, h, seed, metadata_path=None): + """从训练数据中随机采样一帧（用于首 chunk 的 I2V context，放右侧）。返回 PIL 或 None。""" + import csv + candidates = [] + if metadata_path and os.path.isfile(metadata_path): + try: + with open(metadata_path, "r", encoding="utf-8") as f: + reader = csv.DictReader(f) + for row in reader: + vn = row.get("video_name", "").strip() + sf = row.get("start_frame", "") + if vn and str(sf).strip(): + try: + candidates.append((vn, int(sf))) + except ValueError: + continue + except Exception: + pass + if not candidates: + frames_dir = os.path.join(dataset_base, "frames") + if os.path.isdir(frames_dir): + for vn in sorted(os.listdir(frames_dir)): + vd = os.path.join(frames_dir, vn) + if not os.path.isdir(vd): + continue + pngs = [f for f in os.listdir(vd) if f.endswith(".png")] + if not pngs: + continue + for p in pngs[:3]: + try: + frame_idx = int(os.path.splitext(p)[0]) + candidates.append((vn, frame_idx)) + except ValueError: + continue + if not candidates: + return None + rng = random.Random(seed) + vn, frame_idx = rng.choice(candidates) + img_p = os.path.join(dataset_base, "frames", vn, f"{frame_idx:04d}.png") + if not os.path.isfile(img_p): + img_p = os.path.join(dataset_base, "frames", vn, f"{frame_idx}.png") + if os.path.isfile(img_p): + return Image.open(img_p).convert("RGB").resize((w, h)) + return None + + +def _angle_distance_deg(a: float, b: float) -> float: + """Smallest absolute yaw distance in degrees.""" + return abs((float(a) - float(b) + 180.0) % 360.0 - 180.0) + + +def fov_history_context_from_generated_frames(frames_list, yaw_list, K: int, target_yaw: float): + """Select replay context by yaw/FOV proxy from generated history. + + The first context frame remains the most recent frame for short-term continuity. + The remaining frames are selected from generated history by closest world-yaw + distance to the next chunk's midpoint yaw. Context actions are handled by the + caller and intentionally remain unchanged in this first version. + """ + n = min(len(frames_list), len(yaw_list)) + if n <= 0 or K <= 0: + return [], [] + if K == 1 or n == 1: + return [frames_list[n - 1]], [n - 1] + + forced_idx = n - 1 + need = min(int(K) - 1, n - 1) + scored = [] + for idx in range(n - 1): + dist = _angle_distance_deg(yaw_list[idx], target_yaw) + # Prefer recent frames as a tie-breaker while keeping FOV/yaw match primary. + scored.append((dist, -idx, idx)) + scored.sort() + selected = [idx for _dist, _neg_idx, idx in scored[:need]] + return [frames_list[forced_idx]] + [frames_list[i] for i in selected], [forced_idx] + selected + + +def _world_yaw_rt(yaw: float): + return compute_rotation_list([0.0, 0.0, 0.0, float(yaw)]) + + +def context_actions_from_world_yaws(selected_yaws, ref_yaw: float): + """Convert selected world-yaw RTs into RTs relative to the next chunk start.""" + from src.model_training.fov_retrieval import convert_rt_to_relative + + ref_rt = _world_yaw_rt(ref_yaw) + selected_rts = [_world_yaw_rt(y) for y in selected_yaws] + return convert_rt_to_relative(selected_rts, ref_rt) + + +def trim_continuation_first_frame(chunk_frames_list, yaw_history, chunk_frames=81): + """与训练对齐：训练时 context[0]=target[0]（同一帧），续段首帧=上一段末帧。拼接时去掉续段第 0 帧避免重复/跳帧。 + 返回 (trimmed_chunk_frames_list, trimmed_yaw)：chunk0 保留 81 帧，chunk1 及之后只保留 [1:81] 共 80 帧。""" + if not chunk_frames_list or len(chunk_frames_list) <= 1: + return chunk_frames_list, yaw_history + trimmed_chunks = [list(chunk_frames_list[0])] + trimmed_yaw = [] + offset = 0 + for i, gen_frames in enumerate(chunk_frames_list): + n_use = len(gen_frames) if i == 0 else max(0, len(gen_frames) - 1) + if i == 0: + trimmed_yaw.extend(yaw_history[offset : offset + n_use]) + else: + trimmed_yaw.extend(yaw_history[offset + 1 : offset + 1 + n_use]) + offset += len(gen_frames) + if i > 0 and len(gen_frames) > 1: + trimmed_chunks.append(list(gen_frames[1:])) + elif i > 0: + trimmed_chunks.append([]) + return trimmed_chunks, trimmed_yaw + + +def build_right_panel_and_yaw(chunk_frames_list, yaw_history, context_frames_per_chunk, chunk_frames=81, w=640, h=352, frame_to_pil_fn=None): + """从每段生成帧 + 每段后的 context 帧拼出右栏；并扩展 yaw 与右栏帧数一致（context 帧沿用上一 yaw）。 + 支持变长 chunk（如 trim 后首段 81、续段 80），按 yaw_history 顺序逐帧对齐。""" + fp = frame_to_pil_fn or _frame_to_pil + right_frames = [] + yaw_extended = [] + yaw_idx = 0 + for i, gen_frames in enumerate(chunk_frames_list): + for j, f in enumerate(gen_frames): + right_frames.append(f) + yaw_extended.append(yaw_history[yaw_idx] if yaw_idx < len(yaw_history) else (yaw_history[-1] if yaw_history else 0.0)) + yaw_idx += 1 + ctx_list = context_frames_per_chunk[i] if i < len(context_frames_per_chunk) else [] + last_yaw = yaw_extended[-1] if yaw_extended else 0.0 + for ctx in ctx_list: + right_frames.append(ctx if isinstance(ctx, Image.Image) else fp(ctx, w, h)) + yaw_extended.append(last_yaw) + return right_frames, yaw_extended + + +def _frame_to_pil(f, tw, th): + if hasattr(f, "convert") and hasattr(f, "resize"): + return f.convert("RGB").resize((tw, th)) + if isinstance(f, np.ndarray): + if f.dtype != np.uint8: + f = (f * 255).astype(np.uint8) if f.max() <= 1.0 else f.astype(np.uint8) + return Image.fromarray(f).convert("RGB").resize((tw, th)) + if isinstance(f, torch.Tensor): + fn = f.cpu().numpy() + if len(fn.shape) == 3 and fn.shape[0] == 3: + fn = fn.transpose(1, 2, 0) + fn = (fn * 255).clip(0, 255).astype(np.uint8) if fn.max() <= 1.0 else fn.clip(0, 255).astype(np.uint8) + return Image.fromarray(fn).convert("RGB").resize((tw, th)) + return f + + +def run_one_chunk( + pipe, + prompt, + use_negative_prompt, + action_path=None, + cam_pose_actions=None, + context_latents=None, + num_context_frames=1, + context_actions_t=None, + chunk_frames=81, + h=352, + w=640, + seed=0, + sigma_shift=15.0, + num_inference_steps=50, + cfg_scale=5.0, + inference_noise_level=0.0, + omit_context_actions=False, # kept for backward compat, no longer used + log_prefix="[Loop]", + **_extra, +): + """Generate one chunk. VWM-aligned action injection via cam_pose_actions (latent-frame-aligned 12-D RT).""" + device = pipe.device + kwargs_common = dict( + prompt=prompt, + negative_prompt=use_negative_prompt, + height=h, width=w, num_frames=chunk_frames, + num_inference_steps=num_inference_steps, + seed=seed, + cfg_scale=cfg_scale, + sigma_shift=sigma_shift, + denoising_strength=1.0, + ) + if action_path is not None: + kwargs_common["action_path"] = action_path + elif cam_pose_actions is not None: + kwargs_common["cam_pose_actions"] = cam_pose_actions + + has_action_mlp = hasattr(pipe.dit.blocks[0], 'action_mlp') if len(pipe.dit.blocks) > 0 else False + if (action_path or cam_pose_actions is not None) and not has_action_mlp: + print(f"{log_prefix} 警告: dit.blocks[0] 无 action_mlp，action 可能未注入（请确认 ckpt 含 action_mlp 权重）", flush=True) + print(f"{log_prefix} run_one_chunk sigma_shift={sigma_shift} steps={num_inference_steps} (ctx={num_context_frames}) inference_noise={inference_noise_level} context_mem={context_latents is not None} action_mlp={has_action_mlp}") + if context_latents is not None: + pipe_kw = dict( + **kwargs_common, + enable_context_memory=True, + context_latents=context_latents, + num_context_frames=num_context_frames, + context_position="suffix", + cfg_target_only=True, + inference_noise_level=inference_noise_level, + ) + if context_actions_t is not None: + pipe_kw["context_actions"] = context_actions_t + with torch.no_grad(): + vid = pipe(**pipe_kw) + else: + with torch.no_grad(): + vid = pipe(**kwargs_common, enable_context_memory=False) + return vid if isinstance(vid, list) else [vid] + + +def run_roundtrip_2chunk( + pipe, + dataset_base, + output_dir, + video_name, + start_frame, + deg=30.0, + chunk_frames=81, + context_frames=1, + h=352, + w=640, + sigma_shift=15.0, + num_inference_steps=50, + cfg_scale=5.0, + seed=42, + inference_noise_level=0.0, + metadata_path=None, + keep_action_jsons=False, + omit_context_actions=True, +): + """2 chunk 折返：匀速 顺时针 deg° 再 逆时针 deg°（回起点）。""" + print(f"[Roundtrip] 匀速 顺时针{deg}° then 逆时针{deg}° sigma_shift={sigma_shift} omit_ctx_act={omit_context_actions}") + json_file = os.path.join(dataset_base, "jsons", f"{video_name}.json") + prompt = irc.load_prompt_for_video(dataset_base, video_name) or "A scene." + use_negative_prompt = getattr(irc, "DEFAULT_NEGATIVE_PROMPT", "oversaturated colors, overexposed, static, blurry details") + + actions_ch1, actions_ch2 = build_action_cw_then_ccw(deg, chunk_frames) + subdir = os.path.join(output_dir, f"loop_{video_name}_start{start_frame}") + os.makedirs(subdir, exist_ok=True) + path_ch1 = os.path.join(subdir, "_ch1_cw.json") + path_ch2 = os.path.join(subdir, "_ch2_ccw.json") + with open(path_ch1, "w") as f: + json.dump(actions_ch1, f, indent=2) + with open(path_ch2, "w") as f: + json.dump(actions_ch2, f, indent=2) + print(f"[Roundtrip] Chunk1 CW 0->-{deg}° Chunk2 CCW 0->+{deg}° (匀速)") + + denom = max(1, chunk_frames - 1) + yaw_history = [-(i / denom) * deg for i in range(chunk_frames)] # chunk1: 0 -> -deg + last_y1 = -deg + yaw_history += [last_y1 + (i / denom) * deg for i in range(chunk_frames)] # chunk2: -deg -> 0 + + chunk_frames_list = [] + context_frames_per_chunk = [] + + # Chunk 1: context = 当前样本首帧（与 Replay 一致，同视频同场景），避免随机帧导致糊；若无则回退随机帧 + ctx_pil_0 = load_sample_first_frame(dataset_base, video_name, start_frame, w, h) + used_sample_first = ctx_pil_0 is not None + if ctx_pil_0 is None: + ctx_pil_0 = sample_random_frame_from_dataset(dataset_base, w, h, seed, metadata_path) + if ctx_pil_0 is not None: + print(f"[Roundtrip] Chunk1 context: {'sample first frame ' + str((video_name, start_frame)) if used_sample_first else 'random frame (fallback)'}") + if ctx_pil_0 is not None: + ctx_pil_0 = [ctx_pil_0] + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + ctx_latents_0 = encode_context_frames_per_frame(pipe, ctx_pil_0, pipe.device) + identity_rt = [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0] + ctx_actions_t_0 = torch.tensor([identity_rt], dtype=torch.float32) + frames_ch1 = run_one_chunk( + pipe, prompt, use_negative_prompt, path_ch1, + context_latents=ctx_latents_0, + num_context_frames=ctx_latents_0.shape[2], + context_actions_t=ctx_actions_t_0, + chunk_frames=chunk_frames, h=h, w=w, seed=seed, + sigma_shift=sigma_shift, num_inference_steps=num_inference_steps, + cfg_scale=cfg_scale, inference_noise_level=inference_noise_level, + omit_context_actions=omit_context_actions, + ) + context_frames_per_chunk.append([ctx_pil_0[0]]) + else: + frames_ch1 = run_one_chunk( + pipe, prompt, use_negative_prompt, path_ch1, + chunk_frames=chunk_frames, h=h, w=w, seed=seed, + sigma_shift=sigma_shift, num_inference_steps=num_inference_steps, cfg_scale=cfg_scale, + omit_context_actions=omit_context_actions, + ) + context_frames_per_chunk.append([_frame_to_pil(frames_ch1[-1], w, h)]) + chunk_frames_list.append(frames_ch1) + + # Chunk 2: condition = 上一 chunk，顺序与训练一致 [last_frame, ctx1, ...] 紧挨噪声后 + n_ctx = min(context_frames, len(frames_ch1)) + prev_frames = replay_context_from_generated_frames(frames_ch1, n_ctx) + prev_pil = [_frame_to_pil(f, w, h) for f in prev_frames] + identity_rt = [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0] + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + context_latents = encode_context_frames_per_frame(pipe, prev_pil, pipe.device) + num_ctx_tokens = context_latents.shape[2] + context_actions_t = torch.tensor([identity_rt] * num_ctx_tokens, dtype=torch.float32) + print(f"[Loop] continuation chunk: len(prev_pil)={len(prev_pil)} num_ctx_tokens={num_ctx_tokens} (应等于 ctx)") + frames_ch2 = run_one_chunk( + pipe, prompt, use_negative_prompt, path_ch2, + context_latents=context_latents, + num_context_frames=num_ctx_tokens, + context_actions_t=context_actions_t, + chunk_frames=chunk_frames, h=h, w=w, seed=seed + 1, + sigma_shift=sigma_shift, num_inference_steps=num_inference_steps, + cfg_scale=cfg_scale, inference_noise_level=inference_noise_level, + omit_context_actions=omit_context_actions, + ) + chunk_frames_list.append(frames_ch2) + context_frames_per_chunk.append(list(prev_pil)) + + if not keep_action_jsons: + for p in [path_ch1, path_ch2]: + if os.path.exists(p): + try: + os.remove(p) + except Exception: + pass + return chunk_frames_list, yaw_history, context_frames_per_chunk + + +def run_single_chunk_rotation( + pipe, + dataset_base, + output_dir, + video_name, + start_frame, + deg=45.0, + clockwise=False, + chunk_frames=81, + h=352, + w=640, + sigma_shift=15.0, + num_inference_steps=50, + cfg_scale=5.0, + seed=42, + inference_noise_level=0.0, + metadata_path=None, + keep_action_jsons=False, + sampling_action_dir=None, + omit_context_actions=True, + context_image_path=None, +): + """旋转原子操作：单 chunk 左转(CCW)或右转(CW) deg°。优先使用与训练采样相同的 action JSON。omit_context_actions 与训练 ctx 设置对齐。context_image_path：泛化检查时用指定图片作为首帧 context。""" + label = "右转(CW)" if clockwise else "左转(CCW)" + print(f"[SingleChunk] 1 chunk {label} {deg}° sigma_shift={sigma_shift}") + prompt = irc.load_prompt_for_video(dataset_base, video_name) or "A scene." + use_negative_prompt = getattr(irc, "DEFAULT_NEGATIVE_PROMPT", "oversaturated colors, overexposed, static, blurry details") + + # 与采样对齐：45°、81 帧时优先用采样同款 JSON（generate_rotation_actions.py 生成），注入方式一致 + path_ch = None + if deg == 45.0 and chunk_frames == 81: + action_dir = sampling_action_dir if sampling_action_dir else _script_dir + if clockwise: + candidate = os.path.join(action_dir, "action_rotation_right_45.json") + else: + candidate = os.path.join(action_dir, "action_rotation_left_45.json") + if os.path.isfile(candidate): + path_ch = candidate + print(f"[SingleChunk] 使用采样同款 action: {os.path.basename(path_ch)} (与训练采样注入一致)", flush=True) + + if path_ch is None: + actions, yaw_chunk = build_action_chunk(deg, clockwise, chunk_frames) + yaw0 = _yaw_deg_from_rt(actions["0"]) + yaw_last = _yaw_deg_from_rt(actions[str(chunk_frames - 1)]) + print(f"[SingleChunk] action 首帧 yaw={yaw0:.1f}° 末帧 yaw={yaw_last:.1f}° (预期: 左转 0→+{deg}° 右转 0→-{deg}°)", flush=True) + subdir = os.path.join(output_dir, f"loop_{video_name}_start{start_frame}") + os.makedirs(subdir, exist_ok=True) + suffix = "cw_right" if clockwise else "ccw_left" + path_ch = os.path.join(subdir, f"_single_{suffix}.json") + with open(path_ch, "w") as f: + json.dump(actions, f, indent=2) + yaw_history = list(yaw_chunk) + delete_path_after = not keep_action_jsons + else: + with open(path_ch, "r") as f: + actions = json.load(f) + yaw_history = [_yaw_deg_from_rt(actions.get(str(i), [0] * 12)) for i in range(min(chunk_frames, len(actions)))] + if len(yaw_history) < chunk_frames: + yaw_history += [yaw_history[-1]] * (chunk_frames - len(yaw_history)) + delete_path_after = False + + subdir = os.path.join(output_dir, f"loop_{video_name}_start{start_frame}") + os.makedirs(subdir, exist_ok=True) + yaw_history = yaw_history[:chunk_frames] if len(yaw_history) > chunk_frames else yaw_history + chunk_frames_list = [] + context_frames_per_chunk = [] + + if context_image_path and os.path.isfile(context_image_path): + ctx_pil_0 = Image.open(context_image_path).convert("RGB").resize((w, h)) + print(f"[SingleChunk] 使用指定 context 图: {context_image_path}", flush=True) + else: + ctx_pil_0 = load_sample_first_frame(dataset_base, video_name, start_frame, w, h) + if ctx_pil_0 is None: + ctx_pil_0 = sample_random_frame_from_dataset(dataset_base, w, h, seed, metadata_path) + if ctx_pil_0 is not None: + ctx_pil_0 = [ctx_pil_0] + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + ctx_latents_0 = encode_context_frames_per_frame(pipe, ctx_pil_0, pipe.device) + identity_rt = [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0] + ctx_actions_t_0 = torch.tensor([identity_rt], dtype=torch.float32) + frames_ch = run_one_chunk( + pipe, prompt, use_negative_prompt, path_ch, + context_latents=ctx_latents_0, + num_context_frames=ctx_latents_0.shape[2], + context_actions_t=ctx_actions_t_0, + chunk_frames=chunk_frames, h=h, w=w, seed=seed, + sigma_shift=sigma_shift, num_inference_steps=num_inference_steps, + cfg_scale=cfg_scale, inference_noise_level=inference_noise_level, + omit_context_actions=omit_context_actions, + ) + context_frames_per_chunk.append([ctx_pil_0[0]]) + else: + frames_ch = run_one_chunk( + pipe, prompt, use_negative_prompt, path_ch, + chunk_frames=chunk_frames, h=h, w=w, seed=seed, + sigma_shift=sigma_shift, num_inference_steps=num_inference_steps, cfg_scale=cfg_scale, + omit_context_actions=omit_context_actions, + ) + context_frames_per_chunk.append([_frame_to_pil(frames_ch[-1], w, h)]) + chunk_frames_list.append(frames_ch) + + if delete_path_after and os.path.exists(path_ch): + try: + os.remove(path_ch) + except Exception: + pass + return chunk_frames_list, yaw_history, context_frames_per_chunk + + +def run_left_right_2chunk( + pipe, + dataset_base, + output_dir, + video_name, + start_frame, + deg=45.0, + chunk_frames=81, + context_frames=1, + h=352, + w=640, + sigma_shift=15.0, + num_inference_steps=50, + cfg_scale=5.0, + seed=42, + inference_noise_level=0.0, + metadata_path=None, + keep_action_jsons=False, + sampling_action_dir=None, + omit_context_actions=True, +): + """回环：1 chunk 左转(CCW) deg°，1 chunk 右转(CW) deg°。45°×81 时优先用采样同款 left_45/right_45.json。""" + print(f"[LeftRight 2chunk] 左转(CCW){deg}° then 右转(CW){deg}° sigma_shift={sigma_shift} omit_ctx_act={omit_context_actions} context_frames={context_frames} (续段 ctx 数)") + prompt = irc.load_prompt_for_video(dataset_base, video_name) or "A scene." + use_negative_prompt = getattr(irc, "DEFAULT_NEGATIVE_PROMPT", "oversaturated colors, overexposed, static, blurry details") + + subdir = os.path.join(output_dir, f"loop_{video_name}_start{start_frame}") + os.makedirs(subdir, exist_ok=True) + action_dir = sampling_action_dir if sampling_action_dir else _script_dir + path_ch1 = path_ch2 = None + delete_ch1 = delete_ch2 = True + if deg == 45.0 and chunk_frames == 81: + p_left = os.path.join(action_dir, "action_rotation_left_45.json") + p_right = os.path.join(action_dir, "action_rotation_right_45.json") + if os.path.isfile(p_left) and os.path.isfile(p_right): + path_ch1, path_ch2 = p_left, p_right + delete_ch1 = delete_ch2 = False + print(f"[LeftRight 2chunk] 使用采样同款 action: left_45 + right_45 (与训练采样注入一致)", flush=True) + if path_ch1 is None: + actions_ccw, actions_cw = build_action_ccw_cw(deg, chunk_frames) + path_ch1 = os.path.join(subdir, "_ch1_ccw_left.json") + path_ch2 = os.path.join(subdir, "_ch2_cw_right.json") + with open(path_ch1, "w") as f: + json.dump(actions_ccw, f, indent=2) + with open(path_ch2, "w") as f: + json.dump(actions_cw, f, indent=2) + delete_ch1 = delete_ch2 = not keep_action_jsons + + if not delete_ch1 and path_ch1 and path_ch2: + with open(path_ch1, "r") as f: + a1 = json.load(f) + with open(path_ch2, "r") as f: + a2 = json.load(f) + yaw_left = [_yaw_deg_from_rt(a1.get(str(i), [0] * 12)) for i in range(min(chunk_frames, len(a1)))] + yaw_right = [_yaw_deg_from_rt(a2.get(str(i), [0] * 12)) for i in range(min(chunk_frames, len(a2)))] + if len(yaw_left) < chunk_frames: + yaw_left += [yaw_left[-1]] * (chunk_frames - len(yaw_left)) + if len(yaw_right) < chunk_frames: + yaw_right += [yaw_right[-1]] * (chunk_frames - len(yaw_right)) + base = yaw_left[-1] if yaw_left else 45.0 + yaw_history = yaw_left[:chunk_frames] + [base + yaw_right[i] for i in range(chunk_frames)] + else: + denom = max(1, chunk_frames - 1) + yaw_history = [(i / denom) * deg for i in range(chunk_frames)] + yaw_history += [deg - (i / denom) * deg for i in range(chunk_frames)] + + chunk_frames_list = [] + context_frames_per_chunk = [] + + ctx_pil_0 = load_sample_first_frame(dataset_base, video_name, start_frame, w, h) + used_sample_first = ctx_pil_0 is not None + if ctx_pil_0 is None: + ctx_pil_0 = sample_random_frame_from_dataset(dataset_base, w, h, seed, metadata_path) + if ctx_pil_0 is not None: + ctx_pil_0 = [ctx_pil_0] + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + ctx_latents_0 = encode_context_frames_per_frame(pipe, ctx_pil_0, pipe.device) + identity_rt = [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0] + ctx_actions_t_0 = torch.tensor([identity_rt], dtype=torch.float32) + frames_ch1 = run_one_chunk( + pipe, prompt, use_negative_prompt, path_ch1, + context_latents=ctx_latents_0, + num_context_frames=ctx_latents_0.shape[2], + context_actions_t=ctx_actions_t_0, + chunk_frames=chunk_frames, h=h, w=w, seed=seed, + sigma_shift=sigma_shift, num_inference_steps=num_inference_steps, + cfg_scale=cfg_scale, inference_noise_level=inference_noise_level, + omit_context_actions=omit_context_actions, + ) + context_frames_per_chunk.append([ctx_pil_0[0]]) + else: + frames_ch1 = run_one_chunk( + pipe, prompt, use_negative_prompt, path_ch1, + chunk_frames=chunk_frames, h=h, w=w, seed=seed, + sigma_shift=sigma_shift, num_inference_steps=num_inference_steps, cfg_scale=cfg_scale, + omit_context_actions=omit_context_actions, + ) + context_frames_per_chunk.append([_frame_to_pil(frames_ch1[-1], w, h)]) + chunk_frames_list.append(frames_ch1) + + n_ctx = min(context_frames, len(frames_ch1)) + prev_frames = replay_context_from_generated_frames(frames_ch1, n_ctx) + prev_pil = [_frame_to_pil(f, w, h) for f in prev_frames] + identity_rt = [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0] + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + context_latents = encode_context_frames_per_frame(pipe, prev_pil, pipe.device) + num_ctx_tokens = context_latents.shape[2] + context_actions_t = torch.tensor([identity_rt] * num_ctx_tokens, dtype=torch.float32) + print(f"[Loop] continuation chunk: len(prev_pil)={len(prev_pil)} num_ctx_tokens={num_ctx_tokens} (应等于 ctx)") + frames_ch2 = run_one_chunk( + pipe, prompt, use_negative_prompt, path_ch2, + context_latents=context_latents, + num_context_frames=num_ctx_tokens, + context_actions_t=context_actions_t, + chunk_frames=chunk_frames, h=h, w=w, seed=seed + 1, + sigma_shift=sigma_shift, num_inference_steps=num_inference_steps, + cfg_scale=cfg_scale, inference_noise_level=inference_noise_level, + omit_context_actions=omit_context_actions, + ) + chunk_frames_list.append(frames_ch2) + context_frames_per_chunk.append(list(prev_pil)) + + if delete_ch1 and path_ch1 and os.path.exists(path_ch1): + try: + os.remove(path_ch1) + except Exception: + pass + if delete_ch2 and path_ch2 and os.path.exists(path_ch2): + try: + os.remove(path_ch2) + except Exception: + pass + return chunk_frames_list, yaw_history, context_frames_per_chunk + + +def run_translation_4chunk( + pipe, + dataset_base, + output_dir, + video_name, + start_frame, + direction="forward", + translation_delta=0.1, + chunk_frames=81, + context_frames=1, + h=352, + w=640, + sigma_shift=15.0, + num_inference_steps=50, + cfg_scale=5.0, + seed=42, + inference_noise_level=0.0, + metadata_path=None, + omit_context_actions=True, +): + """仅纯平移 4chunk：同一方向（forward/backward/left/right）连续 4 段，R=I，无旋转。仅用于 --run_atomic_translation_4chunk。""" + _prefix = "[atomic_translation_4chunk]" + print(f"{_prefix} 纯平移 direction={direction} delta={translation_delta} sigma_shift={sigma_shift} omit_ctx_act={omit_context_actions} context_frames={context_frames} (续段 ctx 数)") + prompt = irc.load_prompt_for_video(dataset_base, video_name) or "A scene." + use_negative_prompt = getattr(irc, "DEFAULT_NEGATIVE_PROMPT", "oversaturated colors, overexposed, static, blurry details") + + yaw_history = [] + chunk_frames_list = [] + context_frames_per_chunk = [] + subdir = os.path.join(output_dir, f"loop_{video_name}_start{start_frame}") + os.makedirs(subdir, exist_ok=True) + identity_rt = [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0] + context_latents = None + context_actions_t = None + + for ch in range(4): + actions, yaw_chunk = build_action_translation_only(direction, translation_delta, chunk_frames) + path_ch = os.path.join(subdir, f"_ch{ch}_trans_{direction}.json") + with open(path_ch, "w") as f: + json.dump(actions, f, indent=2) + try: + if ch == 0: + ctx_pil_0 = load_sample_first_frame(dataset_base, video_name, start_frame, w, h) + if ctx_pil_0 is None: + ctx_pil_0 = sample_random_frame_from_dataset(dataset_base, w, h, seed, metadata_path) + if ctx_pil_0 is not None: + ctx_pil_0 = [ctx_pil_0] + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + context_latents = encode_context_frames_per_frame(pipe, ctx_pil_0, pipe.device) + context_actions_t = torch.tensor([identity_rt], dtype=torch.float32) + context_frames_per_chunk.append([ctx_pil_0[0]]) + else: + context_frames_per_chunk.append([]) + + if context_latents is not None and context_actions_t is not None: + frames_ch = run_one_chunk( + pipe, prompt, use_negative_prompt, path_ch, + context_latents=context_latents, + num_context_frames=context_latents.shape[2], + context_actions_t=context_actions_t, + chunk_frames=chunk_frames, h=h, w=w, seed=seed + ch, + sigma_shift=sigma_shift, num_inference_steps=num_inference_steps, + cfg_scale=cfg_scale, inference_noise_level=inference_noise_level, + omit_context_actions=omit_context_actions, + log_prefix=_prefix, + ) + else: + frames_ch = run_one_chunk( + pipe, prompt, use_negative_prompt, path_ch, + chunk_frames=chunk_frames, h=h, w=w, seed=seed + ch, + sigma_shift=sigma_shift, num_inference_steps=num_inference_steps, cfg_scale=cfg_scale, + omit_context_actions=omit_context_actions, + log_prefix=_prefix, + ) + if ch == 0 and not context_frames_per_chunk[0]: + context_frames_per_chunk[0] = [_frame_to_pil(frames_ch[-1], w, h)] + + for y in yaw_chunk: + yaw_history.append(y) + chunk_frames_list.append(frames_ch) + + if ch < 3: + n_ctx = min(context_frames, len(frames_ch)) + prev_frames = context_frames_for_next_chunk(frames_ch, n_ctx) if n_ctx else [frames_ch[-1]] + prev_pil = [_frame_to_pil(f, w, h) for f in prev_frames] + context_frames_per_chunk.append(list(prev_pil)) + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + context_latents = encode_context_frames_per_frame(pipe, prev_pil, pipe.device) + num_ctx_tokens = context_latents.shape[2] + context_actions_t = torch.tensor([identity_rt] * num_ctx_tokens, dtype=torch.float32) + print(f"{_prefix} continuation chunk ch={ch}: len(prev_pil)={len(prev_pil)} num_ctx_tokens={num_ctx_tokens} (应等于 ctx)") + finally: + if os.path.exists(path_ch): + try: + os.remove(path_ch) + except Exception: + pass + + return chunk_frames_list, yaw_history, context_frames_per_chunk + + +def run_left2_right2_4chunk( + pipe, + dataset_base, + output_dir, + video_name, + start_frame, + deg_per_chunk=45.0, + chunk_frames=81, + context_frames=1, + h=352, + w=640, + sigma_shift=15.0, + num_inference_steps=50, + cfg_scale=5.0, + seed=42, + inference_noise_level=0.0, + metadata_path=None, + sampling_action_dir=None, + omit_context_actions=True, + multi_ctx_all_history=False, + fov_history_context=False, + fov_last_target=False, + fov_context_rt=False, +): + """回环：先左转 2 chunk（各 deg° CCW），再右转 2 chunk（各 deg° CW）。 + + 默认行为：与 2chunk 回环一致，续段 context 仅来自上一 chunk 的末尾若干帧。 + 当 multi_ctx_all_history=True 时：续段 context 第一帧必须为上一 chunk 的最后一帧； + 其余 (ctx-1) 帧从「前面所有已生成帧」均匀采样（保持时间序），用于记忆机制对比。 + 当 fov_history_context=True 时：续段 context 第一帧仍为上一 chunk 的最后一帧； + 其余 (ctx-1) 帧按下一 chunk 中点 world-yaw，从所有已生成历史帧中检索最接近的帧。 + 当 fov_last_target=True 时：仅第 4 个 chunk 的检索 target 改用该 chunk 末尾 world-yaw。 + 当 fov_context_rt=True 时：FOV/yaw 检索出的 context actions 转成相对下一 chunk 起始 world-yaw 的 RT。 + """ + print(f"[Left2Right2 4chunk] 左转2chunk then 右转2chunk sigma_shift={sigma_shift} omit_ctx_act={omit_context_actions} context_frames={context_frames} (续段 ctx 数)") + prompt = irc.load_prompt_for_video(dataset_base, video_name) or "A scene." + use_negative_prompt = getattr(irc, "DEFAULT_NEGATIVE_PROMPT", "oversaturated colors, overexposed, static, blurry details") + + yaw_history = [] + chunk_frames_list = [] + context_frames_per_chunk = [] + all_prev_frames = [] + subdir = os.path.join(output_dir, f"loop_{video_name}_start{start_frame}") + os.makedirs(subdir, exist_ok=True) + identity_rt = [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0] + cumulative_yaw = 0.0 + context_latents = None + context_actions_t = None + + # 45°×81 时直接使用采样同款 JSON，多 chunk 多次导入同一文件即可 + action_dir = sampling_action_dir if sampling_action_dir else _script_dir + path_left = os.path.join(action_dir, "action_rotation_left_45.json") + path_right = os.path.join(action_dir, "action_rotation_right_45.json") + use_sampling_files = (deg_per_chunk == 45.0 and chunk_frames == 81 and + os.path.isfile(path_left) and os.path.isfile(path_right)) + if use_sampling_files: + print(f"[Left2Right2 4chunk] 使用采样同款 action: left_45 x2 + right_45 x2 (与训练采样注入一致)", flush=True) + with open(path_left, "r") as f: + _actions_left = json.load(f) + with open(path_right, "r") as f: + _actions_right = json.load(f) + _yaw_left = [_yaw_deg_from_rt(_actions_left.get(str(i), [0] * 12)) for i in range(min(chunk_frames, len(_actions_left)))] + _yaw_right = [_yaw_deg_from_rt(_actions_right.get(str(i), [0] * 12)) for i in range(min(chunk_frames, len(_actions_right)))] + if len(_yaw_left) < chunk_frames: + _yaw_left += [_yaw_left[-1]] * (chunk_frames - len(_yaw_left)) + if len(_yaw_right) < chunk_frames: + _yaw_right += [_yaw_right[-1]] * (chunk_frames - len(_yaw_right)) + + for ch in range(4): + clockwise = ch >= 2 + if use_sampling_files: + path_ch = path_right if clockwise else path_left + else: + actions, yaw_chunk = build_action_chunk(deg_per_chunk, clockwise, chunk_frames) + path_ch = os.path.join(subdir, f"_ch{ch}_left2right2.json") + with open(path_ch, "w") as f: + json.dump(actions, f, indent=2) + + # 与 2chunk 一致：ch=0 用首帧 context；ch>=1 用上一 chunk 输出做 context（上一轮末尾已写好 context_latents/context_actions_t） + if ch == 0: + ctx_pil_0 = load_sample_first_frame(dataset_base, video_name, start_frame, w, h) + if ctx_pil_0 is None: + ctx_pil_0 = sample_random_frame_from_dataset(dataset_base, w, h, seed, metadata_path) + if ctx_pil_0 is not None: + ctx_pil_0 = [ctx_pil_0] + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + context_latents = encode_context_frames_per_frame(pipe, ctx_pil_0, pipe.device) + context_actions_t = torch.tensor([identity_rt], dtype=torch.float32) + context_frames_per_chunk.append([ctx_pil_0[0]]) + else: + context_frames_per_chunk.append([]) + + if context_latents is not None and context_actions_t is not None: + frames_ch = run_one_chunk( + pipe, prompt, use_negative_prompt, path_ch, + context_latents=context_latents, + num_context_frames=context_latents.shape[2], + context_actions_t=context_actions_t, + chunk_frames=chunk_frames, h=h, w=w, seed=seed + ch, + sigma_shift=sigma_shift, num_inference_steps=num_inference_steps, + cfg_scale=cfg_scale, inference_noise_level=inference_noise_level, + omit_context_actions=omit_context_actions, + ) + else: + frames_ch = run_one_chunk( + pipe, prompt, use_negative_prompt, path_ch, + chunk_frames=chunk_frames, h=h, w=w, seed=seed + ch, + sigma_shift=sigma_shift, num_inference_steps=num_inference_steps, cfg_scale=cfg_scale, + omit_context_actions=omit_context_actions, + ) + if ch == 0 and not context_frames_per_chunk[0]: + context_frames_per_chunk[0] = [_frame_to_pil(frames_ch[-1], w, h)] + + if use_sampling_files: + yaw_chunk = _yaw_right if clockwise else _yaw_left + else: + yaw_chunk = list(yaw_chunk) + for y in yaw_chunk: + yaw_history.append(cumulative_yaw + y) + cumulative_yaw = yaw_history[-1] + chunk_frames_list.append(frames_ch) + all_prev_frames.extend(frames_ch) + + # 续段 context： + # - 默认：与 2chunk 一致，仅用上一 chunk 的 last n 帧 + # - fov_history_context=True：第一帧必须为上一 chunk 最后一帧；其余 (ctx-1) 帧按下一 chunk 中点 world-yaw 做 FOV/yaw proxy 检索。 + # - multi_ctx_all_history=True：第一帧必须为上一 chunk 最后一帧；其余 (ctx-1) 帧从「前面所有帧」均匀采样，保持时间序。 + if ch < 3: + next_context_actions = None + if context_frames <= 0: + prev_frames = [frames_ch[-1]] + elif fov_history_context and len(all_prev_frames) > 0: + next_ch = ch + 1 + next_clockwise = next_ch >= 2 + if use_sampling_files: + next_yaw_chunk = _yaw_right if next_clockwise else _yaw_left + else: + _unused_actions, next_yaw_chunk = build_action_chunk(deg_per_chunk, next_clockwise, chunk_frames) + target_idx = min(max(0, chunk_frames // 2), len(next_yaw_chunk) - 1) + if fov_last_target and next_ch == 3: + target_idx = len(next_yaw_chunk) - 1 + target_mid_yaw = cumulative_yaw + float(next_yaw_chunk[target_idx]) + prev_frames, picked_indices = fov_history_context_from_generated_frames( + all_prev_frames, + yaw_history, + context_frames, + target_mid_yaw, + ) + picked_yaws = [float(yaw_history[i]) for i in picked_indices if i < len(yaw_history)] + if fov_context_rt and picked_yaws: + next_context_actions = context_actions_from_world_yaws(picked_yaws, cumulative_yaw) + print( + f"[Loop] fov_history_context ch={ch}->next={next_ch}: " + f"target_idx={target_idx} target_yaw={target_mid_yaw:.2f} picked_indices={picked_indices} " + f"picked_yaws={[round(y, 2) for y in picked_yaws]} " + f"context_rt={bool(next_context_actions)} ref_yaw={cumulative_yaw:.2f}", + flush=True, + ) + elif multi_ctx_all_history and len(all_prev_frames) > 0: + total = len(all_prev_frames) + if total == 1 or context_frames == 1: + prev_frames = [all_prev_frames[-1]] + else: + # 第一帧 = 上一 chunk 最后一帧 (all_prev_frames[-1])；其余 (ctx-1) 帧从 all_prev_frames[0..total-2] 均匀采样 + n_pick = min(context_frames - 1, total - 1) + if n_pick <= 0: + prev_frames = [all_prev_frames[-1]] + else: + pool_size = total - 1 # 可选下标 0..total-2（前面所有帧，不含已单独占位的最后一帧） + indices = [] + for i in range(n_pick): + pos = int(round(i * (pool_size - 1) / max(n_pick - 1, 1))) + indices.append(max(0, min(pool_size - 1, pos))) + uniq_indices = sorted(set(indices)) + while len(uniq_indices) < n_pick: + uniq_indices.append(pool_size - 1 if pool_size > 0 else 0) + uniq_indices = sorted(uniq_indices)[:n_pick] + chosen = [all_prev_frames[i] for i in uniq_indices] + prev_frames = [all_prev_frames[-1]] + chosen + else: + n_ctx = min(context_frames, len(frames_ch)) + prev_frames = context_frames_for_next_chunk(frames_ch, n_ctx) if n_ctx else [frames_ch[-1]] + + prev_pil = [_frame_to_pil(f, w, h) for f in prev_frames] + context_frames_per_chunk.append(list(prev_pil)) + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + context_latents = encode_context_frames_per_frame(pipe, prev_pil, pipe.device) + num_ctx_tokens = context_latents.shape[2] + if next_context_actions is not None: + if len(next_context_actions) < num_ctx_tokens: + next_context_actions = next_context_actions + [next_context_actions[-1]] * (num_ctx_tokens - len(next_context_actions)) + context_actions_t = torch.tensor(next_context_actions[:num_ctx_tokens], dtype=torch.float32) + else: + context_actions_t = torch.tensor([identity_rt] * num_ctx_tokens, dtype=torch.float32) + print( + f"[Loop] continuation chunk ch={ch}: len(prev_pil)={len(prev_pil)} num_ctx_tokens={num_ctx_tokens} " + f"(multi_ctx_all_history={multi_ctx_all_history} fov_history_context={fov_history_context} fov_context_rt={fov_context_rt})" + ) + + if not use_sampling_files and os.path.exists(path_ch): + try: + os.remove(path_ch) + except Exception: + pass + + return chunk_frames_list, yaw_history, context_frames_per_chunk + + +def run_replay_4chunk( + pipe, + dataset_base, + output_dir, + video_name, + start_frame, + chunk_frames=81, + context_frames=1, + h=352, + w=640, + sigma_shift=15.0, + num_inference_steps=50, + cfg_scale=5.0, + seed=42, + inference_noise_level=0.0, + omit_context_actions=True, +): + """4 chunk 参考训练：每段用同视频连续 4 段的 GT 轨迹，逐 chunk 对比。需 start_frame 起至少 4*81 帧。""" + print(f"[Replay 4chunk] {video_name} start{start_frame} (4 段 GT 轨迹) context_frames={context_frames} (续段 ctx 数)") + json_file = os.path.join(dataset_base, "jsons", f"{video_name}.json") + if not os.path.isfile(json_file): + return None + num_chunks = 4 + need_frames = num_chunks * chunk_frames # 324 + # 检查是否有足够帧（用 pose 存在性粗略判断） + try: + last_pose = irc.load_pose_rt(json_file, start_frame + need_frames - 1) + if last_pose is None or len(last_pose) < 12: + return None + except Exception: + return None + prompt = irc.load_prompt_for_video(dataset_base, video_name) or "A scene." + use_negative_prompt = getattr(irc, "DEFAULT_NEGATIVE_PROMPT", "oversaturated colors, overexposed, static, blurry details") + identity_rt = [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0] + subdir = os.path.join(output_dir, f"loop_{video_name}_start{start_frame}") + os.makedirs(subdir, exist_ok=True) + chunk_frames_list = [] + context_frames_per_chunk = [] + yaw_history = [] + cumulative_yaw = 0.0 + context_latents = None + context_actions_t = None + + for ch in range(num_chunks): + seg_start = start_frame + ch * chunk_frames + ch1_actions = build_gt_trajectory_actions(dataset_base, video_name, seg_start, chunk_frames, json_file) + if ch1_actions is None: + return None + path_ch = os.path.join(subdir, f"_ch{ch}_replay.json") + with open(path_ch, "w") as f: + json.dump(ch1_actions, f, indent=2) + if ch == 0: + ctx_pil_0 = load_sample_first_frame(dataset_base, video_name, seg_start, w, h) + if ctx_pil_0 is None: + return None + ctx_pil_0 = [ctx_pil_0] + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + context_latents = encode_context_frames_per_frame(pipe, ctx_pil_0, pipe.device) + context_actions_t = torch.tensor([identity_rt], dtype=torch.float32) + context_frames_per_chunk.append([ctx_pil_0[0]]) + else: + # 下一 chunk 的 condition = 上一 chunk 的 last n 帧（生成结果） + n_ctx = min(context_frames, len(chunk_frames_list[-1])) + prev_frames = context_frames_for_next_chunk(chunk_frames_list[-1], n_ctx) if n_ctx else [chunk_frames_list[-1][-1]] + prev_pil = [_frame_to_pil(f, w, h) for f in prev_frames] + if ch < num_chunks: + context_frames_per_chunk.append(list(prev_pil)) + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + context_latents = encode_context_frames_per_frame(pipe, prev_pil, pipe.device) + context_actions_t = torch.tensor([identity_rt] * context_latents.shape[2], dtype=torch.float32) + print(f"[Loop] continuation chunk ch={ch}: len(prev_pil)={len(prev_pil)} num_ctx_tokens={context_latents.shape[2]} (应等于 ctx)") + + frames_ch = run_one_chunk( + pipe, prompt, use_negative_prompt, path_ch, + context_latents=context_latents, + num_context_frames=context_latents.shape[2], + context_actions_t=context_actions_t, + chunk_frames=chunk_frames, h=h, w=w, seed=seed + ch * 100, + sigma_shift=sigma_shift, num_inference_steps=num_inference_steps, + cfg_scale=cfg_scale, inference_noise_level=inference_noise_level, + omit_context_actions=omit_context_actions, + ) + for i in range(chunk_frames): + yaw_history.append(cumulative_yaw + _yaw_deg_from_rt(ch1_actions[str(i)])) + cumulative_yaw = yaw_history[-1] + chunk_frames_list.append(frames_ch) + if os.path.exists(path_ch): + try: + os.remove(path_ch) + except Exception: + pass + return chunk_frames_list, yaw_history, context_frames_per_chunk + + +def run_one_direction_4chunk( + pipe, + dataset_base, + output_dir, + video_name, + start_frame, + deg_per_chunk=15.0, + clockwise=True, + chunk_frames=81, + context_frames=1, + h=352, + w=640, + sigma_shift=15.0, + num_inference_steps=50, + cfg_scale=5.0, + seed=42, + inference_noise_level=0.0, + metadata_path=None, + omit_context_actions=True, +): + """4 chunk 单向匀速：共 4*deg_per_chunk 度。omit_context_actions 与训练对齐。""" + print(f"[Onedir 4chunk] 匀速 总{4*deg_per_chunk:.0f}° {'CW' if clockwise else 'CCW'} sigma_shift={sigma_shift} omit_ctx_act={omit_context_actions} context_frames={context_frames} (续段 ctx 数)") + prompt = irc.load_prompt_for_video(dataset_base, video_name) or "A scene." + use_negative_prompt = getattr(irc, "DEFAULT_NEGATIVE_PROMPT", "oversaturated colors, overexposed, static, blurry details") # 与训练一致 + + yaw_history = [] + chunk_frames_list = [] + context_frames_per_chunk = [] + subdir = os.path.join(output_dir, f"loop_{video_name}_start{start_frame}") + os.makedirs(subdir, exist_ok=True) + identity_rt = [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0] + cumulative_yaw_start = 0.0 + context_latents = None + context_actions_t = None + + for ch in range(4): + actions, yaw_chunk = build_action_chunk(deg_per_chunk, clockwise, chunk_frames) + path_ch = os.path.join(subdir, f"_ch{ch}_onedir.json") + with open(path_ch, "w") as f: + json.dump(actions, f, indent=2) + + if ch == 0: + ctx_pil_0 = load_sample_first_frame(dataset_base, video_name, start_frame, w, h) + if ctx_pil_0 is None: + ctx_pil_0 = sample_random_frame_from_dataset(dataset_base, w, h, seed, metadata_path) + if ctx_pil_0 is not None: + ctx_pil_0 = [ctx_pil_0] + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + context_latents = encode_context_frames_per_frame(pipe, ctx_pil_0, pipe.device) + context_actions_t = torch.tensor([identity_rt], dtype=torch.float32) + context_frames_per_chunk.append([ctx_pil_0[0]]) + else: + context_frames_per_chunk.append([]) + + if context_latents is not None and context_actions_t is not None: + frames_ch = run_one_chunk( + pipe, prompt, use_negative_prompt, path_ch, + context_latents=context_latents, + num_context_frames=context_latents.shape[2], + context_actions_t=context_actions_t, + chunk_frames=chunk_frames, h=h, w=w, seed=seed + ch, + sigma_shift=sigma_shift, num_inference_steps=num_inference_steps, + cfg_scale=cfg_scale, inference_noise_level=inference_noise_level, + omit_context_actions=omit_context_actions, + ) + else: + frames_ch = run_one_chunk( + pipe, prompt, use_negative_prompt, path_ch, + chunk_frames=chunk_frames, h=h, w=w, seed=seed + ch, + sigma_shift=sigma_shift, num_inference_steps=num_inference_steps, cfg_scale=cfg_scale, + omit_context_actions=omit_context_actions, + ) + if ch == 0 and not context_frames_per_chunk[0]: + context_frames_per_chunk[0] = [_frame_to_pil(frames_ch[-1], w, h)] + + for y in yaw_chunk: + yaw_history.append(cumulative_yaw_start + y) + cumulative_yaw_start = yaw_history[-1] + chunk_frames_list.append(frames_ch) + + # 与 2chunk 一致：仅当有下一 chunk 时用本 chunk 输出做 context（last n 帧 → 逐帧 VAE） + if ch < 3: + n_ctx = min(context_frames, len(frames_ch)) + prev_frames = context_frames_for_next_chunk(frames_ch, n_ctx) if n_ctx else [frames_ch[-1]] + prev_pil = [_frame_to_pil(f, w, h) for f in prev_frames] + context_frames_per_chunk.append(list(prev_pil)) + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + context_latents = encode_context_frames_per_frame(pipe, prev_pil, pipe.device) + num_ctx_tokens = context_latents.shape[2] + context_actions_t = torch.tensor([identity_rt] * num_ctx_tokens, dtype=torch.float32) + print(f"[Loop] next continuation chunk: len(prev_pil)={len(prev_pil)} num_ctx_tokens={num_ctx_tokens} (应等于 ctx)") + + if os.path.exists(path_ch): + try: + os.remove(path_ch) + except Exception: + pass + + return chunk_frames_list, yaw_history, context_frames_per_chunk + + +def run_random_4chunk( + pipe, + dataset_base, + output_dir, + video_name, + start_frame, + deg_min=15.0, + deg_max=30.0, + chunk_frames=81, + context_frames=1, + h=352, + w=640, + sigma_shift=15.0, + num_inference_steps=50, + cfg_scale=5.0, + seed=42, + inference_noise_level=0.0, + metadata_path=None, + omit_context_actions=True, +): + """4 chunk 随机。首 chunk context=随机 1 帧；之后 context=上一 chunk last n。""" + prompt = irc.load_prompt_for_video(dataset_base, video_name) or "A scene." + use_negative_prompt = getattr(irc, "DEFAULT_NEGATIVE_PROMPT", "oversaturated colors, overexposed, static, blurry details") # 与训练一致 + + rng = random.Random(seed) + yaw_history = [] + chunk_frames_list = [] + context_frames_per_chunk = [] + subdir = os.path.join(output_dir, f"loop_{video_name}_start{start_frame}") + os.makedirs(subdir, exist_ok=True) + identity_rt = [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0] + cumulative_yaw_start = 0.0 + context_latents = None + context_actions_t = None + + for ch in range(4): + deg = rng.uniform(deg_min, deg_max) + clockwise = rng.choice([True, False]) + actions, yaw_chunk = build_action_chunk(deg, clockwise, chunk_frames) + path_ch = os.path.join(subdir, f"_ch{ch}_rand.json") + with open(path_ch, "w") as f: + json.dump(actions, f, indent=2) + + if ch == 0: + ctx_pil_0 = load_sample_first_frame(dataset_base, video_name, start_frame, w, h) + if ctx_pil_0 is None: + ctx_pil_0 = sample_random_frame_from_dataset(dataset_base, w, h, seed, metadata_path) + if ctx_pil_0 is not None: + ctx_pil_0 = [ctx_pil_0] + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + context_latents = encode_context_frames_per_frame(pipe, ctx_pil_0, pipe.device) + context_actions_t = torch.tensor([identity_rt], dtype=torch.float32) + context_frames_per_chunk.append([ctx_pil_0[0]]) + else: + context_frames_per_chunk.append([]) + + if context_latents is not None and context_actions_t is not None: + frames_ch = run_one_chunk( + pipe, prompt, use_negative_prompt, path_ch, + context_latents=context_latents, + num_context_frames=context_latents.shape[2], + context_actions_t=context_actions_t, + chunk_frames=chunk_frames, h=h, w=w, seed=seed + ch * 7, + sigma_shift=sigma_shift, num_inference_steps=num_inference_steps, + cfg_scale=cfg_scale, inference_noise_level=inference_noise_level, + omit_context_actions=omit_context_actions, + ) + else: + frames_ch = run_one_chunk( + pipe, prompt, use_negative_prompt, path_ch, + chunk_frames=chunk_frames, h=h, w=w, seed=seed + ch * 7, + sigma_shift=sigma_shift, num_inference_steps=num_inference_steps, cfg_scale=cfg_scale, + omit_context_actions=omit_context_actions, + ) + if ch == 0 and not context_frames_per_chunk[0]: + context_frames_per_chunk[0] = [_frame_to_pil(frames_ch[-1], w, h)] + + for y in yaw_chunk: + yaw_history.append(cumulative_yaw_start + y) + cumulative_yaw_start = yaw_history[-1] + chunk_frames_list.append(frames_ch) + + # 与 2chunk 一致：仅当有下一 chunk 时用本 chunk 输出做 context（last n 帧 → 逐帧 VAE） + if ch < 3: + n_ctx = min(context_frames, len(frames_ch)) + prev_frames = context_frames_for_next_chunk(frames_ch, n_ctx) if n_ctx else [frames_ch[-1]] + prev_pil = [_frame_to_pil(f, w, h) for f in prev_frames] + context_frames_per_chunk.append(list(prev_pil)) + pipe.load_models_to_device(["vae"]) + with torch.no_grad(): + context_latents = encode_context_frames_per_frame(pipe, prev_pil, pipe.device) + num_ctx_tokens = context_latents.shape[2] + context_actions_t = torch.tensor([identity_rt] * num_ctx_tokens, dtype=torch.float32) + print(f"[Loop] next continuation chunk: len(prev_pil)={len(prev_pil)} num_ctx_tokens={num_ctx_tokens} (应等于 ctx)") + + if os.path.exists(path_ch): + try: + os.remove(path_ch) + except Exception: + pass + + return chunk_frames_list, yaw_history, context_frames_per_chunk + + +def main(): + p = argparse.ArgumentParser(description="Loop: 两栏 [输入轨迹|生成视频]。首帧随机采样训练数据，之后每段 context=上一 chunk last n 帧，context 放右侧") + p.add_argument("--ckpt", required=True, help="Step-15000.safetensors path") + p.add_argument("--dataset_base", default=os.environ.get("DATASET_BASE_PATH", "data/Context-as-Memory-Dataset")) + p.add_argument("--output_dir", required=True) + p.add_argument("--num_samples", type=int, default=4) + p.add_argument("--seed", type=int, default=42) + p.add_argument("--chunk_frames", type=int, default=81) + p.add_argument("--height", type=int, default=352) + p.add_argument("--width", type=int, default=640) + p.add_argument("--sigma_shift", type=float, default=5.0, help="与训练 timestep_shift 一致：pre_qkv noise_5→5, noise_10→10, noise_3→3") + p.add_argument("--num_inference_steps", type=int, default=50, help="与训练 sampling_num_inference_steps 一致（如 50）") + p.add_argument("--cfg_scale", type=float, default=5.0) + p.add_argument("--inference_noise_level", type=float, default=0.0, help="与训练 context_fixed_noise_std 一致（通常 0）") + p.add_argument("--no_omit_context_actions", action="store_true", help="训练使用 context_actions 时加此参数；默认 omit（与 ctx=1 训练一致）") + p.add_argument("--deg_per_chunk", type=float, default=45.0, help="回环每 chunk 转角（左转/右转均用此值，默认 45°）") + p.add_argument("--skip_left_right_2chunk", action="store_true", help="跳过 1左1右 2chunk 回环") + p.add_argument("--skip_left2_right2_4chunk", action="store_true", help="跳过 2左2右 4chunk 回环") + p.add_argument("--only_single_left_45", action="store_true", help="仅跑旋转原子：单 chunk 左转 45°") + p.add_argument("--only_single_right_45", action="store_true", help="仅跑旋转原子：单 chunk 右转 45°") + p.add_argument("--sampling_action_dir", type=str, default=None, help="与采样一致的 action JSON 目录（含 action_rotation_left_45.json/right_45.json）；默认用本脚本所在目录") + p.add_argument("--context_image", type=str, default=None, help="泛化检查：用指定图片（如 image.png）作为单帧 context 做旋转原子采样，与训练 action 形式一致") + p.add_argument("--run_legacy_loop", action="store_true", help="跑旧版三组：roundtrip/onedir/replay，与下面 skip 配合") + p.add_argument("--roundtrip_deg", type=float, default=30.0, help="[legacy] 2chunk 折返 顺/逆 角度") + p.add_argument("--onedir_total_deg", type=float, default=60.0, help="[legacy] 4chunk 单向总角度") + p.add_argument("--keep_action_jsons", action="store_true", help="保留临时 action json 便于检查") + p.add_argument("--skip_roundtrip", action="store_true", help="[legacy] 跳过 2chunk 折返") + p.add_argument("--skip_onedir", action="store_true", help="[legacy] 跳过 4chunk 单向 60°") + p.add_argument("--skip_replay_4chunk", action="store_true", help="[legacy] 跳过 4chunk Replay") + p.add_argument("--w_traj", type=int, default=320, help="轨迹图宽度") + p.add_argument("--dataset_metadata_path", type=str, default=None, help="Optional CSV; also used for random first-frame sampling") + p.add_argument("--context_frames", type=int, default=1, help="后续 chunk 的 context 用上一 chunk 的 last n 帧，n=context_frames；--run_ctx5_multi_chunk 时表示 K（K-1 均匀+最后 1 帧）") + p.add_argument("--run_ctx5_multi_chunk", action="store_true", help="跑 ctx5 风格：chunk1 无 context，chunk2 用 K-1 均匀+最后 1 帧；多场景 2chunk 前进/后退/平移/左45右45") + p.add_argument("--run_atomic_translation_4chunk", action="store_true", help="原子操作：仅跑纯平移 4chunk（向前/向后/向左/向右各 4chunk，镜头不旋转）") + p.add_argument("--translation_delta", type=float, default=0.1, help="纯平移每 chunk 位移量（单轴），默认 0.1") + # Memory baselines runtime flags (must match training for multichunk consistency) + p.add_argument("--use_framepack_memory", action="store_true", help="Enable FAR/FramePack-style context reweighting during multichunk inference") + p.add_argument("--context_temporal_decay", type=float, default=1.0, help="FramePack/FAR: per-frame decay for context") + p.add_argument("--context_attention_weight", type=float, default=1.0, help="FramePack/FAR: global scale for all context tokens") + p.add_argument("--use_framepack_length_compress", action="store_true", help="FramePack: compress context length K->K' during multichunk inference") + p.add_argument("--framepack_ratio", type=int, default=2, help="FramePack temporal ratio r") + p.add_argument("--use_spatial_memory", action="store_true", help="Enable spatial memory baseline during multichunk inference") + p.add_argument("--use_spatial_memory_legacy", action="store_true", help="Non-learnable adaptive pool (if no SpatialGridMemory in ckpt, auto-enabled)") + p.add_argument("--spatial_memory_tokens", type=int, default=64, help="Spatial memory baseline: number of pooled spatial tokens") + p.add_argument( + "--spatial_memory_inject_mode", + type=str, + default=None, + choices=("concat_text", "cross_attn_readout", "none"), + help="Spatial memory inject mode; must match training", + ) + p.add_argument("--num_gpus", type=int, default=1, help="总 GPU 数，与 --rank 配合做多卡并行：每卡只推理 rank, rank+num_gpus, ... 的样本") + p.add_argument("--rank", type=int, default=0, help="当前进程的 rank（0 到 num_gpus-1），由启动脚本设 CUDA_VISIBLE_DEVICES=rank 并传 --rank") + p.add_argument( + "--multi_ctx_4chunk_all_history", + action="store_true", + help="4chunk 回环：续段 ctx 时，除最后 1 帧固定为上一 chunk 的最后一帧，其余 (ctx-1) 帧从所有已生成 chunk 的历史帧中均匀抽取，用于多 chunk 记忆机制实验", + ) + p.add_argument( + "--multi_ctx_4chunk_fov_history", + action="store_true", + help="4chunk 回环：续段 ctx 时，除上一 chunk 最后一帧外，其余帧按下一 chunk 中点 world-yaw 从已生成历史帧中检索（仅改 context 帧，context_actions 仍保持 identity）", + ) + p.add_argument( + "--multi_ctx_4chunk_fov_last_target", + action="store_true", + help="配合 --multi_ctx_4chunk_fov_history：仅第 4 个 chunk 的检索 target 改用该 chunk 末尾 world-yaw，便于回到起点时检索 chunk1 开头帧", + ) + p.add_argument( + "--multi_ctx_4chunk_fov_context_rt", + action="store_true", + help="配合 --multi_ctx_4chunk_fov_history：为检索出的 context 帧注入相对下一 chunk 起始 world-yaw 的 RT，而不是 identity", + ) + # Camera encoder 与注入节点：须与训练一致（见 INFERENCE_ALIGNMENT.md） + p.add_argument("--camera_inject_mode", type=str, default=None, help="与训练一致：post|pre_norm|pre_qkv|pre_qkv_post；不设则从 ckpt 路径推断（如含 pre_qkv_post）") + p.add_argument("--camera_encoder_shallow", action="store_true", default=None, help="与训练一致：单层 Linear(12,D)。不设则从 ckpt 自动推断") + p.add_argument("--no_camera_encoder_shallow", action="store_true", help="明确不用 shallow（3 层 MLP）") + p.add_argument("--camera_encoder_separate_t_r", action="store_true", default=None, help="与训练一致：t/R 分路编码。不设则从 ckpt 自动推断") + p.add_argument("--no_camera_encoder_separate_t_r", action="store_true", help="明确不用 separate_t_r") + p.add_argument("--no_camera_encoder_zero_init", action="store_true", help="与训练一致：encoder scale 非 0 初始化时加此参数") + p.add_argument("--camera_encoder_explicit_yaw", action="store_true", help="与训练一致：显式 yaw 分支（dir_abl_A 等）") + p.add_argument("--camera_encoder_sincos_yaw", action="store_true", help="与训练一致：sin/cos yaw 分支") + p.add_argument("--add_camera_outside_gate", action="store_true", help="与训练一致：camera 加在 gate 外（abl 实验）") + p.add_argument( + "--base_model", + type=str, + default=None, + help="Wan2.1 base model dir; default: $WAN_BASE_MODEL", + ) + args = p.parse_args() + # 原子平移评估用独立前缀，与回环 [Loop] 区分 + _log_prefix = "[atomic_translation_4chunk]" if getattr(args, "run_atomic_translation_4chunk", False) else "[Loop]" + # 与训练 setting 对齐：ctx=1 时训练用 omit_context_actions=True + args.omit_context_actions = not getattr(args, "no_omit_context_actions", False) + # context_k* / ctx_*: infer context frames from released HF folders and legacy names. + ckpt_lower = (args.ckpt or "").lower() + if "ctx_20" in ckpt_lower or "context_k20" in ckpt_lower or "ctx20" in ckpt_lower: + if getattr(args, "context_frames", 1) < 20: + args.context_frames = 20 + print(f"{_log_prefix} 从 ckpt 路径识别 context_k20，将 context_frames 设为 20（续段 20 帧逐帧 VAE）", flush=True) + elif "ctx_5" in ckpt_lower or "context_k5" in ckpt_lower or "ctx5" in ckpt_lower: + if getattr(args, "context_frames", 1) < 5: + args.context_frames = 5 + print(f"{_log_prefix} 从 ckpt 路径识别 context_k5，将 context_frames 设为 5（续段 5 帧）", flush=True) + # 兼容：run_ctx5_multi_chunk 时若尚未被上面推断，也设为 5 + if getattr(args, "run_ctx5_multi_chunk", False) and getattr(args, "context_frames", 1) == 1: + args.context_frames = 5 + print(f"{_log_prefix} run_ctx5_multi_chunk 已启用，将 context_frames 设为 5（续段 ctx=5）", flush=True) + print(f"{_log_prefix} context_frames={args.context_frames}（续段用上一 chunk 的 last n 帧；首 chunk 始终 1 帧）", flush=True) + + # camera_inject_mode：未显式指定时从 ckpt 路径推断（与训练脚本命名一致） + camera_inject_mode = (getattr(args, "camera_inject_mode", None) or "").strip() + if not camera_inject_mode: + env_cam = (os.environ.get("CAMERA_INJECT_MODE") or "").strip().lower() + if env_cam in ("pre_qkv_post", "pre_qkv", "pre_norm", "post"): + camera_inject_mode = env_cam + print(f"{_log_prefix} 从环境 CAMERA_INJECT_MODE 使用 camera_inject_mode={camera_inject_mode}", flush=True) + if not camera_inject_mode: + ckpt_lower = (args.ckpt or "").lower() + for mode in ("pre_qkv_post", "pre_qkv", "pre_norm", "post"): + if mode.replace("_", "") in ckpt_lower or mode in ckpt_lower: + camera_inject_mode = mode + print(f"{_log_prefix} 从 ckpt 路径推断 camera_inject_mode={camera_inject_mode}", flush=True) + break + if not camera_inject_mode: + # 与 memory_baselines_basic 默认训练脚本一致（旧默认 post 易与 pre_qkv 训练 ckpt 错位） + camera_inject_mode = "pre_qkv" + print(f"{_log_prefix} camera_inject_mode 未推断，使用默认 pre_qkv（与 train/memory_baselines_basic 对齐）", flush=True) + # encoder 结构、SSM：均由 load_pipeline_and_ckpt 按 ckpt 自动推断并注入，无需额外参数 + load_kw = dict( + action_inject_after_spatial_attn=True, + add_action_attn=True, + action_use_temporal_attention=True, + camera_inject_mode=camera_inject_mode, + add_camera_outside_gate=getattr(args, "add_camera_outside_gate", False), + no_camera_encoder_zero_init=getattr(args, "no_camera_encoder_zero_init", False), + camera_encoder_explicit_yaw=getattr(args, "camera_encoder_explicit_yaw", False), + camera_encoder_sincos_yaw=getattr(args, "camera_encoder_sincos_yaw", False), + ) + if getattr(args, "camera_encoder_shallow", False): + load_kw["camera_encoder_shallow"] = True + if getattr(args, "no_camera_encoder_shallow", False): + load_kw["camera_encoder_shallow"] = False + if getattr(args, "camera_encoder_separate_t_r", False): + load_kw["camera_encoder_separate_t_r"] = True + if getattr(args, "no_camera_encoder_separate_t_r", False): + load_kw["camera_encoder_separate_t_r"] = False + + if args.num_gpus > 1: + cuda_vis = os.environ.get("CUDA_VISIBLE_DEVICES", "not set") + print(f"{_log_prefix} rank={args.rank} CUDA_VISIBLE_DEVICES={cuda_vis}", flush=True) + + base_model = ( + getattr(args, "base_model", None) + or os.environ.get("WAN_BASE_MODEL") + ) + if not base_model: + raise ValueError("Set --base_model or WAN_BASE_MODEL to the Wan2.1 base model directory.") + dit_path = os.path.join(base_model, "diffusion_pytorch_model.safetensors") + text_path = os.path.join(base_model, "models_t5_umt5-xxl-enc-bf16.pth") + vae_path = os.path.join(base_model, "Wan2.1_VAE.pth") + for _p in (dit_path, text_path, vae_path): + if not os.path.isfile(_p): + raise FileNotFoundError( + f"Missing Wan2.1 base weight: {_p} (set --base_model or WAN_BASE_MODEL to override)" + ) + + pipe = irc.load_pipeline_and_ckpt( + args.ckpt, + dit_path, + text_path, + vae_path, + **load_kw, + ) + # Runtime memory flags (must align with the checkpoint training flags) + pipe.use_framepack_memory = bool(getattr(args, "use_framepack_memory", False)) + pipe.context_temporal_decay = float(getattr(args, "context_temporal_decay", 1.0) or 1.0) + pipe.context_attention_weight = float(getattr(args, "context_attention_weight", 1.0) or 1.0) + pipe.use_framepack_length_compress = bool(getattr(args, "use_framepack_length_compress", False)) + pipe.framepack_ratio = int(getattr(args, "framepack_ratio", 2) or 2) + pipe.use_spatial_memory = bool(getattr(args, "use_spatial_memory", False)) + pipe.spatial_memory_tokens = int(getattr(args, "spatial_memory_tokens", 64) or 64) + if getattr(args, "spatial_memory_inject_mode", None): + pipe.spatial_memory_inject_mode = str(getattr(args, "spatial_memory_inject_mode")) + pipe.use_spatial_memory_legacy = bool(getattr(args, "use_spatial_memory_legacy", False)) + # Ckpt may omit SpatialGridMemory weights: fall back to legacy adaptive pool for correct shapes + if pipe.use_spatial_memory and not pipe.use_spatial_memory_legacy and getattr(pipe, "spatial_memory_module", None) is None: + pipe.use_spatial_memory_legacy = True + print(f"{_log_prefix} use_spatial_memory but no spatial_memory_module in ckpt -> use_spatial_memory_legacy=True", flush=True) + if args.num_gpus > 1 and torch.cuda.is_available(): + print(f"{_log_prefix} rank={args.rank} device_count={torch.cuda.device_count()} current_device={torch.cuda.current_device()}", flush=True) + + if args.dataset_metadata_path and os.path.isfile(args.dataset_metadata_path): + import csv + candidates = [] + with open(args.dataset_metadata_path, "r", encoding="utf-8") as f: + reader = csv.DictReader(f) + for row in reader: + vn = row.get("video_name", "").strip() + sf = row.get("start_frame", "") + if vn and str(sf).strip(): + try: + candidates.append((vn, int(sf))) + except ValueError: + continue + rng = random.Random(args.seed) + if len(candidates) <= args.num_samples: + samples = candidates + else: + samples = [candidates[i] for i in rng.sample(range(len(candidates)), args.num_samples)] + else: + samples = irc.sample_trajectory_samples_from_dataset( + args.dataset_base, + num_samples=args.num_samples, + num_frames=args.chunk_frames, + seed=args.seed, + ) + if not samples: + print(f"{_log_prefix} No samples from dataset.") + return + + # 多卡：每卡只跑自己 rank 的样本（样本顺序一致，按 idx % num_gpus == rank 划分） + if args.num_gpus > 1: + samples = [s for i, s in enumerate(samples) if i % args.num_gpus == args.rank] + print(f"{_log_prefix} num_gpus={args.num_gpus} rank={args.rank} -> 本进程跑 {len(samples)} 个样本") + if not samples: + print(f"{_log_prefix} 本 rank 无样本，退出") + return + + os.makedirs(args.output_dir, exist_ok=True) + h, w = args.height, args.width + + # 日志同时写入 output_dir 下的文件 + log_path = os.path.join(args.output_dir, f"loop_run_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log") + _log_file = open(log_path, "w", encoding="utf-8") + _stdout_orig = sys.stdout + + class _Tee: + def __init__(self, stream, f): + self._stream = stream + self._file = f + def write(self, data): + self._stream.write(data) + self._stream.flush() + if self._file and not self._file.closed: + self._file.write(data) + self._file.flush() + def flush(self): + self._stream.flush() + if self._file and not self._file.closed: + self._file.flush() + + sys.stdout = _Tee(_stdout_orig, _log_file) + try: + print(f"{_log_prefix} Log file: {log_path}") + if getattr(args, "run_atomic_translation_4chunk", False): + print(f"{_log_prefix} 模式: 仅原子操作纯平移 4chunk (forward/backward/left/right)，无旋转", flush=True) + elif getattr(args, "run_ctx5_multi_chunk", False): + print(f"{_log_prefix} 模式: ctx5 多 chunk 2chunk 多场景 (含旋转)", flush=True) + else: + print(f"{_log_prefix} 模式: 回环 1左1右 + 2左2右 旋转 (若只要纯平移请加 --run_atomic_translation_4chunk)", flush=True) + + for idx, (video_name, start_frame) in enumerate(samples): + subdir = os.path.join(args.output_dir, f"loop_{video_name}_start{start_frame}") + os.makedirs(subdir, exist_ok=True) + # 同一样本 (video_name, start_frame) 使用确定性 seed，与 loop_traj_fov_gen / evals_ep0 等调用一致，避免偏移 + sample_seed = seed_for_sample(args.seed, video_name, start_frame) + print(f"{_log_prefix} [{idx+1}/{len(samples)}] {video_name} start{start_frame} sigma_shift={args.sigma_shift} sample_seed={sample_seed}") + + def save_composite(name, chunk_frames_list, yaw_history, context_frames_per_chunk): + # 与训练对齐：续段首帧=上一段末帧，拼接时去掉续段第 0 帧，避免重复/跳帧（chunk0 保留 81，chunk1+ 各 80） + if len(chunk_frames_list) > 1: + trim_chunks, trim_yaw = trim_continuation_first_frame(chunk_frames_list, yaw_history, args.chunk_frames) + else: + trim_chunks, trim_yaw = chunk_frames_list, yaw_history + right_frames, yaw_ext = build_right_panel_and_yaw( + trim_chunks, trim_yaw, context_frames_per_chunk, + chunk_frames=args.chunk_frames, w=w, h=h, + ) + traj_frames = draw_trajectory_frames(yaw_ext, args.w_traj, h, total_frames=len(right_frames)) + comp = composite_two_panel(traj_frames, right_frames, w_traj=args.w_traj, h_traj=h, w_gen=w, h_gen=h) + save_video(comp, os.path.join(subdir, f"{name}_traj_gen.mp4"), fps=15, quality=5) + gen_only = [f for ch in trim_chunks for f in ch] + save_video(gen_only, os.path.join(subdir, f"{name}_gen_only.mp4"), fps=15, quality=5) + + meta = getattr(args, "dataset_metadata_path", None) + try: + # 原子操作：仅纯平移 4chunk（无旋转），与回环左/右转彻底互斥 + if getattr(args, "run_atomic_translation_4chunk", False): + delta = getattr(args, "translation_delta", 0.1) + for direction in ("forward", "backward", "left", "right"): + chunks, yaw, ctx_per_chunk = run_translation_4chunk( + pipe, args.dataset_base, args.output_dir, video_name, start_frame, + direction=direction, translation_delta=delta, chunk_frames=args.chunk_frames, + context_frames=args.context_frames, h=h, w=w, + sigma_shift=args.sigma_shift, num_inference_steps=args.num_inference_steps, + cfg_scale=args.cfg_scale, seed=sample_seed, inference_noise_level=args.inference_noise_level, + metadata_path=meta, omit_context_actions=args.omit_context_actions, + ) + save_composite(f"{direction}_4chunk", chunks, yaw, ctx_per_chunk) + # 旋转原子：仅单 chunk 左转 45° 或 右转 45° + elif getattr(args, "only_single_left_45", False): + chunks, yaw, ctx_per_chunk = run_single_chunk_rotation( + pipe, args.dataset_base, args.output_dir, video_name, start_frame, + deg=args.deg_per_chunk, clockwise=False, chunk_frames=args.chunk_frames, + h=h, w=w, sigma_shift=args.sigma_shift, num_inference_steps=args.num_inference_steps, + cfg_scale=args.cfg_scale, seed=sample_seed, inference_noise_level=args.inference_noise_level, + metadata_path=meta, keep_action_jsons=getattr(args, "keep_action_jsons", False), + sampling_action_dir=getattr(args, "sampling_action_dir", None), + omit_context_actions=args.omit_context_actions, + context_image_path=getattr(args, "context_image", None), + ) + save_composite("single_left_45", chunks, yaw, ctx_per_chunk) + elif getattr(args, "only_single_right_45", False): + chunks, yaw, ctx_per_chunk = run_single_chunk_rotation( + pipe, args.dataset_base, args.output_dir, video_name, start_frame, + deg=args.deg_per_chunk, clockwise=True, chunk_frames=args.chunk_frames, + h=h, w=w, sigma_shift=args.sigma_shift, num_inference_steps=args.num_inference_steps, + cfg_scale=args.cfg_scale, seed=sample_seed, inference_noise_level=args.inference_noise_level, + metadata_path=meta, keep_action_jsons=getattr(args, "keep_action_jsons", False), + sampling_action_dir=getattr(args, "sampling_action_dir", None), + omit_context_actions=args.omit_context_actions, + context_image_path=getattr(args, "context_image", None), + ) + save_composite("single_right_45", chunks, yaw, ctx_per_chunk) + # 默认：回环两场景 1左1右(45°)、2左2右(每chunk 45°)。--run_legacy_loop 时跑旧三组 + elif not getattr(args, "run_legacy_loop", False): + if not getattr(args, "skip_left_right_2chunk", False): + chunks, yaw, ctx_per_chunk = run_left_right_2chunk( + pipe, args.dataset_base, args.output_dir, video_name, start_frame, + deg=args.deg_per_chunk, chunk_frames=args.chunk_frames, context_frames=args.context_frames, + h=h, w=w, sigma_shift=args.sigma_shift, num_inference_steps=args.num_inference_steps, + cfg_scale=args.cfg_scale, seed=sample_seed, inference_noise_level=args.inference_noise_level, + metadata_path=meta, keep_action_jsons=getattr(args, "keep_action_jsons", False), + sampling_action_dir=getattr(args, "sampling_action_dir", None), + omit_context_actions=args.omit_context_actions, + ) + save_composite("left_right_2chunk", chunks, yaw, ctx_per_chunk) + if not getattr(args, "skip_left2_right2_4chunk", False): + chunks, yaw, ctx_per_chunk = run_left2_right2_4chunk( + pipe, args.dataset_base, args.output_dir, video_name, start_frame, + deg_per_chunk=args.deg_per_chunk, chunk_frames=args.chunk_frames, context_frames=args.context_frames, + h=h, w=w, sigma_shift=args.sigma_shift, num_inference_steps=args.num_inference_steps, + cfg_scale=args.cfg_scale, seed=sample_seed + 100, inference_noise_level=args.inference_noise_level, + metadata_path=meta, sampling_action_dir=getattr(args, "sampling_action_dir", None), + omit_context_actions=args.omit_context_actions, + multi_ctx_all_history=getattr(args, "multi_ctx_4chunk_all_history", False), + fov_history_context=getattr(args, "multi_ctx_4chunk_fov_history", False), + fov_last_target=getattr(args, "multi_ctx_4chunk_fov_last_target", False), + fov_context_rt=getattr(args, "multi_ctx_4chunk_fov_context_rt", False), + ) + save_composite("left2_right2_4chunk", chunks, yaw, ctx_per_chunk) + else: + if not args.skip_roundtrip: + chunks, yaw, ctx_per_chunk = run_roundtrip_2chunk( + pipe, args.dataset_base, args.output_dir, video_name, start_frame, + deg=args.roundtrip_deg, chunk_frames=args.chunk_frames, context_frames=args.context_frames, + h=h, w=w, sigma_shift=args.sigma_shift, num_inference_steps=args.num_inference_steps, + cfg_scale=args.cfg_scale, seed=sample_seed, inference_noise_level=args.inference_noise_level, + metadata_path=meta, keep_action_jsons=getattr(args, "keep_action_jsons", False), + omit_context_actions=args.omit_context_actions, + ) + save_composite("roundtrip_2chunk", chunks, yaw, ctx_per_chunk) + if not args.skip_onedir: + deg_per = args.onedir_total_deg / 4.0 + chunks, yaw, ctx_per_chunk = run_one_direction_4chunk( + pipe, args.dataset_base, args.output_dir, video_name, start_frame, + deg_per_chunk=deg_per, clockwise=True, chunk_frames=args.chunk_frames, + context_frames=args.context_frames, h=h, w=w, + sigma_shift=args.sigma_shift, num_inference_steps=args.num_inference_steps, + cfg_scale=args.cfg_scale, seed=sample_seed + 100, inference_noise_level=args.inference_noise_level, + metadata_path=meta, omit_context_actions=args.omit_context_actions, + ) + save_composite("onedir_4chunk_60", chunks, yaw, ctx_per_chunk) + if not args.skip_replay_4chunk: + result = run_replay_4chunk( + pipe, args.dataset_base, args.output_dir, video_name, start_frame, + chunk_frames=args.chunk_frames, context_frames=args.context_frames, + h=h, w=w, sigma_shift=args.sigma_shift, num_inference_steps=args.num_inference_steps, + cfg_scale=args.cfg_scale, seed=sample_seed + 200, inference_noise_level=args.inference_noise_level, + omit_context_actions=args.omit_context_actions, + ) + if result is not None: + chunks, yaw, ctx_per_chunk = result + save_composite("replay_4chunk", chunks, yaw, ctx_per_chunk) + else: + print(f"[Loop] Skip replay_4chunk for {video_name} start{start_frame} (need 4*81 frames)") + + except Exception as e: + print(f"[Loop] ERROR {video_name} start{start_frame}: {e}") + import traceback + traceback.print_exc() + + if getattr(args, "only_single_left_45", False): + mode = "旋转原子 单chunk 左转45°" + elif getattr(args, "only_single_right_45", False): + mode = "旋转原子 单chunk 右转45°" + elif getattr(args, "run_legacy_loop", False): + mode = "legacy" + else: + mode = "回环 1左1右 + 2左2右 (每chunk 45°)" + print(f"{_log_prefix} Done. Mode: {mode}. Outputs under {args.output_dir}") + finally: + sys.stdout = _stdout_orig + _log_file.close() + + +if __name__ == "__main__": + main() diff --git a/examples/1774363417.png b/examples/1774363417.png new file mode 100644 index 0000000000000000000000000000000000000000..c8585d172148610829910ac0f3e3a826db7a0faa --- /dev/null +++ b/examples/1774363417.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fc3f9f9dfe2d5b2239347cdd085d18760b2b72ae85558dc596fba58c800ae4b7 +size 2145143 diff --git a/examples/1774363487.png b/examples/1774363487.png new file mode 100644 index 0000000000000000000000000000000000000000..a3daca7a1d82b622b0e7d7564e7dce6bb994066c --- /dev/null +++ b/examples/1774363487.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bb6d48951402c0d7a1630dd28a5fdeae0082b7e1082522d27effee085c5d7030 +size 2199217 diff --git a/examples/1774363572.png b/examples/1774363572.png new file mode 100644 index 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+opencv-python +peft +pillow +protobuf +pyyaml +safetensors +scikit-image +scipy +sentencepiece +timm +torchvision +transformers +tqdm \ No newline at end of file diff --git a/src/data/__init__.py b/src/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..01c4482392f81ea64352569834bf3914a523d21e --- /dev/null +++ b/src/data/__init__.py @@ -0,0 +1 @@ +"""Data preprocessing utilities for Echo-Memory.""" diff --git a/src/data/preprocess_cam_dataset.py b/src/data/preprocess_cam_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..3c2232b95567ec825e77298ac9eacb6e80a046cc --- /dev/null +++ b/src/data/preprocess_cam_dataset.py @@ -0,0 +1,232 @@ +#!/usr/bin/env python3 +""" +Preprocess Context-as-Memory dataset folders into Echo-Memory metadata CSV. + +Expected dataset layout: +- frames/: frame images organized by video +- jsons/: camera pose information for each video +- overlap_labels/: FOV overlap information for memory retrieval +- captions.txt: video segment captions +""" + +import argparse +import csv +import json +import os +from typing import Dict, List, Tuple + + +def parse_caption_line(line: str) -> Tuple[str, str]: + """ + Parse a line from captions.txt. + + Format: "video_name/start_end.mp4\tcaption text..." + Returns: (video_path, caption) + """ + parts = line.strip().split("\t", 1) + if len(parts) != 2: + return None, None + video_path = parts[0] + caption = parts[1] + return video_path, caption + + +def load_captions(captions_file: str) -> Dict[str, str]: + """Load captions.txt as video_name -> caption.""" + captions = {} + if not os.path.exists(captions_file): + print(f"Warning: Captions file not found: {captions_file}") + return captions + + with open(captions_file, "r", encoding="utf-8") as f: + for line in f: + video_path, caption = parse_caption_line(line) + if video_path and caption: + video_name = video_path.split("/")[0] + if video_name not in captions: + captions[video_name] = [] + captions[video_name].append(caption) + + for video_name in captions: + captions[video_name] = captions[video_name][0] if captions[video_name] else "" + + return captions + + +def get_frame_files(frames_dir: str, video_name: str) -> List[str]: + """Get sorted frame paths for one video, relative to frames_dir.""" + video_frames_dir = os.path.join(frames_dir, video_name) + if not os.path.exists(video_frames_dir): + return [] + + frame_files = [] + for frame_file in sorted(os.listdir(video_frames_dir)): + if frame_file.endswith(".png"): + frame_files.append(os.path.join(video_name, frame_file)) + + return frame_files + + +def load_camera_poses(json_file: str) -> Dict: + """Load camera poses from a JSON file.""" + if not os.path.exists(json_file): + return {} + + with open(json_file, "r", encoding="utf-8") as f: + data = json.load(f) + + if "CineCameraActor" in data: + return data["CineCameraActor"] + if isinstance(data, dict): + return data + return {} + + +def load_overlap_labels(overlap_dir: str, video_name: str, frame_idx: int) -> List[int]: + """Load overlapping frame indices for a given frame.""" + overlap_file = os.path.join(overlap_dir, video_name, f"{frame_idx}.json") + if not os.path.exists(overlap_file): + return [] + + try: + with open(overlap_file, "r", encoding="utf-8") as f: + data = json.load(f) + overlapping_frames = data.get("overlapping_frames", []) + return [int(frame) for frame in overlapping_frames if str(frame).isdigit()] + except Exception: + return [] + + +def create_metadata_csv( + dataset_base_path: str, + output_csv: str, + segment_length: int = 81, + context_frames: int = 5, +): + """ + Create metadata CSV for the Context-as-Memory dataset. + + Args: + dataset_base_path: root of the dataset. + output_csv: output CSV path. + segment_length: frames per training segment. + context_frames: context frames reserved by downstream workflows. + """ + frames_dir = os.path.join(dataset_base_path, "frames") + captions_file = os.path.join(dataset_base_path, "captions.txt") + + captions = load_captions(captions_file) + + if not os.path.exists(frames_dir): + print(f"Error: Frames directory not found: {frames_dir}") + return + + video_names = [ + d for d in os.listdir(frames_dir) + if os.path.isdir(os.path.join(frames_dir, d)) + ] + + print(f"Found {len(video_names)} videos") + print(f"Context frames: {context_frames}") + + output_dir = os.path.dirname(output_csv) + if output_dir: + os.makedirs(output_dir, exist_ok=True) + + with open(output_csv, "w", newline="", encoding="utf-8") as csvfile: + fieldnames = [ + "video", + "prompt", + "video_name", + "start_frame", + "end_frame", + ] + writer = csv.DictWriter(csvfile, fieldnames=fieldnames) + writer.writeheader() + + total_segments = 0 + + for video_name in sorted(video_names): + print(f"Processing video: {video_name}") + + frame_files = get_frame_files(frames_dir, video_name) + if len(frame_files) < segment_length: + print( + f" Skipping {video_name}: only {len(frame_files)} frames " + f"(need at least {segment_length})" + ) + continue + + prompt = captions.get(video_name, f"A scene from {video_name}") + step = max(1, segment_length // 2) + video_segments = 0 + + for start_idx in range(0, len(frame_files) - segment_length + 1, step): + end_idx = start_idx + segment_length - 1 + segment_frames = frame_files[start_idx:end_idx + 1] + + if len(segment_frames) < segment_length: + continue + + frame_paths = "|".join(segment_frames) + video_path = os.path.join("frames", frame_paths) + + writer.writerow({ + "video": video_path, + "prompt": prompt, + "video_name": video_name, + "start_frame": start_idx, + "end_frame": end_idx, + }) + + total_segments += 1 + video_segments += 1 + + print(f" Created {video_segments} segments for {video_name}") + + print(f"\nTotal segments created: {total_segments}") + print(f"Metadata CSV saved to: {output_csv}") + + +def main(): + parser = argparse.ArgumentParser(description="Preprocess Context-as-Memory Dataset") + parser.add_argument( + "--dataset_base_path", + type=str, + required=True, + help="Base path to Context-as-Memory dataset", + ) + parser.add_argument( + "--output_csv", + type=str, + default="metadata.csv", + help="Output CSV file path (default: metadata.csv)", + ) + parser.add_argument( + "--segment_length", + type=int, + default=81, + help="Length of video segments (default: 81 frames)", + ) + parser.add_argument( + "--context_frames", + type=int, + default=5, + help="Number of context frames (default: 5)", + ) + + args = parser.parse_args() + + if not os.path.isabs(args.output_csv): + args.output_csv = os.path.join(args.dataset_base_path, args.output_csv) + + create_metadata_csv( + dataset_base_path=args.dataset_base_path, + output_csv=args.output_csv, + segment_length=args.segment_length, + context_frames=args.context_frames, + ) + + +if __name__ == "__main__": + main() diff --git a/src/model_training/fov_retrieval.py b/src/model_training/fov_retrieval.py new file mode 100644 index 0000000000000000000000000000000000000000..d01366081fa0f2c3887a2df6f446d80eb6e7ed38 --- /dev/null +++ b/src/model_training/fov_retrieval.py @@ -0,0 +1,1509 @@ +""" +FOV (Field of View) Overlap-based Memory Retrieval Module +Implements geometric retrieval based on camera pose overlap for Context-as-Memory + +Aligned with the Context-as-Memory paper [2506.03141]. +""" + +import os +import json +import random +import sys +from concurrent.futures import ThreadPoolExecutor, as_completed +import numpy as np +from typing import List, Dict, Tuple, Optional +from pathlib import Path + +import torch +from PIL import Image + +def degrees_to_radians(degrees: float) -> float: + """Convert degrees to radians.""" + return degrees * np.pi / 180 + + +def compute_rotation_list_z_only(x: float, y: float, z: float, yaw_degrees: float) -> List[float]: + """ + Paper-aligned: rotation only around Z-axis (yaw). 4 parameters. + R = R_z(yaw). No roll/pitch. + + Returns: + [t_x, t_y, t_z, R_11, R_12, ..., R_33] (12 elements) + """ + yaw_rad = degrees_to_radians(yaw_degrees) + c, s = np.cos(yaw_rad), np.sin(yaw_rad) + R_z = np.array([ + [c, -s, 0], + [s, c, 0], + [0, 0, 1], + ], dtype=np.float64) + return [float(x), float(y), float(z)] + R_z.flatten().tolist() + + +def compute_rotation_list_yaw_pitch( + x: float, y: float, z: float, yaw_degrees: float, pitch_degrees: float +) -> List[float]: + """ + Five-parameter rotation: R = R_z(pitch) @ R_y(yaw). Same convention as external pipelines. + - R_y: rotation around Y (yaw in degrees). + - R_z: rotation around Z (pitch in degrees). + Returns [t_x, t_y, t_z, R_11, ..., R_33] (12 elements), encoder-compatible. + """ + yaw_rad = degrees_to_radians(yaw_degrees) + pitch_rad = degrees_to_radians(pitch_degrees) + cy, sy = np.cos(yaw_rad), np.sin(yaw_rad) + cp, sp = np.cos(pitch_rad), np.sin(pitch_rad) + R_y = np.array([ + [cy, 0, sy], + [0, 1, 0], + [-sy, 0, cy], + ], dtype=np.float64) + R_z = np.array([ + [cp, -sp, 0], + [sp, cp, 0], + [0, 0, 1], + ], dtype=np.float64) + R = R_z @ R_y + return [float(x), float(y), float(z)] + R.flatten().tolist() + + +def compute_rotation_list(params: List[float]) -> List[float]: + """ + Compute rotation matrix from camera parameters and flatten to list. + - 4 params [x, y, z, yaw]: Z-only, R = R_z(yaw) (paper default). + - 5 params [x, y, z, yaw, pitch]: R = R_z(pitch) @ R_y(yaw). + Returns [t_x, t_y, t_z, R_11, ..., R_33] (12 elements). + """ + if len(params) >= 5: + x, y, z = float(params[0]), float(params[1]), float(params[2]) + yaw = float(params[3]) + pitch = float(params[4]) + return compute_rotation_list_yaw_pitch(x, y, z, yaw, pitch) + if len(params) >= 4: + x, y, z = float(params[0]), float(params[1]), float(params[2]) + yaw = float(params[3]) + return compute_rotation_list_z_only(x, y, z, yaw) + x = y = z = yaw = 0.0 + return compute_rotation_list_z_only(x, y, z, yaw) + + +def yaw_deg_from_rt(rt: List[float]) -> float: + """Extract yaw (degrees) from 12-dim RT. Z-only: yaw = atan2(R_21, R_11).""" + if rt is None or len(rt) < 12: + return 0.0 + R = np.array(rt[3:12]).reshape(3, 3) + return float(np.degrees(np.arctan2(R[1, 0], R[0, 0]))) + + +def flip_yaw_rt(rt: List[float]) -> List[float]: + """Return RT with yaw negated (CW<->CCW). rt is 12 elements [t_x,t_y,t_z, R_11..R_33].""" + if rt is None or len(rt) < 12: + return list(rt) if rt else [] + tx, ty, tz = rt[0], rt[1], rt[2] + yaw_deg = yaw_deg_from_rt(rt) + return compute_rotation_list_z_only(tx, ty, tz, -yaw_deg) + + +def flip_yaw_rt_list(rt_list: List[List[float]]) -> List[List[float]]: + """Flip yaw for each RT in list (data aug for direction sensitivity).""" + return [flip_yaw_rt(rt) for rt in rt_list] + + +def convert_rt_to_relative(rt_list_all: List[List[float]], ref_rt: List[float]) -> List[List[float]]: + """ + Convert RT (rotation-translation) poses to relative coordinates. + + This aligns with the Context-as-Memory paper's use of relative camera poses + for better geometric consistency in context frame retrieval. + + Args: + rt_list_all: List of RT poses, each is [t_x, t_y, t_z, R_11, R_12, ..., R_33] (12 elements) + ref_rt: Reference RT pose [t_x, t_y, t_z, R_11, R_12, ..., R_33] (12 elements) + + Returns: + new_rt_list: List of relative RT poses in the same format + """ + def parse_rt(rt: List[float]) -> tuple: + """Parse RT list into rotation matrix R and translation vector t.""" + t = np.array(rt[:3]).reshape((3, 1)) + R = np.array(rt[3:]).reshape((3, 3)) + return R, t + + R_ref, T_ref = parse_rt(ref_rt) + R_ref_inv = R_ref.T + T_ref_inv = -R_ref_inv @ T_ref + + new_rt_list = [] + + for rt in rt_list_all: + R_i, T_i = parse_rt(rt) + + # Convert to relative coordinates + R_new = R_ref_inv @ R_i + T_new = R_ref_inv @ T_i + T_ref_inv + + # Flatten back to list format: [t_x, t_y, t_z, R_11, R_12, ..., R_33] + rt_new = T_new.flatten().tolist() + R_new.flatten().tolist() + new_rt_list.append(rt_new) + + return new_rt_list + + +def pose_to_rt(pose: Dict, constrain_to_xy: bool = True) -> Optional[List[float]]: + """ + Convert camera pose dict to RT format. Paper-aligned by default. + + - 2D plane (constrain_to_xy=True): use position x, y only; z=0. + - Rotation: Z-axis only; use rotation[2] as yaw (degrees). Roll/pitch ignored. + + Args: + pose: Dict with 'position' [x, y, z] and 'rotation' [roll, pitch, yaw] in degrees + constrain_to_xy: If True (default), set z=0 for strict XY-plane displacement (paper). + + Returns: + RT list: [t_x, t_y, t_z, R_11, R_12, ..., R_33] (12 elements) or None if invalid + """ + if pose is None: + return None + + pos = pose.get('position', [0, 0, 0]) + rot = pose.get('rotation', [0, 0, 0]) + + if len(pos) < 2: + return None + + x = float(pos[0]) + y = float(pos[1]) + z = 0.0 if constrain_to_xy else (float(pos[2]) if len(pos) >= 3 else 0.0) + # Paper: rotation only around Z-axis -> use yaw (index 2) only + yaw = float(rot[2]) if len(rot) > 2 else 0.0 + + return compute_rotation_list([x, y, z, yaw]) + + +def rt_to_pose(rt: List[float]) -> Optional[Dict]: + """ + Convert RT format back to pose dict. + + Args: + rt: RT list [t_x, t_y, t_z, R_11, R_12, ..., R_33] (12 elements) + + Returns: + pose dict with 'position' and 'rotation' or None if invalid + """ + if rt is None or len(rt) < 12: + return None + + t = np.array(rt[:3]) + R = np.array(rt[3:]).reshape((3, 3)) + + # Extract Euler angles from rotation matrix + # Using ZYX convention (yaw-pitch-roll) + sy = np.sqrt(R[0, 0] * R[0, 0] + R[1, 0] * R[1, 0]) + singular = sy < 1e-6 + + if not singular: + roll = np.arctan2(R[2, 1], R[2, 2]) + pitch = np.arctan2(-R[2, 0], sy) + yaw = np.arctan2(R[1, 0], R[0, 0]) + else: + roll = np.arctan2(-R[1, 2], R[1, 1]) + pitch = np.arctan2(-R[2, 0], sy) + yaw = 0 + + return { + 'position': t.tolist(), + 'rotation': [np.degrees(roll), np.degrees(pitch), np.degrees(yaw)] + } + + + +def _parse_poses_dict(data: dict) -> dict: + """Extract poses dict from JSON data (CineCameraActor or flat dict).""" + if 'CineCameraActor' in data: + return data['CineCameraActor'] + return data if isinstance(data, dict) else {} + + +def load_poses_dict(json_file: str) -> dict: + """ + Load full camera poses dict from JSON file (one read for all frames). + + Args: + json_file: Path to camera pose JSON file + + Returns: + Dict mapping frame_idx (str) -> pose dict, or {} if failed + """ + if not os.path.exists(json_file): + return {} + try: + with open(json_file, 'r') as f: + data = json.load(f) + return _parse_poses_dict(data) + except Exception as e: + print(f"Error loading poses from {json_file}: {e}") + return {} + + +def load_camera_pose(json_file: str, frame_idx: int) -> Optional[Dict]: + """ + Load camera pose for a specific frame from JSON file. + + Args: + json_file: Path to camera pose JSON file + frame_idx: Frame index + + Returns: + Dict with 'position' and 'rotation' keys, or None if not found + """ + poses = load_poses_dict(json_file) + frame_key = str(frame_idx) + return poses.get(frame_key) + + +def load_camera_poses_batch(json_file: str, frame_indices: List[int]) -> List[Optional[Dict]]: + """ + Load camera poses for multiple frames in one JSON read. + + Args: + json_file: Path to camera pose JSON file + frame_indices: List of frame indices + + Returns: + List of pose dicts (or None) in same order as frame_indices + """ + poses = load_poses_dict(json_file) + return [poses.get(str(fi)) for fi in frame_indices] + + +def load_overlap_frames(overlap_labels_dir: str, video_name: str, frame_idx: int) -> List[int]: + """ + Load overlapping frame indices for a given frame from overlap_labels. + + Args: + overlap_labels_dir: Base directory for overlap labels + video_name: Name of the video + frame_idx: Current frame index + + Returns: + List of overlapping frame indices + """ + overlap_file = os.path.join(overlap_labels_dir, video_name, f"{frame_idx}.json") + if not os.path.exists(overlap_file): + return [] + + try: + # Add distributed training safety - timeout protection + import signal + import torch.distributed as dist + + def timeout_handler(signum, frame): + raise TimeoutError(f"Timeout loading overlap file: {overlap_file}") + + # Set 10-second timeout for file operations in distributed training + if dist.is_available() and dist.is_initialized() and dist.get_world_size() > 1: + signal.signal(signal.SIGALRM, timeout_handler) + signal.alarm(10) + + try: + with open(overlap_file, 'r') as f: + data = json.load(f) + overlapping_frames = data.get('overlapping_frames', []) + # Convert string indices to integers + result = [int(f) for f in overlapping_frames if f.isdigit() or isinstance(f, int)] + if dist.is_available() and dist.is_initialized() and dist.get_world_size() > 1: + signal.alarm(0) # Cancel alarm + return result + finally: + if dist.is_available() and dist.is_initialized() and dist.get_world_size() > 1: + signal.alarm(0) # Always cancel alarm + + except TimeoutError as e: + print(f"Warning: Timeout loading overlap file {overlap_file} (distributed training): {e}") + return [] + except Exception as e: + print(f"Error loading overlap labels from {overlap_file}: {e}") + return [] + + +def compute_fov_overlap_3d( + pose1: Dict, + pose2: Dict, + fov_degrees: float = 52.67, + max_distance: float = 500.0 # Increased default from 50.0 to 500.0 for large scenes +) -> float: + """ + Compute FOV overlap score between two camera poses using 3D geometry. + + This implementation uses full 6-DoF camera poses (position + rotation) + to compute more accurate FOV overlap, as described in Context-as-Memory. + + Args: + pose1: First camera pose with 'position' [x, y, z] and 'rotation' [roll, pitch, yaw] in degrees + pose2: Second camera pose with 'position' [x, y, z] and 'rotation' [roll, pitch, yaw] in degrees + fov_degrees: Field of view in degrees (default: 52.67 from paper) + max_distance: Maximum distance to consider (meters) + + Returns: + Overlap score between 0 and 1 + """ + if pose1 is None or pose2 is None: + return 0.0 + + pos1 = np.array(pose1.get('position', [0, 0, 0]), dtype=np.float32) + pos2 = np.array(pose2.get('position', [0, 0, 0]), dtype=np.float32) + + # Distance between cameras + distance = np.linalg.norm(pos2 - pos1) + # Instead of returning 0.0 for distances > max_distance, we use a soft threshold + # that still gives some score based on direction similarity even for far cameras + distance_exceeds_max = distance > max_distance + + if distance < 1e-6: + # Same position - high overlap + return 1.0 + + # Extract rotation angles (assuming [roll, pitch, yaw] or [x, y, z] rotation in degrees) + rot1 = pose1.get('rotation', [0, 0, 0]) + rot2 = pose2.get('rotation', [0, 0, 0]) + + # Convert to numpy array + rot1 = np.array(rot1, dtype=np.float32) + rot2 = np.array(rot2, dtype=np.float32) + + # Compute rotation matrices from Euler angles + # Note: The rotation order may vary by dataset. Common conventions: + # - ZYX (yaw-pitch-roll): R = R_z(yaw) * R_y(pitch) * R_x(roll) + # - XYZ (roll-pitch-yaw): R = R_x(roll) * R_y(pitch) * R_z(yaw) + # Based on Context-as-Memory dataset, rotation[2] is yaw (rotation around Z-axis) + # We'll use ZYX convention: yaw (Z), pitch (Y), roll (X) + + def euler_to_rotation_matrix(euler_angles): + """Convert Euler angles [roll, pitch, yaw] in degrees to rotation matrix (ZYX order)""" + roll, pitch, yaw = np.radians(euler_angles) + + # Rotation around X-axis (roll) + Rx = np.array([ + [1, 0, 0], + [0, np.cos(roll), -np.sin(roll)], + [0, np.sin(roll), np.cos(roll)] + ]) + + # Rotation around Y-axis (pitch) + Ry = np.array([ + [np.cos(pitch), 0, np.sin(pitch)], + [0, 1, 0], + [-np.sin(pitch), 0, np.cos(pitch)] + ]) + + # Rotation around Z-axis (yaw) + Rz = np.array([ + [np.cos(yaw), -np.sin(yaw), 0], + [np.sin(yaw), np.cos(yaw), 0], + [0, 0, 1] + ]) + + # ZYX order: R = Rz * Ry * Rx + R = Rz @ Ry @ Rx + return R + + # Handle different rotation formats + if len(rot1) >= 3: + # Euler angles [roll, pitch, yaw] or [x, y, z] + R1 = euler_to_rotation_matrix([rot1[0], rot1[1], rot1[2]]) + elif len(rot1) == 9: + # Rotation matrix flattened (3x3 = 9 elements) + R1 = rot1.reshape(3, 3) + else: + # Fallback: only yaw + R1 = euler_to_rotation_matrix([0, 0, rot1[2] if len(rot1) > 2 else 0]) + + if len(rot2) >= 3: + R2 = euler_to_rotation_matrix([rot2[0], rot2[1], rot2[2]]) + elif len(rot2) == 9: + R2 = rot2.reshape(3, 3) + else: + R2 = euler_to_rotation_matrix([0, 0, rot2[2] if len(rot2) > 2 else 0]) + + # Camera forward vector (typically Z-axis in camera coordinate system) + # In OpenCV/OpenGL convention, forward is usually -Z or +Z + # Based on Context-as-Memory dataset, we assume forward is +Z (third column) + forward1 = R1[:, 2] # Third column of rotation matrix + forward2 = R2[:, 2] + + # Vector from camera1 to camera2 + vec_1_to_2 = pos2 - pos1 + vec_1_to_2_norm = np.linalg.norm(vec_1_to_2) + vec_1_to_2_unit = vec_1_to_2 / (vec_1_to_2_norm + 1e-6) + + # FOV half-angle threshold (cosine of half FOV) + fov_rad = np.radians(fov_degrees) + fov_half_cos = np.cos(fov_rad / 2) + + # Check if camera1 can see camera2's position (within FOV) + # cos(angle) = dot(forward, vec_to_target) + # angle < fov/2 => cos(angle) > cos(fov/2) + dot1 = np.dot(forward1, vec_1_to_2_unit) + can_1_see_2 = dot1 > fov_half_cos + + # Check if camera2 can see camera1's position (within FOV) + dot2 = np.dot(forward2, -vec_1_to_2_unit) # Negative because looking back + can_2_see_1 = dot2 > fov_half_cos + + # Compute overlap score based on mutual visibility and distance + # Normalize distance for scoring (use max_distance as reference, but don't hard-cut) + normalized_distance = min(1.0, distance / max_distance) if max_distance > 0 else 1.0 + + if can_1_see_2 and can_2_see_1: + # Both cameras can see each other - high overlap + # Score decreases with distance, but never goes to 0 + distance_factor = 1.0 - normalized_distance * 0.5 + overlap = 0.8 + 0.2 * distance_factor + elif can_1_see_2 or can_2_see_1: + # One camera can see the other - medium overlap + distance_factor = 1.0 - normalized_distance * 0.6 + overlap = 0.4 + 0.3 * distance_factor + else: + # Check if cameras are looking in similar directions (even if not directly at each other) + # This handles the case where both cameras see the same scene from different angles + forward_similarity = np.dot(forward1, forward2) + if forward_similarity > 0.7: # Cameras looking in similar directions + # Even for far cameras, if they're looking in similar directions, there's some overlap + distance_factor = 1.0 - normalized_distance * 0.7 + overlap = 0.2 + 0.3 * distance_factor + elif forward_similarity > 0.0: + # Cameras looking in somewhat similar directions + distance_factor = 1.0 - normalized_distance * 0.8 + overlap = 0.05 + 0.15 * distance_factor * forward_similarity + else: + # Cameras looking away from each other - very low overlap + # But still give some score based on distance (closer = slightly better) + overlap = max(0.0, 0.01 - normalized_distance * 0.01) + + # Apply distance penalty for cameras exceeding max_distance (soft penalty) + if distance_exceeds_max: + # Reduce score by distance penalty, but don't make it zero + distance_penalty = min(0.5, (distance - max_distance) / max_distance * 0.3) + overlap = overlap * (1.0 - distance_penalty) + + return np.clip(overlap, 0.0, 1.0) + + +# Keep the simple version as fallback +def compute_fov_overlap_simple( + pose1: Dict, + pose2: Dict, + fov_degrees: float = 52.67, + max_distance: float = 50.0 +) -> float: + """ + Simplified FOV overlap computation (fallback). + Use compute_fov_overlap_3d for more accurate results. + """ + return compute_fov_overlap_3d(pose1, pose2, fov_degrees, max_distance) + + +class FOVMemoryRetriever: + """ + FOV-based Memory Retriever for Context-as-Memory. + + Retrieves relevant historical frames based on FOV overlap with current frame. + """ + + def __init__( + self, + dataset_base_path: str, + fov_degrees: float = 52.67, + max_distance: float = 50.0, + use_precomputed_overlaps: bool = True + ): + """ + Initialize FOV Memory Retriever. + + Args: + dataset_base_path: Base path to Context-as-Memory dataset + fov_degrees: Field of view in degrees (default from paper: 52.67) + max_distance: Maximum distance to consider for overlap (meters) + use_precomputed_overlaps: Whether to use precomputed overlap_labels if available + """ + self.dataset_base_path = dataset_base_path + self.fov_degrees = fov_degrees + self.max_distance = max_distance + self.use_precomputed_overlaps = use_precomputed_overlaps + + self.jsons_dir = os.path.join(dataset_base_path, 'jsons') + self.overlap_labels_dir = os.path.join(dataset_base_path, 'overlap_labels') + + # Cache for loaded poses + self._pose_cache: Dict[str, Dict] = {} + + def retrieve_frames( + self, + video_name: str, + current_frame_idx: int, + candidate_frame_indices: List[int], + top_k: int = 5, + include_last_frame: bool = True, + use_relative_poses: bool = False # Experiment 1_4_2: use RT relative conversion + ) -> List[int]: + """ + Retrieve top-k most relevant frames based on FOV overlap. + + According to Context-as-Memory, for temporal coherence, we should: + 1. Always include the last frame (current_frame_idx - 1) as short-term memory + 2. Retrieve top-(k-1) frames from history as long-term memory + + Args: + video_name: Name of the video + current_frame_idx: Index of current frame to generate + candidate_frame_indices: List of candidate frame indices to consider + top_k: Number of frames to retrieve + include_last_frame: Whether to force include the last frame (default: True, per Context-as-Memory) + use_relative_poses: Whether to use RT relative conversion (experiment 1_4_2, aligned with paper) + + Returns: + List of top-k frame indices sorted by relevance (last frame first if included) + """ + if not candidate_frame_indices: + return [] + + retrieved_frames = [] + + # Step 1: Force include last frame for short-term memory (Context-as-Memory requirement) + if include_last_frame and current_frame_idx > 0: + last_frame_idx = current_frame_idx - 1 + if last_frame_idx in candidate_frame_indices: + retrieved_frames.append(last_frame_idx) + # Remove from candidates to avoid duplication + candidate_frame_indices = [idx for idx in candidate_frame_indices if idx != last_frame_idx] + + # Calculate how many more frames we need + remaining_k = top_k - len(retrieved_frames) + if remaining_k <= 0: + return retrieved_frames[:top_k] + + # Step 2: Retrieve long-term memory frames using FOV overlap + # If precomputed overlaps are available, use them + if self.use_precomputed_overlaps: + overlap_frames = load_overlap_frames( + self.overlap_labels_dir, + video_name, + current_frame_idx + ) + + # Filter to only include candidate frames (and exclude already included last frame) + overlap_frames = [f for f in overlap_frames + if f in candidate_frame_indices and f not in retrieved_frames] + + if overlap_frames: + # Take top remaining_k frames + retrieved_frames.extend(overlap_frames[:remaining_k]) + return retrieved_frames[:top_k] + + # Step 3: Compute FOV overlap using camera poses (if precomputed not available) + current_pose = self._load_pose(video_name, current_frame_idx) + if current_pose is None: + # Fallback: return first k candidates + retrieved_frames.extend(candidate_frame_indices[:remaining_k]) + return retrieved_frames[:top_k] + + # Experiment 1_4_2: Convert to relative poses if enabled (aligned with Context-as-Memory) + if use_relative_poses: + # Convert current pose to RT format + ref_rt = pose_to_rt(current_pose) + if ref_rt is None: + # Fallback to absolute poses if conversion fails + use_relative_poses = False + + # Compute overlap scores for all candidates + overlap_scores = [] + for candidate_idx in candidate_frame_indices: + if candidate_idx in retrieved_frames: + continue # Skip already included frames + + candidate_pose = self._load_pose(video_name, candidate_idx) + if candidate_pose is None: + continue + + # Experiment 1_4_2: Use relative poses for FOV overlap computation + if use_relative_poses and ref_rt is not None: + # Convert candidate pose to RT format + candidate_rt = pose_to_rt(candidate_pose) + if candidate_rt is not None: + # Convert to relative coordinates + relative_rt_list = convert_rt_to_relative([candidate_rt], ref_rt) + if relative_rt_list: + # Convert back to pose format for FOV overlap computation + relative_pose = rt_to_pose(relative_rt_list[0]) + if relative_pose is not None: + # Use relative pose for overlap computation + # Reference pose in relative coordinates is identity (origin) + ref_relative_pose = {'position': [0, 0, 0], 'rotation': [0, 0, 0]} + score = compute_fov_overlap_3d( + ref_relative_pose, + relative_pose, + self.fov_degrees, + self.max_distance + ) + overlap_scores.append((candidate_idx, score)) + continue + + # Fallback: Use absolute poses (original method) + score = compute_fov_overlap_3d( + current_pose, + candidate_pose, + self.fov_degrees, + self.max_distance + ) + overlap_scores.append((candidate_idx, score)) + + # Sort by score (descending) and take top remaining_k + overlap_scores.sort(key=lambda x: x[1], reverse=True) + retrieved_frames.extend([idx for idx, _ in overlap_scores[:remaining_k]]) + + return retrieved_frames[:top_k] + + def _load_pose(self, video_name: str, frame_idx: int) -> Optional[Dict]: + """Load and cache camera pose.""" + cache_key = f"{video_name}_{frame_idx}" + if cache_key in self._pose_cache: + return self._pose_cache[cache_key] + + json_file = os.path.join(self.jsons_dir, f"{video_name}.json") + pose = load_camera_pose(json_file, frame_idx) + + if pose is not None: + self._pose_cache[cache_key] = pose + + return pose + + def clear_cache(self): + """Clear pose cache.""" + self._pose_cache.clear() + + +def create_fov_retriever(dataset_base_path: str) -> Optional[FOVMemoryRetriever]: + """ + Create FOV retriever if dataset has camera pose information. + + Args: + dataset_base_path: Base path to dataset + + Returns: + FOVMemoryRetriever instance or None if dataset doesn't support it + """ + jsons_dir = os.path.join(dataset_base_path, 'jsons') + if not os.path.exists(jsons_dir): + return None + + return FOVMemoryRetriever(dataset_base_path) + + +# Context/FOV retrieval integration helpers. +def _load_frame_png(frame_file: str) -> Optional[Image.Image]: + """Load a single frame from PNG file.""" + if os.path.exists(frame_file): + try: + return Image.open(frame_file).convert('RGB') + except Exception: + pass + return None + + + +def retrieve_simple_context_frames( + data: Dict, + dataset_base_path: str, + top_k: int = 4, # Number of overlap frames to retrieve. First Frame will be added automatically. + drop_overlap_probability: float = 0.1, # 10% probability to drop overlap frames (paper strategy) + use_rt_relative: bool = False, # Experiment 1_4_2: use RT relative conversion (aligned with Context-as-Memory) +) -> Tuple[List[Image.Image], List, List[int], int, str, str]: + """ + Retrieve context frames according to the Context-as-Memory paper [2506.03141]. + + Data Structure (Precomputed Retrieval Results): + - Each JSON file: overlap_labels/{video_name}/{frame_index}.json + - JSON structure: { + "frame_index": "0", # First Frame (short-term memory) + "overlapping_frames": ["2796", "2797", ..., "3839", ...] # Long-term memory (sample 4) + } + - frame_index and the next 80 frames constitute GT (target frames to generate) + - Iterating through all JSON files = one epoch + + Design principles: + 1. First Frame (frame_index from JSON) as Immediate Condition: + - The frame_index in JSON is the First Frame (short-term memory) + - Provides immediate visual and temporal starting point (Image-to-Video mode) + - Always included as context + - GT: frame_index and the next 80 frames (81 frames total) + + 2. Overlap Frames (from overlapping_frames in JSON) as Long-term Memory: + - Retrieved from precomputed overlap_labels JSON files + - Precomputed lists may be very long (e.g., [2796, 3839, 4183, ..., 6339]) + - Random uniform sampling: sample top_k (4) frames from overlapping_frames list + - Provides long-term consistency information + - Memory frames are unordered snapshots (no temporal sequence) + + 3. Context Composition: + - Order: [First Frame, Overlap Frame 1, Overlap Frame 2, Overlap Frame 3, Overlap Frame 4] + - Total: 1 First Frame + top_k Overlap Frames = top_k + 1 frames (e.g., 5 frames) + - Context frames are concatenated with target frames in temporal dimension + + 4. 10% Probability Drop Strategy: + - With 10% probability, drop all Overlap Frames, only use First Frame + - Simulates video generation starting stage (no historical memory) + - Forces model to generate reasonable videos without long-term memory assistance + + 5. Epoch Definition: + - One epoch = iterate through all JSON files in overlap_labels/{video_name}/ + - Each JSON file = one training sample + + 6. Positional Encoding Note: + - Memory frames should NOT use original absolute time positions + - They should be treated as unordered image collection or use memory ID encoding only + - First Frame should use explicit "Ref Frame" encoding + + Args: + data: Training data dict containing video frames and metadata + dataset_base_path: Base path to Context-as-Memory dataset + top_k: Number of overlap frames to retrieve (default: 4). First Frame will be added automatically. + drop_overlap_probability: Probability to drop overlap frames (default: 0.1 = 10%) + + Returns: + Tuple of: + (context_frames, context_actions, context_indices, current_frame_idx, video_name, source) + """ + video_frames = data.get("video", []) + # Get segment boundaries + start_frame = data.get("start_frame", 0) + end_frame = data.get("end_frame", None) + + # Use frame_idx if available, otherwise calculate from segment (middle of segment) + # This ensures we can find previous frames for context retrieval + if "frame_idx" in data: + current_frame_idx = data.get("frame_idx") + else: + # Calculate middle of segment as reference frame (same as training convention) + if end_frame is not None: + current_frame_idx = (start_frame + end_frame) // 2 + else: + # Fallback: use middle of video_frames if available + if len(video_frames) > 0: + current_frame_idx = len(video_frames) // 2 + else: + current_frame_idx = 0 + + # First Frame: current segment's first frame (start_frame) - ALWAYS included + first_frame_idx = start_frame + + video_name = data.get("video_name", "") + context_frames: List[Image.Image] = [] + context_actions: List = [] + context_indices: List[int] = [] + source = "none" + + # Get video frames from data + if not isinstance(video_frames, list): + video_frames = [] + + if not video_name: + # Try to infer from data + if "video_path" in data: + video_name = os.path.basename(data["video_path"]).replace(".mp4", "").replace(".avi", "") + elif "file_path" in data: + video_name = os.path.basename(data["file_path"]).replace(".mp4", "").replace(".avi", "") + + # Step 1: Load First Frame (current segment's first frame) - ALWAYS included + # According to JSON structure: frame_index in JSON is the First Frame (short-term memory) + # frame_index and the next 80 frames constitute GT (target frames to generate) + # First Frame provides immediate visual and temporal starting point (Image-to-Video mode) + frames_dir = os.path.join(dataset_base_path, 'frames', video_name) + first_frame_loaded = False + + # Experiment 1_4_2: Load camera poses once for all context frames (one JSON read) + json_file = os.path.join(dataset_base_path, "jsons", f"{video_name}.json") + poses_dict = load_poses_dict(json_file) + first_frame_pose_rt = None + first_frame_pose = poses_dict.get(str(first_frame_idx)) + if first_frame_pose is not None: + first_frame_pose_rt = pose_to_rt(first_frame_pose) + if first_frame_pose_rt is not None: + # Experiment 1_4_2: When use_rt_relative, first frame = reference frame = identity RT + # Target actions use ref=first_frame, so context first frame must also be identity + # to align with target's coordinate system (same frame = same RT representation) + if use_rt_relative: + # Identity RT: [t=0,0,0, R=eye(3)] = [0,0,0,1,0,0,0,1,0,0,0,1] + context_actions.append([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]) + else: + context_actions.append(first_frame_pose_rt) + else: + context_actions.append([0.0] * 12) + else: + context_actions.append([0.0] * 12) + + if os.path.exists(frames_dir): + first_frame_file = os.path.join(frames_dir, f"{first_frame_idx:04d}.png") + if os.path.exists(first_frame_file): + try: + first_frame = Image.open(first_frame_file).convert('RGB') + context_frames.append(first_frame) + context_indices.append(first_frame_idx) + first_frame_loaded = True + except Exception as e: + pass # Frame loading failed, skip + + # Fallback: use first frame from video_frames if file not found + if not first_frame_loaded and len(video_frames) > 0: + if isinstance(video_frames[0], Image.Image): + context_frames.append(video_frames[0]) + context_indices.append(start_frame) + first_frame_loaded = True + elif isinstance(video_frames[0], str): + try: + first_frame = Image.open(video_frames[0]).convert('RGB') + context_frames.append(first_frame) + context_indices.append(start_frame) + first_frame_loaded = True + except Exception as e: + pass # Fallback frame loading failed, skip + + # Step 2: Retrieve Overlap Frames (long-term memory) - with 10% probability drop + # According to the data structure: + # - Each JSON file in overlap_labels/{video_name}/{frame_index}.json contains: + # - frame_index: First Frame (short-term memory) - this is the current segment's start + # - overlapping_frames: List of historical frames (long-term memory) - sample 4 from this list + # - frame_index and the next 80 frames constitute GT (target frames to generate) + # - Iterating through all JSON files = one epoch + + drop_overlap = random.random() < drop_overlap_probability + + if not drop_overlap: + # Use precomputed overlap_labels for FOV overlap-based selection + overlap_labels_dir = os.path.join(dataset_base_path, 'overlap_labels') + if os.path.exists(overlap_labels_dir): + # Load overlapping_frames from JSON file: overlap_labels/{video_name}/{current_frame_idx}.json + # The JSON structure: {"frame_index": "...", "overlapping_frames": ["2796", "2797", ...]} + overlapping_indices = load_overlap_frames( + overlap_labels_dir, + video_name, + current_frame_idx + ) + # Filter: exclude first_frame_idx, but allow frames with FOV overlap (may include future frames) + # FOV overlap indicates visual similarity, which is valuable for context memory + # even if the overlapping frame is from the future + overlapping_indices = [idx for idx in overlapping_indices + if idx != first_frame_idx] + + # If we have overlap frames, randomly sample top_k from them + # Note: overlap_labels are precomputed FOV overlap results, containing potentially + # very long lists of non-contiguous frame indices (e.g., [2796, 3839, 4183, ...]). + # We cannot use all frames due to memory constraints, so we use random uniform sampling. + # Sampling all JSON files once = one epoch + if overlapping_indices: + # Experiment 1_4_2: Use RT relative conversion for better geometric consistency + # Aligned with Context-as-Memory [2506.03141] - use relative camera poses for FOV overlap + if use_rt_relative: + ref_pose = poses_dict.get(str(current_frame_idx)) + + if ref_pose is not None: + # Convert reference pose to RT format + ref_rt = pose_to_rt(ref_pose) + + if ref_rt is not None: + # Score candidates using relative poses + candidate_scores = [] + for candidate_idx in overlapping_indices: + candidate_pose = poses_dict.get(str(candidate_idx)) + if candidate_pose is None: + continue + + # Convert to RT and compute relative pose + candidate_rt = pose_to_rt(candidate_pose) + if candidate_rt is not None: + relative_rt_list = convert_rt_to_relative([candidate_rt], ref_rt) + if relative_rt_list: + relative_pose = rt_to_pose(relative_rt_list[0]) + if relative_pose is not None: + # Compute FOV overlap using relative poses + ref_relative_pose = {'position': [0, 0, 0], 'rotation': [0, 0, 0]} + score = compute_fov_overlap_3d( + ref_relative_pose, + relative_pose, + fov_degrees=52.67, + max_distance=500.0 + ) + candidate_scores.append((candidate_idx, score)) + + # Sort by score and select top_k + if candidate_scores: + candidate_scores.sort(key=lambda x: x[1], reverse=True) + sampled_overlap_indices = [idx for idx, _ in candidate_scores[:top_k]] + else: + # Fallback to random sampling if RT conversion fails + num_overlap_frames = max(1, top_k) + sampled_overlap_indices = random.sample( + overlapping_indices, + min(len(overlapping_indices), num_overlap_frames) + ) + else: + # Fallback to random sampling if RT conversion fails + num_overlap_frames = max(1, top_k) + sampled_overlap_indices = random.sample( + overlapping_indices, + min(len(overlapping_indices), num_overlap_frames) + ) + else: + # Fallback to random sampling if reference pose not found + num_overlap_frames = max(1, top_k) + sampled_overlap_indices = random.sample( + overlapping_indices, + min(len(overlapping_indices), num_overlap_frames) + ) + else: + # Original strategy: Random Uniform Sampling (recommended for robustness) + # This allows the model to learn from diverse temporal spans of memory. + # The precomputed overlapping_frames list may contain hundreds or thousands of indices, + # but we only sample top_k (e.g., 4) frames due to memory constraints. + num_overlap_frames = max(1, top_k) + + # Random uniform sampling from the precomputed overlapping_frames list + # This treats memory frames as an unordered image collection + # IMPORTANT: Do NOT sort sampled_indices - they are unordered snapshots, not a temporal sequence + # Positional encoding should NOT use original absolute time positions for memory frames + sampled_overlap_indices = random.sample( + overlapping_indices, + min(len(overlapping_indices), num_overlap_frames) + ) + + # Load overlap frames (memory frames) - long-term memory + # Experiment 1_4_2: Use first_frame (start_frame) as reference for ALL context RTs + # Target actions use ref=first_frame; context must use same ref for trajectory alignment + ref_pose_for_rt = first_frame_pose_rt if use_rt_relative else None + + if os.path.exists(frames_dir): + to_load = [(idx, os.path.join(frames_dir, f"{idx:04d}.png")) for idx in sampled_overlap_indices[:top_k]] + frames_loaded = {} + with ThreadPoolExecutor(max_workers=max(1, min(5, len(to_load)))) as ex: + futures = {ex.submit(_load_frame_png, fp): idx for idx, fp in to_load} + for fut in as_completed(futures): + frame_idx = futures[fut] + frame = fut.result() + if frame is not None: + frames_loaded[frame_idx] = frame + for frame_idx in sampled_overlap_indices[:top_k]: + if frame_idx not in frames_loaded: + continue + frame = frames_loaded[frame_idx] + context_frames.append(frame) + context_indices.append(frame_idx) + pose = poses_dict.get(str(frame_idx)) + if pose is not None: + rt_pose = pose_to_rt(pose) + if rt_pose is not None: + if use_rt_relative and ref_pose_for_rt is not None: + relative_rt_list = convert_rt_to_relative([rt_pose], ref_pose_for_rt) + context_actions.append(relative_rt_list[0] if relative_rt_list else rt_pose) + else: + context_actions.append(rt_pose) + else: + context_actions.append([0.0] * 12) + else: + context_actions.append([0.0] * 12) + + source = "overlap_labels_random" + else: + # No overlap frames found, will use fallback below + source = "first_frame_only" + else: + # No overlap_labels directory, will use fallback below + source = "first_frame_only" + else: + # 10% probability: drop overlap frames, only use First Frame + # This simulates video generation starting stage (no historical memory) + source = "first_frame_only_dropped" + # Step 3: Fallback if we don't have enough overlap frames (and not dropped) + # Only fill if we haven't dropped overlap frames and need more frames + if source not in ["first_frame_only_dropped"] and len(context_frames) < top_k + 1: + # Try random fallback: use random previous frames before current_frame_idx + max_prev_frame = max(1, current_frame_idx - 1) + if max_prev_frame > 1: + # Exclude first_frame_idx from random sampling + candidate_indices = [idx for idx in range(max_prev_frame) + if idx != first_frame_idx and idx < current_frame_idx] + if candidate_indices: + num_needed = top_k + 1 - len(context_frames) + num_random_frames = min(len(candidate_indices), num_needed) + if num_random_frames > 0: + sampled_indices = random.sample(candidate_indices, num_random_frames) + + # Load random frames + if os.path.exists(frames_dir): + for frame_idx in sampled_indices: + frame_file = os.path.join(frames_dir, f"{frame_idx:04d}.png") + if os.path.exists(frame_file): + try: + frame = Image.open(frame_file).convert('RGB') + context_frames.append(frame) + context_indices.append(frame_idx) + pose = poses_dict.get(str(frame_idx)) + if pose is not None: + rt_pose = pose_to_rt(pose) + if rt_pose is not None: + # Convert to relative RT if enabled + if use_rt_relative and first_frame_pose_rt is not None: + relative_rt_list = convert_rt_to_relative([rt_pose], first_frame_pose_rt) + context_actions.append(relative_rt_list[0] if relative_rt_list else rt_pose) + else: + context_actions.append(rt_pose) + else: + context_actions.append([0.0] * 12) + else: + context_actions.append([0.0] * 12) + except Exception as e: + pass # Frame loading failed, skip + + if source == "first_frame_only": + source = "random_fallback" + + # Step 4: Final fallback - use additional frames from current segment if needed + # Target is top_k + 1 frames (1 First Frame + top_k Overlap/Random Frames) + target_total_frames = top_k + 1 + if len(context_frames) < target_total_frames and len(video_frames) > 0: + num_needed = target_total_frames - len(context_frames) + # Use additional frames from current segment (after first frame) + segment_start_idx = 1 if len(context_frames) > 0 else 0 + for i in range(segment_start_idx, min(segment_start_idx + num_needed, len(video_frames))): + frame_idx_seg = start_frame + i + if isinstance(video_frames[i], Image.Image): + context_frames.append(video_frames[i]) + context_indices.append(frame_idx_seg) + elif isinstance(video_frames[i], str): + # If it's a path, load it + try: + frame = Image.open(video_frames[i]).convert('RGB') + context_frames.append(frame) + context_indices.append(frame_idx_seg) + except: + pass + + pose = poses_dict.get(str(frame_idx_seg)) + if pose is not None: + rt_pose = pose_to_rt(pose) + if rt_pose is not None: + # Convert to relative RT if enabled + if use_rt_relative and first_frame_pose_rt is not None: + relative_rt_list = convert_rt_to_relative([rt_pose], first_frame_pose_rt) + context_actions.append(relative_rt_list[0] if relative_rt_list else rt_pose) + else: + context_actions.append(rt_pose) + else: + context_actions.append([0.0] * 12) + else: + context_actions.append([0.0] * 12) + + # Update source if we used segment frames + if len(context_frames) >= num_needed and source in ["first_frame_only", "none"]: + source = "segment_fallback" + + # Step 5: Ensure we return exactly top_k + 1 frames + # If we have some frames but not enough, pad by repeating the last frame + if len(context_frames) < target_total_frames: + if context_frames: + last_frame = context_frames[-1] + last_idx = context_indices[-1] if context_indices else first_frame_idx + last_action = context_actions[-1] if context_actions else [0.0] * 12 + while len(context_frames) < target_total_frames: + context_frames.append(last_frame) + context_indices.append(last_idx) + context_actions.append(last_action) # Always pad with pose data + + # Limit to top_k + 1 (in case we have more) + context_frames = context_frames[:target_total_frames] + if context_indices: + context_indices = context_indices[:target_total_frames] + if context_actions: + context_actions = context_actions[:target_total_frames] + + # Ensure context_actions length matches context_frames (pad with zeros if needed) + # Always ensure context_actions are provided (not just when use_rt_relative=True) + while len(context_actions) < len(context_frames): + context_actions.append([0.0] * 12) + + return context_frames, context_actions, context_indices, current_frame_idx, video_name, source + + +def retrieve_fov_context_frames( + data: Dict, + dataset_base_path: str, + fov_retriever=None, + top_k: int = 4, # Number of overlap frames to retrieve. First Frame will be added automatically. + use_precomputed_overlaps: bool = True, + use_rt_relative: bool = False, # Experiment 1_4_2: Use RT relative conversion (aligned with Context-as-Memory) + strict_overlap_labels: bool = False, + allow_realtime_fallback: bool = True, + allow_segment_fallback: bool = True, + drop_overlap_probability: float = 0.1, # 10% probability to drop overlap frames (paper strategy) +): + """ + Backward-compatible wrapper. + We use FOV overlap scoring to select top-k overlap frames (as in Context-as-Memory). + + According to Context-as-Memory [2506.03141]: + - First Frame (current segment's first frame) is always included as immediate condition + - Overlap Frames are retrieved as long-term memory + - With 10% probability, drop overlap frames to simulate starting stage + + Experiment 1_4_2: Uses RT relative conversion for better geometric consistency. + """ + context_frames, context_actions, context_indices, cur_idx, video_name, source = retrieve_simple_context_frames( + data=data, + dataset_base_path=dataset_base_path, + top_k=top_k, # top_k is number of overlap frames (4), First Frame will be added automatically (total: 5) + use_rt_relative=use_rt_relative, # Experiment 1_4_2: RT relative conversion + drop_overlap_probability=drop_overlap_probability, # 10% probability to drop overlap frames + ) + # Check if we have top_k + 1 frames (1 First Frame + top_k Overlap Frames) + target_total_frames = top_k + 1 + if strict_overlap_labels and len(context_frames) < target_total_frames: + return [], [], [], cur_idx, video_name, "overlap_labels_insufficient" + return context_frames, context_actions, context_indices, cur_idx, video_name, source + + +def save_sampling_jsonl( + output_path: str, + video_name: str, + frame_index: int, + context_indices: List[int], + prompt: Optional[str] = None, + start_frame: Optional[int] = None, + end_frame: Optional[int] = None, + source: Optional[str] = None, + append: bool = True, +) -> None: + """ + Save context sampling result to JSONL file for eval consistency. + + Format: + { + "video_name": "AncientTempleEnv_0", + "frame_index": 0, # First Frame (short-term memory) + "context_indices": [0, 2796, 3839, 4183, 6339], # First Frame + 4 Overlap Frames + "prompt": "...", # Optional + "start_frame": 0, # Optional: GT segment start + "end_frame": 80, # Optional: GT segment end + "source": "overlap_labels_random" # Optional: sampling source + } + + Args: + output_path: Path to JSONL file + video_name: Video name + frame_index: First Frame index (from JSON file) + context_indices: List of context frame indices [first_frame, overlap1, overlap2, ...] + prompt: Optional prompt text + start_frame: Optional GT segment start frame + end_frame: Optional GT segment end frame + source: Optional sampling source + append: Whether to append to existing file (default: True) + """ + os.makedirs(os.path.dirname(output_path), exist_ok=True) + + item = { + "video_name": video_name, + "frame_index": frame_index, + "context_indices": context_indices, + } + + if prompt is not None: + item["prompt"] = prompt + if start_frame is not None: + item["start_frame"] = start_frame + if end_frame is not None: + item["end_frame"] = end_frame + if source is not None: + item["source"] = source + + mode = "a" if append else "w" + with open(output_path, mode, encoding="utf-8") as f: + f.write(json.dumps(item, ensure_ascii=False) + "\n") + + +def load_sampling_jsonl(jsonl_path: str) -> List[Dict]: + """ + Load context sampling results from JSONL file. + + Args: + jsonl_path: Path to JSONL file + + Returns: + List of sampling items, each containing: + { + "video_name": str, + "frame_index": int, + "context_indices": List[int], + "prompt": Optional[str], + "start_frame": Optional[int], + "end_frame": Optional[int], + "source": Optional[str] + } + """ + if not os.path.exists(jsonl_path): + return [] + + items = [] + with open(jsonl_path, "r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if not line: + continue + try: + item = json.loads(line) + items.append(item) + except json.JSONDecodeError as e: + print(f"Warning: Failed to parse JSONL line: {e}") + continue + + return items + + +def setup_fov_retriever_for_training( + dataset_base_path: str, + enable_fov_retrieval: bool = True, +) -> Optional[object]: + """ + Setup FOV retriever for training (simplified version). + Context frames are selected by FOV overlap scoring from precomputed `overlap_labels`. + """ + return None + + +_WARN_ONCE_KEYS = set() + + +def _warn_once(key: str, msg: str) -> None: + if key in _WARN_ONCE_KEYS: + return + _WARN_ONCE_KEYS.add(key) + print(f"[context_retrieval] WARN: {msg}", file=sys.stderr, flush=True) + + +def _load_latent(latent_dir: str, video_name: str, frame_idx: int): + """Load a single-frame latent. Expects latent_dir/video_name/{frame_idx:04d}.pt or .pt with key 'latent' or raw tensor.""" + base = os.path.join(latent_dir, video_name) + for fmt in (f"{frame_idx:04d}.pt", f"{frame_idx}.pt"): + path = os.path.join(base, fmt) + if os.path.isfile(path): + try: + try: + x = torch.load(path, map_location="cpu", weights_only=True) + except TypeError: + x = torch.load(path, map_location="cpu") + if isinstance(x, dict) and "latent" in x: + z = x["latent"] + else: + z = x + if hasattr(z, "shape"): + # (C, 1, H, W) or (C, H, W) -> flatten for similarity + return z.flatten() + return None + except Exception: + pass + return None + + +def latent_sim_rank( + video_name: str, + first_frame_idx: int, + overlapping_indices: List[int], + dataset_base_path: str, + top_k: int, + latent_dir: Optional[str] = None, + use_cosine: bool = True, +) -> List[int]: + """ + Rank overlapping frame indices by latent similarity to the first (reference) frame. + If latent_dir is None or latents are missing, falls back to random sample. + Expects per-frame latents under latent_dir/video_name/{frame_idx:04d}.pt (or {frame_idx}.pt). + """ + if not overlapping_indices or top_k <= 0: + return [] + if latent_dir is None or not os.path.isdir(latent_dir): + return random.sample(overlapping_indices, min(top_k, len(overlapping_indices))) + + ref = _load_latent(latent_dir, video_name, first_frame_idx) + if ref is None: + return random.sample(overlapping_indices, min(top_k, len(overlapping_indices))) + + ref = ref.float().unsqueeze(0) + scores = [] + for idx in overlapping_indices: + cand = _load_latent(latent_dir, video_name, idx) + if cand is None: + continue + cand = cand.float().unsqueeze(0) + if use_cosine: + sim = torch.nn.functional.cosine_similarity(ref, cand, dim=1).item() + else: + sim = -((ref - cand) ** 2).sum().item() + scores.append((idx, sim)) + if not scores: + return random.sample(overlapping_indices, min(top_k, len(overlapping_indices))) + scores.sort(key=lambda x: x[1], reverse=True) + return [idx for idx, _ in scores[:top_k]] + + +def retrieve_context_frames_advanced( + data: Dict, + dataset_base_path: str, + top_k: int = 4, + drop_overlap_probability: float = 0.1, + use_rt_relative: bool = False, + retrieval_method: str = "fov", + latent_retrieval_dir: Optional[str] = None, + strict_overlap_labels: bool = False, +) -> Tuple[List, List, List[int], int, str, str]: + """ + Retrieve context frames with pluggable retrieval method. + Interface matches retrieve_fov_context_frames return: + (context_frames, context_actions, context_indices, cur_idx, video_name, source). + + retrieval_method: + - "fov": use existing FOV/overlap_labels logic (random or RT-scored from overlap). + - "latent_sim": rank overlap candidates by latent similarity to first frame (requires latent_retrieval_dir). + When latent_sim is used but latent_retrieval_dir is missing or latents absent, falls back to FOV behavior. + """ + if retrieval_method == "latent_sim" and not latent_retrieval_dir: + _warn_once( + "latent_sim_missing_dir", + "retrieval_method=latent_sim but latent_retrieval_dir is not set; fallback to FOV retrieval.", + ) + if retrieval_method == "fov" or (retrieval_method == "latent_sim" and not latent_retrieval_dir): + return retrieve_simple_context_frames( + data=data, + dataset_base_path=dataset_base_path, + top_k=top_k, + drop_overlap_probability=drop_overlap_probability, + use_rt_relative=use_rt_relative, + ) + if retrieval_method == "latent_sim" and latent_retrieval_dir and not os.path.isdir(latent_retrieval_dir): + _warn_once( + "latent_sim_bad_dir", + f"latent_retrieval_dir not found: {latent_retrieval_dir}; latent_sim will degrade to random-overlap selection.", + ) + + # latent_sim: we need to inject a custom ranking into the flow. We do a minimal duplicate of the + # overlap selection step then reuse the rest via a wrapper around retrieve_simple_context_frames + # by passing a custom rank function. Since retrieve_simple_context_frames doesn't support that yet, + # we implement a full path here that mirrors it but uses latent_sim_rank for overlap selection. + + video_frames = data.get("video", []) + start_frame = data.get("start_frame", 0) + end_frame = data.get("end_frame", None) + if "frame_idx" in data: + current_frame_idx = data.get("frame_idx") + else: + current_frame_idx = (start_frame + end_frame) // 2 if end_frame is not None else (len(video_frames) // 2 if video_frames else 0) + first_frame_idx = start_frame + video_name = data.get("video_name", "") + if not video_name and "video_path" in data: + video_name = os.path.basename(data["video_path"]).replace(".mp4", "").replace(".avi", "") + elif not video_name and "file_path" in data: + video_name = os.path.basename(data["file_path"]).replace(".mp4", "").replace(".avi", "") + + frames_dir = os.path.join(dataset_base_path, "frames", video_name) + json_file = os.path.join(dataset_base_path, "jsons", f"{video_name}.json") + poses_dict = load_poses_dict(json_file) if os.path.isfile(json_file) else {} + first_frame_pose = poses_dict.get(str(first_frame_idx)) + first_frame_pose_rt = pose_to_rt(first_frame_pose) if first_frame_pose is not None and pose_to_rt else None + + context_frames: List[Image.Image] = [] + context_actions: List = [] + context_indices: List[int] = [] + source = "none" + + def _append_pose(frame_idx: int): + pose = poses_dict.get(str(frame_idx)) + if pose is not None and pose_to_rt and use_rt_relative and first_frame_pose_rt is not None: + rt = pose_to_rt(pose) + if rt is not None and convert_rt_to_relative: + rel = convert_rt_to_relative([rt], first_frame_pose_rt) + context_actions.append(rel[0] if rel else [0.0] * 12) + else: + context_actions.append([0.0] * 12) + elif pose is not None and pose_to_rt: + rt = pose_to_rt(pose) + context_actions.append(rt if rt is not None else [0.0] * 12) + else: + context_actions.append([0.0] * 12) + + if first_frame_pose_rt is not None and use_rt_relative: + context_actions.append([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]) + else: + context_actions.append(first_frame_pose_rt if first_frame_pose_rt is not None else [0.0] * 12) + + if os.path.isdir(frames_dir): + first_path = os.path.join(frames_dir, f"{first_frame_idx:04d}.png") + if os.path.isfile(first_path): + try: + context_frames.append(Image.open(first_path).convert("RGB")) + context_indices.append(first_frame_idx) + except Exception: + pass + if not context_frames and video_frames: + if isinstance(video_frames[0], Image.Image): + context_frames.append(video_frames[0]) + context_indices.append(start_frame) + elif isinstance(video_frames[0], str) and os.path.isfile(video_frames[0]): + try: + context_frames.append(Image.open(video_frames[0]).convert("RGB")) + context_indices.append(start_frame) + except Exception: + pass + + drop_overlap = random.random() < drop_overlap_probability + if drop_overlap: + target_total = top_k + 1 + if strict_overlap_labels and len(context_frames) < target_total: + return [], [], [], current_frame_idx, video_name, "overlap_labels_insufficient" + return context_frames, context_actions, context_indices, current_frame_idx, video_name, "first_frame_only_dropped" + + overlap_labels_dir = os.path.join(dataset_base_path, "overlap_labels") + overlapping_indices = [] + if os.path.isdir(overlap_labels_dir): + overlapping_indices = load_overlap_frames(overlap_labels_dir, video_name, current_frame_idx) + overlapping_indices = [i for i in overlapping_indices if i != first_frame_idx] + + if not overlapping_indices: + source = "first_frame_only" + if strict_overlap_labels and len(context_frames) < top_k + 1: + return [], [], [], current_frame_idx, video_name, "overlap_labels_insufficient" + return context_frames, context_actions, context_indices, current_frame_idx, video_name, source + + sampled_overlap_indices = latent_sim_rank( + video_name, first_frame_idx, overlapping_indices, dataset_base_path, top_k, + latent_dir=latent_retrieval_dir, use_cosine=True, + ) + + def _load_frame(path: str): + if os.path.isfile(path): + try: + return Image.open(path).convert("RGB") + except Exception: + pass + return None + + to_load = [(idx, os.path.join(frames_dir, f"{idx:04d}.png")) for idx in sampled_overlap_indices[:top_k]] + with ThreadPoolExecutor(max_workers=max(1, min(5, len(to_load)))) as ex: + futures = {ex.submit(_load_frame, path): idx for idx, path in to_load} + for fut in futures: + idx = futures[fut] + frame = fut.result() + if frame is not None: + context_frames.append(frame) + context_indices.append(idx) + _append_pose(idx) + + source = "latent_sim" + if strict_overlap_labels and len(context_frames) < top_k + 1: + return [], [], [], current_frame_idx, video_name, "overlap_labels_insufficient" + return context_frames, context_actions, context_indices, current_frame_idx, video_name, source diff --git a/src/model_training/multichunk_sample_utils.py b/src/model_training/multichunk_sample_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..eb0f6da56f17055003b1e9ed6d4ac60fdd31ef2e --- /dev/null +++ b/src/model_training/multichunk_sample_utils.py @@ -0,0 +1,877 @@ +""" +Shared multichunk sampling for training monitor and replay scripts. + +Two-chunk path matches run_replay_loop_two_chunk: chunk1 with 1-frame context, +chunk2 with context_frames_for_next_chunk from chunk1 output. +No hidden state is carried across chunks (each pipe() is independent diffusion), only PIL frames + latents. +""" +from __future__ import annotations + +import argparse +import json +import os +import random +import traceback +from typing import Any, Dict, List, Optional, Sequence, Tuple + +import numpy as np +import torch +from PIL import Image +from diffsynth import save_video +from diffsynth.pipelines.wan_video_new import ModelConfig, WanVideoPipeline +from src.model_training.transformers_compat import patch_transformers_hybrid_cache + +patch_transformers_hybrid_cache() + +from diffsynth.trainers.utils import VideoDataset +from safetensors.torch import load_file as safe_load_file + +from src.model_training.fov_retrieval import load_camera_poses_batch +from src.model_training.fov_retrieval import convert_rt_to_relative, pose_to_rt + + +FrameType = Any + + +def context_frames_for_next_chunk(frames_list: Sequence[FrameType], K: int) -> List[FrameType]: + """Select K context frames from a finished chunk for the next chunk (replay-style). + + Order is [last_frame, ...]: last frame first (adjacent to target), then K-1 uniformly + spaced frames from indices [0 .. n-2]. + + - K==1: [last] + - K>1: [last] + (K-1) uniform samples from [0, n-2] + """ + n = len(frames_list) + if n <= 0 or K <= 0: + return [] + if K == 1: + return [frames_list[-1]] + n_ctx = min(K, n) + if n_ctx == 1: + return [frames_list[-1]] + last = frames_list[-1] + num_rest = n_ctx - 1 + if num_rest <= 0: + return [last] + if num_rest == 1: + return [last, frames_list[0]] + indices = [int(round(i * (n - 2) / (num_rest - 1))) for i in range(num_rest)] + rest = [frames_list[i] for i in indices] + return [last] + rest + + +def replay_context_global_indices(n_frames: int, K: int) -> List[int]: + """Indices into frames_list matching context_frames_for_next_chunk order (for tests/debug).""" + if n_frames <= 0 or K <= 0: + return [] + if K == 1: + return [n_frames - 1] + n_ctx = min(K, n_frames) + if n_ctx == 1: + return [n_frames - 1] + num_rest = n_ctx - 1 + if num_rest == 1: + return [n_frames - 1, 0] + indices = [int(round(i * (n_frames - 2) / (num_rest - 1))) for i in range(num_rest)] + return [n_frames - 1] + indices + + +def replay_context_from_generated_frames( + frames_list: Sequence[FrameType], + n_ctx: int, +) -> List[FrameType]: + """Single replay-style context selection entrypoint used by callsites. + + Keep legacy semantics: + - n_ctx > 0: replay sampling rule (last + uniform historical) + - n_ctx <= 0: fallback to last frame only + """ + n_ctx = int(n_ctx) + if n_ctx > 0: + return context_frames_for_next_chunk(frames_list, n_ctx) + return [frames_list[-1]] + + +def prev_chunk_tail_global_indices(start_frame: int, N: int, *, nearest_first: bool = False) -> Optional[List[int]]: + """Strict consecutive globals with configurable order. + + - nearest_first=False: [start_frame - N, ..., start_frame - 1] (oldest -> newest) + - nearest_first=True: [start_frame - 1, ..., start_frame - N] (newest -> oldest) + None if start_frame < N. + """ + if N <= 0: + return [] + if start_frame < N: + return None + if nearest_first: + return list(range(int(start_frame) - 1, int(start_frame) - N - 1, -1)) + return list(range(int(start_frame) - N, int(start_frame))) + + +def load_prev_chunk_tail_from_disk( + dataset_base_path: str, + video_name: str, + start_frame: int, + N: int, + *, + nearest_first: bool = False, +) -> Tuple[Optional[List[Any]], Optional[List[int]]]: + """Load N frames before start_frame in configured order.""" + + idxs = prev_chunk_tail_global_indices(int(start_frame), int(N), nearest_first=nearest_first) + if idxs is None: + return None, None + if not idxs: + return [], [] + vn = str(video_name) + if vn.endswith((".mp4", ".avi")): + vn = os.path.splitext(vn)[0] + frames_root = os.path.join(dataset_base_path, "frames", vn) + out: List[Any] = [] + for idx in idxs: + path = os.path.join(frames_root, f"{int(idx):04d}.png") + if not os.path.isfile(path): + return None, None + try: + out.append(Image.open(path).convert("RGB")) + except Exception: + return None, None + return out, idxs + + +def synthetic_replay_context_from_segment( + video_frames: Sequence[FrameType], + chunk_frames: int, + K: int, +) -> Optional[List[FrameType]]: + """Use first `chunk_frames` of video_frames as virtual chunk1; context for 'chunk2' via replay rule. + + Requires len(video_frames) >= chunk_frames. Returns None otherwise. + """ + if len(video_frames) < chunk_frames or K <= 0: + return None + chunk1 = list(video_frames[:chunk_frames]) + return context_frames_for_next_chunk(chunk1, K) + + +def replay_context_actions_from_segment_actions( + actions: Sequence[Sequence[float]], + n_frames: int, + K: int, +) -> Optional[List[List[float]]]: + """Align RT/action rows with context_frames_for_next_chunk order (same indices as replay_context_global_indices).""" + idxs = replay_context_global_indices(int(n_frames), int(K)) + if not idxs: + return [] + need_max = max(idxs) + if need_max >= len(actions): + return None + return [list(actions[i]) for i in idxs] + + +def load_prev_chunk_tail_rt_actions( + dataset_base_path: str, + video_name: str, + start_frame: int, + N: int, + *, + use_rt_relative: bool = True, + nearest_first: bool = False, +) -> Tuple[Optional[List[List[float]]], Optional[List[int]]]: + """Load RT poses in configured order, relative to first context frame.""" + idxs = prev_chunk_tail_global_indices(int(start_frame), int(N), nearest_first=nearest_first) + if idxs is None: + return None, None + if not idxs: + return [], [] + + vn = str(video_name) + if vn.endswith((".mp4", ".avi")): + vn = os.path.splitext(vn)[0] + json_file = os.path.join(dataset_base_path, "jsons", f"{vn}.json") + if not os.path.isfile(json_file): + return None, None + poses = load_camera_poses_batch(json_file, idxs) + rt_list = [pose_to_rt(p) if p else None for p in poses] + if not rt_list or any(r is None for r in rt_list): + return None, None + ref_rt = rt_list[0] + if use_rt_relative: + out = convert_rt_to_relative(rt_list, ref_rt) + else: + out = [list(r) for r in rt_list] + return out, idxs + +def encode_context_frames(pipe, pil_list, device, dtype=torch.bfloat16, per_frame: bool = False): + """Encode context frames to latents aligned with training behavior. + + per_frame=False: encode the whole clip once (default training path, temporal downsample). + per_frame=True: encode each frame separately and concat on latent time. + """ + if not pil_list: + return None + if not per_frame: + context_video = pipe.preprocess_video(pil_list).to(device=device) + if context_video.dim() == 5: + context_video = context_video.squeeze(0) + context_latents = pipe.vae.encode([context_video], device=pipe.device, tiled=False, tile_size=None, tile_stride=None) + return context_latents.to(dtype=dtype, device=device) + + encoded = [] + for pil in pil_list: + frame_video = pipe.preprocess_video([pil]).to(device=device) + frame_sq = frame_video.squeeze(0) if frame_video.dim() == 5 else frame_video + if frame_sq.dim() == 3: + frame_sq = frame_sq.unsqueeze(0) + lat_one = pipe.vae.encode([frame_sq], device=pipe.device, tiled=False, tile_size=None, tile_stride=None) + encoded.append(lat_one) + context_latents = torch.cat(encoded, dim=2).to(dtype=dtype, device=device) + return context_latents + + +def _frame_to_pil(f, tw, th): + if hasattr(f, "convert") and hasattr(f, "resize"): + return f.convert("RGB").resize((tw, th)) + if isinstance(f, np.ndarray): + if f.dtype != np.uint8: + f = (f * 255).astype(np.uint8) if f.max() <= 1.0 else f.astype(np.uint8) + return Image.fromarray(f).convert("RGB").resize((tw, th)) + if isinstance(f, torch.Tensor): + fn = f.cpu().numpy() + if len(fn.shape) == 3 and fn.shape[0] == 3: + fn = fn.transpose(1, 2, 0) + fn = (fn * 255).clip(0, 255).astype(np.uint8) if fn.max() <= 1.0 else fn.clip(0, 255).astype(np.uint8) + return Image.fromarray(fn).convert("RGB").resize((tw, th)) + return f + + +def run_one_chunk( + pipe, + prompt: str, + use_negative_prompt: str, + action_path: Optional[str] = None, + *, + cam_pose_actions=None, + context_latents=None, + num_context_frames: int = 1, + context_actions_t=None, + chunk_frames: int = 81, + h: int = 352, + w: int = 640, + seed: int = 0, + sigma_shift: float = 5.0, + num_inference_steps: int = 50, + cfg_scale: float = 5.0, + inference_noise_level: float = 0.0, + omit_context_actions: bool = False, # kept for backward compat, no longer used + context_position: str = "suffix", + log_prefix: str = "[multichunk]", +) -> List[Any]: + """Single chunk generation with explicit context position. VWM-aligned action injection.""" + device = pipe.device + kwargs_common = dict( + prompt=prompt, + negative_prompt=use_negative_prompt, + height=h, + width=w, + num_frames=chunk_frames, + num_inference_steps=num_inference_steps, + seed=seed, + cfg_scale=cfg_scale, + sigma_shift=sigma_shift, + denoising_strength=1.0, + ) + if action_path is not None: + kwargs_common["action_path"] = action_path + elif cam_pose_actions is not None: + kwargs_common["cam_pose_actions"] = cam_pose_actions + + if context_latents is not None: + pipe_kw = dict( + **kwargs_common, + enable_context_memory=True, + context_latents=context_latents, + num_context_frames=num_context_frames, + context_position=context_position, + cfg_target_only=True, + inference_noise_level=inference_noise_level, + ) + if context_actions_t is not None: + pipe_kw["context_actions"] = context_actions_t + with torch.no_grad(): + vid = pipe(**pipe_kw) + else: + with torch.no_grad(): + vid = pipe(**kwargs_common, enable_context_memory=False) + return vid if isinstance(vid, list) else [vid] + + +def _load_actions_tensor_from_json( + action_path: Optional[str], + *, + device: torch.device, + dtype: torch.dtype = torch.float32, +) -> Optional[torch.Tensor]: + if not action_path or not os.path.exists(action_path): + return None + try: + with open(action_path, "r", encoding="utf-8") as f: + data = json.load(f) + seq = data.get("actions", data) + items = sorted( + ((int(k), v) for k, v in seq.items() if str(k).isdigit()), + key=lambda x: x[0], + ) + if not items: + return None + rows = [] + for _, v in items: + if isinstance(v, (list, tuple)) and len(v) >= 12: + rows.append([float(x) for x in v[:12]]) + if not rows: + return None + return torch.tensor(rows, device=device, dtype=dtype) + except Exception: + return None + + +def _tail_context_actions( + src_actions: Optional[torch.Tensor], + num_ctx: int, + *, + device: torch.device, + dtype: torch.dtype = torch.float32, + nearest_first: bool = False, +) -> Optional[torch.Tensor]: + if num_ctx <= 0: + return None + if src_actions is None or src_actions.numel() == 0: + return torch.zeros(num_ctx, 12, device=device, dtype=dtype) + if src_actions.dim() == 3: + src_actions = src_actions[0] + if src_actions.shape[0] >= num_ctx: + out = src_actions[-num_ctx:] + if nearest_first: + out = torch.flip(out, dims=[0]) + return out.to(device=device, dtype=dtype) + pad_n = num_ctx - src_actions.shape[0] + pad = src_actions[-1:, :].expand(pad_n, src_actions.shape[1]) + out = torch.cat([src_actions, pad], dim=0) + if nearest_first: + out = torch.flip(out, dims=[0]) + return out.to(device=device, dtype=dtype) + + +def sync_pipe_memory_from_training_module(pipe, unwrapped_model: Any) -> Dict[str, Any]: + """Copy memory-related flags from WanTrainingModule.pipe onto pipe (defensive if pipe handle diverges).""" + log: Dict[str, Any] = {} + p = pipe + m = unwrapped_model + src = getattr(m, "pipe", None) or p + + def _g(attr, default=None): + v = getattr(src, attr, None) + if v is None: + v = getattr(p, attr, None) + if v is None: + v = getattr(m, attr, default) + return v + + p.use_framepack_memory = bool(_g("use_framepack_memory", False)) + p.context_temporal_decay = float(_g("context_temporal_decay", 1.0) or 1.0) + p.context_attention_weight = float(_g("context_attention_weight", 1.0) or 1.0) + p.use_framepack_length_compress = bool(_g("use_framepack_length_compress", False)) + p.framepack_ratio = int(_g("framepack_ratio", 1) or 1) + p.framepack_length_strategy = str(_g("framepack_length_strategy", "distance_merge") or "distance_merge") + p.framepack_recent_keep_ratio = float(_g("framepack_recent_keep_ratio", 0.5) or 0.5) + p.framepack_multiscale_w2 = float(_g("framepack_multiscale_w2", 0.25) or 0.25) + p.framepack_multiscale_w4 = float(_g("framepack_multiscale_w4", 0.15) or 0.15) + p.use_spatial_memory = bool(_g("use_spatial_memory", False)) + p.spatial_memory_tokens = int(_g("spatial_memory_tokens", 64) or 64) + p.use_spatial_memory_legacy = bool(_g("use_spatial_memory_legacy", False)) + p.spatial_memory_inject_mode = str(_g("spatial_memory_inject_mode", "concat_text") or "concat_text") + sm = getattr(m, "spatial_memory_module", None) or getattr(src, "spatial_memory_module", None) or getattr(p, "spatial_memory_module", None) + p.spatial_memory_module = sm + srm = getattr(m, "spatial_memory_readout_module", None) or getattr(src, "spatial_memory_readout_module", None) or getattr(p, "spatial_memory_readout_module", None) + p.spatial_memory_readout_module = srm + dit = getattr(p, "dit", None) + bl0 = dit.blocks[0] if dit is not None and hasattr(dit, "blocks") and len(dit.blocks) > 0 else None + log.update( + { + "use_framepack_memory": p.use_framepack_memory, + "use_framepack_length_compress": p.use_framepack_length_compress, + "framepack_ratio": p.framepack_ratio, + "framepack_length_strategy": p.framepack_length_strategy, + "use_spatial_memory": p.use_spatial_memory, + "use_spatial_memory_legacy": p.use_spatial_memory_legacy, + "spatial_memory_inject_mode": p.spatial_memory_inject_mode, + "spatial_module": sm is not None, + "spatial_readout_module": srm is not None, + "dit_block0_use_block_wise_ssm": bool(getattr(bl0, "use_block_wise_ssm", False)), + "dit_block0_use_videossm_hybrid": bool(getattr(bl0, "use_videossm_hybrid", False)), + } + ) + return log + + +def run_two_chunk_memory_monitor( + pipe, + *, + prompt: str, + negative_prompt: str, + action_path: Optional[str], + chunk0_action_path: Optional[str] = None, + chunk1_action_path: Optional[str] = None, + first_frame_pil, + context_memory_frames: int, + chunk_frames: int = 81, + h: int = 352, + w: int = 640, + seed: int = 42, + sigma_shift: float = 5.0, + num_inference_steps: int = 50, + cfg_scale: float = 5.0, + inference_noise_level: float = 0.0, + omit_context_actions: bool = False, + context_source: str = "replay", + context_position: str = "suffix", + context_per_frame_vae: bool = False, + device=None, + dtype=torch.bfloat16, + log_prefix: str = "[two_chunk_mem]", +) -> Tuple[List[Any], List[Any], Dict[str, Any]]: + """ + Chunk1: 1-frame context. Chunk2 context follows context_source: + - replay: context_frames_for_next_chunk + - prev_chunk_tail: strict tail frames (nearest-first) + + Returns (frames_ch0, frames_ch1, meta). chunk0 defaults left_45 and chunk1 defaults right_45 when provided by caller. + """ + device = device or pipe.device + context_source = (context_source or "replay").strip().lower() + if context_source not in ("replay", "prev_chunk_tail"): + context_source = "replay" + context_position = (context_position or "suffix").strip().lower() + if context_position not in ("prefix", "suffix"): + context_position = "suffix" + meta: Dict[str, Any] = { + "n_ctx": int(context_memory_frames), + "chunk_frames": chunk_frames, + "context_source": context_source, + "context_position": context_position, + "context_per_frame_vae": bool(context_per_frame_vae), + } + + ff = first_frame_pil + if isinstance(ff, Image.Image): + ff = ff.convert("RGB").resize((w, h), Image.Resampling.LANCZOS) + else: + ff = _frame_to_pil(ff, w, h) + + ctx_lat_0 = encode_context_frames(pipe, [ff], device, dtype=dtype, per_frame=bool(context_per_frame_vae)) + num_ctx0 = int(ctx_lat_0.shape[2]) if ctx_lat_0 is not None else 1 + meta["chunk0_num_context_latent"] = num_ctx0 + + use_omit_ch0 = omit_context_actions or (num_ctx0 <= 1) + act0 = chunk0_action_path or action_path + act1 = chunk1_action_path or action_path + src_actions0 = _load_actions_tensor_from_json(act0, device=device, dtype=torch.float32) + meta["chunk0_action_path"] = act0 + meta["chunk1_action_path"] = act1 + + frames_ch0 = run_one_chunk( + pipe, + prompt, + negative_prompt, + act0, + context_latents=ctx_lat_0, + num_context_frames=num_ctx0, + context_actions_t=None, + chunk_frames=chunk_frames, + h=h, + w=w, + seed=seed, + sigma_shift=sigma_shift, + num_inference_steps=num_inference_steps, + cfg_scale=cfg_scale, + inference_noise_level=inference_noise_level, + omit_context_actions=use_omit_ch0, + context_position=context_position, + log_prefix=log_prefix + " ch0", + ) + + pil_ch0 = [_frame_to_pil(f, w, h) for f in frames_ch0] + n_ctx = int(context_memory_frames) + if n_ctx <= 0: + n_ctx = 1 + if context_source == "prev_chunk_tail": + tail = pil_ch0[-n_ctx:] + prev_pil = list(reversed(tail)) if context_position == "suffix" else tail + else: + prev_pil = context_frames_for_next_chunk(pil_ch0, n_ctx) + meta["chunk1_context_count"] = len(prev_pil) + + ctx_lat_1 = encode_context_frames(pipe, prev_pil, device, dtype=dtype, per_frame=bool(context_per_frame_vae)) + num_ctx1 = int(ctx_lat_1.shape[2]) if ctx_lat_1 is not None else len(prev_pil) + meta["chunk1_num_context_latent"] = num_ctx1 + + # Align with training: when context has only 1 latent frame, context actions are omitted. + # train.py sets omit_context_actions=True when context_memory_frames == 1. + use_omit_ch1 = omit_context_actions or (num_ctx1 <= 1) + ca1 = None + if not use_omit_ch1 and num_ctx1 > 0: + ca1 = _tail_context_actions( + src_actions0, + num_ctx1, + device=device, + dtype=torch.float32, + nearest_first=(context_source == "prev_chunk_tail" and context_position == "suffix"), + ) + meta["chunk1_context_actions_count"] = int(ca1.shape[0]) if ca1 is not None else 0 + + frames_ch1 = run_one_chunk( + pipe, + prompt, + negative_prompt, + act1, + context_latents=ctx_lat_1, + num_context_frames=num_ctx1, + context_actions_t=ca1, + chunk_frames=chunk_frames, + h=h, + w=w, + seed=seed + 1, + sigma_shift=sigma_shift, + num_inference_steps=num_inference_steps, + cfg_scale=cfg_scale, + inference_noise_level=inference_noise_level, + omit_context_actions=use_omit_ch1, + context_position=context_position, + log_prefix=log_prefix + " ch1", + ) + + meta["note"] = "No cross-chunk SSM/RNN state; only frame-conditioned second chunk (same as replay eval)." + return frames_ch0, frames_ch1, meta + +def load_model(checkpoint_path, model_paths, lora_path=None, lora_alpha=1.0, device="cuda"): + """Load model from checkpoint""" + print(f"Loading model from checkpoint: {checkpoint_path}") + + # Load base pipeline + pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device=device, + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], + ) + + # Load LoRA if specified + if lora_path and os.path.exists(lora_path): + print(f"Loading LoRA from: {lora_path}") + pipe.load_lora(pipe.dit, lora_path, alpha=lora_alpha) + + # Load checkpoint if specified + if checkpoint_path and os.path.exists(checkpoint_path): + print(f"Loading checkpoint from: {checkpoint_path}") + checkpoint = safe_load_file(checkpoint_path) + pipe.dit.load_state_dict(checkpoint, strict=False) + + pipe.enable_vram_management() + pipe.eval() + + return pipe + + +def sample_prompts_from_dataset(dataset, num_prompts=5): + """Randomly sample prompts from dataset""" + prompts = [] + dataset_size = len(dataset) + + if dataset_size == 0: + print("Warning: Dataset is empty, using default prompts") + return ["A cyberpunk city game scene, a character walking through neon-lit streets"] * num_prompts + + # Sample random indices + indices = random.sample(range(dataset_size), min(num_prompts, dataset_size)) + + print(f"Sampling {len(indices)} prompts from dataset (size: {dataset_size})...") + for idx in indices: + try: + sample = dataset[idx] + if isinstance(sample, dict): + prompt = sample.get("description") or sample.get("prompt") or sample.get("text", "") + if prompt: + prompts.append(prompt) + else: + print(f"Warning: Sample {idx} has no prompt field, skipping") + else: + print(f"Warning: Sample {idx} is not a dict, skipping") + except Exception as e: + print(f"Warning: Failed to load sample {idx}: {e}, skipping") + + # Fill with default if not enough prompts + while len(prompts) < num_prompts: + prompts.append("A cyberpunk city game scene, a character walking through neon-lit streets") + + return prompts[:num_prompts] + + +def encode_frames_to_latents(pipe, frames): + """Encode frames to latents using VAE""" + pipe.load_models_to_device(["vae"]) + vae = pipe.vae + + latents_list = [] + for frame in frames: + vid = pipe.preprocess_video([frame]).squeeze(0) + with torch.no_grad(): + lat = vae.encode([vid], device=pipe.device)[0].unsqueeze(0) + latents_list.append(lat) + + if latents_list: + return torch.cat(latents_list, dim=2) + return None + + +def generate_long_video( + pipe, + prompt, + negative_prompt="oversaturated colors, overexposed, static, blurry details", + output_dir="./long_video_output", + video_name="long_video", + context_memory_frames=4, + frames_per_segment=81, + target_frames=450, # 30 seconds at 15fps + height=352, + width=640, + num_inference_steps=20, + cfg_scale=5.0, + timestep_shift=1.0, + seed=42, + fps=15, +): + """ + Generate long video using iterative context-based generation + + Args: + pipe: WanVideoPipeline instance + prompt: Text prompt for generation + negative_prompt: Negative prompt + output_dir: Output directory for videos + video_name: Base name for output video + context_memory_frames: Number of context frames to use (K) + frames_per_segment: Frames to generate per segment (default: 81) + target_frames: Target total frames (default: 450 for 30s at 15fps) + height: Video height + width: Video width + num_inference_steps: Number of inference steps + cfg_scale: CFG scale + timestep_shift: Timestep shift + seed: Random seed + fps: FPS for output video + """ + os.makedirs(output_dir, exist_ok=True) + + # Set environment variable for concatenation inference + os.environ["USE_CONCATENATION_INFERENCE"] = "true" + + all_frames = [] + current_context_latents = None + current_context_frames = [] + + # Calculate number of segments needed + num_segments = (target_frames + frames_per_segment - 1) // frames_per_segment + + print(f"Generating long video: {target_frames} frames in {num_segments} segments") + print(f" - Frames per segment: {frames_per_segment}") + print(f" - Context frames: {context_memory_frames}") + print(f" - Prompt: {prompt[:100]}...") + + torch.manual_seed(seed) + + for segment_idx in range(num_segments): + # Calculate frames to generate for this segment + remaining_frames = target_frames - len(all_frames) + frames_to_generate = min(frames_per_segment, remaining_frames) + + if frames_to_generate <= 0: + break + + print(f"\n[{segment_idx + 1}/{num_segments}] Generating {frames_to_generate} frames...") + + # Prepare sampling kwargs + # First segment: no context (generate from scratch) + # Subsequent segments: use context from previous segment + has_context = current_context_latents is not None and segment_idx > 0 + + sampling_kwargs = { + "prompt": prompt, + "negative_prompt": negative_prompt, + "height": height, + "width": width, + "num_frames": frames_to_generate, + "num_inference_steps": num_inference_steps, + "seed": seed + segment_idx, # Different seed for each segment + "cfg_scale": cfg_scale, + "sigma_shift": timestep_shift, + "denoising_strength": 1.0, + } + + # Add context memory only if we have context + if has_context: + sampling_kwargs["enable_context_memory"] = True + sampling_kwargs["context_latents"] = current_context_latents + sampling_kwargs["num_context_frames"] = len(current_context_frames) + + try: + # Generate frames + if has_context: + print(f" Using {len(current_context_frames)} context frames from previous segment...") + + generated_frames = pipe(**sampling_kwargs) + + if isinstance(generated_frames, list): + segment_frames = generated_frames + else: + segment_frames = [generated_frames] if hasattr(generated_frames, '__iter__') else [generated_frames] + + # Add to all frames + all_frames.extend(segment_frames) + + # Update context: use last K frames from generated segment + # These will be used as context for the next segment + if len(segment_frames) >= context_memory_frames: + context_frames = segment_frames[-context_memory_frames:] + current_context_frames = context_frames + + # Encode context frames to latents + print(f" Encoding last {context_memory_frames} frames as context for next segment...") + current_context_latents = encode_frames_to_latents(pipe, context_frames) + else: + # If not enough frames, use all frames as context + current_context_frames = segment_frames + current_context_latents = encode_frames_to_latents(pipe, segment_frames) + + print(f" Generated {len(segment_frames)} frames (total: {len(all_frames)}/{target_frames})") + + except Exception as e: + print(f" Error generating segment {segment_idx + 1}: {e}") + traceback.print_exc() + break + + # Save final video + if len(all_frames) > 0: + output_path = os.path.join(output_dir, f"{video_name}.mp4") + print(f"\nSaving video to: {output_path}") + print(f" Total frames: {len(all_frames)}") + print(f" Duration: {len(all_frames) / fps:.2f} seconds") + + save_video(all_frames, output_path, fps=fps, quality=5) + print(f"Video saved: {output_path}") + + # Save prompt + prompt_path = os.path.join(output_dir, f"{video_name}_prompt.txt") + with open(prompt_path, 'w', encoding='utf-8') as f: + f.write(prompt) + + return output_path + else: + print("Error: No frames generated") + return None + + +def main(): + parser = argparse.ArgumentParser(description="Generate long videos using iterative context-based generation") + + # Model paths + parser.add_argument("--checkpoint_path", type=str, default=None, help="Path to model checkpoint") + parser.add_argument("--lora_path", type=str, default=None, help="Path to LoRA weights") + parser.add_argument("--lora_alpha", type=float, default=1.0, help="LoRA alpha") + parser.add_argument("--model_paths", type=str, default=None, help="JSON string of model paths (not used if checkpoint_path is set)") + + # Dataset + parser.add_argument("--dataset_base_path", type=str, required=True, help="Base path to dataset") + parser.add_argument("--dataset_metadata_path", type=str, required=True, help="Path to dataset metadata CSV") + parser.add_argument("--num_prompts", type=int, default=5, help="Number of prompts to sample from dataset") + + # Generation parameters + parser.add_argument("--output_dir", type=str, default="./long_video_output", help="Output directory") + parser.add_argument("--context_memory_frames", type=int, default=4, help="Number of context frames (K)") + parser.add_argument("--frames_per_segment", type=int, default=81, help="Frames per segment (default: 81)") + parser.add_argument("--target_frames", type=int, default=450, help="Target total frames (30s at 15fps)") + parser.add_argument("--height", type=int, default=352, help="Video height") + parser.add_argument("--width", type=int, default=640, help="Video width") + parser.add_argument("--num_inference_steps", type=int, default=20, help="Number of inference steps") + parser.add_argument("--cfg_scale", type=float, default=5.0, help="CFG scale") + parser.add_argument("--timestep_shift", type=float, default=1.0, help="Timestep shift") + parser.add_argument("--seed", type=int, default=42, help="Random seed") + parser.add_argument("--fps", type=int, default=15, help="FPS for output video") + parser.add_argument("--device", type=str, default="cuda", help="Device (cuda/cpu)") + + args = parser.parse_args() + + # Load dataset for prompt sampling + print("Loading dataset...") + dataset_args = wan_parser.parse_args([]) # Create minimal args + dataset_args.dataset_base_path = args.dataset_base_path + dataset_args.dataset_metadata_path = args.dataset_metadata_path + dataset_args.height = args.height + dataset_args.width = args.width + + dataset = VideoDataset(args=dataset_args) + print(f"Dataset loaded: {len(dataset)} samples") + + # Sample prompts + prompts = sample_prompts_from_dataset(dataset, args.num_prompts) + print(f"Sampled {len(prompts)} prompts") + + # Load model + model_paths = None + if args.model_paths: + model_paths = json.loads(args.model_paths) + + pipe = load_model( + checkpoint_path=args.checkpoint_path, + model_paths=model_paths, + lora_path=args.lora_path, + lora_alpha=args.lora_alpha, + device=args.device, + ) + + # Generate videos for each prompt + output_paths = [] + for idx, prompt in enumerate(prompts): + print(f"\n{'='*80}") + print(f"Generating video {idx + 1}/{len(prompts)}") + print(f"{'='*80}") + + video_name = f"long_video_{idx + 1:03d}" + + output_path = generate_long_video( + pipe=pipe, + prompt=prompt, + output_dir=args.output_dir, + video_name=video_name, + context_memory_frames=args.context_memory_frames, + frames_per_segment=args.frames_per_segment, + target_frames=args.target_frames, + height=args.height, + width=args.width, + num_inference_steps=args.num_inference_steps, + cfg_scale=args.cfg_scale, + timestep_shift=args.timestep_shift, + seed=args.seed + idx, # Different seed for each video + fps=args.fps, + ) + + if output_path: + output_paths.append(output_path) + + print(f"\n{'='*80}") + print(f"Generation completed: {len(output_paths)} videos generated") + print(f"Output directory: {args.output_dir}") + print(f"{'='*80}") diff --git a/src/model_training/train.py b/src/model_training/train.py new file mode 100644 index 0000000000000000000000000000000000000000..a376ea8d8ccc72fe1471fd40152310f68f2235f0 --- /dev/null +++ b/src/model_training/train.py @@ -0,0 +1,453 @@ +import os, sys, re +import torch +import torch.nn as nn +import logging + +logger = logging.getLogger(__name__) + +_rank_env = os.environ.get("RANK") or os.environ.get("LOCAL_RANK") or os.environ.get("ACCELERATE_PROCESS_INDEX") or "0" +_rank = int(str(_rank_env)) +_level = logging.INFO if _rank == 0 else logging.WARNING +logging.basicConfig( + level=_level, + format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + force=True, +) +logger.setLevel(_level) + +current_file_abs = os.path.abspath(__file__) +project_root = os.path.dirname(os.path.dirname(os.path.dirname(current_file_abs))) + +if project_root not in sys.path: + sys.path.insert(0, project_root) + +from src.model_training.transformers_compat import patch_transformers_hybrid_cache + +patch_transformers_hybrid_cache() + +from diffsynth.trainers.utils import VideoDataset, CamVideoDataset, wan_parser + +import diffsynth.trainers.utils as utils_module + +utils_file = utils_module.__file__ +if 'site-packages' in utils_file: + logger.warning(f"Using INSTALLED diffsynth package from: {utils_file}") +else: + logger.info(f"[VERIFIED] Using LOCAL diffsynth code from: {utils_file}") + +import random +import numpy as np +os.environ["TOKENIZERS_PARALLELISM"] = "false" +from safetensors.torch import load_file as safe_load_file +from src.model_training.fov_retrieval import setup_fov_retriever_for_training +from src.model_training.training_modules import DiTBlock_w_Action, WanTrainingModule + + +def set_seed(seed=42): + """Set random seeds for reproducible training.""" + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + os.environ['PYTHONHASHSEED'] = str(seed) + os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8' + logger.info(f"Random seed set to {seed}") + + +def _log_dit_freeze_summary(dit: torch.nn.Module) -> None: + by_module: dict[str, tuple[int, bool]] = {} + for name, p in dit.named_parameters(): + numel = p.numel() + trainable = p.requires_grad + parts = name.split(".") + prefix = ".".join(parts[:-1]) if len(parts) > 1 else name + if prefix not in by_module: + by_module[prefix] = (0, False) + prev_numel, prev_trainable = by_module[prefix] + by_module[prefix] = (prev_numel + numel, prev_trainable or trainable) + trainable_list = [(k, v[0]) for k, v in by_module.items() if v[1]] + frozen_list = [(k, v[0]) for k, v in by_module.items() if not v[1]] + trainable_list.sort(key=lambda x: x[0]) + frozen_list.sort(key=lambda x: x[0]) + total_trainable = sum(n for _, n in trainable_list) + total_frozen = sum(n for _, n in frozen_list) + examples = ", ".join(name for name, _ in trainable_list[:8]) + logger.info( + f"[DiT freeze] trainable={total_trainable:,} ({len(trainable_list)} groups), " + f"frozen={total_frozen:,} ({len(frozen_list)} groups), examples=[{examples}]" + ) + + +set_seed(42) + + +from src.model_training.training_modules.model_logger import ModelLogger +from src.model_training.training_modules.training_loop import launch_training_task + + +if __name__ == "__main__": + parser = wan_parser() + def _add_arg_if_missing(*args, **kwargs): + if args and args[0] in parser._option_string_actions: + return + parser.add_argument(*args, **kwargs) + + for name, kwargs in [ + ("--tokenizer_path", dict(type=str, default=None, help="Local tokenizer path.")), + ("--wandb_run_name", dict(type=str, default=None)), + ("--ckpt_interval", dict(type=int, default=None)), + ("--trainable_dit_modules", dict(type=str, default=None, help="Comma-separated DiT modules to unfreeze.")), + ("--num_workers", dict(type=int, default=0, help="DataLoader workers.")), + ("--max_train_steps", dict(type=int, default=0, help="Stop after N optimizer steps.")), + ("--progress_total_steps", dict(type=int, default=0, help="tqdm total steps override.")), + ("--resume_from_checkpoint", dict(type=str, default=None)), + ("--context_memory_frames", dict(type=int, default=8)), + ("--training_mode", dict(type=str, default="predict", choices=["predict", "context", "condition"])), + ("--context_drop_prob", dict(type=float, default=0.0)), + ("--retrieval_method", dict(type=str, default="fov", choices=["fov", "latent_sim"])), + ("--latent_retrieval_dir", dict(type=str, default=None)), + ("--fov_top_k", dict(type=int, default=4)), + ("--context_attention_weight", dict(type=float, default=1.0)), + ("--context_temporal_decay", dict(type=float, default=1.0)), + ("--spike_threshold", dict(type=float, default=5.0)), + ("--spatial_memory_tokens", dict(type=int, default=64)), + ("--spatial_memory_grid", dict(type=int, default=8)), + ("--spatial_memory_inject_mode", dict(type=str, default="concat_text", choices=["concat_text", "none", "cross_attn_readout"])), + ("--framepack_ratio", dict(type=int, default=2)), + ("--framepack_length_strategy", dict(type=str, default="distance_merge", choices=["distance_merge", "mean", "uniform", "recent_weighted", "weighted_recent", "packed_multiscale"])), + ("--framepack_recent_keep_ratio", dict(type=float, default=0.5)), + ("--framepack_multiscale_w2", dict(type=float, default=0.25)), + ("--framepack_multiscale_w4", dict(type=float, default=0.15)), + ("--context_source", dict(type=str, default="fov", choices=["fov", "replay", "prev_chunk_tail"])), + ("--ssm_num_blocks_hint", dict(type=int, default=21)), + ("--ssm_every_n_blocks", dict(type=int, default=4)), + ("--videossm_kernel_size", dict(type=int, default=3)), + ("--videossm_expand", dict(type=int, default=2)), + ("--videossm_every_n_blocks", dict(type=int, default=4)), + ("--sampling_interval_steps", dict(type=int, default=0)), + ("--sampling_negative_prompt", dict(type=str, default="oversaturated colors, overexposed, static, blurry details")), + ("--sampling_height", dict(type=int, default=352)), + ("--sampling_width", dict(type=int, default=640)), + ("--sampling_num_frames", dict(type=int, default=81)), + ("--sampling_num_inference_steps", dict(type=int, default=50)), + ("--sampling_action_path", dict(type=str, default=None)), + ("--sampling_two_chunk_action_path", dict(type=str, default=None)), + ("--sampling_eval_dataset_base", dict(type=str, default=None)), + ("--sampling_eval_metadata_path", dict(type=str, default=None)), + ("--samples_per_epoch", dict(type=int, default=0)), + ("--camera_encoder_scale", dict(type=float, default=1.0)), + ("--camera_inject_mode", dict(type=str, default="post", choices=["post", "pre_norm", "pre_qkv", "pre_qkv_post", "pre_modulate", "pre_qkv_gated"])), + ]: + _add_arg_if_missing(name, **kwargs) + + for name in [ + "--save_full_model", "--add_action_attn", "--action_use_temporal_attention", + "--action_inject_after_spatial_attn", "--use_camera_encoder", "--camera_encoder_shallow", + "--camera_encoder_separate_t_r", "--camera_encoder_explicit_yaw", "--yaw_flip_aug", + "--camera_encoder_sincos_yaw", "--camera_encoder_r_mlp_no_layernorm", + "--add_camera_outside_gate", "--no_camera_encoder_zero_init", + "--camera_encoder_full_zero_init", "--enable_context_memory", "--context_per_frame_vae", + "--cfg_target_only", "--enable_fov_retrieval", "--use_rt_relative", + "--strict_overlap_context", "--use_anchor_frame", "--use_spatial_memory", + "--use_spatial_memory_legacy", "--use_framepack_memory", "--use_framepack_length_compress", + "--use_block_wise_ssm", "--use_videossm_hybrid", "--sampling_two_chunk_memory", + ]: + _add_arg_if_missing(name, action="store_true") + + for name, kwargs in [ + ("--per_device_train_batch_size", dict(type=int, default=None)), + ("--timestep_shift", dict(type=float, default=1.0)), + ("--action_base_path", dict(type=str, default=None)), + ("--ckpt_path", dict(type=str, default=None)), + ("--cam_position_scale", dict(type=float, default=0.01)), + ("--resume_from", dict(type=str, default=None)), + ("--verify_ckpt_step", dict(type=int, default=0)), + ("--verify_high_noise_first_steps", dict(type=int, default=0)), + ("--moc_temperature", dict(type=float, default=1.0)), + ("--moc_top_k", dict(type=int, default=0)), + ("--prev_chunk_frames", dict(type=int, default=81)), + ("--implicit_type", dict(type=str, default="summary")), + ("--context_compressor_ratio", dict(type=int, default=2)), + ("--episodic_buffer_size", dict(type=int, default=0)), + ("--episodic_replay_interval", dict(type=int, default=0)), + ("--episodic_replay_weight", dict(type=float, default=0.0)), + ]: + _add_arg_if_missing(name, **kwargs) + for name in [ + "--enable_video_sampling", "--sampling_atomic_left_right", "--sampling_four_prompts", + "--sampling_two_prompts", "--train_action_module", "--train_cam_pose", + "--action_module_only", "--use_moc", "--unified_implicit", "--use_implicit_memory", + "--use_memory_v2v_compressor", "--use_slow_fast_memory", "--use_entity_memory", + "--use_episodic_memory", + ]: + _add_arg_if_missing(name, action="store_true") + args = parser.parse_args() + def _arg(name, default=None): + return getattr(args, name, default) + + def _normalize_and_validate_args(): + # Backward-compat mappings + if _arg("per_device_train_batch_size", None) is None: + args.per_device_train_batch_size = int(_arg("batch_size", 1)) + if _arg("sampling_atomic_left_right", False) and not _arg("sampling_two_chunk_memory", False): + # Legacy monitor intent maps to current two-chunk monitor. + args.sampling_two_chunk_memory = True + if _arg("enable_video_sampling", False) and int(_arg("sampling_interval_steps", 0)) <= 0: + args.sampling_interval_steps = 1000 + + # Keep paper-style block-wise SSM and legacy VideoSSM hybrid explicitly separated. + if _arg("use_block_wise_ssm", False) and _arg("use_videossm_hybrid", False): + raise ValueError( + "--use_block_wise_ssm and --use_videossm_hybrid are mutually exclusive; " + "use block-wise SSM for paper-aligned runs or VideoSSM hybrid for legacy baselines." + ) + + # Explicit retrieval strategy visibility: default fov, latent_sim degrades to fov when cache dir is absent. + if _arg("retrieval_method", "fov") == "latent_sim": + if not _arg("latent_retrieval_dir", None): + logger.warning("retrieval_method=latent_sim but latent_retrieval_dir is empty; runtime will fallback to fov retrieval.") + else: + logger.info(f"retrieval_method=latent_sim latent_retrieval_dir={args.latent_retrieval_dir}") + else: + logger.info("retrieval_method=fov") + + # 2-chunk sampling defaults: keep left/right_45 semantics compatible with existing shell wrappers. + if _arg("sampling_two_chunk_action_path", None) in (None, ""): + args.sampling_two_chunk_action_path = _arg("sampling_action_path", None) + + _normalize_and_validate_args() + + resume_step_count = 0 + if args.resume_from_checkpoint is not None: + if (_arg('trainable_dit_modules', None) or "").strip() or _arg('resume_weights_only', False): + logger.info("resume_from_checkpoint used for weights only (trainable_dit_modules set or resume_weights_only), step count starts from 0, no skip data") + resume_step_count = 0 + else: + checkpoint_filename = os.path.basename(args.resume_from_checkpoint) + step_match = re.search(r'Step-(\d+)', checkpoint_filename) + epoch_match = re.search(r'epoch-(\d+)', checkpoint_filename) + if step_match: + resume_step_count = int(step_match.group(1)) + logger.info(f"Resuming from step {resume_step_count} (extracted from checkpoint filename)") + elif epoch_match: + logger.info(f"Resuming from epoch checkpoint (epoch-{epoch_match.group(1)}), step count will start from 0") + resume_step_count = 0 + else: + logger.warning("Could not extract step count from checkpoint filename, starting from step 0") + + set_seed(42) + + args.enable_icl = False + args.icl_num_examples = 2 + args.icl_context_frames = 8 + + if _arg('train_cam_pose', False): + dataset = CamVideoDataset(args=args) + else: + dataset = VideoDataset(args=args, action_base_path=args.action_base_path) + + def _log_dataset_validation(ds): + ds_size = len(ds) + ds_repeat = _arg('dataset_repeat', 1) + logger.info( + f"[Dataset] size={ds_size}, repeat={ds_repeat}, " + f"epochs={args.num_epochs}, total_samples={ds_size * ds_repeat * args.num_epochs}" + ) + + _log_dataset_validation(dataset) + + model = WanTrainingModule( + model_paths=args.model_paths, + model_id_with_origin_paths=args.model_id_with_origin_paths, + tokenizer_path=_arg('tokenizer_path', None), + trainable_models=_arg('trainable_models', None), + lora_base_model=args.lora_base_model, + lora_target_modules=args.lora_target_modules, + lora_rank=args.lora_rank, + use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload, + extra_inputs=args.extra_inputs, + resume_from_checkpoint=args.resume_from_checkpoint, + dataset_base_path=_arg('dataset_base_path', None), + enable_context_memory=_arg('enable_context_memory', False), + context_drop_prob=_arg('context_drop_prob', 0.0), + context_drop_seed=42, + omit_context_actions=_arg('omit_context_actions', False) or (_arg('context_memory_frames', 8) == 1), # ctx=1: no context action injection + context_noise_prob=_arg('context_noise_prob', 0.0), + context_noise_std=_arg('context_noise_std', 0.02), + context_fixed_noise_std=_arg('context_fixed_noise_std', None), + context_memory_frames=_arg('context_memory_frames', 8), + context_per_frame_vae=_arg('context_per_frame_vae', False), + training_mode=_arg('training_mode', 'predict'), + teacher_forcing_prob=_arg('teacher_forcing_prob', 0.0), + yaw_flip_aug=_arg('yaw_flip_aug', False), + context_source=_arg('context_source', 'fov'), + use_framepack_memory=_arg('use_framepack_memory', False), + context_temporal_decay=_arg('context_temporal_decay', 1.0), + context_attention_weight=_arg('context_attention_weight', 1.0), + use_framepack_length_compress=_arg('use_framepack_length_compress', False), + framepack_ratio=_arg('framepack_ratio', 2), + framepack_length_strategy=_arg('framepack_length_strategy', 'distance_merge'), + framepack_recent_keep_ratio=_arg('framepack_recent_keep_ratio', 0.5), + framepack_multiscale_w2=_arg('framepack_multiscale_w2', 0.25), + framepack_multiscale_w4=_arg('framepack_multiscale_w4', 0.15), + use_spatial_memory=_arg('use_spatial_memory', False), + use_spatial_memory_legacy=_arg('use_spatial_memory_legacy', False), + spatial_memory_tokens=_arg('spatial_memory_tokens', 64), + spatial_memory_grid=_arg('spatial_memory_grid', 8), + spatial_memory_inject_mode=_arg('spatial_memory_inject_mode', 'concat_text'), + timestep_shift=float(_arg('timestep_shift', 1.0)), + ) + + # ── VWM-style: Replace DiT blocks with DiTBlock_w_Action ── + _use_cam_pose = bool(_arg('train_cam_pose', False)) + if _arg('train_action_module', False) or _use_cam_pose: + dit = model.pipe.dit + old_blocks = dit.blocks + has_image_input = dit.has_image_input + dim = dit.dim + num_heads = dit.num_heads + ffn_dim = dit.ffn_dim + eps = 1e-6 + + block_dtype = next(old_blocks[0].parameters()).dtype + + use_block_wise_ssm = bool(_arg('use_block_wise_ssm', False)) + use_videossm_hybrid = bool(_arg('use_videossm_hybrid', False)) + ssm_every_n = max(int(_arg('ssm_every_n_blocks', 4)), 1) + videossm_every_n = max(int(_arg('videossm_every_n_blocks', 4)), 1) + + new_blocks = nn.ModuleList() + for block_id, old_block in enumerate(old_blocks): + attach_block_ssm = use_block_wise_ssm and (block_id % ssm_every_n == 0) + attach_videossm = use_videossm_hybrid and (block_id % videossm_every_n == 0) + new_block = DiTBlock_w_Action( + has_image_input=has_image_input, + dim=dim, num_heads=num_heads, ffn_dim=ffn_dim, eps=eps, + add_action_attn=_arg('add_action_attn', False), + action_use_temporal_attention=_arg('action_use_temporal_attention', False), + use_cam_pose=_use_cam_pose, + use_block_wise_ssm=attach_block_ssm, + use_videossm_hybrid=attach_videossm, + videossm_kernel_size=int(_arg('videossm_kernel_size', 3)), + videossm_expand=int(_arg('videossm_expand', 2)), + ) + new_block = new_block.to(dtype=block_dtype, device=next(old_block.parameters()).device) + for attr in ("self_attn", "cross_attn", "norm1", "norm2", "norm3", "ffn"): + getattr(new_block, attr).load_state_dict(getattr(old_block, attr).state_dict()) + with torch.no_grad(): + new_block.modulation.copy_(old_block.modulation.to(dtype=block_dtype)) + new_blocks.append(new_block) + + dit.blocks = new_blocks + _mlp_type = "MLP_CamPose" if _use_cam_pose else "MLP_Action" + logger.info(f"[VWM-style] Replaced {len(new_blocks)} DiT blocks with DiTBlock_w_Action ({_mlp_type}, zero-init)") + if use_block_wise_ssm: + logger.info(f"[Block-wise SSM] attached to every {ssm_every_n} DiT block(s)") + if use_videossm_hybrid: + logger.info(f"[VideoSSM hybrid] attached to every {videossm_every_n} DiT block(s)") + + device = next(dit.parameters()).device + _ckpt_path = _arg('ckpt_path', None) or _arg('resume_from_checkpoint', None) + if _ckpt_path is not None: + ckpt = safe_load_file(_ckpt_path) + missing, unexpected = dit.load_state_dict(ckpt, strict=False) + dit.to(device=device) + logger.info(f"[VWM-style] Loaded ckpt: {len(ckpt)} keys, missing={len(missing)}, unexpected={len(unexpected)}") + + if _arg('action_module_only', False): + if _arg('add_action_attn', False): + for block in dit.blocks: + for name, param in block.named_parameters(): + if ("action_mlp" in name) or ("self_attn_with_action" in name) or ("block_wise_ssm" in name) or ("videossm_hybrid" in name): + param.requires_grad = True + else: + param.requires_grad = False + else: + for block in dit.blocks: + for name, param in block.named_parameters(): + if "action_mlp" in name or "self_attn" in name or "block_wise_ssm" in name or "videossm_hybrid" in name: + param.requires_grad = True + else: + param.requires_grad = False + else: + for block in dit.blocks: + for name, param in block.named_parameters(): + if "action_mlp" in name or "self_attn_with_action" in name or "block_wise_ssm" in name or "videossm_hybrid" in name: + param.requires_grad = True + else: + param.requires_grad = False + _log_dit_freeze_summary(dit) + + _resume_from = _arg('resume_from', None) + if _resume_from: + logger.info(f"Loading full resume checkpoint: {_resume_from}") + ckpt = safe_load_file(_resume_from) + model.pipe.dit.load_state_dict(ckpt, strict=False) + logger.info(f"Checkpoint loaded, resuming from step {resume_step_count}") + + model_logger = ModelLogger( + args.output_path, + remove_prefix_in_ckpt=args.remove_prefix_in_ckpt, + wandb_run_name=args.wandb_run_name, + ckpt_interval=args.ckpt_interval, + resume_step_count=resume_step_count, + save_full_model=_arg('save_full_model', False), + context_drop_prob=float(_arg("context_drop_prob", 0.0)), + enable_video_sampling=_arg("enable_video_sampling", False), + sampling_interval_steps=int(_arg("sampling_interval_steps", 0)), + sampling_two_chunk_memory=_arg("sampling_two_chunk_memory", False), + sampling_action_path=_arg("sampling_action_path", None), + sampling_two_chunk_action_path=_arg("sampling_two_chunk_action_path", None), + sampling_negative_prompt=_arg("sampling_negative_prompt", ""), + sampling_height=int(_arg("sampling_height", 352)), + sampling_width=int(_arg("sampling_width", 640)), + sampling_num_frames=int(_arg("sampling_num_frames", 81)), + sampling_num_inference_steps=int(_arg("sampling_num_inference_steps", 50)), + context_memory_frames=int(_arg("context_memory_frames", 1)), + context_source=_arg("context_source", "replay"), + context_per_frame_vae=_arg("context_per_frame_vae", False), + ) + + optimizer = torch.optim.AdamW(model.trainable_modules(), lr=args.learning_rate) + scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer) + + # Setup FOV retriever for context-based memory training (also for ModelLogger sampling) + enable_fov_retrieval = _arg('enable_fov_retrieval', False) + fov_retriever = None + dataset_base_path = _arg('dataset_base_path', None) + if enable_fov_retrieval: + fov_retriever = setup_fov_retriever_for_training( + dataset_base_path=dataset_base_path, + enable_fov_retrieval=True + ) + + launch_training_task( + dataset, model, model_logger, optimizer, scheduler, + num_epochs=args.num_epochs, + gradient_accumulation_steps=args.gradient_accumulation_steps, + per_device_train_batch_size=int(_arg("per_device_train_batch_size", 1)), + spike_threshold=_arg('spike_threshold', 5.0), + resume_step_count=resume_step_count, + enable_fov_retrieval=enable_fov_retrieval, + retrieval_method=_arg('retrieval_method', 'fov'), + latent_retrieval_dir=_arg('latent_retrieval_dir', None), + dataset_base_path=_arg('dataset_base_path', None), + fov_retriever=fov_retriever, + context_memory_frames=_arg('context_memory_frames', 8), + prev_chunk_frames=int(_arg('prev_chunk_frames', 81)), + fov_top_k=_arg('fov_top_k', 4), # Number of overlap frames (4), GT frame 0 added automatically + use_rt_relative=_arg('use_rt_relative', False), # Experiment 1_4_2: RT relative conversion + strict_overlap_context=_arg('strict_overlap_context', False), + dataset_repeat=_arg('dataset_repeat', 1), # Pass dataset_repeat for step calculation + use_camera_encoder=_arg('use_camera_encoder', False), # exp1_4_3: DDP find_unused_parameters + num_workers=_arg('num_workers', 0), + context_source=_arg('context_source', 'fov'), + max_train_steps=int(_arg('max_train_steps', 0)), + progress_total_steps=int(_arg('progress_total_steps', 0)), + ) + + model_logger.finish() \ No newline at end of file diff --git a/src/model_training/training_modules/__init__.py b/src/model_training/training_modules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3ace1b53769225070083b99e63e8edfe8d434d51 --- /dev/null +++ b/src/model_training/training_modules/__init__.py @@ -0,0 +1,13 @@ +from .action_blocks import DiTBlock_w_Action, MLP_Action, MLP_CamPose +from .model_logger import ModelLogger +from .training_loop import launch_training_task +from .wan_training_module import WanTrainingModule + +__all__ = [ + "DiTBlock_w_Action", + "MLP_Action", + "MLP_CamPose", + "ModelLogger", + "WanTrainingModule", + "launch_training_task", +] diff --git a/src/model_training/training_modules/action_blocks.py b/src/model_training/training_modules/action_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..3553e402937d8f5061a0a774c7d591a7810922c1 --- /dev/null +++ b/src/model_training/training_modules/action_blocks.py @@ -0,0 +1,127 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from diffsynth.models.memory.block_wise_ssm import BlockWiseStateSpaceMemory +from diffsynth.models.memory.videossm_hybrid import HybridStateSpaceMemory +from diffsynth.models.wan_video_dit import SelfAttention, CrossAttention, GateModule, modulate + + +class MLP_Action(nn.Module): + def __init__(self, out_dim, sliding_window_size=3, r=4): + super().__init__() + self.proj_action = nn.Linear(r * sliding_window_size * 10, out_dim) + nn.init.zeros_(self.proj_action.weight) + nn.init.zeros_(self.proj_action.bias) + self.sliding_window_size = sliding_window_size + self.r = r + + def forward(self, x): + bs, nr, act_dim = x.shape + r = self.r + n = nr // r + actions = x.reshape(bs, n, r, act_dim) + actions = F.pad(actions, (0, 0, 0, 0, self.sliding_window_size - 1, 1), mode="replicate") + action_windows = [] + for i in range(self.sliding_window_size): + action_windows.append(actions[:, i:i + n + 1]) + actions = torch.cat(action_windows, dim=2) + actions = actions.reshape(bs, n + 1, -1) + actions = self.proj_action(actions) + return actions + + +class MLP_CamPose(nn.Module): + def __init__(self, out_dim, pose_dim=12): + super().__init__() + self.proj = nn.Linear(pose_dim, out_dim) + nn.init.zeros_(self.proj.weight) + nn.init.zeros_(self.proj.bias) + + def forward(self, x): + return self.proj(x) + + +class DiTBlock_w_Action(nn.Module): + def __init__(self, has_image_input: bool, dim: int, num_heads: int, ffn_dim: int, + eps: float = 1e-6, add_action_attn=False, + action_use_temporal_attention: bool = True, use_cam_pose: bool = False, + use_block_wise_ssm: bool = False, use_videossm_hybrid: bool = False, + videossm_kernel_size: int = 3, videossm_expand: int = 2): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.ffn_dim = ffn_dim + + if add_action_attn: + self.self_attn_with_action = SelfAttention(dim, num_heads, eps) + nn.init.zeros_(self.self_attn_with_action.o.weight) + nn.init.zeros_(self.self_attn_with_action.o.bias) + if use_cam_pose: + self.action_mlp = MLP_CamPose(dim) + else: + self.action_mlp = MLP_Action(dim) + + self.self_attn = SelfAttention(dim, num_heads, eps) + self.cross_attn = CrossAttention(dim, num_heads, eps, has_image_input=has_image_input) + self.norm1 = nn.LayerNorm(dim, eps=eps, elementwise_affine=False) + self.norm2 = nn.LayerNorm(dim, eps=eps, elementwise_affine=False) + self.norm3 = nn.LayerNorm(dim, eps=eps) + self.ffn = nn.Sequential(nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'), nn.Linear(ffn_dim, dim)) + self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5) + self.gate = GateModule() + self.action_use_temporal_attention = action_use_temporal_attention + self.use_block_wise_ssm = bool(use_block_wise_ssm) + self.use_videossm_hybrid = bool(use_videossm_hybrid) + if use_block_wise_ssm: + self.block_wise_ssm = BlockWiseStateSpaceMemory(dim) + if use_videossm_hybrid: + self.videossm_hybrid = HybridStateSpaceMemory( + dim, kernel_size=videossm_kernel_size, expand=videossm_expand + ) + + def forward(self, x, context, t_mod, freqs, actions=None): + has_seq = len(t_mod.shape) == 4 + chunk_dim = 2 if has_seq else 1 + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( + self.modulation.to(dtype=t_mod.dtype, device=t_mod.device) + t_mod).chunk(6, dim=chunk_dim) + if has_seq: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( + shift_msa.squeeze(2), scale_msa.squeeze(2), gate_msa.squeeze(2), + shift_mlp.squeeze(2), scale_mlp.squeeze(2), gate_mlp.squeeze(2), + ) + + num_frames = None + if actions is not None: + original_x = x + actions = self.action_mlp(actions.to(x.dtype)).to(x.dtype) + bs, num_frames, dim = actions.shape + actions = actions.reshape(bs, num_frames, 1, dim) + x = x.reshape(bs, num_frames, -1, dim) + x = x + actions + if hasattr(self, "self_attn_with_action"): + if not self.action_use_temporal_attention: + x = x.reshape(bs, -1, dim) + x = original_x + self.self_attn_with_action(x, freqs) + else: + from einops import rearrange + x = rearrange(x, "b f p d -> (b p) f d") + attn_out = self.self_attn_with_action(x) + attn_out = rearrange(attn_out, "(b p) f d -> b f p d", b=bs) + x = original_x + attn_out.reshape(bs, -1, dim) + else: + x = x.reshape(bs, -1, dim) + + input_x = modulate(self.norm1(x), shift_msa, scale_msa) + x = self.gate(x, gate_msa, self.self_attn(input_x, freqs)) + if num_frames is not None: + if hasattr(self, "block_wise_ssm"): + x = self.block_wise_ssm(x, f=num_frames) + if hasattr(self, "videossm_hybrid"): + spatial = x.shape[1] // int(num_frames) if int(num_frames) > 0 else 0 + x = self.videossm_hybrid(x, f=num_frames, h=1, w=spatial) + x = x + self.cross_attn(self.norm3(x), context) + input_x = modulate(self.norm2(x), shift_mlp, scale_mlp) + x = self.gate(x, gate_mlp, self.ffn(input_x)) + return x + diff --git a/src/model_training/training_modules/model_logger.py b/src/model_training/training_modules/model_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..84c90e0d9ab431b4aa0f58e3de482235d737c3df --- /dev/null +++ b/src/model_training/training_modules/model_logger.py @@ -0,0 +1,171 @@ +import json +import os +from typing import Optional + +import wandb + +from src.model_training.transformers_compat import patch_transformers_hybrid_cache + +patch_transformers_hybrid_cache() + +from diffsynth.trainers.utils import ModelLogger as BaseModelLogger + + +class ModelLogger(BaseModelLogger): + """Compatibility wrapper for legacy training scripts.""" + + def __init__( + self, + output_path, + remove_prefix_in_ckpt=None, + state_dict_converter=lambda x: x, + wandb_run_name=None, + ckpt_interval=None, + resume_step_count=0, + save_full_model=False, + context_drop_prob: float = 0.0, + enable_video_sampling=False, + sampling_interval_steps: int = 0, + sampling_two_chunk_memory: bool = False, + sampling_action_path: Optional[str] = None, + sampling_two_chunk_action_path: Optional[str] = None, + sampling_negative_prompt: str = "oversaturated colors, overexposed, static, blurry details", + sampling_height: int = 352, + sampling_width: int = 640, + sampling_num_frames: int = 81, + sampling_num_inference_steps: int = 50, + context_memory_frames: int = 1, + context_source: str = "replay", + context_per_frame_vae: bool = False, + ): + super().__init__(output_path, remove_prefix_in_ckpt=remove_prefix_in_ckpt, state_dict_converter=state_dict_converter) + self.wandb_run_name = wandb_run_name + self.ckpt_interval = int(ckpt_interval) if ckpt_interval else None + self.step_count = int(resume_step_count) + self.save_full_model = bool(save_full_model) + self.total_steps = None + self.context_drop_prob = float(context_drop_prob) + self.enable_video_sampling = bool(enable_video_sampling) + self.sampling_interval_steps = int(sampling_interval_steps) + self.sampling_two_chunk_memory = bool(sampling_two_chunk_memory) + self.sampling_action_path = sampling_action_path + self.sampling_two_chunk_action_path = sampling_two_chunk_action_path + self.sampling_negative_prompt = sampling_negative_prompt + self.sampling_height = int(sampling_height) + self.sampling_width = int(sampling_width) + self.sampling_num_frames = int(sampling_num_frames) + self.sampling_num_inference_steps = int(sampling_num_inference_steps) + self.context_memory_frames = int(context_memory_frames) + self.context_source = context_source.strip().lower() + self.context_per_frame_vae = bool(context_per_frame_vae) + self.wandb_logger = None + if self.wandb_run_name: + self.wandb_logger = wandb.init(project="wan-cam", name=self.wandb_run_name, reinit=True) + + def _save_step_or_epoch_ckpt(self, accelerator, model, path: str): + state_dict = None + unwrapped = accelerator.unwrap_model(model) + if self.save_full_model: + # Save full DiT (including action/camera/memory modules), not whole pipeline. + state_dict = accelerator.get_state_dict(unwrapped.pipe.dit) + for module_name in ("spatial_memory_module", "spatial_memory_readout_module"): + module = getattr(unwrapped, module_name, None) + if module is not None: + state_dict.update( + { + f"{module_name}.{name}": param + for name, param in accelerator.get_state_dict(module).items() + } + ) + if state_dict is None: + full_state = accelerator.get_state_dict(model) + state_dict = unwrapped.export_trainable_state_dict(full_state, remove_prefix=self.remove_prefix_in_ckpt) + state_dict = self.state_dict_converter(state_dict) + os.makedirs(self.output_path, exist_ok=True) + accelerator.save(state_dict, path, safe_serialization=True) + + def _maybe_sample_paper_process(self, accelerator=None, model=None, current_batch=None): + if not ( + self.enable_video_sampling + and self.sampling_two_chunk_memory + and self.sampling_interval_steps > 0 + and self.step_count % self.sampling_interval_steps == 0 + and accelerator is not None + and model is not None + and current_batch is not None + ): + return + from diffsynth import save_video + from src.model_training.multichunk_sample_utils import ( + run_two_chunk_memory_monitor, + sync_pipe_memory_from_training_module, + ) + + sample = current_batch[0] if isinstance(current_batch, list) else current_batch + first_frame = sample["video"][0] + unwrapped = accelerator.unwrap_model(model) + pipe = unwrapped.pipe + sync_pipe_memory_from_training_module(pipe, unwrapped) + action0 = self.sampling_two_chunk_action_path or self.sampling_action_path + action1 = self.sampling_action_path + frames0, frames1, meta = run_two_chunk_memory_monitor( + pipe, + prompt=sample.get("prompt") or sample.get("description") or "A scene.", + negative_prompt=self.sampling_negative_prompt, + action_path=self.sampling_action_path, + chunk0_action_path=action0, + chunk1_action_path=action1, + first_frame_pil=first_frame, + context_memory_frames=self.context_memory_frames, + chunk_frames=self.sampling_num_frames, + h=self.sampling_height, + w=self.sampling_width, + seed=42 + self.step_count + accelerator.process_index, + sigma_shift=5.0, + num_inference_steps=self.sampling_num_inference_steps, + cfg_scale=5.0, + inference_noise_level=0.0, + omit_context_actions=False, + context_source=self.context_source, + context_position=os.environ.get("CONTEXT_POSITION", "suffix"), + context_per_frame_vae=self.context_per_frame_vae, + device=pipe.device, + log_prefix=f"[paper-sampling][step={self.step_count}]", + ) + out_dir = os.path.join(self.output_path, "paper_process_sampling") + os.makedirs(out_dir, exist_ok=True) + tag = f"step_{self.step_count:07d}_rank{accelerator.process_index}" + save_video(list(frames0) + list(frames1), os.path.join(out_dir, f"{tag}_pred.mp4"), fps=15, quality=5) + with open(os.path.join(out_dir, f"{tag}_meta.json"), "w", encoding="utf-8") as f: + json.dump(meta, f, ensure_ascii=False, indent=2) + + def on_step_end(self, loss, accelerator=None, model=None, current_batch=None): + self.step_count += 1 + if self.wandb_logger is not None: + if accelerator is None or accelerator.is_main_process: + loss_v = float(loss.detach().float().item()) + self.wandb_logger.log({"train/loss": loss_v, "step": self.step_count}) + if accelerator is not None and accelerator.is_main_process: + self._maybe_sample_paper_process(accelerator, model, current_batch) + if accelerator is not None and self.enable_video_sampling and self.sampling_two_chunk_memory and self.sampling_interval_steps > 0: + accelerator.wait_for_everyone() + if ( + self.ckpt_interval + and accelerator is not None + and model is not None + and (self.step_count % self.ckpt_interval == 0) + ): + accelerator.wait_for_everyone() + if accelerator.is_main_process: + path = os.path.join(self.output_path, f"Step-{self.step_count}.safetensors") + self._save_step_or_epoch_ckpt(accelerator, model, path) + + def on_epoch_end(self, accelerator, model, epoch_id): + accelerator.wait_for_everyone() + if accelerator.is_main_process: + path = os.path.join(self.output_path, f"epoch-{epoch_id}.safetensors") + self._save_step_or_epoch_ckpt(accelerator, model, path) + + def finish(self): + if self.wandb_logger is not None: + wandb.finish() diff --git a/src/model_training/training_modules/training_loop.py b/src/model_training/training_modules/training_loop.py new file mode 100644 index 0000000000000000000000000000000000000000..5f84ea0d934a0757fd376ab3d9614d61fd08dc21 --- /dev/null +++ b/src/model_training/training_modules/training_loop.py @@ -0,0 +1,360 @@ +import logging +import os +from typing import Optional + +import torch +import torch.distributed as dist +from accelerate import Accelerator +from tqdm import tqdm + +from src.model_training.transformers_compat import patch_transformers_hybrid_cache + +patch_transformers_hybrid_cache() + +from diffsynth.trainers.utils import DiffusionTrainingModule +from src.model_training.fov_retrieval import FOVMemoryRetriever +from src.model_training.fov_retrieval import retrieve_context_frames_advanced, retrieve_fov_context_frames +from src.model_training.training_modules.model_logger import ModelLogger + +logger = logging.getLogger(__name__) + + +def launch_training_task( + dataset: torch.utils.data.Dataset, + model: DiffusionTrainingModule, + model_logger: ModelLogger, + optimizer: torch.optim.Optimizer, + scheduler: torch.optim.lr_scheduler.LRScheduler, + num_epochs: int = 1, + gradient_accumulation_steps: int = 1, + per_device_train_batch_size: int = 1, + seed: int = 42, + spike_threshold: float = 5.0, + resume_step_count: int = 0, + enable_fov_retrieval: bool = False, + retrieval_method: str = "fov", # fov | latent_sim + latent_retrieval_dir: Optional[str] = None, + dataset_base_path: str = None, + fov_retriever: Optional[FOVMemoryRetriever] = None, + context_memory_frames: int = 5, + prev_chunk_frames: int = 81, + fov_top_k: int = 4, # Number of overlap frames to retrieve. GT frame 0 will be added automatically. + use_rt_relative: bool = False, # Experiment 1_4_2: Use RT relative conversion (aligned with Context-as-Memory) + strict_overlap_context: bool = False, + dataset_repeat: int = 1, # Add dataset_repeat parameter for step calculation + use_camera_encoder: bool = False, # exp1_4_3: use CameraEncoder (action_mlp unused -> need find_unused_parameters) + num_workers: int = 0, # DataLoader workers: 0=main process, >0=parallel preload (recommend 4 for video) + context_source: str = "fov", + max_train_steps: int = 0, + progress_total_steps: int = 0, +): + prev_chunk_frames = int(prev_chunk_frames) + # VideoDataset can return None when file loading fails; keep distributed batches aligned. + def collate_fn(batch): + valid_batch = [item for item in batch if item is not None] + return valid_batch or None + + num_workers = max(0, int(num_workers)) + dataloader = torch.utils.data.DataLoader( + dataset, + batch_size=per_device_train_batch_size, + shuffle=True, + collate_fn=collate_fn, + num_workers=num_workers, + drop_last=True, + persistent_workers=(num_workers > 0), + pin_memory=(num_workers > 0 and torch.cuda.is_available()), + ) + if num_workers > 0: + logger.info(f"[DataLoader] num_workers={num_workers}, persistent_workers=True, pin_memory={torch.cuda.is_available()} (data preload parallel to GPU)") + + timeout_seconds = int(os.environ.get('TORCH_DISTRIBUTED_DEFAULT_TIMEOUT', 2400)) + os.environ['TORCH_DISTRIBUTED_DEFAULT_TIMEOUT'] = str(timeout_seconds) + logger.info(f"[Timeout Config] Setting TORCH_DISTRIBUTED_DEFAULT_TIMEOUT={timeout_seconds} seconds ({timeout_seconds/60:.1f} minutes)") + + # Conditional context paths can leave parameters unused on some iterations. + need_find_unused = bool(use_camera_encoder) or model_logger.context_drop_prob > 0.0 + if need_find_unused: + from accelerate import DistributedDataParallelKwargs + ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True) + accelerator = Accelerator(gradient_accumulation_steps=gradient_accumulation_steps, kwargs_handlers=[ddp_kwargs]) + logger.info("[DDP] find_unused_parameters=True (conditional modules / context_drop_prob enabled)") + else: + accelerator = Accelerator(gradient_accumulation_steps=gradient_accumulation_steps) + model, optimizer, dataloader, scheduler = accelerator.prepare(model, optimizer, dataloader, scheduler) + + if model_logger.enable_video_sampling and model_logger.total_steps is not None: + dataset_size = len(dataset) + num_processes = accelerator.num_processes + effective_dataset_size = dataset_size * dataset_repeat + total_steps_per_gpu = (effective_dataset_size * num_epochs) // (gradient_accumulation_steps * num_processes * per_device_train_batch_size) + total_steps_global = total_steps_per_gpu * num_processes + model_logger.total_steps = total_steps_global + + if accelerator.is_main_process: + logger.info("="*80) + logger.info("[Step Calculation] Corrected total_steps after accelerator.init") + logger.info("="*80) + logger.info(f" Dataset size (unique samples): {dataset_size}") + logger.info(f" Dataset repeat: {dataset_repeat}") + logger.info(f" Effective dataset size: {effective_dataset_size} (unique * repeat)") + logger.info(f" Number of epochs: {num_epochs}") + logger.info(f" Number of GPUs: {num_processes}") + logger.info(f" Gradient accumulation steps: {gradient_accumulation_steps}") + logger.info(f" Per-device batch size: {per_device_train_batch_size}") + logger.info(f" Total samples to process: {effective_dataset_size * num_epochs}") + logger.info(f" Steps per GPU: ~{total_steps_per_gpu}") + logger.info(f" Total steps (global): {total_steps_global}") + logger.info("") + logger.info(f" ✓ Each GPU will process ~{total_steps_per_gpu} steps") + logger.info(f" ✓ This ensures traversal of all {effective_dataset_size} samples") + logger.info(f" ({dataset_size} unique samples × {dataset_repeat} repeats)") + logger.info(f" ✓ Over {num_epochs} epoch(s)") + logger.info("="*80) + + step = resume_step_count + traj_loss = 0.0 + if resume_step_count > 0: + adaptation_steps = max(200, resume_step_count // 100) + spike_detection_start_step = resume_step_count + adaptation_steps + logger.info(f"Resuming from step {resume_step_count}, spike detection will start at step {spike_detection_start_step} (after {adaptation_steps} adaptation steps)") + else: + spike_detection_start_step = 100 + + for epoch_id in range(num_epochs): + epoch_seed = seed + epoch_id + torch.manual_seed(epoch_seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(epoch_seed) + torch.cuda.manual_seed_all(epoch_seed) + + if resume_step_count > 0 and epoch_id == 0: + estimated_skip = resume_step_count // gradient_accumulation_steps + if estimated_skip > 0: + logger.info(f"Skipping {estimated_skip} data samples to resume from step {resume_step_count}...") + dataloader_iter = iter(dataloader) + for _ in tqdm(range(estimated_skip), desc="Skipping data", unit="samples", leave=False): + try: + next(dataloader_iter) + except StopIteration: + break + dataloader = dataloader_iter + logger.info(f"Successfully skipped {estimated_skip} data samples, resuming training...") + + # Track consecutive None data to detect if we're stuck in a loop + consecutive_none_count = 0 + max_consecutive_none = 100 # If we get 100 consecutive None values, something is wrong + + progress_total = int(progress_total_steps) + if progress_total <= 0: + progress_total = len(dataloader) + progress_bar = tqdm( + dataloader, + total=progress_total, + initial=resume_step_count if progress_total_steps else 0, + desc="Training steps", + unit="step", + ) + for data_idx, data in enumerate(progress_bar): + # Handle None data (can happen if all files in batch fail to load) + if data is None: + consecutive_none_count += 1 + if consecutive_none_count >= max_consecutive_none: + logger.error(f"Received {max_consecutive_none} consecutive None data samples. This suggests a serious dataset issue. Stopping training.") + raise ValueError(f"Too many consecutive None data samples ({max_consecutive_none}). Check dataset files.") + + # Log warning but continue (will skip this step) + if consecutive_none_count <= 10 or consecutive_none_count % 10 == 0: + logger.warning(f"Received None data at index {data_idx} (consecutive: {consecutive_none_count}). This may indicate missing or corrupted files. Skipping...") + + # Still increment step to keep step_count synchronized + step += 1 + dummy_loss = torch.tensor(0.0, device=accelerator.device, requires_grad=False) + model_logger.on_step_end(dummy_loss, accelerator, model, current_batch=samples) + continue + + # Reset consecutive None counter when we get valid data + consecutive_none_count = 0 + + # Normalize to list of samples for batch processing (per_device_train_batch_size > 1) + samples = data if isinstance(data, list) else [data] + + # Simplified context-based retrieval OR replay/prev_chunk_tail (aligned with multichunk eval) + context_retrieval_success = True # Set False if any sample fails (for strict mode) + _umodel = accelerator.unwrap_model(model) + _cm_frames = int(_umodel.context_memory_frames) + _cs = context_source.strip().lower() + if _cs not in ("fov", "replay", "prev_chunk_tail"): + _cs = "fov" + + if _cs == "replay" and dataset_base_path: + from src.model_training.multichunk_sample_utils import ( + replay_context_actions_from_segment_actions, + replay_context_global_indices, + synthetic_replay_context_from_segment, + ) + for d in samples: + vf = d.get("video") or [] + n_seg = min(int(prev_chunk_frames), len(vf)) if vf else 0 + ctx_pil = synthetic_replay_context_from_segment(vf, n_seg, _cm_frames) if n_seg > 0 else None + if not ctx_pil: + context_retrieval_success = False + break + d["context_frames"] = ctx_pil + d["context_source"] = "replay_synthetic" + acts = d.get("actions") + if isinstance(acts, list) and len(acts) >= n_seg: + ra = replay_context_actions_from_segment_actions(acts[:n_seg], n_seg, _cm_frames) + if ra is not None: + d["context_actions"] = ra + sf = int(d.get("start_frame", 0) or 0) + idxs = replay_context_global_indices(n_seg, _cm_frames) + d["context_frame_indices"] = [sf + int(i) for i in idxs] + + elif _cs == "prev_chunk_tail" and dataset_base_path: + from src.model_training.multichunk_sample_utils import load_prev_chunk_tail_from_disk, load_prev_chunk_tail_rt_actions + _ctx_pos = os.environ.get("CONTEXT_POSITION", "suffix").strip().lower() + _nearest_first = (_ctx_pos == "suffix") + for d in samples: + sf = int(d.get("start_frame", 0) or 0) + vn = d.get("video_name", "") + pil_list, idxs = load_prev_chunk_tail_from_disk( + dataset_base_path, str(vn), sf, _cm_frames, nearest_first=_nearest_first + ) + if not pil_list: + context_retrieval_success = False + break + d["context_frames"] = pil_list + d["context_frame_indices"] = list(idxs) if idxs else [] + d["context_source"] = "prev_chunk_tail" + ra, _ = load_prev_chunk_tail_rt_actions( + dataset_base_path, + str(vn), + sf, + _cm_frames, + use_rt_relative=use_rt_relative, + nearest_first=_nearest_first, + ) + if ra: + d["context_actions"] = ra + + elif enable_fov_retrieval and dataset_base_path: + for d in samples: + if retrieval_method == "latent_sim": + ( + context_frames, + context_actions, + context_indices, + ref_frame_idx, + video_name, + source, + ) = retrieve_context_frames_advanced( + data=d, + dataset_base_path=dataset_base_path, + top_k=fov_top_k, + drop_overlap_probability=0.1, + use_rt_relative=use_rt_relative, + retrieval_method="latent_sim", + latent_retrieval_dir=latent_retrieval_dir, + strict_overlap_labels=strict_overlap_context, + ) + else: + ( + context_frames, + context_actions, + context_indices, + ref_frame_idx, + video_name, + source, + ) = retrieve_fov_context_frames( + data=d, + dataset_base_path=dataset_base_path, + fov_retriever=fov_retriever, # unused in simplified retrieval, kept for compat + top_k=fov_top_k, # fov_top_k is number of overlap frames (4), GT frame 0 will be added automatically + use_precomputed_overlaps=True, + strict_overlap_labels=strict_overlap_context, + allow_realtime_fallback=(not strict_overlap_context), + allow_segment_fallback=(not strict_overlap_context), + ) + + if context_frames and len(context_frames) > 0: + # Use retrieved frames as context + d["context_frames"] = context_frames + if context_actions: + d["context_actions"] = context_actions + # Store retrieval metadata for visualization/debugging + d["context_frame_indices"] = context_indices + d["context_ref_frame_idx"] = ref_frame_idx + d["context_video_name"] = video_name + d["context_source"] = source + else: + context_retrieval_success = False + break + + # Strict mode: if we require context but retrieval failed, skip this step + _need_ctx_strict = ( + strict_overlap_context + and (not context_retrieval_success) + and ( + enable_fov_retrieval + or context_source.strip().lower() in ("replay", "prev_chunk_tail") + ) + ) + if _need_ctx_strict: + if step % 50 == 0 and accelerator.is_main_process: + logger.warning(f"[CONTEXT][STRICT] No context at step={step}, skipping this training sample.") + step += 1 + dummy_loss = torch.tensor(0.0, device=accelerator.device, requires_grad=False) + model_logger.on_step_end(dummy_loss, accelerator, model, current_batch=samples) + continue + + with accelerator.accumulate(model): + optimizer.zero_grad() + # One forward over full batch: data is list of B dicts when per_device_train_batch_size > 1 + # Main loss on current batch + loss = model(data) + + step += 1 + if traj_loss == 0.0: + traj_loss = loss.item() + else: + alpha = 0.01 + traj_loss = (1 - alpha) * traj_loss + alpha * loss.item() + + if step >= spike_detection_start_step and traj_loss > 0: + relative_loss = loss.item() / traj_loss + if resume_step_count > 0 and step < resume_step_count + 500: + effective_threshold = spike_threshold * 1.5 + else: + effective_threshold = spike_threshold + + should_skip = relative_loss > effective_threshold + # Keep the skip decision identical across ranks to avoid DDP hangs. + skip_t = torch.tensor(1.0 if should_skip else 0.0, device=accelerator.device, dtype=torch.float32) + if accelerator.num_processes > 1: + dist.all_reduce(skip_t, op=dist.ReduceOp.MAX) + skip_global = skip_t.item() > 0.5 + + if skip_global: + if accelerator.is_main_process: + logger.warning(f"Spike detected at step {step} (loss={loss.item():.4f}, traj_loss={traj_loss:.4f}, ratio={relative_loss:.2f}), sync skip across all ranks") + dummy_loss = torch.tensor(0.0, device=accelerator.device, requires_grad=False) + model_logger.on_step_end(dummy_loss, accelerator, model, current_batch=samples) + del loss + torch.cuda.empty_cache() + continue + accelerator.backward(loss) + optimizer.step() + model_logger.on_step_end(loss, accelerator, model, current_batch=samples) + scheduler.step() + + if max_train_steps and step >= max_train_steps: + if progress_total_steps: + progress_bar.n = min(step, progress_bar.total) if progress_bar.total is not None else step + progress_bar.refresh() + if accelerator.is_main_process: + logger.info(f"[TRAIN] Reached max_train_steps={max_train_steps}; stopping without epoch checkpoint.") + accelerator.wait_for_everyone() + return + + model_logger.on_epoch_end(accelerator, model, epoch_id) diff --git a/src/model_training/training_modules/wan_training_module.py b/src/model_training/training_modules/wan_training_module.py new file mode 100644 index 0000000000000000000000000000000000000000..62c5686146239999ef10e519402c995205ea4ae3 --- /dev/null +++ b/src/model_training/training_modules/wan_training_module.py @@ -0,0 +1,630 @@ +import hashlib +import json +import logging +import os +import random +from typing import Any, Dict, Optional + +import torch +from safetensors.torch import load_file as safe_load_file + +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from src.model_training.transformers_compat import patch_transformers_hybrid_cache + +patch_transformers_hybrid_cache() +from diffsynth.trainers.utils import DiffusionTrainingModule +from diffsynth.models.memory.spatial_grid_memory import SpatialCrossAttnReadout, SpatialGridMemory +from src.model_training.fov_retrieval import flip_yaw_rt_list + +logger = logging.getLogger(__name__) + + +class WanTrainingModule(DiffusionTrainingModule): + def __init__( + self, + model_paths=None, model_id_with_origin_paths=None, + tokenizer_path=None, + trainable_models=None, + lora_base_model=None, lora_target_modules="q,k,v,o,ffn.0,ffn.2", lora_rank=32, + use_gradient_checkpointing=True, + use_gradient_checkpointing_offload=False, + extra_inputs=None, + timestep_shift=1.0, + resume_from_checkpoint=None, + dataset_base_path: Optional[str] = None, + enable_context_memory=False, + context_memory_frames=8, + training_mode="context", # "context" mode for Context Memory (inpainting) + context_drop_prob: float = 0.0, + context_drop_seed: int = 42, + omit_context_actions: bool = False, # Context-as-Memory: no context RT injection + context_noise_prob=0.0, + context_noise_std=0.02, + context_fixed_noise_std=None, # Experiment 7: Fixed noise std (e.g., 0.1) to align training-inference + teacher_forcing_prob=0.0, + yaw_flip_aug: bool = False, # 50% prob flip yaw (ACTION_FOLLOWING direction sensitivity) + context_per_frame_vae: bool = False, # Encode each context frame separately (1 latent per raw frame) + context_source: str = "fov", # fov | replay | prev_chunk_tail (multichunk-aligned context construction) + use_framepack_memory: bool = False, + context_temporal_decay: float = 1.0, + context_attention_weight: float = 1.0, + use_framepack_length_compress: bool = False, + framepack_ratio: int = 2, + framepack_length_strategy: str = "distance_merge", + framepack_recent_keep_ratio: float = 0.5, + framepack_multiscale_w2: float = 0.25, + framepack_multiscale_w4: float = 0.15, + use_spatial_memory: bool = False, + use_spatial_memory_legacy: bool = False, + spatial_memory_tokens: int = 64, + spatial_memory_grid: int = 8, + spatial_memory_inject_mode: str = "concat_text", + # Note: Self-forcing parameters removed - using standard training only + ): + super().__init__() + # Load models + model_configs = [] + if model_paths is not None: + model_paths = json.loads(model_paths) + model_configs += [ModelConfig(path=path) for path in model_paths] + if model_id_with_origin_paths is not None: + model_id_with_origin_paths = model_id_with_origin_paths.split(",") + model_configs += [ModelConfig(model_id=i.split(":")[0], origin_file_pattern=i.split(":")[1]) for i in model_id_with_origin_paths] + from_pretrained_kw = {"torch_dtype": torch.bfloat16, "device": "cpu", "model_configs": model_configs} + if tokenizer_path: + from_pretrained_kw["tokenizer_config"] = ModelConfig(path=tokenizer_path) + self.pipe = WanVideoPipeline.from_pretrained(**from_pretrained_kw) + + # Store timestep_shift for later use (e.g., after video sampling) + self.timestep_shift = timestep_shift + + # Reset training scheduler + self.pipe.scheduler.set_timesteps(1000, training=True, shift=timestep_shift) + + # Freeze untrainable models + self.pipe.freeze_except([] if trainable_models is None else trainable_models.split(",")) + + # Add LoRA to the base models + if lora_base_model is not None: + model = self.add_lora_to_model( + getattr(self.pipe, lora_base_model), + target_modules=lora_target_modules.split(","), + lora_rank=lora_rank + ) + setattr(self.pipe, lora_base_model, model) + + # Load checkpoint if provided + if resume_from_checkpoint is not None: + logger.info(f"Loading LoRA checkpoint from: {resume_from_checkpoint}") + if not os.path.exists(resume_from_checkpoint): + raise FileNotFoundError(f"Checkpoint file not found: {resume_from_checkpoint}") + checkpoint_state_dict = safe_load_file(resume_from_checkpoint) + logger.info(f"Checkpoint contains {len(checkpoint_state_dict)} parameters") + # The checkpoint was saved with remove_prefix_in_ckpt, so keys don't have the prefix + # The model (pipe.dit) state_dict keys also don't have the prefix, so they should match + # Use strict=False to allow partial loading + missing_keys, unexpected_keys = model.load_state_dict(checkpoint_state_dict, strict=False) + if missing_keys: + logger.warning(f"{len(missing_keys)} keys were missing when loading checkpoint") + if len(missing_keys) <= 10: + logger.debug(f"Missing keys: {missing_keys}") + if unexpected_keys: + logger.warning(f"{len(unexpected_keys)} unexpected keys in checkpoint (will be ignored)") + if len(unexpected_keys) <= 10: + logger.debug(f"Unexpected keys: {unexpected_keys}") + loaded_count = len(checkpoint_state_dict) - len(missing_keys) - len(unexpected_keys) + logger.info(f"Successfully loaded {loaded_count} parameters from checkpoint!") + + # Store other configs + self.use_gradient_checkpointing = use_gradient_checkpointing + self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload + self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else [] + self.dataset_base_path = dataset_base_path + + # Context Memory (Context as Memory) configuration + self.enable_context_memory = enable_context_memory + self.context_memory_frames = context_memory_frames + self.training_mode = training_mode # "predict", "context", or "condition" + self.context_drop_prob = float(context_drop_prob or 0.0) + self.context_drop_seed = int(context_drop_seed or 42) + self.omit_context_actions = bool(omit_context_actions) + self.context_per_frame_vae = bool(context_per_frame_vae) + self.context_source = (context_source or "fov").strip().lower() + if self.context_source not in ("fov", "replay", "prev_chunk_tail"): + self.context_source = "fov" + self.context_noise_prob = context_noise_prob + self.context_noise_std = context_noise_std + self.context_fixed_noise_std = context_fixed_noise_std # Experiment 7: Fixed noise for training-inference alignment + self.teacher_forcing_prob = teacher_forcing_prob + self.teacher_forcing_enabled = teacher_forcing_prob > 0.0 + self.yaw_flip_aug = bool(yaw_flip_aug) + # Memory baselines runtime flags (train + sampling path shared). + self.use_framepack_memory = bool(use_framepack_memory) + self.context_temporal_decay = float(context_temporal_decay or 1.0) + self.context_attention_weight = float(context_attention_weight or 1.0) + self.use_framepack_length_compress = bool(use_framepack_length_compress) + self.framepack_ratio = int(framepack_ratio or 2) + self.framepack_length_strategy = str(framepack_length_strategy or "distance_merge").lower() + self.framepack_recent_keep_ratio = float(framepack_recent_keep_ratio or 0.5) + self.framepack_multiscale_w2 = float(framepack_multiscale_w2 or 0.25) + self.framepack_multiscale_w4 = float(framepack_multiscale_w4 or 0.15) + # Mirror key flags to pipe for inference-time sampling monitor. + self.pipe.use_framepack_memory = self.use_framepack_memory + self.pipe.context_temporal_decay = self.context_temporal_decay + self.pipe.context_attention_weight = self.context_attention_weight + self.pipe.use_framepack_length_compress = self.use_framepack_length_compress + self.pipe.framepack_ratio = self.framepack_ratio + self.pipe.framepack_length_strategy = self.framepack_length_strategy + self.pipe.framepack_recent_keep_ratio = self.framepack_recent_keep_ratio + self.pipe.framepack_multiscale_w2 = self.framepack_multiscale_w2 + self.pipe.framepack_multiscale_w4 = self.framepack_multiscale_w4 + self.pipe.use_spatial_memory = bool(use_spatial_memory) + self.pipe.use_spatial_memory_legacy = bool(use_spatial_memory_legacy) + self.pipe.spatial_memory_tokens = int(spatial_memory_tokens or 64) + self.pipe.spatial_memory_inject_mode = str(spatial_memory_inject_mode or "concat_text") + self.spatial_memory_module = None + self.spatial_memory_readout_module = None + if self.pipe.use_spatial_memory and not self.pipe.use_spatial_memory_legacy: + dim = int(getattr(self.pipe.dit, "dim")) + grid_size = int(spatial_memory_grid or 8) + self.pipe.spatial_memory_grid = grid_size + self.spatial_memory_module = SpatialGridMemory( + dim=dim, + grid_size=grid_size, + num_tokens=self.pipe.spatial_memory_tokens, + ) + self.pipe.spatial_memory_module = self.spatial_memory_module + if self.pipe.spatial_memory_inject_mode == "cross_attn_readout": + self.spatial_memory_readout_module = SpatialCrossAttnReadout(dim=dim, num_heads=8) + self.pipe.spatial_memory_readout_module = self.spatial_memory_readout_module + else: + self.pipe.spatial_memory_module = None + self.pipe.spatial_memory_readout_module = None + # Note: Self-forcing removed - using standard training only + self.current_step = 0 # Track current training step (for logging/debugging) + + def _forward_preprocess_batch(self, samples: list) -> dict: + """Batch preprocessing for Stage 1 Interactive (no context). data is list of sample dicts.""" + if not samples: + raise ValueError("samples cannot be empty in _forward_preprocess_batch") + batch_size = len(samples) + prompts = [] + video_frames_list = [] + actions_list = [] + for s in samples: + p = s.get("prompt") + if p is None: + raise ValueError("sample['prompt'] is missing or None") + prompts.append(str(p) if not isinstance(p, str) else p) + video_frames_list.append(s["video"]) + if "actions" in s and s["actions"] is not None: + acts = s["actions"] + if getattr(self, 'yaw_flip_aug', False) and isinstance(acts, list) and len(acts) > 0 and isinstance(acts[0], (list, tuple)) and len(acts[0]) >= 12 and random.random() < 0.5: + acts = flip_yaw_rt_list(acts) + if isinstance(acts, torch.Tensor): + actions_list.append(acts) + elif isinstance(acts, list) and len(acts) > 0: + actions_list.append(torch.tensor(acts, dtype=torch.float32)) + else: + actions_list.append(None) + else: + actions_list.append(None) + + # input_video: list of lists (each inner list = PIL images for one video) + input_video = video_frames_list + first = samples[0] + h, w = first["video"][0].size[1], first["video"][0].size[0] + num_frames = len(first["video"]) + + inputs_posi = {"prompt": prompts} + inputs_nega = {} + inputs_shared = { + "input_video": input_video, + "height": h, + "width": w, + "num_frames": num_frames, + "batch_size": batch_size, + "cfg_scale": 1, + "tiled": False, + "rand_device": self.pipe.device, + "use_gradient_checkpointing": self.use_gradient_checkpointing, + "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload, + "cfg_merge": False, + "vace_scale": 1, + } + + ref_action = next((a for a in actions_list if a is not None), None) + if ref_action is not None and batch_size == 1: + inputs_shared["actions"] = ref_action.detach().cpu().tolist() if isinstance(ref_action, torch.Tensor) else ref_action + elif ref_action is not None: + device = self.pipe.device + dtype = ref_action.dtype + stacked = [] + for a in actions_list: + if a is not None: + stacked.append(a.to(device=device)) + else: + stacked.append(torch.zeros_like(ref_action, device=device, dtype=dtype)) + inputs_shared["actions"] = torch.stack(stacked) + else: + inputs_shared["actions"] = None + + for unit in self.pipe.units: + inputs_shared, inputs_posi, inputs_nega = self.pipe.unit_runner(unit, self.pipe, inputs_shared, inputs_posi, inputs_nega) + return {**inputs_shared, **inputs_posi} + + def _build_context_with_anchor(self, context_frames, context_actions=None, expected_k=None): + """Training-side anchor helper: keep last frame as mandatory anchor and keep action length aligned.""" + frames = list(context_frames or []) + actions = list(context_actions or []) if context_actions is not None else [] + if not frames or not getattr(self, "use_anchor_frame", False): + return frames, actions + k = int(expected_k) if (expected_k is not None and int(expected_k) > 0) else len(frames) + if len(frames) > k: + frames = frames[-k:] + if actions: + actions = actions[-k:] + if actions: + if len(actions) < len(frames): + actions = actions + [actions[-1]] * (len(frames) - len(actions)) + elif len(actions) > len(frames): + actions = actions[:len(frames)] + return frames, actions + + def _forward_preprocess_batch_context(self, samples: list) -> dict: + """Batch preprocessing for Stage 2 Context Memory. Batch-level drop: if drop, all samples get no context.""" + if not samples: + raise ValueError("samples cannot be empty in _forward_preprocess_batch_context") + batch_size = len(samples) + first = samples[0] + + def _should_drop_context(_data) -> bool: + p = float(getattr(self, "context_drop_prob", 0.0) or 0.0) + if p <= 0.0: + return False + if p >= 1.0: + return True + vn = str(_data.get("video_name", "")) + sf = str(_data.get("start_frame", "")) + key = f"{int(getattr(self, 'context_drop_seed', 42))}|{vn}|{sf}" + h = hashlib.md5(key.encode("utf-8")).hexdigest() + u = int(h[:8], 16) / 0xFFFFFFFF + return u < p + + # Batch-level drop: use first sample to decide for whole batch + dropped_context = _should_drop_context(first) + # IMPORTANT (DDP safety): ensure all ranks make the same drop decision. + # If some ranks drop context while others keep it, modules conditioned on context + # (e.g. implicit encoder / compressor) become unused on a subset of ranks and can + # deadlock gradient sync / trigger NCCL watchdog timeouts. + try: + import torch.distributed as dist + if dist.is_available() and dist.is_initialized(): + flag = torch.tensor([1 if dropped_context else 0], device=self.pipe.device, dtype=torch.int64) + dist.broadcast(flag, src=0) + dropped_context = bool(int(flag.item())) + except Exception: + pass + + prompts = [] + video_frames_list = [] + actions_list = [] + context_latents_list = [] + context_actions_list = [] + expected_k = self.context_memory_frames + training_mode = getattr(self, 'training_mode', 'context') + + target_h = first["video"][0].size[1] + target_w = first["video"][0].size[0] + num_frames = len(first["video"]) + + from PIL import Image + + for s in samples: + p = s.get("prompt") + if p is None: + raise ValueError("sample['prompt'] is missing or None") + prompts.append(str(p) if not isinstance(p, str) else p) + video_frames_list.append(s["video"]) + + if "actions" in s and s["actions"] is not None: + acts = s["actions"] + if getattr(self, 'yaw_flip_aug', False) and isinstance(acts, list) and len(acts) > 0 and isinstance(acts[0], (list, tuple)) and len(acts[0]) >= 12 and random.random() < 0.5: + acts = flip_yaw_rt_list(acts) + if isinstance(acts, torch.Tensor): + actions_list.append(acts) + elif isinstance(acts, list) and len(acts) > 0: + actions_list.append(torch.tensor(acts, dtype=torch.float32)) + else: + actions_list.append(None) + else: + actions_list.append(None) + + if dropped_context: + context_latents_list.append(None) + context_actions_list.append(None) + continue + + ctx_frames = s.get("context_frames") or [] + ctx_actions = [] if getattr(self, "omit_context_actions", False) else (s.get("context_actions") or []) # ctx=1: no context action + context_indices = s.get("context_frame_indices", []) + start_frame = s.get("start_frame", None) + end_frame = s.get("end_frame", None) + + if ctx_frames and context_indices and start_frame is not None and end_frame is not None: + filtered_frames, filtered_actions = [ctx_frames[0]], [] + if ctx_actions: + filtered_actions.append(ctx_actions[0]) + for i in range(1, len(ctx_frames)): + idx = context_indices[i] if i < len(context_indices) else None + if idx is None or idx < start_frame or idx > end_frame: + filtered_frames.append(ctx_frames[i]) + if ctx_actions and i < len(ctx_actions): + filtered_actions.append(ctx_actions[i]) + ctx_frames, ctx_actions = filtered_frames, filtered_actions if filtered_actions else ctx_actions + + if not ctx_frames and len(s["video"]) > expected_k: + ctx_frames = s["video"][:expected_k] + if s.get("actions") and len(s["actions"]) >= expected_k: + ctx_actions = s["actions"][:expected_k] + + if not ctx_frames: + context_latents_list.append(None) + context_actions_list.append(None) + continue + + resized = [] + for f in ctx_frames: + if hasattr(f, 'resize') and hasattr(f, 'size'): + w, h = f.size + if h != target_h or w != target_w: + f = f.resize((target_w, target_h), Image.Resampling.LANCZOS) + resized.append(f) + ctx_frames = resized + + if len(ctx_frames) < expected_k: + last = ctx_frames[-1] if ctx_frames else Image.new('RGB', (target_w, target_h), (0, 0, 0)) + ctx_frames = ctx_frames + [last] * (expected_k - len(ctx_frames)) + if ctx_actions: + ctx_actions = ctx_actions + [ctx_actions[-1]] * (expected_k - len(ctx_actions)) + elif len(ctx_frames) > expected_k: + ctx_frames = ctx_frames[:expected_k] + ctx_actions = ctx_actions[:expected_k] if ctx_actions else [] + + ctx_frames, ctx_actions = self._build_context_with_anchor( + ctx_frames, + context_actions=ctx_actions, + expected_k=expected_k, + ) + + with torch.no_grad(): + if getattr(self, "context_per_frame_vae", False): + # Each context frame -> 1 latent token (no temporal downsample); context_actions remain one per raw frame + context_latents_per_sample = [] + for f in ctx_frames: + frame_video = self.pipe.preprocess_video([f]) # (1, C, 1, H, W) + frame_sq = frame_video.squeeze(0) # (C, 1, H, W) + lat_one = self.pipe.vae.encode([frame_sq], device=self.pipe.device, tiled=False, tile_size=None, tile_stride=None) + context_latents_per_sample.append(lat_one) + lat = torch.cat(context_latents_per_sample, dim=2) # (1, C, K, H//8, W//8) + else: + ctx_video = self.pipe.preprocess_video(ctx_frames) + if ctx_video.dim() == 4: + ctx_video = ctx_video.unsqueeze(0) + lat = self.pipe.vae.encode([ctx_video[i] for i in range(ctx_video.shape[0])], device=self.pipe.device, tiled=False, tile_size=None, tile_stride=None) + context_latents_list.append(lat.to(dtype=self.pipe.torch_dtype, device=self.pipe.device)) + + if ctx_actions: + if isinstance(ctx_actions[0], (list, tuple)): + context_actions_list.append(torch.tensor(ctx_actions, dtype=torch.float32)) + else: + context_actions_list.append(torch.tensor(ctx_actions, dtype=torch.float32)) + else: + context_actions_list.append(None) + + input_video = video_frames_list + inputs_posi = {"prompt": prompts} + inputs_nega = {} + inputs_shared = { + "input_video": input_video, + "height": target_h, + "width": target_w, + "num_frames": num_frames, + "batch_size": batch_size, + "cfg_scale": 1, + "tiled": False, + "rand_device": self.pipe.device, + "use_gradient_checkpointing": self.use_gradient_checkpointing, + "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload, + "cfg_merge": False, + "vace_scale": 1, + } + + # DDP safety: ensure *all* ranks either have context (and thus use context-conditioned modules) + # or all ranks drop it. Using an all-reduce MIN means if any rank lacks context, we drop globally. + has_context_step = (not dropped_context) and any(x is not None for x in context_latents_list) + try: + import torch.distributed as dist + if dist.is_available() and dist.is_initialized(): + flag = torch.tensor([1 if has_context_step else 0], device=self.pipe.device, dtype=torch.int64) + dist.all_reduce(flag, op=dist.ReduceOp.MIN) + has_context_step = bool(int(flag.item())) + except Exception: + pass + if not has_context_step: + dropped_context = True + + if not dropped_context and any(x is not None for x in context_latents_list): + valid = [x for x in context_latents_list if x is not None] + if valid: + ref = valid[0] + device, dtype = self.pipe.device, ref.dtype + stacked_ctx = [] + for x in context_latents_list: + if x is not None: + stacked_ctx.append(x.to(device=device)) + else: + stacked_ctx.append(torch.zeros_like(ref, device=device, dtype=dtype)) + inputs_shared["context_latents"] = torch.cat(stacked_ctx, dim=0) + inputs_shared["num_context_frames"] = ref.shape[2] + inputs_shared["training_mode"] = training_mode + inputs_shared["context_noise_prob"] = getattr(self, 'context_noise_prob', 0.0) + inputs_shared["context_noise_std"] = getattr(self, 'context_noise_std', 0.02) + if self.context_fixed_noise_std is not None: + inputs_shared["context_fixed_noise_std"] = self.context_fixed_noise_std + inputs_shared["context_position"] = os.environ.get("CONTEXT_POSITION", "suffix") + inputs_shared["omit_context_actions"] = getattr(self, "omit_context_actions", False) + inputs_shared["context_attention_weight"] = getattr(self, "context_attention_weight", 1.0) + inputs_shared["use_anchor_frame"] = getattr(self, "use_anchor_frame", False) + inputs_shared["context_temporal_decay"] = getattr(self, "context_temporal_decay", 1.0) + inputs_shared["use_spatial_memory"] = getattr(self.pipe, "use_spatial_memory", False) + inputs_shared["spatial_memory_tokens"] = int(getattr(self.pipe, "spatial_memory_tokens", 64) or 64) + inputs_shared["use_spatial_memory_legacy"] = bool(getattr(self.pipe, "use_spatial_memory_legacy", False)) + inputs_shared["spatial_memory_module"] = getattr(self.pipe, "spatial_memory_module", None) + inputs_shared["spatial_memory_inject_mode"] = getattr(self.pipe, "spatial_memory_inject_mode", "concat_text") + inputs_shared["spatial_memory_readout_module"] = getattr(self.pipe, "spatial_memory_readout_module", None) + inputs_shared["use_framepack_memory"] = bool(getattr(self, "use_framepack_memory", False)) + nf_list = [s.get("non_fov_frames") or [] for s in samples] + if any(nf for nf in nf_list): + inputs_shared["non_fov_frames_list"] = nf_list + + ctx_acts_valid = [a for a in context_actions_list if a is not None] + if not getattr(self, "omit_context_actions", False) and ctx_acts_valid: + ref_act = ctx_acts_valid[0] + target_len = ref_act.shape[0] # num_context_frames (K) + stacked_ca = [] + for a in context_actions_list: + if a is not None: + a = a.to(device=device) + if a.shape[0] != target_len: + if a.shape[0] > target_len: + a = a[:target_len] + else: + pad = a.new_zeros(target_len - a.shape[0], a.shape[-1]) + a = torch.cat([a, pad], dim=0) + stacked_ca.append(a) + else: + stacked_ca.append(torch.zeros_like(ref_act, device=device, dtype=ref_act.dtype)) + inputs_shared["context_actions"] = torch.stack(stacked_ca) + + ref_action = next((a for a in actions_list if a is not None), None) + if ref_action is not None and batch_size == 1: + inputs_shared["actions"] = ref_action.detach().cpu().tolist() if isinstance(ref_action, torch.Tensor) else ref_action + elif ref_action is not None: + device = self.pipe.device + dtype = ref_action.dtype + stacked = [] + for a in actions_list: + if a is not None: + stacked.append(a.to(device=device)) + else: + stacked.append(torch.zeros_like(ref_action, device=device, dtype=dtype)) + inputs_shared["actions"] = torch.stack(stacked) + else: + inputs_shared["actions"] = None + + for unit in self.pipe.units: + inputs_shared, inputs_posi, inputs_nega = self.pipe.unit_runner(unit, self.pipe, inputs_shared, inputs_posi, inputs_nega) + return {**inputs_shared, **inputs_posi} + + @staticmethod + def _translate_condition_keys(d): + """Map VWM CamVideoDataset condition_* keys to context-memory keys.""" + if not isinstance(d, dict): + return d + if "condition_frames" in d and "context_frames" not in d: + d["context_frames"] = d.pop("condition_frames") + if "condition_actions" in d and "context_actions" not in d: + d["context_actions"] = d.pop("condition_actions") + if "condition_frame_indices" in d and "context_frame_indices" not in d: + d["context_frame_indices"] = d.pop("condition_frame_indices") + if "use_condition_context_frames" in d: + d.pop("use_condition_context_frames") + if "condition_source" in d: + d.pop("condition_source", None) + return d + + def forward_preprocess(self, data): + if data is None: + raise ValueError("data cannot be None in forward_preprocess") + samples = data if isinstance(data, list) else [data] + samples = [self._translate_condition_keys(d) for d in samples] + if self.enable_context_memory: + return self._forward_preprocess_batch_context(samples) + return self._forward_preprocess_batch(samples) + + def _ensure_input_latents(self, inputs: Dict[str, Any], *, strict: bool = False) -> Dict[str, Any]: + if "input_latents" in inputs: + return inputs + import warnings + video_obj = inputs.get("input_video", None) + if video_obj is None: + video_obj = inputs.get("video", None) + vae = getattr(self.pipe, "vae", None) + if video_obj is not None and vae is not None and hasattr(vae, "encode"): + try: + if isinstance(video_obj, list): + video_tensor = self.pipe.preprocess_video(video_obj) + else: + video_tensor = video_obj + if hasattr(video_tensor, "dim"): + video_sq = video_tensor.squeeze(0) if video_tensor.dim() == 5 else video_tensor + with torch.no_grad(): + try: + lat = vae.encode(video_tensor, device=self.pipe.device, tiled=False, tile_size=None, tile_stride=None) + except Exception: + lat = vae.encode([video_sq], device=self.pipe.device, tiled=False, tile_size=None, tile_stride=None) + if isinstance(lat, (list, tuple)): + lat = lat[0] + if hasattr(lat, "dim") and lat.dim() == 4: + lat = lat.unsqueeze(0) + inputs["input_latents"] = lat.to(dtype=torch.bfloat16, device=self.pipe.device) + return inputs + except Exception as e: + warnings.warn(f"Failed to rebuild input_latents: {e}") + msg = ( + "input_latents missing and auto-rebuild failed. " + f"available input keys={sorted(list(inputs.keys()))}" + ) + if strict: + raise KeyError(msg) + warnings.warn(msg) + return inputs + + + def forward(self, data, inputs=None): + if inputs is None: + inputs = self.forward_preprocess(data) + models = {name: getattr(self.pipe, name) for name in self.pipe.in_iteration_models} + if self.enable_context_memory and "context_latents" in inputs: + return self._training_loss_with_context(**models, **inputs) + inputs = self._ensure_input_latents(inputs, strict=True) + return self.pipe.training_loss(**models, **inputs) + + def _training_loss_with_context(self, **kwargs): + context_latents = kwargs.pop("context_latents", None) + num_context_frames = kwargs.pop("num_context_frames", 0) + models = {k: v for k, v in kwargs.items() if k in self.pipe.in_iteration_models} + inputs = {k: v for k, v in kwargs.items() if k not in self.pipe.in_iteration_models} + if context_latents is not None: + inputs.update({ + "context_latents": context_latents, + "num_context_frames": num_context_frames, + "context_noise_prob": self.context_noise_prob, + "context_noise_std": self.context_noise_std, + "context_attention_weight": getattr(self, "context_attention_weight", 1.0), + "use_anchor_frame": getattr(self, "use_anchor_frame", False), + "context_temporal_decay": getattr(self, "context_temporal_decay", 1.0), + "use_spatial_memory": getattr(self.pipe, "use_spatial_memory", False), + "spatial_memory_tokens": int(getattr(self.pipe, "spatial_memory_tokens", 64) or 64), + "use_spatial_memory_legacy": bool(getattr(self.pipe, "use_spatial_memory_legacy", False)), + "spatial_memory_module": getattr(self.pipe, "spatial_memory_module", None), + "spatial_memory_inject_mode": getattr(self.pipe, "spatial_memory_inject_mode", "concat_text"), + "spatial_memory_readout_module": getattr(self.pipe, "spatial_memory_readout_module", None), + "use_framepack_memory": bool(getattr(self, "use_framepack_memory", False)), + }) + if self.context_fixed_noise_std is not None: + inputs["context_fixed_noise_std"] = self.context_fixed_noise_std + inputs = self._ensure_input_latents(inputs, strict=True) + return self.pipe.training_loss(**models, **inputs) + diff --git a/src/model_training/transformers_compat.py b/src/model_training/transformers_compat.py new file mode 100644 index 0000000000000000000000000000000000000000..aa1d34ba6f83a9c1fca00ffcff4f2edb8aafedd1 --- /dev/null +++ b/src/model_training/transformers_compat.py @@ -0,0 +1,25 @@ +def patch_transformers_hybrid_cache() -> None: + """Map missing transformers.HybridCache to DynamicCache for newer peft.""" + try: + import transformers + except Exception: + return + + if hasattr(transformers, "HybridCache"): + return + + lazy_module_cls = type(transformers) + original_getattr = getattr(lazy_module_cls, "__getattr__", None) + if original_getattr is not None and not getattr(lazy_module_cls, "_echo_memory_hybrid_cache_patch", False): + def _compat_getattr(self, name): + if name == "HybridCache": + return original_getattr(self, "DynamicCache") + return original_getattr(self, name) + + lazy_module_cls.__getattr__ = _compat_getattr + lazy_module_cls._echo_memory_hybrid_cache_patch = True + + try: + transformers.HybridCache = transformers.DynamicCache + except Exception: + pass