| import math |
| from typing import TYPE_CHECKING |
|
|
| if TYPE_CHECKING: |
| from modules.prompt_parser import SdConditioning |
|
|
| import torch |
| from huggingface_guess import model_list |
|
|
| from backend import memory_management |
| from backend.args import dynamic_args |
| from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects |
| from backend.modules.k_prediction import PredictionDiscreteFlow |
| from backend.patcher.clip import CLIP |
| from backend.patcher.unet import UnetPatcher |
| from backend.patcher.vae import VAE |
| from backend.text_processing.qwen_engine import QwenTextProcessingEngine |
| from modules.shared import opts |
|
|
|
|
| class QwenImage(ForgeDiffusionEngine): |
| matched_guesses = [model_list.QwenImage] |
|
|
| def __init__(self, estimated_config, huggingface_components): |
| super().__init__(estimated_config, huggingface_components) |
| self.is_inpaint = False |
|
|
| clip = CLIP(model_dict={"qwen25_7b": huggingface_components["text_encoder"]}, tokenizer_dict={"qwen25_7b": huggingface_components["tokenizer"]}) |
|
|
| vae = VAE(model=huggingface_components["vae"], is_wan=True) |
| vae.first_stage_model.latent_format = self.model_config.latent_format |
|
|
| k_predictor = PredictionDiscreteFlow(estimated_config) |
|
|
| unet = UnetPatcher.from_model(model=huggingface_components["transformer"], diffusers_scheduler=None, k_predictor=k_predictor, config=estimated_config) |
|
|
| self.text_processing_engine_qwen = QwenTextProcessingEngine( |
| text_encoder=clip.cond_stage_model.qwen25_7b, |
| tokenizer=clip.tokenizer.qwen25_7b, |
| ) |
|
|
| self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None) |
| self.forge_objects_original = self.forge_objects.shallow_copy() |
| self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy() |
|
|
| self.is_wan = True |
|
|
| self.images_vl = [] |
| self.ref_latents = [] |
| self.image_prompt = "" |
|
|
| @torch.inference_mode() |
| def get_learned_conditioning(self, prompt: "SdConditioning"): |
| memory_management.load_model_gpu(self.forge_objects.clip.patcher) |
| if not prompt.is_negative_prompt: |
| if self.image_prompt: |
| return self.get_learned_conditioning_with_image(prompt) |
| else: |
| dynamic_args["ref_latents"].clear() |
| self.ref_latents.clear() |
| self.image_prompt = "" |
| return self.text_processing_engine_qwen(prompt) |
|
|
| @torch.inference_mode() |
| def get_learned_conditioning_with_image(self, prompt: list[str]): |
| cond = self.text_processing_engine_qwen([self.image_prompt + "".join(prompt)], images=self.images_vl) |
| self.images_vl.clear() |
| dynamic_args["ref_latents"] = self.ref_latents.copy() |
| self.ref_latents.clear() |
| self.image_prompt = "" |
| return cond |
|
|
| @torch.inference_mode() |
| def get_prompt_lengths_on_ui(self, prompt): |
| token_count = len(self.text_processing_engine_qwen.tokenize([prompt])[0]) |
| return token_count, max(999, token_count) |
|
|
| @torch.inference_mode() |
| def encode_vision(self, image: torch.Tensor): |
| samples = image.movedim(-1, 1) |
|
|
| total = int(384 * 384) |
| scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) |
| width = round(samples.shape[3] * scale_by) |
| height = round(samples.shape[2] * scale_by) |
|
|
| s = torch.nn.functional.interpolate(samples, size=(height, width), mode="area") |
| self.images_vl.append(s.movedim(1, -1)) |
|
|
| if opts.qwen_vae_resize: |
| total = int(1024 * 1024) |
| scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) |
| width = round(samples.shape[3] * scale_by / 32.0) * 32 |
| height = round(samples.shape[2] * scale_by / 32.0) * 32 |
|
|
| s = torch.nn.functional.interpolate(samples, size=(height, width), mode="area") |
| else: |
| s = samples.clone() |
| sample = self.forge_objects.vae.encode(s.movedim(1, -1)[:, :, :, :3]) |
| self.ref_latents.append(self.forge_objects.vae.first_stage_model.process_in(sample)) |
|
|
| self.image_prompt += f"Picture {len(self.images_vl)}: <|vision_start|><|image_pad|><|vision_end|>" |
|
|
| @torch.inference_mode() |
| def encode_first_stage(self, x): |
| if x.size(0) > 1: |
| x = x[0].unsqueeze(0) |
| start_image = x.movedim(1, -1) * 0.5 + 0.5 |
| self.encode_vision(start_image) |
| sample = self.forge_objects.vae.encode(start_image) |
| sample = self.forge_objects.vae.first_stage_model.process_in(sample) |
| return sample.to(x) |
|
|
| @torch.inference_mode() |
| def decode_first_stage(self, x): |
| sample = self.forge_objects.vae.first_stage_model.process_out(x) |
| sample = self.forge_objects.vae.decode(sample).movedim(-1, 2) * 2.0 - 1.0 |
| return sample.to(x) |
|
|
