| |
| import json |
| import os |
| import sys |
| from typing import Sequence, Mapping, Any, Union |
|
|
| ALIGN_MODEL_TYPE = "SDXL" |
| ALIGN_SCHEDULER = "AlignYourSteps" |
| GENERATION = 2 |
| ANIMA_CLIP = "2_qwen_3_06b_base.safetensors" |
| GRID_SIZE = 5 |
| LATENT_SCALE = 8 |
| REGIONAL_FEATHER = .5 |
| DETAILER_GUIDE_SIZE = 768 |
| DETAILER_MAX_SIZE = 1024 |
| DETAILER_THRESHOLD = .5 |
| DETAILER_DILATION = 10 |
| DETAILER_CROP = 3 |
| DETAILER_FEATHER = 5 |
| DETAILER_DROP_SIZE = 10 |
| STYLE_IPADAPTER = "2_ip-adapter-plus_sdxl_vit-h.safetensors" |
| STYLE_CLIP_VISION = "1_CLIP-ViT-H-fp16.safetensors" |
| STYLE_WEIGHT_TYPE = "style transfer" |
| STYLE_EMBEDS_SCALING = "V only" |
| STYLE_IMAGE_SIZE = 1024 |
|
|
|
|
| def upscale_size(width, height, scale): |
| return tuple( |
| round(value / LATENT_SCALE * scale) * LATENT_SCALE |
| for value in (width, height) |
| ) |
|
|
|
|
| def mask_box(x, y, width, height, image_width, image_height): |
| x1 = round(x * image_width) |
| y1 = round(y * image_height) |
| x2 = round((x + width) * image_width) |
| y2 = round((y + height) * image_height) |
| box_width = x2 - x1 |
| box_height = y2 - y1 |
| x_edges = int(x1 > 0) + int(x2 < image_width) |
| y_edges = int(y1 > 0) + int(y2 < image_height) |
| feather_x = min( |
| round(image_width / GRID_SIZE * REGIONAL_FEATHER), |
| box_width // max(1, x_edges), |
| ) |
| feather_y = min( |
| round(image_height / GRID_SIZE * REGIONAL_FEATHER), |
| box_height // max(1, y_edges), |
| ) |
| return ( |
| x1, |
| y1, |
| box_width, |
| box_height, |
| feather_x if x1 else 0, |
| feather_y if y1 else 0, |
| feather_x if x2 < image_width else 0, |
| feather_y if y2 < image_height else 0, |
| ) |
|
|
|
|
| def is_anima_model(name): |
| number, separator, model = ( |
| name.rsplit("/", 1)[-1].casefold().partition("_") |
| ) |
| return ( |
| bool(separator) |
| and number.isdigit() |
| and model.startswith("anima") |
| and not model.startswith("animag") |
| ) |
|
|
|
|
| def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any: |
| """Return a sequence or mapping result item by index.""" |
| try: |
| return obj[index] |
| except KeyError: |
| return obj["result"][index] |
|
|
|
|
| def get_comfyui_path() -> str: |
| """Return the configured ComfyUI path, preferring COMFYUI_PATH when set.""" |
| comfyui_path = os.environ.get("COMFYUI_PATH") |
| if comfyui_path: |
| return comfyui_path |
| return find_path("ComfyUI") |
|
|
|
|
| def find_path(name: str, path: str = None) -> str: |
| """Recursively search parent folders until the named entry is found.""" |
| if path is None: |
| path = os.getcwd() |
|
|
| if name in os.listdir(path): |
| path_name = os.path.join(path, name) |
| print(f"{name} found: {path_name}") |
| return path_name |
|
|
| parent_directory = os.path.dirname(path) |
| if parent_directory == path: |
| return None |
|
|
| return find_path(name, parent_directory) |
|
|
|
|
| def add_comfyui_directory_to_sys_path() -> None: |
| """Add the ComfyUI checkout to sys.path.""" |
| comfyui_path = get_comfyui_path() |
| if comfyui_path is not None and os.path.isdir(comfyui_path): |
| if comfyui_path in sys.path: |
| sys.path.remove(comfyui_path) |
| sys.path.insert(0, comfyui_path) |
| print(f"'{comfyui_path}' added to sys.path") |
|
|
|
|
| def add_extra_model_paths() -> None: |
| """Load ComfyUI extra model paths configuration when available.""" |
| try: |
| from main import load_extra_path_config |
| except ImportError: |
| print( |
| "Could not import load_extra_path_config from main.py. Looking in utils.extra_config instead." |
| ) |
| from utils.extra_config import load_extra_path_config |
|
|
| extra_model_paths = find_path("extra_model_paths.yaml") |
| if extra_model_paths is not None: |
| load_extra_path_config(extra_model_paths) |
| else: |
| print("Could not find the extra_model_paths config file.") |
|
|
|
|
| def bootstrap_comfyui_runtime() -> None: |
| """Mirror the allocator-related ComfyUI startup steps before torch import.""" |
| add_comfyui_directory_to_sys_path() |
|
|
| import comfy.options |
|
|
| comfy.options.enable_args_parsing() |
|
|
| from comfy.cli_args import args |
|
|
| if os.name == "nt": |
| os.environ["MIMALLOC_PURGE_DELAY"] = "0" |
|
|
| if args.default_device is not None: |
| default_dev = args.default_device |
| devices = list(range(32)) |
| devices.remove(default_dev) |
| devices.insert(0, default_dev) |
| devices = ",".join(map(str, devices)) |
| os.environ["CUDA_VISIBLE_DEVICES"] = str(devices) |
| os.environ["HIP_VISIBLE_DEVICES"] = str(devices) |
|
|
| if args.cuda_device is not None: |
| os.environ["CUDA_VISIBLE_DEVICES"] = str(args.cuda_device) |
| os.environ["HIP_VISIBLE_DEVICES"] = str(args.cuda_device) |
| os.environ["ASCEND_RT_VISIBLE_DEVICES"] = str(args.cuda_device) |
|
|
| if args.oneapi_device_selector is not None: |
| os.environ["ONEAPI_DEVICE_SELECTOR"] = args.oneapi_device_selector |
|
|
| if args.deterministic and "CUBLAS_WORKSPACE_CONFIG" not in os.environ: |
| os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" |
|
|
| import cuda_malloc |
|
|
| if "rocm" in cuda_malloc.get_torch_version_noimport(): |
| os.environ["OCL_SET_SVM_SIZE"] = "262144" |
|
|
|
|
| def cleanup_comfyui_runtime(unload_models: bool | None = None) -> None: |
| """Best-effort cleanup for embedded or repeated generated-script execution.""" |
| import gc |
|
|
| def run_cleanup_hook(name: str, should_run: bool = True) -> None: |
| if not should_run or not hasattr(model_management, name): |
| return |
| cleanup_fn = getattr(model_management, name) |
| try: |
| cleanup_fn() |
| except Exception as exc: |
| warnings.warn( |
| f"ComfyUI cleanup hook {name} failed during teardown: {exc}", |
| RuntimeWarning, |
| stacklevel=2, |
| ) |
|
|
| should_unload = unload_models |
| if should_unload is None: |
| should_unload = os.environ.get( |
| "COMFYUI_TOPYTHON_UNLOAD_MODELS", "" |
| ).lower() in { |
| "1", |
| "true", |
| "yes", |
| "on", |
| } |
|
|
| try: |
| import comfy.model_management as model_management |
| except ModuleNotFoundError: |
| gc.collect() |
| return |
|
|
| run_cleanup_hook("cleanup_models_gc") |
| run_cleanup_hook("unload_all_models", should_run=should_unload) |
| run_cleanup_hook("soft_empty_cache") |
| gc.collect() |
|
|
|
|
| def import_custom_nodes() -> None: |
| """Initialize ComfyUI custom nodes in the exporter runtime.""" |
| comfyui_path = get_comfyui_path() |
| if comfyui_path and comfyui_path not in sys.path: |
| sys.path.insert(0, comfyui_path) |
|
|
| import asyncio |
| import execution |
| from nodes import init_extra_nodes |
|
|
| if comfyui_path in sys.path: |
| sys.path.remove(comfyui_path) |
| sys.path.insert(0, comfyui_path) |
|
|
| import server |
| from app.assets.manager import default_asset_manager |
|
|
| loop = asyncio.new_event_loop() |
| asyncio.set_event_loop(loop) |
| try: |
| server_instance = server.PromptServer(loop, default_asset_manager()) |
| execution.PromptQueue(server_instance) |
| loop.run_until_complete(init_extra_nodes()) |
| finally: |
| asyncio.set_event_loop(None) |
| loop.close() |
|
|
|
|
| |
| def build_workflow() -> dict[str, Any]: |
| return { |
| "1": { |
| "inputs": {"ckpt_name": "52_novaAnimeXL_ilV190.safetensors"}, |
| "class_type": "CheckpointLoaderSimple", |
| "_meta": {"title": "Loader"}, |
| }, |
| "2": { |
| "inputs": {"text": ["118", 0], "clip": ["117", 1]}, |
| "class_type": "CLIPTextEncode", |
| "_meta": {"title": "CLIP Text Encode (Prompt)"}, |
| }, |
| "3": { |
| "inputs": { |
| "text": "(censored, mosaic censoring, bar censor:1.1), bad " |
| "quality, worst quality, worst detail, bad anatomy, " |
| "extra fingers, extra toes, extra legs, 4 toes, 6 " |
| "toes, 4 fingers, 6 fingers, malformed fingers, " |
| "extra limbs, missing fingers, extra arms, censored, " |
| "deformed, disfigured, text, (multiple views:1.1)", |
| "clip": ["28", 1], |
| }, |
| "class_type": "CLIPTextEncode", |
| "_meta": {"title": "CLIP Text Encode (Prompt)"}, |
| }, |
| "5": { |
| "inputs": { |
| "seed": 809278554234612, |
| "steps": 16, |
| "cfg": 4, |
| "sampler_name": "euler_ancestral", |
| "scheduler": "karras", |
| "denoise": 1, |
| "model": ["117", 0], |
| "positive": ["2", 0], |
| "negative": ["3", 0], |
| "latent_image": ["27", 0], |
| }, |
| "class_type": "KSampler", |
| "_meta": {"title": "KSampler"}, |
| }, |
| "10": { |
| "inputs": { |
| "lora_name": "8_bikabaka.safetensors", |
| "strength_model": 0.3, |
| "strength_clip": 0, |
| "model": ["1", 0], |
| "clip": ["1", 1], |
| }, |
| "class_type": "LoraLoader", |
| "_meta": {"title": "Load LoRA"}, |
| }, |
| "27": { |
| "inputs": {"width": 1152, "height": 896, "batch_size": 1}, |
| "class_type": "EmptyLatentImage", |
| "_meta": {"title": "Empty Landscape"}, |
| }, |
| "28": { |
| "inputs": { |
| "lora_name": "43_5cm-illustriousXL_v01_V1-CAME-000035.safetensors", |
| "strength_model": 0.4, |
| "strength_clip": 0, |
| "model": ["38", 0], |
| "clip": ["38", 1], |
| }, |
| "class_type": "LoraLoader", |
| "_meta": {"title": "Load LoRA"}, |
| }, |
| "38": { |
| "inputs": { |
| "lora_name": "42_アップスケール_remacri_original.pt", |
| "strength_model": 0.4, |
| "strength_clip": 0, |
| "model": ["10", 0], |
| "clip": ["10", 1], |
| }, |
| "class_type": "LoraLoader", |
| "_meta": {"title": "Load LoRA"}, |
| }, |
| "45": { |
| "inputs": {"vae_name": "3_sdxlVAE_sdxlVAE.safetensors"}, |
| "class_type": "VAELoader", |
| "_meta": {"title": "Load VAE"}, |
| }, |
| "56": { |
| "inputs": {"samples": ["5", 0], "vae": ["45", 0]}, |
| "class_type": "VAEDecode", |
| "_meta": {"title": "VAE Decode"}, |
| }, |
| "72": { |
| "inputs": {"images": ["56", 0]}, |
| "class_type": "PreviewImage", |
| "_meta": {"title": "Preview Image"}, |
| }, |
| "117": { |
| "inputs": { |
| "lora_name": "5_add_saturation_XL.safetensors", |
| "strength_model": -1.4, |
| "strength_clip": 0, |
| "model": ["28", 0], |
| "clip": ["28", 1], |
| }, |
| "class_type": "LoraLoader", |
| "_meta": {"title": "Load LoRA (Model and CLIP)"}, |
| }, |
| "118": { |
| "inputs": { |
| "string_a": "%prompt%", |
| "string_b": "", |
| "delimiter": "", |
| }, |
| "class_type": "StringConcatenate", |
| "_meta": {"title": "Concatenate Text"}, |
| }, |
| } |
|
|
|
|
| def build_extra_pnginfo() -> dict[str, Any] | None: |
| return { |
| "workflow": { |
| "id": "e69619af-5ceb-4a83-821d-68180291905e", |
| "revision": 0, |
| "last_node_id": 121, |
| "last_link_id": 57, |
| "nodes": [ |
| { |
| "id": 27, |
| "type": "EmptyLatentImage", |
| "pos": [100, 358], |
| "size": [270, 106], |
| "flags": {}, |
| "order": 0, |
| "mode": 0, |
| "inputs": [], |
| "outputs": [{"name": "LATENT", "type": "LATENT", "links": [24]}], |
| "title": "Empty Landscape", |
| "properties": {"Node name for S&R": "EmptyLatentImage"}, |
| "widgets_values": [1152, 896, 1], |
| }, |
| { |
| "id": 45, |
| "type": "VAELoader", |
| "pos": [100, 594], |
| "size": [270, 58], |
| "flags": {}, |
| "order": 1, |
| "mode": 0, |
| "inputs": [], |
| "outputs": [{"name": "VAE", "type": "VAE", "links": [32]}], |
| "properties": {"Node name for S&R": "VAELoader"}, |
| "widgets_values": ["3_sdxlVAE_sdxlVAE.safetensors"], |
| }, |
| { |
| "id": 56, |
| "type": "VAEDecode", |
| "pos": [2948.649165895271, 134.76727061509087], |
| "size": [140, 46], |
| "flags": {}, |
| "order": 11, |
| "mode": 0, |
| "inputs": [ |
| {"name": "samples", "type": "LATENT", "link": 31}, |
| {"name": "vae", "type": "VAE", "link": 32}, |
| ], |
| "outputs": [{"name": "IMAGE", "type": "IMAGE", "links": [33]}], |
| "properties": {"Node name for S&R": "VAEDecode"}, |
| "widgets_values": [], |
| }, |
| { |
| "id": 10, |
| "type": "LoraLoader", |
| "pos": [600, 130], |
| "size": [290.43334045410154, 126], |
| "flags": {}, |
| "order": 4, |
| "mode": 0, |
| "inputs": [ |
| {"name": "model", "type": "MODEL", "link": 25}, |
| {"name": "clip", "type": "CLIP", "link": 26}, |
| ], |
| "outputs": [ |
| {"name": "MODEL", "type": "MODEL", "links": [29]}, |
| {"name": "CLIP", "type": "CLIP", "links": [30]}, |
| ], |
| "title": "Load LoRA", |
| "properties": {"Node name for S&R": "LoraLoader"}, |
| "widgets_values": ["8_bikabaka.safetensors", 0.3, 0], |
| }, |
| { |
| "id": 72, |
| "type": "PreviewImage", |
| "pos": [3188.649165895271, 134.76727061509087], |
| "size": [285.77604360195164, 258], |
| "flags": {}, |
| "order": 12, |
| "mode": 0, |
| "inputs": [{"name": "images", "type": "IMAGE", "link": 33}], |
| "outputs": [{"name": "images", "type": "IMAGE", "links": None}], |
| "properties": {"Node name for S&R": "PreviewImage"}, |
| "widgets_values": [], |
| }, |
| { |
| "id": 1, |
| "type": "CheckpointLoaderSimple", |
| "pos": [100, 130], |
| "size": [270, 98], |
| "flags": {}, |
| "order": 2, |
| "mode": 0, |
| "inputs": [], |
| "outputs": [ |
| {"name": "MODEL", "type": "MODEL", "links": [25]}, |
| {"name": "CLIP", "type": "CLIP", "links": [26]}, |
| {"name": "VAE", "type": "VAE", "links": None}, |
| ], |
| "title": "Loader", |
| "properties": {"Node name for S&R": "CheckpointLoaderSimple"}, |
| "widgets_values": ["52_novaAnimeXL_ilV190.safetensors"], |
| }, |
| { |
| "id": 2, |
| "type": "CLIPTextEncode", |
| "pos": [1880.866680908203, 130], |
| "size": [400, 200], |
| "flags": {}, |
| "order": 9, |
| "mode": 0, |
| "inputs": [ |
| {"name": "clip", "type": "CLIP", "link": 49}, |
| { |
| "name": "text", |
| "type": "STRING", |
| "widget": {"name": "text"}, |
| "link": 54, |
| }, |
| ], |
| "outputs": [ |
| {"name": "CONDITIONING", "type": "CONDITIONING", "links": [39]} |
| ], |
| "properties": {"Node name for S&R": "CLIPTextEncode"}, |
| "widgets_values": [""], |
| }, |
| { |
| "id": 5, |
| "type": "KSampler", |
| "pos": [2578.649165895271, 134.76727061509087], |
| "size": [270, 262], |
| "flags": {}, |
| "order": 10, |
| "mode": 0, |
| "inputs": [ |
| {"name": "model", "type": "MODEL", "link": 50}, |
| {"name": "positive", "type": "CONDITIONING", "link": 39}, |
| {"name": "negative", "type": "CONDITIONING", "link": 23}, |
| {"name": "latent_image", "type": "LATENT", "link": 24}, |
| ], |
| "outputs": [{"name": "LATENT", "type": "LATENT", "links": [31]}], |
| "properties": {"Node name for S&R": "KSampler"}, |
| "widgets_values": [ |
| 809278554234612, |
| "randomize", |
| 16, |
| 4, |
| "euler_ancestral", |
| "karras", |
| 1, |
| ], |
| }, |
| { |
| "id": 38, |
| "type": "LoraLoader", |
| "pos": [990, 130], |
| "size": [290.43334045410154, 126], |
| "flags": {}, |
| "order": 5, |
| "mode": 0, |
| "inputs": [ |
| {"name": "model", "type": "MODEL", "link": 29}, |
| {"name": "clip", "type": "CLIP", "link": 30}, |
| ], |
| "outputs": [ |
| {"name": "MODEL", "type": "MODEL", "links": [27]}, |
| {"name": "CLIP", "type": "CLIP", "links": [28]}, |
| ], |
| "title": "Load LoRA", |
| "properties": {"Node name for S&R": "LoraLoader"}, |
| "widgets_values": ["42_アップスケール_remacri_original.pt", 0.4, 0], |
| }, |
| { |
| "id": 28, |
| "type": "LoraLoader", |
| "pos": [1380, 130], |
| "size": [290.43334045410154, 126], |
| "flags": {}, |
| "order": 6, |
| "mode": 0, |
| "inputs": [ |
| {"name": "model", "type": "MODEL", "link": 27}, |
| {"name": "clip", "type": "CLIP", "link": 28}, |
| ], |
| "outputs": [ |
| {"name": "MODEL", "type": "MODEL", "links": [47]}, |
| {"name": "CLIP", "type": "CLIP", "links": [20, 48]}, |
| ], |
| "title": "Load LoRA", |
| "properties": {"Node name for S&R": "LoraLoader"}, |
| "widgets_values": [ |
| "43_5cm-illustriousXL_v01_V1-CAME-000035.safetensors", |
| 0.4, |
| 0, |
| ], |
| }, |
| { |
| "id": 117, |
| "type": "LoraLoader", |
| "pos": [1523.7427746854546, 341.08039710943746], |
| "size": [290.43334045410154, 126], |
| "flags": {}, |
| "order": 8, |
| "mode": 0, |
| "inputs": [ |
| {"name": "model", "type": "MODEL", "link": 47}, |
| {"name": "clip", "type": "CLIP", "link": 48}, |
| ], |
| "outputs": [ |
| {"name": "MODEL", "type": "MODEL", "links": [50]}, |
| {"name": "CLIP", "type": "CLIP", "links": [49]}, |
| ], |
| "properties": {"Node name for S&R": "LoraLoader"}, |
| "widgets_values": ["5_add_saturation_XL.safetensors", -1.4, 0], |
| }, |
| { |
| "id": 118, |
| "type": "StringConcatenate", |
| "pos": [1363.7030337022063, -256.7007293998441], |
| "size": [400, 200], |
| "flags": {}, |
| "order": 3, |
| "mode": 0, |
| "inputs": [], |
| "outputs": [{"name": "STRING", "type": "STRING", "links": [54]}], |
| "properties": {"Node name for S&R": "StringConcatenate"}, |
| "widgets_values": [ |
| "%prompt%", |
| "", |
| "", |
| ], |
| }, |
| { |
| "id": 3, |
| "type": "CLIPTextEncode", |
| "pos": [1881.5394309031356, 459.32725000506747], |
| "size": [400, 200], |
| "flags": {}, |
| "order": 7, |
| "mode": 0, |
| "inputs": [{"name": "clip", "type": "CLIP", "link": 20}], |
| "outputs": [ |
| {"name": "CONDITIONING", "type": "CONDITIONING", "links": [23]} |
| ], |
| "properties": {"Node name for S&R": "CLIPTextEncode"}, |
| "widgets_values": [ |
| "(censored, mosaic censoring, bar " |
| "censor:1.1), bad quality, worst " |
| "quality, worst detail, bad " |
| "anatomy, extra fingers, extra " |
| "toes, extra legs, 4 toes, 6 toes, " |
| "4 fingers, 6 fingers, malformed " |
| "fingers, extra limbs, missing " |
| "fingers, extra arms, censored, " |
| "deformed, disfigured, text, " |
| "(multiple views:1.1)" |
| ], |
| }, |
| ], |
| "links": [ |
| [20, 28, 1, 3, 0, "CLIP"], |
| [23, 3, 0, 5, 2, "CONDITIONING"], |
| [24, 27, 0, 5, 3, "LATENT"], |
| [25, 1, 0, 10, 0, "MODEL"], |
| [26, 1, 1, 10, 1, "CLIP"], |
| [27, 38, 0, 28, 0, "MODEL"], |
| [28, 38, 1, 28, 1, "CLIP"], |
| [29, 10, 0, 38, 0, "MODEL"], |
| [30, 10, 1, 38, 1, "CLIP"], |
| [31, 5, 0, 56, 0, "LATENT"], |
| [32, 45, 0, 56, 1, "VAE"], |
| [33, 56, 0, 72, 0, "IMAGE"], |
| [39, 2, 0, 5, 1, "CONDITIONING"], |
| [47, 28, 0, 117, 0, "MODEL"], |
| [48, 28, 1, 117, 1, "CLIP"], |
| [49, 117, 1, 2, 0, "CLIP"], |
| [50, 117, 0, 5, 0, "MODEL"], |
| [54, 118, 0, 2, 1, "STRING"], |
| ], |
| "groups": [], |
| "config": {}, |
| "extra": { |
| "ds": { |
| "scale": 0.6303940863128564, |
| "offset": [-602.8886169463092, 486.2591310892753], |
| }, |
| "frontendVersion": "1.45.20", |
| }, |
| "version": 0.4, |
| } |
| } |
|
|
|
|
| def image_metadata( |
| config, |
| seeds, |
| detailer_seeds, |
| detailer_vaes, |
| vaes, |
| regions, |
| environment_start, |
| global_strength, |
| ): |
| api = {} |
|
|
| def add(class_type, inputs): |
| node_id = str(len(api) + 1) |
| api[node_id] = {"inputs": inputs, "class_type": class_type} |
| return node_id |
|
|
| style_images = {} |
| style_pipeline = None |
| style_clip_vision = None |
|
|
| def style_config(stage): |
| if stage == "second" and config.get("second_style_images"): |
| return ( |
| "second", |
| config["second_style_images"], |
| config["second_style_weight"], |
| config["second_style_end"], |
| ) |
| images = config.get("style_images") |
| scope = config.get("style_scope", "generation") |
| enabled = ( |
| stage == "first" |
| or stage == "second" and scope in ("generation", "all") |
| or stage == "detailer" and scope == "all" |
| ) |
| if images and enabled: |
| return "first", images, config["style_weight"], config["style_end"] |
| return None |
|
|
| def apply_style(model, stage): |
| nonlocal style_pipeline, style_clip_vision |
| values = style_config(stage) |
| if values is None: |
| return model |
| key, names, weight, end = values |
| if key not in style_images: |
| style_image = None |
| for name in names: |
| image = [add("LoadImage", {"image": name}), 0] |
| image = [add("ImageScale", { |
| "image": image, |
| "upscale_method": "lanczos", |
| "width": STYLE_IMAGE_SIZE, |
| "height": STYLE_IMAGE_SIZE, |
| "crop": "center", |
| }), 0] |
| if style_image is None: |
| style_image = image |
| else: |
| style_image = [add("ImageBatch", { |
| "image1": style_image, |
| "image2": image, |
| }), 0] |
| style_images[key] = style_image |
| if style_pipeline is None: |
| style_pipeline = [add("IPAdapterModelLoader", { |
| "ipadapter_file": STYLE_IPADAPTER, |
| }), 0] |
| style_clip_vision = [add("CLIPVisionLoader", { |
| "clip_name": STYLE_CLIP_VISION, |
| }), 0] |
| return [add("IPAdapterAdvanced", { |
| "model": model, |
| "ipadapter": style_pipeline, |
| "clip_vision": style_clip_vision, |
| "image": style_images[key], |
| "weight": weight, |
| "weight_type": STYLE_WEIGHT_TYPE, |
| "combine_embeds": "average", |
| "start_at": 0, |
| "end_at": end, |
| "embeds_scaling": STYLE_EMBEDS_SCALING, |
| }), 0] |
|
|
| def load_chain(model_name, loras): |
| if is_anima_model(model_name): |
| model = [add("UNETLoader", { |
| "unet_name": model_name, |
| "weight_dtype": "default", |
| }), 0] |
| clip = [add("CLIPLoader", { |
| "clip_name": ANIMA_CLIP, |
| "type": "stable_diffusion", |
| "device": "default", |
| }), 0] |
| else: |
| node_id = add( |
| "CheckpointLoaderSimple", |
| {"ckpt_name": model_name}, |
| ) |
| model = [node_id, 0] |
| clip = [node_id, 1] |
| for lora in loras: |
| node_id = add("LoraLoader", { |
| "lora_name": lora["name"], |
| "strength_model": lora["strength"], |
| "strength_clip": lora["clip"], |
| "model": model, |
| "clip": clip, |
| }) |
| model = [node_id, 0] |
| clip = [node_id, 1] |
| return model, clip |
|
|
| def custom_sampler(sampler_name, model): |
| prefix, separator, name = sampler_name.partition(":") |
| if not separator: |
| return None |
| if prefix == "ppm-dyn": |
| return [add("DynSamplerSelect", { |
| "sampler_name": name, |
| "eta": 1, |
| "s_dy_pow": -1, |
| "s_extra_steps": False, |
| }), 0] |
| if prefix == "ppm-cfgpp": |
| return [add("CFGPPSamplerSelect", { |
| "sampler_name": name, |
| "eta": 1, |
| "s_gamma_start": 0, |
| "s_gamma_end": 1, |
| "s_extra_steps": False, |
| }), 0] |
| if prefix == "ppm": |
| return [add("PPMSamplerSelect", { |
| "sampler_name": name, |
| "model": model, |
| "cfg_pp": False, |
| "s_sigma_diff": 2, |
| }), 0] |
| return None |
|
|
| def sample( |
| model, |
| seed, |
| steps, |
| cfg, |
| sampler_name, |
| scheduler, |
| positive, |
| negative, |
| latent, |
| denoise, |
| ): |
| sampler = custom_sampler(sampler_name, model) |
| if scheduler != ALIGN_SCHEDULER and sampler is None: |
| return [add("KSampler", { |
| "seed": seed, |
| "steps": steps, |
| "cfg": cfg, |
| "sampler_name": sampler_name, |
| "scheduler": scheduler, |
| "denoise": denoise, |
| "model": model, |
| "positive": positive, |
| "negative": negative, |
| "latent_image": latent, |
| }), 0] |
|
|
| if scheduler == ALIGN_SCHEDULER: |
| sigmas = [add("AlignYourStepsScheduler", { |
| "model_type": ALIGN_MODEL_TYPE, |
| "steps": steps, |
| "denoise": denoise, |
| }), 0] |
| else: |
| sigmas = [add("BasicScheduler", { |
| "model": model, |
| "scheduler": scheduler, |
| "steps": steps, |
| "denoise": denoise, |
| }), 0] |
| if sampler is None: |
| sampler = [add("KSamplerSelect", { |
| "sampler_name": sampler_name, |
| }), 0] |
| return [add("SamplerCustom", { |
| "model": model, |
| "add_noise": True, |
| "noise_seed": seed, |
| "cfg": cfg, |
| "positive": positive, |
| "negative": negative, |
| "sampler": sampler, |
| "sigmas": sigmas, |
| "latent_image": latent, |
| }), 0] |
|
|
| def encode_positive(model, clip, prompt, image_width, image_height): |
| mask_width = image_width // LATENT_SCALE |
| mask_height = image_height // LATENT_SCALE |
| positive = [add("CLIPTextEncode", { |
| "text": prompt, "clip": clip, |
| }), 0] |
| regional_mode = config.get("regional_mode", "conditioning") |
| if regions: |
| positive = [add("ConditioningSetAreaStrength", { |
| "conditioning": positive, |
| "strength": global_strength, |
| }), 0] |
| if regions and regional_mode == "conditioning": |
| positive = [add("ConditioningSetTimestepRange", { |
| "conditioning": positive, |
| "start": environment_start, |
| "end": 1, |
| }), 0] |
| regional_inputs = {} |
| for index, (prompt, x, y, width, height, strength) in enumerate( |
| regions, |
| 1, |
| ): |
| conditioning = [add("CLIPTextEncode", { |
| "text": prompt, "clip": clip, |
| }), 0] |
| x, y, width, height, left, top, right, bottom = mask_box( |
| x, |
| y, |
| width, |
| height, |
| mask_width, |
| mask_height, |
| ) |
| mask = [add("SolidMask", { |
| "value": 1, |
| "width": width, |
| "height": height, |
| }), 0] |
| if any((left, top, right, bottom)): |
| mask = [add("FeatherMask", { |
| "mask": mask, |
| "left": left, |
| "top": top, |
| "right": right, |
| "bottom": bottom, |
| }), 0] |
| background = [add("SolidMask", { |
| "value": 0, |
| "width": mask_width, |
| "height": mask_height, |
| }), 0] |
| mask = [add("MaskComposite", { |
| "destination": background, |
| "source": mask, |
| "x": x, |
| "y": y, |
| "operation": "add", |
| }), 0] |
| if regional_mode == "attention": |
| conditioning = [add("ConditioningSetAreaStrength", { |
| "conditioning": conditioning, |
| "strength": strength, |
| }), 0] |
| regional_inputs[f"cond_{index}"] = conditioning |
| regional_inputs[f"mask_{index}"] = mask |
| else: |
| conditioning = [add("ConditioningSetMask", { |
| "conditioning": conditioning, |
| "mask": mask, |
| "strength": strength, |
| "set_cond_area": "mask bounds", |
| }), 0] |
| positive = [add("ConditioningCombine", { |
| "conditioning_1": positive, |
| "conditioning_2": conditioning, |
| }), 0] |
| if regional_inputs: |
| base_mask = [add("SolidMask", { |
| "value": 1, |
| "width": mask_width, |
| "height": mask_height, |
| }), 0] |
| model = [add("AttentionCouplePPM", { |
| "model": model, |
| "base_cond": positive, |
| "base_mask": base_mask, |
| **regional_inputs, |
| }), 0] |
| return model, positive |
|
|
| first_model = config["model"] |
| second_model = config["second_model"] or first_model |
| first_vae, second_vae = vaes |
| base_model, clip = load_chain(first_model, config["loras"]) |
| model = apply_style(base_model, "first") |
| model, positive = encode_positive( |
| model, |
| clip, |
| config["prompt"], |
| config["width"], |
| config["height"], |
| ) |
| negative = [add("CLIPTextEncode", { |
| "text": config["negative"], "clip": clip, |
| }), 0] |
| latent = [add("EmptyLatentImage", { |
| "width": config["width"], |
| "height": config["height"], |
| "batch_size": config["batch_size"], |
| }), 0] |
| samples = sample( |
| model, |
| seeds[0], |
| config["steps"], |
| config["cfg"], |
| config["sampler"], |
| config["scheduler"], |
| positive, |
| negative, |
| latent, |
| 1, |
| ) |
|
|
| if config["upscale"]: |
| width, height = upscale_size( |
| config["width"], |
| config["height"], |
| config["upscale_scale"], |
| ) |
| samples = [add("LatentUpscale", { |
| "upscale_method": config["upscale_method"], |
| "width": width, |
| "height": height, |
| "crop": "disabled", |
| "samples": samples, |
| }), 0] |
| if is_anima_model(first_model) != is_anima_model(second_model): |
| source_vae = [add("VAELoader", {"vae_name": first_vae}), 0] |
| image = [add("VAEDecode", { |
| "samples": samples, |
| "vae": source_vae, |
| }), 0] |
| target_vae = [add("VAELoader", {"vae_name": second_vae}), 0] |
| samples = [add("VAEEncode", { |
| "pixels": image, |
| "vae": target_vae, |
| }), 0] |
| if config["second_model"]: |
| base_model, clip = load_chain( |
| config["second_model"], config["second_loras"], |
| ) |
| model = apply_style(base_model, "second") |
| model, positive = encode_positive( |
| model, |
| clip, |
| config.get("second_prompt") or config["prompt"], |
| width, |
| height, |
| ) |
| negative = [add("CLIPTextEncode", { |
| "text": config.get("second_negative") or config["negative"], |
| "clip": clip, |
| }), 0] |
| samples = sample( |
| model, |
| seeds[1], |
| config["second_steps"], |
| config["second_cfg"], |
| config["second_sampler"], |
| config["second_scheduler"], |
| positive, |
| negative, |
| samples, |
| config["denoise"], |
| ) |
|
|
| vae = second_vae if config["upscale"] else first_vae |
| vae_node = add("VAELoader", {"vae_name": vae}) |
| image = add("VAEDecode", {"samples": samples, "vae": [vae_node, 0]}) |
| base_clip, base_vae = clip, [vae_node, 0] |
| final_prompt = config.get("second_prompt") or config["prompt"] \ |
| if config["upscale"] else config["prompt"] |
| final_negative = config.get("second_negative") or config["negative"] \ |
| if config["upscale"] else config["negative"] |
| for detailer, seed, detailer_vae in zip( |
| config["detailers"], |
| detailer_seeds, |
| detailer_vaes, |
| ): |
| if detailer["model"]: |
| model, clip = load_chain(detailer["model"], []) |
| vae = [add("VAELoader", { |
| "vae_name": detailer_vae, |
| }), 0] |
| prompt, negative_prompt = config["prompt"], config["negative"] |
| else: |
| model, clip, vae = base_model, base_clip, base_vae |
| prompt, negative_prompt = final_prompt, final_negative |
| model = apply_style(model, "detailer") |
| positive = [add("CLIPTextEncode", { |
| "text": detailer["prompt"] or prompt, "clip": clip, |
| }), 0] |
| negative = [add("CLIPTextEncode", { |
| "text": detailer["negative"] or negative_prompt, "clip": clip, |
| }), 0] |
| detector = add("UltralyticsDetectorProvider", { |
| "model_name": f"bbox/{detailer['detector']}", |
| }) |
| image = add("FaceDetailer", { |
| "image": [image, 0], |
| "model": model, |
| "clip": clip, |
| "vae": vae, |
| "guide_size": DETAILER_GUIDE_SIZE, |
| "guide_size_for": True, |
| "max_size": DETAILER_MAX_SIZE, |
| "seed": seed, |
| "steps": detailer["steps"], |
| "cfg": detailer["cfg"], |
| "sampler_name": detailer["sampler"], |
| "scheduler": detailer["scheduler"], |
| "positive": positive, |
| "negative": negative, |
| "denoise": detailer["denoise"], |
| "feather": DETAILER_FEATHER, |
| "noise_mask": True, |
| "force_inpaint": True, |
| "bbox_threshold": DETAILER_THRESHOLD, |
| "bbox_dilation": DETAILER_DILATION, |
| "bbox_crop_factor": DETAILER_CROP, |
| "sam_detection_hint": "none", |
| "sam_dilation": 0, |
| "sam_threshold": .93, |
| "sam_bbox_expansion": 0, |
| "sam_mask_hint_threshold": .7, |
| "sam_mask_hint_use_negative": "False", |
| "drop_size": DETAILER_DROP_SIZE, |
| "bbox_detector": [detector, 0], |
| "wildcard": "", |
| "cycle": 1, |
| }) |
| if config["upscale"] and config["upscale_model"]: |
| upscale_model = [add("UpscaleModelLoader", { |
| "model_name": config["upscale_model"], |
| }), 0] |
| image = add("ImageUpscaleWithModel", { |
| "upscale_model": upscale_model, |
| "image": [image, 0], |
| }) |
| add("PreviewImage", {"images": [image, 0]}) |
| return { |
| "prompt": json.dumps(api, separators=(",", ":")), |
| "parameters": json.dumps(config, separators=(",", ":")), |
| } |
|
|
|
|
| workflow = build_workflow() |
| prompt = json.loads(json.dumps(workflow)) |
| extra_pnginfo = build_extra_pnginfo() |
|
|
|
|
| |
| def main(unload_models: bool | None = None): |
| bootstrap_comfyui_runtime() |
| add_extra_model_paths() |
| import_custom_nodes() |
|
|
| |
| from nodes import ( |
| CLIPTextEncode, |
| CheckpointLoaderSimple, |
| EmptyLatentImage, |
| KSampler, |
| LoraLoader, |
| NODE_CLASS_MAPPINGS, |
| VAEDecode, |
| VAELoader, |
| ) |
|
|
| import torch |
|
|
| try: |
| with torch.inference_mode(): |
| checkpointloadersimple = CheckpointLoaderSimple() |
| checkpointloadersimple_1 = checkpointloadersimple.load_checkpoint( |
| ckpt_name="52_novaAnimeXL_ilV190.safetensors" |
| ) |
| stringconcatenate = NODE_CLASS_MAPPINGS["StringConcatenate"]() |
| stringconcatenate_118 = stringconcatenate.EXECUTE_NORMALIZED( |
| string_a="%prompt%", |
| string_b="", |
| delimiter="", |
| ) |
| loraloader = LoraLoader() |
| loraloader_10 = loraloader.load_lora( |
| lora_name="8_bikabaka.safetensors", |
| strength_model=0.3, |
| strength_clip=0, |
| model=get_value_at_index(checkpointloadersimple_1, 0), |
| clip=get_value_at_index(checkpointloadersimple_1, 1), |
| ) |
| loraloader_38 = loraloader.load_lora( |
| lora_name="42_\u30a2\u30c3\u30d7\u30b9\u30b1\u30fc\u30eb_remacri_original.pt", |
| strength_model=0.4, |
| strength_clip=0, |
| model=get_value_at_index(loraloader_10, 0), |
| clip=get_value_at_index(loraloader_10, 1), |
| ) |
| loraloader_28 = loraloader.load_lora( |
| lora_name="43_5cm-illustriousXL_v01_V1-CAME-000035.safetensors", |
| strength_model=0.4, |
| strength_clip=0, |
| model=get_value_at_index(loraloader_38, 0), |
| clip=get_value_at_index(loraloader_38, 1), |
| ) |
| loraloader_117 = loraloader.load_lora( |
| lora_name="5_add_saturation_XL.safetensors", |
| strength_model=-1.4, |
| strength_clip=0, |
| model=get_value_at_index(loraloader_28, 0), |
| clip=get_value_at_index(loraloader_28, 1), |
| ) |
| cliptextencode = CLIPTextEncode() |
| cliptextencode_2 = cliptextencode.encode( |
| text=get_value_at_index(stringconcatenate_118, 0), |
| clip=get_value_at_index(loraloader_117, 1), |
| ) |
| cliptextencode_3 = cliptextencode.encode( |
| text="(censored, mosaic censoring, bar censor:1.1), bad quality, worst quality, worst detail, bad anatomy, extra fingers, extra toes, extra legs, 4 toes, 6 toes, 4 fingers, 6 fingers, malformed fingers, extra limbs, missing fingers, extra arms, censored, deformed, disfigured, text, (multiple views:1.1)", |
| clip=get_value_at_index(loraloader_28, 1), |
| ) |
| emptylatentimage = EmptyLatentImage() |
| emptylatentimage_27 = emptylatentimage.generate( |
| width=1152, height=896, batch_size=1 |
| ) |
| vaeloader = VAELoader() |
| vaeloader_45 = vaeloader.load_vae(vae_name="3_sdxlVAE_sdxlVAE.safetensors") |
| ksampler = KSampler() |
| vaedecode = VAEDecode() |
| for q in range(1): |
| node_5_seed = prompt["5"]["inputs"]["seed"] = GENERATION |
| ksampler_5 = ksampler.sample( |
| seed=node_5_seed, |
| steps=16, |
| cfg=4, |
| sampler_name="euler_ancestral", |
| scheduler="karras", |
| denoise=1, |
| model=get_value_at_index(loraloader_117, 0), |
| positive=get_value_at_index(cliptextencode_2, 0), |
| negative=get_value_at_index(cliptextencode_3, 0), |
| latent_image=get_value_at_index(emptylatentimage_27, 0), |
| ) |
| vaedecode_56 = vaedecode.decode( |
| samples=get_value_at_index(ksampler_5, 0), |
| vae=get_value_at_index(vaeloader_45, 0), |
| ) |
| finally: |
| cleanup_comfyui_runtime(unload_models=unload_models) |
|
|
|
|
| |
| if __name__ == "__main__": |
| main() |
|
|
