Upload FINAL_Workflow.py

FINAL_Workflow.py

@@ -0,0 +1,646 @@
+import os
+import random
+import sys
+import json
+import argparse
+import contextlib
+from typing import Sequence, Mapping, Any, Union
+import torch
+
+
+def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
+    """Returns the value at the given index of a sequence or mapping.
+
+    If the object is a sequence (like list or string), returns the value at the given index.
+    If the object is a mapping (like a dictionary), returns the value at the index-th key.
+
+    Some return a dictionary, in these cases, we look for the "results" key
+
+    Args:
+        obj (Union[Sequence, Mapping]): The object to retrieve the value from.
+        index (int): The index of the value to retrieve.
+
+    Returns:
+        Any: The value at the given index.
+
+    Raises:
+        IndexError: If the index is out of bounds for the object and the object is not a mapping.
+    """
+    try:
+        return obj[index]
+    except KeyError:
+        return obj["result"][index]
+
+
+def find_path(name: str, path: str = None) -> str:
+    """
+    Recursively looks at parent folders starting from the given path until it finds the given name.
+    Returns the path as a Path object if found, or None otherwise.
+    """
+    # If no path is given, use the current working directory
+    if path is None:
+        if args is None or args.comfyui_directory is None:
+            path = os.getcwd()
+        else:
+            path = args.comfyui_directory
+
+    # Check if the current directory contains the name
+    if name in os.listdir(path):
+        path_name = os.path.join(path, name)
+        print(f"{name} found: {path_name}")
+        return path_name
+
+    # Get the parent directory
+    parent_directory = os.path.dirname(path)
+
+    # If the parent directory is the same as the current directory, we've reached the root and stop the search
+    if parent_directory == path:
+        return None
+
+    # Recursively call the function with the parent directory
+    return find_path(name, parent_directory)
+
+
+def add_comfyui_directory_to_sys_path() -> None:
+    """
+    Add 'ComfyUI' to the sys.path
+    """
+    comfyui_path = find_path("ComfyUI")
+    if comfyui_path is not None and os.path.isdir(comfyui_path):
+        sys.path.append(comfyui_path)
+
+        manager_path = os.path.join(
+            comfyui_path, "custom_nodes", "ComfyUI-Manager", "glob"
+        )
+
+        if os.path.isdir(manager_path) and os.listdir(manager_path):
+            sys.path.append(manager_path)
+            global has_manager
+            has_manager = True
+
+        import __main__
+
+        if getattr(__main__, "__file__", None) is None:
+            __main__.__file__ = os.path.join(comfyui_path, "main.py")
+
+        print(f"'{comfyui_path}' added to sys.path")
+
+
+def add_extra_model_paths() -> None:
+    """
+    Parse the optional extra_model_paths.yaml file and add the parsed paths to the sys.path.
+    """
+    from comfy.options import enable_args_parsing
+
+    enable_args_parsing()
+    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 import_custom_nodes() -> None:
+    """Find all custom nodes in the custom_nodes folder and add those node objects to NODE_CLASS_MAPPINGS
+
+    This function sets up a new asyncio event loop, initializes the PromptServer,
+    creates a PromptQueue, and initializes the custom nodes.
+    """
+    if has_manager:
+        try:
+            import manager_core as manager
+        except ImportError:
+            print("Could not import manager_core, proceeding without it.")
+            return
+        else:
+            if hasattr(manager, "get_config"):
+                print("Patching manager_core.get_config to enforce offline mode.")
+                try:
+                    get_config = manager.get_config
+
+                    def _get_config(*args, **kwargs):
+                        config = get_config(*args, **kwargs)
+                        config["network_mode"] = "offline"
+                        return config
+
+                    manager.get_config = _get_config
+                except Exception as e:
+                    print("Failed to patch manager_core.get_config:", e)
+
+    import asyncio
+    import execution
+    from nodes import init_extra_nodes
+    import server
+
+    # Creating a new event loop and setting it as the default loop
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+
+    async def inner():
+        # Creating an instance of PromptServer with the loop
+        server_instance = server.PromptServer(loop)
+        execution.PromptQueue(server_instance)
+
+        # Initializing custom nodes
+        await init_extra_nodes(init_custom_nodes=True)
+
+    loop.run_until_complete(inner())
+
+
+def save_image_wrapper(context, cls):
+    if args.output is None:
+        return cls
+
+    from PIL import Image, ImageOps, ImageSequence
+    from PIL.PngImagePlugin import PngInfo
+
+    import numpy as np
+
+    class WrappedSaveImage(cls):
+        counter = 0
+
+        def save_images(
+            self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None
+        ):
+            if args.output is None:
+                return super().save_images(
+                    images, filename_prefix, prompt, extra_pnginfo
+                )
+            else:
+                if len(images) > 1 and args.output == "-":
+                    raise ValueError("Cannot save multiple images to stdout")
+                filename_prefix += self.prefix_append
+
+                results = list()
+                for batch_number, image in enumerate(images):
+                    i = 255.0 * image.cpu().numpy()
+                    img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+                    metadata = None
+                    if not args.disable_metadata:
+                        metadata = PngInfo()
+                        if prompt is not None:
+                            metadata.add_text("prompt", json.dumps(prompt))
+                        if extra_pnginfo is not None:
+                            for x in extra_pnginfo:
+                                metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+
+                    if args.output == "-":
+                        # Hack to briefly restore stdout
+                        if context is not None:
+                            context.__exit__(None, None, None)
+                        try:
+                            img.save(
+                                sys.stdout.buffer,
+                                format="png",
+                                pnginfo=metadata,
+                                compress_level=self.compress_level,
+                            )
+                        finally:
+                            if context is not None:
+                                context.__enter__()
+                    else:
+                        subfolder = ""
+                        if len(images) == 1:
+                            if os.path.isdir(args.output):
+                                subfolder = args.output
+                                file = "output.png"
+                            else:
+                                subfolder, file = os.path.split(args.output)
+                                if subfolder == "":
+                                    subfolder = os.getcwd()
+                        else:
+                            if os.path.isdir(args.output):
+                                subfolder = args.output
+                                file = filename_prefix
+                            else:
+                                subfolder, file = os.path.split(args.output)
+
+                            if subfolder == "":
+                                subfolder = os.getcwd()
+
+                            files = os.listdir(subfolder)
+                            file_pattern = file
+                            while True:
+                                filename_with_batch_num = file_pattern.replace(
+                                    "%batch_num%", str(batch_number)
+                                )
+                                file = (
+                                    f"{filename_with_batch_num}_{self.counter:05}.png"
+                                )
+                                self.counter += 1
+
+                                if file not in files:
+                                    break
+
+                        img.save(
+                            os.path.join(subfolder, file),
+                            pnginfo=metadata,
+                            compress_level=self.compress_level,
+                        )
+                        print("Saved image to", os.path.join(subfolder, file))
+                        results.append(
+                            {
+                                "filename": file,
+                                "subfolder": subfolder,
+                                "type": self.type,
+                            }
+                        )
+
+                return {"ui": {"images": results}}
+
+    return WrappedSaveImage
+
+
+def parse_arg(s: Any, default: Any = None) -> Any:
+    """Parses a JSON string, returning it unchanged if the parsing fails."""
+    if __name__ == "__main__" or not isinstance(s, str):
+        return s
+
+    try:
+        return json.loads(s)
+    except json.JSONDecodeError:
+        return s
+
+
+parser = argparse.ArgumentParser(
+    description="A converted ComfyUI workflow. Node inputs listed below. Values passed should be valid JSON (assumes string if not valid JSON)."
+)
+parser.add_argument(
+    "--width1",
+    default=5176,
+    help='Argument 0, input `width` for node "Empty Latent Image" id 5 (autogenerated)',
+)
+
+parser.add_argument(
+    "--height2",
+    default=3784,
+    help='Argument 1, input `height` for node "Empty Latent Image" id 5 (autogenerated)',
+)
+
+parser.add_argument(
+    "--batch_size3",
+    default=1,
+    help='Argument 2, input `batch_size` for node "Empty Latent Image" id 5 (autogenerated)',
+)
+
+parser.add_argument(
+    "--ckpt_name4",
+    default="SDXLCheckpoint.safetensors",
+    help='Argument 0, input `ckpt_name` for node "Load Checkpoint" id 14 (autogenerated)',
+)
+
+parser.add_argument(
+    "--lora_name5",
+    default="dmd2_sdxl_4step_lora_fp16.safetensors",
+    help='Argument 2, input `lora_name` for node "Load LoRA" id 17 (autogenerated)',
+)
+
+parser.add_argument(
+    "--strength_model6",
+    default=1,
+    help='Argument 3, input `strength_model` for node "Load LoRA" id 17 (autogenerated)',
+)
+
+parser.add_argument(
+    "--strength_clip7",
+    default=1,
+    help='Argument 4, input `strength_clip` for node "Load LoRA" id 17 (autogenerated)',
+)
+
+parser.add_argument(
+    "--text8",
+    default="Xx_negative_xX",
+    help='Argument 0, input `text` for node "CLIP Text Encode (Prompt)" id 7 (autogenerated)',
+)
+
+parser.add_argument(
+    "--text9",
+    default="Xx_positive_xX",
+    help='Argument 0, input `text` for node "CLIP Text Encode (Prompt)" id 18 (autogenerated)',
+)
+
+parser.add_argument(
+    "--block_number10",
+    default=3,
+    help='Argument 1, input `block_number` for node "PatchModelAddDownscale (Kohya Deep Shrink)" id 16 (autogenerated)',
+)
+
+parser.add_argument(
+    "--downscale_factor11",
+    default=2,
+    help='Argument 2, input `downscale_factor` for node "PatchModelAddDownscale (Kohya Deep Shrink)" id 16 (autogenerated)',
+)
+
+parser.add_argument(
+    "--start_percent12",
+    default=0,
+    help='Argument 3, input `start_percent` for node "PatchModelAddDownscale (Kohya Deep Shrink)" id 16 (autogenerated)',
+)
+
+parser.add_argument(
+    "--end_percent13",
+    default=0.5000000000000001,
+    help='Argument 4, input `end_percent` for node "PatchModelAddDownscale (Kohya Deep Shrink)" id 16 (autogenerated)',
+)
+
+parser.add_argument(
+    "--downscale_after_skip14",
+    default=True,
+    help='Argument 5, input `downscale_after_skip` for node "PatchModelAddDownscale (Kohya Deep Shrink)" id 16 (autogenerated)',
+)
+
+parser.add_argument(
+    "--downscale_method15",
+    default="bicubic",
+    help='Argument 6, input `downscale_method` for node "PatchModelAddDownscale (Kohya Deep Shrink)" id 16 (autogenerated)',
+)
+
+parser.add_argument(
+    "--upscale_method16",
+    default="bicubic",
+    help='Argument 7, input `upscale_method` for node "PatchModelAddDownscale (Kohya Deep Shrink)" id 16 (autogenerated)',
+)
+
+parser.add_argument(
+    "--seed17",
+    default=64836095259134,
+    help='Argument 1, input `seed` for node "KSampler" id 3 (autogenerated)',
+)
+
+parser.add_argument(
+    "--steps18",
+    default=8,
+    help='Argument 2, input `steps` for node "KSampler" id 3 (autogenerated)',
+)
+
+parser.add_argument(
+    "--cfg19",
+    default=1,
+    help='Argument 3, input `cfg` for node "KSampler" id 3 (autogenerated)',
+)
+
+parser.add_argument(
+    "--sampler_name20",
+    default="lcm",
+    help='Argument 4, input `sampler_name` for node "KSampler" id 3 (autogenerated)',
+)
+
+parser.add_argument(
+    "--scheduler21",
+    default="beta",
+    help='Argument 5, input `scheduler` for node "KSampler" id 3 (autogenerated)',
+)
+
+parser.add_argument(
+    "--denoise22",
+    default=1,
+    help='Argument 9, input `denoise` for node "KSampler" id 3 (autogenerated)',
+)
+
+parser.add_argument(
+    "--filename_prefix23",
+    default="Fast",
+    help='Argument 1, input `filename_prefix` for node "Save Image" id 9 (autogenerated)',
+)
+
+parser.add_argument(
+    "--queue-size",
+    "-q",
+    type=int,
+    default=1,
+    help="How many times the workflow will be executed (default: 1)",
+)
+
+parser.add_argument(
+    "--comfyui-directory",
+    "-c",
+    default=None,
+    help="Where to look for ComfyUI (default: current directory)",
+)
+
+parser.add_argument(
+    "--output",
+    "-o",
+    default=None,
+    help="The location to save the output image. Either a file path, a directory, or - for stdout (default: the ComfyUI output directory)",
+)
+
+parser.add_argument(
+    "--disable-metadata",
+    action="store_true",
+    help="Disables writing workflow metadata to the outputs",
+)
+
+
+comfy_args = [sys.argv[0]]
+if __name__ == "__main__" and "--" in sys.argv:
+    idx = sys.argv.index("--")
+    comfy_args += sys.argv[idx + 1 :]
+    sys.argv = sys.argv[:idx]
+
+args = None
+if __name__ == "__main__":
+    args = parser.parse_args()
+    sys.argv = comfy_args
+if args is not None and args.output is not None and args.output == "-":
+    ctx = contextlib.redirect_stdout(sys.stderr)
+else:
+    ctx = contextlib.nullcontext()
+
+PROMPT_DATA = json.loads(
+    '{"3": {"inputs": {"seed": 64836095259134, "steps": 8, "cfg": 1, "sampler_name": "lcm", "scheduler": "beta", "denoise": 1, "model": ["16", 0], "positive": ["18", 0], "negative": ["7", 0], "latent_image": ["5", 0]}, "class_type": "KSampler", "_meta": {"title": "KSampler"}}, "5": {"inputs": {"width": 5176, "height": 3784, "batch_size": 1}, "class_type": "EmptyLatentImage", "_meta": {"title": "Empty Latent Image"}}, "7": {"inputs": {"text": "Xx_negative_xX", "clip": ["17", 1]}, "class_type": "CLIPTextEncode", "_meta": {"title": "CLIP Text Encode (Prompt)"}}, "8": {"inputs": {"samples": ["3", 0], "vae": ["14", 2]}, "class_type": "VAEDecode", "_meta": {"title": "VAE Decode"}}, "9": {"inputs": {"filename_prefix": "Fast", "images": ["8", 0]}, "class_type": "SaveImage", "_meta": {"title": "Save Image"}}, "14": {"inputs": {"ckpt_name": "SDXLCheckpoint.safetensors"}, "class_type": "CheckpointLoaderSimple", "_meta": {"title": "Load Checkpoint"}}, "16": {"inputs": {"block_number": 3, "downscale_factor": 2, "start_percent": 0, "end_percent": 0.5000000000000001, "downscale_after_skip": true, "downscale_method": "bicubic", "upscale_method": "bicubic", "model": ["17", 0]}, "class_type": "PatchModelAddDownscale", "_meta": {"title": "PatchModelAddDownscale (Kohya Deep Shrink)"}}, "17": {"inputs": {"lora_name": "dmd2_sdxl_4step_lora_fp16.safetensors", "strength_model": 1, "strength_clip": 1, "model": ["14", 0], "clip": ["14", 1]}, "class_type": "LoraLoader", "_meta": {"title": "Load LoRA"}}, "18": {"inputs": {"text": "Xx_positive_xX", "clip": ["17", 1]}, "class_type": "CLIPTextEncode", "_meta": {"title": "CLIP Text Encode (Prompt)"}}}'
+)
+
+
+def import_custom_nodes() -> None:
+    """Find all custom nodes in the custom_nodes folder and add those node objects to NODE_CLASS_MAPPINGS
+
+    This function sets up a new asyncio event loop, initializes the PromptServer,
+    creates a PromptQueue, and initializes the custom nodes.
+    """
+    if has_manager:
+        try:
+            import manager_core as manager
+        except ImportError:
+            print("Could not import manager_core, proceeding without it.")
+            return
+        else:
+            if hasattr(manager, "get_config"):
+                print("Patching manager_core.get_config to enforce offline mode.")
+                try:
+                    get_config = manager.get_config
+
+                    def _get_config(*args, **kwargs):
+                        config = get_config(*args, **kwargs)
+                        config["network_mode"] = "offline"
+                        return config
+
+                    manager.get_config = _get_config
+                except Exception as e:
+                    print("Failed to patch manager_core.get_config:", e)
+
+    import asyncio
+    import execution
+    from nodes import init_extra_nodes
+    import server
+
+    # Creating a new event loop and setting it as the default loop
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+
+    async def inner():
+        # Creating an instance of PromptServer with the loop
+        server_instance = server.PromptServer(loop)
+        execution.PromptQueue(server_instance)
+
+        # Initializing custom nodes
+        await init_extra_nodes(init_custom_nodes=True)
+
+    loop.run_until_complete(inner())
+
+
+_custom_nodes_imported = False
+_custom_path_added = False
+
+
+def main(*func_args, **func_kwargs):
+    global args, _custom_nodes_imported, _custom_path_added
+    if __name__ == "__main__":
+        if args is None:
+            args = parser.parse_args()
+    else:
+        defaults = dict(
+            (arg, parser.get_default(arg))
+            for arg in ["queue_size", "comfyui_directory", "output", "disable_metadata"]
+            + [
+                "width1",
+                "height2",
+                "batch_size3",
+                "ckpt_name4",
+                "lora_name5",
+                "strength_model6",
+                "strength_clip7",
+                "text8",
+                "text9",
+                "block_number10",
+                "downscale_factor11",
+                "start_percent12",
+                "end_percent13",
+                "downscale_after_skip14",
+                "downscale_method15",
+                "upscale_method16",
+                "seed17",
+                "steps18",
+                "cfg19",
+                "sampler_name20",
+                "scheduler21",
+                "denoise22",
+                "filename_prefix23",
+            ]
+        )
+
+        all_args = dict()
+        all_args.update(defaults)
+        all_args.update(func_kwargs)
+
+        args = argparse.Namespace(**all_args)
+
+    with ctx:
+        if not _custom_path_added:
+            add_comfyui_directory_to_sys_path()
+            add_extra_model_paths()
+
+            _custom_path_added = True
+
+        if not _custom_nodes_imported:
+            import_custom_nodes()
+
+            _custom_nodes_imported = True
+
+        from nodes import NODE_CLASS_MAPPINGS
+
+    with torch.inference_mode(), ctx:
+        emptylatentimage = NODE_CLASS_MAPPINGS["EmptyLatentImage"]()
+        emptylatentimage_5 = emptylatentimage.generate(
+            width=parse_arg(args.width1),
+            height=parse_arg(args.height2),
+            batch_size=parse_arg(args.batch_size3),
+        )
+
+        checkpointloadersimple = NODE_CLASS_MAPPINGS["CheckpointLoaderSimple"]()
+        checkpointloadersimple_14 = checkpointloadersimple.load_checkpoint(
+            ckpt_name=parse_arg(args.ckpt_name4)
+        )
+
+        loraloader = NODE_CLASS_MAPPINGS["LoraLoader"]()
+        loraloader_17 = loraloader.load_lora(
+            lora_name=parse_arg(args.lora_name5),
+            strength_model=parse_arg(args.strength_model6),
+            strength_clip=parse_arg(args.strength_clip7),
+            model=get_value_at_index(checkpointloadersimple_14, 0),
+            clip=get_value_at_index(checkpointloadersimple_14, 1),
+        )
+
+        cliptextencode = NODE_CLASS_MAPPINGS["CLIPTextEncode"]()
+        cliptextencode_7 = cliptextencode.encode(
+            text=parse_arg(args.text8), clip=get_value_at_index(loraloader_17, 1)
+        )
+
+        cliptextencode_18 = cliptextencode.encode(
+            text=parse_arg(args.text9), clip=get_value_at_index(loraloader_17, 1)
+        )
+
+        patchmodeladddownscale = NODE_CLASS_MAPPINGS["PatchModelAddDownscale"]()
+        ksampler = NODE_CLASS_MAPPINGS["KSampler"]()
+        vaedecode = NODE_CLASS_MAPPINGS["VAEDecode"]()
+        saveimage = save_image_wrapper(ctx, NODE_CLASS_MAPPINGS["SaveImage"])()
+        for q in range(args.queue_size):
+            patchmodeladddownscale_16 = patchmodeladddownscale.patch(
+                block_number=parse_arg(args.block_number10),
+                downscale_factor=parse_arg(args.downscale_factor11),
+                start_percent=parse_arg(args.start_percent12),
+                end_percent=parse_arg(args.end_percent13),
+                downscale_after_skip=parse_arg(args.downscale_after_skip14),
+                downscale_method=parse_arg(args.downscale_method15),
+                upscale_method=parse_arg(args.upscale_method16),
+                model=get_value_at_index(loraloader_17, 0),
+            )
+
+            ksampler_3 = ksampler.sample(
+                seed=parse_arg(args.seed17),
+                steps=parse_arg(args.steps18),
+                cfg=parse_arg(args.cfg19),
+                sampler_name=parse_arg(args.sampler_name20),
+                scheduler=parse_arg(args.scheduler21),
+                denoise=parse_arg(args.denoise22),
+                model=get_value_at_index(patchmodeladddownscale_16, 0),
+                positive=get_value_at_index(cliptextencode_18, 0),
+                negative=get_value_at_index(cliptextencode_7, 0),
+                latent_image=get_value_at_index(emptylatentimage_5, 0),
+            )
+
+            vaedecode_8 = vaedecode.decode(
+                samples=get_value_at_index(ksampler_3, 0),
+                vae=get_value_at_index(checkpointloadersimple_14, 2),
+            )
+
+            if __name__ != "__main__":
+                return dict(
+                    filename_prefix=parse_arg(args.filename_prefix23),
+                    images=get_value_at_index(vaedecode_8, 0),
+                    prompt=PROMPT_DATA,
+                )
+            else:
+                saveimage_9 = saveimage.save_images(
+                    filename_prefix=parse_arg(args.filename_prefix23),
+                    images=get_value_at_index(vaedecode_8, 0),
+                    prompt=PROMPT_DATA,
+                )
+
+
+if __name__ == "__main__":
+    for i in range(repeats):
+        main()