app.py

import os
import random
import sys
from typing import Sequence, Mapping, Any, Union
import torch
import gradio as gr
from huggingface_hub import hf_hub_download
import spaces
from comfy import model_management

from huggingface_hub import hf_hub_download


hf_hub_download(
    repo_id="Madespace/clip", 
    filename="google_t5-v1_1-xxl_encoderonly-fp8_e4m3fn.safetensors", 
    local_dir="models/clip"              
)
hf_hub_download(
    repo_id="ezioruan/inswapper_128.onnx",        
    filename="inswapper_128.onnx",       
    local_dir="models/insightface"              
)
hf_hub_download(
    repo_id="gmk123/GFPGAN",           
    filename="GFPGANv1.4.pth",           
    local_dir="models/facerestore_models" 
)
hf_hub_download(
    repo_id="gemasai/4x_NMKD-Superscale-SP_178000_G",          
    filename="4x_NMKD-Superscale-SP_178000_G.pth",  
    local_dir="models/upscale_models" 
)


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:
        path = os.getcwd()

    # 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)
        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.
    """
    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 ut.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.")


add_comfyui_directory_to_sys_path()
add_extra_model_paths()


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.
    """
    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)

    # Creating an instance of PromptServer with the loop
    server_instance = server.PromptServer(loop)
    execution.PromptQueue(server_instance)

    # Initializing custom nodes
    init_extra_nodes()

from nodes import NODE_CLASS_MAPPINGS

#TO be added to "model_loaders" as it loads a model
# downloadandloadcogvideomodel = NODE_CLASS_MAPPINGS[
#     "DownloadAndLoadCogVideoModel"
# ]()
# downloadandloadcogvideomodel_1 = downloadandloadcogvideomodel.loadmodel(
#     model="THUDM/CogVideoX-5b",
#     precision="bf16",
#     quantization="disabled",
#     enable_sequential_cpu_offload=True,
#     attention_mode="sdpa",
#     load_device="main_device",
# )
# loadimage = NODE_CLASS_MAPPINGS["LoadImage"]()
# cliploader = NODE_CLASS_MAPPINGS["CLIPLoader"]()
# cliploader_20 = cliploader.load_clip(
#     clip_name="t5/google_t5-v1_1-xxl_encoderonly-fp8_e4m3fn.safetensors",
#     type="sd3",
#     device="default",
# )
# emptylatentimage = NODE_CLASS_MAPPINGS["EmptyLatentImage"]()

# cogvideotextencode = NODE_CLASS_MAPPINGS["CogVideoTextEncode"]()
# cogvideosampler = NODE_CLASS_MAPPINGS["CogVideoSampler"]()
# cogvideodecode = NODE_CLASS_MAPPINGS["CogVideoDecode"]()
# reactorfaceswap = NODE_CLASS_MAPPINGS["ReActorFaceSwap"]()
# cr_upscale_image = NODE_CLASS_MAPPINGS["CR Upscale Image"]()
# vhs_videocombine = NODE_CLASS_MAPPINGS["VHS_VideoCombine"]()

# #Add all the models that load a safetensors file
# model_loaders = [downloadandloadcogvideomodel_1, cliploader_20]

# # Check which models are valid and how to best load them
# valid_models = [
#     getattr(loader[0], 'patcher', loader[0]) 
#     for loader in model_loaders
#     if not isinstance(loader[0], dict) and not isinstance(getattr(loader[0], 'patcher', None), dict)
# ]

# #Finally loads the models
# model_management.load_models_gpu(valid_models)

#Run ComfyUI Workflow
@spaces.GPU(duration=800)
def generate_video(positive_prompt, num_frames, input_image):
    
    print("Positive Prompt:", positive_prompt)
    print("Number of Frames:", num_frames)
    print("Input Image:", input_image)

    progress = gr.Progress(track_tqdm=True)
    import_custom_nodes()
    with torch.inference_mode():
        downloadandloadcogvideomodel = NODE_CLASS_MAPPINGS[
            "DownloadAndLoadCogVideoModel"
        ]()
        downloadandloadcogvideomodel_1 = downloadandloadcogvideomodel.loadmodel(
            model="THUDM/CogVideoX-5b",
            precision="bf16",
            quantization="disabled",
            enable_sequential_cpu_offload=True,
            attention_mode="sdpa",
            load_device="main_device",
        )

        loadimage = NODE_CLASS_MAPPINGS["LoadImage"]()
        loadimage_8 = loadimage.load_image(image=input_image)

        cliploader = NODE_CLASS_MAPPINGS["CLIPLoader"]()
        cliploader_20 = cliploader.load_clip(
            clip_name="google_t5-v1_1-xxl_encoderonly-fp8_e4m3fn.safetensors",
            type="sd3",
            device="default",
        )

        emptylatentimage = NODE_CLASS_MAPPINGS["EmptyLatentImage"]()
        emptylatentimage_161 = emptylatentimage.generate(
            width=480, #reduce this to avoid OOM error
            height=480, #reduce this to avoid OOM error
            batch_size=1 #reduce this to avoid OOM error
        )

        cogvideotextencode = NODE_CLASS_MAPPINGS["CogVideoTextEncode"]()
        cogvideosampler = NODE_CLASS_MAPPINGS["CogVideoSampler"]()
        cogvideodecode = NODE_CLASS_MAPPINGS["CogVideoDecode"]()
        reactorfaceswap = NODE_CLASS_MAPPINGS["ReActorFaceSwap"]()
        cr_upscale_image = NODE_CLASS_MAPPINGS["CR Upscale Image"]()
        vhs_videocombine = NODE_CLASS_MAPPINGS["VHS_VideoCombine"]()

        for q in range(1):
            cogvideotextencode_30 = cogvideotextencode.process(
                prompt=positive_prompt,
                strength=1,
                force_offload=True,
                clip=get_value_at_index(cliploader_20, 0),
            )

            cogvideotextencode_31 = cogvideotextencode.process(
                prompt='',
                strength=1,
                force_offload=True,
                clip=get_value_at_index(cogvideotextencode_30, 1),
            )

            cogvideosampler_155 = cogvideosampler.process(
                num_frames=num_frames,
                steps=30, #reduce this to avoid OOM error
                cfg=6,
                seed=random.randint(1, 2**64),
                scheduler="CogVideoXDDIM",
                denoise_strength=1,
                model=get_value_at_index(downloadandloadcogvideomodel_1, 0),
                positive=get_value_at_index(cogvideotextencode_30, 0),
                negative=get_value_at_index(cogvideotextencode_31, 0),
                samples=get_value_at_index(emptylatentimage_161, 0),
            )

            cogvideodecode_11 = cogvideodecode.decode(
                enable_vae_tiling=False,
                tile_sample_min_height=240,#reduce this to avoid OOM error
                tile_sample_min_width=240,#reduce this to avoid OOM error
                tile_overlap_factor_height=0.2,
                tile_overlap_factor_width=0.2,
                auto_tile_size=True,
                vae=get_value_at_index(downloadandloadcogvideomodel_1, 1),
                samples=get_value_at_index(cogvideosampler_155, 0),
            )

            reactorfaceswap_3 = reactorfaceswap.execute(
                enabled=True,
                swap_model="inswapper_128.onnx",
                facedetection="retinaface_resnet50",
                face_restore_model="GFPGANv1.4.pth",
                face_restore_visibility=1,
                codeformer_weight=0.75,
                detect_gender_input="no",
                detect_gender_source="no",
                input_faces_index="0",
                source_faces_index="0",
                console_log_level=1,
                input_image=get_value_at_index(cogvideodecode_11, 0),
                source_image=get_value_at_index(loadimage_8, 0),
            )

            cr_upscale_image_151 = cr_upscale_image.upscale(
                upscale_model="4x_NMKD-Superscale-SP_178000_G.pth",
                mode="rescale",
                rescale_factor=4,
                resize_width=720,
                resampling_method="lanczos",
                supersample="true",
                rounding_modulus=16,
                image=get_value_at_index(reactorfaceswap_3, 0),
            )

            vhs_videocombine_154 = vhs_videocombine.combine_video(
                frame_rate=8,
                loop_count=0,
                filename_prefix="AnimateDiff",
                format="video/h264-mp4",
                pix_fmt="yuv420p",
                crf=19,
                save_metadata=True,
                trim_to_audio=False,
                pingpong=True,
                save_output=True,
                images=get_value_at_index(cr_upscale_image_151, 0),
                unique_id=7214086815220268849,
            )
            video_path = f"output/{vhs_videocombine_154['ui']['gifs'][0]['filename']}"
            image_path = f"output/{vhs_videocombine_154['result'][0][1][0].split('/')[-1]}"
            
            print(vhs_videocombine_154)
            print(video_path, image_path)

            return video_path, image_path


if __name__ == "__main__":

    with gr.Blocks() as app:
        with gr.Row():
            positive_prompt = gr.Textbox(label="Positive Prompt", value="A young Asian man with shoulder-length black hair, wearing a stylish black outfit, playing an acoustic guitar on a dimly lit stage. His full face is visible, showing a calm and focused expression as he strums the guitar. A microphone stand is positioned near him, and a music stand with sheet music is in front of him. The stage lighting casts a soft, warm glow on his face, and the background features an intimate live music setting with visible metal beams and soft blue ambient lighting. The scene captures the artistic mood of a live performance, emphasizing the details of the guitar, the musician’s fingers on the strings, and the relaxed yet passionate vibe of the moment.", lines=2)
        with gr.Row():
            num_frames = gr.Number(label="Number of Frames", value=10)
        with gr.Row():
            input_image = gr.Image(label="Input Image", type="filepath")
        submit = gr.Button("Submit")
        output_video = gr.Video(label="Output Video")
        output_image = gr.Image(label="Output Image")
        
        submit.click(
            fn=generate_video, 
            inputs=[positive_prompt, num_frames, input_image],
            outputs=[output_video, output_image]
        )

    app.launch(share=True,show_error=True)