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 subprocess
import logging
import spaces

# Initialize logging
logging.basicConfig(level=logging.INFO)

# List of GitHub repositories
custom_nodes = [
    "https://github.com/rgthree/rgthree-comfy",
    "https://github.com/yolain/ComfyUI-Easy-Use",
    "https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite",
    "https://github.com/chrisgoringe/cg-use-everywhere",
    "https://github.com/alt-key-project/comfyui-dream-project",
    "https://github.com/giriss/comfy-image-saver",
    "https://github.com/facok/ComfyUI-HunyuanVideoMultiLora"
]

custom_nodes_dir = "models/custom_nodes"
os.makedirs(custom_nodes_dir, exist_ok=True)


# Clone or update repositories
for repo in custom_nodes:
    repo_name = repo.split("/")[-1]
    repo_path = os.path.join(custom_nodes_dir, repo_name)

    if os.path.exists(repo_path):
        logging.info(f"🔄 Updating {repo_name}...")
        subprocess.run(["git", "-C", repo_path, "pull"], check=True)
    else:
        logging.info(f"⬇️ Cloning {repo_name}...")
        subprocess.run(["git", "clone", repo, repo_path], check=True)

logging.info("✅ All custom nodes downloaded successfully!")

# Create a symlink if missing
if not os.path.exists("custom_nodes"):
    os.symlink(custom_nodes_dir, "custom_nodes")
    logging.info("✅ Symlink created: custom_nodes → models/custom_nodes")


os.makedirs("models/diffusion_models", exist_ok=True)
os.makedirs("models/text_encoders", exist_ok=True)
os.makedirs("models/vae", exist_ok=True)
os.makedirs("models/lora", exist_ok=True)

import shutil
from huggingface_hub import hf_hub_download
from pathlib import Path

# Function to download and move files to the correct directory
def download_and_move(repo_id, filename, local_dir):
    # Download the file
    file_path = hf_hub_download(repo_id=repo_id, filename=filename, local_dir=local_dir)
    
    # Calculate the target path without the intermediate folders
    src_path = Path(file_path)
    dest_path = Path(local_dir) / Path(filename).name
    
    # Move the file to the correct directory
    shutil.move(src_path, dest_path)
    print(f"Model moved to: {dest_path}")

# Download VAE model
download_and_move(
    repo_id="Comfy-Org/HunyuanVideo_repackaged",
    filename="split_files/vae/hunyuan_video_vae_bf16.safetensors",
    local_dir="models/vae"
)

# Download Diffusion Model
download_and_move(
    repo_id="Comfy-Org/HunyuanVideo_repackaged",
    filename="split_files/diffusion_models/hunyuan_video_t2v_720p_bf16.safetensors",
    local_dir="models/diffusion_models"
)

# Download Text Encoder - Clip
download_and_move(
    repo_id="Comfy-Org/HunyuanVideo_repackaged",
    filename="split_files/text_encoders/clip_l.safetensors",
    local_dir="models/text_encoders"
)

# Download Text Encoder - LLaVA
download_and_move(
    repo_id="Comfy-Org/HunyuanVideo_repackaged",
    filename="split_files/text_encoders/llava_llama3_fp8_scaled.safetensors",
    local_dir="models/text_encoders"
)

# Download LoRA model
download_and_move(
    repo_id="alexShangeeth/huny_lora",
    filename="boreal-hl-v1.safetensors",
    local_dir="models/loras"
)


















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 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.")


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

@spaces.GPU(duration=1000)
def generate_image(prompt,frames,lora_strenth,width,height,frame_rate):
    import_custom_nodes()
    with torch.inference_mode():
        vaeloader = NODE_CLASS_MAPPINGS["VAELoader"]()
        vaeloader_10 = vaeloader.load_vae(vae_name="hunyuan_video_vae_bf16.safetensors")

        dualcliploader = NODE_CLASS_MAPPINGS["DualCLIPLoader"]()
        dualcliploader_11 = dualcliploader.load_clip(
            clip_name1="clip_l.safetensors",
            clip_name2="llava_llama3_fp8_scaled.safetensors",
            type="hunyuan_video",
            device="default",
        )

        unetloader = NODE_CLASS_MAPPINGS["UNETLoader"]()
        unetloader_12 = unetloader.load_unet(
            unet_name="hunyuan_video_t2v_720p_bf16.safetensors",
            weight_dtype="fp8_e4m3fn",
        )

        ksamplerselect = NODE_CLASS_MAPPINGS["KSamplerSelect"]()
        ksamplerselect_16 = ksamplerselect.get_sampler(
            sampler_name="gradient_estimation"
        )

        randomnoise = NODE_CLASS_MAPPINGS["RandomNoise"]()
        randomnoise_25 = randomnoise.get_noise(noise_seed=random.randint(1, 2**64))

        cliptextencode = NODE_CLASS_MAPPINGS["CLIPTextEncode"]()
        cliptextencode_44 = cliptextencode.encode(
            text=prompt,
            clip=get_value_at_index(dualcliploader_11, 0),
        )

        int_literal = NODE_CLASS_MAPPINGS["Int Literal"]()
        int_literal_295 = int_literal.get_int(int=frames)

        hunyuanvideoloraloader = NODE_CLASS_MAPPINGS["HunyuanVideoLoraLoader"]()
        modelsamplingsd3 = NODE_CLASS_MAPPINGS["ModelSamplingSD3"]()
        fluxguidance = NODE_CLASS_MAPPINGS["FluxGuidance"]()
        basicguider = NODE_CLASS_MAPPINGS["BasicGuider"]()
        basicscheduler = NODE_CLASS_MAPPINGS["BasicScheduler"]()
        emptyhunyuanlatentvideo = NODE_CLASS_MAPPINGS["EmptyHunyuanLatentVideo"]()
        samplercustomadvanced = NODE_CLASS_MAPPINGS["SamplerCustomAdvanced"]()
        big_latent_switch_dream = NODE_CLASS_MAPPINGS["Big Latent Switch [Dream]"]()
        vaedecodetiled = NODE_CLASS_MAPPINGS["VAEDecodeTiled"]()
        imagesharpen = NODE_CLASS_MAPPINGS["ImageSharpen"]()
        vhs_videocombine = NODE_CLASS_MAPPINGS["VHS_VideoCombine"]()
        anything_everywhere3 = NODE_CLASS_MAPPINGS["Anything Everywhere3"]()
        easy_cleangpuused = NODE_CLASS_MAPPINGS["easy cleanGpuUsed"]()

        


        for q in range(1):
            hunyuanvideoloraloader_255 = hunyuanvideoloraloader.load_lora(
                lora_name="boreal-hl-v1.safetensors",
                strength=lora_strenth,
                blocks_type="all",
                model=get_value_at_index(unetloader_12, 0),
            )

            modelsamplingsd3_67 = modelsamplingsd3.patch(
                shift=9, model=get_value_at_index(hunyuanvideoloraloader_255, 0)
            )

            fluxguidance_26 = fluxguidance.append(
                guidance=12, conditioning=get_value_at_index(cliptextencode_44, 0)
            )

            basicguider_22 = basicguider.get_guider(
                model=get_value_at_index(modelsamplingsd3_67, 0),
                conditioning=get_value_at_index(fluxguidance_26, 0),
            )

            basicscheduler_17 = basicscheduler.get_sigmas(
                scheduler="simple",
                steps=40,
                denoise=1,
                model=get_value_at_index(hunyuanvideoloraloader_255, 0),
            )

            emptyhunyuanlatentvideo_232 = emptyhunyuanlatentvideo.generate(
                width=width,
                height=height,
                length=get_value_at_index(int_literal_295, 0),
                batch_size=1,
            )

            samplercustomadvanced_13 = samplercustomadvanced.sample(
                noise=get_value_at_index(randomnoise_25, 0),
                guider=get_value_at_index(basicguider_22, 0),
                sampler=get_value_at_index(ksamplerselect_16, 0),
                sigmas=get_value_at_index(basicscheduler_17, 0),
                latent_image=get_value_at_index(emptyhunyuanlatentvideo_232, 0),
            )

            big_latent_switch_dream_243 = big_latent_switch_dream.pick(
                select=0,
                on_missing="next",
                input_2=get_value_at_index(samplercustomadvanced_13, 1),
            )

            vaedecodetiled_73 = vaedecodetiled.decode(
                tile_size=128,
                overlap=64,
                temporal_size=64,
                temporal_overlap=8,
                samples=get_value_at_index(big_latent_switch_dream_243, 0),
                vae=get_value_at_index(vaeloader_10, 0),
            )

            imagesharpen_106 = imagesharpen.sharpen(
                sharpen_radius=1,
                sigma=0.43,
                alpha=0.5,
                image=get_value_at_index(vaedecodetiled_73, 0),
            )

            vhs_videocombine_82 = vhs_videocombine.combine_video(
                frame_rate=frame_rate,
                loop_count=0,
                filename_prefix="HunyuanVideo",
                format="video/h264-mp4",
                pix_fmt="yuv420p",
                crf=10,
                save_metadata=True,
                trim_to_audio=False,
                pingpong=False,
                save_output=True,
                images=get_value_at_index(imagesharpen_106, 0),
                vae=get_value_at_index(vaeloader_10, 0),
                unique_id=3348895206324303610,
            )

            anything_everywhere3_180 = anything_everywhere3.func(
                CLIP=get_value_at_index(dualcliploader_11, 0),
                VAE=get_value_at_index(vaeloader_10, 0),
            )

            easy_cleangpuused_182 = easy_cleangpuused.empty_cache(
                anything=get_value_at_index(big_latent_switch_dream_243, 0),
                unique_id=16583500820061639415,
            )



            #saved_path = f"output/{vhs_videocombine_82['ui']['filename_prefix'][0]}"
            #return saved_path

            def get_latest_video(directory="output"):
                files = [os.path.join(directory, f) for f in os.listdir(directory) if f.endswith(".mp4")]
                if not files:
                    raise FileNotFoundError("No video files found in the output directory.")
                latest_file = max(files, key=os.path.getmtime)  # Get file with the latest modification time
                return latest_file

            # Get the latest video file based on modification time
            saved_path = get_latest_video()
            return saved_path

    



import gradio as gr

# Placeholder functions for demonstration purposes
def generate_video(prompt, frames, lora_strength, width, height, frame_rate):
    # Integrate with your generation logic here
    return "Generated Video Placeholder"

with gr.Blocks(theme="soft") as app:

    gr.Markdown("# 🌟 FLUX Style Shaping 🌟")

    with gr.Row():
        with gr.Column():
            prompt_input = gr.Textbox(label="📝 Prompt", placeholder="Enter your prompt here...")
            frames_input = gr.Number(label="🎞 Frames", value=30, minimum=1)
            lora_strength_input = gr.Slider(label="🔧 LoRA Strength", minimum=0.0, maximum=1.0, value=0.5, step=0.01)
            width_input = gr.Number(label="📏 Width", value=512, minimum=256, step=64)
            height_input = gr.Number(label="📐 Height", value=512, minimum=256, step=64)
            frame_rate_input = gr.Number(label="🎯 Frame Rate", value=24, minimum=1)
            generate_btn = gr.Button("🚀 Generate Video")

        with gr.Column():
            output_video = gr.Video(label="🎬 Generated Video", interactive=False)

    generate_btn.click(
        fn=generate_image,
        inputs=[prompt_input, frames_input, lora_strength_input, width_input, height_input, frame_rate_input],
        outputs=[output_video]
    )

app.launch(share=True)