app.py

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

# Download required models from huggingface
hf_token = os.environ.get("HF_TOKEN")
hf_hub_download(
    repo_id="Comfy-Org/stable-diffusion-v1-5-archive",
    filename="v1-5-pruned-emaonly-fp16.safetensors",
    local_dir="models/checkpoints",
    token=hf_token,
)


def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
    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:
    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:
    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

import_custom_nodes()


checkpointloadersimple = NODE_CLASS_MAPPINGS["CheckpointLoaderSimple"]()
cliptextencode = NODE_CLASS_MAPPINGS["CLIPTextEncode"]()
vaeencode = NODE_CLASS_MAPPINGS["VAEEncode"]()
ksampler = NODE_CLASS_MAPPINGS["KSampler"]()
vaedecode = NODE_CLASS_MAPPINGS["VAEDecode"]()
framercomfysaveimagenode = NODE_CLASS_MAPPINGS["FramerComfySaveImageNode"]()


checkpointloadersimple_14 = checkpointloadersimple.load_checkpoint(
    ckpt_name="v1-5-pruned-emaonly-fp16.safetensors"
)


model_loaders = [checkpointloadersimple_14]

model_management.load_models_gpu(
    [
        loader[0].patcher if hasattr(loader[0], "patcher") else loader[0]
        for loader in model_loaders
    ]
)


@spaces.GPU
def run_workflow(prompt, negative_prompt, image_input) -> Tuple[Any, ...]:
    with torch.inference_mode():
        cliptextencode_6 = cliptextencode.encode(
            text=prompt, clip=get_value_at_index(checkpointloadersimple_14, 1)
        )

        cliptextencode_7 = cliptextencode.encode(
            text=negative_prompt, clip=get_value_at_index(checkpointloadersimple_14, 1)
        )

        vaeencode_12 = vaeencode.encode(
            pixels=image_input, vae=get_value_at_index(checkpointloadersimple_14, 2)
        )

        ksampler_3 = ksampler.sample(
            seed=random.randint(1, 2**64),
            steps=20,
            cfg=8,
            sampler_name="dpmpp_2m",
            scheduler="normal",
            denoise=0.8700000000000001,
            model=get_value_at_index(checkpointloadersimple_14, 0),
            positive=get_value_at_index(cliptextencode_6, 0),
            negative=get_value_at_index(cliptextencode_7, 0),
            latent_image=get_value_at_index(vaeencode_12, 0),
        )

        vaedecode_8 = vaedecode.decode(
            samples=get_value_at_index(ksampler_3, 0),
            vae=get_value_at_index(checkpointloadersimple_14, 2),
        )

        framercomfysaveimagenode_18 = framercomfysaveimagenode.save_images(
            filename_prefix="ComfyUI",
            output_name="result_image",
            images=get_value_at_index(vaedecode_8, 0),
        )

    framercomfysaveimagenode_18_path = (
        "output/" + framercomfysaveimagenode_18["ui"]["images"][0]["filename"]
    )
    return framercomfysaveimagenode_18_path


# Create Gradio interface
image18_output = gr.Image(label="Generated Image18")

with gr.Blocks() as app:
    with gr.Row():
        with gr.Column():
            prompt_input = gr.Textbox(
                label="Prompt",
                value="None" if "None" else None,
                placeholder=f"Enter prompt here...",
            )
            negative_prompt_input = gr.Textbox(
                label="Negative_Prompt",
                value="None" if "None" else None,
                placeholder=f"Enter negative_prompt here...",
            )
            generate_btn = gr.Button("Generate")
        with gr.Column():
            image18_output.render()
    generate_btn.click(
        fn=run_workflow,
        inputs=[prompt_input, negative_prompt_input],
        outputs=[image18_output],
    )

if __name__ == "__main__":
    app.launch(share=True)