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

hf_hub_download(repo_id="Comfy-Org/stable-diffusion-v1-5-archive", filename="v1-5-pruned-emaonly-fp16.safetensors", local_dir="models/checkpoints")


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

checkpointloadersimple = NODE_CLASS_MAPPINGS["CheckpointLoaderSimple"]()
checkpointloadersimple_4 = checkpointloadersimple.load_checkpoint(
    ckpt_name="v1-5-pruned-emaonly-fp16.safetensors"
)


#Add all the models that load a safetensors file
model_loaders = [checkpointloadersimple_4]

# 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)

@spaces.GPU(duration=60) #modify the duration for the average it takes for your worflow to run, in seconds
def generate_image(prompt):
    import_custom_nodes()
    with torch.inference_mode():

        # checkpointloadersimple_4 = checkpointloadersimple.load_checkpoint(
        #     ckpt_name="v1-5-pruned.safetensors"
        # )

        emptylatentimage = NODE_CLASS_MAPPINGS["EmptyLatentImage"]()
        emptylatentimage_5 = emptylatentimage.generate(
            width=512, height=512, batch_size=1
        )

        cliptextencode = NODE_CLASS_MAPPINGS["CLIPTextEncode"]()
        cliptextencode_6 = cliptextencode.encode(
            text=prompt, clip=get_value_at_index(checkpointloadersimple_4, 1)
        )

        cliptextencode_7 = cliptextencode.encode(
            text="(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)",
            clip=get_value_at_index(checkpointloadersimple_4, 1),
        )

        ksampler = NODE_CLASS_MAPPINGS["KSampler"]()
        vaedecode = NODE_CLASS_MAPPINGS["VAEDecode"]()
        saveimage = NODE_CLASS_MAPPINGS["SaveImage"]()

        for q in range(1):
            ksampler_3 = ksampler.sample(
                seed=random.randint(1, 2**64),
                steps=35,
                cfg=7,
                sampler_name="dpmpp_2m",
                scheduler="karras",
                denoise=1,
                model=get_value_at_index(checkpointloadersimple_4, 0),
                positive=get_value_at_index(cliptextencode_6, 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_4, 2),
            )

            saveimage_9 = saveimage.save_images(
                filename_prefix="ComfyUI", images=get_value_at_index(vaedecode_8, 0)
            )

            saved_path = f"output/{saveimage_9['ui']['images'][0]['filename']}"
            return saved_path


# if __name__ == "__main__":
#     main()

if __name__ == "__main__":
    # Comment out the main() call in the exported Python code
    
    # Start your Gradio app
    with gr.Blocks() as app:
        # Add a title
        gr.Markdown("# Simple Example")

        with gr.Row():
            with gr.Column():
                # Add an input
                prompt_input = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...")
                # Add a `Row` to include the groups side by side 
                # with gr.Row():
                #     # First group includes structure image and depth strength
                #     with gr.Group():
                #         # structure_image = gr.Image(label="Structure Image", type="filepath")
                #         # depth_strength = gr.Slider(minimum=0, maximum=50, value=15, label="Depth Strength")
                #     # Second group includes style image and style strength
                #     # with gr.Group():
                #     #     style_image = gr.Image(label="Style Image", type="filepath")
                #     #     style_strength = gr.Slider(minimum=0, maximum=1, value=0.5, label="Style Strength")
                
                # The generate button
                generate_btn = gr.Button("Generate")
            
            with gr.Column():
                # The output image
                output_image = gr.Image(label="Generated Image")

            # When clicking the button, it will trigger the `generate_image` function, with the respective inputs
            # and the output an image
            generate_btn.click(
                fn=generate_image,
                # inputs=[prompt_input, structure_image, style_image, depth_strength, style_strength],
                inputs=[prompt_input],
                outputs=[output_image]
            )
        app.launch(share=True)