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)