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)