app.py

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

print("Starting Space...")

os.makedirs('models/ultralytics/bbox',exist_ok=True)
os.makedirs('models/inpaint',exist_ok=True)
os.makedirs('models/sams',exist_ok=True)

def download_model(repo_id, filename, local_dir):
    local_path = os.path.join(local_dir, filename)
    if not os.path.exists(local_path):
        hf_hub_download(repo_id=repo_id, filename=filename, local_dir=local_dir, force_download=True)
    else:
        print(f"Model {filename} already exists. Skipping download.")


download_model("Acly/MAT", "MAT_Places512_G_fp16.safetensors", "models/inpaint")

download_model("stabilityai/sd-vae-ft-mse-original", "vae-ft-mse-840000-ema-pruned.safetensors", "models/vae")

download_model("Ultralytics/YOLOv8", "yolov8m.pt", "models/ultralytics/bbox")

download_model("xingren23/comfyflow-models", "sams/sam_vit_b_01ec64.pth", "models/sams")


# ------ JIT's Files --------

download_model("camenduru/YoloWorld-EfficientSAM", "efficient_sam_s_cpu.jit", "custom_nodes/ComfyUI-YoloWorld-EfficientSAM")

download_model("camenduru/YoloWorld-EfficientSAM", "efficient_sam_s_gpu.jit", "custom_nodes/ComfyUI-YoloWorld-EfficientSAM")


print("Downloaded Models") 

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.
    """
    if obj is None:
        raise ValueError("Object is None, cannot retrieve index.")
    try:
        return obj[index]
    except (KeyError, IndexError, TypeError):
        return obj.get("result", [])[index] if isinstance(obj, dict) and "result" in obj else None


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 and os.path.isdir(comfyui_path):
        sys.path.append(comfyui_path)
        print(f"'{comfyui_path}' added to sys.path")
    else:
        print("⚠️ Warning: 'ComfyUI' directory not found. Ensure it is installed properly.")


def add_extra_model_paths() -> None:
    load_extra_path_config = None
    try:
        from main import load_extra_path_config
    except ImportError:
        print("Could not import load_extra_path_config from main.py.")
        try:
            from utils.extra_config import load_extra_path_config
        except ImportError:
            print("Could not import load_extra_path_config from utils.extra_config. Skipping extra model paths.")
    extra_model_paths = find_path("extra_model_paths.yaml")
    if extra_model_paths is not None and load_extra_path_config is not None:
        load_extra_path_config(extra_model_paths)
    else:
        print("Skipping extra model paths: config file or loader function not found.")


# add_comfyui_directory_to_sys_path()
add_extra_model_paths()

print("Model paths added")

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)

    NODE_CLASS_MAPPINGS.clear()  # Clear previous mappings
    print("Reloading custom nodes...")

    # Creating an instance of PromptServer with the loop
    server_instance = server.PromptServer(loop)
    execution.PromptQueue(server_instance)

    # Initializing custom nodes
    init_extra_nodes()

    print("Custom Nodes Loaded:", NODE_CLASS_MAPPINGS.keys())

import_custom_nodes()

from nodes import NODE_CLASS_MAPPINGS

@spaces.GPU(duration=120)
def object_remover(object_name, image) :
    import_custom_nodes()
    with torch.inference_mode():
        loadimage_1 = loadimage.load_image(image=image)

        yoloworld_esam_zho = NODE_CLASS_MAPPINGS["Yoloworld_ESAM_Zho"]()
        yoloworld_esam_zho_2 = yoloworld_esam_zho.yoloworld_esam_image(
            categories=object_name,
            confidence_threshold=0.10000000000000002,
            iou_threshold=0.10000000000000002,
            box_thickness=2,
            text_thickness=2,
            text_scale=1,
            with_confidence=True,
            with_class_agnostic_nms=False,
            with_segmentation=True,
            mask_combined=True,
            mask_extracted=True,
            mask_extracted_index=0,
            yolo_world_model=get_value_at_index(yoloworld_modelloader_zho_3, 0),
            esam_model=get_value_at_index(esam_modelloader_zho_4, 0),
            image=get_value_at_index(loadimage_1, 0),
        )

        growmask = NODE_CLASS_MAPPINGS["GrowMask"]()
        growmask_8 = growmask.expand_mask(
            expand=30,
            tapered_corners=True,
            mask=get_value_at_index(yoloworld_esam_zho_2, 1),
        )


        loadimage = NODE_CLASS_MAPPINGS["LoadImage"]()

        inpaint_inpaintwithmodel = NODE_CLASS_MAPPINGS["INPAINT_InpaintWithModel"]()
        inpaint_inpaintwithmodel_12 = inpaint_inpaintwithmodel.inpaint(
            seed=random.randint(1, 2**64),
            inpaint_model=get_value_at_index(inpaint_loadinpaintmodel_13, 0),
            image=get_value_at_index(loadimage_1, 0),
            mask=get_value_at_index(growmask_8, 0),
        )


        vaeencode = NODE_CLASS_MAPPINGS["VAEEncode"]()
        vaeencode_18 = vaeencode.encode(
            pixels=get_value_at_index(inpaint_inpaintwithmodel_12, 0),
            vae=get_value_at_index(vaeloader_20, 0),
        )

        masktoimage = NODE_CLASS_MAPPINGS["MaskToImage"]()
        vaedecode = NODE_CLASS_MAPPINGS["VAEDecode"]()
        image_comparer_rgthree = NODE_CLASS_MAPPINGS["Image Comparer (rgthree)"]()
        saveimage = NODE_CLASS_MAPPINGS["SaveImage"]()

        for q in range(1):
            masktoimage_9 = masktoimage.mask_to_image(
                mask=get_value_at_index(growmask_8, 0)
            )

            vaedecode_19 = vaedecode.decode(
                samples=get_value_at_index(vaeencode_18, 0),
                vae=get_value_at_index(vaeloader_20, 0),
            )

            image_comparer_rgthree_17 = image_comparer_rgthree.compare_images(
                image_a=get_value_at_index(vaedecode_19, 0),
                image_b=get_value_at_index(loadimage_1, 0),
            )

            saveimage_24 = saveimage.save_images(
                filename_prefix="ComfyUI",
                images=get_value_at_index(inpaint_inpaintwithmodel_12, 0),
            )   
            if saveimage_24 and "ui" in saveimage_24 and "images" in saveimage_24["ui"]:
                output_dir = "output"
                os.makedirs(output_dir, exist_ok=True)
                saved_path = os.path.join(output_dir, saveimage_24['ui']['images'][0]['filename'])
                return saved_path
            else:
                raise ValueError("Image generation failed. Check model inputs.")
    return "Error: Image generation failed."


# yoloworld_modelloader_zho = NODE_CLASS_MAPPINGS["Yoloworld_ModelLoader_Zho"]()
if "Yoloworld_ModelLoader_Zho" in NODE_CLASS_MAPPINGS:
    yoloworld_modelloader_zho = NODE_CLASS_MAPPINGS["Yoloworld_ModelLoader_Zho"]()
    yoloworld_modelloader_zho_3 = yoloworld_modelloader_zho.load_yolo_world_model(
    yolo_world_model="yolo_world/m"
    )
else:
    raise KeyError("❌ Error: 'Yoloworld_ModelLoader_Zho' not found. Check if custom nodes are loaded.")

esam_modelloader_zho = NODE_CLASS_MAPPINGS["ESAM_ModelLoader_Zho"]()
esam_modelloader_zho_4 = esam_modelloader_zho.load_esam_model(device="CUDA")

inpaint_loadinpaintmodel = NODE_CLASS_MAPPINGS["INPAINT_LoadInpaintModel"]()
inpaint_loadinpaintmodel_13 = inpaint_loadinpaintmodel.load(
    model_name="MAT_Places512_G_fp16.safetensors"
)

vaeloader = NODE_CLASS_MAPPINGS["VAELoader"]()
vaeloader_20 = vaeloader.load_vae(
    vae_name="vae-ft-mse-840000-ema-pruned.safetensors"
)


loadimage = NODE_CLASS_MAPPINGS["LoadImage"]()
yoloworld_esam_zho = NODE_CLASS_MAPPINGS["Yoloworld_ESAM_Zho"]()
growmask = NODE_CLASS_MAPPINGS["GrowMask"]()
inpaint_inpaintwithmodel = NODE_CLASS_MAPPINGS["INPAINT_InpaintWithModel"]()
vaeloader = NODE_CLASS_MAPPINGS["VAELoader"]()
vaeencode = NODE_CLASS_MAPPINGS["VAEEncode"]()
masktoimage = NODE_CLASS_MAPPINGS["MaskToImage"]()
vaedecode = NODE_CLASS_MAPPINGS["VAEDecode"]()
image_comparer_rgthree = NODE_CLASS_MAPPINGS["Image Comparer (rgthree)"]()
saveimage = NODE_CLASS_MAPPINGS["SaveImage"]()



#Add all the models that load a safetensors file
model_loaders = [yoloworld_modelloader_zho_3,esam_modelloader_zho_4,inpaint_loadinpaintmodel_13,vaeloader_20]

# 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
device = "cuda" if torch.cuda.is_available() else "cpu"
model_management.load_models_gpu(valid_models) if device == "cuda" else model_management.load_models_cpu(valid_models)


with gr.Blocks() as app:
    gr.Markdown("# Object Remover")

    with gr.Column():
        image = gr.Image(label="Image",type="filepath")
        object_name = gr.Textbox(label="Obj_name")
    generate = gr.Button("Generate")
    with gr.Column():
        output = gr.Image(label="output")
    generate.click(fn=object_remover,inputs=[image,object_name],outputs=[output])


print("Launching app...")
app.launch(share=True, debug=True)