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="black-forest-labs/FLUX.1-Redux-dev", filename="flux1-redux-dev.safetensors", local_dir="models/style_models")
hf_hub_download(repo_id="black-forest-labs/FLUX.1-Depth-dev", filename="flux1-depth-dev.safetensors", local_dir="models/diffusion_models")
hf_hub_download(repo_id="Comfy-Org/sigclip_vision_384", filename="sigclip_vision_patch14_384.safetensors", local_dir="models/clip_vision")
hf_hub_download(repo_id="Kijai/DepthAnythingV2-safetensors", filename="depth_anything_v2_vitl_fp32.safetensors", local_dir="models/depthanything")
hf_hub_download(repo_id="black-forest-labs/FLUX.1-dev", filename="ae.safetensors", local_dir="models/vae/FLUX1")
hf_hub_download(repo_id="comfyanonymous/flux_text_encoders", filename="clip_l.safetensors", local_dir="models/text_encoders")
hf_hub_download(repo_id="comfyanonymous/flux_text_encoders", filename="t5xxl_fp16.safetensors", local_dir="models/text_encoders/t5")

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

intconstant = NODE_CLASS_MAPPINGS["INTConstant"]()
dualcliploader = NODE_CLASS_MAPPINGS["DualCLIPLoader"]()

#To be added to `model_loaders` as it loads a model
dualcliploader_357 = dualcliploader.load_clip(
    clip_name1="t5/t5xxl_fp16.safetensors",
    clip_name2="clip_l.safetensors",
    type="flux",
)
cr_clip_input_switch = NODE_CLASS_MAPPINGS["CR Clip Input Switch"]()
cliptextencode = NODE_CLASS_MAPPINGS["CLIPTextEncode"]()
loadimage = NODE_CLASS_MAPPINGS["LoadImage"]()
imageresize = NODE_CLASS_MAPPINGS["ImageResize+"]()
getimagesizeandcount = NODE_CLASS_MAPPINGS["GetImageSizeAndCount"]()
vaeloader = NODE_CLASS_MAPPINGS["VAELoader"]()

#To be added to `model_loaders` as it loads a model
vaeloader_359 = vaeloader.load_vae(vae_name="FLUX1/ae.safetensors")

vaeencode = NODE_CLASS_MAPPINGS["VAEEncode"]()
unetloader = NODE_CLASS_MAPPINGS["UNETLoader"]()

#To be added to `model_loaders` as it loads a model
unetloader_358 = unetloader.load_unet(
    unet_name="flux1-depth-dev.safetensors", weight_dtype="default"
)
ksamplerselect = NODE_CLASS_MAPPINGS["KSamplerSelect"]()
randomnoise = NODE_CLASS_MAPPINGS["RandomNoise"]()
fluxguidance = NODE_CLASS_MAPPINGS["FluxGuidance"]()
depthanything_v2 = NODE_CLASS_MAPPINGS["DepthAnything_V2"]()
downloadandloaddepthanythingv2model = NODE_CLASS_MAPPINGS[
    "DownloadAndLoadDepthAnythingV2Model"
]()

#To be added to `model_loaders` as it loads a model
downloadandloaddepthanythingv2model_437 = (
    downloadandloaddepthanythingv2model.loadmodel(
        model="depth_anything_v2_vitl_fp32.safetensors"
    )
)
instructpixtopixconditioning = NODE_CLASS_MAPPINGS[
    "InstructPixToPixConditioning"
]()
text_multiline_454 = text_multiline.text_multiline(text="FLUX_Redux")
clipvisionloader = NODE_CLASS_MAPPINGS["CLIPVisionLoader"]()

#To be added to `model_loaders` as it loads a model
clipvisionloader_438 = clipvisionloader.load_clip(
    clip_name="sigclip_vision_patch14_384.safetensors"
)
clipvisionencode = NODE_CLASS_MAPPINGS["CLIPVisionEncode"]()
stylemodelloader = NODE_CLASS_MAPPINGS["StyleModelLoader"]()

#To be added to `model_loaders` as it loads a model
stylemodelloader_441 = stylemodelloader.load_style_model(
    style_model_name="flux1-redux-dev.safetensors"
)
text_multiline = NODE_CLASS_MAPPINGS["Text Multiline"]()
emptylatentimage = NODE_CLASS_MAPPINGS["EmptyLatentImage"]()
cr_conditioning_input_switch = NODE_CLASS_MAPPINGS[
    "CR Conditioning Input Switch"
]()
cr_model_input_switch = NODE_CLASS_MAPPINGS["CR Model Input Switch"]()
stylemodelapplyadvanced = NODE_CLASS_MAPPINGS["StyleModelApplyAdvanced"]()
basicguider = NODE_CLASS_MAPPINGS["BasicGuider"]()
basicscheduler = NODE_CLASS_MAPPINGS["BasicScheduler"]()
samplercustomadvanced = NODE_CLASS_MAPPINGS["SamplerCustomAdvanced"]()
vaedecode = NODE_CLASS_MAPPINGS["VAEDecode"]()
saveimage = NODE_CLASS_MAPPINGS["SaveImage"]()
imagecrop = NODE_CLASS_MAPPINGS["ImageCrop+"]()

#Add all the models that load a safetensors file
model_loaders = [dualcliploader_357, vaeloader_359, unetloader_358, clipvisionloader_438, stylemodelloader_441, downloadandloaddepthanythingv2model_437]

# 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)
def generate_image(prompt, structure_image, style_image, depth_strength, style_strength):
    import_custom_nodes()
    with torch.inference_mode():
        
        intconstant_83 = intconstant.get_value(value=1024)

        intconstant_84 = intconstant.get_value(value=1024)

        cr_clip_input_switch_319 = cr_clip_input_switch.switch(
            Input=1,
            clip1=get_value_at_index(dualcliploader_357, 0),
            clip2=get_value_at_index(dualcliploader_357, 0),
        )

        cliptextencode_174 = cliptextencode.encode(
            text=prompt,
            clip=get_value_at_index(cr_clip_input_switch_319, 0),
        )

        cliptextencode_175 = cliptextencode.encode(
            text="purple", clip=get_value_at_index(cr_clip_input_switch_319, 0)
        )

        loadimage_429 = loadimage.load_image(image=structure_image)

        imageresize_72 = imageresize.execute(
            width=get_value_at_index(intconstant_83, 0),
            height=get_value_at_index(intconstant_84, 0),
            interpolation="bicubic",
            method="keep proportion",
            condition="always",
            multiple_of=16,
            image=get_value_at_index(loadimage_429, 0),
        )

        getimagesizeandcount_360 = getimagesizeandcount.getsize(
            image=get_value_at_index(imageresize_72, 0)
        )

        vaeencode_197 = vaeencode.encode(
            pixels=get_value_at_index(getimagesizeandcount_360, 0),
            vae=get_value_at_index(vaeloader_359, 0),
        )

        ksamplerselect_363 = ksamplerselect.get_sampler(sampler_name="euler")

        randomnoise_365 = randomnoise.get_noise(noise_seed=random.randint(1, 2**64))

        
        fluxguidance_430 = fluxguidance.append(
            guidance=15, conditioning=get_value_at_index(cliptextencode_174, 0)
        )

        depthanything_v2_436 = depthanything_v2.process(
            da_model=get_value_at_index(downloadandloaddepthanythingv2model_437, 0),
            images=get_value_at_index(getimagesizeandcount_360, 0),
        )

        instructpixtopixconditioning_431 = instructpixtopixconditioning.encode(
            positive=get_value_at_index(fluxguidance_430, 0),
            negative=get_value_at_index(cliptextencode_175, 0),
            vae=get_value_at_index(vaeloader_359, 0),
            pixels=get_value_at_index(depthanything_v2_436, 0),
        )

        loadimage_440 = loadimage.load_image(image=style_image)
        
        clipvisionencode_439 = clipvisionencode.encode(
            crop="center",
            clip_vision=get_value_at_index(clipvisionloader_438, 0),
            image=get_value_at_index(loadimage_440, 0),
        )
        

        emptylatentimage_10 = emptylatentimage.generate(
            width=get_value_at_index(imageresize_72, 1),
            height=get_value_at_index(imageresize_72, 2),
            batch_size=1,
        )

        cr_conditioning_input_switch_271 = cr_conditioning_input_switch.switch(
            Input=1,
            conditioning1=get_value_at_index(instructpixtopixconditioning_431, 0),
            conditioning2=get_value_at_index(instructpixtopixconditioning_431, 0),
        )

        cr_conditioning_input_switch_272 = cr_conditioning_input_switch.switch(
            Input=1,
            conditioning1=get_value_at_index(instructpixtopixconditioning_431, 1),
            conditioning2=get_value_at_index(instructpixtopixconditioning_431, 1),
        )

        cr_model_input_switch_320 = cr_model_input_switch.switch(
            Input=1,
            model1=get_value_at_index(unetloader_358, 0),
            model2=get_value_at_index(unetloader_358, 0),
        )

        stylemodelapplyadvanced_442 = stylemodelapplyadvanced.apply_stylemodel(
            strength=style_strength,
            conditioning=get_value_at_index(instructpixtopixconditioning_431, 0),
            style_model=get_value_at_index(stylemodelloader_441, 0),
            clip_vision_output=get_value_at_index(clipvisionencode_439, 0),
        )

        basicguider_366 = basicguider.get_guider(
            model=get_value_at_index(cr_model_input_switch_320, 0),
            conditioning=get_value_at_index(stylemodelapplyadvanced_442, 0),
        )

        basicscheduler_364 = basicscheduler.get_sigmas(
            scheduler="simple",
            steps=28,
            denoise=1,
            model=get_value_at_index(cr_model_input_switch_320, 0),
        )

        samplercustomadvanced_362 = samplercustomadvanced.sample(
            noise=get_value_at_index(randomnoise_365, 0),
            guider=get_value_at_index(basicguider_366, 0),
            sampler=get_value_at_index(ksamplerselect_363, 0),
            sigmas=get_value_at_index(basicscheduler_364, 0),
            latent_image=get_value_at_index(emptylatentimage_10, 0),
        )

        vaedecode_321 = vaedecode.decode(
            samples=get_value_at_index(samplercustomadvanced_362, 0),
            vae=get_value_at_index(vaeloader_359, 0),
        )

        saveimage_327 = saveimage.save_images(
            filename_prefix=get_value_at_index(text_multiline_454, 0),
            images=get_value_at_index(vaedecode_321, 0),
        )


        fluxguidance_382 = fluxguidance.append(
            guidance=depth_strength,
            conditioning=get_value_at_index(cr_conditioning_input_switch_272, 0),
        )

        imagecrop_447 = imagecrop.execute(
            width=2000,
            height=2000,
            position="top-center",
            x_offset=0,
            y_offset=0,
            image=get_value_at_index(loadimage_440, 0),
        )

        saved_path = f"output/{saveimage_327['ui']['images'][0]['filename']}"
        return saved_path

if __name__ == "__main__":
    # Comment out the main() call
    
    # Start your Gradio app
    with gr.Blocks() as app:
        # Add a title
        gr.Markdown("# FLUX Style Shaping")

        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],
                outputs=[output_image]
            )
        app.launch(share=True)