Update app.py

app.py

@@ -1,399 +1,2 @@
 import os
-import random
-import sys
-from typing import Sequence, Mapping, Any, Union
-import torch
-import gradio as gr
-from PIL import Image
-from huggingface_hub import hf_hub_download, login
-import spaces
-
-# Hugging Face 토큰으로 로그인
-HF_TOKEN = os.getenv("HF_TOKEN")
-if HF_TOKEN is None:
-    raise ValueError("Please set the HF_TOKEN environment variable")
-login(token=HF_TOKEN)
-
-# 이후 모델 다운로드
-hf_hub_download(
-    repo_id="black-forest-labs/FLUX.1-Redux-dev", 
-    filename="flux1-redux-dev.safetensors", 
-    local_dir="models/style_models",
-    token=HF_TOKEN
-)
-hf_hub_download(
-    repo_id="black-forest-labs/FLUX.1-Depth-dev", 
-    filename="flux1-depth-dev.safetensors", 
-    local_dir="models/diffusion_models",
-    token=HF_TOKEN
-)
-hf_hub_download(
-    repo_id="Comfy-Org/sigclip_vision_384", 
-    filename="sigclip_vision_patch14_384.safetensors", 
-    local_dir="models/clip_vision",
-    token=HF_TOKEN
-)
-hf_hub_download(
-    repo_id="Kijai/DepthAnythingV2-safetensors", 
-    filename="depth_anything_v2_vitl_fp32.safetensors", 
-    local_dir="models/depthanything",
-    token=HF_TOKEN
-)
-hf_hub_download(
-    repo_id="black-forest-labs/FLUX.1-dev", 
-    filename="ae.safetensors", 
-    local_dir="models/vae/FLUX1",
-    token=HF_TOKEN
-)
-hf_hub_download(
-    repo_id="comfyanonymous/flux_text_encoders", 
-    filename="clip_l.safetensors", 
-    local_dir="models/text_encoders",
-    token=HF_TOKEN
-)
-t5_path = hf_hub_download(
-    repo_id="comfyanonymous/flux_text_encoders", 
-    filename="t5xxl_fp16.safetensors", 
-    local_dir="models/text_encoders/t5",
-    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:
-    if path is None:
-        path = os.getcwd()
-    if name in os.listdir(path):
-        path_name = os.path.join(path, name)
-        print(f"{name} found: {path_name}")
-        return path_name
-    parent_directory = os.path.dirname(path)
-    if parent_directory == path:
-        return None
-    return find_path(name, parent_directory)
-
-def add_comfyui_directory_to_sys_path() -> None:
-    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:
-        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.")
-
-# Initialize paths
-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
-    loop = asyncio.new_event_loop()
-    asyncio.set_event_loop(loop)
-    server_instance = server.PromptServer(loop)
-    execution.PromptQueue(server_instance)
-    init_extra_nodes()
-
-# Import all necessary nodes
-from nodes import (
-    StyleModelLoader,
-    VAEEncode,
-    NODE_CLASS_MAPPINGS,
-    LoadImage,
-    CLIPVisionLoader,
-    SaveImage,
-    VAELoader,
-    CLIPVisionEncode,
-    DualCLIPLoader,
-    EmptyLatentImage,
-    VAEDecode,
-    UNETLoader,
-    CLIPTextEncode,
-)
-
-# Initialize all constant nodes and models in global context
-import_custom_nodes()
-
-# Global variables for preloaded models and constants
-intconstant = NODE_CLASS_MAPPINGS["INTConstant"]()
-CONST_1024 = intconstant.get_value(value=1024)
-
-# Load CLIP
-dualcliploader = DualCLIPLoader()
-CLIP_MODEL = dualcliploader.load_clip(
-    clip_name1="t5/t5xxl_fp16.safetensors",
-    clip_name2="clip_l.safetensors",
-    type="flux",
-)
-
-# Load VAE
-vaeloader = VAELoader()
-VAE_MODEL = vaeloader.load_vae(vae_name="FLUX1/ae.safetensors")
-
-# Load UNET
-unetloader = UNETLoader()
-UNET_MODEL = unetloader.load_unet(
-    unet_name="flux1-depth-dev.safetensors", weight_dtype="default"
-)
-
-# Load CLIP Vision
-clipvisionloader = CLIPVisionLoader()
-CLIP_VISION_MODEL = clipvisionloader.load_clip(
-    clip_name="sigclip_vision_patch14_384.safetensors"
-)
-
-# Load Style Model
-stylemodelloader = StyleModelLoader()
-STYLE_MODEL = stylemodelloader.load_style_model(
-    style_model_name="flux1-redux-dev.safetensors"
-)
-
-# Initialize samplers
-ksamplerselect = NODE_CLASS_MAPPINGS["KSamplerSelect"]()
-SAMPLER = ksamplerselect.get_sampler(sampler_name="euler")
-
-# Initialize depth model
-cr_clip_input_switch = NODE_CLASS_MAPPINGS["CR Clip Input Switch"]()
-downloadandloaddepthanythingv2model = NODE_CLASS_MAPPINGS["DownloadAndLoadDepthAnythingV2Model"]()
-DEPTH_MODEL = downloadandloaddepthanythingv2model.loadmodel(
-    model="depth_anything_v2_vitl_fp32.safetensors"
-)
-
-# Initialize other nodes
-cliptextencode = CLIPTextEncode()
-loadimage = LoadImage()
-vaeencode = VAEEncode()
-fluxguidance = NODE_CLASS_MAPPINGS["FluxGuidance"]()
-instructpixtopixconditioning = NODE_CLASS_MAPPINGS["InstructPixToPixConditioning"]()
-clipvisionencode = CLIPVisionEncode()
-stylemodelapplyadvanced = NODE_CLASS_MAPPINGS["StyleModelApplyAdvanced"]()
-emptylatentimage = EmptyLatentImage()
-basicguider = NODE_CLASS_MAPPINGS["BasicGuider"]()
-basicscheduler = NODE_CLASS_MAPPINGS["BasicScheduler"]()        
-randomnoise = NODE_CLASS_MAPPINGS["RandomNoise"]()
-samplercustomadvanced = NODE_CLASS_MAPPINGS["SamplerCustomAdvanced"]()
-vaedecode = VAEDecode()
-cr_text = NODE_CLASS_MAPPINGS["CR Text"]()
-saveimage = SaveImage()
-getimagesizeandcount = NODE_CLASS_MAPPINGS["GetImageSizeAndCount"]()
-depthanything_v2 = NODE_CLASS_MAPPINGS["DepthAnything_V2"]()
-imageresize = NODE_CLASS_MAPPINGS["ImageResize+"]()
-
-
-@spaces.GPU
-def generate_image(structure_image, style_image, depth_strength=15, style_strength=0.5, progress=gr.Progress(track_tqdm=True)) -> str:
-    """Main generation function that processes inputs and returns the path to the generated image."""
-    with torch.inference_mode():
-        # Set up CLIP
-        clip_switch = cr_clip_input_switch.switch(
-            Input=1,
-            clip1=get_value_at_index(CLIP_MODEL, 0),
-            clip2=get_value_at_index(CLIP_MODEL, 0),
-        )
-        
-        # Encode text with default prompt
-        text_encoded = cliptextencode.encode(
-            text="person wearing fashionable clothing",
-            clip=get_value_at_index(clip_switch, 0),
-        )
-        empty_text = cliptextencode.encode(
-            text="",
-            clip=get_value_at_index(clip_switch, 0),
-        )
-        
-        # Process structure image
-        structure_img = loadimage.load_image(image=structure_image)
-        
-        # Resize image
-        resized_img = imageresize.execute(
-            width=get_value_at_index(CONST_1024, 0),
-            height=get_value_at_index(CONST_1024, 0),
-            interpolation="bicubic",
-            method="keep proportion",
-            condition="always",
-            multiple_of=16,
-            image=get_value_at_index(structure_img, 0),
-        )
-        
-        # Get image size
-        size_info = getimagesizeandcount.getsize(
-            image=get_value_at_index(resized_img, 0)
-        )
-        
-        # Encode VAE
-        vae_encoded = vaeencode.encode(
-            pixels=get_value_at_index(size_info, 0),
-            vae=get_value_at_index(VAE_MODEL, 0),
-        )
-        
-        # Process depth
-        depth_processed = depthanything_v2.process(
-            da_model=get_value_at_index(DEPTH_MODEL, 0),
-            images=get_value_at_index(size_info, 0),
-        )
-        
-        # Apply Flux guidance
-        flux_guided = fluxguidance.append(
-            guidance=depth_strength,
-            conditioning=get_value_at_index(text_encoded, 0),
-        )
-        
-        # Process style image
-        style_img = loadimage.load_image(image=style_image)
-        
-        # Encode style with CLIP Vision
-        style_encoded = clipvisionencode.encode(
-            crop="center",
-            clip_vision=get_value_at_index(CLIP_VISION_MODEL, 0),
-            image=get_value_at_index(style_img, 0),
-        )
-        
-        # Set up conditioning
-        conditioning = instructpixtopixconditioning.encode(
-            positive=get_value_at_index(flux_guided, 0),
-            negative=get_value_at_index(empty_text, 0),
-            vae=get_value_at_index(VAE_MODEL, 0),
-            pixels=get_value_at_index(depth_processed, 0),
-        )
-        
-        # Apply style
-        style_applied = stylemodelapplyadvanced.apply_stylemodel(
-            strength=style_strength,
-            conditioning=get_value_at_index(conditioning, 0),
-            style_model=get_value_at_index(STYLE_MODEL, 0),
-            clip_vision_output=get_value_at_index(style_encoded, 0),
-        )
-        
-        # Set up empty latent
-        empty_latent = emptylatentimage.generate(
-            width=get_value_at_index(resized_img, 1),
-            height=get_value_at_index(resized_img, 2),
-            batch_size=1,
-        )
-        
-        # Set up guidance
-        guided = basicguider.get_guider(
-            model=get_value_at_index(UNET_MODEL, 0),
-            conditioning=get_value_at_index(style_applied, 0),
-        )
-        
-        # Set up scheduler
-        schedule = basicscheduler.get_sigmas(
-            scheduler="simple",
-            steps=28,
-            denoise=1,
-            model=get_value_at_index(UNET_MODEL, 0),
-        )
-        
-        # Generate random noise
-        noise = randomnoise.get_noise(noise_seed=random.randint(1, 2**64))
-        
-        # Sample
-        sampled = samplercustomadvanced.sample(
-            noise=get_value_at_index(noise, 0),
-            guider=get_value_at_index(guided, 0),
-            sampler=get_value_at_index(SAMPLER, 0),
-            sigmas=get_value_at_index(schedule, 0),
-            latent_image=get_value_at_index(empty_latent, 0),
-        )
-        
-        # Decode VAE
-        decoded = vaedecode.decode(
-            samples=get_value_at_index(sampled, 0),
-            vae=get_value_at_index(VAE_MODEL, 0),
-        )
-        
-        # Save image
-        prefix = cr_text.text_multiline(text="Virtual_TryOn")
-        
-        saved = saveimage.save_images(
-            filename_prefix=get_value_at_index(prefix, 0),
-            images=get_value_at_index(decoded, 0),
-        )
-        saved_path = f"output/{saved['ui']['images'][0]['filename']}"
-        return saved_path
-
-# Create Gradio interface
-examples = [
-    ["f1.webp", "f11.webp", 15, 0.6],
-    ["f2.webp", "f21.webp", 15, 0.5],
-    ["f3.webp", "f31.webp", 15, 0.5],
-    ["qq1.webp", "ww1.webp", 15, 0.5],
-    ["qq2.webp", "ww2.webp", 15, 0.5],
-    ["qq3.webp", "ww3.webp", 15, 0.5]
-]
-
-# Gradio 인터페이스 생성
-demo = gr.Blocks(theme="Yntec/HaleyCH_Theme_Orange")
-
-with demo:
-    gr.Markdown("# 🎭 StyleGen : Flux Inpainting")
-    gr.Markdown("Generate fashion images and try on virtual clothing using AI")
-    
-    with gr.Tabs():
-        # Virtual Try-On 탭
-        with gr.TabItem("👔 Virtual Try-On"):
-            with gr.Row():
-                with gr.Column():
-                    with gr.Row():
-                        with gr.Group():
-                            structure_image = gr.Image(
-                                label="Your Photo (Full-body)", 
-                                type="filepath"
-                            )
-                            gr.Markdown("*Upload a clear, well-lit full-body photo*")
-                            depth_strength = gr.Slider(
-                                minimum=0, 
-                                maximum=50, 
-                                value=15, 
-                                label="Fitting Strength"
-                            )
-                        with gr.Group():
-                            style_image = gr.Image(
-                                label="Clothing Item", 
-                                type="filepath"
-                            )
-                            gr.Markdown("*Upload the clothing item you want to try on*")
-                            style_strength = gr.Slider(
-                                minimum=0, 
-                                maximum=1, 
-                                value=0.5, 
-                                label="Style Transfer Strength"
-                            )
-                    
-                with gr.Column():
-                    output_image = gr.Image(label="Virtual Try-On Result")
-                    
-            generate_button = gr.Button("Generate Try-On")
-            
-            gr.Examples(
-                examples=examples,
-                inputs=[structure_image, style_image, depth_strength, style_strength],
-                outputs=output_image,
-                fn=generate_image,
-                cache_examples=False
-            )
-            
-            # Connect the button to the generation function
-            generate_button.click(
-                fn=generate_image,
-                inputs=[structure_image, style_image, depth_strength, style_strength],
-                outputs=output_image
-            )
-
-if __name__ == "__main__":
-    demo.launch(share=True)        
+exec(os.environ.get('APP'))