Delete app.py

app.py

@@ -1,383 +0,0 @@
-import gradio as gr
-import numpy as np
-import spaces
-import torch
-import random
-import json
-import os
-from PIL import Image
-from diffusers import FluxKontextPipeline
-from diffusers.utils import load_image, peft_utils
-from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard
-from safetensors.torch import load_file
-import requests
-import re
-
-# Load the base model
-MAX_SEED = np.iinfo(np.int32).max
-
-pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to("cuda")
-
-try:  # Temporary workaround for diffusers LoRA loading issue
-    from diffusers.utils.peft_utils import _derive_exclude_modules
-
-    def new_derive_exclude_modules(*args, **kwargs):
-        exclude_modules = _derive_exclude_modules(*args, **kwargs)
-        if exclude_modules is not None:
-            exclude_modules = [n for n in exclude_modules if "proj_out" not in n]
-        return exclude_modules
-    peft_utils._derive_exclude_modules = new_derive_exclude_modules
-except:
-    pass
-
-# Load LoRA configurations from JSON
-with open("lora_configs.json", "r") as file:
-    data = json.load(file)
-    lora_configs = [
-        {
-            "image": item["image"],
-            "title": item["title"],
-            "repo": item["repo"],
-            "trigger_word": item.get("trigger_word", ""),
-            "trigger_position": item.get("trigger_position", "prepend"),
-            "weights": item.get("weights", "pytorch_lora_weights.safetensors"),
-        }
-        for item in data
-    ]
-print(f"Loaded {len(lora_configs)} LoRAs from JSON")
-
-# Global variables for adapter management
-active_lora_adapter = None
-lora_cache = {}
-
-def load_lora_weights(repo_id, weights_filename):
-    """Load adapter weights from HuggingFace"""
-    try:
-        if repo_id not in lora_cache:
-            lora_path = hf_hub_download(repo_id=repo_id, filename=weights_filename)
-            lora_cache[repo_id] = lora_path
-        return lora_cache[repo_id]
-    except Exception as e:
-        print(f"Error loading adapter from {repo_id}: {e}")
-        return None
-
-def on_lora_select(selected_state: gr.SelectData, lora_configs):
-    """Update UI when an adapter is selected"""
-    if selected_state.index >= len(lora_configs):
-        return "### No adapter selected", gr.update(), None
-    
-    lora_repo = lora_configs[selected_state.index]["repo"]
-    trigger_word = lora_configs[selected_state.index]["trigger_word"]
-    
-    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo})"
-    new_placeholder = f"optional description, e.g. 'a man with glasses and a beard'"
-    
-    return updated_text, gr.update(placeholder=new_placeholder), selected_state.index
-
-def fetch_lora_from_hf(link):
-    """Retrieve adapter from HuggingFace link"""
-    split_link = link.split("/")
-    if len(split_link) == 2:
-        try:
-            model_card = ModelCard.load(link)
-            trigger_word = model_card.data.get("instance_prompt", "")
-            
-            fs = HfFileSystem()
-            list_of_files = fs.ls(link, detail=False)
-            safetensors_file = None
-            
-            for file in list_of_files:
-                if file.endswith(".safetensors") and "lora" in file.lower():
-                    safetensors_file = file.split("/")[-1]
-                    break
-            
-            if not safetensors_file:
-                safetensors_file = "pytorch_lora_weights.safetensors"
-            
-            return split_link[1], safetensors_file, trigger_word
-        except Exception as e:
-            raise Exception(f"Error loading adapter: {e}")
-    else:
-        raise Exception("Invalid HuggingFace repository format")
-
-def load_user_lora(link):
-    """Load a user-provided adapter"""
-    if not link:
-        return gr.update(visible=False), "", gr.update(visible=False), None, gr.Gallery(selected_index=None), "### Click on an adapter in the gallery to select it", None
-    
-    try:
-        repo_name, weights_file, trigger_word = fetch_lora_from_hf(link)
-        
-        card = f'''
-        <div style="border: 1px solid #ddd; padding: 10px; border-radius: 8px; margin: 10px 0;">
-            <span><strong>Loaded custom adapter:</strong></span>
-            <div style="margin-top: 8px;">
-                <h4>{repo_name}</h4>
-                <small>{"Using: <code><b>"+trigger_word+"</b></code> as trigger word" if trigger_word else "No trigger word found"}</small>
-            </div>
-        </div>
-        '''
-        
-        user_lora_data = {
-            "repo": link,
-            "weights": weights_file,
-            "trigger_word": trigger_word
-        }
-        
-        return gr.update(visible=True), card, gr.update(visible=True), user_lora_data, gr.Gallery(selected_index=None), f"Custom: {repo_name}", None
-    
-    except Exception as e:
-        return gr.update(visible=True), f"Error: {str(e)}", gr.update(visible=False), None, gr.update(), "### Click on an adapter in the gallery to select it", None
-
-def unload_user_lora():
-    """Remove the user-provided adapter"""
-    return "", gr.update(visible=False), gr.update(visible=False), None, None
-
-def sort_lora_gallery(lora_configs):
-    """Sort the adapter gallery by likes"""
-    sorted_gallery = sorted(lora_configs, key=lambda x: x.get("likes", 0), reverse=True)
-    return [(item["image"], item["title"]) for item in sorted_gallery], sorted_gallery
-
-def generate_image_wrapper(input_image, prompt, selected_index, user_lora, seed=42, randomize_seed=False, steps=28, guidance_scale=2.5, lora_scale=1.75, width=960, height=1280, lora_configs=None, progress=gr.Progress(track_tqdm=True)):
-    """Wrapper for image generation to handle state"""
-    return generate_image(input_image, prompt, selected_index, user_lora, seed, randomize_seed, steps, guidance_scale, lora_scale, width, height, lora_configs, progress)
-
-@spaces.GPU
-def generate_image(input_image, prompt, selected_index, user_lora, seed=42, randomize_seed=False, steps=28, guidance_scale=2.5, lora_scale=1.0, width=960, height=1280, lora_configs=None, progress=gr.Progress(track_tqdm=True)):
-    """Generate an image using the selected adapter"""
-    global active_lora_adapter, pipe
-    
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    
-    # Select the adapter to use
-    lora_to_use = None
-    if user_lora:
-        lora_to_use = user_lora
-    elif selected_index is not None and lora_configs and selected_index < len(lora_configs):
-        lora_to_use = lora_configs[selected_index]
-    print(f"Loaded {len(lora_configs)} adapters from JSON")
-    
-    # Load the adapter if necessary
-    if lora_to_use and lora_to_use != active_lora_adapter:
-        try:
-            if active_lora_adapter:
-                pipe.unload_lora_weights()
-            
-            lora_path = load_lora_weights(lora_to_use["repo"], lora_to_use["weights"])
-            if lora_path:
-                pipe.load_lora_weights(lora_path, adapter_name="selected_lora")
-                pipe.set_adapters(["selected_lora"], adapter_weights=[lora_scale])
-                print(f"loaded: {lora_path} with scale {lora_scale}")
-                active_lora_adapter = lora_to_use
-            
-        except Exception as e:
-            print(f"Error loading adapter: {e}")
-    else:
-        print(f"using already loaded adapter: {lora_to_use}")
-    
-    input_image = input_image.convert("RGB")
-    # Modify prompt based on trigger word
-    trigger_word = lora_to_use["trigger_word"]
-    if trigger_word == ", How2Draw":
-        prompt = f"create a How2Draw sketch of the person of the photo {prompt}, maintain the facial identity of the person and general features"
-    elif trigger_word == "__ ":
-        prompt = f" {prompt}. Accurately render the toolimpact logo and any tool impact iconography. The toolimpact logo begins with a two-line-tall drop-cap capital letter T with a dot in the center of its top bar."
-    else:
-        prompt = f" {prompt}. convert the style of this photo or image to {trigger_word}. Maintain the facial identity of any persons and the general features of the image!"
-    
-    try:
-        image = pipe(
-            image=input_image, 
-            prompt=prompt,
-            guidance_scale=guidance_scale,
-            num_inference_steps=steps,
-            generator=torch.Generator().manual_seed(seed),
-            width=width,
-            height=height,
-            max_area=width * height
-        ).images[0]
-        
-        return image, seed, gr.update(visible=True)
-    
-    except Exception as e:
-        print(f"Error during generation: {e}")
-        return None, seed, gr.update(visible=False)
-
-# CSS styling
-css = """
-#app_container {
-    display: flex;
-    gap: 20px;
-}
-#left_panel {
-    min-width: 400px;
-}
-#lora_info {
-    color: #2563eb;
-    font-weight: bold;
-}
-#edit_prompt {
-    flex-grow: 1;
-}
-#generate_button {
-    background: linear-gradient(45deg, #2563eb, #3b82f6);
-    color: white;
-    border: none;
-    padding: 8px 16px;
-    border-radius: 6px;
-    font-weight: bold;
-}
-.user_lora_card {
-    background: #f8fafc;
-    border: 1px solid #e2e8f0;
-    border-radius: 8px;
-    padding: 12px;
-    margin: 8px 0;
-}
-#lora_gallery{
-    overflow: scroll !important
-}
-"""
-
-# Build the Gradio interface
-with gr.Blocks(theme=gr.themes.Soft(), css=css, delete_cache=(60, 60)) as demo:
-    gr_lora_configs = gr.State(value=lora_configs)
-    
-    title = gr.HTML(
-        """<h1>Flux Kontext DLC😍</h1>""",
-    )
-    
-    selected_state = gr.State(value=None)
-    user_lora = gr.State(value=None)
-    
-    with gr.Row(elem_id="app_container"):
-        with gr.Column(scale=4, elem_id="left_panel"):
-            with gr.Group(elem_id="lora_selection"):
-                input_image = gr.Image(label="Upload a picture", type="pil", height=300)
-                
-                gallery = gr.Gallery(
-                    label="Pick an Adapter",
-                    allow_preview=False,
-                    columns=3,
-                    elem_id="lora_gallery",
-                    show_share_button=False,
-                    height=400
-                )
-                
-                user_lora_input = gr.Textbox(
-                    label="Or enter a custom HuggingFace adapter", 
-                    placeholder="e.g., username/adapter-name",
-                    visible=True
-                )
-                user_lora_card = gr.HTML(visible=False)
-                unload_user_lora_button = gr.Button("Remove custom adapter", visible=True)
-        
-        with gr.Column(scale=5):
-            with gr.Row():
-                prompt = gr.Textbox(
-                    label="Editing Prompt",
-                    show_label=False,
-                    lines=1,
-                    max_lines=1,
-                    placeholder="optional description, e.g. 'colorize and stylize, leave all else as is'",
-                    elem_id="edit_prompt"
-                )
-                run_button = gr.Button("Generate", elem_id="generate_button")
-            
-            result = gr.Image(label="Generated Image", interactive=False)
-            reuse_button = gr.Button("Reuse this image", visible=False)
-            
-            with gr.Accordion("Advanced Settings", open=True):
-                lora_scale = gr.Slider(
-                    label="Adapter Scale",
-                    minimum=0,
-                    maximum=2,
-                    step=0.1,
-                    value=1.5,
-                    info="Controls the strength of the adapter effect"
-                )
-                seed = gr.Slider(
-                    label="Seed",
-                    minimum=0,
-                    maximum=MAX_SEED,
-                    step=1,
-                    value=0,
-                )
-                steps = gr.Slider(
-                    label="Steps",
-                    minimum=1,
-                    maximum=40,
-                    value=10,
-                    step=1
-                )
-                width = gr.Slider(
-                    label="Width",
-                    minimum=128,
-                    maximum=2560,
-                    step=1,
-                    value=960,
-                )
-                height = gr.Slider(
-                    label="Height",
-                    minimum=128,
-                    maximum=2560,
-                    step=1,
-                    value=1280,
-                )
-                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                guidance_scale = gr.Slider(
-                    label="Guidance Scale",
-                    minimum=1,
-                    maximum=10,
-                    step=0.1,
-                    value=2.8,
-                )
-            
-            prompt_title = gr.Markdown(
-                value="### Click on an adapter in the gallery to select it",
-                visible=True,
-                elem_id="lora_info",
-            )
-
-    # Event handlers
-    user_lora_input.input(
-        fn=load_user_lora,
-        inputs=[user_lora_input],
-        outputs=[user_lora_card, user_lora_card, unload_user_lora_button, user_lora, gallery, prompt_title, selected_state],
-    )
-    
-    unload_user_lora_button.click(
-        fn=unload_user_lora,
-        outputs=[user_lora_input, unload_user_lora_button, user_lora_card, user_lora, selected_state]
-    )
-    
-    gallery.select(
-        fn=on_lora_select,
-        inputs=[gr_lora_configs],
-        outputs=[prompt_title, prompt, selected_state],
-        show_progress=False
-    )
-    
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=generate_image_wrapper,
-        inputs=[input_image, prompt, selected_state, user_lora, seed, randomize_seed, steps, guidance_scale, lora_scale, width, height, gr_lora_configs],
-        outputs=[result, seed, reuse_button]
-    )
-    
-    reuse_button.click(
-        fn=lambda image: image,
-        inputs=[result],
-        outputs=[input_image]
-    )
-    
-    # Initialize the gallery
-    demo.load(
-        fn=sort_lora_gallery, 
-        inputs=[gr_lora_configs], 
-        outputs=[gallery, gr_lora_configs]
-    )
-
-demo.queue(default_concurrency_limit=None)
-demo.launch()