app.py

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()