app.py

import os
import gradio as gr
import json
import logging
import torch
from PIL import Image
import spaces
from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPipelineForImage2Image, FluxControlNetModel
from diffusers.pipelines import FluxControlNetPipeline
from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
from diffusers.utils import load_image
from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
import copy
import random
import time
import requests
import pandas as pd
from transformers import pipeline
from gradio_imageslider import ImageSlider
import numpy as np
import warnings


huggingface_token = os.getenv("HUGGINFACE_TOKEN")

# 번역 모델 로드
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en", device=0 if torch.cuda.is_available() else -1)

#Load prompts for randomization
df = pd.read_csv('prompts.csv', header=None)
prompt_values = df.values.flatten()

# Load LoRAs from JSON file
with open('loras.json', 'r') as f:
    loras = json.load(f)

# Initialize the base model
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"

# 공통 FLUX 모델 로드
base_model = "black-forest-labs/FLUX.1-dev"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype)
pipe.to(device)  # 여기서 한 번만 device로 이동

# LoRA를 위한 설정
taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype)
good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype)

# Image-to-Image 파이프라인 설정
pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
    base_model,
    vae=good_vae,
    transformer=pipe.transformer,
    text_encoder=pipe.text_encoder,
    tokenizer=pipe.tokenizer,
    text_encoder_2=pipe.text_encoder_2,
    tokenizer_2=pipe.tokenizer_2,
    torch_dtype=dtype
)

controlnet = FluxControlNetModel.from_pretrained(
    "jasperai/Flux.1-dev-Controlnet-Upscaler", torch_dtype=torch.bfloat16
)

print("Available attributes in FLUX pipeline:", pipe.__dict__.keys())

# Upscale 파이프라인 설정 (FLUX 모델의 실제 구조에 맞게 수정)
pipe_upscale = FluxControlNetPipeline(
    vae=pipe.vae,
    text_encoder=pipe.text_encoder,
    text_encoder_2=pipe.text_encoder_2,
    tokenizer=pipe.tokenizer,
    tokenizer_2=pipe.tokenizer_2,
    transformer=pipe.transformer,
    scheduler=pipe.scheduler,
    controlnet=controlnet
)

# 추가 속성 설정 (있는 경우에만)
if hasattr(pipe, 'image_processor'):
    pipe_upscale.image_processor = pipe.image_processor

# 모든 파이프라인을 device로 이동
pipe_upscale.to(device)


MAX_SEED = 2**32 - 1
MAX_PIXEL_BUDGET = 1024 * 1024

pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)

class calculateDuration:
    def __init__(self, activity_name=""):
        self.activity_name = activity_name

    def __enter__(self):
        self.start_time = time.time()
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        self.end_time = time.time()
        self.elapsed_time = self.end_time - self.start_time
        if self.activity_name:
            print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
        else:
            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")

def download_file(url, directory=None):
    if directory is None:
        directory = os.getcwd()  # Use current working directory if not specified
    
    # Get the filename from the URL
    filename = url.split('/')[-1]
    
    # Full path for the downloaded file
    filepath = os.path.join(directory, filename)
    
    # Download the file
    response = requests.get(url)
    response.raise_for_status()  # Raise an exception for bad status codes
    
    # Write the content to the file
    with open(filepath, 'wb') as file:
        file.write(response.content)
    
    return filepath
            
def update_selection(evt: gr.SelectData, selected_indices, loras_state, width, height):
    selected_index = evt.index
    selected_indices = selected_indices or []
    if selected_index in selected_indices:
        selected_indices.remove(selected_index)
    else:
        if len(selected_indices) < 2:
            selected_indices.append(selected_index)
        else:
            gr.Warning("You can select up to 2 LoRAs, remove one to select a new one.")
            return gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), width, height, gr.update(), gr.update()

    selected_info_1 = "Select a LoRA 1"
    selected_info_2 = "Select a LoRA 2"
    lora_scale_1 = 1.15
    lora_scale_2 = 1.15
    lora_image_1 = None
    lora_image_2 = None
    if len(selected_indices) >= 1:
        lora1 = loras_state[selected_indices[0]]
        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co./{lora1['repo']}) ✨"
        lora_image_1 = lora1['image']
    if len(selected_indices) >= 2:
        lora2 = loras_state[selected_indices[1]]
        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co./{lora2['repo']}) ✨"
        lora_image_2 = lora2['image']

    if selected_indices:
        last_selected_lora = loras_state[selected_indices[-1]]
        new_placeholder = f"Type a prompt for {last_selected_lora['title']}"
    else:
        new_placeholder = "Type a prompt after selecting a LoRA"

    return gr.update(placeholder=new_placeholder), selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, width, height, lora_image_1, lora_image_2

def remove_lora_1(selected_indices, loras_state):
    if len(selected_indices) >= 1:
        selected_indices.pop(0)
    selected_info_1 = "Select a LoRA 1"
    selected_info_2 = "Select a LoRA 2"
    lora_scale_1 = 1.15
    lora_scale_2 = 1.15
    lora_image_1 = None
    lora_image_2 = None
    if len(selected_indices) >= 1:
        lora1 = loras_state[selected_indices[0]]
        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
        lora_image_1 = lora1['image']
    if len(selected_indices) >= 2:
        lora2 = loras_state[selected_indices[1]]
        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
        lora_image_2 = lora2['image']
    return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2

def remove_lora_2(selected_indices, loras_state):
    if len(selected_indices) >= 2:
        selected_indices.pop(1)
    selected_info_1 = "Select LoRA 1"
    selected_info_2 = "Select LoRA 2"
    lora_scale_1 = 1.15
    lora_scale_2 = 1.15
    lora_image_1 = None
    lora_image_2 = None
    if len(selected_indices) >= 1:
        lora1 = loras_state[selected_indices[0]]
        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
        lora_image_1 = lora1['image']
    if len(selected_indices) >= 2:
        lora2 = loras_state[selected_indices[1]]
        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
        lora_image_2 = lora2['image']
    return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2

def randomize_loras(selected_indices, loras_state):
    if len(loras_state) < 2:
        raise gr.Error("Not enough LoRAs to randomize.")
    selected_indices = random.sample(range(len(loras_state)), 2)
    lora1 = loras_state[selected_indices[0]]
    lora2 = loras_state[selected_indices[1]]
    selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co./{lora1['repo']}) ✨"
    selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co./{lora2['repo']}) ✨"
    lora_scale_1 = 1.15
    lora_scale_2 = 1.15
    lora_image_1 = lora1['image']
    lora_image_2 = lora2['image']
    random_prompt = random.choice(prompt_values)
    return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2, random_prompt

def add_custom_lora(custom_lora, selected_indices, current_loras):
    if custom_lora:
        try:
            title, repo, path, trigger_word, image = check_custom_model(custom_lora)
            print(f"Loaded custom LoRA: {repo}")
            existing_item_index = next((index for (index, item) in enumerate(current_loras) if item['repo'] == repo), None)
            if existing_item_index is None:
                if repo.endswith(".safetensors") and repo.startswith("http"):
                    repo = download_file(repo)
                new_item = {
                    "image": image if image else "/home/user/app/custom.png",
                    "title": title,
                    "repo": repo,
                    "weights": path,
                    "trigger_word": trigger_word
                }
                print(f"New LoRA: {new_item}")
                existing_item_index = len(current_loras)
                current_loras.append(new_item)
            
            # Update gallery
            gallery_items = [(item["image"], item["title"]) for item in current_loras]
            # Update selected_indices if there's room
            if len(selected_indices) < 2:
                selected_indices.append(existing_item_index)
            else:
                gr.Warning("You can select up to 2 LoRAs, remove one to select a new one.")

            # Update selected_info and images
            selected_info_1 = "Select a LoRA 1"
            selected_info_2 = "Select a LoRA 2"
            lora_scale_1 = 1.15
            lora_scale_2 = 1.15
            lora_image_1 = None
            lora_image_2 = None
            if len(selected_indices) >= 1:
                lora1 = current_loras[selected_indices[0]]
                selected_info_1 = f"### LoRA 1 Selected: {lora1['title']} ✨"
                lora_image_1 = lora1['image'] if lora1['image'] else None
            if len(selected_indices) >= 2:
                lora2 = current_loras[selected_indices[1]]
                selected_info_2 = f"### LoRA 2 Selected: {lora2['title']} ✨"
                lora_image_2 = lora2['image'] if lora2['image'] else None
            print("Finished adding custom LoRA")
            return (
                current_loras,
                gr.update(value=gallery_items),
                selected_info_1, 
                selected_info_2,
                selected_indices,
                lora_scale_1,
                lora_scale_2,
                lora_image_1,
                lora_image_2
            )
        except Exception as e:
            print(e)
            gr.Warning(str(e))
            return current_loras, gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), gr.update()
    else:
        return current_loras, gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), gr.update()

def remove_custom_lora(selected_indices, current_loras):
    if current_loras:
        custom_lora_repo = current_loras[-1]['repo']
        # Remove from loras list
        current_loras = current_loras[:-1]
        # Remove from selected_indices if selected
        custom_lora_index = len(current_loras)
        if custom_lora_index in selected_indices:
            selected_indices.remove(custom_lora_index)
    # Update gallery
    gallery_items = [(item["image"], item["title"]) for item in current_loras]
    # Update selected_info and images
    selected_info_1 = "Select a LoRA 1"
    selected_info_2 = "Select a LoRA 2"
    lora_scale_1 = 1.15
    lora_scale_2 = 1.15
    lora_image_1 = None
    lora_image_2 = None
    if len(selected_indices) >= 1:
        lora1 = current_loras[selected_indices[0]]
        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
        lora_image_1 = lora1['image']
    if len(selected_indices) >= 2:
        lora2 = current_loras[selected_indices[1]]
        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
        lora_image_2 = lora2['image']
    return (
        current_loras,
        gr.update(value=gallery_items),
        selected_info_1,
        selected_info_2,
        selected_indices,
        lora_scale_1,
        lora_scale_2,
        lora_image_1,
        lora_image_2
    )

@spaces.GPU(duration=75)
def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress):
    print("Generating image...")
    pipe.to("cuda")
    generator = torch.Generator(device="cuda").manual_seed(seed)
    with calculateDuration("Generating image"):
        # Generate image
        for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
            prompt=prompt_mash,
            num_inference_steps=steps,
            guidance_scale=cfg_scale,
            width=width,
            height=height,
            generator=generator,
            joint_attention_kwargs={"scale": 1.0},
            output_type="pil",
            good_vae=good_vae,
        ):
            yield img

@spaces.GPU(duration=75)
def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, seed):
    pipe_i2i.to("cuda")
    generator = torch.Generator(device="cuda").manual_seed(seed)
    image_input = load_image(image_input_path)
    final_image = pipe_i2i(
        prompt=prompt_mash,
        image=image_input,
        strength=image_strength,
        num_inference_steps=steps,
        guidance_scale=cfg_scale,
        width=width,
        height=height,
        generator=generator,
        joint_attention_kwargs={"scale": 1.0},
        output_type="pil",
    ).images[0]
    return final_image

def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state, progress=gr.Progress(track_tqdm=True)):
    # 한글 감지 및 번역
    if any('\u3131' <= char <= '\u318E' or '\uAC00' <= char <= '\uD7A3' for char in prompt):
        translated = translator(prompt, max_length=512)[0]['translation_text']
        print(f"Original prompt: {prompt}")
        print(f"Translated prompt: {translated}")
        prompt = translated

    if not selected_indices:
        raise gr.Error("You must select at least one LoRA before proceeding.")

    selected_loras = [loras_state[idx] for idx in selected_indices]

    # Build the prompt with trigger words
    prepends = []
    appends = []
    for lora in selected_loras:
        trigger_word = lora.get('trigger_word', '')
        if trigger_word:
            if lora.get("trigger_position") == "prepend":
                prepends.append(trigger_word)
            else:
                appends.append(trigger_word)
    prompt_mash = " ".join(prepends + [prompt] + appends)
    print("Prompt Mash: ", prompt_mash)

    # Unload previous LoRA weights
    with calculateDuration("Unloading LoRA"):
        pipe.unload_lora_weights()
        pipe_i2i.unload_lora_weights()
        
    print(pipe.get_active_adapters())
    # Load LoRA weights with respective scales
    lora_names = []
    lora_weights = []
    with calculateDuration("Loading LoRA weights"):
        for idx, lora in enumerate(selected_loras):
            lora_name = f"lora_{idx}"
            lora_names.append(lora_name)
            lora_weights.append(lora_scale_1 if idx == 0 else lora_scale_2)
            lora_path = lora['repo']
            weight_name = lora.get("weights")
            print(f"Lora Path: {lora_path}")
            if image_input is not None:
                if weight_name:
                    pipe_i2i.load_lora_weights(lora_path, weight_name=weight_name, low_cpu_mem_usage=True, adapter_name=lora_name)
                else:
                    pipe_i2i.load_lora_weights(lora_path, low_cpu_mem_usage=True, adapter_name=lora_name)
            else:
                if weight_name:
                    pipe.load_lora_weights(lora_path, weight_name=weight_name, low_cpu_mem_usage=True, adapter_name=lora_name)
                else:
                    pipe.load_lora_weights(lora_path, low_cpu_mem_usage=True, adapter_name=lora_name)
        print("Loaded LoRAs:", lora_names)
        print("Adapter weights:", lora_weights)
        if image_input is not None:
            pipe_i2i.set_adapters(lora_names, adapter_weights=lora_weights)
        else:
            pipe.set_adapters(lora_names, adapter_weights=lora_weights)
    print(pipe.get_active_adapters())
    # Set random seed for reproducibility
    with calculateDuration("Randomizing seed"):
        if randomize_seed:
            seed = random.randint(0, MAX_SEED)

    # Generate image
    if image_input is not None:
        final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, seed)
        yield final_image, seed, gr.update(visible=False)
    else:
        image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
        # Consume the generator to get the final image
        final_image = None
        step_counter = 0
        for image in image_generator:
            step_counter += 1
            final_image = image
            progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
            yield image, seed, gr.update(value=progress_bar, visible=True)
        
        if final_image is None:
            raise gr.Error("Failed to generate image")
        
        yield final_image, seed, gr.update(value=progress_bar, visible=False)

run_lora.zerogpu = True

def get_huggingface_safetensors(link):
    split_link = link.split("/")
    if len(split_link) == 2:
        model_card = ModelCard.load(link)
        base_model = model_card.data.get("base_model")
        print(f"Base model: {base_model}")
        if base_model not in ["black-forest-labs/FLUX.1-dev", "black-forest-labs/FLUX.1-schnell"]:
            raise Exception("Not a FLUX LoRA!")
        image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
        trigger_word = model_card.data.get("instance_prompt", "")
        image_url = f"https://huggingface.co./{link}/resolve/main/{image_path}" if image_path else None
        fs = HfFileSystem()
        safetensors_name = None
        try:
            list_of_files = fs.ls(link, detail=False)
            for file in list_of_files:
                if file.endswith(".safetensors"):
                    safetensors_name = file.split("/")[-1]
                if not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp")):
                    image_elements = file.split("/")
                    image_url = f"https://huggingface.co./{link}/resolve/main/{image_elements[-1]}"
        except Exception as e:
            print(e)
            raise gr.Error("Invalid Hugging Face repository with a *.safetensors LoRA")
        if not safetensors_name:
            raise gr.Error("No *.safetensors file found in the repository")
        return split_link[1], link, safetensors_name, trigger_word, image_url
    else:
        raise gr.Error("Invalid Hugging Face repository link")

def check_custom_model(link):
    if link.endswith(".safetensors"):
        # Treat as direct link to the LoRA weights
        title = os.path.basename(link)
        repo = link
        path = None  # No specific weight name
        trigger_word = ""
        image_url = None
        return title, repo, path, trigger_word, image_url
    elif link.startswith("https://"):
        if "huggingface.co" in link:
            link_split = link.split("huggingface.co/")
            return get_huggingface_safetensors(link_split[1])
        else:
            raise Exception("Unsupported URL")
    else:
        # Assume it's a Hugging Face model path
        return get_huggingface_safetensors(link)

def update_history(new_image, history):
    """Updates the history gallery with the new image."""
    if history is None:
        history = []
    history.insert(0, new_image)
    return history

css = '''
#gen_btn{height: 100%}
#title{text-align: center}
#title h1{font-size: 3em; display:inline-flex; align-items:center}
#title img{width: 100px; margin-right: 0.25em}
#gallery .grid-wrap{height: 5vh}
#lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%}
.custom_lora_card{margin-bottom: 1em}
.card_internal{display: flex;height: 100px;margin-top: .5em}
.card_internal img{margin-right: 1em}
.styler{--form-gap-width: 0px !important}
#progress{height:30px}
#progress .generating{display:none}
.progress-container {width: 100%;height: 30px;background-color: #f0f0f0;border-radius: 15px;overflow: hidden;margin-bottom: 20px}
.progress-bar {height: 100%;background-color: #4f46e5;width: calc(var(--current) / var(--total) * 100%);transition: width 0.5s ease-in-out}
#component-8, .button_total{height: 100%; align-self: stretch;}
#loaded_loras [data-testid="block-info"]{font-size:80%}
#custom_lora_structure{background: var(--block-background-fill)}
#custom_lora_btn{margin-top: auto;margin-bottom: 11px}
#random_btn{font-size: 300%}
#component-11{align-self: stretch;}
footer {visibility: hidden;}
'''

# 업스케일 관련 함수 추가
def process_input(input_image, upscale_factor, **kwargs):
    w, h = input_image.size
    w_original, h_original = w, h
    aspect_ratio = w / h

    was_resized = False

    if w * h * upscale_factor**2 > MAX_PIXEL_BUDGET:
        warnings.warn(
            f"Requested output image is too large ({w * upscale_factor}x{h * upscale_factor}). Resizing to ({int(aspect_ratio * MAX_PIXEL_BUDGET ** 0.5 // upscale_factor), int(MAX_PIXEL_BUDGET ** 0.5 // aspect_ratio // upscale_factor)}) pixels."
        )
        gr.Info(
            f"Requested output image is too large ({w * upscale_factor}x{h * upscale_factor}). Resizing input to ({int(aspect_ratio * MAX_PIXEL_BUDGET ** 0.5 // upscale_factor), int(MAX_PIXEL_BUDGET ** 0.5 // aspect_ratio // upscale_factor)}) pixels budget."
        )
        input_image = input_image.resize(
            (
                int(aspect_ratio * MAX_PIXEL_BUDGET**0.5 // upscale_factor),
                int(MAX_PIXEL_BUDGET**0.5 // aspect_ratio // upscale_factor),
            )
        )
        was_resized = True

    # resize to multiple of 8
    w, h = input_image.size
    w = w - w % 8
    h = h - h % 8

    return input_image.resize((w, h)), w_original, h_original, was_resized

@spaces.GPU
def infer_upscale(
    seed,
    randomize_seed,
    input_image,
    num_inference_steps,
    upscale_factor,
    controlnet_conditioning_scale,
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    true_input_image = input_image
    input_image, w_original, h_original, was_resized = process_input(
        input_image, upscale_factor
    )

    # rescale with upscale factor
    w, h = input_image.size
    control_image = input_image.resize((w * upscale_factor, h * upscale_factor))

    generator = torch.Generator().manual_seed(seed)

    gr.Info("Upscaling image...")
    image = pipe_upscale(
        prompt="",
        control_image=control_image,
        controlnet_conditioning_scale=controlnet_conditioning_scale,
        num_inference_steps=num_inference_steps,
        guidance_scale=3.5,
        height=control_image.size[1],
        width=control_image.size[0],
        generator=generator,
    ).images[0]

    if was_resized:
        gr.Info(
            f"Resizing output image to targeted {w_original * upscale_factor}x{h_original * upscale_factor} size."
        )

    # resize to target desired size
    image = image.resize((w_original * upscale_factor, h_original * upscale_factor))
    image.save("output.jpg")
    # convert to numpy
    return [true_input_image, image, seed]



with gr.Blocks(theme=gr.themes.Default(), css=css) as app:
    loras_state = gr.State(loras)
    selected_indices = gr.State([])

    with gr.Row():
        with gr.Column(scale=3):
            prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA")
        with gr.Column(scale=1):
            generate_button = gr.Button("Generate", variant="primary")

    with gr.Row():
        with gr.Column(scale=1, min_width=25):
            randomize_button = gr.Button("🎲", variant="secondary")
        with gr.Column(scale=8):
            with gr.Row():
                with gr.Column(scale=0, min_width=50):
                    lora_image_1 = gr.Image(label="LoRA 1 Image", interactive=False, width=50, height=50)
                with gr.Column(scale=3, min_width=100):
                    selected_info_1 = gr.Markdown("Select a LoRA 1")
                with gr.Column(scale=5, min_width=50):
                    lora_scale_1 = gr.Slider(label="LoRA 1 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
            remove_button_1 = gr.Button("Remove", size="sm")
        with gr.Column(scale=8):
            with gr.Row():
                with gr.Column(scale=0, min_width=50):
                    lora_image_2 = gr.Image(label="LoRA 2 Image", interactive=False, width=50, height=50)
                with gr.Column(scale=3, min_width=100):
                    selected_info_2 = gr.Markdown("Select a LoRA 2")
                with gr.Column(scale=5, min_width=50):
                    lora_scale_2 = gr.Slider(label="LoRA 2 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
            remove_button_2 = gr.Button("Remove", size="sm")

    with gr.Row():
        with gr.Column():
            with gr.Group():
                with gr.Row():
                    custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path or *.safetensors public URL", placeholder="ginipick/flux-lora-eric-cat", scale=3)
                    add_custom_lora_button = gr.Button("Add Custom LoRA", scale=2)
                remove_custom_lora_button = gr.Button("Remove Custom LoRA", visible=False)
                gr.Markdown("[Check the list of FLUX LoRAs](https://huggingface.co./models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)")
            gallery = gr.Gallery(
                [(item["image"], item["title"]) for item in loras],
                label="Or pick from the LoRA Explorer gallery",
                columns=4,
            )
        with gr.Column():
            progress_bar = gr.Markdown(visible=False)
            result = gr.Image(label="Generated Image", interactive=False)
            with gr.Accordion("History", open=False):
                history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", interactive=False)

    with gr.Accordion("Advanced Settings", open=False):
        with gr.Row():
            input_image = gr.Image(label="Input image", type="filepath")
            image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
        with gr.Row():
            cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
            steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
        with gr.Row():
            width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
            height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
        with gr.Row():
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)

    # Upscale related UI
    with gr.Row():
        upscale_button = gr.Button("Upscale")
    
    with gr.Row():
        with gr.Column(scale=4):
            upscale_input = gr.Image(label="Input Image for Upscaling", type="pil")
        with gr.Column(scale=1):
            upscale_steps = gr.Slider(label="Number of Inference Steps for Upscaling", minimum=8, maximum=50, step=1, value=28)
            upscale_factor = gr.Slider(label="Upscale Factor", minimum=1, maximum=4, step=1, value=4)
            controlnet_conditioning_scale = gr.Slider(label="Controlnet Conditioning Scale", minimum=0.1, maximum=1.5, step=0.1, value=0.6)
            upscale_seed = gr.Slider(label="Seed for Upscaling", minimum=0, maximum=MAX_SEED, step=1, value=42)
            upscale_randomize_seed = gr.Checkbox(label="Randomize seed for Upscaling", value=True)

    with gr.Row():
        upscale_result = gr.Image(label="Input / Output for Upscaling", type="pil", interactive=True)

    # Event handlers
    gallery.select(
        update_selection,
        inputs=[selected_indices, loras_state, width, height],
        outputs=[prompt, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, width, height, lora_image_1, lora_image_2]
    )
    remove_button_1.click(
        remove_lora_1,
        inputs=[selected_indices, loras_state],
        outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
    )
    remove_button_2.click(
        remove_lora_2,
        inputs=[selected_indices, loras_state],
        outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
    )
    randomize_button.click(
        randomize_loras,
        inputs=[selected_indices, loras_state],
        outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2, prompt]
    )
    add_custom_lora_button.click(
        add_custom_lora,
        inputs=[custom_lora, selected_indices, loras_state],
        outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
    )
    remove_custom_lora_button.click(
        remove_custom_lora,
        inputs=[selected_indices, loras_state],
        outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
    )
    generate_button.click(
        run_lora,
        inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state],
        outputs=[result, seed, progress_bar]
    )
    upscale_button.click(
        infer_upscale,
        inputs=[upscale_seed, upscale_randomize_seed, upscale_input, upscale_steps, upscale_factor, controlnet_conditioning_scale],
        outputs=upscale_result
    )

if __name__ == "__main__":
    app.queue(concurrency_count=3, max_size=20)
    app.launch(debug=True)