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
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("HF_TOKEN")


translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en", device="cpu")


 
#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).to(device)

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

# 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
).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) < 3:
 selected_indices.append(selected_index)
 else:
 gr.Warning("You can select up to 3 LoRAs, remove one to select a new one.")
 return gr.update(), gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), width, height, gr.update(), gr.update(), gr.update()

 selected_info_1 = "Select LoRA 1"
 selected_info_2 = "Select LoRA 2"
 selected_info_3 = "Select LoRA 3"
 
 lora_scale_1 = 1.15
 lora_scale_2 = 1.15
 lora_scale_3 = 1.15 
 lora_image_1 = None
 lora_image_2 = None
 lora_image_3 = 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 len(selected_indices) >= 3:
 lora3 = loras_state[selected_indices[2]]
 selected_info_3 = f"### LoRA 3 Selected: [{lora3['title']}](https://huggingface.co/{lora3['repo']}) ✨"
 lora_image_3 = lora3['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_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, width, height, lora_image_1, lora_image_2, lora_image_3

def remove_lora(selected_indices, loras_state, index_to_remove):
 if len(selected_indices) > index_to_remove:
 selected_indices.pop(index_to_remove)
 
 selected_info_1 = "Select LoRA 1"
 selected_info_2 = "Select LoRA 2"
 selected_info_3 = "Select LoRA 3"
 lora_scale_1 = 1.15
 lora_scale_2 = 1.15
 lora_scale_3 = 1.15
 lora_image_1 = None
 lora_image_2 = None
 lora_image_3 = None
 
 for i, idx in enumerate(selected_indices):
 lora = loras_state[idx]
 if i == 0:
 selected_info_1 = f"### LoRA 1 Selected: [{lora['title']}]({lora['repo']}) ✨"
 lora_image_1 = lora['image']
 elif i == 1:
 selected_info_2 = f"### LoRA 2 Selected: [{lora['title']}]({lora['repo']}) ✨"
 lora_image_2 = lora['image']
 elif i == 2:
 selected_info_3 = f"### LoRA 3 Selected: [{lora['title']}]({lora['repo']}) ✨"
 lora_image_3 = lora['image']
 
 return selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3

def remove_lora_1(selected_indices, loras_state):
 return remove_lora(selected_indices, loras_state, 0)

def remove_lora_2(selected_indices, loras_state):
 return remove_lora(selected_indices, loras_state, 1)

def remove_lora_3(selected_indices, loras_state):
 return remove_lora(selected_indices, loras_state, 2)

def randomize_loras(selected_indices, loras_state):
 try:
 if len(loras_state) < 3:
 raise gr.Error("Not enough LoRAs to randomize.")
 selected_indices = random.sample(range(len(loras_state)), 3)
 lora1 = loras_state[selected_indices[0]]
 lora2 = loras_state[selected_indices[1]]
 lora3 = loras_state[selected_indices[2]]
 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']}) ✨"
 selected_info_3 = f"### LoRA 3 Selected: [{lora3['title']}](https://huggingface.co/{lora3['repo']}) ✨"
 lora_scale_1 = 1.15
 lora_scale_2 = 1.15
 lora_scale_3 = 1.15
 lora_image_1 = lora1.get('image', 'path/to/default/image.png')
 lora_image_2 = lora2.get('image', 'path/to/default/image.png')
 lora_image_3 = lora3.get('image', 'path/to/default/image.png')
 random_prompt = random.choice(prompt_values)
 return selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3, random_prompt
 except Exception as e:
 print(f"Error in randomize_loras: {str(e)}")
 return "Error", "Error", "Error", [], 1.15, 1.15, 1.15, 'path/to/default/image.png', 'path/to/default/image.png', 'path/to/default/image.png', ""

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) < 3:
 selected_indices.append(existing_item_index)
 else:
 gr.Warning("You can select up to 3 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"
 selected_info_3 = "Select a LoRA 3"
 lora_scale_1 = 1.15
 lora_scale_2 = 1.15
 lora_scale_3 = 1.15
 lora_image_1 = None
 lora_image_2 = None
 lora_image_3 = 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
 if len(selected_indices) >= 3:
 lora3 = current_loras[selected_indices[2]]
 selected_info_3 = f"### LoRA 3 Selected: {lora3['title']} ✨"
 lora_image_3 = lora3['image'] if lora3['image'] else None
 print("Finished adding custom LoRA")
 return (
 current_loras,
 gr.update(value=gallery_items),
 selected_info_1, 
 selected_info_2,
 selected_info_3,
 selected_indices,
 lora_scale_1,
 lora_scale_2,
 lora_scale_3,
 lora_image_1,
 lora_image_2,
 lora_image_3
 )
 except Exception as e:
 print(e)
 gr.Warning(str(e))
 return current_loras, gr.update(), gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update()
 else:
 return current_loras, gr.update(), gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), 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"
 selected_info_3 = "Select a LoRA 3"
 lora_scale_1 = 1.15
 lora_scale_2 = 1.15
 lora_scale_3 = 1.15
 lora_image_1 = None
 lora_image_2 = None
 lora_image_3 = 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']
 if len(selected_indices) >= 3:
 lora3 = current_loras[selected_indices[2]]
 selected_info_3 = f"### LoRA 3 Selected: [{lora3['title']}]({lora3['repo']}) ✨"
 lora_image_3 = lora3['image']
 return (
 current_loras,
 gr.update(value=gallery_items),
 selected_info_1,
 selected_info_2,
 selected_info_3,
 selected_indices,
 lora_scale_1,
 lora_scale_2,
 lora_scale_3,
 lora_image_1,
 lora_image_2,
 lora_image_3
 )

@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, lora_scale_3, randomize_seed, seed, width, height, loras_state, progress=gr.Progress(track_tqdm=True)):
 try:
 # 한글 감지 및 번역 (이 부분은 그대로 유지)
 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 (이 부분은 그대로 유지)
 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(f"Active adapters before loading: {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):
 try:
 lora_name = f"lora_{idx}"
 lora_path = lora['repo']
 weight_name = lora.get("weights")
 print(f"Loading LoRA {lora_name} from {lora_path}")
 if image_input is not None:
 if weight_name:
 pipe_i2i.load_lora_weights(lora_path, weight_name=weight_name, adapter_name=lora_name)
 else:
 pipe_i2i.load_lora_weights(lora_path, adapter_name=lora_name)
 else:
 if weight_name:
 pipe.load_lora_weights(lora_path, weight_name=weight_name, adapter_name=lora_name)
 else:
 pipe.load_lora_weights(lora_path, adapter_name=lora_name)
 lora_names.append(lora_name)
 lora_weights.append(lora_scale_1 if idx == 0 else lora_scale_2 if idx == 1 else lora_scale_3)
 except Exception as e:
 print(f"Failed to load LoRA {lora_name}: {str(e)}")

 print("Loaded LoRAs:", lora_names)
 print("Adapter weights:", lora_weights)
 
 if lora_names:
 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)
 else:
 print("No LoRAs were successfully loaded.")
 return None, seed, gr.update(visible=False)

 print(f"Active adapters after loading: {pipe.get_active_adapters()}")

 # 여기서부터 이미지 생성 로직 (이 부분은 그대로 유지)
 with calculateDuration("Randomizing seed"):
 if randomize_seed:
 seed = random.randint(0, MAX_SEED)

 if image_input is not None:
 final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, seed)
 else:
 image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
 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 Exception("Failed to generate image")
 
 return final_image, seed, gr.update(visible=False)

 except Exception as e:
 print(f"Error in run_lora: {str(e)}")
 return None, seed, gr.update(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 = []
 if new_image is not None:
 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;}
'''

with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css, delete_cache=(60, 3600)) 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", elem_classes=["button_total"])

 with gr.Row(elem_id="loaded_loras"):
 with gr.Column(scale=1, min_width=25):
 randomize_button = gr.Button("🎲", variant="secondary", scale=1, elem_id="random_btn")
 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, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, 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)
 with gr.Row():
 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, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, 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)
 with gr.Row():
 remove_button_2 = gr.Button("Remove", size="sm")

 with gr.Column(scale=8):
 with gr.Row():
 with gr.Column(scale=0, min_width=50):
 lora_image_3 = gr.Image(label="LoRA 3 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50)
 with gr.Column(scale=3, min_width=100):
 selected_info_3 = gr.Markdown("Select a LoRA 3")
 with gr.Column(scale=5, min_width=50):
 lora_scale_3 = gr.Slider(label="LoRA 3 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
 with gr.Row():
 remove_button_3 = gr.Button("Remove", size="sm")
 
 with gr.Row():
 with gr.Column():
 with gr.Group():
 with gr.Row(elem_id="custom_lora_structure"):
 custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path or *.safetensors public URL", placeholder="ginipick/flux-lora-eric-cat", scale=3, min_width=150)
 add_custom_lora_button = gr.Button("Add Custom LoRA", elem_id="custom_lora_btn", scale=2, min_width=150)
 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)", elem_id="lora_list")
 gallery = gr.Gallery(
 [(item["image"], item["title"]) for item in loras],
 label="Or pick from the LoRA Explorer gallery",
 allow_preview=False,
 columns=4,
 elem_id="gallery"
 )
 with gr.Column():
 progress_bar = gr.Markdown(elem_id="progress", 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.Row():
 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.Column():
 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(True, label="Randomize seed")
 seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)

 gallery.select(
 update_selection,
 inputs=[selected_indices, loras_state, width, height],
 outputs=[prompt, selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, width, height, lora_image_1, lora_image_2, lora_image_3]
 )

 remove_button_1.click(
 remove_lora_1,
 inputs=[selected_indices, loras_state],
 outputs=[selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3]
 )
 
 remove_button_2.click(
 remove_lora_2,
 inputs=[selected_indices, loras_state],
 outputs=[selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3]
 )
 
 remove_button_3.click(
 remove_lora_3,
 inputs=[selected_indices, loras_state],
 outputs=[selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3]
 )

 randomize_button.click(
 randomize_loras,
 inputs=[selected_indices, loras_state],
 outputs=[selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3, 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_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3]
 )

 remove_custom_lora_button.click(
 remove_custom_lora,
 inputs=[selected_indices, loras_state],
 outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3]
 )

 gr.on(
 triggers=[generate_button.click, prompt.submit],
 fn=run_lora,
 inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, randomize_seed, seed, width, height, loras_state],
 outputs=[result, seed, progress_bar]
 ).then(
 fn=lambda x, history: update_history(x, history) if x is not None else history,
 inputs=[result, history_gallery],
 outputs=history_gallery,
 )

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