Delete app.py

app.py

@@ -1,654 +0,0 @@
-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 huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
-from transformers import AutoModelForCausalLM, CLIPTokenizer, CLIPProcessor, CLIPModel, LongformerTokenizer, LongformerModel
-import copy
-import random
-import time
-import requests
-import pandas as pd
-import torch.nn as nn
-
-# Disable tokenizer parallelism
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-# Initialize the CLIP tokenizer and model
-clip_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch16")
-clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16")
-clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch16")
-
-# Initialize the Longformer tokenizer and model
-longformer_tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
-longformer_model = LongformerModel.from_pretrained("allenai/longformer-base-4096")
-
-#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"
-base_model = "black-forest-labs/FLUX.1-dev"
-
-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)
-pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)
-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
-)
-MAX_SEED = 2**32 - 1
-
-pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
-# Replace text embedding generation with pre-computed Longformer embeddings
-pipeline.text_encoder = None  # Disable the default CLIP text encoder
-pipeline.longformer_embeddings = transformed_embeddings  # Inject your embeddings
-
-def process_input(input_text):
-    # Tokenize and truncate input
-    #inputs = clip_processor(text=input_text, return_tensors="pt", padding=True, truncation=True, max_length=77)
-    #return inputs
-    #Change clip_processor to longformer
-    inputs = longformer_tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=4096)
-    return inputs
-
-# Example usage
-input_text = "Your long prompt goes here..."
-inputs = process_input(input_text)
-
-# Get Longformer embeddings
-with torch.no_grad():
-    longformer_embeddings = longformer_model(**encoded_input).last_hidden_state
-
-# Create a transformation layer to match CLIP's embedding dimension
-transform_layer = nn.Linear(pooled_embeddings.size(-1), 512)
-transformed_embeddings = transform_layer(pooled_embeddings)  # Shape: [batch_size, 512]    
-
-# Pass your embeddings to the pipeline during generation
-image = pipeline(prompt=None, text_embeddings=pipeline.longformer_embeddings)
-
-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 Celebrity as LoRA 1"
-    selected_info_2 = "Select a LoRA 2"
-    lora_scale_1 = 0.6
-    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 Celebrity as LoRA 1"
-    selected_info_2 = "Select a LoRA 2"
-    lora_scale_1 = 0.6
-    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 a Celebrity as LoRA 1"
-    selected_info_2 = "Select LoRA 2"
-    lora_scale_1 = 0.6
-    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, gallery):
-    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 Celebrity as LoRA 1"
-            selected_info_2 = "Select a LoRA 2"
-            lora_scale_1 = 0.6
-            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, gallery):
-    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 Celebrity as LoRA 1"
-    selected_info_2 = "Select a LoRA 2"
-    lora_scale_1 = 0.6
-    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
-    )
-
-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
-
-#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 img
-
-@spaces.GPU(duration=75)
-def run_lora(prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state, progress=gr.Progress(track_tqdm=True)):
-    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)
-            print(f"Lora Name: {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}")
-            pipe.load_lora_weights(
-                lora_path, 
-                weight_name=weight_name if weight_name else None,
-                low_cpu_mem_usage=True,
-                adapter_name=lora_name
-            )
-#            if image_input is not None: pipe_i2i = pipe_to_use
-#            else: pipe = pipe_to_use
-        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, 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)
-#            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;}
-'''
-
-with gr.Blocks(css=css, delete_cache=(240, 240)) as app:
-    title = gr.HTML(
-        """<h1><img src="Keltezaa/Celebrity_LoRa_Mix" alt=" "> Celebrity LoRa Mix</h1><br><span style="
-    margin-top: -25px !important;
-    display: block;
-    margin-left: 37px;
-">SFW & NSFW FLUX LoRAs</span>""",
-        elem_id="title",
-    )
-    loras_state = gr.State(loras)
-    selected_indices = gr.State([])
-    trigger_word_display = gr.Markdown("", elem_id="trigger_word")
-
-    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.Row(elem_id="loaded_loras"):
-                   
-        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, 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=0.05, value=0.5)
-            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, 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=0.05, value=0.5)
-            with gr.Row():
-                remove_button_2 = gr.Button("Remove", size="sm")
-            
-        with gr.Column(scale=1,min_width=50):
-            randomize_button = gr.Button("🎲", variant="secondary", scale=1, elem_id="random_btn")
-                
-#    with gr.Row(elem_id="loaded_loras"):            
-#        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, 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=0.05, value=0.5)
-#            with gr.Row():
-#                remove_button_3 = gr.Button("Remove", size="sm")
-#        with gr.Column(scale=8):
-#            with gr.Row():
-#                with gr.Column(scale=0, min_width=50):
-#                    lora_image_4 = gr.Image(label="LoRA 4 Image", interactive=False, 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_4 = gr.Markdown("Select a LoRA 4")
-#                with gr.Column(scale=5, min_width=150):
-#                    lora_scale_4 = gr.Slider(label="LoRA 4 Scale", minimum=0, maximum=3, step=0.05, value=0.5)
-#            with gr.Row():
-#                remove_button_4 = gr.Button("Remove", size="sm")
-
-    with gr.Row():
-        with gr.Accordion("Advanced Settings", open=True):
-            #with gr.Row():
-            #    input_image = gr.Image(label="Input image", type="filepath", show_share_button=False)
-            #    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=7.5)
-                    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=768)
-                    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)
-
-    with gr.Row():
-        with gr.Column(scale=3):
-            generate_button = gr.Button("Generate", variant="primary", elem_classes=["button_total"])
-            
-    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="multimodalart/vintage-ads-flux", 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 gallery",
-                allow_preview=False,
-                columns=5,
-                elem_id="gallery",
-                show_share_button=False,
-                interactive=False
-            )
-        with gr.Column():
-            progress_bar = gr.Markdown(elem_id="progress", visible=False)
-            result = gr.Image(label="Generated Image", interactive=False, show_share_button=False)
-#            with gr.Accordion("History", open=False):
-#                history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", interactive=False)  
-
-    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, gallery],
-        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, gallery],
-        outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
-    )
-    gr.on(
-        triggers=[generate_button.click, prompt.submit],
-        fn=run_lora,
-        inputs=[prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state],
-        outputs=[result, seed, progress_bar]
-    )#.then(
-     #   fn=lambda x, history: update_history(x, history),
-     #   inputs=[result, history_gallery],
-     #   outputs=history_gallery,
-    #)
-
-app.queue()
-app.launch()