Update app.py

app.py

@@ -4,6 +4,7 @@ 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
@@ -13,7 +14,6 @@ import random
 import time
 import requests
 import pandas as pd
-import spaces
 
 # Load prompts for randomization
 df = pd.read_csv('prompts.csv', header=None)
@@ -81,472 +81,10 @@ def download_file(url, directory=None):
         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) < 6:
-            selected_indices.append(selected_index)
-        else:
-            gr.Warning("You can select up to 6 LoRAs, remove one to select a new one.")
-            return gr.update(), "Select a LoRA 1", "Select a LoRA 2", "Select a LoRA 3", "Select a LoRA 4", "Select a LoRA 5", "Select a LoRA 6", selected_indices, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, width, height, None, None, None, None, None, None
-
-    # Initialize defaults
-    selected_info_1 = "Select a LoRA 1"
-    selected_info_2 = "Select a LoRA 2"
-    selected_info_3 = "Select a LoRA 3"
-    selected_info_4 = "Select a LoRA 4"
-    selected_info_5 = "Select a LoRA 5"
-    selected_info_6 = "Select a LoRA 6"
-    lora_scale_1 = 1.15
-    lora_scale_2 = 1.15
-    lora_scale_3 = 1.15
-    lora_scale_4 = 1.15
-    lora_scale_5 = 1.15
-    lora_scale_6 = 1.15
-    lora_image_1 = None
-    lora_image_2 = None
-    lora_image_3 = None
-    lora_image_4 = None
-    lora_image_5 = None
-    lora_image_6 = 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 len(selected_indices) >= 4:
-        lora4 = loras_state[selected_indices[3]]
-        selected_info_4 = f"### LoRA 4 Selected: [{lora4['title']}](https://huggingface.co/{lora4['repo']}) ✨"
-        lora_image_4 = lora4['image']
-    if len(selected_indices) >= 5:
-        lora5 = loras_state[selected_indices[4]]
-        selected_info_5 = f"### LoRA 5 Selected: [{lora5['title']}](https://huggingface.co/{lora5['repo']}) ✨"
-        lora_image_5 = lora5['image']
-    if len(selected_indices) >= 6:
-        lora6 = loras_state[selected_indices[5]]
-        selected_info_6 = f"### LoRA 6 Selected: [{lora6['title']}](https://huggingface.co/{lora6['repo']}) ✨"
-        lora_image_6 = lora6['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_info_4,
-        selected_info_5,
-        selected_info_6,
-        selected_indices,
-        lora_scale_1,
-        lora_scale_2,
-        lora_scale_3,
-        lora_scale_4,
-        lora_scale_5,
-        lora_scale_6,
-        width,
-        height,
-        lora_image_1,
-        lora_image_2,
-        lora_image_3,
-        lora_image_4,
-        lora_image_5,
-        lora_image_6
-    )
-
-def remove_lora_1(selected_indices, loras_state):
-    if len(selected_indices) >= 1:
-        selected_indices.pop(0)
-    return update_selection_after_removal(selected_indices, loras_state)
-
-def remove_lora_2(selected_indices, loras_state):
-    if len(selected_indices) >= 2:
-        selected_indices.pop(1)
-    return update_selection_after_removal(selected_indices, loras_state)
-
-def remove_lora_3(selected_indices, loras_state):
-    if len(selected_indices) >= 3:
-        selected_indices.pop(2)
-    return update_selection_after_removal(selected_indices, loras_state)
-
-def remove_lora_4(selected_indices, loras_state):
-    if len(selected_indices) >= 4:
-        selected_indices.pop(3)
-    return update_selection_after_removal(selected_indices, loras_state)
-
-def remove_lora_5(selected_indices, loras_state):
-    if len(selected_indices) >= 5:
-        selected_indices.pop(4)
-    return update_selection_after_removal(selected_indices, loras_state)
-
-def remove_lora_6(selected_indices, loras_state):
-    if len(selected_indices) >= 6:
-        selected_indices.pop(5)
-    return update_selection_after_removal(selected_indices, loras_state)
-
-def update_selection_after_removal(selected_indices, loras_state):
-    # Reinitialize defaults
-    selected_info_1 = "Select a LoRA 1"
-    selected_info_2 = "Select a LoRA 2"
-    selected_info_3 = "Select a LoRA 3"
-    selected_info_4 = "Select a LoRA 4"
-    selected_info_5 = "Select a LoRA 5"
-    selected_info_6 = "Select a LoRA 6"
-    lora_scale_1 = 1.15
-    lora_scale_2 = 1.15
-    lora_scale_3 = 1.15
-    lora_scale_4 = 1.15
-    lora_scale_5 = 1.15
-    lora_scale_6 = 1.15
-    lora_image_1 = None
-    lora_image_2 = None
-    lora_image_3 = None
-    lora_image_4 = None
-    lora_image_5 = None
-    lora_image_6 = None
-
-    # Update selected LoRAs
-    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']
-    if len(selected_indices) >= 3:
-        lora3 = loras_state[selected_indices[2]]
-        selected_info_3 = f"### LoRA 3 Selected: [{lora3['title']}]({lora3['repo']}) ✨"
-        lora_image_3 = lora3['image']
-    if len(selected_indices) >= 4:
-        lora4 = loras_state[selected_indices[3]]
-        selected_info_4 = f"### LoRA 4 Selected: [{lora4['title']}]({lora4['repo']}) ✨"
-        lora_image_4 = lora4['image']
-    if len(selected_indices) >= 5:
-        lora5 = loras_state[selected_indices[4]]
-        selected_info_5 = f"### LoRA 5 Selected: [{lora5['title']}]({lora5['repo']}) ✨"
-        lora_image_5 = lora5['image']
-    if len(selected_indices) >= 6:
-        lora6 = loras_state[selected_indices[5]]
-        selected_info_6 = f"### LoRA 6 Selected: [{lora6['title']}]({lora6['repo']}) ✨"
-        lora_image_6 = lora6['image']
-
-    return (
-        selected_info_1,
-        selected_info_2,
-        selected_info_3,
-        selected_info_4,
-        selected_info_5,
-        selected_info_6,
-        selected_indices,
-        lora_scale_1,
-        lora_scale_2,
-        lora_scale_3,
-        lora_scale_4,
-        lora_scale_5,
-        lora_scale_6,
-        lora_image_1,
-        lora_image_2,
-        lora_image_3,
-        lora_image_4,
-        lora_image_5,
-        lora_image_6
-    )
-
-def randomize_loras(selected_indices, loras_state):
-    num_loras = min(6, len(loras_state))
-    selected_indices = random.sample(range(len(loras_state)), num_loras)
-    selected_info_1 = "Select a LoRA 1"
-    selected_info_2 = "Select a LoRA 2"
-    selected_info_3 = "Select a LoRA 3"
-    selected_info_4 = "Select a LoRA 4"
-    selected_info_5 = "Select a LoRA 5"
-    selected_info_6 = "Select a LoRA 6"
-    lora_scale_1 = 1.15
-    lora_scale_2 = 1.15
-    lora_scale_3 = 1.15
-    lora_scale_4 = 1.15
-    lora_scale_5 = 1.15
-    lora_scale_6 = 1.15
-    lora_image_1 = None
-    lora_image_2 = None
-    lora_image_3 = None
-    lora_image_4 = None
-    lora_image_5 = None
-    lora_image_6 = None
-
-    for idx, sel_idx in enumerate(selected_indices):
-        lora = loras_state[sel_idx]
-        if idx == 0:
-            selected_info_1 = f"### LoRA 1 Selected: [{lora['title']}](https://huggingface.co/{lora['repo']}) ✨"
-            lora_image_1 = lora['image']
-        elif idx == 1:
-            selected_info_2 = f"### LoRA 2 Selected: [{lora['title']}](https://huggingface.co/{lora['repo']}) ✨"
-            lora_image_2 = lora['image']
-        elif idx == 2:
-            selected_info_3 = f"### LoRA 3 Selected: [{lora['title']}](https://huggingface.co/{lora['repo']}) ✨"
-            lora_image_3 = lora['image']
-        elif idx == 3:
-            selected_info_4 = f"### LoRA 4 Selected: [{lora['title']}](https://huggingface.co/{lora['repo']}) ✨"
-            lora_image_4 = lora['image']
-        elif idx == 4:
-            selected_info_5 = f"### LoRA 5 Selected: [{lora['title']}](https://huggingface.co/{lora['repo']}) ✨"
-            lora_image_5 = lora['image']
-        elif idx == 5:
-            selected_info_6 = f"### LoRA 6 Selected: [{lora['title']}](https://huggingface.co/{lora['repo']}) ✨"
-            lora_image_6 = lora['image']
-
-    random_prompt = random.choice(prompt_values)
-    return (
-        selected_info_1,
-        selected_info_2,
-        selected_info_3,
-        selected_info_4,
-        selected_info_5,
-        selected_info_6,
-        selected_indices,
-        lora_scale_1,
-        lora_scale_2,
-        lora_scale_3,
-        lora_scale_4,
-        lora_scale_5,
-        lora_scale_6,
-        lora_image_1,
-        lora_image_2,
-        lora_image_3,
-        lora_image_4,
-        lora_image_5,
-        lora_image_6,
-        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) < 6:
-                selected_indices.append(existing_item_index)
-            else:
-                gr.Warning("You can select up to 6 LoRAs, remove one to select a new one.")
-
-            # Update selected_info and images
-            return update_selection_after_removal(selected_indices, current_loras)[:20] + (current_loras, gr.update(value=gallery_items))
-
-        except Exception as e:
-            print(e)
-            gr.Warning(str(e))
-            return current_loras, gr.update(), "Select a LoRA 1", "Select a LoRA 2", "Select a LoRA 3", "Select a LoRA 4", "Select a LoRA 5", "Select a LoRA 6", selected_indices, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, None, None, None, None, None, None
-    else:
-        return current_loras, gr.update(), "Select a LoRA 1", "Select a LoRA 2", "Select a LoRA 3", "Select a LoRA 4", "Select a LoRA 5", "Select a LoRA 6", selected_indices, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, None, None, None, None, None, None
-
-@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()
-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
-
-
-@spaces.GPU()
-def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_scale_4, lora_scale_5, lora_scale_6, 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 = []
-    lora_scales = [
-        lora_scale_1,
-        lora_scale_2,
-        lora_scale_3,
-        lora_scale_4,
-        lora_scale_5,
-        lora_scale_6
-    ]
-    with calculateDuration("Loading LoRA weights"):
-        for idx, lora in enumerate(selected_loras):
-            if idx >= 6:
-                break
-            lora_name = f"lora_{idx}"
-            lora_names.append(lora_name)
-            print(f"Lora Name: {lora_name}")
-            lora_weights.append(lora_scales[idx])
-            lora_path = lora['repo']
-            weight_name = lora.get("weights")
-            print(f"Lora Path: {lora_path}")
-            pipe_to_use = pipe_i2i if image_input is not None else pipe
-            pipe_to_use.load_lora_weights(
-                lora_path, 
-                weight_name=weight_name if weight_name else None,
-                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)
-        yield final_image, seed, gr.update(value=progress_bar, visible=False)
-
-
-run_lora.zerogpu = True
-@spaces.GPU()
-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)
+    # Your existing implementation of check_custom_model
+    # ...
 
 def update_history(new_image, history):
     """Updates the history gallery with the new image."""
@@ -578,7 +116,7 @@ css = '''
 #component-11{align-self: stretch;}
 '''
 
-with gr.Blocks(css=css, delete_cache=(60, 60)) as app:
+with gr.Blocks(css=css, theme=gr.themes.Default(), delete_cache=(60, 60)) as app:
     title = gr.HTML(
         """<h1><img src="https://i.imgur.com/wMh2Oek.png" alt="LoRA"> LoRA Lab [beta]</h1><br><span style="
     margin-top: -25px !important;
@@ -589,222 +127,250 @@ with gr.Blocks(css=css, delete_cache=(60, 60)) as app:
     )
     loras_state = gr.State(loras)
     selected_indices = gr.State([])
+
+    # Define UI components
     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")
-        # LoRA 1
-        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",interactive=True)
-        # LoRA 2
-        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")
-        # LoRA 3
-        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")
-        # LoRA 4
-        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, 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_4 = gr.Markdown("Select a LoRA 4")
-                with gr.Column(scale=5, min_width=50):
-                    lora_scale_4 = gr.Slider(label="LoRA 4 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
-            with gr.Row():
-                remove_button_4 = gr.Button("Remove", size="sm")
-        # LoRA 5
-        with gr.Column(scale=8):
-            with gr.Row():
-                with gr.Column(scale=0, min_width=50):
-                    lora_image_5 = gr.Image(label="LoRA 5 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_5 = gr.Markdown("Select a LoRA 5")
-                with gr.Column(scale=5, min_width=50):
-                    lora_scale_5 = gr.Slider(label="LoRA 5 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
-            with gr.Row():
-                remove_button_5 = gr.Button("Remove", size="sm")
-        # LoRA 6
-        with gr.Column(scale=8):
-            with gr.Row():
-                with gr.Column(scale=0, min_width=50):
-                    lora_image_6 = gr.Image(label="LoRA 6 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_6 = gr.Markdown("Select a LoRA 6")
-                with gr.Column(scale=5, min_width=50):
-                    lora_scale_6 = gr.Slider(label="LoRA 6 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
-            with gr.Row():
-                remove_button_6 = 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="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 LoRA Explorer gallery",
-                allow_preview=False,
-                columns=5,
-                elem_id="gallery",
-                show_share_button=False,
-                interactive=False
-            )
+        randomize_button = gr.Button("🎲", variant="secondary", scale=1, elem_id="random_btn")
+        # We'll dynamically render the LoRA selections below using @gr.render
+
+    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 LoRA Explorer gallery",
+        allow_preview=False,
+        columns=5,
+        elem_id="gallery",
+        show_share_button=False,
+        interactive=True  # Set to True to allow selection
+    )
+    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)
+
+    with gr.Accordion("Advanced Settings", open=False):
+        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():
-            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)
-
-    with gr.Row():
-        with gr.Accordion("Advanced Settings", open=False):
             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=3.5)
-                    steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
+                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():
+                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)
+
+    # Define states for LoRA selections
+    selected_loras_state = gr.State([])  # List of selected LoRA indices
+    lora_scales_state = gr.State([])     # List of corresponding scales
+
+    # Function to handle gallery selection
+    @gr.render(inputs=[gallery], outputs=[], triggers=[gallery.select])
+    def update_lora_selection(selected_gallery):
+        selected_index = selected_gallery.index
+        selected_indices = selected_loras_state.value.copy()
+        if selected_index in selected_indices:
+            selected_indices.remove(selected_index)
+        else:
+            if len(selected_indices) < 6:
+                selected_indices.append(selected_index)
+            else:
+                gr.Warning("You can select up to 6 LoRAs. Remove one to select a new one.")
+        selected_loras_state.value = selected_indices
+
+    # Function to render the LoRA selection components dynamically
+    @gr.render(inputs=[selected_loras_state], outputs=[], triggers=[selected_loras_state.change])
+    def render_lora_selections(selected_indices):
+        lora_scales = lora_scales_state.value
+        if len(lora_scales) != len(selected_indices):
+            lora_scales = [1.15] * len(selected_indices)
+            lora_scales_state.value = lora_scales
+
+        # Clear previous components
+        gr.Markdown("### Selected LoRAs")
+        if not selected_indices:
+            gr.Markdown("No LoRAs selected.")
+        else:
+            for idx, sel_idx in enumerate(selected_indices):
+                lora = loras_state.value[sel_idx]
                 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.Column(scale=1, min_width=50):
+                        gr.Image(value=lora['image'], interactive=False, show_label=False, height=50)
+                    with gr.Column(scale=3):
+                        gr.Markdown(f"### LoRA {idx+1}: [{lora['title']}](https://huggingface.co/{lora['repo']}) ✨")
+                    with gr.Column(scale=2):
+                        scale_slider = gr.Slider(label=f"Scale {idx+1}", minimum=0, maximum=3, step=0.01, value=lora_scales[idx])
+                        scale_slider.change(lambda val, idx=idx: update_lora_scale(idx, val), inputs=[scale_slider], outputs=[])
+                    with gr.Column(scale=1, min_width=50):
+                        remove_btn = gr.Button("Remove", size="sm")
+                        remove_btn.click(lambda idx=idx: remove_lora(idx), inputs=[], outputs=[])
+
+    # Helper function to update LoRA scales
+    def update_lora_scale(idx, value):
+        lora_scales = lora_scales_state.value
+        lora_scales[idx] = value
+        lora_scales_state.value = lora_scales
+
+    # Helper function to remove a LoRA
+    def remove_lora(idx):
+        selected_indices = selected_loras_state.value
+        lora_scales = lora_scales_state.value
+        if idx < len(selected_indices):
+            selected_indices.pop(idx)
+            lora_scales.pop(idx)
+            selected_loras_state.value = selected_indices
+            lora_scales_state.value = lora_scales
+
+    # Randomize LoRAs
+    def randomize_loras():
+        num_loras = min(6, len(loras_state.value))
+        selected_indices = random.sample(range(len(loras_state.value)), num_loras)
+        lora_scales = [1.15] * num_loras
+        selected_loras_state.value = selected_indices
+        lora_scales_state.value = lora_scales
+        random_prompt = random.choice(prompt_values)
+        prompt.value = random_prompt
+
+    randomize_button.click(randomize_loras, inputs=[], outputs=[])
+
+    # Add custom LoRA
+    def add_custom_lora_fn(custom_lora_input):
+        if custom_lora_input:
+            try:
+                title, repo, path, trigger_word, image = check_custom_model(custom_lora_input)
+                existing_item_index = next((index for (index, item) in enumerate(loras_state.value) 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
+                    }
+                    existing_item_index = len(loras_state.value)
+                    loras_state.value.append(new_item)
+                    # Update gallery
+                    gallery.value = [(item["image"], item["title"]) for item in loras_state.value]
+                if len(selected_loras_state.value) < 6:
+                    selected_loras_state.value.append(existing_item_index)
+                    lora_scales_state.value.append(1.15)
+                else:
+                    gr.Warning("You can select up to 6 LoRAs. Remove one to select a new one.")
+            except Exception as e:
+                gr.Warning(str(e))
+
+    add_custom_lora_button.click(add_custom_lora_fn, inputs=[custom_lora], outputs=[])
+
+    # Run the LoRA generation
+    @spaces.GPU(duration=75)
+    def run_lora(prompt_text, image_input_path, image_strength_value, cfg_scale_value, steps_value, randomize_seed_value, seed_value, width_value, height_value):
+        selected_indices = selected_loras_state.value
+        lora_scales = lora_scales_state.value
+
+        if not selected_indices:
+            raise gr.Error("You must select at least one LoRA before proceeding.")
+
+        selected_loras = [loras_state.value[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_text] + 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_scales[idx])
+                lora_path = lora['repo']
+                weight_name = lora.get("weights")
+                print(f"Lora Path: {lora_path}")
+                pipe_to_use = pipe_i2i if image_input_path is not None else pipe
+                pipe_to_use.load_lora_weights(
+                    lora_path, 
+                    weight_name=weight_name if weight_name else None,
+                    low_cpu_mem_usage=True,
+                    adapter_name=lora_name
+                )
+            print("Loaded LoRAs:", lora_names)
+            print("Adapter weights:", lora_weights)
+            if image_input_path 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_value:
+                seed_value = random.randint(0, MAX_SEED)
 
-    gallery.select(
-        fn=update_selection,
-        inputs=[selected_indices, loras_state, width, height],
-        outputs=[
-            prompt,
-            selected_info_1, selected_info_2, selected_info_3, selected_info_4, selected_info_5, selected_info_6,
-            selected_indices,
-            lora_scale_1, lora_scale_2, lora_scale_3, lora_scale_4, lora_scale_5, lora_scale_6,
-            width, height,
-            lora_image_1, lora_image_2, lora_image_3, lora_image_4, lora_image_5, lora_image_6
-        ])
-    remove_lora_1event = remove_button_1.click(
-        fn=remove_lora_1,
-        inputs=[0, selected_indices, loras_state],
-        outputs=[
-            selected_info_1, selected_info_2, selected_info_3, selected_info_4, selected_info_5, selected_info_6,
-            selected_indices,
-            lora_scale_1, lora_scale_2, lora_scale_3, lora_scale_4, lora_scale_5, lora_scale_6,
-            lora_image_1, lora_image_2, lora_image_3, lora_image_4, lora_image_5, lora_image_6
-        ]
-    )
-    remove_button_2.click(
-        remove_lora_2,
-        inputs=[selected_indices, loras_state],
-        outputs=[
-            selected_info_1, selected_info_2, selected_info_3, selected_info_4, selected_info_5, selected_info_6,
-            selected_indices,
-            lora_scale_1, lora_scale_2, lora_scale_3, lora_scale_4, lora_scale_5, lora_scale_6,
-            lora_image_1, lora_image_2, lora_image_3, lora_image_4, lora_image_5, lora_image_6
-        ]
-    )
-    remove_button_3.click(
-        remove_lora_3,
-        inputs=[selected_indices, loras_state],
-        outputs=[
-            selected_info_1, selected_info_2, selected_info_3, selected_info_4, selected_info_5, selected_info_6,
-            selected_indices,
-            lora_scale_1, lora_scale_2, lora_scale_3, lora_scale_4, lora_scale_5, lora_scale_6,
-            lora_image_1, lora_image_2, lora_image_3, lora_image_4, lora_image_5, lora_image_6
-        ]
-    )
-    remove_button_4.click(
-        remove_lora_4,
-        inputs=[selected_indices, loras_state],
-        outputs=[
-            selected_info_1, selected_info_2, selected_info_3, selected_info_4, selected_info_5, selected_info_6,
-            selected_indices,
-            lora_scale_1, lora_scale_2, lora_scale_3, lora_scale_4, lora_scale_5, lora_scale_6,
-            lora_image_1, lora_image_2, lora_image_3, lora_image_4, lora_image_5, lora_image_6
-        ]
-    )
-    remove_button_5.click(
-        remove_lora_5,
-        inputs=[selected_indices, loras_state],
-        outputs=[
-            selected_info_1, selected_info_2, selected_info_3, selected_info_4, selected_info_5, selected_info_6,
-            selected_indices,
-            lora_scale_1, lora_scale_2, lora_scale_3, lora_scale_4, lora_scale_5, lora_scale_6,
-            lora_image_1, lora_image_2, lora_image_3, lora_image_4, lora_image_5, lora_image_6
-        ]
-    )
-    remove_button_6.click(
-        remove_lora_6,
-        inputs=[selected_indices, loras_state],
-        outputs=[
-            selected_info_1, selected_info_2, selected_info_3, selected_info_4, selected_info_5, selected_info_6,
-            selected_indices,
-            lora_scale_1, lora_scale_2, lora_scale_3, lora_scale_4, lora_scale_5, lora_scale_6,
-            lora_image_1, lora_image_2, lora_image_3, lora_image_4, lora_image_5, lora_image_6
-        ]
-    )
-    randomize_button.click(
-        randomize_loras,
-        inputs=[selected_indices, loras_state],
-        outputs=[
-            selected_info_1, selected_info_2, selected_info_3, selected_info_4, selected_info_5, selected_info_6,
-            selected_indices,
-            lora_scale_1, lora_scale_2, lora_scale_3, lora_scale_4, lora_scale_5, lora_scale_6,
-            lora_image_1, lora_image_2, lora_image_3, lora_image_4, lora_image_5, lora_image_6,
-            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_info_3, selected_info_4, selected_info_5, selected_info_6,
-            selected_indices,
-            lora_scale_1, lora_scale_2, lora_scale_3, lora_scale_4, lora_scale_5, lora_scale_6,
-            lora_image_1, lora_image_2, lora_image_3, lora_image_4, lora_image_5, lora_image_6
-        ]
-    )
-    gr.on(
-        triggers=[generate_button.click, prompt.submit],
-        fn=run_lora,
+        # Generate image
+        if image_input_path is not None:
+            final_image = generate_image_to_image(prompt_mash, image_input_path, image_strength_value, steps_value, cfg_scale_value, width_value, height_value, seed_value)
+            yield final_image, seed_value, gr.update(visible=False)
+        else:
+            image_generator = generate_image(prompt_mash, steps_value, seed_value, cfg_scale_value, width_value, height_value, progress=gr.Progress(track_tqdm=True))
+            # 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_html = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps_value};"></div></div>'
+                yield image, seed_value, gr.update(value=progress_bar_html, visible=True)
+            yield final_image, seed_value, gr.update(value=progress_bar_html, visible=False)
+
+    run_lora.zerogpu = True
+
+    # Bind the generate button to run_lora function
+    generate_button.click(
+        run_lora,
         inputs=[
-            prompt, input_image, image_strength, cfg_scale, steps, selected_indices,
-            lora_scale_1, lora_scale_2, lora_scale_3, lora_scale_4, lora_scale_5, lora_scale_6,
-            randomize_seed, seed, width, height, loras_state],
+            prompt,
+            input_image,
+            image_strength,
+            cfg_scale,
+            steps,
+            randomize_seed,
+            seed,
+            width,
+            height
+        ],
         outputs=[result, seed, progress_bar]
     ).then(
         fn=lambda x, history: update_history(x, history),