Upload Do not edit app.py

Do not edit app.py

@@ -0,0 +1,586 @@
+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
+
+#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)
+
+class calculateDuration:
+    def __init__(self, activity_name=""):
+        self.activity_name = activity_name
+
+    def __enter__(self):
+        self.start_time = time.time()
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.end_time = time.time()
+        self.elapsed_time = self.end_time - self.start_time
+        if self.activity_name:
+            print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
+        else:
+            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")
+
+def download_file(url, directory=None):
+    if directory is None:
+        directory = os.getcwd()  # Use current working directory if not specified
+    
+    # Get the filename from the URL
+    filename = url.split('/')[-1]
+    
+    # Full path for the downloaded file
+    filepath = os.path.join(directory, filename)
+    
+    # Download the file
+    response = requests.get(url)
+    response.raise_for_status()  # Raise an exception for bad status codes
+    
+    # Write the content to the file
+    with open(filepath, 'wb') as file:
+        file.write(response.content)
+    
+    return filepath
+            
+def update_selection(evt: gr.SelectData, selected_indices, loras_state, width, height):
+    selected_index = evt.index
+    selected_indices = selected_indices or []
+    if selected_index in selected_indices:
+        selected_indices.remove(selected_index)
+    else:
+        if len(selected_indices) < 2:
+            selected_indices.append(selected_index)
+        else:
+            gr.Warning("You can select up to 2 LoRAs, remove one to select a new one.")
+            return gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), width, height, gr.update(), gr.update()
+
+    selected_info_1 = "Select a LoRA 1"
+    selected_info_2 = "Select a LoRA 2"
+    lora_scale_1 = 1.15
+    lora_scale_2 = 1.15
+    lora_image_1 = None
+    lora_image_2 = None
+    if len(selected_indices) >= 1:
+        lora1 = loras_state[selected_indices[0]]
+        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨"
+        lora_image_1 = lora1['image']
+    if len(selected_indices) >= 2:
+        lora2 = loras_state[selected_indices[1]]
+        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨"
+        lora_image_2 = lora2['image']
+
+    if selected_indices:
+        last_selected_lora = loras_state[selected_indices[-1]]
+        new_placeholder = f"Type a prompt for {last_selected_lora['title']}"
+    else:
+        new_placeholder = "Type a prompt after selecting a LoRA"
+
+    return gr.update(placeholder=new_placeholder), selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, width, height, lora_image_1, lora_image_2
+
+def remove_lora_1(selected_indices, loras_state):
+    if len(selected_indices) >= 1:
+        selected_indices.pop(0)
+    selected_info_1 = "Select a LoRA 1"
+    selected_info_2 = "Select a LoRA 2"
+    lora_scale_1 = 1.15
+    lora_scale_2 = 1.15
+    lora_image_1 = None
+    lora_image_2 = None
+    if len(selected_indices) >= 1:
+        lora1 = loras_state[selected_indices[0]]
+        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
+        lora_image_1 = lora1['image']
+    if len(selected_indices) >= 2:
+        lora2 = loras_state[selected_indices[1]]
+        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
+        lora_image_2 = lora2['image']
+    return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2
+
+def remove_lora_2(selected_indices, loras_state):
+    if len(selected_indices) >= 2:
+        selected_indices.pop(1)
+    selected_info_1 = "Select LoRA 1"
+    selected_info_2 = "Select LoRA 2"
+    lora_scale_1 = 1.15
+    lora_scale_2 = 1.15
+    lora_image_1 = None
+    lora_image_2 = None
+    if len(selected_indices) >= 1:
+        lora1 = loras_state[selected_indices[0]]
+        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
+        lora_image_1 = lora1['image']
+    if len(selected_indices) >= 2:
+        lora2 = loras_state[selected_indices[1]]
+        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
+        lora_image_2 = lora2['image']
+    return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2
+
+def randomize_loras(selected_indices, loras_state):
+    if len(loras_state) < 2:
+        raise gr.Error("Not enough LoRAs to randomize.")
+    selected_indices = random.sample(range(len(loras_state)), 2)
+    lora1 = loras_state[selected_indices[0]]
+    lora2 = loras_state[selected_indices[1]]
+    selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨"
+    selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨"
+    lora_scale_1 = 1.15
+    lora_scale_2 = 1.15
+    lora_image_1 = lora1['image']
+    lora_image_2 = lora2['image']
+    random_prompt = random.choice(prompt_values)
+    return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2, random_prompt
+
+def add_custom_lora(custom_lora, selected_indices, current_loras, 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 LoRA 1"
+            selected_info_2 = "Select a LoRA 2"
+            lora_scale_1 = 1.15
+            lora_scale_2 = 1.15
+            lora_image_1 = None
+            lora_image_2 = None
+            if len(selected_indices) >= 1:
+                lora1 = current_loras[selected_indices[0]]
+                selected_info_1 = f"### LoRA 1 Selected: {lora1['title']} ✨"
+                lora_image_1 = lora1['image'] if lora1['image'] else None
+            if len(selected_indices) >= 2:
+                lora2 = current_loras[selected_indices[1]]
+                selected_info_2 = f"### LoRA 2 Selected: {lora2['title']} ✨"
+                lora_image_2 = lora2['image'] if lora2['image'] else None
+            print("Finished adding custom LoRA")
+            return (
+                current_loras,
+                gr.update(value=gallery_items),
+                selected_info_1, 
+                selected_info_2,
+                selected_indices,
+                lora_scale_1,
+                lora_scale_2,
+                lora_image_1,
+                lora_image_2
+            )
+        except Exception as e:
+            print(e)
+            gr.Warning(str(e))
+            return current_loras, gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), gr.update()
+    else:
+        return current_loras, gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), gr.update()
+
+def remove_custom_lora(selected_indices, current_loras, 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 LoRA 1"
+    selected_info_2 = "Select a LoRA 2"
+    lora_scale_1 = 1.15
+    lora_scale_2 = 1.15
+    lora_image_1 = None
+    lora_image_2 = None
+    if len(selected_indices) >= 1:
+        lora1 = current_loras[selected_indices[0]]
+        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
+        lora_image_1 = lora1['image']
+    if len(selected_indices) >= 2:
+        lora2 = current_loras[selected_indices[1]]
+        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
+        lora_image_2 = lora2['image']
+    return (
+        current_loras,
+        gr.update(value=gallery_items),
+        selected_info_1,
+        selected_info_2,
+        selected_indices,
+        lora_scale_1,
+        lora_scale_2,
+        lora_image_1,
+        lora_image_2
+    )
+
+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 final_image
+
+@spaces.GPU(duration=75)
+def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state, progress=gr.Progress(track_tqdm=True)):
+    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_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
+            )
+#            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, 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
+
+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=(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;
+    display: block;
+    margin-left: 37px;
+">Mix and match any FLUX[dev] LoRAs</span>""",
+        elem_id="title",
+    )
+    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.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
+            )
+        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)
+
+                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_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, input_image, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state],
+        outputs=[result, seed, progress_bar]
+    ).then(
+        fn=lambda x, history: update_history(x, history),
+        inputs=[result, history_gallery],
+        outputs=history_gallery,
+    )
+
+app.queue()
+app.launch()