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| 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 | |
| # 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 update_selection(evt: gr.SelectData, selected_indices, width, height): | |
| selected_index = evt.index | |
| selected_indices = selected_indices or [] | |
| if selected_index in selected_indices: | |
| # LoRA is already selected, remove it | |
| selected_indices.remove(selected_index) | |
| else: | |
| if len(selected_indices) < 2: | |
| selected_indices.append(selected_index) | |
| else: | |
| raise gr.Error("You can select up to 2 LoRAs only.") | |
| # Initialize outputs | |
| selected_info_1 = "" | |
| selected_info_2 = "" | |
| lora_scale_1 = 0.95 | |
| lora_scale_2 = 0.95 | |
| lora_image_1 = None | |
| lora_image_2 = None | |
| if len(selected_indices) >= 1: | |
| lora1 = loras[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[selected_indices[1]] | |
| selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨" | |
| lora_image_2 = lora2['image'] | |
| # Update prompt placeholder based on last selected LoRA | |
| if selected_indices: | |
| last_selected_lora = loras[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): | |
| selected_indices = selected_indices or [] | |
| if len(selected_indices) >= 1: | |
| selected_indices.pop(0) | |
| # Update selected_info_1 and selected_info_2 | |
| selected_info_1 = "" | |
| selected_info_2 = "" | |
| lora_scale_1 = 0.95 | |
| lora_scale_2 = 0.95 | |
| lora_image_1 = None | |
| lora_image_2 = None | |
| if len(selected_indices) >= 1: | |
| lora1 = loras[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[selected_indices[1]] | |
| selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{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): | |
| selected_indices = selected_indices or [] | |
| if len(selected_indices) >= 2: | |
| selected_indices.pop(1) | |
| # Update selected_info_1 and selected_info_2 | |
| selected_info_1 = "" | |
| selected_info_2 = "" | |
| lora_scale_1 = 0.95 | |
| lora_scale_2 = 0.95 | |
| lora_image_1 = None | |
| lora_image_2 = None | |
| if len(selected_indices) >= 1: | |
| lora1 = loras[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[selected_indices[1]] | |
| selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{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): | |
| if len(loras) < 2: | |
| raise gr.Error("Not enough LoRAs to randomize.") | |
| selected_indices = random.sample(range(len(loras)), 2) | |
| lora1 = loras[selected_indices[0]] | |
| lora2 = loras[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 = 0.95 | |
| lora_scale_2 = 0.95 | |
| lora_image_1 = lora1['image'] | |
| 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 generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress): | |
| 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): | |
| generator = torch.Generator(device="cuda").manual_seed(seed) | |
| pipe_i2i.to("cuda") | |
| image_input = load_image(image_input_path) | |
| final_image = pipe_i2i( | |
| prompt=prompt_mash, | |
| image=image_input, | |
| strength=image_strength, | |
| num_inference_steps=steps, | |
| guidance_scale=cfg_scale, | |
| width=width, | |
| height=height, | |
| generator=generator, | |
| joint_attention_kwargs={"scale": 1.0}, | |
| output_type="pil", | |
| ).images[0] | |
| return final_image | |
| def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, 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[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) | |
| # Unload previous LoRA weights | |
| with calculateDuration("Unloading LoRA"): | |
| pipe.unload_lora_weights() | |
| pipe_i2i.unload_lora_weights() | |
| # Load LoRA weights with respective scales | |
| lora_names = [] | |
| with calculateDuration("Loading LoRA weights"): | |
| for idx, lora in enumerate(selected_loras): | |
| lora_name = f"lora_{idx}" | |
| lora_names.append(lora_name) | |
| lora_path = lora['repo'] | |
| scale = lora_scale_1 if idx == 0 else lora_scale_2 | |
| if image_input is not None: | |
| if "weights" in lora: | |
| pipe_i2i.load_lora_weights(lora_path, weight_name=lora["weights"], low_cpu_mem_usage=True, adapter_name=lora_name) | |
| else: | |
| pipe_i2i.load_lora_weights(lora_path, low_cpu_mem_usage=True, adapter_name=lora_name) | |
| else: | |
| if "weights" in lora: | |
| pipe.load_lora_weights(lora_path, weight_name=lora["weights"], low_cpu_mem_usage=True, adapter_name=lora_name) | |
| else: | |
| pipe.load_lora_weights(lora_path, low_cpu_mem_usage=True, adapter_name=lora_name) | |
| pipeline.set_adapters(lora_names, adapter_weights=[lora_scale_1, lora_scale_2]) | |
| # 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) | |
| 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(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 Exception("Invalid Hugging Face repository with a *.safetensors LoRA") | |
| if not safetensors_name: | |
| raise Exception("No *.safetensors file found in the repository") | |
| return split_link[1], link, safetensors_name, trigger_word, image_url | |
| def check_custom_model(link): | |
| if link.startswith("https://"): | |
| if link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co"): | |
| link_split = link.split("huggingface.co/") | |
| return get_huggingface_safetensors(link_split[1]) | |
| else: | |
| return get_huggingface_safetensors(link) | |
| def add_custom_lora(custom_lora, selected_indices): | |
| global loras | |
| if custom_lora: | |
| try: | |
| title, repo, path, trigger_word, image = check_custom_model(custom_lora) | |
| print(f"Loaded custom LoRA: {repo}") | |
| card = f''' | |
| <div class="custom_lora_card"> | |
| <span>Loaded custom LoRA:</span> | |
| <div class="card_internal"> | |
| <img src="{image}" /> | |
| <div> | |
| <h3>{title}</h3> | |
| <small>{"Using: <code><b>"+trigger_word+"</code></b> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}<br></small> | |
| </div> | |
| </div> | |
| </div> | |
| ''' | |
| existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None) | |
| if existing_item_index is None: | |
| new_item = { | |
| "image": image, | |
| "title": title, | |
| "repo": repo, | |
| "weights": path, | |
| "trigger_word": trigger_word | |
| } | |
| print(new_item) | |
| existing_item_index = len(loras) | |
| loras.append(new_item) | |
| # Update gallery | |
| gallery_items = [(item["image"], item["title"]) for item in loras] | |
| # Update selected_indices if there's room | |
| if len(selected_indices) < 2: | |
| selected_indices.append(existing_item_index) | |
| selected_info_1 = "" | |
| selected_info_2 = "" | |
| lora_scale_1 = 0.95 | |
| lora_scale_2 = 0.95 | |
| lora_image_1 = None | |
| lora_image_2 = None | |
| if len(selected_indices) >= 1: | |
| lora1 = loras[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[selected_indices[1]] | |
| selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨" | |
| lora_image_2 = lora2['image'] | |
| return (gr.update(visible=True, value=card), gr.update(visible=True), 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) | |
| else: | |
| return (gr.update(visible=True, value=card), gr.update(visible=True), gr.update(value=gallery_items), | |
| gr.NoChange(), gr.NoChange(), selected_indices, gr.NoChange(), gr.NoChange(), gr.NoChange(), gr.NoChange()) | |
| except Exception as e: | |
| print(e) | |
| return gr.update(visible=True, value=str(e)), gr.update(visible=True), gr.NoChange(), gr.NoChange(), gr.NoChange(), selected_indices, gr.NoChange(), gr.NoChange(), gr.NoChange(), gr.NoChange() | |
| else: | |
| return gr.update(visible=False), gr.update(visible=False), gr.NoChange(), gr.NoChange(), gr.NoChange(), selected_indices, gr.NoChange(), gr.NoChange(), gr.NoChange(), gr.NoChange() | |
| def remove_custom_lora(custom_lora_info, custom_lora_button, selected_indices): | |
| global loras | |
| if loras: | |
| custom_lora_repo = loras[-1]['repo'] | |
| # Remove from loras list | |
| loras = loras[:-1] | |
| # Remove from selected_indices if selected | |
| custom_lora_index = len(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 loras] | |
| # Update selected_info and images | |
| selected_info_1 = "" | |
| selected_info_2 = "" | |
| lora_scale_1 = 0.95 | |
| lora_scale_2 = 0.95 | |
| lora_image_1 = None | |
| lora_image_2 = None | |
| if len(selected_indices) >= 1: | |
| lora1 = loras[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[selected_indices[1]] | |
| selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨" | |
| lora_image_2 = lora2['image'] | |
| return gr.update(visible=False), gr.update(visible=False), 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 | |
| run_lora.zerogpu = True | |
| 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: 0.5em} | |
| #gallery .grid-wrap{height: 10vh} | |
| #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} | |
| button{height: 100%} | |
| #loaded_loras [data-testid="block-info"]{font-size:80%} | |
| ''' | |
| with gr.Blocks(theme=gr.themes.Soft(), css=css, delete_cache=(60, 3600)) as app: | |
| title = gr.HTML( | |
| """<h1><img src="https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer/resolve/main/flux_lora.png" alt="LoRA"> LoRA Lab</h1>""", | |
| elem_id="title", | |
| ) | |
| selected_indices = gr.State([]) | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA") | |
| with gr.Column(scale=1): | |
| generate_button = gr.Button("Generate", variant="primary") | |
| with gr.Row(elem_id="loaded_loras"): | |
| with gr.Column(scale=1, min_width=25): | |
| randomize_button = gr.Button("🎲", variant="secondary", scale=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=7): | |
| selected_info_1 = gr.Markdown("Select a LoRA 1") | |
| with gr.Row(): | |
| with gr.Column(scale=2, min_width=50): | |
| lora_scale_1 = gr.Slider(label="LoRA 1 Scale", minimum=0, maximum=3, step=0.01, value=0.95) | |
| with gr.Column(scale=1, min_width=50): | |
| remove_button_1 = gr.Button("Remove") | |
| 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=7): | |
| selected_info_2 = gr.Markdown("Select a LoRA 2") | |
| with gr.Row(): | |
| with gr.Column(scale=2, min_width=50): | |
| lora_scale_2 = gr.Slider(label="LoRA 2 Scale", minimum=0, maximum=3, step=0.01, value=0.95) | |
| with gr.Column(scale=1, min_width=50): | |
| remove_button_2 = gr.Button("Remove") | |
| with gr.Row(): | |
| with gr.Column(): | |
| gallery = gr.Gallery( | |
| [(item["image"], item["title"]) for item in loras], | |
| label="LoRA Gallery", | |
| allow_preview=False, | |
| columns=3, | |
| elem_id="gallery" | |
| ) | |
| with gr.Group(): | |
| custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path", placeholder="multimodalart/vintage-ads-flux") | |
| 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") | |
| custom_lora_info = gr.HTML(visible=False) | |
| custom_lora_button = gr.Button("Remove custom LoRA", visible=False) | |
| with gr.Column(): | |
| progress_bar = gr.Markdown(elem_id="progress", visible=False) | |
| result = gr.Image(label="Generated Image") | |
| with gr.Row(): | |
| with gr.Accordion("Advanced Settings", open=False): | |
| with gr.Row(): | |
| input_image = gr.Image(label="Input image", type="filepath") | |
| image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75) | |
| with gr.Column(): | |
| with gr.Row(): | |
| cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5) | |
| steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28) | |
| with gr.Row(): | |
| width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024) | |
| height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024) | |
| with gr.Row(): | |
| randomize_seed = gr.Checkbox(True, label="Randomize seed") | |
| seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True) | |
| gallery.select( | |
| update_selection, | |
| inputs=[selected_indices, 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], | |
| 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], | |
| 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], | |
| outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2] | |
| ) | |
| custom_lora.change( | |
| add_custom_lora, | |
| inputs=[custom_lora, selected_indices], | |
| outputs=[custom_lora_info, custom_lora_button, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2] | |
| ) | |
| custom_lora_button.click( | |
| remove_custom_lora, | |
| inputs=[custom_lora_info, custom_lora_button, selected_indices], | |
| outputs=[custom_lora_info, custom_lora_button, 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], | |
| outputs=[result, seed, progress_bar] | |
| ) | |
| app.queue() | |
| app.launch() | |