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import os |
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import gradio as gr |
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import json |
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import logging |
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import torch |
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from PIL import Image |
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import spaces |
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from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPipelineForImage2Image |
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from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images |
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from diffusers.utils import load_image |
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from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download |
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import copy |
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import random |
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import time |
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with open('loras.json', 'r') as f: |
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loras = json.load(f) |
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dtype = torch.bfloat16 |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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base_model = "black-forest-labs/FLUX.1-dev" |
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taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device) |
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good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device) |
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pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device) |
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pipe_i2i = AutoPipelineForImage2Image.from_pretrained(base_model, |
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vae=good_vae, |
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transformer=pipe.transformer, |
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text_encoder=pipe.text_encoder, |
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tokenizer=pipe.tokenizer, |
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text_encoder_2=pipe.text_encoder_2, |
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tokenizer_2=pipe.tokenizer_2, |
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torch_dtype=dtype |
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) |
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MAX_SEED = 2**32-1 |
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pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe) |
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class calculateDuration: |
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def __init__(self, activity_name=""): |
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self.activity_name = activity_name |
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def __enter__(self): |
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self.start_time = time.time() |
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return self |
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def __exit__(self, exc_type, exc_value, traceback): |
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self.end_time = time.time() |
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self.elapsed_time = self.end_time - self.start_time |
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if self.activity_name: |
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print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds") |
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else: |
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print(f"Elapsed time: {self.elapsed_time:.6f} seconds") |
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def update_selection(evt: gr.SelectData, selected_indices, width, height): |
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selected_index = evt.index |
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selected_indices = selected_indices or [] |
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if selected_index in selected_indices: |
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selected_indices.remove(selected_index) |
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else: |
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if len(selected_indices) < 2: |
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selected_indices.append(selected_index) |
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else: |
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raise gr.Error("You can select up to 2 LoRAs only.") |
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selected_info_1 = "" |
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selected_info_2 = "" |
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lora_scale_1 = 0.95 |
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lora_scale_2 = 0.95 |
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lora_image_1 = None |
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lora_image_2 = None |
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if len(selected_indices) >= 1: |
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lora1 = loras[selected_indices[0]] |
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selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨" |
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lora_image_1 = lora1['image'] |
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if len(selected_indices) >= 2: |
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lora2 = loras[selected_indices[1]] |
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selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨" |
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lora_image_2 = lora2['image'] |
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if selected_indices: |
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last_selected_lora = loras[selected_indices[-1]] |
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new_placeholder = f"Type a prompt for {last_selected_lora['title']}" |
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else: |
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new_placeholder = "Type a prompt after selecting a LoRA" |
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return ( |
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gr.update(placeholder=new_placeholder), |
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selected_info_1, |
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selected_info_2, |
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selected_indices, |
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lora_scale_1, |
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lora_scale_2, |
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width, |
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height, |
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lora_image_1, |
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lora_image_2, |
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) |
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def remove_lora_1(selected_indices): |
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selected_indices = selected_indices or [] |
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if len(selected_indices) >= 1: |
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selected_indices.pop(0) |
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selected_info_1 = "" |
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selected_info_2 = "" |
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lora_scale_1 = 0.95 |
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lora_scale_2 = 0.95 |
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lora_image_1 = None |
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lora_image_2 = None |
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if len(selected_indices) >= 1: |
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lora1 = loras[selected_indices[0]] |
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selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨" |
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lora_image_1 = lora1['image'] |
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if len(selected_indices) >= 2: |
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lora2 = loras[selected_indices[1]] |
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selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨" |
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lora_image_2 = lora2['image'] |
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return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2 |
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def remove_lora_2(selected_indices): |
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selected_indices = selected_indices or [] |
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if len(selected_indices) >= 2: |
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selected_indices.pop(1) |
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selected_info_1 = "" |
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selected_info_2 = "" |
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lora_scale_1 = 0.95 |
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lora_scale_2 = 0.95 |
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lora_image_1 = None |
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lora_image_2 = None |
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if len(selected_indices) >= 1: |
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lora1 = loras[selected_indices[0]] |
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selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨" |
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lora_image_1 = lora1['image'] |
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if len(selected_indices) >= 2: |
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lora2 = loras[selected_indices[1]] |
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selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨" |
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lora_image_2 = lora2['image'] |
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return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2 |
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def randomize_loras(selected_indices): |
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if len(loras) < 2: |
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raise gr.Error("Not enough LoRAs to randomize.") |
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selected_indices = random.sample(range(len(loras)), 2) |
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lora1 = loras[selected_indices[0]] |
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lora2 = loras[selected_indices[1]] |
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selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨" |
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selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨" |
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lora_scale_1 = 0.95 |
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lora_scale_2 = 0.95 |
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lora_image_1 = lora1['image'] |
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lora_image_2 = lora2['image'] |
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return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2 |
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run_lora.zerogpu = True |
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css = ''' |
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#gen_btn{height: 100%} |
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#title{text-align: center} |
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#title h1{font-size: 3em; display:inline-flex; align-items:center} |
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#title img{width: 100px; margin-right: 0.5em} |
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#gallery .grid-wrap{height: 10vh} |
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#lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%} |
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.custom_lora_card{margin-bottom: 1em} |
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.card_internal{display: flex;height: 100px;margin-top: .5em} |
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.card_internal img{margin-right: 1em} |
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.styler{--form-gap-width: 0px !important} |
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#progress{height:30px} |
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#progress .generating{display:none} |
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.progress-container {width: 100%;height: 30px;background-color: #f0f0f0;border-radius: 15px;overflow: hidden;margin-bottom: 20px} |
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.progress-bar {height: 100%;background-color: #4f46e5;width: calc(var(--current) / var(--total) * 100%);transition: width 0.5s ease-in-out} |
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''' |
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with gr.Blocks(theme=gr.themes.Soft(), css=css, delete_cache=(60, 3600)) as app: |
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title = gr.HTML( |
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"""<h1><img src="https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer/resolve/main/flux_lora.png" alt="LoRA"> LoRA Lab</h1>""", |
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elem_id="title", |
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) |
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selected_indices = gr.State([]) |
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with gr.Row(): |
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with gr.Column(scale=3): |
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prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA") |
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with gr.Column(scale=1): |
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generate_button = gr.Button("Generate", variant="primary") |
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with gr.Row(): |
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with gr.Column(scale=1): |
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randomize_button = gr.Button("🎲", variant="secondary", scale=1, min_width=50) |
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with gr.Column(scale=4): |
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lora_image_1 = gr.Image(label="LoRA 1 Image", interactive=False) |
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selected_info_1 = gr.Markdown("Select a LoRA 1") |
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lora_scale_1 = gr.Slider(label="LoRA 1 Scale", minimum=0, maximum=3, step=0.01, value=0.95) |
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remove_button_1 = gr.Button("Remove LoRA 1") |
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with gr.Column(scale=4): |
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lora_image_2 = gr.Image(label="LoRA 2 Image", interactive=False) |
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selected_info_2 = gr.Markdown("Select a LoRA 2") |
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lora_scale_2 = gr.Slider(label="LoRA 2 Scale", minimum=0, maximum=3, step=0.01, value=0.95) |
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remove_button_2 = gr.Button("Remove LoRA 2") |
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with gr.Row(): |
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with gr.Column(): |
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gallery = gr.Gallery( |
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[(item["image"], item["title"]) for item in loras], |
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label="LoRA Gallery", |
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allow_preview=False, |
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columns=3, |
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elem_id="gallery" |
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) |
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with gr.Group(): |
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custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path", placeholder="multimodalart/vintage-ads-flux") |
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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") |
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custom_lora_info = gr.HTML(visible=False) |
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custom_lora_button = gr.Button("Remove custom LoRA", visible=False) |
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with gr.Column(): |
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progress_bar = gr.Markdown(elem_id="progress", visible=False) |
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result = gr.Image(label="Generated Image") |
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with gr.Row(): |
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with gr.Accordion("Advanced Settings", open=False): |
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with gr.Row(): |
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input_image = gr.Image(label="Input image", type="filepath") |
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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) |
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with gr.Column(): |
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with gr.Row(): |
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cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5) |
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steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28) |
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with gr.Row(): |
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width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024) |
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height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024) |
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with gr.Row(): |
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randomize_seed = gr.Checkbox(True, label="Randomize seed") |
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seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True) |
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gallery.select( |
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update_selection, |
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inputs=[selected_indices, width, height], |
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outputs=[prompt, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, width, height, lora_image_1, lora_image_2] |
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) |
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remove_button_1.click( |
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remove_lora_1, |
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inputs=[selected_indices], |
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outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2] |
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) |
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remove_button_2.click( |
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remove_lora_2, |
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inputs=[selected_indices], |
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outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2] |
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) |
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randomize_button.click( |
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randomize_loras, |
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inputs=[selected_indices], |
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outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2] |
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) |
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custom_lora.change( |
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add_custom_lora, |
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inputs=[custom_lora, selected_indices], |
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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] |
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) |
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custom_lora_button.click( |
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remove_custom_lora, |
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inputs=[custom_lora_info, custom_lora_button, selected_indices], |
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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] |
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) |
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gr.on( |
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triggers=[generate_button.click, prompt.submit], |
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fn=run_lora, |
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inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height], |
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outputs=[result, seed, progress_bar] |
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) |
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app.queue() |
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app.launch() |
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