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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 |
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import copy |
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with open('loras.json', 'r') as f: |
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loras = json.load(f) |
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base_model = "black-forest-labs/FLUX.1-dev" |
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pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) |
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pipe.to("cuda") |
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MAX_SEED = 2**32-1 |
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def update_selection(evt: gr.SelectData): |
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selected_lora = loras[evt.index] |
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new_placeholder = f"Type a prompt for {selected_lora['title']}" |
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lora_repo = selected_lora["repo"] |
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updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨" |
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return ( |
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gr.update(placeholder=new_placeholder), |
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updated_text, |
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evt.index |
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) |
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@spaces.GPU(duration=90) |
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def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)): |
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if selected_index is None: |
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raise gr.Error("You must select a LoRA before proceeding.") |
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selected_lora = loras[selected_index] |
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lora_path = selected_lora["repo"] |
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trigger_word = selected_lora["trigger_word"] |
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if "weights" in selected_lora: |
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pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"]) |
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else: |
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pipe.load_lora_weights(lora_path) |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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generator = torch.Generator(device="cuda").manual_seed(seed) |
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image = pipe( |
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prompt=f"{prompt} {trigger_word}", |
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num_inference_steps=steps, |
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guidance_scale=cfg_scale, |
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width=width, |
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height=height, |
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generator=generator, |
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joint_attention_kwargs={"scale": lora_scale}, |
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).images[0] |
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yield image |
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pipe.unload_lora_weights() |
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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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''' |
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with gr.Blocks(theme=gr.themes.Soft(), css=css) as app: |
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title = gr.HTML( |
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"""<h1><img src="https://i.imgur.com/vT48NAO.png" alt="LoRA"> FLUX LoRA the Explorer</h1>""", |
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elem_id="title", |
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) |
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selected_index = gr.State(None) |
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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, elem_id="gen_column"): |
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generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn") |
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with gr.Row(): |
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with gr.Column(scale=3): |
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selected_info = gr.Markdown("") |
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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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) |
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with gr.Column(scale=4): |
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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.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=100, step=1, value=30) |
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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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lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1, step=0.01, value=0.85) |
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gallery.select(update_selection, outputs=[prompt, selected_info, selected_index]) |
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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, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale], |
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outputs=[result] |
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) |
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app.queue() |
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app.launch() |