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import random |
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import gradio as gr |
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import numpy as np |
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import spaces |
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import torch |
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from diffusers import DiffusionPipeline |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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repo_id = "black-forest-labs/FLUX.1-dev" |
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adapter_id = "alvarobartt/ghibli-characters-flux-lora" |
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pipeline = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16) |
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pipeline.load_lora_weights(adapter_id) |
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pipeline = pipeline.to(device) |
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MAX_SEED = np.iinfo(np.int32).max |
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MAX_IMAGE_SIZE = 1024 |
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@spaces.GPU(duration=120) |
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def inference( |
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prompt: str, |
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seed: int, |
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randomize_seed: bool, |
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width: int, |
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height: int, |
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guidance_scale: float, |
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num_inference_steps: int, |
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lora_scale: float, |
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progress: gr.Progress = gr.Progress(track_tqdm=True), |
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): |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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generator = torch.Generator().manual_seed(seed) |
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image = pipeline( |
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prompt=prompt, |
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guidance_scale=guidance_scale, |
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num_inference_steps=num_inference_steps, |
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width=width, |
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height=height, |
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generator=generator, |
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lora_scale=lora_scale, |
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).images[0] |
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return image, seed |
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examples = [ |
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( |
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"Ghibli style futuristic stormtrooper with glossy white armor and a sleek helmet," |
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" standing heroically on a lush alien planet, vibrant flowers blooming around, soft" |
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" sunlight illuminating the scene, a gentle breeze rustling the leaves" |
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) |
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] |
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css = """ |
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#col-container { |
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margin: 0 auto; |
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max-width: 640px; |
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} |
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""" |
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with gr.Blocks(css=css) as demo: |
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with gr.Column(elem_id="col-container"): |
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gr.Markdown("# FLUX.1 Ghibli Studio LoRA") |
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with gr.Row(): |
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prompt = gr.Text( |
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label="Prompt", |
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show_label=False, |
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max_lines=1, |
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placeholder="Enter your prompt", |
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container=False, |
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) |
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run_button = gr.Button("Run", scale=0) |
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result = gr.Image(label="Result", show_label=False) |
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with gr.Accordion("Advanced Settings", open=False): |
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seed = gr.Slider( |
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label="Seed", |
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minimum=0, |
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maximum=MAX_SEED, |
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step=1, |
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value=42, |
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) |
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
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with gr.Row(): |
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width = gr.Slider( |
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label="Width", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=32, |
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value=1024, |
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) |
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height = gr.Slider( |
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label="Height", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=32, |
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value=768, |
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) |
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with gr.Row(): |
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guidance_scale = gr.Slider( |
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label="Guidance scale", |
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minimum=0.0, |
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maximum=10.0, |
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step=0.1, |
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value=3.5, |
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) |
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lora_scale = gr.Slider( |
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label="LoRA scale", |
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minimum=0.0, |
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maximum=1.0, |
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step=0.1, |
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value=1.0, |
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) |
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num_inference_steps = gr.Slider( |
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label="Number of inference steps", |
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minimum=1, |
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maximum=50, |
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step=1, |
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value=30, |
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) |
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gr.Examples(examples=examples, inputs=[prompt]) |
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gr.on( |
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triggers=[run_button.click, prompt.submit], |
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fn=inference, |
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inputs=[ |
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prompt, |
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seed, |
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randomize_seed, |
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width, |
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height, |
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guidance_scale, |
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num_inference_steps, |
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], |
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outputs=[result, seed], |
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) |
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demo.queue().launch() |
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