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import gradio as gr |
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import PIL.Image |
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import torch |
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import torchvision.transforms.functional as TF |
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from model import Model |
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from utils import ( |
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DEFAULT_STYLE_NAME, |
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MAX_SEED, |
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STYLE_NAMES, |
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apply_style, |
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randomize_seed_fn, |
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) |
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def create_demo(model: Model) -> gr.Blocks: |
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def run( |
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image: PIL.Image.Image, |
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prompt: str, |
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negative_prompt: str, |
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style_name: str = DEFAULT_STYLE_NAME, |
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num_steps: int = 25, |
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guidance_scale: float = 5, |
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adapter_conditioning_scale: float = 0.8, |
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adapter_conditioning_factor: float = 0.8, |
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seed: int = 0, |
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progress=gr.Progress(track_tqdm=True), |
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) -> PIL.Image.Image: |
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image = image.convert("RGB") |
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image = TF.to_tensor(image) > 0.5 |
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image = TF.to_pil_image(image.to(torch.float32)) |
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prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt) |
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return model.run( |
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image=image, |
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prompt=prompt, |
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negative_prompt=negative_prompt, |
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adapter_name="sketch", |
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num_inference_steps=num_steps, |
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guidance_scale=guidance_scale, |
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adapter_conditioning_scale=adapter_conditioning_scale, |
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adapter_conditioning_factor=adapter_conditioning_factor, |
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seed=seed, |
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apply_preprocess=False, |
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)[1] |
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with gr.Blocks() as demo: |
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with gr.Row(): |
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with gr.Column(): |
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with gr.Group(): |
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image = gr.Image( |
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source="canvas", |
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tool="sketch", |
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type="pil", |
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image_mode="L", |
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invert_colors=True, |
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shape=(1024, 1024), |
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brush_radius=4, |
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height=600, |
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) |
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prompt = gr.Textbox(label="Prompt") |
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style = gr.Dropdown(label="Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME) |
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run_button = gr.Button("Run") |
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with gr.Accordion("Advanced options", open=False): |
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negative_prompt = gr.Textbox( |
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label="Negative prompt", |
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value=" extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured", |
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) |
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num_steps = gr.Slider( |
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label="Number of steps", |
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minimum=1, |
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maximum=50, |
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step=1, |
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value=25, |
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) |
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guidance_scale = gr.Slider( |
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label="Guidance scale", |
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minimum=0.1, |
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maximum=10.0, |
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step=0.1, |
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value=5, |
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) |
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adapter_conditioning_scale = gr.Slider( |
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label="Adapter conditioning scale", |
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minimum=0.5, |
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maximum=1, |
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step=0.1, |
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value=0.8, |
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) |
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adapter_conditioning_factor = gr.Slider( |
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label="Adapter conditioning factor", |
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info="Fraction of timesteps for which adapter should be applied", |
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minimum=0.5, |
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maximum=1, |
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step=0.1, |
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value=0.8, |
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) |
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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=0, |
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) |
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
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with gr.Column(): |
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result = gr.Image(label="Result", height=600) |
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inputs = [ |
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image, |
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prompt, |
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negative_prompt, |
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style, |
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num_steps, |
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guidance_scale, |
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adapter_conditioning_scale, |
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adapter_conditioning_factor, |
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seed, |
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] |
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prompt.submit( |
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fn=randomize_seed_fn, |
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inputs=[seed, randomize_seed], |
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outputs=seed, |
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queue=False, |
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api_name=False, |
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).then( |
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fn=run, |
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inputs=inputs, |
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outputs=result, |
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api_name=False, |
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) |
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negative_prompt.submit( |
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fn=randomize_seed_fn, |
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inputs=[seed, randomize_seed], |
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outputs=seed, |
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queue=False, |
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api_name=False, |
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).then( |
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fn=run, |
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inputs=inputs, |
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outputs=result, |
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api_name=False, |
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) |
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run_button.click( |
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fn=randomize_seed_fn, |
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inputs=[seed, randomize_seed], |
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outputs=seed, |
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queue=False, |
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api_name=False, |
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).then( |
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fn=run, |
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inputs=inputs, |
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outputs=result, |
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api_name=False, |
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) |
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return demo |
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if __name__ == "__main__": |
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model = Model("sketch") |
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demo = create_demo(model) |
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demo.queue(max_size=20).launch() |
