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Running
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A10G
Running
on
A10G
| #!/usr/bin/env python | |
| import gradio as gr | |
| import PIL.Image | |
| import torch | |
| import torchvision.transforms.functional as TF | |
| from model import Model | |
| from utils import ( | |
| DEFAULT_STYLE_NAME, | |
| MAX_SEED, | |
| STYLE_NAMES, | |
| apply_style, | |
| randomize_seed_fn, | |
| ) | |
| def create_demo(model: Model) -> gr.Blocks: | |
| def run( | |
| image: PIL.Image.Image, | |
| prompt: str, | |
| negative_prompt: str, | |
| style_name: str = DEFAULT_STYLE_NAME, | |
| num_steps: int = 25, | |
| guidance_scale: float = 5, | |
| adapter_conditioning_scale: float = 0.8, | |
| adapter_conditioning_factor: float = 0.8, | |
| seed: int = 0, | |
| progress=gr.Progress(track_tqdm=True), | |
| ) -> PIL.Image.Image: | |
| image = image.convert("RGB") | |
| image = TF.to_tensor(image) > 0.5 | |
| image = TF.to_pil_image(image.to(torch.float32)) | |
| prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt) | |
| return model.run( | |
| image=image, | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| adapter_name="sketch", | |
| num_inference_steps=num_steps, | |
| guidance_scale=guidance_scale, | |
| adapter_conditioning_scale=adapter_conditioning_scale, | |
| adapter_conditioning_factor=adapter_conditioning_factor, | |
| seed=seed, | |
| apply_preprocess=False, | |
| )[1] | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Group(): | |
| image = gr.Image( | |
| source="canvas", | |
| tool="sketch", | |
| type="pil", | |
| image_mode="L", | |
| invert_colors=True, | |
| shape=(1024, 1024), | |
| brush_radius=4, | |
| height=600, | |
| ) | |
| prompt = gr.Textbox(label="Prompt") | |
| style = gr.Dropdown(label="Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME) | |
| run_button = gr.Button("Run") | |
| with gr.Accordion("Advanced options", open=False): | |
| negative_prompt = gr.Textbox( | |
| label="Negative prompt", | |
| value=" extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured", | |
| ) | |
| num_steps = gr.Slider( | |
| label="Number of steps", | |
| minimum=1, | |
| maximum=50, | |
| step=1, | |
| value=25, | |
| ) | |
| guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| minimum=0.1, | |
| maximum=10.0, | |
| step=0.1, | |
| value=5, | |
| ) | |
| adapter_conditioning_scale = gr.Slider( | |
| label="Adapter conditioning scale", | |
| minimum=0.5, | |
| maximum=1, | |
| step=0.1, | |
| value=0.8, | |
| ) | |
| adapter_conditioning_factor = gr.Slider( | |
| label="Adapter conditioning factor", | |
| info="Fraction of timesteps for which adapter should be applied", | |
| minimum=0.5, | |
| maximum=1, | |
| step=0.1, | |
| value=0.8, | |
| ) | |
| seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=0, | |
| ) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| with gr.Column(): | |
| result = gr.Image(label="Result", height=600) | |
| inputs = [ | |
| image, | |
| prompt, | |
| negative_prompt, | |
| style, | |
| num_steps, | |
| guidance_scale, | |
| adapter_conditioning_scale, | |
| adapter_conditioning_factor, | |
| seed, | |
| ] | |
| prompt.submit( | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=run, | |
| inputs=inputs, | |
| outputs=result, | |
| api_name=False, | |
| ) | |
| negative_prompt.submit( | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=run, | |
| inputs=inputs, | |
| outputs=result, | |
| api_name=False, | |
| ) | |
| run_button.click( | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=run, | |
| inputs=inputs, | |
| outputs=result, | |
| api_name=False, | |
| ) | |
| return demo | |
| if __name__ == "__main__": | |
| model = Model("sketch") | |
| demo = create_demo(model) | |
| demo.queue(max_size=20).launch() | |