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Running
on
A10G
Running
on
A10G
| #!/usr/bin/env python | |
| import os | |
| import gradio as gr | |
| import PIL.Image | |
| from diffusers.utils import load_image | |
| from model import ADAPTER_NAMES, Model | |
| from utils import ( | |
| DEFAULT_STYLE_NAME, | |
| MAX_SEED, | |
| STYLE_NAMES, | |
| apply_style, | |
| randomize_seed_fn, | |
| ) | |
| CACHE_EXAMPLES = os.environ.get("CACHE_EXAMPLES") == "1" | |
| def create_demo(model: Model) -> gr.Blocks: | |
| def run( | |
| image: PIL.Image.Image, | |
| prompt: str, | |
| negative_prompt: str, | |
| adapter_name: str, | |
| style_name: str = DEFAULT_STYLE_NAME, | |
| num_inference_steps: int = 30, | |
| guidance_scale: float = 5.0, | |
| adapter_conditioning_scale: float = 1.0, | |
| adapter_conditioning_factor: float = 1.0, | |
| seed: int = 0, | |
| apply_preprocess: bool = True, | |
| progress=gr.Progress(track_tqdm=True), | |
| ) -> list[PIL.Image.Image]: | |
| prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt) | |
| return model.run( | |
| image=image, | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| adapter_name=adapter_name, | |
| num_inference_steps=num_inference_steps, | |
| guidance_scale=guidance_scale, | |
| adapter_conditioning_scale=adapter_conditioning_scale, | |
| adapter_conditioning_factor=adapter_conditioning_factor, | |
| seed=seed, | |
| apply_preprocess=apply_preprocess, | |
| ) | |
| def process_example( | |
| image_url: str, | |
| prompt: str, | |
| adapter_name: str, | |
| guidance_scale: float, | |
| adapter_conditioning_scale: float, | |
| seed: int, | |
| apply_preprocess: bool, | |
| ) -> list[PIL.Image.Image]: | |
| image = load_image(image_url) | |
| return run( | |
| image=image, | |
| prompt=prompt, | |
| negative_prompt="extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured", | |
| adapter_name=adapter_name, | |
| style_name="(No style)", | |
| guidance_scale=guidance_scale, | |
| adapter_conditioning_scale=adapter_conditioning_scale, | |
| seed=seed, | |
| apply_preprocess=apply_preprocess, | |
| ) | |
| examples = [ | |
| [ | |
| "assets/org_canny.jpg", | |
| "Mystical fairy in real, magic, 4k picture, high quality", | |
| "canny", | |
| 7.5, | |
| 0.75, | |
| 42, | |
| True, | |
| ], | |
| [ | |
| "assets/org_sketch.png", | |
| "a robot, mount fuji in the background, 4k photo, highly detailed", | |
| "sketch", | |
| 7.5, | |
| 1.0, | |
| 42, | |
| True, | |
| ], | |
| [ | |
| "assets/org_lin.jpg", | |
| "Ice dragon roar, 4k photo", | |
| "lineart", | |
| 7.5, | |
| 0.8, | |
| 42, | |
| True, | |
| ], | |
| [ | |
| "assets/org_mid.jpg", | |
| "A photo of a room, 4k photo, highly detailed", | |
| "depth-midas", | |
| 7.5, | |
| 1.0, | |
| 42, | |
| True, | |
| ], | |
| [ | |
| "assets/org_zoe.jpg", | |
| "A photo of a orchid, 4k photo, highly detailed", | |
| "depth-zoe", | |
| 5.0, | |
| 1.0, | |
| 42, | |
| True, | |
| ], | |
| [ | |
| "assets/people.jpg", | |
| "A couple, 4k photo, highly detailed", | |
| "openpose", | |
| 5.0, | |
| 1.0, | |
| 42, | |
| True, | |
| ], | |
| [ | |
| "assets/depth-midas-image.png", | |
| "stormtrooper lecture, 4k photo, highly detailed", | |
| "depth-midas", | |
| 7.5, | |
| 1.0, | |
| 42, | |
| False, | |
| ], | |
| [ | |
| "assets/openpose-image.png", | |
| "spiderman, 4k photo, highly detailed", | |
| "openpose", | |
| 5.0, | |
| 1.0, | |
| 42, | |
| False, | |
| ], | |
| ] | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Group(): | |
| image = gr.Image(label="Input image", type="pil", height=600) | |
| prompt = gr.Textbox(label="Prompt") | |
| with gr.Row(): | |
| adapter_name = gr.Dropdown(label="Adapter name", choices=ADAPTER_NAMES, value=ADAPTER_NAMES[0]) | |
| style = gr.Dropdown(label="Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME) | |
| run_button = gr.Button("Run") | |
| with gr.Accordion("Advanced options", open=False): | |
| apply_preprocess = gr.Checkbox(label="Apply preprocess", value=True) | |
| negative_prompt = gr.Textbox( | |
| label="Negative prompt", | |
| value=" extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured", | |
| ) | |
| num_inference_steps = gr.Slider( | |
| label="Number of steps", | |
| minimum=1, | |
| maximum=Model.MAX_NUM_INFERENCE_STEPS, | |
| step=1, | |
| value=25, | |
| ) | |
| guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| minimum=0.1, | |
| maximum=30.0, | |
| step=0.1, | |
| value=5.0, | |
| ) | |
| adapter_conditioning_scale = gr.Slider( | |
| label="Adapter conditioning scale", | |
| minimum=0.5, | |
| maximum=1, | |
| step=0.1, | |
| value=1.0, | |
| ) | |
| adapter_conditioning_factor = gr.Slider( | |
| label="Adapter conditioning factor", | |
| info="Fraction of timesteps for which adapter should be applied", | |
| minimum=0.5, | |
| maximum=1.0, | |
| step=0.1, | |
| value=1.0, | |
| ) | |
| seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=42, | |
| ) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=False) | |
| with gr.Column(): | |
| result = gr.Gallery(label="Result", columns=2, height=600, object_fit="scale-down", show_label=False) | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[ | |
| image, | |
| prompt, | |
| adapter_name, | |
| guidance_scale, | |
| adapter_conditioning_scale, | |
| seed, | |
| apply_preprocess, | |
| ], | |
| outputs=result, | |
| fn=process_example, | |
| cache_examples=CACHE_EXAMPLES, | |
| ) | |
| inputs = [ | |
| image, | |
| prompt, | |
| negative_prompt, | |
| adapter_name, | |
| style, | |
| num_inference_steps, | |
| guidance_scale, | |
| adapter_conditioning_scale, | |
| adapter_conditioning_factor, | |
| seed, | |
| apply_preprocess, | |
| ] | |
| 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="run", | |
| ) | |
| return demo | |
| if __name__ == "__main__": | |
| model = Model(ADAPTER_NAMES[0]) | |
| demo = create_demo(model) | |
| demo.queue(max_size=20).launch() | |