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()