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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 PIL.Image |
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import torch |
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from diffusers import DDPMScheduler, StableDiffusionXLAdapterPipeline, T2IAdapter |
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DESCRIPTION = "# T2I-Adapter-SDXL Sketch" |
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if not torch.cuda.is_available(): |
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DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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if torch.cuda.is_available(): |
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model_id = "stabilityai/stable-diffusion-xl-base-1.0" |
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adapter = T2IAdapter.from_pretrained( |
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"Adapter/t2iadapter", |
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subfolder="sketch_sdxl_1.0", |
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torch_dtype=torch.float16, |
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adapter_type="full_adapter_xl", |
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) |
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scheduler = DDPMScheduler.from_pretrained(model_id, subfolder="scheduler") |
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pipe = StableDiffusionXLAdapterPipeline.from_pretrained( |
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model_id, |
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adapter=adapter, |
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safety_checker=None, |
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torch_dtype=torch.float16, |
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variant="fp16", |
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scheduler=scheduler, |
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) |
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pipe.to(device) |
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else: |
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pipe = None |
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MAX_SEED = np.iinfo(np.int32).max |
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def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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return seed |
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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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num_steps=50, |
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guidance_scale=7.5, |
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seed=0, |
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) -> PIL.Image.Image: |
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image = image.convert("L") |
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generator = torch.Generator(device=device).manual_seed(seed) |
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out = pipe( |
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prompt=prompt, |
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negative_prompt=negative_prompt, |
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image=image, |
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num_inference_steps=num_steps, |
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generator=generator, |
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guidance_scale=guidance_scale, |
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).images[0] |
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return out |
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with gr.Blocks() as demo: |
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gr.Markdown(DESCRIPTION) |
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with gr.Row(): |
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with gr.Column(): |
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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="1", |
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invert_colors=True, |
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shape=(1024, 1024), |
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brush_radius=20, |
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height=600, |
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) |
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prompt = gr.Textbox(label="Prompt") |
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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", value="extra digit, fewer digits, cropped, worst quality, low quality" |
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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=100, |
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step=1, |
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value=50, |
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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=30.0, |
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step=0.1, |
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value=7.5, |
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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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num_steps, |
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guidance_scale, |
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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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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="run", |
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) |
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if __name__ == "__main__": |
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demo.queue(max_size=20).launch() |
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