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import os |
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import uuid |
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import gradio as gr |
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import spaces |
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import torch |
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from diffusers import DiffusionPipeline |
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DESCRIPTION = """# Playground v2.5""" |
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if not torch.cuda.is_available(): |
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DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>" |
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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pipe = DiffusionPipeline.from_pretrained( |
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"playgroundai/playground-v2.5-1024px-aesthetic", |
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torch_dtype=torch.float16, |
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use_safetensors=True, |
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add_watermarker=False, |
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variant="fp16" |
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) |
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pipe.to(device) |
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def save_image(img): |
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unique_name = str(uuid.uuid4()) + ".png" |
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img.save(unique_name) |
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return unique_name |
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@spaces.GPU(enable_queue=True) |
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def generate( |
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prompt: str, |
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negative_prompt: str = "", |
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use_negative_prompt: bool = False, |
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seed: int = 0, |
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width: int = 1024, |
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height: int = 1024, |
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guidance_scale: float = 3, |
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randomize_seed: bool = False, |
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): |
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pipe.to(device) |
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seed = random.randint(0, np.iinfo(np.int32).max) if randomize_seed else seed |
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generator = torch.Generator().manual_seed(seed) |
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images = pipe( |
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prompt=prompt, |
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negative_prompt=negative_prompt if use_negative_prompt else None, |
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width=width, |
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height=height, |
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guidance_scale=guidance_scale, |
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num_inference_steps=25, |
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generator=generator, |
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).images |
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image_paths = [save_image(img) for img in images] |
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return image_paths, seed |
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with gr.Blocks() as demo: |
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gr.Markdown(DESCRIPTION) |
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with gr.Group(): |
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with gr.Row(): |
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prompt = gr.Textbox(label="Prompt") |
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run_button = gr.Button("Run") |
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result = gr.Gallery(label="Result") |
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with gr.Accordion("Advanced options", open=False): |
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use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False) |
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negative_prompt = gr.Textbox(label="Negative prompt") |
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seed = gr.Slider(label="Seed", minimum=0, maximum=np.iinfo(np.int32).max, step=1, value=0) |
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
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width = gr.Slider(label="Width", minimum=256, maximum=1536, step=32, value=1024) |
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height = gr.Slider(label="Height", minimum=256, maximum=1536, step=32, value=1024) |
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guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=20, step=0.1, value=3.0) |
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gr.on( |
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triggers=[prompt.submit, negative_prompt.submit, run_button.click], |
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fn=generate, |
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inputs=[prompt, negative_prompt, use_negative_prompt, seed, width, height, guidance_scale, randomize_seed], |
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outputs=[result, seed], |
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api_name="run", |
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) |
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if __name__ == "__main__": |
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demo.launch() |
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