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from diffusers import StableDiffusionXLPipeline |
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import torch |
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from gradio import Interface, Image, Dropdown, Slider |
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import gradio as gr |
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import spaces |
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model_id = "RunDiffusion/Juggernaut-X-v10" |
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pipe = StableDiffusionXLPipeline.from_pretrained(model_id, torch_dtype=torch.float16) |
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pipe = pipe.to("cuda") |
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@spaces.GPU() |
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def text_to_image(prompt, negative_prompt, steps, guidance_scale, progress=gr.Progress(track_tqdm=True)): |
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image = pipe(prompt, negative_prompt=negative_prompt, num_inference_steps=steps, guidance_scale=guidance_scale).images[0] |
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return image |
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gradio_interface = Interface( |
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fn=text_to_image, |
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inputs=[ |
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gr.Textbox(label="Prompt", lines=2, placeholder="Enter your prompt here..."), |
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gr.Textbox(label="Negative Prompt", lines=2, placeholder="What to exclude from the image..."), |
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gr.Slider(minimum=1, maximum=65, value=50, label="Steps", step=1), |
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gr.Slider(minimum=1, maximum=20, value=7.5, label="Guidance Scale", step=0.1) |
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], |
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outputs=Image(type="pil", show_download_button=True), |
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examples=[ |
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["magical kitten, 4k, high quality, (masterpiece)"], |
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], |
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cache_examples=False, |
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theme=gr.themes.Soft() |
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) |
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gradio_interface.launch() |
