import gradio as gr import spaces import torch from diffusers import StableDiffusionXLPipeline pipeline = StableDiffusionXLPipeline.from_pretrained("glides/animexl").to("cuda") @spaces.GPU def generate(prompt, negative_prompt, width, height, sample_steps): return pipeline(prompt=prompt, negative_prompt=negative_prompt, width=width, height=height, num_inference_steps=sample_steps).images[0] with gr.Blocks() as interface: with gr.Column(): with gr.Row(): with gr.Column(): prompt = gr.Textbox(label="Prompt", info="What do you want?", value="A perfectly red apple, 32k HDR, studio lighting", lines=4, interactive=True) negative_prompt = gr.Textbox(label="Negative Prompt", info="What do you want to exclude from the image?", value="ugly, low quality", lines=4, interactive=True) with gr.Column(): generate_button = gr.Button("Generate") output = gr.Image() with gr.Row(): with gr.Accordion(label="Advanced Settings", open=False): with gr.Row(): with gr.Column(): width = gr.Slider(label="Width", info="The width in pixels of the generated image.", value=1024, minimum=128, maximum=4096, step=64, interactive=True) height = gr.Slider(label="Height", info="The height in pixels of the generated image.", value=1024, minimum=128, maximum=4096, step=64, interactive=True) with gr.Column(): sampling_steps = gr.Slider(label="Sampling Steps", info="The number of denoising steps.", value=20, minimum=4, maximum=50, step=1, interactive=True) generate_button.click(fn=generate, inputs=[prompt, negative_prompt, width, height, sampling_steps], outputs=[output]) if __name__ == "__main__": interface.launch()