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| import gradio as gr | |
| import spaces | |
| import torch | |
| from diffusers import StableDiffusionPipeline | |
| pipeline = StableDiffusionPipeline.from_pretrained( | |
| "nroggendorff/zelda-diffusion", use_safetensors=True#, variant="fp16"#, torch_dtype=torch.float16 | |
| ).to("cuda") | |
| 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 woman in a blue dress, 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=512, minimum=128, maximum=4096, step=64, interactive=True) | |
| height = gr.Slider(label="Height", info="The height in pixels of the generated image.", value=512, 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=50, 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() |