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import torch |
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from diffusers import StableDiffusionPipeline, DDIMScheduler |
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import gradio as gr |
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pipe = StableDiffusionPipeline.from_pretrained("MVRL/GeoSynth") |
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def process(prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta): |
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generator = torch.manual_seed(seed) |
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output_images = pipe(prompt, |
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height=image_resolution, |
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width=image_resolution, |
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num_inference_steps=ddim_steps, |
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guidance_scale=scale, |
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negative_prompt=n_prompt, |
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num_images_per_prompt=num_samples, |
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eta=eta, |
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generator=generator, |
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).images |
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return output_images |
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block = gr.Blocks().queue() |
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with block: |
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with gr.Row(): |
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gr.Markdown( |
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""" |
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# GeoSynth: Contextually-Aware High-Resolution Satellite Image Synthesis |
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Srikumar Sastry*, Subash Khanal, Aayush Dhakal, Nathan Jacobs (*Corresponding Author)<br> |
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""" |
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) |
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with gr.Row(): |
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with gr.Column(): |
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prompt = gr.Textbox(label="Prompt") |
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run_button = gr.Button(value="Run") |
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with gr.Accordion("Advanced options", open=True): |
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num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1) |
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image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=64) |
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ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1) |
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scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1) |
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seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True) |
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eta = gr.Number(label="eta (DDIM)", value=0.0) |
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n_prompt = gr.Textbox(label="Negative Prompt", |
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value='') |
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with gr.Column(): |
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result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery") |
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ips = [prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta] |
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run_button.click(fn=process, inputs=ips, outputs=[result_gallery]) |
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block.launch() |
