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