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Running
on
Zero
| import gradio as gr | |
| from src.utils import * | |
| theme = gr.themes.Soft(primary_hue="amber", secondary_hue="orange", font=[gr.themes.GoogleFont("Source Sans 3", weights=(400, 600)),'arial']) | |
| with gr.Blocks(theme=theme) as demo: | |
| with gr.Column(elem_classes="header"): | |
| gr.Markdown("# 🗾 COP-GEN-Beta: Unified Generative Modelling of COPernicus Imagery Thumbnails") | |
| gr.Markdown("### Miguel Espinosa, Valerio Marsocci, Yuru Jia, Elliot J. Crowley, Mikolaj Czerkawski") | |
| gr.Markdown('[[Website](https://miquel-espinosa.github.io/cop-gen-beta/)] [[GitHub](https://github.com/miquel-espinosa/COP-GEN-Beta)] [[Model](https://huggingface.co/mespinosami/COP-GEN-Beta)] [[Dataset](https://huggingface.co/Major-TOM)]') | |
| with gr.Column(elem_classes="abstract"): | |
| with gr.Accordion("Abstract", open=False) as abstract: | |
| gr.Markdown("In remote sensing, multi-modal data from various sensors capturing the same scene offers rich opportunities, but learning a unified representation across these modalities remains a significant challenge. Traditional methods have often been limited to single or dual-modality approaches. In this paper, we introduce COP-GEN-Beta, a generative diffusion model trained on optical, radar, and elevation data from the Major TOM dataset. What sets COP-GEN-Beta apart is its ability to map any subset of modalities to any other, enabling zero-shot modality translation after training. This is achieved through a sequence-based diffusion transformer, where each modality is controlled by its own timestep embedding. We extensively evaluate COP-GEN-Beta on thumbnail images from the Major TOM dataset, demonstrating its effectiveness in generating high-quality samples. Qualitative and quantitative evaluations validate the model's performance, highlighting its potential as a powerful pre-trained model for future remote sensing tasks.") # Replace with your abstract text | |
| with gr.Accordion("Instructions", open=False) as abstract: | |
| gr.Markdown("1. **Define input**: You can upload your thumbnails manually or you can get a random sample from Major TOM by clicking the button.") | |
| gr.Markdown("2. **Select conditions**: Each input image can be used as a **conditioning** by selecting the `Active` checkbox. If no checkbox is selected, then you will observe **unconditional generation**.") | |
| gr.Markdown("3. **Generate**: Click the `Generate` button to synthesize the output. The outputs will be shown below.") | |
| with gr.Column(): | |
| with gr.Row(): | |
| gr.Markdown("## Inputs (Optional)") | |
| load_button = gr.Button("Load a random sample from Major TOM 🗺", variant="secondary") | |
| with gr.Row(): | |
| with gr.Column(): | |
| s2l1c_input = gr.Image(label="S2 L1C (Optical - Top of Atmosphere)", interactive=True) | |
| s2l1c_active = gr.Checkbox(value=False, label="Active", interactive=True) | |
| with gr.Column(): | |
| s2l2a_input = gr.Image(label="S2 L2A (Optical - Bottom of Atmosphere)", interactive=True) | |
| s2l2a_active = gr.Checkbox(value=False, label="Active", interactive=True) | |
| with gr.Column(): | |
| s1rtc_input = gr.Image(label="S1 RTC (SAR)", interactive=True) | |
| s1rtc_active = gr.Checkbox(value=False, label="Active", interactive=True) | |
| with gr.Column(): | |
| dem_input = gr.Image(label="DEM (Elevation)", interactive=True) | |
| dem_active = gr.Checkbox(value=False, label="Active", interactive=True) | |
| generate_button = gr.Button("Generate", variant="primary") | |
| gr.Markdown("## Outputs") | |
| with gr.Row(): | |
| s2l1c_output = gr.Image(label="S2 L1C (Optical - Top of Atmosphere)", interactive=False) | |
| s2l2a_output = gr.Image(label="S2 L2A (Optical - Bottom of Atmosphere)", interactive=False) | |
| s1rtc_output = gr.Image(label="S1 RTC (SAR)", interactive=False) | |
| dem_output = gr.Image(label="DEM (Elevation)", interactive=False) | |
| with gr.Accordion("Advanced Options", open=False) as advanced_options: | |
| num_inference_steps_slider = gr.Slider(minimum=10, maximum=1000, step=10, value=50, label="Inference Steps") | |
| guidance_scale_slider = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, value=7.5, label="Guidance Scale") | |
| with gr.Row(): | |
| seed_number = gr.Number(value=6378, label="Seed") | |
| seed_checkbox = gr.Checkbox(value=True, label="Random") | |
| load_button.click( | |
| fn=sample_shuffle, | |
| outputs=[s2l1c_input, s2l1c_active, s2l2a_input,s2l2a_active, s1rtc_input, s1rtc_active, dem_input, dem_active] | |
| ) | |
| generate_button.click( | |
| #fn=generate_output, | |
| inputs=[s2l1c_input, s2l2a_input, s1rtc_input, dem_input, num_inference_steps_slider, guidance_scale_slider, seed_number, seed_checkbox], | |
| outputs=[s2l1c_output, s2l2a_output, s1rtc_output, dem_output], | |
| ) | |
| demo.launch() | |
| demo.launch(share=True) |