| | --- |
| | title: DiffViews |
| | emoji: 🔬 |
| | colorFrom: purple |
| | colorTo: blue |
| | sdk: gradio |
| | sdk_version: 4.44.0 |
| | app_file: app.py |
| | python_version: "3.10" |
| | pinned: false |
| | license: mit |
| | --- |
| | |
| | # DiffViews - Diffusion Activation Visualizer |
| |
|
| | Interactive visualization of diffusion model activations projected to 2D via UMAP. |
| |
|
| | ## Features |
| | - Explore activation space of diffusion models |
| | - Select points and find nearest neighbors |
| | - Generate images from averaged neighbor activations |
| | - Visualize denoising trajectories |
| |
|
| | ## Usage |
| | 1. Hover over points to preview samples |
| | 2. Click to select a point |
| | 3. Click nearby points or use "Suggest KNN" to add neighbors |
| | 4. Click "Generate from Neighbors" to create new images |
| |
|
| | ## Note |
| | First launch downloads ~2.5GB of data and checkpoints. Generation on CPU takes ~30-60s per image. |
| |
|
