| license: cc-by-nc-4.0 | |
| tags: [diffusion, dit, synthetic-data, generalization] | |
| # Visual Generative Lab (VGL) checkpoints | |
| Checkpoints behind Table 2 of *Do Diffusion Models Learn to Generalize Basic Visual Skills?* | |
| Code, datasets and evaluation: https://github.com/AmishSethi/visual-generative-lab | |
| Layout: `table2/<skill>/<variant>/seed_<n>/final.pt` with the run's `run_config.json` beside it. | |
| Skills: size, position, rotation, count. Variants: baseline, sinusoidal, rotary, adaln, vae, flow, unet, dit_large | |
| (rotation also has `baseline_nz`, the ten-seed zero-null baseline). Each `.pt` holds `model`, `ema`, `opt` and the | |
| training state; load with the training scripts in the repository. License CC BY-NC 4.0 (inherited from DiT). | |