| --- |
| library_name: pytorch |
| license: cc-by-nc-4.0 |
| tags: |
| - super-resolution |
| - latent-diffusion |
| - pytorch |
| - rocm |
| - research |
| --- |
| |
| # sr-diffusion |
|
|
| Research checkpoint storage for the `sr-diffusion` project. |
|
|
| GitHub: https://github.com/BitIntx/sr-diffusion |
|
|
| This is a public source-available, non-commercial research project. It trains a |
| vision-only x4 latent diffusion super-resolution pipeline from scratch and does |
| not use a pretrained text-to-image diffusion model. |
|
|
| Current artifacts are study/research checkpoints. They are not a production SR |
| model. |
|
|
| ## License |
|
|
| - Checkpoints, generated samples, metrics, and other non-code artifacts: |
| CC BY-NC 4.0. |
| - Source code: PolyForm Noncommercial License 1.0.0. |
|
|
| Commercial use is not permitted without separate written permission. This |
| includes paid hosted inference, resale, and integration into commercial |
| products. |
|
|
| Training data is not redistributed in this repository. Dataset license |
| constraints should be reviewed before training or redistributing derived |
| weights. |
|
|
| ## Artifacts |
|
|
| | Path | Source | SHA256 | |
| | --- | --- | --- | |
| | `LICENSE` | `LICENSE` | `c635a1fa2c80` | |
| | `CHECKPOINT_LICENSE.md` | `CHECKPOINT_LICENSE.md` | `da3b25759abb` | |
| | `configs/diffusion_photo10k_b32.yaml` | `diffusion_photo10k_b32.yaml` | `0fa6c0eeec85` | |
| | `checkpoints/stage3_diffusion_b32_best_eval_noise.pt` | `best_eval_noise.pt` | `ea4b458d668c` | |
| | `checkpoints/stage3_diffusion_b32_step_0025000.pt` | `step_0025000.pt` | `38d2f44adf65` | |
| | `metrics/stage3_b32_step_0024000_metrics.json` | `step_0024000_metrics.json` | `7df33601c289` | |
| | `metrics/stage3_b32_step_0025000_metrics.json` | `step_0025000_metrics.json` | `bebb82e594da` | |
|
|
| ## Stages |
|
|
| - Stage 1: factor-4 VAE / Autoencoder over 512px HR crops. |
| - Stage 2: deterministic LR-to-HR-latent pretraining with the Stage 1 VAE frozen. |
| - Stage 3: conditional latent diffusion U-Net, planned. |
|
|
