| # sfp4_v4_sparse09_hpo_on_ours_p_init2050 checkpoint-700 |
| |
| This upload contains the consolidated WanTransformer3DModel transformer weights |
| from: |
| |
| `checkpoints/sfp4_v4_sparse09_hpo_on_ours_p_init2050_1n_interactive/checkpoint-700` |
|
|
| Contents: |
|
|
| - `transformer/config.json` |
| - `transformer/diffusion_pytorch_model.safetensors` |
| - `backend_snapshot/` |
| - `standalone_inference/` |
|
|
| Training run: |
|
|
| - run name: `sfp4_v4_sparse09_hpo_on_ours_p_init2050_1n_interactive` |
| - source init: `sfp4_v4_sparse06_hpo_on_ours_p_1n_interactive_v2 checkpoint-2050` |
| - attention backend: `SPARSE_FP4_OURS_P_ATTN` |
| - high precision output for backward: enabled |
| - VSA sparsity: `0.9` |
|
|
| This package does not include the distributed optimizer/training-state |
| checkpoint. Use the original `distributed_checkpoint/` directory if exact |
| training resume state is required. |
|
|
| `backend_snapshot/` contains the local FastVideo backend code used by this |
| checkpoint, including `SPARSE_FP4_OURS_P_ATTN`, its Triton forward/backward |
| kernel, FP4 quant helpers, VSA metadata helper, backend wiring, and the exact |
| SFT launch scripts. |
|
|
| It also includes the inference entrypoint snapshot and an example script: |
|
|
| - `backend_snapshot/scripts/inference/run_sfp4_ours_p_checkpoint_700.sh` |
| - `backend_snapshot/training_attention_settings.json` |
| - `standalone_inference/` |
|
|
| Attention setup for this checkpoint: |
|
|
| - self-attention: `SPARSE_FP4_OURS_P_ATTN`, FP4 Q/K/V, sparse 64-token VSA |
| tiles, group-local P quant, dropped-tile mean compensation |
| - cross-attention: dense SDPA fallback, not FP4/sparse |
| - force-dense paths: dense SDPA |
|
|
| `standalone_inference/` is a portable helper for normal inference. It contains |
| an overlay installer, a runner that downloads/loads the checkpoint-700 |
| transformer weights, and the sparse FP4 backend files required by this |
| checkpoint. |
|
|
