File size: 2,105 Bytes
1f125c6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | # step_04000 — SpatialAV2AV checkpoint
**Latest & best-tested full fine-tune weights** for LTX-2.3 (22B) spatial audio-video editing.
## Provenance
| Field | Value |
|-------|-------|
| Source path | `/apdcephfs_zwfy11/share_305172035/helensliang/Projects/LTX-2-SpatialAV2AV/outputs_SpatialAV2AV_train/2026.06.19-17.33.11/checkpoints/model_weights_step_04000.safetensors` |
| Training run | `2026.06.19-17.33.11` |
| Step | **4000 / 20000** (checkpoints saved every 1000 steps) |
| Checkpoint written | 2026-06-22 14:05 |
| File size | 37,979,154,726 bytes (~36 GiB) |
| Base model | `LTX-2.3/ltx-2.3-22b-dev.safetensors` (full-parameter fine-tune, FSDP FULL_SHARD, 8×H20) |
| Training time to this step | ~341 h wall (60 s/step) |
## Why this checkpoint
Last checkpoint of the run and the one used in the final inference tests
(`test8_step4000_*` / `realtest8_step4000_*`, run 2026-07-05). Video-dominant loss stayed low
and stable; stereo L≠R verified on decode.
## Loss (video weight 0.85 / audio weight 0.15)
| Step | Loss | v_loss |
|------|------|--------|
| 1000 | 0.1089 | 0.0937 |
| 2000 | 0.1528 | 0.1555 |
| 3000 | 0.1099 | 0.1051 |
| **4000** | **0.1191** | **0.1221** |
## Key training config
- `learning_rate: 1.0e-05`, `steps: 20000`, `batch_size: 1`, gradient checkpointing on
- `timestep_sampling_mode: shifted_logit_normal`
- `loss_video_weight: 0.85`, `loss_audio_weight: 0.15`, `with_audio: true`, `use_ref_audio: false`
- `edit_sample_n_frames: 113`, `edit_min_size: 288`
- `resolution_buckets: 288x384x113; 384x288x113; 320x320x113; 352x352x113`
- `training_strategy.name: spatialav2av`
- data: `SpatialAV2AV_full/all.list` (source→edit stereo pairs)
## ⚠️ Format quirk — read before loading
This `.safetensors` file is **actually a `torch.save` (zip/pickle) file**, not safetensors.
Magic bytes = `PK\x03\x04`.
- ❌ `safetensors` loaders fail with `HeaderTooLarge`.
- ✅ `torch.load()` + `transformer.load_state_dict(..., strict=False)` → `missing=0 unexpected=0`.
Use `SpatialAV2AV_inference.py --full_weights <this file>` (it handles the injection).
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