| # step_04000 — SpatialAV2AV checkpoint |
| |
| **Latest & best-tested full fine-tune weights** for LTX-2.3 (22B) spatial audio-video editing. |
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| ## 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) | |
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| ## Why this checkpoint |
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| 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) |
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| | Step | Loss | v_loss | |
| |------|------|--------| |
| | 1000 | 0.1089 | 0.0937 | |
| | 2000 | 0.1528 | 0.1555 | |
| | 3000 | 0.1099 | 0.1051 | |
| | **4000** | **0.1191** | **0.1221** | |
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| ## Key training config |
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| - `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) |
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| ## ⚠️ Format quirk — read before loading |
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| This `.safetensors` file is **actually a `torch.save` (zip/pickle) file**, not safetensors. |
| Magic bytes = `PK\x03\x04`. |
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| - ❌ `safetensors` loaders fail with `HeaderTooLarge`. |
| - ✅ `torch.load()` + `transformer.load_state_dict(..., strict=False)` → `missing=0 unexpected=0`. |
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| Use `SpatialAV2AV_inference.py --full_weights <this file>` (it handles the injection). |
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