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README.md

@@ -1,3 +1,76 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+library_name: pytorch
+base_model: facebook/VGGT-1B
+tags:
+  - vggt
+  - depth-estimation
+  - 3d-vision
+  - camera-pose
+  - test-time-training
+  - lact
+pipeline_tag: depth-estimation
+---
+
+# VGGT LaCT (stage 1) — slim adapter weights
+
+These files are **LaCT-block weights only** (~200 MB), not a full VGGT checkpoint. They plug into the public **[facebook/VGGT-1B](https://huggingface.co/facebook/VGGT-1B)** backbone: DINOv2 patch embed, frame-wise attention, and prediction heads stay at Meta’s pretrained VGGT-1B; only the **global-attention layers are replaced** by LaCT-style fast-weight GLU blocks trained with stage-1 distillation against the frozen teacher.
+
+**Code:** [github.com/Akrao9/vggt_ttt](https://github.com/Akrao9/vggt_ttt) (install `vggt` from [facebookresearch/vggt](https://github.com/facebookresearch/vggt) as in that README).
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `vggt_ttt_lact_stage1.pt` | Stage 1 distilled LaCT state dict (`lact_state_dict()` format). Keys are prefixed with `aggregator.lact_blocks.`. |
+
+## Load (Python)
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+# From the vggt_ttt repo (with `vggt` installed per upstream README):
+from model.vggt_ttt import VGGT_TTT
+from model.io_utils import torch_load_checkpoint
+
+ckpt_path = hf_hub_download("akrao9/VGGT-LACT", "vggt_ttt_lact_stage1.pt")
+device = "cuda"
+model = VGGT_TTT.from_pretrained("facebook/VGGT-1B", chunk_size=16).to(device).eval()
+state = torch_load_checkpoint(ckpt_path, map_location=device)
+model.load_lact_state_dict(state, strict=True)
+```
+
+Use a local path instead of `hf_hub_download` if you already downloaded the `.pt` file.
+
+## Inference CLI
+
+From the [vggt_ttt](https://github.com/Akrao9/vggt_ttt) repo, after downloading this checkpoint locally:
+
+```bash
+python scripts/run_inference.py \
+  --input path/to/video.mp4 --fps 2 \
+  --checkpoint ./vggt_ttt_lact_stage1.pt \
+  --out ./out
+```
+
+(`--checkpoint` accepts this LaCT-only dict; see `scripts/run_inference.py`.)
+
+## Training summary
+
+- **Stage 1:** distillation from frozen `facebook/VGGT-1B` (pose / depth / world points), trainable parameters confined to the 24 LaCT blocks; `c_proj` zero-init for a near-identity start.
+- **Checkpoints:** saved with `torch.save(model.lact_state_dict(), path)` — same tensor layout as this Hub file.
+
+## Hardware / scaling
+
+LaCT path is aimed at **longer frame sequences** with more favorable VRAM scaling than full global attention; see the GitHub README for benchmark tables (DL3DV-style eval).
+
+## License and attribution
+
+- This **adapter** repository and the training code release are under **Apache 2.0** (see project `LICENSE` / `NOTICE` on GitHub).
+- **VGGT-1B** is subject to Meta’s license and terms on its model card; you must comply with those when using the backbone.
+- Method builds on **VGGT** and **LaCT**-style components as described in the upstream README.
+
+## Citation
+
+If you use these weights or the [vggt_ttt](https://github.com/Akrao9/vggt_ttt) codebase, cite the original **VGGT** paper/repo and credit this adapter as appropriate for your venue.