---
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.