---
library_name: pytorch
pipeline_tag: image-to-video
tags:
- gae
- text-to-video
- camera-control
- 3d-generation
---

# GAE-D64-1B

This repository contains the GAE-D64-1B model presented in [GAE: Learning a Geometry-Native Latent Space for 3D-Consistent World Generation](https://huggingface.co/papers/2609.24981).

- Project page: https://jiah-cloud.github.io/GAE.github.io/
- Code: https://github.com/TencentARC/GAE-GeometricAutoEncoder

Weights for GAE-64: a ~1B-parameter temporal DiT
(`hidden_size=[768, 2048]`, `depth=[28, 6]`) over a **64-channel**
geometry-native latent, at **672×378** and **V=81**, with metric Plücker
camera conditioning and Qwen3-0.6B text.

This repo is self-contained for GAE-64 inference: codec, per-channel latent
statistics, DA3 feature-level stats, and the flow transformer. The frozen
DA3-GIANT encoder / DPT head comes from
[`depth-anything/DA3-GIANT-1.1`](https://huggingface.co/depth-anything/DA3-GIANT-1.1).

From the GAE code repository:

```bash
scripts/run_demo.sh
```

Or, after the files are in `ckpts/`:

```bash
python scripts/generate.py \
    --image examples/scenes/forest_lake_trail.jpg \
    --prompt-file examples/scenes/forest_lake_trail.txt \
    --flow-ckpt ckpts/flow_gae64.pt \
    --codec-ckpt ckpts/gae_64.pt \
    --output results/forest
```

Image-to-video uses 50 Euler steps and CFG 2 with one reference view.

Layout:

- `codec/` — safetensors + `config.json`
- `gae_64.pt` — drop-in checkpoint for `gae.load_codec`
- `latent_stats_gae_64.pt` — per-channel latent mean/std
- `da3_stats_giant_5ds.tar` — DA3 feature-level normalization stats
- `transformer/` — flow safetensors + `config.json`
- `flow_gae64.pt` — drop-in checkpoint for `gae.load_flow`