---
license: other
license_name: upstream-weight-license-not-specified
license_link: https://huggingface.co/gatilin/openvision3_base_latent_visual_encoder/blob/main/LICENSE
library_name: pytorch
base_model: UCSC-VLAA/openvision3-vit-base-patch2-32
tags:
- openvision3
- vision-encoder
- latent-representation
---
# OpenVision 3 Base latent visual encoder

All 150 trained entries from [the pinned official checkpoint](https://huggingface.co/UCSC-VLAA/openvision3-vit-base-patch2-32/tree/7c5fbd37faab5091a1fa29d3ab4f6760d7c87c28), exported as safetensors with original names, shapes, dtypes and logical values. Noncontiguous source storage is packed contiguously without transposing values. The complete original PyTorch file matches its official LFS SHA-256; every logical tensor digest is recorded in tensor_sha256.json.

This checkpoint is a latent ViT encoder: 12 layers, width 768, 16 input channels, a 32x32 latent grid, 2x2 patches, 256 output tokens and a pooled 768-dimensional projection. It contains no RGB VAE, reconstruction decoder, diffusion model or language model. Its representation is intended for both understanding and generation; this package alone is not a complete generation pipeline.

## Usage

Install requirements.txt and download the repository files:

```python
import torch
from component import load_model
model = load_model('.')
latents = torch.randn(1, 16, 32, 32)
with torch.inference_mode():
    pooled, tokens = model(latents)
print(pooled.shape, tokens.shape)  # (1, 768), (1, 256, 768)
```

The official RGB example first applies an external FLUX VAE, using its posterior mean multiplied by its scaling factor. That pinned example does not apply a latent shift. External VAE and matching image-reconstruction decoder weights are not included.

## Verification

validation.json records nine passed CPU float32 checks covering original LFS checksum, all 150 values/dtypes, strict native loading, input/output geometry, finite embeddings, exact native pooling/projection and batch independence. The bundled model is the exact official custom OpenVision VisionTransformer, not a substituted RGB OpenCLIP model.

Run `python verify_release.py` to verify every original tensor hash, strict loading, full encoder inference, pooling and batch independence. recovery_verification.json records the same checks on this publication copy after local cache recovery. Tests use synthetic latent inputs and do not establish RGB reconstruction or generation quality.

## Source and license metadata

Model attribution: UCSC-VLAA/OpenVision 3 authors; original repository and revision linked above. The inspected upstream checkpoint repository does not declare a weight license. This export preserves that status and asserts no additional weight-license grant (LICENSE). Official project code is Apache 2.0 (CODE_LICENSE); original headers and code_provenance.json retain attribution. The code license is not presented as a new declaration of the checkpoint weight license.

Inherited OpenCLIP and Meta MAE source notices are additionally retained in OPENCLIP_LICENSE and MAE_SOURCE_LICENSE; the original module headers remain intact.