| --- |
| 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. |
|
|
