UniSpace is a unified visual representation framework spanning image
understanding, reconstruction, generation, and instruction-based editing. This
repository provides the selected inference checkpoints and normalization
statistics associated with the paper.
Overview
UniSpace contains two related components:
Patch-reparameterized vision encoders. PR-SigLIP2, PR-DINOv2, and
PR-Qwen-ViT retain the semantic representation of pretrained vision
encoders while adding the image detail needed for faithful reconstruction
and high-quality generation.
The UniSpace multimodal model. A Qwen-based patch-reparameterized visual
tokenizer is coupled with a Qwen3-8B Mixture-of-Transformers model for visual
understanding, text-to-image generation, and image editing.
The public code release is inference and evaluation only. UniSpace training
code, optimizer states, private data pipelines, and cluster-specific files are
not included.
Checkpoints
Artifact
Role
Used for
encoders/pr-siglip2-tokenizer.pt
Unified PR-SigLIP2 tokenizer
Reconstruction and ImageNet generation
encoders/pr-dinov2-tokenizer.pt
PR-DINOv2 tokenizer
Reconstruction and generation
encoders/pr-qwen-vit-tokenizer.pt
PR-Qwen-ViT tokenizer
Reconstruction and UniSpace initialization
encoders/pr-siglip2-dit.pt
Class-conditional DiT
PR-SigLIP2 ImageNet generation
encoders/pr-dinov2-dit.pt
Class-conditional DiT
PR-DINOv2 ImageNet generation
stats/*.pt
Latent normalization statistics
Matching encoder evaluation configs
unispace-sft-0012000/model-*.safetensors
Final selected UniSpace SFT checkpoint (17 standard shards)
Understanding, generation, and editing
The selected UniSpace model is SFT step 0012000, initialized from stage-3
step 0060000. Earlier training checkpoints are not required for inference.
File digests are provided in SHA256SUMS.
The verified environment uses Python 3.10, PyTorch 2.8.0, torchvision 0.23.0,
Transformers 4.57.3, and Accelerate 1.12.0. See the
GitHub reproduction guide for
dataset preparation, model dependencies, distributed launch commands, and
canonical evaluator setup.
Reproduce encoder reconstruction
Prepare ImageNet-1K in its standard class-directory layout and set the paths
described in the code repository. For example:
Each run evaluates EMA weights and reports PSNR, SSIM, rFID, and sample count.
The complete generation and multimodal benchmark commands are documented in
the main README.
Judge-based editing scores may vary with the evaluator endpoint and model
version. The clean ImgEdit rerun generated all 737 expected outputs and scored
4.25 overall; category differences from the paper result were within 0.30.
Intended use and limitations
The release is intended for research on visual representation learning,
multimodal inference, image generation, image editing, and reproducible
benchmarking. Generated or edited images may contain factual, compositional,
text-rendering, or perceptual errors. Users should independently assess
outputs before using them in consequential settings. The release does not add
application-specific safety guarantees beyond those of its base models.
Citation
bibtex
1@article{yan2026unispace,
2 title = {UniSpace: Unified Visual Representation and Scalable Multimodal Modeling},
3 author = {Yan, Jinbo and Qiao, Limeng and Qin, Jie and He, Jun-Yan and Wu, Feize and Wan, Guanglu},
4 journal = {arXiv preprint arXiv:2608.08676},
5 year = {2026}
6}
License and third-party components
Repository code is distributed under the licenses included in the GitHub
release. Base-model and benchmark components retain their respective licenses
and terms. See
THIRD_PARTY.md
before redistribution or commercial use.
