KVAE-Image 2.0 is the image tokenizer from KVAE 2.0, a family of image and video tokenizers designed to provide latent representations for diffusion models. It compresses RGB images by 8 x 8 into continuous 32-channel latents and reconstructs them with high fidelity. A new revision `v2.1` is available and `main` points to it (`from_pretrained(..., revision="v2.0")` for the initial release); reconstruction quality on COCO val2017 (5000 images, native resolution, center crop to a multiple of 32, bf16): In a side-by-side human evaluation of text-to-image generations, KVAE-Image 2.1 is preferred over 2.0 with an overall win rate of 54/46, driven by prompt following (56/44) and semantic quality (56/44), with visual quality on par (51/49).

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KVAE-3D-2.0-t4s16

kandinskylab

KVAE-Video 2.0 t4s16 is the higher-compression 4 x 16 x 16 video tokenizer in the KVAE 2.0 family. It is designed to provide the latent space for diffusion models, encoding videos into continuous 64-channel representations and reconstructing them with high fidelity. Its causal, fully convolutional architecture supports long videos through segment-wise processing with temporal caching. Run from the KVAE source repository root. The reference environment uses Python 3.11, PyTorch 2.8.0, and CUDA 12.8. Temporal segments are processed through internal block caches. Do not interleave independent videos on the same model instance; use one `KVAEVideo` instance per concurrent stream.

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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