KBlueLeaf / TTVidT-decoders

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Introduction of TTVidT-decoders

Model Details of TTVidT-decoders

TT-VidT pretrained DiT decoders

Pretrained DiT decoders for TT-VidT ( Decoupling the Temporal Axis for Efficient Motion-Centric Video Pretraining , NeurIPS 2026). Each TT-VidT encoder pretraining run initialises its decoder from one of these; the training code downloads them automatically through DECODER_PRETRAINED , e.g. "KBlueLeaf/TTVidT-decoders/pretrain_video_S_qknorm" .

The decoders work in the 32x32x4 latent space of the frozen f8 frame VAE KBlueLeaf/latentmaid-vae and are conditioned on DINOv3 ViT-B/16 features (768-d).

  • ImageNet decoders reconstruct an ImageNet image from its DINOv3 class token.
  • Video decoders reconstruct a later video frame from its class token plus, through cross-attention, the spatial features of an earlier frame.
Files

Each decoder is <name>.safetensors plus <name>.json , which holds the decoder config ( decoder_config ), the latent size and the latent statistics used in training.

pretrain_{imgnet,video}_{S,B,L}_<variant> :

Size Hidden Layers Heads
S 768 12 12
B 1024 16 16
L 1152 28 16
Variant Meaning
qknorm QK-norm attention, diffusion head
qknorm_nofinal as above, without the final norm
qknorm_reg , qknorm_nofinal_reg latent-regression head instead of diffusion
768d12l , 1024d16l , 1152d28l ( _80k ) earlier runs without QK-norm

The ones used in the paper's configs:

Decoder Used by
pretrain_imgnet_S_qknorm objective / encoder sweep
pretrain_video_S_qknorm TT-VidT
pretrain_imgnet_S_qknorm_nofinal DisMo-2D3D with dual augmentation
pretrain_{imgnet,video}_B_qknorm_nofinal , pretrain_{imgnet,video}_L_qknorm decoder ablation

Recipe: 100k steps (80k for _80k ), global batch 256, AdamW lr 1e-4. See the code repository for training and usage.

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More Information About TTVidT-decoders huggingface.co Model

More TTVidT-decoders license Visit here:

https://choosealicense.com/licenses/apache-2.0

TTVidT-decoders huggingface.co

TTVidT-decoders huggingface.co is an AI model on huggingface.co that provides TTVidT-decoders's model effect (), which can be used instantly with this KBlueLeaf TTVidT-decoders model. huggingface.co supports a free trial of the TTVidT-decoders model, and also provides paid use of the TTVidT-decoders. Support call TTVidT-decoders model through api, including Node.js, Python, http.

TTVidT-decoders huggingface.co Url

https://huggingface.co/KBlueLeaf/TTVidT-decoders

KBlueLeaf TTVidT-decoders online free

TTVidT-decoders huggingface.co is an online trial and call api platform, which integrates TTVidT-decoders's modeling effects, including api services, and provides a free online trial of TTVidT-decoders, you can try TTVidT-decoders online for free by clicking the link below.

KBlueLeaf TTVidT-decoders online free url in huggingface.co:

https://huggingface.co/KBlueLeaf/TTVidT-decoders

TTVidT-decoders install

TTVidT-decoders is an open source model from GitHub that offers a free installation service, and any user can find TTVidT-decoders on GitHub to install. At the same time, huggingface.co provides the effect of TTVidT-decoders install, users can directly use TTVidT-decoders installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

TTVidT-decoders install url in huggingface.co:

https://huggingface.co/KBlueLeaf/TTVidT-decoders

Url of TTVidT-decoders

TTVidT-decoders huggingface.co Url

Provider of TTVidT-decoders huggingface.co

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