Skip to page content
SAVRN
Search Plan a project

Open-weight model · Video to video

slither-wam-pretrain

by Seth Lupo mundoamundo/slither-wam-pretrain

slither-wam-pretrain is an open-weight model for video to video from Seth Lupo. Its published files total 22.2 MB.

The six-H200 full-corpus run is in progress. Its rolling latest checkpoint is in a mutable Storage Bucket, with the verified step recorded in latest.json. The final weights will be published in this model repository.

Parameters—
Context—
Weights22.2 MB
License—
AccessOpen weights
Monthly Downloads—

Model Card

The six-H200 full-corpus run is in progress. Its rolling latest checkpoint is in a mutable Storage Bucket, with the verified step recorded in latest.json. The final weights will be published in this model repository. Live training and held-out validation metrics are updated throughout the run. The model initializes from OpenWAM-α at revision 52df4e66c82c5c8b480adcc8d01f4db7415dfb56. Its Slither adapter replaces the 80-dimensional robot action head with three outputs: cosine and sine of estimated mouse angle, and binary boost. It removes robot proprioception and uses fixed empty-prompt conditioning. Each example uses 33 frames at 30 Hz and 32 transition actions; the nine video frames are…

Excerpt from the card by Seth Lupo.

Identity and Version

Repository
mundoamundo/slither-wam-pretrain
Publisher
Seth Lupo
Task
Video to video
Modality
Other
Library
openwam
Parameters
Not stated by the source
Languages
Not stated by the source
Revision
d3aaab89444af03bae87c2c891689bcba0b96196
First published
2026-09-30
Last updated
2026-10-01

Files and Weights

9 files, 22.2 MB in total.

Configuration2 files · 9.6 KB
Tokenizer3 files · 21.4 MB
Documentation1 file · 1.7 KB
Other2 files · 704.6 KB
Repository1 file · 1.7 KB
Every file
FileTypeSizeSHA-256
config.yamlConfiguration3.0 KB —
tokenizer/google/umt5-xxl/special_tokens_map.jsonConfiguration6.6 KB —
README.mdDocumentation1.7 KB —
metrics/live/train_metrics.jsonlOther518.4 KB —
metrics/live/validation.jsonlOther186.2 KB —
.gitattributesRepository1.7 KB —
tokenizer/google/umt5-xxl/spiece.modelTokenizer4.5 MB e3909a67b780
tokenizer/google/umt5-xxl/tokenizer.jsonTokenizer16.8 MB 6e197b4d3dbd
tokenizer/google/umt5-xxl/tokenizer_config.jsonTokenizer61.7 KB —

License and Download

License
Not stated by the source
Access
Open weights, no gate
Download from Seth Lupo

Released by Seth Lupo through its official repository on Hugging Face.

Similar Models

Model · Video to video

Matrix-Game-4.0

Skywork

Scene-specialized causal video renderers for Medieval Castle, Ancient Egypt, and City Racing. An executable game determines gameplay, physics and input, while the model re-renders clean low-poly RGB. HUD and sound remain separate, frame-aligned presentation layers. Each scene checkpoint has 5,001,016,512 BF16 parameters. The weights require the Matrix-Game 4.0 inference source; they are not drop-in Diffusers pipelines. Training code and training data are not included. Shared encoders/codecs are installed once for all scenes. See game asset details. Use the source release's manifest downloader with a pinned Hub commit revision and --repo-id Skywork/Matrix-Game-4.0 --repo-type model for both…

Open weights apache-2.0

Model · Video to video

SwiftVR

H-oliday

SwiftVR is a streaming one-step generative video restoration (VR) framework presented in SwiftVR: Real-Time One-Step Generative Video Restoration. - [2026/06] Release the inference code and pretrained weights LightX2V brings faster inference and lower GPU memory usage to SwiftVR (1.91× speedup and 65.71% lower peak GPU memory per request on a single H100). It also supports multi-GPU acceleration. Ready-to-use scripts cover image and video super-resolution, offline inference, and API serving. Get started with LightX2V → - Mask-free shifted-window self-attention (MFSWA). Each spatial window is pre-gathered into a dense tensor, so every attention call reduces to a single standard…

Open weights apache-2.0 5B parameters diffusers

Model · Video to video

Viggle-Animate

Viggle AI

Built on MiniMaxAI/MiniMax-H3 this repository. The cast of a two-minute music video replaced through its fast cuts, from at most one repainted frame per shot. AI-generated parody: the people shown did not take part in, approve or endorse it. Viggle-Animate replaces the character in a video with whatever you paint into one of its own frames — motion, camera and timing untouched. You prepare that one frame in an image editor; from there the video stage runs no pose estimator, segmenter, face tracker or text encoder. Two inputs, three forward passes, 26 seconds a shot on one GPU. It is strongest where replacement is hardest: fast motion, and pose transfer accurate enough to follow it. Whipping…

Open weights other 33.1B parameters minimax-h3