AI models and licences
The 37 AI models the AI nodes can download, with licences and tiers.
The AI nodes run open models in a separate AI worker process on your GPU. Nothing is sent to a cloud service. Models are downloaded to your machine the first time a node needs them and checked against the sizes and checksums below. Oink never redistributes model weights.
Tiers:
- bundled: downloaded when a node first needs it; permissive licence
- optional: you must accept its licence in the app before it downloads
- research: the research pack: NON-COMMERCIAL use only; each user accepts its licence in the node panel before it downloads, and a studio policy can forbid it (see Research pack)
birefnet-hr-matting
BiRefNet HR matting (automatic, 2048 px), licence MIT, tier bundled.
Provenance: Zheng Peng et al., BiRefNet (CAAI AIR 2024), Swin-L backbone, HR matting fine-tune at 2048x2048. Trained on academic matting datasets (P3M-10k, AM-2k, Distinctions-646, TR-humans etc.) — dataset provenance flagged for legal review. Needs the repo's birefnet.py (MIT, trust_remote_code; timm.layers shimmed by opencomp_ai/ops/matte.py).
Weights: Hugging Face ZhengPeng7/BiRefNet_HR-matting at 5d6b6f8adcb5. Registry file opencomp_ai/models/birefnet-hr-matting.json.
cuda-nvcc-12.8
CUDA 12.8 compiler (nvcc + runtime + CCCL headers) for SplatTrain's one-time gsplat build, licence NVIDIA CUDA Toolkit EULA, tier bundled.
Provenance: NVIDIA CUDA 12.8.1 redistributable archives (developer.download.nvidia.com/compute/cuda/redist, redistrib_12.8.1.json). Downloaded on first SplatTrain run, never shipped with Oink. Matches the torch cu128 build.
dinov2-small
DINOv2 ViT-S/14 (image embeddings for Prep Show key shots), licence Apache-2.0, tier bundled.
Provenance: Meta AI DINOv2 (Oquab et al. 2023), self-supervised ViT-S/14 trained on LVD-142M (curated web images, no labels).
Weights: Hugging Face facebook/dinov2-small at ed25f3a31f01. Registry file opencomp_ai/models/dinov2-small.json.
edgetam
EdgeTAM (fast preview), licence Apache-2.0, tier bundled.
Provenance: Meta Reality Labs EdgeTAM (SAM 2 distilled to a RepViT encoder), HF-transformers conversion by the transformers team (yonigozlan). Trained on SA-1B + SA-V.
Weights: Hugging Face yonigozlan/EdgeTAM-hf at c266ce53b3fc. Registry file opencomp_ai/models/edgetam.json.
efficienttam-s
EfficientTAM-S (light video tracker, ViT-S image encoder, 34M params), licence Apache-2.0, tier bundled.
Apache-2.0 for the code and the checkpoints (README: 'Efficient track anything checkpoints and codebase are licensed under Apache 2.0'). Commercial use allowed; keep the licence + NOTICE with redistributions.
Provenance: Meta / yformer EfficientTAM (Efficient Track Anything, arXiv:2411.18933): SAM 2 architecture with a plain non-hierarchical ViT image encoder and efficient memory cross-attention, trained on SA-1B + SA-V like SAM 2. Weights: HF yunyangx/efficient-track-anything. Code: the official repo at a pinned commit, downloaded as a tarball next to the weights, never vendored.
Weights: Hugging Face yunyangx/efficient-track-anything at 9bdd8ab585b1. Registry file opencomp_ai/models/efficienttam-s.json.
efficienttam-ti
EfficientTAM-TI (light video tracker, ViT-Ti image encoder, 18M params), licence Apache-2.0, tier bundled.
Apache-2.0 for the code and the checkpoints (README: 'Efficient track anything checkpoints and codebase are licensed under Apache 2.0'). Commercial use allowed; keep the licence + NOTICE with redistributions.
Provenance: Meta / yformer EfficientTAM (Efficient Track Anything, arXiv:2411.18933): SAM 2 architecture with a plain non-hierarchical ViT image encoder and efficient memory cross-attention, trained on SA-1B + SA-V like SAM 2. Weights: HF yunyangx/efficient-track-anything. Code: the official repo at a pinned commit, downloaded as a tarball next to the weights, never vendored.
Weights: Hugging Face yunyangx/efficient-track-anything at 9bdd8ab585b1. Registry file opencomp_ai/models/efficienttam-ti.json.
flux2-klein-4b
FLUX.2 [klein] 4B (distilled, 4 steps, inpaint + reference), licence Apache-2.0, tier bundled.
Provenance: black-forest-labs/FLUX.2-klein-4B @e7b7dc2 (Apache-2.0), diffusers transformer + VAE downloaded once to the shared cache; its Qwen3-4B text encoder is byte-identical to the ComfyUI qwen_3_4b.safetensors (checked tensor by tensor) and is reused from there.
Weights: Hugging Face black-forest-labs/FLUX.2-klein-4B at e7b7dc27f91d. Registry file opencomp_ai/models/flux2-klein-4b.json.
grounding-dino-base
Grounding DINO Base, licence Apache-2.0, tier bundled.
Provenance: IDEA Research. Trained on O365, GoldG, Cap4M, COCO, RefCOCO (academic/web datasets; see paper).
Weights: Hugging Face IDEA-Research/grounding-dino-base at 12bdfa3120f3. Registry file opencomp_ai/models/grounding-dino-base.json.
map-anything-apache
MapAnything (Apache checkpoint): multi-view focal, camera poses, metric depth, licence Apache-2.0, tier bundled.
Checkpoint Apache-2.0 (the 'apache' variant, trained only on data whose licences allow it; the default facebook/map-anything checkpoint is CC-BY-NC and NOT used). Code: map-anything Apache-2.0, uniception BSD-3-Clause, DINOv2 code Apache-2.0.
Provenance: Meta Reality Labs / CMU MapAnything (Keetha et al. 2025, arXiv:2509.13414). DINOv2-giant encoder + alternating-attention transformer, trained on the Apache-compatible subset of its 13 datasets. Code pinned: facebookresearch/map-anything@3d10cf7, uniception 0.1.7 (PyPI wheel), facebookresearch/dinov2@7764ea0 (hub code only, no DINOv2 weights downloaded).
Weights: Hugging Face facebook/map-anything-apache at 00f9c245bbcb. Registry file opencomp_ai/models/map-anything-apache.json.
moge-2-vitb-normal
MoGe-2 ViT-B (metric geometry, FOV, normals), licence MIT, tier bundled.
MIT (model card metadata; code github.com/microsoft/MoGe MIT). DINOv2 backbone code Apache-2.0.
Provenance: Microsoft Research MoGe-2 (Wang et al. 2025), DINOv2 ViT-B/14 encoder fine-tuned; trained on a mix of synthetic and real metric datasets. Code vendored in opencomp_ai/vendor/moge (MIT).
Weights: Hugging Face Ruicheng/moge-2-vitb-normal at ca5f0e07ff01. Registry file opencomp_ai/models/moge-2-vitb-normal.json.
qwen-image-edit-q8
Qwen-Image-Edit 20B (Aug 2025) Q8_0 GGUF + Lightning 8-step LoRA, licence Apache-2.0, tier bundled.
Provenance: Qwen/Qwen-Image-Edit (Aug 2025) MMDiT, Q8_0 GGUF by QuantStack (Qwen-Image-Edit-GGUF @acab6f9); text encoder Qwen2.5-VL-7B bf16 and VAE as repackaged by Comfy-Org; optional lightx2v Qwen-Image-Edit-Lightning 8-step LoRA (Apache-2.0). Big files reused from the ComfyUI models dir (external). Configs pinned from Qwen/Qwen-Image-Edit.
Weights: Hugging Face Qwen/Qwen-Image-Edit at ac7f9318f633. Registry file opencomp_ai/models/qwen-image-edit-q8.json.
realesrgan-x2plus
Real-ESRGAN x2plus (fast, per frame), licence BSD-3-Clause, tier bundled.
Provenance: Real-ESRGAN (Wang et al., Tencent ARC, 2021), RRDBNet generator trained on DIV2K + Flickr2K + OutdoorSceneTraining with synthetic degradations. Weights from the official GitHub release.
realesrgan-x4plus
Real-ESRGAN x4plus (fast, per frame), licence BSD-3-Clause, tier bundled.
Provenance: Real-ESRGAN (Wang et al., Tencent ARC, 2021), RRDBNet generator trained on DIV2K + Flickr2K + OutdoorSceneTraining with synthetic degradations. Weights from the official GitHub release.
rife-4.26
RIFE 4.26 (frame interpolation), licence MIT, tier bundled.
Provenance: hzwer Practical-RIFE v4.26 (2024-09-21). Official release is on Google Drive; this HF mirror (lopi999/rife_v4.26) is byte-identical to it (sha256 of flownet.pkl checked against the official zip, 2026-09-25). Trained on Vimeo90K + in-house video (per the authors).
Weights: Hugging Face lopi999/rife_v4.26 at 93b1dd7b5447. Registry file opencomp_ai/models/rife-4.26.json.
rvm-mobilenetv3
Robust Video Matting (humans, no prompt), licence GPL-3.0, tier bundled.
Provenance: Lin et al., Robust High-Resolution Video Matting with Temporal Guidance (WACV 2022), MobileNetV3 variant. Official weights are on GitHub releases (v1.0.0); this HF mirror serves the same file (sha256 pinned). Model code vendored in opencomp_ai/vendor/rvm (GPL-3). Trained on VideoMatte240K, Distinctions-646, AM-2k, COCO/YouTubeVIS segmentation.
Weights: Hugging Face SJTU-TES/RobustVideoMatting at 3c88b194e7e4. Registry file opencomp_ai/models/rvm-mobilenetv3.json.
sam2.1-hiera-large
SAM 2.1 Hiera Large, licence Apache-2.0, tier bundled.
Provenance: Meta FAIR. Trained on SA-1B + SA-V (Meta, licensed/collected video) per the SAM 2 paper.
Weights: Hugging Face facebook/sam2.1-hiera-large at 665f8e2ad61c. Registry file opencomp_ai/models/sam2.1-hiera-large.json.
sea-raft-m
SEA-RAFT M (optical flow), licence BSD-3-Clause, tier bundled.
Provenance: Princeton Vision & Learning Lab, SEA-RAFT (ECCV 2024) medium, checkpoint Tartan-C-T-TSKH-spring540x960-M. Trained on TartanAir, FlyingChairs, FlyingThings3D, Sintel, KITTI, HD1K, Spring (synthetic + academic benchmark data).
Weights: Hugging Face MemorySlices/Tartan-C-T-TSKH-spring540x960-M at eb97ef34ba5d. Registry file opencomp_ai/models/sea-raft-m.json.
sea-raft-s
SEA-RAFT S (optical flow, fast), licence BSD-3-Clause, tier bundled.
Provenance: Princeton Vision & Learning Lab, SEA-RAFT (ECCV 2024) small, checkpoint Tartan-C-T-TSKH-spring540x960-S. Same training data as sea-raft-m.
Weights: Hugging Face MemorySlices/Tartan-C-T-TSKH-spring540x960-S at 31b9b4b711bd. Registry file opencomp_ai/models/sea-raft-s.json.
seedvr2-3b
SeedVR2 3B (one-step video restoration, temporal), licence Apache-2.0, tier bundled.
Provenance: ByteDance Seed SeedVR2-3B (one-step diffusion video restoration, adversarial post-training). FP8 e4m3fn DiT + fp16 VAE repack by numz (ComfyUI-SeedVR2_VideoUpscaler, Apache-2.0); inference code pinned from that repo. Training data: undisclosed large-scale image/video corpus (see paper arXiv:2506.05301).
Weights: Hugging Face numz/SeedVR2_comfyUI at 09ced7102363. Registry file opencomp_ai/models/seedvr2-3b.json.
shot-sr
Shot-trained upscaler (fidelity, trained on your plate), licence GPL-3.0, tier bundled.
Oink's own code (opencomp_ai/ops/upscale.py). No downloaded weights: a small residual CNN is trained on the shot itself, on this machine.
Provenance: Zero-shot super-resolution (Shocher et al. 2018, method only): trained on the plate's own frame (downscaled -> Lanczos -> the frame). No external training data; the weights stay in the local cache (shot-sr/).
tapnextpp
TAPNext++ (point tracking through occlusion, re-detection), licence Apache-2.0, tier bundled.
Code and all released checkpoints (TAP-Net, TAPIR, BootsTAPIR, TAPNext, TAPNext++) Apache-2.0 per the tapnet README.
Provenance: Google DeepMind TAPNext++ (BootsTAPNext fine-tuned on PointOdyssey v1.2 + Kubric-1024 synthetic sequences; BootsTAP self-training used real YouTube video without labels). Torch checkpoint from storage.googleapis.com/dm-tapnet; code pinned google-deepmind/tapnet@730cda1.
video-depth-anything-small
Video Depth Anything Small (temporal relative depth), licence Apache-2.0, tier bundled.
Apache-2.0 (model card metadata + upstream README: 'Video-Depth-Anything-Small model is under the Apache-2.0 license'; Base/Large are CC-BY-NC and are not listed).
Provenance: ByteDance Video Depth Anything (Chen et al. 2025), DINOv2 ViT-S encoder + temporal DPT head; trained on synthetic video depth + Depth Anything V2 pseudo-labelled images. Code vendored in opencomp_ai/vendor/vda (Apache-2.0).
Weights: Hugging Face depth-anything/Video-Depth-Anything-Small at 256875362cff. Registry file opencomp_ai/models/video-depth-anything-small.json.
vitmatte-base
ViTMatte base (trimap, Composition-1k), licence Apache-2.0, tier bundled.
Provenance: HUST VL ViTMatte (Yao et al. 2023), ViT-B DINO backbone, trained on Adobe Composition-1k (academic dataset; flag for legal review). Only a pickle .bin is published (loaded with torch weights_only).
Weights: Hugging Face hustvl/vitmatte-base-composition-1k at bf486d01a7d9. Registry file opencomp_ai/models/vitmatte-base.json.
vitmatte-small
ViTMatte small (trimap, Composition-1k), licence Apache-2.0, tier bundled.
Provenance: HUST VL ViTMatte (Yao et al. 2023), ViT-S DINO backbone, trained on Adobe Composition-1k (academic dataset; flag for legal review). HF transformers VitMatteForImageMatting.
Weights: Hugging Face hustvl/vitmatte-small-composition-1k at 6a58ad764640. Registry file opencomp_ai/models/vitmatte-small.json.
wan2.1-vace-1.3b
Wan 2.1 VACE 1.3B (video inpainting, ComfyUI split files), licence Apache-2.0, tier bundled.
Provenance: Wan 2.1 VACE 1.3B (Alibaba Tongyi Wan team, 2025): all-in-one video creation/editing DiT (masked video-to-video). umT5-XXL text encoder (fp8 scaled), Wan 2.1 VAE. Weights as repackaged by Comfy-Org (fp16 DiT), configs/tokenizer from Wan-AI/Wan2.1-VACE-1.3B-diffusers; reused from the user's ComfyUI models dir when present. Training data: undisclosed large-scale image/video corpus (Wan tech report arXiv:2503.20314).
Weights: Hugging Face Comfy-Org/Wan_2.1_ComfyUI_repackaged at 123acf1cc74b. Registry file opencomp_ai/models/wan2.1-vace-1.3b.json.
wan2.2-i2v-a14b
Wan 2.2 I2V A14B (MoE high/low noise, fp8 scaled), licence Apache-2.0, tier bundled.
Provenance: Wan-AI/Wan2.2-I2V-A14B (Apache-2.0): two 14B experts (high / low noise) in ComfyUI fp8 e4m3 scaled form (weights x per-tensor scale, upcast per layer), Wan 2.1 VAE and umt5-xxl fp8-scaled as repackaged by Comfy-Org (reused from the ComfyUI models dir); diffusers configs pinned from Wan-AI/Wan2.2-I2V-A14B-Diffusers.
Weights: Hugging Face Wan-AI/Wan2.2-I2V-A14B-Diffusers at 596658fd9ca6. Registry file opencomp_ai/models/wan2.2-i2v-a14b.json.
wan2.2-ti2v-5b
Wan 2.2 TI2V 5B (text/image to video, 720p 24 fps), licence Apache-2.0, tier bundled.
Provenance: Wan-AI/Wan2.2-TI2V-5B (Apache-2.0): fp16 DiT, Wan 2.2 VAE (16x16x4) and umt5-xxl fp8-scaled text encoder as repackaged by Comfy-Org (reused from the ComfyUI models dir); diffusers configs pinned from Wan-AI/Wan2.2-TI2V-5B-Diffusers. The Wan2.2 VAE comes from the diffusers repo (diffusers 0.39 cannot convert the ComfyUI wan2.2_vae single file: residual decoder layout).
Weights: Hugging Face Wan-AI/Wan2.2-TI2V-5B-Diffusers at b8fff7315c76. Registry file opencomp_ai/models/wan2.2-ti2v-5b.json.
z-image-turbo
Z-Image Turbo (6B, 8-step text/img2img, ComfyUI split files), licence Apache-2.0, tier bundled.
Provenance: Z-Image Turbo (Tongyi-MAI / Alibaba, 2025), distilled 6B single-stream DiT; Qwen3-4B text encoder; FLUX.1 autoencoder (ae.safetensors, Apache-2.0). Files as repackaged by Comfy-Org; reused from the user's ComfyUI models dir when present (external), hashes = Hugging Face LFS sha256.
Weights: Hugging Face Comfy-Org/z_image_turbo at 6fc90a3b1b65. Registry file opencomp_ai/models/z-image-turbo.json.
cosmos-diffusion-renderer-forward
Cosmos DiffusionRenderer 7B (forward renderer: G-buffers + HDR environment map -> relit video), licence NVIDIA-Open-Model-License, tier optional.
NVIDIA Open Model License: commercial use permitted; you may create and distribute derivative models; NVIDIA claims no ownership of outputs. Conditions: keep the notice 'Licensed by NVIDIA Corporation under the NVIDIA Open Model License'; rights terminate if you bypass or disable the model's safety guardrails without a substantially similar replacement, or if you start patent/copyright litigation over the model. Weights are not redistributed by Oink. Read the full text at the licence URL before accepting.
Provenance: NVIDIA Toronto AI Lab, Cosmos-Transfer1-DiffusionRenderer (Liang et al., CVPR 2025, arXiv:2501.18590). 7B Cosmos-Predict1 DiT fine-tuned; trained on 150k synthetic videos (rendered G-buffers) + 150k auto-labelled real videos, 57 frames at 1280x704. Latent tokenizer: nvidia/Cosmos-Tokenize1-CV8x8x8-720p (same licence). Inference code (Apache-2.0) re-implemented in pure PyTorch in opencomp_ai/vendor/cosmos_dr (no transformer-engine / megatron).
Weights: Hugging Face nvidia/Diffusion_Renderer_Forward_Cosmos_7B at 7d5f9b81affa. Registry file opencomp_ai/models/cosmos-diffusion-renderer-forward.json.
cosmos-diffusion-renderer-inverse
Cosmos DiffusionRenderer 7B (inverse renderer: video -> base colour, normals, depth, roughness, metallic), licence NVIDIA-Open-Model-License, tier optional.
NVIDIA Open Model License: commercial use permitted; you may create and distribute derivative models; NVIDIA claims no ownership of outputs. Conditions: keep the notice 'Licensed by NVIDIA Corporation under the NVIDIA Open Model License'; rights terminate if you bypass or disable the model's safety guardrails without a substantially similar replacement, or if you start patent/copyright litigation over the model. Weights are not redistributed by Oink. Read the full text at the licence URL before accepting.
Provenance: NVIDIA Toronto AI Lab, Cosmos-Transfer1-DiffusionRenderer (Liang et al., CVPR 2025, arXiv:2501.18590). 7B Cosmos-Predict1 DiT fine-tuned; trained on 150k synthetic videos (rendered G-buffers) + 150k auto-labelled real videos, 57 frames at 1280x704. Latent tokenizer: nvidia/Cosmos-Tokenize1-CV8x8x8-720p (same licence). Inference code (Apache-2.0) re-implemented in pure PyTorch in opencomp_ai/vendor/cosmos_dr (no transformer-engine / megatron).
Weights: Hugging Face nvidia/Diffusion_Renderer_Inverse_Cosmos_7B at 41f63abccfb7. Registry file opencomp_ai/models/cosmos-diffusion-renderer-inverse.json.
ltx-2.3-22b-distilled
LTX-2.3 22B distilled (fp8, video + audio), licence LTX-2 Community License, tier optional.
Free for entities with < $10M annual revenue; above that a commercial licence from Lightricks is required. Acceptable-use restrictions apply. You must accept before the weights are used.
Provenance: Lightricks/LTX-2.3 @5948be4 22B distilled DiT (fp8 v2 as used by ComfyUI) + LTX-2.3 video VAE, reused from the ComfyUI models dir; text encoder Gemma-3-12B-it (Google Gemma terms) from Comfy-Org/ltx-2.
Weights: Hugging Face Lightricks/LTX-2.3 at 5948be4ced3a. Registry file opencomp_ai/models/ltx-2.3-22b-distilled.json.
moge-2-vitl-normal
MoGe-2 ViT-L (metric geometry, FOV, normals; larger), licence MIT, tier optional.
MIT (model card metadata; code github.com/microsoft/MoGe MIT). DINOv2 backbone code Apache-2.0.
Provenance: Microsoft Research MoGe-2 (Wang et al. 2025), DINOv2 ViT-L/14 encoder fine-tuned; the larger sibling of moge-2-vitb-normal. Code vendored in opencomp_ai/vendor/moge (MIT).
Weights: Hugging Face Ruicheng/moge-2-vitl-normal at cb0e8bbd6b1e. Registry file opencomp_ai/models/moge-2-vitl-normal.json.
sam3
SAM 3 (text prompts + points / box / mask, per-object tracker), licence SAM-License, tier optional.
Meta SAM License (custom, not OSI). Allows: use, copy, modify, distribute, derivative works, commercial use (royalty-free, no user/revenue cap). Conditions: redistribution only under the same agreement with a copy of it; acknowledge SAM in published research; comply with laws, trade controls and privacy law; no ITAR / military / warfare / nuclear / espionage / weapons use; do not reverse engineer the SAM Materials; indemnify Meta; licence ends if you sue Meta over SAM IP; Meta may amend the terms. Weights and code are downloaded to your machine after you accept; Oink never redistributes them. Read the full text at the licence URL before accepting.
Provenance: Meta FAIR SAM 3 (Segment Anything with Concepts, arXiv:2511.16719), 848M params. Gated on Hugging Face (manual approval). Trained on SA-Co (Meta) + SA-1B + SA-V. Inference code: official facebookresearch/sam3 at a pinned commit, fetched as a tarball (same SAM License), never vendored.
Weights: Hugging Face facebook/sam3 at 3c879f39826c. Registry file opencomp_ai/models/sam3.json.
sam3.1
SAM 3.1 (text prompts, Object Multiplex joint multi-object tracking), licence SAM-License, tier optional.
Meta SAM License (custom, not OSI). Allows: use, copy, modify, distribute, derivative works, commercial use (royalty-free, no user/revenue cap). Conditions: redistribution only under the same agreement with a copy of it; acknowledge SAM in published research; comply with laws, trade controls and privacy law; no ITAR / military / warfare / nuclear / espionage / weapons use; do not reverse engineer the SAM Materials; indemnify Meta; licence ends if you sue Meta over SAM IP; Meta may amend the terms. Weights and code are downloaded to your machine after you accept; Oink never redistributes them. Read the full text at the licence URL before accepting.
Provenance: Meta FAIR SAM 3.1 (SAM 3 + Object Multiplex: up to 16 objects per shared-memory bucket). Gated on Hugging Face (manual approval). Inference code: official facebookresearch/sam3 at a pinned commit, fetched as a tarball (same SAM License), never vendored. Checkpoint only on HF (no transformers integration).
Weights: Hugging Face facebook/sam3.1 at daa63191845a. Registry file opencomp_ai/models/sam3.1.json.
flux2-dev
FLUX.2 [dev] 32B fp8mixed (non-commercial), licence FLUX [dev] Non-Commercial License, tier research.
Non-commercial use only (research / evaluation). Outputs may not be used commercially without a paid licence from Black Forest Labs. Hidden unless the studio enables the research tier.
Provenance: black-forest-labs/FLUX.2-dev as repackaged by Comfy-Org (fp8mixed transformer, Mistral-3-Small fp8 text encoder, VAE); reused from the ComfyUI models dir (external).
Weights: Hugging Face Comfy-Org/flux2-dev at ed33133cd564. Registry file opencomp_ai/models/flux2-dev.json.
matanyone2
MatAnyone 2 (video matting from a rough mask) — RESEARCH PACK, non-commercial, licence S-Lab-License-1.0 (non-commercial), tier research.
NON-COMMERCIAL USE ONLY. NTU S-Lab License 1.0 covers the code and the weights: redistribution and use are permitted for non-commercial purposes only, keeping the copyright notice; commercial use needs written permission from the authors (Peiqing Yang, Shangchen Zhou, Chen Change Loy, NTU S-Lab). Not usable on paid / client work. Code and weights download to your machine after you accept; Oink never bundles or redistributes them.
Provenance: NTU S-Lab MatAnyone 2 (Scaling Video Matting via a Learned Quality Evaluator, arXiv:2512.11782). Weights: Hugging Face PeiqingYang/MatAnyone2 (model.safetensors 141 MB; the repo's LICENSE file is empty, the licence is the GitHub LICENSE.txt). Inference code: pq-yang/MatAnyone2 at a pinned commit, fetched as a tarball after acceptance, never vendored.
Weights: Hugging Face PeiqingYang/MatAnyone2 at 40c894a6f68d. Registry file opencomp_ai/models/matanyone2.json.
minimax-remover
MiniMax-Remover (video object removal) — RESEARCH PACK, non-commercial, licence CC-BY-NC-4.0 (weights); code: no licence, tier research.
NON-COMMERCIAL USE ONLY. The weights (Hugging Face zibojia/minimax-remover) are Creative Commons Attribution-NonCommercial 4.0: share and adapt for non-commercial purposes, with attribution (Bojia Zi, Weixuan Peng et al., arXiv:2505.24873). The inference code (github.com/zibojia/MiniMax-Remover) has NO licence file, so its authors reserve all rights; it is fetched to your machine for your own non-commercial use only. Built on the Wan 2.1 architecture/VAE (Apache-2.0). Not usable on paid / client work.
Provenance: MiniMax-Remover (Zi, Peng, Qi, Wang, Zhao, Xiao, Wong; arXiv:2505.24873): simplified Wan 2.1 1.3B DiT without text conditioning, minimax-distilled for 6-12 steps without CFG. Training data per the paper: public video datasets with synthetic masks (not audited). Code: two files at a pinned commit, fetched after acceptance, never vendored.
Weights: Hugging Face zibojia/minimax-remover at 889e41651d90. Registry file opencomp_ai/models/minimax-remover.json.