Instructions to use mlx-community/SeedVR2-3B-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/SeedVR2-3B-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir SeedVR2-3B-mlx mlx-community/SeedVR2-3B-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
SeedVR2-3B (MLX-Swift) β fp16
MLX-Swift weights for SeedVR2-3B, ByteDance's one-step diffusion super-resolution /
restoration model (ICLR 2026). For on-device upscaling on Apple Silicon via the
seedvr2-mlx-swift package (built for
MLXEngine / ForgeUpscaler). int8 variant: SeedVR2-3B-mlx-int8.
- Files:
transformer.safetensors(DiT, fp16, ~7.9 GB) Β·vae.safetensors(3D-causal-conv VAE, fp16) Β·pos_emb.safetensors(precomputed text embedding) Β·config.json. - Precision: fp16. Parity vs the mflux reference (CPU): transformer
t_outmax_abs 2.1e-4, VAE encode/decode 3.5e-3 / 7.2e-3, RNG/scheduler 0.0.
Usage
import SeedVR2MLX // github.com/xocialize/seedvr2-mlx-swift
let upscaler = try SeedVR2Upscaler(directory: weightsDir) // downloaded from this repo
let out = upscaler.upscale(processedImage: img, seed: 42) // [-1,1], dims padded to /16
(Preprocess β resolution/softness bicubic resize β and LAB color-correction are host-side; VAE tiling for large images is handled by the host, e.g. ForgeUpscaler's tile processor.)
Provenance & license
Chain: ByteDance Seed β SeedVR2: One-Step Video Restoration via Diffusion Adversarial
Post-Training (ICLR 2026), ByteDance-Seed/SeedVR,
Apache-2.0 β PyTorch fp16 redistribution numz/SeedVR2_comfyUI
β MLX reference impl filipstrand/mflux β MLX-Swift
port + weight conversion by MVS Collective (xocialize). These are format-converted weight
artifacts (not a new model); Apache-2.0 applies. Credit ByteDance Seed (original), cite the paper.
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