Instructions to use mlx-community/Bernini-R-1.3B-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Bernini-R-1.3B-bf16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Bernini-R-1.3B-bf16 mlx-community/Bernini-R-1.3B-bf16
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 1,160 Bytes
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Apache MLX port of ByteDance Bernini-R-1.3B (the Bernini Renderer, small tier).
This work is licensed under the Apache License, Version 2.0.
It is derived from and depends on the following Apache-2.0 works; their notices
and attributions are retained here:
- ByteDance/Bernini-R-1.3B — the Bernini Renderer, 1.3B tier (weights + reference
inference code).
https://github.com/bytedance/Bernini · https://huggingface.co/ByteDance/Bernini-R-1.3B-Diffusers
Paper: "Bernini: Latent Semantic Planning for Video Diffusion" (arXiv:2605.22344).
- Wan-AI/Wan2.1-T2V-1.3B — the base DiT, 16-channel causal VAE, and UMT5 text encoder
that Bernini-R-1.3B fine-tunes / reuses. https://github.com/Wan-Video/Wan2.1
- Qwen2.5-VL-7B-Instruct — the Bernini *planner* (NOT used here; not released as weights).
- mlx-video (Blaizzy/mlx-video) — the MLX Wan backbone reused by this port.
Scope note: only the Bernini *Renderer* is open-sourced upstream. The MLLM semantic
planner (the paper's "latent semantic planning") is not released, so this port runs with
UMT5 text conditioning only; the planner-feature channel is absent.
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