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license: apache-2.0
library_name: mlx
pipeline_tag: text-to-video
tags:
- mlx
- apple-silicon
- text-to-video
- text-to-image
- bernini
- wan2.2
- qwen2.5-vl
base_model:
- ByteDance/Bernini-Diffusers
- Wan-AI/Wan2.2-T2V-A14B
---
# Bernini — MLX (full planner + renderer)
Native **Apple Silicon / MLX** conversion of ByteDance **Bernini** (the full pipeline), packaged for
in-process generation in [SceneWorks](https://github.com/michaeltrefry). Bernini is a
**Latent Semantic Planning** model: a Qwen2.5-VL-7B semantic planner (MAR loop) drives a
**Wan2.2-T2V-A14B** dual-expert renderer.
This is a turnkey, self-contained snapshot — no diffusers source or separate Wan base is needed at
runtime. It loads directly via `mlx_gen::load("bernini")` (mlx-gen-bernini) and is quantized to
**Q4 (default) / Q8 (opt-in)** at load time.
## Contents
- `qwen2_5_vl.safetensors` + `qwen2_5_vl_config.json` — Qwen2.5-VL-7B planner backbone + vision tower
- `connector.safetensors`, `vit_decoder.safetensors`, `mask_tokens.safetensors` — MLP connector, ViT
decoder (clip-diff flow head), MAR mask token
- `high_noise_model.safetensors` + `low_noise_model.safetensors` — the Wan2.2 dual-expert renderer DiTs
- `t5_encoder.safetensors` + `tokenizer.json` — UMT5-XXL text encoder + tokenizer
- `vae.safetensors` — z16 AutoencoderKLWan
- `mllm/` — Qwen ChatML tokenizer/config; `*.json` sidecars — config + planner/renderer knobs
`dtype`: bf16. Validated on a 128 GB Apple Silicon Mac for **text-to-image** and **text-to-video**
(~44 GB peak at Q4).
## Credits & license
Derived from [ByteDance/Bernini-Diffusers](https://huggingface.co/ByteDance/Bernini-Diffusers) and
[Wan-AI/Wan2.2-T2V-A14B](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B) (the renderer's stock UMT5/VAE),
both Apache-2.0. Conversion/packaging by SceneWorks; released under Apache-2.0.
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