| --- |
| 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. |
|
|
