Bernini-v2 (bf16, MLX)
Apple-MLX conversion of ByteDance/Bernini-Diffusers-v2
(revision 399cf6a) — the full unified Bernini: MLLM semantic planner + dual-expert
Wan2.2-A14B DiT renderer. Converted 2026-08-18. Apache-2.0, same as upstream.
Contents
| File | Component | Notes |
|---|---|---|
high_noise_model.safetensors | Wan2.2-A14B high-noise expert (bf16) | retrained vs Bernini-R (co-trained with the planner) — not interchangeable with mlx-community/Bernini-R-bf16 |
low_noise_model.safetensors | Wan2.2-A14B low-noise expert (bf16) | ditto |
mllm/ | Qwen2.5-VL-7B semantic planner (bf16, HF layout) | Bernini-trained weights (scratch_mllm), not stock Qwen; configs + tokenizer from upstream |
vit_decoder.safetensors | DiffLoss_FM flow-match head (bf16) | SimpleMLPAdaLN, width 4096, depth 16 |
planner_glue.safetensors | MLPConnector + mask_tokens | keys verbatim upstream |
t5_encoder.safetensors | umT5-XXL (bf16) | bit-identical to the stock Wan2.2 encoder (verified vs upstream) |
vae.safetensors | 16-ch WanVAE | bit-identical to stock Wan2.2 (verified vs upstream) |
config.json | wan-core runtime config (dual-expert A14B) | |
conversion.json | conversion provenance |
Conversion notes
- Experts: upstream fp32 masters → diffusers→original-Wan key premap → mlx-video sanitize → bf16. Key set verified bijective against the established Bernini-R MLX layout; value probes bit-exact (RNE) against the fp32 masters.
- The planner (
mllm.*) is saved in standard HF Qwen2.5-VL layout for direct consumption by MLX Qwen2.5-VL loaders. - The upstream in-checkpoint fp32 T5 copy was skipped; the standalone bf16 encoder (verified bit-identical) is shipped instead.
Usage
The renderer is drop-in for the Bernini-R MLX stack (same layout as
mlx-community/Bernini-R-bf16) — e.g. bernini-r-mlx
pipeline_mlx.t2v/t2i, or the Swift bernini-r-mlx-swift/wan-core stack.
The planner plane (mllm / vit_decoder / connector / mask_tokens) implements the
MaskGIT-style semantic planning of the Bernini paper (arXiv 2605.22344); a Swift-MLX planner
integration is in progress in bernini-r-mlx-swift. Until then these files carry the released
weights for downstream use.
Provenance & license
Upstream: ByteDance/Bernini-Diffusers-v2 (Apache-2.0). All credit for the model to the Bernini authors — see the Bernini repository and paper. This conversion changes dtype/layout only (plus the key renames described above); no weights were fine-tuned.