Model reference · open weights
Bernini-Wan2.2-scaled is an open-weight video model from attashe, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
Bernini (full) Wan2.2 renderer — fp8 e4m3 scaled (ComfyUI) The two DiT renderer transformers of the full ByteDance Bernini pipeline (diffdec = high-noise expert, diffdeclow = low-noise expert), quantized to fp8 e4m3 scaled in the ComfyUI format. The layout is byte-for-byte structurally identical to Comfy-Org/Bernini-R's wan2.2berninirfp8scaled.safetensors (verified: same 1815 keys, shapes, dtypes, and metadata) — the difference is only the weights, which here are the full Bernini renderer (jointly trained with the MLLM planner) rather than the renderer-only Bernini-R. Files Drop them into ComfyUI/models/diffusionmodels/ and use them anywhere the Bernini-R fp8scaled files work (same modeltype, same keys). Quantization details - Format marker per quantized weight: comfyquant = {"format": "float8e4m3fn"}. - Quantized Linears: selfattn.{q,k,v,o}, crossattn.{q,k,v} (cross-attn o kept in fp16), ffn.0, ffn.2 — 9 per block × 40 = 360 weights per expert. - For each quantized weight W: scale = max(|W|)/448, Wfp8 = (W/scale).clamp(±448).to(float8e4m3fn), stored alongside a scalar weightscale (fp32). Dequant: W ≈ Wfp8.to(dtype) weightscale. - Everything else (norms, modulation, patchembedding, text/timeembedding, timeprojection, head, all biases) is kept in fp16. - Mean per-tensor reconstruction error ≈ 2.2%. - Source: extracted from ByteDance/Bernini-Diffusers (bernini/ checkpoint, fp32), with diffusers WanTransformer3DModel keys remapped to the original Wan / ComfyUI naming. License: Apache-2.0, inherited from the upstream Bernini release.
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | attashe |
|---|---|
| Type | Video models |
| Variants | 1 |
| Runs with | diffusers |
| Based on | ByteDance/Bernini-Diffusers |
| Released | 2026-06-16 |
| Popularity | 8k downloads / month |
| Likes | 2 |
| Licence | Open weights |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| Bernini-Wan2.2-fp8-scaled | — | FP8 | — | — | Weights ↗ |
Using it via the API
Once AxForge deploys bernini-wan2-2-scaled for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (bernini-wan2-2-scaled below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/videos/generations \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"bernini-wan2-2-scaled","prompt":"a drone shot over a forest"}'
Details
Tags
Licence
Open weights under apache-2.0 — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗
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