Instructions to use mlx-community/AnimeGen-T2V-A14B-Lightning-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/AnimeGen-T2V-A14B-Lightning-int4 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir AnimeGen-T2V-A14B-Lightning-int4 mlx-community/AnimeGen-T2V-A14B-Lightning-int4
- Wan2.2
How to use mlx-community/AnimeGen-T2V-A14B-Lightning-int4 with Wan2.2:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
AnimeGen-T2V-A14B-Lightning-int4 (MLX)
An MLX (Apple Silicon) conversion of aidealab/AnimeGen-T2V — an anime-specialized full fine-tune of Wan2.2-T2V-A14B (dual-expert MoE) — quantized to int4 and shipped as a pre-merged Lightning checkpoint for few-step, CFG-free inference.
This variant is built for the Swift-MLX wan-core / Bernini-R runtime: it uses the exact flat, 1095-key A14B
checkpoint layout that runtime loads, so the DiT experts drop in unchanged.
What's in here
Flat checkpoint (stock-Wan VAE + umT5 reused from Wan2.2-T2V-A14B, byte-identical):
| File | What |
|---|---|
high_noise_model.safetensors |
fine-tuned high-noise expert, int4-quantized (attention + FFN linears) |
low_noise_model.safetensors |
fine-tuned low-noise expert, int4-quantized |
vae.safetensors |
16-channel Wan VAE (stock Wan2.2) |
t5_encoder.safetensors |
umT5-XXL text encoder (stock Wan2.2) |
config.json |
Wan2.2-A14B config + quantization metadata |
How it was made
- The two fine-tuned experts from
aidealab/AnimeGen-T2V(original-Wan key naming, bf16) were converted 1:1 into the MLX A14B layout (sanitize_wan_transformer_weights; the key set is asserted equal to the base A14B contract, 1095 keys, 0 added / 0 dropped). - AIdeaLab's acceleration recipe — the lightx2v Wan2.2-Lightning
Wan2.2-T2V-A14B-4steps-lora-250928LoRA — was merged offline per-expert with AIdeaLab's weighting (set_adapters([high, low], [2.0, 1.0]); kohyaW += strength·(alpha/rank)·(up @ down)). - The attention + FFN linears of both experts were quantized to int4 (group size 64); VAE + umT5 kept full precision.
Recommended inference recipe (from the AnimeGen model card)
- Sampler: euler, CFG-free (guidance 1.0)
- 8 steps, shift 3.0 (FlowMatchEuler)
- 832×480 or 1280×720, frames
16·seconds + 1(must be 4n+1), 16 fps
Footprint
int4 measured ~42 GB peak at 832×480 / 17 frames / 8 steps on an M-series GPU (fits a 64 GB Mac).
License & attribution
Apache-2.0. Derived from aidealab/AnimeGen-T2V (AIdeaLab, developed with GENIAC/METI support) and the lightx2v
Lightning LoRA. This is an unofficial community MLX conversion; not affiliated with AIdeaLab.
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Quantized
Model tree for mlx-community/AnimeGen-T2V-A14B-Lightning-int4
Base model
Wan-AI/Wan2.2-T2V-A14B