Instructions to use mlx-community/AnimeGen-I2V-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-I2V-A14B-Lightning-int4 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir AnimeGen-I2V-A14B-Lightning-int4 mlx-community/AnimeGen-I2V-A14B-Lightning-int4
- Wan2.2
How to use mlx-community/AnimeGen-I2V-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-I2V-A14B-Lightning-int4 (MLX)
An MLX (Apple Silicon) conversion of aidealab/AnimeGen-I2V — an anime-specialized image-to-video fine-tune of Wan2.2-I2V-A14B (dual-expert MoE) — quantized to int4 and shipped as a pre-merged Lightning checkpoint for few-step, CFG-free inference.
Built for the Swift-MLX wan-core / Bernini-R runtime. The i2v sibling of
AnimeGen-T2V-A14B-Lightning-int4.
What's in here
Flat checkpoint (stock-Wan VAE + umT5 reused from Wan2.2, byte-identical):
| File | What |
|---|---|
high_noise_model.safetensors |
fine-tuned high-noise expert, int4 (in_dim=36 = 16 noise + 4 mask + 16 image latent) |
low_noise_model.safetensors |
fine-tuned low-noise expert, int4 |
vae.safetensors |
16-channel Wan VAE |
t5_encoder.safetensors |
umT5-XXL text encoder |
config.json |
Wan2.2-I2V-A14B config (model_type: i2v, in_dim: 36) + quantization metadata |
Image conditioning (channel-concat, no CLIP)
The DiT input is [noisy_latent(16), mask(4), image_latent(16)] = 36 channels. Encode a video whose first frame is
the conditioning image and the rest zeros through the 16-ch WanVAE → z_video(16); build a first-frame temporal
mask(4); y = [mask, z_video](20) is constant across steps and concatenated onto the 16-ch noisy latent each
denoise step. No separate image encoder (image_dim is null).
Recommended inference recipe (from the AnimeGen-I2V model card)
- Sampler: euler, CFG-free (guidance 1.0)
- 4 steps, shift 3.0 (FlowMatchEuler)
- 832×480, frames
16·seconds + 1(must be 4n+1), 16 fps - Prompt prefix:
"Japanese anime style, "
How it was made
Experts converted 1:1 from aidealab/AnimeGen-I2V (original-Wan keys → sanitize; key set == the A14B 1095-key
contract). The lightx2v Wan2.2-I2V-A14B-4steps-lora-rank64-Seko-V1
LoRA merged offline per-expert (set_adapters([high,low],[1.0,1.0]), kohya). Attention + FFN linears quantized int4/g64.
Footprint
int4 measured ~43 GB peak at 832×480 / int4 / 4 steps on an M-series GPU (fits a 64 GB Mac).
License & attribution
Apache-2.0. Derived from aidealab/AnimeGen-I2V (AIdeaLab / GENIAC) + the lightx2v Lightning LoRA. Unofficial
community MLX conversion; not affiliated with AIdeaLab.
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Quantized
Model tree for mlx-community/AnimeGen-I2V-A14B-Lightning-int4
Base model
Wan-AI/Wan2.2-I2V-A14B
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir AnimeGen-I2V-A14B-Lightning-int4 mlx-community/AnimeGen-I2V-A14B-Lightning-int4