docs/source/en/api/models/autoencoderkl_minimax_h3_audio.md
The audio autoencoder used in MiniMax-H3 by MiniMax. It is waveform in and waveform out, with no mel front-end and no separate vocoder: a DAC-lineage strided convolutional encoder, a causal-attention projection onto the diffusion latent width, and a BigVGAN decoder.
The encoder hops 800 samples at 32 kHz, i.e. 40 latents per second, so a waveform of 800 * n samples encodes to n latents. Waveforms that are not a whole number of hops are right-padded.
The causal-attention projection goes through the attention dispatcher, so set_attention_backend applies to it; its mask is is_causal=True, which every backend honours except _native_npu, whose kernel takes no causal flag.
The autoencoder is mono, and it normalizes latents per channel with latents_mean / latents_std rather than a scalar scaling_factor. MiniMax-H3 carries stereo as two batch items, and it always consumes the posterior mean (latent_dist.mode()), never a sample.
import torch
from diffusers import AutoencoderKLMiniMaxH3Audio
audio_vae = AutoencoderKLMiniMaxH3Audio.from_pretrained(
"MiniMaxAI/MiniMax-H3", subfolder="audio_vae", dtype=torch.float32
).to("cuda")
[[autodoc]] AutoencoderKLMiniMaxH3Audio - encode - decode - all