# AceStepTransformer1DModel

A 1D Diffusion Transformer for music generation from [ACE-Step 1.5](https://github.com/ace-step/ACE-Step-1.5). The model operates on the 25 Hz stereo latents produced by [AutoencoderOobleck](/docs/diffusers/main/en/api/models/autoencoder_oobleck#diffusers.AutoencoderOobleck) using flow matching, and is trained with a Qwen3-derived backbone (grouped-query attention, rotary position embedding, RMSNorm, AdaLN-Zero timestep conditioning) plus cross-attention to the text / lyric / timbre conditions built by `AceStepConditionEncoder`.

## AceStepTransformer1DModel[[diffusers.AceStepTransformer1DModel]]

Diffusion Transformer for ACE-Step 1.5 music generation.

Generates audio latents conditioned on text, lyrics, and timbre. Uses 1D patch embedding (`Conv1d` with stride
`patch_size`) followed by a stack of `AceStepTransformerBlock`s with alternating sliding-window / full attention on
the self-attention branch. Cross-attention consumes the packed `encoder_hidden_states` produced by
`AceStepConditionEncoder`.

- **hidden_states** (`torch.Tensor` of shape `(batch_size, seq_len, channels)`) --
  Noisy latent input for the diffusion process.
- **timestep** (`torch.Tensor` of shape `(batch_size,)`) --
  Current diffusion timestep `t`.
- **timestep_r** (`torch.Tensor` of shape `(batch_size,)`) --
  Reference timestep `r` (set equal to `t` for standard inference).
- **encoder_hidden_states** (`torch.Tensor` of shape `(batch_size, encoder_seq_len, hidden_size)`) --
  Conditioning embeddings from the condition encoder (text + lyrics + timbre).
- **context_latents** (`torch.Tensor` of shape `(batch_size, seq_len, context_dim)`) --
  Context latents (source latents concatenated with chunk masks) — fed to the patchify conv alongside
  `hidden_states`.
- **return_dict** (`bool`, defaults to `True`) --
  Whether to return a `Transformer2DModelOutput` or a plain tuple.`Transformer2DModelOutput` or `tuple`The predicted velocity field.
The [AceStepTransformer1DModel](/docs/diffusers/main/en/api/models/ace_step_transformer#diffusers.AceStepTransformer1DModel) forward method.