diffusers/pr_13881/en/api/models/ace_step_transformer.md

AceStepTransformer1DModel

A 1D Diffusion Transformer for music generation from ACE-Step 1.5. The model operates on the 25 Hz stereo latents produced by 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 AceStepTransformerBlocks 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 tupleThe predicted velocity field. The AceStepTransformer1DModel forward method.