Diffusers 0.38.0: New image and audio pipelines, Core library improvements, and more
LLaDA2 is a family of discrete diffusion language models that generate text through block-wise iterative refinement. Instead of autoregressive token-by-token generation, LLaDA2 starts with a fully masked sequence and progressively unmasks tokens by confidence over multiple refinement steps.
NucleusMoE-Image is a 2B active 17B parameter model trained with efficiency at its core. Our novel architecture highlights the scalability of a sparse MoE architecture for Image generation.
Thanks to @sippycoder for the contribution.
ERNIE-Image is a powerful and highly efficient image generation model with 8B parameters.
Thanks to @HsiaWinter for the contribution.
LongCat-AudioDiT is a text-to-audio diffusion model from Meituan LongCat.
Thanks to @RuixiangMa for the contribution.
ACE-Step 1.5 generates variable-length stereo audio at 48 kHz (10 seconds to 10 minutes) from text prompts and optional lyrics. The full system pairs a Language Model planner with a Diffusion Transformer (DiT) synthesizer; this pipeline wraps the DiT half of that stack, and consists of three components: an AutoencoderOobleck VAE that compresses waveforms into 25 Hz stereo latents, a Qwen3-based text encoder for prompt and lyric conditioning, and an AceStepTransformer1DModel DiT that operates in the VAE latent space using flow matching.
Thanks to @ChuxiJ for the contribution.
Make your Flux.2 decoding faster with this new small decoder model from the Black Forest Labs. You can check it out here. It was contributed by @huemin-art in this PR.
We added modular support for LTX-2 and Hunyuan 1.5.
ring_anything as a new CP backendlru_cache warnings during torch.compile by @jiqing-feng in #13384--with_prior_preservation by @chenyangzhu1 in #133960.8.0-rc.0 by @McPatate in #13470trust_remote_code by @hlky in #13448The following contributors have made significant changes to the library over the last release:
trust_remote_code (#13448)Fetched May 1, 2026
Modular Diffusers Modular Diffusers introduces a new way to build diffusion pipelines by composing reusable blocks. Instead of writing enti…