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Diffusers 0.39.0: New image and video pipelines, core library improvements, and more

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@sayakpaul sayakpaul released this 03 Jul 08:55
v0.39.0
a3608b5

New Pipelines

Cosmos 3

Cosmos 3 is NVIDIA's unified world foundation model (WFM) for Physical AI — a single omni-model built on a Mixture-of-Transformers (MoT) architecture that combines world generation, physical reasoning, and action generation, replacing the separate Predict, Reason, and Transfer models from earlier Cosmos releases. A single Cosmos3OmniTransformer runs a Qwen-style language model in parallel with a diffusion generation pathway, joined by a 3D multimodal RoPE. This release also lands video-to-video and action-conditioned generation, and a sound encoder.

Thanks to @atharvajoshi10, @yzhautouskay, and @MaciejBalaNV for the contributions.

Ideogram 4

Ideogram 4 is a flow-matching text-to-image model that uses a multimodal text encoder and an asymmetric classifier-free guidance scheme: a dedicated unconditional_transformer produces the negative branch with zeroed text features, while the main transformer consumes the full packed text + image sequence. The pipeline ships with structured prompt upsampling and LoRA loading support.

Thanks to @JinLiIdeogram for the contribution.

Krea 2

Krea 2 (K2) is a flow-matching text-to-image model built around a single-stream MMDiT with grouped-query attention. A Qwen3-VL text encoder provides the conditioning — hidden states from twelve decoder layers are tapped per token and fused inside the transformer by a small text-fusion stage — and images are decoded with the Qwen-Image VAE. Both the base (midtrain) and TDM (distilled, few-step) checkpoints are supported, alongside a LoRA DreamBooth trainer.

Thanks to @EleaZhong and @Abhinay1997 for the contribution.

DreamLite

DreamLite is a text-to-image and image-editing model from ByteDance. It pairs a custom 2D U-Net (DreamLiteUNetModel) with the Qwen3-VL multimodal encoder as its prompt / image-instruction encoder, and uses an AutoencoderTiny (TAESD-style) VAE for fast latent encode/decode. A distilled DreamLiteMobilePipeline targets on-device, low-latency generation.

Thanks to @Carlofkl for the contribution.

PRX Pixel

PRXPixel is a pixel-space text-to-image generation model by Photoroom. A ~7B PRXTransformer2DModel denoises raw RGB images directly — no VAE is needed. The model is conditioned on a Qwen3-VL text encoder and uses flow matching where the transformer predicts the clean image at each step (x-prediction).

Thanks to @DavidBert for the contribution.

Motif-Video

Motif-Video is a 2B parameter diffusion transformer for text-to-video and image-to-video generation. It features a three-stage architecture (12 dual-stream + 16 single-stream + 8 DDT decoder layers), Shared Cross-Attention for stable text-video alignment over long sequences, a T5Gemma2 text encoder, and rectified flow matching for velocity prediction.

Thanks to @waitingcheung for the contribution.

AnyFlow

AnyFlow from NVIDIA, NUS, and MIT is the first any-step video diffusion framework built on flow maps, enabling a single model (bidirectional or causal) to adapt to arbitrary inference budgets. It ships both bidirectional and FAR causal pipelines built on Wan2.1 backbones, covering text-to-video, image-to-video, and video-to-video.

Thanks to @Enderfga for the contribution.

JoyAI-Image-Edit

JoyAI-Image is a unified multimodal foundation model for image understanding, text-to-image generation, and instruction-guided image editing. It combines an 8B Multimodal LLM with a 16B Multimodal Diffusion Transformer (MMDiT). JoyImageEditPipeline supports general image editing as well as spatial editing capabilities including object move, object rotation, and camera control.

Thanks to @Moran232 for the contribution.

DiffusionGemma

DiffusionGemma is a block-diffusion encoder-decoder language model. A causal encoder reads the clean prompt (and any previously generated blocks) into a KV cache, and a bidirectional decoder denoises a fixed-size "canvas" of tokens by cross-attending to that cache, committing the most confident tokens via the new BlockRefinementScheduler. The released checkpoint is google/diffusiongemma-26B-A4B-it.

Anima

Anima is a 2 billion parameter text-to-image model created via a collaboration between CircleStone Labs and Comfy Org. It is focused mainly on anime concepts, characters, and styles, but is also capable of generating a wide variety of other non-photorealistic content.

It reuses the CosmosTransformer3DModel with a Qwen3 text encoder, a T5-token text conditioner, and the AutoencoderKLQwenImage VAE.

Thanks to @rmatif for the contribution.

LTX-2.X IC LoRA and HDR Pipelines

New LTX2InContextPipeline (in-context LoRA) and LTX2HDRPipeline extend the LTX-2 family with in-context conditioning and HDR video generation.

Modular Pipeline Support

  • We added a modular pipeline for Stable Diffusion 3 (SD3) in #13324 (thanks to @AlanPonnachan).
  • We added a modular pipeline for Anima in #13732 (thanks to @rmatif).
  • LoRA loading is now enabled on ErnieImageModularPipeline (#13948) and Ideogram4ModularPipeline (#13980), thanks to @SamuelTallet.

Core Library

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Contributors

kashif, tarekziade, and 55 other contributors
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Diffusers 0.38.0: New image and audio pipelines, Core library improvements, and more

Diffusers 0.38.0: New image and audio pipelines, Core library improvements, and more

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@sayakpaul sayakpaul released this 01 May 05:52
v0.38.0
275869d

New Pipelines

LLaDA2

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.

Nucleus-MoE

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

ERNIE-Image is a powerful and highly efficient image generation model with 8B parameters.

Thanks to @HsiaWinter for the contribution.

LongCat-AudioDiT

LongCat-AudioDiT is a text-to-audio diffusion model from Meituan LongCat.

Thanks to @RuixiangMa for the contribution.

Ace-Step 1.5

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.

Flux.2 Small Decoder

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.

Modular Pipeline Support

We added modular support for LTX-2 and Hunyuan 1.5.

Core Library

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