Diffusers

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.

• PR: https://github.com/huggingface/diffusers/pull/13818
• Docs: https://huggingface.co/docs/diffusers/main/api/pipelines/cosmos3

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.

• PR: https://github.com/huggingface/diffusers/pull/13859
• Docs: https://huggingface.co/docs/diffusers/main/api/pipelines/ideogram4

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.

• PR: https://github.com/huggingface/diffusers/pull/14045
• Docs: https://huggingface.co/docs/diffusers/main/api/pipelines/krea2

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.

• PR: https://github.com/huggingface/diffusers/pull/13815
• Docs: https://huggingface.co/docs/diffusers/main/api/pipelines/dreamlite

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).

• PR: https://github.com/huggingface/diffusers/pull/13928
• Docs: https://huggingface.co/docs/diffusers/main/api/pipelines/prx_pixel

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.

• PR: https://github.com/huggingface/diffusers/pull/13551
• Docs: https://huggingface.co/docs/diffusers/main/api/pipelines/motif_video

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.

• PR: https://github.com/huggingface/diffusers/pull/13745
• Docs: https://huggingface.co/docs/diffusers/main/api/pipelines/anyflow

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.

• PR: https://github.com/huggingface/diffusers/pull/13444
• Docs: https://huggingface.co/docs/diffusers/main/api/pipelines/joyimage_edit

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.

• PR: https://github.com/huggingface/diffusers/pull/13986
• Docs: https://huggingface.co/docs/diffusers/main/api/pipelines/diffusion_gemma

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.

• PR: https://github.com/huggingface/diffusers/pull/13732
• Docs: https://huggingface.co/docs/diffusers/main/api/pipelines/anima

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.

• PR: https://github.com/huggingface/diffusers/pull/13572
• Docs: https://huggingface.co/docs/diffusers/main/api/pipelines/ltx2

Modular Pipeline Support

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

Core Library

• AutoRound quantization integration
• safetensors support in the TorchAO backend and _dequantize for the TorchAO quantizer
• BitsAndBytes quantization on MPS
• AutoPipelineForText2Audio
• AWS Neuron (Trainium/Inferentia) as an officially supported device with torch.compile compatibility
• Bump safetensors to 0.8.0
• Minimum supported torch version is now 2.6
• Eliminate GPU sync overhead and CPU→GPU transfers across the LTX-2 pipeline

All commits

• [CI] Update all workflows with permissions by @DN6 in #13672
• [agents docs] update models.md with class attributes and attention mask by @yiyixuxu in #13665
• Fix ignored generator in FlowMatchEulerDiscreteScheduler by @RobbinMarcus in #13678
• [core] remove txt_seq_lens from qwen transformer. by @sayakpaul in #13674
• [tests] fix lora tests involving clip. by @sayakpaul in #13675
• post release 0.38.0 by @sayakpaul in #13670
• Fix NameError in ZImageOmniPipeline when guidance_scale=0 by @Ricardo-M-L in #13527
• Enable TorchAO int4wo quantization tests on XPU by @jiqing-feng in #13537
• [CI] QOL improvement for PR size labeler by @DN6 in #13554
• Fix BucketBatchSampler cache alignment in DreamBooth scripts by @azolotenkov in #13353
• chore: update pr_labeler.yml by @hf-security-analysis[bot] in #13685
• Address ernie-image review findings #13577 by @akshan-main in #13663
• feat: Add Modular Pipeline for Stable Diffusion 3 (SD3) by @AlanPonnachan in #13324
• Update attention_backends.md to update FA3 minimum support to Ampere by @sayakpaul in #13283
• [CI] Bump style-bot SHA + switch to GitHub App by @paulinebm in #13690
• [feat] JoyAI-JoyImage-Edit support by @Moran232 in #13444
• Add LoRA support for Cosmos Predict 2.5 and fix pipeline to match official Cosmos repo by @terarachang in #13664
• Eliminate GPU sync overhead and CPU→GPU transfers across LTX2 pipeline by @ViktoriiaRomanova in #13564
• Gate deep imports from torch.distributed by @hlky in #13673
• Bump diffusers from 0.20.1 to 0.38.0 in /examples/research_projects/realfill by @dependabot[bot] in #13692
• Reduce WanAnimate TorchAO test input sizes to prevent OOM by @jiqing-feng in #13541
• add SP support for flash_varlen_hub backend by @zhtmike in #13479
• [ci] allow claude to open PRs for certain instructions. by @sayakpaul in #13536
• [ci] remove compel. by @sayakpaul in #13715
• styling fix. by @sayakpaul (direct commit on v0.39.0-release)
• better usage of UV_PRERELEASE=allow by @sayakpaul in #13716
• [docs] add magcache to caching api listing by @sayakpaul in #13714
• [tests] refactor autoencoderkl tests by @sayakpaul in #13368
• [docs] add docs for JoyAI-Image-Edit by @feice-huang in #13726
• [tests] add attention backend tests. by @sayakpaul in #13174
• Install transformers from main for doc and staging by @sayakpaul in #13723
• Update Flax removal version by @DN6 in #13729
• examples/dreambooth: fix LR scheduler step count for multi-GPU in train_dreambooth_lora_sd3.py by @Dev-X25874 in #13731
• Serge reviewer by @sayakpaul in #13735
• [ci] switch to a more unique name by @sayakpaul in #13738
• fix autoencoder memory tests by @sayakpaul in #13734
• Fix GGUF to Work Better with modules_to_not_convert / keep_in_fp32_modules by @dg845 in #13697
• [tests] refactor ltx2 autoencoder tests to use latest mixins by @sayakpaul in #13739
• feat: Add Motif-Video model and pipelines by @waitingcheung in #13551
• Update contribution guidelines by @DN6 in #13753
• [agents] add a section on tests in the ai skill and integration guides. by @sayakpaul in #13752
• Add LTX-2.X IC LoRA and HDR Pipelines by @dg845 in #13572
• [tests] Fix controlnet tests by @sayakpaul in #13736
• [tests] fix bitsandbytes compile tests for flux. by @sayakpaul in #13750
• [core] minimum torch version is 2.6 by @sayakpaul in #13725
• [tests] fix lora checkpoint serialization issues by @sayakpaul in #13676
• fix(randn_tensor): compare device.type, not torch.device, when suppressing MPS info log by @Ricardo-M-L in #13508
• [LLADA2] Fix llada2 review #13598 by @kashif in #13698
• fix lfs pointer rejection problems for hub tests by @sayakpaul in #13733
• Fix training gradient underflow in quantization tests by @jiqing-feng in #13539
• examples/dreambooth: fix missing weighting chunk when using prior preservation in Flux and SD3 LoRA training by @Dev-X25874 in #13743
• Implement _dequantize for TorchAO quantizer by @jiqing-feng in #13538
• fix device mismatch issue for HiDreamTransformerTests by @kaixuanliu in #13766
• [docs] remove pipeline examples section by @stevhliu in #13771
• [CI] Replace print_env step in CI with diffusers-cli env by @DN6 in #13662
• update safetensors.torch._tobytes to safetensors.torch._to_ndarray by @sywangyi in #13770
• [agents docs] update pipelines.md: by @yiyixuxu in #13570
• fix(gguf): correct mismatched-shape error message in check_quantized_param_shape by @Ricardo-M-L in #13504
• [CI] claude_review: target source PR's branch for follow-up PRs by @yiyixuxu in #13774
• [WIP] chore: add utilities to check if call/forward methods are documented. by @sayakpaul in #13758
• Fix OOM in WanAnimate BitsAndBytes Training Test by @jiqing-feng in #13777
• ci: use uv overrides to make sure tokenizers install from <=0.23.0 under subs by @sayakpaul in #13767
• [LTX 2.3] update docs by @linoytsaban in #13788
• [docs] fix ace step checkpoint id. by @sayakpaul in #13787
• Add AnyFlow Any-Step Video Diffusion Pipelines (Bidirectional + FAR Causal) by @Enderfga in #13745
• Initialize ZImage pad tokens deterministically by @sywangyi in #13805
• note: torch.zeros -> torch.empty by @sayakpaul in #13807
• chore: enable Dependabot weekly GitHub Actions bumps by @hf-dependantbot-rollout[bot] in #13812
• [ci] shorten serge name. by @sayakpaul in #13795
• Adding Cosmos 3 to Diffusers by @atharvajoshi10 in #13818
• This PR updates the Stable Diffusion IP-Adapter integration by @sywangyi in #13810
• [AnyFlow] FAR: standalone causal-mask builder + torch.compile follow-up by @Enderfga in #13792
• Update repo_id for FLASH_4_HUB in attention_dispatch by @WaterKnight1998 in #13822
• Pin torchvision, torch, and torchaudio versions by @sayakpaul in #13757
• [docs] Follow ups for consistent forward docstrings by @sayakpaul in #13779
• refactor sana transformer tests by @akshan-main in #13826
• Fix redundant Z-Image terminal timestep by @rootonchair in #13730
• override torch stuff to prevent them from getting updated by @sayakpaul in #13831
• Add Anima modular pipeline by @rmatif in #13732
• [Feat] support AutoPipelineForText2Audio by @RuixiangMa in #13511
• moved to a webhook by @tarekziade in #13836
• refactor autoencoder tests (asymmetric_kl, ltx_video) by @akshan-main in #13845
• Fix duplicate safetensors.load_file call in _onload_from_disk when st… by @gagandhakrey in #13851
• Fix AttributeError in onnxruntime train_unconditional (args.report_to → args.logger) by @Ricardo-M-L in #13524
• [fix] CLIPTextModel with transformers >= 5.6 and from_single_file by @asomoza in #13843
• [tests] migrate group offloading tests to pytest by @sayakpaul in #13234
• [tests] refactor caching tests. by @sayakpaul in #13235
• Allow bucket reshuffling with DreamBooth caches by @azolotenkov in #13712
• [Neuron] Add AWS Neuron (Trainium/Inferentia) as an officially supported device by @JingyaHuang in #13289
• refactor autoencoder_magvit tests by @akshan-main in #13834
• refactor autoencoder_hunyuan_video tests by @akshan-main in #13835
• refactor autoencoder_kl_cogvideox tests by @akshan-main in #13840
• refactor autoencoder tests (vq, kvae_video, oobleck, consistency_decoder, tiny, vidtok) by @akshan-main in #13849
• updatge the test marigold to make it pass in xpu by @sywangyi in #13856
• [CI] Fix torch_device import in AutoencoderTesterMixin by @DN6 in #13852
• Add Ideogram 4 by @apolinario in #13859
• Add structured prompt upsampling to Ideogram4 by @apolinario in #13860
• [ci] add hook tests to our CI. by @sayakpaul in #13848
• fix kvae gradient checkpointing tests by @sayakpaul (direct commit on v0.39.0-release)
• Revert "fix kvae gradient checkpointing tests" by @sayakpaul (direct commit on v0.39.0-release)
• [tests] fix anyflow tests by @sayakpaul in #13855
• [CI] Refactor LTX Transformer Tests by @DN6 in #13254
• [CI] Refactor Bria Transformer Tests by @DN6 in #13341
• [CI] Refactor Chronoedit, PRX, EasyAnimate, Ovis transformer tests by @DN6 in #13347
• Add Cosmos3 action generation support by @yzhautouskay in #13823
• [docs] update philosophy.md (finally) by @yiyixuxu in #13808
• fix kvae gradient checkpointing tests by @sayakpaul in #13865
• [tests] Improve ideogram4 tests by @sayakpaul in #13862
• [tests] migrate test_hooks.py to pytest by @sayakpaul in #13242
• fix chronoedit tests on PRs by @sayakpaul in #13870
• Fix the QwenImage Attention mask under Ulysses SP by @zhtmike in #13756
• Add from_single_file support to ErnieImageTransformer2DModel by @akshan-main in #13727
• switch to a webhook by @tarekziade in #13884
• [chore] fix styling by @sayakpaul in #13885
• [cli] report all quant backends in diffusers-cli env. by @sayakpaul in #13728
• fix marigold depth failure in xpu and A100 by @sywangyi in #13886
• refactor autoencoder tests (temporal decoder, cosmos, kvae, mochi) by @akshan-main in #13832
• refactor controlnet_cosmos tests by @akshan-main in #13847
• refactor unet_spatiotemporal tests by @akshan-main in #13891
• Fix fp16 LoRA unscale crash after validation in train_dreambooth_lora.py by @HaozheZhang6 in #13895
• [CI] Refactor Chroma , LongCat and HiDream Transformer Tests by @DN6 in #13345
• [CI] Refactor Skyreels, Lumina, Ominigen, Mochi transformer tests by @DN6 in #13348
• [CI] Refactor SD3 Transformer Test by @DN6 in #13340
• refactor unet tests (3d_condition, motion, controlnetxs) by @akshan-main in #13897
• refactor unet_1d tests by @akshan-main in #13898
• refactor unet_2d tests by @akshan-main in #13901
• [chore] log quant config to the user_agent by @sayakpaul in #13850
• Integrate AutoRound into Diffusers by @xin3he in #13552
• [tests] refactor UNet model tests to align with the new pattern by @sayakpaul in #13153
• [tests] fix vidtok tests by @sayakpaul in #13894
• quant config logging by @sayakpaul in #13906
• Use device_map="auto" in single file tests to support large models on limited GPU memory by @jiqing-feng in #13816
• Fix incorrect batch temporal IDs for cond_model_input in Flux2 Klein img2img training by @HaozheZhang6 in #13923
• Incorporate safetensors support to TorchAO by @hlky in #13719
• [Pipelines] Add DreamLite text-to-image and image-edit pipelines by @Carlofkl in #13815
• [.ai] add self-review skill by @yiyixuxu in #13917
• update PR template and highlight AI-agent setup for contributors by @yiyixuxu in #13913
• [CI] implement a bot to remind prs to link issues if not. by @sayakpaul in #13744
• Point "Coding with AI agents" links at the rendered docs site by @yiyixuxu in #13952
• [tests] fix consistency decoder tests by @sayakpaul in #13905
• Add tutorial translations in Chinese by @liwd190019 in #13932
• Make root PHILOSOPHY.md a symlink to the docs philosophy page by @yiyixuxu in #13954
• fix(flux): enable true CFG with precomputed negative embeds by @akshan-main in #13957
• Enable LoRA loading on ErnieImageModularPipeline by @SamuelTallet in #13948
• Fix typo in AutoModel by @neo in #13889
• keep the agent symlinks by @yiyixuxu in #13968
• [CI] allow running tests as PR comments through a bot by @sayakpaul in #13873
• Add Cosmos3 video2video generation support by @yzhautouskay in #13896
• [CI] Refactor Z Image Transformer Tests by @DN6 in #13253
• fix untrusted fork secret mixing by @sayakpaul in #13970
• start by @sayakpaul (direct commit on v0.39.0-release)
• Revert "start" by @sayakpaul (direct commit on v0.39.0-release)
• Add Sound Encoder to Cosmos3 by @MaciejBalaNV in #13911
• Add PRXPixelPipeline: pixel-space PRX text-to-image pipeline by @DavidBert in #13928
• [tests] port final set of model tests and others by @sayakpaul in #13974
• Add Ideogram4LoraLoaderMixin (LoRA loading for Ideogram4) by @linoytsaban in #13921
• Enable LoRA loading on Ideogram4ModularPipeline by @SamuelTallet in #13980
• [Neuron] Enable torch.compile compatibility with Neuron device by @JingyaHuang in #13485
• ci: don't remind on prs from admins, etc. by @sayakpaul in #13965
• ci: use hosted runners by @tarekziade in #13987
• Fix LTX2 connector token/register layout (regression from #13564) by @Boffee in #13931
• Fix Ideogram4MRoPE collapsing under torch.autocast (compute rotary in float32) by @HaozheZhang6 in #13922
• [Fix] Fix three final_layer LoRA conversion bugs in _convert_sd_scripts_to_ai_toolkit by @lcheng321 in #14001
• Add Krea 2 (K2) text-to-image pipeline and transformer by @yiyixuxu in #14045
• [.ai doc] Refine .ai attention-mask and component-mutation guidance by @yiyixuxu in #13982
• Enable BitsAndBytes quantization in MPS by @LucasSte in #13915
• fix(flux): tighten check_inputs validation by @akshan-main in #13955
• Krea 2 LoRA DreamBooth trainer by @apolinario in #14046
• Fix model cuda tests by @sayakpaul in #13975
• [.ai] document single-file model layout and "don't reimplement Diffus… by @yiyixuxu in #14048
• fix claude code review fix in PRs. by @sayakpaul in #14058
• fix(bria_fibo): fix guidance_embeds, prompt_embeds, tensor-image and multi-image crashes by @akshan-main in #13981
• [tests] implement base model output caching in model-level tests by @sayakpaul in #14059
• [discrete diffusion] Add DiffusionGemma pipeline and schedulers by @kashif in #13986
• Add from_single_file support for SkyReelsV2 and ChronoEdit transformers by @HaozheZhang6 in #13946
• multi-GPU VAE Fix for Cosmos 3 by @atharvajoshi10 in #13924
• docs: fix repeated word typo in set_timesteps docstring by @ramkumar27072006 in #13876
• feat: bump safetensors to 0.8.0 by @porunov in #13971
• Fix DreamLite legacy block type aliases by @ElectricGoal in #14066
• Fix Kohya UNet LoRA key conversion for conv_in/conv_out/time_embedding by @dxqb in #14006
• [Tests] Skip layerwise casting tests on devices without float8_e4m3fn support by @GiGiKoneti in #14073
• [lora] add non-diffusers LoRA loading support for Krea 2 LoRAs by @linoytsaban in #14074
• Add doc pages for the DiffusionGemma schedulers by @kashif in #14092
• [chore] update to 2026 finally. by @sayakpaul in #14079
• fix #14063 for Kandinsky5 pipeline load with device_map=balanced by @kaixuanliu in #14050
• Complete Kohya LoRA conversion for Qwen and Z-Image by @dxqb in #14080
• Ideogram4 lora training by @apolinario in #13861
• ovis_image: fix guidance_scale / max_sequence_length / batched CFG / precomputed embeds + add pipeline test by @HaozheZhang6 in #13944
• [docs] fix qwen tokenizer in docstrings. by @sayakpaul in #14098
• Bump transformers from 4.47.0 to 5.3.0 in /examples/cogview4-control by @dependabot[bot] in #14109
• Fix mutable default args in lora_base.py by @PrakshaaleJain in #14064
• Fix FA3 varlen wrapper when hub kernel returns single tensor by @ in #14102
• support loading pipeline from transformer style (flat) repo by @yiyixuxu in #14096
• diffusers test installation package by @sayakpaul in #14078
• [tests] fix test_from_save_pretrained_dtype_inference by @sayakpaul in #13872
• Release: v0.39.0-release by @sayakpaul (direct commit on v0.39.0-release)

Significant community contributions

The following contributors have made significant changes to the library over the last release:

• @DN6
  • [CI] Update all workflows with permissions (#13672)
  • [CI] QOL improvement for PR size labeler (#13554)
  • Update Flax removal version (#13729)
  • Update contribution guidelines (#13753)
  • [CI] Replace print_env step in CI with diffusers-cli env (#13662)
  • [CI] Fix torch_device import in AutoencoderTesterMixin (#13852)
  • [CI] Refactor LTX Transformer Tests (#13254)
  • [CI] Refactor Bria Transformer Tests (#13341)
  • [CI] Refactor Chronoedit, PRX, EasyAnimate, Ovis transformer tests (#13347)
  • [CI] Refactor Chroma , LongCat and HiDream Transformer Tests (#13345)
  • [CI] Refactor Skyreels, Lumina, Ominigen, Mochi transformer tests (#13348)
  • [CI] Refactor SD3 Transformer Test (#13340)
  • [CI] Refactor Z Image Transformer Tests (#13253)
• @yiyixuxu
  • [agents docs] update models.md with class attributes and attention mask (#13665)
  • [agents docs] update pipelines.md: (#13570)
  • [CI] claude_review: target source PR's branch for follow-up PRs (#13774)
  • [docs] update philosophy.md (finally) (#13808)
  • [.ai] add self-review skill (#13917)
  • update PR template and highlight AI-agent setup for contributors (#13913)
  • Point "Coding with AI agents" links at the rendered docs site (#13952)
  • Make root PHILOSOPHY.md a symlink to the docs philosophy page (#13954)
  • keep the agent symlinks (#13968)
  • Add Krea 2 (K2) text-to-image pipeline and transformer (#14045)
  • [.ai doc] Refine .ai attention-mask and component-mutation guidance (#13982)
  • [.ai] document single-file model layout and "don't reimplement Diffus… (#14048)
  • support loading pipeline from transformer style (flat) repo (#14096)
• @akshan-main
  • Address ernie-image review findings #13577 (#13663)
  • refactor sana transformer tests (#13826)
  • refactor autoencoder tests (asymmetric_kl, ltx_video) (#13845)
  • refactor autoencoder_magvit tests (#13834)
  • refactor autoencoder_hunyuan_video tests (#13835)
  • refactor autoencoder_kl_cogvideox tests (#13840)
  • refactor autoencoder tests (vq, kvae_video, oobleck, consistency_decoder, tiny, vidtok) (#13849)
  • Add from_single_file support to ErnieImageTransformer2DModel (#13727)
  • refactor autoencoder tests (temporal decoder, cosmos, kvae, mochi) (#13832)
  • refactor controlnet_cosmos tests (#13847)
  • refactor unet_spatiotemporal tests (#13891)
  • refactor unet tests (3d_condition, motion, controlnetxs) (#13897)
  • refactor unet_1d tests (#13898)
  • refactor unet_2d tests (#13901)
  • fix(flux): enable true CFG with precomputed negative embeds (#13957)
  • fix(flux): tighten check_inputs validation (#13955)
  • fix(bria_fibo): fix guidance_embeds, prompt_embeds, tensor-image and multi-image crashes (#13981)
• @AlanPonnachan
  • feat: Add Modular Pipeline for Stable Diffusion 3 (SD3) (#13324)
• @Moran232
  • [feat] JoyAI-JoyImage-Edit support (#13444)
• @terarachang
  • Add LoRA support for Cosmos Predict 2.5 and fix pipeline to match official Cosmos repo (#13664)
• @dg845
  • Fix GGUF to Work Better with modules_to_not_convert / keep_in_fp32_modules (#13697)
  • Add LTX-2.X IC LoRA and HDR Pipelines (#13572)
• @waitingcheung
  • feat: Add Motif-Video model and pipelines (#13551)
• @kashif
  • [LLADA2] Fix llada2 review #13598 (#13698)
  • [discrete diffusion] Add DiffusionGemma pipeline and schedulers (#13986)
  • Add doc pages for the DiffusionGemma schedulers (#14092)
• @linoytsaban
  • [LTX 2.3] update docs (#13788)
  • Add Ideogram4LoraLoaderMixin (LoRA loading for Ideogram4) (#13921)
  • [lora] add non-diffusers LoRA loading support for Krea 2 LoRAs (#14074)
• @Enderfga
  • Add AnyFlow Any-Step Video Diffusion Pipelines (Bidirectional + FAR Causal) (#13745)
  • [AnyFlow] FAR: standalone causal-mask builder + torch.compile follow-up (#13792)
• @atharvajoshi10
  • Adding Cosmos 3 to Diffusers (#13818)
  • multi-GPU VAE Fix for Cosmos 3 (#13924)
• @rmatif
  • Add Anima modular pipeline (#13732)
• @JingyaHuang
  • [Neuron] Add AWS Neuron (Trainium/Inferentia) as an officially supported device (#13289)
  • [Neuron] Enable torch.compile compatibility with Neuron device (#13485)
• @apolinario
  • Add Ideogram 4 (#13859)
  • Add structured prompt upsampling to Ideogram4 (#13860)
  • Krea 2 LoRA DreamBooth trainer (#14046)
  • Ideogram4 lora training (#13861)
• @yzhautouskay
  • Add Cosmos3 action generation support (#13823)
  • Add Cosmos3 video2video generation support (#13896)
• @xin3he
  • Integrate AutoRound into Diffusers (#13552)
• @Carlofkl
  • [Pipelines] Add DreamLite text-to-image and image-edit pipelines (#13815)
• @liwd190019
  • Add tutorial translations in Chinese (#13932)
• @MaciejBalaNV
  • Add Sound Encoder to Cosmos3 (#13911)
• @DavidBert
  • Add PRXPixelPipeline: pixel-space PRX text-to-image pipeline (#13928)

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.

• PR: https://github.com/huggingface/diffusers/pull/13226
• Docs: https://huggingface.co/docs/diffusers/main/api/pipelines/llada2

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.

• PR: https://github.com/huggingface/diffusers/pull/13317
• Docs: https://huggingface.co/docs/diffusers/main/api/pipelines/nucleusmoe_image

Thanks to @sippycoder for the contribution.

Ernie-Image

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

• PR: https://github.com/huggingface/diffusers/pull/13432
• Docs: https://huggingface.co/docs/diffusers/main/api/pipelines/ernie_image

Thanks to @HsiaWinter for the contribution.

LongCat-AudioDiT

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

• PR: https://github.com/huggingface/diffusers/pull/13483
• Docs: https://huggingface.co/docs/diffusers/main/api/pipelines/longcat_audio_dit

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.

• PR: https://github.com/huggingface/diffusers/pull/13095
• Docs: https://huggingface.co/docs/diffusers/main/api/pipelines/ace_step

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

• Flash Attention 4 backend
• FlashPack loading
• Group offloading + TorchAO
• ring_anything as a new CP backend
• Profiling pipelines in Diffusers

All commits

• [Discrete Diffusion] Add LLaDA2 pipeline by @kashif in #13226
• [LLADA2] documentation fixes by @kashif in #13333
• [ci] claude in ci. by @sayakpaul in #13297
• [docs] kernels by @stevhliu in #13139
• [tests] Tests for conditional pipeline blocks by @sayakpaul in #13247
• avoid hardcode device in flux-control example by @kaixuanliu in #13336
• fix claude workflow to include id-token with write. by @sayakpaul in #13338
• Update LTX-2 Docs to Cover LTX-2.3 Models by @dg845 in #13337
• remove str option for quantization config in torchao by @howardzhang-cv in #13291
• [ci] include checkout step in claude review workflow by @sayakpaul in #13352
• change minimum version guard for torchao to 0.15.0 by @howardzhang-cv in #13355
• [ci] move to assert instead of self.Assert* by @sayakpaul in #13366
• [docs] refactor model skill by @stevhliu in #13334
• Fix Ulysses SP backward with SDPA by @zhtmike in #13328
• Add train flux2 series lora config by @tcaimm in #13011
• [docs] Add NeMo Automodel training guide by @pthombre in #13306
• Fix: ensure consistent dtype and eval mode in pipeline save/load tests by @YangKai0616 in #13339
• [ci] support claude reviewing on forks. by @sayakpaul in #13365
• Fix MotionConv2d to cast blur_kernel to input dtype instead of reverse by @YangKai0616 in #13364
• chore: update claude_review.yml by @hf-security-analysis[bot] in #13374
• corrects single file path validation logic by @andrew-w-ross in #13363
• [docs] deprecate pipelines by @stevhliu in #13157
• 🔒 Pin GitHub Actions to commit SHAs by @paulinebm in #13385
• [docs] add auto docstring and parameter templates documentation for m… by @yiyixuxu in #13382
• Fix typos and grammar errors in documentation by @GalacticAvenger in #13391
• fix(ddim): validate eta is in [0, 1] in DDIMPipeline by @NIK-TIGER-BILL in #13367
• Fix Dynamo lru_cache warnings during torch.compile by @jiqing-feng in #13384
• [tests] refactor wan autoencoder tests by @sayakpaul in #13371
• NucleusMoE-Image by @sippycoder in #13317
• Add examples on how to profile a pipeline by @sayakpaul in #13356
• Update README.md of the profiling guide by @sayakpaul in #13400
• [CI] Refactor Cosmos Transformer Tests by @DN6 in #13335
• [tests] refactor autoencoderdc tests by @sayakpaul in #13369
• [CI] Hunyuan Transformer Tests Refactor by @DN6 in #13342
• Fix VAE offload encode device mismatch in DreamBooth scripts by @azolotenkov in #13417
• Remove references to torchao's AffineQuantizedTensor by @andrewor14 in #13405
• [tests] fix autoencoderdc tests by @sayakpaul in #13424
• [core] fix group offloading when using torchao by @sayakpaul in #13276
• Fix IndexError in HunyuanVideo I2V pipeline by @kaixuanliu in #13244
• improve Claude CI by @yiyixuxu in #13397
• FLUX.2 small decoder by @huemin-art in #13428
• [CI] Add PR/Issue Auto Labeler by @DN6 in #13380
• [CI] Add GLM Image Transformer Model Tests by @DN6 in #13344
• [CI] Use finegrained token for Issue Labeler by @DN6 in #13433
• Handle prompt embedding concat in Qwen dreambooth example by @chenyangzhu1 in #13387
• fix(qwen-image dreambooth): correct prompt embed repeats when using --with_prior_preservation by @chenyangzhu1 in #13396
• Cache RoPE freqs on device to avoid repeated CPU-GPU copy in QwenImage by @akshan-main in #13406
• [tests] tighten dependency testing. by @sayakpaul in #13332
• Fix grammar in LoRA documentation by @Xyc2016 in #13423
• Fix HunyuanVideo 1.5 I2V by preprocessing image at pixel resolution i… by @akshan-main in #13440
• [modular] Add LTX Video modular pipeline by @akshan-main in #13378
• Add ernie image by @HsiaWinter in #13432
• [core] fix fa4 integration by @sayakpaul in #13443
• FlashPack by @hlky in #12700
• [ptxla] fix pytorch xla inference on TPUs. by @entrpn in #13463
• fix some dtype issue for gguf / some gpu backends by @HsiaWinter in #13464
• Fix Qwen Image DreamBooth prior-preservation batch ordering by @azolotenkov in #13441
• [tests] fix deprecated attention processor testing. by @sayakpaul in #13469
• [tests] xfail clip related issues. by @sayakpaul in #13454
• [agent] add modular doc by @yiyixuxu in #13410
• [tests] fix training tests by @sayakpaul in #13442
• fix(profiling): preserve instance isolation when decorating methods by @Akash504-ai in #13471
• [Feat] Adds LongCat-AudioDiT pipeline by @RuixiangMa in #13390
• Fix Flux2 DreamBooth prior preservation prompt repeats by @azolotenkov in #13415
• chore: bump doc-builder SHA for PR upload workflow by @rtrompier in #13476
• Remove compile bottlenecks from ZImage pipeline by @hitchhiker3010 in #13461
• [chore] Add diffusers-format example to LongCatAudioDiTPipeline by @RuixiangMa in #13483
• [core] fix autoencoderkl qwenimage for xla by @sayakpaul in #13480
• add PR fork workable by @paulinebm in #13438
• Add modular pipeline for HunyuanVideo 1.5 by @akshan-main in #13389
• [agents docs] add float64 gotcha by @yiyixuxu in #13472
• fix(ernie-image): avoid locals() comprehension scope issue in callback kwargs by @songh11 in #13478
• [Bugfix] Fix shape mismatch in LongCatAudioDiTTransformer conversion by @RuixiangMa in #13494
• feat: bump safetensors to 0.8.0-rc.0 by @McPatate in #13470
• fix(qwen): fix CFG failing when passing neg prompt embeds with none mask by @Sunhill666 in #13379
• add an example of spmd for flux on v5e-8 by @sayakpaul in #13474
• Add FLUX.2 Klein Inpaint Pipeline by @adi776borate in #13050
• [docs] add a mention of torchao and other backends in speed memory docs. by @sayakpaul in #13499
• Fix Flux2 non-diffusers guidance LoRA conversion by @yadferhad in #13486
• add _native_npu_attention support mask shape like [B,1,1,S] by @chang-zhijie in #13490
• fix(freeu): run FFT in float32 for float16 inputs to avoid ComplexHalf by @Ricardo-M-L in #13503
• Fix non-deterministic T5 outputs in HiDream pipeline tests by @kaixuanliu in #13534
• Fix AuraFlow attn processors applying norm_added_q to key projection by @Ricardo-M-L in #13533
• add _repeated_blocks for ErnieImageTransformer2DModel by @kaixuanliu in #13496
• [CI] Fix BnB tests by @DN6 in #13481
• [tests] fix group offloading with disk tests by @sayakpaul in #13491
• [ci] feat: have pr labeler label for closing issues. by @sayakpaul in #13548
• Improve trust_remote_code by @hlky in #13448
• chore: bump doc-builder SHA for main doc build workflow by @rtrompier in #13555
• [ci] simplify release workflow. by @sayakpaul in #13329
• [attention backends] fix ring CP for flash and flash 3 by @sayakpaul in #13182
• [agents docs] add pipelines.md etc by @yiyixuxu in #13567
• Add Ernie-Image modular pipeline by @akshan-main in #13498
• [agents docs] update modular.md by @yiyixuxu in #13568
• [docs] fix typo in AutoencoderOobleck docs by @ivnvalex in #13642)
• Fix ErnieImagePipeline pre-computed prompt_embeds + num_images_per_prompt shape mismatch by @Ricardo-M-L in #13532
• feat: support ring attention with arbitrary KV sequence lengths by @songh11 in #13545
• [ci] use tokenizers stable installtion in CI. by @sayakpaul in #13562
• NucleusMoE docs by @sayakpaul in #13661
• Fix UniPC scheduler device mismatch when using offloading by @ParamChordiya in #13489
• [Ernie-Image] Add lora support by @asomoza in #13575
• Add ACE-Step pipeline for text-to-music generation by @ChuxiJ in #13095
• Fix missing latents_bn_std dtype cast in VAE normalization by @adi776borate in #13299
• Release: v0.38.0-release by @sayakpaul (direct commit on v0.38.0-release)

Significant community contributions

The following contributors have made significant changes to the library over the last release:

• @kashif
  • [Discrete Diffusion] Add LLaDA2 pipeline (#13226)
  • [LLADA2] documentation fixes (#13333)
• @howardzhang-cv
  • remove str option for quantization config in torchao (#13291)
  • change minimum version guard for torchao to 0.15.0 (#13355)
• @sippycoder
  • NucleusMoE-Image (#13317)
• @DN6
  • [CI] Refactor Cosmos Transformer Tests (#13335)
  • [CI] Hunyuan Transformer Tests Refactor (#13342)
  • [CI] Add PR/Issue Auto Labeler (#13380)
  • [CI] Add GLM Image Transformer Model Tests (#13344)
  • [CI] Use finegrained token for Issue Labeler (#13433)
  • [CI] Fix BnB tests (#13481)
• @akshan-main
  • Cache RoPE freqs on device to avoid repeated CPU-GPU copy in QwenImage (#13406)
  • Fix HunyuanVideo 1.5 I2V by preprocessing image at pixel resolution i… (#13440)
  • [modular] Add LTX Video modular pipeline (#13378)
  • Add modular pipeline for HunyuanVideo 1.5 (#13389)
  • Add Ernie-Image modular pipeline (#13498)
• @HsiaWinter
  • Add ernie image (#13432)
  • fix some dtype issue for gguf / some gpu backends (#13464)
• @hlky
  • FlashPack (#12700)
  • Improve trust_remote_code (#13448)
• @RuixiangMa
  • [Feat] Adds LongCat-AudioDiT pipeline (#13390)
  • [chore] Add diffusers-format example to LongCatAudioDiTPipeline (#13483)
  • [Bugfix] Fix shape mismatch in LongCatAudioDiTTransformer conversion (#13494)
• @adi776borate
  • Add FLUX.2 Klein Inpaint Pipeline (#13050)
  • Fix missing latents_bn_std dtype cast in VAE normalization (#13299)
• @ChuxiJ
  • Add ACE-Step pipeline for text-to-music generation (#13095)

• Fix for loading ModularPipelines with AutoModel type hints in their modular_model_index.json #13271
• Fix Flux Klein LoRA loading #13313
• Fix unguarded torchvision import in Cosmos Predict 2.5 #13321

Modular Diffusers

Modular Diffusers introduces a new way to build diffusion pipelines by composing reusable blocks. Instead of writing entire pipelines from scratch, you can now mix and match building blocks to create custom workflows tailored to your specific needs! This complements the existing DiffusionPipeline class, providing a more flexible way to create custom diffusion pipelines.

Find more details on how to get started with Modular Diffusers here, and also check out the announcement post.

New Pipelines and Models

Image 🌆

• Z Image Omni Base: Z-Image is the foundation model of the Z-Image family, engineered for good quality, robust generative diversity, broad stylistic coverage, and precise prompt adherence. While Z-Image-Turbo is built for speed, Z-Image is a full-capacity, undistilled transformer designed to be the backbone for creators, researchers, and developers who require the highest level of creative freedom. Thanks to @RuoyiDufor for contributing this in #12857.
• Flux2 Klein:FLUX.2 [Klein] unifies generation and editing in a single compact architecture, delivering state-of-the-art quality with end-to-end inference in as low as under a second. Built for applications that require real-time image generation without sacrificing quality, and runs on consumer hardware, with as little as 13GB VRAM.
• Qwen Image Layered: Qwen-Image-Layered is a model capable of decomposing an image into multiple RGBA layers. This layered representation unlocks inherent editability: each layer can be independently manipulated without affecting other content. Thanks to @naykun for contributing this in #12853.
• FIBO Edit: Fibo Edit is an 8B parameter image-to-image model that introduces a new paradigm of structured control, operating on JSON inputs paired with source images to enable deterministic and repeatable editing workflows. Featuring native masking for granular precision, it moves beyond simple prompt-based diffusion to offer explicit, interpretable control optimized for production environments. Its lightweight architecture is designed for deep customization, empowering researchers to build specialized “Edit” models for domain-specific tasks while delivering top-tier aesthetic quality. Thanks galbria for contributing it in https://github.com/huggingface/diffusers/pull/12930.
• Cosmos Predict2.5: Cosmos-Predict2.5, the latest version of the Cosmos World Foundation Models (WFMs) family, specialized for simulating and predicting the future state of the world. Thanks to @miguelmartin75 for contributing it in #12852.
• Cosmos Transfer2.5: Cosmos-Transfer2.5 is a conditional world generation model with adaptive multimodal control, that produces high-quality world simulations conditioned on multiple control inputs. These inputs can take different modalities—including edges, blurred video, segmentation maps, and depth maps. Thanks to @miguelmartin75 for contributing it in #13066.
• GLM-Image: GLM-Image is an image generation model adopts a hybrid autoregressive + diffusion decoder architecture, effectively pushing the upper bound of visual fidelity and fine-grained details. In general image generation quality, it aligns with industry-standard LDM-based approaches, while demonstrating significant advantages in knowledge-intensive image generation scenarios. Thanks to @zRzRzRzRzRzRzR for contributing it in https://github.com/huggingface/diffusers/pull/12973.
• RAE: Representation Autoencoders (aka RAE) are an exciting alternative to traditional VAEs, typically used in the area of latent-space diffusion models of image generation. RAEs leverage pre-trained vision encoders and train lightweight decoders for the task of reconstruction.

Video + audio 🎥 🎼

• LTX-2: LTX-2 is an audio-conditioned text-to-video generation model that can generate videos with synced audio. Full and distilled model inference, as well as two-stage inference with spatial sampling, is supported. We also support a conditioning pipeline that allows for passing different conditions (such as images, series of images, etc.). Check out the docs to learn more!
• Helios: Helios is a 14B video generation model that runs at 17 FPS on a single NVIDIA H100 GPU and supports minute-scale generation while matching a strong baseline in quality. Thanks to @SHYuanBest for contributing this in https://github.com/huggingface/diffusers/pull/13208.

Improvements to Core Library

New caching methods

• MagCache — thanks to @AlanPonnachan!
• TaylorSeer — thanks to @toilaluan!

New context-parallelism (CP) backends

• Unified Sequence Parallel attention — thanks to @Bissmella!
• Ulysses Anything Attention — thanks to @DefTruth!

Misc

• Mambo-G Guidance: New guider implementation (#12862)
• Laplace Scheduler for DDPM (#11320)
• Custom Sigmas in UniPCMultistepScheduler (#12109)
• MultiControlNet support for SD3 Inpainting (#11251)
• Context parallel in native flash attention (#12829)
• NPU Ulysses Attention Support (#12919)
• Fix Wan 2.1 I2V Context Parallel Inference (#12909)
• Fix Qwen-Image Context Parallel Inference (#12970)
• Introduction to @apply_lora_scale decorator for simplifying model definitions (#12994)
• Introduction of pipeline-level “cpu” device_map (#12811)
• Enable CP for kernels-based attention backends (#12812)
• Diffusers is fully functional with Transformers V5 (https://github.com/huggingface/diffusers/pull/12976)

A lot of the above features/improvements came as part of the MVP program we have been running. Immense thanks to the contributors!

Bug Fixes

• Fix QwenImageEditPlus on NPU (#13017)
• Fix MT5Tokenizer → use T5Tokenizer for Transformers v5.0+ compatibility (#12877)
• Fix Wan/WanI2V patchification (#13038)
• Fix LTX-2 inference with num_videos_per_prompt > 1 and CFG (#13121)
• Fix Flux2 img2img prediction (#12855)
• Fix QwenImage txt_seq_lens handling (#12702)
• Fix prefix_token_len bug (#12845)
• Fix ftfy imports in Wan and SkyReels-V2 (#12314, #13113)
• Fix is_fsdp determination (#12960)
• Fix GLM-Image get_image_features API (#13052)
• Fix Wan 2.2 when either transformer isn't present (#13055)
• Fix guider issue (#13147)
• Fix torchao quantizer for new versions (#12901)
• Fix GGUF for unquantized types with unquantize kernels (#12498)
• Make Qwen hidden states contiguous for torchao (#13081)
• Make Flux hidden states contiguous (#13068)
• Fix Kandinsky 5 hardcoded CUDA autocast (#12814)
• Fix aiter availability check (#13059)
• Fix attention mask check for unsupported backends (#12892)
• Allow prompt and prior_token_ids simultaneously in GlmImagePipeline (#13092)
• GLM-Image batch support (#13007)
• Cosmos 2.5 Video2World frame extraction fix (#13018)
• ResNet: only use contiguous in training mode (#12977)

All commits

• [PRX] Improve model compilation by @WaterKnight1998 in #12787
• Improve docstrings and type hints in scheduling_dpmsolver_singlestep.py by @delmalih in #12798
• [Modular]z-image by @yiyixuxu in #12808
• Fix Qwen Edit Plus modular for multi-image input by @sayakpaul in #12601
• [WIP] Add Flux2 modular by @DN6 in #12763
• [docs] improve distributed inference cp docs. by @sayakpaul in #12810
• post release 0.36.0 by @sayakpaul in #12804
• Update distributed_inference.md to correct syntax by @sayakpaul in #12827
• [lora] Remove lora docs unneeded and add " # Copied from ..." by @sayakpaul in #12824
• support CP in native flash attention by @sywangyi in #12829
• [qwen-image] edit 2511 support by @naykun in #12839
• fix pytest tests/pipelines/pixart_sigma/test_pixart.py::PixArtSigmaPi… by @sywangyi in #12842
• Support for control-lora by @lavinal712 in #10686
• Add support for LongCat-Image by @junqiangwu in #12828
• fix the prefix_token_len bug by @junqiangwu in #12845
• extend TorchAoTest::test_model_memory_usage to other platform by @sywangyi in #12768
• Qwen Image Layered Support by @naykun in #12853
• Z-Image-Turbo ControlNet by @hlky in #12792
• Cosmos Predict2.5 Base: inference pipeline, scheduler & chkpt conversion by @miguelmartin75 in #12852
• more update in modular by @yiyixuxu in #12560
• Feature: Add Mambo-G Guidance as Guider by @MatrixTeam-AI in #12862
• Add OvisImagePipeline in AUTO_TEXT2IMAGE_PIPELINES_MAPPING by @alvarobartt in #12876
• Cosmos Predict2.5 14b Conversion by @miguelmartin75 in #12863
• Use T5Tokenizer instead of MT5Tokenizer (removed in Transformers v5.0+) by @alvarobartt in #12877
• Add z-image-omni-base implementation by @RuoyiDu in #12857
• fix torchao quantizer for new torchao versions by @vkuzo in #12901
• fix Qwen Image Transformer single file loading mapping function to be consistent with other loader APIs by @mbalabanski in #12894
• Z-Image-Turbo from_single_file fix by @hlky in #12888
• chore: fix dev version in setup.py by @DefTruth in #12904
• Community Pipeline: Add z-image differential img2img by @r4inm4ker in #12882
• Fix typo in src/diffusers/pipelines/cosmos/pipeline_cosmos2_5_predict.py by @miguelmartin75 in #12914
• Fix wan 2.1 i2v context parallel by @DefTruth in #12909
• fix the use of device_map in CP docs by @sayakpaul in #12902
• [core] remove unneeded autoencoder methods when subclassing from AutoencoderMixin by @sayakpaul in #12873
• Detect 2.0 vs 2.1 ZImageControlNetModel by @hlky in #12861
• Refactor environment variable assignments in workflow by @paulinebm in #12916
• Add codeQL workflow by @paulinebm in #12917
• Delete .github/workflows/codeql.yml by @paulinebm (direct commit on v0.37.0-release)
• CodeQL workflow for security analysis by @paulinebm (direct commit on v0.37.0-release)
• Check for attention mask in backends that don't support it by @dxqb in #12892
• [Flux.1] improve pos embed for ascend npu by computing on npu by @zhangtao0408 in #12897
• LTX Video 0.9.8 long multi prompt by @yaoqih in #12614
• Add FSDP option for Flux2 by @leisuzz in #12860
• Add transformer cache context for SkyReels-V2 pipelines & Update docs by @tolgacangoz in #12837
• [docs] fix torchao typo. by @sayakpaul in #12883
• Update wan.md to remove unneeded hfoptions by @sayakpaul in #12890
• Improve docstrings and type hints in scheduling_edm_euler.py by @delmalih in #12871
• [Modular] Video for Mellon by @asomoza in #12924
• Add LTX 2.0 Video Pipelines by @dg845 in #12915
• Add environment variables to checkout step by @paulinebm in #12927
• Improve docstrings and type hints in scheduling_consistency_decoder.py by @delmalih in #12928
• Fix: Remove hardcoded CUDA autocast in Kandinsky 5 to fix import warning by @adi776borate in #12814
• Upgrade GitHub Actions for Node 24 compatibility by @salmanmkc in #12865
• fix the warning torch_dtype is deprecated by @msdsm in #12841
• [NPU] npu attention enable ulysses by @TmacAaron in #12919
• Torchao floatx version guard by @howardzhang-cv in #12923
• Bugfix for dreambooth flux2 img2img2 by @leisuzz in #12825
• [Modular] qwen refactor by @yiyixuxu in #12872
• [modular] Tests for custom blocks in modular diffusers by @sayakpaul in #12557
• [chore] remove controlnet implementations outside controlnet module. by @sayakpaul in #12152
• [core] Handle progress bar and logging in distributed environments by @sayakpaul in #12806
• Improve docstrings and type hints in scheduling_consistency_models.py by @delmalih in #12931
• [Feature] MultiControlNet support for SD3Impainting by @ishan-modi in #11251
• Laplace Scheduler for DDPM by @gapatron in #11320
• Store vae.config.scaling_factor to prevent missing attr reference (sdxl advanced dreambooth training script) by @Teriks in #12346
• Add thread-safe wrappers for components in pipeline (examples/server-async/utils/requestscopedpipeline.py) by @FredyRivera-dev in #12515
• [Research] Latent Perceptual Loss (LPL) for Stable Diffusion XL by @kashif in #11573
• Change timestep device to cpu for xla by @bhavya01 in #11501
• [LoRA] add lora_alpha to sana README by @linoytsaban in #11780
• Fix wrong param types, docs, and handles noise=None in scale_noise of FlowMatching schedulers by @Promisery in #11669
• [docs] Remote inference by @stevhliu in #12372
• Align HunyuanVideoConditionEmbedding with CombinedTimestepGuidanceTextProjEmbeddings by @samutamm in #12316
• [Fix] syntax in QwenImageEditPlusPipeline by @SahilCarterr in #12371
• Fix ftfy name error in Wan pipeline by @dsocek in #12314
• [modular] error early in enable_auto_cpu_offload by @sayakpaul in #12578
• [ChronoEdit] support multiple loras by @zhangjiewu in #12679
• fix how is_fsdp is determined by @sayakpaul in #12960
• [LoRA] add LoRA support to LTX-2 by @sayakpaul in #12933
• Fix: typo in autoencoder_dc.py by @tvelovraf in #12687
• [Modular] better docstring by @yiyixuxu in #12932
• [docs] polish caching docs. by @sayakpaul in #12684
• Fix typos by @omahs in #12705
• Fix link to diffedit implementation reference by @JuanFKurucz in #12708
• Fix QwenImage txt_seq_lens handling by @kashif in #12702
• Bugfix for flux2 img2img2 prediction by @leisuzz in #12855
• Add Flag to PeftLoraLoaderMixinTests to Enable/Disable Text Encoder LoRA Tests by @dg845 in #12962
• Add Unified Sequence Parallel attention by @Bissmella in #12693
• [Modular] Changes for using WAN I2V by @asomoza in #12959
• Z rz rz rz rz rz rz r cogview by @sayakpaul in #12973
• Update distributed_inference.md to reposition sections by @sayakpaul in #12971
• [chore] make transformers version check stricter for glm image. by @sayakpaul in #12974
• Remove 8bit device restriction by @SunMarc in #12972
• disable_mmap in pipeline from_pretrained by @hlky in #12854
• [Modular] mellon utils by @yiyixuxu in #12978
• LongCat Image pipeline: Allow offloading/quantization of text_encoder component by @Yahweasel in #12963
• Add ChromaInpaintPipeline by @hameerabbasi in #12848
• fix Qwen-Image series context parallel by @DefTruth in #12970
• Flux2 klein by @yiyixuxu in #12982
• [modular] fix a bug in mellon param & improve docstrings by @yiyixuxu in #12980
• add klein docs. by @sayakpaul in #12984
• LTX 2 Single File Support by @dg845 in #12983
• [core] gracefully error out when attn-backend x cp combo isn't supported. by @sayakpaul in #12832
• Improve docstrings and type hints in scheduling_cosine_dpmsolver_multistep.py by @delmalih in #12936
• [Docs] Replace root CONTRIBUTING.md with symlink to source docs by @delmalih in #12986
• make style && make quality by @sayakpaul (direct commit on v0.37.0-release)
• Revert "make style && make quality" by @sayakpaul (direct commit on v0.37.0-release)
• [chore] make style to push new changes. by @sayakpaul in #12998
• Fibo edit pipeline by @galbria in #12930
• Fix variable name in docstring for PeftAdapterMixin.set_adapters by @geekuillaume in #13003
• Improve docstrings and type hints in scheduling_ddim_cogvideox.py by @delmalih in #12992
• [scheduler] Support custom sigmas in UniPCMultistepScheduler by @a-r-r-o-w in #12109
• feat: accelerate longcat-image with regional compile by @lgyStoic in #13019
• Improve docstrings and type hints in scheduling_ddim_flax.py by @delmalih in #13010
• Improve docstrings and type hints in scheduling_ddim_inverse.py by @delmalih in #13020
• fix Dockerfiles for cuda and xformers. by @sayakpaul in #13022
• Resnet only use contiguous in training mode. by @jiqing-feng in #12977
• feat: add qkv projection fuse for longcat transformers by @lgyStoic in #13021
• Improve docstrings and type hints in scheduling_ddim_parallel.py by @delmalih in #13023
• Improve docstrings and type hints in scheduling_ddpm_flax.py by @delmalih in #13024
• Improve docstrings and type hints in scheduling_ddpm_parallel.py by @delmalih in #13027
• Remove *pooled_* mentions from Chroma inpaint by @hameerabbasi in #13026
• Flag Flax schedulers as deprecated by @delmalih in #13031
• [modular] add auto_docstring & more doc related refactors by @yiyixuxu in #12958
• Upgrade GitHub Actions to latest versions by @salmanmkc in #12866
• [From Single File] support from_single_file method for WanAnimateTransformer3DModel by @samadwar in #12691
• Fix: Cosmos2.5 Video2World frame extraction and add default negative prompt by @adi776borate in #13018
• [GLM-Image] Add batch support for GlmImagePipeline by @JaredforReal in #13007
• [Qwen] avoid creating attention masks when there is no padding by @kashif in #12987
• [modular]support klein by @yiyixuxu in #13002
• [QwenImage] fix prompt isolation tests by @sayakpaul in #13042
• fast tok update by @itazap in #13036
• change to CUDA 12.9. by @sayakpaul in #13045
• remove torchao autoquant from diffusers docs by @vkuzo in #13048
• docs: improve docstring scheduling_dpm_cogvideox.py by @delmalih in #13044
• Fix Wan/WanI2V patchification by @Jayce-Ping in #13038
• LTX2 distilled checkpoint support by @rootonchair in #12934
• [wan] fix layerwise upcasting tests on CPU by @sayakpaul in #13039
• [ci] uniform run times and wheels for pytorch cuda. by @sayakpaul in #13047
• docs: fix grammar in fp16_safetensors CLI warning by @Olexandr88 in #13040
• [wan] fix wan 2.2 when either of the transformers isn't present. by @sayakpaul in #13055
• [bug fix] GLM-Image fit new get_image_features API by @JaredforReal in #13052
• Fix aiter availability check by @lauri9 in #13059
• [Modular]add a real quick start guide by @yiyixuxu in #13029
• feat: support Ulysses Anything Attention by @DefTruth in #12996
• Refactor Model Tests by @DN6 in #12822
• [Flux2] Fix LoRA loading for Flux2 Klein by adaptively enumerating transformer blocks by @songkey in #13030
• [Modular] loader related by @yiyixuxu in #13025
• [Modular] mellon doc etc by @yiyixuxu in #13051
• [modular] change the template modular pipeline card by @sayakpaul in #13072
• Add support for Magcache by @AlanPonnachan in #12744
• [docs] Fix syntax error in quantization configuration by @sayakpaul in #13076
• docs: improve docstring scheduling_dpmsolver_multistep_inverse.py by @delmalih in #13083
• [core] make flux hidden states contiguous by @sayakpaul in #13068
• [core] make qwen hidden states contiguous to make torchao happy. by @sayakpaul in #13081
• Feature/zimage inpaint pipeline by @CalamitousFelicitousness in #13006
• GGUF fix for unquantized types when using unquantize kernels by @dxqb in #12498
• docs: improve docstring scheduling_dpmsolver_multistep_inverse.py by @delmalih in #13085
• [modular]simplify components manager doc by @yiyixuxu in #13088
• ZImageControlNet cfg by @hlky in #13080
• [Modular] refactor Wan: modular pipelines by task etc by @yiyixuxu in #13063
• [Modular] guard ModularPipeline.blocks attribute by @yiyixuxu in #13014
• LTX 2 Improve encode_video by Accepting More Input Types by @dg845 in #13057
• Z image lora training by @linoytsaban in #13056
• [modular] add modular tests for Z-Image and Wan by @sayakpaul in #13078
• [Docs] Add guide for AutoModel with custom code by @DN6 in #13099
• [SkyReelsV2] Fix ftfy import by @asomoza in #13113
• [lora] fix non-diffusers lora key handling for flux2 by @sayakpaul in #13119
• [CI] Refactor Wan Model Tests by @DN6 in #13082
• docs: improve docstring scheduling_edm_dpmsolver_multistep.py by @delmalih in #13122
• [Fix]Allow prompt and prior_token_ids to be provided simultaneously in GlmImagePipeline by @JaredforReal in #13092
• docs: improve docstring scheduling_flow_match_euler_discrete.py by @delmalih in #13127
• Cosmos Transfer2.5 inference pipeline: general/{seg, depth, blur, edge} by @miguelmartin75 in #13066
• [modular] add tests for robust model loading. by @sayakpaul in #13120
• Fix LTX-2 Inference when num_videos_per_prompt > 1 and CFG is Enabled by @dg845 in #13121
• [CI] Fix setuptools pkg_resources Errors by @dg845 in #13129
• docs: improve docstring scheduling_flow_match_heun_discrete.py by @delmalih in #13130
• [CI] Fix setuptools pkg_resources Bug for PR GPU Tests by @dg845 in #13132
• fix cosmos transformer typing. by @sayakpaul in #13134
• Sunset Python 3.8 & get rid of explicit typing exports where possible by @sayakpaul in #12524
• feat: implement apply_lora_scale to remove boilerplate. by @sayakpaul in #12994
• [docs] fix ltx2 i2v docstring. by @sayakpaul in #13135
• [Modular] add different pipeine blocks to init by @yiyixuxu in #13145
• fix MT5Tokenizer by @yiyixuxu in #13146
• fix guider by @yiyixuxu in #13147
• [Modular] update doc for ModularPipeline by @yiyixuxu in #13100
• [Modular] add explicit workflow support by @yiyixuxu in #13028
• [LTX2] Fix wrong lora mixin by @asomoza in #13144
• [Pipelines] Remove k-diffusion by @DN6 in #13152
• [tests] accept recompile_limit from the user in tests by @sayakpaul in #13150
• [core] support device type device_maps to work with offloading. by @sayakpaul in #12811
• [Bug] Fix QwenImageEditPlus Series on NPU by @zhangtao0408 in #13017
• [CI] Add ftfy as a test dependency by @DN6 in #13155
• docs: improve docstring scheduling_flow_match_lcm.py by @delmalih in #13160
• [docs] add docs for qwenimagelayered by @stevhliu in #13158
• Flux2: Tensor tuples can cause issues for checkpointing by @dxqb in #12777
• [CI] Revert setuptools CI Fix as the Failing Pipelines are Deprecated by @dg845 in #13149
• Fix ftfy import for PRX Pipeline by @dg845 in #13154
• [core] Enable CP for kernels-based attention backends by @sayakpaul in #12812
• remove deps related to test from ci by @sayakpaul in #13164
• [CI] Fix new LoRAHotswap tests by @DN6 in #13163
• [gguf][torch.compile time] Convert to plain tensor earlier in dequantize_gguf_tensor by @anijain2305 in #13166
• Support Flux Klein peft (fal) lora format by @asomoza in #13169
• Fix T5GemmaEncoder loading for transformers 5.x composite T5GemmaConfig by @DavidBert in #13143
• Allow Automodel to use from_config with custom code. by @DN6 in #13123
• Fix AutoModel typing Import Error by @dg845 in #13178
• migrate to transformers v5 by @sayakpaul in #12976
• fix: graceful fallback when attention backends fail to import by @sym-bot in #13060
• [docs] Fix torchrun command argument order in docs by @sayakpaul in #13181
• [attention backends] use dedicated wrappers from fa3 for cp. by @sayakpaul in #13165
• Cosmos Transfer2.5 Auto-Regressive Inference Pipeline by @miguelmartin75 in #13114
• Fix wrong do_classifier_free_guidance threshold in ZImagePipeline by @kirillsst in #13183
• Fix Flash Attention 3 interface for new FA3 return format by @veeceey in #13173
• Fix LTX-2 image-to-video generation failure in two stages generation by @Songrui625 in #13187
• Fixing Kohya loras loading: Flux.1-dev loras with TE ("lora_te1_" prefix) by @christopher5106 in #13188
• [Modular] update the auto pipeline blocks doc by @yiyixuxu in #13148
• [tests] consistency tests for modular index by @sayakpaul in #13192
• [modular] fallback to default_blocks_name when loading base block classes in ModularPipeline by @yiyixuxu in #13193
• [chore] updates in the pypi publication workflow. by @sayakpaul in #12805
• [tests] enable cpu offload test in torchao without compilation. by @sayakpaul in #12704
• remove db utils from benchmarking by @sayakpaul in #13199
• [AutoModel] Fix bug with subfolders and local model paths when loading custom code by @DN6 in #13197
• [AutoModel] Allow registering auto_map to model config by @DN6 in #13186
• [Modular] Save Modular Pipeline weights to Hub by @DN6 in #13168
• docs: improve docstring scheduling_ipndm.py by @delmalih in #13198
• Clean up accidental files by @DN6 in #13202
• [modular]Update model card to include workflow by @yiyixuxu in #13195
• [modular] not pass trust_remote_code to external repos by @yiyixuxu in #13204
• [Modular] implement requirements validation for custom blocks by @sayakpaul in #12196
• cogvideo example: Distribute VAE video encoding across processes in CogVideoX LoRA training by @jiqing-feng in #13207
• Fix group-offloading bug by @SHYuanBest in #13211
• Add Helios-14B Video Generation Pipelines by @dg845 in #13208
• [Z-Image] Fix more do_classifier_free_guidance thresholds by @asomoza in #13212
• [lora] fix zimage lora conversion to support for more lora. by @sayakpaul in #13209
• adding lora support to z-image controlnet pipelines by @christopher5106 in #13200
• Add LTX2 Condition Pipeline by @dg845 in #13058
• Fix Helios paper link in documentation by @SHYuanBest in #13213
• [attention backends] change to updated repo and version. by @sayakpaul in #13161
• feat: implement rae autoencoder. by @Ando233 in #13046
• Release: v0.37.0-release by @sayakpaul (direct commit on v0.37.0-release)

Significant community contributions

The following contributors have made significant changes to the library over the last release:

• @delmalih
  • Improve docstrings and type hints in scheduling_dpmsolver_singlestep.py (#12798)
  • Improve docstrings and type hints in scheduling_edm_euler.py (#12871)
  • Improve docstrings and type hints in scheduling_consistency_decoder.py (#12928)
  • Improve docstrings and type hints in scheduling_consistency_models.py (#12931)
  • Improve docstrings and type hints in scheduling_cosine_dpmsolver_multistep.py (#12936)
  • [Docs] Replace root CONTRIBUTING.md with symlink to source docs (#12986)
  • Improve docstrings and type hints in scheduling_ddim_cogvideox.py (#12992)
  • Improve docstrings and type hints in scheduling_ddim_flax.py (#13010)
  • Improve docstrings and type hints in scheduling_ddim_inverse.py (#13020)
  • Improve docstrings and type hints in scheduling_ddim_parallel.py (#13023)
  • Improve docstrings and type hints in scheduling_ddpm_flax.py (#13024)
  • Improve docstrings and type hints in scheduling_ddpm_parallel.py (#13027)
  • Flag Flax schedulers as deprecated (#13031)
  • docs: improve docstring scheduling_dpm_cogvideox.py (#13044)
  • docs: improve docstring scheduling_dpmsolver_multistep_inverse.py (#13083)
  • docs: improve docstring scheduling_dpmsolver_multistep_inverse.py (#13085)
  • docs: improve docstring scheduling_edm_dpmsolver_multistep.py (#13122)
  • docs: improve docstring scheduling_flow_match_euler_discrete.py (#13127)
  • docs: improve docstring scheduling_flow_match_heun_discrete.py (#13130)
  • docs: improve docstring scheduling_flow_match_lcm.py (#13160)
  • docs: improve docstring scheduling_ipndm.py (#13198)
• @yiyixuxu
  • [Modular]z-image (#12808)
  • more update in modular (#12560)
  • [Modular] qwen refactor (#12872)
  • [Modular] better docstring (#12932)
  • [Modular] mellon utils (#12978)
  • Flux2 klein (#12982)
  • [modular] fix a bug in mellon param & improve docstrings (#12980)
  • [modular] add auto_docstring & more doc related refactors (#12958)
  • [modular]support klein (#13002)
  • [Modular]add a real quick start guide (#13029)
  • [Modular] loader related (#13025)
  • [Modular] mellon doc etc (#13051)
  • [modular]simplify components manager doc (#13088)
  • [Modular] refactor Wan: modular pipelines by task etc (#13063)
  • [Modular] guard ModularPipeline.blocks attribute (#13014)
  • [Modular] add different pipeine blocks to init (#13145)
  • fix MT5Tokenizer (#13146)
  • fix guider (#13147)
  • [Modular] update doc for ModularPipeline (#13100)
  • [Modular] add explicit workflow support (#13028)
  • [Modular] update the auto pipeline blocks doc (#13148)
  • [modular] fallback to default_blocks_name when loading base block classes in ModularPipeline (#13193)
  • [modular]Update model card to include workflow (#13195)
  • [modular] not pass trust_remote_code to external repos (#13204)
• @sayakpaul
  • Fix Qwen Edit Plus modular for multi-image input (#12601)
  • [docs] improve distributed inference cp docs. (#12810)
  • post release 0.36.0 (#12804)
  • Update distributed_inference.md to correct syntax (#12827)
  • [lora] Remove lora docs unneeded and add " # Copied from ..." (#12824)
  • fix the use of device_map in CP docs (#12902)
  • [core] remove unneeded autoencoder methods when subclassing from AutoencoderMixin (#12873)
  • [docs] fix torchao typo. (#12883)
  • Update wan.md to remove unneeded hfoptions (#12890)
  • [modular] Tests for custom blocks in modular diffusers (#12557)
  • [chore] remove controlnet implementations outside controlnet module. (#12152)
  • [core] Handle progress bar and logging in distributed environments (#12806)
  • [modular] error early in enable_auto_cpu_offload (#12578)
  • fix how is_fsdp is determined (#12960)
  • [LoRA] add LoRA support to LTX-2 (#12933)
  • [docs] polish caching docs. (#12684)
  • Z rz rz rz rz rz rz r cogview (#12973)
  • Update distributed_inference.md to reposition sections (#12971)
  • [chore] make transformers version check stricter for glm image. (#12974)
  • add klein docs. (#12984)
  • [core] gracefully error out when attn-backend x cp combo isn't supported. (#12832)
  • make style && make quality
  • Revert "make style && make quality"
  • [chore] make style to push new changes. (#12998)
  • fix Dockerfiles for cuda and xformers. (#13022)
  • [QwenImage] fix prompt isolation tests (#13042)
  • change to CUDA 12.9. (#13045)
  • [wan] fix layerwise upcasting tests on CPU (#13039)
  • [ci] uniform run times and wheels for pytorch cuda. (#13047)
  • [wan] fix wan 2.2 when either of the transformers isn't present. (#13055)
  • [modular] change the template modular pipeline card (#13072)
  • [docs] Fix syntax error in quantization configuration (#13076)
  • [core] make flux hidden states contiguous (#13068)
  • [core] make qwen hidden states contiguous to make torchao happy. (#13081)
  • [modular] add modular tests for Z-Image and Wan (#13078)
  • [lora] fix non-diffusers lora key handling for flux2 (#13119)
  • [modular] add tests for robust model loading. (#13120)
  • fix cosmos transformer typing. (#13134)
  • Sunset Python 3.8 & get rid of explicit typing exports where possible (#12524)
  • feat: implement apply_lora_scale to remove boilerplate. (#12994)
  • [docs] fix ltx2 i2v docstring. (#13135)
  • [tests] accept recompile_limit from the user in tests (#13150)
  • [core] support device type device_maps to work with offloading. (#12811)
  • [core] Enable CP for kernels-based attention backends (#12812)
  • remove deps related to test from ci (#13164)
  • migrate to transformers v5 (#12976)
  • [docs] Fix torchrun command argument order in docs (#13181)
  • [attention backends] use dedicated wrappers from fa3 for cp. (#13165)
  • [tests] consistency tests for modular index (#13192)
  • [chore] updates in the pypi publication workflow. (#12805)
  • [tests] enable cpu offload test in torchao without compilation. (#12704)
  • remove db utils from benchmarking (#13199)
  • [Modular] implement requirements validation for custom blocks (#12196)
  • [lora] fix zimage lora conversion to support for more lora. (#13209)
  • [attention backends] change to updated repo and version. (#13161)
  • Release: v0.37.0-release
• @DN6
  • [WIP] Add Flux2 modular (#12763)
  • Refactor Model Tests (#12822)
  • [Docs] Add guide for AutoModel with custom code (#13099)
  • [CI] Refactor Wan Model Tests (#13082)
  • [Pipelines] Remove k-diffusion (#13152)
  • [CI] Add ftfy as a test dependency (#13155)
  • [CI] Fix new LoRAHotswap tests (#13163)
  • Allow Automodel to use from_config with custom code. (#13123)
  • [AutoModel] Fix bug with subfolders and local model paths when loading custom code (#13197)
  • [AutoModel] Allow registering auto_map to model config (#13186)
  • [Modular] Save Modular Pipeline weights to Hub (#13168)
  • Clean up accidental files (#13202)
• @naykun
  • [qwen-image] edit 2511 support (#12839)
  • Qwen Image Layered Support (#12853)
• @junqiangwu
  • Add support for LongCat-Image (#12828)
  • fix the prefix_token_len bug (#12845)
• @hlky
  • Z-Image-Turbo ControlNet (#12792)
  • Z-Image-Turbo from_single_file fix (#12888)
  • Detect 2.0 vs 2.1 ZImageControlNetModel (#12861)
  • disable_mmap in pipeline from_pretrained (#12854)
  • ZImageControlNet cfg (#13080)
• @miguelmartin75
  • Cosmos Predict2.5 Base: inference pipeline, scheduler & chkpt conversion (#12852)
  • Cosmos Predict2.5 14b Conversion (#12863)
  • Fix typo in src/diffusers/pipelines/cosmos/pipeline_cosmos2_5_predict.py (#12914)
  • Cosmos Transfer2.5 inference pipeline: general/{seg, depth, blur, edge} (#13066)
  • Cosmos Transfer2.5 Auto-Regressive Inference Pipeline (#13114)
• @RuoyiDu
  • Add z-image-omni-base implementation (#12857)
• @r4inm4ker
  • Community Pipeline: Add z-image differential img2img (#12882)
• @yaoqih
  • LTX Video 0.9.8 long multi prompt (#12614)
• @dg845
  • Add LTX 2.0 Video Pipelines (#12915)
  • Add Flag to PeftLoraLoaderMixinTests to Enable/Disable Text Encoder LoRA Tests (#12962)
  • LTX 2 Single File Support (#12983)
  • LTX 2 Improve encode_video by Accepting More Input Types (#13057)
  • Fix LTX-2 Inference when num_videos_per_prompt > 1 and CFG is Enabled (#13121)
  • [CI] Fix setuptools pkg_resources Errors (#13129)
  • [CI] Fix setuptools pkg_resources Bug for PR GPU Tests (#13132)
  • [CI] Revert setuptools CI Fix as the Failing Pipelines are Deprecated (#13149)
  • Fix ftfy import for PRX Pipeline (#13154)
  • Fix AutoModel typing Import Error (#13178)
  • Add Helios-14B Video Generation Pipelines (#13208)
  • Add LTX2 Condition Pipeline (#13058)
• @kashif
  • [Research] Latent Perceptual Loss (LPL) for Stable Diffusion XL (#11573)
  • Fix QwenImage txt_seq_lens handling (#12702)
  • [Qwen] avoid creating attention masks when there is no padding (#12987)
• @bhavya01
  • Change timestep device to cpu for xla (#11501)
• @linoytsaban
  • [LoRA] add lora_alpha to sana README (#11780)
  • Z image lora training (#13056)
• @stevhliu
  • [docs] Remote inference (#12372)
  • [docs] add docs for qwenimagelayered (#13158)
• @hameerabbasi
  • Add ChromaInpaintPipeline (#12848)
  • Remove *pooled_* mentions from Chroma inpaint (#13026)
• @galbria
  • Fibo edit pipeline (#12930)
• @JaredforReal
  • [GLM-Image] Add batch support for GlmImagePipeline (#13007)
  • [bug fix] GLM-Image fit new get_image_features API (#13052)
  • [Fix]Allow prompt and prior_token_ids to be provided simultaneously in GlmImagePipeline (#13092)
• @rootonchair
  • LTX2 distilled checkpoint support (#12934)
• @AlanPonnachan
  • Add support for Magcache (#12744)
• @CalamitousFelicitousness
  • Feature/zimage inpaint pipeline (#13006)
• @Ando233
  • feat: implement rae autoencoder. (#13046)

The release features a number of new image and video pipelines, a new caching method, a new training script, new kernels - powered attention backends, and more. It is quite packed with a lot of new stuff, so make sure you read the release notes fully 🚀

New image pipelines

• Flux2: Flux2 is the latest generation of image generation and editing model from Black Forest Labs. It’s capable of taking multiple input images as reference, making it versatile for different use cases.
• Z-Image: Z-Image is a best-of-its-kind image generation model in the 6B param regime. Thanks to @JerryWu-code in https://github.com/huggingface/diffusers/pull/12703.
• QwenImage Edit Plus: It’s an upgrade of QwenImage Edit and is capable of taking multiple input images as references. It can act as both a generation and an editing model. Thanks to @naykun for contributing in https://github.com/huggingface/diffusers/issues/12357.
• Bria FIBO: FIBO is trained on structured JSON captions up to 1,000+ words and designed to understand and control different visual parameters such as lighting, composition, color, and camera settings, enabling precise and reproducible outputs. Thanks to @galbria for contributing this in https://github.com/huggingface/diffusers/pull/12545.
• Kandinsky Image Lite: Kandinsky 5.0 Image Lite is a lightweight image generation model (6B parameters). Thanks to @leffff for contributing this in https://github.com/huggingface/diffusers/pull/12664.
• ChronoEdit: ChronoEdit reframes image editing as a video generation task, using input and edited images as start/end frames to leverage pretrained video models with temporal consistency. A temporal reasoning stage introduces reasoning tokens to ensure physically plausible edits and visualize the editing trajectory. Thanks to @zhangjiewu for contributing this in https://github.com/huggingface/diffusers/pull/12593.

New video pipelines

• Sana-Video: Sana-Video is a fast and efficient video generation model, equipped to handle long video sequences, thanks to its incorporation of linear attention. Thanks to @lawrence-cj for contributing this in https://github.com/huggingface/diffusers/pull/12634.
• Kandinsky 5: Kandinsky 5.0 T2V Lite is a lightweight video generation model (2B parameters) that ranks #1 among open-source models in its class. It outperforms larger models and offers the best understanding of Russian concepts in the open-source ecosystem. Thanks to @leffff for contributing this in https://github.com/huggingface/diffusers/pull/12478.
• Hunyuan 1.5: HunyuanVideo-1.5 is a lightweight yet powerful video generation model that achieves state-of-the-art visual quality and motion coherence with only 8.3 billion parameters, enabling efficient inference on consumer-grade GPUs.
• Wan Animate: Wan-Animate is a state-of-the-art character animation and replacement video model based on Wan2.1. Given a reference character image and driving motion video, it can either animate the character with motion from the driving video, or replace the existing character in that video with that character.

New kernels-powered attention backends

The kernels library helps you save a lot of time by providing pre-built kernel interfaces for various environments and accelerators. This release features three new kernels-powered attention backends:

• Flash Attention 3 (+ its varlen variant)
• Flash Attention 2 (+ its varlen variant)
• SAGE

This means if any of the above backend is supported by your development environment, you should be able to skip the manual process of building the corresponding kernels and just use:

# Make sure you have `kernels` installed: `pip install kernels`.
# You can choose `flash_hub` or `sage_hub`, too.
pipe.transformer.set_attention_backend("_flash_3_hub")

For more details, check out the documentation.

TaylorSeer cache

TaylorSeer is now supported in Diffusers, delivering upto 3x speedups with negligible-to-none quality compromise. Thanks to @toilaluan for contributing this in https://github.com/huggingface/diffusers/pull/12648. Check out the documentation here.

New training script

Our Flux.2 integration features a LoRA fine-tuning script that you can check out here. We provide a number of optimizations to help make it run on consumer GPUs.

Misc

• Reusing AttentionMixin: Making certain compatible models subclass from the AttentionMixin class helped us get rid of 2K LoC. Going forward, users can expect more such refactorings that will help make the library leaner and simpler. Check out https://github.com/huggingface/diffusers/pull/12463 for more details.
• Diffusers backend in SGLang: https://github.com/sgl-project/sglang/pull/14112.
• We started the Diffusers MVP program to work with talented community members who will help us improve the library across multiple fronts. Check out the link for more information.

All commits

• remove unneeded checkpoint imports. by @sayakpaul in #12488
• [tests] fix clapconfig for text backbone in audioldm2 by @sayakpaul in #12490
• ltx0.9.8 (without IC lora, autoregressive sampling) by @yiyixuxu in #12493
• [docs] Attention checks by @stevhliu in #12486
• [CI] Check links by @stevhliu in #12491
• [ci] xfail more incorrect transformer imports. by @sayakpaul in #12455
• [tests] introduce VAETesterMixin to consolidate tests for slicing and tiling by @sayakpaul in #12374
• docs: cleanup of runway model by @EazyAl in #12503
• Kandinsky 5 is finally in Diffusers! by @leffff in #12478
• Remove Qwen Image Redundant RoPE Cache by @dg845 in #12452
• Raise warning instead of error when imports are missing for custom code by @DN6 in #12513
• Fix: Use incorrect temporary variable key when replacing adapter name… by @FeiXie8 in #12502
• [docs] Organize toctree by modality by @stevhliu in #12514
• styling issues. by @sayakpaul in #12522
• Add Photon model and pipeline support by @DavidBert in #12456
• purge HF_HUB_ENABLE_HF_TRANSFER; promote Xet by @Vaibhavs10 in #12497
• Prx by @DavidBert in #12525
• [core] AutoencoderMixin to abstract common methods by @sayakpaul in #12473
• Kandinsky5 No cfg fix by @asomoza in #12527
• Fix: Add _skip_keys for AutoencoderKLWan by @yiyixuxu in #12523
• [CI] xfail the test_wuerstchen_prior test by @sayakpaul in #12530
• [tests] Test attention backends by @sayakpaul in #12388
• fix CI bug for kandinsky3_img2img case by @kaixuanliu in #12474
• Fix MPS compatibility in get_1d_sincos_pos_embed_from_grid #12432 by @Aishwarya0811 in #12449
• Handle deprecated transformer classes by @DN6 in #12517
• fix constants.py to user upper() by @sayakpaul in #12479
• HunyuanImage21 by @yiyixuxu in #12333
• Loose the criteria tolerance appropriately for Intel XPU devices by @kaixuanliu in #12460
• Deprecate Stable Cascade by @DN6 in #12537
• [chore] Move guiders experimental warning by @sayakpaul in #12543
• Fix Chroma attention padding order and update docs to use lodestones/Chroma1-HD by @josephrocca in #12508
• Add AITER attention backend by @lauri9 in #12549
• Fix small inconsistency in output dimension of "_get_t5_prompt_embeds" function in sd3 pipeline by @alirezafarashah in #12531
• Kandinsky 5 10 sec (NABLA suport) by @leffff in #12520
• Improve pos embed for Flux.1 inference on Ascend NPU by @gameofdimension in #12534
• support latest few-step wan LoRA. by @sayakpaul in #12541
• [Pipelines] Enable Wan VACE to run since single transformer by @DN6 in #12428
• fix crash if tiling mode is enabled by @sywangyi in #12521
• Fix typos in kandinsky5 docs by @Meatfucker in #12552
• [ci] don't run sana layerwise casting tests in CI. by @sayakpaul in #12551
• Bria fibo by @galbria in #12545
• Avoiding graph break by changing the way we infer dtype in vae.decoder by @ppadjinTT in #12512
• [Modular] Fix for custom block kwargs by @DN6 in #12561
• [Modular] Allow custom blocks to be saved to local_dir by @DN6 in #12381
• Fix Stable Diffusion 3.x pooled prompt embedding with multiple images by @friedrich in #12306
• Fix custom code loading in Automodel by @DN6 in #12571
• [modular] better warn message by @yiyixuxu in #12573
• [tests] add tests for flux modular (t2i, i2i, kontext) by @sayakpaul in #12566
• [modular]pass hub_kwargs to load_config by @yiyixuxu in #12577
• ulysses enabling in native attention path by @sywangyi in #12563
• Kandinsky 5.0 Docs fixes by @leffff in #12582
• [docs] sort doc by @sayakpaul in #12586
• [LoRA] add support for more Qwen LoRAs by @linoytsaban in #12581
• [Modular] Allow ModularPipeline to load from revisions by @DN6 in #12592
• Add optional precision-preserving preprocessing for examples/unconditional_image_generation/train_unconditional.py by @turian in #12596
• [SANA-Video] Adding 5s pre-trained 480p SANA-Video inference by @lawrence-cj in #12584
• Fix overflow and dtype handling in rgblike_to_depthmap (NumPy + PyTorch) by @MohammadSadeghSalehi in #12546
• [Modular] Some clean up for Modular tests by @DN6 in #12579
• feat: enable attention dispatch for huanyuan video by @DefTruth in #12591
• fix the crash in Wan-AI/Wan2.2-TI2V-5B-Diffusers if CP is enabled by @sywangyi in #12562
• [CI] Push test fix by @DN6 in #12617
• add ChronoEdit by @zhangjiewu in #12593
• [modular] wan! by @yiyixuxu in #12611
• [CI] Fix typo in uv install by @DN6 in #12618
• fix: correct import path for load_model_dict_into_meta in conversion scripts by @yashwantbezawada in #12616
• Fix Context Parallel validation checks by @DN6 in #12446
• [Modular] Clean up docs by @DN6 in #12604
• Fix: update type hints for Tuple parameters across multiple files to support variable-length tuples by @cesaryuan in #12544
• [CI] Remove unittest dependency from testing_utils.py by @DN6 in #12621
• Fix rotary positional embedding dimension mismatch in Wan and SkyReels V2 transformers by @charchit7 in #12594
• fix copies by @yiyixuxu in #12637
• Add MLU Support. by @a120092009 in #12629
• fix dispatch_attention_fn check by @yiyixuxu in #12636
• [modular] add tests for qwen modular by @sayakpaul in #12585
• ArXiv -> HF Papers by @qgallouedec in #12583
• [docs] Update install instructions by @stevhliu in #12626
• [modular] add a check by @yiyixuxu in #12628
• Improve docstrings and type hints in scheduling_amused.py by @delmalih in #12623
• [WIP]Add Wan2.2 Animate Pipeline (Continuation of #12442 by tolgacangoz) by @dg845 in #12526
• adjust unit tests for test_save_load_float16 by @kaixuanliu in #12500
• skip autoencoderdl layerwise casting memory by @sayakpaul in #12647
• [utils] Update check_doc_toc by @stevhliu in #12642
• [docs] AutoModel by @stevhliu in #12644
• Improve docstrings and type hints in scheduling_ddim.py by @delmalih in #12622
• Improve docstrings and type hints in scheduling_ddpm.py by @delmalih in #12651
• [Modular] Add Custom Blocks guide to doc by @DN6 in #12339
• Improve docstrings and type hints in scheduling_euler_discrete.py by @delmalih in #12654
• Update Wan Animate Docs by @dg845 in #12658
• Rope in float32 for mps or npu compatibility by @DavidBert in #12665
• [PRX pipeline]: add 1024 resolution ratio bins by @DavidBert in #12670
• SANA-Video Image to Video pipeline SanaImageToVideoPipeline support by @lawrence-cj in #12634
• [CI] Make CI logs less verbose by @DN6 in #12674
• Revert AutoencoderKLWan's dim_mult default value back to list by @dg845 in #12640
• [CI] Temporarily pin transformers by @DN6 in #12677
• [core] Refactor hub attn kernels by @sayakpaul in #12475
• [CI] Fix indentation issue in workflow files by @DN6 in #12685
• [CI] Fix failing Pipeline CPU tests by @DN6 in #12681
• Improve docstrings and type hints in scheduling_pndm.py by @delmalih in #12676
• Community Pipeline: FluxFillControlNetInpaintPipeline for FLUX Fill-Based Inpainting with ControlNet by @pratim4dasude in #12649
• Improve docstrings and type hints in scheduling_lms_discrete.py by @delmalih in #12678
• Add FluxLoraLoaderMixin to Fibo pipeline by @SwayStar123 in #12688
• bugfix: fix chrono-edit context parallel by @DefTruth in #12660
• [core] support sage attention + FA2 through kernels by @sayakpaul in #12439
• [i8n-pt] Fix grammar and expand Portuguese documentation by @cdutr in #12598
• Fix variable naming typos in community FluxControlNetFillInpaintPipeline by @sqhuang in #12701
• fix typo in docs by @lawrence-cj in #12675
• Add Support for Z-Image Series by @JerryWu-code in #12703
• let's go Flux2 🚀 by @sayakpaul in #12711
• Update script names in README for Flux2 training by @anvilarth in #12713
• [lora]: Fix Flux2 LoRA NaN test by @sayakpaul in #12714
• [docs] Correct flux2 links by @sayakpaul in #12716
• [docs] put autopipeline after overview and hunyuanimage in images by @sayakpaul in #12548
• Improve docstrings and type hints in scheduling_dpmsolver_multistep.py by @delmalih in #12710
• Support unittest for Z-image ⚡️ by @JerryWu-code in #12715
• [chore] remove torch.save from remnant code. by @sayakpaul in #12717
• Enable regional compilation on z-image transformer model by @sayakpaul in #12736
• Fix examples not loading LoRA adapter weights from checkpoint by @SurAyush in #12690
• [Modular] Add single file support to Modular by @DN6 in #12383
• fix type-check for z-image transformer by @DefTruth in #12739
• Hunyuanvideo15 by @yiyixuxu in #12696
• [Docs] Update Imagen Video paper link in schedulers by @delmalih in #12724
• Improve docstrings and type hints in scheduling_heun_discrete.py by @delmalih in #12726
• Improve docstrings and type hints in scheduling_euler_ancestral_discrete.py by @delmalih in #12766
• fix FLUX.2 context parallel by @DefTruth in #12737
• Rename BriaPipeline to BriaFiboPipeline in documentation by @galbria in #12758
• Update bria_fibo.md with minor fixes by @sayakpaul in #12731
• [feat]: implement "local" caption upsampling for Flux.2 by @sayakpaul in #12718
• Add ZImage LoRA support and integrate into ZImagePipeline by @CalamitousFelicitousness in #12750
• Add support for Ovis-Image by @DoctorKey in #12740
• Fix TPU (torch_xla) compatibility Error about tensor repeat func along with empty dim. by @JerryWu-code in #12770
• Fixes #12673. record_stream in group offloading is not working properly by @KimbingNg in #12721
• [core] start varlen variants for attn backend kernels. by @sayakpaul in #12765
• [core] reuse AttentionMixin for compatible classes by @sayakpaul in #12463
• Deprecate upcast_vae in SDXL based pipelines by @DN6 in #12619
• Kandinsky 5.0 Video Pro and Image Lite by @leffff in #12664
• Fix: leaf_level offloading breaks after delete_adapters by @adi776borate in #12639
• [tests] fix hunuyanvideo 1.5 offloading tests. by @sayakpaul in #12782
• [Z-Image] various small changes, Z-Image transformer tests, etc. by @sayakpaul in #12741
• Z-Image-Turbo from_single_file by @hlky in #12756
• Update attention_backends.md to format kernels by @sayakpaul in #12757
• Improve docstrings and type hints in scheduling_unipc_multistep.py by @delmalih in #12767
• fix spatial compression ratio error for AutoEncoderKLWan doing tiled encode by @jerry2102 in #12753
• [lora] support more ZImage LoRAs by @sayakpaul in #12790
• PRX Set downscale_freq_shift to 0 for consistency with internal implementation by @DavidBert in #12791
• Fix broken group offloading with block_level for models with standalone layers by @rycerzes in #12692
• [Docs] Add Z-Image docs by @asomoza in #12775
• move kandisnky docs. by @sayakpaul (direct commit on v0.36.0-release)
• [docs] minor fixes to kandinsky docs by @sayakpaul in #12797
• Improve docstrings and type hints in scheduling_deis_multistep.py by @delmalih in #12796
• [Feat] TaylorSeer Cache by @toilaluan in #12648
• Update the TensorRT-ModelOPT to Nvidia-ModelOPT by @jingyu-ml in #12793
• add post init for safty checker by @jiqing-feng in #12794
• [HunyuanVideo1.5] support step-distilled by @yiyixuxu in #12802
• Add ZImageImg2ImgPipeline by @CalamitousFelicitousness in #12751
• Release: v0.36.0-release by @sayakpaul (direct commit on v0.36.0-release)

Significant community contributions

The following contributors have made significant changes to the library over the last release:

• @yiyixuxu
  • ltx0.9.8 (without IC lora, autoregressive sampling) (#12493)
  • Fix: Add _skip_keys for AutoencoderKLWan (#12523)
  • HunyuanImage21 (#12333)
  • [modular] better warn message (#12573)
  • [modular]pass hub_kwargs to load_config (#12577)
  • [modular] wan! (#12611)
  • fix copies (#12637)
  • fix dispatch_attention_fn check (#12636)
  • [modular] add a check (#12628)
  • Hunyuanvideo15 (#12696)
  • [HunyuanVideo1.5] support step-distilled (#12802)
• @leffff
  • Kandinsky 5 is finally in Diffusers! (#12478)
  • Kandinsky 5 10 sec (NABLA suport) (#12520)
  • Kandinsky 5.0 Docs fixes (#12582)
  • Kandinsky 5.0 Video Pro and Image Lite (#12664)
• @dg845
  • Remove Qwen Image Redundant RoPE Cache (#12452)
  • [WIP]Add Wan2.2 Animate Pipeline (Continuation of #12442 by tolgacangoz) (#12526)
  • Update Wan Animate Docs (#12658)
  • Revert AutoencoderKLWan's dim_mult default value back to list (#12640)
• @DN6
  • Raise warning instead of error when imports are missing for custom code (#12513)
  • Handle deprecated transformer classes (#12517)
  • Deprecate Stable Cascade (#12537)
  • [Pipelines] Enable Wan VACE to run since single transformer (#12428)
  • [Modular] Fix for custom block kwargs (#12561)
  • [Modular] Allow custom blocks to be saved to local_dir (#12381)
  • Fix custom code loading in Automodel (#12571)
  • [Modular] Allow ModularPipeline to load from revisions (#12592)
  • [Modular] Some clean up for Modular tests (#12579)
  • [CI] Push test fix (#12617)
  • [CI] Fix typo in uv install (#12618)
  • Fix Context Parallel validation checks (#12446)
  • [Modular] Clean up docs (#12604)
  • [CI] Remove unittest dependency from testing_utils.py (#12621)
  • [Modular] Add Custom Blocks guide to doc (#12339)
  • [CI] Make CI logs less verbose (#12674)
  • [CI] Temporarily pin transformers (#12677)
  • [CI] Fix indentation issue in workflow files (#12685)
  • [CI] Fix failing Pipeline CPU tests (#12681)
  • [Modular] Add single file support to Modular (#12383)
  • Deprecate upcast_vae in SDXL based pipelines (#12619)
• @DavidBert
  • Add Photon model and pipeline support (#12456)
  • Prx (#12525)
  • Rope in float32 for mps or npu compatibility (#12665)
  • [PRX pipeline]: add 1024 resolution ratio bins (#12670)
  • PRX Set downscale_freq_shift to 0 for consistency with internal implementation (#12791)
• @galbria
  • Bria fibo (#12545)
  • Rename BriaPipeline to BriaFiboPipeline in documentation (#12758)
• @lawrence-cj
  • [SANA-Video] Adding 5s pre-trained 480p SANA-Video inference (#12584)
  • SANA-Video Image to Video pipeline SanaImageToVideoPipeline support (#12634)
  • fix typo in docs (#12675)
• @zhangjiewu
  • add ChronoEdit (#12593)
• @delmalih
  • Improve docstrings and type hints in scheduling_amused.py (#12623)
  • Improve docstrings and type hints in scheduling_ddim.py (#12622)
  • Improve docstrings and type hints in scheduling_ddpm.py (#12651)
  • Improve docstrings and type hints in scheduling_euler_discrete.py (#12654)
  • Improve docstrings and type hints in scheduling_pndm.py (#12676)
  • Improve docstrings and type hints in scheduling_lms_discrete.py (#12678)
  • Improve docstrings and type hints in scheduling_dpmsolver_multistep.py (#12710)
  • [Docs] Update Imagen Video paper link in schedulers (#12724)
  • Improve docstrings and type hints in scheduling_heun_discrete.py (#12726)
  • Improve docstrings and type hints in scheduling_euler_ancestral_discrete.py (#12766)
  • Improve docstrings and type hints in scheduling_unipc_multistep.py (#12767)
  • Improve docstrings and type hints in scheduling_deis_multistep.py (#12796)
• @pratim4dasude
  • Community Pipeline: FluxFillControlNetInpaintPipeline for FLUX Fill-Based Inpainting with ControlNet (#12649)
• @JerryWu-code
  • Add Support for Z-Image Series (#12703)
  • Support unittest for Z-image ⚡️ (#12715)
  • Fix TPU (torch_xla) compatibility Error about tensor repeat func along with empty dim. (#12770)
• @CalamitousFelicitousness
  • Add ZImage LoRA support and integrate into ZImagePipeline (#12750)
  • Add ZImageImg2ImgPipeline (#12751)
• @DoctorKey
  • Add support for Ovis-Image (#12740)

All commits

• Release: v0.35.1-patch by @sayakpaul (direct commit on v0.35.2-patch)
• handle offload_state_dict when initing transformers models by @sayakpaul in #12438
• [CI] Fix TRANSFORMERS_FLAX_WEIGHTS_NAME import issue by @DN6 in #12354
• Fix PyTorch 2.3.1 compatibility: add version guard for torch.library.… by @Aishwarya0811 in #12206
• fix scale_shift_factor being on cpu for wan and ltx by @vladmandic in #12347
• Release: v0.35.2-patch by @sayakpaul (direct commit on v0.35.2-patch)

Thanks to @naykun for the following PRs that improve Qwen-Image Edit:

• https://github.com/huggingface/diffusers/pull/12188
• https://github.com/huggingface/diffusers/pull/12190

This release comes packed with new image generation and editing pipelines, a new video pipeline, new training scripts, quality-of-life improvements, and much more. Read the rest of the release notes fully to not miss out on the fun stuff.

New pipelines 🧨

We welcomed new pipelines in this release:

• Wan 2.2
• Flux-Kontext
• Qwen-Image
• Qwen-Image-Edit

Wan 2.2 📹

This update to Wan provides significant improvements in video fidelity, prompt adherence, and style. Please check out the official doc to learn more.

Flux-Kontext 🎇

Flux-Kontext is a 12-billion-parameter rectified flow transformer capable of editing images based on text instructions. Please check out the official doc to learn more about it.

Qwen-Image 🌅

After a successful run of delivering language models and vision-language models, the Qwen team is back with an image generation model, which is Apache-2.0 licensed! It achieves significant advances in complex text rendering and precise image editing. To learn more about this powerful model, refer to our docs.

Thanks to @naykun for contributing both Qwen-Image and Qwen-Image-Edit via this PR and this PR.

New training scripts 🎛️

Make these newly added models your own with our training scripts:

• Kontext trainer
• Qwen-Image trainer

Single-file modeling implementations

Following the 🤗 Transformers’ philosophy of single-file modeling implementations, we have started implementing modeling code in single and self-contained files. The Flux Transformer code is one example of this.

Attention refactor

We have massively refactored how we do attention in the models. This allows us to provide support for different attention backends (such as PyTorch native scaled_dot_product_attention, Flash Attention 3, SAGE attention, etc.) in the library seamlessly.

Having attention supported this way also allows us to integrate different parallelization mechanisms, which we’re actively working on. Follow this PR if you’re interested.

Users shouldn’t be affected at all by these changes. Please open an issue if you face any problems.

Regional compilation

Regional compilation trims cold-start latency by only compiling the small and frequently-repeated block(s) of a model - typically a transformer layer - and enables reusing compiled artifacts for every subsequent occurrence. For many diffusion architectures, this delivers the same runtime speedups as full-graph compilation and reduces compile time by 8–10x. Refer to this doc to learn more.

Thanks to @anijain2305 for contributing this feature in this PR.

We have also authored a number of posts that center around the use of torch.compile. You can check them out at the links below:

• Presenting Flux Fast: Making Flux go brrr on H100s
• torch.compile and Diffusers: A Hands-On Guide to Peak Performance
• Fast LoRA inference for Flux with Diffusers and PEFT

Faster pipeline loading ⚡️

Users can now load pipelines directly on an accelerator device leading to significantly faster load times. This particularly becomes evident when loading large pipelines like Wan and Qwen-Image.

from diffusers import DiffusionPipeline
import torch 

ckpt_id = "Qwen/Qwen-Image"
pipe = DiffusionPipeline.from_pretrained(
-    ckpt_id, torch_dtype=torch.bfloat16
- ).to("cuda")
+    ckpt_id, torch_dtype=torch.bfloat16, device_map="cuda"
+ )				

You can speed up loading even more by enabling parallelized loading of state dict shards. This is particularly helpful when you’re working with large models like Wan and Qwen-Image, where the model state dicts are typically sharded across multiple files.

import os
os.environ["HF_ENABLE_PARALLEL_LOADING"] = "yes"

# rest of the loading code
....

Better GGUF integration

@Isotr0py contributed support for native GGUF CUDA kernels in this PR. This should provide an approximately 10% improvement in inference speed.

We have also worked on a tool for converting regular checkpoints to GGUF, letting the community easily share their GGUF checkpoints. Learn more here.

We now support loading of Diffusers format GGUF checkpoints.

You can learn more about all of this in our GGUF official docs.

Modular Diffusers (Experimental)

Modular Diffusers is a system for building diffusion pipelines pipelines with individual pipeline blocks. It is highly customisable, with blocks that can be mixed and matched to adapt to or create a pipeline for a specific workflow or multiple workflows.

The API is currently in active development and is being released as an experimental feature. Learn more in our docs.

All commits

• [tests] skip instead of returning. by @sayakpaul in #11793
• adjust to get CI test cases passed on XPU by @kaixuanliu in #11759
• fix deprecation in lora after 0.34.0 release by @sayakpaul in #11802
• [chore] post release v0.34.0 by @sayakpaul in #11800
• Follow up for Group Offload to Disk by @DN6 in #11760
• [rfc][compile] compile method for DiffusionPipeline by @anijain2305 in #11705
• [tests] add a test on torch compile for varied resolutions by @sayakpaul in #11776
• adjust tolerance criteria for test_float16_inference in unit test by @kaixuanliu in #11809
• Flux Kontext by @a-r-r-o-w in #11812
• Kontext training by @sayakpaul in #11813
• Kontext fixes by @a-r-r-o-w in #11815
• remove syncs before denoising in Kontext by @sayakpaul in #11818
• [CI] disable onnx, mps, flax from the CI by @sayakpaul in #11803
• TorchAO compile + offloading tests by @a-r-r-o-w in #11697
• Support dynamically loading/unloading loras with group offloading by @a-r-r-o-w in #11804
• [lora] fix: lora unloading behvaiour by @sayakpaul in #11822
• [lora]feat: use exclude modules to loraconfig. by @sayakpaul in #11806
• ENH: Improve speed of function expanding LoRA scales by @BenjaminBossan in #11834
• Remove print statement in SCM Scheduler by @a-r-r-o-w in #11836
• [tests] add test for hotswapping + compilation on resolution changes by @sayakpaul in #11825
• reset deterministic in tearDownClass by @jiqing-feng in #11785
• [tests] Fix failing float16 cuda tests by @a-r-r-o-w in #11835
• [single file] Cosmos by @a-r-r-o-w in #11801
• [docs] fix single_file example. by @sayakpaul in #11847
• Use real-valued instead of complex tensors in Wan2.1 RoPE by @mjkvaak-amd in #11649
• [docs] Batch generation by @stevhliu in #11841
• [docs] Deprecated pipelines by @stevhliu in #11838
• fix norm not training in train_control_lora_flux.py by @Luo-Yihang in #11832
• [From Single File] support from_single_file method for WanVACE3DTransformer by @J4BEZ in #11807
• [lora] tests for exclude_modules with Wan VACE by @sayakpaul in #11843
• update: FluxKontextInpaintPipeline support by @vuongminh1907 in #11820
• [Flux Kontext] Support Fal Kontext LoRA by @linoytsaban in #11823
• [docs] Add a note of _keep_in_fp32_modules by @a-r-r-o-w in #11851
• [benchmarks] overhaul benchmarks by @sayakpaul in #11565
• FIX set_lora_device when target layers differ by @BenjaminBossan in #11844
• Fix Wan AccVideo/CausVid fuse_lora by @a-r-r-o-w in #11856
• [chore] deprecate blip controlnet pipeline. by @sayakpaul in #11877
• [docs] fix references in flux pipelines. by @sayakpaul in #11857
• [tests] remove tests for deprecated pipelines. by @sayakpaul in #11879
• [docs] LoRA metadata by @stevhliu in #11848
• [training ] add Kontext i2i training by @sayakpaul in #11858
• [CI] Fix big GPU test marker by @DN6 in #11786
• First Block Cache by @a-r-r-o-w in #11180
• [tests] annotate compilation test classes with bnb by @sayakpaul in #11715
• Update chroma.md by @shm4r7 in #11891
• [CI] Speed up GPU PR Tests by @DN6 in #11887
• Pin k-diffusion for CI by @sayakpaul in #11894
• [Docker] update doc builder dockerfile to include quant libs. by @sayakpaul in #11728
• [tests] Remove more deprecated tests by @sayakpaul in #11895
• [tests] mark the wanvace lora tester flaky by @sayakpaul in #11883
• [tests] add compile + offload tests for GGUF. by @sayakpaul in #11740
• feat: add multiple input image support in Flux Kontext by @Net-Mist in #11880
• Fix unique memory address when doing group-offloading with disk by @sayakpaul in #11767
• [SD3] CFG Cutoff fix and official callback by @asomoza in #11890
• The Modular Diffusers by @yiyixuxu in #9672
• [quant] QoL improvements for pipeline-level quant config by @sayakpaul in #11876
• Bump torch from 2.4.1 to 2.7.0 in /examples/server by @dependabot[bot] in #11429
• [LoRA] fix: disabling hooks when loading loras. by @sayakpaul in #11896
• [utils] account for MPS when available in get_device(). by @sayakpaul in #11905
• [ControlnetUnion] Multiple Fixes by @asomoza in #11888
• Avoid creating tensor in CosmosAttnProcessor2_0 by @chenxiao111222 in #11761)
• [tests] Unify compilation + offloading tests in quantization by @sayakpaul in #11910
• Speedup model loading by 4-5x ⚡ by @a-r-r-o-w in #11904
• [docs] torch.compile blog post by @stevhliu in #11837
• Flux: pass joint_attention_kwargs when using gradient_checkpointing by @piercus in #11814
• Fix: Align VAE processing in ControlNet SD3 training with inference by @Henry-Bi in #11909
• Bump aiohttp from 3.10.10 to 3.12.14 in /examples/server by @dependabot[bot] in #11924
• [tests] Improve Flux tests by @a-r-r-o-w in #11919
• Remove device synchronization when loading weights by @a-r-r-o-w in #11927
• Remove forced float64 from onnx stable diffusion pipelines by @lostdisc in #11054
• Fixed bug: Uncontrolled recursive calls that caused an infinite loop when loading certain pipelines containing Transformer2DModel by @lengmo1996 in #11923
• [ControlnetUnion] Propagate #11888 to img2img by @asomoza in #11929
• enable flux pipeline compatible with unipc and dpm-solver by @gameofdimension in #11908
• [training] add an offload utility that can be used as a context manager. by @sayakpaul in #11775
• Add SkyReels V2: Infinite-Length Film Generative Model by @tolgacangoz in #11518
• [refactor] Flux/Chroma single file implementation + Attention Dispatcher by @a-r-r-o-w in #11916
• [docs] clarify the mapping between Transformer2DModel and finegrained variants. by @sayakpaul in #11947
• [Modular] Updates for Custom Pipeline Blocks by @DN6 in #11940
• [docs] Update toctree by @stevhliu in #11936
• [docs] include bp link. by @sayakpaul in #11952
• Fix kontext finetune issue when batch size >1 by @mymusise in #11921
• [tests] Add test slices for Hunyuan Video by @a-r-r-o-w in #11954
• [tests] Add test slices for Cosmos by @a-r-r-o-w in #11955
• [tests] Add fast test slices for HiDream-Image by @a-r-r-o-w in #11953
• [Modular] update the collection behavior by @yiyixuxu in #11963
• fix "Expected all tensors to be on the same device, but found at least two devices" error by @yao-matrix in #11690
• Remove logger warnings for attention backends and hard error during runtime instead by @a-r-r-o-w in #11967
• [Examples] Uniform notations in train_flux_lora by @tomguluson92 in #10011
• fix style by @yiyixuxu in #11975
• [tests] Add test slices for Wan by @a-r-r-o-w in #11920
• [docs] update guidance_scale docstring for guidance_distilled models. by @sayakpaul in #11935
• [tests] enforce torch version in the compilation tests. by @sayakpaul in #11979
• [modular diffusers] Wan by @a-r-r-o-w in #11913
• [compile] logger statements create unnecessary guards during dynamo tracing by @a-r-r-o-w in #11987
• enable quantcompile test on xpu by @yao-matrix in #11988
• [WIP] Wan2.2 by @yiyixuxu in #12004
• [refactor] some shared parts between hooks + docs by @a-r-r-o-w in #11968
• [refactor] Wan single file implementation by @a-r-r-o-w in #11918
• Fix huggingface-hub failing tests by @asomoza in #11994
• feat: add flux kontext by @jlonge4 in #11985
• [modular] add Modular flux for text-to-image by @sayakpaul in #11995
• [docs] include lora fast post. by @sayakpaul in #11993
• [docs] quant_kwargs by @stevhliu in #11712
• [docs] Fix link by @stevhliu in #12018
• [wan2.2] add 5b i2v by @yiyixuxu in #12006
• wan2.2 i2v FirstBlockCache fix by @okaris in #12013
• [core] support attention backends for LTX by @sayakpaul in #12021
• [docs] Update index by @stevhliu in #12020
• [Fix] huggingface-cli to hf missed files by @asomoza in #12008
• [training-scripts] Make pytorch examples UV-compatible by @sayakpaul in #12000
• [wan2.2] fix vae patches by @yiyixuxu in #12041
• Allow SD pipeline to use newer schedulers, eg: FlowMatch by @ppbrown in #12015
• [LoRA] support lightx2v lora in wan by @sayakpaul in #12040
• Fix type of force_upcast to bool by @BerndDoser in #12046
• Update autoencoder_kl_cosmos.py by @tanuj-rai in #12045
• Qwen-Image by @naykun in #12055
• [wan2.2] follow-up by @yiyixuxu in #12024
• tests + minor refactor for QwenImage by @a-r-r-o-w in #12057
• Cross attention module to Wan Attention by @samuelt0 in #12058
• fix(qwen-image): update vae license by @naykun in #12063
• CI fixing by @paulinebm in #12059
• enable all gpus when running ci. by @sayakpaul in #12062
• fix the rest for all GPUs in CI by @sayakpaul in #12064
• [docs] Install by @stevhliu in #12026
• [wip] feat: support lora in qwen image and training script by @sayakpaul in #12056
• [docs] small corrections to the example in the Qwen docs by @sayakpaul in #12068
• [tests] Fix Qwen test_inference slices by @a-r-r-o-w in #12070
• [tests] deal with the failing AudioLDM2 tests by @sayakpaul in #12069
• optimize QwenImagePipeline to reduce unnecessary CUDA synchronization by @chengzeyi in #12072
• Add cuda kernel support for GGUF inference by @Isotr0py in #11869
• fix input shape for WanGGUFTexttoVideoSingleFileTests by @jiqing-feng in #12081
• [refactor] condense group offloading by @a-r-r-o-w in #11990
• Fix group offloading synchronization bug for parameter-only GroupModule's by @a-r-r-o-w in #12077
• Helper functions to return skip-layer compatible layers by @a-r-r-o-w in #12048
• Make prompt_2 optional in Flux Pipelines by @DN6 in #12073
• [tests] tighten compilation tests for quantization by @sayakpaul in #12002
• Implement Frequency-Decoupled Guidance (FDG) as a Guider by @dg845 in #11976
• fix flux type hint by @DefTruth in #12089
• [qwen] device typo by @yiyixuxu in #12099
• [lora] adapt new LoRA config injection method by @sayakpaul in #11999
• lora_conversion_utils: replace lora up/down with a/b even if transformer. in key by @Beinsezii in #12101
• [tests] device placement for non-denoiser components in group offloading LoRA tests by @sayakpaul in #12103
• [Modular] Fast Tests by @yiyixuxu in #11937
• [GGUF] feat: support loading diffusers format gguf checkpoints. by @sayakpaul in #11684
• [docs] diffusers gguf checkpoints by @sayakpaul in #12092
• [core] add modular support for Flux I2I by @sayakpaul in #12086
• [lora] support loading loras from lightx2v/Qwen-Image-Lightning by @sayakpaul in #12119
• [Modular] More Updates for Custom Code Loading by @DN6 in #11969
• enable compilation in qwen image. by @sayakpaul in #12061
• [tests] Add inference test slices for SD3 and remove unnecessary tests by @a-r-r-o-w in #12106
• [chore] complete the licensing statement. by @sayakpaul in #12001
• [docs] Cache link by @stevhliu in #12105
• [Modular] Add experimental feature warning for Modular Diffusers by @DN6 in #12127
• Add low_cpu_mem_usage option to from_single_file to align with from_pretrained by @IrisRainbowNeko in #12114
• [docs] Modular diffusers by @stevhliu in #11931
• [Bugfix] typo fix in NPU FA by @leisuzz in #12129
• Add QwenImage Inpainting and Img2Img pipeline by @Trgtuan10 in #12117
• [core] parallel loading of shards by @sayakpaul in #12028
• try to use deepseek with an agent to auto i18n to zh by @SamYuan1990 in #12032
• [docs] Refresh effective and efficient doc by @stevhliu in #12134
• Fix bf15/fp16 for pipeline_wan_vace.py by @SlimRG in #12143
• make parallel loading flag a part of constants. by @sayakpaul in #12137
• [docs] Parallel loading of shards by @stevhliu in #12135
• feat: cuda device_map for pipelines. by @sayakpaul in #12122
• [core] respect local_files_only=True when using sharded checkpoints by @sayakpaul in #12005
• support hf_quantizer in cache warmup. by @sayakpaul in #12043
• make test_gguf all pass on xpu by @yao-matrix in #12158
• [docs] Quickstart by @stevhliu in #12128
• Qwen Image Edit Support by @naykun in #12164
• remove silu for CogView4 by @lambertwjh in #12150
• [qwen] Qwen image edit followups by @sayakpaul in #12166
• Minor modification to support DC-AE-turbo by @chenjy2003 in #12169
• [Docs] typo error in qwen image by @leisuzz in #12144
• fix: caching allocator behaviour for quantization. by @sayakpaul in #12172
• fix(training_utils): wrap device in list for DiffusionPipeline by @MengAiDev in #12178
• [docs] Clarify guidance scale in Qwen pipelines by @sayakpaul in #12181
• [LoRA] feat: support more Qwen LoRAs from the community. by @sayakpaul in #12170
• Update README.md by @Taechai in #12182
• [chore] add lora button to qwenimage docs by @sayakpaul in #12183
• [Wan 2.2 LoRA] add support for 2nd transformer lora loading + wan 2.2 lightx2v lora by @linoytsaban in #12074
• Release: v0.35.0 by @sayakpaul (direct commit on v0.35.0-release)

Significant community contributions

The following contributors have made significant changes to the library over the last release:

• @vuongminh1907
  • update: FluxKontextInpaintPipeline support (#11820)
• @Net-Mist
  • feat: add multiple input image support in Flux Kontext (#11880)
• @tolgacangoz
  • Add SkyReels V2: Infinite-Length Film Generative Model (#11518)
• @naykun
  • Qwen-Image (#12055)
  • fix(qwen-image): update vae license (#12063)
  • Qwen Image Edit Support (#12164)
• @Trgtuan10
  • Add QwenImage Inpainting and Img2Img pipeline (#12117)
• @SamYuan1990
  • try to use deepseek with an agent to auto i18n to zh (#12032)

📹 New video generation pipelines

Wan VACE

Wan VACE supports various generation techniques which achieve controllable video generation. It comes in two variants: a 1.3B model for fast iteration & prototyping, and a 14B for high quality generation. Some of the capabilities include:

• Control to Video (Depth, Pose, Sketch, Flow, Grayscale, Scribble, Layout, Boundary Box, etc.). Recommended library for preprocessing videos to obtain control videos: huggingface/controlnet_aux
• Image/Video to Video (first frame, last frame, starting clip, ending clip, random clips)
• Inpainting and Outpainting
• Subject to Video (faces, object, characters, etc.)
• Composition to Video (reference anything, animate anything, swap anything, expand anything, move anything, etc.)

The code snippets available in this pull request demonstrate some examples of how videos can be generated with controllability signals.

Check out the docs to learn more.

Cosmos Predict2 Video2World

Cosmos-Predict2 is a key branch of the Cosmos World Foundation Models (WFMs) ecosystem for Physical AI, specializing in future state prediction through advanced world modeling. It offers two powerful capabilities: text-to-image generation for creating high-quality images from text descriptions, and video-to-world generation for producing visual simulations from video inputs.

The Video2World model comes in a 2B and 14B variant. Check out the docs to learn more.

LTX 0.9.7 and Distilled

LTX 0.9.7 and its distilled variants are the latest in the family of models released by Lightricks.

Check out the docs to learn more.

Hunyuan Video Framepack and F1

Framepack is a novel method for enabling long video generation. There are two released variants of Hunyuan Video trained using this technique. Check out the docs to learn more.

FusionX

The FusionX family of models and LoRAs, built on top of Wan2.1-14B, should already be supported. To load the model, use from_single_file():

from diffusers import WanTransformer3DModel

transformer = WanTransformer3DModel.from_single_file(
    "https://huggingface.co/vrgamedevgirl84/Wan14BT2VFusioniX/blob/main/Wan14Bi2vFusioniX_fp16.safetensors",
    torch_dtype=torch.bfloat16
)

To load the LoRAs, use load_lora_weights():

pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-14B-Diffusers",
    torch_dtype=torch.bfloat16
).to("cuda")
pipe.load_lora_weights(
    "vrgamedevgirl84/Wan14BT2VFusioniX", weight_name="FusionX_LoRa/Wan2.1_T2V_14B_FusionX_LoRA.safetensors"
)

AccVideo and CausVid (only LoRAs)

AccVideo and CausVid are two novel distillation techniques that speed up the generation time of video diffusion models while preserving quality. Diffusers supports loading their extracted LoRAs with their respective models.

🌠 New image generation pipelines

Cosmos Predict2 Text2Image

Text-to-image models from the Cosmos-Predict2 release. The models comes in a 2B and 14B variant. Check out the docs to learn more.

Chroma

Chroma is a 8.9B parameter model based on FLUX.1-schnell. It’s fully Apache 2.0 licensed, ensuring that anyone can use, modify, and build on top of it. Checkout the docs to learn more

Thanks to @Ednaordinary for contributing it in this PR!

VisualCloze

VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning is an innovative in-context learning framework based universal image generation framework that offers key capabilities:

1. Support for various in-domain tasks
2. Generalization to unseen tasks through in-context learning
3. Unify multiple tasks into one step and generate both target image and intermediate results
4. Support reverse-engineering conditions from target images

Check out the docs to learn more. Thanks to @lzyhha for contributing this in this PR!

Better torch.compile support

We have worked with the PyTorch team to improve how we provide torch.compile() compatibility throughout the library. More specifically, we now test the widely used models like Flux for any recompilation and graph break issues which can get in the way of fully realizing torch.compile() benefits. Refer to the following links to learn more:

• https://github.com/huggingface/diffusers/pull/11085
• https://github.com/huggingface/diffusers/issues/11430

Additionally, users can combine offloading with compilation to get a better speed-memory trade-off. Below is an example:

import torch
from diffusers import DiffusionPipeline
torch._dynamo.config.cache_size_limit = 10000

pipeline = DiffusionPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipline.enable_model_cpu_offload()
# Compile.
pipeline.transformer.compile()

image = pipeline(
    prompt="An astronaut riding a horse on Mars",
    guidance_scale=0.,
    height=768,
    width=1360,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
print(f"Max memory reserved: {torch.cuda.max_memory_allocated() / 1024**3:.2f} GB")

This is compatible with group offloading, too. Interested readers can check out the concerned PRs below:

• https://github.com/huggingface/diffusers/pull/11605
• https://github.com/huggingface/diffusers/pull/11670

You can substantially reduce memory requirements by combining quantization with offloading and then improving speed with torch.compile(). Below is an example:

from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig
from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig
from diffusers import AutoModel, FluxPipeline
from transformers import T5EncoderModel

import torch
torch._dynamo.config.recompile_limit = 1000 

quant_kwargs = {"load_in_4bit": True, "bnb_4bit_compute_dtype": torch_dtype, "bnb_4bit_quant_type": "nf4"}
text_encoder_2_quant_config = TransformersBitsAndBytesConfig(**quant_kwargs)
dit_quant_config = DiffusersBitsAndBytesConfig(**quant_kwargs)

ckpt_id = "black-forest-labs/FLUX.1-dev"
text_encoder_2 = T5EncoderModel.from_pretrained(
    ckpt_id,
    subfolder="text_encoder_2",
    quantization_config=text_encoder_2_quant_config,
    torch_dtype=torch_dtype,
)
transformer = AutoModel.from_pretrained(
    ckpt_id,
    subfolder="transformer",
    quantization_config=dit_quant_config,
    torch_dtype=torch_dtype,
)
pipe = FluxPipeline.from_pretrained(
    ckpt_id,
    transformer=transformer,
    text_encoder_2=text_encoder_2,
    torch_dtype=torch_dtype,
)
pipe.enable_model_cpu_offload()
pipe.transformer.compile()

image = pipeline(
    prompt="An astronaut riding a horse on Mars",
    guidance_scale=3.5,
    height=768,
    width=1360,
    num_inference_steps=28,
    max_sequence_length=512,
).images[0]

Starting from bitsandbytes==0.46.0 onwards, bnb-quantized models should be fully compatible with torch.compile() without graph-breaks. This means that when compiling a bnb-quantized model, users can do: model.compile(fullgraph=True). This can significantly improve speed while still providing memory benefits. The figure below provides a comparison with Flux.1-Dev. Refer to this benchmarking script to learn more.

image

Note that for 4bit bnb models, it’s currently needed to install PyTorch nightly if fullgraph=True is specified during compilation.

Huge shoutout to @anijain2305 and @StrongerXi from the PyTorch team for the incredible support.

PipelineQuantizationConfig

Users can now provide a quantization config while initializing a pipeline:

import torch
from diffusers import DiffusionPipeline
from diffusers.quantizers import PipelineQuantizationConfig

pipeline_quant_config = PipelineQuantizationConfig(
     quant_backend="bitsandbytes_4bit",
     quant_kwargs={"load_in_4bit": True, "bnb_4bit_quant_type": "nf4", "bnb_4bit_compute_dtype": torch.bfloat16},
     components_to_quantize=["transformer", "text_encoder_2"],
)
pipe = DiffusionPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    quantization_config=pipeline_quant_config,
    torch_dtype=torch.bfloat16,
).to("cuda")

image = pipe("photo of a cute dog").images[0]

This reduces the barrier to entry for our users willing to use quantization without having to write too much code. Refer to the documentation to learn more about different configurations allowed through PipelineQuantizationConfig.

Group offloading with disk

In the previous release, we shipped “group offloading” which lets you offload blocks/nodes within a model, optimizing its memory consumption. It also lets you overlap this offloading with computation, providing a good speed-memory trade-off, especially in low VRAM environments.

However, you still need a considerable amount of system RAM to make offloading work effectively. So, low VRAM and low RAM environments would still not work.

Starting this release, users will additionally have the option to offload to disk instead of RAM, further lowering memory consumption. Set the offload_to_disk_path to enable this feature.

pipeline.transformer.enable_group_offload(
    onload_device="cuda", 
    offload_device="cpu", 
    offload_type="leaf_level", 
    offload_to_disk_path="path/to/disk"
)

Refer to these two tables to compare the speed and memory trade-offs.

LoRA metadata parsing

It is beneficial to include the LoraConfig in a LoRA state dict that was used to train the LoRA. In its absence, users were restricted to using the same LoRA alpha as the LoRA rank. We have modified the most popular training scripts to allow passing custom lora_alpha through the CLI. Refer to this thread for more updates. Refer to this comment for some extended clarifications.

New training scripts

• We now have a capable training script for training robust timestep-distilled models through the SANA Sprint framework. Check out this resource for more details. Thanks to @scxue and @lawrence-cj for contributing it in this PR.
• HiDream LoRA DreamBooth training script (docs). The script supports training with quantization. HiDream is an MIT-licensed model. So, make it yours with this training script.

Updates on educational materials on quantization

We have worked on a two-part series discussing the support of quantization in Diffusers. Check them out:

• Exploring Quantization Backends in Diffusers
• (LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware

All commits

• [LoRA] support musubi wan loras. by @sayakpaul in #11243
• fix test_vanilla_funetuning failure on XPU and A100 by @yao-matrix in #11263
• make test_stable_diffusion_inpaint_fp16 pass on XPU by @yao-matrix in #11264
• make test_dict_tuple_outputs_equivalent pass on XPU by @yao-matrix in #11265
• add onnxruntime-qnn & onnxruntime-cann by @xieofxie in #11269
• make test_instant_style_multiple_masks pass on XPU by @yao-matrix in #11266
• [BUG] Fix convert_vae_pt_to_diffusers bug by @lavinal712 in #11078
• Fix LTX 0.9.5 single file by @hlky in #11271
• [Tests] Cleanup lora tests utils by @sayakpaul in #11276
• [CI] relax tolerance for unclip further by @sayakpaul in #11268
• do not use DIFFUSERS_REQUEST_TIMEOUT for notification bot by @sayakpaul in #11273
• Fix incorrect tile_latent_min_width calculation in AutoencoderKLMochi by @kuantuna in #11294
• HiDream Image by @hlky in #11231
• flow matching lcm scheduler by @quickjkee in #11170
• Update autoencoderkl_allegro.md by @Forbu in #11303
• Hidream refactoring follow ups by @a-r-r-o-w in #11299
• Fix incorrect tile_latent_min_width calculations by @kuantuna in #11305
• [ControlNet] Adds controlnet for SanaTransformer by @ishan-modi in #11040
• make KandinskyV22PipelineInpaintCombinedFastTests::test_float16_inference pass on XPU by @yao-matrix in #11308
• make test_stable_diffusion_karras_sigmas pass on XPU by @yao-matrix in #11310
• make KolorsPipelineFastTests::test_inference_batch_single_identical pass on XPU by @faaany in #11313
• [LoRA] support more SDXL loras. by @sayakpaul in #11292
• [HiDream] code example by @linoytsaban in #11317
• import for FlowMatchLCMScheduler by @asomoza in #11318
• Use float32 on mps or npu in transformer_hidream_image's rope by @hlky in #11316
• Add skrample section to community_projects.md by @Beinsezii in #11319
• [docs] Promote AutoModel usage by @sayakpaul in #11300
• [LoRA] Add LoRA support to AuraFlow by @hameerabbasi in #10216
• Fix vae.Decoder prev_output_channel by @hlky in #11280
• fix CPU offloading related fail cases on XPU by @yao-matrix in #11288
• [docs] fix hidream docstrings. by @sayakpaul in #11325
• Rewrite AuraFlowPatchEmbed.pe_selection_index_based_on_dim to be torch.compile compatible by @AstraliteHeart in #11297
• post release 0.33.0 by @sayakpaul in #11255
• another fix for FlowMatchLCMScheduler forgotten import by @asomoza in #11330
• Fix Hunyuan I2V for transformers>4.47.1 by @DN6 in #11293
• unpin torch versions for onnx Dockerfile by @sayakpaul in #11290
• [single file] enable telemetry for single file loading when using GGUF. by @sayakpaul in #11284
• [docs] add a snippet for compilation in the auraflow docs. by @sayakpaul in #11327
• Hunyuan I2V fast tests fix by @DN6 in #11341
• [BUG] fixed _toctree.yml alphabetical ordering by @ishan-modi in #11277
• Fix wrong dtype argument name as torch_dtype by @nPeppon in #11346
• [chore] fix lora docs utils by @sayakpaul in #11338
• [docs] add note about use_duck_shape in auraflow docs. by @sayakpaul in #11348
• [LoRA] Propagate hotswap better by @sayakpaul in #11333
• [Hi Dream] follow-up by @yiyixuxu in #11296
• [bitsandbytes] improve dtype mismatch handling for bnb + lora. by @sayakpaul in #11270
• Update controlnet_flux.py by @haofanwang in #11350
• enable 2 test cases on XPU by @yao-matrix in #11332
• [BNB] Fix test_moving_to_cpu_throws_warning by @SunMarc in #11356
• support Wan-FLF2V by @yiyixuxu in #11353
• Fix: StableDiffusionXLControlNetAdapterInpaintPipeline incorrectly inherited StableDiffusionLoraLoaderMixin by @Kazuki-Yoda in #11357
• update output for Hidream transformer by @yiyixuxu in #11366
• [Wan2.1-FLF2V] update conversion script by @yiyixuxu in #11365
• [Flux LoRAs] fix lr scheduler bug in distributed scenarios by @linoytsaban in #11242
• [train_dreambooth_lora_sdxl.py] Fix the LR Schedulers when num_train_epochs is passed in a distributed training env by @kghamilton89 in #11240
• fix issue that training flux controlnet was unstable and validation r… by @PromeAIpro in #11373
• Fix Wan I2V prepare_latents dtype by @a-r-r-o-w in #11371
• [BUG] fixes in kadinsky pipeline by @ishan-modi in #11080
• Add Serialized Type Name kwarg in Model Output by @anzr299 in #10502
• [cogview4][feat] Support attention mechanism with variable-length support and batch packing by @OleehyO in #11349
• Support different-length pos/neg prompts for FLUX.1-schnell variants like Chroma by @josephrocca in #11120
• [Refactor] Minor Improvement for import utils by @ishan-modi in #11161
• Add stochastic sampling to FlowMatchEulerDiscreteScheduler by @apolinario in #11369
• [LoRA] add LoRA support to HiDream and fine-tuning script by @linoytsaban in #11281
• Update modeling imports by @a-r-r-o-w in #11129
• [HiDream] move deprecation to 0.35.0 by @yiyixuxu in #11384
• Update README_hidream.md by @AMEERAZAM08 in #11386
• Fix group offloading with block_level and use_stream=True by @a-r-r-o-w in #11375
• [train_dreambooth_flux] Add LANCZOS as the default interpolation mode for image resizing by @ishandutta0098 in #11395
• [Feature] Added Xlab Controlnet support by @ishan-modi in #11249
• Kolors additional pipelines, community contrib by @Teriks in #11372
• [HiDream LoRA] optimizations + small updates by @linoytsaban in #11381
• Fix Flux IP adapter argument in the pipeline example by @AeroDEmi in #11402
• [BUG] fixed WAN docstring by @ishan-modi in #11226
• Fix typos in strings and comments by @co63oc in #11407
• [train_dreambooth_lora.py] Set LANCZOS as default interpolation mode for resizing by @merterbak in #11421
• [tests] add tests to check for graph breaks, recompilation, cuda syncs in pipelines during torch.compile() by @sayakpaul in #11085
• enable group_offload cases and quanto cases on XPU by @yao-matrix in #11405
• enable test_layerwise_casting_memory cases on XPU by @yao-matrix in #11406
• [tests] fix import. by @sayakpaul in #11434
• [train_text_to_image] Better image interpolation in training scripts follow up by @tongyu0924 in #11426
• [train_text_to_image_lora] Better image interpolation in training scripts follow up by @tongyu0924 in #11427
• enable 28 GGUF test cases on XPU by @yao-matrix in #11404
• [Hi-Dream LoRA] fix bug in validation by @linoytsaban in #11439
• Fixing missing provider options argument by @urpetkov-amd in #11397
• Set LANCZOS as the default interpolation for image resizing in ControlNet training by @YoulunPeng in #11449
• Raise warning instead of error for block offloading with streams by @a-r-r-o-w in #11425
• enable marigold_intrinsics cases on XPU by @yao-matrix in #11445
• torch.compile fullgraph compatibility for Hunyuan Video by @a-r-r-o-w in #11457
• enable consistency test cases on XPU, all passed by @yao-matrix in #11446
• enable unidiffuser test cases on xpu by @yao-matrix in #11444
• Add generic support for Intel Gaudi accelerator (hpu device) by @dsocek in #11328
• Add StableDiffusion3InstructPix2PixPipeline by @xduzhangjiayu in #11378
• make safe diffusion test cases pass on XPU and A100 by @yao-matrix in #11458
• [test_models_transformer_hunyuan_video] help us test torch.compile() for impactful models by @tongyu0924 in #11431
• Add LANCZOS as default interplotation mode. by @Va16hav07 in #11463
• make autoencoders. controlnet_flux and wan_transformer3d_single_file pass on xpu by @yao-matrix in #11461
• [WAN] fix recompilation issues by @sayakpaul in #11475
• Fix typos in docs and comments by @co63oc in #11416
• [tests] xfail recent pipeline tests for specific methods. by @sayakpaul in #11469
• cache packages_distributions by @vladmandic in #11453
• [docs] Memory optims by @stevhliu in #11385
• [docs] Adapters by @stevhliu in #11331
• [train_dreambooth_lora_sdxl_advanced] Add LANCZOS as the default interpolation mode for image resizing by @yuanjua in #11471
• [train_dreambooth_lora_flux_advanced] Add LANCZOS as the default interpolation mode for image resizing by @ysurs in #11472
• enable semantic diffusion and stable diffusion panorama cases on XPU by @yao-matrix in #11459
• [Feature] Implement tiled VAE encoding/decoding for Wan model. by @c8ef in #11414
• [train_text_to_image_sdxl]Add LANCZOS as default interpolation mode for image resizing by @ParagEkbote in #11455
• [train_dreambooth_lora_sdxl] Add --image_interpolation_mode option for image resizing (default to lanczos) by @MinJu-Ha in #11490
• [train_dreambooth_lora_lumina2] Add LANCZOS as the default interpolation mode for image resizing by @cjfghk5697 in #11491
• [training] feat: enable quantization for hidream lora training. by @sayakpaul in #11494
• Set LANCZOS as the default interpolation method for image resizing. by @yijun-lee in #11492
• Update training script for txt to img sdxl with lora supp with new interpolation. by @RogerSinghChugh in #11496
• Fix torchao docs typo for fp8 granular quantization by @a-r-r-o-w in #11473
• Update setup.py to pin min version of peft by @sayakpaul in #11502
• update dep table. by @sayakpaul in #11504
• [LoRA] use removeprefix to preserve sanity. by @sayakpaul in #11493
• Hunyuan Video Framepack by @a-r-r-o-w in #11428
• enable lora cases on XPU by @yao-matrix in #11506
• [lora_conversion] Enhance key handling for OneTrainer components in LORA conversion utility by @iamwavecut in #11441)
• [docs] minor updates to bitsandbytes docs. by @sayakpaul in #11509
• Cosmos by @a-r-r-o-w in #10660
• clean up the Init for stable_diffusion by @yiyixuxu in #11500
• fix audioldm by @sayakpaul (direct commit on v0.34.0-release)
• Revert "fix audioldm" by @sayakpaul (direct commit on v0.34.0-release)
• [LoRA] make lora alpha and dropout configurable by @linoytsaban in #11467
• Add cross attention type for Sana-Sprint training in diffusers. by @scxue in #11514
• Conditionally import torchvision in Cosmos transformer by @a-r-r-o-w in #11524
• [tests] fix audioldm2 for transformers main. by @sayakpaul in #11522
• feat: pipeline-level quantization config by @sayakpaul in #11130
• [Tests] Enable more general testing for torch.compile() with LoRA hotswapping by @sayakpaul in #11322
• [LoRA] support non-diffusers hidream loras by @sayakpaul in #11532
• enable 7 cases on XPU by @yao-matrix in #11503
• [LTXPipeline] Update latents dtype to match VAE dtype by @james-p-xu in #11533
• enable dit integration cases on xpu by @yao-matrix in #11523
• enable print_env on xpu by @yao-matrix in #11507
• Change Framepack transformer layer initialization order by @a-r-r-o-w in #11535
• [tests] add tests for framepack transformer model. by @sayakpaul in #11520
• Hunyuan Video Framepack F1 by @a-r-r-o-w in #11534
• enable several pipeline integration tests on XPU by @yao-matrix in #11526
• [test_models_transformer_ltx.py] help us test torch.compile() for impactful models by @cjfghk5697 in #11512
• Add VisualCloze by @lzyhha in #11377
• Fix typo in train_diffusion_orpo_sdxl_lora_wds.py by @Meeex2 in #11541
• fix: remove torch_dtype="auto" option from docstrings by @johannaSommer in #11513
• [train_dreambooth.py] Fix the LR Schedulers when num_train_epochs is passed in a distributed training env by @kghamilton89 in #11239
• [LoRA] small change to support Hunyuan LoRA Loading for FramePack by @linoytsaban in #11546
• LTX Video 0.9.7 by @a-r-r-o-w in #11516
• [tests] Enable testing for HiDream transformer by @sayakpaul in #11478
• Update pipeline_flux_img2img.py to add missing vae_slicing and vae_tiling calls. by @Meatfucker in #11545
• Fix deprecation warnings in test_ltx_image2video.py by @AChowdhury1211 in #11538
• [tests] Add torch.compile test for UNet2DConditionModel by @olccihyeon in #11537
• [Single File] GGUF/Single File Support for HiDream by @DN6 in #11550
• [gguf] Refactor torch_function to avoid unnecessary computation by @anijain2305 in #11551
• [tests] add tests for combining layerwise upcasting and groupoffloading. by @sayakpaul in #11558
• [docs] Regional compilation docs by @sayakpaul in #11556
• enhance value guard of _device_agnostic_dispatch by @yao-matrix in #11553
• Doc update by @Player256 in #11531
• Revert error to warning when loading LoRA from repo with multiple weights by @apolinario in #11568
• [docs] tip for group offloding + quantization by @sayakpaul in #11576
• [LoRA] support non-diffusers LTX-Video loras by @linoytsaban in #11572
• [WIP][LoRA] start supporting kijai wan lora. by @sayakpaul in #11579
• [Single File] Fix loading for LTX 0.9.7 transformer by @DN6 in #11578
• Use HF Papers by @qgallouedec in #11567
• LTX 0.9.7-distilled; documentation improvements by @a-r-r-o-w in #11571
• [LoRA] kijai wan lora support for I2V by @linoytsaban in #11588
• docs: fix invalid links by @osrm in #11505
• [docs] Remove fast diffusion tutorial by @stevhliu in #11583
• RegionalPrompting: Inherit from Stable Diffusion by @b-sai in #11525
• [chore] allow string device to be passed to randn_tensor. by @sayakpaul in #11559
• Type annotation fix by @DN6 in #11597
• [LoRA] minor fix for load_lora_weights() for Flux and a test by @sayakpaul in #11595
• Update Intel Gaudi doc by @regisss in #11479
• enable pipeline test cases on xpu by @yao-matrix in #11527
• [Feature] AutoModel can load components using model_index.json by @ishan-modi in #11401
• [docs] Pipeline-level quantization by @stevhliu in #11604
• Fix bug when variant and safetensor file does not match by @kaixuanliu in #11587
• [tests] Changes to the torch.compile() CI and tests by @sayakpaul in #11508
• Fix mixed variant downloading by @DN6 in #11611
• fix security issue in build docker ci by @sayakpaul in #11614
• Make group offloading compatible with torch.compile() by @sayakpaul in #11605
• [training docs] smol update to README files by @linoytsaban in #11616
• Adding NPU for get device function by @leisuzz in #11617
• [LoRA] improve LoRA fusion tests by @sayakpaul in #11274
• [Sana Sprint] add image-to-image pipeline by @linoytsaban in #11602
• [CI] fix the filename for displaying failures in lora ci. by @sayakpaul in #11600
• [docs] PyTorch 2.0 by @stevhliu in #11618
• [textual_inversion_sdxl.py] fix lr scheduler steps count by @yuanjua in #11557
• Fix wrong indent for examples of controlnet script by @Justin900429 in #11632
• removing unnecessary else statement by @YanivDorGalron in #11624
• enable group_offloading and PipelineDeviceAndDtypeStabilityTests on XPU, all passed by @yao-matrix in #11620
• Bug: Fixed Image 2 Image example by @vltmedia in #11619
• typo fix in pipeline_flux.py by @YanivDorGalron in #11623
• Fix typos in strings and comments by @co63oc in #11476
• [docs] update torchao doc link by @sayakpaul in #11634
• Use float32 RoPE freqs in Wan with MPS backends by @hvaara in #11643
• [chore] misc changes in the bnb tests for consistency. by @sayakpaul in #11355
• [tests] chore: rename lora model-level tests. by @sayakpaul in #11481
• [docs] Caching methods by @stevhliu in #11625
• [docs] Model cards by @stevhliu in #11112
• [CI] Some improvements to Nightly reports summaries by @DN6 in #11166
• [chore] bring PipelineQuantizationConfig at the top of the import chain. by @sayakpaul in #11656
• [examples] flux-control: use num_training_steps_for_scheduler by @Markus-Pobitzer in #11662
• use deterministic to get stable result by @jiqing-feng in #11663
• [tests] add test for torch.compile + group offloading by @sayakpaul in #11670
• Wan VACE by @a-r-r-o-w in #11582
• fixed axes_dims_rope init (huggingface#11641) by @sofinvalery in #11678
• [tests] Fix how compiler mixin classes are used by @sayakpaul in #11680
• Introduce DeprecatedPipelineMixin to simplify pipeline deprecation process by @DN6 in #11596
• Add community class StableDiffusionXL_T5Pipeline by @ppbrown in #11626
• Update pipeline_flux_inpaint.py to fix padding_mask_crop returning only the inpainted area by @Meatfucker in #11658
• Allow remote code repo names to contain "." by @akasharidas in #11652
• [LoRA] support Flux Control LoRA with bnb 8bit. by @sayakpaul in #11655
• [Wan] Fix VAE sampling mode in WanVideoToVideoPipeline by @tolgacangoz in #11639
• enable torchao test cases on XPU and switch to device agnostic APIs for test cases by @yao-matrix in #11654
• [tests] tests for compilation + quantization (bnb) by @sayakpaul in #11672
• [tests] model-level device_map clarifications by @sayakpaul in #11681
• Improve Wan docstrings by @a-r-r-o-w in #11689
• Set _torch_version to N/A if torch is disabled. by @rasmi in #11645
• Avoid DtoH sync from access of nonzero() item in scheduler by @jbschlosser in #11696
• Apply Occam's Razor in position embedding calculation by @tolgacangoz in #11562
• [docs] add compilation bits to the bitsandbytes docs. by @sayakpaul in #11693
• swap out token for style bot. by @sayakpaul in #11701
• [docs] mention fp8 benefits on supported hardware. by @sayakpaul in #11699
• Support Wan AccVideo lora by @a-r-r-o-w in #11704
• [LoRA] parse metadata from LoRA and save metadata by @sayakpaul in #11324
• Cosmos Predict2 by @a-r-r-o-w in #11695
• Chroma Pipeline by @Ednaordinary in #11698
• [LoRA ]fix flux lora loader when return_metadata is true for non-diffusers by @sayakpaul in #11716
• [training] show how metadata stuff should be incorporated in training scripts. by @sayakpaul in #11707
• Fix misleading comment by @carlthome in #11722
• Add Pruna optimization framework documentation by @davidberenstein1957 in #11688
• Support more Wan loras (VACE) by @a-r-r-o-w in #11726
• [LoRA training] update metadata use for lora alpha + README by @linoytsaban in #11723
• ⚡️ Speed up method AutoencoderKLWan.clear_cache by 886% by @misrasaurabh1 in #11665
• [training] add ds support to lora hidream by @leisuzz in #11737
• [tests] device_map tests for all models. by @sayakpaul in #11708
• [chore] change to 2025 licensing for remaining by @sayakpaul in #11741
• Chroma Follow Up by @DN6 in #11725
• [Quantizers] add is_compileable property to quantizers. by @sayakpaul in #11736
• Update more licenses to 2025 by @a-r-r-o-w in #11746
• Add missing HiDream license by @a-r-r-o-w in #11747
• Bump urllib3 from 2.2.3 to 2.5.0 in /examples/server by @dependabot[bot] in #11748
• [LoRA] refactor lora loading at the model-level by @sayakpaul in #11719
• [CI] Fix WAN VACE tests by @DN6 in #11757
• [CI] Fix SANA tests by @DN6 in #11756
• Fix HiDream pipeline test module by @DN6 in #11754
• make group offloading work with disk/nvme transfers by @sayakpaul in #11682
• Update Chroma Docs by @DN6 in #11753
• fix invalid component handling behaviour in PipelineQuantizationConfig by @sayakpaul in #11750
• Fix failing cpu offload test for LTX Latent Upscale by @DN6 in #11755
• [docs] Quantization + torch.compile + offloading by @stevhliu in #11703
• [docs] device_map by @stevhliu in #11711
• [docs] LoRA scale scheduling by @stevhliu in #11727
• Fix dimensionalities in apply_rotary_emb functions' comments by @tolgacangoz in #11717
• enable deterministic in bnb 4 bit tests by @jiqing-feng in #11738
• enable cpu offloading of new pipelines on XPU & use device agnostic empty to make pipelines work on XPU by @yao-matrix in #11671
• [tests] properly skip tests instead of return by @sayakpaul in #11771
• [CI] Skip ONNX Upscale tests by @DN6 in #11774
• [Wan] Fix mask padding in Wan VACE pipeline. by @bennyguo in #11778
• Add --lora_alpha and metadata handling to train_dreambooth_lora_sana.py by @imbr92 in #11744
• [docs] minor cleanups in the lora docs. by @sayakpaul in #11770
• [lora] only remove hooks that we add back by @yiyixuxu in #11768
• [tests] Fix HunyuanVideo Framepack device tests by @a-r-r-o-w in #11789
• [chore] raise as early as possible in group offloading by @sayakpaul in #11792
• [tests] Fix group offloading and layerwise casting test interaction by @a-r-r-o-w in #11796
• guard omnigen processor. by @sayakpaul in #11799
• Release: v0.34.0 by @sayakpaul (direct commit on v0.34.0-release)

Significant community contributions

The following contributors have made significant changes to the library over the last release:

• @yao-matrix
  • fix test_vanilla_funetuning failure on XPU and A100 (#11263)
  • make test_stable_diffusion_inpaint_fp16 pass on XPU (#11264)
  • make test_dict_tuple_outputs_equivalent pass on XPU (#11265)
  • make test_instant_style_multiple_masks pass on XPU (#11266)
  • make KandinskyV22PipelineInpaintCombinedFastTests::test_float16_inference pass on XPU (#11308)
  • make test_stable_diffusion_karras_sigmas pass on XPU (#11310)
  • fix CPU offloading related fail cases on XPU (#11288)
  • enable 2 test cases on XPU (#11332)
  • enable group_offload cases and quanto cases on XPU (#11405)
  • enable test_layerwise_casting_memory cases on XPU (#11406)
  • enable 28 GGUF test cases on XPU (#11404)
  • enable marigold_intrinsics cases on XPU (#11445)
  • enable consistency test cases on XPU, all passed (#11446)
  • enable unidiffuser test cases on xpu (#11444)
  • make safe diffusion test cases pass on XPU and A100 (#11458)
  • make autoencoders. controlnet_flux and wan_transformer3d_single_file pass on xpu (#11461)
  • enable semantic diffusion and stable diffusion panorama cases on XPU (#11459)
  • enable lora cases on XPU (#11506)
  • enable 7 cases on XPU (#11503)
  • enable dit integration cases on xpu (#11523)
  • enable print_env on xpu (#11507)
  • enable several pipeline integration tests on XPU (#11526)
  • enhance value guard of _device_agnostic_dispatch (#11553)
  • enable pipeline test cases on xpu (#11527)
  • enable group_offloading and PipelineDeviceAndDtypeStabilityTests on XPU, all passed (#11620)
  • enable torchao test cases on XPU and switch to device agnostic APIs for test cases (#11654)
  • enable cpu offloading of new pipelines on XPU & use device agnostic empty to make pipelines work on XPU (#11671)
• @hlky
  • Fix LTX 0.9.5 single file (#11271)
  • HiDream Image (#11231)
  • Use float32 on mps or npu in transformer_hidream_image's rope (#11316)
  • Fix vae.Decoder prev_output_channel (#11280)
• @quickjkee
  • flow matching lcm scheduler (#11170)
• @ishan-modi
  • [ControlNet] Adds controlnet for SanaTransformer (#11040)
  • [BUG] fixed _toctree.yml alphabetical ordering (#11277)
  • [BUG] fixes in kadinsky pipeline (#11080)
  • [Refactor] Minor Improvement for import utils (#11161)
  • [Feature] Added Xlab Controlnet support (#11249)
  • [BUG] fixed WAN docstring (#11226)
  • [Feature] AutoModel can load components using model_index.json (#11401)
• @linoytsaban
  • [HiDream] code example (#11317)
  • [Flux LoRAs] fix lr scheduler bug in distributed scenarios (#11242)
  • [LoRA] add LoRA support to HiDream and fine-tuning script (#11281)
  • [HiDream LoRA] optimizations + small updates (#11381)
  • [Hi-Dream LoRA] fix bug in validation (#11439)
  • [LoRA] make lora alpha and dropout configurable (#11467)
  • [LoRA] small change to support Hunyuan LoRA Loading for FramePack (#11546)
  • [LoRA] support non-diffusers LTX-Video loras (#11572)
  • [LoRA] kijai wan lora support for I2V (#11588)
  • [training docs] smol update to README files (#11616)
  • [Sana Sprint] add image-to-image pipeline (#11602)
  • [LoRA training] update metadata use for lora alpha + README (#11723)
• @hameerabbasi
  • [LoRA] Add LoRA support to AuraFlow (#10216)
• @DN6
  • Fix Hunyuan I2V for transformers>4.47.1 (#11293)
  • Hunyuan I2V fast tests fix (#11341)
  • [Single File] GGUF/Single File Support for HiDream (#11550)
  • [Single File] Fix loading for LTX 0.9.7 transformer (#11578)
  • Type annotation fix (#11597)
  • Fix mixed variant downloading (#11611)
  • [CI] Some improvements to Nightly reports summaries (#11166)
  • Introduce DeprecatedPipelineMixin to simplify pipeline deprecation process (#11596)
  • Chroma Follow Up (#11725)
  • [CI] Fix WAN VACE tests (#11757)
  • [CI] Fix SANA tests (#11756)
  • Fix HiDream pipeline test module (#11754)
  • Update Chroma Docs (#11753)
  • Fix failing cpu offload test for LTX Latent Upscale (#11755)
  • [CI] Skip ONNX Upscale tests (#11774)
• @yiyixuxu
  • [Hi Dream] follow-up (#11296)
  • support Wan-FLF2V (#11353)
  • update output for Hidream transformer (#11366)
  • [Wan2.1-FLF2V] update conversion script (#11365)
  • [HiDream] move deprecation to 0.35.0 (#11384)
  • clean up the Init for stable_diffusion (#11500)
  • [lora] only remove hooks that we add back (#11768)
• @Teriks
  • Kolors additional pipelines, community contrib (#11372)
• @co63oc
  • Fix typos in strings and comments (#11407)
  • Fix typos in docs and comments (#11416)
  • Fix typos in strings and comments (#11476)
• @xduzhangjiayu
  • Add StableDiffusion3InstructPix2PixPipeline (#11378)
• @scxue
  • Add cross attention type for Sana-Sprint training in diffusers. (#11514)
• @lzyhha
  • Add VisualCloze (#11377)
• @b-sai
  • RegionalPrompting: Inherit from Stable Diffusion (#11525)
• @Ednaordinary
  • Chroma Pipeline (#11698)

All commits

• fix ftfy import for wan pipelines by @yiyixuxu in #11262