Bump diffusers from 0.35.2 to 0.36.0 in /tests/python_tests

[dependabot]

Bumps diffusers from 0.35.2 to 0.36.0.

Release notes

Sourced from diffusers's releases.

Diffusers 0.36.0: Pipelines galore, new caching method, training scripts, and more 🎄

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 huggingface/diffusers#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 huggingface/diffusers#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 huggingface/diffusers#12545.
• Kandinsky Image Lite: Kandinsky 5.0 Image Lite is a lightweight image generation model (6B parameters). Thanks to @​leffff for contributing this in huggingface/diffusers#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 huggingface/diffusers#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 huggingface/diffusers#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 huggingface/diffusers#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 huggingface/diffusers#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 huggingface/diffusers#12463 for more details.
• Diffusers backend in SGLang: sgl-project/sglang#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.

... (truncated)

Commits

• 9380e58 Release: v0.36.0-release
• 2246d2c Add ZImageImg2ImgPipeline (#12751)
• 671149e [HunyuanVideo1.5] support step-distilled (#12802)
• f67639b add post init for safty checker (#12794)
• 5a74319 Update the TensorRT-ModelOPT to Nvidia-ModelOPT (#12793)
• 6290fdf [Feat] TaylorSeer Cache (#12648)
• 256e010 Improve docstrings and type hints in scheduling_deis_multistep.py (#12796)
• 8430ac2 [docs] minor fixes to kandinsky docs (#12797)
• bb9e713 move kandisnky docs.
• c98c157 [Docs] Add Z-Image docs (#12775)
• Additional commits viewable in compare view

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