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.github/workflows/nightly_tests.yml

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- backend: "optimum_quanto"
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test_location: "quanto"
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additional_deps: []
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- backend: "nvidia_modelopt"
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test_location: "modelopt"
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additional_deps: []
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runs-on:
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group: aws-g6e-xlarge-plus
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container:

.github/workflows/pr_flax_dependency_test.yml

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README.md

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## Installation
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We recommend installing 🤗 Diffusers in a virtual environment from PyPI or Conda. For more details about installing [PyTorch](https://pytorch.org/get-started/locally/) and [Flax](https://flax.readthedocs.io/en/latest/#installation), please refer to their official documentation.
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We recommend installing 🤗 Diffusers in a virtual environment from PyPI or Conda. For more details about installing [PyTorch](https://pytorch.org/get-started/locally/), please refer to their official documentation.
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### PyTorch
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conda install -c conda-forge diffusers
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```
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### Flax
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With `pip` (official package):
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```bash
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pip install --upgrade diffusers[flax]
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```
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### Apple Silicon (M1/M2) support
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Please refer to the [How to use Stable Diffusion in Apple Silicon](https://huggingface.co/docs/diffusers/optimization/mps) guide.

docker/diffusers-flax-cpu/Dockerfile

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docker/diffusers-flax-tpu/Dockerfile

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docs/source/en/_toctree.yml

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- local: stable_diffusion
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title: Basic performance
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- title: DiffusionPipeline
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- title: Pipelines
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isExpanded: false
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sections:
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- local: using-diffusers/loading
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title: Load pipelines
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title: DiffusionPipeline
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- local: tutorials/autopipeline
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title: AutoPipeline
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- local: using-diffusers/custom_pipeline_overview
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title: Load community pipelines and components
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title: Community pipelines and components
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- local: using-diffusers/callback
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title: Pipeline callbacks
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- local: using-diffusers/reusing_seeds
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title: Reproducible pipelines
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title: Reproducibility
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- local: using-diffusers/schedulers
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title: Load schedulers and models
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- local: using-diffusers/models
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title: Models
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- local: using-diffusers/scheduler_features
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title: Scheduler features
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- local: using-diffusers/other-formats
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title: Model files and layouts
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- local: using-diffusers/push_to_hub
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title: Push files to the Hub
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title: Sharing pipelines and models
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- title: Adapters
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title: Batch inference
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- local: training/distributed_inference
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title: Distributed inference
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- local: using-diffusers/scheduler_features
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title: Scheduler features
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- local: using-diffusers/callback
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title: Pipeline callbacks
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- local: using-diffusers/reusing_seeds
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title: Reproducible pipelines
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- local: using-diffusers/image_quality
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title: Controlling image quality
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- title: Inference optimization
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- local: optimization/memory
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title: Reduce memory usage
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- local: optimization/speed-memory-optims
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title: Compile and offloading quantized models
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title: Compiling and offloading quantized models
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- title: Community optimizations
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sections:
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- local: optimization/pruna
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title: xDiT
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- local: optimization/para_attn
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title: ParaAttention
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- local: using-diffusers/image_quality
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title: FreeU
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- title: Hybrid Inference
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title: torchao
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- local: quantization/quanto
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title: quanto
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- local: quantization/modelopt
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title: NVIDIA ModelOpt
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- title: Model accelerators and hardware
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isExpanded: false
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sections:
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- local: using-diffusers/stable_diffusion_jax_how_to
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title: JAX/Flax
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- local: optimization/onnx
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title: ONNX
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title: AllegroTransformer3DModel
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- local: api/models/aura_flow_transformer2d
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title: AuraFlowTransformer2DModel
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- local: api/models/bria_transformer
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title: BriaTransformer2DModel
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- local: api/models/chroma_transformer
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title: ChromaTransformer2DModel
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title: AutoPipeline
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- local: api/pipelines/blip_diffusion
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title: BLIP-Diffusion
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- local: api/pipelines/bria_3_2
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title: Bria 3.2
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- local: api/pipelines/chroma
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title: Chroma
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- local: api/pipelines/cogvideox

docs/source/en/api/image_processor.md

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[[autodoc]] image_processor.VaeImageProcessor
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## InpaintProcessor
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The [`InpaintProcessor`] accepts `mask` and `image` inputs and process them together. Optionally, it can accept padding_mask_crop and apply mask overlay.
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[[autodoc]] image_processor.InpaintProcessor
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The [`VaeImageProcessorLDM3D`] accepts RGB and depth inputs and returns RGB and depth outputs.

docs/source/en/api/models/autoencoderkl.md

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## DecoderOutput
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[[autodoc]] models.autoencoders.vae.DecoderOutput
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## FlaxAutoencoderKL
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[[autodoc]] FlaxAutoencoderKL
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## FlaxAutoencoderKLOutput
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[[autodoc]] models.vae_flax.FlaxAutoencoderKLOutput
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## FlaxDecoderOutput
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[[autodoc]] models.vae_flax.FlaxDecoderOutput
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<!--Copyright 2025 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# BriaTransformer2DModel
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A modified flux Transformer model from [Bria](https://huggingface.co/briaai/BRIA-3.2)
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## BriaTransformer2DModel
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[[autodoc]] BriaTransformer2DModel

docs/source/en/api/models/controlnet.md

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## ControlNetOutput
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[[autodoc]] models.controlnets.controlnet.ControlNetOutput
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## FlaxControlNetModel
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[[autodoc]] FlaxControlNetModel
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## FlaxControlNetOutput
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[[autodoc]] models.controlnets.controlnet_flax.FlaxControlNetOutput

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