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docs/source/en/_toctree.yml

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title: DiTTransformer2DModel
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- local: api/models/easyanimate_transformer3d
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title: EasyAnimateTransformer3DModel
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- local: api/models/flux2_transformer
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title: Flux2Transformer2DModel
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- local: api/models/flux_transformer
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title: FluxTransformer2DModel
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- local: api/models/hidream_image_transformer
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title: EasyAnimate
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- local: api/pipelines/flux
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title: Flux
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- local: api/pipelines/flux2
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title: Flux2
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- local: api/pipelines/control_flux_inpaint
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title: FluxControlInpaint
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- local: api/pipelines/hidream

docs/source/en/api/loaders/lora.md

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- [`CogView4LoraLoaderMixin`] provides similar functions for [CogView4](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogview4).
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- [`AmusedLoraLoaderMixin`] is for the [`AmusedPipeline`].
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- [`HiDreamImageLoraLoaderMixin`] provides similar functions for [HiDream Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hidream)
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- [`QwenImageLoraLoaderMixin`] provides similar functions for [Qwen Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/qwen)
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- [`QwenImageLoraLoaderMixin`] provides similar functions for [Qwen Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/qwen).
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- [`Flux2LoraLoaderMixin`] provides similar functions for [Flux2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux2).
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- [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.
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> [!TIP]
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[[autodoc]] loaders.lora_pipeline.FluxLoraLoaderMixin
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## Flux2LoraLoaderMixin
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[[autodoc]] loaders.lora_pipeline.Flux2LoraLoaderMixin
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## CogVideoXLoraLoaderMixin
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[[autodoc]] loaders.lora_pipeline.CogVideoXLoraLoaderMixin
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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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# Flux2Transformer2DModel
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A Transformer model for image-like data from [Flux2](https://hf.co/black-forest-labs/FLUX.2-dev).
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## Flux2Transformer2DModel
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[[autodoc]] Flux2Transformer2DModel
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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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# Flux2
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<div class="flex flex-wrap space-x-1">
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<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
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<img alt="MPS" src="https://img.shields.io/badge/MPS-000000?style=flat&logo=apple&logoColor=white%22">
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</div>
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Flux.2 is the recent series of image generation models from Black Forest Labs, preceded by the [Flux.1](./flux.md) series. It is an entirely new model with a new architecture and pre-training done from scratch!
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Original model checkpoints for Flux can be found [here](https://huggingface.co/black-forest-labs). Original inference code can be found [here](https://github.com/black-forest-labs/flux2-dev).
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> [!TIP]
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> Flux2 can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to [this blog post](https://huggingface.co/blog/quanto-diffusers) to learn more.
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>
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> [Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
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## Flux2Pipeline
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[[autodoc]] Flux2Pipeline
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- all
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- __call__

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