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‎docs/source/en/_toctree.yml‎

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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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- local: api/models/cogvideox_transformer3d
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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/models/bria_transformer.md‎

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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/pipelines/bria_3_2.md‎

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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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# Bria 3.2
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Bria 3.2 is the next-generation commercial-ready text-to-image model. With just 4 billion parameters, it provides exceptional aesthetics and text rendering, evaluated to provide on par results to leading open-source models, and outperforming other licensed models.
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In addition to being built entirely on licensed data, 3.2 provides several advantages for enterprise and commercial use:
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- Efficient Compute - the model is X3 smaller than the equivalent models in the market (4B parameters vs 12B parameters other open source models)
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- Architecture Consistency: Same architecture as 3.1—ideal for users looking to upgrade without disruption.
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- Fine-tuning Speedup: 2x faster fine-tuning on L40S and A100.
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Original model checkpoints for Bria 3.2 can be found [here](https://huggingface.co/briaai/BRIA-3.2).
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Github repo for Bria 3.2 can be found [here](https://github.com/Bria-AI/BRIA-3.2).
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If you want to learn more about the Bria platform, and get free traril access, please visit [bria.ai](https://bria.ai).
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## Usage
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_As the model is gated, before using it with diffusers you first need to go to the [Bria 3.2 Hugging Face page](https://huggingface.co/briaai/BRIA-3.2), fill in the form and accept the gate. Once you are in, you need to login so that your system knows you’ve accepted the gate._
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Use the command below to log in:
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```bash
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hf auth login
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```
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## BriaPipeline
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[[autodoc]] BriaPipeline
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- all
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- __call__
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‎docs/source/en/api/pipelines/overview.md‎

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| [AudioLDM2](audioldm2) | text2audio |
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| [AuraFlow](auraflow) | text2image |
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| [BLIP Diffusion](blip_diffusion) | text2image |
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| [Bria 3.2](bria_3_2) | text2image |
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| [CogVideoX](cogvideox) | text2video |
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| [Consistency Models](consistency_models) | unconditional image generation |
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| [ControlNet](controlnet) | text2image, image2image, inpainting |

‎docs/source/zh/_toctree.yml‎

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- local: using-diffusers/schedulers
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title: Load schedulers and models
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- title: Inference
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isExpanded: false
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sections:
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- local: training/distributed_inference
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title: Distributed inference
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- title: Inference optimization
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isExpanded: false
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sections:
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- local: optimization/fp16
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title: Accelerate inference
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- local: optimization/cache
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title: Caching
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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: Community optimizations
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sections:
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- local: optimization/pruna
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title: Pruna
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- local: optimization/xformers
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title: xFormers
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- local: optimization/tome
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title: Token merging
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- local: optimization/deepcache
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title: DeepCache
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- local: optimization/tgate
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title: TGATE
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- local: optimization/xdit
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title: xDiT
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- local: optimization/para_attn
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title: ParaAttention
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- title: Hybrid Inference
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isExpanded: false
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sections:
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- local: hybrid_inference/overview
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title: Overview
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- local: hybrid_inference/vae_encode
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title: VAE Encode
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- local: hybrid_inference/api_reference
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title: API Reference
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- title: Modular Diffusers
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isExpanded: false
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sections:
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- local: modular_diffusers/overview
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title: Overview
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- local: modular_diffusers/quickstart
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title: Quickstart
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- local: modular_diffusers/modular_diffusers_states
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title: States
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- local: modular_diffusers/pipeline_block
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title: ModularPipelineBlocks
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- local: modular_diffusers/sequential_pipeline_blocks
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title: SequentialPipelineBlocks
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- local: modular_diffusers/loop_sequential_pipeline_blocks
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title: LoopSequentialPipelineBlocks
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- local: modular_diffusers/auto_pipeline_blocks
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title: AutoPipelineBlocks
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- local: modular_diffusers/modular_pipeline
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title: ModularPipeline
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- local: modular_diffusers/components_manager
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title: ComponentsManager
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- local: modular_diffusers/guiders
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title: Guiders
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- title: Training
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- local: training/text2image
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title: Text-to-image
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- local: training/kandinsky
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title: Kandinsky 2.2
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- local: training/wuerstchen
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title: Wuerstchen
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- local: training/controlnet
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title: ControlNet
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- local: training/instructpix2pix
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title: InstructPix2Pix
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- title: Methods
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- local: training/text_inversion
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title: Textual Inversion
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- local: training/dreambooth
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title: DreamBooth
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- local: training/lora
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title: LoRA
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- local: optimization/onnx
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title: ONNX
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- local: optimization/open_vino
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title: OpenVINO
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- local: optimization/coreml
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title: Core ML
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- local: optimization/mps
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title: Metal Performance Shaders (MPS)
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- local: optimization/habana
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title: Intel Gaudi
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- local: optimization/neuron
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title: AWS Neuron
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- title: Task recipes
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sections:
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- local: community_projects
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title: Projects built with Diffusers
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- local: conceptual/philosophy
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title: Philosophy
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- local: conceptual/contribution

‎docs/source/zh/community_projects.md‎

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<!--版权 2025 The HuggingFace Team。保留所有权利。
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根据Apache许可证，版本2.0（"许可证"）授权；除非符合许可证，否则不得使用此文件。您可以在
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http://www.apache.org/licenses/LICENSE-2.0
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获取许可证的副本。
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除非适用法律要求或书面同意，根据许可证分发的软件是按"原样"分发的，没有任何形式的明示或暗示的担保或条件。有关许可证的特定语言，请参阅许可证。
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-->
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# 社区项目
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欢迎来到社区项目。这个空间致力于展示我们充满活力的社区使用`diffusers`库创建的令人难以置信的工作和创新应用。
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本节旨在：
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- 突出使用`diffusers`构建的多样化和鼓舞人心的项目
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- 促进我们社区内的知识共享
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- 提供如何利用`diffusers`的实际例子
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探索愉快，感谢您成为Diffusers社区的一部分！
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<table>
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<tr>
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<th>项目名称</th>
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<th>描述</th>
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</tr>
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<tr style="border-top: 2px solid black">
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<td><a href="https://github.com/carson-katri/dream-textures"> dream-textures </a></td>
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<td>Stable Diffusion内置到Blender</td>
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</tr>
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<tr style="border-top: 2px solid black">
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<td><a href="https://github.com/megvii-research/HiDiffusion"> HiDiffusion </a></td>
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<td>仅通过添加一行代码即可提高扩散模型的分辨率和速度</td>
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</tr>
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<tr style="border-top: 2px solid black">
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<td><a href="https://github.com/lllyasviel/IC-Light"> IC-Light </a></td>
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<td>IC-Light是一个用于操作图像照明的项目</td>
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</tr>
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<tr style="border-top: 2px solid black">
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<td><a href="https://github.com/InstantID/InstantID"> InstantID </a></td>
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<td>InstantID：零样本身份保留生成在几秒钟内</td>
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</tr>
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<tr style="border-top: 2px solid black">
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<td><a href="https://github.com/Sanster/IOPaint"> IOPaint </a></td>
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<td>由SOTA AI模型驱动的图像修复工具。从您的图片中移除任何不需要的物体、缺陷、人物，或擦除并替换（由stable_diffusion驱动）图片上的任何内容。</td>
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</tr>
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<tr style="border-top: 2px solid black">
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<td><a href="https://github.com/bmaltais/kohya_ss"> Kohya </a></td>
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<td>Kohya的Stable Diffusion训练器的Gradio GUI</td>
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</tr>
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<tr style="border-top: 2px solid black">
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<td><a href="https://github.com/magic-research/magic-animate"> MagicAnimate </a></td>
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<td>MagicAnimate：使用扩散模型进行时间一致的人体图像动画</td>
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</tr>
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<tr style="border-top: 2px solid black">
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<td><a href="https://github.com/levihsu/OOTDiffusion"> OOTDiffusion </a></td>
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<td>基于潜在扩散的虚拟试穿控制</td>
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</tr>
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<tr style="border-top: 2px solid black">
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<td><a href="https://github.com/vladmandic/automatic"> SD.Next </a></td>
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<td>SD.Next: Stable Diffusion 和其他基于Diffusion的生成图像模型的高级实现</td>
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</tr>
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<tr style="border-top: 2px solid black">
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<td><a href="https://github.com/ashawkey/stable-dreamfusion"> stable-dreamfusion </a></td>
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<td>使用 NeRF + Diffusion 进行文本到3D & 图像到3D & 网格导出</td>
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</tr>
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<tr style="border-top: 2px solid black">
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<td><a href="https://github.com/HVision-NKU/StoryDiffusion"> StoryDiffusion </a></td>
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<td>StoryDiffusion 可以通过生成一致的图像和视频来创造一个神奇的故事。</td>
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</tr>
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<tr style="border-top: 2px solid black">
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<td><a href="https://github.com/cumulo-autumn/StreamDiffusion"> StreamDiffusion </a></td>
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<td>实时交互生成的管道级解决方案</td>
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</tr>
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<tr style="border-top: 2px solid black">
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<td><a href="https://github.com/Netwrck/stable-diffusion-server"> Stable Diffusion Server </a></td>
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<td>配置用于使用一个 stable diffusion 模型进行修复/生成/img2img 的服务器</td>
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</tr>
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<tr style="border-top: 2px solid black">
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<td><a href="https://github.com/suzukimain/auto_diffusers"> Model Search </a></td>
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<td>在 Civitai 和 Hugging Face 上搜索模型</td>
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</tr>
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<tr style="border-top: 2px solid black">
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<td><a href="https://github.com/beinsezii/skrample"> Skrample </a></td>
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<td>完全模块化的调度器功能，具有一流的 diffusers 集成。</td>
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</tr>
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</table>

‎docs/source/zh/hybrid_inference/api_reference.md‎

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# 混合推理 API 参考
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## 远程解码
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[[autodoc]] utils.remote_utils.remote_decode
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## 远程编码
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[[autodoc]] utils.remote_utils.remote_encode

‎docs/source/zh/hybrid_inference/overview.md‎

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<!--版权 2025 HuggingFace 团队。保留所有权利。
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根据 Apache 许可证 2.0 版本（"许可证"）授权；除非遵守许可证，否则不得使用此文件。
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您可以在以下网址获取许可证副本：
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http://www.apache.org/licenses/LICENSE-2.0
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除非适用法律要求或书面同意，否则根据许可证分发的软件按"原样"分发，不附带任何明示或暗示的担保或条件。请参阅许可证以了解具体的语言管理权限和限制。
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-->
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# 混合推理
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**通过混合推理赋能本地 AI 构建者**
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> [!TIP]
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> 混合推理是一项[实验性功能](https://huggingface.co/blog/remote_vae)。
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> 可以在此处提供反馈[此处](https://github.com/huggingface/diffusers/issues/new?template=remote-vae-pilot-feedback.yml)。
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## 为什么使用混合推理？
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混合推理提供了一种快速简单的方式来卸载本地生成需求。
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- 🚀 **降低要求：** 无需昂贵硬件即可访问强大模型。
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- 💎 **无妥协：** 在不牺牲性能的情况下实现最高质量。
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- 💰 **成本效益高：** 它是免费的！🤑
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- 🎯 **多样化用例：** 与 Diffusers � 和更广泛的社区完全兼容。
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- 🔧 **开发者友好：** 简单请求，快速响应。
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---
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## 可用模型
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* **VAE 解码 🖼️：** 快速将潜在表示解码为高质量图像，不影响性能或工作流速度。
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* **VAE 编码 🔢：** 高效将图像编码为潜在表示，用于生成和训练。
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* **文本编码器 📃（即将推出）：** 快速准确地计算提示的文本嵌入，确保流畅高质量的工作流。
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---
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## 集成
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* **[SD.Next](https://github.com/vladmandic/sdnext)：** 一体化 UI，直接支持混合推理。
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* **[ComfyUI-HFRemoteVae](https://github.com/kijai/ComfyUI-HFRemoteVae)：** 用于混合推理的 ComfyUI 节点。
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## 更新日志
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- 2025 年 3 月 10 日：添加了 VAE 编码
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- 2025 年 3 月 2 日：初始发布，包含 VAE 解码
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## 内容
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文档分为三个部分：
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* **VAE 解码** 学习如何使用混合推理进行 VAE 解码的基础知识。
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* **VAE 编码** 学习如何使用混合推理进行 VAE 编码的基础知识。
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* **API 参考** 深入了解任务特定设置和参数。

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