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docker/diffusers-onnxruntime-cuda/Dockerfile

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# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
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RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
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python3.10 -m uv pip install --no-cache-dir \
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"torch<2.5.0" \
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torch \
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torchvision \
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torchaudio \
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"onnxruntime-gpu>=1.13.1" \

docker/diffusers-pytorch-compile-cuda/Dockerfile

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# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
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RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
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python3.10 -m uv pip install --no-cache-dir \
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"torch<2.5.0" \
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torch \
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torchvision \
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torchaudio \
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invisible_watermark && \

docker/diffusers-pytorch-cpu/Dockerfile

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# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
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RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
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python3.10 -m uv pip install --no-cache-dir \
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"torch<2.5.0" \
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torch \
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torchvision \
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torchaudio \
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invisible_watermark \

docker/diffusers-pytorch-cuda/Dockerfile

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# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
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RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
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python3.10 -m uv pip install --no-cache-dir \
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"torch<2.5.0" \
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torch \
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torchvision \
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torchaudio \
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invisible_watermark && \

docker/diffusers-pytorch-xformers-cuda/Dockerfile

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# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
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RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
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python3.10 -m pip install --no-cache-dir \
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"torch<2.5.0" \
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torch \
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torchvision \
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torchaudio \
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invisible_watermark && \

docs/source/en/_toctree.yml

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- sections:
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- local: using-diffusers/overview_techniques
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title: Overview
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- local: using-diffusers/create_a_server
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title: Create a server
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- local: training/distributed_inference
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title: Distributed inference
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- local: using-diffusers/merge_loras
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title: LatteTransformer3DModel
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- local: api/models/lumina_nextdit2d
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title: LuminaNextDiT2DModel
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- local: api/models/mochi_transformer3d
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title: MochiTransformer3DModel
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- local: api/models/pixart_transformer2d
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title: PixArtTransformer2DModel
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- local: api/models/prior_transformer
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title: AutoencoderKLAllegro
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- local: api/models/autoencoderkl_cogvideox
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title: AutoencoderKLCogVideoX
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- local: api/models/autoencoderkl_mochi
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title: AutoencoderKLMochi
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- local: api/models/asymmetricautoencoderkl
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title: AsymmetricAutoencoderKL
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- local: api/models/consistency_decoder_vae
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title: Lumina-T2X
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- local: api/pipelines/marigold
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title: Marigold
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- local: api/pipelines/mochi
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title: Mochi
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- local: api/pipelines/panorama
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title: MultiDiffusion
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- local: api/pipelines/musicldm
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<!-- Copyright 2024 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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# AutoencoderKLMochi
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The 3D variational autoencoder (VAE) model with KL loss used in [Mochi](https://github.com/genmoai/models) was introduced in [Mochi 1 Preview](https://huggingface.co/genmo/mochi-1-preview) by Tsinghua University & ZhipuAI.
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The model can be loaded with the following code snippet.
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```python
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from diffusers import AutoencoderKLMochi
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vae = AutoencoderKLMochi.from_pretrained("genmo/mochi-1-preview", subfolder="vae", torch_dtype=torch.float32).to("cuda")
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```
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## AutoencoderKLMochi
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[[autodoc]] AutoencoderKLMochi
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- decode
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- all
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## DecoderOutput
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[[autodoc]] models.autoencoders.vae.DecoderOutput

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

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

docs/source/en/api/models/controlnet_sd3.md

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## SD3ControlNetOutput
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[[autodoc]] models.controlnet_sd3.SD3ControlNetOutput
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[[autodoc]] models.controlnets.controlnet_sd3.SD3ControlNetOutput
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<!-- Copyright 2024 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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# MochiTransformer3DModel
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A Diffusion Transformer model for 3D video-like data was introduced in [Mochi-1 Preview](https://huggingface.co/genmo/mochi-1-preview) by Genmo.
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The model can be loaded with the following code snippet.
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```python
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from diffusers import MochiTransformer3DModel
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vae = MochiTransformer3DModel.from_pretrained("genmo/mochi-1-preview", subfolder="transformer", torch_dtype=torch.float16).to("cuda")
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```
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## MochiTransformer3DModel
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[[autodoc]] MochiTransformer3DModel
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## Transformer2DModelOutput
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[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

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