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.github/workflows/build_documentation.yml

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jobs:
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build:
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uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@2430c1ec91d04667414e2fa31ecfc36c153ea391 # main
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uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@e60a538eea9817ab312196d0d233604b01697265 # main
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with:
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commit_sha: ${{ github.sha }}
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install_libgl1: true
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package: diffusers
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notebook_folder: diffusers_doc
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languages: en ko zh ja pt
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custom_container: diffusers/diffusers-doc-builder
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pre_command: uv pip uninstall transformers huggingface_hub && UV_PRERELEASE=allow uv pip install -U transformers@git+https://github.com/huggingface/transformers.git
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secrets:
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token: ${{ secrets.HUGGINGFACE_PUSH }}

.github/workflows/build_pr_documentation.yml

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build:
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needs: check-links
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uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@90b4ee2c10b81b5c1a6367c4e6fc9e2fb510a7e3 # main
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uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@e60a538eea9817ab312196d0d233604b01697265 # main
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with:
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commit_sha: ${{ github.event.pull_request.head.sha }}
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pr_number: ${{ github.event.number }}
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install_libgl1: true
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package: diffusers
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languages: en ko zh ja pt
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custom_container: diffusers/diffusers-doc-builder
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pre_command: uv pip uninstall transformers huggingface_hub && UV_PRERELEASE=allow uv pip install -U transformers@git+https://github.com/huggingface/transformers.git

docker/diffusers-pytorch-cuda/Dockerfile

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@@ -40,11 +40,6 @@ RUN uv pip install --no-cache-dir \
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torchaudio==2.10.0 \
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--index-url https://download.pytorch.org/whl/cu129
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# Install compatible versions of numba/llvmlite for Python 3.10+
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RUN uv pip install --no-cache-dir \
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"llvmlite>=0.40.0" \
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"numba>=0.57.0"
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RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
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# Extra dependencies

docs/source/en/_toctree.yml

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title: HunyuanVideoTransformer3DModel
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- local: api/models/ideogram4_transformer2d
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title: Ideogram4Transformer2DModel
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- local: api/models/transformer_joyimage_edit_plus
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title: JoyImageEditPlusTransformer3DModel
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- local: api/models/transformer_joyimage
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title: JoyImageEditTransformer3DModel
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- local: api/models/krea2_transformer2d
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title: InstructPix2Pix
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- local: api/pipelines/joyimage_edit
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title: JoyImage Edit
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- local: api/pipelines/joyimage_edit_plus
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title: JoyImage Edit Plus
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- local: api/pipelines/kandinsky
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title: Kandinsky 2.1
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- local: api/pipelines/kandinsky_v22
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title: DDPMScheduler
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- local: api/schedulers/deis
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title: DEISMultistepScheduler
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- local: api/schedulers/discrete_ddim
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title: DiscreteDDIMScheduler
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- local: api/schedulers/multistep_dpm_solver_inverse
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title: DPMSolverMultistepInverse
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- local: api/schedulers/multistep_dpm_solver
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title: EDMDPMSolverMultistepScheduler
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- local: api/schedulers/edm_euler
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title: EDMEulerScheduler
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- local: api/schedulers/entropy_bound
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title: EntropyBoundScheduler
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- local: api/schedulers/euler_ancestral
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title: EulerAncestralDiscreteScheduler
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- local: api/schedulers/euler
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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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# JoyImageEditPlusTransformer3DModel
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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 JoyImageEditPlusTransformer3DModel
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transformer = JoyImageEditPlusTransformer3DModel.from_pretrained("jdopensource/JoyAI-Image-Edit-Plus-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
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```
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## JoyImageEditPlusTransformer3DModel
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[[autodoc]] JoyImageEditPlusTransformer3DModel
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## Transformer2DModelOutput
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[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

docs/source/en/api/pipelines/animatediff.md

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pipe.scheduler = scheduler
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# enable memory savings
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pipe.enable_vae_slicing()
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pipe.vae.enable_slicing()
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pipe.enable_model_cpu_offload()
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output = pipe(
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).to("cuda")
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# enable memory savings
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pipe.enable_vae_slicing()
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pipe.enable_vae_tiling()
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pipe.vae.enable_slicing()
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pipe.vae.enable_tiling()
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output = pipe(
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prompt="a panda surfing in the ocean, realistic, high quality",
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pipe.scheduler = scheduler
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# enable memory savings
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pipe.enable_vae_slicing()
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pipe.vae.enable_slicing()
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pipe.enable_model_cpu_offload()
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# helper function to load videos
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pipe.scheduler = scheduler
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pipe.enable_vae_slicing()
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pipe.vae.enable_slicing()
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pipe.scheduler = scheduler
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pipe.vae.enable_slicing()
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pipe.enable_vae_slicing()
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pipe.enable_vae_tiling()
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pipe.vae.enable_slicing()
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pipe.vae.enable_tiling()
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docs/source/en/api/pipelines/controlnet.md

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- __call__
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- enable_attention_slicing
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- disable_attention_slicing
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- enable_vae_slicing
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- disable_vae_slicing
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- enable_xformers_memory_efficient_attention
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- disable_xformers_memory_efficient_attention
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- load_textual_inversion
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- __call__
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- enable_attention_slicing
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- disable_attention_slicing
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- enable_vae_slicing
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- disable_vae_slicing
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- enable_xformers_memory_efficient_attention
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- load_textual_inversion
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- __call__
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- disable_attention_slicing
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- enable_vae_slicing
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- disable_vae_slicing
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- enable_xformers_memory_efficient_attention
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- disable_xformers_memory_efficient_attention
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- load_textual_inversion

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