update

init
2025-01-02 15:35:58 +01:00 · 2025-01-02 13:11:44 +01:00
674 changed files with 4967 additions and 18462 deletions
@@ -34,7 +34,7 @@ jobs:
        id: file_changes
        uses: jitterbit/get-changed-files@v1
        with:
-          format: "space-delimited"
+          format: 'space-delimited'
          token: ${{ secrets.GITHUB_TOKEN }}

      - name: Build Changed Docker Images
@@ -67,7 +67,6 @@ jobs:
          - diffusers-pytorch-cuda
          - diffusers-pytorch-compile-cuda
          - diffusers-pytorch-xformers-cuda
-          - diffusers-pytorch-minimum-cuda
          - diffusers-flax-cpu
          - diffusers-flax-tpu
          - diffusers-onnxruntime-cpu
@@ -235,64 +235,7 @@ jobs:
        run: |
          pip install slack_sdk tabulate
          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
-          
-  torch_minimum_version_cuda_tests:
-    name: Torch Minimum Version CUDA Tests
-    runs-on:
-      group: aws-g4dn-2xlarge
-    container:
-      image: diffusers/diffusers-pytorch-minimum-cuda
-      options: --shm-size "16gb" --ipc host --gpus 0
-    defaults:
-      run:
-        shell: bash
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2

-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-
-      - name: Environment
-        run: |
-          python utils/print_env.py
-
-      - name: Run PyTorch CUDA tests
-        env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-          CUBLAS_WORKSPACE_CONFIG: :16:8
-        run: |
-          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -s -v -k "not Flax and not Onnx" \
-            --make-reports=tests_torch_minimum_version_cuda \
-            tests/models/test_modeling_common.py \
-            tests/pipelines/test_pipelines_common.py \
-            tests/pipelines/test_pipeline_utils.py \
-            tests/pipelines/test_pipelines.py \
-            tests/pipelines/test_pipelines_auto.py \
-            tests/schedulers/test_schedulers.py \
-            tests/others
-
-      - name: Failure short reports
-        if: ${{ failure() }}
-        run: |
-          cat reports/tests_torch_minimum_version_cuda_stats.txt
-          cat reports/tests_torch_minimum_version_cuda_failures_short.txt
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: torch_minimum_version_cuda_test_reports
-          path: reports
- 
  run_flax_tpu_tests:
    name: Nightly Flax TPU Tests
    runs-on:
@@ -505,7 +448,7 @@ jobs:
 #        shell: arch -arch arm64 bash {0}
 #        env:
 #          HF_HOME: /System/Volumes/Data/mnt/cache
-#          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+#          HF_TOKEN: ${{ secrets.HF_TOKEN }}
 #        run: |
 #          ${CONDA_RUN} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps \
 #            --report-log=tests_torch_mps.log \
@@ -561,7 +504,7 @@ jobs:
 #        shell: arch -arch arm64 bash {0}
 #        env:
 #          HF_HOME: /System/Volumes/Data/mnt/cache
-#          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+#          HF_TOKEN: ${{ secrets.HF_TOKEN }}
 #        run: |
 #          ${CONDA_RUN} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps \
 #            --report-log=tests_torch_mps.log \
@@ -266,7 +266,6 @@ jobs:
        # TODO (sayakpaul, DN6): revisit `--no-deps`
        python -m pip install -U peft@git+https://github.com/huggingface/peft.git --no-deps
        python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
-        python -m uv pip install -U tokenizers
        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps

    - name: Environment
@@ -83,7 +83,7 @@ jobs:
          python utils/print_env.py
      - name: PyTorch CUDA checkpoint tests on Ubuntu
        env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
          CUBLAS_WORKSPACE_CONFIG: :16:8
        run: |
@@ -137,7 +137,7 @@ jobs:

    - name: Run PyTorch CUDA tests
      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
        # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
        CUBLAS_WORKSPACE_CONFIG: :16:8
      run: |
@@ -187,7 +187,7 @@ jobs:

    - name: Run Flax TPU tests
      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
      run: |
        python -m pytest -n 0 \
          -s -v -k "Flax" \
@@ -235,7 +235,7 @@ jobs:

    - name: Run ONNXRuntime CUDA tests
      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
      run: |
        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
          -s -v -k "Onnx" \
@@ -283,7 +283,7 @@ jobs:
        python utils/print_env.py
    - name: Run example tests on GPU
      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
        RUN_COMPILE: yes
      run: |
        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
@@ -326,7 +326,7 @@ jobs:
        python utils/print_env.py
    - name: Run example tests on GPU
      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
      run: |
        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
    - name: Failure short reports
@@ -372,7 +372,7 @@ jobs:

    - name: Run example tests on GPU
      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
      run: |
        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
        python -m uv pip install timm
@@ -81,7 +81,7 @@ jobs:
          python utils/print_env.py
      - name: Slow PyTorch CUDA checkpoint tests on Ubuntu
        env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
          CUBLAS_WORKSPACE_CONFIG: :16:8
        run: |
@@ -135,7 +135,7 @@ jobs:

    - name: Run PyTorch CUDA tests
      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
        # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
        CUBLAS_WORKSPACE_CONFIG: :16:8
      run: |
@@ -157,63 +157,6 @@ jobs:
        name: torch_cuda_${{ matrix.module }}_test_reports
        path: reports

-  torch_minimum_version_cuda_tests:
-    name: Torch Minimum Version CUDA Tests
-    runs-on:
-      group: aws-g4dn-2xlarge
-    container:
-      image: diffusers/diffusers-pytorch-minimum-cuda
-      options: --shm-size "16gb" --ipc host --gpus 0
-    defaults:
-      run:
-        shell: bash
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-
-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-
-      - name: Environment
-        run: |
-          python utils/print_env.py
-
-      - name: Run PyTorch CUDA tests
-        env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-          CUBLAS_WORKSPACE_CONFIG: :16:8
-        run: |
-          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -s -v -k "not Flax and not Onnx" \
-            --make-reports=tests_torch_minimum_cuda \
-            tests/models/test_modeling_common.py \
-            tests/pipelines/test_pipelines_common.py \
-            tests/pipelines/test_pipeline_utils.py \
-            tests/pipelines/test_pipelines.py \
-            tests/pipelines/test_pipelines_auto.py \
-            tests/schedulers/test_schedulers.py \
-            tests/others
-
-      - name: Failure short reports
-        if: ${{ failure() }}
-        run: |
-          cat reports/tests_torch_minimum_version_cuda_stats.txt
-          cat reports/tests_torch_minimum_version_cuda_failures_short.txt
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: torch_minimum_version_cuda_test_reports
-          path: reports
-          
  flax_tpu_tests:
    name: Flax TPU Tests
    runs-on: docker-tpu
@@ -241,7 +184,7 @@ jobs:

    - name: Run slow Flax TPU tests
      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
      run: |
        python -m pytest -n 0 \
          -s -v -k "Flax" \
@@ -289,7 +232,7 @@ jobs:

    - name: Run slow ONNXRuntime CUDA tests
      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
      run: |
        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
          -s -v -k "Onnx" \
@@ -337,7 +280,7 @@ jobs:
        python utils/print_env.py
    - name: Run example tests on GPU
      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
        RUN_COMPILE: yes
      run: |
        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
@@ -380,7 +323,7 @@ jobs:
        python utils/print_env.py
    - name: Run example tests on GPU
      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
      run: |
        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
    - name: Failure short reports
@@ -426,7 +369,7 @@ jobs:

    - name: Run example tests on GPU
      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
      run: |
        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
        python -m uv pip install timm
@@ -1,53 +0,0 @@
-FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
-LABEL maintainer="Hugging Face"
-LABEL repository="diffusers"
-
-ENV DEBIAN_FRONTEND=noninteractive
-ENV MINIMUM_SUPPORTED_TORCH_VERSION="2.1.0"
-ENV MINIMUM_SUPPORTED_TORCHVISION_VERSION="0.16.0"
-ENV MINIMUM_SUPPORTED_TORCHAUDIO_VERSION="2.1.0"
-
-RUN apt-get -y update \
-    && apt-get install -y software-properties-common \
-    && add-apt-repository ppa:deadsnakes/ppa
-
-RUN apt install -y bash \
-    build-essential \
-    git \
-    git-lfs \
-    curl \
-    ca-certificates \
-    libsndfile1-dev \
-    libgl1 \
-    python3.10 \
-    python3.10-dev \
-    python3-pip \
-    python3.10-venv && \
-    rm -rf /var/lib/apt/lists
-
-# make sure to use venv
-RUN python3.10 -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
-
-# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
-    python3.10 -m uv pip install --no-cache-dir \
-    torch==$MINIMUM_SUPPORTED_TORCH_VERSION \
-    torchvision==$MINIMUM_SUPPORTED_TORCHVISION_VERSION \
-    torchaudio==$MINIMUM_SUPPORTED_TORCHAUDIO_VERSION \
-    invisible_watermark && \
-    python3.10 -m pip install --no-cache-dir \
-    accelerate \
-    datasets \
-    hf-doc-builder \
-    huggingface-hub \
-    hf_transfer \
-    Jinja2 \
-    librosa \
-    numpy==1.26.4 \
-    scipy \
-    tensorboard \
-    transformers \
-    hf_transfer
-
-CMD ["/bin/bash"]
@@ -79,8 +79,6 @@
 - sections:
  - local: using-diffusers/cogvideox
    title: CogVideoX
-  - local: using-diffusers/consisid
-    title: ConsisID
  - local: using-diffusers/sdxl
    title: Stable Diffusion XL
  - local: using-diffusers/sdxl_turbo
@@ -181,8 +179,6 @@
    title: TGATE
  - local: optimization/xdit
    title: xDiT
-  - local: optimization/para_attn
-    title: ParaAttention
  - sections:
    - local: using-diffusers/stable_diffusion_jax_how_to
      title: JAX/Flax
@@ -272,8 +268,6 @@
        title: AuraFlowTransformer2DModel
      - local: api/models/cogvideox_transformer3d
        title: CogVideoXTransformer3DModel
-      - local: api/models/consisid_transformer3d
-        title: ConsisIDTransformer3DModel
      - local: api/models/cogview3plus_transformer2d
        title: CogView3PlusTransformer2DModel
      - local: api/models/dit_transformer2d
@@ -376,8 +370,6 @@
      title: CogVideoX
    - local: api/pipelines/cogview3
      title: CogView3
-    - local: api/pipelines/consisid
-      title: ConsisID
    - local: api/pipelines/consistency_models
      title: Consistency Models
    - local: api/pipelines/controlnet
@@ -598,8 +590,6 @@
      title: Attention Processor
    - local: api/activations
      title: Custom activation functions
-    - local: api/cache
-      title: Caching methods
    - local: api/normalization
      title: Custom normalization layers
    - local: api/utilities
@@ -1,49 +0,0 @@
-<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# Caching methods
-
-## Pyramid Attention Broadcast
-
-[Pyramid Attention Broadcast](https://huggingface.co/papers/2408.12588) from Xuanlei Zhao, Xiaolong Jin, Kai Wang, Yang You.
-
-Pyramid Attention Broadcast (PAB) is a method that speeds up inference in diffusion models by systematically skipping attention computations between successive inference steps and reusing cached attention states. The attention states are not very different between successive inference steps. The most prominent difference is in the spatial attention blocks, not as much in the temporal attention blocks, and finally the least in the cross attention blocks. Therefore, many cross attention computation blocks can be skipped, followed by the temporal and spatial attention blocks. By combining other techniques like sequence parallelism and classifier-free guidance parallelism, PAB achieves near real-time video generation.
-
-Enable PAB with [`~PyramidAttentionBroadcastConfig`] on any pipeline. For some benchmarks, refer to [this](https://github.com/huggingface/diffusers/pull/9562) pull request.
-
-```python
-import torch
-from diffusers import CogVideoXPipeline, PyramidAttentionBroadcastConfig
-
-pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-
-# Increasing the value of `spatial_attention_timestep_skip_range[0]` or decreasing the value of
-# `spatial_attention_timestep_skip_range[1]` will decrease the interval in which pyramid attention
-# broadcast is active, leader to slower inference speeds. However, large intervals can lead to
-# poorer quality of generated videos.
-config = PyramidAttentionBroadcastConfig(
-    spatial_attention_block_skip_range=2,
-    spatial_attention_timestep_skip_range=(100, 800),
-    current_timestep_callback=lambda: pipe.current_timestep,
-)
-pipe.transformer.enable_cache(config)
-```
-
-### CacheMixin
-
-[[autodoc]] CacheMixin
-
-### PyramidAttentionBroadcastConfig
-
-[[autodoc]] PyramidAttentionBroadcastConfig
-
-[[autodoc]] apply_pyramid_attention_broadcast
@@ -18,7 +18,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import AllegroTransformer3DModel

-transformer = AllegroTransformer3DModel.from_pretrained("rhymes-ai/Allegro", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
+vae = AllegroTransformer3DModel.from_pretrained("rhymes-ai/Allegro", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
 ```

 ## AllegroTransformer3DModel
@@ -18,7 +18,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import CogVideoXTransformer3DModel

-transformer = CogVideoXTransformer3DModel.from_pretrained("THUDM/CogVideoX-2b", subfolder="transformer", torch_dtype=torch.float16).to("cuda")
+vae = CogVideoXTransformer3DModel.from_pretrained("THUDM/CogVideoX-2b", subfolder="transformer", torch_dtype=torch.float16).to("cuda")
 ```

 ## CogVideoXTransformer3DModel
@@ -18,7 +18,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import CogView3PlusTransformer2DModel

-transformer = CogView3PlusTransformer2DModel.from_pretrained("THUDM/CogView3Plus-3b", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
+vae = CogView3PlusTransformer2DModel.from_pretrained("THUDM/CogView3Plus-3b", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
 ```

 ## CogView3PlusTransformer2DModel
@@ -1,30 +0,0 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# ConsisIDTransformer3DModel
-
-A Diffusion Transformer model for 3D data from [ConsisID](https://github.com/PKU-YuanGroup/ConsisID) was introduced in [Identity-Preserving Text-to-Video Generation by Frequency Decomposition](https://arxiv.org/pdf/2411.17440) by Peking University & University of Rochester & etc.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import ConsisIDTransformer3DModel
-
-transformer = ConsisIDTransformer3DModel.from_pretrained("BestWishYsh/ConsisID-preview", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
-```
-
-## ConsisIDTransformer3DModel
-
-[[autodoc]] ConsisIDTransformer3DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput
@@ -18,7 +18,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import MochiTransformer3DModel

-transformer = MochiTransformer3DModel.from_pretrained("genmo/mochi-1-preview", subfolder="transformer", torch_dtype=torch.float16).to("cuda")
+vae = MochiTransformer3DModel.from_pretrained("genmo/mochi-1-preview", subfolder="transformer", torch_dtype=torch.float16).to("cuda")
 ```

 ## MochiTransformer3DModel
@@ -19,7 +19,7 @@ The abstract from the paper is:

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -803,7 +803,7 @@ FreeInit is not really free - the improved quality comes at the cost of extra co

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -22,7 +22,7 @@ You can find additional information about Attend-and-Excite on the [project page

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -37,7 +37,7 @@ During inference:

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -60,7 +60,7 @@ The following example demonstrates how to construct good music and speech genera

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -62,33 +62,6 @@ image = pipeline(prompt).images[0]
 image.save("auraflow.png")
 ```

-Loading [GGUF checkpoints](https://huggingface.co/docs/diffusers/quantization/gguf) are also supported:
-
-```py
-import torch
-from diffusers import (
-    AuraFlowPipeline,
-    GGUFQuantizationConfig,
-    AuraFlowTransformer2DModel,
-)
-
-transformer = AuraFlowTransformer2DModel.from_single_file(
-    "https://huggingface.co/city96/AuraFlow-v0.3-gguf/blob/main/aura_flow_0.3-Q2_K.gguf",
-    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
-    torch_dtype=torch.bfloat16,
-)
-
-pipeline = AuraFlowPipeline.from_pretrained(
-    "fal/AuraFlow-v0.3",
-    transformer=transformer,
-    torch_dtype=torch.bfloat16,
-)
-
-prompt = "a cute pony in a field of flowers"
-image = pipeline(prompt).images[0]
-image.save("auraflow.png")
-```
-
 ## AuraFlowPipeline

 [[autodoc]] AuraFlowPipeline
@@ -25,7 +25,7 @@ The original codebase can be found at [salesforce/LAVIS](https://github.com/sale

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -23,7 +23,7 @@ The abstract from the paper is:

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -23,7 +23,7 @@ The abstract from the paper is:

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -1,60 +0,0 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-->
-
-# ConsisID
-
-[Identity-Preserving Text-to-Video Generation by Frequency Decomposition](https://arxiv.org/abs/2411.17440) from Peking University & University of Rochester & etc, by Shenghai Yuan, Jinfa Huang, Xianyi He, Yunyang Ge, Yujun Shi, Liuhan Chen, Jiebo Luo, Li Yuan.
-
-The abstract from the paper is:
-
-*Identity-preserving text-to-video (IPT2V) generation aims to create high-fidelity videos with consistent human identity. It is an important task in video generation but remains an open problem for generative models. This paper pushes the technical frontier of IPT2V in two directions that have not been resolved in the literature: (1) A tuning-free pipeline without tedious case-by-case finetuning, and (2) A frequency-aware heuristic identity-preserving Diffusion Transformer (DiT)-based control scheme. To achieve these goals, we propose **ConsisID**, a tuning-free DiT-based controllable IPT2V model to keep human-**id**entity **consis**tent in the generated video. Inspired by prior findings in frequency analysis of vision/diffusion transformers, it employs identity-control signals in the frequency domain, where facial features can be decomposed into low-frequency global features (e.g., profile, proportions) and high-frequency intrinsic features (e.g., identity markers that remain unaffected by pose changes). First, from a low-frequency perspective, we introduce a global facial extractor, which encodes the reference image and facial key points into a latent space, generating features enriched with low-frequency information. These features are then integrated into the shallow layers of the network to alleviate training challenges associated with DiT. Second, from a high-frequency perspective, we design a local facial extractor to capture high-frequency details and inject them into the transformer blocks, enhancing the model's ability to preserve fine-grained features. To leverage the frequency information for identity preservation, we propose a hierarchical training strategy, transforming a vanilla pre-trained video generation model into an IPT2V model. Extensive experiments demonstrate that our frequency-aware heuristic scheme provides an optimal control solution for DiT-based models. Thanks to this scheme, our **ConsisID** achieves excellent results in generating high-quality, identity-preserving videos, making strides towards more effective IPT2V. The model weight of ConsID is publicly available at https://github.com/PKU-YuanGroup/ConsisID.*
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.md) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-This pipeline was contributed by [SHYuanBest](https://github.com/SHYuanBest). The original codebase can be found [here](https://github.com/PKU-YuanGroup/ConsisID). The original weights can be found under [hf.co/BestWishYsh](https://huggingface.co/BestWishYsh).
-
-There are two official ConsisID checkpoints for identity-preserving text-to-video.
-
-| checkpoints | recommended inference dtype |
-|:---:|:---:|
-| [`BestWishYsh/ConsisID-preview`](https://huggingface.co/BestWishYsh/ConsisID-preview) | torch.bfloat16 |
-| [`BestWishYsh/ConsisID-1.5`](https://huggingface.co/BestWishYsh/ConsisID-preview) | torch.bfloat16 |
-
-### Memory optimization
-
-ConsisID requires about 44 GB of GPU memory to decode 49 frames (6 seconds of video at 8 FPS) with output resolution 720x480 (W x H), which makes it not possible to run on consumer GPUs or free-tier T4 Colab. The following memory optimizations could be used to reduce the memory footprint. For replication, you can refer to [this](https://gist.github.com/SHYuanBest/bc4207c36f454f9e969adbb50eaf8258) script.
-
-| Feature (overlay the previous) | Max Memory Allocated | Max Memory Reserved |
-| :----------------------------- | :------------------- | :------------------ |
-| -                              | 37 GB                | 44 GB               |
-| enable_model_cpu_offload       | 22 GB                | 25 GB               |
-| enable_sequential_cpu_offload  | 16 GB                | 22 GB               |
-| vae.enable_slicing             | 16 GB                | 22 GB               |
-| vae.enable_tiling              | 5 GB                 | 7 GB                |
-
-## ConsisIDPipeline
-
-[[autodoc]] ConsisIDPipeline
-
-  - all
-  - __call__
-
-## ConsisIDPipelineOutput
-
-[[autodoc]] pipelines.consisid.pipeline_output.ConsisIDPipelineOutput
@@ -26,7 +26,7 @@ The original codebase can be found at [lllyasviel/ControlNet](https://github.com

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -42,7 +42,7 @@ XLabs ControlNets are also supported, which was contributed by the [XLabs team](

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -26,7 +26,7 @@ This code is implemented by Tencent Hunyuan Team. You can find pre-trained check

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -36,7 +36,7 @@ This controlnet code is mainly implemented by [The InstantX Team](https://huggin

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -32,7 +32,7 @@ If you don't see a checkpoint you're interested in, you can train your own SDXL

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -26,7 +26,7 @@ This model was contributed by [UmerHA](https://twitter.com/UmerHAdil). ❤️

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -32,7 +32,7 @@ This model was contributed by [UmerHA](https://twitter.com/UmerHAdil). ❤️

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -19,7 +19,7 @@ Dance Diffusion is the first in a suite of generative audio tools for producers

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -22,7 +22,7 @@ The original codebase can be found at [hohonathanho/diffusion](https://github.co

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -22,7 +22,7 @@ The original codebase can be found at [facebookresearch/dit](https://github.com/

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -305,57 +305,6 @@ image = control_pipe(
 image.save("output.png")
 ```

-## Note about `unload_lora_weights()` when using Flux LoRAs
-
-When unloading the Control LoRA weights, call `pipe.unload_lora_weights(reset_to_overwritten_params=True)` to reset the `pipe.transformer` completely back to its original form. The resultant pipeline can then be used with methods like [`DiffusionPipeline.from_pipe`]. More details about this argument are available in [this PR](https://github.com/huggingface/diffusers/pull/10397).
-
-## IP-Adapter
-
-<Tip>
-
-Check out [IP-Adapter](../../../using-diffusers/ip_adapter) to learn more about how IP-Adapters work.
-
-</Tip>
-
-An IP-Adapter lets you prompt Flux with images, in addition to the text prompt. This is especially useful when describing complex concepts that are difficult to articulate through text alone and you have reference images.
-
-```python
-import torch
-from diffusers import FluxPipeline
-from diffusers.utils import load_image
-
-pipe = FluxPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16
-).to("cuda")
-
-image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flux_ip_adapter_input.jpg").resize((1024, 1024))
-
-pipe.load_ip_adapter(
-    "XLabs-AI/flux-ip-adapter",
-    weight_name="ip_adapter.safetensors",
-    image_encoder_pretrained_model_name_or_path="openai/clip-vit-large-patch14"
-)
-pipe.set_ip_adapter_scale(1.0)
-
-image = pipe(
-    width=1024,
-    height=1024,
-    prompt="wearing sunglasses",
-    negative_prompt="",
-    true_cfg=4.0,
-    generator=torch.Generator().manual_seed(4444),
-    ip_adapter_image=image,
-).images[0]
-
-image.save('flux_ip_adapter_output.jpg')
-```
-
-<div class="justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flux_ip_adapter_output.jpg"/>
-    <figcaption class="mt-2 text-sm text-center text-gray-500">IP-Adapter examples with prompt "wearing sunglasses"</figcaption>
-</div>
-
-
 ## Running FP16 inference

 Flux can generate high-quality images with FP16 (i.e. to accelerate inference on Turing/Volta GPUs) but produces different outputs compared to FP32/BF16. The issue is that some activations in the text encoders have to be clipped when running in FP16, which affects the overall image. Forcing text encoders to run with FP32 inference thus removes this output difference. See [here](https://github.com/huggingface/diffusers/pull/9097#issuecomment-2272292516) for details.
@@ -414,7 +363,7 @@ transformer_8bit = FluxTransformer2DModel.from_pretrained(

 pipeline = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
-    text_encoder_2=text_encoder_8bit,
+    text_encoder=text_encoder_8bit,
    transformer=transformer_8bit,
    torch_dtype=torch.float16,
    device_map="balanced",
@@ -16,11 +16,11 @@

 [HunyuanVideo](https://www.arxiv.org/abs/2412.03603) by Tencent.

-*Recent advancements in video generation have significantly impacted daily life for both individuals and industries. However, the leading video generation models remain closed-source, resulting in a notable performance gap between industry capabilities and those available to the public. In this report, we introduce HunyuanVideo, an innovative open-source video foundation model that demonstrates performance in video generation comparable to, or even surpassing, that of leading closed-source models. HunyuanVideo encompasses a comprehensive framework that integrates several key elements, including data curation, advanced architectural design, progressive model scaling and training, and an efficient infrastructure tailored for large-scale model training and inference. As a result, we successfully trained a video generative model with over 13 billion parameters, making it the largest among all open-source models. We conducted extensive experiments and implemented a series of targeted designs to ensure high visual quality, motion dynamics, text-video alignment, and advanced filming techniques. According to evaluations by professionals, HunyuanVideo outperforms previous state-of-the-art models, including Runway Gen-3, Luma 1.6, and three top-performing Chinese video generative models. By releasing the code for the foundation model and its applications, we aim to bridge the gap between closed-source and open-source communities. This initiative will empower individuals within the community to experiment with their ideas, fostering a more dynamic and vibrant video generation ecosystem. The code is publicly available at [this https URL](https://github.com/tencent/HunyuanVideo).*
+*Recent advancements in video generation have significantly impacted daily life for both individuals and industries. However, the leading video generation models remain closed-source, resulting in a notable performance gap between industry capabilities and those available to the public. In this report, we introduce HunyuanVideo, an innovative open-source video foundation model that demonstrates performance in video generation comparable to, or even surpassing, that of leading closed-source models. HunyuanVideo encompasses a comprehensive framework that integrates several key elements, including data curation, advanced architectural design, progressive model scaling and training, and an efficient infrastructure tailored for large-scale model training and inference. As a result, we successfully trained a video generative model with over 13 billion parameters, making it the largest among all open-source models. We conducted extensive experiments and implemented a series of targeted designs to ensure high visual quality, motion dynamics, text-video alignment, and advanced filming techniques. According to evaluations by professionals, HunyuanVideo outperforms previous state-of-the-art models, including Runway Gen-3, Luma 1.6, and three top-performing Chinese video generative models. By releasing the code for the foundation model and its applications, we aim to bridge the gap between closed-source and open-source communities. This initiative will empower individuals within the community to experiment with their ideas, fostering a more dynamic and vibrant video generation ecosystem. The code is publicly available at [this https URL](https://github.com/Tencent/HunyuanVideo).*

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](https://huggingface.co/docs/diffusers/main/en/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -45,14 +45,14 @@ from diffusers.utils import export_to_video

 quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
 transformer_8bit = HunyuanVideoTransformer3DModel.from_pretrained(
-    "hunyuanvideo-community/HunyuanVideo",
+    "tencent/HunyuanVideo",
    subfolder="transformer",
    quantization_config=quant_config,
-    torch_dtype=torch.bfloat16,
+    torch_dtype=torch.float16,
 )

 pipeline = HunyuanVideoPipeline.from_pretrained(
-    "hunyuanvideo-community/HunyuanVideo",
+    "tencent/HunyuanVideo",
    transformer=transformer_8bit,
    torch_dtype=torch.float16,
    device_map="balanced",
@@ -30,7 +30,7 @@ HunyuanDiT has the following components:

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](https://huggingface.co/docs/diffusers/main/en/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -22,7 +22,7 @@ The original codebase can be found [here](https://github.com/ali-vilab/i2vgen-xl

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. Also, to know more about reducing the memory usage of this pipeline, refer to the ["Reduce memory usage"] section [here](../../using-diffusers/svd#reduce-memory-usage).
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines. Also, to know more about reducing the memory usage of this pipeline, refer to the ["Reduce memory usage"] section [here](../../using-diffusers/svd#reduce-memory-usage).

 </Tip>

@@ -25,7 +25,7 @@ Check out the [Kandinsky Community](https://huggingface.co/kandinsky-community)

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -32,7 +32,7 @@ Check out the [Kandinsky Community](https://huggingface.co/kandinsky-community)

 <Tip>

-Make sure to check out the schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -25,7 +25,7 @@ Check out the [Kandinsky Community](https://huggingface.co/kandinsky-community)

 <Tip>

-Make sure to check out the schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -22,7 +22,7 @@ The original codebase can be found at [CompVis/latent-diffusion](https://github.

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -28,7 +28,7 @@ This pipeline was contributed by [maxin-cn](https://github.com/maxin-cn). The or

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -18,7 +18,7 @@

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -47,7 +47,7 @@ This pipeline was contributed by [PommesPeter](https://github.com/PommesPeter).

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -43,7 +43,7 @@ The original checkpoints can be found under the [PRS-ETH](https://huggingface.co

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. Also, to know more about reducing the memory usage of this pipeline, refer to the ["Reduce memory usage"] section [here](../../using-diffusers/svd#reduce-memory-usage).
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines. Also, to know more about reducing the memory usage of this pipeline, refer to the ["Reduce memory usage"] section [here](../../using-diffusers/svd#reduce-memory-usage).

 </Tip>

@@ -115,7 +115,7 @@ export_to_video(frames, "mochi.mp4", fps=30)

 ## Reproducing the results from the Genmo Mochi repo

-The [Genmo Mochi implementation](https://github.com/genmoai/mochi/tree/main) uses different precision values for each stage in the inference process. The text encoder and VAE use `torch.float32`, while the DiT uses `torch.bfloat16` with the [attention kernel](https://pytorch.org/docs/stable/generated/torch.nn.attention.sdpa_kernel.html#torch.nn.attention.sdpa_kernel) set to `EFFICIENT_ATTENTION`. Diffusers pipelines currently do not support setting different `dtypes` for different stages of the pipeline. In order to run inference in the same way as the original implementation, please refer to the following example.
+The [Genmo Mochi implementation](https://github.com/genmoai/mochi/tree/main) uses different precision values for each stage in the inference process. The text encoder and VAE use `torch.float32`, while the DiT uses `torch.bfloat16` with the [attention kernel](https://pytorch.org/docs/stable/generated/torch.nn.attention.sdpa_kernel.html#torch.nn.attention.sdpa_kernel) set to `EFFICIENT_ATTENTION`. Diffusers pipelines currently do not support setting different `dtypes` for different stages of the pipeline. In order to run inference in the same way as the the original implementation, please refer to the following example.

 <Tip>
 The original Mochi implementation zeros out empty prompts. However, enabling this option and placing the entire pipeline under autocast can lead to numerical overflows with the T5 text encoder.
@@ -42,7 +42,7 @@ During inference:

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -26,7 +26,7 @@ Paint by Example is supported by the official [Fantasy-Studio/Paint-by-Example](

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -37,7 +37,7 @@ But with circular padding, the right and the left parts are matching (`circular_

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -22,7 +22,7 @@ You can find additional information about InstructPix2Pix on the [project page](

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -31,7 +31,7 @@ Some notes about this pipeline:

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -22,7 +22,7 @@ The abstract from the paper is:

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -59,10 +59,10 @@ Refer to the [Quantization](../../quantization/overview) overview to learn more
 ```py
 import torch
 from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, SanaTransformer2DModel, SanaPipeline
-from transformers import BitsAndBytesConfig as BitsAndBytesConfig, AutoModel
+from transformers import BitsAndBytesConfig as BitsAndBytesConfig, AutoModelForCausalLM

 quant_config = BitsAndBytesConfig(load_in_8bit=True)
-text_encoder_8bit = AutoModel.from_pretrained(
+text_encoder_8bit = AutoModelForCausalLM.from_pretrained(
    "Efficient-Large-Model/Sana_1600M_1024px_diffusers",
    subfolder="text_encoder",
    quantization_config=quant_config,
@@ -22,7 +22,7 @@ You can find additional information about Self-Attention Guidance on the [projec

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -21,7 +21,7 @@ The abstract from the paper is:

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -19,7 +19,7 @@ The original codebase can be found at [openai/shap-e](https://github.com/openai/

 <Tip>

-See the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+See the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -77,7 +77,7 @@ from diffusers import StableDiffusion3Pipeline
 from transformers import SiglipVisionModel, SiglipImageProcessor

 image_encoder_id = "google/siglip-so400m-patch14-384"
-ip_adapter_id = "guiyrt/InstantX-SD3.5-Large-IP-Adapter-diffusers"
+ip_adapter_id = "InstantX/SD3.5-Large-IP-Adapter"

 feature_extractor = SiglipImageProcessor.from_pretrained(
    image_encoder_id,
@@ -97,7 +97,7 @@ image

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -175,7 +175,7 @@ Check out the [Text or image-to-video](text-img2vid) guide for more details abou

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -284,7 +284,7 @@ You can filter out some available DreamBooth-trained models with [this link](htt

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -19,7 +19,7 @@ You can find lucidrains' DALL-E 2 recreation at [lucidrains/DALLE2-pytorch](http

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -192,7 +192,7 @@ print(final_prompt)

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -30,7 +30,7 @@ The script to run the model is available [here](https://github.com/huggingface/d

 <Tip>

-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.

 </Tip>

@@ -41,7 +41,3 @@ Utility and helper functions for working with 🤗 Diffusers.
 ## randn_tensor

 [[autodoc]] utils.torch_utils.randn_tensor
-
-## apply_layerwise_casting
-
-[[autodoc]] hooks.layerwise_casting.apply_layerwise_casting
@@ -23,60 +23,32 @@ You should install 🤗 Diffusers in a [virtual environment](https://docs.python
 If you're unfamiliar with Python virtual environments, take a look at this [guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
 A virtual environment makes it easier to manage different projects and avoid compatibility issues between dependencies.

-Create a virtual environment with Python or [uv](https://docs.astral.sh/uv/) (refer to [Installation](https://docs.astral.sh/uv/getting-started/installation/) for installation instructions), a fast Rust-based Python package and project manager.
-
-<hfoptions id="install">
-<hfoption id="uv">
+Start by creating a virtual environment in your project directory:

 ```bash
-uv venv my-env
-source my-env/bin/activate
+python -m venv .env
 ```

-</hfoption>
-<hfoption id="Python">
+Activate the virtual environment:

 ```bash
-python -m venv my-env
-source my-env/bin/activate
+source .env/bin/activate
 ```

-</hfoption>
-</hfoptions>
-
-You should also install 🤗 Transformers because 🤗 Diffusers relies on its models.
+You should also install 🤗 Transformers because 🤗 Diffusers relies on its models:


 <frameworkcontent>
 <pt>
-
-PyTorch only supports Python 3.8 - 3.11 on Windows. Install Diffusers with uv.
-
-```bash
-uv install diffusers["torch"] transformers
-```
-
-You can also install Diffusers with pip.
-
+Note - PyTorch only supports Python 3.8 - 3.11 on Windows.
 ```bash
 pip install diffusers["torch"] transformers
 ```
-
 </pt>
 <jax>
-
-Install Diffusers with uv.
-
-```bash
-uv pip install diffusers["flax"] transformers
-```
-
-You can also install Diffusers with pip.
-
 ```bash
 pip install diffusers["flax"] transformers
 ```
-
 </jax>
 </frameworkcontent>

@@ -158,43 +158,6 @@ In order to properly offload models after they're called, it is required to run

 </Tip>

-## FP8 layerwise weight-casting
-
-PyTorch supports `torch.float8_e4m3fn` and `torch.float8_e5m2` as weight storage dtypes, but they can't be used for computation in many different tensor operations due to unimplemented kernel support. However, you can use these dtypes to store model weights in fp8 precision and upcast them on-the-fly when the layers are used in the forward pass. This is known as layerwise weight-casting.
-
-Typically, inference on most models is done with `torch.float16` or `torch.bfloat16` weight/computation precision. Layerwise weight-casting cuts down the memory footprint of the model weights by approximately half.
-
-```python
-import torch
-from diffusers import CogVideoXPipeline, CogVideoXTransformer3DModel
-from diffusers.utils import export_to_video
-
-model_id = "THUDM/CogVideoX-5b"
-
-# Load the model in bfloat16 and enable layerwise casting
-transformer = CogVideoXTransformer3DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.bfloat16)
-transformer.enable_layerwise_casting(storage_dtype=torch.float8_e4m3fn, compute_dtype=torch.bfloat16)
-
-# Load the pipeline
-pipe = CogVideoXPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-
-prompt = (
-    "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. "
-    "The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other "
-    "pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, "
-    "casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. "
-    "The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical "
-    "atmosphere of this unique musical performance."
-)
-video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
-export_to_video(video, "output.mp4", fps=8)
-```
-
-In the above example, layerwise casting is enabled on the transformer component of the pipeline. By default, certain layers are skipped from the FP8 weight casting because it can lead to significant degradation of generation quality. The normalization and modulation related weight parameters are also skipped by default.
-
-However, you gain more control and flexibility by directly utilizing the [`~hooks.layerwise_casting.apply_layerwise_casting`] function instead of [`~ModelMixin.enable_layerwise_casting`].
-
 ## Channels-last memory format

 The channels-last memory format is an alternative way of ordering NCHW tensors in memory to preserve dimension ordering. Channels-last tensors are ordered in such a way that the channels become the densest dimension (storing images pixel-per-pixel). Since not all operators currently support the channels-last format, it may result in worst performance but you should still try and see if it works for your model.
@@ -1,497 +0,0 @@
-# ParaAttention
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/flux-performance.png">
-</div>
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/hunyuan-video-performance.png">
-</div>
-
-
-Large image and video generation models, such as [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) and [HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo), can be an inference challenge for real-time applications and deployment because of their size.
-
-[ParaAttention](https://github.com/chengzeyi/ParaAttention) is a library that implements **context parallelism** and **first block cache**, and can be combined with other techniques (torch.compile, fp8 dynamic quantization), to accelerate inference.
-
-This guide will show you how to apply ParaAttention to FLUX.1-dev and HunyuanVideo on NVIDIA L20 GPUs.
-No optimizations are applied for our baseline benchmark, except for HunyuanVideo to avoid out-of-memory errors.
-
-Our baseline benchmark shows that FLUX.1-dev is able to generate a 1024x1024 resolution image in 28 steps in 26.36 seconds, and HunyuanVideo is able to generate 129 frames at 720p resolution in 30 steps in 3675.71 seconds.
-
-> [!TIP]
-> For even faster inference with context parallelism, try using NVIDIA A100 or H100 GPUs (if available) with NVLink support, especially when there is a large number of GPUs.
-
-## First Block Cache
-
-Caching the output of the transformers blocks in the model and reusing them in the next inference steps reduces the computation cost and makes inference faster.
-
-However, it is hard to decide when to reuse the cache to ensure quality generated images or videos. ParaAttention directly uses the **residual difference of the first transformer block output** to approximate the difference among model outputs. When the difference is small enough, the residual difference of previous inference steps is reused. In other words, the denoising step is skipped.
-
-This achieves a 2x speedup on FLUX.1-dev and HunyuanVideo inference with very good quality.
-
-<figure>
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/ada-cache.png" alt="Cache in Diffusion Transformer" />
-    <figcaption>How AdaCache works, First Block Cache is a variant of it</figcaption>
-</figure>
-
-<hfoptions id="first-block-cache">
-<hfoption id="FLUX-1.dev">
-
-To apply first block cache on FLUX.1-dev, call `apply_cache_on_pipe` as shown below. 0.08 is the default residual difference value for FLUX models.
-
-```python
-import time
-import torch
-from diffusers import FluxPipeline
-
-pipe = FluxPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-dev",
-    torch_dtype=torch.bfloat16,
-).to("cuda")
-
-from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe
-
-apply_cache_on_pipe(pipe, residual_diff_threshold=0.08)
-
-# Enable memory savings
-# pipe.enable_model_cpu_offload()
-# pipe.enable_sequential_cpu_offload()
-
-begin = time.time()
-image = pipe(
-    "A cat holding a sign that says hello world",
-    num_inference_steps=28,
-).images[0]
-end = time.time()
-print(f"Time: {end - begin:.2f}s")
-
-print("Saving image to flux.png")
-image.save("flux.png")
-```
-
-| Optimizations | Original | FBCache rdt=0.06 | FBCache rdt=0.08 | FBCache rdt=0.10 | FBCache rdt=0.12 |
-| - | - | - | - | - | - |
-| Preview | ![Original](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/flux-original.png) | ![FBCache rdt=0.06](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/flux-fbc-0.06.png) | ![FBCache rdt=0.08](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/flux-fbc-0.08.png) | ![FBCache rdt=0.10](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/flux-fbc-0.10.png) | ![FBCache rdt=0.12](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/flux-fbc-0.12.png) |
-| Wall Time (s) | 26.36 | 21.83 | 17.01 | 16.00 | 13.78 |
-
-First Block Cache reduced the inference speed to 17.01 seconds compared to the baseline, or 1.55x faster, while maintaining nearly zero quality loss.
-
-</hfoption>
-<hfoption id="HunyuanVideo">
-
-To apply First Block Cache on HunyuanVideo, `apply_cache_on_pipe` as shown below. 0.06 is the default residual difference value for HunyuanVideo models.
-
-```python
-import time
-import torch
-from diffusers import HunyuanVideoPipeline, HunyuanVideoTransformer3DModel
-from diffusers.utils import export_to_video
-
-model_id = "tencent/HunyuanVideo"
-transformer = HunyuanVideoTransformer3DModel.from_pretrained(
-    model_id,
-    subfolder="transformer",
-    torch_dtype=torch.bfloat16,
-    revision="refs/pr/18",
-)
-pipe = HunyuanVideoPipeline.from_pretrained(
-    model_id,
-    transformer=transformer,
-    torch_dtype=torch.float16,
-    revision="refs/pr/18",
-).to("cuda")
-
-from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe
-
-apply_cache_on_pipe(pipe, residual_diff_threshold=0.6)
-
-pipe.vae.enable_tiling()
-
-begin = time.time()
-output = pipe(
-    prompt="A cat walks on the grass, realistic",
-    height=720,
-    width=1280,
-    num_frames=129,
-    num_inference_steps=30,
-).frames[0]
-end = time.time()
-print(f"Time: {end - begin:.2f}s")
-
-print("Saving video to hunyuan_video.mp4")
-export_to_video(output, "hunyuan_video.mp4", fps=15)
-```
-
-<video controls>
-  <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/hunyuan-video-original.mp4" type="video/mp4">
-  Your browser does not support the video tag.
-</video>
-
-<small> HunyuanVideo without FBCache </small>
-
-<video controls>
-  <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/hunyuan-video-fbc.mp4" type="video/mp4">
-  Your browser does not support the video tag.
-</video>
-
-<small> HunyuanVideo with FBCache </small>
-
-First Block Cache reduced the inference speed to 2271.06 seconds compared to the baseline, or 1.62x faster, while maintaining nearly zero quality loss.
-
-</hfoption>
-</hfoptions>
-
-## fp8 quantization
-
-fp8 with dynamic quantization further speeds up inference and reduces memory usage. Both the activations and weights must be quantized in order to use the 8-bit [NVIDIA Tensor Cores](https://www.nvidia.com/en-us/data-center/tensor-cores/).
-
-Use `float8_weight_only` and `float8_dynamic_activation_float8_weight` to quantize the text encoder and transformer model.
-
-The default quantization method is per tensor quantization, but if your GPU supports row-wise quantization, you can also try it for better accuracy.
-
-Install [torchao](https://github.com/pytorch/ao/tree/main) with the command below.
-
-```bash
-pip3 install -U torch torchao
-```
-
-[torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) with `mode="max-autotune-no-cudagraphs"` or `mode="max-autotune"` selects the best kernel for performance. Compilation can take a long time if it's the first time the model is called, but it is worth it once the model has been compiled.
-
-This example only quantizes the transformer model, but you can also quantize the text encoder to reduce memory usage even more.
-
-> [!TIP]
-> Dynamic quantization can significantly change the distribution of the model output, so you need to change the `residual_diff_threshold` to a larger value for it to take effect.
-
-<hfoptions id="fp8-quantization">
-<hfoption id="FLUX-1.dev">
-
-```python
-import time
-import torch
-from diffusers import FluxPipeline
-
-pipe = FluxPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-dev",
-    torch_dtype=torch.bfloat16,
-).to("cuda")
-
-from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe
-
-apply_cache_on_pipe(
-    pipe,
-    residual_diff_threshold=0.12,  # Use a larger value to make the cache take effect
-)
-
-from torchao.quantization import quantize_, float8_dynamic_activation_float8_weight, float8_weight_only
-
-quantize_(pipe.text_encoder, float8_weight_only())
-quantize_(pipe.transformer, float8_dynamic_activation_float8_weight())
-pipe.transformer = torch.compile(
-   pipe.transformer, mode="max-autotune-no-cudagraphs",
-)
-
-# Enable memory savings
-# pipe.enable_model_cpu_offload()
-# pipe.enable_sequential_cpu_offload()
-
-for i in range(2):
-    begin = time.time()
-    image = pipe(
-        "A cat holding a sign that says hello world",
-        num_inference_steps=28,
-    ).images[0]
-    end = time.time()
-    if i == 0:
-        print(f"Warm up time: {end - begin:.2f}s")
-    else:
-        print(f"Time: {end - begin:.2f}s")
-
-print("Saving image to flux.png")
-image.save("flux.png")
-```
-
-fp8 dynamic quantization and torch.compile reduced the inference speed to 7.56 seconds compared to the baseline, or 3.48x faster.
-
-</hfoption>
-<hfoption id="HunyuanVideo">
-
-```python
-import time
-import torch
-from diffusers import HunyuanVideoPipeline, HunyuanVideoTransformer3DModel
-from diffusers.utils import export_to_video
-
-model_id = "tencent/HunyuanVideo"
-transformer = HunyuanVideoTransformer3DModel.from_pretrained(
-    model_id,
-    subfolder="transformer",
-    torch_dtype=torch.bfloat16,
-    revision="refs/pr/18",
-)
-pipe = HunyuanVideoPipeline.from_pretrained(
-    model_id,
-    transformer=transformer,
-    torch_dtype=torch.float16,
-    revision="refs/pr/18",
-).to("cuda")
-
-from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe
-
-apply_cache_on_pipe(pipe)
-
-from torchao.quantization import quantize_, float8_dynamic_activation_float8_weight, float8_weight_only
-
-quantize_(pipe.text_encoder, float8_weight_only())
-quantize_(pipe.transformer, float8_dynamic_activation_float8_weight())
-pipe.transformer = torch.compile(
-   pipe.transformer, mode="max-autotune-no-cudagraphs",
-)
-
-# Enable memory savings
-pipe.vae.enable_tiling()
-# pipe.enable_model_cpu_offload()
-# pipe.enable_sequential_cpu_offload()
-
-for i in range(2):
-    begin = time.time()
-    output = pipe(
-        prompt="A cat walks on the grass, realistic",
-        height=720,
-        width=1280,
-        num_frames=129,
-        num_inference_steps=1 if i == 0 else 30,
-    ).frames[0]
-    end = time.time()
-    if i == 0:
-        print(f"Warm up time: {end - begin:.2f}s")
-    else:
-        print(f"Time: {end - begin:.2f}s")
-
-print("Saving video to hunyuan_video.mp4")
-export_to_video(output, "hunyuan_video.mp4", fps=15)
-```
-
-A NVIDIA L20 GPU only has 48GB memory and could face out-of-memory (OOM) errors after compilation and if `enable_model_cpu_offload` isn't called because HunyuanVideo has very large activation tensors when running with high resolution and large number of frames. For GPUs with less than 80GB of memory, you can try reducing the resolution and number of frames to avoid OOM errors.
-
-Large video generation models are usually bottlenecked by the attention computations rather than the fully connected layers. These models don't significantly benefit from quantization and torch.compile.
-
-</hfoption>
-</hfoptions>
-
-## Context Parallelism
-
-Context Parallelism parallelizes inference and scales with multiple GPUs. The ParaAttention compositional design allows you to combine Context Parallelism with First Block Cache and dynamic quantization.
-
-> [!TIP]
-> Refer to the [ParaAttention](https://github.com/chengzeyi/ParaAttention/tree/main) repository for detailed instructions and examples of how to scale inference with multiple GPUs.
-
-If the inference process needs to be persistent and serviceable, it is suggested to use [torch.multiprocessing](https://pytorch.org/docs/stable/multiprocessing.html) to write your own inference processor. This can eliminate the overhead of launching the process and loading and recompiling the model.
-
-<hfoptions id="context-parallelism">
-<hfoption id="FLUX-1.dev">
-
-The code sample below combines First Block Cache, fp8 dynamic quantization, torch.compile, and Context Parallelism for the fastest inference speed.
-
-```python
-import time
-import torch
-import torch.distributed as dist
-from diffusers import FluxPipeline
-
-dist.init_process_group()
-
-torch.cuda.set_device(dist.get_rank())
-
-pipe = FluxPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-dev",
-    torch_dtype=torch.bfloat16,
-).to("cuda")
-
-from para_attn.context_parallel import init_context_parallel_mesh
-from para_attn.context_parallel.diffusers_adapters import parallelize_pipe
-from para_attn.parallel_vae.diffusers_adapters import parallelize_vae
-
-mesh = init_context_parallel_mesh(
-    pipe.device.type,
-    max_ring_dim_size=2,
-)
-parallelize_pipe(
-    pipe,
-    mesh=mesh,
-)
-parallelize_vae(pipe.vae, mesh=mesh._flatten())
-
-from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe
-
-apply_cache_on_pipe(
-    pipe,
-    residual_diff_threshold=0.12,  # Use a larger value to make the cache take effect
-)
-
-from torchao.quantization import quantize_, float8_dynamic_activation_float8_weight, float8_weight_only
-
-quantize_(pipe.text_encoder, float8_weight_only())
-quantize_(pipe.transformer, float8_dynamic_activation_float8_weight())
-torch._inductor.config.reorder_for_compute_comm_overlap = True
-pipe.transformer = torch.compile(
-   pipe.transformer, mode="max-autotune-no-cudagraphs",
-)
-
-# Enable memory savings
-# pipe.enable_model_cpu_offload(gpu_id=dist.get_rank())
-# pipe.enable_sequential_cpu_offload(gpu_id=dist.get_rank())
-
-for i in range(2):
-    begin = time.time()
-    image = pipe(
-        "A cat holding a sign that says hello world",
-        num_inference_steps=28,
-        output_type="pil" if dist.get_rank() == 0 else "pt",
-    ).images[0]
-    end = time.time()
-    if dist.get_rank() == 0:
-        if i == 0:
-            print(f"Warm up time: {end - begin:.2f}s")
-        else:
-            print(f"Time: {end - begin:.2f}s")
-
-if dist.get_rank() == 0:
-    print("Saving image to flux.png")
-    image.save("flux.png")
-
-dist.destroy_process_group()
-```
-
-Save to `run_flux.py` and launch it with [torchrun](https://pytorch.org/docs/stable/elastic/run.html).
-
-```bash
-# Use --nproc_per_node to specify the number of GPUs
-torchrun --nproc_per_node=2 run_flux.py
-```
-
-Inference speed is reduced to 8.20 seconds compared to the baseline, or 3.21x faster, with 2 NVIDIA L20 GPUs. On 4 L20s, inference speed is 3.90 seconds, or 6.75x faster.
-
-</hfoption>
-<hfoption id="HunyuanVideo">
-
-The code sample below combines First Block Cache and Context Parallelism for the fastest inference speed.
-
-```python
-import time
-import torch
-import torch.distributed as dist
-from diffusers import HunyuanVideoPipeline, HunyuanVideoTransformer3DModel
-from diffusers.utils import export_to_video
-
-dist.init_process_group()
-
-torch.cuda.set_device(dist.get_rank())
-
-model_id = "tencent/HunyuanVideo"
-transformer = HunyuanVideoTransformer3DModel.from_pretrained(
-    model_id,
-    subfolder="transformer",
-    torch_dtype=torch.bfloat16,
-    revision="refs/pr/18",
-)
-pipe = HunyuanVideoPipeline.from_pretrained(
-    model_id,
-    transformer=transformer,
-    torch_dtype=torch.float16,
-    revision="refs/pr/18",
-).to("cuda")
-
-from para_attn.context_parallel import init_context_parallel_mesh
-from para_attn.context_parallel.diffusers_adapters import parallelize_pipe
-from para_attn.parallel_vae.diffusers_adapters import parallelize_vae
-
-mesh = init_context_parallel_mesh(
-    pipe.device.type,
-)
-parallelize_pipe(
-    pipe,
-    mesh=mesh,
-)
-parallelize_vae(pipe.vae, mesh=mesh._flatten())
-
-from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe
-
-apply_cache_on_pipe(pipe)
-
-# from torchao.quantization import quantize_, float8_dynamic_activation_float8_weight, float8_weight_only
-#
-# torch._inductor.config.reorder_for_compute_comm_overlap = True
-#
-# quantize_(pipe.text_encoder, float8_weight_only())
-# quantize_(pipe.transformer, float8_dynamic_activation_float8_weight())
-# pipe.transformer = torch.compile(
-#    pipe.transformer, mode="max-autotune-no-cudagraphs",
-# )
-
-# Enable memory savings
-pipe.vae.enable_tiling()
-# pipe.enable_model_cpu_offload(gpu_id=dist.get_rank())
-# pipe.enable_sequential_cpu_offload(gpu_id=dist.get_rank())
-
-for i in range(2):
-    begin = time.time()
-    output = pipe(
-        prompt="A cat walks on the grass, realistic",
-        height=720,
-        width=1280,
-        num_frames=129,
-        num_inference_steps=1 if i == 0 else 30,
-        output_type="pil" if dist.get_rank() == 0 else "pt",
-    ).frames[0]
-    end = time.time()
-    if dist.get_rank() == 0:
-        if i == 0:
-            print(f"Warm up time: {end - begin:.2f}s")
-        else:
-            print(f"Time: {end - begin:.2f}s")
-
-if dist.get_rank() == 0:
-    print("Saving video to hunyuan_video.mp4")
-    export_to_video(output, "hunyuan_video.mp4", fps=15)
-
-dist.destroy_process_group()
-```
-
-Save to `run_hunyuan_video.py` and launch it with [torchrun](https://pytorch.org/docs/stable/elastic/run.html).
-
-```bash
-# Use --nproc_per_node to specify the number of GPUs
-torchrun --nproc_per_node=8 run_hunyuan_video.py
-```
-
-Inference speed is reduced to 649.23 seconds compared to the baseline, or 5.66x faster, with 8 NVIDIA L20 GPUs.
-
-</hfoption>
-</hfoptions>
-
-## Benchmarks
-
-<hfoptions id="conclusion">
-<hfoption id="FLUX-1.dev">
-
-| GPU Type | Number of GPUs | Optimizations | Wall Time (s) | Speedup |
-| - | - | - | - | - |
-| NVIDIA L20 | 1 | Baseline | 26.36 | 1.00x |
-| NVIDIA L20 | 1 | FBCache (rdt=0.08) | 17.01 | 1.55x |
-| NVIDIA L20 | 1 | FP8 DQ | 13.40 | 1.96x |
-| NVIDIA L20 | 1 | FBCache (rdt=0.12) + FP8 DQ | 7.56 | 3.48x |
-| NVIDIA L20 | 2 | FBCache (rdt=0.12) + FP8 DQ + CP | 4.92 | 5.35x |
-| NVIDIA L20 | 4 | FBCache (rdt=0.12) + FP8 DQ + CP | 3.90 | 6.75x |
-
-</hfoption>
-<hfoption id="HunyuanVideo">
-
-| GPU Type | Number of GPUs | Optimizations | Wall Time (s) | Speedup |
-| - | - | - | - | - |
-| NVIDIA L20 | 1 | Baseline | 3675.71 | 1.00x |
-| NVIDIA L20 | 1 | FBCache | 2271.06 | 1.62x |
-| NVIDIA L20 | 2 | FBCache + CP | 1132.90 | 3.24x |
-| NVIDIA L20 | 4 | FBCache + CP | 718.15 | 5.12x |
-| NVIDIA L20 | 8 | FBCache + CP | 649.23 | 5.66x |
-
-</hfoption>
-</hfoptions>
@@ -56,7 +56,7 @@ image

 With the `adapter_name` parameter, it is really easy to use another adapter for inference! Load the [nerijs/pixel-art-xl](https://huggingface.co/nerijs/pixel-art-xl) adapter that has been fine-tuned to generate pixel art images and call it `"pixel"`.

-The pipeline automatically sets the first loaded adapter (`"toy"`) as the active adapter, but you can activate the `"pixel"` adapter with the [`~loaders.peft.PeftAdapterMixin.set_adapters`] method:
+The pipeline automatically sets the first loaded adapter (`"toy"`) as the active adapter, but you can activate the `"pixel"` adapter with the [`~PeftAdapterMixin.set_adapters`] method:

 ```python
 pipe.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
@@ -85,7 +85,7 @@ By default, if the most up-to-date versions of PEFT and Transformers are detecte

 You can also merge different adapter checkpoints for inference to blend their styles together.

-Once again, use the [`~loaders.peft.PeftAdapterMixin.set_adapters`] method to activate the `pixel` and `toy` adapters and specify the weights for how they should be merged.
+Once again, use the [`~PeftAdapterMixin.set_adapters`] method to activate the `pixel` and `toy` adapters and specify the weights for how they should be merged.

 ```python
 pipe.set_adapters(["pixel", "toy"], adapter_weights=[0.5, 1.0])
@@ -114,7 +114,7 @@ Impressive! As you can see, the model generated an image that mixed the characte
 > [!TIP]
 > Through its PEFT integration, Diffusers also offers more efficient merging methods which you can learn about in the [Merge LoRAs](../using-diffusers/merge_loras) guide!

-To return to only using one adapter, use the [`~loaders.peft.PeftAdapterMixin.set_adapters`] method to activate the `"toy"` adapter:
+To return to only using one adapter, use the [`~PeftAdapterMixin.set_adapters`] method to activate the `"toy"` adapter:

 ```python
 pipe.set_adapters("toy")
@@ -127,7 +127,7 @@ image = pipe(
 image
 ```

-Or to disable all adapters entirely, use the [`~loaders.peft.PeftAdapterMixin.disable_lora`] method to return the base model.
+Or to disable all adapters entirely, use the [`~PeftAdapterMixin.disable_lora`] method to return the base model.

 ```python
 pipe.disable_lora()
@@ -141,7 +141,7 @@ image

 ### Customize adapters strength

-For even more customization, you can control how strongly the adapter affects each part of the pipeline. For this, pass a dictionary with the control strengths (called "scales") to [`~loaders.peft.PeftAdapterMixin.set_adapters`].
+For even more customization, you can control how strongly the adapter affects each part of the pipeline. For this, pass a dictionary with the control strengths (called "scales") to [`~PeftAdapterMixin.set_adapters`].

 For example, here's how you can turn on the adapter for the `down` parts, but turn it off for the `mid` and `up` parts:
 ```python
@@ -214,7 +214,7 @@ list_adapters_component_wise
 {"text_encoder": ["toy", "pixel"], "unet": ["toy", "pixel"], "text_encoder_2": ["toy", "pixel"]}
 ```

-The [`~loaders.peft.PeftAdapterMixin.delete_adapters`] function completely removes an adapter and their LoRA layers from a model.
+The [`~PeftAdapterMixin.delete_adapters`] function completely removes an adapter and their LoRA layers from a model.

 ```py
 pipe.delete_adapters("toy")
@@ -1,96 +0,0 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
-->
-# ConsisID
-
-[ConsisID](https://github.com/PKU-YuanGroup/ConsisID) is an identity-preserving text-to-video generation model that keeps the face consistent in the generated video by frequency decomposition. The main features of ConsisID are:
-
- Frequency decomposition: The characteristics of the DiT architecture are analyzed from the frequency domain perspective, and based on these characteristics, a reasonable control information injection method is designed.
- Consistency training strategy: A coarse-to-fine training strategy, dynamic masking loss, and dynamic cross-face loss further enhance the model's generalization ability and identity preservation performance.
- Inference without finetuning: Previous methods required case-by-case finetuning of the input ID before inference, leading to significant time and computational costs. In contrast, ConsisID is tuning-free.
-
-This guide will walk you through using ConsisID for use cases.
-
-## Load Model Checkpoints
-
-Model weights may be stored in separate subfolders on the Hub or locally, in which case, you should use the [`~DiffusionPipeline.from_pretrained`] method.
-
-```python
-# !pip install consisid_eva_clip insightface facexlib
-import torch
-from diffusers import ConsisIDPipeline
-from diffusers.pipelines.consisid.consisid_utils import prepare_face_models, process_face_embeddings_infer
-from huggingface_hub import snapshot_download
-
-# Download ckpts
-snapshot_download(repo_id="BestWishYsh/ConsisID-preview", local_dir="BestWishYsh/ConsisID-preview")
-
-# Load face helper model to preprocess input face image
-face_helper_1, face_helper_2, face_clip_model, face_main_model, eva_transform_mean, eva_transform_std = prepare_face_models("BestWishYsh/ConsisID-preview", device="cuda", dtype=torch.bfloat16)
-
-# Load consisid base model
-pipe = ConsisIDPipeline.from_pretrained("BestWishYsh/ConsisID-preview", torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-```
-
-## Identity-Preserving Text-to-Video
-
-For identity-preserving text-to-video, pass a text prompt and an image contain clear face (e.g., preferably half-body or full-body). By default, ConsisID generates a 720x480 video for the best results.
-
-```python
-from diffusers.utils import export_to_video
-
-prompt = "The video captures a boy walking along a city street, filmed in black and white on a classic 35mm camera. His expression is thoughtful, his brow slightly furrowed as if he's lost in contemplation. The film grain adds a textured, timeless quality to the image, evoking a sense of nostalgia. Around him, the cityscape is filled with vintage buildings, cobblestone sidewalks, and softly blurred figures passing by, their outlines faint and indistinct. Streetlights cast a gentle glow, while shadows play across the boy's path, adding depth to the scene. The lighting highlights the boy's subtle smile, hinting at a fleeting moment of curiosity. The overall cinematic atmosphere, complete with classic film still aesthetics and dramatic contrasts, gives the scene an evocative and introspective feel."
-image = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_input.png?download=true"
-
-id_cond, id_vit_hidden, image, face_kps = process_face_embeddings_infer(face_helper_1, face_clip_model, face_helper_2, eva_transform_mean, eva_transform_std, face_main_model, "cuda", torch.bfloat16, image, is_align_face=True)
-
-video = pipe(image=image, prompt=prompt, num_inference_steps=50, guidance_scale=6.0, use_dynamic_cfg=False, id_vit_hidden=id_vit_hidden, id_cond=id_cond, kps_cond=face_kps, generator=torch.Generator("cuda").manual_seed(42))
-export_to_video(video.frames[0], "output.mp4", fps=8)
-```
-<table>
-  <tr>
-    <th style="text-align: center;">Face Image</th>
-    <th style="text-align: center;">Video</th>
-    <th style="text-align: center;">Description</th
-  </tr>
-  <tr>
-    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_image_0.png?download=true" style="height: auto; width: 600px;"></td>
-    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_output_0.gif?download=true" style="height: auto; width: 2000px;"></td>
-    <td>The video, in a beautifully crafted animated style, features a confident woman riding a horse through a lush forest clearing. Her expression is focused yet serene as she adjusts her wide-brimmed hat with a practiced hand. She wears a flowy bohemian dress, which moves gracefully with the rhythm of the horse, the fabric flowing fluidly in the animated motion. The dappled sunlight filters through the trees, casting soft, painterly patterns on the forest floor. Her posture is poised, showing both control and elegance as she guides the horse with ease. The animation's gentle, fluid style adds a dreamlike quality to the scene, with the woman’s calm demeanor and the peaceful surroundings evoking a sense of freedom and harmony.</td>
-  </tr>
-  <tr>
-    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_image_1.png?download=true" style="height: auto; width: 600px;"></td>
-    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_output_1.gif?download=true" style="height: auto; width: 2000px;"></td>
-    <td>The video, in a captivating animated style, shows a woman standing in the center of a snowy forest, her eyes narrowed in concentration as she extends her hand forward. She is dressed in a deep blue cloak, her breath visible in the cold air, which is rendered with soft, ethereal strokes. A faint smile plays on her lips as she summons a wisp of ice magic, watching with focus as the surrounding trees and ground begin to shimmer and freeze, covered in delicate ice crystals. The animation’s fluid motion brings the magic to life, with the frost spreading outward in intricate, sparkling patterns. The environment is painted with soft, watercolor-like hues, enhancing the magical, dreamlike atmosphere. The overall mood is serene yet powerful, with the quiet winter air amplifying the delicate beauty of the frozen scene.</td>
-  </tr>
-  <tr>
-    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_image_2.png?download=true" style="height: auto; width: 600px;"></td>
-    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_output_2.gif?download=true" style="height: auto; width: 2000px;"></td>
-    <td>The animation features a whimsical portrait of a balloon seller standing in a gentle breeze, captured with soft, hazy brushstrokes that evoke the feel of a serene spring day. His face is framed by a gentle smile, his eyes squinting slightly against the sun, while a few wisps of hair flutter in the wind. He is dressed in a light, pastel-colored shirt, and the balloons around him sway with the wind, adding a sense of playfulness to the scene. The background blurs softly, with hints of a vibrant market or park, enhancing the light-hearted, yet tender mood of the moment.</td>
-  </tr>
-  <tr>
-    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_image_3.png?download=true" style="height: auto; width: 600px;"></td>
-    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_output_3.gif?download=true" style="height: auto; width: 2000px;"></td>
-    <td>The video captures a boy walking along a city street, filmed in black and white on a classic 35mm camera. His expression is thoughtful, his brow slightly furrowed as if he's lost in contemplation. The film grain adds a textured, timeless quality to the image, evoking a sense of nostalgia. Around him, the cityscape is filled with vintage buildings, cobblestone sidewalks, and softly blurred figures passing by, their outlines faint and indistinct. Streetlights cast a gentle glow, while shadows play across the boy's path, adding depth to the scene. The lighting highlights the boy's subtle smile, hinting at a fleeting moment of curiosity. The overall cinematic atmosphere, complete with classic film still aesthetics and dramatic contrasts, gives the scene an evocative and introspective feel.</td>
-  </tr>
-  <tr>
-    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_image_4.png?download=true" style="height: auto; width: 600px;"></td>
-    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_output_4.gif?download=true" style="height: auto; width: 2000px;"></td>
-    <td>The video features a baby wearing a bright superhero cape, standing confidently with arms raised in a powerful pose. The baby has a determined look on their face, with eyes wide and lips pursed in concentration, as if ready to take on a challenge. The setting appears playful, with colorful toys scattered around and a soft rug underfoot, while sunlight streams through a nearby window, highlighting the fluttering cape and adding to the impression of heroism. The overall atmosphere is lighthearted and fun, with the baby's expressions capturing a mix of innocence and an adorable attempt at bravery, as if truly ready to save the day.</td>
-  </tr>
-</table>
-
-## Resources
-
-Learn more about ConsisID with the following resources.
- A [video](https://www.youtube.com/watch?v=PhlgC-bI5SQ) demonstrating ConsisID's main features.
- The research paper, [Identity-Preserving Text-to-Video Generation by Frequency Decomposition](https://hf.co/papers/2411.17440) for more details.
@@ -240,46 +240,6 @@ Benefits of using a single-file layout include:
 1. Easy compatibility with diffusion interfaces such as [ComfyUI](https://github.com/comfyanonymous/ComfyUI) or [Automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) which commonly use a single-file layout.
 2. Easier to manage (download and share) a single file.

-### DDUF
-
-> [!WARNING]
-> DDUF is an experimental file format and APIs related to it can change in the future.
-
-DDUF (**D**DUF **D**iffusion **U**nified **F**ormat) is a file format designed to make storing, distributing, and using diffusion models much easier. Built on the ZIP file format, DDUF offers a standardized, efficient, and flexible way to package all parts of a diffusion model into a single, easy-to-manage file. It provides a balance between Diffusers multi-folder format and the widely popular single-file format.
-
-Learn more details about DDUF on the Hugging Face Hub [documentation](https://huggingface.co/docs/hub/dduf).
-
-Pass a checkpoint to the `dduf_file` parameter to load it in [`DiffusionPipeline`].
-
-```py
-from diffusers import DiffusionPipeline
-import torch
-
-pipe = DiffusionPipeline.from_pretrained(
-    "DDUF/FLUX.1-dev-DDUF", dduf_file="FLUX.1-dev.dduf", torch_dtype=torch.bfloat16
-).to("cuda")
-image = pipe(
-    "photo a cat holding a sign that says Diffusers", num_inference_steps=50, guidance_scale=3.5
-).images[0]
-image.save("cat.png")
-```
-
-To save a pipeline as a `.dduf` checkpoint, use the [`~huggingface_hub.export_folder_as_dduf`] utility, which takes care of all the necessary file-level validations.
-
-```py
-from huggingface_hub import export_folder_as_dduf
-from diffusers import DiffusionPipeline
-import torch 
-
-pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
-
-save_folder = "flux-dev"
-pipe.save_pretrained("flux-dev")
-export_folder_as_dduf("flux-dev.dduf", folder_path=save_folder)
-
-> [!TIP]
-> Packaging and loading quantized checkpoints in the DDUF format is supported as long as they respect the multi-folder structure.
-
 ## Convert layout and files

 Diffusers provides many scripts and methods to convert storage layouts and file formats to enable broader support across the diffusion ecosystem.
@@ -78,10 +78,10 @@ from diffusers import HunyuanVideoPipeline, HunyuanVideoTransformer3DModel
 from diffusers.utils import export_to_video

 transformer = HunyuanVideoTransformer3DModel.from_pretrained(
-    "hunyuanvideo-community/HunyuanVideo", subfolder="transformer", torch_dtype=torch.bfloat16
+    "tencent/HunyuanVideo", subfolder="transformer", torch_dtype=torch.bfloat16
 )
 pipe = HunyuanVideoPipeline.from_pretrained(
-  "hunyuanvideo-community/HunyuanVideo", transformer=transformer, torch_dtype=torch.float16
+  "tencent/HunyuanVideo", transformer=transformer, torch_dtype=torch.float16
 )

 # reduce memory requirements
@@ -5,8 +5,6 @@
    title: 快速入门
  - local: stable_diffusion
    title: 有效和高效的扩散
-  - local: consisid 
-    title: 身份保持的文本到视频生成
  - local: installation
    title: 安装
  title: 开始
@@ -1,100 +0,0 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
-->
-# ConsisID
-
-[ConsisID](https://github.com/PKU-YuanGroup/ConsisID)是一种身份保持的文本到视频生成模型，其通过频率分解在生成的视频中保持面部一致性。它具有以下特点：
-
- 基于频率分解：将人物ID特征解耦为高频和低频部分，从频域的角度分析DIT架构的特性，并且基于此特性设计合理的控制信息注入方式。
-
- 一致性训练策略：我们提出粗到细训练策略、动态掩码损失、动态跨脸损失，进一步提高了模型的泛化能力和身份保持效果。
-
-
- 推理无需微调：之前的方法在推理前，需要对输入id进行case-by-case微调，时间和算力开销较大，而我们的方法是tuning-free的。
-
-
-本指南将指导您使用 ConsisID 生成身份保持的视频。
-
-## Load Model Checkpoints
-模型权重可以存储在Hub上或本地的单独子文件夹中，在这种情况下，您应该使用 [`~DiffusionPipeline.from_pretrained`] 方法。
-
-
-```python
-# !pip install consisid_eva_clip insightface facexlib
-import torch
-from diffusers import ConsisIDPipeline
-from diffusers.pipelines.consisid.consisid_utils import prepare_face_models, process_face_embeddings_infer
-from huggingface_hub import snapshot_download
-
-# Download ckpts
-snapshot_download(repo_id="BestWishYsh/ConsisID-preview", local_dir="BestWishYsh/ConsisID-preview")
-
-# Load face helper model to preprocess input face image
-face_helper_1, face_helper_2, face_clip_model, face_main_model, eva_transform_mean, eva_transform_std = prepare_face_models("BestWishYsh/ConsisID-preview", device="cuda", dtype=torch.bfloat16)
-
-# Load consisid base model
-pipe = ConsisIDPipeline.from_pretrained("BestWishYsh/ConsisID-preview", torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-```
-
-## Identity-Preserving Text-to-Video
-对于身份保持的文本到视频生成，需要输入文本提示和包含清晰面部（例如，最好是半身或全身）的图像。默认情况下，ConsisID 会生成 720x480 的视频以获得最佳效果。
-
-```python
-from diffusers.utils import export_to_video
-
-prompt = "The video captures a boy walking along a city street, filmed in black and white on a classic 35mm camera. His expression is thoughtful, his brow slightly furrowed as if he's lost in contemplation. The film grain adds a textured, timeless quality to the image, evoking a sense of nostalgia. Around him, the cityscape is filled with vintage buildings, cobblestone sidewalks, and softly blurred figures passing by, their outlines faint and indistinct. Streetlights cast a gentle glow, while shadows play across the boy's path, adding depth to the scene. The lighting highlights the boy's subtle smile, hinting at a fleeting moment of curiosity. The overall cinematic atmosphere, complete with classic film still aesthetics and dramatic contrasts, gives the scene an evocative and introspective feel."
-image = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_input.png?download=true"
-
-id_cond, id_vit_hidden, image, face_kps = process_face_embeddings_infer(face_helper_1, face_clip_model, face_helper_2, eva_transform_mean, eva_transform_std, face_main_model, "cuda", torch.bfloat16, image, is_align_face=True)
-
-video = pipe(image=image, prompt=prompt, num_inference_steps=50, guidance_scale=6.0, use_dynamic_cfg=False, id_vit_hidden=id_vit_hidden, id_cond=id_cond, kps_cond=face_kps, generator=torch.Generator("cuda").manual_seed(42))
-export_to_video(video.frames[0], "output.mp4", fps=8)
-```
-<table>
-  <tr>
-    <th style="text-align: center;">Face Image</th>
-    <th style="text-align: center;">Video</th>
-    <th style="text-align: center;">Description</th
-  </tr>
-  <tr>
-    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_image_0.png?download=true" style="height: auto; width: 600px;"></td>
-    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_output_0.gif?download=true" style="height: auto; width: 2000px;"></td>
-    <td>The video, in a beautifully crafted animated style, features a confident woman riding a horse through a lush forest clearing. Her expression is focused yet serene as she adjusts her wide-brimmed hat with a practiced hand. She wears a flowy bohemian dress, which moves gracefully with the rhythm of the horse, the fabric flowing fluidly in the animated motion. The dappled sunlight filters through the trees, casting soft, painterly patterns on the forest floor. Her posture is poised, showing both control and elegance as she guides the horse with ease. The animation's gentle, fluid style adds a dreamlike quality to the scene, with the woman’s calm demeanor and the peaceful surroundings evoking a sense of freedom and harmony.</td>
-  </tr>
-  <tr>
-    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_image_1.png?download=true" style="height: auto; width: 600px;"></td>
-    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_output_1.gif?download=true" style="height: auto; width: 2000px;"></td>
-    <td>The video, in a captivating animated style, shows a woman standing in the center of a snowy forest, her eyes narrowed in concentration as she extends her hand forward. She is dressed in a deep blue cloak, her breath visible in the cold air, which is rendered with soft, ethereal strokes. A faint smile plays on her lips as she summons a wisp of ice magic, watching with focus as the surrounding trees and ground begin to shimmer and freeze, covered in delicate ice crystals. The animation’s fluid motion brings the magic to life, with the frost spreading outward in intricate, sparkling patterns. The environment is painted with soft, watercolor-like hues, enhancing the magical, dreamlike atmosphere. The overall mood is serene yet powerful, with the quiet winter air amplifying the delicate beauty of the frozen scene.</td>
-  </tr>
-  <tr>
-    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_image_2.png?download=true" style="height: auto; width: 600px;"></td>
-    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_output_2.gif?download=true" style="height: auto; width: 2000px;"></td>
-    <td>The animation features a whimsical portrait of a balloon seller standing in a gentle breeze, captured with soft, hazy brushstrokes that evoke the feel of a serene spring day. His face is framed by a gentle smile, his eyes squinting slightly against the sun, while a few wisps of hair flutter in the wind. He is dressed in a light, pastel-colored shirt, and the balloons around him sway with the wind, adding a sense of playfulness to the scene. The background blurs softly, with hints of a vibrant market or park, enhancing the light-hearted, yet tender mood of the moment.</td>
-  </tr>
-  <tr>
-    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_image_3.png?download=true" style="height: auto; width: 600px;"></td>
-    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_output_3.gif?download=true" style="height: auto; width: 2000px;"></td>
-    <td>The video captures a boy walking along a city street, filmed in black and white on a classic 35mm camera. His expression is thoughtful, his brow slightly furrowed as if he's lost in contemplation. The film grain adds a textured, timeless quality to the image, evoking a sense of nostalgia. Around him, the cityscape is filled with vintage buildings, cobblestone sidewalks, and softly blurred figures passing by, their outlines faint and indistinct. Streetlights cast a gentle glow, while shadows play across the boy's path, adding depth to the scene. The lighting highlights the boy's subtle smile, hinting at a fleeting moment of curiosity. The overall cinematic atmosphere, complete with classic film still aesthetics and dramatic contrasts, gives the scene an evocative and introspective feel.</td>
-  </tr>
-  <tr>
-    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_image_4.png?download=true" style="height: auto; width: 600px;"></td>
-    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_output_4.gif?download=true" style="height: auto; width: 2000px;"></td>
-    <td>The video features a baby wearing a bright superhero cape, standing confidently with arms raised in a powerful pose. The baby has a determined look on their face, with eyes wide and lips pursed in concentration, as if ready to take on a challenge. The setting appears playful, with colorful toys scattered around and a soft rug underfoot, while sunlight streams through a nearby window, highlighting the fluttering cape and adding to the impression of heroism. The overall atmosphere is lighthearted and fun, with the baby's expressions capturing a mix of innocence and an adorable attempt at bravery, as if truly ready to save the day.</td>
-  </tr>
-</table>
-
-## Resources
-
-通过以下资源了解有关 ConsisID 的更多信息：
-
- 一段 [视频](https://www.youtube.com/watch?v=PhlgC-bI5SQ) 演示了 ConsisID 的主要功能；
- 有关更多详细信息，请参阅研究论文 [Identity-Preserving Text-to-Video Generation by Frequency Decomposition](https://hf.co/papers/2411.17440)。
@@ -67,17 +67,6 @@ write_basic_config()
 When running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.
 Note also that we use PEFT library as backend for LoRA training, make sure to have `peft>=0.6.0` installed in your environment.

-Lastly, we recommend logging into your HF account so that your trained LoRA is automatically uploaded to the hub:
-```bash
-huggingface-cli login
-```
-This command will prompt you for a token. Copy-paste yours from your [settings/tokens](https://huggingface.co/settings/tokens),and press Enter.
-
-> [!NOTE]
-> In the examples below we use `wandb` to document the training runs. To do the same, make sure to install `wandb`:
-> `pip install wandb`
-> Alternatively, you can use other tools / train without reporting by modifying the flag  `--report_to="wandb"`.
-
 ### Pivotal Tuning
 **Training with text encoder(s)**

@@ -65,17 +65,6 @@ write_basic_config()
 When running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.
 Note also that we use PEFT library as backend for LoRA training, make sure to have `peft>=0.6.0` installed in your environment.

-Lastly, we recommend logging into your HF account so that your trained LoRA is automatically uploaded to the hub:
-```bash
-huggingface-cli login
-```
-This command will prompt you for a token. Copy-paste yours from your [settings/tokens](https://huggingface.co/settings/tokens),and press Enter.
-
-> [!NOTE]
-> In the examples below we use `wandb` to document the training runs. To do the same, make sure to install `wandb`:
-> `pip install wandb`
-> Alternatively, you can use other tools / train without reporting by modifying the flag  `--report_to="wandb"`.
-
 ### Target Modules
 When LoRA was first adapted from language models to diffusion models, it was applied to the cross-attention layers in the Unet that relate the image representations with the prompts that describe them. 
 More recently, SOTA text-to-image diffusion models replaced the Unet with a diffusion Transformer(DiT). With this change, we may also want to explore 
@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 # coding=utf-8
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 # coding=utf-8
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -160,7 +160,7 @@ to trigger concept `{key}` → use `{tokens}` in your prompt \n
 from diffusers import AutoPipelineForText2Image
 import torch
 {diffusers_imports_pivotal}
-pipeline = AutoPipelineForText2Image.from_pretrained('stable-diffusion-v1-5/stable-diffusion-v1-5', torch_dtype=torch.float16).to('cuda')
+pipeline = AutoPipelineForText2Image.from_pretrained('runwayml/stable-diffusion-v1-5', torch_dtype=torch.float16).to('cuda')
 pipeline.load_lora_weights('{repo_id}', weight_name='pytorch_lora_weights.safetensors')
 {diffusers_example_pivotal}
 image = pipeline('{validation_prompt if validation_prompt else instance_prompt}').images[0]
@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 # coding=utf-8
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -1,5 +1,5 @@
 # coding=utf-8
-# Copyright 2025 The HuggingFace Inc. team.
+# Copyright 2024 The HuggingFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -33,12 +33,12 @@ Please also check out our [Community Scripts](https://github.com/huggingface/dif
 | Bit Diffusion                                                                                                                         | Diffusion on discrete data                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | [Bit Diffusion](#bit-diffusion)                                                           | -  |                       [Stuti R.](https://github.com/kingstut) |
 | K-Diffusion Stable Diffusion                                                                                                          | Run Stable Diffusion with any of [K-Diffusion's samplers](https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/sampling.py)                                                                                                                                                                                                                                                                                                                                                                  | [Stable Diffusion with K Diffusion](#stable-diffusion-with-k-diffusion)                   | -  |    [Patrick von Platen](https://github.com/patrickvonplaten/) |
 | Checkpoint Merger Pipeline                                                                                                            | Diffusion Pipeline that enables merging of saved model checkpoints                                                                                                                                                                                                                                                                                                                                                                                                                                       | [Checkpoint Merger Pipeline](#checkpoint-merger-pipeline)                                 | -                                                                                                                                                                                                                  | [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) |
-| Stable Diffusion v1.1-1.4 Comparison                                                                                                  | Run all 4 model checkpoints for Stable Diffusion and compare their results together                                                                                                                                                                                                                                                                                                                                                                                                                      | [Stable Diffusion Comparison](#stable-diffusion-comparisons)                              | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/stable_diffusion_comparison.ipynb) |        [Suvaditya Mukherjee](https://github.com/suvadityamuk) |
+| Stable Diffusion v1.1-1.4 Comparison                                                                                                  | Run all 4 model checkpoints for Stable Diffusion and compare their results together                                                                                                                                                                                                                                                                                                                                                                                                                      | [Stable Diffusion Comparison](#stable-diffusion-comparisons)                              | - |        [Suvaditya Mukherjee](https://github.com/suvadityamuk) |
 | MagicMix                                                                                                                              | Diffusion Pipeline for semantic mixing of an image and a text prompt                                                                                                                                                                                                                                                                                                                                                                                                                                     | [MagicMix](#magic-mix)                                                                    | - |                    [Partho Das](https://github.com/daspartho) |
-| Stable UnCLIP                                                                                                                         | Diffusion Pipeline for combining prior model (generate clip image embedding from text, UnCLIPPipeline `"kakaobrain/karlo-v1-alpha"`) and decoder pipeline (decode clip image embedding to image, StableDiffusionImageVariationPipeline `"lambdalabs/sd-image-variations-diffusers"` ).                                                                                                                                                                                                                   | [Stable UnCLIP](#stable-unclip)                                                           | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/stable_unclip.ipynb)  |                                [Ray Wang](https://wrong.wang) |
-| UnCLIP Text Interpolation Pipeline                                                                                                    | Diffusion Pipeline that allows passing two prompts and produces images while interpolating between the text-embeddings of the two prompts                                                                                                                                                                                                                                                                                                                                                                | [UnCLIP Text Interpolation Pipeline](#unclip-text-interpolation-pipeline)                 | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/unclip_text_interpolation.ipynb)| [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) |
+| Stable UnCLIP                                                                                                                         | Diffusion Pipeline for combining prior model (generate clip image embedding from text, UnCLIPPipeline `"kakaobrain/karlo-v1-alpha"`) and decoder pipeline (decode clip image embedding to image, StableDiffusionImageVariationPipeline `"lambdalabs/sd-image-variations-diffusers"` ).                                                                                                                                                                                                                   | [Stable UnCLIP](#stable-unclip)                                                           | -  |                                [Ray Wang](https://wrong.wang) |
+| UnCLIP Text Interpolation Pipeline                                                                                                    | Diffusion Pipeline that allows passing two prompts and produces images while interpolating between the text-embeddings of the two prompts                                                                                                                                                                                                                                                                                                                                                                | [UnCLIP Text Interpolation Pipeline](#unclip-text-interpolation-pipeline)                 | -                                                                                                                                                                                                                  | [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) |
 | UnCLIP Image Interpolation Pipeline                                                                                                   | Diffusion Pipeline that allows passing two images/image_embeddings and produces images while interpolating between their image-embeddings                                                                                                                                                                                                                                                                                                                                                                | [UnCLIP Image Interpolation Pipeline](#unclip-image-interpolation-pipeline)               | -                                                                                                                                                                                                                  | [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) |
-| DDIM Noise Comparative Analysis Pipeline                                                                                              | Investigating how the diffusion models learn visual concepts from each noise level (which is a contribution of [P2 weighting (CVPR 2022)](https://arxiv.org/abs/2204.00227))                                                                                                                                                                                                                                                                                                                             | [DDIM Noise Comparative Analysis Pipeline](#ddim-noise-comparative-analysis-pipeline)     | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/ddim_noise_comparative_analysis.ipynb)|              [Aengus (Duc-Anh)](https://github.com/aengusng8) |
+| DDIM Noise Comparative Analysis Pipeline                                                                                              | Investigating how the diffusion models learn visual concepts from each noise level (which is a contribution of [P2 weighting (CVPR 2022)](https://arxiv.org/abs/2204.00227))                                                                                                                                                                                                                                                                                                                             | [DDIM Noise Comparative Analysis Pipeline](#ddim-noise-comparative-analysis-pipeline)     | - |              [Aengus (Duc-Anh)](https://github.com/aengusng8) |
 | CLIP Guided Img2Img Stable Diffusion Pipeline                                                                                         | Doing CLIP guidance for image to image generation with Stable Diffusion                                                                                                                                                                                                                                                                                                                                                                                                                                  | [CLIP Guided Img2Img Stable Diffusion](#clip-guided-img2img-stable-diffusion)             | - |               [Nipun Jindal](https://github.com/nipunjindal/) |
 | TensorRT Stable Diffusion Text to Image Pipeline                                                                                                    | Accelerates the Stable Diffusion Text2Image Pipeline using TensorRT                                                                                                                                                                                                                                                                                                                                                                                                                                      | [TensorRT Stable Diffusion Text to Image Pipeline](#tensorrt-text2image-stable-diffusion-pipeline)      | - |              [Asfiya Baig](https://github.com/asfiyab-nvidia) |
 | EDICT Image Editing Pipeline                                                                                                          | Diffusion pipeline for text-guided image editing                                                                                                                                                                                                                                                                                                                                                                                                                                                         | [EDICT Image Editing Pipeline](#edict-image-editing-pipeline)                             | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/edict_image_pipeline.ipynb) |                    [Joqsan Azocar](https://github.com/Joqsan) |
@@ -50,7 +50,7 @@ Please also check out our [Community Scripts](https://github.com/huggingface/dif
 |   IADB Pipeline                                                                                                    | Implementation of [Iterative α-(de)Blending: a Minimalist Deterministic Diffusion Model](https://arxiv.org/abs/2305.03486)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [IADB Pipeline](#iadb-pipeline)      | - |              [Thomas Chambon](https://github.com/tchambon)
 |   Zero1to3 Pipeline                                                                                                    | Implementation of [Zero-1-to-3: Zero-shot One Image to 3D Object](https://arxiv.org/abs/2303.11328)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [Zero1to3 Pipeline](#zero1to3-pipeline)      | - |              [Xin Kong](https://github.com/kxhit) |
 | Stable Diffusion XL Long Weighted Prompt Pipeline | A pipeline support unlimited length of prompt and negative prompt, use A1111 style of prompt weighting | [Stable Diffusion XL Long Weighted Prompt Pipeline](#stable-diffusion-xl-long-weighted-prompt-pipeline) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1LsqilswLR40XLLcp6XFOl5nKb_wOe26W?usp=sharing) | [Andrew Zhu](https://xhinker.medium.com/) |
-| FABRIC - Stable Diffusion with feedback Pipeline | pipeline supports feedback from liked and disliked images | [Stable Diffusion Fabric Pipeline](#stable-diffusion-fabric-pipeline) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/stable_diffusion_fabric.ipynb)| [Shauray Singh](https://shauray8.github.io/about_shauray/) |
+| FABRIC - Stable Diffusion with feedback Pipeline | pipeline supports feedback from liked and disliked images | [Stable Diffusion Fabric Pipeline](#stable-diffusion-fabric-pipeline) | - | [Shauray Singh](https://shauray8.github.io/about_shauray/) |
 | sketch inpaint - Inpainting with non-inpaint Stable Diffusion | sketch inpaint much like in automatic1111 | [Masked Im2Im Stable Diffusion Pipeline](#stable-diffusion-masked-im2im) | - | [Anatoly Belikov](https://github.com/noskill) |
 | sketch inpaint xl - Inpainting with non-inpaint Stable Diffusion | sketch inpaint much like in automatic1111 | [Masked Im2Im Stable Diffusion XL Pipeline](#stable-diffusion-xl-masked-im2im) | - | [Anatoly Belikov](https://github.com/noskill) |
 | prompt-to-prompt | change parts of a prompt and retain image structure (see [paper page](https://prompt-to-prompt.github.io/)) | [Prompt2Prompt Pipeline](#prompt2prompt-pipeline) | - | [Umer H. Adil](https://twitter.com/UmerHAdil) |
@@ -77,7 +77,6 @@ Please also check out our [Community Scripts](https://github.com/huggingface/dif
 PIXART-α Controlnet pipeline | Implementation of the controlnet model for pixart alpha and its diffusers pipeline | [PIXART-α Controlnet pipeline](#pixart-α-controlnet-pipeline) | - | [Raul Ciotescu](https://github.com/raulc0399/) |
 | HunyuanDiT Differential Diffusion Pipeline | Applies [Differential Diffusion](https://github.com/exx8/differential-diffusion) to [HunyuanDiT](https://github.com/huggingface/diffusers/pull/8240). | [HunyuanDiT with Differential Diffusion](#hunyuandit-with-differential-diffusion) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1v44a5fpzyr4Ffr4v2XBQ7BajzG874N4P?usp=sharing) | [Monjoy Choudhury](https://github.com/MnCSSJ4x) |
 | [🪆Matryoshka Diffusion Models](https://huggingface.co/papers/2310.15111) | A diffusion process that denoises inputs at multiple resolutions jointly and uses a NestedUNet architecture where features and parameters for small scale inputs are nested within those of the large scales. See [original codebase](https://github.com/apple/ml-mdm). | [🪆Matryoshka Diffusion Models](#matryoshka-diffusion-models) | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/pcuenq/mdm) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/tolgacangoz/1f54875fc7aeaabcf284ebde64820966/matryoshka_hf.ipynb) | [M. Tolga Cangöz](https://github.com/tolgacangoz) |
-| Stable Diffusion XL Attentive Eraser Pipeline |[[AAAI2025 Oral] Attentive Eraser](https://github.com/Anonym0u3/AttentiveEraser) is a novel tuning-free method that enhances object removal capabilities in pre-trained diffusion models.|[Stable Diffusion XL Attentive Eraser Pipeline](#stable-diffusion-xl-attentive-eraser-pipeline)|-|[Wenhao Sun](https://github.com/Anonym0u3) and [Benlei Cui](https://github.com/Benny079)|

 To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.

@@ -4586,8 +4585,8 @@ image = pipe(
 ```

 | ![Gradient](https://github.com/user-attachments/assets/e38ce4d5-1ae6-4df0-ab43-adc1b45716b5) | ![Input](https://github.com/user-attachments/assets/9c95679c-e9d7-4f5a-90d6-560203acd6b3) | ![Output](https://github.com/user-attachments/assets/5313ff64-a0c4-418b-8b55-a38f1a5e7532) |
-| -------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ |
-| Gradient                                                                                     | Input                                                                                     | Output                                                                                     |
+| ------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------- |
+| Gradient                                                                                   | Input                                                                                   | Output                                                                                   |

 A colab notebook demonstrating all results can be found [here](https://colab.research.google.com/drive/1v44a5fpzyr4Ffr4v2XBQ7BajzG874N4P?usp=sharing). Depth Maps have also been added in the same colab.

@@ -4635,93 +4634,6 @@ make_image_grid(image, rows=1, cols=len(image))
 # 50+, 100+, and 250+ num_inference_steps are recommended for nesting levels 0, 1, and 2 respectively.
 ```

-### Stable Diffusion XL Attentive Eraser Pipeline
-<img src="https://raw.githubusercontent.com/Anonym0u3/Images/refs/heads/main/fenmian.png"  width="600" />
-
-**Stable Diffusion XL Attentive Eraser Pipeline** is an advanced object removal pipeline that leverages SDXL for precise content suppression and seamless region completion. This pipeline uses **self-attention redirection guidance** to modify the model’s self-attention mechanism, allowing for effective removal and inpainting across various levels of mask precision, including semantic segmentation masks, bounding boxes, and hand-drawn masks. If you are interested in more detailed information and have any questions, please refer to the [paper](https://arxiv.org/abs/2412.12974) and [official implementation](https://github.com/Anonym0u3/AttentiveEraser).
-
-#### Key features
-
- **Tuning-Free**: No additional training is required, making it easy to integrate and use.
- **Flexible Mask Support**: Works with different types of masks for targeted object removal.
- **High-Quality Results**: Utilizes the inherent generative power of diffusion models for realistic content completion.
-
-#### Usage example
-To use the Stable Diffusion XL Attentive Eraser Pipeline, you can initialize it as follows:
-```py
-import torch
-from diffusers import DDIMScheduler, DiffusionPipeline
-from diffusers.utils import load_image
-import torch.nn.functional as F
-from torchvision.transforms.functional import to_tensor, gaussian_blur
-
-dtype = torch.float16
-device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") 
-
-scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False)
-pipeline = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0",
-    custom_pipeline="pipeline_stable_diffusion_xl_attentive_eraser",
-    scheduler=scheduler,
-    variant="fp16",
-    use_safetensors=True,
-    torch_dtype=dtype,
-).to(device)
-
-
-def preprocess_image(image_path, device):
-    image = to_tensor((load_image(image_path)))
-    image = image.unsqueeze_(0).float() * 2 - 1 # [0,1] --> [-1,1]
-    if image.shape[1] != 3:
-        image = image.expand(-1, 3, -1, -1)
-        image = F.interpolate(image, (1024, 1024))
-        image = image.to(dtype).to(device)
-        return image
-
-def preprocess_mask(mask_path, device):
-    mask = to_tensor((load_image(mask_path, convert_method=lambda img: img.convert('L'))))
-    mask = mask.unsqueeze_(0).float()  # 0 or 1
-    mask = F.interpolate(mask, (1024, 1024))
-    mask = gaussian_blur(mask, kernel_size=(77, 77))
-    mask[mask < 0.1] = 0
-    mask[mask >= 0.1] = 1
-    mask = mask.to(dtype).to(device)
-    return mask
-
-prompt = "" # Set prompt to null
-seed=123 
-generator = torch.Generator(device=device).manual_seed(seed)
-source_image_path = "https://raw.githubusercontent.com/Anonym0u3/Images/refs/heads/main/an1024.png"
-mask_path = "https://raw.githubusercontent.com/Anonym0u3/Images/refs/heads/main/an1024_mask.png"
-source_image = preprocess_image(source_image_path, device)
-mask = preprocess_mask(mask_path, device)
-
-image = pipeline(
-    prompt=prompt, 
-    image=source_image,
-    mask_image=mask,
-    height=1024,
-    width=1024,
-    AAS=True, # enable AAS
-    strength=0.8, # inpainting strength
-    rm_guidance_scale=9, # removal guidance scale
-    ss_steps = 9, # similarity suppression steps
-    ss_scale = 0.3, # similarity suppression scale
-    AAS_start_step=0, # AAS start step
-    AAS_start_layer=34, # AAS start layer
-    AAS_end_layer=70, # AAS end layer
-    num_inference_steps=50, # number of inference steps # AAS_end_step = int(strength*num_inference_steps)
-    generator=generator,
-    guidance_scale=1,
-).images[0]
-image.save('./removed_img.png')
-print("Object removal completed")
-```
-
-| Source Image                                                                                   | Mask                                                                                        | Output                                                                                              |
-| ---------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- |
-| ![Source Image](https://raw.githubusercontent.com/Anonym0u3/Images/refs/heads/main/an1024.png) | ![Mask](https://raw.githubusercontent.com/Anonym0u3/Images/refs/heads/main/an1024_mask.png) | ![Output](https://raw.githubusercontent.com/Anonym0u3/Images/refs/heads/main/AE_step40_layer34.png) |
-
 # Perturbed-Attention Guidance

 [Project](https://ku-cvlab.github.io/Perturbed-Attention-Guidance/) / [arXiv](https://arxiv.org/abs/2403.17377) / [GitHub](https://github.com/KU-CVLAB/Perturbed-Attention-Guidance)
@@ -372,7 +372,7 @@ class AdaptiveMaskInpaintPipeline(
        self.register_adaptive_mask_model()
        self.register_adaptive_mask_settings()

-        if scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 1:
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
@@ -386,7 +386,7 @@ class AdaptiveMaskInpaintPipeline(
            new_config["steps_offset"] = 1
            scheduler._internal_dict = FrozenDict(new_config)

-        if scheduler is not None and getattr(scheduler.config, "skip_prk_steps", True) is False:
+        if hasattr(scheduler.config, "skip_prk_steps") and scheduler.config.skip_prk_steps is False:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} has not set the configuration"
                " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
@@ -416,14 +416,10 @@ class AdaptiveMaskInpaintPipeline(
                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
            )

-        is_unet_version_less_0_9_0 = (
-            unet is not None
-            and hasattr(unet.config, "_diffusers_version")
-            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
-        )
-        is_unet_sample_size_less_64 = (
-            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        )
+        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
+            version.parse(unet.config._diffusers_version).base_version
+        ) < version.parse("0.9.0.dev0")
+        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
            deprecation_message = (
                "The configuration file of the unet has set the default `sample_size` to smaller than"
@@ -442,7 +438,7 @@ class AdaptiveMaskInpaintPipeline(
            unet._internal_dict = FrozenDict(new_config)

        # Check shapes, assume num_channels_latents == 4, num_channels_mask == 1, num_channels_masked == 4
-        if unet is not None and unet.config.in_channels != 9:
+        if unet.config.in_channels != 9:
            logger.info(f"You have loaded a UNet with {unet.config.in_channels} input channels which.")

        self.register_modules(
@@ -454,7 +450,7 @@ class AdaptiveMaskInpaintPipeline(
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
        self.register_to_config(requires_safety_checker=requires_safety_checker)

@@ -89,7 +89,7 @@ class ComposableStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin)
    ):
        super().__init__()

-        if scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 1:
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
@@ -103,7 +103,7 @@ class ComposableStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin)
            new_config["steps_offset"] = 1
            scheduler._internal_dict = FrozenDict(new_config)

-        if scheduler is not None and getattr(scheduler.config, "clip_sample", False) is True:
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
@@ -132,14 +132,10 @@ class ComposableStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin)
                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
            )

-        is_unet_version_less_0_9_0 = (
-            unet is not None
-            and hasattr(unet.config, "_diffusers_version")
-            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
-        )
-        is_unet_sample_size_less_64 = (
-            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        )
+        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
+            version.parse(unet.config._diffusers_version).base_version
+        ) < version.parse("0.9.0.dev0")
+        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
            deprecation_message = (
                "The configuration file of the unet has set the default `sample_size` to smaller than"
@@ -166,7 +162,7 @@ class ComposableStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin)
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.register_to_config(requires_safety_checker=requires_safety_checker)

    def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
@@ -35,7 +35,7 @@ class EDICTPipeline(DiffusionPipeline):
            scheduler=scheduler,
        )

-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)

    def _encode_prompt(
@@ -404,11 +404,10 @@ def my_forward(
            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
            # This would be a good case for the `match` statement (Python 3.10+)
            is_mps = sample.device.type == "mps"
-            is_npu = sample.device.type == "npu"
            if isinstance(timestep, float):
-                dtype = torch.float32 if (is_mps or is_npu) else torch.float64
+                dtype = torch.float32 if is_mps else torch.float64
            else:
-                dtype = torch.int32 if (is_mps or is_npu) else torch.int64
+                dtype = torch.int32 if is_mps else torch.int64
            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
        elif len(timesteps.shape) == 0:
            timesteps = timesteps[None].to(sample.device)
@@ -1343,7 +1342,7 @@ class FrescoV2VPipeline(StableDiffusionControlNetImg2ImgPipeline):
            feature_extractor=feature_extractor,
            image_encoder=image_encoder,
        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)
        self.control_image_processor = VaeImageProcessor(
            vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
@@ -221,7 +221,7 @@ class GlueGenStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin, St
            language_adapter=language_adapter,
            tensor_norm=tensor_norm,
        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
        self.register_to_config(requires_safety_checker=requires_safety_checker)

@@ -95,7 +95,7 @@ class ImageToImageInpaintingPipeline(DiffusionPipeline):
    ):
        super().__init__()

-        if scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 1:
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
@@ -109,7 +109,7 @@ class InstaFlowPipeline(
    ):
        super().__init__()

-        if scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 1:
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
@@ -123,7 +123,7 @@ class InstaFlowPipeline(
            new_config["steps_offset"] = 1
            scheduler._internal_dict = FrozenDict(new_config)

-        if scheduler is not None and getattr(scheduler.config, "clip_sample", False) is True:
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
@@ -152,14 +152,10 @@ class InstaFlowPipeline(
                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
            )

-        is_unet_version_less_0_9_0 = (
-            unet is not None
-            and hasattr(unet.config, "_diffusers_version")
-            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
-        )
-        is_unet_sample_size_less_64 = (
-            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        )
+        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
+            version.parse(unet.config._diffusers_version).base_version
+        ) < version.parse("0.9.0.dev0")
+        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
            deprecation_message = (
                "The configuration file of the unet has set the default `sample_size` to smaller than"
@@ -186,7 +182,7 @@ class InstaFlowPipeline(
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
        self.register_to_config(requires_safety_checker=requires_safety_checker)

@@ -86,7 +86,7 @@ class StableDiffusionWalkPipeline(DiffusionPipeline, StableDiffusionMixin):
    ):
        super().__init__()

-        if scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 1:
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
@@ -191,7 +191,7 @@ class IPAdapterFaceIDStableDiffusionPipeline(
    ):
        super().__init__()

-        if scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 1:
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
@@ -205,7 +205,7 @@ class IPAdapterFaceIDStableDiffusionPipeline(
            new_config["steps_offset"] = 1
            scheduler._internal_dict = FrozenDict(new_config)

-        if scheduler is not None and getattr(scheduler.config, "clip_sample", False) is True:
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
@@ -234,14 +234,10 @@ class IPAdapterFaceIDStableDiffusionPipeline(
                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
            )

-        is_unet_version_less_0_9_0 = (
-            unet is not None
-            and hasattr(unet.config, "_diffusers_version")
-            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
-        )
-        is_unet_sample_size_less_64 = (
-            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        )
+        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
+            version.parse(unet.config._diffusers_version).base_version
+        ) < version.parse("0.9.0.dev0")
+        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
            deprecation_message = (
                "The configuration file of the unet has set the default `sample_size` to smaller than"
@@ -269,7 +265,7 @@ class IPAdapterFaceIDStableDiffusionPipeline(
            feature_extractor=feature_extractor,
            image_encoder=image_encoder,
        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
        self.register_to_config(requires_safety_checker=requires_safety_checker)

@@ -463,6 +463,6 @@ class StableDiffusionHighResFixPipeline(StableDiffusionPipeline):
            feature_extractor=feature_extractor,
            image_encoder=image_encoder,
        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
        self.register_to_config(requires_safety_checker=requires_safety_checker)
@@ -69,7 +69,7 @@ class LatentConsistencyModelImg2ImgPipeline(DiffusionPipeline):
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)

    def _encode_prompt(
@@ -273,7 +273,7 @@ class LatentConsistencyModelWalkPipeline(
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
        self.register_to_config(requires_safety_checker=requires_safety_checker)

@@ -67,7 +67,7 @@ class LatentConsistencyModelPipeline(DiffusionPipeline):
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)

    def _encode_prompt(
@@ -336,7 +336,7 @@ class LLMGroundedDiffusionPipeline(
        # This is copied from StableDiffusionPipeline, with hook initizations for LMD+.
        super().__init__()

-        if scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 1:
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
@@ -350,7 +350,7 @@ class LLMGroundedDiffusionPipeline(
            new_config["steps_offset"] = 1
            scheduler._internal_dict = FrozenDict(new_config)

-        if scheduler is not None and getattr(scheduler.config, "clip_sample", False) is True:
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
@@ -379,14 +379,10 @@ class LLMGroundedDiffusionPipeline(
                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
            )

-        is_unet_version_less_0_9_0 = (
-            unet is not None
-            and hasattr(unet.config, "_diffusers_version")
-            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
-        )
-        is_unet_sample_size_less_64 = (
-            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        )
+        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
+            version.parse(unet.config._diffusers_version).base_version
+        ) < version.parse("0.9.0.dev0")
+        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
            deprecation_message = (
                "The configuration file of the unet has set the default `sample_size` to smaller than"
@@ -414,7 +410,7 @@ class LLMGroundedDiffusionPipeline(
            feature_extractor=feature_extractor,
            image_encoder=image_encoder,
        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
        self.register_to_config(requires_safety_checker=requires_safety_checker)

@@ -496,7 +496,7 @@ class StableDiffusionLongPromptWeightingPipeline(
    ):
        super().__init__()

-        if scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 1:
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
@@ -510,7 +510,7 @@ class StableDiffusionLongPromptWeightingPipeline(
            new_config["steps_offset"] = 1
            scheduler._internal_dict = FrozenDict(new_config)

-        if scheduler is not None and getattr(scheduler.config, "clip_sample", False) is True:
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
@@ -539,14 +539,10 @@ class StableDiffusionLongPromptWeightingPipeline(
                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
            )

-        is_unet_version_less_0_9_0 = (
-            unet is not None
-            and hasattr(unet.config, "_diffusers_version")
-            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
-        )
-        is_unet_sample_size_less_64 = (
-            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        )
+        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
+            version.parse(unet.config._diffusers_version).base_version
+        ) < version.parse("0.9.0.dev0")
+        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
            deprecation_message = (
                "The configuration file of the unet has set the default `sample_size` to smaller than"
@@ -572,7 +568,7 @@ class StableDiffusionLongPromptWeightingPipeline(
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)

        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
        self.register_to_config(
@@ -673,16 +673,12 @@ class SDXLLongPromptWeightingPipeline(
            image_encoder=image_encoder,
        )
        self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
        self.mask_processor = VaeImageProcessor(
            vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True
        )
-        self.default_sample_size = (
-            self.unet.config.sample_size
-            if hasattr(self, "unet") and self.unet is not None and hasattr(self.unet.config, "sample_size")
-            else 128
-        )
+        self.default_sample_size = self.unet.config.sample_size

        add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()

@@ -831,9 +827,7 @@ class SDXLLongPromptWeightingPipeline(
                )

                # We are only ALWAYS interested in the pooled output of the final text encoder
-                if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
-                    pooled_prompt_embeds = prompt_embeds[0]
-
+                pooled_prompt_embeds = prompt_embeds[0]
                prompt_embeds = prompt_embeds.hidden_states[-2]

                prompt_embeds_list.append(prompt_embeds)
@@ -885,8 +879,7 @@ class SDXLLongPromptWeightingPipeline(
                    output_hidden_states=True,
                )
                # We are only ALWAYS interested in the pooled output of the final text encoder
-                if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
-                    negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]

                negative_prompt_embeds_list.append(negative_prompt_embeds)
@@ -80,6 +80,7 @@ from diffusers.utils import (
    USE_PEFT_BACKEND,
    BaseOutput,
    deprecate,
+    is_torch_version,
    is_torch_xla_available,
    logging,
    replace_example_docstring,
@@ -868,7 +869,23 @@ class CrossAttnDownBlock2D(nn.Module):

        for i, (resnet, attn) in enumerate(blocks):
            if torch.is_grad_enabled() and self.gradient_checkpointing:
-                hidden_states = self._gradient_checkpointing_func(resnet, hidden_states, temb)
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet),
+                    hidden_states,
+                    temb,
+                    **ckpt_kwargs,
+                )
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
@@ -1013,6 +1030,17 @@ class UNetMidBlock2DCrossAttn(nn.Module):
        hidden_states = self.resnets[0](hidden_states, temb)
        for attn, resnet in zip(self.attentions, self.resnets[1:]):
            if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
@@ -1021,7 +1049,12 @@ class UNetMidBlock2DCrossAttn(nn.Module):
                    encoder_attention_mask=encoder_attention_mask,
                    return_dict=False,
                )[0]
-                hidden_states = self._gradient_checkpointing_func(resnet, hidden_states, temb)
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet),
+                    hidden_states,
+                    temb,
+                    **ckpt_kwargs,
+                )
            else:
                hidden_states = attn(
                    hidden_states,
@@ -1159,7 +1192,23 @@ class CrossAttnUpBlock2D(nn.Module):
            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

            if torch.is_grad_enabled() and self.gradient_checkpointing:
-                hidden_states = self._gradient_checkpointing_func(resnet, hidden_states, temb)
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet),
+                    hidden_states,
+                    temb,
+                    **ckpt_kwargs,
+                )
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
@@ -1233,6 +1282,10 @@ class MatryoshkaTransformer2DModel(LegacyModelMixin, LegacyConfigMixin):
            ]
        )

+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
    def forward(
        self,
        hidden_states: torch.Tensor,
@@ -1312,8 +1365,19 @@ class MatryoshkaTransformer2DModel(LegacyModelMixin, LegacyConfigMixin):
        # Blocks
        for block in self.transformer_blocks:
            if torch.is_grad_enabled() and self.gradient_checkpointing:
-                hidden_states = self._gradient_checkpointing_func(
-                    block,
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
                    hidden_states,
                    attention_mask,
                    encoder_hidden_states,
@@ -1321,6 +1385,7 @@ class MatryoshkaTransformer2DModel(LegacyModelMixin, LegacyConfigMixin):
                    timestep,
                    cross_attention_kwargs,
                    class_labels,
+                    **ckpt_kwargs,
                )
            else:
                hidden_states = block(
@@ -2659,6 +2724,10 @@ class MatryoshkaUNet2DConditionModel(
        for module in self.children():
            fn_recursive_set_attention_slice(module, reversed_slice_size)

+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
    def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
        r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.

@@ -2737,11 +2806,10 @@ class MatryoshkaUNet2DConditionModel(
            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
            # This would be a good case for the `match` statement (Python 3.10+)
            is_mps = sample.device.type == "mps"
-            is_npu = sample.device.type == "npu"
            if isinstance(timestep, float):
-                dtype = torch.float32 if (is_mps or is_npu) else torch.float64
+                dtype = torch.float32 if is_mps else torch.float64
            else:
-                dtype = torch.int32 if (is_mps or is_npu) else torch.int64
+                dtype = torch.int32 if is_mps else torch.int64
            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
        elif len(timesteps.shape) == 0:
            timesteps = timesteps[None].to(sample.device)
@@ -3698,7 +3766,7 @@ class MatryoshkaPipeline(
        else:
            raise ValueError("Currently, nesting levels 0, 1, and 2 are supported.")

-        if scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 1:
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
@@ -3712,7 +3780,7 @@ class MatryoshkaPipeline(
            new_config["steps_offset"] = 1
            scheduler._internal_dict = FrozenDict(new_config)

-        # if scheduler is not None and getattr(scheduler.config, "clip_sample", False) is True:
+        # if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
        #     deprecation_message = (
        #         f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
        #         " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
@@ -3725,14 +3793,10 @@ class MatryoshkaPipeline(
        #     new_config["clip_sample"] = False
        #     scheduler._internal_dict = FrozenDict(new_config)

-        is_unet_version_less_0_9_0 = (
-            unet is not None
-            and hasattr(unet.config, "_diffusers_version")
-            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
-        )
-        is_unet_sample_size_less_64 = (
-            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        )
+        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
+            version.parse(unet.config._diffusers_version).base_version
+        ) < version.parse("0.9.0.dev0")
+        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
            deprecation_message = (
                "The configuration file of the unet has set the default `sample_size` to smaller than"
@@ -98,7 +98,7 @@ class MultilingualStableDiffusion(DiffusionPipeline, StableDiffusionMixin):
    ):
        super().__init__()

-        if scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 1:
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
@@ -188,7 +188,7 @@ class AnimateDiffControlNetPipeline(
            feature_extractor=feature_extractor,
            image_encoder=image_encoder,
        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
        self.control_image_processor = VaeImageProcessor(
            vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
@@ -308,7 +308,7 @@ class AnimateDiffImgToVideoPipeline(
            feature_extractor=feature_extractor,
            image_encoder=image_encoder,
        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt
@@ -162,7 +162,7 @@ class AnimateDiffPipelineIpex(
            feature_extractor=feature_extractor,
            image_encoder=image_encoder,
        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.video_processor = VideoProcessor(do_resize=False, vae_scale_factor=self.vae_scale_factor)

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Aryan	a64118206a	update	2025-01-02 15:35:58 +01:00
Aryan	13d5af7649	init	2025-01-02 13:11:44 +01:00