Merge branch 'main' into gpu-pr-test

.github/workflows/benchmark.yml

@@ -25,7 +25,7 @@ jobs:
       group: aws-g6e-4xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3

.github/workflows/mirror_community_pipeline.yml

@@ -79,14 +79,14 @@ jobs:
 
       # Check secret is set
       - name: whoami
-        run: hf auth whoami
+        run: huggingface-cli whoami
         env:
             HF_TOKEN: ${{ secrets.HF_TOKEN_MIRROR_COMMUNITY_PIPELINES }}
 
       # Push to HF! (under subfolder based on checkout ref)
       # https://huggingface.co/datasets/diffusers/community-pipelines-mirror
       - name: Mirror community pipeline to HF
-        run: hf upload diffusers/community-pipelines-mirror ./examples/community ${PATH_IN_REPO} --repo-type dataset
+        run: huggingface-cli upload diffusers/community-pipelines-mirror ./examples/community ${PATH_IN_REPO} --repo-type dataset
         env:
             PATH_IN_REPO: ${{ env.PATH_IN_REPO }}
             HF_TOKEN: ${{ secrets.HF_TOKEN_MIRROR_COMMUNITY_PIPELINES }}

.github/workflows/nightly_tests.yml

@@ -61,7 +61,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -107,7 +107,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     defaults:
       run:
         shell: bash
@@ -178,7 +178,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -222,7 +222,7 @@ jobs:
       group: aws-g6e-xlarge-plus
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -270,7 +270,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-minimum-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     defaults:
       run:
         shell: bash
@@ -333,21 +333,18 @@ jobs:
             additional_deps: ["peft"]
           - backend: "gguf"
             test_location: "gguf"
-            additional_deps: ["peft", "kernels"]
+            additional_deps: ["peft"]
           - backend: "torchao"
             test_location: "torchao"
             additional_deps: []
           - backend: "optimum_quanto"
             test_location: "quanto"
             additional_deps: []
-          - backend: "nvidia_modelopt"
-            test_location: "modelopt"
-            additional_deps: []
     runs-on:
       group: aws-g6e-xlarge-plus
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "20gb" --ipc host --gpus all
+      options: --shm-size "20gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -399,7 +396,7 @@ jobs:
       group: aws-g6e-xlarge-plus
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "20gb" --ipc host --gpus all
+      options: --shm-size "20gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3

.github/workflows/pr_flax_dependency_test.yml

@@ -0,0 +1,38 @@
+name: Run Flax dependency tests
+
+on:
+  pull_request:
+    branches:
+      - main
+    paths:
+      - "src/diffusers/**.py"
+  push:
+    branches:
+      - main
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  check_flax_dependencies:
+    runs-on: ubuntu-22.04
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.8"
+      - name: Install dependencies
+        run: |
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m pip install --upgrade pip uv
+          python -m uv pip install -e .
+          python -m uv pip install "jax[cpu]>=0.2.16,!=0.3.2"
+          python -m uv pip install "flax>=0.4.1"
+          python -m uv pip install "jaxlib>=0.1.65"
+          python -m uv pip install pytest
+      - name: Check for soft dependencies
+        run: |
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          pytest tests/others/test_dependencies.py

.github/workflows/pr_modular_tests.yml

@@ -1,141 +0,0 @@
-name: Fast PR tests for Modular
-
-on:
-  pull_request:
-    branches: [main]
-    paths:
-      - "src/diffusers/modular_pipelines/**.py"
-      - "src/diffusers/models/modeling_utils.py"
-      - "src/diffusers/models/model_loading_utils.py"
-      - "src/diffusers/pipelines/pipeline_utils.py"
-      - "src/diffusers/pipeline_loading_utils.py"
-      - "src/diffusers/loaders/lora_base.py"
-      - "src/diffusers/loaders/lora_pipeline.py"
-      - "src/diffusers/loaders/peft.py"
-      - "tests/modular_pipelines/**.py"
-      - ".github/**.yml"
-      - "utils/**.py"
-      - "setup.py"
-  push:
-    branches:
-      - ci-*
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-env:
-  DIFFUSERS_IS_CI: yes
-  HF_HUB_ENABLE_HF_TRANSFER: 1
-  OMP_NUM_THREADS: 4
-  MKL_NUM_THREADS: 4
-  PYTEST_TIMEOUT: 60
-
-jobs:
-  check_code_quality:
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.10"
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install .[quality]
-      - name: Check quality
-        run: make quality
-      - name: Check if failure
-        if: ${{ failure() }}
-        run: |
-          echo "Quality check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make style && make quality'" >> $GITHUB_STEP_SUMMARY
-
-  check_repository_consistency:
-    needs: check_code_quality
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.10"
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install .[quality]
-      - name: Check repo consistency
-        run: |
-          python utils/check_copies.py
-          python utils/check_dummies.py
-          python utils/check_support_list.py
-          make deps_table_check_updated
-      - name: Check if failure
-        if: ${{ failure() }}
-        run: |
-          echo "Repo consistency check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make fix-copies'" >> $GITHUB_STEP_SUMMARY
-
-  run_fast_tests:
-    needs: [check_code_quality, check_repository_consistency]
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          - name: Fast PyTorch Modular Pipeline CPU tests
-            framework: pytorch_pipelines
-            runner: aws-highmemory-32-plus
-            image: diffusers/diffusers-pytorch-cpu
-            report: torch_cpu_modular_pipelines
-
-    name: ${{ matrix.config.name }}
-
-    runs-on:
-      group: ${{ matrix.config.runner }}
-
-    container:
-      image: ${{ matrix.config.image }}
-      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
-
-    defaults:
-      run:
-        shell: bash
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
-
-    - name: Environment
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python utils/print_env.py
-
-    - name: Run fast PyTorch Pipeline CPU tests
-      if: ${{ matrix.config.framework == 'pytorch_pipelines' }}
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m pytest -n 8 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "not Flax and not Onnx" \
-          --make-reports=tests_${{ matrix.config.report }} \
-          tests/modular_pipelines
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: pr_${{ matrix.config.framework }}_${{ matrix.config.report }}_test_reports
-        path: reports
-
-

.github/workflows/pr_tests_gpu.yml

@@ -13,7 +13,6 @@ on:
       - "src/diffusers/loaders/peft.py"
       - "tests/pipelines/test_pipelines_common.py"
       - "tests/models/test_modeling_common.py"
-      - "examples/**/*.py"
   workflow_dispatch:
 
 concurrency:
@@ -118,7 +117,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -183,7 +182,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     defaults:
       run:
         shell: bash
@@ -253,7 +252,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host
     steps:
     - name: Checkout diffusers
       uses: actions/checkout@v3

.github/workflows/push_tests.yml

@@ -64,7 +64,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -109,7 +109,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     defaults:
       run:
         shell: bash
@@ -167,7 +167,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -210,7 +210,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-xformers-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -252,7 +252,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host
     steps:
     - name: Checkout diffusers
       uses: actions/checkout@v3

.github/workflows/release_tests_fast.yml

@@ -62,7 +62,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -107,7 +107,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     defaults:
       run:
         shell: bash
@@ -163,7 +163,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-minimum-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     defaults:
       run:
         shell: bash
@@ -222,7 +222,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -265,7 +265,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-xformers-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -307,7 +307,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers

.github/workflows/run_tests_from_a_pr.yml

@@ -30,7 +30,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: ${{ github.event.inputs.docker_image }}
-      options: --gpus all --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      options: --gpus 0 --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
 
     steps:
       - name: Validate test files input

.github/workflows/ssh-runner.yml

@@ -31,7 +31,7 @@ jobs:
       group: "${{ github.event.inputs.runner_type }}"
     container:
       image: ${{ github.event.inputs.docker_image }}
-      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --gpus all --privileged
+      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --gpus 0 --privileged
 
     steps:
       - name: Checkout diffusers

README.md

@@ -37,7 +37,7 @@ limitations under the License.
 
 ## Installation
 
-We recommend installing 🤗 Diffusers in a virtual environment from PyPI or Conda. For more details about installing [PyTorch](https://pytorch.org/get-started/locally/), please refer to their official documentation.
+We recommend installing 🤗 Diffusers in a virtual environment from PyPI or Conda. For more details about installing [PyTorch](https://pytorch.org/get-started/locally/) and [Flax](https://flax.readthedocs.io/en/latest/#installation), please refer to their official documentation.
 
 ### PyTorch
 
@@ -53,6 +53,14 @@ With `conda` (maintained by the community):
 conda install -c conda-forge diffusers
 ```
 
+### Flax
+
+With `pip` (official package):
+
+```bash
+pip install --upgrade diffusers[flax]
+```
+
 ### Apple Silicon (M1/M2) support
 
 Please refer to the [How to use Stable Diffusion in Apple Silicon](https://huggingface.co/docs/diffusers/optimization/mps) guide.

benchmarks/README.md

@@ -31,7 +31,7 @@ pip install -r requirements.txt
 We need to be authenticated to access some of the checkpoints used during benchmarking:
 
 ```sh
-hf auth login
+huggingface-cli login
 ```
 
 We use an L40 GPU with 128GB RAM to run the benchmark CI. As such, the benchmarks are configured to run on NVIDIA GPUs. So, make sure you have access to a similar machine (or modify the benchmarking scripts accordingly).

docker/diffusers-doc-builder/Dockerfile

@@ -47,10 +47,6 @@ RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         tensorboard \
         transformers \
         matplotlib \
-        setuptools==69.5.1 \
-        bitsandbytes \
-        torchao \
-        gguf \
-        optimum-quanto
+        setuptools==69.5.1
 
 CMD ["/bin/bash"]

docker/diffusers-flax-cpu/Dockerfile

@@ -0,0 +1,49 @@
+FROM ubuntu:20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+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-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)
+# follow the instructions here: https://cloud.google.com/tpu/docs/run-in-container#train_a_jax_model_in_a_docker_container
+RUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
+    python3 -m uv pip install --upgrade --no-cache-dir \
+        clu \
+        "jax[cpu]>=0.2.16,!=0.3.2" \
+        "flax>=0.4.1" \
+        "jaxlib>=0.1.65" && \
+    python3 -m uv pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        Jinja2 \
+        librosa \
+        numpy==1.26.4 \
+        scipy \
+        tensorboard \
+        transformers \
+        hf_transfer
+
+CMD ["/bin/bash"]

docker/diffusers-flax-tpu/Dockerfile

@@ -0,0 +1,51 @@
+FROM ubuntu:20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+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-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)
+# follow the instructions here: https://cloud.google.com/tpu/docs/run-in-container#train_a_jax_model_in_a_docker_container
+RUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
+    python3 -m pip install --no-cache-dir \
+        "jax[tpu]>=0.2.16,!=0.3.2" \
+        -f https://storage.googleapis.com/jax-releases/libtpu_releases.html && \
+    python3 -m uv pip install --upgrade --no-cache-dir \
+        clu \
+        "flax>=0.4.1" \
+        "jaxlib>=0.1.65" && \
+    python3 -m uv pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        Jinja2 \
+        librosa \
+        numpy==1.26.4 \
+        scipy \
+        tensorboard \
+        transformers \
+        hf_transfer
+
+CMD ["/bin/bash"]

docs/source/en/_toctree.yml

@@ -1,41 +1,36 @@
-- title: Get started
-  sections:
+- sections:
   - local: index
-    title: Diffusers
+    title: 🧨 Diffusers
+  - local: quicktour
+    title: Quicktour
+  - local: stable_diffusion
+    title: Effective and efficient diffusion
   - local: installation
     title: Installation
-  - local: quicktour
-    title: Quickstart
-  - local: stable_diffusion
-    title: Basic performance
-
-- title: Pipelines
-  isExpanded: false
-  sections:
-  - local: using-diffusers/loading
-    title: DiffusionPipeline
+  title: Get started
+- sections:
+  - local: tutorials/tutorial_overview
+    title: Overview
+  - local: using-diffusers/write_own_pipeline
+    title: Understanding pipelines, models and schedulers
   - local: tutorials/autopipeline
     title: AutoPipeline
+  - local: tutorials/basic_training
+    title: Train a diffusion model
+  title: Tutorials
+- sections:
+  - local: using-diffusers/loading
+    title: Load pipelines
   - local: using-diffusers/custom_pipeline_overview
-    title: Community pipelines and components
-  - local: using-diffusers/callback
-    title: Pipeline callbacks
-  - local: using-diffusers/reusing_seeds
-    title: Reproducibility
+    title: Load community pipelines and components
   - local: using-diffusers/schedulers
     title: Load schedulers and models
-  - local: using-diffusers/models
-    title: Models
-  - local: using-diffusers/scheduler_features
-    title: Scheduler features
   - local: using-diffusers/other-formats
     title: Model files and layouts
   - local: using-diffusers/push_to_hub
-    title: Sharing pipelines and models
-
-- title: Adapters
-  isExpanded: false
-  sections:
+    title: Push files to the Hub
+  title: Load pipelines and adapters
+- sections:
   - local: tutorials/using_peft_for_inference
     title: LoRA
   - local: using-diffusers/ip_adapter
@@ -48,52 +43,47 @@
     title: DreamBooth
   - local: using-diffusers/textual_inversion_inference
     title: Textual inversion
-
-- title: Inference
+  title: Adapters
   isExpanded: false
-  sections:
-  - local: using-diffusers/weighted_prompts
-    title: Prompt techniques
+- sections:
+  - local: using-diffusers/unconditional_image_generation
+    title: Unconditional image generation
+  - local: using-diffusers/conditional_image_generation
+    title: Text-to-image
+  - local: using-diffusers/img2img
+    title: Image-to-image
+  - local: using-diffusers/inpaint
+    title: Inpainting
+  - local: using-diffusers/text-img2vid
+    title: Video generation
+  - local: using-diffusers/depth2img
+    title: Depth-to-image
+  title: Generative tasks
+- sections:
+  - local: using-diffusers/overview_techniques
+    title: Overview
   - local: using-diffusers/create_a_server
     title: Create a server
   - local: using-diffusers/batched_inference
     title: Batch inference
   - local: training/distributed_inference
     title: Distributed inference
-
-- title: Inference optimization
-  isExpanded: false
-  sections:
-  - local: optimization/fp16
-    title: Accelerate inference
-  - local: optimization/cache
-    title: Caching
-  - local: optimization/memory
-    title: Reduce memory usage
-  - local: optimization/speed-memory-optims
-    title: Compiling and offloading quantized models
-  - title: Community optimizations
-    sections:
-    - local: optimization/pruna
-      title: Pruna
-    - local: optimization/xformers
-      title: xFormers
-    - local: optimization/tome
-      title: Token merging
-    - local: optimization/deepcache
-      title: DeepCache
-    - local: optimization/tgate
-      title: TGATE
-    - local: optimization/xdit
-      title: xDiT
-    - local: optimization/para_attn
-      title: ParaAttention
-    - local: using-diffusers/image_quality
-      title: FreeU
-
-- title: Hybrid Inference
-  isExpanded: false
-  sections:
+  - local: using-diffusers/scheduler_features
+    title: Scheduler features
+  - local: using-diffusers/callback
+    title: Pipeline callbacks
+  - local: using-diffusers/reusing_seeds
+    title: Reproducible pipelines
+  - local: using-diffusers/image_quality
+    title: Controlling image quality
+  - local: using-diffusers/weighted_prompts
+    title: Prompt techniques
+  title: Inference techniques
+- sections:
+  - local: advanced_inference/outpaint
+    title: Outpainting
+  title: Advanced inference
+- sections:
   - local: hybrid_inference/overview
     title: Overview
   - local: hybrid_inference/vae_decode
@@ -102,112 +92,8 @@
     title: VAE Encode
   - local: hybrid_inference/api_reference
     title: API Reference
-
-- title: Modular Diffusers
-  isExpanded: false
-  sections:
-  - local: modular_diffusers/overview
-    title: Overview
-  - local: modular_diffusers/quickstart
-    title: Quickstart
-  - local: modular_diffusers/modular_diffusers_states
-    title: States
-  - local: modular_diffusers/pipeline_block
-    title: ModularPipelineBlocks
-  - local: modular_diffusers/sequential_pipeline_blocks
-    title: SequentialPipelineBlocks
-  - local: modular_diffusers/loop_sequential_pipeline_blocks
-    title: LoopSequentialPipelineBlocks
-  - local: modular_diffusers/auto_pipeline_blocks
-    title: AutoPipelineBlocks
-  - local: modular_diffusers/modular_pipeline
-    title: ModularPipeline
-  - local: modular_diffusers/components_manager
-    title: ComponentsManager
-  - local: modular_diffusers/guiders
-    title: Guiders
-
-- title: Training
-  isExpanded: false
-  sections:
-  - local: training/overview
-    title: Overview
-  - local: training/create_dataset
-    title: Create a dataset for training
-  - local: training/adapt_a_model
-    title: Adapt a model to a new task
-  - local: tutorials/basic_training
-    title: Train a diffusion model
-  - title: Models
-    sections:
-    - local: training/unconditional_training
-      title: Unconditional image generation
-    - local: training/text2image
-      title: Text-to-image
-    - local: training/sdxl
-      title: Stable Diffusion XL
-    - local: training/kandinsky
-      title: Kandinsky 2.2
-    - local: training/wuerstchen
-      title: Wuerstchen
-    - local: training/controlnet
-      title: ControlNet
-    - local: training/t2i_adapters
-      title: T2I-Adapters
-    - local: training/instructpix2pix
-      title: InstructPix2Pix
-    - local: training/cogvideox
-      title: CogVideoX
-  - title: Methods
-    sections:
-    - local: training/text_inversion
-      title: Textual Inversion
-    - local: training/dreambooth
-      title: DreamBooth
-    - local: training/lora
-      title: LoRA
-    - local: training/custom_diffusion
-      title: Custom Diffusion
-    - local: training/lcm_distill
-      title: Latent Consistency Distillation
-    - local: training/ddpo
-      title: Reinforcement learning training with DDPO
-
-- title: Quantization
-  isExpanded: false
-  sections:
-  - local: quantization/overview
-    title: Getting started
-  - local: quantization/bitsandbytes
-    title: bitsandbytes
-  - local: quantization/gguf
-    title: gguf
-  - local: quantization/torchao
-    title: torchao
-  - local: quantization/quanto
-    title: quanto
-  - local: quantization/modelopt
-    title: NVIDIA ModelOpt
-
-- title: Model accelerators and hardware
-  isExpanded: false
-  sections:
-  - local: optimization/onnx
-    title: ONNX
-  - local: optimization/open_vino
-    title: OpenVINO
-  - local: optimization/coreml
-    title: Core ML
-  - local: optimization/mps
-    title: Metal Performance Shaders (MPS)
-  - local: optimization/habana
-    title: Intel Gaudi
-  - local: optimization/neuron
-    title: AWS Neuron
-
-- title: Specific pipeline examples
-  isExpanded: false
-  sections:
+  title: Hybrid Inference
+- sections:
   - local: using-diffusers/consisid
     title: ConsisID
   - local: using-diffusers/sdxl
@@ -232,30 +118,106 @@
     title: Stable Video Diffusion
   - local: using-diffusers/marigold_usage
     title: Marigold Computer Vision
-
-- title: Resources
-  isExpanded: false
-  sections:
-  - title: Task recipes
+  title: Specific pipeline examples
+- sections:
+  - local: training/overview
+    title: Overview
+  - local: training/create_dataset
+    title: Create a dataset for training
+  - local: training/adapt_a_model
+    title: Adapt a model to a new task
+  - isExpanded: false
     sections:
-    - local: using-diffusers/unconditional_image_generation
+    - local: training/unconditional_training
       title: Unconditional image generation
-    - local: using-diffusers/conditional_image_generation
+    - local: training/text2image
       title: Text-to-image
-    - local: using-diffusers/img2img
-      title: Image-to-image
-    - local: using-diffusers/inpaint
-      title: Inpainting
-    - local: advanced_inference/outpaint
-      title: Outpainting
-    - local: using-diffusers/text-img2vid
-      title: Video generation
-    - local: using-diffusers/depth2img
-      title: Depth-to-image
-  - local: using-diffusers/write_own_pipeline
-    title: Understanding pipelines, models and schedulers
-  - local: community_projects
-    title: Projects built with Diffusers
+    - local: training/sdxl
+      title: Stable Diffusion XL
+    - local: training/kandinsky
+      title: Kandinsky 2.2
+    - local: training/wuerstchen
+      title: Wuerstchen
+    - local: training/controlnet
+      title: ControlNet
+    - local: training/t2i_adapters
+      title: T2I-Adapters
+    - local: training/instructpix2pix
+      title: InstructPix2Pix
+    - local: training/cogvideox
+      title: CogVideoX
+    title: Models
+  - isExpanded: false
+    sections:
+    - local: training/text_inversion
+      title: Textual Inversion
+    - local: training/dreambooth
+      title: DreamBooth
+    - local: training/lora
+      title: LoRA
+    - local: training/custom_diffusion
+      title: Custom Diffusion
+    - local: training/lcm_distill
+      title: Latent Consistency Distillation
+    - local: training/ddpo
+      title: Reinforcement learning training with DDPO
+    title: Methods
+  title: Training
+- sections:
+  - local: quantization/overview
+    title: Getting Started
+  - local: quantization/bitsandbytes
+    title: bitsandbytes
+  - local: quantization/gguf
+    title: gguf
+  - local: quantization/torchao
+    title: torchao
+  - local: quantization/quanto
+    title: quanto
+  title: Quantization Methods
+- sections:
+  - local: optimization/fp16
+    title: Accelerate inference
+  - local: optimization/cache
+    title: Caching
+  - local: optimization/memory
+    title: Reduce memory usage
+  - local: optimization/speed-memory-optims
+    title: Compile and offloading quantized models
+  - local: optimization/pruna
+    title: Pruna
+  - local: optimization/xformers
+    title: xFormers
+  - local: optimization/tome
+    title: Token merging
+  - local: optimization/deepcache
+    title: DeepCache
+  - local: optimization/tgate
+    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
+    - local: optimization/onnx
+      title: ONNX
+    - local: optimization/open_vino
+      title: OpenVINO
+    - local: optimization/coreml
+      title: Core ML
+    title: Optimized model formats
+  - sections:
+    - local: optimization/mps
+      title: Metal Performance Shaders (MPS)
+    - local: optimization/habana
+      title: Intel Gaudi
+    - local: optimization/neuron
+      title: AWS Neuron
+    title: Optimized hardware
+  title: Accelerate inference and reduce memory
+- sections:
   - local: conceptual/philosophy
     title: Philosophy
   - local: using-diffusers/controlling_generation
@@ -266,11 +228,13 @@
     title: Diffusers' Ethical Guidelines
   - local: conceptual/evaluation
     title: Evaluating Diffusion Models
-
-- title: API
-  isExpanded: false
-  sections:
-  - title: Main Classes
+  title: Conceptual Guides
+- sections:
+  - local: community_projects
+    title: Projects built with Diffusers
+  title: Community Projects
+- sections:
+  - isExpanded: false
     sections:
     - local: api/configuration
       title: Configuration
@@ -280,19 +244,8 @@
       title: Outputs
     - local: api/quantization
       title: Quantization
-  - title: Modular
-    sections:
-    - local: api/modular_diffusers/pipeline
-      title: Pipeline
-    - local: api/modular_diffusers/pipeline_blocks
-      title: Blocks
-    - local: api/modular_diffusers/pipeline_states
-      title: States
-    - local: api/modular_diffusers/pipeline_components
-      title: Components and configs
-    - local: api/modular_diffusers/guiders
-      title: Guiders
-  - title: Loaders
+    title: Main Classes
+  - isExpanded: false
     sections:
     - local: api/loaders/ip_adapter
       title: IP-Adapter
@@ -308,14 +261,14 @@
       title: SD3Transformer2D
     - local: api/loaders/peft
       title: PEFT
-  - title: Models
+    title: Loaders
+  - isExpanded: false
     sections:
     - local: api/models/overview
       title: Overview
     - local: api/models/auto_model
       title: AutoModel
-    - title: ControlNets
-      sections:
+    - sections:
       - local: api/models/controlnet
         title: ControlNetModel
       - local: api/models/controlnet_union
@@ -330,14 +283,12 @@
         title: SD3ControlNetModel
       - local: api/models/controlnet_sparsectrl
         title: SparseControlNetModel
-    - title: Transformers
-      sections:
+      title: ControlNets
+    - sections:
       - local: api/models/allegro_transformer3d
         title: AllegroTransformer3DModel
       - local: api/models/aura_flow_transformer2d
         title: AuraFlowTransformer2DModel
-      - local: api/models/bria_transformer
-        title: BriaTransformer2DModel
       - local: api/models/chroma_transformer
         title: ChromaTransformer2DModel
       - local: api/models/cogvideox_transformer3d
@@ -378,14 +329,10 @@
         title: PixArtTransformer2DModel
       - local: api/models/prior_transformer
         title: PriorTransformer
-      - local: api/models/qwenimage_transformer2d
-        title: QwenImageTransformer2DModel
       - local: api/models/sana_transformer2d
         title: SanaTransformer2DModel
       - local: api/models/sd3_transformer2d
         title: SD3Transformer2DModel
-      - local: api/models/skyreels_v2_transformer_3d
-        title: SkyReelsV2Transformer3DModel
       - local: api/models/stable_audio_transformer
         title: StableAudioDiTModel
       - local: api/models/transformer2d
@@ -394,8 +341,8 @@
         title: TransformerTemporalModel
       - local: api/models/wan_transformer_3d
         title: WanTransformer3DModel
-    - title: UNets
-      sections:
+      title: Transformers
+    - sections:
       - local: api/models/stable_cascade_unet
         title: StableCascadeUNet
       - local: api/models/unet
@@ -410,8 +357,8 @@
         title: UNetMotionModel
       - local: api/models/uvit2d
         title: UViT2DModel
-    - title: VAEs
-      sections:
+      title: UNets
+    - sections:
       - local: api/models/asymmetricautoencoderkl
         title: AsymmetricAutoencoderKL
       - local: api/models/autoencoder_dc
@@ -432,8 +379,6 @@
         title: AutoencoderKLMagvit
       - local: api/models/autoencoderkl_mochi
         title: AutoencoderKLMochi
-      - local: api/models/autoencoderkl_qwenimage
-        title: AutoencoderKLQwenImage
       - local: api/models/autoencoder_kl_wan
         title: AutoencoderKLWan
       - local: api/models/consistency_decoder_vae
@@ -444,7 +389,9 @@
         title: Tiny AutoEncoder
       - local: api/models/vq
         title: VQModel
-  - title: Pipelines
+      title: VAEs
+    title: Models
+  - isExpanded: false
     sections:
     - local: api/pipelines/overview
       title: Overview
@@ -466,8 +413,6 @@
       title: AutoPipeline
     - local: api/pipelines/blip_diffusion
       title: BLIP-Diffusion
-    - local: api/pipelines/bria_3_2
-      title: Bria 3.2
     - local: api/pipelines/chroma
       title: Chroma
     - local: api/pipelines/cogvideox
@@ -572,8 +517,6 @@
       title: PixArt-α
     - local: api/pipelines/pixart_sigma
       title: PixArt-Σ
-    - local: api/pipelines/qwenimage
-      title: QwenImage
     - local: api/pipelines/sana
       title: Sana
     - local: api/pipelines/sana_sprint
@@ -584,14 +527,11 @@
       title: Semantic Guidance
     - local: api/pipelines/shap_e
       title: Shap-E
-    - local: api/pipelines/skyreels_v2
-      title: SkyReels-V2
     - local: api/pipelines/stable_audio
       title: Stable Audio
     - local: api/pipelines/stable_cascade
       title: Stable Cascade
-    - title: Stable Diffusion
-      sections:
+    - sections:
       - local: api/pipelines/stable_diffusion/overview
         title: Overview
       - local: api/pipelines/stable_diffusion/depth2img
@@ -628,6 +568,7 @@
         title: T2I-Adapter
       - local: api/pipelines/stable_diffusion/text2img
         title: Text-to-image
+      title: Stable Diffusion
     - local: api/pipelines/stable_unclip
       title: Stable unCLIP
     - local: api/pipelines/text_to_video
@@ -646,7 +587,8 @@
       title: Wan
     - local: api/pipelines/wuerstchen
       title: Wuerstchen
-  - title: Schedulers
+    title: Pipelines
+  - isExpanded: false
     sections:
     - local: api/schedulers/overview
       title: Overview
@@ -716,7 +658,8 @@
       title: UniPCMultistepScheduler
     - local: api/schedulers/vq_diffusion
       title: VQDiffusionScheduler
-  - title: Internal classes
+    title: Schedulers
+  - isExpanded: false
     sections:
     - local: api/internal_classes_overview
       title: Overview
@@ -734,3 +677,5 @@
       title: VAE Image Processor
     - local: api/video_processor
       title: Video Processor
+    title: Internal classes
+  title: API

docs/source/en/api/configuration.md

@@ -16,7 +16,7 @@ Schedulers from [`~schedulers.scheduling_utils.SchedulerMixin`] and models from
 
 <Tip>
 
-To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `hf auth login`.
+To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `huggingface-cli login`.
 
 </Tip>
 

docs/source/en/api/image_processor.md

@@ -20,12 +20,6 @@ All pipelines with [`VaeImageProcessor`] accept PIL Image, PyTorch tensor, or Nu
 
 [[autodoc]] image_processor.VaeImageProcessor
 
-## InpaintProcessor
-
-The [`InpaintProcessor`] accepts `mask` and `image` inputs and process them together. Optionally, it can accept padding_mask_crop and apply mask overlay.
-
-[[autodoc]] image_processor.InpaintProcessor
-
 ## VaeImageProcessorLDM3D
 
 The [`VaeImageProcessorLDM3D`] accepts RGB and depth inputs and returns RGB and depth outputs.

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

@@ -26,11 +26,9 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 - [`HunyuanVideoLoraLoaderMixin`] provides similar functions for [HunyuanVideo](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hunyuan_video).
 - [`Lumina2LoraLoaderMixin`] provides similar functions for [Lumina2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/lumina2).
 - [`WanLoraLoaderMixin`] provides similar functions for [Wan](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan).
-- [`SkyReelsV2LoraLoaderMixin`] provides similar functions for [SkyReels-V2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/skyreels_v2).
 - [`CogView4LoraLoaderMixin`] provides similar functions for [CogView4](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogview4).
 - [`AmusedLoraLoaderMixin`] is for the [`AmusedPipeline`].
 - [`HiDreamImageLoraLoaderMixin`] provides similar functions for [HiDream Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hidream)
-- [`QwenImageLoraLoaderMixin`] provides similar functions for [Qwen Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/qwen)
 - [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.
 
 <Tip>
@@ -94,10 +92,6 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse
 
 [[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin
 
-## SkyReelsV2LoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.SkyReelsV2LoraLoaderMixin
-
 ## AmusedLoraLoaderMixin
 
 [[autodoc]] loaders.lora_pipeline.AmusedLoraLoaderMixin
@@ -106,10 +100,6 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse
 
 [[autodoc]] loaders.lora_pipeline.HiDreamImageLoraLoaderMixin
 
-## QwenImageLoraLoaderMixin
+## WanLoraLoaderMixin
 
-[[autodoc]] loaders.lora_pipeline.QwenImageLoraLoaderMixin
-
-## LoraBaseMixin
-
-[[autodoc]] loaders.lora_base.LoraBaseMixin
+[[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin

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

@@ -44,3 +44,15 @@ model = AutoencoderKL.from_single_file(url)
 ## DecoderOutput
 
 [[autodoc]] models.autoencoders.vae.DecoderOutput
+
+## FlaxAutoencoderKL
+
+[[autodoc]] FlaxAutoencoderKL
+
+## FlaxAutoencoderKLOutput
+
+[[autodoc]] models.vae_flax.FlaxAutoencoderKLOutput
+
+## FlaxDecoderOutput
+
+[[autodoc]] models.vae_flax.FlaxDecoderOutput

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

@@ -1,35 +0,0 @@
-<!-- Copyright 2025 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. -->
-
-# AutoencoderKLQwenImage
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLQwenImage
-
-vae = AutoencoderKLQwenImage.from_pretrained("Qwen/QwenImage-20B", subfolder="vae")
-```
-
-## AutoencoderKLQwenImage
-
-[[autodoc]] AutoencoderKLQwenImage
-    - decode
-    - encode
-    - all
-
-## AutoencoderKLOutput
-
-[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

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

@@ -40,3 +40,11 @@ pipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=contro
 ## ControlNetOutput
 
 [[autodoc]] models.controlnets.controlnet.ControlNetOutput
+
+## FlaxControlNetModel
+
+[[autodoc]] FlaxControlNetModel
+
+## FlaxControlNetOutput
+
+[[autodoc]] models.controlnets.controlnet_flax.FlaxControlNetOutput

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

@@ -19,6 +19,10 @@ All models are built from the base [`ModelMixin`] class which is a [`torch.nn.Mo
 ## ModelMixin
 [[autodoc]] ModelMixin
 
+## FlaxModelMixin
+
+[[autodoc]] FlaxModelMixin
+
 ## PushToHubMixin
 
 [[autodoc]] utils.PushToHubMixin

docs/source/en/api/models/qwenimage_transformer2d.md

@@ -1,28 +0,0 @@
-<!-- Copyright 2025 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. -->
-
-# QwenImageTransformer2DModel
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import QwenImageTransformer2DModel
-
-transformer = QwenImageTransformer2DModel.from_pretrained("Qwen/QwenImage-20B", subfolder="transformer", torch_dtype=torch.bfloat16)
-```
-
-## QwenImageTransformer2DModel
-
-[[autodoc]] QwenImageTransformer2DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

docs/source/en/api/models/skyreels_v2_transformer_3d.md

@@ -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. -->
-
-# SkyReelsV2Transformer3DModel
-
-A Diffusion Transformer model for 3D video-like data was introduced in [SkyReels-V2](https://github.com/SkyworkAI/SkyReels-V2) by the Skywork AI.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import SkyReelsV2Transformer3DModel
-
-transformer = SkyReelsV2Transformer3DModel.from_pretrained("Skywork/SkyReels-V2-DF-1.3B-540P-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
-```
-
-## SkyReelsV2Transformer3DModel
-
-[[autodoc]] SkyReelsV2Transformer3DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

docs/source/en/api/models/unet2d-cond.md

@@ -23,3 +23,9 @@ The abstract from the paper is:
 
 ## UNet2DConditionOutput
 [[autodoc]] models.unets.unet_2d_condition.UNet2DConditionOutput
+
+## FlaxUNet2DConditionModel
+[[autodoc]] models.unets.unet_2d_condition_flax.FlaxUNet2DConditionModel
+
+## FlaxUNet2DConditionOutput
+[[autodoc]] models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput

docs/source/en/api/modular_diffusers/guiders.md

@@ -1,39 +0,0 @@
-# Guiders
-
-Guiders are components in Modular Diffusers that control how the diffusion process is guided during generation. They implement various guidance techniques to improve generation quality and control.
-
-## BaseGuidance
-
-[[autodoc]] diffusers.guiders.guider_utils.BaseGuidance
-
-## ClassifierFreeGuidance
-
-[[autodoc]] diffusers.guiders.classifier_free_guidance.ClassifierFreeGuidance
-
-## ClassifierFreeZeroStarGuidance
-
-[[autodoc]] diffusers.guiders.classifier_free_zero_star_guidance.ClassifierFreeZeroStarGuidance
-
-## SkipLayerGuidance
-
-[[autodoc]] diffusers.guiders.skip_layer_guidance.SkipLayerGuidance
-
-## SmoothedEnergyGuidance
-
-[[autodoc]] diffusers.guiders.smoothed_energy_guidance.SmoothedEnergyGuidance
-
-## PerturbedAttentionGuidance
-
-[[autodoc]] diffusers.guiders.perturbed_attention_guidance.PerturbedAttentionGuidance
-
-## AdaptiveProjectedGuidance
-
-[[autodoc]] diffusers.guiders.adaptive_projected_guidance.AdaptiveProjectedGuidance
-
-## AutoGuidance
-
-[[autodoc]] diffusers.guiders.auto_guidance.AutoGuidance
-
-## TangentialClassifierFreeGuidance
-
-[[autodoc]] diffusers.guiders.tangential_classifier_free_guidance.TangentialClassifierFreeGuidance

docs/source/en/api/modular_diffusers/pipeline.md

@@ -1,5 +0,0 @@
-# Pipeline
-
-## ModularPipeline
-
-[[autodoc]] diffusers.modular_pipelines.modular_pipeline.ModularPipeline

docs/source/en/api/modular_diffusers/pipeline_blocks.md

@@ -1,17 +0,0 @@
-# Pipeline blocks
-
-## ModularPipelineBlocks
-
-[[autodoc]] diffusers.modular_pipelines.modular_pipeline.ModularPipelineBlocks
-
-## SequentialPipelineBlocks
-
-[[autodoc]] diffusers.modular_pipelines.modular_pipeline.SequentialPipelineBlocks
-
-## LoopSequentialPipelineBlocks
-
-[[autodoc]] diffusers.modular_pipelines.modular_pipeline.LoopSequentialPipelineBlocks
-
-## AutoPipelineBlocks
-
-[[autodoc]] diffusers.modular_pipelines.modular_pipeline.AutoPipelineBlocks

docs/source/en/api/modular_diffusers/pipeline_components.md

@@ -1,17 +0,0 @@
-# Components and configs
-
-## ComponentSpec
-
-[[autodoc]] diffusers.modular_pipelines.modular_pipeline.ComponentSpec
-
-## ConfigSpec
-
-[[autodoc]] diffusers.modular_pipelines.modular_pipeline.ConfigSpec
-
-## ComponentsManager
-
-[[autodoc]] diffusers.modular_pipelines.components_manager.ComponentsManager
-
-## InsertableDict
-
-[[autodoc]] diffusers.modular_pipelines.modular_pipeline_utils.InsertableDict

docs/source/en/api/modular_diffusers/pipeline_states.md

@@ -1,9 +0,0 @@
-# Pipeline states
-
-## PipelineState
-
-[[autodoc]] diffusers.modular_pipelines.modular_pipeline.PipelineState
-
-## BlockState
-
-[[autodoc]] diffusers.modular_pipelines.modular_pipeline.BlockState 

docs/source/en/api/outputs.md

@@ -54,6 +54,10 @@ To check a specific pipeline or model output, refer to its corresponding API doc
 
 [[autodoc]] pipelines.ImagePipelineOutput
 
+## FlaxImagePipelineOutput
+
+[[autodoc]] pipelines.pipeline_flax_utils.FlaxImagePipelineOutput
+
 ## AudioPipelineOutput
 
 [[autodoc]] pipelines.AudioPipelineOutput

docs/source/en/api/pipelines/bria_3_2.md

@@ -1,44 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# Bria 3.2
-
-Bria 3.2 is the next-generation commercial-ready text-to-image model. With just 4 billion parameters, it provides exceptional aesthetics and text rendering, evaluated to provide on par results to leading open-source models, and outperforming other licensed models.
-In addition to being built entirely on licensed data, 3.2 provides several advantages for enterprise and commercial use:
-
-- Efficient Compute - the model is X3 smaller than the equivalent models in the market (4B parameters vs 12B parameters other open source models)
-- Architecture Consistency: Same architecture as 3.1—ideal for users looking to upgrade without disruption.
-- Fine-tuning Speedup: 2x faster fine-tuning on L40S and A100.
-
-Original model checkpoints for Bria 3.2 can be found [here](https://huggingface.co/briaai/BRIA-3.2).
-Github repo for Bria 3.2 can be found [here](https://github.com/Bria-AI/BRIA-3.2).
-
-If you want to learn more about the Bria platform, and get free traril access, please visit [bria.ai](https://bria.ai).
-
-
-## Usage
-
-_As the model is gated, before using it with diffusers you first need to go to the [Bria 3.2 Hugging Face page](https://huggingface.co/briaai/BRIA-3.2), fill in the form and accept the gate. Once you are in, you need to login so that your system knows you’ve accepted the gate._
-
-Use the command below to log in:
-
-```bash
-hf auth login
-```
-
-
-## BriaPipeline
-
-[[autodoc]] BriaPipeline
-	- all
-	- __call__
-

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

@@ -36,7 +36,7 @@ import torch
 from diffusers import ChromaPipeline
 
 pipe = ChromaPipeline.from_pretrained("lodestones/Chroma", torch_dtype=torch.bfloat16)
-pipe.enable_model_cpu_offload()
+pipe.enabe_model_cpu_offload()
 
 prompt = [
     "A high-fashion close-up portrait of a blonde woman in clear sunglasses. The image uses a bold teal and red color split for dramatic lighting. The background is a simple teal-green. The photo is sharp and well-composed, and is designed for viewing with anaglyph 3D glasses for optimal effect. It looks professionally done."

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

@@ -50,7 +50,7 @@ from diffusers.utils import export_to_video
 pipeline_quant_config = PipelineQuantizationConfig(
   quant_backend="torchao",
   quant_kwargs={"quant_type": "int8wo"},
-  components_to_quantize="transformer"
+  components_to_quantize=["transformer"]
 )
 
 # fp8 layerwise weight-casting

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

@@ -72,3 +72,11 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers)
 
 ## StableDiffusionPipelineOutput
 [[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
+
+## FlaxStableDiffusionControlNetPipeline
+[[autodoc]] FlaxStableDiffusionControlNetPipeline
+	- all
+	- __call__
+
+## FlaxStableDiffusionControlNetPipelineOutput
+[[autodoc]] pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput

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

@@ -25,8 +25,6 @@ Original model checkpoints for Flux can be found [here](https://huggingface.co/b
 
 Flux can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to [this blog post](https://huggingface.co/blog/quanto-diffusers) to learn more.  For an exhaustive list of resources, check out [this gist](https://gist.github.com/sayakpaul/b664605caf0aa3bf8585ab109dd5ac9c).
 
-[Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
-
 </Tip>
 
 Flux comes in the following variants:
@@ -316,67 +314,6 @@ if integrity_checker.test_image(image_):
     raise ValueError("Your image has been flagged. Choose another prompt/image or try again.")
 ```
 
-### Kontext Inpainting
-`FluxKontextInpaintPipeline` enables image modification within a fixed mask region. It currently supports both text-based conditioning and image-reference conditioning.
-<hfoptions id="kontext-inpaint">
-<hfoption id="text-only">
-
-
-```python
-import torch
-from diffusers import FluxKontextInpaintPipeline
-from diffusers.utils import load_image
-
-prompt = "Change the yellow dinosaur to green one"
-img_url = (
-    "https://github.com/ZenAI-Vietnam/Flux-Kontext-pipelines/blob/main/assets/dinosaur_input.jpeg?raw=true"
-)
-mask_url = (
-    "https://github.com/ZenAI-Vietnam/Flux-Kontext-pipelines/blob/main/assets/dinosaur_mask.png?raw=true"
-)
-
-source = load_image(img_url)
-mask = load_image(mask_url)
-
-pipe = FluxKontextInpaintPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16
-)
-pipe.to("cuda")
-
-image = pipe(prompt=prompt, image=source, mask_image=mask, strength=1.0).images[0]
-image.save("kontext_inpainting_normal.png")
-```
-</hfoption>
-<hfoption id="image conditioning">
-
-```python
-import torch
-from diffusers import FluxKontextInpaintPipeline
-from diffusers.utils import load_image
-
-pipe = FluxKontextInpaintPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16
-)
-pipe.to("cuda")
-
-prompt = "Replace this ball"
-img_url = "https://images.pexels.com/photos/39362/the-ball-stadion-football-the-pitch-39362.jpeg?auto=compress&cs=tinysrgb&dpr=1&w=500"
-mask_url = "https://github.com/ZenAI-Vietnam/Flux-Kontext-pipelines/blob/main/assets/ball_mask.png?raw=true"
-image_reference_url = "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTah3x6OL_ECMBaZ5ZlJJhNsyC-OSMLWAI-xw&s"
-
-source = load_image(img_url)
-mask = load_image(mask_url)
-image_reference = load_image(image_reference_url)
-
-mask = pipe.mask_processor.blur(mask, blur_factor=12)
-image = pipe(
-    prompt=prompt, image=source, mask_image=mask, image_reference=image_reference, strength=1.0
-).images[0]
-image.save("kontext_inpainting_ref.png")
-```
-</hfoption>
-</hfoptions>
-
 ## Combining Flux Turbo LoRAs with Flux Control, Fill, and Redux
 
 We can combine Flux Turbo LoRAs with Flux Control and other pipelines like Fill and Redux to enable few-steps' inference. The example below shows how to do that for Flux Control LoRA for depth and turbo LoRA from [`ByteDance/Hyper-SD`](https://hf.co/ByteDance/Hyper-SD).
@@ -707,15 +644,3 @@ image.save("flux-fp8-dev.png")
 [[autodoc]] FluxFillPipeline
 	- all
 	- __call__
-
-## FluxKontextPipeline
-
-[[autodoc]] FluxKontextPipeline
-	- all
-	- __call__
-
-## FluxKontextInpaintPipeline
-
-[[autodoc]] FluxKontextInpaintPipeline
-	- all
-	- __call__

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

@@ -18,7 +18,7 @@
 
 <Tip>
 
-[Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
+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.
 
 </Tip>
 

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

@@ -54,7 +54,7 @@ pipeline_quant_config = PipelineQuantizationConfig(
       "bnb_4bit_quant_type": "nf4",
       "bnb_4bit_compute_dtype": torch.bfloat16
       },
-    components_to_quantize="transformer"
+    components_to_quantize=["transformer"]
 )
 
 pipeline = HunyuanVideoPipeline.from_pretrained(
@@ -91,7 +91,7 @@ pipeline_quant_config = PipelineQuantizationConfig(
       "bnb_4bit_quant_type": "nf4",
       "bnb_4bit_compute_dtype": torch.bfloat16
       },
-    components_to_quantize="transformer"
+    components_to_quantize=["transformer"]
 )
 
 pipeline = HunyuanVideoPipeline.from_pretrained(
@@ -139,7 +139,7 @@ export_to_video(video, "output.mp4", fps=15)
         "bnb_4bit_quant_type": "nf4",
         "bnb_4bit_compute_dtype": torch.bfloat16
         },
-      components_to_quantize="transformer"
+      components_to_quantize=["transformer"]
   )
 
   pipeline = HunyuanVideoPipeline.from_pretrained(

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

@@ -88,7 +88,7 @@ export_to_video(video, "output.mp4", fps=24)
 </hfoption>
 <hfoption id="inference speed">
 
-[Compilation](../../optimization/fp16#torchcompile) is slow the first time but subsequent calls to the pipeline are faster. [Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
+[Compilation](../../optimization/fp16#torchcompile) is slow the first time but subsequent calls to the pipeline are faster.
 
 ```py
 import torch

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

@@ -37,7 +37,6 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an
 | [AudioLDM2](audioldm2) | text2audio |
 | [AuraFlow](auraflow) | text2image |
 | [BLIP Diffusion](blip_diffusion) | text2image |
-| [Bria 3.2](bria_3_2) | text2image |
 | [CogVideoX](cogvideox) | text2video |
 | [Consistency Models](consistency_models) | unconditional image generation |
 | [ControlNet](controlnet) | text2image, image2image, inpainting |
@@ -106,20 +105,10 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an
 
 [[autodoc]] pipelines.StableDiffusionMixin.disable_freeu
 
+## FlaxDiffusionPipeline
+
+[[autodoc]] pipelines.pipeline_flax_utils.FlaxDiffusionPipeline
+
 ## PushToHubMixin
 
 [[autodoc]] utils.PushToHubMixin
-
-## Callbacks
-
-[[autodoc]] callbacks.PipelineCallback
-
-[[autodoc]] callbacks.SDCFGCutoffCallback
-
-[[autodoc]] callbacks.SDXLCFGCutoffCallback
-
-[[autodoc]] callbacks.SDXLControlnetCFGCutoffCallback
-
-[[autodoc]] callbacks.IPAdapterScaleCutoffCallback
-
-[[autodoc]] callbacks.SD3CFGCutoffCallback

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

@@ -1,135 +0,0 @@
-<!-- Copyright 2025 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. -->
-
-# QwenImage
-
-<div class="flex flex-wrap space-x-1">
-  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
-</div>
-
-Qwen-Image from the Qwen team is an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing. Experiments show strong general capabilities in both image generation and editing, with exceptional performance in text rendering, especially for Chinese.
-
-Qwen-Image comes in the following variants:
-
-| model type | model id |
-|:----------:|:--------:|
-| Qwen-Image | [`Qwen/Qwen-Image`](https://huggingface.co/Qwen/Qwen-Image) |
-| Qwen-Image-Edit | [`Qwen/Qwen-Image-Edit`](https://huggingface.co/Qwen/Qwen-Image-Edit) |
-
-<Tip>
-
-[Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
-
-</Tip>
-
-## LoRA for faster inference
-
-Use a LoRA from `lightx2v/Qwen-Image-Lightning` to speed up inference by reducing the
-number of steps. Refer to the code snippet below:
-
-<details>
-<summary>Code</summary>
-
-```py
-from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
-import torch 
-import math
-
-ckpt_id = "Qwen/Qwen-Image"
-
-# From
-# https://github.com/ModelTC/Qwen-Image-Lightning/blob/342260e8f5468d2f24d084ce04f55e101007118b/generate_with_diffusers.py#L82C9-L97C10
-scheduler_config = {
-    "base_image_seq_len": 256,
-    "base_shift": math.log(3),  # We use shift=3 in distillation
-    "invert_sigmas": False,
-    "max_image_seq_len": 8192,
-    "max_shift": math.log(3),  # We use shift=3 in distillation
-    "num_train_timesteps": 1000,
-    "shift": 1.0,
-    "shift_terminal": None,  # set shift_terminal to None
-    "stochastic_sampling": False,
-    "time_shift_type": "exponential",
-    "use_beta_sigmas": False,
-    "use_dynamic_shifting": True,
-    "use_exponential_sigmas": False,
-    "use_karras_sigmas": False,
-}
-scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config)
-pipe = DiffusionPipeline.from_pretrained(
-    ckpt_id, scheduler=scheduler, torch_dtype=torch.bfloat16
-).to("cuda")
-pipe.load_lora_weights(
-    "lightx2v/Qwen-Image-Lightning", weight_name="Qwen-Image-Lightning-8steps-V1.0.safetensors"
-)
-
-prompt = "a tiny astronaut hatching from an egg on the moon, Ultra HD, 4K, cinematic composition."
-negative_prompt = " "
-image = pipe(
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    width=1024,
-    height=1024,
-    num_inference_steps=8,
-    true_cfg_scale=1.0,
-    generator=torch.manual_seed(0),
-).images[0]
-image.save("qwen_fewsteps.png")
-```
-
-</details>
-
-<Tip>
-
-The `guidance_scale` parameter in the pipeline is there to support future guidance-distilled models when they come up. Note that passing `guidance_scale` to the pipeline is ineffective. To enable classifier-free guidance, please pass `true_cfg_scale` and `negative_prompt` (even an empty negative prompt like " ") should enable classifier-free guidance computations.
-
-</Tip>
-
-## QwenImagePipeline
-
-[[autodoc]] QwenImagePipeline
-  - all
-  - __call__
-
-## QwenImageImg2ImgPipeline
-
-[[autodoc]] QwenImageImg2ImgPipeline
-  - all
-  - __call__
-
-## QwenImageInpaintPipeline
-
-[[autodoc]] QwenImageInpaintPipeline
-  - all
-  - __call__
-
-## QwenImageEditPipeline
-
-[[autodoc]] QwenImageEditPipeline
-  - all
-  - __call__
-
-## QwenImageEditInpaintPipeline
-
-[[autodoc]] QwenImageEditInpaintPipeline
-  - all
-  - __call__
-
-## QwenImaggeControlNetPipeline
-  - all
-  - __call__
-
-## QwenImagePipelineOutput
-
-[[autodoc]] pipelines.qwenimage.pipeline_output.QwenImagePipelineOutput

docs/source/en/api/pipelines/skyreels_v2.md

@@ -1,346 +0,0 @@
-<!-- Copyright 2025 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. -->
-
-<div style="float: right;">
-  <div class="flex flex-wrap space-x-1">
-    <a href="https://huggingface.co/docs/diffusers/main/en/tutorials/using_peft_for_inference" target="_blank" rel="noopener">
-      <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
-    </a>
-  </div>
-</div>
-
-# SkyReels-V2: Infinite-length Film Generative model
-
-[SkyReels-V2](https://huggingface.co/papers/2504.13074) by the SkyReels Team from Skywork AI.
-
-*Recent advances in video generation have been driven by diffusion models and autoregressive frameworks, yet critical challenges persist in harmonizing prompt adherence, visual quality, motion dynamics, and duration: compromises in motion dynamics to enhance temporal visual quality, constrained video duration (5-10 seconds) to prioritize resolution, and inadequate shot-aware generation stemming from general-purpose MLLMs' inability to interpret cinematic grammar, such as shot composition, actor expressions, and camera motions. These intertwined limitations hinder realistic long-form synthesis and professional film-style generation. To address these limitations, we propose SkyReels-V2, an Infinite-length Film Generative Model, that synergizes Multi-modal Large Language Model (MLLM), Multi-stage Pretraining, Reinforcement Learning, and Diffusion Forcing Framework. Firstly, we design a comprehensive structural representation of video that combines the general descriptions by the Multi-modal LLM and the detailed shot language by sub-expert models. Aided with human annotation, we then train a unified Video Captioner, named SkyCaptioner-V1, to efficiently label the video data. Secondly, we establish progressive-resolution pretraining for the fundamental video generation, followed by a four-stage post-training enhancement: Initial concept-balanced Supervised Fine-Tuning (SFT) improves baseline quality; Motion-specific Reinforcement Learning (RL) training with human-annotated and synthetic distortion data addresses dynamic artifacts; Our diffusion forcing framework with non-decreasing noise schedules enables long-video synthesis in an efficient search space; Final high-quality SFT refines visual fidelity. All the code and models are available at [this https URL](https://github.com/SkyworkAI/SkyReels-V2).*
-
-You can find all the original SkyReels-V2 checkpoints under the [Skywork](https://huggingface.co/collections/Skywork/skyreels-v2-6801b1b93df627d441d0d0d9) organization.
-
-The following SkyReels-V2 models are supported in Diffusers:
-- [SkyReels-V2 DF 1.3B - 540P](https://huggingface.co/Skywork/SkyReels-V2-DF-1.3B-540P-Diffusers)
-- [SkyReels-V2 DF 14B - 540P](https://huggingface.co/Skywork/SkyReels-V2-DF-14B-540P-Diffusers)
-- [SkyReels-V2 DF 14B - 720P](https://huggingface.co/Skywork/SkyReels-V2-DF-14B-720P-Diffusers)
-- [SkyReels-V2 T2V 14B - 540P](https://huggingface.co/Skywork/SkyReels-V2-T2V-14B-540P-Diffusers)
-- [SkyReels-V2 T2V 14B - 720P](https://huggingface.co/Skywork/SkyReels-V2-T2V-14B-720P-Diffusers)
-- [SkyReels-V2 I2V 1.3B - 540P](https://huggingface.co/Skywork/SkyReels-V2-I2V-1.3B-540P-Diffusers)
-- [SkyReels-V2 I2V 14B - 540P](https://huggingface.co/Skywork/SkyReels-V2-I2V-14B-540P-Diffusers)
-- [SkyReels-V2 I2V 14B - 720P](https://huggingface.co/Skywork/SkyReels-V2-I2V-14B-720P-Diffusers)
-- [SkyReels-V2 FLF2V 1.3B - 540P](https://huggingface.co/Skywork/SkyReels-V2-FLF2V-1.3B-540P-Diffusers)
-
-> [!TIP]
-> Click on the SkyReels-V2 models in the right sidebar for more examples of video generation.
-
-### A _Visual_ Demonstration
-
-The example below has the following parameters:
-
-- `base_num_frames=97`
-- `num_frames=97`
-- `num_inference_steps=30`
-- `ar_step=5`
-- `causal_block_size=5`
-
-With `vae_scale_factor_temporal=4`, expect `5` blocks of `5` frames each as calculated by:
-
-`num_latent_frames: (97-1)//vae_scale_factor_temporal+1 = 25 frames -> 5 blocks of 5 frames each`
-
-And the maximum context length in the latent space is calculated with `base_num_latent_frames`:
-
-`base_num_latent_frames = (97-1)//vae_scale_factor_temporal+1 = 25 -> 25//5 = 5 blocks`
-
-Asynchronous Processing Timeline:
-```text
-┌─────────────────────────────────────────────────────────────────┐
-│ Steps:    1    6   11   16   21   26   31   36   41   46   50   │
-│ Block 1: [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■]                       │
-│ Block 2:      [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■]                  │
-│ Block 3:           [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■]             │
-│ Block 4:                [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■]        │
-│ Block 5:                     [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■]   │
-└─────────────────────────────────────────────────────────────────┘
-```
-
-For Long Videos (`num_frames` > `base_num_frames`):
-`base_num_frames` acts as the "sliding window size" for processing long videos.
-
-Example: `257`-frame video with `base_num_frames=97`, `overlap_history=17`
-```text
-┌──── Iteration 1 (frames 1-97) ────┐
-│ Processing window: 97 frames      │ → 5 blocks,
-│ Generates: frames 1-97            │   async processing
-└───────────────────────────────────┘
-            ┌────── Iteration 2 (frames 81-177) ──────┐
-            │ Processing window: 97 frames            │
-            │ Overlap: 17 frames (81-97) from prev    │ → 5 blocks,
-            │ Generates: frames 98-177                │   async processing
-            └─────────────────────────────────────────┘
-                        ┌────── Iteration 3 (frames 161-257) ──────┐
-                        │ Processing window: 97 frames             │
-                        │ Overlap: 17 frames (161-177) from prev   │ → 5 blocks,
-                        │ Generates: frames 178-257                │   async processing
-                        └──────────────────────────────────────────┘
-```
-
-Each iteration independently runs the asynchronous processing with its own `5` blocks.
-`base_num_frames` controls:
-1. Memory usage (larger window = more VRAM)
-2. Model context length (must match training constraints)
-3. Number of blocks per iteration (`base_num_latent_frames // causal_block_size`)
-
-Each block takes `30` steps to complete denoising.
-Block N starts at step: `1 + (N-1) x ar_step`
-Total steps: `30 + (5-1) x 5 = 50` steps
-
-
-Synchronous mode (`ar_step=0`) would process all blocks/frames simultaneously:
-```text
-┌──────────────────────────────────────────────┐
-│ Steps:       1            ...            30  │
-│ All blocks: [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■] │
-└──────────────────────────────────────────────┘
-```
-Total steps: `30` steps
-
-
-An example on how the step matrix is constructed for asynchronous processing:
-Given the parameters: (`num_inference_steps=30, flow_shift=8, num_frames=97, ar_step=5, causal_block_size=5`)
-```
-- num_latent_frames = (97 frames - 1) // (4 temporal downsampling) + 1 = 25
-- step_template = [999, 995, 991, 986, 980, 975, 969, 963, 956, 948,
-                   941, 932, 922, 912, 901, 888, 874, 859, 841, 822,
-                   799, 773, 743, 708, 666, 615, 551, 470, 363, 216]
-```
-
-The algorithm creates a `50x25` `step_matrix` where:
-```
-- Row 1:  [999×5, 999×5, 999×5, 999×5, 999×5]
-- Row 2:  [995×5, 999×5, 999×5, 999×5, 999×5]
-- Row 3:  [991×5, 999×5, 999×5, 999×5, 999×5]
-- ...
-- Row 7:  [969×5, 995×5, 999×5, 999×5, 999×5]
-- ...
-- Row 21: [799×5, 888×5, 941×5, 975×5, 999×5]
-- ...
-- Row 35: [  0×5, 216×5, 666×5, 822×5, 901×5]
-- ...
-- Row 42: [  0×5,   0×5,   0×5, 551×5, 773×5]
-- ...
-- Row 50: [  0×5,   0×5,   0×5,   0×5, 216×5]
-```
-
-Detailed Row `6` Analysis:
-```
-- step_matrix[5]:      [ 975×5,  999×5,   999×5,   999×5,   999×5]
-- step_index[5]:       [   6×5,    1×5,     0×5,     0×5,     0×5]
-- step_update_mask[5]: [True×5, True×5, False×5, False×5, False×5]
-- valid_interval[5]:   (0, 25)
-```
-
-Key Pattern: Block `i` lags behind Block `i-1` by exactly `ar_step=5` timesteps, creating the
-staggered "diffusion forcing" effect where later blocks condition on cleaner earlier blocks.
-
-
-### Text-to-Video Generation
-
-The example below demonstrates how to generate a video from text.
-
-<hfoptions id="T2V usage">
-<hfoption id="T2V memory">
-
-Refer to the [Reduce memory usage](../../optimization/memory) guide for more details about the various memory saving techniques.
-
-From the original repo:
->You can use --ar_step 5 to enable asynchronous inference. When asynchronous inference, --causal_block_size 5 is recommended while it is not supposed to be set for synchronous generation... Asynchronous inference will take more steps to diffuse the whole sequence which means it will be SLOWER than synchronous mode. In our experiments, asynchronous inference may improve the instruction following and visual consistent performance.
-
-```py
-import torch
-from diffusers import AutoModel, SkyReelsV2DiffusionForcingPipeline, UniPCMultistepScheduler
-from diffusers.utils import export_to_video
-
-
-model_id = "Skywork/SkyReels-V2-DF-1.3B-540P-Diffusers"
-vae = AutoModel.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
-
-pipeline = SkyReelsV2DiffusionForcingPipeline.from_pretrained(
-    model_id,
-    vae=vae,
-    torch_dtype=torch.bfloat16,
-)
-pipeline.to("cuda")
-flow_shift = 8.0  # 8.0 for T2V, 5.0 for I2V
-pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config, flow_shift=flow_shift)
-
-prompt = "A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window."
-
-output = pipeline(
-    prompt=prompt,
-    num_inference_steps=30,
-    height=544,  # 720 for 720P
-    width=960,   # 1280 for 720P
-    num_frames=97,
-    base_num_frames=97,  # 121 for 720P
-    ar_step=5,  # Controls asynchronous inference (0 for synchronous mode)
-    causal_block_size=5,  # Number of frames in each block for asynchronous processing
-    overlap_history=None,  # Number of frames to overlap for smooth transitions in long videos; 17 for long video generations
-    addnoise_condition=20,  # Improves consistency in long video generation
-).frames[0]
-export_to_video(output, "video.mp4", fps=24, quality=8)
-```
-
-</hfoption>
-</hfoptions>
-
-### First-Last-Frame-to-Video Generation
-
-The example below demonstrates how to use the image-to-video pipeline to generate a video using a text description, a starting frame, and an ending frame.
-
-<hfoptions id="FLF2V usage">
-<hfoption id="usage">
-
-```python
-import numpy as np
-import torch
-import torchvision.transforms.functional as TF
-from diffusers import AutoencoderKLWan, SkyReelsV2DiffusionForcingImageToVideoPipeline, UniPCMultistepScheduler
-from diffusers.utils import export_to_video, load_image
-
-
-model_id = "Skywork/SkyReels-V2-DF-1.3B-720P-Diffusers"
-vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
-pipeline = SkyReelsV2DiffusionForcingImageToVideoPipeline.from_pretrained(
-    model_id, vae=vae, torch_dtype=torch.bfloat16
-)
-pipeline.to("cuda")
-flow_shift = 5.0  # 8.0 for T2V, 5.0 for I2V
-pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config, flow_shift=flow_shift)
-
-first_frame = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_first_frame.png")
-last_frame = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_last_frame.png")
-
-def aspect_ratio_resize(image, pipeline, max_area=720 * 1280):
-    aspect_ratio = image.height / image.width
-    mod_value = pipeline.vae_scale_factor_spatial * pipeline.transformer.config.patch_size[1]
-    height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
-    width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
-    image = image.resize((width, height))
-    return image, height, width
-
-def center_crop_resize(image, height, width):
-    # Calculate resize ratio to match first frame dimensions
-    resize_ratio = max(width / image.width, height / image.height)
-
-    # Resize the image
-    width = round(image.width * resize_ratio)
-    height = round(image.height * resize_ratio)
-    size = [width, height]
-    image = TF.center_crop(image, size)
-
-    return image, height, width
-
-first_frame, height, width = aspect_ratio_resize(first_frame, pipeline)
-if last_frame.size != first_frame.size:
-    last_frame, _, _ = center_crop_resize(last_frame, height, width)
-
-prompt = "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird's feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective."
-
-output = pipeline(
-    image=first_frame, last_image=last_frame, prompt=prompt, height=height, width=width, guidance_scale=5.0
-).frames[0]
-export_to_video(output, "video.mp4", fps=24, quality=8)
-```
-
-</hfoption>
-</hfoptions>
-
-
-### Video-to-Video Generation
-
-<hfoptions id="V2V usage">
-<hfoption id="usage">
-
-`SkyReelsV2DiffusionForcingVideoToVideoPipeline` extends a given video.
-
-```python
-import numpy as np
-import torch
-import torchvision.transforms.functional as TF
-from diffusers import AutoencoderKLWan, SkyReelsV2DiffusionForcingVideoToVideoPipeline, UniPCMultistepScheduler
-from diffusers.utils import export_to_video, load_video
-
-
-model_id = "Skywork/SkyReels-V2-DF-1.3B-720P-Diffusers"
-vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
-pipeline = SkyReelsV2DiffusionForcingVideoToVideoPipeline.from_pretrained(
-    model_id, vae=vae, torch_dtype=torch.bfloat16
-)
-pipeline.to("cuda")
-flow_shift = 5.0  # 8.0 for T2V, 5.0 for I2V
-pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config, flow_shift=flow_shift)
-
-video = load_video("input_video.mp4")
-
-prompt = "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird's feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective."
-
-output = pipeline(
-    video=video, prompt=prompt, height=720, width=1280, guidance_scale=5.0, overlap_history=17,
-    num_inference_steps=30, num_frames=257, base_num_frames=121#, ar_step=5, causal_block_size=5,
-).frames[0]
-export_to_video(output, "video.mp4", fps=24, quality=8)
-# Total frames will be the number of frames of the given video + 257
-```
-
-</hfoption>
-</hfoptions>
-
-## Notes
-
-- SkyReels-V2 supports LoRAs with [`~loaders.SkyReelsV2LoraLoaderMixin.load_lora_weights`].
-
-`SkyReelsV2Pipeline` and `SkyReelsV2ImageToVideoPipeline` are also available without Diffusion Forcing framework applied.
-
-
-## SkyReelsV2DiffusionForcingPipeline
-
-[[autodoc]] SkyReelsV2DiffusionForcingPipeline
-  - all
-  - __call__
-
-## SkyReelsV2DiffusionForcingImageToVideoPipeline
-
-[[autodoc]] SkyReelsV2DiffusionForcingImageToVideoPipeline
-  - all
-  - __call__
-
-## SkyReelsV2DiffusionForcingVideoToVideoPipeline
-
-[[autodoc]] SkyReelsV2DiffusionForcingVideoToVideoPipeline
-  - all
-  - __call__
-
-## SkyReelsV2Pipeline
-
-[[autodoc]] SkyReelsV2Pipeline
-  - all
-  - __call__
-
-## SkyReelsV2ImageToVideoPipeline
-
-[[autodoc]] SkyReelsV2ImageToVideoPipeline
-  - all
-  - __call__
-
-## SkyReelsV2PipelineOutput
-
-[[autodoc]] pipelines.skyreels_v2.pipeline_output.SkyReelsV2PipelineOutput

docs/source/en/api/pipelines/stable_diffusion/img2img.md

@@ -47,3 +47,13 @@ Make sure to check out the Stable Diffusion [Tips](overview#tips) section to lea
 ## StableDiffusionPipelineOutput
 
 [[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
+
+## FlaxStableDiffusionImg2ImgPipeline
+
+[[autodoc]] FlaxStableDiffusionImg2ImgPipeline
+	- all
+	- __call__
+
+## FlaxStableDiffusionPipelineOutput
+
+[[autodoc]] pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput

docs/source/en/api/pipelines/stable_diffusion/inpaint.md

@@ -49,3 +49,13 @@ If you're interested in using one of the official checkpoints for a task, explor
 ## StableDiffusionPipelineOutput
 
 [[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
+
+## FlaxStableDiffusionInpaintPipeline
+
+[[autodoc]] FlaxStableDiffusionInpaintPipeline
+	- all
+	- __call__
+
+## FlaxStableDiffusionPipelineOutput
+
+[[autodoc]] pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput

docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_3.md

@@ -31,7 +31,7 @@ _As the model is gated, before using it with diffusers you first need to go to t
 Use the command below to log in:
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 <Tip>

docs/source/en/api/pipelines/stable_diffusion/text2img.md

@@ -51,3 +51,13 @@ If you're interested in using one of the official checkpoints for a task, explor
 ## StableDiffusionPipelineOutput
 
 [[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
+
+## FlaxStableDiffusionPipeline
+
+[[autodoc]] FlaxStableDiffusionPipeline
+	- all
+	- __call__
+
+## FlaxStableDiffusionPipelineOutput
+
+[[autodoc]] pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput

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

@@ -20,7 +20,7 @@
   </div>
 </div>
 
-# Wan
+# Wan2.1
 
 [Wan-2.1](https://huggingface.co/papers/2503.20314) by the Wan Team.
 
@@ -29,7 +29,6 @@
 You can find all the original Wan2.1 checkpoints under the [Wan-AI](https://huggingface.co/Wan-AI) organization.
 
 The following Wan models are supported in Diffusers:
-
 - [Wan 2.1 T2V 1.3B](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers)
 - [Wan 2.1 T2V 14B](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B-Diffusers)
 - [Wan 2.1 I2V 14B - 480P](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P-Diffusers)
@@ -37,12 +36,9 @@ The following Wan models are supported in Diffusers:
 - [Wan 2.1 FLF2V 14B - 720P](https://huggingface.co/Wan-AI/Wan2.1-FLF2V-14B-720P-diffusers)
 - [Wan 2.1 VACE 1.3B](https://huggingface.co/Wan-AI/Wan2.1-VACE-1.3B-diffusers)
 - [Wan 2.1 VACE 14B](https://huggingface.co/Wan-AI/Wan2.1-VACE-14B-diffusers)
-- [Wan 2.2 T2V 14B](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B-Diffusers)
-- [Wan 2.2 I2V 14B](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers)
-- [Wan 2.2 TI2V 5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers)
 
 > [!TIP]
-> Click on the Wan models in the right sidebar for more examples of video generation.
+> Click on the Wan2.1 models in the right sidebar for more examples of video generation.
 
 ### Text-to-Video Generation
 
@@ -119,7 +115,7 @@ export_to_video(output, "output.mp4", fps=16)
 </hfoption>
 <hfoption id="T2V inference speed">
 
-[Compilation](../../optimization/fp16#torchcompile) is slow the first time but subsequent calls to the pipeline are faster. [Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
+[Compilation](../../optimization/fp16#torchcompile) is slow the first time but subsequent calls to the pipeline are faster.
 
 ```py
 # pip install ftfy
@@ -331,10 +327,6 @@ The general rule of thumb to keep in mind when preparing inputs for the VACE pip
 
 - Try lower `shift` values (`2.0` to `5.0`) for lower resolution videos and higher `shift` values (`7.0` to `12.0`) for higher resolution images.
 
-- Wan 2.1 and 2.2 support using [LightX2V LoRAs](https://huggingface.co/Kijai/WanVideo_comfy/tree/main/Lightx2v) to speed up inference. Using them on Wan 2.2 is slightly more involed. Refer to [this code snippet](https://github.com/huggingface/diffusers/pull/12040#issuecomment-3144185272) to learn more.
-
-- Wan 2.2 has two denoisers. By default, LoRAs are only loaded into the first denoiser. One can set `load_into_transformer_2=True` to load LoRAs into the second denoiser. Refer to [this](https://github.com/huggingface/diffusers/pull/12074#issue-3292620048) and [this](https://github.com/huggingface/diffusers/pull/12074#issuecomment-3155896144) examples to learn more.
-
 ## WanPipeline
 
 [[autodoc]] WanPipeline

docs/source/en/api/quantization.md

@@ -27,19 +27,19 @@ Learn how to quantize models in the [Quantization](../quantization/overview) gui
 
 ## BitsAndBytesConfig
 
-[[autodoc]] quantizers.quantization_config.BitsAndBytesConfig
+[[autodoc]] BitsAndBytesConfig
 
 ## GGUFQuantizationConfig
 
-[[autodoc]] quantizers.quantization_config.GGUFQuantizationConfig
+[[autodoc]] GGUFQuantizationConfig
 
 ## QuantoConfig
 
-[[autodoc]] quantizers.quantization_config.QuantoConfig
+[[autodoc]] QuantoConfig
 
 ## TorchAoConfig
 
-[[autodoc]] quantizers.quantization_config.TorchAoConfig
+[[autodoc]] TorchAoConfig
 
 ## DiffusersQuantizer
 

docs/source/en/index.md

@@ -12,24 +12,37 @@ specific language governing permissions and limitations under the License.
 
 <p align="center">
     <br>
-    <img src="https://raw.githubusercontent.com/huggingface/diffusers/77aadfee6a891ab9fcfb780f87c693f7a5beeb8e/docs/source/imgs/diffusers_library.jpg" width="400" style="border: none;"/>
+    <img src="https://raw.githubusercontent.com/huggingface/diffusers/77aadfee6a891ab9fcfb780f87c693f7a5beeb8e/docs/source/imgs/diffusers_library.jpg" width="400"/>
     <br>
 </p>
 
 # Diffusers
 
-Diffusers is a library of state-of-the-art pretrained diffusion models for generating videos, images, and audio.
+🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or want to train your own diffusion model, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on [usability over performance](conceptual/philosophy#usability-over-performance), [simple over easy](conceptual/philosophy#simple-over-easy), and [customizability over abstractions](conceptual/philosophy#tweakable-contributorfriendly-over-abstraction).
 
-The library revolves around the [`DiffusionPipeline`], an API designed for:
+The library has three main components:
 
-- easy inference with only a few lines of code
-- flexibility to mix-and-match pipeline components (models, schedulers)
-- loading and using adapters like LoRA
+- State-of-the-art diffusion pipelines for inference with just a few lines of code. There are many pipelines in 🤗 Diffusers, check out the table in the pipeline [overview](api/pipelines/overview) for a complete list of available pipelines and the task they solve.
+- Interchangeable [noise schedulers](api/schedulers/overview) for balancing trade-offs between generation speed and quality.
+- Pretrained [models](api/models) that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.
 
-Diffusers also comes with optimizations - such as offloading and quantization - to ensure even the largest models are accessible on memory-constrained devices. If memory is not an issue, Diffusers supports torch.compile to boost inference speed.
-
-Get started right away with a Diffusers model on the [Hub](https://huggingface.co/models?library=diffusers&sort=trending) today!
-
-## Learn
-
-If you're a beginner, we recommend starting with the [Hugging Face Diffusion Models Course](https://huggingface.co/learn/diffusion-course/unit0/1). You'll learn the theory behind diffusion models, and learn how to use the Diffusers library to generate images, fine-tune your own models, and more.
+<div class="mt-10">
+  <div class="w-full flex flex-col space-y-4 md:space-y-0 md:grid md:grid-cols-2 md:gap-y-4 md:gap-x-5">
+    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./tutorials/tutorial_overview"
+      ><div class="w-full text-center bg-gradient-to-br from-blue-400 to-blue-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Tutorials</div>
+      <p class="text-gray-700">Learn the fundamental skills you need to start generating outputs, build your own diffusion system, and train a diffusion model. We recommend starting here if you're using 🤗 Diffusers for the first time!</p>
+    </a>
+    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./using-diffusers/loading_overview"
+      ><div class="w-full text-center bg-gradient-to-br from-indigo-400 to-indigo-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">How-to guides</div>
+      <p class="text-gray-700">Practical guides for helping you load pipelines, models, and schedulers. You'll also learn how to use pipelines for specific tasks, control how outputs are generated, optimize for inference speed, and different training techniques.</p>
+    </a>
+    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./conceptual/philosophy"
+      ><div class="w-full text-center bg-gradient-to-br from-pink-400 to-pink-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Conceptual guides</div>
+      <p class="text-gray-700">Understand why the library was designed the way it was, and learn more about the ethical guidelines and safety implementations for using the library.</p>
+   </a>
+    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./api/models/overview"
+      ><div class="w-full text-center bg-gradient-to-br from-purple-400 to-purple-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Reference</div>
+      <p class="text-gray-700">Technical descriptions of how 🤗 Diffusers classes and methods work.</p>
+    </a>
+  </div>
+</div>

docs/source/en/installation.md

@@ -12,135 +12,183 @@ specific language governing permissions and limitations under the License.
 
 # Installation
 
-Diffusers is tested on Python 3.8+ and PyTorch 1.4+. Install [PyTorch](https://pytorch.org/get-started/locally/) according to your system and setup.
+🤗 Diffusers is tested on Python 3.8+, PyTorch 1.7.0+, and Flax. Follow the installation instructions below for the deep learning library you are using:
 
-Create a [virtual environment](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/) for easier management of separate projects and to avoid compatibility issues between dependencies. Use [uv](https://docs.astral.sh/uv/), a Rust-based Python package and project manager, to create a virtual environment and install Diffusers.
+- [PyTorch](https://pytorch.org/get-started/locally/) installation instructions
+- [Flax](https://flax.readthedocs.io/en/latest/) installation instructions
+
+## Install with pip
+
+You should install 🤗 Diffusers in a [virtual environment](https://docs.python.org/3/library/venv.html).
+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">
 
 ```bash
 uv venv my-env
 source my-env/bin/activate
 ```
 
-Install Diffusers with one of the following methods.
-
-<hfoptions id="install">
-<hfoption id="pip">
-
-PyTorch only supports Python 3.8 - 3.11 on Windows.
-
-```bash
-uv pip install diffusers["torch"] transformers
-```
-
 </hfoption>
-<hfoption id="conda">
+<hfoption id="Python">
 
 ```bash
-conda install -c conda-forge diffusers
-```
-
-</hfoption>
-<hfoption id="source">
-
-A source install installs the `main` version instead of the latest `stable` version. The `main` version is useful for staying updated with the latest changes but it may not always be stable. If you run into a problem, open an [Issue](https://github.com/huggingface/diffusers/issues/new/choose) and we will try to resolve it as soon as possible.
-
-Make sure [Accelerate](https://huggingface.co/docs/accelerate/index) is installed.
-
-```bash
-uv pip install accelerate
-```
-
-Install Diffusers from source with the command below.
-
-```bash
-uv pip install git+https://github.com/huggingface/diffusers
+python -m venv my-env
+source my-env/bin/activate
 ```
 
 </hfoption>
 </hfoptions>
 
+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.
+
+```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>
+
+## Install with conda
+
+After activating your virtual environment, with `conda` (maintained by the community):
+
+```bash
+conda install -c conda-forge diffusers
+```
+
+## Install from source
+
+Before installing 🤗 Diffusers from source, make sure you have PyTorch and 🤗 Accelerate installed.
+
+To install 🤗 Accelerate:
+
+```bash
+pip install accelerate
+```
+
+Then install 🤗 Diffusers from source:
+
+```bash
+pip install git+https://github.com/huggingface/diffusers
+```
+
+This command installs the bleeding edge `main` version rather than the latest `stable` version.
+The `main` version is useful for staying up-to-date with the latest developments.
+For instance, if a bug has been fixed since the last official release but a new release hasn't been rolled out yet.
+However, this means the `main` version may not always be stable.
+We strive to keep the `main` version operational, and most issues are usually resolved within a few hours or a day.
+If you run into a problem, please open an [Issue](https://github.com/huggingface/diffusers/issues/new/choose) so we can fix it even sooner!
+
 ## Editable install
 
-An editable install is recommended for development workflows or if you're using the `main` version of the source code. A special link is created between the cloned repository and the Python library paths. This avoids reinstalling a package after every change.
+You will need an editable install if you'd like to:
 
-Clone the repository and install Diffusers with the following commands.
+* Use the `main` version of the source code.
+* Contribute to 🤗 Diffusers and need to test changes in the code.
+
+Clone the repository and install 🤗 Diffusers with the following commands:
 
 ```bash
 git clone https://github.com/huggingface/diffusers.git
 cd diffusers
-uv pip install -e ".[torch]"
 ```
 
-> [!WARNING]
-> You must keep the `diffusers` folder if you want to keep using the library with the editable install.
+<frameworkcontent>
+<pt>
+```bash
+pip install -e ".[torch]"
+```
+</pt>
+<jax>
+```bash
+pip install -e ".[flax]"
+```
+</jax>
+</frameworkcontent>
 
-Update your cloned repository to the latest version of Diffusers with the command below.
+These commands will link the folder you cloned the repository to and your Python library paths.
+Python will now look inside the folder you cloned to in addition to the normal library paths.
+For example, if your Python packages are typically installed in `~/anaconda3/envs/main/lib/python3.10/site-packages/`, Python will also search the `~/diffusers/` folder you cloned to.
+
+<Tip warning={true}>
+
+You must keep the `diffusers` folder if you want to keep using the library.
+
+</Tip>
+
+Now you can easily update your clone to the latest version of 🤗 Diffusers with the following command:
 
 ```bash
 cd ~/diffusers/
 git pull
 ```
 
+Your Python environment will find the `main` version of 🤗 Diffusers on the next run.
+
 ## Cache
 
-Model weights and files are downloaded from the Hub to a cache, which is usually your home directory. Change the cache location with the [HF_HOME](https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfhome) or [HF_HUB_CACHE](https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfhubcache) environment variables or configuring the `cache_dir` parameter in methods like [`~DiffusionPipeline.from_pretrained`].
+Model weights and files are downloaded from the Hub to a cache which is usually your home directory. You can change the cache location by specifying the `HF_HOME` or `HUGGINFACE_HUB_CACHE` environment variables or configuring the `cache_dir` parameter in methods like [`~DiffusionPipeline.from_pretrained`].
 
-<hfoptions id="cache">
-<hfoption id="env variable">
-
-```bash
-export HF_HOME="/path/to/your/cache"
-export HF_HUB_CACHE="/path/to/your/hub/cache"
-```
-
-</hfoption>
-<hfoption id="from_pretrained">
-
-```py
-from diffusers import DiffusionPipeline
-
-pipeline = DiffusionPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-dev",
-    cache_dir="/path/to/your/cache"
-)
-```
-
-</hfoption>
-</hfoptions>
-
-Cached files allow you to use Diffusers offline. Set the [HF_HUB_OFFLINE](https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfhuboffline) environment variable to `1` to prevent Diffusers from connecting to the internet.
+Cached files allow you to run 🤗 Diffusers offline. To prevent 🤗 Diffusers from connecting to the internet, set the `HF_HUB_OFFLINE` environment variable to `1` and 🤗 Diffusers will only load previously downloaded files in the cache.
 
 ```shell
 export HF_HUB_OFFLINE=1
 ```
 
-For more details about managing and cleaning the cache, take a look at the [Understand caching](https://huggingface.co/docs/huggingface_hub/guides/manage-cache) guide.
+For more details about managing and cleaning the cache, take a look at the [caching](https://huggingface.co/docs/huggingface_hub/guides/manage-cache) guide.
 
 ## Telemetry logging
 
-Diffusers gathers telemetry information during [`~DiffusionPipeline.from_pretrained`] requests.
-The data gathered includes the Diffusers and PyTorch version, the requested model or pipeline class,
-and the path to a pretrained checkpoint if it is hosted on the Hub.
-
+Our library gathers telemetry information during [`~DiffusionPipeline.from_pretrained`] requests.
+The data gathered includes the version of 🤗 Diffusers and PyTorch/Flax, the requested model or pipeline class,
+and the path to a pretrained checkpoint if it is hosted on the Hugging Face Hub.
 This usage data helps us debug issues and prioritize new features.
 Telemetry is only sent when loading models and pipelines from the Hub,
 and it is not collected if you're loading local files.
 
-Opt-out and disable telemetry collection with the [HF_HUB_DISABLE_TELEMETRY](https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfhubdisabletelemetry) environment variable.
+We understand that not everyone wants to share additional information,and we respect your privacy.
+You can disable telemetry collection by setting the `HF_HUB_DISABLE_TELEMETRY` environment variable from your terminal:
 
-<hfoptions id="telemetry">
-<hfoption id="Linux/macOS">
+On Linux/MacOS:
 
 ```bash
 export HF_HUB_DISABLE_TELEMETRY=1
 ```
 
-</hfoption>
-<hfoption id="Windows">
+On Windows:
 
 ```bash
 set HF_HUB_DISABLE_TELEMETRY=1
 ```
-
-</hfoption>
-</hfoptions>

docs/source/en/modular_diffusers/auto_pipeline_blocks.md

@@ -1,156 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# AutoPipelineBlocks
-
-[`~modular_pipelines.AutoPipelineBlocks`] are a multi-block type containing blocks that support different workflows. It automatically selects which sub-blocks to run based on the input provided at runtime. This is typically used to package multiple workflows - text-to-image, image-to-image, inpaint - into a single pipeline for convenience.
-
-This guide shows how to create [`~modular_pipelines.AutoPipelineBlocks`].
-
-Create three [`~modular_pipelines.ModularPipelineBlocks`] for text-to-image, image-to-image, and inpainting. These represent the different workflows available in the pipeline.
-
-<hfoptions id="auto">
-<hfoption id="text-to-image">
-
-```py
-import torch
-from diffusers.modular_pipelines import ModularPipelineBlocks, InputParam, OutputParam
-
-class TextToImageBlock(ModularPipelineBlocks):
-    model_name = "text2img"
-
-    @property
-    def inputs(self):
-        return [InputParam(name="prompt")]
-
-    @property
-    def intermediate_outputs(self):
-        return []
-
-    @property
-    def description(self):
-        return "I'm a text-to-image workflow!"
-
-    def __call__(self, components, state):
-        block_state = self.get_block_state(state)
-        print("running the text-to-image workflow")
-        # Add your text-to-image logic here
-        # For example: generate image from prompt
-        self.set_block_state(state, block_state)
-        return components, state
-```
-
-
-</hfoption>
-<hfoption id="image-to-image">
-
-```py
-class ImageToImageBlock(ModularPipelineBlocks):
-    model_name = "img2img"
-
-    @property
-    def inputs(self):
-        return [InputParam(name="prompt"), InputParam(name="image")]
-
-    @property
-    def intermediate_outputs(self):
-        return []
-
-    @property
-    def description(self):
-        return "I'm an image-to-image workflow!"
-
-    def __call__(self, components, state):
-        block_state = self.get_block_state(state)
-        print("running the image-to-image workflow")
-        # Add your image-to-image logic here
-        # For example: transform input image based on prompt
-        self.set_block_state(state, block_state)
-        return components, state
-```
-
-
-</hfoption>
-<hfoption id="inpaint">
-
-```py
-class InpaintBlock(ModularPipelineBlocks):
-    model_name = "inpaint"
-
-    @property
-    def inputs(self):
-        return [InputParam(name="prompt"), InputParam(name="image"), InputParam(name="mask")]
-
-    @property
-    def intermediate_outputs(self):
-        return []
-
-    @property
-    def description(self):
-        return "I'm an inpaint workflow!"
-
-    def __call__(self, components, state):
-        block_state = self.get_block_state(state)
-        print("running the inpaint workflow")
-        # Add your inpainting logic here
-        # For example: fill masked areas based on prompt
-        self.set_block_state(state, block_state)
-        return components, state
-```
-
-</hfoption>
-</hfoptions>
-
-Create an [`~modular_pipelines.AutoPipelineBlocks`] class that includes a list of the sub-block classes and their corresponding block names.
-
-You also need to include `block_trigger_inputs`, a list of input names that trigger the corresponding block. If a trigger input is provided at runtime, then that block is selected to run. Use `None` to specify the default block to run if no trigger inputs are detected.
-
-Lastly, it is important to include a `description` that clearly explains which inputs trigger which workflow. This helps users understand how to run specific workflows.
-
-```py
-from diffusers.modular_pipelines import AutoPipelineBlocks
-
-class AutoImageBlocks(AutoPipelineBlocks):
-    # List of sub-block classes to choose from
-    block_classes = [block_inpaint_cls, block_i2i_cls, block_t2i_cls]
-    # Names for each block in the same order
-    block_names = ["inpaint", "img2img", "text2img"]
-    # Trigger inputs that determine which block to run
-    # - "mask" triggers inpaint workflow
-    # - "image" triggers img2img workflow (but only if mask is not provided)
-    # - if none of above, runs the text2img workflow (default)
-    block_trigger_inputs = ["mask", "image", None]
-    # Description is extremely important for AutoPipelineBlocks
-
-    def description(self):
-        return (
-            "Pipeline generates images given different types of conditions!\n"
-            + "This is an auto pipeline block that works for text2img, img2img and inpainting tasks.\n"
-            + " - inpaint workflow is run when `mask` is provided.\n"
-            + " - img2img workflow is run when `image` is provided (but only when `mask` is not provided).\n"
-            + " - text2img workflow is run when neither `image` nor `mask` is provided.\n"
-        )
-```
-
-It is **very** important to include a `description` to avoid any confusion over how to run a block and what inputs are required. While [`~modular_pipelines.AutoPipelineBlocks`] are convenient, it's conditional logic may be difficult to figure out if it isn't properly explained.
-
-Create an instance of `AutoImageBlocks`.
-
-```py
-auto_blocks = AutoImageBlocks()
-```
-
-For more complex compositions, such as nested [`~modular_pipelines.AutoPipelineBlocks`] blocks when they're used as sub-blocks in larger pipelines, use the [`~modular_pipelines.SequentialPipelineBlocks.get_execution_blocks`] method to extract the a block that is actually run based on your input.
-
-```py
-auto_blocks.get_execution_blocks("mask")
-```

docs/source/en/modular_diffusers/components_manager.md

@@ -1,190 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# ComponentsManager
-
-The [`ComponentsManager`] is a model registry and management system for Modular Diffusers. It adds and tracks models, stores useful metadata (model size, device placement, adapters), prevents duplicate model instances, and supports offloading.
-
-This guide will show you how to use [`ComponentsManager`] to manage components and device memory.
-
-## Add a component
-
-The [`ComponentsManager`] should be created alongside a [`ModularPipeline`] in either [`~ModularPipeline.from_pretrained`] or [`~ModularPipelineBlocks.init_pipeline`].
-
-> [!TIP]
-> The `collection` parameter is optional but makes it easier to organize and manage components.
-
-<hfoptions id="create">
-<hfoption id="from_pretrained">
-
-```py
-from diffusers import ModularPipeline, ComponentsManager
-
-comp = ComponentsManager()
-pipe = ModularPipeline.from_pretrained("YiYiXu/modular-demo-auto", components_manager=comp, collection="test1")
-```
-
-</hfoption>
-<hfoption id="init_pipeline">
-
-```py
-from diffusers import ComponentsManager
-from diffusers.modular_pipelines import SequentialPipelineBlocks
-from diffusers.modular_pipelines.stable_diffusion_xl import TEXT2IMAGE_BLOCKS
-
-t2i_blocks = SequentialPipelineBlocks.from_blocks_dict(TEXT2IMAGE_BLOCKS)
-
-modular_repo_id = "YiYiXu/modular-loader-t2i-0704"
-components = ComponentsManager()
-t2i_pipeline = t2i_blocks.init_pipeline(modular_repo_id, components_manager=components)
-```
-
-</hfoption>
-</hfoptions>
-
-Components are only loaded and registered when using [`~ModularPipeline.load_components`] or [`~ModularPipeline.load_components`]. The example below uses [`~ModularPipeline.load_components`] to create a second pipeline that reuses all the components from the first one, and assigns it to a different collection
-
-```py
-pipe.load_components()
-pipe2 = ModularPipeline.from_pretrained("YiYiXu/modular-demo-auto", components_manager=comp, collection="test2")
-```
-
-Use the [`~ModularPipeline.null_component_names`] property to identify any components that need to be loaded, retrieve them with [`~ComponentsManager.get_components_by_names`], and then call [`~ModularPipeline.update_components`] to add the missing components.
-
-```py
-pipe2.null_component_names 
-['text_encoder', 'text_encoder_2', 'tokenizer', 'tokenizer_2', 'image_encoder', 'unet', 'vae', 'scheduler', 'controlnet']
-
-comp_dict = comp.get_components_by_names(names=pipe2.null_component_names)
-pipe2.update_components(**comp_dict)
-```
-
-To add individual components, use the [`~ComponentsManager.add`] method. This registers a component with a unique id.
-
-```py
-from diffusers import AutoModel
-
-text_encoder = AutoModel.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder")
-component_id = comp.add("text_encoder", text_encoder)
-comp
-```
-
-Use [`~ComponentsManager.remove`] to remove a component using their id.
-
-```py
-comp.remove("text_encoder_139917733042864")
-```
-
-## Retrieve a component
-
-The [`ComponentsManager`] provides several methods to retrieve registered components.
-
-### get_one
-
-The [`~ComponentsManager.get_one`] method returns a single component and supports pattern matching for the `name` parameter. If multiple components match, [`~ComponentsManager.get_one`] returns an error.
-
-| Pattern     | Example                          | Description                               |
-|-------------|----------------------------------|-------------------------------------------|
-| exact       | `comp.get_one(name="unet")`      | exact name match                          |
-| wildcard    | `comp.get_one(name="unet*")`     | names starting with "unet"                |
-| exclusion   | `comp.get_one(name="!unet")`     | exclude components named "unet"           |
-| or          | `comp.get_one(name="unet&#124;vae")`  | name is "unet" or "vae"                   |
-
-[`~ComponentsManager.get_one`] also filters components by the `collection` argument or `load_id` argument.
-
-```py
-comp.get_one(name="unet", collection="sdxl")
-```
-
-### get_components_by_names
-
-The [`~ComponentsManager.get_components_by_names`] method accepts a list of names and returns a dictionary mapping names to components. This is especially useful with [`ModularPipeline`] since they provide lists of required component names and the returned dictionary can be passed directly to [`~ModularPipeline.update_components`].
-
-```py
-component_dict = comp.get_components_by_names(names=["text_encoder", "unet", "vae"])
-{"text_encoder": component1, "unet": component2, "vae": component3}
-```
-
-## Duplicate detection
-
-It is recommended to load model components with [`ComponentSpec`] to assign components with a unique id that encodes their loading parameters. This allows [`ComponentsManager`] to automatically detect and prevent duplicate model instances even when different objects represent the same underlying checkpoint.
-
-```py
-from diffusers import ComponentSpec, ComponentsManager
-from transformers import CLIPTextModel
-
-comp = ComponentsManager()
-
-# Create ComponentSpec for the first text encoder
-spec = ComponentSpec(name="text_encoder", repo="stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder", type_hint=AutoModel)
-# Create ComponentSpec for a duplicate text encoder (it is same checkpoint, from the same repo/subfolder)
-spec_duplicated = ComponentSpec(name="text_encoder_duplicated", repo="stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder", type_hint=CLIPTextModel)
-
-# Load and add both components - the manager will detect they're the same model
-comp.add("text_encoder", spec.load())
-comp.add("text_encoder_duplicated", spec_duplicated.load())
-```
-
-This returns a warning with instructions for removing the duplicate.
-
-```py
-ComponentsManager: adding component 'text_encoder_duplicated_139917580682672', but it has duplicate load_id 'stabilityai/stable-diffusion-xl-base-1.0|text_encoder|null|null' with existing components: text_encoder_139918506246832. To remove a duplicate, call `components_manager.remove('<component_id>')`.
-'text_encoder_duplicated_139917580682672'
-```
-
-You could also add a component without using [`ComponentSpec`] and duplicate detection still works in most cases even if you're adding the same component under a different name.
-
-However, [`ComponentManager`] can't detect duplicates when you load the same component into different objects. In this case, you should load a model with [`ComponentSpec`].
-
-```py
-text_encoder_2 = AutoModel.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder")
-comp.add("text_encoder", text_encoder_2)
-'text_encoder_139917732983664'
-```
-
-## Collections
-
-Collections are labels assigned to components for better organization and management. Add a component to a collection with the `collection` argument in [`~ComponentsManager.add`].
-
-Only one component per name is allowed in each collection. Adding a second component with the same name automatically removes the first component.
-
-```py
-from diffusers import ComponentSpec, ComponentsManager
-
-comp = ComponentsManager()
-# Create ComponentSpec for the first UNet
-spec = ComponentSpec(name="unet", repo="stabilityai/stable-diffusion-xl-base-1.0", subfolder="unet", type_hint=AutoModel)
-# Create ComponentSpec for a different UNet
-spec2 = ComponentSpec(name="unet", repo="RunDiffusion/Juggernaut-XL-v9", subfolder="unet", type_hint=AutoModel, variant="fp16")
-
-# Add both UNets to the same collection - the second one will replace the first
-comp.add("unet", spec.load(), collection="sdxl")
-comp.add("unet", spec2.load(), collection="sdxl")
-```
-
-This makes it convenient to work with node-based systems because you can:
-
-- Mark all models as loaded from one node with the `collection` label.
-- Automatically replace models when new checkpoints are loaded under the same name.
-- Batch delete all models in a collection when a node is removed.
-
-## Offloading
-
-The [`~ComponentsManager.enable_auto_cpu_offload`] method is a global offloading strategy that works across all models regardless of which pipeline is using them. Once enabled, you don't need to worry about device placement if you add or remove components.
-
-```py
-comp.enable_auto_cpu_offload(device="cuda")
-```
-
-All models begin on the CPU and [`ComponentsManager`] moves them to the appropriate device right before they're needed, and moves other models back to the CPU when GPU memory is low.
-
-You can set your own rules for which models to offload first.

docs/source/en/modular_diffusers/guiders.md

@@ -1,175 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# Guiders
-
-[Classifier-free guidance](https://huggingface.co/papers/2207.12598) steers model generation that better match a prompt and is commonly used to improve generation quality, control, and adherence to prompts. There are different types of guidance methods, and in Diffusers, they are known as *guiders*. Like blocks, it is easy to switch and use different guiders for different use cases without rewriting the pipeline.
-
-This guide will show you how to switch guiders, adjust guider parameters, and load and share them to the Hub.
-
-## Switching guiders
-
-[`ClassifierFreeGuidance`] is the default guider and created when a pipeline is initialized with [`~ModularPipelineBlocks.init_pipeline`]. It is created by `from_config` which means it doesn't require loading specifications from a modular repository. A guider won't be listed in `modular_model_index.json`.
-
-Use [`~ModularPipeline.get_component_spec`] to inspect a guider.
-
-```py
-t2i_pipeline.get_component_spec("guider")
-ComponentSpec(name='guider', type_hint=<class 'diffusers.guiders.classifier_free_guidance.ClassifierFreeGuidance'>, description=None, config=FrozenDict([('guidance_scale', 7.5), ('guidance_rescale', 0.0), ('use_original_formulation', False), ('start', 0.0), ('stop', 1.0), ('_use_default_values', ['start', 'guidance_rescale', 'stop', 'use_original_formulation'])]), repo=None, subfolder=None, variant=None, revision=None, default_creation_method='from_config')
-```
-
-Switch to a different guider by passing the new guider to [`~ModularPipeline.update_components`].
-
-> [!TIP]
-> Changing guiders will return text letting you know you're changing the guider type.
-> ```bash
-> ModularPipeline.update_components: adding guider with new type: PerturbedAttentionGuidance, previous type: ClassifierFreeGuidance
-> ```
-
-```py
-from diffusers import LayerSkipConfig, PerturbedAttentionGuidance
-
-config = LayerSkipConfig(indices=[2, 9], fqn="mid_block.attentions.0.transformer_blocks", skip_attention=False, skip_attention_scores=True, skip_ff=False)
-guider = PerturbedAttentionGuidance(
-    guidance_scale=5.0, perturbed_guidance_scale=2.5, perturbed_guidance_config=config
-)
-t2i_pipeline.update_components(guider=guider)
-```
-
-Use [`~ModularPipeline.get_component_spec`] again to verify the guider type is different.
-
-```py
-t2i_pipeline.get_component_spec("guider")
-ComponentSpec(name='guider', type_hint=<class 'diffusers.guiders.perturbed_attention_guidance.PerturbedAttentionGuidance'>, description=None, config=FrozenDict([('guidance_scale', 5.0), ('perturbed_guidance_scale', 2.5), ('perturbed_guidance_start', 0.01), ('perturbed_guidance_stop', 0.2), ('perturbed_guidance_layers', None), ('perturbed_guidance_config', LayerSkipConfig(indices=[2, 9], fqn='mid_block.attentions.0.transformer_blocks', skip_attention=False, skip_attention_scores=True, skip_ff=False, dropout=1.0)), ('guidance_rescale', 0.0), ('use_original_formulation', False), ('start', 0.0), ('stop', 1.0), ('_use_default_values', ['perturbed_guidance_start', 'use_original_formulation', 'perturbed_guidance_layers', 'stop', 'start', 'guidance_rescale', 'perturbed_guidance_stop']), ('_class_name', 'PerturbedAttentionGuidance'), ('_diffusers_version', '0.35.0.dev0')]), repo=None, subfolder=None, variant=None, revision=None, default_creation_method='from_config')
-```
-
-## Loading custom guiders
-
-Guiders that are already saved on the Hub with a `modular_model_index.json` file are considered a `from_pretrained` component now instead of a `from_config` component.
-
-```json
-{
-  "guider": [
-    null,
-    null,
-    {
-      "repo": "YiYiXu/modular-loader-t2i-guider",
-      "revision": null,
-      "subfolder": "pag_guider",
-      "type_hint": [
-        "diffusers",
-        "PerturbedAttentionGuidance"
-      ],
-      "variant": null
-    }
-  ]
-}
-```
-
-The guider is only created after calling [`~ModularPipeline.load_components`] based on the loading specification in `modular_model_index.json`.
-
-```py
-t2i_pipeline = t2i_blocks.init_pipeline("YiYiXu/modular-doc-guider")
-# not created during init
-assert t2i_pipeline.guider is None
-t2i_pipeline.load_components()
-# loaded as PAG guider
-t2i_pipeline.guider
-```
-
-
-## Changing guider parameters
-
-The guider parameters can be adjusted with either the [`~ComponentSpec.create`] method or with [`~ModularPipeline.update_components`]. The example below changes the `guidance_scale` value.
-
-<hfoptions id="switch">
-<hfoption id="create">
-
-```py
-guider_spec = t2i_pipeline.get_component_spec("guider")
-guider = guider_spec.create(guidance_scale=10)
-t2i_pipeline.update_components(guider=guider)
-```
-
-</hfoption>
-<hfoption id="update_components">
-
-```py
-guider_spec = t2i_pipeline.get_component_spec("guider")
-guider_spec.config["guidance_scale"] = 10
-t2i_pipeline.update_components(guider=guider_spec)
-```
-
-</hfoption>
-</hfoptions>
-
-## Uploading custom guiders
-
-Call the [`~utils.PushToHubMixin.push_to_hub`] method on a custom guider to share it to the Hub.
-
-```py
-guider.push_to_hub("YiYiXu/modular-loader-t2i-guider", subfolder="pag_guider")
-```
-
-To make this guider available to the pipeline, either modify the `modular_model_index.json` file or use the [`~ModularPipeline.update_components`] method.
-
-<hfoptions id="upload">
-<hfoption id="modular_model_index.json">
-
-Edit the `modular_model_index.json` file and add a loading specification for the guider by pointing to a folder containing the guider config.
-
-```json
-{
-  "guider": [
-    "diffusers",
-    "PerturbedAttentionGuidance",
-    {
-      "repo": "YiYiXu/modular-loader-t2i-guider",
-      "revision": null,
-      "subfolder": "pag_guider",
-      "type_hint": [
-        "diffusers",
-        "PerturbedAttentionGuidance"
-      ],
-      "variant": null
-    }
-  ],
-```
-
-</hfoption>
-<hfoption id="update_components">
-
-Change the [`~ComponentSpec.default_creation_method`] to `from_pretrained` and use [`~ModularPipeline.update_components`] to update the guider and component specifications as well as the pipeline config.
-
-> [!TIP]
-> Changing the creation method will return text letting you know you're changing the creation type to `from_pretrained`.
-> ```bash
-> ModularPipeline.update_components: changing the default_creation_method of guider from from_config to from_pretrained.
-> ```
-
-```py
-guider_spec = t2i_pipeline.get_component_spec("guider")
-guider_spec.default_creation_method="from_pretrained"
-guider_spec.repo="YiYiXu/modular-loader-t2i-guider"
-guider_spec.subfolder="pag_guider"
-pag_guider = guider_spec.load()
-t2i_pipeline.update_components(guider=pag_guider)
-```
-
-To make it the default guider for a pipeline, call [`~utils.PushToHubMixin.push_to_hub`]. This is an optional step and not necessary if you are only experimenting locally.
-
-```py
-t2i_pipeline.push_to_hub("YiYiXu/modular-doc-guider")
-```
-
-</hfoption>
-</hfoptions>

docs/source/en/modular_diffusers/loop_sequential_pipeline_blocks.md

@@ -1,93 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# LoopSequentialPipelineBlocks
-
-[`~modular_pipelines.LoopSequentialPipelineBlocks`] are a multi-block type that composes other [`~modular_pipelines.ModularPipelineBlocks`] together in a loop. Data flows circularly, using `intermediate_inputs` and `intermediate_outputs`, and each block is run iteratively. This is typically used to create a denoising loop which is iterative by default.
-
-This guide shows you how to create [`~modular_pipelines.LoopSequentialPipelineBlocks`].
-
-## Loop wrapper
-
-[`~modular_pipelines.LoopSequentialPipelineBlocks`], is also known as the *loop wrapper* because it defines the loop structure, iteration variables, and configuration. Within the loop wrapper, you need the following variables.
-
-- `loop_inputs` are user provided values and equivalent to [`~modular_pipelines.ModularPipelineBlocks.inputs`].
-- `loop_intermediate_inputs` are intermediate variables from the [`~modular_pipelines.PipelineState`] and equivalent to [`~modular_pipelines.ModularPipelineBlocks.intermediate_inputs`].
-- `loop_intermediate_outputs` are new intermediate variables created by the block and added to the [`~modular_pipelines.PipelineState`]. It is equivalent to [`~modular_pipelines.ModularPipelineBlocks.intermediate_outputs`].
-- `__call__` method defines the loop structure and iteration logic.
-
-```py
-import torch
-from diffusers.modular_pipelines import LoopSequentialPipelineBlocks, ModularPipelineBlocks, InputParam, OutputParam
-
-class LoopWrapper(LoopSequentialPipelineBlocks):
-    model_name = "test"
-    @property
-    def description(self):
-        return "I'm a loop!!"
-    @property
-    def loop_inputs(self):
-        return [InputParam(name="num_steps")]
-    @torch.no_grad()
-    def __call__(self, components, state):
-        block_state = self.get_block_state(state)
-        # Loop structure - can be customized to your needs
-        for i in range(block_state.num_steps):
-            # loop_step executes all registered blocks in sequence
-            components, block_state = self.loop_step(components, block_state, i=i)
-        self.set_block_state(state, block_state)
-        return components, state
-```
-
-The loop wrapper can pass additional arguments, like current iteration index, to the loop blocks.
-
-## Loop blocks
-
-A loop block is a [`~modular_pipelines.ModularPipelineBlocks`], but the `__call__` method behaves differently.
-
-- It recieves the iteration variable from the loop wrapper.
-- It works directly with the [`~modular_pipelines.BlockState`] instead of the [`~modular_pipelines.PipelineState`].
-- It doesn't require retrieving or updating the [`~modular_pipelines.BlockState`].
-
-Loop blocks share the same [`~modular_pipelines.BlockState`] to allow values to accumulate and change for each iteration in the loop.
-
-```py
-class LoopBlock(ModularPipelineBlocks):
-    model_name = "test"
-    @property
-    def inputs(self):
-        return [InputParam(name="x")]
-    @property
-    def intermediate_outputs(self):
-        # outputs produced by this block
-        return [OutputParam(name="x")]
-    @property
-    def description(self):
-        return "I'm a block used inside the `LoopWrapper` class"
-    def __call__(self, components, block_state, i: int):
-        block_state.x += 1
-        return components, block_state
-```
-
-## LoopSequentialPipelineBlocks
-
-Use the [`~modular_pipelines.LoopSequentialPipelineBlocks.from_blocks_dict`] method to add the loop block to the loop wrapper to create [`~modular_pipelines.LoopSequentialPipelineBlocks`].
-
-```py
-loop = LoopWrapper.from_blocks_dict({"block1": LoopBlock})
-```
-
-Add more loop blocks to run within each iteration with [`~modular_pipelines.LoopSequentialPipelineBlocks.from_blocks_dict`]. This allows you to modify the blocks without changing the loop logic itself.
-
-```py
-loop = LoopWrapper.from_blocks_dict({"block1": LoopBlock(), "block2": LoopBlock})
-```

docs/source/en/modular_diffusers/modular_diffusers_states.md

@@ -1,75 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# States
-
-Blocks rely on the [`~modular_pipelines.PipelineState`] and [`~modular_pipelines.BlockState`] data structures for communicating and sharing data.
-
-| State | Description |
-|-------|-------------|
-| [`~modular_pipelines.PipelineState`] | Maintains the overall data required for a pipeline's execution and allows blocks to read and update its data. |
-| [`~modular_pipelines.BlockState`] | Allows each block to perform its computation with the necessary data from `inputs`|
-
-This guide explains how states work and how they connect blocks.
-
-## PipelineState
-
-The [`~modular_pipelines.PipelineState`] is a global state container for all blocks. It maintains the complete runtime state of the pipeline and provides a structured way for blocks to read from and write to shared data.
-
-There are two dict's in [`~modular_pipelines.PipelineState`] for structuring data.
-
-- The `values` dict is a **mutable** state containing a copy of user provided input values and intermediate output values generated by blocks. If a block modifies an `input`, it will be reflected in the `values` dict after calling `set_block_state`.
-
-```py
-PipelineState(
-  values={
-    'prompt': 'a cat'
-    'guidance_scale': 7.0
-    'num_inference_steps': 25
-    'prompt_embeds': Tensor(dtype=torch.float32, shape=torch.Size([1, 1, 1, 1]))
-    'negative_prompt_embeds': None
-  },
-)
-```
-
-## BlockState
-
-The [`~modular_pipelines.BlockState`] is a local view of the relevant variables an individual block needs from [`~modular_pipelines.PipelineState`] for performing it's computations.
-
-Access these variables directly as attributes like `block_state.image`.
-
-```py
-BlockState(
-    image: <PIL.Image.Image image mode=RGB size=512x512 at 0x7F3ECC494640>
-)
-```
-
-When a block's `__call__` method is executed, it retrieves the [`BlockState`] with `self.get_block_state(state)`, performs it's operations, and updates [`~modular_pipelines.PipelineState`] with `self.set_block_state(state, block_state)`.
-
-```py
-def __call__(self, components, state):
-    # retrieve BlockState
-    block_state = self.get_block_state(state)
-
-    # computation logic on inputs
-
-    # update PipelineState
-    self.set_block_state(state, block_state)
-    return components, state
-```
-
-## State interaction
-
-[`~modular_pipelines.PipelineState`] and [`~modular_pipelines.BlockState`] interaction is defined by a block's `inputs`, and `intermediate_outputs`.
-
-- `inputs`, a block can modify an input - like `block_state.image` - and this change can be propagated globally to [`~modular_pipelines.PipelineState`] by calling `set_block_state`.
-- `intermediate_outputs`, is a new variable that a block creates. It is added to the [`~modular_pipelines.PipelineState`]'s `values` dict and is available as for subsequent blocks or accessed by users as a final output from the pipeline.

docs/source/en/modular_diffusers/modular_pipeline.md

@@ -1,358 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# ModularPipeline
-
-[`ModularPipeline`] converts [`~modular_pipelines.ModularPipelineBlocks`]'s into an executable pipeline that loads models and performs the computation steps defined in the block. It is the main interface for running a pipeline and it is very similar to the [`DiffusionPipeline`] API.
-
-The main difference is to include an expected `output` argument in the pipeline.
-
-<hfoptions id="example">
-<hfoption id="text-to-image">
-
-```py
-import torch
-from diffusers.modular_pipelines import SequentialPipelineBlocks
-from diffusers.modular_pipelines.stable_diffusion_xl import TEXT2IMAGE_BLOCKS
-
-blocks = SequentialPipelineBlocks.from_blocks_dict(TEXT2IMAGE_BLOCKS)
-
-modular_repo_id = "YiYiXu/modular-loader-t2i-0704"
-pipeline = blocks.init_pipeline(modular_repo_id)
-
-pipeline.load_components(torch_dtype=torch.float16)
-pipeline.to("cuda")
-
-image = pipeline(prompt="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", output="images")[0]
-image.save("modular_t2i_out.png")
-```
-
-</hfoption>
-<hfoption id="image-to-image">
-
-```py
-import torch
-from diffusers.modular_pipelines import SequentialPipelineBlocks
-from diffusers.modular_pipelines.stable_diffusion_xl import IMAGE2IMAGE_BLOCKS
-
-blocks = SequentialPipelineBlocks.from_blocks_dict(IMAGE2IMAGE_BLOCKS)
-
-modular_repo_id = "YiYiXu/modular-loader-t2i-0704"
-pipeline = blocks.init_pipeline(modular_repo_id)
-
-pipeline.load_components(torch_dtype=torch.float16)
-pipeline.to("cuda")
-
-url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png"
-init_image = load_image(url)
-prompt = "a dog catching a frisbee in the jungle"
-image = pipeline(prompt=prompt, image=init_image, strength=0.8, output="images")[0]
-image.save("modular_i2i_out.png")
-```
-
-</hfoption>
-<hfoption id="inpainting">
-
-```py
-import torch
-from diffusers.modular_pipelines import SequentialPipelineBlocks
-from diffusers.modular_pipelines.stable_diffusion_xl import INPAINT_BLOCKS
-from diffusers.utils import load_image
-
-blocks = SequentialPipelineBlocks.from_blocks_dict(INPAINT_BLOCKS)
-
-modular_repo_id = "YiYiXu/modular-loader-t2i-0704"
-pipeline = blocks.init_pipeline(modular_repo_id)
-
-pipeline.load_components(torch_dtype=torch.float16)
-pipeline.to("cuda")
-
-img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png"
-mask_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-inpaint-mask.png"
-
-init_image = load_image(img_url)
-mask_image = load_image(mask_url)
-
-prompt = "A deep sea diver floating"
-image = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, strength=0.85, output="images")[0]
-image.save("moduar_inpaint_out.png")
-```
-
-</hfoption>
-</hfoptions>
-
-This guide will show you how to create a [`ModularPipeline`] and manage the components in it.
-
-## Adding blocks
-
-Blocks are [`InsertableDict`] objects that can be inserted at specific positions, providing a flexible way to mix-and-match blocks.
-
-Use [`~modular_pipelines.modular_pipeline_utils.InsertableDict.insert`] on either the block class or `sub_blocks` attribute to add a block.
-
-```py
-# BLOCKS is dict of block classes, you need to add class to it
-BLOCKS.insert("block_name", BlockClass, index)
-# sub_blocks attribute contains instance, add a block instance to the  attribute
-t2i_blocks.sub_blocks.insert("block_name", block_instance, index)
-```
-
-Use [`~modular_pipelines.modular_pipeline_utils.InsertableDict.pop`] on either the block class or `sub_blocks` attribute to remove a block.
-
-```py
-# remove a block class from preset
-BLOCKS.pop("text_encoder")
-# split out a block instance on its own
-text_encoder_block = t2i_blocks.sub_blocks.pop("text_encoder")
-```
-
-Swap blocks by setting the existing block to the new block.
-
-```py
-# Replace block class in preset
-BLOCKS["prepare_latents"] = CustomPrepareLatents
-# Replace in sub_blocks attribute using an block instance
-t2i_blocks.sub_blocks["prepare_latents"] = CustomPrepareLatents()
-```
-
-## Creating a pipeline
-
-There are two ways to create a [`ModularPipeline`]. Assemble and create a pipeline from [`ModularPipelineBlocks`] or load an existing pipeline with [`~ModularPipeline.from_pretrained`].
-
-You should also initialize a [`ComponentsManager`] to handle device placement and memory and component management.
-
-> [!TIP]
-> Refer to the [ComponentsManager](./components_manager) doc for more details about how it can help manage components across different workflows.
-
-<hfoptions id="create">
-<hfoption id="ModularPipelineBlocks">
-
-Use the [`~ModularPipelineBlocks.init_pipeline`] method to create a [`ModularPipeline`] from the component and configuration specifications. This method loads the *specifications* from a `modular_model_index.json` file, but it doesn't load the *models* yet.
-
-```py
-from diffusers import ComponentsManager
-from diffusers.modular_pipelines import SequentialPipelineBlocks
-from diffusers.modular_pipelines.stable_diffusion_xl import TEXT2IMAGE_BLOCKS
-
-t2i_blocks = SequentialPipelineBlocks.from_blocks_dict(TEXT2IMAGE_BLOCKS)
-
-modular_repo_id = "YiYiXu/modular-loader-t2i-0704"
-components = ComponentsManager()
-t2i_pipeline = t2i_blocks.init_pipeline(modular_repo_id, components_manager=components)
-```
-
-</hfoption>
-<hfoption id="from_pretrained">
-
-The [`~ModularPipeline.from_pretrained`] method creates a [`ModularPipeline`] from a modular repository on the Hub.
-
-```py
-from diffusers import ModularPipeline, ComponentsManager
-
-components = ComponentsManager()
-pipeline = ModularPipeline.from_pretrained("YiYiXu/modular-loader-t2i-0704", components_manager=components)
-```
-
-Add the `trust_remote_code` argument to load a custom [`ModularPipeline`].
-
-```py
-from diffusers import ModularPipeline, ComponentsManager
-
-components = ComponentsManager()
-modular_repo_id = "YiYiXu/modular-diffdiff-0704"
-diffdiff_pipeline = ModularPipeline.from_pretrained(modular_repo_id, trust_remote_code=True, components_manager=components)
-```
-
-</hfoption>
-</hfoptions>
-
-## Loading components
-
-A [`ModularPipeline`] doesn't automatically instantiate with components. It only loads the configuration and component specifications. You can load all components with [`~ModularPipeline.load_components`] or only load specific components with [`~ModularPipeline.load_components`].
-
-<hfoptions id="load">
-<hfoption id="load_components">
-
-```py
-import torch
-
-t2i_pipeline.load_components(torch_dtype=torch.float16)
-t2i_pipeline.to("cuda")
-```
-
-</hfoption>
-<hfoption id="load_components">
-
-The example below only loads the UNet and VAE.
-
-```py
-import torch
-
-t2i_pipeline.load_components(names=["unet", "vae"], torch_dtype=torch.float16)
-```
-
-</hfoption>
-</hfoptions>
-
-Print the pipeline to inspect the loaded pretrained components.
-
-```py
-t2i_pipeline
-```
-
-This should match the `modular_model_index.json` file from the modular repository a pipeline is initialized from. If a pipeline doesn't need a component, it won't be included even if it exists in the modular repository.
-
-To modify where components are loaded from, edit the `modular_model_index.json` file in the repository and change it to your desired loading path. The example below loads a UNet from a different repository.
-
-```json
-# original
-"unet": [
-  null, null,
-  {
-    "repo": "stabilityai/stable-diffusion-xl-base-1.0",
-    "subfolder": "unet",
-    "variant": "fp16"
-  }
-]
-
-# modified
-"unet": [
-  null, null,
-  {
-    "repo": "RunDiffusion/Juggernaut-XL-v9",
-    "subfolder": "unet",
-    "variant": "fp16"
-  }
-]
-```
-
-### Component loading status
-
-The pipeline properties below provide more information about which components are loaded.
-
-Use `component_names` to return all expected components.
-
-```py
-t2i_pipeline.component_names
-['text_encoder', 'text_encoder_2', 'tokenizer', 'tokenizer_2', 'guider', 'scheduler', 'unet', 'vae', 'image_processor']
-```
-
-Use `null_component_names` to return components that aren't loaded yet. Load these components with [`~ModularPipeline.from_pretrained`].
-
-```py
-t2i_pipeline.null_component_names
-['text_encoder', 'text_encoder_2', 'tokenizer', 'tokenizer_2', 'scheduler']
-```
-
-Use `pretrained_component_names` to return components that will be loaded from pretrained models.
-
-```py
-t2i_pipeline.pretrained_component_names
-['text_encoder', 'text_encoder_2', 'tokenizer', 'tokenizer_2', 'scheduler', 'unet', 'vae']
-```
-
-Use `config_component_names` to return components that are created with the default config (not loaded from a modular repository). Components from a config aren't included because they are already initialized during pipeline creation. This is why they aren't listed in `null_component_names`.
-
-```py
-t2i_pipeline.config_component_names
-['guider', 'image_processor']
-```
-
-## Updating components
-
-Components may be updated depending on whether it is a *pretrained component* or a *config component*.
-
-> [!WARNING]
-> A component may change from pretrained to config when updating a component. The component type is initially defined in a block's `expected_components` field.
-
-A pretrained component is updated with [`ComponentSpec`] whereas a config component is updated by eihter passing the object directly or with [`ComponentSpec`].
-
-The [`ComponentSpec`] shows `default_creation_method="from_pretrained"` for a pretrained component shows `default_creation_method="from_config` for a config component.
-
-To update a pretrained component, create a [`ComponentSpec`] with the name of the component and where to load it from. Use the [`~ComponentSpec.load`] method to load the component.
-
-```py
-from diffusers import ComponentSpec, UNet2DConditionModel
-
-unet_spec = ComponentSpec(name="unet",type_hint=UNet2DConditionModel, repo="stabilityai/stable-diffusion-xl-base-1.0", subfolder="unet", variant="fp16")
-unet = unet_spec.load(torch_dtype=torch.float16)
-```
-
-The [`~ModularPipeline.update_components`] method replaces the component with a new one.
-
-```py
-t2i_pipeline.update_components(unet=unet2)
-```
-
-When a component is updated, the loading specifications are also updated in the pipeline config.
-
-### Component extraction and modification
-
-When you use [`~ComponentSpec.load`], the new component maintains its loading specifications. This makes it possible to extract the specification and recreate the component.
-
-```py
-spec = ComponentSpec.from_component("unet", unet2)
-spec
-ComponentSpec(name='unet', type_hint=<class 'diffusers.models.unets.unet_2d_condition.UNet2DConditionModel'>, description=None, config=None, repo='stabilityai/stable-diffusion-xl-base-1.0', subfolder='unet', variant='fp16', revision=None, default_creation_method='from_pretrained')
-unet2_recreated = spec.load(torch_dtype=torch.float16)
-```
-
-The [`~ModularPipeline.get_component_spec`] method gets a copy of the current component specification to modify or update.
-
-```py
-unet_spec = t2i_pipeline.get_component_spec("unet")
-unet_spec
-ComponentSpec(
-    name='unet',
-    type_hint=<class 'diffusers.models.unets.unet_2d_condition.UNet2DConditionModel'>,
-    repo='RunDiffusion/Juggernaut-XL-v9',
-    subfolder='unet',
-    variant='fp16',
-    default_creation_method='from_pretrained'
-)
-
-# modify to load from a different repository
-unet_spec.repo = "stabilityai/stable-diffusion-xl-base-1.0"
-
-# load component with modified spec
-unet = unet_spec.load(torch_dtype=torch.float16)
-```
-
-## Modular repository
-
-A repository is required if the pipeline blocks use *pretrained components*. The repository supplies loading specifications and metadata.
-
-[`ModularPipeline`] specifically requires *modular repositories* (see [example repository](https://huggingface.co/YiYiXu/modular-diffdiff)) which are more flexible than a typical repository. It contains a `modular_model_index.json` file containing the following 3 elements.
-
-- `library` and `class` shows which library the component was loaded from and it's class. If `null`, the component hasn't been loaded yet.
-- `loading_specs_dict` contains the information required to load the component such as the repository and subfolder it is loaded from.
-
-Unlike standard repositories, a modular repository can fetch components from different repositories based on the `loading_specs_dict`. Components don't need to exist in the same repository.
-
-A modular repository may contain custom code for loading a [`ModularPipeline`]. This allows you to use specialized blocks that aren't native to Diffusers.
-
-```
-modular-diffdiff-0704/
-├── block.py                    # Custom pipeline blocks implementation
-├── config.json                 # Pipeline configuration and auto_map
-└── modular_model_index.json    # Component loading specifications
-```
-
-The [config.json](https://huggingface.co/YiYiXu/modular-diffdiff-0704/blob/main/config.json) file contains an `auto_map` key that points to where a custom block is defined in `block.py`.
-
-```json
-{
-  "_class_name": "DiffDiffBlocks",
-  "auto_map": {
-    "ModularPipelineBlocks": "block.DiffDiffBlocks"
-  }
-}
-```

docs/source/en/modular_diffusers/overview.md

@@ -1,41 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# Overview
-
-> [!WARNING]
-> Modular Diffusers is under active development and it's API may change.
-
-Modular Diffusers is a unified pipeline system that simplifies your workflow with *pipeline blocks*.
-
-- Blocks are reusable and you only need to create new blocks that are unique to your pipeline.
-- Blocks can be mixed and matched to adapt to or create a pipeline for a specific workflow or multiple workflows.
-
-The Modular Diffusers docs are organized as shown below.
-
-## Quickstart
-
-- A [quickstart](./quickstart) demonstrating how to implement an example workflow with Modular Diffusers.
-
-## ModularPipelineBlocks
-
-- [States](./modular_diffusers_states) explains how data is shared and communicated between blocks and [`ModularPipeline`].
-- [ModularPipelineBlocks](./pipeline_block) is the most basic unit of a [`ModularPipeline`] and this guide shows you how to create one.
-- [SequentialPipelineBlocks](./sequential_pipeline_blocks) is a type of block that chains multiple blocks so they run one after another, passing data along the chain. This guide shows you how to create [`~modular_pipelines.SequentialPipelineBlocks`] and how they connect and work together.
-- [LoopSequentialPipelineBlocks](./loop_sequential_pipeline_blocks) is a type of block that runs a series of blocks in a loop. This guide shows you how to create [`~modular_pipelines.LoopSequentialPipelineBlocks`].
-- [AutoPipelineBlocks](./auto_pipeline_blocks) is a type of block that automatically chooses which blocks to run based on the input. This guide shows you how to create [`~modular_pipelines.AutoPipelineBlocks`].
-
-## ModularPipeline
-
-- [ModularPipeline](./modular_pipeline) shows you how to create and convert pipeline blocks into an executable [`ModularPipeline`].
-- [ComponentsManager](./components_manager) shows you how to manage and reuse components across multiple pipelines.
-- [Guiders](./guiders) shows you how to use different guidance methods in the pipeline.

docs/source/en/modular_diffusers/pipeline_block.md

@@ -1,115 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# ModularPipelineBlocks
-
-[`~modular_pipelines.ModularPipelineBlocks`] is the basic block for building a [`ModularPipeline`]. It defines what components, inputs/outputs, and computation a block should perform for a specific step in a pipeline. A [`~modular_pipelines.ModularPipelineBlocks`] connects with other blocks, using [state](./modular_diffusers_states), to enable the modular construction of workflows.
-
-A [`~modular_pipelines.ModularPipelineBlocks`] on it's own can't be executed. It is a blueprint for what a step should do in a pipeline. To actually run and execute a pipeline, the [`~modular_pipelines.ModularPipelineBlocks`] needs to be converted into a [`ModularPipeline`].
-
-This guide will show you how to create a [`~modular_pipelines.ModularPipelineBlocks`].
-
-## Inputs and outputs
-
-> [!TIP]
-> Refer to the [States](./modular_diffusers_states) guide if you aren't familiar with how state works in Modular Diffusers.
-
-A [`~modular_pipelines.ModularPipelineBlocks`] requires `inputs`, and `intermediate_outputs`.
-
-- `inputs` are values provided by a user and retrieved from the [`~modular_pipelines.PipelineState`]. This is useful because some workflows resize an image, but the original image is still required. The [`~modular_pipelines.PipelineState`] maintains the original image.
-
-    Use `InputParam` to define `inputs`.
-
-    ```py
-    from diffusers.modular_pipelines import InputParam
-
-    user_inputs = [
-        InputParam(name="image", type_hint="PIL.Image", description="raw input image to process")
-    ]
-    ```
-
-- `intermediate_inputs` are values typically created from a previous block but it can also be directly provided if no preceding block generates them. Unlike `inputs`, `intermediate_inputs` can be modified.
-
-    Use `InputParam` to define `intermediate_inputs`.
-
-    ```py
-    user_intermediate_inputs = [
-        InputParam(name="processed_image", type_hint="torch.Tensor", description="image that has been preprocessed and normalized"),
-    ]
-    ```
-
-- `intermediate_outputs` are new values created by a block and added to the [`~modular_pipelines.PipelineState`]. The `intermediate_outputs` are available as `intermediate_inputs` for subsequent blocks or available as the final output from running the pipeline.
-
-    Use `OutputParam` to define `intermediate_outputs`.
-
-    ```py
-    from diffusers.modular_pipelines import OutputParam
-
-        user_intermediate_outputs = [
-        OutputParam(name="image_latents", description="latents representing the image")
-    ]
-    ```
-
-The intermediate inputs and outputs share data to connect blocks. They are accessible at any point, allowing you to track the workflow's progress.
-
-## Computation logic
-
-The computation a block performs is defined in the `__call__` method and it follows a specific structure.
-
-1. Retrieve the [`~modular_pipelines.BlockState`] to get a local view of the `inputs` and `intermediate_inputs`.
-2. Implement the computation logic on the `inputs` and `intermediate_inputs`.
-3. Update [`~modular_pipelines.PipelineState`] to push changes from the local [`~modular_pipelines.BlockState`] back to the global [`~modular_pipelines.PipelineState`].
-4. Return the components and state which becomes available to the next block.
-
-```py
-def __call__(self, components, state):
-    # Get a local view of the state variables this block needs
-    block_state = self.get_block_state(state)
-
-    # Your computation logic here
-    # block_state contains all your inputs and intermediate_inputs
-    # Access them like: block_state.image, block_state.processed_image
-
-    # Update the pipeline state with your updated block_states
-    self.set_block_state(state, block_state)
-    return components, state
-```
-
-### Components and configs
-
-The components and pipeline-level configs a block needs are specified in [`ComponentSpec`] and [`~modular_pipelines.ConfigSpec`].
-
-- [`ComponentSpec`] contains the expected components used by a block. You need the `name` of the component and ideally a `type_hint` that specifies exactly what the component is.
-- [`~modular_pipelines.ConfigSpec`] contains pipeline-level settings that control behavior across all blocks.
-
-```py
-from diffusers import ComponentSpec, ConfigSpec
-
-expected_components = [
-    ComponentSpec(name="unet", type_hint=UNet2DConditionModel),
-    ComponentSpec(name="scheduler", type_hint=EulerDiscreteScheduler)
-]
-
-expected_config = [
-    ConfigSpec("force_zeros_for_empty_prompt", True)
-]
-```
-
-When the blocks are converted into a pipeline, the components become available to the block as the first argument in `__call__`.
-
-```py
-def __call__(self, components, state):
-    # Access components using dot notation
-    unet = components.unet
-    vae = components.vae
-    scheduler = components.scheduler
-```

docs/source/en/modular_diffusers/quickstart.md

@@ -1,344 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# Quickstart
-
-Modular Diffusers is a framework for quickly building flexible and customizable pipelines. At the core of Modular Diffusers are [`ModularPipelineBlocks`] that can be combined with other blocks to adapt to new workflows. The blocks are converted into a [`ModularPipeline`], a friendly user-facing interface developers can use.
-
-This doc will show you how to implement a [Differential Diffusion](https://differential-diffusion.github.io/) pipeline with the modular framework.
-
-## ModularPipelineBlocks
-
-[`ModularPipelineBlocks`] are *definitions* that specify the components, inputs, outputs, and computation logic for a single step in a pipeline. There are four types of blocks.
-
-- [`ModularPipelineBlocks`] is the most basic block for a single step.
-- [`SequentialPipelineBlocks`] is a multi-block that composes other blocks linearly. The outputs of one block are the inputs to the next block.
-- [`LoopSequentialPipelineBlocks`] is a multi-block that runs iteratively and is designed for iterative workflows.
-- [`AutoPipelineBlocks`] is a collection of blocks for different workflows and it selects which block to run based on the input. It is designed to conveniently package multiple workflows into a single pipeline.
-
-[Differential Diffusion](https://differential-diffusion.github.io/) is an image-to-image workflow. Start with the `IMAGE2IMAGE_BLOCKS` preset, a collection of `ModularPipelineBlocks` for image-to-image generation.
-
-```py
-from diffusers.modular_pipelines.stable_diffusion_xl import IMAGE2IMAGE_BLOCKS
-IMAGE2IMAGE_BLOCKS = InsertableDict([
-    ("text_encoder", StableDiffusionXLTextEncoderStep),
-    ("image_encoder", StableDiffusionXLVaeEncoderStep),
-    ("input", StableDiffusionXLInputStep),
-    ("set_timesteps", StableDiffusionXLImg2ImgSetTimestepsStep),
-    ("prepare_latents", StableDiffusionXLImg2ImgPrepareLatentsStep),
-    ("prepare_add_cond", StableDiffusionXLImg2ImgPrepareAdditionalConditioningStep),
-    ("denoise", StableDiffusionXLDenoiseStep),
-    ("decode", StableDiffusionXLDecodeStep)
-])
-```
-
-## Pipeline and block states
-
-Modular Diffusers uses *state* to communicate data between blocks. There are two types of states.
-
-- [`PipelineState`] is a global state that can be used to track all inputs and outputs across all blocks.
-- [`BlockState`] is a local view of relevant variables from [`PipelineState`] for an individual block.
-
-## Customizing blocks
-
-[Differential Diffusion](https://differential-diffusion.github.io/) differs from standard image-to-image in its `prepare_latents` and `denoise` blocks. All the other blocks can be reused, but you'll need to modify these two.
-
-Create placeholder `ModularPipelineBlocks` for `prepare_latents` and `denoise` by copying and modifying the existing ones.
-
-Print the `denoise` block to see that it is composed of [`LoopSequentialPipelineBlocks`] with three sub-blocks, `before_denoiser`, `denoiser`, and `after_denoiser`. Only the `before_denoiser` sub-block needs to be modified to prepare the latent input for the denoiser based on the change map.
-
-```py
-denoise_blocks = IMAGE2IMAGE_BLOCKS["denoise"]()
-print(denoise_blocks)
-```
-
-Replace the `StableDiffusionXLLoopBeforeDenoiser` sub-block with the new `SDXLDiffDiffLoopBeforeDenoiser` block.
-
-```py
-# Copy existing blocks as placeholders
-class SDXLDiffDiffPrepareLatentsStep(ModularPipelineBlocks):
-    """Copied from StableDiffusionXLImg2ImgPrepareLatentsStep - will modify later"""
-    # ... same implementation as StableDiffusionXLImg2ImgPrepareLatentsStep
-
-class SDXLDiffDiffDenoiseStep(StableDiffusionXLDenoiseLoopWrapper):
-    block_classes = [SDXLDiffDiffLoopBeforeDenoiser, StableDiffusionXLLoopDenoiser, StableDiffusionXLLoopAfterDenoiser]
-    block_names = ["before_denoiser", "denoiser", "after_denoiser"]
-```
-
-### prepare_latents
-
-The `prepare_latents` block requires the following changes.
-
-- a processor to process the change map
-- a new `inputs` to accept the user-provided change map, `timestep` for precomputing all the latents and `num_inference_steps` to create the mask for updating the image regions
-- update the computation in the `__call__` method for processing the change map and creating the masks, and storing it in the [`BlockState`]
-
-```diff
-class SDXLDiffDiffPrepareLatentsStep(ModularPipelineBlocks):
-    @property
-    def expected_components(self) -> List[ComponentSpec]:
-        return [
-            ComponentSpec("vae", AutoencoderKL),
-            ComponentSpec("scheduler", EulerDiscreteScheduler),
-+           ComponentSpec("mask_processor", VaeImageProcessor, config=FrozenDict({"do_normalize": False, "do_convert_grayscale": True}))
-        ]
-    @property
-    def inputs(self) -> List[Tuple[str, Any]]:
-        return [
-            InputParam("generator"),
-+           InputParam("diffdiff_map", required=True),
--           InputParam("latent_timestep", required=True, type_hint=torch.Tensor),
-+           InputParam("timesteps", type_hint=torch.Tensor),
-+           InputParam("num_inference_steps", type_hint=int),
-        ]
-
-    @property
-    def intermediate_outputs(self) -> List[OutputParam]:
-        return [
-+           OutputParam("original_latents", type_hint=torch.Tensor),
-+           OutputParam("diffdiff_masks", type_hint=torch.Tensor),
-        ]
-    def __call__(self, components, state: PipelineState):
-        # ... existing logic ...
-+       # Process change map and create masks
-+       diffdiff_map = components.mask_processor.preprocess(block_state.diffdiff_map, height=latent_height, width=latent_width)
-+       thresholds = torch.arange(block_state.num_inference_steps, dtype=diffdiff_map.dtype) / block_state.num_inference_steps
-+       block_state.diffdiff_masks = diffdiff_map > (thresholds + (block_state.denoising_start or 0))
-+       block_state.original_latents = block_state.latents
-```
-
-### denoise
-
-The `before_denoiser` sub-block requires the following changes.
-
-- a new `inputs` to accept a `denoising_start` parameter,  `original_latents` and `diffdiff_masks` from the `prepare_latents` block
-- update the computation in the `__call__` method for applying Differential Diffusion
-
-```diff
-class SDXLDiffDiffLoopBeforeDenoiser(ModularPipelineBlocks):
-    @property
-    def description(self) -> str:
-        return (
-            "Step within the denoising loop for differential diffusion that prepare the latent input for the denoiser"
-        )
-
-    @property
-    def inputs(self) -> List[str]:
-        return [
-            InputParam("latents", required=True, type_hint=torch.Tensor),
-+           InputParam("denoising_start"),
-+           InputParam("original_latents", type_hint=torch.Tensor),
-+           InputParam("diffdiff_masks", type_hint=torch.Tensor),
-        ]
-
-    def __call__(self, components, block_state, i, t):
-+       # Apply differential diffusion logic
-+       if i == 0 and block_state.denoising_start is None:
-+           block_state.latents = block_state.original_latents[:1]
-+       else:
-+           block_state.mask = block_state.diffdiff_masks[i].unsqueeze(0).unsqueeze(1)
-+           block_state.latents = block_state.original_latents[i] * block_state.mask + block_state.latents * (1 - block_state.mask)
-
-        # ... rest of existing logic ...
-```
-
-## Assembling the blocks
-
-You should have all the blocks you need at this point to create a [`ModularPipeline`].
-
-Copy the existing `IMAGE2IMAGE_BLOCKS` preset and for the `set_timesteps` block, use the `set_timesteps` from the `TEXT2IMAGE_BLOCKS` because Differential Diffusion doesn't require a `strength` parameter.
-
-Set the `prepare_latents` and `denoise` blocks to the `SDXLDiffDiffPrepareLatentsStep` and `SDXLDiffDiffDenoiseStep` blocks you just modified.
-
-Call [`SequentialPipelineBlocks.from_blocks_dict`] on the blocks to create a `SequentialPipelineBlocks`.
-
-```py
-DIFFDIFF_BLOCKS = IMAGE2IMAGE_BLOCKS.copy()
-DIFFDIFF_BLOCKS["set_timesteps"] = TEXT2IMAGE_BLOCKS["set_timesteps"]
-DIFFDIFF_BLOCKS["prepare_latents"] = SDXLDiffDiffPrepareLatentsStep
-DIFFDIFF_BLOCKS["denoise"] = SDXLDiffDiffDenoiseStep
-
-dd_blocks = SequentialPipelineBlocks.from_blocks_dict(DIFFDIFF_BLOCKS)
-print(dd_blocks)
-```
-
-## ModularPipeline
-
-Convert the [`SequentialPipelineBlocks`] into a [`ModularPipeline`] with the [`ModularPipeline.init_pipeline`] method. This initializes the expected components to load from a `modular_model_index.json` file. Explicitly load the components by calling [`ModularPipeline.load_components`].
-
-It is a good idea to initialize the [`ComponentManager`] with the pipeline to help manage the different components. Once you call [`~ModularPipeline.load_components`], the components are registered to the [`ComponentManager`] and can be shared between workflows. The example below uses the `collection` argument to assign the components a `"diffdiff"` label for better organization.
-
-```py
-from diffusers.modular_pipelines import ComponentsManager
-
-components = ComponentManager()
-
-dd_pipeline = dd_blocks.init_pipeline("YiYiXu/modular-demo-auto", components_manager=components, collection="diffdiff")
-dd_pipeline.load_default_componenets(torch_dtype=torch.float16)
-dd_pipeline.to("cuda")
-```
-
-## Adding workflows
-
-Other workflows can be added to the [`ModularPipeline`] to support additional features without rewriting the entire pipeline from scratch.
-
-This section demonstrates how to add an IP-Adapter or ControlNet.
-
-### IP-Adapter
-
-Stable Diffusion XL already has a preset IP-Adapter block that you can use and doesn't require any changes to the existing Differential Diffusion pipeline.
-
-```py
-from diffusers.modular_pipelines.stable_diffusion_xl.encoders import StableDiffusionXLAutoIPAdapterStep
-
-ip_adapter_block = StableDiffusionXLAutoIPAdapterStep()
-```
-
-Use the [`sub_blocks.insert`] method to insert it into the [`ModularPipeline`]. The example below inserts the `ip_adapter_block` at position `0`. Print the pipeline to see that the `ip_adapter_block` is added and it requires an `ip_adapter_image`. This also added two components to the pipeline, the `image_encoder` and `feature_extractor`.
-
-```py
-dd_blocks.sub_blocks.insert("ip_adapter", ip_adapter_block, 0)
-```
-
-Call [`~ModularPipeline.init_pipeline`] to initialize a [`ModularPipeline`] and use [`~ModularPipeline.load_components`] to load the model components. Load and set the IP-Adapter to run the pipeline.
-
-```py
-dd_pipeline = dd_blocks.init_pipeline("YiYiXu/modular-demo-auto", collection="diffdiff")
-dd_pipeline.load_components(torch_dtype=torch.float16)
-dd_pipeline.loader.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
-dd_pipeline.loader.set_ip_adapter_scale(0.6)
-dd_pipeline = dd_pipeline.to(device)
-
-ip_adapter_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/diffdiff_orange.jpeg")
-image = load_image("https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/20240329211129_4024911930.png?download=true")
-mask = load_image("https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/gradient_mask.png?download=true")
-
-prompt = "a green pear"
-negative_prompt = "blurry"
-generator = torch.Generator(device=device).manual_seed(42)
-
-image = dd_pipeline(
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    num_inference_steps=25,
-    generator=generator,
-    ip_adapter_image=ip_adapter_image,
-    diffdiff_map=mask,
-    image=image,
-    output="images"
-)[0]
-```
-
-### ControlNet
-
-Stable Diffusion XL already has a preset ControlNet block that can readily be used.
-
-```py
-from diffusers.modular_pipelines.stable_diffusion_xl.modular_blocks import StableDiffusionXLAutoControlNetInputStep
-
-control_input_block = StableDiffusionXLAutoControlNetInputStep()
-```
-
-However, it requires modifying the `denoise` block because that's where the ControlNet injects the control information into the UNet.
-
-Modify the `denoise` block by replacing the `StableDiffusionXLLoopDenoiser` sub-block with the `StableDiffusionXLControlNetLoopDenoiser`.
-
-```py
-class SDXLDiffDiffControlNetDenoiseStep(StableDiffusionXLDenoiseLoopWrapper):
-    block_classes = [SDXLDiffDiffLoopBeforeDenoiser, StableDiffusionXLControlNetLoopDenoiser, StableDiffusionXLDenoiseLoopAfterDenoiser]
-    block_names = ["before_denoiser", "denoiser", "after_denoiser"]
-
-controlnet_denoise_block = SDXLDiffDiffControlNetDenoiseStep()
-```
-
-Insert the `controlnet_input` block and replace the `denoise` block with the new `controlnet_denoise_block`. Initialize a [`ModularPipeline`] and [`~ModularPipeline.load_components`] into it.
-
-```py
-dd_blocks.sub_blocks.insert("controlnet_input", control_input_block, 7)
-dd_blocks.sub_blocks["denoise"] = controlnet_denoise_block
-
-dd_pipeline = dd_blocks.init_pipeline("YiYiXu/modular-demo-auto", collection="diffdiff")
-dd_pipeline.load_components(torch_dtype=torch.float16)
-dd_pipeline = dd_pipeline.to(device)
-
-control_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/diffdiff_tomato_canny.jpeg")
-image = load_image("https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/20240329211129_4024911930.png?download=true")
-mask = load_image("https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/gradient_mask.png?download=true")
-
-prompt = "a green pear"
-negative_prompt = "blurry"
-generator = torch.Generator(device=device).manual_seed(42)
-
-image = dd_pipeline(
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    num_inference_steps=25,
-    generator=generator,
-    control_image=control_image,
-    controlnet_conditioning_scale=0.5,
-    diffdiff_map=mask,
-    image=image,
-    output="images"
-)[0]
-```
-
-### AutoPipelineBlocks
-
-The Differential Diffusion, IP-Adapter, and ControlNet workflows can be bundled into a single [`ModularPipeline`] by using [`AutoPipelineBlocks`]. This allows automatically selecting which sub-blocks to run based on the inputs like `control_image` or `ip_adapter_image`. If none of these inputs are passed, then it defaults to the Differential Diffusion.
-
-Use `block_trigger_inputs` to only run the `SDXLDiffDiffControlNetDenoiseStep` block if a `control_image` input is provided. Otherwise, the `SDXLDiffDiffDenoiseStep` is used.
-
-```py
-class SDXLDiffDiffAutoDenoiseStep(AutoPipelineBlocks):
-    block_classes = [SDXLDiffDiffControlNetDenoiseStep, SDXLDiffDiffDenoiseStep]
-    block_names = ["controlnet_denoise", "denoise"]
-    block_trigger_inputs = ["controlnet_cond", None]
-```
-
-Add the `ip_adapter` and `controlnet_input` blocks.
-
-```py
-DIFFDIFF_AUTO_BLOCKS = IMAGE2IMAGE_BLOCKS.copy()
-DIFFDIFF_AUTO_BLOCKS["prepare_latents"] = SDXLDiffDiffPrepareLatentsStep
-DIFFDIFF_AUTO_BLOCKS["set_timesteps"] = TEXT2IMAGE_BLOCKS["set_timesteps"]
-DIFFDIFF_AUTO_BLOCKS["denoise"] = SDXLDiffDiffAutoDenoiseStep
-DIFFDIFF_AUTO_BLOCKS.insert("ip_adapter", StableDiffusionXLAutoIPAdapterStep, 0)
-DIFFDIFF_AUTO_BLOCKS.insert("controlnet_input",StableDiffusionXLControlNetAutoInput, 7)
-```
-
-Call [`SequentialPipelineBlocks.from_blocks_dict`] to create a [`SequentialPipelineBlocks`] and create a [`ModularPipeline`] and load in the model components to run.
-
-```py
-dd_auto_blocks = SequentialPipelineBlocks.from_blocks_dict(DIFFDIFF_AUTO_BLOCKS)
-dd_pipeline = dd_auto_blocks.init_pipeline("YiYiXu/modular-demo-auto", collection="diffdiff")
-dd_pipeline.load_components(torch_dtype=torch.float16)
-```
-
-## Share
-
-Add your [`ModularPipeline`] to the Hub with [`~ModularPipeline.save_pretrained`] and set `push_to_hub` argument to `True`.
-
-```py
-dd_pipeline.save_pretrained("YiYiXu/test_modular_doc", push_to_hub=True)
-```
-
-Other users can load the [`ModularPipeline`] with [`~ModularPipeline.from_pretrained`].
-
-```py
-import torch
-from diffusers.modular_pipelines import ModularPipeline, ComponentsManager
-
-components = ComponentsManager()
-
-diffdiff_pipeline = ModularPipeline.from_pretrained("YiYiXu/modular-diffdiff-0704", trust_remote_code=True, components_manager=components, collection="diffdiff")
-diffdiff_pipeline.load_components(torch_dtype=torch.float16)
-```

docs/source/en/modular_diffusers/sequential_pipeline_blocks.md

@@ -1,113 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# SequentialPipelineBlocks
-
-[`~modular_pipelines.SequentialPipelineBlocks`] are a multi-block type that composes other [`~modular_pipelines.ModularPipelineBlocks`] together in a sequence. Data flows linearly from one block to the next using `intermediate_inputs` and `intermediate_outputs`. Each block in [`~modular_pipelines.SequentialPipelineBlocks`] usually represents a step in the pipeline, and by combining them, you gradually build a pipeline.
-
-This guide shows you how to connect two blocks into a [`~modular_pipelines.SequentialPipelineBlocks`].
-
-Create two [`~modular_pipelines.ModularPipelineBlocks`]. The first block, `InputBlock`, outputs a `batch_size` value and the second block, `ImageEncoderBlock` uses `batch_size` as `intermediate_inputs`.
-
-<hfoptions id="sequential">
-<hfoption id="InputBlock">
-
-```py
-from diffusers.modular_pipelines import ModularPipelineBlocks, InputParam, OutputParam
-
-class InputBlock(ModularPipelineBlocks):
-
-    @property
-    def inputs(self):
-        return [
-            InputParam(name="prompt", type_hint=list, description="list of text prompts"),
-            InputParam(name="num_images_per_prompt", type_hint=int, description="number of images per prompt"),
-        ]
-
-    @property
-    def intermediate_outputs(self):
-        return [
-            OutputParam(name="batch_size", description="calculated batch size"),
-        ]
-
-    @property
-    def description(self):
-        return "A block that determines batch_size based on the number of prompts and num_images_per_prompt argument."
-
-    def __call__(self, components, state):
-        block_state = self.get_block_state(state)
-        batch_size = len(block_state.prompt)
-        block_state.batch_size = batch_size * block_state.num_images_per_prompt
-        self.set_block_state(state, block_state)
-        return components, state
-```
-
-</hfoption>
-<hfoption id="ImageEncoderBlock">
-
-```py
-import torch
-from diffusers.modular_pipelines import ModularPipelineBlocks, InputParam, OutputParam
-
-class ImageEncoderBlock(ModularPipelineBlocks):
-
-    @property
-    def inputs(self):
-        return [
-            InputParam(name="image", type_hint="PIL.Image", description="raw input image to process"),
-            InputParam(name="batch_size", type_hint=int),
-        ]
-
-    @property
-    def intermediate_outputs(self):
-        return [
-            OutputParam(name="image_latents", description="latents representing the image"),
-        ]
-
-    @property
-    def description(self):
-        return "Encode raw image into its latent presentation"
-
-    def __call__(self, components, state):
-        block_state = self.get_block_state(state)
-        # Simulate processing the image
-        # This will change the state of the image from a PIL image to a tensor for all blocks
-        block_state.image = torch.randn(1, 3, 512, 512)
-        block_state.batch_size = block_state.batch_size * 2
-        block_state.image_latents = torch.randn(1, 4, 64, 64)
-        self.set_block_state(state, block_state)
-        return components, state
-```
-
-</hfoption>
-</hfoptions>
-
-Connect the two blocks by defining an [`InsertableDict`] to map the block names to the block instances. Blocks are executed in the order they're registered in `blocks_dict`.
-
-Use [`~modular_pipelines.SequentialPipelineBlocks.from_blocks_dict`] to create a [`~modular_pipelines.SequentialPipelineBlocks`].
-
-```py
-from diffusers.modular_pipelines import SequentialPipelineBlocks, InsertableDict
-
-blocks_dict = InsertableDict()
-blocks_dict["input"] = input_block
-blocks_dict["image_encoder"] = image_encoder_block
-
-blocks = SequentialPipelineBlocks.from_blocks_dict(blocks_dict)
-```
-
-Inspect the sub-blocks in [`~modular_pipelines.SequentialPipelineBlocks`] by calling `blocks`, and for more details about the inputs and outputs, access the `docs` attribute.
-
-```py
-print(blocks)
-print(blocks.doc)
-```

docs/source/en/optimization/fp16.md

@@ -174,50 +174,53 @@ Feel free to open an issue if dynamic compilation doesn't work as expected for a
 
 ### Regional compilation
 
-[Regional compilation](https://docs.pytorch.org/tutorials/recipes/regional_compilation.html) trims cold-start latency by only compiling the *small and frequently-repeated block(s)* of a model - typically a transformer layer - and enables reusing compiled artifacts for every subsequent occurrence.
-For many diffusion architectures, this delivers the same runtime speedups as full-graph compilation and reduces compile time by 8–10x.
 
-Use the [`~ModelMixin.compile_repeated_blocks`] method, a helper that wraps `torch.compile`, on any component such as the transformer model as shown below.
+[Regional compilation](https://docs.pytorch.org/tutorials/recipes/regional_compilation.html) trims cold-start latency by compiling **only the small, frequently-repeated block(s)** of a model, typically a Transformer layer, enabling reuse of compiled artifacts for every subsequent occurrence.
+For many diffusion architectures this delivers the *same* runtime speed-ups as full-graph compilation yet cuts compile time by **8–10 ×**.
+
+To make this effortless, [`ModelMixin`] exposes [`ModelMixin.compile_repeated_blocks`] API, a helper that wraps `torch.compile` around any sub-modules you designate as repeatable:
 
 ```py
 # pip install -U diffusers
 import torch
 from diffusers import StableDiffusionXLPipeline
 
-pipeline = StableDiffusionXLPipeline.from_pretrained(
+pipe = StableDiffusionXLPipeline.from_pretrained(
     "stabilityai/stable-diffusion-xl-base-1.0",
     torch_dtype=torch.float16,
 ).to("cuda")
 
-# compile only the repeated transformer layers inside the UNet
-pipeline.unet.compile_repeated_blocks(fullgraph=True)
+# Compile only the repeated Transformer layers inside the UNet
+pipe.unet.compile_repeated_blocks(fullgraph=True)
 ```
 
-To enable regional compilation for a new model, add a `_repeated_blocks` attribute to a model class containing the class names (as strings) of the blocks you want to compile.
+To enable a new model with regional compilation, add a `_repeated_blocks` attribute to your model class containing the class names (as strings) of the blocks you want compiled:
+
 
 ```py
 class MyUNet(ModelMixin):
     _repeated_blocks = ("Transformer2DModel",)  # ← compiled by default
 ```
 
-> [!TIP]
-> For more regional compilation examples, see the reference [PR](https://github.com/huggingface/diffusers/pull/11705).
+For more examples, see the reference [PR](https://github.com/huggingface/diffusers/pull/11705).
+
+**Relation to Accelerate compile_regions** There is also a separate API in [accelerate](https://huggingface.co/docs/accelerate/index) - [compile_regions](https://github.com/huggingface/accelerate/blob/273799c85d849a1954a4f2e65767216eb37fa089/src/accelerate/utils/other.py#L78). It takes a fully automatic approach: it walks the module, picks candidate blocks, then compiles the remaining graph separately. That hands-off experience is handy for quick experiments, but it also leaves fewer knobs when you want to fine-tune which blocks are compiled or adjust compilation flags.
+
 
-There is also a [compile_regions](https://github.com/huggingface/accelerate/blob/273799c85d849a1954a4f2e65767216eb37fa089/src/accelerate/utils/other.py#L78) method in [Accelerate](https://huggingface.co/docs/accelerate/index) that automatically selects candidate blocks in a model to compile. The remaining graph is compiled separately. This is useful for quick experiments because there aren't as many options for you to set which blocks to compile or adjust compilation flags.
 
 ```py
 # pip install -U accelerate
 import torch
 from diffusers import StableDiffusionXLPipeline
-from accelerate.utils import compile_regions
+from accelerate.utils import compile regions
 
 pipeline = StableDiffusionXLPipeline.from_pretrained(
     "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
 ).to("cuda")
 pipeline.unet = compile_regions(pipeline.unet, mode="reduce-overhead", fullgraph=True)
 ```
+`compile_repeated_blocks`, by contrast, is intentionally explicit. You list the repeated blocks once (via `_repeated_blocks`) and the helper compiles exactly those, nothing more. In practice this small dose of control hits a sweet spot for diffusion models: predictable behavior, easy reasoning about cache reuse, and still a one-liner for users.
 
-[`~ModelMixin.compile_repeated_blocks`] is intentionally explicit. List the blocks to repeat in `_repeated_blocks` and the helper only compiles those blocks. It offers predictable behavior and easy reasoning about cache reuse in one line of code.
 
 ### Graph breaks
 
@@ -239,12 +242,6 @@ The `step()` function is [called](https://github.com/huggingface/diffusers/blob/
 
 In general, the `sigmas` should [stay on the CPU](https://github.com/huggingface/diffusers/blob/35a969d297cba69110d175ee79c59312b9f49e1e/src/diffusers/schedulers/scheduling_euler_discrete.py#L240) to avoid the communication sync and latency.
 
-<Tip>
-
-Refer to the [torch.compile and Diffusers: A Hands-On Guide to Peak Performance](https://pytorch.org/blog/torch-compile-and-diffusers-a-hands-on-guide-to-peak-performance/) blog post for maximizing performance with `torch.compile` for diffusion models.
-
-</Tip>
-
 ### Benchmarks
 
 Refer to the [diffusers/benchmarks](https://huggingface.co/datasets/diffusers/benchmarks) dataset to see inference latency and memory usage data for compiled pipelines.
@@ -299,11 +296,3 @@ An input is projected into three subspaces, represented by the projection matric
 ```py
 pipeline.fuse_qkv_projections()
 ```
-
-## Resources
-
-- Read the [Presenting Flux Fast: Making Flux go brrr on H100s](https://pytorch.org/blog/presenting-flux-fast-making-flux-go-brrr-on-h100s/) blog post to learn more about how you can combine all of these optimizations with [TorchInductor](https://docs.pytorch.org/docs/stable/torch.compiler.html) and [AOTInductor](https://docs.pytorch.org/docs/stable/torch.compiler_aot_inductor.html) for a ~2.5x speedup using recipes from [flux-fast](https://github.com/huggingface/flux-fast).
-
-    These recipes support AMD hardware and [Flux.1 Kontext Dev](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev).
-- Read the [torch.compile and Diffusers: A Hands-On Guide to Peak Performance](https://pytorch.org/blog/torch-compile-and-diffusers-a-hands-on-guide-to-peak-performance/) blog post
-to maximize performance when using `torch.compile`.

docs/source/en/optimization/memory.md

@@ -291,53 +291,13 @@ Group offloading moves groups of internal layers ([torch.nn.ModuleList](https://
 > [!WARNING]
 > Group offloading may not work with all models if the forward implementation contains weight-dependent device casting of inputs because it may clash with group offloading's device casting mechanism.
 
-Enable group offloading by configuring the `offload_type` parameter to `block_level` or `leaf_level`.
+Call [`~ModelMixin.enable_group_offload`] to enable it for standard Diffusers model components that inherit from [`ModelMixin`]. For other model components that don't inherit from [`ModelMixin`], such as a generic [torch.nn.Module](https://pytorch.org/docs/stable/generated/torch.nn.Module.html), use [`~hooks.apply_group_offloading`] instead.
+
+The `offload_type` parameter can be set to `block_level` or `leaf_level`.
 
 - `block_level` offloads groups of layers based on the `num_blocks_per_group` parameter. For example, if `num_blocks_per_group=2` on a model with 40 layers, 2 layers are onloaded and offloaded at a time (20 total onloads/offloads). This drastically reduces memory requirements.
 - `leaf_level` offloads individual layers at the lowest level and is equivalent to [CPU offloading](#cpu-offloading). But it can be made faster if you use streams without giving up inference speed.
 
-Group offloading is supported for entire pipelines or individual models. Applying group offloading to the entire pipeline is the easiest option while selectively applying it to individual models gives users more flexibility to use different offloading techniques for different models.
-
-<hfoptions id="group-offloading">
-<hfoption id="pipeline">
-
-Call [`~DiffusionPipeline.enable_group_offload`] on a pipeline.
-
-```py
-import torch
-from diffusers import CogVideoXPipeline
-from diffusers.hooks import apply_group_offloading
-from diffusers.utils import export_to_video
-
-onload_device = torch.device("cuda")
-offload_device = torch.device("cpu")
-
-pipeline = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16)
-pipeline.enable_group_offload(
-    onload_device=onload_device,
-    offload_device=offload_device,
-    offload_type="leaf_level",
-    use_stream=True
-)
-
-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 = pipeline(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
-print(f"Max memory reserved: {torch.cuda.max_memory_allocated() / 1024**3:.2f} GB")
-export_to_video(video, "output.mp4", fps=8)
-```
-
-</hfoption>
-<hfoption id="model">
-
-Call [`~ModelMixin.enable_group_offload`] on standard Diffusers model components that inherit from [`ModelMixin`]. For other model components that don't inherit from [`ModelMixin`], such as a generic [torch.nn.Module](https://pytorch.org/docs/stable/generated/torch.nn.Module.html), use [`~hooks.apply_group_offloading`] instead.
-
 ```py
 import torch
 from diffusers import CogVideoXPipeline
@@ -368,9 +328,6 @@ print(f"Max memory reserved: {torch.cuda.max_memory_allocated() / 1024**3:.2f} G
 export_to_video(video, "output.mp4", fps=8)
 ```
 
-</hfoption>
-</hfoptions>
-
 #### CUDA stream
 
 The `use_stream` parameter can be activated for CUDA devices that support asynchronous data transfer streams to reduce overall execution time compared to [CPU offloading](#cpu-offloading). It overlaps data transfer and computation by using layer prefetching. The next layer to be executed is loaded onto the GPU while the current layer is still being executed. It can increase CPU memory significantly so ensure you have 2x the amount of memory as the model size.

docs/source/en/optimization/speed-memory-optims.md

@@ -10,13 +10,10 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# Compiling and offloading quantized models
+# Compile and offloading quantized models
 
 Optimizing models often involves trade-offs between [inference speed](./fp16) and [memory-usage](./memory). For instance, while [caching](./cache) can boost inference speed, it also increases memory consumption since it needs to store the outputs of intermediate attention layers. A more balanced optimization strategy combines quantizing a model, [torch.compile](./fp16#torchcompile) and various [offloading methods](./memory#offloading).
 
-> [!TIP]
-> Check the [torch.compile](./fp16#torchcompile) guide to learn more about compilation and how they can be applied here. For example, regional compilation can significantly reduce compilation time without giving up any speedups. 
-
 For image generation, combining quantization and [model offloading](./memory#model-offloading) can often give the best trade-off between quality, speed, and memory. Group offloading is not as effective for image generation because it is usually not possible to *fully* overlap data transfer if the compute kernel finishes faster. This results in some communication overhead between the CPU and GPU.
 
 For video generation, combining quantization and [group-offloading](./memory#group-offloading) tends to be better because video models are more compute-bound. 
@@ -28,8 +25,7 @@ The table below provides a comparison of optimization strategy combinations and
 | quantization  | 32.602 | 14.9453 |
 | quantization, torch.compile  | 25.847 | 14.9448 |
 | quantization, torch.compile, model CPU offloading | 32.312 | 12.2369 |
-
-<small>These results are benchmarked on Flux with a RTX 4090. The transformer and text_encoder components are quantized. Refer to the <a href="https://gist.github.com/sayakpaul/0db9d8eeeb3d2a0e5ed7cf0d9ca19b7d">benchmarking script</a> if you're interested in evaluating your own model.</small>
+<small>These results are benchmarked on Flux with a RTX 4090. The transformer and text_encoder components are quantized. Refer to the <a href="https://gist.github.com/sayakpaul/0db9d8eeeb3d2a0e5ed7cf0d9ca19b7d" benchmarking script</a> if you're interested in evaluating your own model.</small>
 
 This guide will show you how to compile and offload a quantized model with [bitsandbytes](../quantization/bitsandbytes#torchcompile). Make sure you are using [PyTorch nightly](https://pytorch.org/get-started/locally/) and the latest version of bitsandbytes.
 

docs/source/en/quantization/gguf.md

@@ -53,16 +53,6 @@ image = pipe(prompt, generator=torch.manual_seed(0)).images[0]
 image.save("flux-gguf.png")
 ```
 
-## Using Optimized CUDA Kernels with GGUF
-
-Optimized CUDA kernels can accelerate GGUF quantized model inference by approximately 10%. This functionality requires a compatible GPU with `torch.cuda.get_device_capability` greater than 7 and the kernels library:
-
-```shell
-pip install -U kernels
-```
-
-Once installed, set `DIFFUSERS_GGUF_CUDA_KERNELS=true`  to use optimized kernels when available. Note that CUDA kernels may introduce minor numerical differences compared to the original GGUF implementation, potentially causing subtle visual variations in generated images. To disable CUDA kernel usage, set the environment variable `DIFFUSERS_GGUF_CUDA_KERNELS=false`.
-
 ## Supported Quantization Types
 
 - BF16
@@ -77,44 +67,3 @@ Once installed, set `DIFFUSERS_GGUF_CUDA_KERNELS=true`  to use optimized kernels
 - Q5_K
 - Q6_K
 
-## Convert to GGUF
-
-Use the Space below to convert a Diffusers checkpoint into the GGUF format for inference.
-run conversion:
-
-<iframe
-	src="https://diffusers-internal-dev-diffusers-to-gguf.hf.space"
-	frameborder="0"
-	width="850"
-	height="450"
-></iframe>
-
-
-```py
-import torch
-
-from diffusers import FluxPipeline, FluxTransformer2DModel, GGUFQuantizationConfig
-
-ckpt_path = (
-    "https://huggingface.co/sayakpaul/different-lora-from-civitai/blob/main/flux_dev_diffusers-q4_0.gguf"
-)
-transformer = FluxTransformer2DModel.from_single_file(
-    ckpt_path,
-    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
-    config="black-forest-labs/FLUX.1-dev",
-    subfolder="transformer",
-    torch_dtype=torch.bfloat16,
-)
-pipe = FluxPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-dev",
-    transformer=transformer,
-    torch_dtype=torch.bfloat16,
-)
-pipe.enable_model_cpu_offload()
-prompt = "A cat holding a sign that says hello world"
-image = pipe(prompt, generator=torch.manual_seed(0)).images[0]
-image.save("flux-gguf.png")
-```
-
-When using Diffusers format GGUF checkpoints, it's a must to provide the model `config` path. If the
-model config resides in a `subfolder`, that needs to be specified, too.

docs/source/en/quantization/modelopt.md

@@ -1,141 +0,0 @@
-<!-- Copyright 2025 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. -->
-
-# NVIDIA ModelOpt
-
-[NVIDIA-ModelOpt](https://github.com/NVIDIA/TensorRT-Model-Optimizer) is a unified library of state-of-the-art model optimization techniques like quantization, pruning, distillation, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM or TensorRT to optimize inference speed.
-
-Before you begin, make sure you have nvidia_modelopt installed.
-
-```bash
-pip install -U "nvidia_modelopt[hf]"
-```
-
-Quantize a model by passing [`NVIDIAModelOptConfig`] to [`~ModelMixin.from_pretrained`] (you can also load pre-quantized models). This works for any model in any modality, as long as it supports loading with [Accelerate](https://hf.co/docs/accelerate/index) and contains `torch.nn.Linear` layers.
-
-The example below only quantizes the weights to FP8.
-
-```python
-import torch
-from diffusers import AutoModel, SanaPipeline, NVIDIAModelOptConfig
-
-model_id = "Efficient-Large-Model/Sana_600M_1024px_diffusers"
-dtype = torch.bfloat16
-
-quantization_config = NVIDIAModelOptConfig(quant_type="FP8", quant_method="modelopt")
-transformer = AutoModel.from_pretrained(
-    model_id,
-    subfolder="transformer",
-    quantization_config=quantization_config,
-    torch_dtype=dtype,
-)
-pipe = SanaPipeline.from_pretrained(
-    model_id,
-    transformer=transformer,
-    torch_dtype=dtype,
-)
-pipe.to("cuda")
-
-print(f"Pipeline memory usage: {torch.cuda.max_memory_reserved() / 1024**3:.3f} GB")
-
-prompt = "A cat holding a sign that says hello world"
-image = pipe(
-    prompt, num_inference_steps=50, guidance_scale=4.5, max_sequence_length=512
-).images[0]
-image.save("output.png")
-```
-
-> **Note:**
->
-> The quantization methods in NVIDIA-ModelOpt are designed to reduce the memory footprint of model weights using various QAT (Quantization-Aware Training) and PTQ (Post-Training Quantization) techniques while maintaining model performance. However, the actual performance gain during inference depends on the deployment framework (e.g., TRT-LLM, TensorRT) and the specific hardware configuration.  
-> 
-> More details can be found [here](https://github.com/NVIDIA/TensorRT-Model-Optimizer/tree/main/examples).
-
-## NVIDIAModelOptConfig
-
-The `NVIDIAModelOptConfig` class accepts three parameters:
-- `quant_type`: A string value mentioning one of the quantization types below.
-- `modules_to_not_convert`: A list of module full/partial module names for which quantization should not be performed. For example, to not perform any quantization of the [`SD3Transformer2DModel`]'s pos_embed projection blocks, one would specify: `modules_to_not_convert=["pos_embed.proj.weight"]`.
-- `disable_conv_quantization`: A boolean value which when set to `True` disables quantization for all convolutional layers in the model. This is useful as channel and block quantization generally don't work well with convolutional layers (used with INT4, NF4, NVFP4). If you want to disable quantization for specific convolutional layers, use `modules_to_not_convert` instead.
-- `algorithm`: The algorithm to use for determining scale, defaults to `"max"`. You can check modelopt documentation for more algorithms and details.
-- `forward_loop`: The forward loop function to use for calibrating activation during quantization. If not provided, it relies on static scale values computed using the weights only.
-- `kwargs`: A dict of keyword arguments to pass to the underlying quantization method which will be invoked based on `quant_type`.
-
-## Supported quantization types
-
-ModelOpt supports weight-only, channel and block quantization int8, fp8, int4, nf4, and nvfp4. The quantization methods are designed to reduce the memory footprint of the model weights while maintaining the performance of the model during inference.
-
-Weight-only quantization stores the model weights in a specific low-bit data type but performs computation with a higher-precision data type, like `bfloat16`. This lowers the memory requirements from model weights but retains the memory peaks for activation computation.
-
-The quantization methods supported are as follows:
-
-| **Quantization Type** | **Supported Schemes** | **Required Kwargs** | **Additional Notes** |
-|-----------------------|-----------------------|---------------------|----------------------|
-| **INT8** | `int8 weight only`, `int8 channel quantization`, `int8 block quantization` | `quant_type`, `quant_type + channel_quantize`, `quant_type + channel_quantize + block_quantize` |
-| **FP8** | `fp8 weight only`, `fp8 channel quantization`, `fp8 block quantization` | `quant_type`, `quant_type + channel_quantize`, `quant_type + channel_quantize + block_quantize` |
-| **INT4** | `int4 weight only`, `int4 block quantization` | `quant_type`, `quant_type + channel_quantize + block_quantize` | `channel_quantize = -1 is only supported for now`|
-| **NF4** | `nf4 weight only`, `nf4 double block quantization` | `quant_type`, `quant_type + channel_quantize + block_quantize + scale_channel_quantize` + `scale_block_quantize` | `channel_quantize = -1 and scale_channel_quantize = -1 are only supported for now` |
-| **NVFP4** | `nvfp4 weight only`, `nvfp4 block quantization` | `quant_type`, `quant_type + channel_quantize + block_quantize` | `channel_quantize = -1 is only supported for now`|
-
-
-Refer to the [official modelopt documentation](https://nvidia.github.io/TensorRT-Model-Optimizer/) for a better understanding of the available quantization methods and the exhaustive list of configuration options available.
-
-## Serializing and Deserializing quantized models
-
-To serialize a quantized model in a given dtype, first load the model with the desired quantization dtype and then save it using the [`~ModelMixin.save_pretrained`] method.
-
-```python
-import torch
-from diffusers import AutoModel, NVIDIAModelOptConfig
-from modelopt.torch.opt import enable_huggingface_checkpointing
-
-enable_huggingface_checkpointing()
-
-model_id = "Efficient-Large-Model/Sana_600M_1024px_diffusers"
-quant_config_fp8 = {"quant_type": "FP8", "quant_method": "modelopt"}
-quant_config_fp8 = NVIDIAModelOptConfig(**quant_config_fp8)
-model = AutoModel.from_pretrained(
-    model_id,
-    subfolder="transformer",
-    quantization_config=quant_config_fp8,
-    torch_dtype=torch.bfloat16,
-)
-model.save_pretrained('path/to/sana_fp8', safe_serialization=False)
-```
-
-To load a serialized quantized model, use the [`~ModelMixin.from_pretrained`] method.
-
-```python
-import torch
-from diffusers import AutoModel, NVIDIAModelOptConfig, SanaPipeline
-from modelopt.torch.opt import enable_huggingface_checkpointing
-
-enable_huggingface_checkpointing()
-
-quantization_config = NVIDIAModelOptConfig(quant_type="FP8", quant_method="modelopt")
-transformer = AutoModel.from_pretrained(
-    "path/to/sana_fp8",
-    subfolder="transformer",
-    quantization_config=quantization_config,
-    torch_dtype=torch.bfloat16,
-)
-pipe = SanaPipeline.from_pretrained(
-    "Efficient-Large-Model/Sana_600M_1024px_diffusers",
-    transformer=transformer,
-    torch_dtype=torch.bfloat16,
-)
-pipe.to("cuda")
-prompt = "A cat holding a sign that says hello world"
-image = pipe(
-    prompt, num_inference_steps=50, guidance_scale=4.5, max_sequence_length=512
-).images[0]
-image.save("output.png")
-```

docs/source/en/quantization/overview.md

@@ -11,7 +11,7 @@ specific language governing permissions and limitations under the License.
 
 -->
 
-# Getting started
+# Quantization
 
 Quantization focuses on representing data with fewer bits while also trying to preserve the precision of the original data. This often means converting a data type to represent the same information with fewer bits. For example, if your model weights are stored as 32-bit floating points and they're quantized to 16-bit floating points, this halves the model size which makes it easier to store and reduces memory usage. Lower precision can also speedup inference because it takes less time to perform calculations with fewer bits.
 
@@ -19,26 +19,18 @@ Diffusers supports multiple quantization backends to make large diffusion models
 
 ## Pipeline-level quantization
 
-There are two ways to use [`~quantizers.PipelineQuantizationConfig`] depending on how much customization you want to apply to the quantization configuration. 
+There are two ways you can use [`~quantizers.PipelineQuantizationConfig`] depending on the level of control you want over the quantization specifications of each model in the pipeline.
 
-- for basic use cases, define the `quant_backend`, `quant_kwargs`, and `components_to_quantize` arguments
-- for granular quantization control, define a `quant_mapping` that provides the quantization configuration for individual model components
+- for more basic and simple use cases, you only need to define the `quant_backend`, `quant_kwargs`, and `components_to_quantize`
+- for more granular quantization control, provide a `quant_mapping` that provides the quantization specifications for the individual model components
 
-### Basic quantization
+### Simple quantization
 
 Initialize [`~quantizers.PipelineQuantizationConfig`] with the following parameters.
 
 - `quant_backend` specifies which quantization backend to use. Currently supported backends include: `bitsandbytes_4bit`, `bitsandbytes_8bit`, `gguf`, `quanto`, and `torchao`.
-- `quant_kwargs` specifies the quantization arguments to use.
-
-> [!TIP]
-> These `quant_kwargs` arguments are different for each backend. Refer to the [Quantization API](../api/quantization) docs to view the arguments for each backend.
-
-- `components_to_quantize` specifies which component(s) of the pipeline to quantize. Typically, you should quantize the most compute intensive components like the transformer. The text encoder is another component to consider quantizing if a pipeline has more than one such as [`FluxPipeline`]. The example below quantizes the T5 text encoder in [`FluxPipeline`] while keeping the CLIP model intact.
-
-   `components_to_quantize` accepts either a list for multiple models or a string for a single model.
-
-The example below loads the bitsandbytes backend with the following arguments from [`~quantizers.quantization_config.BitsAndBytesConfig`], `load_in_4bit`, `bnb_4bit_quant_type`, and `bnb_4bit_compute_dtype`.
+- `quant_kwargs` contains the specific quantization arguments to use.
+- `components_to_quantize` specifies which components of the pipeline to quantize. Typically, you should quantize the most compute intensive components like the transformer. The text encoder is another component to consider quantizing if a pipeline has more than one such as [`FluxPipeline`]. The example below quantizes the T5 text encoder in [`FluxPipeline`] while keeping the CLIP model intact.
 
 ```py
 import torch
@@ -64,14 +56,13 @@ pipe = DiffusionPipeline.from_pretrained(
 image = pipe("photo of a cute dog").images[0]
 ```
 
+### quant_mapping
 
-### Advanced quantization
-
-The `quant_mapping` argument provides more options for how to quantize each individual component in a pipeline, like combining different quantization backends.
+The `quant_mapping` argument provides more flexible options for how to quantize each individual component in a pipeline, like combining different quantization backends.
 
 Initialize [`~quantizers.PipelineQuantizationConfig`] and pass a `quant_mapping` to it. The `quant_mapping` allows you to specify the quantization options for each component in the pipeline such as the transformer and text encoder.
 
-The example below uses two quantization backends, [`~quantizers.quantization_config.QuantoConfig`] and [`transformers.BitsAndBytesConfig`], for the transformer and text encoder.
+The example below uses two quantization backends, [`~quantizers.QuantoConfig`] and [`transformers.BitsAndBytesConfig`], for the transformer and text encoder.
 
 ```py
 import torch
@@ -94,7 +85,7 @@ pipeline_quant_config = PipelineQuantizationConfig(
 There is a separate bitsandbytes backend in [Transformers](https://huggingface.co/docs/transformers/main_classes/quantization#transformers.BitsAndBytesConfig). You need to import and use [`transformers.BitsAndBytesConfig`] for components that come from Transformers. For example, `text_encoder_2` in [`FluxPipeline`] is a [`~transformers.T5EncoderModel`] from Transformers so you need to use [`transformers.BitsAndBytesConfig`] instead of [`diffusers.BitsAndBytesConfig`].
 
 > [!TIP]
-> Use the [basic quantization](#basic-quantization) method above if you don't want to manage these distinct imports or aren't sure where each pipeline component comes from.
+> Use the [simple quantization](#simple-quantization) method above if you don't want to manage these distinct imports or aren't sure where each pipeline component comes from.
 
 ```py
 import torch
@@ -138,4 +129,4 @@ Check out the resources below to learn more about quantization.
 
 - The Transformers quantization [Overview](https://huggingface.co/docs/transformers/quantization/overview#when-to-use-what) provides an overview of the pros and cons of different quantization backends.
 
-- Read the [Exploring Quantization Backends in Diffusers](https://huggingface.co/blog/diffusers-quantization) blog post for a brief introduction to each quantization backend, how to choose a backend, and combining quantization with other memory optimizations.
+- Read the [Exploring Quantization Backends in Diffusers](https://huggingface.co/blog/diffusers-quantization) blog post for a brief introduction to each quantization backend, how to choose a backend, and combining quantization with other memory optimizations.

docs/source/en/quicktour.md

@@ -10,223 +10,314 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# Quickstart
+[[open-in-colab]]
 
-Diffusers is a library for developers and researchers that provides an easy inference API for generating images, videos and audio, as well as the building blocks for implementing new workflows.
+# Quicktour
 
-Diffusers provides many optimizations out-of-the-box that makes it possible to load and run large models on setups with limited memory or to accelerate inference.
+Diffusion models are trained to denoise random Gaussian noise step-by-step to generate a sample of interest, such as an image or audio. This has sparked a tremendous amount of interest in generative AI, and you have probably seen examples of diffusion generated images on the internet. 🧨 Diffusers is a library aimed at making diffusion models widely accessible to everyone.
 
-This Quickstart will give you an overview of Diffusers and get you up and generating quickly.
+Whether you're a developer or an everyday user, this quicktour will introduce you to 🧨 Diffusers and help you get up and generating quickly! There are three main components of the library to know about:
 
-> [!TIP]
-> Before you begin, make sure you have a Hugging Face [account](https://huggingface.co/join) in order to use gated models like [Flux](https://huggingface.co/black-forest-labs/FLUX.1-dev).
+* The [`DiffusionPipeline`] is a high-level end-to-end class designed to rapidly generate samples from pretrained diffusion models for inference.
+* Popular pretrained [model](./api/models) architectures and modules that can be used as building blocks for creating diffusion systems.
+* Many different [schedulers](./api/schedulers/overview) - algorithms that control how noise is added for training, and how to generate denoised images during inference.
 
-Follow the [Installation](./installation) guide to install Diffusers if it's not already installed.
+The quicktour will show you how to use the [`DiffusionPipeline`] for inference, and then walk you through how to combine a model and scheduler to replicate what's happening inside the [`DiffusionPipeline`].
+
+<Tip>
+
+The quicktour is a simplified version of the introductory 🧨 Diffusers [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) to help you get started quickly. If you want to learn more about 🧨 Diffusers' goal, design philosophy, and additional details about its core API, check out the notebook!
+
+</Tip>
+
+Before you begin, make sure you have all the necessary libraries installed:
+
+```py
+# uncomment to install the necessary libraries in Colab
+#!pip install --upgrade diffusers accelerate transformers
+```
+
+- [🤗 Accelerate](https://huggingface.co/docs/accelerate/index) speeds up model loading for inference and training.
+- [🤗 Transformers](https://huggingface.co/docs/transformers/index) is required to run the most popular diffusion models, such as [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview).
 
 ## DiffusionPipeline
 
-A diffusion model combines multiple components to generate outputs in any modality based on an input, such as a text description, image or both.
+The [`DiffusionPipeline`] is the easiest way to use a pretrained diffusion system for inference. It is an end-to-end system containing the model and the scheduler. You can use the [`DiffusionPipeline`] out-of-the-box for many tasks. Take a look at the table below for some supported tasks, and for a complete list of supported tasks, check out the [🧨 Diffusers Summary](./api/pipelines/overview#diffusers-summary) table.
 
-For a standard text-to-image model:
+| **Task**                     | **Description**                                                                                              | **Pipeline**
+|------------------------------|--------------------------------------------------------------------------------------------------------------|-----------------|
+| Unconditional Image Generation          | generate an image from Gaussian noise | [unconditional_image_generation](./using-diffusers/unconditional_image_generation) |
+| Text-Guided Image Generation | generate an image given a text prompt | [conditional_image_generation](./using-diffusers/conditional_image_generation) |
+| Text-Guided Image-to-Image Translation     | adapt an image guided by a text prompt | [img2img](./using-diffusers/img2img) |
+| Text-Guided Image-Inpainting          | fill the masked part of an image given the image, the mask and a text prompt | [inpaint](./using-diffusers/inpaint) |
+| Text-Guided Depth-to-Image Translation | adapt parts of an image guided by a text prompt while preserving structure via depth estimation | [depth2img](./using-diffusers/depth2img) |
 
-1. A text encoder turns a prompt into embeddings that guide the denoising process. Some models have more than one text encoder.
-2. A scheduler contains the algorithmic specifics for gradually denoising initial random noise into clean outputs. Different schedulers affect generation speed and quality.
-3. A UNet or diffusion transformer (DiT) is the workhorse of a diffusion model.
+Start by creating an instance of a [`DiffusionPipeline`] and specify which pipeline checkpoint you would like to download.
+You can use the [`DiffusionPipeline`] for any [checkpoint](https://huggingface.co/models?library=diffusers&sort=downloads) stored on the Hugging Face Hub.
+In this quicktour, you'll load the [`stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) checkpoint for text-to-image generation.
 
-  At each step, it performs the denoising predictions, such as how much noise to remove or the general direction in which to steer the noise to generate better quality outputs.
+<Tip warning={true}>
 
-  The UNet or DiT repeats this loop for a set amount of steps to generate the final output.
-  
-4. A variational autoencoder (VAE) encodes and decodes pixels to a spatially compressed latent-space. *Latents* are compressed representations of an image and are more efficient to work with. The UNet or DiT operates on latents, and the clean latents at the end are decoded back into images.
+For [Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion) models, please carefully read the [license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) first before running the model. 🧨 Diffusers implements a [`safety_checker`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) to prevent offensive or harmful content, but the model's improved image generation capabilities can still produce potentially harmful content.
 
-The [`DiffusionPipeline`] packages all these components into a single class for inference. There are several arguments in [`~DiffusionPipeline.__call__`] you can change, such as `num_inference_steps`, that affect the diffusion process. Try different values and arguments to see how they change generation quality or speed.
+</Tip>
 
-Load a model with [`~DiffusionPipeline.from_pretrained`] and describe what you'd like to generate. The example below uses the default argument values.
+Load the model with the [`~DiffusionPipeline.from_pretrained`] method:
 
-<hfoptions id="diffusionpipeline">
-<hfoption id="text-to-image">
+```python
+>>> from diffusers import DiffusionPipeline
 
-Use `.images[0]` to access the generated image output.
-
-```py
-import torch
-from diffusers import DiffusionPipeline
-
-pipeline = DiffusionPipeline.from_pretrained(
-  "Qwen/Qwen-Image", torch_dtype=torch.bfloat16, device_map="cuda"
-)
-
-prompt = """
-cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California
-highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain
-"""
-pipeline(prompt).images[0]
+>>> pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True)
 ```
 
-</hfoption>
-<hfoption id="text-to-video">
-
-Use `.frames[0]` to access the generated video output and [`~utils.export_to_video`] to save the video.
+The [`DiffusionPipeline`] downloads and caches all modeling, tokenization, and scheduling components. You'll see that the Stable Diffusion pipeline is composed of the [`UNet2DConditionModel`] and [`PNDMScheduler`] among other things:
 
 ```py
-import torch
-from diffusers import AutoencoderKLWan, DiffusionPipeline
-from diffusers.quantizers import PipelineQuantizationConfig
-from diffusers.utils import export_to_video
-
-vae = AutoencoderKLWan.from_pretrained(
-  "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
-  subfolder="vae",
-  torch_dtype=torch.float32
-)
-pipeline = DiffusionPipeline.from_pretrained(
-  "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
-  vae=vae
-  torch_dtype=torch.bfloat16,
-  device_map="cuda"
-)
-
-prompt = """
-Cinematic video of a sleek cat lounging on a colorful inflatable in a crystal-clear turquoise pool in Palm Springs, 
-sipping a salt-rimmed margarita through a straw. Golden-hour sunlight glows over mid-century modern homes and swaying palms. 
-Shot in rich Sony a7S III: with moody, glamorous color grading, subtle lens flares, and soft vintage film grain. 
-Ripples shimmer as a warm desert breeze stirs the water, blending luxury and playful charm in an epic, gorgeously composed frame.
-"""
-video = pipeline(prompt=prompt, num_frames=81, num_inference_steps=40).frames[0]
-export_to_video(video, "output.mp4", fps=16)
+>>> pipeline
+StableDiffusionPipeline {
+  "_class_name": "StableDiffusionPipeline",
+  "_diffusers_version": "0.21.4",
+  ...,
+  "scheduler": [
+    "diffusers",
+    "PNDMScheduler"
+  ],
+  ...,
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}
 ```
 
-</hfoption>
-</hfoptions>
+We strongly recommend running the pipeline on a GPU because the model consists of roughly 1.4 billion parameters.
+You can move the generator object to a GPU, just like you would in PyTorch:
 
-## LoRA
+```python
+>>> pipeline.to("cuda")
+```
 
-Adapters insert a small number of trainable parameters to the original base model. Only the inserted parameters are fine-tuned while the rest of the model weights remain frozen. This makes it fast and cheap to fine-tune a model on a new style. Among adapters, [LoRA's](./tutorials/using_peft_for_inference) are the most popular.
+Now you can pass a text prompt to the `pipeline` to generate an image, and then access the denoised image. By default, the image output is wrapped in a [`PIL.Image`](https://pillow.readthedocs.io/en/stable/reference/Image.html?highlight=image#the-image-class) object.
 
-Add a LoRA to a pipeline with the [`~loaders.QwenImageLoraLoaderMixin.load_lora_weights`] method. Some LoRA's require a special word to trigger it, such as `Realism`, in the example below. Check a LoRA's model card to see if it requires a trigger word.
+```python
+>>> image = pipeline("An image of a squirrel in Picasso style").images[0]
+>>> image
+```
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/image_of_squirrel_painting.png"/>
+</div>
+
+Save the image by calling `save`:
+
+```python
+>>> image.save("image_of_squirrel_painting.png")
+```
+
+### Local pipeline
+
+You can also use the pipeline locally. The only difference is you need to download the weights first:
+
+```bash
+!git lfs install
+!git clone https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5
+```
+
+Then load the saved weights into the pipeline:
+
+```python
+>>> pipeline = DiffusionPipeline.from_pretrained("./stable-diffusion-v1-5", use_safetensors=True)
+```
+
+Now, you can run the pipeline as you would in the section above.
+
+### Swapping schedulers
+
+Different schedulers come with different denoising speeds and quality trade-offs. The best way to find out which one works best for you is to try them out! One of the main features of 🧨 Diffusers is to allow you to easily switch between schedulers. For example, to replace the default [`PNDMScheduler`] with the [`EulerDiscreteScheduler`], load it with the [`~diffusers.ConfigMixin.from_config`] method:
 
 ```py
-import torch
-from diffusers import DiffusionPipeline
+>>> from diffusers import EulerDiscreteScheduler
 
-pipeline = DiffusionPipeline.from_pretrained(
-  "Qwen/Qwen-Image", torch_dtype=torch.bfloat16, device_map="cuda"
-)
-pipeline.load_lora_weights(
-  "flymy-ai/qwen-image-realism-lora",
-)
-
-prompt = """
-super Realism cinematic film still of a cat sipping a margarita in a pool in Palm Springs in the style of umempart, California
-highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain
-"""
-pipeline(prompt).images[0]
+>>> pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True)
+>>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
 ```
 
-Check out the [LoRA](./tutorials/using_peft_for_inference) docs or Adapters section to learn more.
+Try generating an image with the new scheduler and see if you notice a difference!
 
-## Quantization
+In the next section, you'll take a closer look at the components - the model and scheduler - that make up the [`DiffusionPipeline`] and learn how to use these components to generate an image of a cat.
 
-[Quantization](./quantization/overview) stores data in fewer bits to reduce memory usage. It may also speed up inference because it takes less time to perform calculations with fewer bits.
+## Models
 
-Diffusers provides several quantization backends and picking one depends on your use case. For example, [bitsandbytes](./quantization/bitsandbytes) and [torchao](./quantization/torchao) are both simple and easy to use for inference, but torchao supports more [quantization types](./quantization/torchao#supported-quantization-types) like fp8.
+Most models take a noisy sample, and at each timestep it predicts the *noise residual* (other models learn to predict the previous sample directly or the velocity or [`v-prediction`](https://github.com/huggingface/diffusers/blob/5e5ce13e2f89ac45a0066cb3f369462a3cf1d9ef/src/diffusers/schedulers/scheduling_ddim.py#L110)), the difference between a less noisy image and the input image. You can mix and match models to create other diffusion systems.
 
-Configure [`PipelineQuantizationConfig`] with the backend to use, the specific arguments (refer to the [API](./api/quantization) reference for available arguments) for that backend, and which components to quantize. The example below quantizes the model to 4-bits and only uses 14.93GB of memory.
+Models are initiated with the [`~ModelMixin.from_pretrained`] method which also locally caches the model weights so it is faster the next time you load the model. For the quicktour, you'll load the [`UNet2DModel`], a basic unconditional image generation model with a checkpoint trained on cat images:
 
 ```py
-import torch
-from diffusers import DiffusionPipeline
-from diffusers.quantizers import PipelineQuantizationConfig
+>>> from diffusers import UNet2DModel
 
-quant_config = PipelineQuantizationConfig(
-  quant_backend="bitsandbytes_4bit",
-  quant_kwargs={"load_in_4bit": True, "bnb_4bit_quant_type": "nf4", "bnb_4bit_compute_dtype": torch.bfloat16},
-  components_to_quantize=["transformer", "text_encoder"],
-)
-pipeline = DiffusionPipeline.from_pretrained(
-  "Qwen/Qwen-Image",
-  torch_dtype=torch.bfloat16,
-  quantization_config=quant_config,
-  device_map="cuda"
-)
-
-prompt = """
-cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California
-highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain
-"""
-pipeline(prompt).images[0]
-print(f"Max memory reserved: {torch.cuda.max_memory_allocated() / 1024**3:.2f} GB")
+>>> repo_id = "google/ddpm-cat-256"
+>>> model = UNet2DModel.from_pretrained(repo_id, use_safetensors=True)
 ```
 
-Take a look at the [Quantization](./quantization/overview) section for more details.
-
-## Optimizations
-
 > [!TIP]
-> Optimization is dependent on hardware specs such as memory. Use this [Space](https://huggingface.co/spaces/diffusers/optimized-diffusers-code) to generate code examples that include all of Diffusers' available memory and speed optimization techniques for any model you're using.
+> Use the [`AutoModel`] API to automatically select a model class if you're unsure of which one to use.
 
-Modern diffusion models are very large and have billions of parameters. The iterative denoising process is also computationally intensive and slow. Diffusers provides techniques for reducing memory usage and boosting inference speed. These techniques can be combined with quantization to optimize for both memory usage and inference speed.
-
-### Memory usage
-
-The text encoders and UNet or DiT can use up as much as ~30GB of memory, exceeding the amount available on many free-tier or consumer GPUs.
-
-Offloading stores weights that aren't currently used on the CPU and only moves them to the GPU when they're needed. There are a few offloading types and the example below uses [model offloading](./optimization/memory#model-offloading). This moves an entire model, like a text encoder or transformer, to the CPU when it isn't actively being used.
-
-Call [`~DiffusionPipeline.enable_model_cpu_offload`] to activate it. By combining quantization and offloading, the following example only requires ~12.54GB of memory.
+To access the model parameters, call `model.config`:
 
 ```py
-import torch
-from diffusers import DiffusionPipeline
-from diffusers.quantizers import PipelineQuantizationConfig
-
-quant_config = PipelineQuantizationConfig(
-  quant_backend="bitsandbytes_4bit",
-  quant_kwargs={"load_in_4bit": True, "bnb_4bit_quant_type": "nf4", "bnb_4bit_compute_dtype": torch.bfloat16},
-  components_to_quantize=["transformer", "text_encoder"],
-)
-pipeline = DiffusionPipeline.from_pretrained(
-  "Qwen/Qwen-Image",
-  torch_dtype=torch.bfloat16,
-  quantization_config=quant_config,
-  device_map="cuda"
-)
-pipeline.enable_model_cpu_offload()
-
-prompt = """
-cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California
-highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain
-"""
-pipeline(prompt).images[0]
-print(f"Max memory reserved: {torch.cuda.max_memory_allocated() / 1024**3:.2f} GB")
+>>> model.config
 ```
 
-Refer to the [Reduce memory usage](./optimization/memory) docs to learn more about other memory reducing techniques.
+The model configuration is a 🧊 frozen 🧊 dictionary, which means those parameters can't be changed after the model is created. This is intentional and ensures that the parameters used to define the model architecture at the start remain the same, while other parameters can still be adjusted during inference.
 
-### Inference speed
+Some of the most important parameters are:
 
-The denoising loop performs a lot of computations and can be slow. Methods like [torch.compile](./optimization/fp16#torchcompile) increases inference speed by compiling the computations into an optimized kernel. Compilation is slow for the first generation but successive generations should be much faster.
+* `sample_size`: the height and width dimension of the input sample.
+* `in_channels`: the number of input channels of the input sample.
+* `down_block_types` and `up_block_types`: the type of down- and upsampling blocks used to create the UNet architecture.
+* `block_out_channels`: the number of output channels of the downsampling blocks; also used in reverse order for the number of input channels of the upsampling blocks.
+* `layers_per_block`: the number of ResNet blocks present in each UNet block.
 
-The example below uses [regional compilation](./optimization/fp16#regional-compilation) to only compile small regions of a model. It reduces cold-start latency while also providing a runtime speed up.
-
-Call [`~ModelMixin.compile_repeated_blocks`] on the model to activate it.
+To use the model for inference, create the image shape with random Gaussian noise. It should have a `batch` axis because the model can receive multiple random noises, a `channel` axis corresponding to the number of input channels, and a `sample_size` axis for the height and width of the image:
 
 ```py
-import torch
-from diffusers import DiffusionPipeline
+>>> import torch
 
-pipeline = DiffusionPipeline.from_pretrained(
-  "Qwen/Qwen-Image", torch_dtype=torch.bfloat16, device_map="cuda"
-)
+>>> torch.manual_seed(0)
 
-pipeline.transformer.compile_repeated_blocks(
-    fullgraph=True,
-)
-prompt = """
-cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California
-highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain
-"""
-pipeline(prompt).images[0]
+>>> noisy_sample = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
+>>> noisy_sample.shape
+torch.Size([1, 3, 256, 256])
 ```
 
-Check out the [Accelerate inference](./optimization/fp16) or [Caching](./optimization/cache) docs for more methods that speed up inference.
+For inference, pass the noisy image and a `timestep` to the model. The `timestep` indicates how noisy the input image is, with more noise at the beginning and less at the end. This helps the model determine its position in the diffusion process, whether it is closer to the start or the end. Use the `sample` method to get the model output:
+
+```py
+>>> with torch.no_grad():
+...     noisy_residual = model(sample=noisy_sample, timestep=2).sample
+```
+
+To generate actual examples though, you'll need a scheduler to guide the denoising process. In the next section, you'll learn how to couple a model with a scheduler.
+
+## Schedulers
+
+Schedulers manage going from a noisy sample to a less noisy sample given the model output - in this case, it is the `noisy_residual`.
+
+<Tip>
+
+🧨 Diffusers is a toolbox for building diffusion systems. While the [`DiffusionPipeline`] is a convenient way to get started with a pre-built diffusion system, you can also choose your own model and scheduler components separately to build a custom diffusion system.
+
+</Tip>
+
+For the quicktour, you'll instantiate the [`DDPMScheduler`] with its [`~diffusers.ConfigMixin.from_config`] method:
+
+```py
+>>> from diffusers import DDPMScheduler
+
+>>> scheduler = DDPMScheduler.from_pretrained(repo_id)
+>>> scheduler
+DDPMScheduler {
+  "_class_name": "DDPMScheduler",
+  "_diffusers_version": "0.21.4",
+  "beta_end": 0.02,
+  "beta_schedule": "linear",
+  "beta_start": 0.0001,
+  "clip_sample": true,
+  "clip_sample_range": 1.0,
+  "dynamic_thresholding_ratio": 0.995,
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "sample_max_value": 1.0,
+  "steps_offset": 0,
+  "thresholding": false,
+  "timestep_spacing": "leading",
+  "trained_betas": null,
+  "variance_type": "fixed_small"
+}
+```
+
+<Tip>
+
+💡 Unlike a model, a scheduler does not have trainable weights and is parameter-free!
+
+</Tip>
+
+Some of the most important parameters are:
+
+* `num_train_timesteps`: the length of the denoising process or, in other words, the number of timesteps required to process random Gaussian noise into a data sample.
+* `beta_schedule`: the type of noise schedule to use for inference and training.
+* `beta_start` and `beta_end`: the start and end noise values for the noise schedule.
+
+To predict a slightly less noisy image, pass the following to the scheduler's [`~diffusers.DDPMScheduler.step`] method: model output, `timestep`, and current `sample`.
+
+```py
+>>> less_noisy_sample = scheduler.step(model_output=noisy_residual, timestep=2, sample=noisy_sample).prev_sample
+>>> less_noisy_sample.shape
+torch.Size([1, 3, 256, 256])
+```
+
+The `less_noisy_sample` can be passed to the next `timestep` where it'll get even less noisy! Let's bring it all together now and visualize the entire denoising process.
+
+First, create a function that postprocesses and displays the denoised image as a `PIL.Image`:
+
+```py
+>>> import PIL.Image
+>>> import numpy as np
+
+
+>>> def display_sample(sample, i):
+...     image_processed = sample.cpu().permute(0, 2, 3, 1)
+...     image_processed = (image_processed + 1.0) * 127.5
+...     image_processed = image_processed.numpy().astype(np.uint8)
+
+...     image_pil = PIL.Image.fromarray(image_processed[0])
+...     display(f"Image at step {i}")
+...     display(image_pil)
+```
+
+To speed up the denoising process, move the input and model to a GPU:
+
+```py
+>>> model.to("cuda")
+>>> noisy_sample = noisy_sample.to("cuda")
+```
+
+Now create a denoising loop that predicts the residual of the less noisy sample, and computes the less noisy sample with the scheduler:
+
+```py
+>>> import tqdm
+
+>>> sample = noisy_sample
+
+>>> for i, t in enumerate(tqdm.tqdm(scheduler.timesteps)):
+...     # 1. predict noise residual
+...     with torch.no_grad():
+...         residual = model(sample, t).sample
+
+...     # 2. compute less noisy image and set x_t -> x_t-1
+...     sample = scheduler.step(residual, t, sample).prev_sample
+
+...     # 3. optionally look at image
+...     if (i + 1) % 50 == 0:
+...         display_sample(sample, i + 1)
+```
+
+Sit back and watch as a cat is generated from nothing but noise! 😻
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/diffusion-quicktour.png"/>
+</div>
+
+## Next steps
+
+Hopefully, you generated some cool images with 🧨 Diffusers in this quicktour! For your next steps, you can:
+
+* Train or finetune a model to generate your own images in the [training](./tutorials/basic_training) tutorial.
+* See example official and community [training or finetuning scripts](https://github.com/huggingface/diffusers/tree/main/examples#-diffusers-examples) for a variety of use cases.
+* Learn more about loading, accessing, changing, and comparing schedulers in the [Using different Schedulers](./using-diffusers/schedulers) guide.
+* Explore prompt engineering, speed and memory optimizations, and tips and tricks for generating higher-quality images with the [Stable Diffusion](./stable_diffusion) guide.
+* Dive deeper into speeding up 🧨 Diffusers with guides on [optimized PyTorch on a GPU](./optimization/fp16), and inference guides for running [Stable Diffusion on Apple Silicon (M1/M2)](./optimization/mps) and [ONNX Runtime](./optimization/onnx).

docs/source/en/stable_diffusion.md

@@ -10,123 +10,252 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+# Effective and efficient diffusion
+
 [[open-in-colab]]
 
-# Basic performance
+Getting the [`DiffusionPipeline`] to generate images in a certain style or include what you want can be tricky. Often times, you have to run the [`DiffusionPipeline`] several times before you end up with an image you're happy with. But generating something out of nothing is a computationally intensive process, especially if you're running inference over and over again.
 
-Diffusion is a random process that is computationally demanding. You may need to run the [`DiffusionPipeline`] several times before getting a desired output. That's why it's important to carefully balance generation speed and memory usage in order to iterate faster,
+This is why it's important to get the most *computational* (speed) and *memory* (GPU vRAM) efficiency from the pipeline to reduce the time between inference cycles so you can iterate faster.
 
-This guide recommends some basic performance tips for using the [`DiffusionPipeline`]. Refer to the Inference Optimization section docs such as [Accelerate inference](./optimization/fp16) or [Reduce memory usage](./optimization/memory) for more detailed performance guides.
+This tutorial walks you through how to generate faster and better with the [`DiffusionPipeline`].
 
-## Memory usage
+Begin by loading the [`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) model:
 
-Reducing the amount of memory used indirectly speeds up generation and can help a model fit on device.
-
-The [`~DiffusionPipeline.enable_model_cpu_offload`] method moves a model to the CPU when it is not in use to save GPU memory.
-
-```py
-import torch
+```python
 from diffusers import DiffusionPipeline
 
-pipeline = DiffusionPipeline.from_pretrained(
-  "stabilityai/stable-diffusion-xl-base-1.0",
-  torch_dtype=torch.bfloat16,
-  device_map="cuda"
-)
-pipeline.enable_model_cpu_offload()
-
-prompt = """
-cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California
-highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain
-"""
-pipeline(prompt).images[0]
-print(f"Max memory reserved: {torch.cuda.max_memory_allocated() / 1024**3:.2f} GB")
+model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+pipeline = DiffusionPipeline.from_pretrained(model_id, use_safetensors=True)
 ```
 
-## Inference speed
+The example prompt you'll use is a portrait of an old warrior chief, but feel free to use your own prompt:
 
-Denoising is the most computationally demanding process during diffusion. Methods that optimizes this process accelerates inference speed. Try the following methods for a speed up.
+```python
+prompt = "portrait photo of a old warrior chief"
+```
 
-- Add `device_map="cuda"` to place the pipeline on a GPU. Placing a model on an accelerator, like a GPU, increases speed because it performs computations in parallel.
-- Set `torch_dtype=torch.bfloat16` to execute the pipeline in half-precision. Reducing the data type precision increases speed because it takes less time to perform computations in a lower precision.
+## Speed
 
-```py
+<Tip>
+
+💡 If you don't have access to a GPU, you can use one for free from a GPU provider like [Colab](https://colab.research.google.com/)!
+
+</Tip>
+
+One of the simplest ways to speed up inference is to place the pipeline on a GPU the same way you would with any PyTorch module:
+
+```python
+pipeline = pipeline.to("cuda")
+```
+
+To make sure you can use the same image and improve on it, use a [`Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) and set a seed for [reproducibility](./using-diffusers/reusing_seeds):
+
+```python
 import torch
-import time
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
 
-pipeline = DiffusionPipeline.from_pretrained(
-  "stabilityai/stable-diffusion-xl-base-1.0",
-  torch_dtype=torch.bfloat16,
-  device_map="cuda
-)
+generator = torch.Generator("cuda").manual_seed(0)
 ```
 
-- Use a faster scheduler, such as [`DPMSolverMultistepScheduler`], which only requires ~20-25 steps.
-- Set `num_inference_steps` to a lower value. Reducing the number of inference steps reduces the overall number of computations. However, this can result in lower generation quality.
+Now you can generate an image:
+
+```python
+image = pipeline(prompt, generator=generator).images[0]
+image
+```
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_1.png">
+</div>
+
+This process took ~30 seconds on a T4 GPU (it might be faster if your allocated GPU is better than a T4). By default, the [`DiffusionPipeline`] runs inference with full `float32` precision for 50 inference steps. You can speed this up by switching to a lower precision like `float16` or running fewer inference steps.
+
+Let's start by loading the model in `float16` and generate an image:
+
+```python
+import torch
+
+pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, use_safetensors=True)
+pipeline = pipeline.to("cuda")
+generator = torch.Generator("cuda").manual_seed(0)
+image = pipeline(prompt, generator=generator).images[0]
+image
+```
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_2.png">
+</div>
+
+This time, it only took ~11 seconds to generate the image, which is almost 3x faster than before!
+
+<Tip>
+
+💡 We strongly suggest always running your pipelines in `float16`, and so far, we've rarely seen any degradation in output quality.
+
+</Tip>
+
+Another option is to reduce the number of inference steps. Choosing a more efficient scheduler could help decrease the number of steps without sacrificing output quality. You can find which schedulers are compatible with the current model in the [`DiffusionPipeline`] by calling the `compatibles` method:
+
+```python
+pipeline.scheduler.compatibles
+[
+    diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler,
+    diffusers.schedulers.scheduling_unipc_multistep.UniPCMultistepScheduler,
+    diffusers.schedulers.scheduling_k_dpm_2_discrete.KDPM2DiscreteScheduler,
+    diffusers.schedulers.scheduling_deis_multistep.DEISMultistepScheduler,
+    diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler,
+    diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler,
+    diffusers.schedulers.scheduling_ddpm.DDPMScheduler,
+    diffusers.schedulers.scheduling_dpmsolver_singlestep.DPMSolverSinglestepScheduler,
+    diffusers.schedulers.scheduling_k_dpm_2_ancestral_discrete.KDPM2AncestralDiscreteScheduler,
+    diffusers.utils.dummy_torch_and_torchsde_objects.DPMSolverSDEScheduler,
+    diffusers.schedulers.scheduling_heun_discrete.HeunDiscreteScheduler,
+    diffusers.schedulers.scheduling_pndm.PNDMScheduler,
+    diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler,
+    diffusers.schedulers.scheduling_ddim.DDIMScheduler,
+]
+```
+
+The Stable Diffusion model uses the [`PNDMScheduler`] by default which usually requires ~50 inference steps, but more performant schedulers like [`DPMSolverMultistepScheduler`], require only ~20 or 25 inference steps. Use the [`~ConfigMixin.from_config`] method to load a new scheduler:
+
+```python
+from diffusers import DPMSolverMultistepScheduler
 
-```py
 pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
-
-prompt = """
-cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California
-highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain
-"""
-
-start_time = time.perf_counter()
-image = pipeline(prompt).images[0]
-end_time = time.perf_counter()
-
-print(f"Image generation took {end_time - start_time:.3f} seconds")
 ```
 
-## Generation quality
+Now set the `num_inference_steps` to 20:
 
-Many modern diffusion models deliver high-quality images out-of-the-box. However, you can still improve generation quality by trying the following.
+```python
+generator = torch.Generator("cuda").manual_seed(0)
+image = pipeline(prompt, generator=generator, num_inference_steps=20).images[0]
+image
+```
 
-- Try a more detailed and descriptive prompt. Include details such as the image medium, subject, style, and aesthetic. A negative prompt may also help by guiding a model away from undesirable features by using words like low quality or blurry.
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_3.png">
+</div>
 
-    ```py
-    import torch
-    from diffusers import DiffusionPipeline
+Great, you've managed to cut the inference time to just 4 seconds! ⚡️
 
-    pipeline = DiffusionPipeline.from_pretrained(
-        "stabilityai/stable-diffusion-xl-base-1.0",
-        torch_dtype=torch.bfloat16,
-        device_map="cuda"
-    )
+## Memory
 
-    prompt = """
-    cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California
-    highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain
-    """
-    negative_prompt = "low quality, blurry, ugly, poor details"
-    pipeline(prompt, negative_prompt=negative_prompt).images[0]
-    ```
+The other key to improving pipeline performance is consuming less memory, which indirectly implies more speed, since you're often trying to maximize the number of images generated per second. The easiest way to see how many images you can generate at once is to try out different batch sizes until you get an `OutOfMemoryError` (OOM).
 
-    For more details about creating better prompts, take a look at the [Prompt techniques](./using-diffusers/weighted_prompts) doc.
+Create a function that'll generate a batch of images from a list of prompts and `Generators`. Make sure to assign each `Generator` a seed so you can reuse it if it produces a good result.
 
-- Try a different scheduler, like [`HeunDiscreteScheduler`] or [`LMSDiscreteScheduler`], that gives up generation speed for quality.
+```python
+def get_inputs(batch_size=1):
+    generator = [torch.Generator("cuda").manual_seed(i) for i in range(batch_size)]
+    prompts = batch_size * [prompt]
+    num_inference_steps = 20
 
-    ```py
-    import torch
-    from diffusers import DiffusionPipeline, HeunDiscreteScheduler
+    return {"prompt": prompts, "generator": generator, "num_inference_steps": num_inference_steps}
+```
 
-    pipeline = DiffusionPipeline.from_pretrained(
-        "stabilityai/stable-diffusion-xl-base-1.0",
-        torch_dtype=torch.bfloat16,
-        device_map="cuda"
-    )
-    pipeline.scheduler = HeunDiscreteScheduler.from_config(pipeline.scheduler.config)
+Start with `batch_size=4` and see how much memory you've consumed:
 
-    prompt = """
-    cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California
-    highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain
-    """
-    negative_prompt = "low quality, blurry, ugly, poor details"
-    pipeline(prompt, negative_prompt=negative_prompt).images[0]
-    ```
+```python
+from diffusers.utils import make_image_grid
+
+images = pipeline(**get_inputs(batch_size=4)).images
+make_image_grid(images, 2, 2)
+```
+
+Unless you have a GPU with more vRAM, the code above probably returned an `OOM` error! Most of the memory is taken up by the cross-attention layers. Instead of running this operation in a batch, you can run it sequentially to save a significant amount of memory. All you have to do is configure the pipeline to use the [`~DiffusionPipeline.enable_attention_slicing`] function:
+
+```python
+pipeline.enable_attention_slicing()
+```
+
+Now try increasing the `batch_size` to 8!
+
+```python
+images = pipeline(**get_inputs(batch_size=8)).images
+make_image_grid(images, rows=2, cols=4)
+```
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_5.png">
+</div>
+
+Whereas before you couldn't even generate a batch of 4 images, now you can generate a batch of 8 images at ~3.5 seconds per image! This is probably the fastest you can go on a T4 GPU without sacrificing quality.
+
+## Quality
+
+In the last two sections, you learned how to optimize the speed of your pipeline by using `fp16`, reducing the number of inference steps by using a more performant scheduler, and enabling attention slicing to reduce memory consumption. Now you're going to focus on how to improve the quality of generated images.
+
+### Better checkpoints
+
+The most obvious step is to use better checkpoints. The Stable Diffusion model is a good starting point, and since its official launch, several improved versions have also been released. However, using a newer version doesn't automatically mean you'll get better results. You'll still have to experiment with different checkpoints yourself, and do a little research (such as using [negative prompts](https://minimaxir.com/2022/11/stable-diffusion-negative-prompt/)) to get the best results.
+
+As the field grows, there are more and more high-quality checkpoints finetuned to produce certain styles. Try exploring the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) and [Diffusers Gallery](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery) to find one you're interested in!
+
+### Better pipeline components
+
+You can also try replacing the current pipeline components with a newer version. Let's try loading the latest [autoencoder](https://huggingface.co/stabilityai/stable-diffusion-2-1/tree/main/vae) from Stability AI into the pipeline, and generate some images:
+
+```python
+from diffusers import AutoencoderKL
+
+vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16).to("cuda")
+pipeline.vae = vae
+images = pipeline(**get_inputs(batch_size=8)).images
+make_image_grid(images, rows=2, cols=4)
+```
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_6.png">
+</div>
+
+### Better prompt engineering
+
+The text prompt you use to generate an image is super important, so much so that it is called *prompt engineering*. Some considerations to keep during prompt engineering are:
+
+- How is the image or similar images of the one I want to generate stored on the internet?
+- What additional detail can I give that steers the model towards the style I want?
+
+With this in mind, let's improve the prompt to include color and higher quality details:
+
+```python
+prompt += ", tribal panther make up, blue on red, side profile, looking away, serious eyes"
+prompt += " 50mm portrait photography, hard rim lighting photography--beta --ar 2:3  --beta --upbeta"
+```
+
+Generate a batch of images with the new prompt:
+
+```python
+images = pipeline(**get_inputs(batch_size=8)).images
+make_image_grid(images, rows=2, cols=4)
+```
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_7.png">
+</div>
+
+Pretty impressive! Let's tweak the second image - corresponding to the `Generator` with a seed of `1` - a bit more by adding some text about the age of the subject:
+
+```python
+prompts = [
+    "portrait photo of the oldest warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3  --beta --upbeta",
+    "portrait photo of an old warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3  --beta --upbeta",
+    "portrait photo of a warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3  --beta --upbeta",
+    "portrait photo of a young warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3  --beta --upbeta",
+]
+
+generator = [torch.Generator("cuda").manual_seed(1) for _ in range(len(prompts))]
+images = pipeline(prompt=prompts, generator=generator, num_inference_steps=25).images
+make_image_grid(images, 2, 2)
+```
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_8.png">
+</div>
 
 ## Next steps
 
-Diffusers offers more advanced and powerful optimizations such as [group-offloading](./optimization/memory#group-offloading) and [regional compilation](./optimization/fp16#regional-compilation). To learn more about how to maximize performance, take a look at the Inference Optimization section.
+In this tutorial, you learned how to optimize a [`DiffusionPipeline`] for computational and memory efficiency as well as improving the quality of generated outputs. If you're interested in making your pipeline even faster, take a look at the following resources:
+
+- Learn how [PyTorch 2.0](./optimization/fp16) and [`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html) can yield 5 - 300% faster inference speed. On an A100 GPU, inference can be up to 50% faster!
+- If you can't use PyTorch 2, we recommend you install [xFormers](./optimization/xformers). Its memory-efficient attention mechanism works great with PyTorch 1.13.1 for faster speed and reduced memory consumption.
+- Other optimization techniques, such as model offloading, are covered in [this guide](./optimization/fp16).

docs/source/en/training/cogvideox.md

@@ -145,10 +145,10 @@ When running `accelerate config`, if you use torch.compile, there can be dramati
 If you would like to push your model to the Hub after training is completed with a neat model card, make sure you're logged in:
 
 ```bash
-hf auth login
+huggingface-cli login
 
 # Alternatively, you could upload your model manually using:
-# hf upload my-cool-account-name/my-cool-lora-name /path/to/awesome/lora
+# huggingface-cli upload my-cool-account-name/my-cool-lora-name /path/to/awesome/lora
 ```
 
 Make sure your data is prepared as described in [Data Preparation](#data-preparation). When ready, you can begin training!

docs/source/en/training/controlnet.md

@@ -14,7 +14,7 @@ specific language governing permissions and limitations under the License.
 
 [ControlNet](https://hf.co/papers/2302.05543) models are adapters trained on top of another pretrained model. It allows for a greater degree of control over image generation by conditioning the model with an additional input image. The input image can be a canny edge, depth map, human pose, and many more.
 
-If you're training on a GPU with limited vRAM, you should try enabling the `gradient_checkpointing`, `gradient_accumulation_steps`, and `mixed_precision` parameters in the training command. You can also reduce your memory footprint by using memory-efficient attention with [xFormers](../optimization/xformers).
+If you're training on a GPU with limited vRAM, you should try enabling the `gradient_checkpointing`, `gradient_accumulation_steps`, and `mixed_precision` parameters in the training command. You can also reduce your memory footprint by using memory-efficient attention with [xFormers](../optimization/xformers). JAX/Flax training is also supported for efficient training on TPUs and GPUs, but it doesn't support gradient checkpointing or xFormers. You should have a GPU with >30GB of memory if you want to train faster with Flax.
 
 This guide will explore the [train_controlnet.py](https://github.com/huggingface/diffusers/blob/main/examples/controlnet/train_controlnet.py) training script to help you become familiar with it, and how you can adapt it for your own use-case.
 
@@ -28,10 +28,45 @@ pip install .
 
 Then navigate to the example folder containing the training script and install the required dependencies for the script you're using:
 
+<hfoptions id="installation">
+<hfoption id="PyTorch">
 ```bash
 cd examples/controlnet
 pip install -r requirements.txt
 ```
+</hfoption>
+<hfoption id="Flax">
+
+If you have access to a TPU, the Flax training script runs even faster! Let's run the training script on the [Google Cloud TPU VM](https://cloud.google.com/tpu/docs/run-calculation-jax). Create a single TPU v4-8 VM and connect to it:
+
+```bash
+ZONE=us-central2-b
+TPU_TYPE=v4-8
+VM_NAME=hg_flax
+
+gcloud alpha compute tpus tpu-vm create $VM_NAME \
+ --zone $ZONE \
+ --accelerator-type $TPU_TYPE \
+ --version  tpu-vm-v4-base
+
+gcloud alpha compute tpus tpu-vm ssh $VM_NAME --zone $ZONE -- \
+```
+
+Install JAX 0.4.5:
+
+```bash
+pip install "jax[tpu]==0.4.5" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html
+```
+
+Then install the required dependencies for the Flax script:
+
+```bash
+cd examples/controlnet
+pip install -r requirements_flax.txt
+```
+
+</hfoption>
+</hfoptions>
 
 <Tip>
 
@@ -85,7 +120,7 @@ Many of the basic and important parameters are described in the [Text-to-image](
 
 ### Min-SNR weighting
 
-The [Min-SNR](https://huggingface.co/papers/2303.09556) weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting `epsilon` (noise) or `v_prediction`, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch.
+The [Min-SNR](https://huggingface.co/papers/2303.09556) weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting `epsilon` (noise) or `v_prediction`, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch and is unavailable in the Flax training script.
 
 Add the `--snr_gamma` parameter and set it to the recommended value of 5.0:
 
@@ -237,6 +272,9 @@ That's it! You don't need to add any additional parameters to your training comm
 </hfoption>
 </hfoptions>
 
+<hfoptions id="training-inference">
+<hfoption id="PyTorch">
+
 ```bash
 export MODEL_DIR="stable-diffusion-v1-5/stable-diffusion-v1-5"
 export OUTPUT_DIR="path/to/save/model"
@@ -254,6 +292,47 @@ accelerate launch train_controlnet.py \
  --push_to_hub
 ```
 
+</hfoption>
+<hfoption id="Flax">
+
+With Flax, you can [profile your code](https://jax.readthedocs.io/en/latest/profiling.html) by adding the `--profile_steps==5` parameter to your training command. Install the Tensorboard profile plugin:
+
+```bash
+pip install tensorflow tensorboard-plugin-profile
+tensorboard --logdir runs/fill-circle-100steps-20230411_165612/
+```
+
+Then you can inspect the profile at [http://localhost:6006/#profile](http://localhost:6006/#profile).
+
+<Tip warning={true}>
+
+If you run into version conflicts with the plugin, try uninstalling and reinstalling all versions of TensorFlow and Tensorboard. The debugging functionality of the profile plugin is still experimental, and not all views are fully functional. The `trace_viewer` cuts off events after 1M, which can result in all your device traces getting lost if for example, you profile the compilation step by accident.
+
+</Tip>
+
+```bash
+python3 train_controlnet_flax.py \
+ --pretrained_model_name_or_path=$MODEL_DIR \
+ --output_dir=$OUTPUT_DIR \
+ --dataset_name=fusing/fill50k \
+ --resolution=512 \
+ --learning_rate=1e-5 \
+ --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
+ --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
+ --validation_steps=1000 \
+ --train_batch_size=2 \
+ --revision="non-ema" \
+ --from_pt \
+ --report_to="wandb" \
+ --tracker_project_name=$HUB_MODEL_ID \
+ --num_train_epochs=11 \
+ --push_to_hub \
+ --hub_model_id=$HUB_MODEL_ID
+```
+
+</hfoption>
+</hfoptions>
+
 Once training is complete, you can use your newly trained model for inference!
 
 ```py

docs/source/en/training/create_dataset.md

@@ -67,7 +67,7 @@ dataset = load_dataset(
 Then use the [`~datasets.Dataset.push_to_hub`] method to upload the dataset to the Hub:
 
 ```python
-# assuming you have ran the hf auth login command in a terminal
+# assuming you have ran the huggingface-cli login command in a terminal
 dataset.push_to_hub("name_of_your_dataset")
 
 # if you want to push to a private repo, simply pass private=True:

docs/source/en/training/distributed_inference.md

@@ -223,7 +223,7 @@ from diffusers.image_processor import VaeImageProcessor
 import torch 
 
 vae = AutoencoderKL.from_pretrained(ckpt_id, subfolder="vae", torch_dtype=torch.bfloat16).to("cuda")
-vae_scale_factor = 2 ** (len(vae.config.block_out_channels) - 1)
+vae_scale_factor = 2 ** (len(vae.config.block_out_channels))
 image_processor = VaeImageProcessor(vae_scale_factor=vae_scale_factor)
 
 with torch.no_grad():

docs/source/en/training/dreambooth.md

@@ -14,7 +14,7 @@ specific language governing permissions and limitations under the License.
 
 [DreamBooth](https://huggingface.co/papers/2208.12242) is a training technique that updates the entire diffusion model by training on just a few images of a subject or style. It works by associating a special word in the prompt with the example images.
 
-If you're training on a GPU with limited vRAM, you should try enabling the `gradient_checkpointing` and `mixed_precision` parameters in the training command. You can also reduce your memory footprint by using memory-efficient attention with [xFormers](../optimization/xformers).
+If you're training on a GPU with limited vRAM, you should try enabling the `gradient_checkpointing` and `mixed_precision` parameters in the training command. You can also reduce your memory footprint by using memory-efficient attention with [xFormers](../optimization/xformers). JAX/Flax training is also supported for efficient training on TPUs and GPUs, but it doesn't support gradient checkpointing or xFormers. You should have a GPU with >30GB of memory if you want to train faster with Flax.
 
 This guide will explore the [train_dreambooth.py](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py) script to help you become more familiar with it, and how you can adapt it for your own use-case.
 
@@ -28,11 +28,25 @@ pip install .
 
 Navigate to the example folder with the training script and install the required dependencies for the script you're using:
 
+<hfoptions id="installation">
+<hfoption id="PyTorch">
+
 ```bash
 cd examples/dreambooth
 pip install -r requirements.txt
 ```
 
+</hfoption>
+<hfoption id="Flax">
+
+```bash
+cd examples/dreambooth
+pip install -r requirements_flax.txt
+```
+
+</hfoption>
+</hfoptions>
+
 <Tip>
 
 🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co/docs/accelerate/quicktour) to learn more.
@@ -96,7 +110,7 @@ Some basic and important parameters to know and specify are:
 
 ### Min-SNR weighting
 
-The [Min-SNR](https://huggingface.co/papers/2303.09556) weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting `epsilon` (noise) or `v_prediction`, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch.
+The [Min-SNR](https://huggingface.co/papers/2303.09556) weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting `epsilon` (noise) or `v_prediction`, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch and is unavailable in the Flax training script.
 
 Add the `--snr_gamma` parameter and set it to the recommended value of 5.0:
 
@@ -297,6 +311,9 @@ That's it! You don't need to add any additional parameters to your training comm
 </hfoption>
 </hfoptions>
 
+<hfoptions id="training-inference">
+<hfoption id="PyTorch">
+
 ```bash
 export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
 export INSTANCE_DIR="./dog"
@@ -317,6 +334,29 @@ accelerate launch train_dreambooth.py \
   --push_to_hub
 ```
 
+</hfoption>
+<hfoption id="Flax">
+
+```bash
+export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
+export INSTANCE_DIR="./dog"
+export OUTPUT_DIR="path-to-save-model"
+
+python train_dreambooth_flax.py \
+  --pretrained_model_name_or_path=$MODEL_NAME  \
+  --instance_data_dir=$INSTANCE_DIR \
+  --output_dir=$OUTPUT_DIR \
+  --instance_prompt="a photo of sks dog" \
+  --resolution=512 \
+  --train_batch_size=1 \
+  --learning_rate=5e-6 \
+  --max_train_steps=400 \
+  --push_to_hub
+```
+
+</hfoption>
+</hfoptions>
+
 Once training is complete, you can use your newly trained model for inference!
 
 <Tip>
@@ -343,6 +383,9 @@ image.save("dog-bucket.png")
 
 </Tip>
 
+<hfoptions id="training-inference">
+<hfoption id="PyTorch">
+
 ```py
 from diffusers import DiffusionPipeline
 import torch
@@ -352,6 +395,39 @@ image = pipeline("A photo of sks dog in a bucket", num_inference_steps=50, guida
 image.save("dog-bucket.png")
 ```
 
+</hfoption>
+<hfoption id="Flax">
+
+```py
+import jax
+import numpy as np
+from flax.jax_utils import replicate
+from flax.training.common_utils import shard
+from diffusers import FlaxStableDiffusionPipeline
+
+pipeline, params = FlaxStableDiffusionPipeline.from_pretrained("path-to-your-trained-model", dtype=jax.numpy.bfloat16)
+
+prompt = "A photo of sks dog in a bucket"
+prng_seed = jax.random.PRNGKey(0)
+num_inference_steps = 50
+
+num_samples = jax.device_count()
+prompt = num_samples * [prompt]
+prompt_ids = pipeline.prepare_inputs(prompt)
+
+# shard inputs and rng
+params = replicate(params)
+prng_seed = jax.random.split(prng_seed, jax.device_count())
+prompt_ids = shard(prompt_ids)
+
+images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
+images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
+image.save("dog-bucket.png")
+```
+
+</hfoption>
+</hfoptions>
+
 ## LoRA
 
 LoRA is a training technique for significantly reducing the number of trainable parameters. As a result, training is faster and it is easier to store the resulting weights because they are a lot smaller (~100MBs). Use the [train_dreambooth_lora.py](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora.py) script to train with LoRA.

docs/source/en/training/kandinsky.md

@@ -88,7 +88,7 @@ Most of the parameters are identical to the parameters in the [Text-to-image](te
 
 ### Min-SNR weighting
 
-The [Min-SNR](https://huggingface.co/papers/2303.09556) weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting `epsilon` (noise) or `v_prediction`, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch.
+The [Min-SNR](https://huggingface.co/papers/2303.09556) weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting `epsilon` (noise) or `v_prediction`, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch and is unavailable in the Flax training script.
 
 Add the `--snr_gamma` parameter and set it to the recommended value of 5.0:
 

docs/source/en/training/lora.md

@@ -38,11 +38,25 @@ pip install .
 
 Navigate to the example folder with the training script and install the required dependencies for the script you're using:
 
+<hfoptions id="installation">
+<hfoption id="PyTorch">
+
 ```bash
 cd examples/text_to_image
 pip install -r requirements.txt
 ```
 
+</hfoption>
+<hfoption id="Flax">
+
+```bash
+cd examples/text_to_image
+pip install -r requirements_flax.txt
+```
+
+</hfoption>
+</hfoptions>
+
 <Tip>
 
 🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co/docs/accelerate/quicktour) to learn more.

docs/source/en/training/overview.md

@@ -23,18 +23,18 @@ Each training script is:
 
 Our current collection of training scripts include:
 
-| Training | SDXL-support | LoRA-support |
-|---|---|---|
-| [unconditional image generation](https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) |  |  |
-| [text-to-image](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image) | 👍 | 👍 |
-| [textual inversion](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb) |  |  |
-| [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb) | 👍 | 👍 |
-| [ControlNet](https://github.com/huggingface/diffusers/tree/main/examples/controlnet) | 👍 |  |
-| [InstructPix2Pix](https://github.com/huggingface/diffusers/tree/main/examples/instruct_pix2pix) | 👍 |  |
-| [Custom Diffusion](https://github.com/huggingface/diffusers/tree/main/examples/custom_diffusion) |  |  |
-| [T2I-Adapters](https://github.com/huggingface/diffusers/tree/main/examples/t2i_adapter) | 👍 |  |
-| [Kandinsky 2.2](https://github.com/huggingface/diffusers/tree/main/examples/kandinsky2_2/text_to_image) |  | 👍 |
-| [Wuerstchen](https://github.com/huggingface/diffusers/tree/main/examples/wuerstchen/text_to_image) |  | 👍 |
+| Training | SDXL-support | LoRA-support | Flax-support |
+|---|---|---|---|
+| [unconditional image generation](https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) |  |  |  |
+| [text-to-image](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image) | 👍 | 👍 | 👍 |
+| [textual inversion](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb) |  |  | 👍 |
+| [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb) | 👍 | 👍 | 👍 |
+| [ControlNet](https://github.com/huggingface/diffusers/tree/main/examples/controlnet) | 👍 |  | 👍 |
+| [InstructPix2Pix](https://github.com/huggingface/diffusers/tree/main/examples/instruct_pix2pix) | 👍 |  |  |
+| [Custom Diffusion](https://github.com/huggingface/diffusers/tree/main/examples/custom_diffusion) |  |  |  |
+| [T2I-Adapters](https://github.com/huggingface/diffusers/tree/main/examples/t2i_adapter) | 👍 |  |  |
+| [Kandinsky 2.2](https://github.com/huggingface/diffusers/tree/main/examples/kandinsky2_2/text_to_image) |  | 👍 |  |
+| [Wuerstchen](https://github.com/huggingface/diffusers/tree/main/examples/wuerstchen/text_to_image) |  | 👍 |  |
 
 These examples are **actively** maintained, so please feel free to open an issue if they aren't working as expected. If you feel like another training example should be included, you're more than welcome to start a [Feature Request](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=) to discuss your feature idea with us and whether it meets our criteria of being self-contained, easy-to-tweak, beginner-friendly, and single-purpose.
 
@@ -48,7 +48,7 @@ cd diffusers
 pip install .
 ```
 
-Then navigate to the folder of the training script (for example, [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth)) and install the `requirements.txt` file. Some training scripts have a specific requirement file for SDXL or LoRA. If you're using one of these scripts, make sure you install its corresponding requirements file.
+Then navigate to the folder of the training script (for example, [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth)) and install the `requirements.txt` file. Some training scripts have a specific requirement file for SDXL, LoRA or Flax. If you're using one of these scripts, make sure you install its corresponding requirements file.
 
 ```bash
 cd examples/dreambooth

docs/source/en/training/sdxl.md

@@ -96,7 +96,7 @@ Most of the parameters are identical to the parameters in the [Text-to-image](te
 
 ### Min-SNR weighting
 
-The [Min-SNR](https://huggingface.co/papers/2303.09556) weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting either `epsilon` (noise) or `v_prediction`, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch.
+The [Min-SNR](https://huggingface.co/papers/2303.09556) weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting either `epsilon` (noise) or `v_prediction`, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch and is unavailable in the Flax training script.
 
 Add the `--snr_gamma` parameter and set it to the recommended value of 5.0:
 

docs/source/en/training/text2image.md

@@ -20,7 +20,7 @@ The text-to-image script is experimental, and it's easy to overfit and run into
 
 Text-to-image models like Stable Diffusion are conditioned to generate images given a text prompt.
 
-Training a model can be taxing on your hardware, but if you enable `gradient_checkpointing` and `mixed_precision`, it is possible to train a model on a single 24GB GPU. If you're training with larger batch sizes or want to train faster, it's better to use GPUs with more than 30GB of memory. You can reduce your memory footprint by enabling memory-efficient attention with [xFormers](../optimization/xformers).
+Training a model can be taxing on your hardware, but if you enable `gradient_checkpointing` and `mixed_precision`, it is possible to train a model on a single 24GB GPU. If you're training with larger batch sizes or want to train faster, it's better to use GPUs with more than 30GB of memory. You can reduce your memory footprint by enabling memory-efficient attention with [xFormers](../optimization/xformers). JAX/Flax training is also supported for efficient training on TPUs and GPUs, but it doesn't support gradient checkpointing, gradient accumulation or xFormers. A GPU with at least 30GB of memory or a TPU v3 is recommended for training with Flax.
 
 This guide will explore the [train_text_to_image.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) training script to help you become familiar with it, and how you can adapt it for your own use-case.
 
@@ -34,10 +34,20 @@ pip install .
 
 Then navigate to the example folder containing the training script and install the required dependencies for the script you're using:
 
+<hfoptions id="installation">
+<hfoption id="PyTorch">
 ```bash
 cd examples/text_to_image
 pip install -r requirements.txt
 ```
+</hfoption>
+<hfoption id="Flax">
+```bash
+cd examples/text_to_image
+pip install -r requirements_flax.txt
+```
+</hfoption>
+</hfoptions>
 
 <Tip>
 
@@ -96,7 +106,7 @@ Some basic and important parameters include:
 
 ### Min-SNR weighting
 
-The [Min-SNR](https://huggingface.co/papers/2303.09556) weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting `epsilon` (noise) or `v_prediction`, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch.
+The [Min-SNR](https://huggingface.co/papers/2303.09556) weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting `epsilon` (noise) or `v_prediction`, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch and is unavailable in the Flax training script.
 
 Add the `--snr_gamma` parameter and set it to the recommended value of 5.0:
 
@@ -145,6 +155,9 @@ Lastly, the [training loop](https://github.com/huggingface/diffusers/blob/8959c5
 
 Once you've made all your changes or you're okay with the default configuration, you're ready to launch the training script! 🚀
 
+<hfoptions id="training-inference">
+<hfoption id="PyTorch">
+
 Let's train on the [Naruto BLIP captions](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions) dataset to generate your own Naruto characters. Set the environment variables `MODEL_NAME` and `dataset_name` to the model and the dataset (either from the Hub or a local path). If you're training on more than one GPU, add the `--multi_gpu` parameter to the `accelerate launch` command.
 
 <Tip>
@@ -174,8 +187,43 @@ accelerate launch --mixed_precision="fp16"  train_text_to_image.py \
   --push_to_hub
 ```
 
+</hfoption>
+<hfoption id="Flax">
+
+Training with Flax can be faster on TPUs and GPUs thanks to [@duongna211](https://github.com/duongna21). Flax is more efficient on a TPU, but GPU performance is also great.
+
+Set the environment variables `MODEL_NAME` and `dataset_name` to the model and the dataset (either from the Hub or a local path).
+
+<Tip>
+
+To train on a local dataset, set the `TRAIN_DIR` and `OUTPUT_DIR` environment variables to the path of the dataset and where to save the model to.
+
+</Tip>
+
+```bash
+export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
+export dataset_name="lambdalabs/naruto-blip-captions"
+
+python train_text_to_image_flax.py \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --dataset_name=$dataset_name \
+  --resolution=512 --center_crop --random_flip \
+  --train_batch_size=1 \
+  --max_train_steps=15000 \
+  --learning_rate=1e-05 \
+  --max_grad_norm=1 \
+  --output_dir="sd-naruto-model" \
+  --push_to_hub
+```
+
+</hfoption>
+</hfoptions>
+
 Once training is complete, you can use your newly trained model for inference:
 
+<hfoptions id="training-inference">
+<hfoption id="PyTorch">
+
 ```py
 from diffusers import StableDiffusionPipeline
 import torch
@@ -186,6 +234,39 @@ image = pipeline(prompt="yoda").images[0]
 image.save("yoda-naruto.png")
 ```
 
+</hfoption>
+<hfoption id="Flax">
+
+```py
+import jax
+import numpy as np
+from flax.jax_utils import replicate
+from flax.training.common_utils import shard
+from diffusers import FlaxStableDiffusionPipeline
+
+pipeline, params = FlaxStableDiffusionPipeline.from_pretrained("path/to/saved_model", dtype=jax.numpy.bfloat16)
+
+prompt = "yoda naruto"
+prng_seed = jax.random.PRNGKey(0)
+num_inference_steps = 50
+
+num_samples = jax.device_count()
+prompt = num_samples * [prompt]
+prompt_ids = pipeline.prepare_inputs(prompt)
+
+# shard inputs and rng
+params = replicate(params)
+prng_seed = jax.random.split(prng_seed, jax.device_count())
+prompt_ids = shard(prompt_ids)
+
+images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
+images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
+image.save("yoda-naruto.png")
+```
+
+</hfoption>
+</hfoptions>
+
 ## Next steps
 
 Congratulations on training your own text-to-image model! To learn more about how to use your new model, the following guides may be helpful:

docs/source/en/training/text_inversion.md

@@ -14,7 +14,7 @@ specific language governing permissions and limitations under the License.
 
 [Textual Inversion](https://hf.co/papers/2208.01618) is a training technique for personalizing image generation models with just a few example images of what you want it to learn. This technique works by learning and updating the text embeddings (the new embeddings are tied to a special word you must use in the prompt) to match the example images you provide.
 
-If you're training on a GPU with limited vRAM, you should try enabling the `gradient_checkpointing` and `mixed_precision` parameters in the training command. You can also reduce your memory footprint by using memory-efficient attention with [xFormers](../optimization/xformers).
+If you're training on a GPU with limited vRAM, you should try enabling the `gradient_checkpointing` and `mixed_precision` parameters in the training command. You can also reduce your memory footprint by using memory-efficient attention with [xFormers](../optimization/xformers). JAX/Flax training is also supported for efficient training on TPUs and GPUs, but it doesn't support gradient checkpointing or xFormers. With the same configuration and setup as PyTorch, the Flax training script should be at least ~70% faster!
 
 This guide will explore the [textual_inversion.py](https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py) script to help you become more familiar with it, and how you can adapt it for your own use-case.
 
@@ -28,10 +28,25 @@ pip install .
 
 Navigate to the example folder with the training script and install the required dependencies for the script you're using:
 
+<hfoptions id="installation">
+<hfoption id="PyTorch">
+
 ```bash
 cd examples/textual_inversion
 pip install -r requirements.txt
 ```
+
+</hfoption>
+<hfoption id="Flax">
+
+```bash
+cd examples/textual_inversion
+pip install -r requirements_flax.txt
+```
+
+</hfoption>
+</hfoptions>
+
 <Tip>
 
 🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co/docs/accelerate/quicktour) to learn more.
@@ -174,6 +189,9 @@ One more thing before you launch the script. If you're interested in following a
 --validation_steps=100
 ```
 
+<hfoptions id="training-inference">
+<hfoption id="PyTorch">
+
 ```bash
 export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
 export DATA_DIR="./cat"
@@ -196,8 +214,36 @@ accelerate launch textual_inversion.py \
   --push_to_hub
 ```
 
+</hfoption>
+<hfoption id="Flax">
+
+```bash
+export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
+export DATA_DIR="./cat"
+
+python textual_inversion_flax.py \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --train_data_dir=$DATA_DIR \
+  --learnable_property="object" \
+  --placeholder_token="<cat-toy>" \
+  --initializer_token="toy" \
+  --resolution=512 \
+  --train_batch_size=1 \
+  --max_train_steps=3000 \
+  --learning_rate=5.0e-04 \
+  --scale_lr \
+  --output_dir="textual_inversion_cat" \
+  --push_to_hub
+```
+
+</hfoption>
+</hfoptions>
+
 After training is complete, you can use your newly trained model for inference like:
 
+<hfoptions id="training-inference">
+<hfoption id="PyTorch">
+
 ```py
 from diffusers import StableDiffusionPipeline
 import torch
@@ -208,6 +254,42 @@ image = pipeline("A <cat-toy> train", num_inference_steps=50).images[0]
 image.save("cat-train.png")
 ```
 
+</hfoption>
+<hfoption id="Flax">
+
+Flax doesn't support the [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] method, but the textual_inversion_flax.py script [saves](https://github.com/huggingface/diffusers/blob/c0f058265161178f2a88849e92b37ffdc81f1dcc/examples/textual_inversion/textual_inversion_flax.py#L636C2-L636C2) the learned embeddings as a part of the model after training. This means you can use the model for inference like any other Flax model:
+
+```py
+import jax
+import numpy as np
+from flax.jax_utils import replicate
+from flax.training.common_utils import shard
+from diffusers import FlaxStableDiffusionPipeline
+
+model_path = "path-to-your-trained-model"
+pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(model_path, dtype=jax.numpy.bfloat16)
+
+prompt = "A <cat-toy> train"
+prng_seed = jax.random.PRNGKey(0)
+num_inference_steps = 50
+
+num_samples = jax.device_count()
+prompt = num_samples * [prompt]
+prompt_ids = pipeline.prepare_inputs(prompt)
+
+# shard inputs and rng
+params = replicate(params)
+prng_seed = jax.random.split(prng_seed, jax.device_count())
+prompt_ids = shard(prompt_ids)
+
+images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
+images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
+image.save("cat-train.png")
+```
+
+</hfoption>
+</hfoptions>
+
 ## Next steps
 
 Congratulations on training your own Textual Inversion model! 🎉 To learn more about how to use your new model, the following guides may be helpful:

docs/source/en/tutorials/autopipeline.md

@@ -12,56 +12,112 @@ specific language governing permissions and limitations under the License.
 
 # AutoPipeline
 
-[AutoPipeline](../api/models/auto_model) is a *task-and-model* pipeline that automatically selects the correct pipeline subclass based on the task. It handles the complexity of loading different pipeline subclasses without needing to know the specific pipeline subclass name.
+Diffusers provides many pipelines for basic tasks like generating images, videos, audio, and inpainting. On top of these, there are specialized pipelines for adapters and features like upscaling, super-resolution, and more. Different pipeline classes can even use the same checkpoint because they share the same pretrained model! With so many different pipelines, it can be overwhelming to know which pipeline class to use.
 
-This is unlike [`DiffusionPipeline`], a *model-only* pipeline that automatically selects the pipeline subclass based on the model.
+The [AutoPipeline](../api/pipelines/auto_pipeline) class is designed to simplify the variety of pipelines in Diffusers. It is a generic *task-first* pipeline that lets you focus on a task ([`AutoPipelineForText2Image`], [`AutoPipelineForImage2Image`], and [`AutoPipelineForInpainting`]) without needing to know the specific pipeline class. The [AutoPipeline](../api/pipelines/auto_pipeline) automatically detects the correct pipeline class to use.
 
-[`AutoPipelineForImage2Image`] returns a specific pipeline subclass, (for example, [`StableDiffusionXLImg2ImgPipeline`]), which can only be used for image-to-image tasks.
+For example, let's use the [dreamlike-art/dreamlike-photoreal-2.0](https://hf.co/dreamlike-art/dreamlike-photoreal-2.0) checkpoint.
+
+Under the hood, [AutoPipeline](../api/pipelines/auto_pipeline):
+
+1. Detects a `"stable-diffusion"` class from the [model_index.json](https://hf.co/dreamlike-art/dreamlike-photoreal-2.0/blob/main/model_index.json) file.
+2. Depending on the task you're interested in, it loads the [`StableDiffusionPipeline`], [`StableDiffusionImg2ImgPipeline`], or [`StableDiffusionInpaintPipeline`]. Any parameter (`strength`, `num_inference_steps`, etc.) you would pass to these specific pipelines can also be passed to the [AutoPipeline](../api/pipelines/auto_pipeline).
+
+<hfoptions id="autopipeline">
+<hfoption id="text-to-image">
 
 ```py
+from diffusers import AutoPipelineForText2Image
 import torch
-from diffusers import AutoPipelineForImage2Image
 
-pipeline = AutoPipelineForImage2Image.from_pretrained(
-  "RunDiffusion/Juggernaut-XL-v9", torch_dtype=torch.bfloat16, device_map="cuda",
-)
-print(pipeline)
-"StableDiffusionXLImg2ImgPipeline {
-  "_class_name": "StableDiffusionXLImg2ImgPipeline",
-  ...
-"
+pipe_txt2img = AutoPipelineForText2Image.from_pretrained(
+    "dreamlike-art/dreamlike-photoreal-2.0", torch_dtype=torch.float16, use_safetensors=True
+).to("cuda")
+
+prompt = "cinematic photo of Godzilla eating sushi with a cat in a izakaya, 35mm photograph, film, professional, 4k, highly detailed"
+generator = torch.Generator(device="cpu").manual_seed(37)
+image = pipe_txt2img(prompt, generator=generator).images[0]
+image
 ```
 
-Loading the same model with [`DiffusionPipeline`] returns the [`StableDiffusionXLPipeline`] subclass. It can be used for text-to-image, image-to-image, or inpainting tasks depending on the inputs.
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/autopipeline-text2img.png"/>
+</div>
+
+</hfoption>
+<hfoption id="image-to-image">
 
 ```py
+from diffusers import AutoPipelineForImage2Image
+from diffusers.utils import load_image
 import torch
-from diffusers import DiffusionPipeline
 
-pipeline = DiffusionPipeline.from_pretrained(
-  "RunDiffusion/Juggernaut-XL-v9", torch_dtype=torch.bfloat16, device_map="cuda",
-)
-print(pipeline)
-"StableDiffusionXLPipeline {
-  "_class_name": "StableDiffusionXLPipeline",
-  ...
-"
+pipe_img2img = AutoPipelineForImage2Image.from_pretrained(
+    "dreamlike-art/dreamlike-photoreal-2.0", torch_dtype=torch.float16, use_safetensors=True
+).to("cuda")
+
+init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/autopipeline-text2img.png")
+
+prompt = "cinematic photo of Godzilla eating burgers with a cat in a fast food restaurant, 35mm photograph, film, professional, 4k, highly detailed"
+generator = torch.Generator(device="cpu").manual_seed(53)
+image = pipe_img2img(prompt, image=init_image, generator=generator).images[0]
+image
 ```
 
-Check the [mappings](https://github.com/huggingface/diffusers/blob/130fd8df54f24ffb006d84787b598d8adc899f23/src/diffusers/pipelines/auto_pipeline.py#L114) to see whether a model is supported or not.
-
-Trying to load an unsupported model returns an error.
+Notice how the [dreamlike-art/dreamlike-photoreal-2.0](https://hf.co/dreamlike-art/dreamlike-photoreal-2.0) checkpoint is used for both text-to-image and image-to-image tasks? To save memory and avoid loading the checkpoint twice, use the [`~DiffusionPipeline.from_pipe`] method.
 
 ```py
+pipe_img2img = AutoPipelineForImage2Image.from_pipe(pipe_txt2img).to("cuda")
+image = pipeline(prompt, image=init_image, generator=generator).images[0]
+image
+```
+
+You can learn more about the [`~DiffusionPipeline.from_pipe`] method in the [Reuse a pipeline](../using-diffusers/loading#reuse-a-pipeline) guide.
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/autopipeline-img2img.png"/>
+</div>
+
+</hfoption>
+<hfoption id="inpainting">
+
+```py
+from diffusers import AutoPipelineForInpainting
+from diffusers.utils import load_image
 import torch
+
+pipeline = AutoPipelineForInpainting.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True
+).to("cuda")
+
+init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/autopipeline-img2img.png")
+mask_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/autopipeline-mask.png")
+
+prompt = "cinematic photo of a owl, 35mm photograph, film, professional, 4k, highly detailed"
+generator = torch.Generator(device="cpu").manual_seed(38)
+image = pipeline(prompt, image=init_image, mask_image=mask_image, generator=generator, strength=0.4).images[0]
+image
+```
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/autopipeline-inpaint.png"/>
+</div>
+
+</hfoption>
+</hfoptions>
+
+## Unsupported checkpoints
+
+The [AutoPipeline](../api/pipelines/auto_pipeline) supports [Stable Diffusion](../api/pipelines/stable_diffusion/overview), [Stable Diffusion XL](../api/pipelines/stable_diffusion/stable_diffusion_xl), [ControlNet](../api/pipelines/controlnet), [Kandinsky 2.1](../api/pipelines/kandinsky.md), [Kandinsky 2.2](../api/pipelines/kandinsky_v22), and [DeepFloyd IF](../api/pipelines/deepfloyd_if) checkpoints.
+
+If you try to load an unsupported checkpoint, you'll get an error.
+
+```py
 from diffusers import AutoPipelineForImage2Image
+import torch
 
 pipeline = AutoPipelineForImage2Image.from_pretrained(
-    "openai/shap-e-img2img", torch_dtype=torch.float16,
+    "openai/shap-e-img2img", torch_dtype=torch.float16, use_safetensors=True
 )
 "ValueError: AutoPipeline can't find a pipeline linked to ShapEImg2ImgPipeline for None"
 ```
-
-There are three types of [AutoPipeline](../api/models/auto_model) classes, [`AutoPipelineForText2Image`], [`AutoPipelineForImage2Image`] and [`AutoPipelineForInpainting`]. Each of these classes have a predefined mapping, linking a pipeline to their task-specific subclass.
-
-When [`~AutoPipelineForText2Image.from_pretrained`] is called, it extracts the class name from the `model_index.json` file and selects the appropriate pipeline subclass for the task based on the mapping.

docs/source/en/tutorials/basic_training.md

@@ -42,7 +42,7 @@ We encourage you to share your model with the community, and in order to do that
 Or login in from the terminal:
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 Since the model checkpoints are quite large, install [Git-LFS](https://git-lfs.com/) to version these large files:

docs/source/en/tutorials/tutorial_overview.md

@@ -0,0 +1,23 @@
+<!--Copyright 2025 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.
+-->
+
+# Overview
+
+Welcome to 🧨 Diffusers! If you're new to diffusion models and generative AI, and want to learn more, then you've come to the right place. These beginner-friendly tutorials are designed to provide a gentle introduction to diffusion models and help you understand the library fundamentals - the core components and how 🧨 Diffusers is meant to be used.
+
+You'll learn how to use a pipeline for inference to rapidly generate things, and then deconstruct that pipeline to really understand how to use the library as a modular toolbox for building your own diffusion systems. In the next lesson, you'll learn how to train your own diffusion model to generate what you want.
+
+After completing the tutorials, you'll have gained the necessary skills to start exploring the library on your own and see how to use it for your own projects and applications.
+
+Feel free to join our community on [Discord](https://discord.com/invite/JfAtkvEtRb) or the [forums](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) to connect and collaborate with other users and developers!
+
+Let's start diffusing! 🧨

docs/source/en/tutorials/using_peft_for_inference.md

@@ -94,7 +94,7 @@ pipeline = AutoPipelineForText2Image.from_pretrained(
 pipeline.unet.load_lora_adapter(
     "jbilcke-hf/sdxl-cinematic-1",
     weight_name="pytorch_lora_weights.safetensors",
-    adapter_name="cinematic",
+    adapter_name="cinematic"
     prefix="unet"
 )
 # use cnmt in the prompt to trigger the LoRA
@@ -319,19 +319,6 @@ If you expect to varied resolutions during inference with this feature, then mak
 
 There are still scenarios where recompulation is unavoidable, such as when the hotswapped LoRA targets more layers than the initial adapter. Try to load the LoRA that targets the most layers *first*. For more details about this limitation, refer to the PEFT [hotswapping](https://huggingface.co/docs/peft/main/en/package_reference/hotswap#peft.utils.hotswap.hotswap_adapter) docs.
 
-<details>
-<summary>Technical details of hotswapping</summary>
-
-The [`~loaders.lora_base.LoraBaseMixin.enable_lora_hotswap`] method converts the LoRA scaling factor from floats to torch.tensors and pads the shape of the weights to the largest required shape to avoid reassigning the whole attribute when the data in the weights are replaced.
-
-This is why the `max_rank` argument is important. The results are unchanged even when the values are padded with zeros. Computation may be slower though depending on the padding size.
-
-Since no new LoRA attributes are added, each subsequent LoRA is only allowed to target the same layers, or subset of layers, the first LoRA targets. Choosing the LoRA loading order is important because if the LoRAs target disjoint layers, you may end up creating a dummy LoRA that targets the union of all target layers.
-
-For more implementation details, take a look at the [`hotswap.py`](https://github.com/huggingface/peft/blob/92d65cafa51c829484ad3d95cf71d09de57ff066/src/peft/utils/hotswap.py) file.
-
-</details>
-
 ## Merge
 
 The weights from each LoRA can be merged together to produce a blend of multiple existing styles. There are several methods for merging LoRAs, each of which differ in *how* the weights are merged (may affect generation quality).
@@ -686,6 +673,4 @@ Browse the [LoRA Studio](https://lorastudio.co/models) for different LoRAs to us
 	height="450"
 ></iframe>
 
-You can find additional LoRAs in the [FLUX LoRA the Explorer](https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer) and [LoRA the Explorer](https://huggingface.co/spaces/multimodalart/LoraTheExplorer) Spaces.
-
-Check out the [Fast LoRA inference for Flux with Diffusers and PEFT](https://huggingface.co/blog/lora-fast) blog post to learn how to optimize LoRA inference with methods like FlashAttention-3 and fp8 quantization.
+You can find additional LoRAs in the [FLUX LoRA the Explorer](https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer) and [LoRA the Explorer](https://huggingface.co/spaces/multimodalart/LoraTheExplorer) Spaces.

docs/source/en/using-diffusers/callback.md

@@ -12,37 +12,52 @@ specific language governing permissions and limitations under the License.
 
 # Pipeline callbacks
 
-A callback is a function that modifies [`DiffusionPipeline`] behavior and it is executed at the end of a denoising step. The changes are propagated to subsequent steps in the denoising process. It is useful for adjusting pipeline attributes or tensor variables to support new features without rewriting the underlying pipeline code.
+The denoising loop of a pipeline can be modified with custom defined functions using the `callback_on_step_end` parameter. The callback function is executed at the end of each step, and modifies the pipeline attributes and variables for the next step. This is really useful for *dynamically* adjusting certain pipeline attributes or modifying tensor variables. This versatility allows for interesting use cases such as changing the prompt embeddings at each timestep, assigning different weights to the prompt embeddings, and editing the guidance scale. With callbacks, you can implement new features without modifying the underlying code!
 
-Diffusers provides several callbacks in the pipeline [overview](../api/pipelines/overview#callbacks).
+> [!TIP]
+> 🤗 Diffusers currently only supports `callback_on_step_end`, but feel free to open a [feature request](https://github.com/huggingface/diffusers/issues/new/choose) if you have a cool use-case and require a callback function with a different execution point!
 
-To enable a callback, configure when the callback is executed after a certain number of denoising steps with one of the following arguments.
+This guide will demonstrate how callbacks work by a few features you can implement with them.
 
-- `cutoff_step_ratio` specifies when a callback is activated as a percentage of the total denoising steps.
-- `cutoff_step_index` specifies the exact step number a callback is activated.
+## Official callbacks
 
-The example below uses `cutoff_step_ratio=0.4`, which means the callback is activated once denoising reaches 40% of the total inference steps. [`~callbacks.SDXLCFGCutoffCallback`] disables classifier-free guidance (CFG) after a certain number of steps, which can help save compute without significantly affecting performance.
+We provide a list of callbacks you can plug into an existing pipeline and modify the denoising loop. This is the current list of official callbacks:
 
-Define a callback with either of the `cutoff` arguments and pass it to the `callback_on_step_end` parameter in the pipeline.
+- `SDCFGCutoffCallback`: Disables the CFG after a certain number of steps for all SD 1.5 pipelines, including text-to-image, image-to-image, inpaint, and controlnet.
+- `SDXLCFGCutoffCallback`: Disables the CFG after a certain number of steps for all SDXL pipelines, including text-to-image, image-to-image, inpaint, and controlnet.
+- `IPAdapterScaleCutoffCallback`: Disables the IP Adapter after a certain number of steps for all pipelines supporting IP-Adapter.
 
-```py
+> [!TIP]
+> If you want to add a new official callback, feel free to open a [feature request](https://github.com/huggingface/diffusers/issues/new/choose) or [submit a PR](https://huggingface.co/docs/diffusers/main/en/conceptual/contribution#how-to-open-a-pr).
+
+To set up a callback, you need to specify the number of denoising steps after which the callback comes into effect. You can do so by using either one of these two arguments
+
+- `cutoff_step_ratio`: Float number with the ratio of the steps.
+- `cutoff_step_index`: Integer number with the exact number of the step.
+
+```python
 import torch
+
 from diffusers import DPMSolverMultistepScheduler, StableDiffusionXLPipeline
 from diffusers.callbacks import SDXLCFGCutoffCallback
 
+
 callback = SDXLCFGCutoffCallback(cutoff_step_ratio=0.4)
-# if using cutoff_step_index
+# can also be used with cutoff_step_index
 # callback = SDXLCFGCutoffCallback(cutoff_step_ratio=None, cutoff_step_index=10)
 
 pipeline = StableDiffusionXLPipeline.from_pretrained(
     "stabilityai/stable-diffusion-xl-base-1.0",
     torch_dtype=torch.float16,
-    device_map="cuda"
-)
+    variant="fp16",
+).to("cuda")
 pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, use_karras_sigmas=True)
 
 prompt = "a sports car at the road, best quality, high quality, high detail, 8k resolution"
-output = pipeline(
+
+generator = torch.Generator(device="cpu").manual_seed(2628670641)
+
+out = pipeline(
     prompt=prompt,
     negative_prompt="",
     guidance_scale=6.5,
@@ -50,16 +65,83 @@ output = pipeline(
     generator=generator,
     callback_on_step_end=callback,
 )
+
+out.images[0].save("official_callback.png")
 ```
 
-If you want to add a new official callback, feel free to open a [feature request](https://github.com/huggingface/diffusers/issues/new/choose) or [submit a PR](https://huggingface.co/docs/diffusers/main/en/conceptual/contribution#how-to-open-a-pr). Otherwise, you can also create your own callback as shown below.
+<div class="flex gap-4">
+  <div>
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/without_cfg_callback.png" alt="generated image of a sports car at the road" />
+    <figcaption class="mt-2 text-center text-sm text-gray-500">without SDXLCFGCutoffCallback</figcaption>
+  </div>
+  <div>
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/with_cfg_callback.png" alt="generated image of a sports car at the road with cfg callback" />
+    <figcaption class="mt-2 text-center text-sm text-gray-500">with SDXLCFGCutoffCallback</figcaption>
+  </div>
+</div>
 
-## Early stopping
+## Dynamic classifier-free guidance
 
-Early stopping is useful if you aren't happy with the intermediate results during generation. This callback sets a hardcoded stop point after which the pipeline terminates by setting the `_interrupt` attribute to `True`.
+Dynamic classifier-free guidance (CFG) is a feature that allows you to disable CFG after a certain number of inference steps which can help you save compute with minimal cost to performance. The callback function for this should have the following arguments:
+
+- `pipeline` (or the pipeline instance) provides access to important properties such as `num_timesteps` and `guidance_scale`. You can modify these properties by updating the underlying attributes. For this example, you'll disable CFG by setting `pipeline._guidance_scale=0.0`.
+- `step_index` and `timestep` tell you where you are in the denoising loop. Use `step_index` to turn off CFG after reaching 40% of `num_timesteps`.
+- `callback_kwargs` is a dict that contains tensor variables you can modify during the denoising loop. It only includes variables specified in the `callback_on_step_end_tensor_inputs` argument, which is passed to the pipeline's `__call__` method. Different pipelines may use different sets of variables, so please check a pipeline's `_callback_tensor_inputs` attribute for the list of variables you can modify. Some common variables include `latents` and `prompt_embeds`. For this function, change the batch size of `prompt_embeds` after setting `guidance_scale=0.0` in order for it to work properly.
+
+Your callback function should look something like this:
+
+```python
+def callback_dynamic_cfg(pipe, step_index, timestep, callback_kwargs):
+        # adjust the batch_size of prompt_embeds according to guidance_scale
+        if step_index == int(pipeline.num_timesteps * 0.4):
+                prompt_embeds = callback_kwargs["prompt_embeds"]
+                prompt_embeds = prompt_embeds.chunk(2)[-1]
+
+                # update guidance_scale and prompt_embeds
+                pipeline._guidance_scale = 0.0
+                callback_kwargs["prompt_embeds"] = prompt_embeds
+        return callback_kwargs
+```
+
+Now, you can pass the callback function to the `callback_on_step_end` parameter and the `prompt_embeds` to `callback_on_step_end_tensor_inputs`.
 
 ```py
-from diffusers import StableDiffusionXLPipeline
+import torch
+from diffusers import StableDiffusionPipeline
+
+pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16)
+pipeline = pipeline.to("cuda")
+
+prompt = "a photo of an astronaut riding a horse on mars"
+
+generator = torch.Generator(device="cuda").manual_seed(1)
+out = pipeline(
+    prompt,
+    generator=generator,
+    callback_on_step_end=callback_dynamic_cfg,
+    callback_on_step_end_tensor_inputs=['prompt_embeds']
+)
+
+out.images[0].save("out_custom_cfg.png")
+```
+
+## Interrupt the diffusion process
+
+> [!TIP]
+> The interruption callback is supported for text-to-image, image-to-image, and inpainting for the [StableDiffusionPipeline](../api/pipelines/stable_diffusion/overview) and [StableDiffusionXLPipeline](../api/pipelines/stable_diffusion/stable_diffusion_xl).
+
+Stopping the diffusion process early is useful when building UIs that work with Diffusers because it allows users to stop the generation process if they're unhappy with the intermediate results. You can incorporate this into your pipeline with a callback.
+
+This callback function should take the following arguments: `pipeline`, `i`, `t`, and `callback_kwargs` (this must be returned). Set the pipeline's `_interrupt` attribute to `True` to stop the diffusion process after a certain number of steps. You are also free to implement your own custom stopping logic inside the callback.
+
+In this example, the diffusion process is stopped after 10 steps even though `num_inference_steps` is set to 50.
+
+```python
+from diffusers import StableDiffusionPipeline
+
+pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
+pipeline.enable_model_cpu_offload()
+num_inference_steps = 50
 
 def interrupt_callback(pipeline, i, t, callback_kwargs):
     stop_idx = 10
@@ -68,11 +150,6 @@ def interrupt_callback(pipeline, i, t, callback_kwargs):
 
     return callback_kwargs
 
-pipeline = StableDiffusionXLPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5"
-)
-num_inference_steps = 50
-
 pipeline(
     "A photo of a cat",
     num_inference_steps=num_inference_steps,
@@ -80,11 +157,92 @@ pipeline(
 )
 ```
 
-## Display intermediate images
+## IP Adapter Cutoff
 
-Visualizing the intermediate images is useful for progress monitoring and assessing the quality of the generated content. This callback decodes the latent tensors at each step and converts them to images.
+IP Adapter is an image prompt adapter that can be used for diffusion models without any changes to the underlying model. We can use the IP Adapter Cutoff Callback to disable the IP Adapter after a certain number of steps. To set up the callback, you need to specify the number of denoising steps after which the callback comes into effect. You can do so by using either one of these two arguments:
 
-[Convert](https://huggingface.co/blog/TimothyAlexisVass/explaining-the-sdxl-latent-space) the Stable Diffusion XL latents from latents (4 channels) to RGB tensors (3 tensors).
+- `cutoff_step_ratio`: Float number with the ratio of the steps.
+- `cutoff_step_index`: Integer number with the exact number of the step.
+
+We need to download the diffusion model and load the ip_adapter for it as follows:
+
+```py
+from diffusers import AutoPipelineForText2Image
+from diffusers.utils import load_image
+import torch
+
+pipeline = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16).to("cuda")
+pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
+pipeline.set_ip_adapter_scale(0.6)
+```
+The setup for the callback should look something like this:
+
+```py
+
+from diffusers import AutoPipelineForText2Image
+from diffusers.callbacks import IPAdapterScaleCutoffCallback
+from diffusers.utils import load_image
+import torch
+ 
+
+pipeline = AutoPipelineForText2Image.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0", 
+    torch_dtype=torch.float16
+).to("cuda")
+
+
+pipeline.load_ip_adapter(
+    "h94/IP-Adapter", 
+    subfolder="sdxl_models", 
+    weight_name="ip-adapter_sdxl.bin"
+)
+
+pipeline.set_ip_adapter_scale(0.6)
+
+
+callback = IPAdapterScaleCutoffCallback(
+    cutoff_step_ratio=None, 
+    cutoff_step_index=5
+)
+
+image = load_image(
+    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_diner.png"
+)
+
+generator = torch.Generator(device="cuda").manual_seed(2628670641)
+
+images = pipeline(
+    prompt="a tiger sitting in a chair drinking orange juice",
+    ip_adapter_image=image,
+    negative_prompt="deformed, ugly, wrong proportion, low res, bad anatomy, worst quality, low quality",
+    generator=generator,
+    num_inference_steps=50,
+    callback_on_step_end=callback,
+).images
+
+images[0].save("custom_callback_img.png")
+```
+
+<div class="flex gap-4">
+  <div>
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/without_callback.png" alt="generated image of a tiger sitting in a chair drinking orange juice" />
+    <figcaption class="mt-2 text-center text-sm text-gray-500">without IPAdapterScaleCutoffCallback</figcaption>
+  </div>
+  <div>
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/with_callback2.png" alt="generated image of a tiger sitting in a chair drinking orange juice with ip adapter callback" />
+    <figcaption class="mt-2 text-center text-sm text-gray-500">with IPAdapterScaleCutoffCallback</figcaption>
+  </div>
+</div>
+
+
+## Display image after each generation step
+
+> [!TIP]
+> This tip was contributed by [asomoza](https://github.com/asomoza).
+
+Display an image after each generation step by accessing and converting the latents after each step into an image. The latent space is compressed to 128x128, so the images are also 128x128 which is useful for a quick preview.
+
+1. Use the function below to convert the SDXL latents (4 channels) to RGB tensors (3 channels) as explained in the [Explaining the SDXL latent space](https://huggingface.co/blog/TimothyAlexisVass/explaining-the-sdxl-latent-space) blog post.
 
 ```py
 def latents_to_rgb(latents):
@@ -102,7 +260,7 @@ def latents_to_rgb(latents):
     return Image.fromarray(image_array)
 ```
 
-Extract the latents and convert the first image in the batch to RGB. Save the image as a PNG file with the step number.
+2. Create a function to decode and save the latents into an image.
 
 ```py
 def decode_tensors(pipe, step, timestep, callback_kwargs):
@@ -114,18 +272,19 @@ def decode_tensors(pipe, step, timestep, callback_kwargs):
     return callback_kwargs
 ```
 
-Use the `callback_on_step_end_tensor_inputs` parameter to specify what input type to modify, which in this case, are the latents.
+3. Pass the `decode_tensors` function to the `callback_on_step_end` parameter to decode the tensors after each step. You also need to specify what you want to modify in the `callback_on_step_end_tensor_inputs` parameter, which in this case are the latents.
 
 ```py
+from diffusers import AutoPipelineForText2Image
 import torch
 from PIL import Image
-from diffusers import AutoPipelineForText2Image
 
 pipeline = AutoPipelineForText2Image.from_pretrained(
     "stabilityai/stable-diffusion-xl-base-1.0",
     torch_dtype=torch.float16,
-    device_map="cuda"
-)
+    variant="fp16",
+    use_safetensors=True
+).to("cuda")
 
 image = pipeline(
     prompt="A croissant shaped like a cute bear.",
@@ -134,3 +293,27 @@ image = pipeline(
     callback_on_step_end_tensor_inputs=["latents"],
 ).images[0]
 ```
+
+<div class="flex gap-4 justify-center">
+  <div>
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/tips_step_0.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">step 0</figcaption>
+  </div>
+  <div>
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/tips_step_19.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">step 19
+    </figcaption>
+  </div>
+  <div>
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/tips_step_29.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">step 29</figcaption>
+  </div>
+  <div>
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/tips_step_39.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">step 39</figcaption>
+  </div>
+  <div>
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/tips_step_49.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">step 49</figcaption>
+  </div>
+</div>

docs/source/en/using-diffusers/custom_pipeline_overview.md

@@ -10,163 +10,376 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+# Load community pipelines and components
+
 [[open-in-colab]]
 
-# Community pipelines and components
+## Community pipelines
 
-Community pipelines are [`DiffusionPipeline`] classes that are different from the original paper implementation. They provide additional functionality or extend the original pipeline implementation.
+> [!TIP] Take a look at GitHub Issue [#841](https://github.com/huggingface/diffusers/issues/841) for more context about why we're adding community pipelines to help everyone easily share their work without being slowed down.
 
-> [!TIP]
-> Check out the community pipelines in [diffusers/examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) with inference and training examples for how to use them.
+Community pipelines are any [`DiffusionPipeline`] class that are different from the original paper implementation (for example, the [`StableDiffusionControlNetPipeline`] corresponds to the [Text-to-Image Generation with ControlNet Conditioning](https://huggingface.co/papers/2302.05543) paper). They provide additional functionality or extend the original implementation of a pipeline.
 
-Community pipelines are either stored on the Hub or the Diffusers' GitHub repository. Hub pipelines are completely customizable (scheduler, models, pipeline code, etc.) while GitHub pipelines are limited to only the custom pipeline code. Further compare the two community pipeline types in the table below.
+There are many cool community pipelines like [Marigold Depth Estimation](https://github.com/huggingface/diffusers/tree/main/examples/community#marigold-depth-estimation) or [InstantID](https://github.com/huggingface/diffusers/tree/main/examples/community#instantid-pipeline), and you can find all the official community pipelines [here](https://github.com/huggingface/diffusers/tree/main/examples/community).
 
-|  | GitHub | Hub |
-|---|---|---|
-| Usage | Same. | Same. |
-| Review process | Open a Pull Request on GitHub and undergo a review process from the Diffusers team before merging. This option is slower. | Upload directly to a Hub repository without a review. This is the fastest option. |
-| Visibility | Included in the official Diffusers repository and docs. | Included on your Hub profile and relies on your own usage and promotion to gain visibility. |
+There are two types of community pipelines, those stored on the Hugging Face Hub and those stored on Diffusers GitHub repository. Hub pipelines are completely customizable (scheduler, models, pipeline code, etc.) while Diffusers GitHub pipelines are only limited to custom pipeline code.
 
-## custom_pipeline
+|                | GitHub community pipeline                                                                                        | HF Hub community pipeline                                                                 |
+|----------------|------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------|
+| usage          | same                                                                                                             | same                                                                                      |
+| review process | open a Pull Request on GitHub and undergo a review process from the Diffusers team before merging; may be slower | upload directly to a Hub repository without any review; this is the fastest workflow      |
+| visibility     | included in the official Diffusers repository and documentation                                                  | included on your HF Hub profile and relies on your own usage/promotion to gain visibility |
 
-Load either community pipeline types by passing the `custom_pipeline` argument to [`~DiffusionPipeline.from_pretrained`].
+<hfoptions id="community">
+<hfoption id="Hub pipelines">
 
-```py
-import torch
-from diffusers import DiffusionPipeline
-
-pipeline = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-3-medium-diffusers",
-    custom_pipeline="pipeline_stable_diffusion_3_instruct_pix2pix",
-    torch_dtype=torch.float16,
-    device_map="cuda"
-)
-```
-
-Add the `custom_revision` argument to [`~DiffusionPipeline.from_pretrained`] to load a community pipeline from a specific version (for example, `v0.30.0` or `main`). By default, community pipelines are loaded from the latest stable version of Diffusers.
-
-```py
-import torch
-from diffusers import DiffusionPipeline
-
-pipeline = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-3-medium-diffusers",
-    custom_pipeline="pipeline_stable_diffusion_3_instruct_pix2pix",
-    custom_revision="main"
-    torch_dtype=torch.float16,
-    device_map="cuda"
-)
-```
+To load a Hugging Face Hub community pipeline, pass the repository id of the community pipeline to the `custom_pipeline` argument and the model repository where you'd like to load the pipeline weights and components from. For example, the example below loads a dummy pipeline from [hf-internal-testing/diffusers-dummy-pipeline](https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py) and the pipeline weights and components from [google/ddpm-cifar10-32](https://huggingface.co/google/ddpm-cifar10-32):
 
 > [!WARNING]
-> While the Hugging Face Hub [scans](https://huggingface.co/docs/hub/security-malware) files, you should still inspect the Hub pipeline code and make sure it is safe.
+> By loading a community pipeline from the Hugging Face Hub, you are trusting that the code you are loading is safe. Make sure to inspect the code online before loading and running it automatically!
 
-There are a few ways to load a community pipeline.
+```py
+from diffusers import DiffusionPipeline
 
-- Pass a path to `custom_pipeline` to load a local community pipeline. The directory must contain a `pipeline.py` file containing the pipeline class.
+pipeline = DiffusionPipeline.from_pretrained(
+    "google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline", use_safetensors=True
+)
+```
 
-  ```py
-  import torch
-  from diffusers import DiffusionPipeline
+</hfoption>
+<hfoption id="GitHub pipelines">
 
-  pipeline = DiffusionPipeline.from_pretrained(
-      "stabilityai/stable-diffusion-3-medium-diffusers",
-      custom_pipeline="path/to/pipeline_directory",
-      torch_dtype=torch.float16,
-      device_map="cuda"
-  )
-  ```
+To load a GitHub community pipeline, pass the repository id of the community pipeline to the `custom_pipeline` argument and the model repository where you you'd like to load the pipeline weights and components from. You can also load model components directly. The example below loads the community [CLIP Guided Stable Diffusion](https://github.com/huggingface/diffusers/tree/main/examples/community#clip-guided-stable-diffusion) pipeline and the CLIP model components.
 
-- The `custom_pipeline` argument is also supported by [`~DiffusionPipeline.from_pipe`], which is useful for [reusing pipelines](./loading#reuse-a-pipeline) without using additional memory. It limits the memory usage to only the largest pipeline loaded.
+```py
+from diffusers import DiffusionPipeline
+from transformers import CLIPImageProcessor, CLIPModel
 
-  ```py
-  import torch
-  from diffusers import DiffusionPipeline
+clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
 
-  pipeline_sd = DiffusionPipeline.from_pretrained("emilianJR/CyberRealistic_V3", torch_dtype=torch.float16, device_map="cuda")
-  pipeline_lpw = DiffusionPipeline.from_pipe(
-      pipeline_sd, custom_pipeline="lpw_stable_diffusion", device_map="cuda"
-  )
-  ```
+feature_extractor = CLIPImageProcessor.from_pretrained(clip_model_id)
+clip_model = CLIPModel.from_pretrained(clip_model_id)
 
-  The [`~DiffusionPipeline.from_pipe`] method is especially useful for loading community pipelines because many of them don't have pretrained weights. Community pipelines generally add a feature on top of an existing pipeline.
+pipeline = DiffusionPipeline.from_pretrained(
+    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    custom_pipeline="clip_guided_stable_diffusion",
+    clip_model=clip_model,
+    feature_extractor=feature_extractor,
+    use_safetensors=True,
+)
+```
+
+</hfoption>
+</hfoptions>
+
+### Load from a local file
+
+Community pipelines can also be loaded from a local file if you pass a file path instead. The path to the passed directory must contain a pipeline.py file that contains the pipeline class.
+
+```py
+pipeline = DiffusionPipeline.from_pretrained(
+    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    custom_pipeline="./path/to/pipeline_directory/",
+    clip_model=clip_model,
+    feature_extractor=feature_extractor,
+    use_safetensors=True,
+)
+```
+
+### Load from a specific version
+
+By default, community pipelines are loaded from the latest stable version of Diffusers. To load a community pipeline from another version, use the `custom_revision` parameter.
+
+<hfoptions id="version">
+<hfoption id="main">
+
+For example, to load from the main branch:
+
+```py
+pipeline = DiffusionPipeline.from_pretrained(
+    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    custom_pipeline="clip_guided_stable_diffusion",
+    custom_revision="main",
+    clip_model=clip_model,
+    feature_extractor=feature_extractor,
+    use_safetensors=True,
+)
+```
+
+</hfoption>
+<hfoption id="older version">
+
+For example, to load from a previous version of Diffusers like v0.25.0:
+
+```py
+pipeline = DiffusionPipeline.from_pretrained(
+    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    custom_pipeline="clip_guided_stable_diffusion",
+    custom_revision="v0.25.0",
+    clip_model=clip_model,
+    feature_extractor=feature_extractor,
+    use_safetensors=True,
+)
+```
+
+</hfoption>
+</hfoptions>
+
+### Load with from_pipe
+
+Community pipelines can also be loaded with the [`~DiffusionPipeline.from_pipe`] method which allows you to load and reuse multiple pipelines without any additional memory overhead (learn more in the [Reuse a pipeline](./loading#reuse-a-pipeline) guide). The memory requirement is determined by the largest single pipeline loaded.
+
+For example, let's load a community pipeline that supports [long prompts with weighting](https://github.com/huggingface/diffusers/tree/main/examples/community#long-prompt-weighting-stable-diffusion) from a Stable Diffusion pipeline.
+
+```py
+import torch
+from diffusers import DiffusionPipeline
+
+pipe_sd = DiffusionPipeline.from_pretrained("emilianJR/CyberRealistic_V3", torch_dtype=torch.float16)
+pipe_sd.to("cuda")
+# load long prompt weighting pipeline
+pipe_lpw = DiffusionPipeline.from_pipe(
+    pipe_sd,
+    custom_pipeline="lpw_stable_diffusion",
+).to("cuda")
+
+prompt = "cat, hiding in the leaves, ((rain)), zazie rainyday, beautiful eyes, macro shot, colorful details, natural lighting, amazing composition, subsurface scattering, amazing textures, filmic, soft light, ultra-detailed eyes, intricate details, detailed texture, light source contrast, dramatic shadows, cinematic light, depth of field, film grain, noise, dark background, hyperrealistic dslr film still, dim volumetric cinematic lighting"
+neg_prompt = "(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation"
+generator = torch.Generator(device="cpu").manual_seed(20)
+out_lpw = pipe_lpw(
+    prompt,
+    negative_prompt=neg_prompt,
+    width=512,
+    height=512,
+    max_embeddings_multiples=3,
+    num_inference_steps=50,
+    generator=generator,
+    ).images[0]
+out_lpw
+```
+
+<div class="flex gap-4">
+  <div>
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/from_pipe_lpw.png" />
+    <figcaption class="mt-2 text-center text-sm text-gray-500">Stable Diffusion with long prompt weighting</figcaption>
+  </div>
+  <div>
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/from_pipe_non_lpw.png" />
+    <figcaption class="mt-2 text-center text-sm text-gray-500">Stable Diffusion</figcaption>
+  </div>
+</div>
+
+## Example community pipelines
+
+Community pipelines are a really fun and creative way to extend the capabilities of the original pipeline with new and unique features. You can find all community pipelines in the [diffusers/examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) folder with inference and training examples for how to use them.
+
+This section showcases a couple of the community pipelines and hopefully it'll inspire you to create your own (feel free to open a PR for your community pipeline and ping us for a review)!
+
+> [!TIP]
+> The [`~DiffusionPipeline.from_pipe`] method is particularly useful for loading community pipelines because many of them don't have pretrained weights and add a feature on top of an existing pipeline like Stable Diffusion or Stable Diffusion XL. You can learn more about the [`~DiffusionPipeline.from_pipe`] method in the [Load with from_pipe](custom_pipeline_overview#load-with-from_pipe) section.
+
+<hfoptions id="community">
+<hfoption id="Marigold">
+
+[Marigold](https://marigoldmonodepth.github.io/) is a depth estimation diffusion pipeline that uses the rich existing and inherent visual knowledge in diffusion models. It takes an input image and denoises and decodes it into a depth map. Marigold performs well even on images it hasn't seen before.
+
+```py
+import torch
+from PIL import Image
+from diffusers import DiffusionPipeline
+from diffusers.utils import load_image
+
+pipeline = DiffusionPipeline.from_pretrained(
+    "prs-eth/marigold-lcm-v1-0",
+    custom_pipeline="marigold_depth_estimation",
+    torch_dtype=torch.float16,
+    variant="fp16",
+)
+
+pipeline.to("cuda")
+image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/community-marigold.png")
+output = pipeline(
+    image,
+    denoising_steps=4,
+    ensemble_size=5,
+    processing_res=768,
+    match_input_res=True,
+    batch_size=0,
+    seed=33,
+    color_map="Spectral",
+    show_progress_bar=True,
+)
+depth_colored: Image.Image = output.depth_colored
+depth_colored.save("./depth_colored.png")
+```
+
+<div class="flex flex-row gap-4">
+  <div class="flex-1">
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/community-marigold.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
+  </div>
+  <div class="flex-1">
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/marigold-depth.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">colorized depth image</figcaption>
+  </div>
+</div>
+
+</hfoption>
+<hfoption id="HD-Painter">
+
+[HD-Painter](https://hf.co/papers/2312.14091) is a high-resolution inpainting pipeline. It introduces a *Prompt-Aware Introverted Attention (PAIntA)* layer to better align a prompt with the area to be inpainted, and *Reweighting Attention Score Guidance (RASG)* to keep the latents more prompt-aligned and within their trained domain to generate realistc images.
+
+```py
+import torch
+from diffusers import DiffusionPipeline, DDIMScheduler
+from diffusers.utils import load_image
+
+pipeline = DiffusionPipeline.from_pretrained(
+    "stable-diffusion-v1-5/stable-diffusion-v1-5-inpainting",
+    custom_pipeline="hd_painter"
+)
+pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
+init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/hd-painter.jpg")
+mask_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/hd-painter-mask.png")
+prompt = "football"
+image = pipeline(prompt, init_image, mask_image, use_rasg=True, use_painta=True, generator=torch.manual_seed(0)).images[0]
+image
+```
+
+<div class="flex flex-row gap-4">
+  <div class="flex-1">
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/hd-painter.jpg"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
+  </div>
+  <div class="flex-1">
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/hd-painter-output.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">generated image</figcaption>
+  </div>
+</div>
+
+</hfoption>
+</hfoptions>
 
 ## Community components
 
-Community components let users build pipelines with custom transformers, UNets, VAEs, and schedulers not supported by Diffusers. These components require Python module implementations. 
+Community components allow users to build pipelines that may have customized components that are not a part of Diffusers. If your pipeline has custom components that Diffusers doesn't already support, you need to provide their implementations as Python modules. These customized components could be a VAE, UNet, and scheduler. In most cases, the text encoder is imported from the Transformers library. The pipeline code itself can also be customized.
 
-This section shows how users can use community components to build a community pipeline using [showlab/show-1-base](https://huggingface.co/showlab/show-1-base) as an example.
+This section shows how users should use community components to build a community pipeline.
 
-1. Load the required components, the scheduler and image processor. The text encoder is generally imported from [Transformers](https://huggingface.co/docs/transformers/index).
+You'll use the [showlab/show-1-base](https://huggingface.co/showlab/show-1-base) pipeline checkpoint as an example.
+
+1. Import and load the text encoder from Transformers:
+
+```python
+from transformers import T5Tokenizer, T5EncoderModel
+
+pipe_id = "showlab/show-1-base"
+tokenizer = T5Tokenizer.from_pretrained(pipe_id, subfolder="tokenizer")
+text_encoder = T5EncoderModel.from_pretrained(pipe_id, subfolder="text_encoder")
+```
+
+2. Load a scheduler:
 
 ```python
-from transformers import T5Tokenizer, T5EncoderModel, CLIPImageProcessor
 from diffusers import DPMSolverMultistepScheduler
 
-pipeline_id = "showlab/show-1-base"
-tokenizer = T5Tokenizer.from_pretrained(pipeline_id, subfolder="tokenizer")
-text_encoder = T5EncoderModel.from_pretrained(pipeline_id, subfolder="text_encoder")
 scheduler = DPMSolverMultistepScheduler.from_pretrained(pipe_id, subfolder="scheduler")
+```
+
+3. Load an image processor:
+
+```python
+from transformers import CLIPImageProcessor
+
 feature_extractor = CLIPImageProcessor.from_pretrained(pipe_id, subfolder="feature_extractor")
 ```
 
-> [!WARNING]
-> In steps 2 and 3, the custom [UNet](https://github.com/showlab/Show-1/blob/main/showone/models/unet_3d_condition.py) and [pipeline](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/unet/showone_unet_3d_condition.py) implementation must match the format shown in their files for this example to work.
+<Tip warning={true}>
 
-2. Load a [custom UNet](https://github.com/showlab/Show-1/blob/main/showone/models/unet_3d_condition.py) which is already implemented in [showone_unet_3d_condition.py](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/unet/showone_unet_3d_condition.py). The [`UNet3DConditionModel`] class name is renamed to the custom implementation, `ShowOneUNet3DConditionModel`, because [`UNet3DConditionModel`] already exists in Diffusers. Any components required for `ShowOneUNet3DConditionModel` class should be placed in `showone_unet_3d_condition.py`.
+In steps 4 and 5, the custom [UNet](https://github.com/showlab/Show-1/blob/main/showone/models/unet_3d_condition.py) and [pipeline](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/unet/showone_unet_3d_condition.py) implementation must match the format shown in their files for this example to work.
+
+</Tip>
+
+4. Now you'll load a [custom UNet](https://github.com/showlab/Show-1/blob/main/showone/models/unet_3d_condition.py), which in this example, has already been implemented in [showone_unet_3d_condition.py](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/unet/showone_unet_3d_condition.py) for your convenience. You'll notice the [`UNet3DConditionModel`] class name is changed to `ShowOneUNet3DConditionModel` because [`UNet3DConditionModel`] already exists in Diffusers. Any components needed for the `ShowOneUNet3DConditionModel` class should be placed in showone_unet_3d_condition.py.
+
+    Once this is done, you can initialize the UNet:
+
+    ```python
+    from showone_unet_3d_condition import ShowOneUNet3DConditionModel
+
+    unet = ShowOneUNet3DConditionModel.from_pretrained(pipe_id, subfolder="unet")
+    ```
+
+5. Finally, you'll load the custom pipeline code. For this example, it has already been created for you in [pipeline_t2v_base_pixel.py](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/pipeline_t2v_base_pixel.py). This script contains a custom `TextToVideoIFPipeline` class for generating videos from text. Just like the custom UNet, any code needed for the custom pipeline to work should go in pipeline_t2v_base_pixel.py.
+
+Once everything is in place, you can initialize the `TextToVideoIFPipeline` with the `ShowOneUNet3DConditionModel`:
 
 ```python
-from showone_unet_3d_condition import ShowOneUNet3DConditionModel
-
-unet = ShowOneUNet3DConditionModel.from_pretrained(pipeline_id, subfolder="unet")
-```
-
-3. Load the custom pipeline code (already implemented in [pipeline_t2v_base_pixel.py](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/pipeline_t2v_base_pixel.py)). This script contains a custom `TextToVideoIFPipeline` class for generating videos from text. Like the custom UNet, any code required for `TextToVideIFPipeline` should be placed in `pipeline_t2v_base_pixel.py`.
-
-Initialize `TextToVideoIFPipeline` with `ShowOneUNet3DConditionModel`.
-
-```python
-import torch
 from pipeline_t2v_base_pixel import TextToVideoIFPipeline
+import torch
 
 pipeline = TextToVideoIFPipeline(
     unet=unet,
     text_encoder=text_encoder,
     tokenizer=tokenizer,
     scheduler=scheduler,
-    feature_extractor=feature_extractor,
-    device_map="cuda",
-    torch_dtype=torch.float16
+    feature_extractor=feature_extractor
 )
+pipeline = pipeline.to(device="cuda")
+pipeline.torch_dtype = torch.float16
 ```
 
-4. Push the pipeline to the Hub to share with the community.
+Push the pipeline to the Hub to share with the community!
 
 ```python
 pipeline.push_to_hub("custom-t2v-pipeline")
 ```
 
-After the pipeline is successfully pushed, make the following changes.
+After the pipeline is successfully pushed, you need to make a few changes:
 
-- Change the `_class_name` attribute in [model_index.json](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/model_index.json#L2) to `"pipeline_t2v_base_pixel"` and `"TextToVideoIFPipeline"`.
-- Upload `showone_unet_3d_condition.py` to the [unet](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/unet/showone_unet_3d_condition.py) subfolder.
-- Upload `pipeline_t2v_base_pixel.py` to the pipeline [repository](https://huggingface.co/sayakpaul/show-1-base-with-code/tree/main).
+1. Change the `_class_name` attribute in [model_index.json](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/model_index.json#L2) to `"pipeline_t2v_base_pixel"` and `"TextToVideoIFPipeline"`.
+2. Upload `showone_unet_3d_condition.py` to the [unet](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/unet/showone_unet_3d_condition.py) subfolder.
+3. Upload `pipeline_t2v_base_pixel.py` to the pipeline [repository](https://huggingface.co/sayakpaul/show-1-base-with-code/tree/main).
 
 To run inference, add the `trust_remote_code` argument while initializing the pipeline to handle all the "magic" behind the scenes.
 
+> [!WARNING]
+> As an additional precaution with `trust_remote_code=True`, we strongly encourage you to pass a commit hash to the `revision` parameter in [`~DiffusionPipeline.from_pretrained`] to make sure the code hasn't been updated with some malicious new lines of code (unless you fully trust the model owners).
+
 ```python
-import torch
 from diffusers import DiffusionPipeline
+import torch
 
 pipeline = DiffusionPipeline.from_pretrained(
     "<change-username>/<change-id>", trust_remote_code=True, torch_dtype=torch.float16
-)
+).to("cuda")
+
+prompt = "hello"
+
+# Text embeds
+prompt_embeds, negative_embeds = pipeline.encode_prompt(prompt)
+
+# Keyframes generation (8x64x40, 2fps)
+video_frames = pipeline(
+    prompt_embeds=prompt_embeds,
+    negative_prompt_embeds=negative_embeds,
+    num_frames=8,
+    height=40,
+    width=64,
+    num_inference_steps=2,
+    guidance_scale=9.0,
+    output_type="pt"
+).frames
 ```
 
-> [!WARNING]
-> As an additional precaution with `trust_remote_code=True`, we strongly encourage passing a commit hash to the `revision` argument in [`~DiffusionPipeline.from_pretrained`] to make sure the code hasn't been updated with new malicious code (unless you fully trust the model owners).
+As an additional reference, take a look at the repository structure of [stabilityai/japanese-stable-diffusion-xl](https://huggingface.co/stabilityai/japanese-stable-diffusion-xl/) which also uses the `trust_remote_code` feature.
 
-## Resources
+```python
+from diffusers import DiffusionPipeline
+import torch
 
-- Take a look at Issue [#841](https://github.com/huggingface/diffusers/issues/841) for more context about why we're adding community pipelines to help everyone easily share their work without being slowed down.
-- Check out the [stabilityai/japanese-stable-diffusion-xl](https://huggingface.co/stabilityai/japanese-stable-diffusion-xl/) repository for an additional example of a community pipeline that also uses the `trust_remote_code` feature.
+pipeline = DiffusionPipeline.from_pretrained(
+    "stabilityai/japanese-stable-diffusion-xl", trust_remote_code=True
+)
+pipeline.to("cuda")
+```

docs/source/en/using-diffusers/image_quality.md

@@ -10,7 +10,13 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# FreeU
+# Controlling image quality
+
+The components of a diffusion model, like the UNet and scheduler, can be optimized to improve the quality of generated images leading to better details. These techniques are especially useful if you don't have the resources to simply use a larger model for inference. You can enable these techniques during inference without any additional training.
+
+This guide will show you how to turn these techniques on in your pipeline and how to configure them to improve the quality of your generated images.
+
+## Details
 
 [FreeU](https://hf.co/papers/2309.11497) improves image details by rebalancing the UNet's backbone and skip connection weights. The skip connections can cause the model to overlook some of the backbone semantics which may lead to unnatural image details in the generated image. This technique does not require any additional training and can be applied on the fly during inference for tasks like image-to-image and text-to-video.
 
@@ -133,7 +139,7 @@ export_to_video(video_frames, "teddy_bear.mp4", fps=10)
 </hfoption>
 </hfoptions>
 
-Call the [`~pipelines.StableDiffusionMixin.disable_freeu`] method to disable FreeU.
+Call the [`pipelines.StableDiffusionMixin.disable_freeu`] method to disable FreeU.
 
 ```py
 pipeline.disable_freeu()

docs/source/en/using-diffusers/loading.md

@@ -10,240 +10,574 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+# Load pipelines
+
 [[open-in-colab]]
 
-# DiffusionPipeline
+Diffusion systems consist of multiple components like parameterized models and schedulers that interact in complex ways. That is why we designed the [`DiffusionPipeline`] to wrap the complexity of the entire diffusion system into an easy-to-use API. At the same time, the [`DiffusionPipeline`] is entirely customizable so you can modify each component to build a diffusion system for your use case.
 
-Diffusion models consists of multiple components like UNets or diffusion transformers (DiTs), text encoders, variational autoencoders (VAEs), and schedulers. The [`DiffusionPipeline`] wraps all of these components into a single easy-to-use API without giving up the flexibility to modify it's components.
+This guide will show you how to load:
 
-This guide will show you how to load a [`DiffusionPipeline`].
+- pipelines from the Hub and locally
+- different components into a pipeline
+- multiple pipelines without increasing memory usage
+- checkpoint variants such as different floating point types or non-exponential mean averaged (EMA) weights
 
-## Loading a pipeline
+## Load a pipeline
 
-[`DiffusionPipeline`] is a base pipeline class that automatically selects and returns an instance of a model's pipeline subclass, like [`QwenImagePipeline`], by scanning the `model_index.json` file for the class name.
+> [!TIP]
+> Skip to the [DiffusionPipeline explained](#diffusionpipeline-explained) section if you're interested in an explanation about how the [`DiffusionPipeline`] class works.
 
-Pass a model id to [`~DiffusionPipeline.from_pretrained`] to load a pipeline.
+There are two ways to load a pipeline for a task:
 
-```py
-import torch
+1. Load the generic [`DiffusionPipeline`] class and allow it to automatically detect the correct pipeline class from the checkpoint.
+2. Load a specific pipeline class for a specific task.
+
+<hfoptions id="pipelines">
+<hfoption id="generic pipeline">
+
+The [`DiffusionPipeline`] class is a simple and generic way to load the latest trending diffusion model from the [Hub](https://huggingface.co/models?library=diffusers&sort=trending). It uses the [`~DiffusionPipeline.from_pretrained`] method to automatically detect the correct pipeline class for a task from the checkpoint, downloads and caches all the required configuration and weight files, and returns a pipeline ready for inference.
+
+```python
 from diffusers import DiffusionPipeline
 
-pipeline = DiffusionPipeline.from_pretrained(
-  "Qwen/Qwen-Image", torch_dtype=torch.bfloat16, device_map="cuda"
-)
+pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True)
 ```
 
-Every model has a specific pipeline subclass that inherits from [`DiffusionPipeline`]. A subclass usually has a narrow focus and are task-specific. See the table below for an example.
-
-| pipeline subclass | task |
-|---|---|
-| [`QwenImagePipeline`] | text-to-image |
-| [`QwenImageImg2ImgPipeline`] | image-to-image |
-| [`QwenImageInpaintPipeline`] | inpaint |
-
-You could use the subclass directly by passing a model id to [`~QwenImagePipeline.from_pretrained`].
+This same checkpoint can also be used for an image-to-image task. The [`DiffusionPipeline`] class can handle any task as long as you provide the appropriate inputs. For example, for an image-to-image task, you need to pass an initial image to the pipeline.
 
 ```py
-import torch
-from diffusers import QwenImagePipeline
-
-pipeline = QwenImagePipeline.from_pretrained(
-  "Qwen/Qwen-Image", torch_dtype=torch.bfloat16, device_map="cuda"
-)
-```
-
-### Local pipelines
-
-Pipelines can also be run locally. Use [`~huggingface_hub.snapshot_download`] to download a model repository.
-
-```py
-from huggingface_hub import snapshot_download
-
-snapshot_download(repo_id="Qwen/Qwen-Image")
-```
-
-The model is downloaded to your [cache](../installation#cache). Pass the folder path to [`~QwenImagePipeline.from_pretrained`] to load it.
-
-```py
-import torch
-from diffusers import QwenImagePipeline
-
-pipeline = QwenImagePipeline.from_pretrained(
-  "path/to/your/cache", torch_dtype=torch.bfloat16, device_map="cuda"
-)
-```
-
-The [`~QwenImagePipeline.from_pretrained`] method won't download files from the Hub when it detects a local path. But this also means it won't download and cache any updates that have been made to the model either.
-
-## Pipeline data types
-
-Use the `torch_dtype` argument in [`~DiffusionPipeline.from_pretrained`] to load a model with a specific data type. This allows you to load different models in different precisions. For example, loading a large transformer model in half-precision reduces the memory required.
-
-Pass the data type for each model as a dictionary to `torch_dtype`. Use the `default` key to set the default data type. If a model isn't in the dictionary and `default` isn't provided, it is loaded in full precision (`torch.float32`).
-
-```py
-import torch
-from diffusers import QwenImagePipeline
-
-pipeline = QwenImagePipeline.from_pretrained(
-  "Qwen/Qwen-Image",
-  torch_dtype={"transformer": torch.bfloat16, "default": torch.float16},
-)
-print(pipeline.transformer.dtype, pipeline.vae.dtype)
-```
-
-You don't need to use a dictionary if you're loading all the models in the same data type.
-
-```py
-import torch
-from diffusers import QwenImagePipeline
-
-pipeline = QwenImagePipeline.from_pretrained(
-  "Qwen/Qwen-Image", torch_dtype=torch.bfloat16
-)
-print(pipeline.transformer.dtype, pipeline.vae.dtype)
-```
-
-## Device placement
-
-The `device_map` argument determines individual model or pipeline placement on an accelerator like a GPU. It is especially helpful when there are multiple GPUs.
-
-A pipeline supports two options for `device_map`, `"cuda"` and `"balanced"`. Refer to the table below to compare the placement strategies.
-
-| parameter | description |
-|---|---|
-| `"cuda"` | places pipeline on a supported accelerator device like CUDA |
-| `"balanced"` | evenly distributes pipeline on all GPUs |
-
-Use the `max_memory` argument in [`~DiffusionPipeline.from_pretrained`] to allocate a maximum amount of memory to use on each device. By default, Diffusers uses the maximum amount available.
-
-```py
-import torch
 from diffusers import DiffusionPipeline
 
-max_memory = {0: "16GB", 1: "16GB"}
-pipeline = DiffusionPipeline.from_pretrained(
-  "Qwen/Qwen-Image", 
-  torch_dtype=torch.bfloat16,
-  device_map="cuda",
-)
+pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True)
+
+init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png")
+prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
+image = pipeline("Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", image=init_image).images[0]
 ```
 
-The `hf_device_map` attribute allows you to access and view the `device_map`.
+</hfoption>
+<hfoption id="specific pipeline">
 
-```py
-print(pipeline.hf_device_map)
-# {'unet': 1, 'vae': 1, 'safety_checker': 0, 'text_encoder': 0}
+Checkpoints can be loaded by their specific pipeline class if you already know it. For example, to load a Stable Diffusion model, use the [`StableDiffusionPipeline`] class.
+
+```python
+from diffusers import StableDiffusionPipeline
+
+pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True)
 ```
 
-Reset a pipeline's `device_map` with the [`~DiffusionPipeline.reset_device_map`] method. This is necessary if you want to use methods such as `.to()`, [`~DiffusionPipeline.enable_sequential_cpu_offload`], and [`~DiffusionPipeline.enable_model_cpu_offload`].
+This same checkpoint may also be used for another task like image-to-image. To differentiate what task you want to use the checkpoint for, you have to use the corresponding task-specific pipeline class. For example, to use the same checkpoint for image-to-image, use the [`StableDiffusionImg2ImgPipeline`] class.
 
 ```py
-pipeline.reset_device_map()
+from diffusers import StableDiffusionImg2ImgPipeline
+
+pipeline = StableDiffusionImg2ImgPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True)
 ```
 
-## Parallel loading
+</hfoption>
+</hfoptions>
 
-Large models are often [sharded](../training/distributed_inference#model-sharding) into smaller files so that they are easier to load. Diffusers supports loading shards in parallel to speed up the loading process.
+Use the Space below to gauge a pipeline's memory requirements before you download and load it to see if it runs on your hardware.
 
-Set `HF_ENABLE_PARALLEL_LOADING` to `"YES"` to enable parallel loading of shards.
+<div class="block dark:hidden">
+	<iframe
+        src="https://diffusers-compute-pipeline-size.hf.space?__theme=light"
+        width="850"
+        height="1600"
+    ></iframe>
+</div>
+<div class="hidden dark:block">
+    <iframe
+        src="https://diffusers-compute-pipeline-size.hf.space?__theme=dark"
+        width="850"
+        height="1600"
+    ></iframe>
+</div>
 
-The `device_map` argument should be set to `"cuda"` to pre-allocate a large chunk of memory based on the model size. This substantially reduces model load time because warming up the memory allocator now avoids many smaller calls to the allocator later.
+### Specifying Component-Specific Data Types
 
-```py
-import os
+You can customize the data types for individual sub-models by passing a dictionary to the `torch_dtype` parameter. This allows you to load different components of a pipeline in different floating point precisions. For instance, if you want to load the transformer with `torch.bfloat16` and all other components with `torch.float16`, you can pass a dictionary mapping:
+
+```python
+from diffusers import HunyuanVideoPipeline
 import torch
+
+pipe = HunyuanVideoPipeline.from_pretrained(
+    "hunyuanvideo-community/HunyuanVideo",
+    torch_dtype={"transformer": torch.bfloat16, "default": torch.float16},
+)
+print(pipe.transformer.dtype, pipe.vae.dtype)  # (torch.bfloat16, torch.float16)
+```
+
+If a component is not explicitly specified in the dictionary and no `default` is provided, it will be loaded with `torch.float32`.
+
+### Local pipeline
+
+To load a pipeline locally, use [git-lfs](https://git-lfs.github.com/) to manually download a checkpoint to your local disk.
+
+```bash
+git-lfs install
+git clone https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5
+```
+
+This creates a local folder, ./stable-diffusion-v1-5, on your disk and you should pass its path to [`~DiffusionPipeline.from_pretrained`].
+
+```python
 from diffusers import DiffusionPipeline
 
-os.environ["HF_ENABLE_PARALLEL_LOADING"] = "YES"
-
-pipeline = DiffusionPipeline.from_pretrained(
-  "Wan-AI/Wan2.2-I2V-A14B-Diffusers", torch_dtype=torch.bfloat16, device_map="cuda"
-)
+stable_diffusion = DiffusionPipeline.from_pretrained("./stable-diffusion-v1-5", use_safetensors=True)
 ```
 
-## Replacing models in a pipeline
+The [`~DiffusionPipeline.from_pretrained`] method won't download files from the Hub when it detects a local path, but this also means it won't download and cache the latest changes to a checkpoint.
 
-[`DiffusionPipeline`] is flexible and accommodates loading different models or schedulers. You can experiment with different schedulers to optimize for generation speed or quality, and you can replace models with more performant ones.
+## Customize a pipeline
 
-The example below uses a more stable VAE version.
+You can customize a pipeline by loading different components into it. This is important because you can:
+
+- change to a scheduler with faster generation speed or higher generation quality depending on your needs (call the `scheduler.compatibles` method on your pipeline to see compatible schedulers)
+- change a default pipeline component to a newer and better performing one
+
+For example, let's customize the default [stabilityai/stable-diffusion-xl-base-1.0](https://hf.co/stabilityai/stable-diffusion-xl-base-1.0) checkpoint with:
+
+- The [`HeunDiscreteScheduler`] to generate higher quality images at the expense of slower generation speed. You must pass the `subfolder="scheduler"` parameter in [`~HeunDiscreteScheduler.from_pretrained`] to load the scheduler configuration into the correct [subfolder](https://hf.co/stabilityai/stable-diffusion-xl-base-1.0/tree/main/scheduler) of the pipeline repository.
+- A more stable VAE that runs in fp16.
 
 ```py
+from diffusers import StableDiffusionXLPipeline, HeunDiscreteScheduler, AutoencoderKL
 import torch
-from diffusers import DiffusionPipeline, AutoModel
 
-vae = AutoModel.from_pretrained(
-  "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
-)
+scheduler = HeunDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16, use_safetensors=True)
+```
 
-pipeline = DiffusionPipeline.from_pretrained(
+Now pass the new scheduler and VAE to the [`StableDiffusionXLPipeline`].
+
+```py
+pipeline = StableDiffusionXLPipeline.from_pretrained(
   "stabilityai/stable-diffusion-xl-base-1.0",
+  scheduler=scheduler,
   vae=vae,
   torch_dtype=torch.float16,
-  device_map="cuda"
-)
+  variant="fp16",
+  use_safetensors=True
+).to("cuda")
 ```
 
-## Reusing models in multiple pipelines
+## Reuse a pipeline
 
-When working with multiple pipelines that use the same model, the [`~DiffusionPipeline.from_pipe`] method enables reusing a model instead of reloading it each time. This allows you to use multiple pipelines without increasing memory usage.
+When you load multiple pipelines that share the same model components, it makes sense to reuse the shared components instead of reloading everything into memory again, especially if your hardware is memory-constrained. For example:
 
-Memory usage is determined by the pipeline with the highest memory requirement regardless of the number of pipelines.
+1. You generated an image with the [`StableDiffusionPipeline`] but you want to improve its quality with the [`StableDiffusionSAGPipeline`]. Both of these pipelines share the same pretrained model, so it'd be a waste of memory to load the same model twice.
+2. You want to add a model component, like a [`MotionAdapter`](../api/pipelines/animatediff#animatediffpipeline), to [`AnimateDiffPipeline`] which was instantiated from an existing [`StableDiffusionPipeline`]. Again, both pipelines share the same pretrained model, so it'd be a waste of memory to load an entirely new pipeline again.
 
-The example below loads a pipeline and then loads a second pipeline with [`~DiffusionPipeline.from_pipe`] to use [perturbed-attention guidance (PAG)](../api/pipelines/pag) to improve generation quality.
+With the [`DiffusionPipeline.from_pipe`] API, you can switch between multiple pipelines to take advantage of their different features without increasing memory-usage. It is similar to turning on and off a feature in your pipeline.
 
-> [!WARNING]
-> Use [`AutoPipelineForText2Image`] because [`DiffusionPipeline`] doesn't support PAG. Refer to the [AutoPipeline](../tutorials/autopipeline) docs to learn more. 
+> [!TIP]
+> To switch between tasks (rather than features), use the [`~DiffusionPipeline.from_pipe`] method with the [AutoPipeline](../api/pipelines/auto_pipeline) class, which automatically identifies the pipeline class based on the task (learn more in the [AutoPipeline](../tutorials/autopipeline) tutorial).
 
-```py
+Let's start with a [`StableDiffusionPipeline`] and then reuse the loaded model components to create a [`StableDiffusionSAGPipeline`] to increase generation quality. You'll use the [`StableDiffusionPipeline`] with an [IP-Adapter](./ip_adapter) to generate a bear eating pizza.
+
+```python
+from diffusers import DiffusionPipeline, StableDiffusionSAGPipeline
 import torch
-from diffusers import AutoPipelineForText2Image
+import gc
+from diffusers.utils import load_image
+from accelerate.utils import compute_module_sizes
 
-pipeline_sdxl = AutoPipelineForText2Image.from_pretrained(
-  "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, device_map="cuda"
-)
-prompt = """
-cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California
-highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain
-"""
-image = pipeline_sdxl(prompt).images[0]
-print(f"Max memory reserved: {torch.cuda.max_memory_allocated() / 1024**3:.2f} GB")
-# Max memory reserved: 10.47 GB
+image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_neg_embed.png")
+
+pipe_sd = DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V6.0_B1_noVAE", torch_dtype=torch.float16)
+pipe_sd.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
+pipe_sd.set_ip_adapter_scale(0.6)
+pipe_sd.to("cuda")
+
+generator = torch.Generator(device="cpu").manual_seed(33)
+out_sd = pipe_sd(
+    prompt="bear eats pizza",
+    negative_prompt="wrong white balance, dark, sketches,worst quality,low quality",
+    ip_adapter_image=image,
+    num_inference_steps=50,
+    generator=generator,
+).images[0]
+out_sd
 ```
 
-Set `enable_pag=True` in the second pipeline to enable PAG. The second pipeline uses the same amount of memory because it shares model weights with the first one.
+<div class="flex justify-center">
+  <img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/from_pipe_out_sd_0.png"/>
+</div>
 
-```py
-pipeline = AutoPipelineForText2Image.from_pipe(
-  pipeline_sdxl, enable_pag=True
-)
-prompt = """
-cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California
-highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain
-"""
-image = pipeline(prompt).images[0]
-print(f"Max memory reserved: {torch.cuda.max_memory_allocated() / 1024**3:.2f} GB")
-# Max memory reserved: 10.47 GB
+For reference, you can check how much memory this process consumed.
+
+```python
+def bytes_to_giga_bytes(bytes):
+    return bytes / 1024 / 1024 / 1024
+print(f"Max memory allocated: {bytes_to_giga_bytes(torch.cuda.max_memory_allocated())} GB")
+"Max memory allocated: 4.406213283538818 GB"
 ```
 
+Now, reuse the same pipeline components from [`StableDiffusionPipeline`] in [`StableDiffusionSAGPipeline`] with the [`~DiffusionPipeline.from_pipe`] method.
+
 > [!WARNING]
-> Pipelines created by [`~DiffusionPipeline.from_pipe`] share the same models and *state*. Modifying the state of a model in one pipeline affects all the other pipelines that share the same model.
+> Some pipeline methods may not function properly on new pipelines created with [`~DiffusionPipeline.from_pipe`]. For instance, the [`~DiffusionPipeline.enable_model_cpu_offload`] method installs hooks on the model components based on a unique offloading sequence for each pipeline. If the models are executed in a different order in the new pipeline, the CPU offloading may not work correctly.
+>
+> To ensure everything works as expected, we recommend re-applying a pipeline method on a new pipeline created with [`~DiffusionPipeline.from_pipe`].
 
-Some methods may not work correctly on pipelines created with [`~DiffusionPipeline.from_pipe`]. For example, [`~DiffusionPipeline.enable_model_cpu_offload`] relies on a unique model execution order, which may differ in the new pipeline. To ensure proper functionality, reapply these methods on the new pipeline.
+```python
+pipe_sag = StableDiffusionSAGPipeline.from_pipe(
+    pipe_sd
+)
+
+generator = torch.Generator(device="cpu").manual_seed(33)
+out_sag = pipe_sag(
+    prompt="bear eats pizza",
+    negative_prompt="wrong white balance, dark, sketches,worst quality,low quality",
+    ip_adapter_image=image,
+    num_inference_steps=50,
+    generator=generator,
+    guidance_scale=1.0,
+    sag_scale=0.75
+).images[0]
+out_sag
+```
+
+<div class="flex justify-center">
+  <img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/from_pipe_out_sag_1.png"/>
+</div>
+
+If you check the memory usage, you'll see it remains the same as before because [`StableDiffusionPipeline`] and [`StableDiffusionSAGPipeline`] are sharing the same pipeline components. This allows you to use them interchangeably without any additional memory overhead.
+
+```py
+print(f"Max memory allocated: {bytes_to_giga_bytes(torch.cuda.max_memory_allocated())} GB")
+"Max memory allocated: 4.406213283538818 GB"
+```
+
+Let's animate the image with the [`AnimateDiffPipeline`] and also add a [`MotionAdapter`] module to the pipeline. For the [`AnimateDiffPipeline`], you need to unload the IP-Adapter first and reload it *after* you've created your new pipeline (this only applies to the [`AnimateDiffPipeline`]).
+
+```py
+from diffusers import AnimateDiffPipeline, MotionAdapter, DDIMScheduler
+from diffusers.utils import export_to_gif
+
+pipe_sag.unload_ip_adapter()
+adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
+
+pipe_animate = AnimateDiffPipeline.from_pipe(pipe_sd, motion_adapter=adapter)
+pipe_animate.scheduler = DDIMScheduler.from_config(pipe_animate.scheduler.config, beta_schedule="linear")
+# load IP-Adapter and LoRA weights again
+pipe_animate.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
+pipe_animate.load_lora_weights("guoyww/animatediff-motion-lora-zoom-out", adapter_name="zoom-out")
+pipe_animate.to("cuda")
+
+generator = torch.Generator(device="cpu").manual_seed(33)
+pipe_animate.set_adapters("zoom-out", adapter_weights=0.75)
+out = pipe_animate(
+    prompt="bear eats pizza",
+    num_frames=16,
+    num_inference_steps=50,
+    ip_adapter_image=image,
+    generator=generator,
+).frames[0]
+export_to_gif(out, "out_animate.gif")
+```
+
+<div class="flex justify-center">
+  <img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/from_pipe_out_animate_3.gif"/>
+</div>
+
+The [`AnimateDiffPipeline`] is more memory-intensive and consumes 15GB of memory (see the [Memory-usage of from_pipe](#memory-usage-of-from_pipe) section to learn what this means for your memory-usage).
+
+```py
+print(f"Max memory allocated: {bytes_to_giga_bytes(torch.cuda.max_memory_allocated())} GB")
+"Max memory allocated: 15.178664207458496 GB"
+```
+
+### Modify from_pipe components
+
+Pipelines loaded with [`~DiffusionPipeline.from_pipe`] can be customized with different model components or methods. However, whenever you modify the *state* of the model components, it affects all the other pipelines that share the same components. For example, if you call [`~diffusers.loaders.IPAdapterMixin.unload_ip_adapter`] on the [`StableDiffusionSAGPipeline`], you won't be able to use IP-Adapter with the [`StableDiffusionPipeline`] because it's been removed from their shared components.
+
+```py
+pipe.sag_unload_ip_adapter()
+
+generator = torch.Generator(device="cpu").manual_seed(33)
+out_sd = pipe_sd(
+    prompt="bear eats pizza",
+    negative_prompt="wrong white balance, dark, sketches,worst quality,low quality",
+    ip_adapter_image=image,
+    num_inference_steps=50,
+    generator=generator,
+).images[0]
+"AttributeError: 'NoneType' object has no attribute 'image_projection_layers'"
+```
+
+### Memory usage of from_pipe
+
+The memory requirement of loading multiple pipelines with [`~DiffusionPipeline.from_pipe`] is determined by the pipeline with the highest memory-usage regardless of the number of pipelines you create.
+
+| Pipeline | Memory usage (GB) |
+|---|---|
+| StableDiffusionPipeline | 4.400 |
+| StableDiffusionSAGPipeline | 4.400 |
+| AnimateDiffPipeline | 15.178 |
+
+The [`AnimateDiffPipeline`] has the highest memory requirement, so the *total memory-usage* is based only on the [`AnimateDiffPipeline`]. Your memory-usage will not increase if you create additional pipelines as long as their memory requirements doesn't exceed that of the [`AnimateDiffPipeline`]. Each pipeline can be used interchangeably without any additional memory overhead.
 
 ## Safety checker
 
-Diffusers provides a [safety checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) for older Stable Diffusion models to prevent generating harmful content. It screens the generated output against a set of hardcoded harmful concepts.
+Diffusers implements a [safety checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) for Stable Diffusion models which can generate harmful content. The safety checker screens the generated output against known hardcoded not-safe-for-work (NSFW) content. If for whatever reason you'd like to disable the safety checker, pass `safety_checker=None` to the [`~DiffusionPipeline.from_pretrained`] method.
 
-If you want to disable the safety checker, pass `safety_checker=None` in [`~DiffusionPipeline.from_pretrained`] as shown below.
-
-```py
+```python
 from diffusers import DiffusionPipeline
 
-pipeline = DiffusionPipeline.from_pretrained(
-  "stable-diffusion-v1-5/stable-diffusion-v1-5", safety_checker=None
-)
+pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", safety_checker=None, use_safetensors=True)
 """
 You have disabled the safety checker for <class 'diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline'> by passing `safety_checker=None`. Ensure that you abide by the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend keeping the safety filter enabled in all public-facing circumstances, disabling it only for use cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .
 """
-```
+```
+
+## Checkpoint variants
+
+A checkpoint variant is usually a checkpoint whose weights are:
+
+- Stored in a different floating point type, such as [torch.float16](https://pytorch.org/docs/stable/tensors.html#data-types), because it only requires half the bandwidth and storage to download. You can't use this variant if you're continuing training or using a CPU.
+- Non-exponential mean averaged (EMA) weights which shouldn't be used for inference. You should use this variant to continue finetuning a model.
+
+> [!TIP]
+> When the checkpoints have identical model structures, but they were trained on different datasets and with a different training setup, they should be stored in separate repositories. For example, [stabilityai/stable-diffusion-2](https://hf.co/stabilityai/stable-diffusion-2) and [stabilityai/stable-diffusion-2-1](https://hf.co/stabilityai/stable-diffusion-2-1) are stored in separate repositories.
+
+Otherwise, a variant is **identical** to the original checkpoint. They have exactly the same serialization format (like [safetensors](./using_safetensors)), model structure, and their weights have identical tensor shapes.
+
+| **checkpoint type** | **weight name**                             | **argument for loading weights** |
+|---------------------|---------------------------------------------|----------------------------------|
+| original            | diffusion_pytorch_model.safetensors         |                                  |
+| floating point      | diffusion_pytorch_model.fp16.safetensors    | `variant`, `torch_dtype`         |
+| non-EMA             | diffusion_pytorch_model.non_ema.safetensors | `variant`                        |
+
+There are two important arguments for loading variants:
+
+- `torch_dtype` specifies the floating point precision of the loaded checkpoint. For example, if you want to save bandwidth by loading a fp16 variant, you should set `variant="fp16"` and `torch_dtype=torch.float16` to *convert the weights* to fp16. Otherwise, the fp16 weights are converted to the default fp32 precision.
+
+  If you only set `torch_dtype=torch.float16`, the default fp32 weights are downloaded first and then converted to fp16.
+
+- `variant` specifies which files should be loaded from the repository. For example, if you want to load a non-EMA variant of a UNet from [stable-diffusion-v1-5/stable-diffusion-v1-5](https://hf.co/stable-diffusion-v1-5/stable-diffusion-v1-5/tree/main/unet), set `variant="non_ema"` to download the `non_ema` file.
+
+<hfoptions id="variants">
+<hfoption id="fp16">
+
+```py
+from diffusers import DiffusionPipeline
+import torch
+
+pipeline = DiffusionPipeline.from_pretrained(
+    "stable-diffusion-v1-5/stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16, use_safetensors=True
+)
+```
+
+</hfoption>
+<hfoption id="non-EMA">
+
+```py
+pipeline = DiffusionPipeline.from_pretrained(
+    "stable-diffusion-v1-5/stable-diffusion-v1-5", variant="non_ema", use_safetensors=True
+)
+```
+
+</hfoption>
+</hfoptions>
+
+Use the `variant` parameter in the [`DiffusionPipeline.save_pretrained`] method to save a checkpoint as a different floating point type or as a non-EMA variant. You should try save a variant to the same folder as the original checkpoint, so you have the option of loading both from the same folder.
+
+<hfoptions id="save">
+<hfoption id="fp16">
+
+```python
+from diffusers import DiffusionPipeline
+
+pipeline.save_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", variant="fp16")
+```
+
+</hfoption>
+<hfoption id="non_ema">
+
+```py
+pipeline.save_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", variant="non_ema")
+```
+
+</hfoption>
+</hfoptions>
+
+If you don't save the variant to an existing folder, you must specify the `variant` argument otherwise it'll throw an `Exception` because it can't find the original checkpoint.
+
+```python
+# 👎 this won't work
+pipeline = DiffusionPipeline.from_pretrained(
+    "./stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
+)
+# 👍 this works
+pipeline = DiffusionPipeline.from_pretrained(
+    "./stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16, use_safetensors=True
+)
+```
+
+## DiffusionPipeline explained
+
+As a class method, [`DiffusionPipeline.from_pretrained`] is responsible for two things:
+
+- Download the latest version of the folder structure required for inference and cache it. If the latest folder structure is available in the local cache, [`DiffusionPipeline.from_pretrained`] reuses the cache and won't redownload the files.
+- Load the cached weights into the correct pipeline [class](../api/pipelines/overview#diffusers-summary) - retrieved from the `model_index.json` file - and return an instance of it.
+
+The pipelines' underlying folder structure corresponds directly with their class instances. For example, the [`StableDiffusionPipeline`] corresponds to the folder structure in [`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5).
+
+```python
+from diffusers import DiffusionPipeline
+
+repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+pipeline = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=True)
+print(pipeline)
+```
+
+You'll see pipeline is an instance of [`StableDiffusionPipeline`], which consists of seven components:
+
+- `"feature_extractor"`: a [`~transformers.CLIPImageProcessor`] from 🤗 Transformers.
+- `"safety_checker"`: a [component](https://github.com/huggingface/diffusers/blob/e55687e1e15407f60f32242027b7bb8170e58266/src/diffusers/pipelines/stable_diffusion/safety_checker.py#L32) for screening against harmful content.
+- `"scheduler"`: an instance of [`PNDMScheduler`].
+- `"text_encoder"`: a [`~transformers.CLIPTextModel`] from 🤗 Transformers.
+- `"tokenizer"`: a [`~transformers.CLIPTokenizer`] from 🤗 Transformers.
+- `"unet"`: an instance of [`UNet2DConditionModel`].
+- `"vae"`: an instance of [`AutoencoderKL`].
+
+```json
+StableDiffusionPipeline {
+  "feature_extractor": [
+    "transformers",
+    "CLIPImageProcessor"
+  ],
+  "safety_checker": [
+    "stable_diffusion",
+    "StableDiffusionSafetyChecker"
+  ],
+  "scheduler": [
+    "diffusers",
+    "PNDMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}
+```
+
+Compare the components of the pipeline instance to the [`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/tree/main) folder structure, and you'll see there is a separate folder for each of the components in the repository:
+
+```
+.
+├── feature_extractor
+│   └── preprocessor_config.json
+├── model_index.json
+├── safety_checker
+│   ├── config.json
+|   ├── model.fp16.safetensors
+│   ├── model.safetensors
+│   ├── pytorch_model.bin
+|   └── pytorch_model.fp16.bin
+├── scheduler
+│   └── scheduler_config.json
+├── text_encoder
+│   ├── config.json
+|   ├── model.fp16.safetensors
+│   ├── model.safetensors
+│   |── pytorch_model.bin
+|   └── pytorch_model.fp16.bin
+├── tokenizer
+│   ├── merges.txt
+│   ├── special_tokens_map.json
+│   ├── tokenizer_config.json
+│   └── vocab.json
+├── unet
+│   ├── config.json
+│   ├── diffusion_pytorch_model.bin
+|   |── diffusion_pytorch_model.fp16.bin
+│   |── diffusion_pytorch_model.f16.safetensors
+│   |── diffusion_pytorch_model.non_ema.bin
+│   |── diffusion_pytorch_model.non_ema.safetensors
+│   └── diffusion_pytorch_model.safetensors
+|── vae
+.   ├── config.json
+.   ├── diffusion_pytorch_model.bin
+    ├── diffusion_pytorch_model.fp16.bin
+    ├── diffusion_pytorch_model.fp16.safetensors
+    └── diffusion_pytorch_model.safetensors
+```
+
+You can access each of the components of the pipeline as an attribute to view its configuration:
+
+```py
+pipeline.tokenizer
+CLIPTokenizer(
+    name_or_path="/root/.cache/huggingface/hub/models--runwayml--stable-diffusion-v1-5/snapshots/39593d5650112b4cc580433f6b0435385882d819/tokenizer",
+    vocab_size=49408,
+    model_max_length=77,
+    is_fast=False,
+    padding_side="right",
+    truncation_side="right",
+    special_tokens={
+        "bos_token": AddedToken("<|startoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=True),
+        "eos_token": AddedToken("<|endoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=True),
+        "unk_token": AddedToken("<|endoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=True),
+        "pad_token": "<|endoftext|>",
+    },
+    clean_up_tokenization_spaces=True
+)
+```
+
+Every pipeline expects a [`model_index.json`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/model_index.json) file that tells the [`DiffusionPipeline`]:
+
+- which pipeline class to load from `_class_name`
+- which version of 🧨 Diffusers was used to create the model in `_diffusers_version`
+- what components from which library are stored in the subfolders (`name` corresponds to the component and subfolder name, `library` corresponds to the name of the library to load the class from, and `class` corresponds to the class name)
+
+```json
+{
+  "_class_name": "StableDiffusionPipeline",
+  "_diffusers_version": "0.6.0",
+  "feature_extractor": [
+    "transformers",
+    "CLIPImageProcessor"
+  ],
+  "safety_checker": [
+    "stable_diffusion",
+    "StableDiffusionSafetyChecker"
+  ],
+  "scheduler": [
+    "diffusers",
+    "PNDMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}
+```

docs/source/en/using-diffusers/models.md

@@ -1,120 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-[[open-in-colab]]
-
-# Models
-
-A diffusion model relies on a few individual models working together to generate an output. These models are responsible for denoising, encoding inputs, and decoding latents into the actual outputs.
-
-This guide will show you how to load models.
-
-## Loading a model
-
-All models are loaded with the [`~ModelMixin.from_pretrained`] method, which downloads and caches the latest model version. If the latest files are available in the local cache, [`~ModelMixin.from_pretrained`] reuses files in the cache.
-
-Pass the `subfolder` argument to [`~ModelMixin.from_pretrained`] to specify where to load the model weights from. Omit the `subfolder` argument if the repository doesn't have a subfolder structure or if you're loading a standalone model.
-
-```py
-from diffusers import QwenImageTransformer2DModel
-
-model = QwenImageTransformer2DModel.from_pretrained("Qwen/Qwen-Image", subfolder="transformer")
-```
-
-## AutoModel
-
-[`AutoModel`] detects the model class from a `model_index.json` file or a model's `config.json` file. It fetches the correct model class from these files and delegates the actual loading to the model class. [`AutoModel`] is useful for automatic model type detection without needing to know the exact model class beforehand.
-
-```py
-from diffusers import AutoModel
-
-model = AutoModel.from_pretrained(
-    "Qwen/Qwen-Image", subfolder="transformer"
-)
-```
-
-## Model data types
-
-Use the `torch_dtype` argument in [`~ModelMixin.from_pretrained`] to load a model with a specific data type. This allows you to load a model in a lower precision to reduce memory usage.
-
-```py
-import torch
-from diffusers import QwenImageTransformer2DModel
-
-model = QwenImageTransformer2DModel.from_pretrained(
-    "Qwen/Qwen-Image",
-    subfolder="transformer",
-    torch_dtype=torch.bfloat16
-)
-```
-
-[nn.Module.to](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.to) can also convert to a specific data type on the fly. However, it converts *all* weights to the requested data type unlike `torch_dtype` which respects `_keep_in_fp32_modules`. This argument preserves layers in `torch.float32` for numerical stability and best generation quality (see example [_keep_in_fp32_modules](https://github.com/huggingface/diffusers/blob/f864a9a352fa4a220d860bfdd1782e3e5af96382/src/diffusers/models/transformers/transformer_wan.py#L374))
-
-```py
-from diffusers import QwenImageTransformer2DModel
-
-model = QwenImageTransformer2DModel.from_pretrained(
-    "Qwen/Qwen-Image", subfolder="transformer"
-)
-model = model.to(dtype=torch.float16) 
-```
-
-## Device placement
-
-Use the `device_map` argument in [`~ModelMixin.from_pretrained`] to place a model on an accelerator like a GPU. It is especially helpful where there are multiple GPUs.
-
-Diffusers currently provides three options to `device_map` for individual models, `"cuda"`, `"balanced"` and `"auto"`. Refer to the table below to compare the three placement strategies.
-
-| parameter | description |
-|---|---|
-| `"cuda"` | places pipeline on a supported accelerator (CUDA) |
-| `"balanced"` | evenly distributes pipeline on all GPUs |
-| `"auto"` | distribute model from fastest device first to slowest |
-
-Use the `max_memory` argument in [`~ModelMixin.from_pretrained`] to allocate a maximum amount of memory to use on each device. By default, Diffusers uses the maximum amount available.
-
-```py
-import torch
-from diffusers import QwenImagePipeline
-
-max_memory = {0: "16GB", 1: "16GB"}
-pipeline = QwenImagePipeline.from_pretrained(
-    "Qwen/Qwen-Image", 
-    torch_dtype=torch.bfloat16,
-    device_map="cuda",
-    max_memory=max_memory
-)
-```
-
-The `hf_device_map` attribute allows you to access and view the `device_map`.
-
-```py
-print(transformer.hf_device_map)
-# {'': device(type='cuda')}
-```
-
-## Saving models
-
-Save a model with the [`~ModelMixin.save_pretrained`] method.
-
-```py
-from diffusers import QwenImageTransformer2DModel
-
-model = QwenImageTransformer2DModel.from_pretrained("Qwen/Qwen-Image", subfolder="transformer")
-model.save_pretrained("./local/model")
-```
-
-For large models, it is helpful to use `max_shard_size` to save a model as multiple shards. A shard can be loaded faster and save memory (refer to the [parallel loading](./loading#parallel-loading) docs for more details), especially if there is more than one GPU.
-
-```py
-model.save_pretrained("./local/model", max_shard_size="5GB")
-```

docs/source/en/using-diffusers/other-formats.md

@@ -176,7 +176,7 @@ Benefits of using the Diffusers-multifolder layout include:
     ).to("cuda")
     turbo_pipeline.scheduler = EulerDiscreteScheduler.from_config(
         turbo_pipeline.scheduler.config,
-        timestep_spacing="trailing"
+        timestep+spacing="trailing"
     )
     image = turbo_pipeline(
         "an astronaut riding a unicorn on mars",
@@ -267,7 +267,6 @@ pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_d
 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.

docs/source/en/using-diffusers/overview_techniques.md

@@ -10,10 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# BriaTransformer2DModel
+# Overview
 
-A modified flux Transformer model from [Bria](https://huggingface.co/briaai/BRIA-3.2)
+The inference pipeline supports and enables a wide range of techniques that are divided into two categories:
 
-## BriaTransformer2DModel
-
-[[autodoc]] BriaTransformer2DModel
+* Pipeline functionality: these techniques modify the pipeline or extend it for other applications. For example, pipeline callbacks add new features to a pipeline and a pipeline can also be extended for distributed inference.
+* Improve inference quality: these techniques increase the visual quality of the generated images. For example, you can enhance your prompts with GPT2 to create better images with lower effort.

docs/source/en/using-diffusers/push_to_hub.md

@@ -10,22 +10,19 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+# Push files to the Hub
+
 [[open-in-colab]]
 
-# Sharing pipelines and models
-
-Share your pipeline or models and schedulers on the Hub with the [`~diffusers.utils.PushToHubMixin`] class. This class:
+🤗 Diffusers provides a [`~diffusers.utils.PushToHubMixin`] for uploading your model, scheduler, or pipeline to the Hub. It is an easy way to store your files on the Hub, and also allows you to share your work with others. Under the hood, the [`~diffusers.utils.PushToHubMixin`]:
 
 1. creates a repository on the Hub
 2. saves your model, scheduler, or pipeline files so they can be reloaded later
 3. uploads folder containing these files to the Hub
 
-This guide will show you how to upload your files to the Hub with the [`~diffusers.utils.PushToHubMixin`] class.
+This guide will show you how to use the [`~diffusers.utils.PushToHubMixin`] to upload your files to the Hub.
 
-Log in to your Hugging Face account with your access [token](https://huggingface.co/settings/tokens).
-
-<hfoptions id="login">
-<hfoption id="notebook">
+You'll need to log in to your Hub account with your access [token](https://huggingface.co/settings/tokens) first:
 
 ```py
 from huggingface_hub import notebook_login
@@ -33,19 +30,9 @@ from huggingface_hub import notebook_login
 notebook_login()
 ```
 
-</hfoption>
-<hfoption id="hf CLI">
-
-```bash
-hf auth login
-```
-
-</hfoption>
-</hfoptions>
-
 ## Models
 
-To push a model to the Hub, call [`~diffusers.utils.PushToHubMixin.push_to_hub`] and specify the repository id of the model.
+To push a model to the Hub, call [`~diffusers.utils.PushToHubMixin.push_to_hub`] and specify the repository id of the model to be stored on the Hub:
 
 ```py
 from diffusers import ControlNetModel
@@ -61,9 +48,15 @@ controlnet = ControlNetModel(
 controlnet.push_to_hub("my-controlnet-model")
 ```
 
-The [`~diffusers.utils.PushToHubMixin.push_to_hub`] method saves the model's `config.json` file and the weights are automatically saved as safetensors files.
+For models, you can also specify the [*variant*](loading#checkpoint-variants) of the weights to push to the Hub. For example, to push `fp16` weights:
 
-Load the model again with [`~DiffusionPipeline.from_pretrained`].
+```py
+controlnet.push_to_hub("my-controlnet-model", variant="fp16")
+```
+
+The [`~diffusers.utils.PushToHubMixin.push_to_hub`] function saves the model's `config.json` file and the weights are automatically saved in the `safetensors` format.
+
+Now you can reload the model from your repository on the Hub:
 
 ```py
 model = ControlNetModel.from_pretrained("your-namespace/my-controlnet-model")
@@ -71,7 +64,7 @@ model = ControlNetModel.from_pretrained("your-namespace/my-controlnet-model")
 
 ## Scheduler
 
-To push a scheduler to the Hub, call [`~diffusers.utils.PushToHubMixin.push_to_hub`] and specify the repository id of the scheduler.
+To push a scheduler to the Hub, call [`~diffusers.utils.PushToHubMixin.push_to_hub`] and specify the repository id of the scheduler to be stored on the Hub:
 
 ```py
 from diffusers import DDIMScheduler
@@ -88,7 +81,7 @@ scheduler.push_to_hub("my-controlnet-scheduler")
 
 The [`~diffusers.utils.PushToHubMixin.push_to_hub`] function saves the scheduler's `scheduler_config.json` file to the specified repository.
 
-Load the scheduler again with [`~SchedulerMixin.from_pretrained`].
+Now you can reload the scheduler from your repository on the Hub:
 
 ```py
 scheduler = DDIMScheduler.from_pretrained("your-namepsace/my-controlnet-scheduler")
@@ -96,7 +89,7 @@ scheduler = DDIMScheduler.from_pretrained("your-namepsace/my-controlnet-schedule
 
 ## Pipeline
 
-To push a pipeline to the Hub, initialize the pipeline components with your desired parameters.
+You can also push an entire pipeline with all it's components to the Hub. For example, initialize the components of a [`StableDiffusionPipeline`] with the parameters you want:
 
 ```py
 from diffusers import (
@@ -150,7 +143,7 @@ text_encoder = CLIPTextModel(text_encoder_config)
 tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
 ```
 
-Pass all components to the pipeline and call [`~diffusers.utils.PushToHubMixin.push_to_hub`].
+Pass all of the components to the [`StableDiffusionPipeline`] and call [`~diffusers.utils.PushToHubMixin.push_to_hub`] to push the pipeline to the Hub:
 
 ```py
 components = {
@@ -167,7 +160,7 @@ pipeline = StableDiffusionPipeline(**components)
 pipeline.push_to_hub("my-pipeline")
 ```
 
-The [`~diffusers.utils.PushToHubMixin.push_to_hub`] method saves each component to a subfolder in the repository. Load the pipeline again with [`~DiffusionPipeline.from_pretrained`].
+The [`~diffusers.utils.PushToHubMixin.push_to_hub`] function saves each component to a subfolder in the repository. Now you can reload the pipeline from your repository on the Hub:
 
 ```py
 pipeline = StableDiffusionPipeline.from_pretrained("your-namespace/my-pipeline")
@@ -175,10 +168,10 @@ pipeline = StableDiffusionPipeline.from_pretrained("your-namespace/my-pipeline")
 
 ## Privacy
 
-Set `private=True` in [`~diffusers.utils.PushToHubMixin.push_to_hub`] to keep a model, scheduler, or pipeline files private.
+Set `private=True` in the [`~diffusers.utils.PushToHubMixin.push_to_hub`] function to keep your model, scheduler, or pipeline files private:
 
 ```py
 controlnet.push_to_hub("my-controlnet-model-private", private=True)
 ```
 
-Private repositories are only visible to you. Other users won't be able to clone the repository and it won't appear in search results. Even if a user has the URL to your private repository, they'll receive a `404 - Sorry, we can't find the page you are looking for`. You must be [logged in](https://huggingface.co/docs/huggingface_hub/quick-start#login) to load a model from a private repository.
+Private repositories are only visible to you, and other users won't be able to clone the repository and your repository won't appear in search results. Even if a user has the URL to your private repository, they'll receive a `404 - Sorry, we can't find the page you are looking for`. You must be [logged in](https://huggingface.co/docs/huggingface_hub/quick-start#login) to load a model from a private repository.

docs/source/en/using-diffusers/reusing_seeds.md

@@ -10,86 +10,129 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# Reproducibility
+# Reproducible pipelines
 
-Diffusion is a random process that generates a different output every time. For certain situations like testing and replicating results, you want to generate the same result each time, across releases and platforms within a certain tolerance range.
+Diffusion models are inherently random which is what allows it to generate different outputs every time it is run. But there are certain times when you want to generate the same output every time, like when you're testing, replicating results, and even [improving image quality](#deterministic-batch-generation). While you can't expect to get identical results across platforms, you can expect reproducible results across releases and platforms within a certain tolerance range (though even this may vary).
 
-This guide will show you how to control sources of randomness and enable deterministic algorithms.
-
-## Generator
-
-Pipelines rely on [torch.randn](https://pytorch.org/docs/stable/generated/torch.randn.html), which uses a different random seed each time, to create the initial noisy tensors. To generate the same output on a CPU or GPU, use a [Generator](https://docs.pytorch.org/docs/stable/generated/torch.Generator.html) to manage how random values are generated.
+This guide will show you how to control randomness for deterministic generation on a CPU and GPU.
 
 > [!TIP]
-> If reproducibility is important to your use case, we recommend always using a CPU `Generator`. The performance loss is often negligible and you'll generate more similar values.
+> We strongly recommend reading PyTorch's [statement about reproducibility](https://pytorch.org/docs/stable/notes/randomness.html):
+>
+> "Completely reproducible results are not guaranteed across PyTorch releases, individual commits, or different platforms. Furthermore, results may not be reproducible between CPU and GPU executions, even when using identical seeds."
 
-<hfoptions id="generator">
-<hfoption id="GPU">
+## Control randomness
 
-The GPU uses a different random number generator than the CPU. Diffusers solves this issue with the [`~utils.torch_utils.randn_tensor`] function to create the random tensor on a CPU and then moving it to the GPU. This function is used everywhere inside the pipeline and you don't need to explicitly call it.
+During inference, pipelines rely heavily on random sampling operations which include creating the
+Gaussian noise tensors to denoise and adding noise to the scheduling step.
 
-Use [manual_seed](https://docs.pytorch.org/docs/stable/generated/torch.manual_seed.html) as shown below to set a seed.
+Take a look at the tensor values in the [`DDIMPipeline`] after two inference steps.
 
-```py
-import torch
-import numpy as np
+```python
 from diffusers import DDIMPipeline
+import numpy as np
 
-ddim = DDIMPipeline.from_pretrained("google/ddpm-cifar10-32", device_map="cuda")
-generator = torch.manual_seed(0)
-image = ddim(num_inference_steps=2, output_type="np", generator=generator).images
+ddim = DDIMPipeline.from_pretrained( "google/ddpm-cifar10-32", use_safetensors=True)
+image = ddim(num_inference_steps=2, output_type="np").images
 print(np.abs(image).sum())
 ```
 
-</hfoption>
+Running the code above prints one value, but if you run it again you get a different value.
+
+Each time the pipeline is run, [torch.randn](https://pytorch.org/docs/stable/generated/torch.randn.html) uses a different random seed to create the Gaussian noise tensors. This leads to a different result each time it is run and enables the diffusion pipeline to generate a different random image each time.
+
+But if you need to reliably generate the same image, that depends on whether you're running the pipeline on a CPU or GPU.
+
+> [!TIP]
+> It might seem unintuitive to pass `Generator` objects to a pipeline instead of the integer value representing the seed. However, this is the recommended design when working with probabilistic models in PyTorch because a `Generator` is a *random state* that can be passed to multiple pipelines in a sequence. As soon as the `Generator` is consumed, the *state* is changed in place which means even if you passed the same `Generator` to a different pipeline, it won't produce the same result because the state is already changed.
+
+<hfoptions id="hardware">
 <hfoption id="CPU">
 
-Set `device="cpu"` in the `Generator` and use [manual_seed](https://docs.pytorch.org/docs/stable/generated/torch.manual_seed.html) to set a seed for generating random numbers.
+To generate reproducible results on a CPU, you'll need to use a PyTorch [Generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) and set a seed. Now when you run the code, it always prints a value of `1491.1711` because the `Generator` object with the seed is passed to all the random functions in the pipeline. You should get a similar, if not the same, result on whatever hardware and PyTorch version you're using.
 
-```py
+```python
 import torch
 import numpy as np
 from diffusers import DDIMPipeline
 
-ddim = DDIMPipeline.from_pretrained("google/ddpm-cifar10-32")
+ddim = DDIMPipeline.from_pretrained("google/ddpm-cifar10-32", use_safetensors=True)
 generator = torch.Generator(device="cpu").manual_seed(0)
 image = ddim(num_inference_steps=2, output_type="np", generator=generator).images
 print(np.abs(image).sum())
 ```
 
 </hfoption>
-</hfoptions>
+<hfoption id="GPU">
 
-The `Generator` object should be passed to the pipeline instead of an integer seed. `Generator` maintains a *random state* that is consumed and modified when used. Once consumed, the same `Generator` object produces different results in subsequent calls, even across different pipelines, because it's *state* has changed.
+Writing a reproducible pipeline on a GPU is a bit trickier, and full reproducibility across different hardware is not guaranteed because matrix multiplication - which diffusion pipelines require a lot of - is less deterministic on a GPU than a CPU. For example, if you run the same code example from the CPU example, you'll get a different result even though the seed is identical. This is because the GPU uses a different random number generator than the CPU.
 
-```py
-generator = torch.manual_seed(0)
+```python
+import torch
+import numpy as np
+from diffusers import DDIMPipeline
 
-for _ in range(5):
--    image = pipeline(prompt, generator=generator)
-+    image = pipeline(prompt, generator=torch.manual_seed(0))
+ddim = DDIMPipeline.from_pretrained("google/ddpm-cifar10-32", use_safetensors=True)
+ddim.to("cuda")
+generator = torch.Generator(device="cuda").manual_seed(0)
+image = ddim(num_inference_steps=2, output_type="np", generator=generator).images
+print(np.abs(image).sum())
 ```
 
+To avoid this issue, Diffusers has a [`~utils.torch_utils.randn_tensor`] function for creating random noise on the CPU, and then moving the tensor to a GPU if necessary. The [`~utils.torch_utils.randn_tensor`] function is used everywhere inside the pipeline. Now you can call [torch.manual_seed](https://pytorch.org/docs/stable/generated/torch.manual_seed.html) which automatically creates a CPU `Generator` that can be passed to the pipeline even if it is being run on a GPU.
+
+```python
+import torch
+import numpy as np
+from diffusers import DDIMPipeline
+
+ddim = DDIMPipeline.from_pretrained("google/ddpm-cifar10-32", use_safetensors=True)
+ddim.to("cuda")
+generator = torch.manual_seed(0)
+image = ddim(num_inference_steps=2, output_type="np", generator=generator).images
+print(np.abs(image).sum())
+```
+
+> [!TIP]
+> If reproducibility is important to your use case, we recommend always passing a CPU `Generator`. The performance loss is often negligible and you'll generate more similar values than if the pipeline had been run on a GPU.
+
+Finally, more complex pipelines such as [`UnCLIPPipeline`], are often extremely
+susceptible to precision error propagation. You'll need to use
+exactly the same hardware and PyTorch version for full reproducibility.
+
+</hfoption>
+</hfoptions>
+
 ## Deterministic algorithms
 
-PyTorch supports [deterministic algorithms](https://docs.pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms) - where available - for certain operations so they produce the same results. Deterministic algorithms may be slower and decrease performance.
+You can also configure PyTorch to use deterministic algorithms to create a reproducible pipeline. The downside is that deterministic algorithms may be slower than non-deterministic ones and you may observe a decrease in performance.
 
-Use Diffusers' [enable_full_determinism](https://github.com/huggingface/diffusers/blob/142f353e1c638ff1d20bd798402b68f72c1ebbdd/src/diffusers/utils/testing_utils.py#L861) function to enable deterministic algorithms.
+Non-deterministic behavior occurs when operations are launched in more than one CUDA stream. To avoid this, set the environment variable [CUBLAS_WORKSPACE_CONFIG](https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility) to `:16:8` to only use one buffer size during runtime.
+
+PyTorch typically benchmarks multiple algorithms to select the fastest one, but if you want reproducibility, you should disable this feature because the benchmark may select different algorithms each time. Set Diffusers [enable_full_determinism](https://github.com/huggingface/diffusers/blob/142f353e1c638ff1d20bd798402b68f72c1ebbdd/src/diffusers/utils/testing_utils.py#L861) to enable deterministic algorithms.
 
 ```py
-import torch
-from diffusers_utils import enable_full_determinism
-
 enable_full_determinism()
 ```
 
-Under the hood, `enable_full_determinism` works by:
+Now when you run the same pipeline twice, you'll get identical results.
 
-- Setting the environment variable [CUBLAS_WORKSPACE_CONFIG](https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility) to `:16:8` to only use one buffer size during rntime. Non-deterministic behavior occurs when operations are used in more than one CUDA stream.
-- Disabling benchmarking to find the fastest convolution operation by setting `torch.backends.cudnn.benchmark=False`. Non-deterministic behavior occurs because the benchmark may select different algorithms each time depending on hardware or benchmarking noise.
-- Disabling TensorFloat32 (TF32) operations in favor of more precise and consistent full-precision operations.
+```py
+import torch
+from diffusers import DDIMScheduler, StableDiffusionPipeline
 
+pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True).to("cuda")
+pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+g = torch.Generator(device="cuda")
 
-## Resources
+prompt = "A bear is playing a guitar on Times Square"
 
-We strongly recommend reading PyTorch's developer notes about [Reproducibility](https://docs.pytorch.org/docs/stable/notes/randomness.html). You can try to limit randomness, but it is not *guaranteed* even with an identical seed.
+g.manual_seed(0)
+result1 = pipe(prompt=prompt, num_inference_steps=50, generator=g, output_type="latent").images
+
+g.manual_seed(0)
+result2 = pipe(prompt=prompt, num_inference_steps=50, generator=g, output_type="latent").images
+
+print("L_inf dist =", abs(result1 - result2).max())
+"L_inf dist = tensor(0., device='cuda:0')"
+```

docs/source/en/using-diffusers/schedulers.md

@@ -165,6 +165,53 @@ image
 
 Most images look very similar and are comparable in quality. Again, it often comes down to your specific use case so a good approach is to run multiple different schedulers and compare the results.
 
+### Flax schedulers
+
+To compare Flax schedulers, you need to additionally load the scheduler state into the model parameters. For example, let's change the default scheduler in [`FlaxStableDiffusionPipeline`] to use the super fast [`FlaxDPMSolverMultistepScheduler`].
+
+> [!WARNING]
+> The [`FlaxLMSDiscreteScheduler`] and [`FlaxDDPMScheduler`] are not compatible with the [`FlaxStableDiffusionPipeline`] yet.
+
+```py
+import jax
+import numpy as np
+from flax.jax_utils import replicate
+from flax.training.common_utils import shard
+from diffusers import FlaxStableDiffusionPipeline, FlaxDPMSolverMultistepScheduler
+
+scheduler, scheduler_state = FlaxDPMSolverMultistepScheduler.from_pretrained(
+    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    subfolder="scheduler"
+)
+pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
+    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    scheduler=scheduler,
+    variant="bf16",
+    dtype=jax.numpy.bfloat16,
+)
+params["scheduler"] = scheduler_state
+```
+
+Then you can take advantage of Flax's compatibility with TPUs to generate a number of images in parallel. You'll need to make a copy of the model parameters for each available device and then split the inputs across them to generate your desired number of images.
+
+```py
+# Generate 1 image per parallel device (8 on TPUv2-8 or TPUv3-8)
+prompt = "A photograph of an astronaut riding a horse on Mars, high resolution, high definition."
+num_samples = jax.device_count()
+prompt_ids = pipeline.prepare_inputs([prompt] * num_samples)
+
+prng_seed = jax.random.PRNGKey(0)
+num_inference_steps = 25
+
+# shard inputs and rng
+params = replicate(params)
+prng_seed = jax.random.split(prng_seed, jax.device_count())
+prompt_ids = shard(prompt_ids)
+
+images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
+images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
+```
+
 ## Models
 
 Models are loaded from the [`ModelMixin.from_pretrained`] method, which downloads and caches the latest version of the model weights and configurations. If the latest files are available in the local cache, [`~ModelMixin.from_pretrained`] reuses files in the cache instead of re-downloading them.

docs/source/en/using-diffusers/stable_diffusion_jax_how_to.md

@@ -0,0 +1,225 @@
+<!--Copyright 2025 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.
+-->
+
+# JAX/Flax
+
+[[open-in-colab]]
+
+🤗 Diffusers supports Flax for super fast inference on Google TPUs, such as those available in Colab, Kaggle or Google Cloud Platform. This guide shows you how to run inference with Stable Diffusion using JAX/Flax.
+
+Before you begin, make sure you have the necessary libraries installed:
+
+```py
+# uncomment to install the necessary libraries in Colab
+#!pip install -q jax==0.3.25 jaxlib==0.3.25 flax transformers ftfy
+#!pip install -q diffusers
+```
+
+You should also make sure you're using a TPU backend. While JAX does not run exclusively on TPUs, you'll get the best performance on a TPU because each server has 8 TPU accelerators working in parallel.
+
+If you are running this guide in Colab, select *Runtime* in the menu above, select the option *Change runtime type*, and then select *TPU* under the *Hardware accelerator* setting. Import JAX and quickly check whether you're using a TPU:
+
+```python
+import jax
+import jax.tools.colab_tpu
+jax.tools.colab_tpu.setup_tpu()
+
+num_devices = jax.device_count()
+device_type = jax.devices()[0].device_kind
+
+print(f"Found {num_devices} JAX devices of type {device_type}.")
+assert (
+    "TPU" in device_type,
+    "Available device is not a TPU, please select TPU from Runtime > Change runtime type > Hardware accelerator"
+)
+# Found 8 JAX devices of type Cloud TPU.
+```
+
+Great, now you can import the rest of the dependencies you'll need:
+
+```python
+import jax.numpy as jnp
+from jax import pmap
+from flax.jax_utils import replicate
+from flax.training.common_utils import shard
+
+from diffusers import FlaxStableDiffusionPipeline
+```
+
+## Load a model
+
+Flax is a functional framework, so models are stateless and parameters are stored outside of them. Loading a pretrained Flax pipeline returns *both* the pipeline and the model weights (or parameters). In this guide, you'll use `bfloat16`, a more efficient half-float type that is supported by TPUs (you can also use `float32` for full precision if you want).
+
+```python
+dtype = jnp.bfloat16
+pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    variant="bf16",
+    dtype=dtype,
+)
+```
+
+## Inference
+
+TPUs usually have 8 devices working in parallel, so let's use the same prompt for each device. This means you can perform inference on 8 devices at once, with each device generating one image. As a result, you'll get 8 images in the same amount of time it takes for one chip to generate a single image!
+
+<Tip>
+
+Learn more details in the [How does parallelization work?](#how-does-parallelization-work) section.
+
+</Tip>
+
+After replicating the prompt, get the tokenized text ids by calling the `prepare_inputs` function on the pipeline. The length of the tokenized text is set to 77 tokens as required by the configuration of the underlying CLIP text model.
+
+```python
+prompt = "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of field, close up, split lighting, cinematic"
+prompt = [prompt] * jax.device_count()
+prompt_ids = pipeline.prepare_inputs(prompt)
+prompt_ids.shape
+# (8, 77)
+```
+
+Model parameters and inputs have to be replicated across the 8 parallel devices. The parameters dictionary is replicated with [`flax.jax_utils.replicate`](https://flax.readthedocs.io/en/latest/api_reference/flax.jax_utils.html#flax.jax_utils.replicate) which traverses the dictionary and changes the shape of the weights so they are repeated 8 times. Arrays are replicated using `shard`.
+
+```python
+# parameters
+p_params = replicate(params)
+
+# arrays
+prompt_ids = shard(prompt_ids)
+prompt_ids.shape
+# (8, 1, 77)
+```
+
+This shape means each one of the 8 devices receives as an input a `jnp` array with shape `(1, 77)`, where `1` is the batch size per device. On TPUs with sufficient memory, you could have a batch size larger than `1` if you want to generate multiple images (per chip) at once.
+
+Next, create a random number generator to pass to the generation function. This is standard procedure in Flax, which is very serious and opinionated about random numbers. All functions that deal with random numbers are expected to receive a generator to ensure reproducibility, even when you're training across multiple distributed devices.
+
+The helper function below uses a seed to initialize a random number generator. As long as you use the same seed, you'll get the exact same results. Feel free to use different seeds when exploring results later in the guide.
+
+```python
+def create_key(seed=0):
+    return jax.random.PRNGKey(seed)
+```
+
+The helper function, or `rng`, is split 8 times so each device receives a different generator and generates a different image.
+
+```python
+rng = create_key(0)
+rng = jax.random.split(rng, jax.device_count())
+```
+
+To take advantage of JAX's optimized speed on a TPU, pass `jit=True` to the pipeline to compile the JAX code into an efficient representation and to ensure the model runs in parallel across the 8 devices.
+
+<Tip warning={true}>
+
+You need to ensure all your inputs have the same shape in subsequent calls, otherwise JAX will need to recompile the code which is slower.
+
+</Tip>
+
+The first inference run takes more time because it needs to compile the code, but subsequent calls (even with different inputs) are much faster. For example, it took more than a minute to compile on a TPU v2-8, but then it takes about **7s** on a future inference run!
+
+```py
+%%time
+images = pipeline(prompt_ids, p_params, rng, jit=True)[0]
+
+# CPU times: user 56.2 s, sys: 42.5 s, total: 1min 38s
+# Wall time: 1min 29s
+```
+
+The returned array has shape `(8, 1, 512, 512, 3)` which should be reshaped to remove the second dimension and get 8 images of `512 × 512 × 3`. Then you can use the [`~utils.numpy_to_pil`] function to convert the arrays into images.
+
+```python
+from diffusers.utils import make_image_grid
+
+images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
+images = pipeline.numpy_to_pil(images)
+make_image_grid(images, rows=2, cols=4)
+```
+
+![img](https://huggingface.co/datasets/YiYiXu/test-doc-assets/resolve/main/stable_diffusion_jax_how_to_cell_38_output_0.jpeg)
+
+## Using different prompts
+
+You don't necessarily have to use the same prompt on all devices. For example, to generate 8 different prompts:
+
+```python
+prompts = [
+    "Labrador in the style of Hokusai",
+    "Painting of a squirrel skating in New York",
+    "HAL-9000 in the style of Van Gogh",
+    "Times Square under water, with fish and a dolphin swimming around",
+    "Ancient Roman fresco showing a man working on his laptop",
+    "Close-up photograph of young black woman against urban background, high quality, bokeh",
+    "Armchair in the shape of an avocado",
+    "Clown astronaut in space, with Earth in the background",
+]
+
+prompt_ids = pipeline.prepare_inputs(prompts)
+prompt_ids = shard(prompt_ids)
+
+images = pipeline(prompt_ids, p_params, rng, jit=True).images
+images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
+images = pipeline.numpy_to_pil(images)
+
+make_image_grid(images, 2, 4)
+```
+
+![img](https://huggingface.co/datasets/YiYiXu/test-doc-assets/resolve/main/stable_diffusion_jax_how_to_cell_43_output_0.jpeg)
+
+## How does parallelization work?
+
+The Flax pipeline in 🤗 Diffusers automatically compiles the model and runs it in parallel on all available devices. Let's take a closer look at how that process works.
+
+JAX parallelization can be done in multiple ways. The easiest one revolves around using the [`jax.pmap`](https://jax.readthedocs.io/en/latest/_autosummary/jax.pmap.html) function to achieve single-program multiple-data (SPMD) parallelization. It means running several copies of the same code, each on different data inputs. More sophisticated approaches are possible, and you can go over to the JAX [documentation](https://jax.readthedocs.io/en/latest/index.html) to explore this topic in more detail if you are interested!
+
+`jax.pmap` does two things:
+
+1. Compiles (or "`jit`s") the code which is similar to `jax.jit()`. This does not happen when you call `pmap`, and only the first time the `pmap`ped function is called.
+2. Ensures the compiled code runs in parallel on all available devices.
+
+To demonstrate, call `pmap` on the pipeline's `_generate` method (this is a private method that generates images and may be renamed or removed in future releases of 🤗 Diffusers):
+
+```python
+p_generate = pmap(pipeline._generate)
+```
+
+After calling `pmap`, the prepared function `p_generate` will:
+
+1. Make a copy of the underlying function, `pipeline._generate`, on each device.
+2. Send each device a different portion of the input arguments (this is why it's necessary to call the *shard* function). In this case, `prompt_ids` has shape `(8, 1, 77, 768)` so the array is split into 8 and each copy of `_generate` receives an input with shape `(1, 77, 768)`.
+
+The most important thing to pay attention to here is the batch size (1 in this example), and the input dimensions that make sense for your code. You don't have to change anything else to make the code work in parallel.
+
+The first time you call the pipeline takes more time, but the calls afterward are much faster. The `block_until_ready` function is used to correctly measure inference time because JAX uses asynchronous dispatch and returns control to the Python loop as soon as it can. You don't need to use that in your code; blocking occurs automatically when you want to use the result of a computation that has not yet been materialized.
+
+```py
+%%time
+images = p_generate(prompt_ids, p_params, rng)
+images = images.block_until_ready()
+
+# CPU times: user 1min 15s, sys: 18.2 s, total: 1min 34s
+# Wall time: 1min 15s
+```
+
+Check your image dimensions to see if they're correct:
+
+```python
+images.shape
+# (8, 1, 512, 512, 3)
+```
+
+## Resources
+
+To learn more about how JAX works with Stable Diffusion, you may be interested in reading:
+
+* [Accelerating Stable Diffusion XL Inference with JAX on Cloud TPU v5e](https://hf.co/blog/sdxl_jax)

docs/source/en/using-diffusers/text-img2vid.md

@@ -98,7 +98,7 @@ pipeline_quant_config = PipelineQuantizationConfig(
     "bnb_4bit_quant_type": "nf4",
     "bnb_4bit_compute_dtype": torch.bfloat16
     },
-  components_to_quantize="transformer"
+  components_to_quantize=["transformer"]
 )
 
 pipeline = HunyuanVideoPipeline.from_pretrained(
@@ -287,7 +287,7 @@ export_to_video(output, "output.mp4", fps=16)
 
 ## Reduce memory usage
 
-Recent video models like [`HunyuanVideoPipeline`] and [`WanPipeline`], which have 10B+ parameters, require a lot of memory and it often exceeds the memory available on consumer hardware. Diffusers offers several techniques for reducing the memory requirements of these large models.
+Recent video models like [`HunyuanVideoPipeline`] and [`WanPipeline`], which have 10B+ parameters, require a lot of memory and it often exceeds the memory availabe on consumer hardware. Diffusers offers several techniques for reducing the memory requirements of these large models.
 
 > [!TIP]
 > Refer to the [Reduce memory usage](../optimization/memory) guide for more details about other memory saving techniques.

docs/source/ko/optimization/mps.md

@@ -37,7 +37,7 @@ Diffusers는 Stable Diffusion 추론을 위해 PyTorch `mps`를 사용해 Apple
 
 
 ```python
-# `hf auth login`에 로그인되어 있음을 확인
+# `huggingface-cli login`에 로그인되어 있음을 확인
 from diffusers import DiffusionPipeline
 
 pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")

docs/source/ko/training/create_dataset.md

@@ -75,7 +75,7 @@ dataset = load_dataset(
 [push_to_hub(https://huggingface.co/docs/datasets/v2.13.1/en/package_reference/main_classes#datasets.Dataset.push_to_hub) 을 사용해서 Hub에 데이터셋을 업로드 합니다:
 
 ```python
-# 터미널에서 hf auth login 커맨드를 이미 실행했다고 가정합니다
+# 터미널에서 huggingface-cli login 커맨드를 이미 실행했다고 가정합니다
 dataset.push_to_hub("name_of_your_dataset")
 
 # 개인 repo로 push 하고 싶다면, `private=True` 을 추가하세요:

docs/source/ko/training/lora.md

@@ -39,7 +39,7 @@ specific language governing permissions and limitations under the License.
 모델을 저장하거나 커뮤니티와 공유하려면 Hugging Face 계정에 로그인하세요(아직 계정이 없는 경우 [생성](https://huggingface.co/join)하세요):
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 ## Text-to-image

docs/source/ko/tutorials/basic_training.md

@@ -42,7 +42,7 @@ Unconditional 이미지 생성은 학습에 사용된 데이터셋과 유사한
 또는 터미널로 로그인할 수 있습니다:
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 모델 체크포인트가 상당히 크기 때문에 [Git-LFS](https://git-lfs.com/)에서 대용량 파일의 버전 관리를 할 수 있습니다.