Merge branch 'main' into flux

.github/workflows/build_docker_images.yml

@@ -34,7 +34,7 @@ jobs:
         id: file_changes
         uses: jitterbit/get-changed-files@v1
         with:
-          format: 'space-delimited'
+          format: "space-delimited"
           token: ${{ secrets.GITHUB_TOKEN }}
 
       - name: Build Changed Docker Images
@@ -67,6 +67,7 @@ jobs:
           - diffusers-pytorch-cuda
           - diffusers-pytorch-compile-cuda
           - diffusers-pytorch-xformers-cuda
+          - diffusers-pytorch-minimum-cuda
           - diffusers-flax-cpu
           - diffusers-flax-tpu
           - diffusers-onnxruntime-cpu

.github/workflows/nightly_tests.yml

@@ -235,7 +235,64 @@ jobs:
         run: |
           pip install slack_sdk tabulate
           python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
+          
+  torch_minimum_version_cuda_tests:
+    name: Torch Minimum Version CUDA Tests
+    runs-on:
+      group: aws-g4dn-2xlarge
+    container:
+      image: diffusers/diffusers-pytorch-minimum-cuda
+      options: --shm-size "16gb" --ipc host --gpus 0
+    defaults:
+      run:
+        shell: bash
+    steps:
+      - name: Checkout diffusers
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 2
+
+      - name: Install dependencies
+        run: |
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
 
+      - name: Environment
+        run: |
+          python utils/print_env.py
+
+      - name: Run PyTorch CUDA tests
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
+          CUBLAS_WORKSPACE_CONFIG: :16:8
+        run: |
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+            -s -v -k "not Flax and not Onnx" \
+            --make-reports=tests_torch_minimum_version_cuda \
+            tests/models/test_modeling_common.py \
+            tests/pipelines/test_pipelines_common.py \
+            tests/pipelines/test_pipeline_utils.py \
+            tests/pipelines/test_pipelines.py \
+            tests/pipelines/test_pipelines_auto.py \
+            tests/schedulers/test_schedulers.py \
+            tests/others
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        run: |
+          cat reports/tests_torch_minimum_version_cuda_stats.txt
+          cat reports/tests_torch_minimum_version_cuda_failures_short.txt
+
+      - name: Test suite reports artifacts
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: torch_minimum_version_cuda_test_reports
+          path: reports
+
   run_flax_tpu_tests:
     name: Nightly Flax TPU Tests
     runs-on:

.github/workflows/pr_tests.yml

@@ -266,6 +266,7 @@ jobs:
         # TODO (sayakpaul, DN6): revisit `--no-deps`
         python -m pip install -U peft@git+https://github.com/huggingface/peft.git --no-deps
         python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
+        python -m uv pip install -U tokenizers
         pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
 
     - name: Environment

.github/workflows/release_tests_fast.yml

@@ -157,6 +157,63 @@ jobs:
         name: torch_cuda_${{ matrix.module }}_test_reports
         path: reports
 
+  torch_minimum_version_cuda_tests:
+    name: Torch Minimum Version CUDA Tests
+    runs-on:
+      group: aws-g4dn-2xlarge
+    container:
+      image: diffusers/diffusers-pytorch-minimum-cuda
+      options: --shm-size "16gb" --ipc host --gpus 0
+    defaults:
+      run:
+        shell: bash
+    steps:
+      - name: Checkout diffusers
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 2
+
+      - name: Install dependencies
+        run: |
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+
+      - name: Environment
+        run: |
+          python utils/print_env.py
+
+      - name: Run PyTorch CUDA tests
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
+          CUBLAS_WORKSPACE_CONFIG: :16:8
+        run: |
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+            -s -v -k "not Flax and not Onnx" \
+            --make-reports=tests_torch_minimum_cuda \
+            tests/models/test_modeling_common.py \
+            tests/pipelines/test_pipelines_common.py \
+            tests/pipelines/test_pipeline_utils.py \
+            tests/pipelines/test_pipelines.py \
+            tests/pipelines/test_pipelines_auto.py \
+            tests/schedulers/test_schedulers.py \
+            tests/others
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        run: |
+          cat reports/tests_torch_minimum_version_cuda_stats.txt
+          cat reports/tests_torch_minimum_version_cuda_failures_short.txt
+
+      - name: Test suite reports artifacts
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: torch_minimum_version_cuda_test_reports
+          path: reports
+
   flax_tpu_tests:
     name: Flax TPU Tests
     runs-on: docker-tpu

docker/diffusers-pytorch-minimum-cuda/Dockerfile

@@ -0,0 +1,53 @@
+FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+ENV MINIMUM_SUPPORTED_TORCH_VERSION="2.1.0"
+ENV MINIMUM_SUPPORTED_TORCHVISION_VERSION="0.16.0"
+ENV MINIMUM_SUPPORTED_TORCHAUDIO_VERSION="2.1.0"
+
+RUN apt-get -y update \
+    && apt-get install -y software-properties-common \
+    && add-apt-repository ppa:deadsnakes/ppa
+
+RUN apt install -y bash \
+    build-essential \
+    git \
+    git-lfs \
+    curl \
+    ca-certificates \
+    libsndfile1-dev \
+    libgl1 \
+    python3.10 \
+    python3.10-dev \
+    python3-pip \
+    python3.10-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3.10 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
+    python3.10 -m uv pip install --no-cache-dir \
+    torch==$MINIMUM_SUPPORTED_TORCH_VERSION \
+    torchvision==$MINIMUM_SUPPORTED_TORCHVISION_VERSION \
+    torchaudio==$MINIMUM_SUPPORTED_TORCHAUDIO_VERSION \
+    invisible_watermark && \
+    python3.10 -m pip install --no-cache-dir \
+    accelerate \
+    datasets \
+    hf-doc-builder \
+    huggingface-hub \
+    hf_transfer \
+    Jinja2 \
+    librosa \
+    numpy==1.26.4 \
+    scipy \
+    tensorboard \
+    transformers \
+    hf_transfer
+
+CMD ["/bin/bash"]

docs/source/en/_toctree.yml

@@ -48,7 +48,7 @@
   - local: using-diffusers/inpaint
     title: Inpainting
   - local: using-diffusers/text-img2vid
-    title: Text or image-to-video
+    title: Video generation
   - local: using-diffusers/depth2img
     title: Depth-to-image
   title: Generative tasks

docs/source/en/api/models/allegro_transformer3d.md

@@ -18,7 +18,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import AllegroTransformer3DModel
 
-vae = AllegroTransformer3DModel.from_pretrained("rhymes-ai/Allegro", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
+transformer = AllegroTransformer3DModel.from_pretrained("rhymes-ai/Allegro", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
 ```
 
 ## AllegroTransformer3DModel

docs/source/en/api/models/cogvideox_transformer3d.md

@@ -18,7 +18,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import CogVideoXTransformer3DModel
 
-vae = CogVideoXTransformer3DModel.from_pretrained("THUDM/CogVideoX-2b", subfolder="transformer", torch_dtype=torch.float16).to("cuda")
+transformer = CogVideoXTransformer3DModel.from_pretrained("THUDM/CogVideoX-2b", subfolder="transformer", torch_dtype=torch.float16).to("cuda")
 ```
 
 ## CogVideoXTransformer3DModel

docs/source/en/api/models/cogview3plus_transformer2d.md

@@ -18,7 +18,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import CogView3PlusTransformer2DModel
 
-vae = CogView3PlusTransformer2DModel.from_pretrained("THUDM/CogView3Plus-3b", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
+transformer = CogView3PlusTransformer2DModel.from_pretrained("THUDM/CogView3Plus-3b", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
 ```
 
 ## CogView3PlusTransformer2DModel

docs/source/en/api/models/mochi_transformer3d.md

@@ -18,7 +18,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import MochiTransformer3DModel
 
-vae = MochiTransformer3DModel.from_pretrained("genmo/mochi-1-preview", subfolder="transformer", torch_dtype=torch.float16).to("cuda")
+transformer = MochiTransformer3DModel.from_pretrained("genmo/mochi-1-preview", subfolder="transformer", torch_dtype=torch.float16).to("cuda")
 ```
 
 ## MochiTransformer3DModel

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

@@ -19,10 +19,55 @@ The abstract from the paper is:
 
 <Tip>
 
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.md) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+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>
 
+## Quantization
+
+Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model.
+
+Refer to the [Quantization](../../quantization/overview) overview to learn more about supported quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized [`AllegroPipeline`] for inference with bitsandbytes.
+
+```py
+import torch
+from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, AllegroTransformer3DModel, AllegroPipeline
+from diffusers.utils import export_to_video
+from transformers import BitsAndBytesConfig as BitsAndBytesConfig, T5EncoderModel
+
+quant_config = BitsAndBytesConfig(load_in_8bit=True)
+text_encoder_8bit = T5EncoderModel.from_pretrained(
+    "rhymes-ai/Allegro",
+    subfolder="text_encoder",
+    quantization_config=quant_config,
+    torch_dtype=torch.float16,
+)
+
+quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
+transformer_8bit = AllegroTransformer3DModel.from_pretrained(
+    "rhymes-ai/Allegro",
+    subfolder="transformer",
+    quantization_config=quant_config,
+    torch_dtype=torch.float16,
+)
+
+pipeline = AllegroPipeline.from_pretrained(
+    "rhymes-ai/Allegro",
+    text_encoder=text_encoder_8bit,
+    transformer=transformer_8bit,
+    torch_dtype=torch.float16,
+    device_map="balanced",
+)
+
+prompt = (
+    "A seaside harbor with bright sunlight and sparkling seawater, with many boats in the water. From an aerial view, "
+    "the boats vary in size and color, some moving and some stationary. Fishing boats in the water suggest that this "
+    "location might be a popular spot for docking fishing boats."
+)
+video = pipeline(prompt, guidance_scale=7.5, max_sequence_length=512).frames[0]
+export_to_video(video, "harbor.mp4", fps=15)
+```
+
 ## AllegroPipeline
 
 [[autodoc]] AllegroPipeline

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

@@ -803,7 +803,7 @@ FreeInit is not really free - the improved quality comes at the cost of extra co
 
 <Tip>
 
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
 
 </Tip>
 

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

@@ -22,7 +22,7 @@ You can find additional information about Attend-and-Excite on the [project page
 
 <Tip>
 
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
 
 </Tip>
 

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

@@ -37,7 +37,7 @@ During inference:
 
 <Tip>
 
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
 
 </Tip>
 

docs/source/en/api/pipelines/audioldm2.md

@@ -60,7 +60,7 @@ The following example demonstrates how to construct good music and speech genera
 
 <Tip>
 
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
 
 </Tip>