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.github/workflows/nightly_tests.yml

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run_flax_tpu_tests:
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name: Nightly Flax TPU Tests
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runs-on: docker-tpu
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runs-on:
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group: gcp-ct5lp-hightpu-8t
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if: github.event_name == 'schedule'
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container:
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image: diffusers/diffusers-flax-tpu
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options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/ --privileged
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options: --shm-size "16gb" --ipc host --privileged ${{ vars.V5_LITEPOD_8_ENV}} -v /mnt/hf_cache:/mnt/hf_cache
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defaults:
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run:
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shell: bash
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pip install slack_sdk tabulate
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python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
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run_nightly_quantization_tests:
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name: Torch quantization nightly tests
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strategy:
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fail-fast: false
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max-parallel: 2
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matrix:
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config:
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- backend: "bitsandbytes"
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test_location: "bnb"
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runs-on:
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group: aws-g6e-xlarge-plus
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container:
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image: diffusers/diffusers-pytorch-cuda
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options: --shm-size "20gb" --ipc host --gpus 0
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steps:
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- name: Checkout diffusers
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uses: actions/checkout@v3
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with:
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fetch-depth: 2
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- name: NVIDIA-SMI
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run: nvidia-smi
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- name: Install dependencies
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run: |
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python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
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python -m uv pip install -e [quality,test]
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python -m uv pip install -U ${{ matrix.config.backend }}
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python -m uv pip install pytest-reportlog
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- name: Environment
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run: |
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python utils/print_env.py
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- name: ${{ matrix.config.backend }} quantization tests on GPU
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env:
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HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
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# https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
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CUBLAS_WORKSPACE_CONFIG: :16:8
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BIG_GPU_MEMORY: 40
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run: |
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python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
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--make-reports=tests_${{ matrix.config.backend }}_torch_cuda \
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--report-log=tests_${{ matrix.config.backend }}_torch_cuda.log \
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tests/quantization/${{ matrix.config.test_location }}
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- name: Failure short reports
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if: ${{ failure() }}
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run: |
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cat reports/tests_${{ matrix.config.backend }}_torch_cuda_stats.txt
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cat reports/tests_${{ matrix.config.backend }}_torch_cuda_failures_short.txt
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- name: Test suite reports artifacts
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if: ${{ always() }}
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uses: actions/upload-artifact@v4
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with:
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name: torch_cuda_${{ matrix.config.backend }}_reports
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path: reports
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- name: Generate Report and Notify Channel
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if: always()
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run: |
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pip install slack_sdk tabulate
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python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
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# M1 runner currently not well supported
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# TODO: (Dhruv) add these back when we setup better testing for Apple Silicon
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# run_nightly_tests_apple_m1:
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# if: always()
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# run: |
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# pip install slack_sdk tabulate
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# python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
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# python utils/log_reports.py >> $GITHUB_STEP_SUMMARY

.github/workflows/pr_test_peft_backend.yml

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.github/workflows/pr_tests.yml

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with:
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name: pr_${{ matrix.config.report }}_test_reports
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path: reports
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run_lora_tests:
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needs: [check_code_quality, check_repository_consistency]
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strategy:
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fail-fast: false
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name: LoRA tests with PEFT main
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runs-on:
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group: aws-general-8-plus
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container:
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image: diffusers/diffusers-pytorch-cpu
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options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
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defaults:
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run:
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shell: bash
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steps:
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- name: Checkout diffusers
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uses: actions/checkout@v3
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with:
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fetch-depth: 2
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- name: Install dependencies
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run: |
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python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
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python -m uv pip install -e [quality,test]
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# TODO (sayakpaul, DN6): revisit `--no-deps`
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python -m pip install -U peft@git+https://github.com/huggingface/peft.git --no-deps
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python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
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pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
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- name: Environment
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run: |
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python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
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python utils/print_env.py
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- name: Run fast PyTorch LoRA tests with PEFT
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run: |
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python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
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python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
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-s -v \
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--make-reports=tests_peft_main \
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tests/lora/
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python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
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-s -v \
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--make-reports=tests_models_lora_peft_main \
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tests/models/ -k "lora"
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- name: Failure short reports
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if: ${{ failure() }}
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run: |
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cat reports/tests_lora_failures_short.txt
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cat reports/tests_models_lora_failures_short.txt
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- name: Test suite reports artifacts
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if: ${{ always() }}
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uses: actions/upload-artifact@v4
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with:
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name: pr_main_test_reports
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path: reports
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.github/workflows/push_tests.yml

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flax_tpu_tests:
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name: Flax TPU Tests
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runs-on: docker-tpu
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runs-on:
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group: gcp-ct5lp-hightpu-8t
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container:
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image: diffusers/diffusers-flax-tpu
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options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ --privileged
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defaults:
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options: --shm-size "16gb" --ipc host --privileged ${{ vars.V5_LITEPOD_8_ENV}} -v /mnt/hf_cache:/mnt/hf_cache defaults:
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run:
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steps:

README.md

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@@ -112,9 +112,9 @@ Check out the [Quickstart](https://huggingface.co/docs/diffusers/quicktour) to l
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| **Documentation** | **What can I learn?** |
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|---------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| [Tutorial](https://huggingface.co/docs/diffusers/tutorials/tutorial_overview) | A basic crash course for learning how to use the library's most important features like using models and schedulers to build your own diffusion system, and training your own diffusion model. |
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| [Loading](https://huggingface.co/docs/diffusers/using-diffusers/loading_overview) | Guides for how to load and configure all the components (pipelines, models, and schedulers) of the library, as well as how to use different schedulers. |
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| [Pipelines for inference](https://huggingface.co/docs/diffusers/using-diffusers/pipeline_overview) | Guides for how to use pipelines for different inference tasks, batched generation, controlling generated outputs and randomness, and how to contribute a pipeline to the library. |
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| [Optimization](https://huggingface.co/docs/diffusers/optimization/opt_overview) | Guides for how to optimize your diffusion model to run faster and consume less memory. |
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| [Loading](https://huggingface.co/docs/diffusers/using-diffusers/loading) | Guides for how to load and configure all the components (pipelines, models, and schedulers) of the library, as well as how to use different schedulers. |
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| [Pipelines for inference](https://huggingface.co/docs/diffusers/using-diffusers/overview_techniques) | Guides for how to use pipelines for different inference tasks, batched generation, controlling generated outputs and randomness, and how to contribute a pipeline to the library. |
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| [Optimization](https://huggingface.co/docs/diffusers/optimization/fp16) | Guides for how to optimize your diffusion model to run faster and consume less memory. |
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| [Training](https://huggingface.co/docs/diffusers/training/overview) | Guides for how to train a diffusion model for different tasks with different training techniques. |
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## Contribution
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docker/diffusers-onnxruntime-cuda/Dockerfile

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

docker/diffusers-pytorch-compile-cuda/Dockerfile

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

docker/diffusers-pytorch-cpu/Dockerfile

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

docker/diffusers-pytorch-cuda/Dockerfile

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

docker/diffusers-pytorch-xformers-cuda/Dockerfile

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

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