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‎.github/workflows/benchmark.yml‎

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

‎.github/workflows/mirror_community_pipeline.yml‎

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

‎.github/workflows/nightly_tests.yml‎

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options: --shm-size "16gb" --ipc host --gpus all
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shell: bash
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additional_deps: ["peft"]
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- backend: "gguf"
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test_location: "gguf"
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additional_deps: ["peft"]
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additional_deps: ["peft", "kernels"]
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- backend: "torchao"
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test_location: "torchao"
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additional_deps: []
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options: --shm-size "20gb" --ipc host --gpus 0
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‎.github/workflows/pr_tests_gpu.yml‎

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- "src/diffusers/loaders/peft.py"
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- "tests/pipelines/test_pipelines_common.py"
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- "tests/models/test_modeling_common.py"
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- "examples/**/*.py"
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workflow_dispatch:
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concurrency:
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‎.github/workflows/push_tests.yml‎

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‎.github/workflows/release_tests_fast.yml‎

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‎.github/workflows/run_tests_from_a_pr.yml‎

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options: --gpus 0 --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
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‎.github/workflows/ssh-runner.yml‎

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‎benchmarks/README.md‎

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We need to be authenticated to access some of the checkpoints used during benchmarking:
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```sh
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huggingface-cli login
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hf auth login
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```
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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).

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