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Merge branch 'main' into sage-kernels

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This commit is contained in:
Sayak Paul
2025-10-13 10:47:20 +05:30
committed by GitHub
parent 3688c9d443 8abc7aeb71
commit 23c173ea58
37 changed files with 964 additions and 440 deletions
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5
.github/workflows/benchmark.yml vendored
View File

@@ -38,9 +38,8 @@ jobs:
run: |
apt update
apt install -y libpq-dev postgresql-client
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test]
python -m uv pip install -r benchmarks/requirements.txt
uv pip install -e ".[quality]"
uv pip install -r benchmarks/requirements.txt
- name: Environment
run: |
python utils/print_env.py

1
.github/workflows/build_docker_images.yml vendored
View File

@@ -72,7 +72,6 @@ jobs:
image-name:
- diffusers-pytorch-cpu
- diffusers-pytorch-cuda
- diffusers-pytorch-cuda
- diffusers-pytorch-xformers-cuda
- diffusers-pytorch-minimum-cuda
- diffusers-doc-builder

2
.github/workflows/mirror_community_pipeline.yml vendored
View File

@@ -74,7 +74,7 @@ jobs:
python-version: "3.10"
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install --upgrade pip
pip install --upgrade huggingface_hub
# Check secret is set

91
.github/workflows/nightly_tests.yml vendored
View File

@@ -71,10 +71,9 @@ jobs:
run: nvidia-smi
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test]
pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
python -m uv pip install pytest-reportlog
uv pip install -e ".[quality]"
uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
uv pip install pytest-reportlog
- name: Environment
run: |
python utils/print_env.py
@@ -84,7 +83,7 @@ jobs:
# https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
CUBLAS_WORKSPACE_CONFIG: :16:8
run: |
python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-s -v -k "not Flax and not Onnx" \
--make-reports=tests_pipeline_${{ matrix.module }}_cuda \
--report-log=tests_pipeline_${{ matrix.module }}_cuda.log \
@@ -124,11 +123,10 @@ jobs:
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$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
python -m uv pip install pytest-reportlog
uv pip install -e ".[quality]"
uv pip install peft@git+https://github.com/huggingface/peft.git
uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
uv pip install pytest-reportlog
- name: Environment
run: python utils/print_env.py
@@ -139,7 +137,7 @@ jobs:
# 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 \
pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-s -v -k "not Flax and not Onnx" \
--make-reports=tests_torch_${{ matrix.module }}_cuda \
--report-log=tests_torch_${{ matrix.module }}_cuda.log \
@@ -152,7 +150,7 @@ jobs:
# 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 \
pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-s -v --make-reports=examples_torch_cuda \
--report-log=examples_torch_cuda.log \
examples/
@@ -191,8 +189,7 @@ jobs:
nvidia-smi
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test,training]
uv pip install -e ".[quality,training]"
- name: Environment
run: |
python utils/print_env.py
@@ -201,7 +198,7 @@ jobs:
HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
RUN_COMPILE: yes
run: |
python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
- name: Failure short reports
if: ${{ failure() }}
run: cat reports/tests_torch_compile_cuda_failures_short.txt
@@ -232,11 +229,10 @@ jobs:
run: nvidia-smi
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$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
python -m uv pip install pytest-reportlog
uv pip install -e ".[quality]"
uv pip install peft@git+https://github.com/huggingface/peft.git
uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
uv pip install pytest-reportlog
- name: Environment
run: |
python utils/print_env.py
@@ -247,7 +243,7 @@ jobs:
CUBLAS_WORKSPACE_CONFIG: :16:8
BIG_GPU_MEMORY: 40
run: |
python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-m "big_accelerator" \
--make-reports=tests_big_gpu_torch_cuda \
--report-log=tests_big_gpu_torch_cuda.log \
@@ -282,10 +278,9 @@ jobs:
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test]
python -m uv pip install peft@git+https://github.com/huggingface/peft.git
pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
uv pip install -e ".[quality]"
uv pip install peft@git+https://github.com/huggingface/peft.git
uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
- name: Environment
run: |
@@ -297,7 +292,7 @@ jobs:
# https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
CUBLAS_WORKSPACE_CONFIG: :16:8
run: |
python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-s -v -k "not Flax and not Onnx" \
--make-reports=tests_torch_minimum_version_cuda \
tests/models/test_modeling_common.py \
@@ -357,13 +352,12 @@ jobs:
run: nvidia-smi
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test]
python -m uv pip install -U ${{ matrix.config.backend }}
uv pip install -e ".[quality]"
uv pip install -U ${{ matrix.config.backend }}
if [ "${{ join(matrix.config.additional_deps, ' ') }}" != "" ]; then
python -m uv pip install ${{ join(matrix.config.additional_deps, ' ') }}
uv pip install ${{ join(matrix.config.additional_deps, ' ') }}
fi
python -m uv pip install pytest-reportlog
uv pip install pytest-reportlog
- name: Environment
run: |
python utils/print_env.py
@@ -374,7 +368,7 @@ jobs:
CUBLAS_WORKSPACE_CONFIG: :16:8
BIG_GPU_MEMORY: 40
run: |
python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
pytest -n 1 --max-worker-restart=0 --dist=loadfile \
--make-reports=tests_${{ matrix.config.backend }}_torch_cuda \
--report-log=tests_${{ matrix.config.backend }}_torch_cuda.log \
tests/quantization/${{ matrix.config.test_location }}
@@ -409,10 +403,9 @@ jobs:
run: nvidia-smi
- 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 -U bitsandbytes optimum_quanto
python -m uv pip install pytest-reportlog
uv pip install -e ".[quality]"
uv pip install -U bitsandbytes optimum_quanto
uv pip install pytest-reportlog
- name: Environment
run: |
python utils/print_env.py
@@ -423,7 +416,7 @@ jobs:
CUBLAS_WORKSPACE_CONFIG: :16:8
BIG_GPU_MEMORY: 40
run: |
python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
pytest -n 1 --max-worker-restart=0 --dist=loadfile \
--make-reports=tests_pipeline_level_quant_torch_cuda \
--report-log=tests_pipeline_level_quant_torch_cuda.log \
tests/quantization/test_pipeline_level_quantization.py
@@ -523,11 +516,11 @@ jobs:
# - name: Install dependencies
# shell: arch -arch arm64 bash {0}
# run: |
# ${CONDA_RUN} python -m pip install --upgrade pip uv
# ${CONDA_RUN} python -m uv pip install -e [quality,test]
# ${CONDA_RUN} python -m uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
# ${CONDA_RUN} python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate
# ${CONDA_RUN} python -m uv pip install pytest-reportlog
# ${CONDA_RUN} pip install --upgrade pip uv
# ${CONDA_RUN} uv pip install -e ".[quality]"
# ${CONDA_RUN} uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
# ${CONDA_RUN} uv pip install accelerate@git+https://github.com/huggingface/accelerate
# ${CONDA_RUN} uv pip install pytest-reportlog
# - name: Environment
# shell: arch -arch arm64 bash {0}
# run: |
@@ -538,7 +531,7 @@ jobs:
# HF_HOME: /System/Volumes/Data/mnt/cache
# HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
# run: |
# ${CONDA_RUN} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps \
# ${CONDA_RUN} pytest -n 1 -s -v --make-reports=tests_torch_mps \
# --report-log=tests_torch_mps.log \
# tests/
# - name: Failure short reports
@@ -579,11 +572,11 @@ jobs:
# - name: Install dependencies
# shell: arch -arch arm64 bash {0}
# run: |
# ${CONDA_RUN} python -m pip install --upgrade pip uv
# ${CONDA_RUN} python -m uv pip install -e [quality,test]
# ${CONDA_RUN} python -m uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
# ${CONDA_RUN} python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate
# ${CONDA_RUN} python -m uv pip install pytest-reportlog
# ${CONDA_RUN} pip install --upgrade pip uv
# ${CONDA_RUN} uv pip install -e ".[quality]"
# ${CONDA_RUN} uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
# ${CONDA_RUN} uv pip install accelerate@git+https://github.com/huggingface/accelerate
# ${CONDA_RUN} uv pip install pytest-reportlog
# - name: Environment
# shell: arch -arch arm64 bash {0}
# run: |
@@ -594,7 +587,7 @@ jobs:
# HF_HOME: /System/Volumes/Data/mnt/cache
# HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
# run: |
# ${CONDA_RUN} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps \
# ${CONDA_RUN} pytest -n 1 -s -v --make-reports=tests_torch_mps \
# --report-log=tests_torch_mps.log \
# tests/
# - name: Failure short reports

9
.github/workflows/pr_dependency_test.yml vendored
View File

@@ -25,11 +25,8 @@ jobs:
python-version: "3.8"
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m pip install --upgrade pip uv
python -m uv pip install -e .
python -m uv pip install pytest
pip install -e .
pip install pytest
- name: Check for soft dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
pytest tests/others/test_dependencies.py
pytest tests/others/test_dependencies.py

15
.github/workflows/pr_modular_tests.yml vendored
View File

@@ -42,7 +42,7 @@ jobs:
python-version: "3.10"
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install --upgrade pip
pip install .[quality]
- name: Check quality
run: make quality
@@ -62,7 +62,7 @@ jobs:
python-version: "3.10"
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install --upgrade pip
pip install .[quality]
- name: Check repo consistency
run: |
@@ -108,21 +108,18 @@ jobs:
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test]
pip uninstall transformers -y && pip uninstall huggingface_hub -y && python -m uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
uv pip install -e ".[quality]"
uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
- name: Environment
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$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 \
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

19
.github/workflows/pr_test_fetcher.yml vendored
View File

@@ -33,8 +33,7 @@ jobs:
fetch-depth: 0
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test]
uv pip install -e ".[quality]"
- name: Environment
run: |
python utils/print_env.py
@@ -90,19 +89,16 @@ jobs:
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m pip install -e [quality,test]
python -m pip install accelerate
uv pip install -e ".[quality]"
uv pip install accelerate
- name: Environment
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python utils/print_env.py
- name: Run all selected tests on CPU
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m pytest -n 2 --dist=loadfile -v --make-reports=${{ matrix.modules }}_tests_cpu ${{ fromJson(needs.setup_pr_tests.outputs.test_map)[matrix.modules] }}
pytest -n 2 --dist=loadfile -v --make-reports=${{ matrix.modules }}_tests_cpu ${{ fromJson(needs.setup_pr_tests.outputs.test_map)[matrix.modules] }}
- name: Failure short reports
if: ${{ failure() }}
@@ -148,19 +144,16 @@ jobs:
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m pip install -e [quality,test]
pip install -e [quality]
- name: Environment
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python utils/print_env.py
- name: Run Hub tests for models, schedulers, and pipelines on a staging env
if: ${{ matrix.config.framework == 'hub_tests_pytorch' }}
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
HUGGINGFACE_CO_STAGING=true python -m pytest \
HUGGINGFACE_CO_STAGING=true pytest \
-m "is_staging_test" \
--make-reports=tests_${{ matrix.config.report }} \
tests

47
.github/workflows/pr_tests.yml vendored
View File

@@ -38,7 +38,7 @@ jobs:
python-version: "3.8"
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install --upgrade pip
pip install .[quality]
- name: Check quality
run: make quality
@@ -58,7 +58,7 @@ jobs:
python-version: "3.8"
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install --upgrade pip
pip install .[quality]
- name: Check repo consistency
run: |
@@ -114,21 +114,18 @@ jobs:
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test]
pip uninstall transformers -y && pip uninstall huggingface_hub -y && python -m uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
uv pip install -e ".[quality]"
uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
- name: Environment
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$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 \
pytest -n 8 --max-worker-restart=0 --dist=loadfile \
-s -v -k "not Flax and not Onnx" \
--make-reports=tests_${{ matrix.config.report }} \
tests/pipelines
@@ -136,8 +133,7 @@ jobs:
- name: Run fast PyTorch Model Scheduler CPU tests
if: ${{ matrix.config.framework == 'pytorch_models' }}
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-s -v -k "not Flax and not Onnx and not Dependency" \
--make-reports=tests_${{ matrix.config.report }} \
tests/models tests/schedulers tests/others
@@ -145,9 +141,8 @@ jobs:
- name: Run example PyTorch CPU tests
if: ${{ matrix.config.framework == 'pytorch_examples' }}
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install peft timm
python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
uv pip install ".[training]"
pytest -n 4 --max-worker-restart=0 --dist=loadfile \
--make-reports=tests_${{ matrix.config.report }} \
examples
@@ -195,19 +190,16 @@ jobs:
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test]
uv pip install -e ".[quality]"
- name: Environment
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python utils/print_env.py
- name: Run Hub tests for models, schedulers, and pipelines on a staging env
if: ${{ matrix.config.framework == 'hub_tests_pytorch' }}
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
HUGGINGFACE_CO_STAGING=true python -m pytest \
HUGGINGFACE_CO_STAGING=true pytest \
-m "is_staging_test" \
--make-reports=tests_${{ matrix.config.report }} \
tests
@@ -249,27 +241,24 @@ jobs:
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test]
uv pip install -e ".[quality]"
# 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 tokenizers
pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
pip uninstall transformers -y && pip uninstall huggingface_hub -y && python -m uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
uv pip install -U peft@git+https://github.com/huggingface/peft.git --no-deps
uv pip install -U tokenizers
uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
- name: Environment
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python utils/print_env.py
- name: Run fast PyTorch LoRA tests with PEFT
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-s -v \
--make-reports=tests_peft_main \
tests/lora/
python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-s -v \
--make-reports=tests_models_lora_peft_main \
tests/models/ -k "lora"

42
.github/workflows/pr_tests_gpu.yml vendored
View File

@@ -39,7 +39,7 @@ jobs:
python-version: "3.8"
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install --upgrade pip
pip install .[quality]
- name: Check quality
run: make quality
@@ -59,7 +59,7 @@ jobs:
python-version: "3.8"
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install --upgrade pip
pip install .[quality]
- name: Check repo consistency
run: |
@@ -88,8 +88,7 @@ jobs:
fetch-depth: 2
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test]
uv pip install -e ".[quality]"
- name: Environment
run: |
python utils/print_env.py
@@ -130,10 +129,9 @@ jobs:
nvidia-smi
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test]
pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
pip uninstall transformers -y && pip uninstall huggingface_hub -y && python -m uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
uv pip install -e ".[quality]"
uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
- name: Environment
run: |
@@ -152,13 +150,13 @@ jobs:
CUBLAS_WORKSPACE_CONFIG: :16:8
run: |
if [ "${{ matrix.module }}" = "ip_adapters" ]; then
python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-s -v -k "not Flax and not Onnx" \
--make-reports=tests_pipeline_${{ matrix.module }}_cuda \
tests/pipelines/${{ matrix.module }}
else
pattern=$(cat ${{ steps.extract_tests.outputs.pattern_file }})
python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-s -v -k "not Flax and not Onnx and $pattern" \
--make-reports=tests_pipeline_${{ matrix.module }}_cuda \
tests/pipelines/${{ matrix.module }}
@@ -200,11 +198,10 @@ jobs:
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$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
pip uninstall transformers -y && pip uninstall huggingface_hub -y && python -m uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
uv pip install -e ".[quality]"
uv pip install peft@git+https://github.com/huggingface/peft.git
uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
- name: Environment
run: |
@@ -225,10 +222,10 @@ jobs:
run: |
pattern=$(cat ${{ steps.extract_tests.outputs.pattern_file }})
if [ -z "$pattern" ]; then
python -m pytest -n 1 -sv --max-worker-restart=0 --dist=loadfile -k "not Flax and not Onnx" tests/${{ matrix.module }} \
pytest -n 1 -sv --max-worker-restart=0 --dist=loadfile -k "not Flax and not Onnx" tests/${{ matrix.module }} \
--make-reports=tests_torch_cuda_${{ matrix.module }}
else
python -m pytest -n 1 -sv --max-worker-restart=0 --dist=loadfile -k "not Flax and not Onnx and $pattern" tests/${{ matrix.module }} \
pytest -n 1 -sv --max-worker-restart=0 --dist=loadfile -k "not Flax and not Onnx and $pattern" tests/${{ matrix.module }} \
--make-reports=tests_torch_cuda_${{ matrix.module }}
fi
@@ -265,22 +262,19 @@ jobs:
nvidia-smi
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
pip uninstall transformers -y && pip uninstall huggingface_hub -y && python -m uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
python -m uv pip install -e [quality,test,training]
uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
uv pip install -e ".[quality,training]"
- name: Environment
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python utils/print_env.py
- name: Run example tests on GPU
env:
HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install timm
python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
uv pip install ".[training]"
pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
- name: Failure short reports
if: ${{ failure() }}

10
.github/workflows/pr_torch_dependency_test.yml vendored
View File

@@ -25,12 +25,8 @@ jobs:
python-version: "3.8"
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m pip install --upgrade pip uv
python -m uv pip install -e .
python -m uv pip install torch torchvision torchaudio
python -m uv pip install pytest
pip install -e .
pip install torch torchvision torchaudio pytest
- name: Check for soft dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
pytest tests/others/test_dependencies.py
pytest tests/others/test_dependencies.py

38
.github/workflows/push_tests.yml vendored
View File

@@ -34,8 +34,7 @@ jobs:
fetch-depth: 2
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test]
uv pip install -e ".[quality]"
- name: Environment
run: |
python utils/print_env.py
@@ -75,9 +74,8 @@ jobs:
nvidia-smi
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test]
pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
uv pip install -e ".[quality]"
uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
- name: Environment
run: |
python utils/print_env.py
@@ -87,7 +85,7 @@ jobs:
# 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 \
pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-s -v -k "not Flax and not Onnx" \
--make-reports=tests_pipeline_${{ matrix.module }}_cuda \
tests/pipelines/${{ matrix.module }}
@@ -126,10 +124,9 @@ jobs:
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test]
python -m uv pip install peft@git+https://github.com/huggingface/peft.git
pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
uv pip install -e ".[quality]"
uv pip install peft@git+https://github.com/huggingface/peft.git
uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
- name: Environment
run: |
@@ -141,7 +138,7 @@ jobs:
# https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
CUBLAS_WORKSPACE_CONFIG: :16:8
run: |
python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-s -v -k "not Flax and not Onnx" \
--make-reports=tests_torch_cuda_${{ matrix.module }} \
tests/${{ matrix.module }}
@@ -180,8 +177,7 @@ jobs:
nvidia-smi
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test,training]
uv pip install -e ".[quality,training]"
- name: Environment
run: |
python utils/print_env.py
@@ -190,7 +186,7 @@ jobs:
HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
RUN_COMPILE: yes
run: |
python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
- name: Failure short reports
if: ${{ failure() }}
run: cat reports/tests_torch_compile_cuda_failures_short.txt
@@ -223,8 +219,7 @@ jobs:
nvidia-smi
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test,training]
uv pip install -e ".[quality,training]"
- name: Environment
run: |
python utils/print_env.py
@@ -232,7 +227,7 @@ jobs:
env:
HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
run: |
python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
- name: Failure short reports
if: ${{ failure() }}
run: cat reports/tests_torch_xformers_cuda_failures_short.txt
@@ -264,21 +259,18 @@ jobs:
nvidia-smi
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test,training]
uv pip install -e ".[quality,training]"
- name: Environment
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python utils/print_env.py
- name: Run example tests on GPU
env:
HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install timm
python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
uv pip install ".[training]"
pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
- name: Failure short reports
if: ${{ failure() }}

12
.github/workflows/push_tests_fast.yml vendored
View File

@@ -60,19 +60,16 @@ jobs:
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test]
uv pip install -e ".[quality]"
- name: Environment
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python utils/print_env.py
- name: Run fast PyTorch CPU tests
if: ${{ matrix.config.framework == 'pytorch' }}
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-s -v -k "not Flax and not Onnx" \
--make-reports=tests_${{ matrix.config.report }} \
tests/
@@ -80,9 +77,8 @@ jobs:
- name: Run example PyTorch CPU tests
if: ${{ matrix.config.framework == 'pytorch_examples' }}
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install peft timm
python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
uv pip install ".[training]"
pytest -n 4 --max-worker-restart=0 --dist=loadfile \
--make-reports=tests_${{ matrix.config.report }} \
examples

47
.github/workflows/release_tests_fast.yml vendored
View File

@@ -32,8 +32,7 @@ jobs:
fetch-depth: 2
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test]
uv pip install -e ".[quality]"
- name: Environment
run: |
python utils/print_env.py
@@ -73,9 +72,8 @@ jobs:
nvidia-smi
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test]
pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
uv pip install -e ".[quality]"
uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
- name: Environment
run: |
python utils/print_env.py
@@ -85,7 +83,7 @@ jobs:
# 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 \
pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-s -v -k "not Flax and not Onnx" \
--make-reports=tests_pipeline_${{ matrix.module }}_cuda \
tests/pipelines/${{ matrix.module }}
@@ -124,10 +122,9 @@ jobs:
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test]
python -m uv pip install peft@git+https://github.com/huggingface/peft.git
pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
uv pip install -e ".[quality]"
uv pip install peft@git+https://github.com/huggingface/peft.git
uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
- name: Environment
run: |
@@ -139,7 +136,7 @@ jobs:
# https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
CUBLAS_WORKSPACE_CONFIG: :16:8
run: |
python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-s -v -k "not Flax and not Onnx" \
--make-reports=tests_torch_${{ matrix.module }}_cuda \
tests/${{ matrix.module }}
@@ -175,10 +172,9 @@ jobs:
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$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
uv pip install -e ".[quality]"
uv pip install peft@git+https://github.com/huggingface/peft.git
uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
- name: Environment
run: |
@@ -190,7 +186,7 @@ jobs:
# 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 \
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 \
@@ -235,8 +231,7 @@ jobs:
nvidia-smi
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test,training]
uv pip install -e ".[quality,training]"
- name: Environment
run: |
python utils/print_env.py
@@ -245,7 +240,7 @@ jobs:
HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
RUN_COMPILE: yes
run: |
python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
- name: Failure short reports
if: ${{ failure() }}
run: cat reports/tests_torch_compile_cuda_failures_short.txt
@@ -278,8 +273,7 @@ jobs:
nvidia-smi
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test,training]
uv pip install -e ".[quality,training]"
- name: Environment
run: |
python utils/print_env.py
@@ -287,7 +281,7 @@ jobs:
env:
HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
run: |
python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
- name: Failure short reports
if: ${{ failure() }}
run: cat reports/tests_torch_xformers_cuda_failures_short.txt
@@ -321,21 +315,18 @@ jobs:
- name: Install dependencies
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install -e [quality,test,training]
uv pip install -e ".[quality,training]"
- name: Environment
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python utils/print_env.py
- name: Run example tests on GPU
env:
HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
run: |
python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
python -m uv pip install timm
python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
uv pip install ".[training]"
pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
- name: Failure short reports
if: ${{ failure() }}

5
.github/workflows/run_tests_from_a_pr.yml vendored
View File

@@ -63,9 +63,8 @@ jobs:
- name: Install pytest
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
uv pip install -e ".[quality]"
uv pip install peft
- name: Run tests
env:

81
docker/diffusers-doc-builder/Dockerfile
View File

@@ -1,56 +1,45 @@
FROM ubuntu:20.04
FROM python:3.10-slim
ENV PYTHONDONTWRITEBYTECODE=1
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-get -y update && apt-get install -y bash \
build-essential \
git \
git-lfs \
curl \
ca-certificates \
libglib2.0-0 \
libsndfile1-dev \
libgl1 \
zip \
wget
RUN apt install -y bash \
build-essential \
git \
git-lfs \
curl \
ca-certificates \
libsndfile1-dev \
python3.10 \
python3-pip \
libgl1 \
zip \
wget \
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"
ENV UV_PYTHON=/usr/local/bin/python
# 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 \
torchvision \
torchaudio \
invisible_watermark \
--extra-index-url https://download.pytorch.org/whl/cpu && \
python3.10 -m uv pip install --no-cache-dir \
accelerate \
datasets \
hf-doc-builder \
huggingface-hub \
Jinja2 \
librosa \
numpy==1.26.4 \
scipy \
tensorboard \
transformers \
matplotlib \
setuptools==69.5.1 \
bitsandbytes \
torchao \
gguf \
optimum-quanto
RUN pip install uv
RUN uv pip install --no-cache-dir \
torch \
torchvision \
torchaudio \
--extra-index-url https://download.pytorch.org/whl/cpu
RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
# Extra dependencies
RUN uv pip install --no-cache-dir \
accelerate \
numpy==1.26.4 \
hf_transfer \
setuptools==69.5.1 \
bitsandbytes \
torchao \
gguf \
optimum-quanto
RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean
CMD ["/bin/bash"]

68
docker/diffusers-pytorch-cpu/Dockerfile
View File

@@ -1,50 +1,38 @@
FROM ubuntu:20.04
FROM python:3.10-slim
ENV PYTHONDONTWRITEBYTECODE=1
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-get -y update && apt-get install -y bash \
build-essential \
git \
git-lfs \
curl \
ca-certificates \
libglib2.0-0 \
libsndfile1-dev \
libgl1
RUN apt install -y bash \
build-essential \
git \
git-lfs \
curl \
ca-certificates \
libsndfile1-dev \
python3.10 \
python3.10-dev \
python3-pip \
libgl1 \
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"
ENV UV_PYTHON=/usr/local/bin/python
# 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 \
torchvision \
torchaudio \
invisible_watermark \
--extra-index-url https://download.pytorch.org/whl/cpu && \
python3.10 -m uv pip install --no-cache-dir \
accelerate \
datasets \
hf-doc-builder \
huggingface-hub \
Jinja2 \
librosa \
numpy==1.26.4 \
scipy \
tensorboard \
transformers matplotlib \
hf_transfer
RUN pip install uv
RUN uv pip install --no-cache-dir \
torch \
torchvision \
torchaudio \
--extra-index-url https://download.pytorch.org/whl/cpu
RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
# Extra dependencies
RUN uv pip install --no-cache-dir \
accelerate \
numpy==1.26.4 \
hf_transfer
RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean
CMD ["/bin/bash"]

44
docker/diffusers-pytorch-cuda/Dockerfile
View File

@@ -2,11 +2,13 @@ FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
LABEL maintainer="Hugging Face"
LABEL repository="diffusers"
ARG PYTHON_VERSION=3.12
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get -y update \
&& apt-get install -y software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa
&& add-apt-repository ppa:deadsnakes/ppa && \
apt-get update
RUN apt install -y bash \
build-essential \
@@ -14,38 +16,34 @@ RUN apt install -y bash \
git-lfs \
curl \
ca-certificates \
libglib2.0-0 \
libsndfile1-dev \
libgl1 \
python3.10 \
python3.10-dev \
python3 \
python3-pip \
python3.10-venv && \
rm -rf /var/lib/apt/lists
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*
# make sure to use venv
RUN python3.10 -m venv /opt/venv
ENV PATH="/opt/venv/bin:$PATH"
RUN curl -LsSf https://astral.sh/uv/install.sh | sh
ENV PATH="/root/.local/bin:$PATH"
ENV VIRTUAL_ENV="/opt/venv"
ENV UV_PYTHON_INSTALL_DIR=/opt/uv/python
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/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 \
RUN uv pip install --no-cache-dir \
torch \
torchvision \
torchaudio \
invisible_watermark && \
python3.10 -m pip install --no-cache-dir \
torchaudio
RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
# Extra dependencies
RUN uv pip install --no-cache-dir \
accelerate \
datasets \
hf-doc-builder \
huggingface-hub \
hf_transfer \
Jinja2 \
librosa \
numpy==1.26.4 \
scipy \
tensorboard \
transformers \
pytorch-lightning \
pytorch-lightning \
hf_transfer
CMD ["/bin/bash"]

43
docker/diffusers-pytorch-minimum-cuda/Dockerfile
View File

@@ -2,6 +2,7 @@ FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
LABEL maintainer="Hugging Face"
LABEL repository="diffusers"
ARG PYTHON_VERSION=3.10
ENV DEBIAN_FRONTEND=noninteractive
ENV MINIMUM_SUPPORTED_TORCH_VERSION="2.1.0"
ENV MINIMUM_SUPPORTED_TORCHVISION_VERSION="0.16.0"
@@ -9,7 +10,8 @@ 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
&& add-apt-repository ppa:deadsnakes/ppa && \
apt-get update
RUN apt install -y bash \
build-essential \
@@ -17,37 +19,34 @@ RUN apt install -y bash \
git-lfs \
curl \
ca-certificates \
libglib2.0-0 \
libsndfile1-dev \
libgl1 \
python3.10 \
python3.10-dev \
python3 \
python3-pip \
python3.10-venv && \
rm -rf /var/lib/apt/lists
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*
# make sure to use venv
RUN python3.10 -m venv /opt/venv
ENV PATH="/opt/venv/bin:$PATH"
RUN curl -LsSf https://astral.sh/uv/install.sh | sh
ENV PATH="/root/.local/bin:$PATH"
ENV VIRTUAL_ENV="/opt/venv"
ENV UV_PYTHON_INSTALL_DIR=/opt/uv/python
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
python3.10 -m uv pip install --no-cache-dir \
RUN uv pip install --no-cache-dir \
torch==$MINIMUM_SUPPORTED_TORCH_VERSION \
torchvision==$MINIMUM_SUPPORTED_TORCHVISION_VERSION \
torchaudio==$MINIMUM_SUPPORTED_TORCHAUDIO_VERSION \
invisible_watermark && \
python3.10 -m pip install --no-cache-dir \
torchaudio==$MINIMUM_SUPPORTED_TORCHAUDIO_VERSION
RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
# Extra dependencies
RUN uv pip install --no-cache-dir \
accelerate \
datasets \
hf-doc-builder \
huggingface-hub \
hf_transfer \
Jinja2 \
librosa \
numpy==1.26.4 \
scipy \
tensorboard \
transformers \
pytorch-lightning \
hf_transfer
CMD ["/bin/bash"]

71
docker/diffusers-pytorch-xformers-cuda/Dockerfile
View File

@@ -2,50 +2,49 @@ FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
LABEL maintainer="Hugging Face"
LABEL repository="diffusers"
ARG PYTHON_VERSION=3.12
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get -y update \
&& apt-get install -y software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa
&& add-apt-repository ppa:deadsnakes/ppa && \
apt-get update
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
build-essential \
git \
git-lfs \
curl \
ca-certificates \
libglib2.0-0 \
libsndfile1-dev \
libgl1 \
python3 \
python3-pip \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*
# make sure to use venv
RUN python3.10 -m venv /opt/venv
ENV PATH="/opt/venv/bin:$PATH"
RUN curl -LsSf https://astral.sh/uv/install.sh | sh
ENV PATH="/root/.local/bin:$PATH"
ENV VIRTUAL_ENV="/opt/venv"
ENV UV_PYTHON_INSTALL_DIR=/opt/uv/python
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/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 pip install --no-cache-dir \
torch \
torchvision \
torchaudio \
invisible_watermark && \
python3.10 -m uv pip install --no-cache-dir \
accelerate \
datasets \
hf-doc-builder \
huggingface-hub \
hf_transfer \
Jinja2 \
librosa \
numpy==1.26.4 \
scipy \
tensorboard \
transformers \
xformers \
hf_transfer
RUN uv pip install --no-cache-dir \
torch \
torchvision \
torchaudio
RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
# Extra dependencies
RUN uv pip install --no-cache-dir \
accelerate \
numpy==1.26.4 \
pytorch-lightning \
hf_transfer \
xformers
CMD ["/bin/bash"]

2
docs/source/en/api/pipelines/marigold.md
View File

@@ -75,7 +75,7 @@ The following is a summary of the recommended checkpoints, all of which produce
| [prs-eth/marigold-depth-v1-1](https://huggingface.co/prs-eth/marigold-depth-v1-1) | Depth | Affine-invariant depth prediction assigns each pixel a value between 0 (near plane) and 1 (far plane), with both planes determined by the model during inference. |
| [prs-eth/marigold-normals-v0-1](https://huggingface.co/prs-eth/marigold-normals-v0-1) | Normals | The surface normals predictions are unit-length 3D vectors in the screen space camera, with values in the range from -1 to 1. |
| [prs-eth/marigold-iid-appearance-v1-1](https://huggingface.co/prs-eth/marigold-iid-appearance-v1-1) | Intrinsics | InteriorVerse decomposition is comprised of Albedo and two BRDF material properties: Roughness and Metallicity. |
| [prs-eth/marigold-iid-lighting-v1-1](https://huggingface.co/prs-eth/marigold-iid-lighting-v1-1) | Intrinsics | HyperSim decomposition of an image &nbsp\\(I\\)&nbsp is comprised of Albedo &nbsp\\(A\\), Diffuse shading &nbsp\\(S\\), and Non-diffuse residual &nbsp\\(R\\): &nbsp\\(I = A*S+R\\). |
| [prs-eth/marigold-iid-lighting-v1-1](https://huggingface.co/prs-eth/marigold-iid-lighting-v1-1) | Intrinsics | HyperSim decomposition of an image $I$ is comprised of Albedo $A$, Diffuse shading $S$, and Non-diffuse residual $R$: $I = A*S+R$. |
> [!TIP]
> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff

3
setup.py
View File

@@ -145,6 +145,7 @@ _deps = [
"black",
"phonemizer",
"opencv-python",
"timm",
]
# this is a lookup table with items like:
@@ -218,7 +219,7 @@ class DepsTableUpdateCommand(Command):
extras = {}
extras["quality"] = deps_list("urllib3", "isort", "ruff", "hf-doc-builder")
extras["docs"] = deps_list("hf-doc-builder")
extras["training"] = deps_list("accelerate", "datasets", "protobuf", "tensorboard", "Jinja2", "peft")
extras["training"] = deps_list("accelerate", "datasets", "protobuf", "tensorboard", "Jinja2", "peft", "timm")
extras["test"] = deps_list(
"compel",
"GitPython",

4
src/diffusers/__init__.py
View File

@@ -386,6 +386,8 @@ else:
_import_structure["modular_pipelines"].extend(
[
"FluxAutoBlocks",
"FluxKontextAutoBlocks",
"FluxKontextModularPipeline",
"FluxModularPipeline",
"QwenImageAutoBlocks",
"QwenImageEditAutoBlocks",
@@ -1050,6 +1052,8 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
else:
from .modular_pipelines import (
FluxAutoBlocks,
FluxKontextAutoBlocks,
FluxKontextModularPipeline,
FluxModularPipeline,
QwenImageAutoBlocks,
QwenImageEditAutoBlocks,

1
src/diffusers/dependency_versions_table.py
View File

@@ -52,4 +52,5 @@ deps = {
"black": "black",
"phonemizer": "phonemizer",
"opencv-python": "opencv-python",
"timm": "timm",
}

5
src/diffusers/hooks/context_parallel.py
View File

@@ -17,7 +17,10 @@ from dataclasses import dataclass
from typing import Dict, List, Type, Union
import torch
import torch.distributed._functional_collectives as funcol
if torch.distributed.is_available():
import torch.distributed._functional_collectives as funcol
from ..models._modeling_parallel import (
ContextParallelConfig,

9
src/diffusers/modular_pipelines/__init__.py
View File

@@ -46,7 +46,12 @@ else:
]
_import_structure["stable_diffusion_xl"] = ["StableDiffusionXLAutoBlocks", "StableDiffusionXLModularPipeline"]
_import_structure["wan"] = ["WanAutoBlocks", "WanModularPipeline"]
_import_structure["flux"] = ["FluxAutoBlocks", "FluxModularPipeline"]
_import_structure["flux"] = [
"FluxAutoBlocks",
"FluxModularPipeline",
"FluxKontextAutoBlocks",
"FluxKontextModularPipeline",
]
_import_structure["qwenimage"] = [
"QwenImageAutoBlocks",
"QwenImageModularPipeline",
@@ -65,7 +70,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
from ..utils.dummy_pt_objects import * # noqa F403
else:
from .components_manager import ComponentsManager
from .flux import FluxAutoBlocks, FluxModularPipeline
from .flux import FluxAutoBlocks, FluxKontextAutoBlocks, FluxKontextModularPipeline, FluxModularPipeline
from .modular_pipeline import (
AutoPipelineBlocks,
BlockState,

15
src/diffusers/modular_pipelines/flux/__init__.py
View File

@@ -25,14 +25,18 @@ else:
_import_structure["modular_blocks"] = [
"ALL_BLOCKS",
"AUTO_BLOCKS",
"AUTO_BLOCKS_KONTEXT",
"FLUX_KONTEXT_BLOCKS",
"TEXT2IMAGE_BLOCKS",
"FluxAutoBeforeDenoiseStep",
"FluxAutoBlocks",
"FluxAutoBlocks",
"FluxAutoDecodeStep",
"FluxAutoDenoiseStep",
"FluxKontextAutoBlocks",
"FluxKontextAutoDenoiseStep",
"FluxKontextBeforeDenoiseStep",
]
_import_structure["modular_pipeline"] = ["FluxModularPipeline"]
_import_structure["modular_pipeline"] = ["FluxKontextModularPipeline", "FluxModularPipeline"]
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
try:
@@ -45,13 +49,18 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
from .modular_blocks import (
ALL_BLOCKS,
AUTO_BLOCKS,
AUTO_BLOCKS_KONTEXT,
FLUX_KONTEXT_BLOCKS,
TEXT2IMAGE_BLOCKS,
FluxAutoBeforeDenoiseStep,
FluxAutoBlocks,
FluxAutoDecodeStep,
FluxAutoDenoiseStep,
FluxKontextAutoBlocks,
FluxKontextAutoDenoiseStep,
FluxKontextBeforeDenoiseStep,
)
from .modular_pipeline import FluxModularPipeline
from .modular_pipeline import FluxKontextModularPipeline, FluxModularPipeline
else:
import sys

84
src/diffusers/modular_pipelines/flux/before_denoise.py
View File

@@ -118,15 +118,6 @@ def retrieve_latents(
raise AttributeError("Could not access latents of provided encoder_output")
# TODO: align this with Qwen patchifier
def _pack_latents(latents, batch_size, num_channels_latents, height, width):
latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
latents = latents.permute(0, 2, 4, 1, 3, 5)
latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4)
return latents
def _get_initial_timesteps_and_optionals(
transformer,
scheduler,
@@ -398,16 +389,15 @@ class FluxPrepareLatentsStep(ModularPipelineBlocks):
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
# TODO: move packing latents code to a patchifier
# TODO: move packing latents code to a patchifier similar to Qwen
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
latents = _pack_latents(latents, batch_size, num_channels_latents, height, width)
latents = FluxPipeline._pack_latents(latents, batch_size, num_channels_latents, height, width)
return latents
@torch.no_grad()
def __call__(self, components: FluxModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
block_state.height = block_state.height or components.default_height
block_state.width = block_state.width or components.default_width
block_state.device = components._execution_device
@@ -557,3 +547,73 @@ class FluxRoPEInputsStep(ModularPipelineBlocks):
self.set_block_state(state, block_state)
return components, state
class FluxKontextRoPEInputsStep(ModularPipelineBlocks):
model_name = "flux-kontext"
@property
def description(self) -> str:
return "Step that prepares the RoPE inputs for the denoising process of Flux Kontext. Should be placed after text encoder and latent preparation steps."
@property
def inputs(self) -> List[InputParam]:
return [
InputParam(name="image_height"),
InputParam(name="image_width"),
InputParam(name="height"),
InputParam(name="width"),
InputParam(name="prompt_embeds"),
]
@property
def intermediate_outputs(self) -> List[OutputParam]:
return [
OutputParam(
name="txt_ids",
kwargs_type="denoiser_input_fields",
type_hint=List[int],
description="The sequence lengths of the prompt embeds, used for RoPE calculation.",
),
OutputParam(
name="img_ids",
kwargs_type="denoiser_input_fields",
type_hint=List[int],
description="The sequence lengths of the image latents, used for RoPE calculation.",
),
]
def __call__(self, components: FluxModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
prompt_embeds = block_state.prompt_embeds
device, dtype = prompt_embeds.device, prompt_embeds.dtype
block_state.txt_ids = torch.zeros(prompt_embeds.shape[1], 3).to(
device=prompt_embeds.device, dtype=prompt_embeds.dtype
)
img_ids = None
if (
getattr(block_state, "image_height", None) is not None
and getattr(block_state, "image_width", None) is not None
):
image_latent_height = 2 * (int(block_state.image_height) // (components.vae_scale_factor * 2))
image_latent_width = 2 * (int(block_state.width) // (components.vae_scale_factor * 2))
img_ids = FluxPipeline._prepare_latent_image_ids(
None, image_latent_height // 2, image_latent_width // 2, device, dtype
)
# image ids are the same as latent ids with the first dimension set to 1 instead of 0
img_ids[..., 0] = 1
height = 2 * (int(block_state.height) // (components.vae_scale_factor * 2))
width = 2 * (int(block_state.width) // (components.vae_scale_factor * 2))
latent_ids = FluxPipeline._prepare_latent_image_ids(None, height // 2, width // 2, device, dtype)
if img_ids is not None:
latent_ids = torch.cat([latent_ids, img_ids], dim=0)
block_state.img_ids = latent_ids
self.set_block_state(state, block_state)
return components, state

107
src/diffusers/modular_pipelines/flux/denoise.py
View File

@@ -109,6 +109,96 @@ class FluxLoopDenoiser(ModularPipelineBlocks):
return components, block_state
class FluxKontextLoopDenoiser(ModularPipelineBlocks):
model_name = "flux-kontext"
@property
def expected_components(self) -> List[ComponentSpec]:
return [ComponentSpec("transformer", FluxTransformer2DModel)]
@property
def description(self) -> str:
return (
"Step within the denoising loop that denoise the latents for Flux Kontext. "
"This block should be used to compose the `sub_blocks` attribute of a `LoopSequentialPipelineBlocks` "
"object (e.g. `FluxDenoiseLoopWrapper`)"
)
@property
def inputs(self) -> List[Tuple[str, Any]]:
return [
InputParam("joint_attention_kwargs"),
InputParam(
"latents",
required=True,
type_hint=torch.Tensor,
description="The initial latents to use for the denoising process. Can be generated in prepare_latent step.",
),
InputParam(
"image_latents",
type_hint=torch.Tensor,
description="Image latents to use for the denoising process. Can be generated in prepare_latent step.",
),
InputParam(
"guidance",
required=True,
type_hint=torch.Tensor,
description="Guidance scale as a tensor",
),
InputParam(
"prompt_embeds",
required=True,
type_hint=torch.Tensor,
description="Prompt embeddings",
),
InputParam(
"pooled_prompt_embeds",
required=True,
type_hint=torch.Tensor,
description="Pooled prompt embeddings",
),
InputParam(
"txt_ids",
required=True,
type_hint=torch.Tensor,
description="IDs computed from text sequence needed for RoPE",
),
InputParam(
"img_ids",
required=True,
type_hint=torch.Tensor,
description="IDs computed from latent sequence needed for RoPE",
),
]
@torch.no_grad()
def __call__(
self, components: FluxModularPipeline, block_state: BlockState, i: int, t: torch.Tensor
) -> PipelineState:
latents = block_state.latents
latent_model_input = latents
image_latents = block_state.image_latents
if image_latents is not None:
latent_model_input = torch.cat([latent_model_input, image_latents], dim=1)
timestep = t.expand(latents.shape[0]).to(latents.dtype)
noise_pred = components.transformer(
hidden_states=latent_model_input,
timestep=timestep / 1000,
guidance=block_state.guidance,
encoder_hidden_states=block_state.prompt_embeds,
pooled_projections=block_state.pooled_prompt_embeds,
joint_attention_kwargs=block_state.joint_attention_kwargs,
txt_ids=block_state.txt_ids,
img_ids=block_state.img_ids,
return_dict=False,
)[0]
noise_pred = noise_pred[:, : latents.size(1)]
block_state.noise_pred = noise_pred
return components, block_state
class FluxLoopAfterDenoiser(ModularPipelineBlocks):
model_name = "flux"
@@ -221,3 +311,20 @@ class FluxDenoiseStep(FluxDenoiseLoopWrapper):
" - `FluxLoopAfterDenoiser`\n"
"This block supports both text2image and img2img tasks."
)
class FluxKontextDenoiseStep(FluxDenoiseLoopWrapper):
model_name = "flux-kontext"
block_classes = [FluxKontextLoopDenoiser, FluxLoopAfterDenoiser]
block_names = ["denoiser", "after_denoiser"]
@property
def description(self) -> str:
return (
"Denoise step that iteratively denoise the latents. \n"
"Its loop logic is defined in `FluxDenoiseLoopWrapper.__call__` method \n"
"At each iteration, it runs blocks defined in `sub_blocks` sequentially:\n"
" - `FluxKontextLoopDenoiser`\n"
" - `FluxLoopAfterDenoiser`\n"
"This block supports both text2image and img2img tasks."
)

107
src/diffusers/modular_pipelines/flux/encoders.py
View File

@@ -20,7 +20,7 @@ import torch
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor
from ...image_processor import VaeImageProcessor, is_valid_image, is_valid_image_imagelist
from ...loaders import FluxLoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL
from ...utils import USE_PEFT_BACKEND, is_ftfy_available, logging, scale_lora_layers, unscale_lora_layers
@@ -83,11 +83,11 @@ def encode_vae_image(vae: AutoencoderKL, image: torch.Tensor, generator: torch.G
class FluxProcessImagesInputStep(ModularPipelineBlocks):
model_name = "Flux"
model_name = "flux"
@property
def description(self) -> str:
return "Image Preprocess step. Resizing is needed in Flux Kontext (will be implemented later.)"
return "Image Preprocess step."
@property
def expected_components(self) -> List[ComponentSpec]:
@@ -106,9 +106,7 @@ class FluxProcessImagesInputStep(ModularPipelineBlocks):
@property
def intermediate_outputs(self) -> List[OutputParam]:
return [
OutputParam(name="processed_image"),
]
return [OutputParam(name="processed_image")]
@staticmethod
def check_inputs(height, width, vae_scale_factor):
@@ -142,13 +140,80 @@ class FluxProcessImagesInputStep(ModularPipelineBlocks):
return components, state
class FluxKontextProcessImagesInputStep(ModularPipelineBlocks):
model_name = "flux-kontext"
def __init__(self, _auto_resize=True):
self._auto_resize = _auto_resize
super().__init__()
@property
def description(self) -> str:
return (
"Image preprocess step for Flux Kontext. The preprocessed image goes to the VAE.\n"
"Kontext works as a T2I model, too, in case no input image is provided."
)
@property
def expected_components(self) -> List[ComponentSpec]:
return [
ComponentSpec(
"image_processor",
VaeImageProcessor,
config=FrozenDict({"vae_scale_factor": 16}),
default_creation_method="from_config",
),
]
@property
def inputs(self) -> List[InputParam]:
return [InputParam("image")]
@property
def intermediate_outputs(self) -> List[OutputParam]:
return [OutputParam(name="processed_image")]
@torch.no_grad()
def __call__(self, components: FluxModularPipeline, state: PipelineState):
from ...pipelines.flux.pipeline_flux_kontext import PREFERRED_KONTEXT_RESOLUTIONS
block_state = self.get_block_state(state)
images = block_state.image
if images is None:
block_state.processed_image = None
else:
multiple_of = components.image_processor.config.vae_scale_factor
if not is_valid_image_imagelist(images):
raise ValueError(f"Images must be image or list of images but are {type(images)}")
if is_valid_image(images):
images = [images]
img = images[0]
image_height, image_width = components.image_processor.get_default_height_width(img)
aspect_ratio = image_width / image_height
if self._auto_resize:
# Kontext is trained on specific resolutions, using one of them is recommended
_, image_width, image_height = min(
(abs(aspect_ratio - w / h), w, h) for w, h in PREFERRED_KONTEXT_RESOLUTIONS
)
image_width = image_width // multiple_of * multiple_of
image_height = image_height // multiple_of * multiple_of
images = components.image_processor.resize(images, image_height, image_width)
block_state.processed_image = components.image_processor.preprocess(images, image_height, image_width)
self.set_block_state(state, block_state)
return components, state
class FluxVaeEncoderDynamicStep(ModularPipelineBlocks):
model_name = "flux"
def __init__(
self,
input_name: str = "processed_image",
output_name: str = "image_latents",
self, input_name: str = "processed_image", output_name: str = "image_latents", sample_mode: str = "sample"
):
"""Initialize a VAE encoder step for converting images to latent representations.
@@ -160,6 +225,7 @@ class FluxVaeEncoderDynamicStep(ModularPipelineBlocks):
Examples: "processed_image" or "processed_control_image"
output_name (str, optional): Name of the output latent tensor. Defaults to "image_latents".
Examples: "image_latents" or "control_image_latents"
sample_mode (str, optional): Sampling mode to be used.
Examples:
# Basic usage with default settings (includes image processor): # FluxImageVaeEncoderDynamicStep()
@@ -170,6 +236,7 @@ class FluxVaeEncoderDynamicStep(ModularPipelineBlocks):
"""
self._image_input_name = input_name
self._image_latents_output_name = output_name
self.sample_mode = sample_mode
super().__init__()
@property
@@ -183,7 +250,7 @@ class FluxVaeEncoderDynamicStep(ModularPipelineBlocks):
@property
def inputs(self) -> List[InputParam]:
inputs = [InputParam(self._image_input_name, required=True), InputParam("generator")]
inputs = [InputParam(self._image_input_name), InputParam("generator")]
return inputs
@property
@@ -199,16 +266,20 @@ class FluxVaeEncoderDynamicStep(ModularPipelineBlocks):
@torch.no_grad()
def __call__(self, components: FluxModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
dtype = components.vae.dtype
image = getattr(block_state, self._image_input_name)
image = image.to(device=device, dtype=dtype)
# Encode image into latents
image_latents = encode_vae_image(image=image, vae=components.vae, generator=block_state.generator)
setattr(block_state, self._image_latents_output_name, image_latents)
if image is None:
setattr(block_state, self._image_latents_output_name, None)
else:
device = components._execution_device
dtype = components.vae.dtype
image = image.to(device=device, dtype=dtype)
# Encode image into latents
image_latents = encode_vae_image(
image=image, vae=components.vae, generator=block_state.generator, sample_mode=self.sample_mode
)
setattr(block_state, self._image_latents_output_name, image_latents)
self.set_block_state(state, block_state)

123
src/diffusers/modular_pipelines/flux/inputs.py
View File

@@ -17,6 +17,7 @@ from typing import List
import torch
from ...pipelines import FluxPipeline
from ...utils import logging
from ..modular_pipeline import ModularPipelineBlocks, PipelineState
from ..modular_pipeline_utils import InputParam, OutputParam
@@ -25,6 +26,9 @@ from ..qwenimage.inputs import calculate_dimension_from_latents, repeat_tensor_t
from .modular_pipeline import FluxModularPipeline
logger = logging.get_logger(__name__)
class FluxTextInputStep(ModularPipelineBlocks):
model_name = "flux"
@@ -234,3 +238,122 @@ class FluxInputsDynamicStep(ModularPipelineBlocks):
self.set_block_state(state, block_state)
return components, state
class FluxKontextInputsDynamicStep(FluxInputsDynamicStep):
model_name = "flux-kontext"
def __call__(self, components: FluxModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
# Process image latent inputs (height/width calculation, patchify, and batch expansion)
for image_latent_input_name in self._image_latent_inputs:
image_latent_tensor = getattr(block_state, image_latent_input_name)
if image_latent_tensor is None:
continue
# 1. Calculate height/width from latents
# Unlike the `FluxInputsDynamicStep`, we don't overwrite the `block.height` and `block.width`
height, width = calculate_dimension_from_latents(image_latent_tensor, components.vae_scale_factor)
if not hasattr(block_state, "image_height"):
block_state.image_height = height
if not hasattr(block_state, "image_width"):
block_state.image_width = width
# 2. Patchify the image latent tensor
# TODO: Implement patchifier for Flux.
latent_height, latent_width = image_latent_tensor.shape[2:]
image_latent_tensor = FluxPipeline._pack_latents(
image_latent_tensor, block_state.batch_size, image_latent_tensor.shape[1], latent_height, latent_width
)
# 3. Expand batch size
image_latent_tensor = repeat_tensor_to_batch_size(
input_name=image_latent_input_name,
input_tensor=image_latent_tensor,
num_images_per_prompt=block_state.num_images_per_prompt,
batch_size=block_state.batch_size,
)
setattr(block_state, image_latent_input_name, image_latent_tensor)
# Process additional batch inputs (only batch expansion)
for input_name in self._additional_batch_inputs:
input_tensor = getattr(block_state, input_name)
if input_tensor is None:
continue
# Only expand batch size
input_tensor = repeat_tensor_to_batch_size(
input_name=input_name,
input_tensor=input_tensor,
num_images_per_prompt=block_state.num_images_per_prompt,
batch_size=block_state.batch_size,
)
setattr(block_state, input_name, input_tensor)
self.set_block_state(state, block_state)
return components, state
class FluxKontextSetResolutionStep(ModularPipelineBlocks):
model_name = "flux-kontext"
def description(self):
return (
"Determines the height and width to be used during the subsequent computations.\n"
"It should always be placed _before_ the latent preparation step."
)
@property
def inputs(self) -> List[InputParam]:
inputs = [
InputParam(name="height"),
InputParam(name="width"),
InputParam(name="max_area", type_hint=int, default=1024**2),
]
return inputs
@property
def intermediate_outputs(self) -> List[OutputParam]:
return [
OutputParam(name="height", type_hint=int, description="The height of the initial noisy latents"),
OutputParam(name="width", type_hint=int, description="The width of the initial noisy latents"),
]
@staticmethod
def check_inputs(height, width, vae_scale_factor):
if height is not None and height % (vae_scale_factor * 2) != 0:
raise ValueError(f"Height must be divisible by {vae_scale_factor * 2} but is {height}")
if width is not None and width % (vae_scale_factor * 2) != 0:
raise ValueError(f"Width must be divisible by {vae_scale_factor * 2} but is {width}")
def __call__(self, components: FluxModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
height = block_state.height or components.default_height
width = block_state.width or components.default_width
self.check_inputs(height, width, components.vae_scale_factor)
original_height, original_width = height, width
max_area = block_state.max_area
aspect_ratio = width / height
width = round((max_area * aspect_ratio) ** 0.5)
height = round((max_area / aspect_ratio) ** 0.5)
multiple_of = components.vae_scale_factor * 2
width = width // multiple_of * multiple_of
height = height // multiple_of * multiple_of
if height != original_height or width != original_width:
logger.warning(
f"Generation `height` and `width` have been adjusted to {height} and {width} to fit the model requirements."
)
block_state.height = height
block_state.width = width
self.set_block_state(state, block_state)
return components, state

215
src/diffusers/modular_pipelines/flux/modular_blocks.py
View File

@@ -18,14 +18,25 @@ from ..modular_pipeline_utils import InsertableDict
from .before_denoise import (
FluxImg2ImgPrepareLatentsStep,
FluxImg2ImgSetTimestepsStep,
FluxKontextRoPEInputsStep,
FluxPrepareLatentsStep,
FluxRoPEInputsStep,
FluxSetTimestepsStep,
)
from .decoders import FluxDecodeStep
from .denoise import FluxDenoiseStep
from .encoders import FluxProcessImagesInputStep, FluxTextEncoderStep, FluxVaeEncoderDynamicStep
from .inputs import FluxInputsDynamicStep, FluxTextInputStep
from .denoise import FluxDenoiseStep, FluxKontextDenoiseStep
from .encoders import (
FluxKontextProcessImagesInputStep,
FluxProcessImagesInputStep,
FluxTextEncoderStep,
FluxVaeEncoderDynamicStep,
)
from .inputs import (
FluxInputsDynamicStep,
FluxKontextInputsDynamicStep,
FluxKontextSetResolutionStep,
FluxTextInputStep,
)
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@@ -33,10 +44,7 @@ logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# vae encoder (run before before_denoise)
FluxImg2ImgVaeEncoderBlocks = InsertableDict(
[
("preprocess", FluxProcessImagesInputStep()),
("encode", FluxVaeEncoderDynamicStep()),
]
[("preprocess", FluxProcessImagesInputStep()), ("encode", FluxVaeEncoderDynamicStep())]
)
@@ -66,6 +74,39 @@ class FluxAutoVaeEncoderStep(AutoPipelineBlocks):
)
# Flux Kontext vae encoder (run before before_denoise)
FluxKontextVaeEncoderBlocks = InsertableDict(
[("preprocess", FluxKontextProcessImagesInputStep()), ("encode", FluxVaeEncoderDynamicStep(sample_mode="argmax"))]
)
class FluxKontextVaeEncoderStep(SequentialPipelineBlocks):
model_name = "flux-kontext"
block_classes = FluxKontextVaeEncoderBlocks.values()
block_names = FluxKontextVaeEncoderBlocks.keys()
@property
def description(self) -> str:
return "Vae encoder step that preprocess andencode the image inputs into their latent representations."
class FluxKontextAutoVaeEncoderStep(AutoPipelineBlocks):
block_classes = [FluxKontextVaeEncoderStep]
block_names = ["img2img"]
block_trigger_inputs = ["image"]
@property
def description(self):
return (
"Vae encoder step that encode the image inputs into their latent representations.\n"
+ "This is an auto pipeline block that works for img2img tasks.\n"
+ " - `FluxKontextVaeEncoderStep` (img2img) is used when only `image` is provided."
+ " - if `image` is not provided, step will be skipped."
)
# before_denoise: text2img
FluxBeforeDenoiseBlocks = InsertableDict(
[
@@ -107,6 +148,7 @@ class FluxImg2ImgBeforeDenoiseStep(SequentialPipelineBlocks):
# before_denoise: all task (text2img, img2img)
class FluxAutoBeforeDenoiseStep(AutoPipelineBlocks):
model_name = "flux-kontext"
block_classes = [FluxImg2ImgBeforeDenoiseStep, FluxBeforeDenoiseStep]
block_names = ["img2img", "text2image"]
block_trigger_inputs = ["image_latents", None]
@@ -121,6 +163,44 @@ class FluxAutoBeforeDenoiseStep(AutoPipelineBlocks):
)
# before_denoise: FluxKontext
FluxKontextBeforeDenoiseBlocks = InsertableDict(
[
("prepare_latents", FluxPrepareLatentsStep()),
("set_timesteps", FluxSetTimestepsStep()),
("prepare_rope_inputs", FluxKontextRoPEInputsStep()),
]
)
class FluxKontextBeforeDenoiseStep(SequentialPipelineBlocks):
block_classes = FluxKontextBeforeDenoiseBlocks.values()
block_names = FluxKontextBeforeDenoiseBlocks.keys()
@property
def description(self):
return (
"Before denoise step that prepare the inputs for the denoise step\n"
"for img2img/text2img task for Flux Kontext."
)
class FluxKontextAutoBeforeDenoiseStep(AutoPipelineBlocks):
block_classes = [FluxKontextBeforeDenoiseStep, FluxBeforeDenoiseStep]
block_names = ["img2img", "text2image"]
block_trigger_inputs = ["image_latents", None]
@property
def description(self):
return (
"Before denoise step that prepare the inputs for the denoise step.\n"
+ "This is an auto pipeline block that works for text2image.\n"
+ " - `FluxBeforeDenoiseStep` (text2image) is used.\n"
+ " - `FluxKontextBeforeDenoiseStep` (img2img) is used when only `image_latents` is provided.\n"
)
# denoise: text2image
class FluxAutoDenoiseStep(AutoPipelineBlocks):
block_classes = [FluxDenoiseStep]
@@ -136,6 +216,23 @@ class FluxAutoDenoiseStep(AutoPipelineBlocks):
)
# denoise: Flux Kontext
class FluxKontextAutoDenoiseStep(AutoPipelineBlocks):
block_classes = [FluxKontextDenoiseStep]
block_names = ["denoise"]
block_trigger_inputs = [None]
@property
def description(self) -> str:
return (
"Denoise step that iteratively denoise the latents for Flux Kontext. "
"This is a auto pipeline block that works for text2image and img2img tasks."
" - `FluxDenoiseStep` (denoise) for text2image and img2img tasks."
)
# decode: all task (text2img, img2img)
class FluxAutoDecodeStep(AutoPipelineBlocks):
block_classes = [FluxDecodeStep]
@@ -165,7 +262,7 @@ class FluxImg2ImgInputStep(SequentialPipelineBlocks):
" - update height/width based `image_latents`, patchify `image_latents`."
class FluxImageAutoInputStep(AutoPipelineBlocks):
class FluxAutoInputStep(AutoPipelineBlocks):
block_classes = [FluxImg2ImgInputStep, FluxTextInputStep]
block_names = ["img2img", "text2image"]
block_trigger_inputs = ["image_latents", None]
@@ -180,16 +277,59 @@ class FluxImageAutoInputStep(AutoPipelineBlocks):
)
# inputs: Flux Kontext
FluxKontextBlocks = InsertableDict(
[
("set_resolution", FluxKontextSetResolutionStep()),
("text_inputs", FluxTextInputStep()),
("additional_inputs", FluxKontextInputsDynamicStep()),
]
)
class FluxKontextInputStep(SequentialPipelineBlocks):
model_name = "flux-kontext"
block_classes = FluxKontextBlocks.values()
block_names = FluxKontextBlocks.keys()
@property
def description(self):
return (
"Input step that prepares the inputs for the both text2img and img2img denoising step. It:\n"
" - make sure the text embeddings have consistent batch size as well as the additional inputs (`image_latents`).\n"
" - update height/width based `image_latents`, patchify `image_latents`."
)
class FluxKontextAutoInputStep(AutoPipelineBlocks):
block_classes = [FluxKontextInputStep, FluxTextInputStep]
# block_classes = [FluxKontextInputStep]
block_names = ["img2img", "text2img"]
# block_names = ["img2img"]
block_trigger_inputs = ["image_latents", None]
# block_trigger_inputs = ["image_latents"]
@property
def description(self):
return (
"Input step that standardize the inputs for the denoising step, e.g. make sure inputs have consistent batch size, and patchified. \n"
" This is an auto pipeline block that works for text2image/img2img tasks.\n"
+ " - `FluxKontextInputStep` (img2img) is used when `image_latents` is provided.\n"
+ " - `FluxKontextInputStep` is also capable of handling text2image task when `image_latent` isn't present."
)
class FluxCoreDenoiseStep(SequentialPipelineBlocks):
model_name = "flux"
block_classes = [FluxImageAutoInputStep, FluxAutoBeforeDenoiseStep, FluxAutoDenoiseStep]
block_classes = [FluxAutoInputStep, FluxAutoBeforeDenoiseStep, FluxAutoDenoiseStep]
block_names = ["input", "before_denoise", "denoise"]
@property
def description(self):
return (
"Core step that performs the denoising process. \n"
+ " - `FluxImageAutoInputStep` (input) standardizes the inputs for the denoising step.\n"
+ " - `FluxAutoInputStep` (input) standardizes the inputs for the denoising step.\n"
+ " - `FluxAutoBeforeDenoiseStep` (before_denoise) prepares the inputs for the denoising step.\n"
+ " - `FluxAutoDenoiseStep` (denoise) iteratively denoises the latents.\n"
+ "This step supports text-to-image and image-to-image tasks for Flux:\n"
@@ -198,6 +338,24 @@ class FluxCoreDenoiseStep(SequentialPipelineBlocks):
)
class FluxKontextCoreDenoiseStep(SequentialPipelineBlocks):
model_name = "flux-kontext"
block_classes = [FluxKontextAutoInputStep, FluxKontextAutoBeforeDenoiseStep, FluxKontextAutoDenoiseStep]
block_names = ["input", "before_denoise", "denoise"]
@property
def description(self):
return (
"Core step that performs the denoising process. \n"
+ " - `FluxKontextAutoInputStep` (input) standardizes the inputs for the denoising step.\n"
+ " - `FluxKontextAutoBeforeDenoiseStep` (before_denoise) prepares the inputs for the denoising step.\n"
+ " - `FluxKontextAutoDenoiseStep` (denoise) iteratively denoises the latents.\n"
+ "This step supports text-to-image and image-to-image tasks for Flux:\n"
+ " - for image-to-image generation, you need to provide `image_latents`\n"
+ " - for text-to-image generation, all you need to provide is prompt embeddings."
)
# Auto blocks (text2image and img2img)
AUTO_BLOCKS = InsertableDict(
[
@@ -208,6 +366,15 @@ AUTO_BLOCKS = InsertableDict(
]
)
AUTO_BLOCKS_KONTEXT = InsertableDict(
[
("text_encoder", FluxTextEncoderStep()),
("image_encoder", FluxKontextAutoVaeEncoderStep()),
("denoise", FluxKontextCoreDenoiseStep()),
("decode", FluxDecodeStep()),
]
)
class FluxAutoBlocks(SequentialPipelineBlocks):
model_name = "flux"
@@ -224,6 +391,13 @@ class FluxAutoBlocks(SequentialPipelineBlocks):
)
class FluxKontextAutoBlocks(FluxAutoBlocks):
model_name = "flux-kontext"
block_classes = AUTO_BLOCKS_KONTEXT.values()
block_names = AUTO_BLOCKS_KONTEXT.keys()
TEXT2IMAGE_BLOCKS = InsertableDict(
[
("text_encoder", FluxTextEncoderStep()),
@@ -250,4 +424,23 @@ IMAGE2IMAGE_BLOCKS = InsertableDict(
]
)
ALL_BLOCKS = {"text2image": TEXT2IMAGE_BLOCKS, "img2img": IMAGE2IMAGE_BLOCKS, "auto": AUTO_BLOCKS}
FLUX_KONTEXT_BLOCKS = InsertableDict(
[
("text_encoder", FluxTextEncoderStep()),
("vae_encoder", FluxVaeEncoderDynamicStep(sample_mode="argmax")),
("input", FluxKontextInputStep()),
("prepare_latents", FluxPrepareLatentsStep()),
("set_timesteps", FluxSetTimestepsStep()),
("prepare_rope_inputs", FluxKontextRoPEInputsStep()),
("denoise", FluxKontextDenoiseStep()),
("decode", FluxDecodeStep()),
]
)
ALL_BLOCKS = {
"text2image": TEXT2IMAGE_BLOCKS,
"img2img": IMAGE2IMAGE_BLOCKS,
"auto": AUTO_BLOCKS,
"auto_kontext": AUTO_BLOCKS_KONTEXT,
"kontext": FLUX_KONTEXT_BLOCKS,
}

10
src/diffusers/modular_pipelines/flux/modular_pipeline.py
View File

@@ -55,3 +55,13 @@ class FluxModularPipeline(ModularPipeline, FluxLoraLoaderMixin, TextualInversion
if getattr(self, "transformer", None):
num_channels_latents = self.transformer.config.in_channels // 4
return num_channels_latents
class FluxKontextModularPipeline(FluxModularPipeline):
"""
A ModularPipeline for Flux Kontext.
> [!WARNING] > This is an experimental feature and is likely to change in the future.
"""
default_blocks_name = "FluxKontextAutoBlocks"

1
src/diffusers/modular_pipelines/modular_pipeline.py
View File

@@ -57,6 +57,7 @@ MODULAR_PIPELINE_MAPPING = OrderedDict(
("stable-diffusion-xl", "StableDiffusionXLModularPipeline"),
("wan", "WanModularPipeline"),
("flux", "FluxModularPipeline"),
("flux-kontext", "FluxKontextModularPipeline"),
("qwenimage", "QwenImageModularPipeline"),
("qwenimage-edit", "QwenImageEditModularPipeline"),
("qwenimage-edit-plus", "QwenImageEditPlusModularPipeline"),

11
src/diffusers/pipelines/marigold/pipeline_marigold_depth.py
View File

@@ -86,15 +86,14 @@ class MarigoldDepthOutput(BaseOutput):
Args:
prediction (`np.ndarray`, `torch.Tensor`):
Predicted depth maps with values in the range [0, 1]. The shape is $numimages \times 1 \times height \times
width$ for `torch.Tensor` or $numimages \times height \times width \times 1$ for `np.ndarray`.
Predicted depth maps with values in the range [0, 1]. The shape is `numimages × 1 × height × width` for
`torch.Tensor` or `numimages × height × width × 1` for `np.ndarray`.
uncertainty (`None`, `np.ndarray`, `torch.Tensor`):
Uncertainty maps computed from the ensemble, with values in the range [0, 1]. The shape is $numimages
\times 1 \times height \times width$ for `torch.Tensor` or $numimages \times height \times width \times 1$
for `np.ndarray`.
Uncertainty maps computed from the ensemble, with values in the range [0, 1]. The shape is `numimages × 1 ×
height × width` for `torch.Tensor` or `numimages × height × width × 1` for `np.ndarray`.
latent (`None`, `torch.Tensor`):
Latent features corresponding to the predictions, compatible with the `latents` argument of the pipeline.
The shape is $numimages * numensemble \times 4 \times latentheight \times latentwidth$.
The shape is `numimages * numensemble × 4 × latentheight × latentwidth`.
"""
prediction: Union[np.ndarray, torch.Tensor]

16
src/diffusers/pipelines/marigold/pipeline_marigold_intrinsics.py
View File

@@ -99,17 +99,17 @@ class MarigoldIntrinsicsOutput(BaseOutput):
Args:
prediction (`np.ndarray`, `torch.Tensor`):
Predicted image intrinsics with values in the range [0, 1]. The shape is $(numimages * numtargets) \times 3
\times height \times width$ for `torch.Tensor` or $(numimages * numtargets) \times height \times width
\times 3$ for `np.ndarray`, where `numtargets` corresponds to the number of predicted target modalities of
the intrinsic image decomposition.
Predicted image intrinsics with values in the range [0, 1]. The shape is `(numimages * numtargets) × 3 ×
height × width` for `torch.Tensor` or `(numimages * numtargets) × height × width × 3` for `np.ndarray`,
where `numtargets` corresponds to the number of predicted target modalities of the intrinsic image
decomposition.
uncertainty (`None`, `np.ndarray`, `torch.Tensor`):
Uncertainty maps computed from the ensemble, with values in the range [0, 1]. The shape is $(numimages *
numtargets) \times 3 \times height \times width$ for `torch.Tensor` or $(numimages * numtargets) \times
height \times width \times 3$ for `np.ndarray`.
Uncertainty maps computed from the ensemble, with values in the range [0, 1]. The shape is `(numimages *
numtargets) × 3 × height × width` for `torch.Tensor` or `(numimages * numtargets) × height × width × 3` for
`np.ndarray`.
latent (`None`, `torch.Tensor`):
Latent features corresponding to the predictions, compatible with the `latents` argument of the pipeline.
The shape is $(numimages * numensemble) \times (numtargets * 4) \times latentheight \times latentwidth$.
The shape is `(numimages * numensemble) × (numtargets * 4) × latentheight × latentwidth`.
"""
prediction: Union[np.ndarray, torch.Tensor]

11
src/diffusers/pipelines/marigold/pipeline_marigold_normals.py
View File

@@ -81,15 +81,14 @@ class MarigoldNormalsOutput(BaseOutput):
Args:
prediction (`np.ndarray`, `torch.Tensor`):
Predicted normals with values in the range [-1, 1]. The shape is $numimages \times 3 \times height \times
width$ for `torch.Tensor` or $numimages \times height \times width \times 3$ for `np.ndarray`.
Predicted normals with values in the range [-1, 1]. The shape is `numimages × 3 × height × width` for
`torch.Tensor` or `numimages × height × width × 3` for `np.ndarray`.
uncertainty (`None`, `np.ndarray`, `torch.Tensor`):
Uncertainty maps computed from the ensemble, with values in the range [0, 1]. The shape is $numimages
\times 1 \times height \times width$ for `torch.Tensor` or $numimages \times height \times width \times 1$
for `np.ndarray`.
Uncertainty maps computed from the ensemble, with values in the range [0, 1]. The shape is `numimages × 1 ×
height × width` for `torch.Tensor` or `numimages × height × width × 1` for `np.ndarray`.
latent (`None`, `torch.Tensor`):
Latent features corresponding to the predictions, compatible with the `latents` argument of the pipeline.
The shape is $numimages * numensemble \times 4 \times latentheight \times latentwidth$.
The shape is `numimages * numensemble × 4 × latentheight × latentwidth`.
"""
prediction: Union[np.ndarray, torch.Tensor]

30
src/diffusers/utils/dummy_torch_and_transformers_objects.py
View File

@@ -17,6 +17,36 @@ class FluxAutoBlocks(metaclass=DummyObject):
requires_backends(cls, ["torch", "transformers"])
class FluxKontextAutoBlocks(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class FluxKontextModularPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class FluxModularPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]