Merge branch 'main' into overhaul-release-workflow

.github/workflows/pypi_publish.yaml

@@ -1,73 +1,45 @@
-# Adapted from https://blog.deepjyoti30.dev/pypi-release-github-action
-
 name: PyPI release
 
 on:
   workflow_dispatch:
   push:
     tags:
-      - "*"
+      - "v*"
 
 jobs:
-  find-and-checkout-latest-branch:
+  build-and-test:
     runs-on: ubuntu-22.04
-    outputs:
-      latest_branch: ${{ steps.set_latest_branch.outputs.latest_branch }}
     steps:
-      - name: Checkout Repo
+      - name: Checkout repo
         uses: actions/checkout@v6
 
       - name: Set up Python
         uses: actions/setup-python@v6
         with:
-          python-version: '3.10'
+          python-version: "3.10"
 
-      - name: Fetch latest branch
-        id: fetch_latest_branch
+      - name: Fetch and checkout latest release branch
         run: |
           pip install -U requests packaging
           LATEST_BRANCH=$(python utils/fetch_latest_release_branch.py)
           echo "Latest branch: $LATEST_BRANCH"
-          echo "latest_branch=$LATEST_BRANCH" >> $GITHUB_ENV
+          git fetch origin "$LATEST_BRANCH"
+          git checkout "$LATEST_BRANCH"
 
-      - name: Set latest branch output
-        id: set_latest_branch
-        run: echo "::set-output name=latest_branch::${{ env.latest_branch }}"
-
-  release:
-    needs: find-and-checkout-latest-branch
-    runs-on: ubuntu-22.04
-
-    steps:
-      - name: Checkout Repo
-        uses: actions/checkout@v6
-        with:
-          ref: ${{ needs.find-and-checkout-latest-branch.outputs.latest_branch }}
-
-      - name: Setup Python
-        uses: actions/setup-python@v6
-        with:
-          python-version: "3.10"
-
-      - name: Install dependencies
+      - name: Install build dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install -U setuptools wheel twine
+          pip install -U build
           pip install -U torch --index-url https://download.pytorch.org/whl/cpu
 
       - name: Build the dist files
-        run: python setup.py bdist_wheel && python setup.py sdist
+        run: python -m build
 
-      - name: Publish to the test PyPI
-        env:
-          TWINE_USERNAME: ${{ secrets.TEST_PYPI_USERNAME }}
-          TWINE_PASSWORD: ${{ secrets.TEST_PYPI_PASSWORD }}
-        run: twine upload dist/* -r pypitest --repository-url=https://test.pypi.org/legacy/
+      - name: Install from built wheel
+        run: pip install dist/*.whl
 
       - name: Test installing diffusers and importing
         run: |
-          pip install diffusers && pip uninstall diffusers -y
-          pip install -i https://test.pypi.org/simple/ diffusers
           pip install -U transformers
           python utils/print_env.py
           python -c "from diffusers import __version__; print(__version__)"
@@ -75,8 +47,26 @@ jobs:
           python -c "from diffusers import DiffusionPipeline; pipe = DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe', safety_checker=None); pipe('ah suh du')"
           python -c "from diffusers import *"
 
+      - name: Upload build artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          name: python-dist
+          path: dist/
+
+  publish-to-pypi:
+    needs: build-and-test
+    if: startsWith(github.ref, 'refs/tags/')
+    runs-on: ubuntu-22.04
+    environment: pypi-release
+    permissions:
+      id-token: write
+
+    steps:
+      - name: Download build artifacts
+        uses: actions/download-artifact@v4
+        with:
+          name: python-dist
+          path: dist/
+
       - name: Publish to PyPI
-        env:
-          TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
-          TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
-        run: twine upload dist/* -r pypi
+        uses: pypa/gh-action-pypi-publish@release/v1

tests/models/transformers/test_models_transformer_sd3.py

@@ -13,63 +13,58 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import unittest
+
 import torch
 
 from diffusers import SD3Transformer2DModel
-from diffusers.utils.torch_utils import randn_tensor
+from diffusers.utils.import_utils import is_xformers_available
 
-from ...testing_utils import enable_full_determinism, torch_device
-from ..testing_utils import (
-    BaseModelTesterConfig,
-    BitsAndBytesTesterMixin,
-    ModelTesterMixin,
-    TorchAoTesterMixin,
-    TorchCompileTesterMixin,
-    TrainingTesterMixin,
+from ...testing_utils import (
+    enable_full_determinism,
+    torch_device,
 )
+from ..test_modeling_common import ModelTesterMixin
 
 
 enable_full_determinism()
 
 
-# ======================== SD3 Transformer ========================
-
-
-class SD3TransformerTesterConfig(BaseModelTesterConfig):
-    @property
-    def model_class(self):
-        return SD3Transformer2DModel
+class SD3TransformerTests(ModelTesterMixin, unittest.TestCase):
+    model_class = SD3Transformer2DModel
+    main_input_name = "hidden_states"
+    model_split_percents = [0.8, 0.8, 0.9]
 
     @property
-    def pretrained_model_name_or_path(self):
-        return "hf-internal-testing/tiny-sd3-pipe"
+    def dummy_input(self):
+        batch_size = 2
+        num_channels = 4
+        height = width = embedding_dim = 32
+        pooled_embedding_dim = embedding_dim * 2
+        sequence_length = 154
 
-    @property
-    def pretrained_model_kwargs(self):
-        return {"subfolder": "transformer"}
+        hidden_states = torch.randn((batch_size, num_channels, height, width)).to(torch_device)
+        encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
+        pooled_prompt_embeds = torch.randn((batch_size, pooled_embedding_dim)).to(torch_device)
+        timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
 
-    @property
-    def main_input_name(self) -> str:
-        return "hidden_states"
-
-    @property
-    def model_split_percents(self) -> list:
-        return [0.8, 0.8, 0.9]
-
-    @property
-    def output_shape(self) -> tuple:
-        return (4, 32, 32)
-
-    @property
-    def input_shape(self) -> tuple:
-        return (4, 32, 32)
-
-    @property
-    def generator(self):
-        return torch.Generator("cpu").manual_seed(0)
-
-    def get_init_dict(self) -> dict:
         return {
+            "hidden_states": hidden_states,
+            "encoder_hidden_states": encoder_hidden_states,
+            "pooled_projections": pooled_prompt_embeds,
+            "timestep": timestep,
+        }
+
+    @property
+    def input_shape(self):
+        return (4, 32, 32)
+
+    @property
+    def output_shape(self):
+        return (4, 32, 32)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {
             "sample_size": 32,
             "patch_size": 1,
             "in_channels": 4,
@@ -84,79 +79,67 @@ class SD3TransformerTesterConfig(BaseModelTesterConfig):
             "dual_attention_layers": (),
             "qk_norm": None,
         }
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
 
-    def get_dummy_inputs(self, batch_size: int = 2) -> dict[str, torch.Tensor]:
-        num_channels = 4
-        height = width = embedding_dim = 32
-        pooled_embedding_dim = embedding_dim * 2
-        sequence_length = 154
+    @unittest.skipIf(
+        torch_device != "cuda" or not is_xformers_available(),
+        reason="XFormers attention is only available with CUDA and `xformers` installed",
+    )
+    def test_xformers_enable_works(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict)
 
-        return {
-            "hidden_states": randn_tensor(
-                (batch_size, num_channels, height, width), generator=self.generator, device=torch_device
-            ),
-            "encoder_hidden_states": randn_tensor(
-                (batch_size, sequence_length, embedding_dim), generator=self.generator, device=torch_device
-            ),
-            "pooled_projections": randn_tensor(
-                (batch_size, pooled_embedding_dim), generator=self.generator, device=torch_device
-            ),
-            "timestep": torch.randint(0, 1000, size=(batch_size,), generator=self.generator).to(torch_device),
-        }
+        model.enable_xformers_memory_efficient_attention()
 
+        assert model.transformer_blocks[0].attn.processor.__class__.__name__ == "XFormersJointAttnProcessor", (
+            "xformers is not enabled"
+        )
 
-class TestSD3Transformer(SD3TransformerTesterConfig, ModelTesterMixin):
-    pass
+    @unittest.skip("SD3Transformer2DModel uses a dedicated attention processor. This test doesn't apply")
+    def test_set_attn_processor_for_determinism(self):
+        pass
 
-
-class TestSD3TransformerTraining(SD3TransformerTesterConfig, TrainingTesterMixin):
     def test_gradient_checkpointing_is_applied(self):
         expected_set = {"SD3Transformer2DModel"}
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
 
 
-class TestSD3TransformerCompile(SD3TransformerTesterConfig, TorchCompileTesterMixin):
-    pass
-
-
-# ======================== SD3.5 Transformer ========================
-
-
-class SD35TransformerTesterConfig(BaseModelTesterConfig):
-    @property
-    def model_class(self):
-        return SD3Transformer2DModel
+class SD35TransformerTests(ModelTesterMixin, unittest.TestCase):
+    model_class = SD3Transformer2DModel
+    main_input_name = "hidden_states"
+    model_split_percents = [0.8, 0.8, 0.9]
 
     @property
-    def pretrained_model_name_or_path(self):
-        return "hf-internal-testing/tiny-sd35-pipe"
+    def dummy_input(self):
+        batch_size = 2
+        num_channels = 4
+        height = width = embedding_dim = 32
+        pooled_embedding_dim = embedding_dim * 2
+        sequence_length = 154
 
-    @property
-    def pretrained_model_kwargs(self):
-        return {"subfolder": "transformer"}
+        hidden_states = torch.randn((batch_size, num_channels, height, width)).to(torch_device)
+        encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
+        pooled_prompt_embeds = torch.randn((batch_size, pooled_embedding_dim)).to(torch_device)
+        timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
 
-    @property
-    def main_input_name(self) -> str:
-        return "hidden_states"
-
-    @property
-    def model_split_percents(self) -> list:
-        return [0.8, 0.8, 0.9]
-
-    @property
-    def output_shape(self) -> tuple:
-        return (4, 32, 32)
-
-    @property
-    def input_shape(self) -> tuple:
-        return (4, 32, 32)
-
-    @property
-    def generator(self):
-        return torch.Generator("cpu").manual_seed(0)
-
-    def get_init_dict(self) -> dict:
         return {
+            "hidden_states": hidden_states,
+            "encoder_hidden_states": encoder_hidden_states,
+            "pooled_projections": pooled_prompt_embeds,
+            "timestep": timestep,
+        }
+
+    @property
+    def input_shape(self):
+        return (4, 32, 32)
+
+    @property
+    def output_shape(self):
+        return (4, 32, 32)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {
             "sample_size": 32,
             "patch_size": 1,
             "in_channels": 4,
@@ -171,56 +154,47 @@ class SD35TransformerTesterConfig(BaseModelTesterConfig):
             "dual_attention_layers": (0,),
             "qk_norm": "rms_norm",
         }
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
 
-    def get_dummy_inputs(self, batch_size: int = 2) -> dict[str, torch.Tensor]:
-        num_channels = 4
-        height = width = embedding_dim = 32
-        pooled_embedding_dim = embedding_dim * 2
-        sequence_length = 154
+    @unittest.skipIf(
+        torch_device != "cuda" or not is_xformers_available(),
+        reason="XFormers attention is only available with CUDA and `xformers` installed",
+    )
+    def test_xformers_enable_works(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict)
 
-        return {
-            "hidden_states": randn_tensor(
-                (batch_size, num_channels, height, width), generator=self.generator, device=torch_device
-            ),
-            "encoder_hidden_states": randn_tensor(
-                (batch_size, sequence_length, embedding_dim), generator=self.generator, device=torch_device
-            ),
-            "pooled_projections": randn_tensor(
-                (batch_size, pooled_embedding_dim), generator=self.generator, device=torch_device
-            ),
-            "timestep": torch.randint(0, 1000, size=(batch_size,), generator=self.generator).to(torch_device),
-        }
+        model.enable_xformers_memory_efficient_attention()
 
+        assert model.transformer_blocks[0].attn.processor.__class__.__name__ == "XFormersJointAttnProcessor", (
+            "xformers is not enabled"
+        )
 
-class TestSD35Transformer(SD35TransformerTesterConfig, ModelTesterMixin):
-    def test_skip_layers(self):
-        init_dict = self.get_init_dict()
-        inputs_dict = self.get_dummy_inputs()
-        model = self.model_class(**init_dict).to(torch_device)
+    @unittest.skip("SD3Transformer2DModel uses a dedicated attention processor. This test doesn't apply")
+    def test_set_attn_processor_for_determinism(self):
+        pass
 
-        output_full = model(**inputs_dict).sample
-
-        inputs_dict_with_skip = inputs_dict.copy()
-        inputs_dict_with_skip["skip_layers"] = [0]
-        output_skip = model(**inputs_dict_with_skip).sample
-
-        assert not torch.allclose(output_full, output_skip, atol=1e-5), "Outputs should differ when layers are skipped"
-        assert output_full.shape == output_skip.shape, "Outputs should have the same shape"
-
-
-class TestSD35TransformerTraining(SD35TransformerTesterConfig, TrainingTesterMixin):
     def test_gradient_checkpointing_is_applied(self):
         expected_set = {"SD3Transformer2DModel"}
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
 
+    def test_skip_layers(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict).to(torch_device)
 
-class TestSD35TransformerCompile(SD35TransformerTesterConfig, TorchCompileTesterMixin):
-    pass
+        # Forward pass without skipping layers
+        output_full = model(**inputs_dict).sample
 
+        # Forward pass with skipping layers 0 (since there's only one layer in this test setup)
+        inputs_dict_with_skip = inputs_dict.copy()
+        inputs_dict_with_skip["skip_layers"] = [0]
+        output_skip = model(**inputs_dict_with_skip).sample
 
-class TestSD35TransformerBitsAndBytes(SD35TransformerTesterConfig, BitsAndBytesTesterMixin):
-    """BitsAndBytes quantization tests for SD3.5 Transformer."""
+        # Check that the outputs are different
+        self.assertFalse(
+            torch.allclose(output_full, output_skip, atol=1e-5), "Outputs should differ when layers are skipped"
+        )
 
-
-class TestSD35TransformerTorchAo(SD35TransformerTesterConfig, TorchAoTesterMixin):
-    """TorchAO quantization tests for SD3.5 Transformer."""
+        # Check that the outputs have the same shape
+        self.assertEqual(output_full.shape, output_skip.shape, "Outputs should have the same shape")