Merge branch 'main' into bria-fibo

tests/lora/test_lora_layers_sana.py

@@ -20,7 +20,7 @@ from transformers import Gemma2Model, GemmaTokenizer
 
 from diffusers import AutoencoderDC, FlowMatchEulerDiscreteScheduler, SanaPipeline, SanaTransformer2DModel
 
-from ..testing_utils import floats_tensor, require_peft_backend
+from ..testing_utils import IS_GITHUB_ACTIONS, floats_tensor, require_peft_backend
 
 
 sys.path.append(".")
@@ -136,3 +136,7 @@ class SanaLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
     @unittest.skip("Text encoder LoRA is not supported in SANA.")
     def test_simple_inference_with_text_lora_save_load(self):
         pass
+
+    @unittest.skipIf(IS_GITHUB_ACTIONS, reason="Skipping test inside GitHub Actions environment")
+    def test_layerwise_casting_inference_denoiser(self):
+        return super().test_layerwise_casting_inference_denoiser()

tests/models/autoencoders/test_models_autoencoder_dc.py

@@ -17,11 +17,7 @@ import unittest
 
 from diffusers import AutoencoderDC
 
-from ...testing_utils import (
-    enable_full_determinism,
-    floats_tensor,
-    torch_device,
-)
+from ...testing_utils import IS_GITHUB_ACTIONS, enable_full_determinism, floats_tensor, torch_device
 from ..test_modeling_common import ModelTesterMixin
 from .testing_utils import AutoencoderTesterMixin
 
@@ -82,3 +78,7 @@ class AutoencoderDCTests(ModelTesterMixin, AutoencoderTesterMixin, unittest.Test
         init_dict = self.get_autoencoder_dc_config()
         inputs_dict = self.dummy_input
         return init_dict, inputs_dict
+
+    @unittest.skipIf(IS_GITHUB_ACTIONS, reason="Skipping test inside GitHub Actions environment")
+    def test_layerwise_casting_inference(self):
+        super().test_layerwise_casting_inference()

tests/pipelines/sana/test_sana.py

@@ -23,6 +23,7 @@ from transformers import Gemma2Config, Gemma2Model, GemmaTokenizer
 from diffusers import AutoencoderDC, FlowMatchEulerDiscreteScheduler, SanaPipeline, SanaTransformer2DModel
 
 from ...testing_utils import (
+    IS_GITHUB_ACTIONS,
     backend_empty_cache,
     enable_full_determinism,
     require_torch_accelerator,
@@ -304,6 +305,10 @@ class SanaPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         # Requires higher tolerance as model seems very sensitive to dtype
         super().test_float16_inference(expected_max_diff=0.08)
 
+    @unittest.skipIf(IS_GITHUB_ACTIONS, reason="Skipping test inside GitHub Actions environment")
+    def test_layerwise_casting_inference(self):
+        super().test_layerwise_casting_inference()
+
 
 @slow
 @require_torch_accelerator

tests/pipelines/sana/test_sana_controlnet.py

@@ -28,10 +28,7 @@ from diffusers import (
 )
 from diffusers.utils.torch_utils import randn_tensor
 
-from ...testing_utils import (
-    enable_full_determinism,
-    torch_device,
-)
+from ...testing_utils import IS_GITHUB_ACTIONS, enable_full_determinism, torch_device
 from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
 from ..test_pipelines_common import PipelineTesterMixin, to_np
 
@@ -326,3 +323,7 @@ class SanaControlNetPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     def test_float16_inference(self):
         # Requires higher tolerance as model seems very sensitive to dtype
         super().test_float16_inference(expected_max_diff=0.08)
+
+    @unittest.skipIf(IS_GITHUB_ACTIONS, reason="Skipping test inside GitHub Actions environment")
+    def test_layerwise_casting_inference(self):
+        super().test_layerwise_casting_inference()

tests/pipelines/sana/test_sana_sprint.py

@@ -21,10 +21,7 @@ from transformers import Gemma2Config, Gemma2Model, GemmaTokenizer
 
 from diffusers import AutoencoderDC, SanaSprintPipeline, SanaTransformer2DModel, SCMScheduler
 
-from ...testing_utils import (
-    enable_full_determinism,
-    torch_device,
-)
+from ...testing_utils import IS_GITHUB_ACTIONS, enable_full_determinism, torch_device
 from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
 from ..test_pipelines_common import PipelineTesterMixin, to_np
 
@@ -300,3 +297,7 @@ class SanaSprintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     def test_float16_inference(self):
         # Requires higher tolerance as model seems very sensitive to dtype
         super().test_float16_inference(expected_max_diff=0.08)
+
+    @unittest.skipIf(IS_GITHUB_ACTIONS, reason="Skipping test inside GitHub Actions environment")
+    def test_layerwise_casting_inference(self):
+        super().test_layerwise_casting_inference()

tests/pipelines/sana/test_sana_sprint_img2img.py

@@ -22,10 +22,7 @@ from transformers import Gemma2Config, Gemma2Model, GemmaTokenizer
 from diffusers import AutoencoderDC, SanaSprintImg2ImgPipeline, SanaTransformer2DModel, SCMScheduler
 from diffusers.utils.torch_utils import randn_tensor
 
-from ...testing_utils import (
-    enable_full_determinism,
-    torch_device,
-)
+from ...testing_utils import IS_GITHUB_ACTIONS, enable_full_determinism, torch_device
 from ..pipeline_params import (
     IMAGE_TO_IMAGE_IMAGE_PARAMS,
     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
@@ -312,3 +309,7 @@ class SanaSprintImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase)
     def test_float16_inference(self):
         # Requires higher tolerance as model seems very sensitive to dtype
         super().test_float16_inference(expected_max_diff=0.08)
+
+    @unittest.skipIf(IS_GITHUB_ACTIONS, reason="Skipping test inside GitHub Actions environment")
+    def test_layerwise_casting_inference(self):
+        super().test_layerwise_casting_inference()

tests/testing_utils.py

@@ -63,6 +63,8 @@ else:
     IS_CUDA_SYSTEM = False
     IS_XPU_SYSTEM = False
 
+IS_GITHUB_ACTIONS = os.getenv("GITHUB_ACTIONS") == "true" and os.getenv("DIFFUSERS_IS_CI") == "yes"
+
 global_rng = random.Random()
 
 logger = get_logger(__name__)