Fix deterministic issue when getting pipeline dtype and device

src/diffusers/pipelines/pipeline_utils.py

@@ -1577,7 +1577,7 @@ def _get_signature_keys(cls, obj):
                 expected_modules.add(name)
                 optional_parameters.remove(name)
 
-        return expected_modules, optional_parameters
+        return sorted(expected_modules), sorted(optional_parameters)
 
     @classmethod
     def _get_signature_types(cls):
@@ -1619,10 +1619,12 @@ def components(self) -> Dict[str, Any]:
             k: getattr(self, k) for k in self.config.keys() if not k.startswith("_") and k not in optional_parameters
         }
 
-        if set(components.keys()) != expected_modules:
+        actual = sorted(set(components.keys()))
+        expected = sorted(expected_modules)
+        if actual != expected:
             raise ValueError(
                 f"{self} has been incorrectly initialized or {self.__class__} is incorrectly implemented. Expected"
-                f" {expected_modules} to be defined, but {components.keys()} are defined."
+                f" {expected} to be defined, but {actual} are defined."
             )
 
         return components

tests/pipelines/test_pipeline_utils.py

@@ -19,7 +19,7 @@
     UNet2DConditionModel,
 )
 from diffusers.pipelines.pipeline_loading_utils import is_safetensors_compatible, variant_compatible_siblings
-from diffusers.utils.testing_utils import torch_device
+from diffusers.utils.testing_utils import require_torch_gpu, torch_device
 
 
 class IsSafetensorsCompatibleTests(unittest.TestCase):
@@ -585,3 +585,104 @@ def test_video_to_video(self):
         with io.StringIO() as stderr, contextlib.redirect_stderr(stderr):
             _ = pipe(**inputs)
             self.assertTrue(stderr.getvalue() == "", "Progress bar should be disabled")
+
+
+@require_torch_gpu
+class PipelineDeviceAndDtypeStabilityTests(unittest.TestCase):
+    expected_pipe_device = torch.device("cuda:0")
+    expected_pipe_dtype = torch.float64
+
+    def get_dummy_components_image_generation(self):
+        cross_attention_dim = 8
+
+        torch.manual_seed(0)
+        unet = UNet2DConditionModel(
+            block_out_channels=(4, 8),
+            layers_per_block=1,
+            sample_size=32,
+            in_channels=4,
+            out_channels=4,
+            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
+            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
+            cross_attention_dim=cross_attention_dim,
+            norm_num_groups=2,
+        )
+        scheduler = DDIMScheduler(
+            beta_start=0.00085,
+            beta_end=0.012,
+            beta_schedule="scaled_linear",
+            clip_sample=False,
+            set_alpha_to_one=False,
+        )
+        torch.manual_seed(0)
+        vae = AutoencoderKL(
+            block_out_channels=[4, 8],
+            in_channels=3,
+            out_channels=3,
+            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
+            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
+            latent_channels=4,
+            norm_num_groups=2,
+        )
+        torch.manual_seed(0)
+        text_encoder_config = CLIPTextConfig(
+            bos_token_id=0,
+            eos_token_id=2,
+            hidden_size=cross_attention_dim,
+            intermediate_size=16,
+            layer_norm_eps=1e-05,
+            num_attention_heads=2,
+            num_hidden_layers=2,
+            pad_token_id=1,
+            vocab_size=1000,
+        )
+        text_encoder = CLIPTextModel(text_encoder_config)
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        components = {
+            "unet": unet,
+            "scheduler": scheduler,
+            "vae": vae,
+            "text_encoder": text_encoder,
+            "tokenizer": tokenizer,
+            "safety_checker": None,
+            "feature_extractor": None,
+            "image_encoder": None,
+        }
+        return components
+
+    def test_deterministic_device(self):
+        components = self.get_dummy_components_image_generation()
+
+        pipe = StableDiffusionPipeline(**components)
+        pipe.to(device=torch_device, dtype=torch.float32)
+
+        pipe.unet.to(device="cpu")
+        pipe.vae.to(device="cuda")
+        pipe.text_encoder.to(device="cuda:0")
+
+        pipe_device = pipe.device
+
+        self.assertEqual(
+            self.expected_pipe_device,
+            pipe_device,
+            f"Wrong expected device. Expected {self.expected_pipe_device}. Got {pipe_device}.",
+        )
+
+    def test_deterministic_dtype(self):
+        components = self.get_dummy_components_image_generation()
+
+        pipe = StableDiffusionPipeline(**components)
+        pipe.to(device=torch_device, dtype=torch.float32)
+
+        pipe.unet.to(dtype=torch.float16)
+        pipe.vae.to(dtype=torch.float32)
+        pipe.text_encoder.to(dtype=torch.float64)
+
+        pipe_dtype = pipe.dtype
+
+        self.assertEqual(
+            self.expected_pipe_dtype,
+            pipe_dtype,
+            f"Wrong expected dtype. Expected {self.expected_pipe_dtype}. Got {pipe_dtype}.",
+        )