more tests

src/diffusers/models/attention.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import math
-import warnings
 from dataclasses import dataclass
 from typing import Optional
 
@@ -23,10 +22,13 @@ from torch import nn
 from ..configuration_utils import ConfigMixin, register_to_config
 from ..modeling_utils import ModelMixin
 from ..models.embeddings import ImagePositionalEmbeddings
-from ..utils import BaseOutput
+from ..utils import BaseOutput, logging
 from ..utils.import_utils import is_xformers_available
 
 
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
 @dataclass
 class Transformer2DModelOutput(BaseOutput):
     """
@@ -450,9 +452,10 @@ class BasicTransformerBlock(nn.Module):
         # if xformers is installed try to use memory_efficient_attention by default
         if is_xformers_available():
             try:
+                logger.info("xformers detected. Memory efficient attention is automatically enabled.")
                 self.set_use_memory_efficient_attention_xformers(True)
             except Exception as e:
-                warnings.warn(
+                logger.warning(
                     "Could not enable memory efficient attention. Make sure xformers is installed"
                     f" correctly and a GPU is available: {e}"
                 )

tests/test_pipelines_common.py

@@ -19,6 +19,9 @@ from diffusers.utils.testing_utils import require_torch, torch_device
 torch.backends.cuda.matmul.allow_tf32 = False
 
 
+ALLOWED_REQUIRED_ARGS = ["source_prompt", "prompt", "image", "mask_image", "example_image"]
+
+
 @require_torch
 class PipelineTesterMixin:
     """
@@ -115,10 +118,138 @@ class PipelineTesterMixin:
         self.assertLess(max_diff, 1e-5)
 
     def test_pipeline_call_implements_required_args(self):
-        required_args = ["num_inference_steps", "generator", "return_dict"]
+        assert hasattr(self.pipeline_class, "__call__"), f"{self.pipeline_class} should have a `__call__` method"
+        parameters = inspect.signature(self.pipeline_class.__call__).parameters
+        required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty}
+        required_parameters.pop("self")
+        required_parameters = set(required_parameters)
+        optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})
 
-        for arg in required_args:
+        for param in required_parameters:
-            self.assertTrue(arg in inspect.signature(self.pipeline_class.__call__).parameters)
+            assert param in ALLOWED_REQUIRED_ARGS
+
+        optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})
+
+        required_optional_params = ["generator", "num_inference_steps", "return_dict"]
+        for param in required_optional_params:
+            assert param in optional_parameters
+
+    def test_inference_batch_image_pil_torch(self):
+        inputs = self.get_dummy_inputs(torch_device)
+
+        allowed_image_args = [v for v in ALLOWED_REQUIRED_ARGS if v != "prompt"]
+
+        if set(allowed_image_args) - set(inputs.keys()) == set(allowed_image_args):
+            # pipeline has no allowed required image args, so no need to test
+            return
+
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        # batchify inputs
+        for batch_size in [2, 4, 13]:
+            batched_inputs = {}
+            for name, value in inputs.items():
+                if name in allowed_image_args:
+                    batched_inputs[name] = batch_size * [value]
+                else:
+                    batched_inputs[name] = value
+
+            batched_inputs["num_inference_steps"] = 2
+            batched_inputs["output_type"] = "np"
+            batched_inputs["generator"] = torch.Generator(torch_device).manual_seed(33)
+            output = pipe(**batched_inputs)
+
+            for name in allowed_image_args:
+                # convert pil to torch
+                if name in batched_inputs:
+                    batched_inputs = torch.tensor(pipe.pil_to_numpy(value), dtype=torch.float32, device=torch_device)
+            batched_inputs["num_inference_steps"] = 2
+            batched_inputs["output_type"] = "np"
+
+            batched_inputs["generator"] = torch.Generator(torch_device).manual_seed(33)
+            output_torch_image = pipe(**batched_inputs)
+
+            max_diff = np.abs(output - output_torch_image).max()
+            self.assertLess(max_diff, 1e-4)
+
+    def test_inference_batch_consistent(self):
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(torch_device)
+
+        # batchify inputs
+        for batch_size in [2, 4, 13]:
+            batched_inputs = {}
+            for name, value in inputs.items():
+                if name in ALLOWED_REQUIRED_ARGS:
+                    # prompt is string
+                    if name == "prompt":
+                        len_prompt = len(value)
+                        # make unequal batch sizes
+                        batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]
+                    # or else we have images
+                    else:
+                        batched_inputs[name] = batch_size * [value]
+                else:
+                    batched_inputs[name] = value
+
+            batched_inputs["num_inference_steps"] = 2
+            batched_inputs["output_type"] = None
+            output = pipe(**batched_inputs)
+
+            assert len(output[0]) == batch_size
+
+            batched_inputs["output_type"] = "np"
+            output = pipe(**batched_inputs)[0]
+
+            assert output.shape[0] == batch_size
+
+    def test_inference_generator_equality(self):
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(torch_device)
+
+        # batchify inputs
+        batched_inputs = {}
+        batch_size = 2
+        for name, value in inputs.items():
+            if name in ALLOWED_REQUIRED_ARGS:
+                # prompt is string
+                if name == "prompt":
+                    len_prompt = len(value)
+                    # make unequal batch sizes
+                    batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]
+                # or else we have images
+                else:
+                    batched_inputs[name] = batch_size * [value]
+            else:
+                batched_inputs[name] = value
+
+        batched_inputs["num_inference_steps"] = 2
+        batched_inputs["output_type"] = "np"
+        seeds = [0, 44]  # make sure length is equal to batch_size
+        generators = [torch.Generator(device=torch_device).manual_seed(s) for s in seeds]
+        batched_inputs["generator"] = generators
+
+        output_batch = pipe(**batched_inputs)[0]
+
+        generators = [torch.Generator(device=torch_device).manual_seed(s) for s in seeds]
+        for i, seed in enumerate(seeds):
+            inputs = {k: v[i] if isinstance(v, list) else v for k, v in batched_inputs.items()}
+            inputs["generator"] = generators[i]
+
+            output_single = pipe(**inputs)[0]
+
+            assert np.abs(output_single - output_batch[i]).sum() < 1e-4
 
     def test_num_inference_steps_consistent(self):
         components = self.get_dummy_components()