finish refactor

src/diffusers/utils/logging.py

@@ -270,7 +270,7 @@ def reset_format() -> None:
 
 def warning_advice(self, *args, **kwargs):
     """
-    This method is identical to `logger.warning()`, but if env var TRANSFORMERS_NO_ADVISORY_WARNINGS=1 is set, this
+    This method is identical to `logger.warninging()`, but if env var TRANSFORMERS_NO_ADVISORY_WARNINGS=1 is set, this
     warning will not be printed
     """
     no_advisory_warnings = os.getenv("TRANSFORMERS_NO_ADVISORY_WARNINGS", False)

tests/test_modeling_utils.py

@@ -19,11 +19,10 @@ import unittest
 
 import torch
 
-from diffusers import GaussianDDPMScheduler, UNetModel, DDIMScheduler
-from diffusers import DDIM, DDPM, LatentDiffusion
+from diffusers import DDIM, DDPM, DDIMScheduler, GaussianDDPMScheduler, LatentDiffusion, UNetModel
 from diffusers.configuration_utils import ConfigMixin
 from diffusers.pipeline_utils import DiffusionPipeline
-from diffusers.testing_utils import floats_tensor, torch_device, slow
+from diffusers.testing_utils import floats_tensor, slow, torch_device
 
 
 torch.backends.cuda.matmul.allow_tf32 = False
@@ -149,6 +148,7 @@ class PipelineTesterMixin(unittest.TestCase):
 
         unet = UNetModel.from_pretrained(model_id)
         noise_scheduler = GaussianDDPMScheduler.from_config(model_id)
+        noise_scheduler = noise_scheduler.set_format("pt")
 
         ddpm = DDPM(unet=unet, noise_scheduler=noise_scheduler)
         image = ddpm(generator=generator)
@@ -165,7 +165,7 @@ class PipelineTesterMixin(unittest.TestCase):
         model_id = "fusing/ddpm-cifar10"
 
         unet = UNetModel.from_pretrained(model_id)
-        noise_scheduler = DDIMScheduler()
+        noise_scheduler = DDIMScheduler(tensor_format="pt")
 
         ddim = DDIM(unet=unet, noise_scheduler=noise_scheduler)
         image = ddim(generator=generator, eta=0.0)

tests/test_scheduler.py

@@ -14,12 +14,13 @@
 # limitations under the License.
 
 
-import torch
-import numpy as np
-import unittest
 import tempfile
+import unittest
 
-from diffusers import GaussianDDPMScheduler, DDIMScheduler
+import numpy as np
+import torch
+
+from diffusers import DDIMScheduler, GaussianDDPMScheduler
 
 
 torch.backends.cuda.matmul.allow_tf32 = False
@@ -38,7 +39,7 @@ class SchedulerCommonTest(unittest.TestCase):
 
         image = np.random.rand(batch_size, num_channels, height, width)
 
-        return torch.tensor(image)
+        return image
 
     @property
     def dummy_image_deter(self):
@@ -53,7 +54,7 @@ class SchedulerCommonTest(unittest.TestCase):
         image = image / num_elems
         image = image.transpose(3, 0, 1, 2)
 
-        return torch.tensor(image)
+        return image
 
     def get_scheduler_config(self):
         raise NotImplementedError
@@ -82,7 +83,7 @@ class SchedulerCommonTest(unittest.TestCase):
             output = scheduler.step(residual, image, time_step, **kwargs)
             new_output = new_scheduler.step(residual, image, time_step, **kwargs)
 
-            assert (output - new_output).abs().sum() < 1e-5, "Scheduler outputs are not identical"
+            assert np.sum(np.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
 
     def check_over_forward(self, time_step=0, **forward_kwargs):
         kwargs = dict(self.forward_default_kwargs)
@@ -103,7 +104,7 @@ class SchedulerCommonTest(unittest.TestCase):
             output = scheduler.step(residual, image, time_step, **kwargs)
             new_output = new_scheduler.step(residual, image, time_step, **kwargs)
 
-            assert (output - new_output).abs().sum() < 1e-5, "Scheduler outputs are not identical"
+            assert np.sum(np.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
 
     def test_from_pretrained_save_pretrained(self):
         kwargs = dict(self.forward_default_kwargs)
@@ -122,7 +123,7 @@ class SchedulerCommonTest(unittest.TestCase):
             output = scheduler.step(residual, image, 1, **kwargs)
             new_output = new_scheduler.step(residual, image, 1, **kwargs)
 
-            assert (output - new_output).abs().sum() < 1e-5, "Scheduler outputs are not identical"
+            assert np.sum(np.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
 
     def test_step_shape(self):
         kwargs = dict(self.forward_default_kwargs)
@@ -140,6 +141,26 @@ class SchedulerCommonTest(unittest.TestCase):
             self.assertEqual(output_0.shape, image.shape)
             self.assertEqual(output_0.shape, output_1.shape)
 
+    def test_pytorch_equal_numpy(self):
+        kwargs = dict(self.forward_default_kwargs)
+
+        for scheduler_class in self.scheduler_classes:
+            image = self.dummy_image
+            residual = 0.1 * image
+
+            image_pt = torch.tensor(image)
+            residual_pt = 0.1 * image_pt
+
+            scheduler_config = self.get_scheduler_config()
+            scheduler = scheduler_class(**scheduler_config)
+
+            scheduler_pt = scheduler_class(tensor_format="pt", **scheduler_config)
+
+            output = scheduler.step(residual, image, 1, **kwargs)
+            output_pt = scheduler_pt.step(residual_pt, image_pt, 1, **kwargs)
+
+            assert np.sum(np.abs(output - output_pt.numpy())) < 1e-5, "Scheduler outputs are not identical"
+
 
 class DDPMSchedulerTest(SchedulerCommonTest):
     scheduler_classes = (GaussianDDPMScheduler,)
@@ -151,7 +172,7 @@ class DDPMSchedulerTest(SchedulerCommonTest):
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "variance_type": "fixed_small",
-            "clip_predicted_image": True
+            "clip_predicted_image": True,
         }
 
         config.update(**kwargs)
@@ -186,9 +207,9 @@ class DDPMSchedulerTest(SchedulerCommonTest):
         scheduler_config = self.get_scheduler_config()
         scheduler = scheduler_class(**scheduler_config)
 
-        assert (scheduler.get_variance(0) - 0.0).abs().sum() < 1e-5
-        assert (scheduler.get_variance(487) - 0.00979).abs().sum() < 1e-5
-        assert (scheduler.get_variance(999) - 0.02).abs().sum() < 1e-5
+        assert np.sum(np.abs(scheduler.get_variance(0) - 0.0)) < 1e-5
+        assert np.sum(np.abs(scheduler.get_variance(487) - 0.00979)) < 1e-5
+        assert np.sum(np.abs(scheduler.get_variance(999) - 0.02)) < 1e-5
 
     def test_full_loop_no_noise(self):
         scheduler_class = self.scheduler_classes[0]
@@ -209,12 +230,12 @@ class DDPMSchedulerTest(SchedulerCommonTest):
 
             if t > 0:
                 noise = self.dummy_image_deter
-                variance = scheduler.get_variance(t).sqrt() * noise
+                variance = scheduler.get_variance(t) ** (0.5) * noise
 
             image = pred_prev_image + variance
 
-        result_sum = image.abs().sum()
-        result_mean = image.abs().mean()
+        result_sum = np.sum(np.abs(image))
+        result_mean = np.mean(np.abs(image))
 
         assert result_sum.item() - 732.9947 < 1e-3
         assert result_mean.item() - 0.9544 < 1e-3
@@ -230,7 +251,7 @@ class DDIMSchedulerTest(SchedulerCommonTest):
             "beta_start": 0.0001,
             "beta_end": 0.02,
             "beta_schedule": "linear",
-            "clip_predicted_image": True
+            "clip_predicted_image": True,
         }
 
         config.update(**kwargs)
@@ -269,12 +290,12 @@ class DDIMSchedulerTest(SchedulerCommonTest):
         scheduler_config = self.get_scheduler_config()
         scheduler = scheduler_class(**scheduler_config)
 
-        assert (scheduler.get_variance(0, 50) - 0.0).abs().sum() < 1e-5
-        assert (scheduler.get_variance(21, 50) - 0.14771).abs().sum() < 1e-5
-        assert (scheduler.get_variance(49, 50) - 0.32460).abs().sum() < 1e-5
-        assert (scheduler.get_variance(0, 1000) - 0.0).abs().sum() < 1e-5
-        assert (scheduler.get_variance(487, 1000) - 0.00979).abs().sum() < 1e-5
-        assert (scheduler.get_variance(999, 1000) - 0.02).abs().sum() < 1e-5
+        assert np.sum(np.abs(scheduler.get_variance(0, 50) - 0.0)) < 1e-5
+        assert np.sum(np.abs(scheduler.get_variance(21, 50) - 0.14771)) < 1e-5
+        assert np.sum(np.abs(scheduler.get_variance(49, 50) - 0.32460)) < 1e-5
+        assert np.sum(np.abs(scheduler.get_variance(0, 1000) - 0.0)) < 1e-5
+        assert np.sum(np.abs(scheduler.get_variance(487, 1000) - 0.00979)) < 1e-5
+        assert np.sum(np.abs(scheduler.get_variance(999, 1000) - 0.02)) < 1e-5
 
     def test_full_loop_no_noise(self):
         scheduler_class = self.scheduler_classes[0]
@@ -297,12 +318,12 @@ class DDIMSchedulerTest(SchedulerCommonTest):
             variance = 0
             if eta > 0:
                 noise = self.dummy_image_deter
-                variance = scheduler.get_variance(t, num_inference_steps).sqrt() * eta * noise
+                variance = scheduler.get_variance(t, num_inference_steps) ** (0.5) * eta * noise
 
             image = pred_prev_image + variance
 
-        result_sum = image.abs().sum()
-        result_mean = image.abs().mean()
+        result_sum = np.sum(np.abs(image))
+        result_mean = np.mean(np.abs(image))
 
         assert result_sum.item() - 270.6214 < 1e-3
         assert result_mean.item() - 0.3524 < 1e-3