Merge branch 'main' into sayakpaul-patch-1

tests/models/autoencoders/test_models_vq.py

@@ -65,9 +65,11 @@ def prepare_init_args_and_inputs_for_common(self):
         inputs_dict = self.dummy_input
         return init_dict, inputs_dict
 
+    @unittest.skip("Test not supported.")
     def test_forward_signature(self):
         pass
 
+    @unittest.skip("Test not supported.")
     def test_training(self):
         pass
 

tests/models/unets/test_models_unet_1d.py

@@ -51,9 +51,11 @@ def input_shape(self):
     def output_shape(self):
         return (4, 14, 16)
 
+    @unittest.skip("Test not supported.")
     def test_ema_training(self):
         pass
 
+    @unittest.skip("Test not supported.")
     def test_training(self):
         pass
 
@@ -126,6 +128,7 @@ def test_output_pretrained(self):
         # fmt: on
         self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3))
 
+    @unittest.skip("Test not supported.")
     def test_forward_with_norm_groups(self):
         # Not implemented yet for this UNet
         pass
@@ -205,9 +208,11 @@ def test_output(self):
         expected_shape = torch.Size((inputs_dict["sample"].shape[0], 1))
         self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
 
+    @unittest.skip("Test not supported.")
     def test_ema_training(self):
         pass
 
+    @unittest.skip("Test not supported.")
     def test_training(self):
         pass
 
@@ -265,6 +270,7 @@ def test_output_pretrained(self):
         # fmt: on
         self.assertTrue(torch.allclose(output, expected_output_slice, rtol=1e-3))
 
+    @unittest.skip("Test not supported.")
     def test_forward_with_norm_groups(self):
         # Not implemented yet for this UNet
         pass

tests/models/unets/test_models_unet_2d.py

@@ -383,6 +383,7 @@ def test_output_pretrained_ve_large(self):
 
         self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-2))
 
+    @unittest.skip("Test not supported.")
     def test_forward_with_norm_groups(self):
         # not required for this model
         pass

tests/models/unets/test_models_unet_controlnetxs.py

@@ -320,6 +320,7 @@ def test_time_embedding_mixing(self):
 
         assert output.shape == output_mix_time.shape
 
+    @unittest.skip("Test not supported.")
     def test_forward_with_norm_groups(self):
         # UNetControlNetXSModel currently only supports StableDiffusion and StableDiffusion-XL, both of which have norm_num_groups fixed at 32. So we don't need to test different values for norm_num_groups.
         pass

tests/pipelines/wuerstchen/test_wuerstchen_combined.py

@@ -232,8 +232,10 @@ def test_inference_batch_single_identical(self):
     def test_float16_inference(self):
         super().test_float16_inference()
 
+    @unittest.skip(reason="Test not supported.")
     def test_callback_inputs(self):
         pass
 
+    @unittest.skip(reason="Test not supported.")
     def test_callback_cfg(self):
         pass

tests/schedulers/test_scheduler_ddim_inverse.py

@@ -1,3 +1,5 @@
+import unittest
+
 import torch
 
 from diffusers import DDIMInverseScheduler
@@ -95,6 +97,7 @@ def test_inference_steps(self):
         for t, num_inference_steps in zip([1, 10, 50], [10, 50, 500]):
             self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps)
 
+    @unittest.skip("Test not supported.")
     def test_add_noise_device(self):
         pass
 

tests/schedulers/test_scheduler_deis.py

@@ -1,4 +1,5 @@
 import tempfile
+import unittest
 
 import torch
 
@@ -57,6 +58,7 @@ def check_over_configs(self, time_step=0, **config):
 
                 assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
 
+    @unittest.skip("Test not supported.")
     def test_from_save_pretrained(self):
         pass
 

tests/schedulers/test_scheduler_dpm_multi.py

@@ -1,4 +1,5 @@
 import tempfile
+import unittest
 
 import torch
 
@@ -67,6 +68,7 @@ def check_over_configs(self, time_step=0, **config):
 
                 assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
 
+    @unittest.skip("Test not supported.")
     def test_from_save_pretrained(self):
         pass
 

tests/schedulers/test_scheduler_dpm_single.py

@@ -1,4 +1,5 @@
 import tempfile
+import unittest
 
 import torch
 
@@ -65,6 +66,7 @@ def check_over_configs(self, time_step=0, **config):
 
                 assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
 
+    @unittest.skip("Test not supported.")
     def test_from_save_pretrained(self):
         pass
 

tests/schedulers/test_scheduler_edm_dpmsolver_multistep.py

@@ -3,9 +3,7 @@
 
 import torch
 
-from diffusers import (
-    EDMDPMSolverMultistepScheduler,
-)
+from diffusers import EDMDPMSolverMultistepScheduler
 
 from .test_schedulers import SchedulerCommonTest
 
@@ -63,6 +61,7 @@ def check_over_configs(self, time_step=0, **config):
 
                 assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
 
+    @unittest.skip("Test not supported.")
     def test_from_save_pretrained(self):
         pass
 
@@ -258,5 +257,6 @@ def test_duplicated_timesteps(self, **config):
             scheduler.set_timesteps(scheduler.config.num_train_timesteps)
             assert len(scheduler.timesteps) == scheduler.num_inference_steps
 
+    @unittest.skip("Test not supported.")
     def test_trained_betas(self):
         pass

tests/schedulers/test_scheduler_flax.py

@@ -675,6 +675,7 @@ def check_over_configs(self, time_step=0, **config):
 
             assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
 
+    @unittest.skip("Test not supported.")
     def test_from_save_pretrained(self):
         pass
 

tests/schedulers/test_scheduler_ipndm.py

@@ -1,4 +1,5 @@
 import tempfile
+import unittest
 
 import torch
 
@@ -50,6 +51,7 @@ def check_over_configs(self, time_step=0, **config):
 
             assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
 
+    @unittest.skip("Test not supported.")
     def test_from_save_pretrained(self):
         pass
 

tests/schedulers/test_scheduler_pndm.py

@@ -1,4 +1,5 @@
 import tempfile
+import unittest
 
 import torch
 
@@ -53,6 +54,7 @@ def check_over_configs(self, time_step=0, **config):
 
             assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
 
+    @unittest.skip("Test not supported.")
     def test_from_save_pretrained(self):
         pass
 

tests/schedulers/test_scheduler_unclip.py

@@ -1,3 +1,5 @@
+import unittest
+
 import torch
 
 from diffusers import UnCLIPScheduler
@@ -130,8 +132,10 @@ def test_full_loop_skip_timesteps(self):
         assert abs(result_sum.item() - 258.2044983) < 1e-2
         assert abs(result_mean.item() - 0.3362038) < 1e-3
 
+    @unittest.skip("Test not supported.")
     def test_trained_betas(self):
         pass
 
+    @unittest.skip("Test not supported.")
     def test_add_noise_device(self):
         pass

tests/schedulers/test_scheduler_vq_diffusion.py

@@ -1,3 +1,5 @@
+import unittest
+
 import torch
 import torch.nn.functional as F
 
@@ -52,5 +54,6 @@ def test_time_indices(self):
         for t in [0, 50, 99]:
             self.check_over_forward(time_step=t)
 
+    @unittest.skip("Test not supported.")
     def test_add_noise_device(self):
         pass