correct paths for tests

tests/test_modeling_utils.py

@@ -365,9 +365,7 @@ class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
         return init_dict, inputs_dict
 
     def test_from_pretrained_hub(self):
-        model, loading_info = UNet2DModel.from_pretrained(
-            "/home/patrick/google_checkpoints/unet-ldm-dummy-update", output_loading_info=True
-        )
+        model, loading_info = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
 
         self.assertIsNotNone(model)
         self.assertEqual(len(loading_info["missing_keys"]), 0)
@@ -378,7 +376,7 @@ class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
         assert image is not None, "Make sure output is not None"
 
     def test_output_pretrained(self):
-        model = UNet2DModel.from_pretrained("/home/patrick/google_checkpoints/unet-ldm-dummy-update")
+        model = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update")
         model.eval()
 
         torch.manual_seed(0)
@@ -472,9 +470,7 @@ class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
         return init_dict, inputs_dict
 
     def test_from_pretrained_hub(self):
-        model, loading_info = UNet2DModel.from_pretrained(
-            "/home/patrick/google_checkpoints/ncsnpp-celebahq-256", output_loading_info=True
-        )
+        model, loading_info = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256", output_loading_info=True)
         self.assertIsNotNone(model)
         self.assertEqual(len(loading_info["missing_keys"]), 0)
 
@@ -487,7 +483,7 @@ class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
         assert image is not None, "Make sure output is not None"
 
     def test_output_pretrained_ve_mid(self):
-        model = UNet2DModel.from_pretrained("/home/patrick/google_checkpoints/ncsnpp-celebahq-256")
+        model = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256")
         model.to(torch_device)
 
         torch.manual_seed(0)
@@ -512,7 +508,7 @@ class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
         self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
 
     def test_output_pretrained_ve_large(self):
-        model = UNet2DModel.from_pretrained("/home/patrick/google_checkpoints/ncsnpp-ffhq-ve-dummy-update")
+        model = UNet2DModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update")
         model.to(torch_device)
 
         torch.manual_seed(0)
@@ -582,9 +578,7 @@ class VQModelTests(ModelTesterMixin, unittest.TestCase):
         pass
 
     def test_from_pretrained_hub(self):
-        model, loading_info = VQModel.from_pretrained(
-            "/home/patrick/google_checkpoints/vqgan-dummy", output_loading_info=True
-        )
+        model, loading_info = VQModel.from_pretrained("fusing/vqgan-dummy", output_loading_info=True)
         self.assertIsNotNone(model)
         self.assertEqual(len(loading_info["missing_keys"]), 0)
 
@@ -594,7 +588,7 @@ class VQModelTests(ModelTesterMixin, unittest.TestCase):
         assert image is not None, "Make sure output is not None"
 
     def test_output_pretrained(self):
-        model = VQModel.from_pretrained("/home/patrick/google_checkpoints/vqgan-dummy")
+        model = VQModel.from_pretrained("fusing/vqgan-dummy")
         model.eval()
 
         torch.manual_seed(0)
@@ -655,9 +649,7 @@ class AutoencoderKLTests(ModelTesterMixin, unittest.TestCase):
         pass
 
     def test_from_pretrained_hub(self):
-        model, loading_info = AutoencoderKL.from_pretrained(
-            "/home/patrick/google_checkpoints/autoencoder-kl-dummy", output_loading_info=True
-        )
+        model, loading_info = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy", output_loading_info=True)
         self.assertIsNotNone(model)
         self.assertEqual(len(loading_info["missing_keys"]), 0)
 
@@ -667,7 +659,7 @@ class AutoencoderKLTests(ModelTesterMixin, unittest.TestCase):
         assert image is not None, "Make sure output is not None"
 
     def test_output_pretrained(self):
-        model = AutoencoderKL.from_pretrained("/home/patrick/google_checkpoints/autoencoder-kl-dummy")
+        model = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy")
         model.eval()
 
         torch.manual_seed(0)
@@ -715,7 +707,7 @@ class PipelineTesterMixin(unittest.TestCase):
 
     @slow
     def test_from_pretrained_hub(self):
-        model_path = "/home/patrick/google_checkpoints/ddpm-cifar10-32"
+        model_path = "google/ddpm-cifar10-32"
 
         ddpm = DDPMPipeline.from_pretrained(model_path)
         ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path)
@@ -733,7 +725,7 @@ class PipelineTesterMixin(unittest.TestCase):
 
     @slow
     def test_output_format(self):
-        model_path = "/home/patrick/google_checkpoints/ddpm-cifar10-32"
+        model_path = "google/ddpm-cifar10-32"
 
         pipe = DDIMPipeline.from_pretrained(model_path)
 
@@ -754,7 +746,7 @@ class PipelineTesterMixin(unittest.TestCase):
 
     @slow
     def test_ddpm_cifar10(self):
-        model_id = "/home/patrick/google_checkpoints/ddpm-cifar10-32"
+        model_id = "google/ddpm-cifar10-32"
 
         unet = UNet2DModel.from_pretrained(model_id)
         scheduler = DDPMScheduler.from_config(model_id)
@@ -773,7 +765,7 @@ class PipelineTesterMixin(unittest.TestCase):
 
     @slow
     def test_ddim_lsun(self):
-        model_id = "/home/patrick/google_checkpoints/ddpm-ema-bedroom-256"
+        model_id = "google/ddpm-ema-bedroom-256"
 
         unet = UNet2DModel.from_pretrained(model_id)
         scheduler = DDIMScheduler.from_config(model_id)
@@ -791,7 +783,7 @@ class PipelineTesterMixin(unittest.TestCase):
 
     @slow
     def test_ddim_cifar10(self):
-        model_id = "/home/patrick/google_checkpoints/ddpm-cifar10-32"
+        model_id = "google/ddpm-cifar10-32"
 
         unet = UNet2DModel.from_pretrained(model_id)
         scheduler = DDIMScheduler(tensor_format="pt")
@@ -809,7 +801,7 @@ class PipelineTesterMixin(unittest.TestCase):
 
     @slow
     def test_pndm_cifar10(self):
-        model_id = "/home/patrick/google_checkpoints/ddpm-cifar10-32"
+        model_id = "google/ddpm-cifar10-32"
 
         unet = UNet2DModel.from_pretrained(model_id)
         scheduler = PNDMScheduler(tensor_format="pt")
@@ -826,7 +818,7 @@ class PipelineTesterMixin(unittest.TestCase):
 
     @slow
     def test_ldm_text2img(self):
-        ldm = LDMTextToImagePipeline.from_pretrained("/home/patrick/google_checkpoints/ldm-text2im-large-256")
+        ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
 
         prompt = "A painting of a squirrel eating a burger"
         generator = torch.manual_seed(0)
@@ -842,7 +834,7 @@ class PipelineTesterMixin(unittest.TestCase):
 
     @slow
     def test_ldm_text2img_fast(self):
-        ldm = LDMTextToImagePipeline.from_pretrained("/home/patrick/google_checkpoints/ldm-text2im-large-256")
+        ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
 
         prompt = "A painting of a squirrel eating a burger"
         generator = torch.manual_seed(0)
@@ -856,13 +848,13 @@ class PipelineTesterMixin(unittest.TestCase):
 
     @slow
     def test_score_sde_ve_pipeline(self):
-        model = UNet2DModel.from_pretrained("/home/patrick/google_checkpoints/ncsnpp-church-256")
+        model = UNet2DModel.from_pretrained("google/ncsnpp-church-256")
 
         torch.manual_seed(0)
         if torch.cuda.is_available():
             torch.cuda.manual_seed_all(0)
 
-        scheduler = ScoreSdeVeScheduler.from_config("/home/patrick/google_checkpoints/ncsnpp-church-256")
+        scheduler = ScoreSdeVeScheduler.from_config("google/ncsnpp-church-256")
 
         sde_ve = ScoreSdeVePipeline(model=model, scheduler=scheduler)
 
@@ -877,7 +869,7 @@ class PipelineTesterMixin(unittest.TestCase):
 
     @slow
     def test_ldm_uncond(self):
-        ldm = LDMPipeline.from_pretrained("/home/patrick/google_checkpoints/ldm-celebahq-256")
+        ldm = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256")
 
         generator = torch.manual_seed(0)
         image = ldm(generator=generator, num_inference_steps=5, output_type="numpy")["sample"]