[CI] fix nightly model tests (#9483)

* check if default attn procs fix it.

* print

* print

* replace

* style./

* replace revision with variant.

* replace with stable-diffusion-v1-5/stable-diffusion-inpainting.

* replace with stable-diffusion-v1-5/stable-diffusion-v1-5.

* fix

tests/models/autoencoders/test_models_vae.py

@@ -651,12 +651,12 @@ class AutoencoderKLIntegrationTests(unittest.TestCase):
             # fmt: off
             [
                 33,
-                [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824],
+                [-0.1556, 0.9848, -0.0410, -0.0642, -0.2685, 0.8381, -0.2004, -0.0700],
                 [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824],
             ],
             [
                 47,
-                [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089],
+                [-0.2376, 0.1200, 0.1337, -0.4830, -0.2504, -0.0759, -0.0486, -0.4077],
                 [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131],
             ],
             # fmt: on
@@ -886,7 +886,7 @@ class AsymmetricAutoencoderKLIntegrationTests(unittest.TestCase):
             # fmt: off
             [
                 33,
-                [-0.0344, 0.2912, 0.1687, -0.0137, -0.3462, 0.3552, -0.1337, 0.1078],
+                [-0.0336, 0.3011, 0.1764, 0.0087, -0.3401, 0.3645, -0.1247, 0.1205],
                 [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824],
             ],
             [

tests/models/unets/test_models_unet_2d_condition.py

@@ -1210,11 +1210,11 @@ class UNet2DConditionModelIntegrationTests(unittest.TestCase):
         return image
 
     def get_unet_model(self, fp16=False, model_id="CompVis/stable-diffusion-v1-4"):
-        revision = "fp16" if fp16 else None
+        variant = "fp16" if fp16 else None
         torch_dtype = torch.float16 if fp16 else torch.float32
 
         model = UNet2DConditionModel.from_pretrained(
-            model_id, subfolder="unet", torch_dtype=torch_dtype, revision=revision
+            model_id, subfolder="unet", torch_dtype=torch_dtype, variant=variant
         )
         model.to(torch_device).eval()
 
@@ -1376,7 +1376,7 @@ class UNet2DConditionModelIntegrationTests(unittest.TestCase):
     @require_torch_accelerator
     @skip_mps
     def test_compvis_sd_v1_5(self, seed, timestep, expected_slice):
-        model = self.get_unet_model(model_id="Jiali/stable-diffusion-1.5")
+        model = self.get_unet_model(model_id="stable-diffusion-v1-5/stable-diffusion-v1-5")
         latents = self.get_latents(seed)
         encoder_hidden_states = self.get_encoder_hidden_states(seed)
 
@@ -1404,7 +1404,7 @@ class UNet2DConditionModelIntegrationTests(unittest.TestCase):
     )
     @require_torch_accelerator_with_fp16
     def test_compvis_sd_v1_5_fp16(self, seed, timestep, expected_slice):
-        model = self.get_unet_model(model_id="Jiali/stable-diffusion-1.5", fp16=True)
+        model = self.get_unet_model(model_id="stable-diffusion-v1-5/stable-diffusion-v1-5", fp16=True)
         latents = self.get_latents(seed, fp16=True)
         encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)
 
@@ -1433,7 +1433,7 @@ class UNet2DConditionModelIntegrationTests(unittest.TestCase):
     @require_torch_accelerator
     @skip_mps
     def test_compvis_sd_inpaint(self, seed, timestep, expected_slice):
-        model = self.get_unet_model(model_id="botp/stable-diffusion-v1-5-inpainting")
+        model = self.get_unet_model(model_id="stable-diffusion-v1-5/stable-diffusion-inpainting")
         latents = self.get_latents(seed, shape=(4, 9, 64, 64))
         encoder_hidden_states = self.get_encoder_hidden_states(seed)
 
@@ -1461,7 +1461,7 @@ class UNet2DConditionModelIntegrationTests(unittest.TestCase):
     )
     @require_torch_accelerator_with_fp16
     def test_compvis_sd_inpaint_fp16(self, seed, timestep, expected_slice):
-        model = self.get_unet_model(model_id="botp/stable-diffusion-v1-5-inpainting", fp16=True)
+        model = self.get_unet_model(model_id="stable-diffusion-v1-5/stable-diffusion-inpainting", fp16=True)
         latents = self.get_latents(seed, shape=(4, 9, 64, 64), fp16=True)
         encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)