make all tests pass

src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py

@@ -4,7 +4,7 @@ import torch
 import torch.nn as nn
 import torch.utils.checkpoint
 
-from tqdm.auto import tqdm
+import tqdm
 from transformers.activations import ACT2FN
 from transformers.configuration_utils import PretrainedConfig
 from transformers.modeling_outputs import BaseModelOutput

src/diffusers/pipelines/pndm/pipeline_pndm.py

@@ -37,7 +37,7 @@ class PNDMPipeline(DiffusionPipeline):
 
         # Sample gaussian noise to begin loop
         image = torch.randn(
-            (batch_size, self.unet.in_channels, self.unet.resolution, self.unet.resolution),
+            (batch_size, self.unet.in_channels, self.unet.image_size, self.unet.image_size),
             generator=generator,
         )
         image = image.to(torch_device)

tests/test_modeling_utils.py

@@ -1011,7 +1011,7 @@ class PipelineTesterMixin(unittest.TestCase):
     def test_pndm_cifar10(self):
         model_id = "google/ddpm-cifar10"
 
-        unet = UNetUnconditionalModel.from_pretrained(model_id, ddpm=True)
+        unet = UNetUnconditionalModel.from_pretrained(model_id)
         scheduler = PNDMScheduler(tensor_format="pt")
 
         pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
@@ -1072,7 +1072,6 @@ class PipelineTesterMixin(unittest.TestCase):
     @slow
     def test_score_sde_ve_pipeline(self):
         model = UNetUnconditionalModel.from_pretrained("fusing/ffhq_ncsnpp", sde=True)
-        model = UNetUnconditionalModel.from_pretrained("google/ffhq_ncsnpp")
 
         torch.manual_seed(0)
         if torch.cuda.is_available():