Final fixes (#118)

final fixes before release

src/diffusers/pipelines/score_sde_ve/pipeline_score_sde_ve.py

@@ -6,19 +6,20 @@ from tqdm.auto import tqdm
 
 
 class ScoreSdeVePipeline(DiffusionPipeline):
-    def __init__(self, model, scheduler):
+    def __init__(self, unet, scheduler):
         super().__init__()
-        self.register_modules(model=model, scheduler=scheduler)
+        self.register_modules(unet=unet, scheduler=scheduler)
 
     @torch.no_grad()
     def __call__(self, batch_size=1, num_inference_steps=2000, generator=None, torch_device=None, output_type="pil"):
+
         if torch_device is None:
             torch_device = "cuda" if torch.cuda.is_available() else "cpu"
 
-        img_size = self.model.config.sample_size
+        img_size = self.unet.config.sample_size
         shape = (batch_size, 3, img_size, img_size)
 
-        model = self.model.to(torch_device)
+        model = self.unet.to(torch_device)
 
         sample = torch.randn(*shape) * self.scheduler.config.sigma_max
         sample = sample.to(torch_device)
@@ -31,7 +32,7 @@ class ScoreSdeVePipeline(DiffusionPipeline):
 
             # correction step
             for _ in range(self.scheduler.correct_steps):
-                model_output = self.model(sample, sigma_t)["sample"]
+                model_output = self.unet(sample, sigma_t)["sample"]
                 sample = self.scheduler.step_correct(model_output, sample)["prev_sample"]
 
             # prediction step
@@ -40,7 +41,7 @@ class ScoreSdeVePipeline(DiffusionPipeline):
 
             sample, sample_mean = output["prev_sample"], output["prev_sample_mean"]
 
-        sample = sample.clamp(0, 1)
+        sample = sample_mean.clamp(0, 1)
         sample = sample.cpu().permute(0, 2, 3, 1).numpy()
         if output_type == "pil":
             sample = self.numpy_to_pil(sample)

tests/test_modeling_utils.py

@@ -848,15 +848,12 @@ class PipelineTesterMixin(unittest.TestCase):
 
     @slow
     def test_score_sde_ve_pipeline(self):
-        model = UNet2DModel.from_pretrained("google/ncsnpp-church-256")
+        model_id = "google/ncsnpp-church-256"
+        model = UNet2DModel.from_pretrained(model_id)
 
-        torch.manual_seed(0)
-        if torch.cuda.is_available():
-            torch.cuda.manual_seed_all(0)
+        scheduler = ScoreSdeVeScheduler.from_config(model_id)
 
-        scheduler = ScoreSdeVeScheduler.from_config("google/ncsnpp-church-256")
-
-        sde_ve = ScoreSdeVePipeline(model=model, scheduler=scheduler)
+        sde_ve = ScoreSdeVePipeline(unet=model, scheduler=scheduler)
 
         torch.manual_seed(0)
         image = sde_ve(num_inference_steps=300, output_type="numpy")["sample"]