Add torch_device to the VE pipeline

src/diffusers/pipelines/score_sde_ve/pipeline_score_sde_ve.py

@@ -11,22 +11,23 @@ class ScoreSdeVePipeline(DiffusionPipeline):
         self.register_modules(model=model, scheduler=scheduler)
 
     @torch.no_grad()
-    def __call__(self, num_inference_steps=2000, generator=None, output_type="pil"):
-        device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    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
-        shape = (1, 3, img_size, img_size)
+        shape = (batch_size, 3, img_size, img_size)
 
-        model = self.model.to(device)
+        model = self.model.to(torch_device)
 
         sample = torch.randn(*shape) * self.scheduler.config.sigma_max
-        sample = sample.to(device)
+        sample = sample.to(torch_device)
 
         self.scheduler.set_timesteps(num_inference_steps)
         self.scheduler.set_sigmas(num_inference_steps)
 
         for i, t in tqdm(enumerate(self.scheduler.timesteps)):
-            sigma_t = self.scheduler.sigmas[i] * torch.ones(shape[0], device=device)
+            sigma_t = self.scheduler.sigmas[i] * torch.ones(shape[0], device=torch_device)
 
             # correction step
             for _ in range(self.scheduler.correct_steps):