add num_inference_steps arg to DDPM (#935)

src/diffusers/pipelines/ddpm/pipeline_ddpm.py

@@ -42,6 +42,7 @@ class DDPMPipeline(DiffusionPipeline):
         self,
         batch_size: int = 1,
         generator: Optional[torch.Generator] = None,
+        num_inference_steps: int = 1000,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
         **kwargs,
@@ -53,6 +54,9 @@ class DDPMPipeline(DiffusionPipeline):
             generator (`torch.Generator`, *optional*):
                 A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
                 deterministic.
+            num_inference_steps (`int`, *optional*, defaults to 1000):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between
                 [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
@@ -73,7 +77,7 @@ class DDPMPipeline(DiffusionPipeline):
         image = image.to(self.device)
 
         # set step values
-        self.scheduler.set_timesteps(1000)
+        self.scheduler.set_timesteps(num_inference_steps)
 
         for t in self.progress_bar(self.scheduler.timesteps):
             # 1. predict noise model_output