made compatible with UNet2DConditionModel

src/diffusers/pipelines/ddpm/pipeline_ddpm.py

@@ -47,9 +47,10 @@ class DDPMPipeline(DiffusionPipeline):
 
     model_cpu_offload_seq = "unet"
 
-    def __init__(self, unet, scheduler):
+    def __init__(self, unet, scheduler, encoder_hidden_states=None):
         super().__init__()
         self.register_modules(unet=unet, scheduler=scheduler)
+        self.encoder_hidden_states=encoder_hidden_states
 
     @torch.no_grad()
     def __call__(
@@ -120,7 +121,9 @@ def __call__(
 
         for t in self.progress_bar(self.scheduler.timesteps):
             # 1. predict noise model_output
-            model_output = self.unet(image, t).sample
+            if self.encoder_hidden_states:
+                model_output = self.unet(image, t, self.encoder_hidden_states).sample    
+            else: model_output = self.unet(image, t).sample
 
             # 2. compute previous image: x_t -> x_t-1
             image = self.scheduler.step(model_output, t, image, generator=generator).prev_sample