always rederive the predicted noise from the clipped x0 for ddim + predict noise objective

dalle2_pytorch/dalle2_pytorch.py

@@ -1334,10 +1334,7 @@ class DiffusionPrior(nn.Module):
 
             # predict noise
 
-            if self.predict_x_start or self.predict_v:
+            pred_noise = self.noise_scheduler.predict_noise_from_start(image_embed, t = time_cond, x0 = x_start)
-                pred_noise = self.noise_scheduler.predict_noise_from_start(image_embed, t = time_cond, x0 = x_start)
-            else:
-                pred_noise = pred
 
             if time_next < 0:
                 image_embed = x_start
@@ -2975,10 +2972,7 @@ class Decoder(nn.Module):
 
                 # predict noise
 
-                if predict_x_start or predict_v:
+                pred_noise = noise_scheduler.predict_noise_from_start(img, t = time_cond, x0 = x_start)
-                    pred_noise = noise_scheduler.predict_noise_from_start(img, t = time_cond, x0 = x_start)
-                else:
-                    pred_noise = pred
 
                 c1 = eta * ((1 - alpha / alpha_next) * (1 - alpha_next) / (1 - alpha)).sqrt()
                 c2 = ((1 - alpha_next) - torch.square(c1)).sqrt()

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '1.12.2'
+__version__ = '1.12.3'