0719-1256

context_unet.py

@@ -330,7 +330,7 @@ class ContextUnet(nn.Module):
             elif image_size == 128:
                 channel_mult = (1, 1, 2, 3, 4)
             elif image_size == 64:
-                channel_mult = (1, 2, 3, 4)#(1, 2, 4, 6, 8)#(1, 2, 2, 4)#(1, 2, 8, 8, 8)#(1, 2, 4)#(1, 2, 2, 4)#(0.5,1,2,2,4,4)#(1, 1, 2, 2, 4, 4)#
+                channel_mult = (1, 1, 2, 2, 4, 4)#(1, 2, 3, 4)#(1, 2, 4, 6, 8)#(1, 2, 2, 4)#(1, 2, 8, 8, 8)#(1, 2, 4)#(1, 2, 2, 4)#(0.5,1,2,2,4,4)#(1, 1, 2, 2, 4, 4)#
             elif image_size == 32:
                 channel_mult = (1, 2, 2, 4)
             elif image_size == 28:

diffusion.py

@@ -669,7 +669,7 @@ if __name__ == "__main__":
     print(f" sampling, world_size = {world_size} ".center(100,'-'))
     # num_train_image_list = [1600,3200,6400,12800,25600]
     # num_train_image_list = [5000]
-    num_new_img_per_gpu = 400
+    num_new_img_per_gpu = 200
     max_num_img_per_gpu = 20
 
     params = torch.tensor([4.4, 131.341])