0725-1147

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 = (1,2,2,4,4)#(1, 2, 4)#(2,4,4,4,8)#(1, 2, 2, 4, 4)#(1, 2, 2, 4, 8)#(1, 1, 2, 2, 4, 4)#(1, 2, 4, 8, 16)#(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 = (2,2,4,4,4)#(1, 2, 4)#(2,4,4,4,8)#(1, 2, 2, 4, 4)#(1, 2, 2, 4, 8)#(1, 1, 2, 2, 4, 4)#(1, 2, 4, 8, 16)#(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

@@ -241,8 +241,8 @@ class TrainConfig:
     dim = 2
     stride = (2,4) if dim == 2 else (2,2,2)
     num_image = 1000#2000#20000#15000#7000#25600#3000#10000#1000#10000#5000#2560#800#2560
-    batch_size = 20#50#20#50#1#2#50#20#2#100 # 10
-    n_epoch = 100#50#100#30#120#5#4# 10#50#20#20#2#5#25 # 120
+    batch_size = 10#50#20#50#1#2#50#20#2#100 # 10
+    n_epoch = 50#100#50#100#30#120#5#4# 10#50#20#20#2#5#25 # 120
     HII_DIM = 64
     num_redshift = 512#64#512#64#256CUDAoom#128#64#512#128#64#512#256#256#64#512#128
     channel = 1