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Files changed (11) hide show
  1. samples/unet_320x576_0.jpg +2 -2
  2. samples/unet_384x576_0.jpg +2 -2
  3. samples/unet_448x576_0.jpg +2 -2
  4. samples/unet_512x576_0.jpg +2 -2
  5. samples/unet_576x320_0.jpg +2 -2
  6. samples/unet_576x384_0.jpg +2 -2
  7. samples/unet_576x448_0.jpg +2 -2
  8. samples/unet_576x512_0.jpg +2 -2
  9. samples/unet_576x576_0.jpg +2 -2
  10. train.py +5 -4
  11. unet/diffusion_pytorch_model.safetensors +1 -1
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train.py CHANGED
@@ -29,8 +29,8 @@ ds_path = "datasets/576"
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  project = "unet"
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  batch_size = 25
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  base_learning_rate = 9.5e-6
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- min_learning_rate = 7e-6
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- num_epochs = 24
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  # samples/save per epoch
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  sample_interval_share = 10
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  use_wandb = True
@@ -51,7 +51,8 @@ dtype = torch.float32
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  save_barrier = 1.03
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  dispersive_temperature=0.5
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  dispersive_weight=0.05
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- percentile_clipping = 90 # 8bit optim
 
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  steps_offset = 1 # Scheduler
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  limit = 0
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  checkpoints_folder = ""
@@ -908,7 +909,7 @@ for epoch in range(start_epoch, start_epoch + num_epochs):
908
  if not fbp:
909
  if accelerator.sync_gradients:
910
  with torch.amp.autocast('cuda', enabled=False):
911
- grad = accelerator.clip_grad_norm_(unet.parameters(), 0.25)
912
  optimizer.step()
913
  lr_scheduler.step()
914
  optimizer.zero_grad(set_to_none=True)
 
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  project = "unet"
30
  batch_size = 25
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  base_learning_rate = 9.5e-6
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+ min_learning_rate = 8.5e-6
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+ num_epochs = 20
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  # samples/save per epoch
35
  sample_interval_share = 10
36
  use_wandb = True
 
51
  save_barrier = 1.03
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  dispersive_temperature=0.5
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  dispersive_weight=0.05
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+ percentile_clipping = 95 # 8bit optim
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+ clip_grad_norm = 0.5
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  steps_offset = 1 # Scheduler
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  limit = 0
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  checkpoints_folder = ""
 
909
  if not fbp:
910
  if accelerator.sync_gradients:
911
  with torch.amp.autocast('cuda', enabled=False):
912
+ grad = accelerator.clip_grad_norm_(unet.parameters(), clip_grad_norm)
913
  optimizer.step()
914
  lr_scheduler.step()
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  optimizer.zero_grad(set_to_none=True)
unet/diffusion_pytorch_model.safetensors CHANGED
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  size 7014306128
 
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