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  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. src/cherrypick.ipynb +2 -2
  11. train.py +5 -6
  12. unet/diffusion_pytorch_model.safetensors +1 -1
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src/cherrypick.ipynb CHANGED
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train.py CHANGED
@@ -28,9 +28,9 @@ import torch.nn.functional as F
28
  ds_path = "datasets/576"
29
  project = "unet"
30
  batch_size = 40
31
- base_learning_rate = 2e-5
32
- min_learning_rate = 9e-6
33
- num_epochs = 16
34
  # samples/save per epoch
35
  sample_interval_share = 5
36
  use_wandb = True
@@ -51,7 +51,7 @@ 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 = 97 # 8bit optim
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  steps_offset = 1 # Scheduler
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  limit = 0
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  checkpoints_folder = ""
@@ -880,7 +880,6 @@ for epoch in range(start_epoch, start_epoch + num_epochs):
880
  dispersive_loss = dispersive_hook.weight * dispersive_hook.compute_dispersive_loss()
881
  if torch.isnan(dispersive_loss) or torch.isinf(dispersive_loss):
882
  print(f"Rank {accelerator.process_index}: Found nan/inf in dispersive_loss: {total_loss}")
883
- #break
884
 
885
  # Итоговый loss
886
  # dispersive_loss должен падать и тотал падать - поэтому плюс
@@ -905,7 +904,7 @@ for epoch in range(start_epoch, start_epoch + num_epochs):
905
  if not fbp:
906
  if accelerator.sync_gradients:
907
  with torch.amp.autocast('cuda', enabled=False):
908
- grad = accelerator.clip_grad_norm_(unet.parameters(), 1.)
909
  optimizer.step()
910
  lr_scheduler.step()
911
  optimizer.zero_grad(set_to_none=True)
 
28
  ds_path = "datasets/576"
29
  project = "unet"
30
  batch_size = 40
31
+ base_learning_rate = 9e-6
32
+ min_learning_rate = 8e-6
33
+ num_epochs = 6
34
  # samples/save per epoch
35
  sample_interval_share = 5
36
  use_wandb = True
 
51
  save_barrier = 1.03
52
  dispersive_temperature=0.5
53
  dispersive_weight=0.05
54
+ percentile_clipping = 95 # 8bit optim
55
  steps_offset = 1 # Scheduler
56
  limit = 0
57
  checkpoints_folder = ""
 
880
  dispersive_loss = dispersive_hook.weight * dispersive_hook.compute_dispersive_loss()
881
  if torch.isnan(dispersive_loss) or torch.isinf(dispersive_loss):
882
  print(f"Rank {accelerator.process_index}: Found nan/inf in dispersive_loss: {total_loss}")
 
883
 
884
  # Итоговый loss
885
  # dispersive_loss должен падать и тотал падать - поэтому плюс
 
904
  if not fbp:
905
  if accelerator.sync_gradients:
906
  with torch.amp.autocast('cuda', enabled=False):
907
+ grad = accelerator.clip_grad_norm_(unet.parameters(), 0.5)
908
  optimizer.step()
909
  lr_scheduler.step()
910
  optimizer.zero_grad(set_to_none=True)
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