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  1. samples/unet_320x640_0.jpg +2 -2
  2. samples/unet_384x640_0.jpg +2 -2
  3. samples/unet_448x640_0.jpg +2 -2
  4. samples/unet_512x640_0.jpg +2 -2
  5. samples/unet_576x640_0.jpg +2 -2
  6. samples/unet_640x320_0.jpg +2 -2
  7. samples/unet_640x384_0.jpg +2 -2
  8. samples/unet_640x448_0.jpg +2 -2
  9. samples/unet_640x512_0.jpg +2 -2
  10. samples/unet_640x576_0.jpg +2 -2
  11. samples/unet_640x640_0.jpg +2 -2
  12. train.py +3 -3
  13. unet/diffusion_pytorch_model.safetensors +1 -1
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train.py CHANGED
@@ -29,8 +29,8 @@ from collections import deque
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  ds_path = "/workspace/sdxs/datasets/640"
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  project = "unet"
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  batch_size = 48
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- base_learning_rate = 5e-5
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- min_learning_rate = 2e-5
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  num_epochs = 50
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  # samples/save per epoch
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  sample_interval_share = 5
@@ -52,7 +52,7 @@ torch.backends.cudnn.allow_tf32 = True
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  torch.backends.cuda.enable_mem_efficient_sdp(False)
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  dtype = torch.float32
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  save_barrier = 1.006
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- warmup_percent = 0.01
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  percentile_clipping = 99 # 8bit optim
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  betta2 = 0.99
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  eps = 1e-8
 
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  ds_path = "/workspace/sdxs/datasets/640"
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  project = "unet"
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  batch_size = 48
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+ base_learning_rate = 3e-5
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+ min_learning_rate = 1e-5
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  num_epochs = 50
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  # samples/save per epoch
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  sample_interval_share = 5
 
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  torch.backends.cuda.enable_mem_efficient_sdp(False)
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  dtype = torch.float32
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  save_barrier = 1.006
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+ warmup_percent = 0.005
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  percentile_clipping = 99 # 8bit optim
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  betta2 = 0.99
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  eps = 1e-8
unet/diffusion_pytorch_model.safetensors CHANGED
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  size 6184944280
 
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