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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
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  5. samples/unet_576x640_0.jpg +2 -2
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  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 +6 -6
  13. unet/config.json +2 -2
  14. unet/diffusion_pytorch_model.safetensors +2 -2
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train.py CHANGED
@@ -29,9 +29,9 @@ from transformers import AutoTokenizer, AutoModel
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  ds_path = "/workspace/sdxs/datasets/ds1234_640"
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  project = "unet"
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  batch_size = 56
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- base_learning_rate = 6e-5
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- min_learning_rate = 2.5e-5
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- num_epochs = 80
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  sample_interval_share = 2
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  use_wandb = True
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  use_comet_ml = False
@@ -51,9 +51,9 @@ 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
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- betta2 = 0.99
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- eps = 1e-8
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  clip_grad_norm = 1.0
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  limit = 0
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  checkpoints_folder = ""
 
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  ds_path = "/workspace/sdxs/datasets/ds1234_640"
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  project = "unet"
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  batch_size = 56
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+ base_learning_rate = 3e-5
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+ min_learning_rate = 3e-5
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+ num_epochs = 50
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  sample_interval_share = 2
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  use_wandb = True
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  use_comet_ml = 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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+ betta2 = 0.998
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+ eps = 1e-6
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  clip_grad_norm = 1.0
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  limit = 0
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  checkpoints_folder = ""
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