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train-wefadoor-master

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3v324v23 commited on
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1 Parent(s): a34661c

276U 320P

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  1. anydoor/image_log/train/conditioning_gs-000000_e-000000_b-000000.png +2 -2
  2. anydoor/image_log/train/control_gs-000000_e-000000_b-000000.png +2 -2
  3. anydoor/image_log/train/reconstruction_gs-000000_e-000000_b-000000.png +2 -2
  4. anydoor/image_log/train/samples_cfg_scale_9.00_gs-000000_e-000000_b-000000.png +2 -2
  5. anydoor/run_train_anydoor.py +1 -1
  6. strip_state.py +2 -2
anydoor/image_log/train/conditioning_gs-000000_e-000000_b-000000.png CHANGED

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  • Pointer size: 131 Bytes
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anydoor/image_log/train/control_gs-000000_e-000000_b-000000.png CHANGED

Git LFS Details

  • SHA256: e36b1d3dd36e29d4537829fd62be998303953d71e32b38eb596d980b433813a1
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anydoor/image_log/train/reconstruction_gs-000000_e-000000_b-000000.png CHANGED

Git LFS Details

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anydoor/image_log/train/samples_cfg_scale_9.00_gs-000000_e-000000_b-000000.png CHANGED

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  • SHA256: 665a5b807213bb5bd5fe99cb009b1587c366eb3f6eaf4c24b24a7bc626cad637
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  • Pointer size: 132 Bytes
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anydoor/run_train_anydoor.py CHANGED
@@ -42,7 +42,7 @@ if save_memory:
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  # accumulate_grad_batches=1
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  # Configs
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- resume_path = '/workspace/train-wefadoor-master/anydoor/lightning_logs/version_0/checkpoints/step_320k.ckpt'
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  batch_size = 8
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  logger_freq = 1000
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  learning_rate = 1e-5
 
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  # accumulate_grad_batches=1
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  # Configs
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+ resume_path = '/workspace/train-wefadoor-master/anydoor/step_300k_u.ckpt'
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  batch_size = 8
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  logger_freq = 1000
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  learning_rate = 1e-5
strip_state.py CHANGED
@@ -1,10 +1,10 @@
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  import torch
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  # Load the checkpoint
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- checkpoint = torch.load('/workspace/train-wefadoor-master/anydoor/step_276500.ckpt', map_location='cpu')
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  # Extract the state dictionary
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  state_dict = checkpoint['state_dict'] if 'state_dict' in checkpoint else checkpoint
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  # Save the state dictionary to a new checkpoint
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- torch.save(state_dict, '/workspace/train-wefadoor-master/anydoor/step_276k.ckpt')
 
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  import torch
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  # Load the checkpoint
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+ checkpoint = torch.load('/workspace/train-wefadoor-master/anydoor/lightning_logs/version_4/checkpoints/300k_u.ckpt', map_location='cpu')
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  # Extract the state dictionary
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  state_dict = checkpoint['state_dict'] if 'state_dict' in checkpoint else checkpoint
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  # Save the state dictionary to a new checkpoint
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+ torch.save(state_dict, '/workspace/train-wefadoor-master/anydoor/lightning_logs/version_4/checkpoints/300k_u.ckpt')