A bug

[LemonC19]

When I'm using the train.py in compvis, this bug come out, and I don't konw how to solve it. Anyone can help me? Thanks!

angogh_painting" --train_size 200 Global seed set to 23 Running on GPUs 0,1 /home/xunaen/anaconda3/envs/ablate/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py:258: LightningDeprecationWarning: pytorch_lightning.utilities.distributed.rank_zero_only has been deprecated in v1.8.1 and will be removed in v2.0.0. You can import it from pytorch_lightning.utilities instead. rank_zero_deprecation( CustomDiffusion: Running in eps-prediction mode DiffusionWrapper has 859.52 M params. making attention of type 'vanilla' with 512 in_channels Working with z of shape (1, 4, 32, 32) = 4096 dimensions. making attention of type 'vanilla' with 512 in_channels Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.18.self_attn.out_proj.bias', 'vision_model.encoder.layers.9.mlp.fc2.weight', 'vision_model.encoder.layers.12.layer_norm2.bias', 'vision_model.encoder.layers.11.mlp.fc1.weight', 'vision_model.encoder.layers.7.self_attn.v_proj.weight', 'vision_model.encoder.layers.15.mlp.fc2.bias', 'vision_model.encoder.layers.4.self_attn.q_proj.weight', 'vision_model.encoder.layers.7.mlp.fc2.bias', 'vision_model.encoder.layers.21.self_attn.out_proj.weight', 'vision_model.encoder.layers.18.self_attn.k_proj.weight', 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• This IS expected if you are initializing CLIPTextModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
• This IS NOT expected if you are initializing CLIPTextModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). Monitoring val/loss_simple_ema as checkpoint metric. Merged modelckpt-cfg: {'target': 'pytorch_lightning.callbacks.ModelCheckpoint', 'params': {'dirpath': 'logs/2024-04-09T21-36-10_vangogh_painting/checkpoints', 'filename': '{epoch:06}', 'verbose': True, 'save_last': True, 'monitor': 'val/loss_simple_ema', 'save_top_k': -1, 'every_n_train_steps': 10000}} GPU available: True (cuda), used: True TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs HPU available: False, using: 0 HPUs

  Data

  train, MaskBase, 200 validation, MaskBase, 200 accumulate_grad_batches = 1 Setting learning rate to 8.00e-06 = 1 (accumulate_grad_batches) 1 (num_gpus) 4 (batchsize) * 2.00e-06 (base_lr) Traceback (most recent call last): File "train.py", line 1171, in Trainer.fit(model, data) File "/home/xunaen/anaconda3/envs/ablate/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 604, in fit model = self._maybe_unwrap_optimized(model) File "/home/xunaen/anaconda3/envs/ablate/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1185, in getattr raise AttributeError("'{}' object has no attribute '{}'".format( AttributeError: 'CustomDiffusion' object has no attribute '_maybe_unwrap_optimized'

[nupurkmr9]

Hi, sorry for the delayed response. This seems like a pytorch_lightning issue. Which version of pytorch_lightning are you using?