Add Support for Python 3.11 and PyTorch 2.0

[inarikami]

Raising some issues I found when running ControlNet with Python 3.11 and PyTorch 2.0. The changes required to run with these versions this are outlined below:

Fix 1:

Encountered a ValueError when running python tool_add_control_sd21.py ./models/v2-1_768-ema-pruned.ckpt ./models/control_sd21_ini.ckpt.

Error message:

ValueError: mutable default <class 'timm.models.maxxvit.MaxxVitConvCfg'> for field conv_cfg is not allowed: use default_factory

Solution:
Implemented the fix from the following commit in HuggingFace's PyTorch Image Models repository:
huggingface/pytorch-image-models@a482365

Alternatively, you can use the pre-release version of timm by running pip install --pre timm.

Fix 2:

Encountered an ImportError for rank_zero_only from pytorch_lightning.utilities.distributed.

Replace:

from pytorch_lightning.utilities.distributed import rank_zero_only

With:

from pytorch_lightning.utilities.rank_zero import rank_zero_only

Fix 3:

remove dataloader_idx param in 'ImageLogger' and 'def on_train_batch_start(self, batch, batch_idx):'

Then you can train successfully with Python 3.11 and PyTorch 2.0, also added cpu core variable for more efficient data loading.

[SamSamhuns]

Did you notice any speedup when using pt 2.0 and compiling the models?

[inarikami]

@SamSamhuns

Most of the speedup during training and inference will be by manually adding the new optimization techniques. This PR serves as a bare bones setup of the latest python/pytorch version and the rest is really up to the end user.

See: https://huggingface.co/docs/diffusers/optimization/torch2.0

[Zhaoyi-Yan]

@tensorneko Hi, I am using version 1.5, and I followed your modifications. It worked fine. I encountered an error related to the missing dataloader_idx, thus I did anything with that part. I am not certain if this issue is due to differences between versions 1.5 and 2.1. Additionally, perhaps you could include your changes in the tutorial_train.py file. Furthermore, maybe torch.compile could be incorporated in your pull request.