VRAM/Speed tests about dreambooth HOT 2 OPEN

A100-SXM4-40GB

• GPU=31142/40536MiB, 32814 after first save, 33302 after 2nd save,

• 1.03s/it training, 3.30s/it inference

• BATCH_SIZE=4

• TRAIN_TEXT_ENCODER

• USE_8BIT_ADAM

• FP16

• GRADIENT_CHECKPOINTING

• GRADIENT_ACCUMULATION_STEPS=1

• USE_EMA=False

• RESOLUTION=512

• Warnings with xformers-0.0.15.dev0+4c06c7 (compiled on A10G)

• https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+4c06c79.d20221205-cp38-cp38-linux_x86_64.whl

• /usr/local/lib/python3.8/dist-packages/xformers/_C.so: undefined symbol: _ZNK3c104impl13OperatorEntry20reportSignatureErrorENS0_12CppSignatureE
  WARNING:xformers:WARNING: /usr/local/lib/python3.8/dist-packages/xformers/_C.so: undefined symbol: _ZNK3c104impl13OperatorEntry20reportSignatureErrorENS0_12CppSignatureE
  Need to compile C++ extensions to get sparse attention support. Please run python setup.py build develop
  */usr/local/lib/python3.8/dist-packages/diffusers/models/attention.py:433: UserWarning: Could not enable memory efficient attention. Make sure xformers is installed correctly and a GPU is available: No such operator xformers::efficient_attention_forward_cutlass - did you forget to build xformers with python setup.py develop?
  warnings.warn(

diffusers==0.9.0
accelerate==0.14.0
torchvision @ https://download.pytorch.org/whl/cu116/torchvision-0.14.0%2Bcu116-cp38-cp38-linux_x86_64.whl
transformers==4.25.1
xformers @ https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+4c06c79.d20221205-cp38-cp38-linux_x86_64.whl

Copy-and-paste the text below in your GitHub issue

• Accelerate version: 0.14.0
• Platform: Linux-5.10.133+-x86_64-with-glibc2.27
• Python version: 3.8.15
• Numpy version: 1.21.6
• PyTorch version (GPU?): 1.13.0+cu116 (True)
• Accelerate default config:
  Not found
• Accelerate version: 0.14.0
• Platform: Linux-5.10.133+-x86_64-with-glibc2.27
• Python version: 3.8.15
• Numpy version: 1.21.6
• PyTorch version (GPU?): 1.13.0+cu116 (True)

from dreambooth.

A100-SXM4-40GB

• GPU=16168/40536MiB
• 1.23s/it training, 5.83 it/s inference
• BATCH_SIZE=4
• TRAIN_TEXT_ENCODER
• USE_8BIT_ADAM
• FP16
• GRADIENT_CHECKPOINTING
• GRADIENT_ACCUMULATION_STEPS=1
• USE_EMA=False
• RESOLUTION=512
• No errors or warnings with 0.0.15.dev0%2B4c06c79/xformers-0.0.15.dev0+4c06c79.d20221205-cp38-cp38-linux_x86_64.whl

Description: Ubuntu 18.04.6 LTS
diffusers==0.9.0
torchvision @ https://download.pytorch.org/whl/cu116/torchvision-0.14.0%2Bcu116-cp38-cp38-linux_x86_64.whl
transformers==4.25.1
xformers @ https://github.com/brian6091/xformers-wheels/releases/download/0.0.15.dev0%2B4c06c79/xformers-0.0.15.dev0+4c06c79.d20221205-cp38-cp38-linux_x86_64.whl
2022-12-08 10:21:20.344739: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.

Copy-and-paste the text below in your GitHub issue

• Accelerate version: 0.14.0
• Platform: Linux-5.10.133+-x86_64-with-glibc2.27
• Python version: 3.8.15
• Numpy version: 1.21.6
• PyTorch version (GPU?): 1.13.0+cu116 (True)

from dreambooth.