Includes:

• Notebooks for Stable Diffusion v2.1 and Stable LM 2 1.6B including (txt2img, depth2img, stablelm).
• General examples in notebooks/learning
• Deepdream notebooks and scripts. DeepDream was really hot a few years back.

• Visit https://colab.research.google.com/ or directly load one of the notebooks:
  • txt2img
  • depth2img
• Select Runtime / Select Runtime type
  • Choose TPU or GPU. One may be out of stock while the other is still available, depending on the time of the day. TODO: Test with TPU.
• Run the first cell to install everything (shift-enter)
  • It will ask for confirmation that you trust the code. Do as you wish. :)
• Run the second cell to generate stuff.

1. Install CUDA from https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=22.04&target_type=deb_network
   • You don't have to register.
2. You can install then confirm it runs:

   sudo apt install cuda-11-8 libcudnn8 tensorrt-libs
   python3 -c "import tensorflow as tf;print(tf.config.list_physical_devices('GPU'))"
   

3. Run ./setup.sh to create the virtual environment and install pip packages
4. To start the server, run ./run.sh
5. Tested on Ubuntu 22.04 with a Nvidia RTX 2060. Works great remotely via a Chromebook!

List memory usage: nvidia-smi

Summary:

nvidia-smi --query-gpu=utilization.gpu,utilization.memory,memory.total,memory.free,memory.used --format=csv

On exceptions, Jupyter tends to leave zombie python processes that will keep GPU VRAM allocations. Kill with:

nvidia-smi | grep 'python' | awk '{ print $5 }' | xargs -n1 kill

1. Get python3.11 from the Microsoft Store until pytorch/pytorch#110436 is fixed and it becomes compatible with 3.12.
2. Get CUDA from https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=11&target_type=exe_network
   • You don't have to register.

• https://huggingface.co/blog/lcm_lora
• https://github.com/huggingface/diffusers/blob/main/examples/consistency_distillation/README_sdxl.md
• https://huggingface.co/latent-consistency/lcm-lora-sdxl/resolve/main/LCM-LoRA-Technical-Report.pdf
• https://github.com/pgvector/pgvector
• https://arxiv.org/pdf/2401.08500
• https://medium.com/@rohanbalkondekar/flow-engineering-is-all-you-need-9046a5e7351d
• https://github.com/LyzrCore/lyzr-experimental-automata
• https://research.google/pubs/hypernetworks-2/
• https://github.com/JiauZhang/hyperdreambooth
• https://medium.com/@jain.sm/hyperdreambooth-a-novel-approach-to-text-to-image-personalization-71b7fe8be42a