chavinlo

# Install bitsandbytes:
# `nvcc --version` to get CUDA version.
# `pip install -i https://test.pypi.org/simple/ bitsandbytes-cudaXXX` to install for current CUDA.
# Example Usage:
# Single GPU: torchrun --nproc_per_node=1 trainer/diffusers_trainer.py --model="CompVis/stable-diffusion-v1-4" --run_name="liminal" --dataset="liminal-dataset" --hf_token="hf_blablabla" --bucket_side_min=64 --use_8bit_adam=True --gradient_checkpointing=True --batch_size=1 --fp16=True --image_log_steps=250 --epochs=20 --resolution=768 --use_ema=True
# Multiple GPUs: torchrun --nproc_per_node=N trainer/diffusers_trainer.py --model="CompVis/stable-diffusion-v1-4" --run_name="liminal" --dataset="liminal-dataset" --hf_token="hf_blablabla" --bucket_side_min=64 --use_8bit_adam=True --gradient_checkpointing=True --batch_size=10 --fp16=True --image_log_steps=250 --epochs=20 --resolution=768 --use_ema=True

import argparse
import socket
import torch

chavinlo

#!/bin/bash

#Install deps
apt-get update -y
apt-get install htop screen psmisc python3-pip unzip wget gcc g++ nano -y

#Install Python deps
wget https://gist.githubusercontent.com/chavinlo/fe8afc02e03d9cc4eb545c4c306c8a73/raw/d9a5ad446fe662dc3e6597163a1f8d5546a8a795/requirements.txt
pip install -r requirements.txt OmegaConf 
pip install triton==2.0.0.dev20221120

chavinlo

#!/bin/bash

#Install deps
apt-get update -y
apt-get install htop screen psmisc python3-pip unzip wget gcc g++ nano -y

#Install Python deps
wget https://gist.githubusercontent.com/chavinlo/fe8afc02e03d9cc4eb545c4c306c8a73/raw/d9a5ad446fe662dc3e6597163a1f8d5546a8a795/requirements.txt
pip install -r requirements.txt OmegaConf 
pip install triton==2.0.0.dev20221120

chavinlo

diffusers>=0.5.1
numpy==1.23.4
wandb==0.13.4
torch
torchvision
transformers>=4.21.0
huggingface-hub>=0.10.0
Pillow==9.2.0
tqdm==4.64.1
ftfy==6.1.1

chavinlo

# Install bitsandbytes:
# `nvcc --version` to get CUDA version.
# `pip install -i https://test.pypi.org/simple/ bitsandbytes-cudaXXX` to install for current CUDA.
# Example Usage:
# Single GPU: torchrun --nproc_per_node=1 trainer/diffusers_trainer.py --model="CompVis/stable-diffusion-v1-4" --run_name="liminal" --dataset="liminal-dataset" --hf_token="hf_blablabla" --bucket_side_min=64 --use_8bit_adam=True --gradient_checkpointing=True --batch_size=1 --fp16=True --image_log_steps=250 --epochs=20 --resolution=768 --use_ema=True
# Multiple GPUs: torchrun --nproc_per_node=N trainer/diffusers_trainer.py --model="CompVis/stable-diffusion-v1-4" --run_name="liminal" --dataset="liminal-dataset" --hf_token="hf_blablabla" --bucket_side_min=64 --use_8bit_adam=True --gradient_checkpointing=True --batch_size=10 --fp16=True --image_log_steps=250 --epochs=20 --resolution=768 --use_ema=True

import argparse
import socket
import torch

chavinlo

# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
# *Only* converts the UNet, VAE, and Text Encoder.
# Does not convert optimizer state or any other thing.

import argparse
import os.path as osp

import torch

chavinlo

#!/bin/bash

#Install deps
apt-get update -y
apt-get install htop screen psmisc python3-pip unzip wget gcc g++ nano -y

#Install Python deps
wget https://gist.githubusercontent.com/chavinlo/41e062890b91cde16ac146719a669308/raw/9ebca64f70379a6012c804e4a39ede1db4a6b665/requirements.txtpip install -r requirements.txt OmegaConf 
pip install triton==2.0.0.dev20221120
conda install xformers -c xformers/label/dev

chavinlo

diffusers>=0.5.1
numpy==1.23.4
wandb==0.13.4
torch
torchvision
transformers>=4.21.0
huggingface-hub>=0.10.0
Pillow==9.2.0
tqdm==4.64.1
ftfy==6.1.1

chavinlo

#!/bin/bash

#Install deps
apt-get update -y
apt-get install htop screen psmisc python3-pip unzip wget gcc g++ -y

#Install Python deps
wget https://gist.githubusercontent.com/chavinlo/04330ffe95223f7a0a42de81526199b7/raw/b1c19df0e694936e1dadc7082d2326d6382b5d15/requirements.txt
pip install -r requirements.txt OmegaConf 

chavinlo

# Install bitsandbytes:
# `nvcc --version` to get CUDA version.
# `pip install -i https://test.pypi.org/simple/ bitsandbytes-cudaXXX` to install for current CUDA.
# Example Usage:
# Single GPU: torchrun --nproc_per_node=1 trainer/diffusers_trainer.py --model="CompVis/stable-diffusion-v1-4" --run_name="liminal" --dataset="liminal-dataset" --hf_token="hf_blablabla" --bucket_side_min=64 --use_8bit_adam=True --gradient_checkpointing=True --batch_size=1 --fp16=True --image_log_steps=250 --epochs=20 --resolution=768 --use_ema=True
# Multiple GPUs: torchrun --nproc_per_node=N trainer/diffusers_trainer.py --model="CompVis/stable-diffusion-v1-4" --run_name="liminal" --dataset="liminal-dataset" --hf_token="hf_blablabla" --bucket_side_min=64 --use_8bit_adam=True --gradient_checkpointing=True --batch_size=10 --fp16=True --image_log_steps=250 --epochs=20 --resolution=768 --use_ema=True

import argparse
import socket
import torch