chavinlo

"""make variations of input image"""

import argparse, os, sys, glob
import PIL
import torch
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice

chavinlo

import torch
from video_diffusion_pytorch import Unet3D, GaussianDiffusion, Trainer

model = Unet3D(
    dim = 64,
    dim_mults = (1, 2, 4, 8),
)

diffusion = GaussianDiffusion(
    model,

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_dist.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
# Multiple GPUs: torchrun --nproc_per_node=N trainer_dist.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

import sys
from flask import Flask, jsonify, request, send_file, Response
from pathlib import Path
from zipfile import ZipFile
import os
import argparse
import time
from io import BytesIO
from datetime import datetime
import threading

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_dist.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
# Multiple GPUs: torchrun --nproc_per_node=N trainer_dist.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

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

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

#!/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

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