Wolfgang Meyers wolfgangmeyers

A guide to fine-tune Stable Diffusion on the Vast.ai service

This guide is based on another more comprehensive one that can be found at https://note.com/kohya_ss/n/nbf7ce8d80f29. All of the necessary environment has been encapsulated into a docker image that can easily run on different cloud providers. (disclaimer: there seems to be a version mismatch with xformers, so we leave it off because it won't work)

Note: the docker image used for this exercise is based on Stable Diffusion 1.5 release and has the updated VAEs. By using the model contained in this image or any fine-tuned models derived from it, you agree to terms of use.

Make sure you have an account with Vast.ai and some credit.
Navigate to https://console.vast.ai/create/ and filter by A100 GPU type (others may work, but probably slower)

Add this snippet to visual studio code as described at https://code.visualstudio.com/docs/editor/userdefinedsnippets

Also see https://gist.github.com/wolfgangmeyers/c7de046903d1fdfcf450440d62779450 for the sublime text version.

Add this as a snippet as described in http://docs.sublimetext.info/en/latest/extensibility/snippets.html

Also see https://gist.github.com/wolfgangmeyers/b169a86357493329035128856b842e66 for the same snippet in visual studio code.

	let text = `paste text of any size here`;

	function splitIntoSegments(inputString) {
	inputString = inputString.replace(/\n/g, " ")
	const words = inputString.split(' ');
	const segments = [];
	let currentSegment = '';

	for (let word of words) {
	// strip each word of whitespace

	# Adapted from https://github.com/Jack000/glid-3-xl
	import clip
	import argparse
	import PIL
	import torch

	def rank(args):
	device = torch.device('cuda:0' if (torch.cuda.is_available() and not args.cpu) else 'cpu')
	clip_model, clip_preprocess = clip.load('ViT-L/14', device=device, jit=False)
	clip_model.eval().requires_grad_(False)

	package retries

	import "time"

	// WithRetries will continue to attempt an operation
	// up to the specified number of retries with an exponential
	// backoff. If the operation is still unsuccessful
	// after all retries are exhausted (operation returns an error),
	// that error is returned.
	func WithRetries(retries int, operation func() error) error {

	#!/usr/bin/env python

	import sys
	import os

	# Dead simple process watcher in python.
	# Restarts a process if and only if it dies with a non-zero exit code.
	# Shuts down if process exits with zero.

	if __name__ == "__main__":