fast-stable-diffusion VS stable-diffusion

I tried using TheLastBen's fast dreambooth trainer. I managed to train a ckpt file but I can't run it.

You have a colab from ThelastBen It uses to be thes best at the time when auto1111 was working in google colab free. https://github.com/TheLastBen/fast-stable-diffusion

Using https://github.com/TheLastBen/fast-stable-diffusion

This repo offers a template how to start with SD on runpod https://github.com/TheLastBen/fast-stable-diffusion. But I know how to code, si I made my own solution.

I can confirm I'm using the latest version of the colab notebook of this repo (https://github.com/TheLastBen/fast-stable-diffusion). Anyone can point to any solutions to this problem? Thanks in advance!

TheLastBen's Fast DB SD Colabs, +25-50% speed increase, AUTOMATIC1111 + DreamBooth

CalledProcessError Traceback (most recent call last) in () 182 wget.download('https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/det.py') 183 print('Detecting model version...') --> 184 Custom_Model_Version=check_output('python det.py '+sftnsr+' --MODEL_PATH '+MODEL_PATH, shell=True).decode('utf-8').replace('\n', '') 185 clear_output() 186 print(''+Custom_Model_Version+' Detected')

have you tried https://github.com/TheLastBen/fast-stable-diffusion ?

> 860M UNet and 123M text encoder

Yep! Less than 1B in total. [0]

[0] https://github.com/CompVis/stable-diffusion/blob/main/README...

Stable Diffusion, developed by Stability AI, is a robust text-to-image generation model designed to create high-quality, creative images based on textual prompts. Project URL: Stable Diffusion on GitHub

Stable Diffusion (GitHub Repository)

Latent diffusion models operate in katent space. This space is generated by an encoder and decoded back into pixel space by a decoder. The encoder-decoder form a generator which is trained to have good visual quality through the use of an adversarial loss.

So the encoder produces a latent space that is more efficient to train a diffusion model on, since diffusion models use Unet-like architecture that must be run many times for a single inference. The latent space is restricted by a KL penalty to a Gaussian shape such that any sample from that shape will map through the decoder to a high-quality image. This makes the generative job of the diffusion model much easier because it can focus on content and semantics rather than pixel-level details

You can see the two optimisers at work in the AutoencoderKL class in the Stable Diffusion source code here: https://github.com/CompVis/stable-diffusion/blob/main/ldm/mo...

Stable Diffusion: It is based on a kind of diffusion model called a latent diffusion model, which is trained to remove noise from images in an iterative process. It is one of the first text-to-image models that can run on consumer hardware and has its code and model weights publicly available.

Which is a 1-click install of Stable Diffusion with an alternative web interface. You can choose a different approach but this one is pretty simple and I am new to this stuff.

an invisible watermarking of the outputs, to help viewers identify the images as machine-generated.

It sounds like you just want to set up Stable Diffusion to run locally. I don't think your computer's specs will be able to do it. You need a graphics card with a decent amount of VRAM. Stable diffusion is in Python as is almost every AI open source project I've seen. If you can get your hands on a system with an Nvidia RTX card with as much VRAM as possible, you're in business. I have an RTX 3060 with 12 gigs of VRAM and I can run stable diffusion and a whole variety of open source LLMs as well as other projects like face swap, Roop, tortoise TTS, sadtalker, etc...

Use specific GPU on multi GPU systems · Issue #87 · CompVis/stable-diffusion · GitHub