How to Use SD 2.1 & Custom Models on Google Colab for Training with Dreambooth & Image Generation

Our Discord : https://discord.gg/HbqgGaZVmr. This video is a follow-up video of : https://youtu.be/mnCY8uM7E50. If I have been of assistance to you and you would like to show your support for my work, please consider becoming a patron on 🥰 https://www.patreon.com/SECourses

Playlist of Stable Diffusion Tutorials, Automatic1111 and Google Colab Guides, DreamBooth, Textual Inversion / Embedding, LoRA, AI Upscaling, Pix2Pix, Img2Img: https://www.youtube.com/playlist?list=PL_pbwdIyffsmclLl0O144nQRnezKlNdx3

In this video, I am demonstrating how you can use #StableDiffusion 2.1 on a #GoogleColab notebook that we have demonstrated in our previous video "Transform Your Selfie into a Stunning AI Avatar with Stable Diffusion - Better than #Lensa for Free". Moreover, I am showing how you can use custom models on this Google Colab notebook as well.

Also, I am showing how you can do training on Stable Diffusion 2.1 or on custom models such as Analog-Diffusion or Anything V3.

Furthermore, I am showing how you can just do inference (image generation) without doing any training. So you can also skip training and just generate images as you wish.

I am also explaining the differences between Stable Diffusion 2.1 and 1.5 and how they work differently. I explain why do you need different codes to run different models.

The link of the Google Colab Notebook used in the first video: https://colab.research.google.com/github/ShivamShrirao/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb

The link of Stable Diffusion 2.1 (768 pixels): https://huggingface.co/stabilityai/stable-diffusion-2-1

The link of best vae file for Stable Diffusion : https://huggingface.co/stabilityai/sd-vae-ft-mse

0:00 How to use different custom models in the Google Colab notebook 1:06 How to fix revision parameter to make custom models work in Stable Diffusion Google Colab Dreambooth notebook 2:16 How to use Stable Diffusion 2.1 on the Google Colab Dreambooth notebook 2:58 How to change vae files to get better quality results with Stable Diffusion 3:24 How to do inference (image generation) with Stable Diffusion 2.1 on the Google Colab Dreambooth notebook 4:03 Necessary code changes to run Version 2.1 on the Stable Diffusion Google Colab Dreambooth notebook 4:40 Generating 768x768 pixels images with SD 2.1 on the Google Colab notebook 5:09 How to return back SD 1.5 version on the Stable Diffusion Google Colab Dreambooth notebook 6:05 You don't have to do training to do inference (generating images) 6:19 You can do training on different custom models as well 6:30 When I say version 1.5 I refer to all models that are trained from 1.x Stable Diffusion models 7:04 The code changes between different prime version numbers 1.x vs 2.x 7:27 Ending speech of the tutorial guide

Image generation using artificial intelligence is a fascinating field that has the potential to revolutionize the way we make, manipulate, and share visual content. With the help of machine learning algorithms, it is now possible for computers to generate high-quality images that are indistinguishable from those made by humans.

One of the most exciting applications of image generation AI is in the field of art. Using deep learning techniques, artists and designers can make unique and visually stunning works of art that would have been impossible to make using traditional methods. For example, a computer program can analyze a large dataset of paintings by a particular artist and then generate a new piece of art that is stylistically similar to the original artist's work.

Another interesting use case for image generation AI is in the field of advertising. Companies can use machine learning algorithms to make personalized ads for their target audience. For example, an ad agency could use image generation AI to make a series of ads featuring a particular product that are customized to appeal to different demographics.

One of the most promising applications of image generation AI is in the field of medicine. Doctors and researchers can use machine learning algorithms to generate detailed 3D models of organs and tissues, which can be used to better understand how the body works and to plan surgeries. Additionally, image generation AI can be used to make training materials for medical students, allowing them to learn about the human body in a more interactive and engaging way.

In conclusion, image generation AI is a rapidly developing field that has the potential to revolutionize many different industries. From art and advertising to medicine and education, the possibilities for this technology are endless. As AI continues to advance, it will be exciting to see what new and innovative uses emerge for this powerful tool. ... https://www.youtube.com/watch?v=2yGGorOxtbA