glid-3-xl-stable VS Dreambooth-Stable-Diffusion

I don't know how you can say this but it's completely different than anything we had before, the only exception was https://github.com/Jack000/glid-3-xl-stable/wiki/Custom-inpainting-model, this model was a finituned version of v1.4 but not having a separate channel for the original image and the mask makes it weaker.

Check out the item by lkwq007 in this list https://www.reddit.com/r/StableDiffusion/comments/wqaizj/list_of_stable_diffusion_systems/ , and also the model for this web app https://replicate.com/devxpy/glid-3-xl-stable , which I believe is this https://github.com/Jack000/glid-3-xl-stable .

Right now I am using different, better optimized model for just outpaiting/inpating using this https://github.com/Jack000/glid-3-xl-stable as base

I'm using a slightly modified and optimized version of https://github.com/Jack000/glid-3-xl-stable for inpainting/outpainting.

For this editor we've integrated Jack Qiao's excellent custom inpainting model from the glid-3-xl-sd project instead. This is a fine-tuned version of Stable Diffusion with significantly better inpainting capabilities than standard SD. You can read more about how it works here along with comparison images between it and regular SD.

you cant. they are different architectures: https://github.com/Jack000/glid-3-xl-stable/issues/17

I am wondering tho, if this one uses https://github.com/Jack000/glid-3-xl-stable/wiki/Custom-inpainting-model glid-3 inpainting?

(also check this custom SD inpainting/outpainting model, it's easily the best i've seen https://github.com/Jack000/glid-3-xl-stable/wiki/Custom-inpainting-model)

Tons of stuff here, no? https://github.com/JoePenna/Dreambooth-Stable-Diffusion/

Joe Penna's Dreambooth (Tutorial|24GB) Most popular DB repo with great results.

1) The https://github.com/JoePenna/Dreambooth-Stable-Diffusion instructions say to use the 1.5 checkpoints - is that the latest? Can I use the 2+ models or?

I quickly turned to the second library, https://github.com/JoePenna/Dreambooth-Stable-Diffusion, because its readme was very encouraging, and its results were the best. Unfortunately, to use it on Colab, you need to sign up for Colab Pro to use advanced GPUs (at least 24GB of VRAM), and training a model requires at least 14 compute units. As a poor Chinese person, I could only buy Colab Pro from a proxy. The results from JoePenna/Dreambooth-Stable-Diffusion were fantastic, and the preparation was straightforward, requiring only <=20 512*512 photos without writing captions. I used it to create many beautiful photos.

I've found EveryDream to be brilliant and have switched from JoePenna's Dreambooth because I've found I get better results so long as I provide good captions for all the images, even if preparing the dataset takes 3x as long (took me 2 hours to crop and label the 54 images).

From what I know you can train with whatever size you want. But you need software that will support it. For example, ShivamShrirao/diffusers repo seems to allow a change of dimension. Also, you need HW that would support the training, because bigger images need more VRAM, for example,Joe Penna repo is using ~23GB with 512x512px so probably it's not a valid option. But the ShivamShrirao repo has optimizations that allow to run it with less VRAM.

This is a good starting place: https://github.com/JoePenna/Dreambooth-Stable-Diffusion