chaiNNer VS Dreambooth-Stable-Diffusion

It is like Comfy UI Jr. but much easier to navigate. Huge amount of useful features, and you can connect it to the WebUI. I highly recommend checking it out: https://github.com/chaiNNer-org/chaiNNer

For the video it turned out a bit too "hairy" compared to many of the still images (I believe because of the long landscape aspect ratio), but I ran out of time to fiddle. I used the Seed Travel extension for the animation and ChaiNNer with the 4x-Valar upscaler.

While at it, check out ChaiNNer -> https://github.com/chaiNNer-org/chaiNNer

If you want to do more, there's chaiNNer and CupScale. You need to download an AI model to use those. There are a lot of anime/cartoon models out, so pick one that you like from here. (Note: Upscaly doesn't support these custom models.)

For local there's cupscale and chainner

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