stable-diffusion-webui VS Dreambooth-Stable-Diffusion

[https://github.com/sd-webui/stable-diffusion-webui/wiki/installation de numéro(https://github.com/sd-webui/stable-diffusion-webui/wiki/installation)

https://github.com/sd-webui/stable-diffusion-webui (prend beaucoup à installer)

you can "animate" with stable diffusion usining text to video https://github.com/nateraw/stable-diffusion-videos or https://github.com/sd-webui/stable-diffusion-webui

I mentioned Automatic1111 on SD-WEBUI and they deleted the comment. I guess this is why. My installation failed on SD-WEBUI and there was no solution for me. I suspect that's why Automatic1111's fork is so popular. He went above and beyond to make sure people with 1660ti's could run SD flawlessly with all the different tools available.

Any way to convert a .pt model to a .ckpt model? Stable-diffusion-webui only seems to support the second type of file but just renaming them does not work:

This is Stable-Diffusion. Here is a UI version https://github.com/sd-webui/stable-diffusion-webui

I dunno about the app. I use this https://github.com/sd-webui/stable-diffusion-webui it's very resource hungry though.

Haven't tried this GUI yet. Can anyone chime in about how it compares to Automatic1111's and sd-webui/HLKY's? There are so many good repos out there that it's getting hard to keep track of them all

Maybe those who joined were not aware that they joined the horde :-)

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