Dreambooth-Stable-Diffusion
Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) by way of Textual Inversion (https://arxiv.org/abs/2208.01618) for Stable Diffusion (https://arxiv.org/abs/2112.10752). Tweaks focused on training faces, objects, and styles. (by JoePenna)
sd-akashic
A compendium of informations regarding Stable Diffusion (SD) (by Maks-s)
| Dreambooth-Stable-Diffusion | sd-akashic | |
|---|---|---|
| 100 | 37 | |
| 3,166 | 1,595 | |
| - | - | |
| 6.8 | 1.4 | |
| 4 months ago | about 1 year ago | |
| Jupyter Notebook | ||
| MIT License | The Unlicense |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Dreambooth-Stable-Diffusion
Posts with mentions or reviews of Dreambooth-Stable-Diffusion.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-09.
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Will there be comprehensive tutorials for fine-tuning SD XL when it comes out?
Tons of stuff here, no? https://github.com/JoePenna/Dreambooth-Stable-Diffusion/
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Useful Links
Joe Penna's Dreambooth (Tutorial|24GB) Most popular DB repo with great results.
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Dreambooth / Custom Training / Model - what's the state of the art?
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?
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My Experience with Training Real-Person Models: A Summary
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 Used Stable Diffusion and Dreambooth to Create an Art Portrait of My Dog
- training
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Training a model on Iwanaga Kotoko (from in/spectre), which step do you guys think the model is at its best?
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).
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Dreambooth training results for face, object and style datasets with various prior regularization settings.
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.
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Starting to get quite good results with Dreambooth. What do you think? (Follow @RokStrnisa on Twitter for more.)
This is a good starting place: https://github.com/JoePenna/Dreambooth-Stable-Diffusion
- I'm a N00b with training stuff. Trying to get runpod with Dreambooth training some images (80 total) and I'm getting this error. Help?
sd-akashic
Posts with mentions or reviews of sd-akashic.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-13.
- [Stable Diffusion] La longueur maximale utilisable d'une invite de texte de diffusion stable est prétendument 77 jetons. Voici ce que cela signifie et comment tester le nombre de jetons dans votre invite de texte.
- [Stablediffusion] Que font exactement les guidance scale ?
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Model Testing - Realistic portraits with a study of various artists (A's)
The very study you linked to also did this same thing, as have multiple others, and they have a visible, attributable, and even measurable effect exactly as I pointed out. Meanwhile yours doesn't, to the point of being attributable to noise on many. Therefor, "Don't be fooled."
- [Stablediffusion] Que fait exactement la Guidance Scale ?
- Is this tutorial legit?
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Hi folks, thought I'd ask for help here because I'm quite a noob when it comes to stable diffusion. I have the problem that I want to generate landscapes and every time I get double mountains. I used negative (((Duplicate mountain))), double mountains,(((extra mountain))) but it doesn't help. Though
I remember referencing this image a lot - https://github.com/Maks-s/sd-akashic/blob/master/img/brbbbq-dimensions.png
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Improving old 3D renders with AI and SD
I've found the keywords to improve the results in this repository along with a lot of usefull info.
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What kind of limits would I have with an RTX 2070?
Picture Ratios
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List of SD Tutorials & Resources
Stable Diffusion Akashic Records
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Intro to Stable Diffusion: Resources and Tutorials
I think the current best list is at https://github.com/Maks-s/sd-akashic. There are now many such lists floating around. (I'm currently starting the hoarding of data and see if I can add to that list the many new links.)
What are some alternatives?
When comparing Dreambooth-Stable-Diffusion and sd-akashic you can also consider the following projects:
Dreambooth-SD-optimized - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion
stable-diffusion - Go to lstein/stable-diffusion for all the best stuff and a stable release. This repository is my testing ground and it's very likely that I've done something that will break it.
Stable-Diffusion-Regularization-Images - For use with fine-tuning, especially the current implementation of "Dreambooth".
rocm-build - build scripts for ROCm
A1111-Web-UI-Installer - Complete installer for Automatic1111's infamous Stable Diffusion WebUI
stable-diffusion - Optimized Stable Diffusion modified to run on lower GPU VRAM
civitai - A repository of models, textual inversions, and more
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/sd-webui/stable-diffusion-webui]
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/Sygil-Dev/sygil-webui]
stable-diffusion - A latent text-to-image diffusion model
InvokeAI - InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.
stable-diffusion-webui - Stable Diffusion web UI
Dreambooth-Stable-Diffusion vs Dreambooth-SD-optimized
sd-akashic vs stable-diffusion
Dreambooth-Stable-Diffusion vs Stable-Diffusion-Regularization-Images
sd-akashic vs rocm-build
Dreambooth-Stable-Diffusion vs A1111-Web-UI-Installer
sd-akashic vs stable-diffusion
Dreambooth-Stable-Diffusion vs civitai
sd-akashic vs stable-diffusion-webui
Dreambooth-Stable-Diffusion vs stable-diffusion-webui
sd-akashic vs stable-diffusion
Dreambooth-Stable-Diffusion vs InvokeAI
sd-akashic vs stable-diffusion-webui