Dreambooth-Stable-Diffusion
Dreambooth-Stable-Diffusion | SD-Regularization-Images-Style-Dreambooth | |
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100 | 7 | |
3,166 | 29 | |
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6.8 | 10.0 | |
4 months ago | over 1 year ago | |
Jupyter Notebook | ||
MIT License | - |
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Dreambooth-Stable-Diffusion
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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-Regularization-Images-Style-Dreambooth
- Comic Diffusion V2. This is a culmination of everything worked towards so far. Trained on 6 styles at the same time, mix and match any number of them to create multiple different unique and consistent styles.
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Question about training styles
I'm using the Joe Penna's repo on runpod and using only 20 training images and 1700 reg images from https://github.com/aitrepreneur/SD-Regularization-Images-Style-Dreambooth to trin styles. I'm getting very good results.
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Classic Disney animation dreambooth model
I'm new to using dreambooth, but I followed the steps in some of the recent trending examples to make a "classic disney" art style. I pulled/cropped/reframed about 50 reference images, and used the style examples [from here](https://github.com/aitrepreneur/SD-Regularization-Images-Style-Dreambooth), trained with 6400 steps. Colors are typically oversaturated, and it's really hard to control. I've also found that adding artists helps balance the composition out a lot. Here are some of the sample outputs!
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Fine-tuned the model on Kurzgesagt videos with DreamBooth. Here are some results.
I've used this repository for regularization images. And these options for training: --class_word "style" --token "kurzgesagt"
- 2D Illustration Styles are scarce on Stable Diffusion so i created a dreambooth model inspired by Hollie Mengert's work
- Hello, i saw that you can train dreambooth for a style, I tried taring dreambooth on vast.ai for a children book illustration style but I got pretty awful result, any ideas what went wrong.
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I've further refined my Studio Ghilbi Model
I used around 20,000 steps (I forgot to look at number of steps when I stopped training). The regulation images I used can be obtained at https://github.com/aitrepreneur/SD-Regularization-Images-Style-Dreambooth
What are some alternatives?
Dreambooth-SD-optimized - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion
Stable-Diffusion-Regularization-Images - For use with fine-tuning, especially the current implementation of "Dreambooth".
Dreambooth-Regularization - All the regs
A1111-Web-UI-Installer - Complete installer for Automatic1111's infamous Stable Diffusion WebUI
civitai - A repository of models, textual inversions, and more
Txt2Vectorgraphics - Custom Script for Automatics1111 StableDiffusion-WebUI.
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/Sygil-Dev/sygil-webui]
Dreambooth-Stable-Diffusion - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion (tweaks focused on training faces)
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.
fast-stable-diffusion - fast-stable-diffusion + DreamBooth