memory_efficient_dreambooth
Dreambooth-Stable-Diffusion
| memory_efficient_dreambooth | Dreambooth-Stable-Diffusion | |
|---|---|---|
| 2 | 100 | |
| 55 | 3,166 | |
| - | - | |
| 10.0 | 6.8 | |
| over 1 year ago | 4 months ago | |
| Python | Jupyter Notebook | |
| - | MIT License |
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memory_efficient_dreambooth
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Dreambooth: Are more images better?
Yeah, all were the same subject. I used this script for running dreambooth https://github.com/matteoserva/memory_efficient_dreambooth and to my understanding this does not use the class images at all. I had another training earlier with 1400 faces only @ 20k steps which had much better results. Maybe I should've used 40k steps? I don't know :)
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Dreambooth in 11GB of VRAM
This is my repository with the updated source and a sample launcher: https://github.com/matteoserva/memory_efficient_dreambooth
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?
What are some alternatives?
stable-dreambooth-optimized - Dreambooth implementation based on Stable Diffusion with minimal code.
Dreambooth-SD-optimized - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion
Dreambooth-Stable-Diffusion-cpu - 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".
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch
A1111-Web-UI-Installer - Complete installer for Automatic1111's infamous Stable Diffusion WebUI
efficient-dreambooth - [Moved to: https://github.com/smy20011/dreambooth-docker]
civitai - A repository of models, textual inversions, and more
fast-stable-diffusion - fast-stable-diffusion + DreamBooth
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/Sygil-Dev/sygil-webui]
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.