efficient-dreambooth
[Moved to: https://github.com/smy20011/dreambooth-docker] (by smy20011)
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)
| efficient-dreambooth | Dreambooth-Stable-Diffusion | |
|---|---|---|
| 9 | 100 | |
| 45 | 3,166 | |
| - | - | |
| 10.0 | 6.8 | |
| over 1 year ago | 4 months ago | |
| Dockerfile | Jupyter Notebook | |
| - | MIT License |
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.
efficient-dreambooth
Posts with mentions or reviews of efficient-dreambooth.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-10-02.
- Want to use your own face ? Uploading a Tutorial today !
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Inserting your face to any picture after Dreambooth (or other faces)
Well, i don't better make a video to install this repo https://github.com/smy20011/efficient-dreambooth in windows, so we all can train our models?
- Automatic1111 with WORKING local textual inversion on 8GB 2090 Super !!!
- Show HN: Train Stable Diffusion Dreambooth on 1080ti
- Train dreambooth on 11GB GPU with prebuilt docker image
- Dreambooth in 11GB of VRAM
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Help with Dreambooth
Option1: you wait for people to push the limits. its currently at 11gb.
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?
What are some alternatives?
When comparing efficient-dreambooth and Dreambooth-Stable-Diffusion you can also consider the following projects:
fast-stable-diffusion - fast-stable-diffusion + DreamBooth
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".
dreambooth-docker
A1111-Web-UI-Installer - Complete installer for Automatic1111's infamous Stable Diffusion WebUI
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch
civitai - A repository of models, textual inversions, and more
stable-diffusion-webui - Stable Diffusion web UI
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/Sygil-Dev/sygil-webui]
stable-dreambooth-optimized - Dreambooth implementation based on Stable Diffusion with minimal code.
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.
efficient-dreambooth vs fast-stable-diffusion
Dreambooth-Stable-Diffusion vs Dreambooth-SD-optimized
efficient-dreambooth vs Dreambooth-Stable-Diffusion-cpu
Dreambooth-Stable-Diffusion vs Stable-Diffusion-Regularization-Images
efficient-dreambooth vs dreambooth-docker
Dreambooth-Stable-Diffusion vs A1111-Web-UI-Installer
efficient-dreambooth vs diffusers
Dreambooth-Stable-Diffusion vs civitai
efficient-dreambooth vs stable-diffusion-webui
Dreambooth-Stable-Diffusion vs stable-diffusion-webui
efficient-dreambooth vs stable-dreambooth-optimized
Dreambooth-Stable-Diffusion vs InvokeAI