dreambooth-docker
dreambooth-training-guide
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dreambooth-docker
- Don't overpay for dreambooth training!
- Stable Diffusion links from around October 5, 2022 that I collected for further processing
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Trained model produces exactly the same result as the original
For reference, I'm using this docker container, not sure if that is related: https://github.com/smy20011/dreambooth-docker
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Want to use your own face ? Uploading a Tutorial today !
I'm using this Repo: https://github.com/smy20011/dreambooth-docker
dreambooth-training-guide
- [Sdforall] L'extension Dreambooth pour Automatic111 est sortie
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Creating own model like the ones on civitai.com
I dont have the time right now, but the rule of thumb for me was 80 unet learning steps for 1 image. Atleast 40 regularization images. Read more about regularization images here: https://github.com/nitrosocke/dreambooth-training-guide
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Image background for LORA training images
This tutorial for dreambooth training has advice with regard to backgrounds which is probably also applicable to LORA. It recommends including images with solid, non-transparent backgrounds but not using them exclusively. Images that focus on the torso and face are probably most important unless your subject has very distinctive legs and feet. Removing other subjects is a must if you're training for a specific subject.
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Non-technical tips for ideal training of Stable Diffusion through Dreambooth?
I found this, I'm going to go through this guide. Seems like I am using far too many images. https://github.com/nitrosocke/dreambooth-training-guide
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Questions about Regularization Images to be used in Dreambooth
Nitrosocke's guide already tells how much and what kind of images to use.
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What’s going to be a problem 20 years from now that people are choosing to ignore?
Dreambooth lets you do it in less than 100 images. https://github.com/nitrosocke/dreambooth-training-guide These folks say it's 5-15 to train on a person but I've not tested myself. https://www.reddit.com/r/StableDiffusion/comments/10tqy88/were\_launching\_a\_lightningfast\_dreambooth\_service/
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We’re launching a lightning-fast Dreambooth service: finetune 1’500 steps in 5min!
See eg this tutorial for styles: https://github.com/nitrosocke/dreambooth-training-guide
- Would it be possible to pretrain generation to mimic my art style?
- Dreambooth model training : dataset labelling
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Introducing Macro Diffusion - A model fine-tuned on over 700 macro images (Link in the comments)
The first time I tried to Dreambooth a style it went poorly. Then I found Nitrosocke's Dreambooth Training Guide and realized my problems were caused by a poorly redacted dataset.
What are some alternatives?
efficient-dreambooth - [Moved to: https://github.com/smy20011/dreambooth-docker]
sd_dreambooth_extension
stable-diffusion-loopback-color-correction-script - A script for AUTOMATIC1111/stable-diffusion-webui that allows advanced color correction options for img2img loopback
StableTuner - Finetuning SD in style.
AI-Horde - A crowdsourced distributed cluster for AI art and text generation
stable-diffusion-webui - Stable Diffusion web UI
Stable-diffusion-webui-video
dreambooth-gui
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch
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.
stablediffusion - High-Resolution Image Synthesis with Latent Diffusion Models