Stable-Diffusion-Regularization-Images Alternatives

Stable-Diffusion-Regularization-Images reviews and mentions

• Clarification regularization for Stable Diffusion
  1 project | /r/learnmachinelearning | 19 Jun 2023

  However, when I look at regularization dataset that people have created, a lot of them are composed by bad quality AI generated pictures, for instance disfigured humans, or images full or artifacts. For instance, this image of train, or this one of a woman.

• Training Picture Source
  1 project | /r/DreamBooth | 28 Dec 2022
• 💡 How train with locally with 1.5 Runwayml Inpainting Model?
  2 projects | /r/DreamBooth | 12 Dec 2022

  BTW, you can find the regularization images (ready to use class images) here.

• Regularization images
  1 project | /r/DreamBooth | 10 Nov 2022

  Have you compared results to using regularization images from an existing repo such as https://github.com/JoePenna/Stable-Diffusion-Regularization-Images

• 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.
  4 projects | /r/StableDiffusion | 7 Nov 2022

  For subjects/people, paste this into the github downloader https://github.com/JoePenna/Stable-Diffusion-Regularization-Images/tree/main/person_ddim

• Good Dreambooth Formula
  1 project | /r/StableDiffusion | 24 Oct 2022

  If you are using person, man or woman as class, you don't need to generate the images as there are a some github repos that have a bunch of them already generated for you to use. Nitrosocke also shared some, check my initial post for the link.

• Custom Model Comparison 1.4 vs 1.5 (something broke)
  1 project | /r/StableDiffusion | 21 Oct 2022
• What should I do when want better results for a person that was already trained in the sd v1.4 version? Train the model, Dreambooth, or textual inversion embeddings?
  1 project | /r/sdforall | 18 Oct 2022

  I did some experiments with dreambooth training. Overall better results were when I have used 1500 "person" class and about 50 training images. It is vital to have different background and different clothes otherwise it will "bake it" into your token (e.g. same sweater will influence all the rendering with color or pattern as it will be "part of your token"). Now I need to test textual inversion and see the difference.

• Any advice on how to use the dream booth colab with automatic?
  1 project | /r/sdforall | 16 Oct 2022

  As for what kind of images to use I've tried actual photos of people and images generated with Stable Diffusion and I've had pretty good results with both. I also tried using exclusively pictures of the person I'm training for everything and even that worked pretty well. All I can really say is that it seems to pay off if you keep an eye on the framing of your images - if the majority of your reference images cut off the upper 10% of the head for example then your model will tend to also produce images that cut off the upper 10% of the head. Oh, and I haven't tried it myself but this Github repository apparently has a ton of images specifically for use in DreamBooth.

• How are you achieving decent results in DreamBooth? My images look terrible!
  2 projects | /r/sdforall | 15 Oct 2022

  I've made sure all my images are only me, and clean images. I have tried using the unsplash regularization images from https://github.com/JoePenna/Stable-Diffusion-Regularization-Images. I've tried generating my own images from SD itself. I've tried 1k, 2k, 3k, 4k steps. I've tried more images of myself and fewer. I've tried using "man", "person", "face" as the class. All of it results in absolute garbage. I get outputs that consistently look like I'm 80 years old or a different ethnicity. Or just wrong... so wrong.

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