Mirrors
/
EveryDream2trainer
mirror of https://github.com/victorchall/EveryDream2trainer.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

docs ofc

This commit is contained in:
Victor Hall 2023-04-03 00:13:10 -04:00
parent 82cda3ca79
commit f0e2e13d3c
2 changed files with 11 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
doc/CONTRIBUTING.md
View File

@ -4,6 +4,8 @@ Thank you for your interest in contributing to EveryDream!
## Way to contribute without code
* Join Discord and help other users with advice and troubleshooting.
* Report bugs. Use the github template or report on discord in #help along with your logs.
* Documentation. Is something confusing or missing? Contibute an update to documentation.
@ -14,13 +16,13 @@ Thank you for your interest in contributing to EveryDream!
* Share your training settings. Did you find a good set of settings for a particular dataset? Share it with others.
* Run A/B experiments. Try different hyperparameters and share your results.
* Run A/B experiments. Try different hyperparameters and share your results on socials or Discord.
## Contributor License Agreement
Please review the [CLA](EveryDream_CLA.txt) before issuing a PR. You will be asked on your first submission to post your agreement for any code changes.
This is not required for simple documentation changes (i.e. spelling mistakes, etc.)
This is not required for simple documentation changes.
## Contributing code
@ -44,7 +46,7 @@ Please update the appropriate document file in `/doc` for your changes. If you
## A few questions to ask yourself before working on enhancements
There is no formation process for contributing to EveryDream, but please consider the following before submitting a PR:
There is no formal process for contributing to EveryDream, but please consider the following before submitting a PR:
* Consider if the change is general enough to be useful to others, or is more specific to your project. Changes should provide value to a broad audience. Sometimes specific project needs can be served by a script for your specific data instead of a change to the trainer behavior, for instance.

12
doc/DATA.md
View File

@ -2,15 +2,14 @@
## Number of images
You should probably start with less than 100 images, until you get a feel for training. When you are ready, ED2 supports up to tens of thousands of images.
You should probably start with less than 100 images, until you get a feel for training. When you are ready, ED2 supports up to hundreds of thousands of images.
## Image size and quality
ED2 supports `.jpg`, `.jpeg`, `.png`, `.bmp`, `.webp`, and `.jfif` image formats.
Current recommendation is _at least_ 1 megapixel (ex 1024x1024, 1100x900, 1300x800, etc). That being said, technology continues to advance rapidly. ED2 has no problem handling 4K images, so it's up to you to pick the appropriate trade-off with disk and network costs.
Scaling images up is not a great idea though, though it may be tolerable as a very small percentage of your data set. If you only have 512x512 images, don't try training at 768.
Bare minimum recommended size is 512x512. Scaling images up is not a great idea though, though it may be tolerable as a very small percentage of your data set. If you only have 512x512 images, don't try to train at 768.
Use high quality, in-focus, low-noise, images, capturing the concept(s) under training with high fidelity wherever possible.
@ -18,20 +17,21 @@ Use high quality, in-focus, low-noise, images, capturing the concept(s) under tr
You can crop your images in an image editor __if it highlights the concept under training__, e.g. to get good close ups of things like faces, or to split images up that contain multiple characters.
**You do not need to crop to a square image**
**You do not need to crop to square**
Aspect ratios between 4:1 and 1:4 are supported; the trainer will handle bucketing and resizing your images as needed.
It is ok to use a full shot of two characters in one image and also a cropped version of each character separately, but make sure every image is captioned appropriately for what is actually present in each image.
## Caption Design
A caption consists of a main prompt, followed by one or more comma-separated tags.
For most use cases, use a sane English sentence to describe the image. Try to put your character or main object name close to the start.
**If you are training on images of humans, there is little benefit in using "unique" names most of the time**. Don't worry so much about using a "rare" toking, or making up gibberish words. Just try generating a few images using your concept names, and make sure there are no serious conflicts.
**If you are training on images of humans, there is little benefit in using "unique" names most of the time**. Don't worry so much about using a "rare" toking, or making up gibberish words. Just try generating a few images using your concept names, and make sure there are no serious conflicts.
**Use normal names for people and characters, such as "cloud strife" or "john david washington" instead of making up weird names like "cldstrf" or "jhndvdwshtn". There's no need for this and it just makes inference less natural and shifts a burden on the user to remember magic names.**
Those training anime models can use booru tags as well using other utilities to generate the captions.