Our latest 8Buff SDXL Model 8Buff_XL_v3 has been released!
Model notes and prompt suggestions:
Based on our 8Buff_XL_V1 model, we have fine tuned, mixed, and trained our model to improve the image quality, diversity, and consistency.
Our models tend to focus on the generation of multiple people. Use terms Woman/Man for singular, and Men/Women for multiple. You can provide a number (3Men) or specify a line of people (line of cheerleaders) also. Stating things like ‘people in background’ will provide people and crowds, but will often be blurry and less defined.
To generate different skin colors and looks, you can use countries names or geographic regions such as; Nigeria, European, Canadian, Japanese, ect…
Use terms like Man/Woman for adults, girl/boy for younger people.
When creating groups of people that should look different from one another use positive prompt terms such as; different face, different hairstyle, different body type, etc. To enforce this you can also use negative prompts; same face, same clothes, etc…
Training done primarily on 1024x1024, 1024x1280, and 1280x1024 images. When mixing with LORA(s) recommend starting with 1024x1024 and adjusting from there.
Sampling method that provides the most consistent results is DPM++ 3M SDE Karras (in Automatic1111). Any of the Exponential methods tend to generate more refined images but less consistency.
Known issues:
Some sample methods tend to paint color streaks over pictures.
Perspective, hands, and eyes are still issues, like most models. Using seed and variation adjustments can help address some of these.
It is biased and tends to be a little thirsty at times. Be selective with your prompts.
Better, but still very bad at spelling, but you can do basic text with prompts like, small sign with handwritten text ‘eightbuff’, with the text you want between the ‘ quote' signs.
Our latest 8Buff SDXL Model 8Buff_XL_v3 has been released!
Model notes and prompt suggestions:
Based on our 8Buff_XL_V1 model, we have fine tuned, mixed, and trained our model to improve the image quality, diversity, and consistency.
Our models tend to focus on the generation of multiple people. Use terms Woman/Man for singular, and Men/Women for multiple. You can provide a number (3Men) or specify a line of people (line of cheerleaders) also. Stating things like ‘people in background’ will provide people and crowds, but will often be blurry and less defined.
To generate different skin colors and looks, you can use countries names or geographic regions such as; Nigeria, European, Canadian, Japanese, ect…
Use terms like Man/Woman for adults, girl/boy for younger people.
When creating groups of people that should look different from one another use positive prompt terms such as; different face, different hairstyle, different body type, etc. To enforce this you can also use negative prompts; same face, same clothes, etc…
Training done primarily on 1024x1024, 1024x1280, and 1280x1024 images. When mixing with LORA(s) recommend starting with 1024x1024 and adjusting from there.
Sampling method that provides the most consistent results is DPM++ 3M SDE Karras (in Automatic1111). Any of the Exponential methods tend to generate more refined images but less consistency.
Known issues:
Some sample methods tend to paint color streaks over pictures.
Perspective, hands, and eyes are still issues, like most models. Using seed and variation adjustments can help address some of these.
It is biased and tends to be a little thirsty at times. Be selective with your prompts.
Better, but still very bad at spelling, but you can do basic text with prompts like, small sign with handwritten text ‘eightbuff’, with the text you want between the ‘ quote' signs.
Works well with LORAs.
Check out our blog https://eightbuff.blogspot.com/ for more details.
For links to download and view samples of all our bots and models:
https://sites.google.com/eightbuff.com/eight-buffalo-media-group-llc/ai-bots-models
All images are from model and unedited