Disclaimer

This model applies Terminal SNR to its training noise schedule, as described here.

This will not work out-of-the-box (as of June 2023) with Automatic1111's Stable Diffusion WebUI.

It works great using a Diffusers pipeline, such as InvokeAI, or a simple Diffusers python script using DDPMScheduler. One such script is available, here.

I hope that Automatic1111's support for terminal SNR pipelines is improved someday, because SDXL will require it.

描述:

This is a checkpoint trained from MJ 5.1 data from a random sampling over a single day's worth of upscaled images.

It was started from stabilityai/stable-diffusion-2-1 with a mix of 22,000 synthetic images and about 7,000 real photos.

MJ data was captioned by its users. Real photos were captioned by BLIP using the Salesforce model on top of the SD 2.1 text encoder.

Some helpful terms:

• UHD

• 8K

• Sharp

• best quality

• midjourney / midjourney's

Here are the most frequent terms in the training data captions:

• in: 7348

• and: 6337

• with: 6110

• style: 3331

• on: 3097

• white: 2643

• background: 2380

• black: 2289

• realistic: 1937

• art: 1589

• an: 1422

• .png: 1344

• beautiful: 1313

• full: 1228

• light: 1200

• is: 1192

• hair: 1192

• detailed: 1189

• woman: 1109

• from: 1108

• for: 1075

• logo: 1013

• wearing: 1013

• body: 1012

• dark: 1004

• to: 998

• high: 987

• blue: 949

• beta: 933

• 8k: 928

• illustration: 915

• man: 915

• red: 904

• cute: 889

• photo: 873

• at: 857

• as: 849

• design: 837

• relaxed.png: 787

• cartoon: 761

• colors: 745

• old: 714

• portrait: 709

• ultra: 692

• fantasy: 682

• eyes: 680

• green: 678

• 4k: 667

训练词语: