I'm not a fan of describing models, but this is my last agony while waiting for SD3, so I'll tell you about savsdxl-5.2.3
- Increased detail and sharpness of lines.
- Great light
- High dynamics.
- Decent nsfw.
Compliance with artistic requests and not quite the right adherence to the promt that you will not even notice, as they say a feature, not a bug, but judges you when you notice it at more than 75 tokens.
In order to avoid questions I will tell you at once: this is a mix of the previous model 4.0.5 between Xi and delta.
In order counting can realize that I created and tested two dozen versions before coming to it. No, I don't need donations, just try this model.
Compare it for yourself with one seed on your favorite queries and favorite sdxl models, maybe you will like its spirit as I liked it!
From experience: 100 steps can give you a good result and just as well you can get a good result in 30 steps. Start your comparison with small.
Please let me know if you have any problems with the samplers, because I didn't have time to test all of them, use mine for starters.
And a secret for all who merge checkpoint: I was not aiming for a certain result, I expected only beauty at the end of generation. In alpha and beta it was RANDOM (R), but when choosing models for merge I expected to get a good result. Choose good models for merge.
Description
- Increased detail and sharpness of lines.
- Great light
- High dynamics.
- Decent nsfw.
Compliance with artistic requests and not quite the right adherence to the promt that you will not even notice, as they say a feature, not a bug, but judges you when you notice it at more than 75 tokens.
In order to avoid questions I will tell you at once: it is a mix of the previous model 4.0.2 between Xi and delta.
In order counting can realize that I created and tested two dozen versions before coming to it. No, I don't need donations, just try this model.
Compare it for yourself with one seed on your favorite queries and favorite sdxl models, maybe you will like its spirit as I liked it!
From experience: 100 steps can give you a good result and just as well you can get a good result in 30 steps. Start your comparison with small.
Please let me know if you have any problems with the samplers, because I didn't have time to test all of them, use mine for starters.
And a secret for all who merge checkpoint: I was not aiming for a certain result, I expected only beauty at the end of generation. In alpha and beta it was RANDOM (R), but when choosing models for merge I expected to get a good result. Choose good models for merge.