ONE FOR ALL «2.5D»

v. Final

v. 3 DPO+VAE

v2.0

v1.0

Last Update：2025-02-23 16:33:25

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ChaosExperience

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ChaosExperience

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ChaosExperience

Creations Number

965

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Download

260

Model Source:

civitai

Type:

Checkpoint

Base Model:

SD 1.5

Tags:

#Anime#disney#3D#Base Model#waifu#Furry#2.5D#bishoujo#美少女#simple prompt#2.5d style#Girl Illustration#Pretty

Trigger Words:

License Scope:

Creative License Scope

Online Image Generation

Merge

Allow Downloads

Commercial License Scope

Sale or Commercial Use of Generated Images

Resale of Models or Their Sale After Merging

Model Parameters:

Review:

That's all folks!!

After thinking about it, I've come to the conclusion that I should do one last version for my SD 1.5 models. The point is that the time and money I've spent and the return I've received have led me to conclude that, once the older models were solidified, it doesn't matter if my model is better or not, I'm unlikely to win over fans of other models. What's more, I've reached a point where I no longer see a big enough evolution on my part in training 1.5 models to justify the time and money spent.

The FINAL model is the result of using the same Dataset that I used for the XL models:

ONE FOR ALL «Plus Ultra» - v 3 DPO+Pony | Stable Diffusion Checkpoint | Civitai

ONE FOR ALL «Pony Fantasy» DPO+VAE - v 2.0 DPO | Stable Diffusion Checkpoint | Civitai

So, in addition to the DPO, you can rely on some of the features of the Pony models. But remember, we're talking about SD 1.5 models, so don't expect the same result as an XL model.

About DPO (Direct Preference Optimization).

DPO fine-tune on datasets scored as highly visually appealing by an aesthetics classifier, to bias the model to visually appealing generations, while maintaining distributional guarantees and improving generic text-image alignment in addition to visual appeal. The training is via gradient descent, automatically validate checkpoints with the 500 unique prompts of the Pick-a-Pic validation set: measuring median PickScore reward of generated images. DPO scheme implicitly learns the differences in between images by taking an expectation over the inner term of the prompt.

So, DPO pay more attention on your prompt and try to generate a better image from it.

I'm very grateful to the authors of the Checkpoints and LoRAs used in the ONE FOR ALL series and I recommend everyone to visit them and appreciate their work (if your name isn't here, it's because any of your assets were been used):

And to all those who in some way collaborated or encouraged this work, I affectionately offer this simple but sincere Special Thanks:

Special Thanks to those creators:

Special Thanks to my amazing followers:

AND YOU WHO IS AMAZING TO READ SO FAR HERE!! WOW!! 😱🤩💓

YOU ARE REALLY AMAZING!! THANK YOU VERY MUCH!! 🥰🥰🥰

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