/upscale
(score_9, score_8_up, score_7_up, score_6_up, score_5_up, score_4_up, source_bishoujo)
50mm lens
aperture opening of f/4.0
sitting on barstool in detailed bar, 1girl, extremely beautiful detailed face, best shadow, medium breasts, orange hair, long hair, Stlouis, female in her Late twenties, (evening gown, plunging neckline), Russian, green eyes, silk dress, ((2.5D:1.4)), 35mm, intricate details, HDR+, intricate details, Detailed Fluffy Fur, Detailed Hair, hyperdetailed, natural fur texture, detailed skin, hyperrealism, sharp (hyperrealistic, cinematic light, depth of field)
Made with ONE FOR ALL checkpoint by Chaos Experience @ https://civitai.com/user/ChaosExperience/
ChaosExperience
About version 3
First of all, the "Plus Ultra" model is an SDXL 1.0 model with DPO. However, given the popularity of Pony models, I took the step of adapting the checkpoint to understand Pony tags and reproduce similar results. However, please note that this is not a Pony model in essence, so some prompts may not generate the intended result. Also, compatibility with LoRAs Pony tends to be perfect, so feel free to use LoRAs for Pony.
Version 3 was made with twice as much training as version 2, reinforcing the DPO and significantly increasing the quality of the generations. However, I would like to point out that the model may need LoRA for more specific NSFW content, especially sexual acts. This is because I preferred to pay attention to other factors when training the base model.
Finally, this is the last version of the ONE FOR ALL series in the XL segment. As explained in version 2, the training and results of the XL models are a real nightmare and end up requiring very high energy consumption and the series as a whole does not generate any financial return, so there is no point in continuing.
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:
PurpleSmartAI - Thank you for the hard work creating the Pony Diffusion.
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