The training of this model and the images it generates are solely for learning purposes.

I did nothing, I'm just a porter.

You could find the example prompts from the images above.

Recommended weight: 1.0~0.6, adjust as needed until the character's appearance meets your requirements.

Upscale value recommendation is around 1.5~2.0, denoising strength is 0.2

Continuous adjustment of the prompt words is needed to achieve a good representation.

If the model tends to exhibit blurriness, that can be alleviated by adjusting the model's weights. Adding 'score_6,score_5,score_4,' to negative prompt, 'highly detailed,highres' to positive prompt should work too.

Manually cropped and enhanced the quality of poor images, a total of 27 images were prepared for the dataset.

Tips: I read about this method in an article on CIVITAI (I forgot which one). The original dataset did not include images of full body or back views, to address this issue, I extracted full body and back view images of a similar style and added them to the dataset. Then, during caption, I only tag the characteristics of the images without adding trigger words. After training, I found that the model's learning effect was good, and the characters could be displayed in full body and back view images normally.