description: <p></p><p><em><span style="color:rgb(64, 192, 87)">The training of this model and the images it generates are solely for learning purposes.</span></em></p><p></p><p><strong><em><span style="color:rgb(64, 192, 87)">I did nothing, I'm just a porter.</span></em></strong></p><p></p><p><em><span style="color:rgb(64, 192, 87)">You could find the example prompts from the images above.</span></em></p><p></p><p>Recommended weight: <em><span style="color:rgb(64, 192, 87)">1.0~0.6</span></em><span style="color:rgb(64, 192, 87)">, </span>adjust as needed until the character's appearance meets your requirements.</p><p></p><p>Upscale value recommendation is around 1.5~2.0, denoising strength is 0.2</p><p></p><p><em><span style="color:rgb(64, 192, 87)">Continuous adjustment of the prompt words is needed to achieve a good representation.</span></em></p><p></p><p>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 <span style="color:rgb(193, 194, 197)">detailed,highres</span>' to positive prompt should work too.</p><p></p><p>Manually cropped and enhanced the quality of poor images, a total of 27 images were prepared for the dataset.</p><p></p><p>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.</p>
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