What is this?

As you can see, this place is called CyberHarem, a centralized repository for anime waifu images dataset and LoRA models.

Currently, we have collected databases of several popular mobile games' characters and crawled datasets of female characters from these games for training. In the future, we may include more characters, not just limited to mobile games, but also from anime series. You can find your waifu with CyberHarem/find_my_waifu.

CyberHarem is a non-profit technical team that works purely out of interest, so we do not charge any fees in any form. However, our computing resources and team members' working time are limited, so we cannot guarantee the delivery time of models in principle. We will do our best to complete them as soon as possible under the circumstances, and we hope for your understanding in this regard.

Where does the dataset come from? What's the format?

• The dataset is automatically crawled from various major image websites like ZeroChan, Anime-Pictures, Danbooru, Rule34, etc.
• In each dataset repository, there are both original data packs and images resized and aligned to a uniform size, along with image tags generated using the SmilingWolf/wd-v1-4-convnextv2-tagger-v2 model.

How are the models trained? What's the format?

LoRA models are trained in batch with corresponding datasets. We use 7eu7d7's HCP-Diffusion training framework for the process.

How to use a1111's WebUI to generate images of anime waifus?

1. Go to the model repository.
2. Check the Model Card and choose a step that looks good visually.
3. Click on the right side's Download to download the model package. The package contains two files: a .pt file and a .safetensors format LoRA file.
4. You need to use both of these models simultaneously. Put the pt file in the embedding path and use the safetensors file as LoRA mount.
5. Use the trigger words (provided in the Model Card) and prompt text to generate images.

Why do some preview images not look very much like the original characters?

The prompt texts used in the preview images are automatically generated using clustering algorithms based on the feature information extracted from the training dataset. The seed for generating images is also randomly generated, and the images are not selected or modified in any way, so there is a probability of such issues.

In reality, according to our internal tests, most models that have this issue perform better in actual use than what you see in the preview images. The only thing you might need to do is fine-tune the tags you use a bit.