Super-squash branch 'main' using huggingface_hub

Co-authored-by: yoinked <yoinked@users.noreply.huggingface.co>
Co-authored-by: narugo1992 <narugo1992@users.noreply.huggingface.co>

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README.md

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+---
+title: README
+emoji: 📉
+colorFrom: gray
+colorTo: gray
+sdk: static
+pinned: false
+license: mit
+---
+
+## Update 2023.9.12
+
+If you want to make a LoRA request, see [this article](https://civitai.com/articles/2186/2023-9-12-open-requests-for-character-lora).
+
+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.
+
+## Update 2023.9.2
+
+Two recent developments:
+
+1. The **automated training process for `v1.4` has been deployed**, and the model's quality has improved significantly compared to before (for more technical details, see: https://civitai.com/articles/2064/2023-8-31-release-of-v14-training-automation-process). We are now in the process of thoroughly cleaning the dataset and retraining the model.
+
+2. We now support LoRA training for characters in anime videos, and the entire process is highly automated.
+
+## 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 (see [Supported Games of GChar Library](https://narugo1992.github.io/gchar/main/best_practice/supported/index.html#supported-games)) 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](https://huggingface.co/spaces/CyberHarem/find_my_waifu).**
+
+## Where does the dataset come from? What's the format?
+
+* The dataset is automatically crawled from various major image websites like [ZeroChan](https://zerochan.net), [Anime-Pictures](https://anime-pictures.net/), [Danbooru](https://danbooru.donmai.us/), [Rule34](https://rule34.xxx/), etc. (see [Supported Sites of GChar Library](https://narugo1992.github.io/gchar/main/best_practice/supported/index.html#supported-sites))
+* 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](https://huggingface.co/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](https://github.com/7eu7d7)'s [HCP-Diffusion](https://github.com/7eu7d7/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.**