v8

肌理更新：強化了以下tag的學習：

Texture Update: 訓練中加強了以下標籤：

realistic, photorealistic, flat color,

shiny skin, matte skin, shiny hair,

請注意，Danbooru數據集中有多個用於描述「照片」或「接近照片風格」的標籤。我在訓練集中將這些圖片統一定義為「photorealistic」。但是使用Danbooru訓練的SDXL模型多數無法很好地繪製寫實圖像，因此建議「photorealistic」只在較低權重使用以調整肌理，「realistic」則適合在較高權重下正常運作。

Please note that Danbooru dataset contains multiple tags to describe "photo" or "photo-like styles". I’ve tagged all such images as “photorealistic” in dataset.

However, most SDXL models trained on the Danbooru dataset do not render realistic images well. “photorealistic” is only recommended at low weight, where it can help adjust texture rather than create realism images. The “realistic” tag can work properly at higher weight.

快速上手 | Quick Start

這是什麼？ | What is this?

• Pony: People's Works （ppw）是一系列實驗性微調模型，約85%的數據來自CivitAI用戶發布的AI生成圖片。早期ppw數據集基於pony v6生成的圖片，因此該系列模型生成的圖片帶有pony diffusion的特徵。

• 本系列模型使用標準Danbooru標籤，主要擅長生成中近景風格化人像。主要功能是在不使用畫師串與減少質量提示詞的條件下，實現相對穩定的圖像質量，節省token空間。
  本模型非風格LoRA，根據不同提示詞和生成條件可能出現微妙畫風差異。

• Pony: People's Works （ppw）是一系列實驗性微調模型，約85%數據集來自CivitAI用戶發布的AI生成圖像。由於早期ppw數據基於Pony V6生成圖像，本系列結果也帶有Pony Diffusion特徵。

• 本系列使用標準Danbooru標籤，優化中近距離風格化人像生成。主效用為使基模型能在無需畫師提示詞和長質量標籤條件下實現相對穩定圖像質量，並為提示詞節省token空間。
  該模型並非風格LoRA，根據不同提示詞和生成條件畫風可能略有差異。

版本信息 | Version Info.

• 本頁發布的是ppw的高維LoCon版本，也是本項目的主頁面。

• LoCon版本的ppw可靈活搭配各類功能LoRA和底模，效果強度更可控。高維度版本具備更強泛化能力及細節表現，但佔用更多存儲和計算資源。

• 主要用於線上生成服務及性能較強用戶的本地生成。

• This page features the high dim LoCon version models of ppw, which also serves as the main page of this project.

• The LoCon versions of ppw can be flexibly combined with various functional LoRAs and checkpoints, offering greater controllability over effect weight. High dimension versions provide stronger generalization and more detailed rendering, but it requires more storage space and computational resources.

• They are mainly intended for online generation services and local use by users with high-performance PCs.

輕量版LoCon | Lightweight LoCon ver.

基礎模型版 | Checkpoint versions (Illustrious)

基礎模型版 | Checkpoint versions (NoobAI)

更多 | more

使用方法 | Usage

正面提示：

masterpiece, best quality, very aesthetic

負面提示：

low quality, displeasing

更新記錄 | Change log

v7

v7版本對數據集結構做出較大調整，並使用不同的訓練參數和策略，因此v7可能不如之前版本穩定。

The v7 version has undergone significant structural adjustments to the dataset, and utilizes different training parameters and strategies. As a result, v7 may be less stable than the previous versions.

v-pred模型在civitAI線上生成器和吐司線上生成表現完全不同，相同參數無法復現。我也不清楚原因......

The v-pred model's performance on the CivitAI online generator is completely different from online generation on TensorArt. The results are entirely unreproducible with a same parameters. I have no idea why...

TensorArt版本 CivitAI相同參數版本 及 更高權重版本

v7版本簡介：

此為基於前作數據集演進的圖像質量LoCon，約有90%-95%的圖片來自CivitAI。

該版本使模型能在無需畫師串並使用較少質量標籤條件下，實現相對穩定的圖像質量，並節省更多token空間，同時修正部分模型固有缺陷（手部問題除外）。

由於數據集選擇，生成圖像會帶有Pony特有質感，但由於不指向任何特定畫師或風格，根據不同提示詞和模型條件可能出現微妙畫風差異。

This is a generation quality LoCon developed based on the dataset from the previous work. About 90%-95% of the image data comes from CivitAI.

It allows models to achieve relatively stable image quality without artist tags or using long quality prompts, freeing up more token space. Additionally, it can fix some inherent generation flaws of the model. (except for hands)

Due to the dataset selection, the generated images exhibit a Pony-like style. However, since it does not reference any specific artist, style, or painting technique, there may be subtle stylistic variations depending on different prompts and checkpoint conditions.

數據集來源及許可證 | Dataset Source & License

• 數據集中每張圖片均由作者親自人工篩選、分類與標註，數百張圖片經過手工編輯與修正。

• 此模型為免費開源模型，用戶可自行在私人設備部署。作者不從售賣模型獲利。作者不限制本系列用於商業生成服務或商業用途生成，但請注意所搭配Checkpoint及其他LoRA的授權條款。

• 約90%-95%數據為AI生成，但約有250+張圖片從公共媒體、新聞及出版物收集用作概念補充。未來版本將逐步替換相關素材。請有商用需求的用戶注意風險。

  數據集未包含獨立畫師資料，亦未標註畫師資訊（但不排除AI錯誤標註的可能）。

• 此外，本模型禁止用於閉源商業、模型販賣及融合入閉源商業模型。對於開源融合模型用於生成服務則無限制，建議標註融合模型來源。

• Every image in the dataset has been manually selected, categorized, and annotated by the author. Additionally, hundreds of the images have been manually edited and corrected.

• This model is free and open-source model, allowing users to deploy it on their personal devices. The author does not receive any compensation from selling the model. The author does not impose restrictions on using this model for commercial image generation services or generating images for commercial purposes. However, please be mindful of the license restrictions of the Checkpoint and other LoRAs used alongside this model.

• Approximately 90%-95% of the dataset consists of AI-generated images. However, around 250+ images have been collected from public media, news outlets, and publications to supplement concepts. Future versions will gradually replace these materials. Users with commercial intentions should be aware of the potential risks.

  This dataset does not include training data from any individual artist, nor does it contain explicit artist attributions (though AI mistagging cannot be entirely ruled out).

• Additionally, this model is not permitted for use in closed-source commercial applications, model resales, or merged into closed-source commercial models. There are no restrictions on open-source merged models being used for image generation services, but it is recommended to credit the sources of any merged models.