v8

肌理更新：強化了以下tag的學習：

Texture Update: The following tags have been reinforced in training:

realistic, photorealistic, flat color,

shiny skin, matte skin, shiny hair,

請注意，在danbooru數據集中有很多個用於描述「照片」和「接近照片風格」的tag。我在訓練集中統一標註這些圖片為「photorealistic」。但是使用danbooru訓練集訓練的SDXL模型多數無法很好地繪製寫實圖像，因此「photorealistic」建議只在較低權重下使用來調整肌理。「realistic」可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）是一個實驗性微調模型系列，數據集約85%來自從CivitAI用戶發佈的AI生成圖片。早期ppw數據集最初建立於pony v6 生成的圖片，因此本系列生成的圖片帶有pony diffusion特徵。

• 本系列模型使用標準Danbooru標籤，擅長生成中景及近景的風格化人像。主要功能是在不使用畫師關鍵詞及減少質量提示詞的條件下，獲得相對穩定的圖像質量，節省提示詞token空間。
  本模型非風格化LoRA，不同提示詞及生成條件下可能有輕微畫風差異。

• Pony: People's Works (ppw) is a experimental fine-tuned model series, approximately 85% of the dataset comes from AI-generated images published by users on CivitAI. Since the earlier ppw dataset was built on images generated by Pony V6, the outputs of this series also carry some characteristics of Pony Diffusion.

• This series uses standard Danbooru tags and is mainly optimized for generating stylized portraits at medium and close range. The primary effect of this model series is to allow the basemodel to achieve relatively stable image quality, without artist keywords or long quality tags, freeing up token space for prompts.
  These models are not style LoRAs. There may be subtle stylistic variations depending on different prompts and generating conditions.

版本資訊 | Version Info.

• 本頁面發布的是ppw的高維LoCon版本，也是本專案主頁面。

• LoCon版本的ppw可以靈活搭配各種功能LoRA及基礎模型，效果強度具較高可控性。高維度LoCon版本具更強泛化及細節表現，但需要更多儲存空間及計算資源。

• 主要用於線上生成服務及性能較強用戶本地生成。

• 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.

精簡版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

正向提示詞：

masterpiece, best quality, very aesthetic

負向提示詞：

low quality, displeasing

更新記錄 | Change log

v7

v7版本對數據集結構做了大幅調整，並使用了不同的訓練參數及策略，因此可能v7比之前版本穩定性低。

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在線生成器上的表現和吐司的在線生成表現完全不同，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 於 CivitAI 高權重版本

v7版本簡介：

這是基於前作數據集開發的影像質量LoCon，約90%-95%的圖片數據來自CivitAI。

它可使模型在不使用畫師關鍵詞及較少質量提示詞下實現相對穩定的圖像質量，節省更多token空間，並可修復部分模型固有生成缺陷（不含手部）。

因數據集挑選原因，生成圖像具有Pony質感。但因不指向特定畫師、風格及繪畫技法，不同提示詞、模型條件下畫風會有細微差異。

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.

數據集來源及授權 | Dataset Source & License

• 數據集中每張圖片均由作者親自篩選、分類和標註編輯，數百張圖片經手動編輯修正。

• 本模型為免費、開源模型，用戶可在私人設備部署。作者不從模型銷售獲取報酬。作者不限制本系列模型用於商業生成服務及商業用途生成圖像，但請留意相關Checkpoint及LoRA的授權限制。

• 數據集約90%-95%為AI生成，但約250+張圖片來源於公共媒體、新聞媒體和出版物作為概念補充，未來版本會逐步替換。商業用戶請自行評估相關風險。

  數據集中未訓練任何獨立畫師資料，且未標註畫師信息（不排除AI誤標註情況）。

• 另外，本模型禁止用於閉源商業應用、模型銷售及閉源商業模型融合。對於開源融合模型用於生成服務不設限制，但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.