v8

肌理更新：強化了以下標籤的學習：

Texture Update: 已加強訓練的標籤包括：

realistic, photorealistic, flat color,

shiny skin, matte skin, shiny hair,

請注意，Danbooru數據集中有許多標籤用於描述“照片”或“接近照片風格”的圖像。我在數據集中將這些圖片統一標記為“photorealistic”。但使用Danbooru訓練的SDXL模型大多無法很好呈現寫實風格，因此“photorealistic”建議以較低權重使用來調整肌理，而非產生真實感。“realistic”標籤可在較高權重下正常發揮效果。

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%的數據集來自用戶在CivitAI上發佈的AI生成圖片。早期ppw數據集最初基於pony v6生成的圖片，因此該系列模型輸出帶有pony diffusion特徵。

• 本系列模型採用標準Danbooru標籤，主要擅長生成中近距離的風格化人像。主要功能是使基模型在無需畫師串、少量質量提示詞條件下獲得相對穩定的圖像品質，以及為提示詞節省token空間。
  本模型非風格LoRA，不同提示詞及生成條件下畫風可能有細微差異。

• 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.

版本信息 | Version Info.

• 本頁面發布的是ppw的高維LoCon版本，也是本專案的主頁面。

• LoCon版本的ppw可靈活結合各種功能LoRA與底模，且效果權重擁有更高可控性。高維版LoCon擁有更強泛化力與細節表現，但佔用更多儲存空間與計算資源。

• 主要用於線上生成服務及高性能用戶本地生成。

• 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

正面提示詞 (positive):

masterpiece, best quality, very aesthetic

反面提示詞 (negative):

low quality, displeasing

更新紀錄 | Change log

v7

v7版本對數據集結構做了大幅調整，並採用了不同的訓練參數及策略，可能導致v7不及舊版穩定。

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線上生成器上的表現與吐司線上生成完全不同，同參數無法複現，原因不明……

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同參數版本 及CivitAI 提高權重版本

v7版本簡介：

本版本基於前作的數據集開發，約90%-95%數據源於CivitAI發布的圖片。

允許模型在無需使用畫師標籤，且使用較少質量提示詞的條件下，取得相對穩定畫質，節省更多token空間，並能修復部分模型固有生成瑕疵（但不包括手部瑕疵）。

由於數據集選擇，輸出具有Pony質感，但因不指向任何特定畫師、風格或繪畫技巧，故根據不同提示詞及模型條件畫風可能略有差異。

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人工篩選、分類及標註，其中數百張經過手工修訂。

• 本模型為免費且開源模型，用戶可自行在私人設備部署。作者不從模型銷售中獲利。作者不限制本系列模型用於商業生成服務或商業用途的生成圖像，但請留意所搭配Checkpoint及其他LoRA的授權限制。

• 約90%-95%數據為AI生成，但約250+張圖像來自公共媒體、新聞及出版物作為概念補充。未來版本將逐步替換相關素材。商業用戶請自覺注意相關風險。

  數據集未包含任何獨立畫師作品，亦無包含畫師標註（但不排除AI誤標）。

• 此外，本模型禁止用於閉源商業應用、模型販售及融合於閉源商業模型。對於用於生成服務的開源融合模型不設限制，但建議標註融合來源。

• 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.