What is this?

• People’s Works is an experimental fine-tuned model series, originally based on a dataset composed of images generated by Pony V6 XL. The dataset consists of several thousand images published by AI community users , along with images synthesized by the author using AI. These images were manually curated, edited, modified, and annotated before being used for training. In addition, there is an auxiliary dataset of over 2,000 images composed of photographs, game CGs, and 3D renders, used to provide supplementary knowledge.

Model features

• The primary function of this series of model is to help the basemodel generate relatively stable, stylized images, without artist keywords or long quality tags, freeing up token space for prompts.

• By using manually selected and annotated datasets, the model strengthens both positive and negative aesthetic tags training.

• The model includes targeted enhancements for specific visual textures and styles, such as flat color and realistic .

• It enables finer control over character attributes, including age, ethnicity, and skin texture.

• A higher training resolution is used, improving the basemodel’s performance during high-res upscaling.

• By manually editing the images, the likelihood of certain flaws appearing in the model’s outputs has been reduced.

Usage

positive:

masterpiece, best quality, very aesthetic

negative:

low quality, displeasing

v9

• The series name has been changed. Starting from this version, images sourced from Pony v6 XL make up less than one third of the training data across all AIGC content. As increasing number of new models are emerging, I plan to expand this series to other models next year. To avoid potential confusion and misunderstanding for users in the future, the series name has been changed starting from this version.

• This version is trained directly as a LoCon, rather than training a checkpoint first and then extracting a LoRA. Compared to the previous versions, the model delivers stronger effects.

• All v9 models use a 1536-resolution training set. When generating images with this LoRA, single-side resolutions from 768 to 1536 are now supported. When using high-res fix, you can also try higher denoise values.

• Color adjustments were applied to images. When no specific color is specified, the model now tends to produce warmer tones, with slightly increased saturation. Darker scenes also have stronger contrast between light and shadow.

• In earlier versions of dataset, nose depiction was inconsistent. Out of personal interest, the author manually modified around 300 images and temporarily excluded about 200 images that could not be edited in time. Characters’ noses now have nose wings.

• Hundreds of outdated, low-quality images from older versions of dataset have been removed.

• A new experimental dataset has been added. Using real photographs as guidance, you can now use the following age and ethnicity tags:

child, teenage, adult, mature

Caucasian, Asian, Indian, African