Updated: Oct 31, 2025
styleEnhancement bundle for Chroma HD
Cover images are raw output from the model, default 1MP resolution, no magical plugins, no upscale, no hands/faces inpainting fixes, no negative prompt.
Make Chroma look better.
Trained with total ~5k images.
Photographs, digital arts, anime images, space images ... everything I can come up with. Many specialized sub datasets, such as close-up clothing, hands, complex ambient lighting ...
Only high resolution images with finest details. Every image is hand-picked by me.
Comprehensive natural language captions from Google LLM. Anime characters are tagged by wd tagger v3 first and then refined to natural language by Google LLM.
Effects
For general realistic image:
It gives you natural texture and details. Better lighting, a little bit cinematic feelings.
Less overexposure and oversaturation.
For anime and 2D art style:
Because Chroma does not support artist tags and quality tags. It's very hard to guide the model to generate high quality anime. I added a default anime style. This default anime style is mainly for reducing noise and overfitted bias that towards low quality image.
Also included a sub dataset full of anime hands. So it should give you better hands as well.
Why a bundle, rather separated LoRAs for different aspects?
Larger datasets is always better. For example, even if you only use this model for anime, the knowledge from photographs can still teach the model how to add more details to anime room and make characters more alive.
Seems no effect?
Yes but also no. Because of the dataset is not small, the model is not overfitted. It does not drastically change the content. But it does can drastically change the details.
For example: (image is compressed, see original image here.)

How to use
Strength 1 is recommended.
For anime, start with "Digital anime illustration." as trigger words.
Recommended settings: I use euler_cfg_pp with cfg 0.5~0.7, beta schedular, 30 steps.
For Civitai online generator, use cfg 3, 30~40 steps.
License
This model is released under Apache License 2.0.
Additional terms: Selling or monetizing models that merged this LoRA is prohibited.
Update logs
(10/4/2025) v0.1 init release.
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