Enhancement bundle for Chroma HD

Cover images are raw output from the model, 1MP resolution, no upscale, no hands/faces inpainting fixes, even no negative prompt. They are not perfect, but 100% "what you see is what you get".

This model

This is an all-in-one (all aspects I can come up with) finetuned model.

Trained with total ~5k images. Mainly photograph and some anime images.

• No AI images. Not on my watch. Every image is hand-picked by me. Only high resolution images with finest details. The whole dataset avg pixels is 3.37 MP, ~1800x1800.

• 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 better natural texture and details.

• Better lighting, a little bit cinematic feelings.

• Less overexposure and oversaturation.

For anime:

• Because Chroma does not support artist tags, and quality tags (not sure though, in my tests, the base model didn't response to those tags.). It's very unstable for anime.

• I added a default anime style. This default anime style is mainly for reducing noise and overfitted bias that towards simple low quality image.

• Also included a sub dataset full of hands.

Why a bundle, rather separated LoRAs for different aspects?

This a mathematic problem. You can ask an AI for a quick answer. "When using an image model, what's the problem if I stacked too many LoRAs on my image model? In mathematic aspect."

If you train all your data and concepts in one go, then there will be no conflicts.

How to use

Simple, this is a LoRA, apply it with strength 1 then you will get the finetuned model.

For anime, use "Digital anime illustration of ..." as trigger words.

Recommended settings: I use euler_cfg_pp with cfg 0.5~0.7, beta schedular, 30 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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