A proof of concept generating captions using Google Gemma 3 on Google Colab Free Tier for captioning prompts akin to training data of FLUX Chroma: https://huggingface.co/lodestones/Chroma

Try the Chroma model at: https://tensor.art/models/891236315830428357

This dataset was built using 200 images from Redcaps : https://huggingface.co/datasets/lodestones/pixelprose

And 200 LLM captioned e621 images: https://huggingface.co/datasets/lodestones/e621-captions/tree/main

The total trained images are just 400 total , randomly selected , so this LoRa adaptation is very basic! You can likely train a better version yourself with listed tools on Google Colab Free Tier T4. 


Want to train your own LoRa from a JSON or .parquet set if data?  Use this notebook found in this repo: https://huggingface.co/codeShare/flux_chroma_image_captioner/blob/main/train_on_parquet.ipynb

//----//

I made some .parquets of the captions here for easier browsing: https://huggingface.co/datasets/codeShare/chroma_prompts

To use this Gemma LoRa adaptation got to the Google Colab Jupyter notebook in this repo: https://huggingface.co/codeShare/flux_chroma_image_captioner/blob/main/gemma_image_captioner.ipynb

To train your own LoRa adaptation of the Gemma on Google Colab Free Tier T4 , visit :  https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_(4B)-Vision.ipynb


---
base_model: unsloth/gemma-3-4b-pt-unsloth-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/gemma-3-4b-pt-unsloth-bnb-4bit
- lora
- sft
- transformers
- trl
- unsloth
---

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