lora

Retro Comic Flux

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Model description

Retro Comic Flux is a Flux LoRA trained on a dataset consisting entirely of public domain images manually sourced, cropped and enhanced. I used ~50 images 3 repeats 15 epochs and 0.0001 learning rate. These were all captioned using Joy Caption Batch.

This is very experimental and I had a difficult time sourcing high quality images of animals and vehicles in this style, so for now it's primarily good with human figures. It work best when you describe the character and a scene for a backdrop. It struggles with hands and eyes sometimes. If something is really not in the dataset it defaults to an ordinary AI illustration style.

USE DEIS SAMPLER FOR THE BEST RESULTS.

I used very similar settings as I have with my previous LoRAs so I won't go into detail for the sake of my time and brevity, low learning rates with less repeats and good data work best for simple styles.

I was careful in my selection of images and preprocessed each image in Photoshop to bring out vivid colors, reduce the yellowing and boost contrast. This helped me to maintain a consistent coloring and I believe it improved the training quality. When I did this I also removed the speech from speech bubbles from some of the training data for more flexibility and to help Flux understand context. You can explicitly state "empty speech bubble" in your prompts and it tends to work. If you want to dive in deeper try using an image captioner to describe a style you want to emulate, the images have rich captions and the model responds well to natural language.

Definitely expect a V2 of this LoRA soon, I have some ideas on how I can improve consistency but I need to find more varied source images.

Trigger keyword: 'c0m1c' 'comic book panel'

Recommended strengths: 0.7 - 0.9

Trigger words

You should use Ce to trigger the image generation.

You should use comic book panel to trigger the image generation.

Download model

Weights for this model are available in Safetensors format.

Download them in the Files & versions tab.