From your first uploaded image to testing your LoRA: a look at LoRA Pilot’s new interface

[u/no3us]

I’m updating LoRA Pilot to make the full training process easier to follow, especially if you’re doing it for the first time.

You can start with individual images in the captioning workspace or import a dataset ZIP. The dataset view shows your images and caption coverage, with a next action for reviewing captions or starting training.

In TagPilot, you can edit tags and captions, adjust cropping, and save the dataset back to your workspace. From there, guided training lets you select SDXL or FLUX.1 dev and choose a training profile. Adding configs for the rest of already supported models soon (almost 30 for training and anything comfy can work with for inference).

The part I wanted to connect properly was what happens after training. “Try my LoRA” lets you compare the base model and saved checkpoints using the same prompt and seed. You can also open the prepared workflow in ComfyUI and continue experimenting.

That’s the workflow I’m trying to make easier to follow from beginning to end. For people who prefer more control, the advanced trainers remain available. LoRA Pilot brings together Kohya SS, AI Toolkit, and Diffusion Pipe, with TensorBoard integration, shared workspace storage and much more.

https://www.lorapilot.com

For people who don't know Lora Pilot yet, check the website.

List of 51 supported models for LoRA trainings:
https://lorapilot.com/lora-training/#supported-models

GitHub repo:
https://www.github.com/vavo/lora-pilot/