I spent months downloading every popular checkpoint on CivitAI. Hundreds of models. Same problem every time — they're either overtrained on one aesthetic, or so general they produce nothing interesting without heavy prompt engineering.

So I started training my own.

The difference was immediate. When you control the training data, you control the output. I could get consistent styles, specific lighting, exact color palettes — stuff that would take 50+ prompt tokens to approximate with a public model.

Here's what I learned along the way:

1. Dataset quality > dataset size. 200 carefully curated images beat 2,000 scraped ones every time. I spend more time selecting and captioning training images than actually training.

2. LoRAs are underrated for style transfer. Everyone's training full checkpoints when a well-made LoRA at the right weight gives you way more control with way less VRAM.

3. The sweet spot for SDXL fine-tuning is shorter than you think. Most people overtrain. I've gotten my best results stopping earlier than feels comfortable and testing aggressively.

I'm now releasing my models exclusively through IrascibleXL. Weekly drops, full download access, recommended settings for every model, and a community of people actually pushing these checkpoints.

If you're tired of generic outputs and want models that actually have a point of view — this is what I'm building.