🔥Choose Your Path: The Modular LoRA Training 🏋️ Odyssey Begins🔥

Hey Friends,
Weeks back you voted for LoRA training—thank you! I hit a small creative block, and then FLUX Kontext and WAN Video 2.2 arrived and shook everything up (in the best way). I decided to pause rather than ship something outdated. The promise still stands—and now it’s stronger.

The pain points we’re leaving behind

• Outdated tutorials from other creators masquerading as “current.”

• Cross-model confusion (SDXL tips copy-pasted onto FLUX/WAN).

• Parameter folklore with no rationale.

• Missing evaluation—no grids, no stress tests, no clarity.

• Overlong videos where the one crucial minute is buried at 28:14.

So I came up with a modular LoRA training approach. Instead of one giant video, I’ll create multiple shorter ones where you can pick your own learning path.

Disclaimer (this should just serve as an example)

LoRA training always comes down to these five steps:

1. Dataset generation (your training images/videos)

2. Dataset annotation/tagging (tell the model what to see and what to expect)

3. Training

4. Testing (detecting overtraining or undertraining)

5. Publishing online (or keeping it private)

I’ll start with 3 videos:

1. Dataset generation + annotation: These belong together. I’ll cover dataset creation for training a Concept, a Style, and a Character—explaining what each means in terms of LoRA and what pitfalls to avoid.

2. Different training setups: Using tools like FLUX Gym or RunPod to train a LoRA. (I’m not yet sure if it will be FLUX Gym in the final cut—there are many good options!)

3. Testing and publishing: How to find out if your LoRA is actually good and how to share it properly.

The advantage of this modular approach is that I can easily showcase different training tools without making 40+ minute videos. (Let’s be honest—no one is going to sit through those.)

Patron-first release
Patreon gets the full cut with extra context and those tiny on-camera fixes that save hours. YouTube will get the streamlined, simplified version later for better flow and easier access. 💛

Best regards,
Chris