Hello everyone,
I’m excited to share what’s coming in the next update for Beyond Experimental Update (Alpha) 10.1
What I learned from the last version
In a previous build, I didn’t have enough balanced data. I had way more anime images than realistic ones, and that imbalance caused the anime style to overwrite the realistic style during training. I did get one model to work, but it wasn’t trained properly, so results could be blurry or disfigured. Looking back, I could’ve fine-tuned with higher-quality images; I just wasn’t thinking in that direction at the time.
What’s new: precise style control
This update introduces clear trigger pairs so you can reliably switch styles without guesswork.
Realistic images
Positive trigger:
source_realisticNegative trigger:
BeyondNegativeR(cleans digital noise, motion blur, etc.)
Example
Positive:
source_realistic, a cinematic photo of a mountain landscape, masterpieceNegative:
BeyondNegativeR
Anime images
Positive trigger:
source_animeNegative trigger:
BeyondNegativeA(cleans messy lines, inconsistent details, etc.)
Example
Positive:
source_anime, a key visual of a magical girl, masterpieceNegative:
BeyondNegativeA
A note on training & feasibility
I want to be transparent: I’m not 100% sure I can run the full training pass in one go. Training ~1,000,000 images across both sources is extremely resource-heavy. My tests push over ~100 GB of VRAM and ~86 GB of system RAM just to get through a single pass.
Most likely plan:
Split the dataset so each half stays under 100 GB VRAM during training.
Train the halves separately.
Fine-tune afterward to keep both styles strong and stable.
This approach should keep the process manageable while preserving quality across realism and anime