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feat: add model training utilities

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MSXYZ-GenAI
2026-05-04 18:22:44 +03:00
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DLAA Trainer/ReadMe.txt
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You can use this tutorial to train a new DLAA model from scratch.
DLAA Model Training Guide
First, load your images into the dataset folder. (5,000-10,000 HD images)
Run Clean_Dataset.bat to filter the images. This will move low-quality images to the 'reject' folder.
You can use these tools to train new DLAA models from scratch.
Run a smoke test first. If everything goes well, proceed with the actual training by running Trainer_DLAA.bat or Trainer_Texture.bat.
1. Prepare the dataset
DLAANet.safetensors — main anti-aliasing refinement model for cleaner edges.
DLAATexture.safetensors — optional texture refinement model for fine detail and micro-texture.
Place your training images in the dataset folder.
The both model files will appear in the 'runs' folder.
If the process stops midway, you can resume it. The best checkpoints are saved automatically.
Recommended dataset size:
5,000 - 10,000 HD images
The final output files will be DLAANet.safetensor and DLAATexture.safetensors.
Run Clean_Dataset.bat to filter the images.
The cleaner checks for weak, broken, low-detail, document-like, or duplicate images.
Rejected images are moved to the _rejected folder instead of being deleted.
2. Run a smoke test
Run the smoke test first before starting a full training run.
If everything works correctly, continue with the actual training.
3. Train the models
Run Trainer_DLAA.bat to train the main DLAA model.
Run Trainer_Texture.bat to train the optional texture refinement model.
4. Output files
DLAANet.safetensors:
Main anti-aliasing refinement model for cleaner edges.
DLAATexture.safetensors:
Optional texture refinement model for fine detail and micro-texture.
Both model files will be saved in the runs folder.
Best checkpoints are saved automatically during training.
If training stops midway, you can resume from the latest checkpoint.
Final output files:
DLAANet.safetensors
DLAATexture.safetensors