48 lines
1.2 KiB
Plaintext
48 lines
1.2 KiB
Plaintext
DLAA Model Training Guide
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You can use these tools to train new DLAA models from scratch.
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Recommended hardware:
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NVIDIA RTX 3090 or better with 24 GB VRAM.
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1. Prepare the dataset
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Place your training images in the dataset folder.
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Recommended dataset size:
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5,000 - 10,000 HD images
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Run Clean_Dataset.bat to filter the images.
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The cleaner checks for weak, broken, low-detail, document-like, or duplicate images.
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Rejected images are moved to the _rejected folder instead of being deleted.
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2. Run a smoke test
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Run the smoke test first before starting a full training run.
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If everything works correctly, continue with the actual training.
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3. Train the models
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Run Trainer_DLAA.bat to train the main DLAA model.
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Run Trainer_Texture.bat to train the optional texture refinement model.
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4. Output files
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DLAANet.safetensors:
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Main anti-aliasing refinement model for cleaner edges.
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DLAATexture.safetensors:
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Optional texture refinement model for fine detail and micro-texture.
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Both model files will be saved in the runs folder.
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Best checkpoints are saved automatically during training.
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If training stops midway, you can resume from the latest checkpoint.
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Final output files:
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DLAANet.safetensors
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DLAATexture.safetensors |