A user has developed and open-sourced a method for training Krea2 LoRA models on hardware with only 16GB of VRAM, bypassing the need for block swapping. The approach involves using an int4 convrot quantized version of Krea2 and modifying the musubi-tuner framework to support this quantization. The user achieved training speeds of 5-7 seconds per iteration by integrating Flash Attention and torch.compile with ComfyUI, though they noted potential V-shaped loss curves that might be related to quantization. AI
IMPACT Enables training of large LoRA models on consumer-grade hardware, potentially democratizing fine-tuning.
RANK_REASON User-developed tool/method for training AI models on limited hardware.
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