Comments (2)
kvablack
commented on August 27, 2024
Sure thing! You don't want training weights to be low-precision, or it can cause instability. Therefore, when not using LoRA, we want to keep the UNet params in full float32. If we're using LoRA, we're not training the UNet params, so we can cast them to low precision. That's why the variable is called inference_dtype.
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GiilDe
commented on August 27, 2024
I see, thanks!
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Related Issues (20)
- Suggest to use larger gradient accumulation steps instead of multi GPUs HOT 3
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