Extracted Loras from Flux.1-Schnell finetunes for Schnell and Dev checkpoints

Model description

LoRAs are extracted from fine-tuned checkpoints (Schnell), and the official Flux.1 Schnell is also used as a basis for comparison. As a result, the resulting differential LoRAs do not contain four-step Schnell optimizations and can therefore be used with both Schnell checkpoints (at 4–6 steps) and DEV checkpoints (with full accuracy and the full number of steps). I did this primarily for myself because LoRAs are easier to work with, including mixing them in real time and setting different weights/strengths. This also saves space on your local computer. Choose a rank according to your taste and the capabilities of your hardware (the lower the rank, the less memory you need). After extraction, the weights been normalized. There is a direct correlation between rank and a LoRA's acceptable strength range. So for r32, keep the LoRA strength at up to 120%, and for r384, at about 250%.

A lot of time, electricity, and compute went into this on my small and not particularly powerful Mac Mini M4. But it was an interesting research project.

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