Quantization of Krea 2 Turbo primarily meant for low VRAM/RAM usage, expect degradation in quality compared to INT8 Convrot only quants or BF16, saves ~64.3% in storage relative to BF16. Layer count 139 layers in convrot_w4a4_mse (it's just convrot_w4a4 but with additional logic for selecting better scales, reduces error quite a lot numerically but I am unsure about visually) 78 layers in int8_convrot 1 layer in convrot_w4a4 Once again, used a simple T2I workflow, no second stage or upscaling done as I'm too lazy for that. Tested on: NVIDIA GTX 1660 Super 6 GB 32 GB System RAM Test settings: 8 steps CFG 1 Euler / Simple 696 × 1048 resolution (0.7 on resolution selector) Approximately 9 s/it on a GTX 1660 Super Quantized using my toolkit Made by - BakaPotatoLord https://civitai.red/models/2935831/krea-2-turbo-int8-mixed-quants