Update README.md

README.md

@@ -233,18 +233,21 @@ image = pipeline(
 
 The VAE in Simple Diffusion utilizes an asymmetric VAE architecture featuring an 8x encoder and a 16x decoder. While a compression factor of 8 is maintained during training, the resolution is effectively doubled during inference through an additional upscaling block. This strategy reduces training costs by an order of magnitude and boosts inference speed without perceptual quality loss. Effectively, this acts as an integrated latent upscaler. To ensure a fair comparison with other VAEs, we downsampled the generated images to match the input resolution for metric evaluation. The SDXS VAE was not trained from scratch but was initialized from weights of FLUX 2 VAE, then redisigned and retrained. We also trained [16 ch vae](https://huggingface.co/AiArtLab/simplevae) with flux.1 quality based on aura vae.
 
-[eval.py](src/eval.py)
 ```
-8x scale factor
-SDXL             | MSE=1.925e-03 PSNR=30.00 LPIPS=0.123 Edge=0.181 KL=32.113
-FLUX.1           | MSE=4.098e-04 PSNR=36.06 LPIPS=0.033 Edge=0.083 KL=13.127
-FLUX.2           | MSE=2.425e-04 PSNR=38.33 LPIPS=0.023 Edge=0.065 KL=2.160
-
-16x scale factor
-Wan2.2-TI2V-5B (2Gb) | MSE=7.034e-04 PSNR=34.65 LPIPS=0.050 Edge=0.115 KL=9.429
-sdxs-1b (200Mb)      | MSE=2.655e-04 PSNR=37.83 LPIPS=0.026 Edge=0.066 KL=2.170
+Wan2.2-T2V-A14B            | MSE=8.908e-04 PSNR=33.21 LPIPS=0.052
+FLUX.1-schnell VAE         | MSE=6.020e-04 PSNR=34.59 LPIPS=0.038
+FLUX.2-klein-9B            | MSE=3.638e-04 PSNR=36.60 LPIPS=0.025
+sdxs-1b                    | MSE=3.491e-04 PSNR=36.95 LPIPS=0.027
+
+=== Percents ===
+| Модель                     |       MSE |      PSNR |     LPIPS |      Edge |    Skew|0 |    Kurt|0 |
+|----------------------------|-----------|-----------|-----------|-----------|-----------|-----------|
+| Wan2.2-T2V-A14B            |      100% |      100% |      100% |      100% |      100% |      100% |
+| FLUX.1-schnell VAE         |    148.0% |    104.2% |    136.7% |    128.8% |    104.9% |    112.2% |
+| FLUX.2-klein-9B            |    244.8% |    110.2% |    209.7% |    165.9% |     93.2% |    118.3% |
+| sdxs-1b                    |    255.2% |    111.2% |    193.7% |    171.6% |     93.3% |    118.2% |
 ```
-To the best of our knowledge, this VAE achieves SOTA for architectures with a 16x scale factor, and is highly competitive overall.
+To the best of our knowledge, this VAE achieves SOTA: [eval.py](src/eval.py)
 
 ### Unet