Update README.md

README.md

@@ -35,6 +35,14 @@ Employing a knowledge distillation strategy, Segmind-Vega leverages the teaching
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62039c2d91d53938a643317d/bZylkXH3PhFhLYJWG6WJ5.png)
 
+## Speed Comparison (Segmind-Vega vs SD-1.5 vs SDXL)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/62039c2d91d53938a643317d/9ec2r_wzlHtAjVhbm1Tpf.png)
+
+## Parameters Comparison (Segmind-Vega vs SD-1.5 vs SDXL)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/62039c2d91d53938a643317d/vjS5mxFuB1PSS7TE5RbUm.png)
+
 ## Usage:
 This model can be used via the 🧨 Diffusers library. 
 
@@ -98,12 +106,6 @@ These are the key hyperparameters used during training:
 - Image resolution: 1024
 - Mixed-precision: fp16
 
-### Speed Comparison
-
-Segmind-Vega has demonstrated an impressive 100% speedup compared to the Base SDXL Model. Below is a comparison on an A100 80GB.
-
-Below are the speed-up metrics on an RTX 4090 GPU.
-
 
 ### Model Sources