Update README.md

README.md

@@ -8,6 +8,9 @@ language:
 CVPR 2025 (Oral)  
 [**Code**](https://github.com/nv-tlabs/Difix3D) | [**Project Page**](https://research.nvidia.com/labs/toronto-ai/difix3d/) | [**Paper**](https://arxiv.org/abs/2503.01774)
 
+## Use the Difix Model
+Please visit the [Difix3D repository](https://github.com/nv-tlabs/Difix3D) to access all relevant files and code needed to use Difix
+
 ## Description: 
 Difix is a single-step image diffusion model trained to enhance and remove artifacts in rendered novel views caused by
 underconstrained regions of 3D representation. The technology behind Difix is based on the concepts outlined in the paper titled
@@ -95,10 +98,6 @@ The following table shows inference time for a single generation across differen
 | NVIDIA A100 |  0.355 sec |
 | NVIDIA H100 | 0.223 sec |
 
-## Use the Difix Model
-Please visit the [Difix3D repository](https://github.com/nv-tlabs/Difix3D) to access all relevant files and code needed to use Difix
-
-
 ## Difix Dataset
 - Data Collection Method: Human
 - Labeling Method by Dataset: Human