Update README.md

README.md

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 # QR Code Conditioned ControlNet Models for Stable Diffusion 1.5 and 2.1
 
-![1](imgs/1.png) ![2](imgs/2.png) ![3](imgs/3.png) ![4](imgs/4.png)
+![1](imgs/1.png)
 
 ## Model Description
 
@@ -17,4 +17,6 @@ The Stable Diffusion 2.1 version is marginally more effective, as it was develop
 
 These models perform quite well in most cases, but please note that they are not 100% accurate. In some instances, the QR code shape might not come through as expected. You can increase the ControlNet weight to emphasize the QR code shape. However, be cautious as this might negatively impact the style of your output.**To optimize for scanning, please generate your QR codes with correction mode 'H' (30%).**
 
-To balance between style and shape, a gentle fine-tuning of the control weight might be required based on the individual input and the desired output, aswell as the correct prompt. Some prompts do not work until you increase the weight by a lot. The process of finding the right balance between these factors is part art and part science. For the best results, it is recommended to generate your artwork at a resolution of 768. This allows for a higher level of detail in the final product, enhancing the quality and effectiveness of the QR code-based artwork.
+To balance between style and shape, a gentle fine-tuning of the control weight might be required based on the individual input and the desired output, aswell as the correct prompt. Some prompts do not work until you increase the weight by a lot. The process of finding the right balance between these factors is part art and part science. For the best results, it is recommended to generate your artwork at a resolution of 768. This allows for a higher level of detail in the final product, enhancing the quality and effectiveness of the QR code-based artwork.
+
+![2](imgs/2.png) ![3](imgs/3.png) ![4](imgs/4.png)