diff --git a/readme.md b/readme.md
index 48b2ef1..97673a0 100644
--- a/readme.md
+++ b/readme.md
@@ -39,7 +39,7 @@ python3 vid2vid.py
 This method is still in development and works on top of ‘Stable Diffusion’ and 'FloweR' - optical flow reconstruction method that is also in a yearly development stage. Do not expect much from it as it is more of a proof of a concept rather than a complete solution. All examples you can see here are originally generated at 512x512 resolution using the 'sd-v1-5-inpainting' model as a base. They were downsized and compressed for better loading speed. You can see them in their original quality in the 'examples' folder. Actual prompts used were stated in the following format "RAW photo, {subject}, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3", only the 'subject' part is described in the table below.
 
 #### Step 1.
-Download 'FloweR_0.1.pth' model from here: [google disc link] and place it in the 'FloweR' folder. 
+Download 'FloweR_0.1.pth' model from here: [Google drive link](https://drive.google.com/file/d/1WhzoVIw6Kdg4EjfK9LaTLqFm5dF-IJ7F/view?usp=share_link) and place it in the 'FloweR' folder. 
 
 #### Step 2.
 Same as with vid2vid case, run web-ui with '--api' flag. It is also better to use '--xformers' flag, as you would need to have the highest resolution possible and using xformers memory optimization will greatly help.