Added thanks message

app.py

@@ -29,6 +29,8 @@ description = """This is a demo on 🧨 ControlNet based on Meta's [Segment Anyt
                 Upload a Segment Anything Segmentation Map, write a prompt, and generate images 🤗 This demo is still a Work in Progress, so don't expect it to work well for now !! 
 
                 ⌛️ It takes about 30~ seconds to generate 4 samples, to get faster results, don't forget to reduce the Nº Samples to 1.
+
+                A huge thanks goes out to @Google Cloud, for providing us with powerful TPUs that enabled us to train this model; and to the @HuggingFace Team for organizing the sprint.
               """
 
 about = """
@@ -41,7 +43,7 @@ about = """
 
         # 💾 About the dataset
 
-        For the training, we generated a segmented dataset based on the [COYO-700M](https://huggingface.co/datasets/kakaobrain/coyo-700m) dataset. The dataset provided us with the images, and the text prompts. For the segmented images, we used [Segment Anything Model](https://github.com/facebookresearch/segment-anything). We then created 8k samples train our model on, which isn't a lot, but as a team, we have been very busy with many other responsibilities and time constraints, which made it challenging to dedicate a lot of time to generating a larger dataset.  Despite the constraints we faced, we have still managed to achieve some nice results 🙌
+        For the training, we generated a segmented dataset based on the [COYO-700M](https://huggingface.co/datasets/kakaobrain/coyo-700m) dataset. The dataset provided us with the images, and the text prompts. For the segmented images, we used [Segment Anything Model](https://github.com/facebookresearch/segment-anything). We then created 8k samples to train our model on, which isn't a lot, but as a team, we have been very busy with many other responsibilities and time constraints, which made it challenging to dedicate a lot of time to generating a larger dataset.  Despite the constraints we faced, we have still managed to achieve some nice results 🙌
 
         You can check the generated datasets below ⬇️
         - [sam-coyo-2k](https://huggingface.co/datasets/mfidabel/sam-coyo-2k)