Update README.md

README.md

@@ -119,7 +119,7 @@ This part is actually really easy. You just train it on the normal flow-matching
 
 FLUX models use a text model called T5-XXL to get most of its conditioning for the text-to-image task. Importantly, they pad the text out to either 256 (schnell) or 512 (dev) tokens. 512 tokens is the maximum trained length for the model. By padding, I mean they repeat the last token until the sequence is this length.
 
-This results in the model using these padding tokens to [store information](https://arxiv.org/abs/2309.16588). When you [visualize the attention maps of the tokens in the padding segment of the text encoder](https://github.com/kaibioinfo/FluxAttentionMap/resolve/main/attentionmap.ipynb), you can see that about 10-40 tokens shortly after the last token of the text and about 10-40 tokens at the end of the padding contain information which the model uses to make images. Because these are normally used to store information, it means that any prompt long enough to not have some of these padding tokens will end up with degraded performance.
+This results in the model using these padding tokens to [store information](https://arxiv.org/abs/2309.16588). When you [visualize the attention maps of the tokens in the padding segment of the text encoder](https://github.com/kaibioinfo/FluxAttentionMap/blob/main/attentionmap.ipynb), you can see that about 10-40 tokens shortly after the last token of the text and about 10-40 tokens at the end of the padding contain information which the model uses to make images. Because these are normally used to store information, it means that any prompt long enough to not have some of these padding tokens will end up with degraded performance.
 
 It's easy to prevent this by masking out these padding token during attention. BFL and their engineers know this, but they probably decided against it because it works as is and most fast implementations of attention only work with causal (LLM) types of padding and so would let them train faster.