Update README.md

Author: lewtun

unit3/README.md

@@ -42,7 +42,7 @@ _Diagram showing the text encoding process which transforms the input prompt int
 
 For this to work, we need to create a numeric representation of the text that captures relevant information about what it describes. To do this, SD leverages a pre-trained transformer model based on something called CLIP. CLIP's text encoder was designed to process image captions into a form that could be used to compare images and text, so it is well suited to the task of creating useful representations from image descriptions. An input prompt is first tokenized (based on a large vocabulary where each word or sub-word is assigned a specific token) and then fed through the CLIP text encoder, producing a 768-dimensional (in the case of SD 1.X) or 1024-dimensional (SD 2.X) vector for each token. To keep things consistent prompts are always padded/truncated to be 77 tokens long, and so the final representation which we use as conditioning is a tensor of shape 77x1024 per prompt.
 
-![conditioning diagram](sd_UNet_color.png)
+![conditioning diagram](sd_unet_color.png)
 
 OK, so how do we actually feed this conditioning information into the UNet for it to use as it makes predictions? The answer is something called cross-attention. Scattered throughout the UNet are cross-attention layers. Each spatial location in the UNet can 'attend' to different tokens in the text conditioning, bringing in relevant information from the prompt. The diagram above shows how this text conditioning (as well as timestep-based conditioning) is fed in at different points. As you can see, at every level the UNet has ample opportunity to make use of this conditioning!