use bold

README.md

@@ -27,7 +27,7 @@ More precisely, 🤗 Diffusers offers:
 
 ## Definitions
 
-**Models**: Neural network that models $p_\theta(x_{t-1}|x_t)$ (see image below) and is trained end-to-end to *denoise* a noisy input to an image.
+**Models**: Neural network that models $p_\theta(\mathbf{x}_{t-1}|\mathbf{x}_t)$ (see image below) and is trained end-to-end to *denoise* a noisy input to an image.
 *Examples*: UNet, Conditioned UNet, 3D UNet, Transformer UNet
 
 ![model_diff_1_50](https://user-images.githubusercontent.com/23423619/171610307-dab0cd8b-75da-4d4e-9f5a-5922072e2bb5.png)

src/diffusers/models/README.md

 # Models
 
-- Models: Neural network that models $p_\theta(x_{t-1}|x_t)$ (see image below) and is trained end-to-end to denoise a noisy input to an image. Examples: UNet, Conditioned UNet, 3D UNet, Transformer UNet
+- Models: Neural network that models $p_\theta(\mathbf{x}_{t-1}|\mathbf{x}_t)$ (see image below) and is trained end-to-end to denoise a noisy input to an image. Examples: UNet, Conditioned UNet, 3D UNet, Transformer UNet
 
 ## API