fix the reaccording to view suggestion

.ci/spellcheck/.pyspelling.wordlist.txt

@@ -13,6 +13,7 @@ al
 Alibaba
 ALiBi
 AlpacaEval
+aMUSEd
 analytics
 AnimeGAN
 api
@@ -29,6 +30,7 @@ audiobooks
 audioldm
 AudioLDM
 autoencoder
+autogenerated
 autoregressive
 autoregressively
 AutoTokenizer
@@ -721,8 +723,10 @@ VM
 Vladlen
 VOC
 Vocoder
+VQ
 VQA
 VQGAN
+VQVAE
 waveform
 waveforms
 Wav
@@ -742,6 +746,7 @@ XCode
 Xeon
 xl
 xvector
+xxl
 XYWH
 Yiqin
 YOLO

notebooks/277-amused-lightweight-text-to-image/README.md

@@ -0,0 +1,32 @@
+# Lightweight image generation with aMUSEd and OpenVINO™
+
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/277-amused-lightweight-text-to-image/277-amused-lightweight-text-to-image.ipynb)
+
+
+<img src="https://huggingface.co/amused/amused-256/resolve/main/assets/collage_small.png" />
+
+[Amused](https://huggingface.co/docs/diffusers/api/pipelines/amused) is a lightweight text to image model based off 
+of the [muse](https://arxiv.org/pdf/2301.00704.pdf) architecture. Amused is particularly useful in applications that 
+require a lightweight and fast model such as generating many images quickly at once.
+
+Amused is a VQVAE token based transformer that can generate an image in fewer forward passes than many diffusion models.
+ In contrast with muse, it uses the smaller text encoder CLIP-L/14 instead of t5-xxl. Due to its small parameter count 
+ and few forward pass generation process, amused can generate many images quickly. This benefit is seen particularly at 
+ larger batch size
+
+## Notebook contents
+The tutorial consists from following steps:
+
+- Prerequisites
+- Load and run the original pipeline
+- Convert the model to OpenVINO IR
+  - Convert the Text Encoder
+  - Convert the U-ViT transformer
+  - Convert VQ-GAN decoder (VQVAE)
+- Compiling models and prepare pipeline
+- Interactive inference
+
+## Installation instructions
+This is a self-contained example that relies solely on its own code.</br>
+We recommend running the notebook in a virtual environment. You only need a Jupyter server to start.
+For details, please refer to [Installation Guide](../../README.md).