add sketch to image notebook (#1991)

.ci/ignore_pip_conflicts.txt

@@ -24,4 +24,5 @@ notebooks/whisper-subtitles-generation/whisper-convert.ipynb # pytube
 notebooks/grounded-segment-anything/grounded-segment-anything.ipynb # deprecated installer
 notebooks/vision-paddlegan-anime/vision-paddlegan-anime.ipynb # old scipy
 notebooks/vision-paddlegan-superresolution/vision-paddlegan-superresolution.ipynb # old scipy and scikit-image
-notebooks/stable-diffusion-torchdynamo-backend/stable-diffusion-torchdynamo-backend.ipynb
+notebooks/stable-diffusion-torchdynamo-backend/stable-diffusion-torchdynamo-backend.ipynb
+notebooks/sketch-to-image-pix2pix-turbo/sketch-to-image-pix2pix-turbo.ipynb

notebooks/sketch-to-image-pix2pix-turbo/README.md

@@ -0,0 +1,23 @@
+# One Step Sketch to Image translation with pix2pix-turbo and OpenVINO
+
+Diffusion models achieve remarkable results in image generation. They are able synthesize high-quality images guided by user instructions. In the same time, majority of diffusion-based image generation approaches are time-consuming due to the iterative denoising process.Pix2Pix-turbo model was proposed in [One-Step Image Translation with Text-to-Image Models paper](https://arxiv.org/abs/2403.12036) for addressing slowness of diffusion process in image-to-image translation task. It is based on [SD-Turbo](https://huggingface.co/stabilityai/sd-turbo), a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation. Using only single inference, pix2pix-turbo achieves comparable by quality results with recent works such as ControlNet for Sketch2Photo and Edge2Image for 50 steps.
+
+![](https://github.com/GaParmar/img2img-turbo/raw/main/assets/gen_variations.jpg)
+
+In this tutorial you will learn how to turn sketches into images using [Pix2Pix-Turbo](https://github.com/GaParmar/img2img-turbo) and OpenVINO.
+
+## Notebook contents
+The tutorial consists from following steps:
+
+- Prerequisites
+- Load PyTorch Model
+- Convert the model to OpenVINO IR
+- Select Inference Device
+- Compile OpenVINO Model
+- Run Model Inference
+- Launch Interactive Demo
+
+## 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).