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README.md

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-# stable_diffusion.openvino
-
-Implementation of Text-To-Image generation using Stable Diffusion on Intel CPU.
-<p align="center">
-  <img src="data/title.png"/>
-</p>
-
-## News
-
-When we started this project, it was just a tiny proof of concept that you can work with state-of-the-art image generators even without access to expensive hardware.
-But, due we get a lot of feedback from you, we decided to make this project something more than a tiny script.
-Currently, we work on the new version of our project, so we can respond to your issues and pool requests with delay.
-
-
-## Requirements
-
-* Linux, Windows, MacOS
-* Python 3.8.+
-* CPU compatible with OpenVINO.
-
-## Install requirements
-
-```bash
-pip install -r requirements.txt
-```
-
-## Generate image from text description
-
-```bash
-usage: demo.py [-h] [--model MODEL] [--seed SEED] [--beta-start BETA_START] [--beta-end BETA_END] [--beta-schedule BETA_SCHEDULE] [--num-inference-steps NUM_INFERENCE_STEPS]
-               [--guidance-scale GUIDANCE_SCALE] [--eta ETA] [--tokenizer TOKENIZER] [--prompt PROMPT] [--init-image INIT_IMAGE] [--strength STRENGTH] [--mask MASK] [--output OUTPUT]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --model MODEL         model name
-  --seed SEED           random seed for generating consistent images per prompt
-  --beta-start BETA_START
-                        LMSDiscreteScheduler::beta_start
-  --beta-end BETA_END   LMSDiscreteScheduler::beta_end
-  --beta-schedule BETA_SCHEDULE
-                        LMSDiscreteScheduler::beta_schedule
-  --num-inference-steps NUM_INFERENCE_STEPS
-                        num inference steps
-  --guidance-scale GUIDANCE_SCALE
-                        guidance scale
-  --eta ETA             eta
-  --tokenizer TOKENIZER
-                        tokenizer
-  --prompt PROMPT       prompt
-  --init-image INIT_IMAGE
-                        path to initial image
-  --strength STRENGTH   how strong the initial image should be noised [0.0, 1.0]
-  --mask MASK           mask of the region to inpaint on the initial image
-  --output OUTPUT       output image name
-  ```
-
-## Examples
-
-### Example Text-To-Image
-```bash
-python demo.py --prompt "Street-art painting of Emilia Clarke in style of Banksy, photorealism"
-```
-
-### Example Image-To-Image
-```bash
-python demo.py --prompt "Photo of Emilia Clarke with a bright red hair" --init-image ./data/input.png --strength 0.5
-```
-
-### Example Inpainting
-```bash
-python demo.py --prompt "Photo of Emilia Clarke with a bright red hair" --init-image ./data/input.png --mask ./data/mask.png --strength 0.5
-```
-
-### Example web demo
-<p align="center">
-  <img src="data/demo_web.png"/>
-</p>
-
-[Example video on YouTube](https://youtu.be/wkbrRr6PPcY)
-
-```bash
-streamlit run demo_web.py
-```
-
-## Performance
-
-| CPU                                                   | Time per iter | Total time |
-|-------------------------------------------------------|---------------|------------|
-| AMD Ryzen Threadripper 1900X                          | 5.34 s/it     | 2.58 min   |
-| Intel(R) Core(TM) i7-4790K  @ 4.00GHz                 | 10.1 s/it     | 5.39 min   |
-| Intel(R) Core(TM) i5-8279U                            | 7.4 s/it      | 3.59 min   |
-| Intel(R) Core(TM) i7-1165G7 @ 2.80GHz                 | 7.4 s/it      | 3.59 min   |
-| Intel(R) Core(TM) i7-11800H @ 2.30GHz (16 threads)    | 2.9 s/it      | 1.54 min   |
-| Intel(R) Xeon(R) Gold 6154 CPU @ 3.00GHz              | 1 s/it        | 33 s       |
-
-## Acknowledgements
-
-* Original implementation of Stable Diffusion: https://github.com/CompVis/stable-diffusion
-* diffusers library: https://github.com/huggingface/diffusers
-
-## Disclaimer
-
-The authors are not responsible for the content generated using this project.
-Please, don't use this project to produce illegal, harmful, offensive etc. content.