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---
language:
- en
tags:
- text-to-image
- stable-diffusion
- safetensors
- stable-diffusion-xl
- animagine-xl
base_model: cagliostrolab/animagine-xl-3.0
---
# AnimagineXL-v3-openvino
This is an *unofficial* [OpenVINO](https://github.com/openvinotoolkit/openvino) variant of [cagliostrolab/animagine-xl-3.0](https://huggingface.co/cagliostrolab/animagine-xl-3.0).
The repo is provided for convenience of running the Animagine XL v3 model on Intel CPU/GPU, as loading & converting a SDXL model to openvino can be pretty slow (dozens of minutes).
Table of contents:
- [Usage](#usage)
- [How the conversion was done](#how-the-conversion-was-done)
- [Appendix](#appendix)
## Usage
Take CPU for example:
```python
from optimum.intel.openvino import OVStableDiffusionXLPipeline
from diffusers import (
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler
)
model_id = "CodeChris/AnimagineXL-v3-openvino"
pipe = OVStableDiffusionXLPipeline.from_pretrained(model_model)
# Fix output image size & batch_size for faster speed
img_w, img_h = 832, 1216 # Example
pipe.reshape(width=img_w, height=img_h,
batch_size=1, num_images_per_prompt=1)
## Change scheduler
# AnimagineXL recommand Euler A:
# pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(
pipe.scheduler.config,
use_karras_sigmas=True,
algorithm_type="dpmsolver++"
) # I prefer DPM++ 2M Karras
# Turn off the filter
pipe.safety_checker = None
# If run on a GPU, you need:
# pipe.to('cuda')
```
After the pipe is prepared, a txt2img task can be executed as below:
```python
prompt = "1girl, dress, day, masterpiece, best quality"
negative_prompt = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name"
images = pipe(
prompt,
negative_prompt,
# If reshaped, image size must equal the reshaped size
width=img_w, height=img_h,
guidance_scale=7,
num_inference_steps=20
)
img = images[0]
img.save('sample.png')
```
For convenience, here is the recommended image sizes from the official AnimagineXL doc:
```
# Or their transpose
896 x 1152
832 x 1216
768 x 1344
640 x 1536
1024 x 1024
```
## How the conversion was done
First, install optimum:
```powershell
pip install --upgrade-strategy eager optimum[openvino,nncf]
```
Then, the repo is converted using the following command:
```powershell
optimum-cli export openvino --model 'cagliostrolab/animagine-xl-3.0' 'models/openvino/AnimagineXL-v3' --task 'stable-diffusion-xl'
```
## Appendix
Push large files **without** git commit the latest changes:
```
git lfs install
huggingface-cli lfs-enable-largefiles .
huggingface-cli upload --commit-message 'Upload model files' 'CodeChris/AnimagineXL-v3-openvino' .
```
Other notes:
* The conversion was done using `optimum==1.16.1` and `openvino==2023.2.0`.
* You may query `optimum-cli export openvino --help` for more usage details.
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