Dedicated for GIMP 3, Python 3 and OpenVINO™.
⭐ ⭐ ⭐ ⭐ are welcome.

[1] Super-Resolution
[2] Semantic-Segmentation
[3] Stable-Diffusion (Supports - SD 1.4, SD 1.5 (landscape, portrait, and Latent Consistency Model), SD 1.5 Inpainting, SD 1.5 Controlnet-OpenPose, SD 1.5 Controlnet-CannyEdge, SD 1.5 Controlnet-Scribble)

[1] Provides a set of OpenVINO™ based plugins that add AI features to GIMP.
[2] Serve as a reference code for how to make use of OpenVino in GIMP application for inferencing on Intel's CPU & GPU.
[3] Add AI to routine image editing workflows.

Welcome people interested in contribution! Please raise a PR for any new features, modifications, or bug fixes.

For detailed steps and tips please refer User guide for Windows.

This plugin is tested on Ubuntu 22.04. Building GIMP from source is recommended.
For detailed steps and tips please refer to Linux Installation Guide.

1. Create or choose a layer
2. Select Stable Diffusion from the drop down list in layers -> OpenVINO-AI-Plugins
3. Choose the desired model and device from the drop down list.
   • note that the Latent Consistency Model is now supported. Choose SD_1.5_square_lcm
4. Click on "Load Models" to compile & load the model on the selected device. Wait for it to complete. Please note that you need to perform this step only if you change the model or device or both. For any subsequent runs just click "Run Inference"
5. Enter prompt and other parameters
6. Click on “Generate”. Wait for the total inference steps to get completed.

1. Create or choose a layer or open an image
2. Follow steps 2,3,4,5 from section A.
3. Select "Use Initial Image"
4. By default the opened image in canvas will be used as initial image to the model. You can also select a different image by browsing from files.
5. Click on “Generate”. Wait for the total inference steps to get completed.

1. Choose a layer or Open an image of size 512x512. (Currently works best with this resolution)
2. Use "Free select tool" to select the area in your image that you wish to change.
3. Right click on your image and click on "Add layer mask". Then choose "Selection" in "Initialize layer Mask to". This should create a mask with your selection.
4. Follow steps 2,3,4,5 from section A. Please note that you will only see "SD_1.5_Inpainting" in model options if you added a mask layer to your image.
5. Click on “Generate”. Wait for the total inference steps to get completed.

1. Open an image with some pose that you want to see in new image.
2. Select Stable Diffusion from the drop down list in layers -> OpenVINO-AI-Plugins
3. Choose the controlnet_openpose model and device from the drop down list.
4. Make sure to select -- "Use Initial Image" option from the GUI. If not selected then it will fail.
5. Follow steps 4,5 from section A.
6. Click on “Generate”. Wait for the total inference steps to get completed.

1. Open an image that you want to use for generating the new image.
2. Select Stable Diffusion from the drop down list in layers -> OpenVINO-AI-Plugins
3. Choose the controlnet_canny model and device from the drop down list.
4. Make sure to select -- "Use Initial Image" option from the GUI. If not selected then it will fail.
5. Follow steps 4,5 from section A.
6. Click on “Generate”. Wait for the total inference steps to get completed.

1. Open an image that you want to use for generating the new image.
2. Select Stable Diffusion from the drop down list in layers -> OpenVINO-AI-Plugins
3. Choose the controlnet_scribble model and device from the drop down list.
4. Make sure to select -- "Use Initial Image" option from the GUI. If not selected then it will fail.
5. Follow steps 4,5 from section A.
6. Click on “Generate”. Wait for the total inference steps to get completed.

With Advanced Setting

With Power Mode in Advanced Setting for SD int8 based models

Note that the screenshots below are based on the previous UI

• Plugin architecture inspired from GIMP-ML - https://github.com/kritiksoman/GIMP-ML/tree/GIMP3-ML
• Stable Diffusion Engine - https://github.com/bes-dev/stable_diffusion.openvino

Apache 2.0

Stable Diffusion’s data model is governed by the Creative ML Open Rail M license, which is not an open source license. https://github.com/CompVis/stable-diffusion. Users are responsible for their own assessment whether their proposed use of the project code and model would be governed by and permissible under this license.