Stable-Diffusion, developed by Stability AI, is an open-source text-to-image generation model based on the Latent Diffusion architecture, capable of producing high-quality visuals from natural language prompts. Trained on billions of text-image pairs, it generates photorealistic, artistic, or abstract outputs across resolutions (e.g., 512x512 to 1024x1024), widely used in creative design, advertising, game asset development, and educational visualization. The open-source framework enables local deployment with customizable parameters (e.g., prompts, sampling steps) for precise control, while supporting extensions like image inpainting and super-resolution. Challenges include balancing output quality with computational demands (mid-to-high-tier GPUs required), mitigating biased/sensitive content generation, and optimizing real-time performance.

The source model can be found here

When the user has fine-tuned the source model, the model conversion process must be performed again.

Users can refer to either of the following two methods to complete the model conversion:

• Using AIMO for model conversion: Click Model Conversion Reference in the Performance Reference section on the right to view the conversion steps.

• Using Qualcomm QNN for model conversion: Please refer to the Qualcomm QNN Documentation.

The model performance benchmarks and example code provided by Model Farm are all implemented based on the APLUX AidLite SDK.

For models in .bin format, you can use either of the following two inference engines to run inference on Qualcomm chips:

Inference using APLUX AidLite: please refer to the APLUX AidLite Developer Documentation

Inference using Qualcomm QNN: Please refer to the Qualcomm QNN Documentation

Inference Example Code

The inference example code is implemented using the AidLite SDK.

Click Model & Code to download the model files and the inference code package. The file structure is as follows:

/model_farm_{model_name}_aidlite
    
    |__ models # folder where model files are stored

    |__ python # aidlite python model inference example

    |__ cpp # aidlite cpp model inference example

    |__ README.md