Introduction to FastSD CPU
FastSD CPU offers an optimized version of the Stable Diffusion model, specifically designed to operate seamlessly on CPU platforms. By leveraging innovative frameworks like the Latent Consistency Models and Adversarial Diffusion Distillation, FastSD CPU dramatically enhances the speed of image generation using CPUs. This project is conducted under the broader umbrella of OpenVINO support, making it a standout solution for fast and reliable image processing.
Key Features
FastSD CPU introduces several exciting features designed to cater to a diverse range of user requirements:
- Interfaces: Users can opt for a desktop GUI for basic operations, a web-based UI for advanced functions (such as Lora and ControlNet), or a command-line interface, depending on their needs.
- OpenVINO Advancement: The integration of OpenVINO boosts processing speeds significantly, allowing the production of a 512x512 image in merely 0.82 seconds on a Core i7-12700.
Supported Platforms
FastSD CPU is compatible across various systems, including:
- Windows
- Linux
- Mac
- Android (with Termux)
- Raspberry PI 4
System Requirements
To run FastSD CPU effectively, different modes demand varying amounts of system RAM. Here are the specifics:
- LCM Mode: Requires at least 2 GB of RAM.
- LCM-LoRA Mode: Needs 4 GB of RAM.
- OpenVINO Mode: Demands a minimum of 11 GB, but this can be reduced to 9 GB through tiny decoder optimization.
Benchmark and Performance
FastSD CPU showcases impressive real-time image generation capabilities:
- Produces fast 1-step inferences with unique models like SDXS-512-0.9 and uses optimized pipelines for rapid image synthesis.
- Compared on i7-12700, the run time for generating 512x512 images is significantly lower when using OpenVINO, reducing latency and boosting speed.
Model Versatility and Compatibility
FastSD CPU supports a robust range of models tailored for different tasks, including:
- LCM and LCM-LoRA Models: These enhance image fidelity through fine-tuning and customization.
- OpenVINO Models: Crafted for efficiency, compressing model sizes for practical CPU use.
- GGUF Models: Focused on flux and efficient memory usage, supported through the stablediffusion.cpp library.
Experimental Features
Pioneering efforts in FastSD CPU have led to the introduction of experimental real-time text-to-image functions. These leverage the CPU capabilities optimally to generate images quickly and efficiently.
OpenVINO Specifics
FastSD CPU makes remarkable use of OpenVINO, achieving faster processing times and supporting a broad array of models while reducing overall system load. Key supported models include:
- Hyper-SD SDXL 1 Step
- SDXL Lightning 2 Steps
GPU and NPU Support
Within the AI PC framework, FastSD CPU now encompasses support for GPU and experimental NPU, enhancing versatility and operational efficiency. This brings forth more opportunities to utilize power-efficient Neural Processing Units for diverse computational tasks.
Conclusion
FastSD CPU stands out as an essential tool for those needing high-speed image processing capabilities without relying on extensive GPU resources. Its adaptability across multiple platforms, experimental features, and performance optimization make it a valuable resource for developers and programmers engaged in machine learning and computer vision fields.