The simplest way to self-host Cyber Realistic. Launch a dedicated cloud GPU server running Lab Station OS to download and serve the model using any compatible app or framework.
Direct Download
Download model weights for local inference. Must be used with a compatible app, notebook, or codebase. May run slowly, or not work at all, depending on your system resources, particularly GPU(s) and available VRAM.
Forge is a platform built on top of Stable Diffusion WebUI to make development easier, optimize resource management, speed up inference, and study experimental features.
Train your own LoRAs and finetunes for Stable Diffusion and Flux using this popular GUI for the Kohya trainers.
Model Report
Cyberdelia / Cyber Realistic
CyberRealistic is a Stable Diffusion model optimized for true-to-life image generation. Available in fp16 (1.99GB) and fp32 (3.97GB) versions, it excels at prompt adherence and supports textual inversions and LoRA implementations. Currently at v7.0, it represents iterative improvements in image fidelity.
Explore the Future of AI
Your server, your data, under your control
CyberRealistic is a photorealistic Stable Diffusion checkpoint model, with its latest version 7.0 released on January 1st, 2025. The model represents a significant advancement in AI image generation, particularly focusing on achieving highly detailed and realistic outputs with minimal prompt engineering required.
Technical Specifications and Versions
The model is available in two primary formats:
Pruned fp16 version (1.99 GB)
fp32 version (3.97 GB)
The CyberRealistic family includes multiple versions from v1.1 through v7.0, with specialized variants such as v5.0-Inpainting offering additional capabilities. Each iteration has brought incremental improvements to the model's performance and capabilities, though specific technical details about these improvements are not publicly documented.
Capabilities and Features
The model demonstrates exceptional ability in generating photorealistic images across various subjects, from portraits to nature scenes. Key features include:
Strong handling of textual inversions and LoRA
Detailed output generation with minimal prompting requirements
Versatile application across different subject matters
The model excels particularly in rendering:
Realistic human features and expressions
Detailed textures and materials
Complex lighting and environmental effects
Natural scenes and wildlife
Usage Guidelines and Optimization
For optimal results, the model includes suggested negative prompt terms to improve image generation:
Common artifacts: "watermark," "text"
Anatomical imperfections: "deformed," etc.
Shorter negative prompts are recommended for more efficient resource usage
Licensing and Distribution
The model is licensed under CreativeML Open RAIL-M with an addendum. Users can access the model through the Hugging Face Model Repository, which serves as a backup location.
