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NectarGraphix

NectarGraphix is a powerful, local image generation studio powered by Stable Diffusion. Run cutting-edge AI models directly on your hardware no cloud dependency, no usage limits. Generate stunning visuals from text prompts with full control over models, settings, and outputs.

NectarGraphix UI

✨ Features

  • Local Stable Diffusion: Run models like SDXL, SD 1.5, or custom fine-tunes offline.
  • Intuitive UI: Drag-and-drop interface for prompts, images, and settings.
  • Model Management: Download, switch, and organize models seamlessly.
  • Advanced Controls: Negative prompts, samplers, CFG scale, steps, and resolution tweaking.
  • Batch Generation: Create multiple images at once.
  • Hardware Optimization: GPU/CPU detection with automatic optimizations (CUDA, ROCm, DirectML).
  • Extensions Support: Compatible with popular LoRAs, ControlNet, and embeddings.

💻 Requirements

  • OS: Windows 10+
  • Python: 3.11.0
  • GPU (recommended):
    • NVIDIA: 4GB+ VRAM (GTX 1060 or newer)
  • RAM: 8GB+ (16GB recommended for larger models)
  • Disk: 10GB+ free space for models and outputs

2. Download Models

Model folder structure:

├── Stable-diffusion/ └── v1-5-pruned-emaonly.safetensors ├── Lora/ └── VAE/

Model Folder Structure

Popular sources: Civitai, Hugging Face

📸 Example Usage

  1. Enter prompt: A cyberpunk cityscape at sunset, neon lights, highly detailed, 8k
  2. Negative prompt: blurry, lowres, text, watermark
  3. Set: Steps=30, CFG=7, Sampler=Euler a, Size=512x512
  4. Hit Generate → Save or upscale results.

📄 License

LICENSE for details.

🙌 Support the Project

  • ⭐ Star on GitHub
  • Share your generations on socials with #NectarGraphix
  • Buy me a coffee: ko-fi.com/zashiron

Built with ❤️ for local AI creators

About

NectarGraphix is a powerful, local image generation studio powered by Stable Diffusion. Run cutting-edge AI models directly on your hardware no cloud dependency, no usage limits. Generate stunning visuals from text prompts with full control over models, settings, and outputs.

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