A high-quality, professional-grade image generation UI powered by GLM-Image.
Run it locally with one click.
This application brings the power of GLM-Image to your desktop. GLM-Image is an open-source image generation model capable of producing stunning, high-resolution visuals from text prompts or existing images.
Built for Pinokio, this app offers a fully-featured Gradio interface that handles model downloading, environment setup, and inference automatically.
- 🎨 Text-to-Image Generation: Create detailed, high-resolution (up to 2048x2048) images from simple text descriptions.
- 🖼️ Image-to-Image Editing: Transform existing images using text prompts. Change styles, backgrounds, or details easily.
- 🎛️ Advanced Controls: Full control over generation parameters:
- Resolution: Adjustable width and height (up to 2K).
- Quality: Tune
Inference StepsandGuidance Scale. - Seed Control: Randomize or lock seeds for reproducible results.
- 💾 Auto-Saving: All generated images are automatically saved to the
output/directory with timestamps. - 🧹 Disk Space Optimizer: Includes a built-in "Save Disk Space" tool to deduplicate redundant library files, saving gigabytes of storage.
- OS: Windows, Linux, or macOS
- GPU: NVIDIA GPU with 16GB+ VRAM recommended (The model is large ~33GB).
- Storage: At least 60GB of free disk space (for model weights and environment).
- Download and install Pinokio.
- Navigate to the Discover page or paste this repository URL into the Pinokio browser:
https://github.com/shinshekai/GLM-Image - Click Install.
- Once installed, click Start.
- Update: If a new version is released, click "Update" in the dashboard.
- Reset: If things break, use the "Reset" button to reinstall the environment (this won't delete your
output/images). - Save Space: Click "Save Disk Space" in the dashboard menu to optimize storage usage.
Software License: This application logic (UI and launcher scripts) is licensed under the terms found in the LICENSE file.
Model License: The GLM-Image model weights are released by Zhipu AI (zai-org) under the MIT License.
- Note: The tokenizer and specific weights derived from
X-Omni-Enare subject to Apache 2.0. - Please verify your usage complies with the model's official license at Hugging Face.
This project is built using: