Generative AI models are packaged into Dockerized web interfaces, simplifying local deployment and interaction for researchers and power users. This project abstracts complex installation and dependency management, offering rapid, low-friction access to cutting-edge AI capabilities directly on local hardware.

How It Works

The project's core strategy involves packaging diverse generative AI models into self-contained, ready-to-run Docker images. This approach ensures consistent, reproducible environments across different user setups, abstracting away intricate dependencies, specific CUDA versions, and model-specific configurations. Users interact with these powerful AI capabilities through user-friendly web UIs, enabling rapid experimentation, local execution, and seamless integration without deep system-level integration efforts.

Quick Start & Requirements

• Install: Requires Docker Desktop. Specific docker run commands are provided for each model UI, typically involving --gpus all and port mapping (e.g., -p 3000:3000).
• Prerequisites: A mandatory NVIDIA GPU is required. Minimum VRAM requirements vary significantly, with many models demanding 24GB (e.g., RTX 3090/4090/5090 tested), while some optimized versions like Z-Image-Turbo (4bit) can run on as little as 6GB.
• Links:
  • Discord: https://discord.gg/k5BwmmvJJU
  • X (Twitter): https://x.com/camenduru
  • Sponsorship: https://github.com/sponsors/camenduru
  • Cloud Service: https://ui.tost.ai

Highlighted Details

• Features a broad spectrum of models, including Trellis 2, Tost Synth v1.0 (4K), SeedVR2, Z-Image-Turbo (with LoRA, 4bit, Upscaler), Qwen Image Edit 2509 (with LoRAs), and Wan 2.2 Image to Video.
• Supports various model optimizations such as 4-bit and 8-bit quantization for reduced VRAM usage, alongside advanced functionalities like LoRA integration, image editing (e.g., Convert to Anime, Face Swap), and image-to-video conversion.
• Extensive testing has been performed on high-end NVIDIA GPUs, specifically the RTX 3090, RTX 4090, and RTX 5090, indicating performance targets and validation for these powerful platforms.

Maintenance & Community

• Project updates and community engagement are primarily facilitated through the author's X (Twitter) account and a dedicated Discord server, providing channels for support and discussion.
• Financial support for the project's development and maintenance can be provided via GitHub Sponsors.

Licensing & Compatibility

• The provided documentation does not specify a software license, leaving its legal status ambiguous.
• Consequently, its suitability for commercial use, integration into proprietary software, or redistribution remains undetermined and requires explicit clarification from the maintainers.

Limitations & Caveats

• The project imposes strict hardware requirements, mandating NVIDIA GPUs with substantial VRAM (often 24GB+) for many models, posing a significant barrier to entry for users without high-end hardware or cloud GPU access.
• All deployments rely exclusively on Docker, necessitating user familiarity with Docker installation, configuration, and management.
• The absence of explicit licensing information presents a critical adoption blocker for any use case requiring legal clarity, particularly in enterprise or commercial settings, or for projects with specific compliance needs.