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3 changes: 2 additions & 1 deletion README.md
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Expand Up @@ -255,9 +255,10 @@ Our core insight behind DMDR is that Reinforcement Learning (RL) and Distributio

## 🎉 Community Works

- [FinalDream](https://github.com/EutropicAI/FinalDream) is a frontend based on zimage-ncnn-vulkan. It provides a user-friendly interface for Z-Image.
- [zimage-ncnn-vulkan](https://github.com/nihui/zimage-ncnn-vulkan) uses ncnn as the inference framework (pure C++), and provides a implementation of Z-Image image generater. It works on most of the mordern computers with vulkan support with BF16 precision.
- [Cache-DiT](https://github.com/vipshop/cache-dit) provides inference acceleration for **Z-Image** and **Z-Image-ControlNet** via DBCache, Context Parallelism and Tensor Parallelism. It achieves nearly **4x** speedup on 4 GPUs with negligible precision loss. Please visit their [example](https://github.com/vipshop/cache-dit/blob/main/examples) for more details.
- [stable-diffusion.cpp](https://github.com/leejet/stable-diffusion.cpp) is a pure C++ diffusion model inference engine that supports fast and memory-efficient Z-Image inference across multiple platforms (CUDA, Vulkan, etc.). You can use stable-diffusion.cpp to generate images with Z-Image on machines with as little as **4GB** of VRAM. For more information, please refer to [How to Use Z‐Image on a GPU with Only 4GB VRAM](https://github.com/leejet/stable-diffusion.cpp/wiki/How-to-Use-Z%E2%80%90Image-on-a-GPU-with-Only-4GB-VRAM).
- [stable-diffusion.cpp](https://github.com/leejet/stable-diffusion.cpp) is a pure C++ diffusion model inference engine that supports fast and memory-efficient Z-Image inference across multiple platforms (CUDA, Vulkan, etc.). You can use stable-diffusion.cpp to generate images with Z-Image on machines with as little as **4GB** of VRAM. For more information, please refer to [How to Use Z‐Image on a GPU with Only 4GB VRAM](https://github.com/leejet/stable-diffusion.cpp/wiki/How-to-Use-Z%E2%80%90Image-on-a-GPU-with-Only-4GB-VRAM).
- [LeMiCa](https://github.com/UnicomAI/LeMiCa) provides a training-free, timestep-level acceleration method that conveniently speeds up Z-Image inference. For more details, see [LeMiCa4Z-Image](https://github.com/UnicomAI/LeMiCa/tree/main/LeMiCa4Z-Image).
- [ComfyUI ZImageLatent](https://github.com/HellerCommaA/ComfyUI-ZImageLatent) provdes an easy to use latent of the official Z-Image resolutions.
- [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) has provided more support for Z-Image, including LoRA training, full training, distillation training, and low-VRAM inference. Please refer to the [document](https://github.com/modelscope/DiffSynth-Studio/blob/main/docs/en/Model_Details/Z-Image.md) of DiffSynth-Studio.
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