Hey everyone! I've been working on TensorSharp, an AI inference engine that supports running different types of AI models locally. I'm happy to share that TensorSharp now supports Qwen Image 2.1 Turbo and LoRA! The attached video demonstrates the new image editing capabilities, including: Loading an existing image and selecting a region to edit using a built-in mask editor. Using natural language prompts to modify the selected area. Running Qwen Image 2.1 Turbo for image editing. Viewing the generated results directly in TensorSharp. One of the main goals of TensorSharp is to provide a unified inference engine for different AI model architectures, including LLMs, multimodal models, and image generation models, while making local inference more accessible. I'm continuing to work on expanding model compatibility, improving inference performance, and optimizing memory management across GPU, CPU, and storage. I'd love to hear feedback from the community, especially from anyone experimenting with local image generation, LoRA, or running large models on memory-constrained hardware. What features or models would you like to see supported next? GitHub: https://github.com/zhongkaifu/TensorSharp   submitted by   /u/fuzhongkai [link]   [comments]