what is comfyui-starnodes-modelconverter fr?

Starnodes Model Converter is a set of custom nodes for ComfyUI, the visual workflow tool used for AI image and video generation. Its main job is to shrink large diffusion models by converting them into more compressed number formats, so they take up less disk space and less GPU memory while trying to keep image quality close to the original. The package includes a few different nodes. The main one, the Ultimate Model Converter, converts a model into formats like FP32, FP16, FP8, INT8, or the newer NVFP4 format, and it uses architecture specific profiles so that the layers most sensitive to quality loss are kept in higher precision while less sensitive layers get compressed harder. There is also a splitter node that takes an all in one checkpoint file, which bundles the diffusion model, text encoder, and VAE together, and separates it into its own individual files. A model layers info node analyzes a model layer by layer and can generate a detailed report, and a Pro version of the converter applies saved quantization profiles with a stricter rule set for predictable results. The project ships with ready made profiles tuned for a number of well known model families, including Flux, Qwen Image, LTX, Wan, and others, each with specific settings meant to preserve quality for that architecture. Some of the newest formats, like NVFP4 and INT4 with block Hadamard rotation, require an NVIDIA GPU and, in some cases, a fairly recent GPU generation. To install it, you clone the repository into ComfyUI's custom_nodes folder and install its Python dependencies, then restart ComfyUI. It needs a recent version of ComfyUI, PyTorch with CUDA support for GPU acceleration, and an additional dependency called comfy kitchen for several of the newer quantization formats. The README also warns that converting large models can use a lot of system memory, and recommends at least 64 gigabytes of RAM plus a large pagefile or swap space if you run into memory errors, with a CPU fallback available for machines with limited GPU memory.