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| title: README |
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| FlowChef introduces a novel approach to controlled image generation by leveraging rectified flow models (RFMs) for **efficient, training-free, inversion-free, and gradient-free steering of denoising trajectories**. |
| Unlike diffusion models, which demand extensive training and computational resources, FlowChef unifies tasks like **classifier guidance, inverse problems, and image editing** without extra training or backpropagation. |
| By model steering facilitated by gradient skipping, FlowChef sets new benchmarks in performance, memory, and efficiency, achieving state-of-the-art results across diverse tasks. |
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| - [**Project page**] [https://flowchef.github.io/](https://flowchef.github.io/) |
| - [**Paper**] [https://flowchef.github.io/static/docs/FlowChef_ArXiv.pdf](https://flowchef.github.io/static/docs/FlowChef_ArXiv.pdf) |
| - [**Demos**] (find below) |
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