We show an extension to 3D and multi-subject editing! 🤯🤯

However, we believe such a straightforward and impactful method could benefit downstream tasks such as video generation. 🚀

🎨 Extending FlowChef for Image Editing

We take FlowChef a step further: enabling image editing without performing an inversion of the source image! 🚀

🔥 Even more exciting, this is one of the first approaches to achieve SOTA results on Flux.

On inverse problems, our method achieves SOTA performance while being the most efficient approach! 💪

Plus, it’s versatile: seamlessly applicable to both pixel and latent space models. 🤯

🎯 Our theoretical insights are backed by empirical observations!

💡 As t → 0, the cosine similarity of gradients for InstaFlow approaches 1️⃣.0️⃣, aligning with our derivations. In contrast, Stable Diffusion gradients behave almost randomly. 📊

Check out the plots below! 👇✨

🔍 In toy settings, vector field, and cost gradients seem orthogonal, but this intuition falters in higher-dimensional ODEs (Prop. 4.1).

⚠️ Gradient-based methods need costly backpropagation in ODESolvers.

💡 We prove rectified flows skip it entirely, ensuring convergence (Lem. 4.2, Thm. 4.3). 🚀

🚨New Paper Alert🚨

🚀 Introducing FlowChef, "Steering Rectified Flow Models in the Vector Field for Controlled Image Generation"! 🌌✨

- Perform image editing, solve inverse problems, and more.
- Achieved inversion-free, gradient-free, & training-free inference time steering! 🤯

👇👇