Summary of D-flow: Differentiating Through Flows For Controlled Generation, by Heli Ben-hamu et al.
D-Flow: Differentiating through Flows for Controlled Generation
by Heli Ben-Hamu, Omri Puny, Itai Gat, Brian Karrer, Uriel Singer, Yaron Lipman
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper introduces D-Flow, a simple yet powerful framework for controlling the generation process in state-of-the-art Diffusion and Flow-Matching (FM) models. By differentiating through the flow and optimizing for the source point, D-Flow unlocks new possibilities for solving inverse problems, conditional generation, and controlled generation. The framework is motivated by the observation that differentiating through the generation process projects gradients on the data manifold, implicitly injecting the prior into the optimization process. The authors validate D-Flow’s effectiveness on various linear and non-linear controlled generation problems, including image and audio inverse problems, and conditional molecule generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary D-Flow is a new way to control how machines generate things like images or music. It’s based on special kinds of computer models called Diffusion and Flow-Matching (FM) models. These models are good at making new things that look or sound like real things. But sometimes we want the machine to make something specific, like a picture of a cat instead of just a random picture. D-Flow makes this possible by changing how the model works from inside. The authors tested D-Flow on many different problems and it did very well. |
Keywords
* Artificial intelligence * Diffusion * Optimization
