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[NeurIPS 2024] ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization

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I'm curious about how the aesthetic scores of the generated images from the paper were calculated. Is it possible to share the code? Thanks!

Very excellent job, if you migrate him to 50-step SD-2-1, can you work well?

Hi there, Thank you for open sourcing your wonderful work. I am just curious, if ReNO would work with SDXL Inpainting pipeline? Thanks

Does he support ControlNet and IP Adapter

Hi, I have a question about the loss function choice. As I see, you have used dot product for similarity of features. Did you experiment using L1, L2 MSE, others...

Hi, I am trying to experience different losses. I Have implement a face similarity loss and disabled all of other losses. But The loss almost does not changed (%1 decreased)....