ReNO
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[NeurIPS 2024] ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization
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!
SD-2.1
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)....