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Compute Resources for Style Mimicry Experimentsby@torts

Compute Resources for Style Mimicry Experiments

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December 15th, 2024
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This section outlines the compute resources used in our experiments, including execution times for generating images and the additional time required by IMPRESS++.
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Academic Research Paper

Academic Research Paper

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Abstract and 1. Introduction

  1. Background and Related Work

  2. Threat Model

  3. Robust Style Mimicry

  4. Experimental Setup

  5. Results

    6.1 Main Findings: All Protections are Easily Circumvented

    6.2 Analysis

  6. Discussion and Broader Impact, Acknowledgements, and References

A. Detailed Art Examples

B. Robust Mimicry Generations

C. Detailed Results

D. Differences with Glaze Finetuning

E. Findings on Glaze 2.0

F. Findings on Mist v2

G. Methods for Style Mimicry

H. Existing Style Mimicry Protections

I. Robust Mimicry Methods

J. Experimental Setup

K. User Study

L. Compute Resources

L Compute Resources

Table 4 reports the compute resources for our experiments.


Table 4: Compute resources for our experiments. Execution time per image / (artist) reports the execution time of the method to compute a single image, or the combined execution time for all samples of an artist, if the method operates on all samples of an artist at once. † Google Cloud ‡ IMPRESS++ requires an additional 2 seconds per image generation.

Table 4: Compute resources for our experiments. Execution time per image / (artist) reports the execution time of the method to compute a single image, or the combined execution time for all samples of an artist, if the method operates on all samples of an artist at once. † Google Cloud ‡ IMPRESS++ requires an additional 2 seconds per image generation.


Authors:

(1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch);

(2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch);

(3) Nicholas Carlini, Google DeepMind;

(4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch).


This paper is available on arxiv under CC BY 4.0 license.


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