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The document presents a study demonstrating that diffusion models outperform GANs in image synthesis quality through architectural improvements and classifier guidance. Achieving state-of-the-art FID scores on various ImageNet datasets, the authors highlight the benefits of diffusion models in terms of diversity and fidelity while maintaining computational efficiency. The research indicates that with fewer forward passes, diffusion models can match the performance of GANs, thereby advancing the field of gen
- Author
- nttu.93tn
- Language
- EN