Translations:Diffusion Models Are Real-Time Game Engines/39/en: Difference between revisions

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We use DDIM sampling (Song et al., [https://arxiv.org/html/2408.14837v1#bib.bib34 2022]). We employ Classifier-Free Guidance (Ho & Salimans, [https://arxiv.org/html/2408.14837v1#bib.bib12 2022]) only for the past observations condition <math>o_{< n}</math>. We didn’t find guidance for the past actions condition <math>a_{< n}</math> to improve quality. The weight we use is relatively small (1.5) as larger weights create artifacts which increase due to our auto-regressive sampling.

We use DDIM sampling (Song et al., 2022). We employ Classifier-Free Guidance (Ho & Salimans, 2022) only for the past observations condition $o_{<n}$ . We didn’t find guidance for the past actions condition $a_{<n}$ to improve quality. The weight we use is relatively small (1.5) as larger weights create artifacts which increase due to our auto-regressive sampling.

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	We use DDIM sampling (Song ~~et al~~., [https://arxiv.org/html/2408.14837v1#bib.bib34 2022]). We employ Classifier-Free Guidance (Ho & Salimans, [https://arxiv.org/html/2408.14837v1#bib.bib12 2022]) only for the past observations condition <math>o_{< n}</math>. We didn’t find guidance for the past actions condition <math>a_{< n}</math> to improve quality. The weight we use is relatively small (1.5) as larger weights create artifacts which increase due to our auto-regressive sampling.		We use DDIM sampling (Song et al., [https://arxiv.org/html/2408.14837v1#bib.bib34 2022]). We employ Classifier-Free Guidance (Ho & Salimans, [https://arxiv.org/html/2408.14837v1#bib.bib12 2022]) only for the past observations condition <math>o_{< n}</math>. We didn’t find guidance for the past actions condition <math>a_{< n}</math> to improve quality. The weight we use is relatively small (1.5) as larger weights create artifacts which increase due to our auto-regressive sampling.