Translations:Diffusion Models Are Real-Time Game Engines/31/zh: Difference between revisions

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where <math>T = {\{ o_{i \leq n},a_{i \leq n}\}} \sim \mathcal{T}_{agent}</math>, <math>x_{0} = {\phi{(o_{n})}}</math>, <math>t \sim {\mathcal{U}{(0,1)}}</math>, <math>\epsilon \sim {\mathcal{N}{(0,\mathbf{I})}}</math>, <math>x_{t} = {{\sqrt{{\overline{\alpha}}_{t}}x_{0}} + {\sqrt{1 - {\overline{\alpha}}_{t}}\epsilon}}</math>, <math>{v{(\epsilon,x_{0},t)}} = {{\sqrt{{\overline{\alpha}}_{t}}\epsilon} - {\sqrt{1 - {\overline{\alpha}}_{t}}x_{0}}}</math>, and <math>v_{\theta^{\prime}}</math> is the v-prediction output of the model <math>f_{\theta}</math>. The noise schedule <math>{\overline{\alpha}}_{t}</math> is linear, similarly to Rombach et al. ([https://arxiv.org/html/2408.14837v1#bib.bib26 2022]).

其中 $T={\{o_{i\leq n},a_{i\leq n}\}}\sim {\mathcal {T}}_{agent}$ ， $x_{0}=\phi {(o_{n})}$ ， $t\sim {\mathcal {U}}{(0,1)}$ ， $\epsilon \sim {\mathcal {N}}{(0,\mathbf {I} )}$ ， $x_{t}={{\sqrt {{\overline {\alpha }}_{t}}}x_{0}+{\sqrt {1-{\overline {\alpha }}_{t}}}\epsilon }$ ， $v{(\epsilon ,x_{0},t)}={{\sqrt {{\overline {\alpha }}_{t}}}\epsilon -{\sqrt {1-{\overline {\alpha }}_{t}}}x_{0}}$ ，而 $v_{\theta ^{\prime }}$ 是模型 $f_{\theta }$ 的 v预测输出。噪声调度 ${\overline {\alpha }}_{t}$ 是线性的，与 Rombach 等（2022）类似。

Revision as of 00:21, 9 September 2024 (view source) Felipefelixarias (talk \| contribs) (Created page with "其中 <math>T = {\{ o_{i \leq n},a_{i \leq n}\}} \sim \mathcal{T}_{代理}</math>，<math>x_{0} = \phi{(o_{n})}</math>，<math>t \sim \mathcal{U}{(0,1)}</math>，<math>\epsilon \sim \mathcal{N}{(0,\mathbf{I})}</math>，<math>x_{t} = {\sqrt{\overline{\alpha}_{t}}x_{0} + \sqrt{1 - \overline{\alpha}_{t}}\epsilon}</math>，<math>v{(\epsilon,x_{0},t)} = {\sqrt{\overline{\alpha}_{t}}\epsilon - \sqrt{1 - \overline{\alpha}_{t}}x_{0}}</math>，而 <math>v_{\theta^{\prime}}</math...")		Latest revision as of 03:06, 9 September 2024 (view source) Felipefelixarias (talk \| contribs) No edit summary
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	其中 <math>T = {\{ o_{i \leq n},a_{i \leq n}\}} \sim \mathcal{T}_{代理}</math>，<math>x_{0} = \phi{(o_{n})}</math>，<math>t \sim \mathcal{U}{(0,1)}</math>，<math>\epsilon \sim \mathcal{N}{(0,\mathbf{I})}</math>，<math>x_{t} = {\sqrt{\overline{\alpha}_{t}}x_{0} + \sqrt{1 - \overline{\alpha}_{t}}\epsilon}</math>，<math>v{(\epsilon,x_{0},t)} = {\sqrt{\overline{\alpha}_{t}}\epsilon - \sqrt{1 - \overline{\alpha}_{t}}x_{0}}</math>，而 <math>v_{\theta^{\prime}}</math> 是模型 <math>f_{\theta}</math> 的 v预测输出。噪声调度 <math>\overline{\alpha}_{t}</math> 是线性的，与 Rombach 等（[https://arxiv.org/html/2408.14837v1#bib.bib26 2022]）类似。		其中 <math>T = {\{ o_{i \leq n},a_{i \leq n}\}} \sim \mathcal{T}_{agent}</math>，<math>x_{0} = \phi{(o_{n})}</math>，<math>t \sim \mathcal{U}{(0,1)}</math>，<math>\epsilon \sim \mathcal{N}{(0,\mathbf{I})}</math>，<math>x_{t} = {\sqrt{\overline{\alpha}_{t}}x_{0} + \sqrt{1 - \overline{\alpha}_{t}}\epsilon}</math>，<math>v{(\epsilon,x_{0},t)} = {\sqrt{\overline{\alpha}_{t}}\epsilon - \sqrt{1 - \overline{\alpha}_{t}}x_{0}}</math>，而 <math>v_{\theta^{\prime}}</math> 是模型 <math>f_{\theta}</math> 的 v预测输出。噪声调度 <math>\overline{\alpha}_{t}</math> 是线性的，与 Rombach 等（[https://arxiv.org/html/2408.14837v1#bib.bib26 2022]）类似。