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| # 2D-Generation | ||||
| Stable Diffusion3 | ||||
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| # 3D-Generation | ||||
| Wonder3D | ||||
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| # Video Generation | ||||
| KLING | ||||
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| # Diffusion | ||||
| Introduced by Ho et al. in Denoising Diffusion Probabilistic Models https://arxiv.org/pdf/2006.11239v2 | ||||
|  | ||||
| ## 1.Description | ||||
| 
 | ||||
| ## 2.Background | ||||
| 各式各样的深度生成模型最近都表现出了高质量样本数据模式:生成对抗网络GANs、自回归模型、流和变分自编码器VAE已经合成出了图像和样本 | ||||
| 
 | ||||
| 扩散模型(Diffusion Probabilistic model)是一个参数化马尔可夫链 | ||||
| 
 | ||||
| ## 3.Papers & Methods | ||||
| *  | ||||
| * | ||||
| * | ||||
| * | ||||
| ## 4.Networks | ||||
| 
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| 
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| ## 5.Comparision | ||||
| | Method | Venue | | | | ||||
| |--|--|--|--| | ||||
| |  |  |  |    | | ||||
| 
 | ||||
| ## 1.Denoising | ||||
| ### 1)Description | ||||
| 
 | ||||
| ### 2) | ||||
| 
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| 
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| ## 2.Image Generation | ||||
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| ## 3.Image Reconstruction | ||||
| 
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| ## 4.Inpainting | ||||
| 
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| ## 5.Video Generation | ||||
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