annotated_deep_learning_paper_implementations/translate_cache/diffusion/ddpm

{
 "<h1><a href=\"https://nn.labml.ai/diffusion/ddpm/index.html\">Denoising Diffusion Probabilistic Models (DDPM)</a></h1>\n<p><a href=\"https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb\"><span translate=no>_^_0_^_</span></a></p>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation/tutorial of the paper <a href=\"https://arxiv.org/abs/2006.11239\">Denoising Diffusion Probabilistic Models</a>.</p>\n<p>In simple terms, we get an image from data and add noise step by step. Then We train a model to predict that noise at each step and use the model to generate images.</p>\n<p>Here is the <a href=\"https://nn.labml.ai/diffusion/ddpm/unet.html\">UNet model</a> that predicts the noise and <a href=\"https://nn.labml.ai/diffusion/ddpm/experiment.html\">training code</a>. <a href=\"https://nn.labml.ai/diffusion/ddpm/evaluate.html\">This file</a> can generate samples and interpolations from a trained model. </p>\n": "<h1><a href=\"https://nn.labml.ai/diffusion/ddpm/index.html\">\u53bb\u566a\u6269\u6563\u6982\u7387\u6a21\u578b (DDPM)</a></h1>\n<p><a href=\"https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb\"><span translate=no>_^_0_^_</span></a></p>\n<p>\u8fd9\u662f\u300a<a href=\"https://arxiv.org/abs/2006.11239\">\u53bb\u566a\u6269\u6563\u6982\u7387\u6a21\u578b</a>\u300b\u8bba\u6587\u7684 <a href=\"https://pytorch.org\">PyTorch</a> \u5b9e\u73b0/\u6559\u7a0b\u3002</p>\n<p>\u7b80\u800c\u8a00\u4e4b\uff0c\u6211\u4eec\u4ece\u6570\u636e\u4e2d\u83b7\u53d6\u56fe\u50cf\u5e76\u9010\u6b65\u6dfb\u52a0\u566a\u70b9\u3002\u7136\u540e\uff0c\u6211\u4eec\u8bad\u7ec3\u4e00\u4e2a\u6a21\u578b\u6765\u9884\u6d4b\u6bcf\u4e2a\u6b65\u9aa4\u7684\u566a\u58f0\uff0c\u5e76\u4f7f\u7528\u8be5\u6a21\u578b\u751f\u6210\u56fe\u50cf\u3002</p>\n<p>\u8fd9\u662f\u9884\u6d4b\u566a\u58f0\u548c<a href=\"https://nn.labml.ai/diffusion/ddpm/experiment.html\">\u8bad\u7ec3\u4ee3\u7801</a>\u7684 <a href=\"https://nn.labml.ai/diffusion/ddpm/unet.html\">UNet \u6a21\u578b</a>\u3002<a href=\"https://nn.labml.ai/diffusion/ddpm/evaluate.html\">\u6b64\u6587\u4ef6</a>\u53ef\u4ee5\u4ece\u7ecf\u8fc7\u8bad\u7ec3\u7684\u6a21\u578b\u751f\u6210\u6837\u672c\u548c\u63d2\u503c\u3002</p>\n",
 "Denoising Diffusion Probabilistic Models (DDPM)": "\u53bb\u566a\u6269\u6563\u6982\u7387\u6a21\u578b (DDPM)"
}