comet links

Author: vpj

docs/diffusion/ddpm/experiment.html

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                 <a href='#section-0'>#</a>
             </div>
             <h1><a href="index.html">Denoising Diffusion Probabilistic Models (DDPM)</a> training</h1>
-<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a> <a href="https://www.comet.ml/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels"><img alt="Open In Comet" src="https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model"></a></p>
+<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a> <a href="https://www.comet.com/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels?utm_source=referral&utm_medium=partner&utm_campaign=labml"><img alt="Open In Comet" src="https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model"></a></p>
 <p>This trains a DDPM based model on CelebA HQ dataset. You can find the download instruction in this <a href="https://forums.fast.ai/t/download-celeba-hq-dataset/45873/3">discussion on fast.ai</a>. Save the images inside <a href="#dataset_path"><code class="highlight"><span></span><span class="n">data</span><span class="o">/</span><span class="n">celebA</span></code>
  folder</a>.</p>
 <p>The paper had used a exponential moving average of the model with a decay of <span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.64444em;vertical-align:0em;"></span><span class="mord">0.9999</span></span></span></span>. We have skipped this for simplicity.</p>

docs/diffusion/ddpm/index.html

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                 <a href='#section-0'>#</a>
             </div>
             <h1>Denoising Diffusion Probabilistic Models (DDPM)</h1>
-<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a> <a href="https://www.comet.ml/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels"><img alt="Open In Comet" src="https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model"></a></p>
+<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a> <a href="https://www.comet.com/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels?utm_source=referral&utm_medium=partner&utm_campaign=labml"><img alt="Open In Comet" src="https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model"></a></p>
 <p>This is a <a href="https://pytorch.org">PyTorch</a> implementation/tutorial of the paper <a href="https://papers.labml.ai/paper/2006.11239">Denoising Diffusion Probabilistic Models</a>.</p>
 <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>
 <p>The following definitions and derivations show how this works. For details please refer to <a href="https://papers.labml.ai/paper/2006.11239">the paper</a>.</p>

docs/diffusion/ddpm/readme.html

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                 <a href='#section-0'>#</a>
             </div>
             <h1><a href="https://nn.labml.ai/diffusion/ddpm/index.html">Denoising Diffusion Probabilistic Models (DDPM)</a></h1>
-<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a> <a href="https://www.comet.ml/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels"><img alt="Open In Comet" src="https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model"></a></p>
+<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a> <a href="https://www.comet.com/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels?utm_source=referral&utm_medium=partner&utm_campaign=labml"><img alt="Open In Comet" src="https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model"></a></p>
 <p>This is a <a href="https://pytorch.org">PyTorch</a> implementation/tutorial of the paper <a href="https://papers.labml.ai/paper/2006.11239">Denoising Diffusion Probabilistic Models</a>.</p>
 <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>
 <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>

docs/sitemap.xml

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     <url>
       <loc>https://nn.labml.ai/diffusion/ddpm/index.html</loc>
-      <lastmod>2022-06-22T16:30:00+00:00</lastmod>
+      <lastmod>2022-07-02T16:30:00+00:00</lastmod>
       <priority>1.00</priority>
     </url>
 

labml_nn/diffusion/ddpm/__init__.py

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 # Denoising Diffusion Probabilistic Models (DDPM)
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb)
-[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.ml/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels)
+[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.com/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels?utm_source=referral&utm_medium=partner&utm_campaign=labml)
 
 This is a [PyTorch](https://pytorch.org) implementation/tutorial of the paper
 [Denoising Diffusion Probabilistic Models](https://papers.labml.ai/paper/2006.11239).

labml_nn/diffusion/ddpm/experiment.ipynb

@@ -11,7 +11,7 @@
    "source": [
     "[![Github](https://img.shields.io/github/stars/labmlai/annotated_deep_learning_paper_implementations?style=social)](https://github.com/labmlai/annotated_deep_learning_paper_implementations)\n",
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb)\n",
-    "[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.ml/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels)\n",
+    "[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.com/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels?utm_source=referral&utm_medium=partner&utm_campaign=labml)\n",
     "\n",
     "## [Denoising Diffusion Probabilistic Models (DDPM)](https://nn.labml.ai/diffusion/ddpm/index.html)\n",
     "\n",

labml_nn/diffusion/ddpm/experiment.py

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 # [Denoising Diffusion Probabilistic Models (DDPM)](index.html) training
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb)
-[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.ml/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels)
+[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.com/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels?utm_source=referral&utm_medium=partner&utm_campaign=labml)
 
 This trains a DDPM based model on CelebA HQ dataset. You can find the download instruction in this
 [discussion on fast.ai](https://forums.fast.ai/t/download-celeba-hq-dataset/45873/3).

labml_nn/diffusion/ddpm/readme.md

@@ -1,7 +1,7 @@
 # [Denoising Diffusion Probabilistic Models (DDPM)](https://nn.labml.ai/diffusion/ddpm/index.html)
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb)
-[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.ml/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels)
+[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.com/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels?utm_source=referral&utm_medium=partner&utm_campaign=labml)
 
 This is a [PyTorch](https://pytorch.org) implementation/tutorial of the paper
 [Denoising Diffusion Probabilistic Models](https://papers.labml.ai/paper/2006.11239).