learning/annotated_deep_learning_paper_implementations
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Varuna Jayasiri
2021-10-09 12:24:03 +05:30
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docs/diffusion/ddpm/index.html
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@ -599,6 +599,18 @@ q(x_t|x_0) &= \mathcal{N} \Big(x_t; \sqrt{\bar\alpha_t} x_0, (1-\bar\alpha_t) \m
<div class="highlight"><pre><span class="lineno">287</span> <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">mse_loss</span><span class="p">(</span><span class="n">noise</span><span class="p">,</span> <span class="n">eps_theta</span><span class="p">)</span></pre></div> <div class="highlight"><pre><span class="lineno">287</span> <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">mse_loss</span><span class="p">(</span><span class="n">noise</span><span class="p">,</span> <span class="n">eps_theta</span><span class="p">)</span></pre></div>
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</div> </div>
<div class='section' id='section-33'>
<div class='docs'>
<div class='section-link'>
<a href='#section-33'>#</a>
</div>
<h2>Here&rsquo;s our tweet with a summary</h2>
<p><blockquote class="twitter-tweet"><p lang="en" dir="ltr">Annotated <a href="https://twitter.com/PyTorch?ref_src=twsrc%5Etfw">@PyTorch</a> implementation of &quot;Denoising Diffusion Probabilistic Models&quot; by <a href="https://twitter.com/hojonathanho?ref_src=twsrc%5Etfw">@hojonathanho</a> <a href="https://twitter.com/ajayj_?ref_src=twsrc%5Etfw">@ajayj_</a> <a href="https://twitter.com/pabbeel?ref_src=twsrc%5Etfw">@pabbeel</a> <a href="https://twitter.com/berkeley_ai?ref_src=twsrc%5Etfw">@berkeley_ai</a><br><br>📝 Annotated code <a href="https://t.co/IxJMNQxJMa">https://t.co/IxJMNQxJMa</a><br>🖥 Github <a href="https://t.co/he5yIZZlB2">https://t.co/he5yIZZlB2</a><br>📎 Paper <a href="https://t.co/FjpamUVhLI">https://t.co/FjpamUVhLI</a><br><br>🧵👇 <a href="https://t.co/5SIZud6OnH">pic.twitter.com/5SIZud6OnH</a></p>&mdash; labml.ai (@labmlai) <a href="https://twitter.com/labmlai/status/1446676487361290240?ref_src=twsrc%5Etfw">October 9, 2021</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script></p>
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<a href="https://papers.labml.ai">Trending Research Papers</a> <a href="https://papers.labml.ai">Trending Research Papers</a>
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@ -281,7 +281,7 @@
<url> <url>
<loc>https://nn.labml.ai/index.html</loc> <loc>https://nn.labml.ai/index.html</loc>
<lastmod>2021-09-21T16:30:00+00:00</lastmod> <lastmod>2021-10-08T16:30:00+00:00</lastmod>
<priority>1.00</priority> <priority>1.00</priority>
</url> </url>
@ -330,7 +330,7 @@
<url> <url>
<loc>https://nn.labml.ai/diffusion/index.html</loc> <loc>https://nn.labml.ai/diffusion/index.html</loc>
<lastmod>2021-10-06T16:30:00+00:00</lastmod> <lastmod>2021-10-08T16:30:00+00:00</lastmod>
<priority>1.00</priority> <priority>1.00</priority>
</url> </url>

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labml_nn/diffusion/ddpm/__init__.py
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@ -285,3 +285,6 @@ class DenoiseDiffusion:
# MSE loss # MSE loss
return F.mse_loss(noise, eps_theta) return F.mse_loss(noise, eps_theta)
# ## Here's our tweet with a summary
# <blockquote class="twitter-tweet"><p lang="en" dir="ltr">Annotated <a href="https://twitter.com/PyTorch?ref_src=twsrc%5Etfw">@PyTorch</a> implementation of &quot;Denoising Diffusion Probabilistic Models&quot; by <a href="https://twitter.com/hojonathanho?ref_src=twsrc%5Etfw">@hojonathanho</a> <a href="https://twitter.com/ajayj_?ref_src=twsrc%5Etfw">@ajayj_</a> <a href="https://twitter.com/pabbeel?ref_src=twsrc%5Etfw">@pabbeel</a> <a href="https://twitter.com/berkeley_ai?ref_src=twsrc%5Etfw">@berkeley_ai</a><br><br>📝 Annotated code <a href="https://t.co/IxJMNQxJMa">https://t.co/IxJMNQxJMa</a><br>🖥 Github <a href="https://t.co/he5yIZZlB2">https://t.co/he5yIZZlB2</a><br>📎 Paper <a href="https://t.co/FjpamUVhLI">https://t.co/FjpamUVhLI</a><br><br>🧵👇 <a href="https://t.co/5SIZud6OnH">pic.twitter.com/5SIZud6OnH</a></p>&mdash; labml.ai (@labmlai) <a href="https://twitter.com/labmlai/status/1446676487361290240?ref_src=twsrc%5Etfw">October 9, 2021</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>