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Denoising Diffusion Probabilistic Models (#98)

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<h1><a href="https://nn.labml.ai/diffusion/ddpm/index.html">Denoising Diffusion Probabilistic Models (DDPM)</a></h1>
<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>
<p><a href="https://app.labml.ai/run/a44333ea251411ec8007d1a1762ed686"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a></p>
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<h1>Utility functions for <a href="index.html">DDPM</a> experiemnt</h1>
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<div class="highlight"><pre><span class="lineno">10</span><span></span><span class="kn">import</span> <span class="nn">torch.utils.data</span></pre></div>
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<p>Gather consts for $t$ and reshape to feature map shape</p>
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<div class="highlight"><pre><span class="lineno">13</span><span class="k">def</span> <span class="nf">gather</span><span class="p">(</span><span class="n">consts</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">t</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span></pre></div>
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<div class="highlight"><pre><span class="lineno">15</span> <span class="n">c</span> <span class="o">=</span> <span class="n">consts</span><span class="o">.</span><span class="n">gather</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">t</span><span class="p">)</span>
<span class="lineno">16</span> <span class="k">return</span> <span class="n">c</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span></pre></div>
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<h1>Diffusion models</h1>
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<li><a href="ddpm/index.html">Denoising Diffusion Probabilistic Models (DDPM)</a></li>
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......@@ -112,6 +112,10 @@ implementations.</p>
<li><a href="gan/wasserstein/gradient_penalty/index.html">Wasserstein GAN with Gradient Penalty</a></li>
<li><a href="gan/stylegan/index.html">StyleGAN 2</a></li>
</ul>
<h4>✨ <a href="diffusion/index.html">Diffusion models</a></h4>
<ul>
<li><a href="diffusion/ddpm/index.html">Denoising Diffusion Probabilistic Models (DDPM)</a></li>
</ul>
<h4>✨ <a href="sketch_rnn/index.html">Sketch RNN</a></h4>
<h4>✨ Graph Neural Networks</h4>
<ul>
......
docs/papers.json
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......@@ -42,6 +42,12 @@
"1503.02531": [
"https://nn.labml.ai/distillation/index.html"
],
"1505.04597": [
"https://nn.labml.ai/diffusion/ddpm/unet.html"
],
"2006.11239": [
"https://nn.labml.ai/diffusion/ddpm/index.html"
],
"2010.07468": [
"https://nn.labml.ai/optimizers/ada_belief.html"
],
......
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<h1><a href="https://nn.labml.ai/rl/dqn/index.html">Deep Q Networks (DQN)</a></h1>
<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of paper
<a href="https://papers.labml.ai/paper/1312.5602">Playing Atari with Deep Reinforcement Learning</a>
along with <a href="https://nn.labml.ai/rl/dqn/model.html">Dueling Network</a>, <a href="https://nn.labml.ai/rl/dqn/replay_buffer.html">Prioritized Replay</a>
and Double Q Network.</p>
<p>Here is the <a href="https://nn.labml.ai/rl/dqn/experiment.html">experiment</a> and <a href="https://nn.labml.ai/rl/dqn/model.html">model</a> implementation.</p>
<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/rl/dqn/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
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function handleImages() {
var images = document.querySelectorAll('p>img')
console.log(images);
for (var i = 0; i < images.length; ++i) {
handleImage(images[i])
}
}
function handleImage(img) {
img.parentElement.style.textAlign = 'center'
var modal = document.createElement('div')
modal.id = 'modal'
var modalContent = document.createElement('div')
modal.appendChild(modalContent)
var modalImage = document.createElement('img')
modalContent.appendChild(modalImage)
var span = document.createElement('span')
span.classList.add('close')
span.textContent = 'x'
modal.appendChild(span)
img.onclick = function () {
console.log('clicked')
document.body.appendChild(modal)
modalImage.src = img.src
}
span.onclick = function () {
document.body.removeChild(modal)
}
}
handleImages()
</script>
</body>
</html>
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docs/sitemap.xml
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</url>
<url>
<loc>https://nn.labml.ai/diffusion/index.html</loc>
<lastmod>2021-10-06T16:30:00+00:00</lastmod>
<priority>1.00</priority>
</url>
<url>
<loc>https://nn.labml.ai/diffusion/ddpm/unet.html</loc>
<lastmod>2021-10-08T16:30:00+00:00</lastmod>
<priority>1.00</priority>
</url>
<url>
<loc>https://nn.labml.ai/diffusion/ddpm/index.html</loc>
<lastmod>2021-10-08T16:30:00+00:00</lastmod>
<priority>1.00</priority>
</url>
<url>
<loc>https://nn.labml.ai/diffusion/ddpm/experiment.html</loc>
<lastmod>2021-10-08T16:30:00+00:00</lastmod>
<priority>1.00</priority>
</url>
<url>
<loc>https://nn.labml.ai/diffusion/ddpm/utils.html</loc>
<lastmod>2021-10-08T16:30:00+00:00</lastmod>
<priority>1.00</priority>
</url>
<url>
<loc>https://nn.labml.ai/diffusion/ddpm/evaluate.html</loc>
<lastmod>2021-10-08T16:30:00+00:00</lastmod>
<priority>1.00</priority>
</url>
<url>
<loc>https://nn.labml.ai/optimizers/adam_warmup.html</loc>
<lastmod>2021-01-13T16:30:00+00:00</lastmod>
......
labml_nn/__init__.py
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......@@ -52,6 +52,10 @@ implementations.
* [Wasserstein GAN with Gradient Penalty](gan/wasserstein/gradient_penalty/index.html)
* [StyleGAN 2](gan/stylegan/index.html)
#### ✨ [Diffusion models](diffusion/index.html)
* [Denoising Diffusion Probabilistic Models (DDPM)](diffusion/ddpm/index.html)
#### ✨ [Sketch RNN](sketch_rnn/index.html)
#### ✨ Graph Neural Networks
......
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"""
---
title: Diffusion models
summary: >
A set of PyTorch implementations/tutorials of diffusion models.
---
# Diffusion models
* [Denoising Diffusion Probabilistic Models (DDPM)](ddpm/index.html)
"""
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# [Denoising Diffusion Probabilistic Models (DDPM)](https://nn.labml.ai/diffusion/ddpm/index.html)
This is a [PyTorch](https://pytorch.org) implementation/tutorial of the paper
[Denoising Diffusion Probabilistic Models](https://papers.labml.ai/paper/2006.11239).
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.
Here is the [UNet model](https://nn.labml.ai/diffusion/ddpm/unet.html) that predicts the noise and
[training code](https://nn.labml.ai/diffusion/ddpm/experiment.html).
[This file](https://nn.labml.ai/diffusion/ddpm/evaluate.html) can generate samples and interpolations
from a trained model.
[![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/a44333ea251411ec8007d1a1762ed686)
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"""
---
title: Utility functions for DDPM experiment
summary: >
Utility functions for DDPM experiment
---
# Utility functions for [DDPM](index.html) experiemnt
"""
import torch.utils.data
def gather(consts: torch.Tensor, t: torch.Tensor):
"""Gather consts for $t$ and reshape to feature map shape"""
c = consts.gather(-1, t)
return c.reshape(-1, 1, 1, 1)
readme.md
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......@@ -57,6 +57,11 @@ implementations almost weekly.
* [Wasserstein GAN with Gradient Penalty](https://nn.labml.ai/gan/wasserstein/gradient_penalty/index.html)
* [StyleGAN 2](https://nn.labml.ai/gan/stylegan/index.html)
#### ✨ [Diffusion models](https://nn.labml.ai/diffusion/index.html)
* [Denoising Diffusion Probabilistic Models (DDPM)](https://nn.labml.ai/diffusion/ddpm/index.html)
#### ✨ [Sketch RNN](https://nn.labml.ai/sketch_rnn/index.html)
#### ✨ Graph Neural Networks
......
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