Diffusion Models: Difference between revisions

Line 22:

<math>E \left[ \frac{\beta^2_t}{2\sigma^2_t \alpha (1-\bar\alpha_t)} \Vert \boldsymbol{\epsilon} - \boldsymbol{\epsilon}_\theta( \sqrt{\bar\alpha_t} \mathbf{x}_0 - \sqrt{1-\bar\alpha_t} \boldsymbol{\epsilon}, t) \Vert^2 \right]</math>

===Super-resolution===

===Super-resolution and other Image-to-image generation===

See [https://iterative-refinement.github.io/ SR3 iterative refinement]<br>

Here we use <math>\mathbf{y}</math> to represent the sequence of priors and we condition on an extra input <math>\mathbf{x}</math> which is the low-resolution image.

Here we use <math>\mathbf{y}</math> to represent the sequence of priors and we condition on an extra input <math>\mathbf{x}</math> which is the low-resolution image.<br>

The neural network <math>f_{\theta}(\mathbf{x}, \mathbf{y}, \gamma)</math> continues to predict the added noise during training the reverse process.

An unofficial PyTorch implementation of SR3 is available at [https://github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement https://github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement].

In addition to SR3, the researchers at Google have also unveiled [https://iterative-refinement.github.io/palette/ Palette] which utilizes the same ideas to perform additional image operations such as colorization, uncropping, and inpainting. These tasks can be performed with a single model.

===Text-to-image===

OpenAI have unveiled two text-to-image models, [https://github.com/openai/glide-text2im GLIDE] and [https://openai.com/dall-e-2/ DALL-E 2], which rely on diffusion models to generate images.<br>

GLIDE has some open-source code which allows you to test a small version.

==Resources==

* [https://ai.googleblog.com/2021/07/high-fidelity-image-generation-using.html Google AI Blog High Fidelity Image Generation Using Diffusion Models] - discusses SR3 and CDM

@@ Line 22: / Line 22: @@
 <math>E \left[ \frac{\beta^2_t}{2\sigma^2_t \alpha (1-\bar\alpha_t)}  \Vert \boldsymbol{\epsilon} - \boldsymbol{\epsilon}_\theta( \sqrt{\bar\alpha_t} \mathbf{x}_0 - \sqrt{1-\bar\alpha_t} \boldsymbol{\epsilon}, t) \Vert^2 \right]</math>
-===Super-resolution===
+===Super-resolution and other Image-to-image generation===
 See [https://iterative-refinement.github.io/ SR3 iterative refinement]<br>
-Here we use <math>\mathbf{y}</math> to represent the sequence of priors and we condition on an extra input <math>\mathbf{x}</math> which is the low-resolution image.
+Here we use <math>\mathbf{y}</math> to represent the sequence of priors and we condition on an extra input <math>\mathbf{x}</math> which is the low-resolution image.<br>
+The neural network <math>f_{\theta}(\mathbf{x}, \mathbf{y}, \gamma)</math> continues to predict the added noise during training the reverse process.
+An unofficial PyTorch implementation of SR3 is available at [https://github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement https://github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement].
+In addition to SR3, the researchers at Google have also unveiled [https://iterative-refinement.github.io/palette/ Palette] which utilizes the same ideas to perform additional image operations such as colorization, uncropping, and inpainting. These tasks can be performed with a single model.
+===Text-to-image===
+OpenAI have unveiled two text-to-image models, [https://github.com/openai/glide-text2im GLIDE] and [https://openai.com/dall-e-2/ DALL-E 2], which rely on diffusion models to generate images.<br>
+GLIDE has some open-source code which allows you to test a small version.
 ==Resources==
 * [https://ai.googleblog.com/2021/07/high-fidelity-image-generation-using.html Google AI Blog High Fidelity Image Generation Using Diffusion Models] - discusses SR3 and CDM