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CS 180 Project: Diffusion Models and Image Processing

Table of Contents

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Part A: Implementation and Analysis

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Part B: Model Training and Development

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1.7.3 Text-Conditional Image-to-image Translation

1.8 Visual Anagrams

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+ The first two pictures were the best visual anagrams I got when combining the prompts, "an oil painting of an old man" and "an oil painting of people around a campfire". + The 3rd picture I have included for fun because the anagram is fully right side up - I think both aspects of the picture are quite clear and meld together surprisingly well. +

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Output with num=10 -
Original
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Old Man + Campfire Anagram
Output with num=10 -
Original
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Old Man + Campfire Anagram
Output with num=10 -
Original
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Old Man + Campfire Right-side Up
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+ I chose to incorporate dogs into sceneries after playing around with some other combinations. Making anagrams combining two sceneries sometimes proved to be difficult, as did combining weaker prompts (eg. a photo of a man, a photo of a hipster barista) with landscapes that tended to dominate. The ones below were the best as the dogs are strikingly clear, especially the second one. + For the third bonus anagram, it honestly felt like both the coast and rocket were quite prominent even without flipping it over so I chose to include it. +

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+ Output with num=10 +
Snowy Village + Dog Anagram
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Amalfi Coast + Dog Anagram
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Amalfi Coast + Rocket Anagram
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1.8 Visual Anagrams

1.9 Hybrid Images

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Skull (Far) + Waterfall (Close)
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Skull (Far) + Waterfall (Close)
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+ Output with num=10 +
Skull (Far) + Waterfall (Close)
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+ Output with num=10 +
Campfire (Close) + Dog (Far)
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+ Output with num=10 +
Campfire (Close) + Rocket (Far)
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Part 1: Training a Single-Step Denoising UNet

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1.1 Implementing the UNet

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1.2 Using the UNet to Train a Denoiser

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1.2.1 Training

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1.2.2 Out-of-Distribution Testing

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Part 2: Training a Diffusion Model

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2.1 Adding Time Conditioning to UNet

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2.2 Training the UNet

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2.3 Sampling from the UNet

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2.4 Adding Class-Conditioning to UNet

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2.5 Sampling from the Class-Conditioned UNet

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Bells and Whistles

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