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21 | 21 | " * [theaisummer blog introduction](https://theaisummer.com/diffusion-models/)\n",
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22 | 22 | " * [Set of resources, paper on code on github of @heejkoo](https://github.com/heejkoo/Awesome-Diffusion-Models)\n",
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23 | 23 | " * [Understanding Diffusion Models: A Unified Perspective](https://arxiv.org/abs/2208.11970)\n",
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24 | | - " * [Beginner's guide to diffusion models](https://towardsdatascience.com/beginners-guide-to-diffusion-models-8c3435ccb4ae)" |
| 24 | + " * [Beginner's guide to diffusion models](https://towardsdatascience.com/beginners-guide-to-diffusion-models-8c3435ccb4ae)\n", |
| 25 | + " * [Tutorial on Diffusion Models for Imaging and Vision](https://arxiv.org/abs/2403.18103)\n", |
| 26 | + "\n", |
| 27 | + "\n", |
| 28 | + "Understanding Diffusion Models: A Unified Perspective:" |
25 | 29 | ]
|
26 | 30 | },
|
27 | 31 | {
|
28 | 32 | "cell_type": "code",
|
29 | | - "execution_count": 2, |
| 33 | + "execution_count": 1, |
30 | 34 | "metadata": {},
|
31 | 35 | "outputs": [],
|
32 | 36 | "source": [
|
|
36 | 40 | {
|
37 | 41 | "cell_type": "markdown",
|
38 | 42 | "metadata": {},
|
39 | | - "source": [] |
| 43 | + "source": [ |
| 44 | + "High-Resolution Image Synthesis with Latent Diffusion Models:" |
| 45 | + ] |
40 | 46 | },
|
41 | 47 | {
|
42 | 48 | "cell_type": "code",
|
43 | | - "execution_count": null, |
| 49 | + "execution_count": 2, |
44 | 50 | "metadata": {},
|
45 | 51 | "outputs": [],
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46 | 52 | "source": [
|
47 | | - "#IFrame(\"doc/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.pdf\", width=1200, height=800)" |
| 53 | + "#IFrame(\"doc/DiffusionModels/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.pdf\", width=1200, height=800)" |
48 | 54 | ]
|
49 | 55 | },
|
50 | 56 | {
|
51 | 57 | "cell_type": "markdown",
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52 | 58 | "metadata": {},
|
53 | | - "source": [] |
| 59 | + "source": [ |
| 60 | + "TACKLING THE GENERATIVE LEARNING TRILEMMA WITH DENOISING DIFFUSION GANS:" |
| 61 | + ] |
54 | 62 | },
|
55 | 63 | {
|
56 | 64 | "cell_type": "code",
|
57 | | - "execution_count": null, |
| 65 | + "execution_count": 3, |
58 | 66 | "metadata": {},
|
59 | 67 | "outputs": [],
|
60 | 68 | "source": [
|
61 | | - "#IFrame(\"doc/2112.07804.pdf\", width=1200, height=800)" |
| 69 | + "#IFrame(\"doc/DiffusionModels/2112.07804.pdf\", width=1200, height=800)" |
62 | 70 | ]
|
63 | 71 | },
|
64 | 72 | {
|
65 | 73 | "cell_type": "markdown",
|
66 | 74 | "metadata": {},
|
67 | | - "source": [] |
| 75 | + "source": [ |
| 76 | + "Denoising Diffusion Probabilistic Models" |
| 77 | + ] |
68 | 78 | },
|
69 | 79 | {
|
70 | 80 | "cell_type": "code",
|
71 | | - "execution_count": null, |
| 81 | + "execution_count": 4, |
72 | 82 | "metadata": {},
|
73 | 83 | "outputs": [],
|
74 | 84 | "source": [
|
75 | | - "#IFrame(\"doc/NeurIPS-2020-denoising-diffusion-probabilistic-models-Paper.pdf\", width=1200, height=800)" |
| 85 | + "#IFrame(\"doc/DiffusionModels/NeurIPS-2020-denoising-diffusion-probabilistic-models-Paper.pdf\", width=1200, height=800)" |
76 | 86 | ]
|
77 | 87 | },
|
78 | 88 | {
|
79 | 89 | "cell_type": "markdown",
|
80 | 90 | "metadata": {},
|
| 91 | + "source": [ |
| 92 | + "Tutorial on Diffusion Models for Imaging and Vision:" |
| 93 | + ] |
| 94 | + }, |
| 95 | + { |
| 96 | + "cell_type": "code", |
| 97 | + "execution_count": 5, |
| 98 | + "metadata": {}, |
| 99 | + "outputs": [], |
| 100 | + "source": [ |
| 101 | + "#IFrame(\"doc/DiffusionModels/2403.18103v1.pdf\", width=1200, height=800)" |
| 102 | + ] |
| 103 | + }, |
| 104 | + { |
| 105 | + "cell_type": "code", |
| 106 | + "execution_count": null, |
| 107 | + "metadata": {}, |
| 108 | + "outputs": [], |
81 | 109 | "source": []
|
82 | 110 | }
|
83 | 111 | ],
|
84 | 112 | "metadata": {
|
85 | 113 | "kernelspec": {
|
86 | | - "display_name": "Python 3", |
| 114 | + "display_name": "Python 3 (ipykernel)", |
87 | 115 | "language": "python",
|
88 | 116 | "name": "python3"
|
89 | 117 | },
|
|
97 | 125 | "name": "python",
|
98 | 126 | "nbconvert_exporter": "python",
|
99 | 127 | "pygments_lexer": "ipython3",
|
100 | | - "version": "3.8.10" |
| 128 | + "version": "3.12.1" |
101 | 129 | }
|
102 | 130 | },
|
103 | 131 | "nbformat": 4,
|
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