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\n\nx_0 is as I understand from real data distribution that could not be Gaussian.\nx_t is a combination of x_0 distribution and Gaussian distribution parametrised by β_t.\n\nSimilarly in the DDIM is not the proof behind the mean and variance definition:\n\"Snímka\n\nThanks.","html":"

Hi,

\n

exists any proof for these equations?

\n\"Snímka\n\n

I understand it as condition probability with multiple conditions, that is defined as:
\"Snímka

\n

x_0 is as I understand from real data distribution that could not be Gaussian.
x_t is a combination of x_0 distribution and Gaussian distribution parametrised by β_t.

\n

Similarly in the DDIM is not the proof behind the mean and variance definition:
\"Snímka

\n

Thanks.

\n","updatedAt":"2024-09-12T10:58:13.479Z","author":{"_id":"642f0983aa1dd0ebdf3c1c1c","avatarUrl":"/avatars/acc0a327882a95676277c168b8c23c74.svg","fullname":"Martin Kubovcik","name":"markub3327","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false}},"numEdits":1,"identifiedLanguage":{"language":"en","probability":0.7536921501159668},"editors":["markub3327"],"editorAvatarUrls":["/avatars/acc0a327882a95676277c168b8c23c74.svg"],"reactions":[],"isReport":false}}],"pinned":false,"locked":false,"collection":"discussions","isPullRequest":false,"isReport":false},"repo":{"name":"google/ddpm-cifar10-32","type":"model"},"activeTab":"discussion","discussionRole":0,"watched":false,"muted":false,"repoDiscussionsLocked":false}">

Variance

#17
by markub3327 - opened
\n\nx_0 is as I understand from real data distribution that could not be Gaussian.\nx_t is a combination of x_0 distribution and Gaussian distribution parametrised by β_t.\n\nSimilarly in the DDIM is not the proof behind the mean and variance definition:\n\"Snímka\n\nThanks.","html":"

Hi,

\n

exists any proof for these equations?

\n\"Snímka\n\n

I understand it as condition probability with multiple conditions, that is defined as:
\"Snímka

\n

x_0 is as I understand from real data distribution that could not be Gaussian.
x_t is a combination of x_0 distribution and Gaussian distribution parametrised by β_t.

\n

Similarly in the DDIM is not the proof behind the mean and variance definition:
\"Snímka

\n

Thanks.

\n","updatedAt":"2024-09-12T10:58:13.479Z","author":{"_id":"642f0983aa1dd0ebdf3c1c1c","avatarUrl":"/avatars/acc0a327882a95676277c168b8c23c74.svg","fullname":"Martin Kubovcik","name":"markub3327","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false}},"numEdits":1,"identifiedLanguage":{"language":"en","probability":0.7536921501159668},"editors":["markub3327"],"editorAvatarUrls":["/avatars/acc0a327882a95676277c168b8c23c74.svg"],"reactions":[],"isReport":false}}],"pinned":false,"locked":false,"collection":"discussions","isPullRequest":false,"isReport":false},"primaryEmailConfirmed":false,"repo":{"name":"google/ddpm-cifar10-32","type":"model"},"discussionRole":0,"acceptLanguages":["*"],"hideComments":true,"repoDiscussionsLocked":false,"isDiscussionAuthor":false}">

Hi,

exists any proof for these equations?

Snímka obrazovky 2024-09-12 o 12 32 56

I understand it as condition probability with multiple conditions, that is defined as:
Snímka obrazovky 2024-09-12 o 12 38 35

x_0 is as I understand from real data distribution that could not be Gaussian.
x_t is a combination of x_0 distribution and Gaussian distribution parametrised by β_t.

Similarly in the DDIM is not the proof behind the mean and variance definition:
Snímka obrazovky 2024-09-12 o 12 57 01

Thanks.

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