DALL-E

[coderman]

DALL·E[1](https://openai.com/blog/dall-e/#fn1)

We decided to name our model using a portmanteau of the artist Salvador Dalí and Pixar’s WALL·E.

is a 12-billion parameter version of[GPT-3](https://arxiv.org/abs/2005.14165) trained to generate images from text descriptions, using a dataset of text–image pairs. We’ve found that it has a diverse set of capabilities, including creating anthropomorphized versions of animals and objects, combining unrelated concepts in plausible ways, rendering text, and applying transformations to existing images.

https://github.com/openai/DALL-E

import

io

import

os

,

sys

import

requests

import

PIL

import

torch

import

torchvision.transforms

as

T

import

torchvision.transforms.functional

as

TF

from

dall_e

import

map_pixels

,

unmap_pixels

,

load_model

from

IPython.display

import

display

,

display_markdown

target_image_size

=

256

def

download_image

(

url

):

resp

=

requests

.

get

(

url

)

resp

.

raise_for_status

()

return

PIL

.

Image

.

open

(

io

.

BytesIO

(

resp

.

content

))

def

preprocess

(

img

):

s

=

min

(

img

.

size

)

if

s

<

target_image_size

:

raise

ValueError

(

f

'min dim for image

{

s

}

<

{

target_image_size

}

'

)

r

=

target_image_size

/

s

s

=

(

round

(

r

*

img

.

size

[

1

]),

round

(

r

*

img

.

size

[

0

]))

img

=

TF

.

resize

(

img

,

s

,

interpolation

=

PIL

.

Image

.

LANCZOS

)

img

=

TF

.

center_crop

(

img

,

output_size

=

2

*

[

target_image_size

])

img

=

torch

.

unsqueeze

(

T

.

ToTensor

()(

img

),

0

)

return

map_pixels

(

img

)

# This can be changed to a GPU, e.g. 'cuda:0'.

dev

=

torch

.

device

(

'cpu'

)

# For faster load times, download these files locally and use the local paths instead.

enc

=

load_model

(

"
https://cdn.openai.com/dall-e/encoder.pkl
"

,

dev

)

dec

=

load_model

(

"
https://cdn.openai.com/dall-e/decoder.pkl
"

,

dev

)

x

=

preprocess

(

download_image

(

'
https://assets.bwbx.io/images/users/iqjWHBFdfxIU/iKIWgaiJUtss/v2/1000x-1.jpg
'

))

display_markdown

(

'Original image:'

)

display

(

T

.

ToPILImage

(

mode

=

'RGB'

)(

x

[

0

]))

import

torch.nn.functional

as

F

z_logits

=

enc

(

x

)

z

=

torch

.

argmax

(

z_logits

,

axis

=

1

)

z

=

F

.

one_hot

(

z

,

num_classes

=

enc

.

vocab_size

)

.

permute

(

0

,

3

,

1

,

2

)

.

float

()

x_stats

=

dec

(

z

)

.

float

()

x_rec

=

unmap_pixels

(

torch

.

sigmoid

(

x_stats

[:,

:

3

]))

x_rec

=

T

.

ToPILImage

(

mode

=

'RGB'

)(

x_rec

[

0

])

display_markdown

(

'Reconstructed image:'

)

display

(

x_rec

)
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