Spaces:
Running
Running
feat: cleanup
Browse files- dev/inference/samples.csv +0 -102
- dev/inference/samples.txt +101 -0
- dev/inference/wandb-backend.ipynb +20 -52
dev/inference/samples.csv
DELETED
@@ -1,102 +0,0 @@
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Caption,Theme
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a cat seats on top of an alligator,Animals
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3 |
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a dog eating worthlessness,Animals
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4 |
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a dog playing with a ball,Animals
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5 |
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a rat holding a red lightsable in a white background,Animals
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6 |
-
A unicorn is passing by a rainbow in a field of flowers,Animals
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7 |
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an elephant made of carrots,Animals
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8 |
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an elephant on a unicycle during a circus,Animals
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9 |
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photography of a penguin watching television,Animals
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10 |
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rat wearing a crown,Animals
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11 |
-
"a background consisting of colors blue, green, and red.",Art
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12 |
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a colorful stairway to heaven,Art
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13 |
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a graphite sketch of a gothic cathedral,Art
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14 |
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a portrait of a nightmare creature watching at you,Art
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15 |
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a white room full of a black substance,Art
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16 |
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epic sword fight,Art
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17 |
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"happy, happiness",Art
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painting of an oniric forest glade surrounded by tall trees,Art
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19 |
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real painting of an alien from Monet,Art
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20 |
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robots taking control over humans,Art
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21 |
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"sad, sadness",Art
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22 |
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still life in the style of Kandinsky,Art
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23 |
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still life in the style of Picasso,Art
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24 |
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the representation of infinity,Art
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25 |
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a cute avocado armchair singing karaoke on stage in front of a crowd of strawberry shaped lamps,Avocado
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26 |
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an armchair in the shape of an avocado,Avocado
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27 |
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an avocado armchair,Avocado
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an avocado armchair flying into space,Avocado
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an illustration of an avocado in a christmas sweater staring at its reflection in a mirror,Avocado
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illustration of an avocado armchair,Avocado
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illustration of an avocado armchair getting married to a pineapple,Avocado
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logo of an avocado armchair,Avocado
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watercolor of the Eiffel tower on the moon,Avocado
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34 |
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a cute pikachu teapot,Culture
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a picture of a castle from minecraft,Culture
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an illustration of pikachu seating on a bench,Culture
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mario eating an avocado while walking his baby koala,Culture
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star wars concept art,Culture
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a cartoon of a superhero bear,Illustrations
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an illustration of a cute skeleton wearing a blue hoodie,Illustrations
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41 |
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Cartoon of a carrot with big eyes,Illustrations
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illustration of a baby shark swimming around corals,Illustrations
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logo of a robot wearing glasses and reading a book,Illustrations
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a beautiful sunset at a beach with a shell on the shore,Landscape
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45 |
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a farmhouse surrounded by beautiful flowers,Landscape
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46 |
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a photo of a fantasy version of New York City,Landscape
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a picture of fantasy kingdoms,Landscape
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a volcano erupting in the middle of New York city,Landscape
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aerial view of the beach at night,Landscape
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aerial view of the beach during daytime,Landscape
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big wave destroying a city,Landscape
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"London in a far future, futuristic London",Landscape
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sunset over green mountains,Landscape
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the last sunrise on earth,Landscape
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55 |
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underwater cathedral,Landscape
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56 |
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white snow covered mountain under blue sky during daytime,Landscape
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a bottle of coca-cola on a table,Objects
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a cactus lifitng weights,Objects
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a living room with two white armchairs and a painting of the collosseum. The painting is mounted above a modern fireplace.,Objects
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a long line of alternating green and red blocks,Objects
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a long line of green blocks on a beach at subset,Objects
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a long line of peaches on a beach at sunset,Objects
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a peanut,Objects
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a photo of a camera from the future,Objects
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a restaurant menu,Objects
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a skeleton with the shape of a spider,Objects
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"looking into the sky, 10 airplanes are seen overhead",Objects
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sheves filled with books and archemy potion bottles,Objects
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the communist statue of liberty,Objects
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this is a detailed high-resolution scan of a human brain,Objects
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71 |
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a collection of glasses is sitting on a table,OpenAI
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72 |
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a cross-section view of a walnut,OpenAI
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73 |
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a painting of a capybara sitting on a mountain during fall in surrealist style,OpenAI
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74 |
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a pentagonal green clock,OpenAI
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a photo of san francisco golden gate bridge,OpenAI
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76 |
-
a pixel art illustration of an eagle sitting in a field in the afternoon,OpenAI
|
77 |
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a professional high-quality emoji of a lovestruck cup of boba,OpenAI
|
78 |
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a small red block sitting on a large green block,OpenAI
|
79 |
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a storefront that has the word 'openai' written on it,OpenAI
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80 |
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a tatoo of a black broccoli,OpenAI
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81 |
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a variety of clocks is sitting on a table,OpenAI
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82 |
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"an emoji of a baby fox wearing a blue hat, blue gloves, red shirt, and red pants",OpenAI
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83 |
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"an emoji of a baby penguin wearing a blue hat, blue gloves, red shirt, and green pants",OpenAI
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84 |
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an extreme close-up view of a capybara sitting in a field,OpenAI
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85 |
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an illustration of a baby cucumber with a mustache playing chess,OpenAI
|
86 |
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an illustration of a baby daikon radish in a tutu walking a dog,OpenAI
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87 |
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an illustration of a baby hedgehog in a cape staring at its reflection in a mirror,OpenAI
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88 |
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an illustration of a baby panda with headphones holding an umbrella in the rain,OpenAI
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89 |
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an illustration of an avocado in a beanie riding a motorcycle,OpenAI
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90 |
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urinals are lined up in a jungle,OpenAI
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91 |
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a human face,People
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92 |
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"a person is holding a phone and a waterbottle, running a marathon.",People
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93 |
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a photograph of Ellen G. White,People
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94 |
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Mohammed Ali and Mike Tyson in a hypothetical match,People
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95 |
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Pele and Maradona in a hypothetical match,People
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96 |
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Young woman riding her bike through the forest,People
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97 |
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a clown wearing a spacesuit floating in space,Space
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98 |
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a photo of the French flag on the planet Saturn,Space
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99 |
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a picture of the eiffel tower on the moon,Space
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100 |
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illustration of an astronaut in a space suit playing guitar,Space
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the moon is a skull,Space
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view of mars from space,Space
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dev/inference/samples.txt
ADDED
@@ -0,0 +1,101 @@
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1 |
+
white snow covered mountain under blue sky during daytime
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2 |
+
aerial view of the beach at night
|
3 |
+
aerial view of the beach during daytime
|
4 |
+
a beautiful sunset at a beach with a shell on the shore
|
5 |
+
a farmhouse surrounded by beautiful flowers
|
6 |
+
a photo of a fantasy version of New York City
|
7 |
+
a picture of fantasy kingdoms
|
8 |
+
a volcano erupting in the middle of San Francisco
|
9 |
+
big wave destroying a city
|
10 |
+
Paris in a far future, futuristic Paris
|
11 |
+
sunset over green mountains
|
12 |
+
the last sunrise on earth
|
13 |
+
underwater cathedral
|
14 |
+
painting of an oniric forest glade surrounded by tall trees
|
15 |
+
real painting of an alien from Monet
|
16 |
+
a graphite sketch of a gothic cathedral
|
17 |
+
still life in the style of Kandinsky
|
18 |
+
still life in the style of Picasso
|
19 |
+
a colorful stairway to heaven
|
20 |
+
a background consisting of colors blue, green, and red
|
21 |
+
the communist statue of liberty
|
22 |
+
robots taking control over humans
|
23 |
+
epic sword fight
|
24 |
+
an avocado armchair
|
25 |
+
an armchair in the shape of an avocado
|
26 |
+
logo of an avocado armchair
|
27 |
+
an avocado armchair flying into space
|
28 |
+
a cute avocado armchair singing karaoke on stage in front of a crowd of strawberry shaped lamps
|
29 |
+
an illustration of an avocado in a christmas sweater staring at its reflection in a mirror
|
30 |
+
illustration of an avocado armchair
|
31 |
+
illustration of an avocado armchair getting married to a pineapple
|
32 |
+
Mohammed Ali and Mike Tyson in a hypothetical match
|
33 |
+
Pele and Maradona in a hypothetical match
|
34 |
+
view of mars from space
|
35 |
+
illustration of an astronaut in a space suit playing guitar
|
36 |
+
a clown wearing a spacesuit floating in space
|
37 |
+
a picture of the eiffel tower on the moon
|
38 |
+
watercolor of the Eiffel tower on the moon
|
39 |
+
a photo of the French flag on the planet Saturn
|
40 |
+
the moon is a skull
|
41 |
+
a dog playing with a ball
|
42 |
+
a cat sits on top of an alligator
|
43 |
+
a rat holding a red lightsaber in a white background
|
44 |
+
A unicorn is passing by a rainbow in a field of flowers
|
45 |
+
a dog eating worthlessness
|
46 |
+
an elephant made of carrots
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47 |
+
an elephant on a unicycle during a circus
|
48 |
+
photography of a penguin watching television
|
49 |
+
rat wearing a crown
|
50 |
+
a portrait of a nightmare creature watching at you
|
51 |
+
a white room full of a black substance
|
52 |
+
happy, happiness
|
53 |
+
sad, sadness
|
54 |
+
the representation of infinity
|
55 |
+
a cute pikachu teapot
|
56 |
+
a picture of a castle from minecraft
|
57 |
+
an illustration of pikachu sitting on a bench
|
58 |
+
mario eating an avocado while walking his baby koala
|
59 |
+
star wars concept art
|
60 |
+
a cartoon of a superhero bear
|
61 |
+
an illustration of a cute skeleton wearing a blue hoodie
|
62 |
+
illustration of a baby shark swimming around corals
|
63 |
+
Cartoon of a carrot with big eyes
|
64 |
+
logo of a robot wearing glasses and reading a book
|
65 |
+
a bottle of coca-cola on a table
|
66 |
+
a cactus lifting weights
|
67 |
+
a living room with two white armchairs and a painting of the collosseum. The painting is mounted above a modern fireplace.
|
68 |
+
a long line of alternating green and red blocks
|
69 |
+
a long line of green blocks on a beach at subset
|
70 |
+
a long line of peaches on a beach at sunset
|
71 |
+
a peanut
|
72 |
+
a photo of a camera from the future
|
73 |
+
a restaurant menu
|
74 |
+
a skeleton with the shape of a spider
|
75 |
+
looking into the sky, 10 airplanes are seen overhead
|
76 |
+
shelves filled with books and alchemy potion bottles
|
77 |
+
this is a detailed high-resolution scan of a human brain
|
78 |
+
a collection of glasses is sitting on a table
|
79 |
+
a cross-section view of a walnut
|
80 |
+
a painting of a capybara sitting on a mountain during fall in surrealist style
|
81 |
+
a pentagonal green clock
|
82 |
+
a photo of san francisco golden gate bridge
|
83 |
+
a pixel art illustration of an eagle sitting in a field in the afternoon
|
84 |
+
a professional high-quality emoji of a lovestruck cup of boba
|
85 |
+
a small red block sitting on a large green block
|
86 |
+
a storefront that has the word 'openai' written on it
|
87 |
+
a tatoo of a black broccoli
|
88 |
+
a variety of clocks is sitting on a table
|
89 |
+
an emoji of a baby fox wearing a blue hat, blue gloves, red shirt, and red pants
|
90 |
+
an emoji of a baby penguin wearing a blue hat, blue gloves, red shirt, and green pants
|
91 |
+
an extreme close-up view of a capybara sitting in a field
|
92 |
+
an illustration of a baby cucumber with a mustache playing chess
|
93 |
+
an illustration of a baby daikon radish in a tutu walking a dog
|
94 |
+
an illustration of a baby hedgehog in a cape staring at its reflection in a mirror
|
95 |
+
an illustration of a baby panda with headphones holding an umbrella in the rain
|
96 |
+
an illustration of an avocado in a beanie riding a motorcycle
|
97 |
+
urinals are lined up in a jungle
|
98 |
+
a human face
|
99 |
+
a person is holding a phone and a waterbottle, running a marathon
|
100 |
+
a photograph of Ellen G. White
|
101 |
+
Young woman riding her bike through the forest
|
dev/inference/wandb-backend.ipynb
CHANGED
@@ -7,7 +7,6 @@
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"metadata": {},
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8 |
"outputs": [],
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9 |
"source": [
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10 |
-
"import csv\n",
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11 |
"import tempfile\n",
|
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"from functools import partial\n",
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13 |
"import random\n",
|
@@ -36,7 +35,8 @@
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"ENTITY, PROJECT = 'dalle-mini', 'dalle-mini' # used only for training run\n",
|
37 |
"VQGAN_REPO, VQGAN_COMMIT_ID = 'dalle-mini/vqgan_imagenet_f16_16384', None\n",
|
38 |
"normalize_text = True\n",
|
39 |
-
"latest_only = False # log only latest or all versions"
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]
|
41 |
},
|
42 |
{
|
@@ -46,11 +46,12 @@
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"metadata": {},
|
47 |
"outputs": [],
|
48 |
"source": [
|
49 |
-
"run_ids = ['
|
50 |
"ENTITY, PROJECT = 'wandb', 'hf-flax-dalle-mini'\n",
|
51 |
"VQGAN_REPO, VQGAN_COMMIT_ID = 'dalle-mini/vqgan_imagenet_f16_16384', None\n",
|
52 |
"normalize_text = False\n",
|
53 |
-
"latest_only = True # log only latest or all versions"
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|
54 |
]
|
55 |
},
|
56 |
{
|
@@ -78,8 +79,8 @@
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|
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"outputs": [],
|
79 |
"source": [
|
80 |
"vqgan = VQModel.from_pretrained(VQGAN_REPO, revision=VQGAN_COMMIT_ID)\n",
|
81 |
-
"clip = FlaxCLIPModel.from_pretrained(\"openai/clip-vit-base-
|
82 |
-
"processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-
|
83 |
"clip_params = replicate(clip.params)\n",
|
84 |
"vqgan_params = replicate(vqgan.params)"
|
85 |
]
|
@@ -108,13 +109,10 @@
|
|
108 |
"metadata": {},
|
109 |
"outputs": [],
|
110 |
"source": [
|
111 |
-
"with open('samples.
|
112 |
-
"
|
113 |
-
" samples = []\n",
|
114 |
-
" for row in reader:\n",
|
115 |
-
" samples.append(row)\n",
|
116 |
" # make list multiple of batch_size by adding elements\n",
|
117 |
-
" samples_to_add = [
|
118 |
" samples.extend(samples_to_add)\n",
|
119 |
" # reshape\n",
|
120 |
" samples = [samples[i:i+batch_size] for i in range(0, len(samples), batch_size)]"
|
@@ -160,7 +158,7 @@
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|
160 |
"# retrieve inference run details\n",
|
161 |
"def get_last_inference_version(run_id):\n",
|
162 |
" try:\n",
|
163 |
-
" inference_run = api.run(f'dalle-mini/dalle-mini/
|
164 |
" return inference_run.summary.get('version', None)\n",
|
165 |
" except:\n",
|
166 |
" return None"
|
@@ -205,37 +203,7 @@
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|
205 |
"execution_count": null,
|
206 |
"id": "bba70f33-af8b-4eb3-9973-7be672301a0b",
|
207 |
"metadata": {},
|
208 |
-
"outputs": [
|
209 |
-
{
|
210 |
-
"name": "stdout",
|
211 |
-
"output_type": "stream",
|
212 |
-
"text": [
|
213 |
-
"Processing artifact: model-4oh3u7ca:v54\n"
|
214 |
-
]
|
215 |
-
},
|
216 |
-
{
|
217 |
-
"name": "stderr",
|
218 |
-
"output_type": "stream",
|
219 |
-
"text": [
|
220 |
-
"\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mborisd13\u001b[0m (use `wandb login --relogin` to force relogin)\n"
|
221 |
-
]
|
222 |
-
},
|
223 |
-
{
|
224 |
-
"data": {
|
225 |
-
"text/html": [
|
226 |
-
"\n",
|
227 |
-
" Syncing run <strong><a href=\"https://wandb.ai/dalle-mini/dalle-mini/runs/inference-4oh3u7ca\" target=\"_blank\">inference-4oh3u7ca</a></strong> to <a href=\"https://wandb.ai/dalle-mini/dalle-mini\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
|
228 |
-
"\n",
|
229 |
-
" "
|
230 |
-
],
|
231 |
-
"text/plain": [
|
232 |
-
"<IPython.core.display.HTML object>"
|
233 |
-
]
|
234 |
-
},
|
235 |
-
"metadata": {},
|
236 |
-
"output_type": "display_data"
|
237 |
-
}
|
238 |
-
],
|
239 |
"source": [
|
240 |
"artifact_versions = get_artifact_versions(run_id, latest_only)\n",
|
241 |
"last_inference_version = get_last_inference_version(run_id)\n",
|
@@ -247,10 +215,11 @@
|
|
247 |
" print(f'Processing artifact: {artifact.name}')\n",
|
248 |
" version = int(artifact.version[1:])\n",
|
249 |
" results = []\n",
|
250 |
-
" columns = ['Caption'
|
251 |
" \n",
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" if latest_only:\n",
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-
"
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" else:\n",
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" if last_inference_version is None:\n",
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" # we should start from v0\n",
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@@ -263,7 +232,7 @@
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"\n",
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" # start/resume corresponding run\n",
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" if run is None:\n",
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-
" run = wandb.init(job_type='inference', entity='dalle-mini', project='dalle-mini', config=training_config, id=f'
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"\n",
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" # work in temporary directory\n",
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" with tempfile.TemporaryDirectory() as tmp:\n",
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@@ -284,8 +253,7 @@
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"\n",
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" # process one batch of captions\n",
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" for batch in tqdm(samples):\n",
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-
"
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-
" processed_prompts = [text_normalizer(x) for x in prompts] if normalize_text else prompts\n",
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"\n",
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" # repeat the prompts to distribute over each device and tokenize\n",
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" processed_prompts = processed_prompts * jax.device_count()\n",
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@@ -306,7 +274,7 @@
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"\n",
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" # get clip scores\n",
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" print('Calculating CLIP scores')\n",
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-
" clip_inputs = processor(text=
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" # each shard will have one prompt, images need to be reorganized to be associated to the correct shard\n",
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" images_per_prompt_indices = np.asarray(range(0, len(images), batch_size))\n",
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" clip_inputs['pixel_values'] = jnp.concatenate(list(clip_inputs['pixel_values'][images_per_prompt_indices + i] for i in range(batch_size)))\n",
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@@ -318,11 +286,11 @@
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"\n",
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" # add to results table\n",
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" for i, (idx, scores, sample) in enumerate(zip(top_scores, logits, batch)):\n",
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-
" if sample
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" cur_images = [images[x] for x in images_per_prompt_indices + i]\n",
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" top_images = [wandb.Image(cur_images[x]) for x in idx]\n",
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" top_scores = [scores[x] for x in idx]\n",
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-
" results.append([sample
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"\n",
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" # log results\n",
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" table = wandb.Table(columns=columns, data=results)\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"import tempfile\n",
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"from functools import partial\n",
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"import random\n",
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"ENTITY, PROJECT = 'dalle-mini', 'dalle-mini' # used only for training run\n",
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"VQGAN_REPO, VQGAN_COMMIT_ID = 'dalle-mini/vqgan_imagenet_f16_16384', None\n",
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"normalize_text = True\n",
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+
"latest_only = False # log only latest or all versions\n",
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+
"suffix = '_1' # mainly for duplicate inference runs with a deleted version"
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]
|
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},
|
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{
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"metadata": {},
|
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"outputs": [],
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"source": [
|
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+
"run_ids = ['3kaut6e8']\n",
|
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"ENTITY, PROJECT = 'wandb', 'hf-flax-dalle-mini'\n",
|
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"VQGAN_REPO, VQGAN_COMMIT_ID = 'dalle-mini/vqgan_imagenet_f16_16384', None\n",
|
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"normalize_text = False\n",
|
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+
"latest_only = True # log only latest or all versions\n",
|
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+
"suffix = '_2' # mainly for duplicate inference runs with a deleted version"
|
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]
|
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},
|
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{
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|
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"outputs": [],
|
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"source": [
|
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"vqgan = VQModel.from_pretrained(VQGAN_REPO, revision=VQGAN_COMMIT_ID)\n",
|
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+
"clip = FlaxCLIPModel.from_pretrained(\"openai/clip-vit-base-patch16\")\n",
|
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+
"processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch16\")\n",
|
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"clip_params = replicate(clip.params)\n",
|
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"vqgan_params = replicate(vqgan.params)"
|
86 |
]
|
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|
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"metadata": {},
|
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"outputs": [],
|
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"source": [
|
112 |
+
"with open('samples.txt', encoding='utf8') as f:\n",
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+
" samples = [l.strip() for l in f.readlines()]\n",
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|
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" # make list multiple of batch_size by adding elements\n",
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+
" samples_to_add = [padding_item] * (-len(samples) % batch_size)\n",
|
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" samples.extend(samples_to_add)\n",
|
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" # reshape\n",
|
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" samples = [samples[i:i+batch_size] for i in range(0, len(samples), batch_size)]"
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|
158 |
"# retrieve inference run details\n",
|
159 |
"def get_last_inference_version(run_id):\n",
|
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" try:\n",
|
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+
" inference_run = api.run(f'dalle-mini/dalle-mini/inf-{run_id}{suffix}')\n",
|
162 |
" return inference_run.summary.get('version', None)\n",
|
163 |
" except:\n",
|
164 |
" return None"
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"execution_count": null,
|
204 |
"id": "bba70f33-af8b-4eb3-9973-7be672301a0b",
|
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"metadata": {},
|
206 |
+
"outputs": [],
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|
207 |
"source": [
|
208 |
"artifact_versions = get_artifact_versions(run_id, latest_only)\n",
|
209 |
"last_inference_version = get_last_inference_version(run_id)\n",
|
|
|
215 |
" print(f'Processing artifact: {artifact.name}')\n",
|
216 |
" version = int(artifact.version[1:])\n",
|
217 |
" results = []\n",
|
218 |
+
" columns = ['Caption'] + [f'Image {i+1}' for i in range(top_k)] + [f'Score {i+1}' for i in range(top_k)]\n",
|
219 |
" \n",
|
220 |
" if latest_only:\n",
|
221 |
+
" pass\n",
|
222 |
+
" #assert last_inference_version is None or version > last_inference_version\n",
|
223 |
" else:\n",
|
224 |
" if last_inference_version is None:\n",
|
225 |
" # we should start from v0\n",
|
|
|
232 |
"\n",
|
233 |
" # start/resume corresponding run\n",
|
234 |
" if run is None:\n",
|
235 |
+
" run = wandb.init(job_type='inference', entity='dalle-mini', project='dalle-mini', config=training_config, id=f'inf-{run_id}{suffix}', resume='allow')\n",
|
236 |
"\n",
|
237 |
" # work in temporary directory\n",
|
238 |
" with tempfile.TemporaryDirectory() as tmp:\n",
|
|
|
253 |
"\n",
|
254 |
" # process one batch of captions\n",
|
255 |
" for batch in tqdm(samples):\n",
|
256 |
+
" processed_prompts = [text_normalizer(x) for x in batch] if normalize_text else list(batch)\n",
|
|
|
257 |
"\n",
|
258 |
" # repeat the prompts to distribute over each device and tokenize\n",
|
259 |
" processed_prompts = processed_prompts * jax.device_count()\n",
|
|
|
274 |
"\n",
|
275 |
" # get clip scores\n",
|
276 |
" print('Calculating CLIP scores')\n",
|
277 |
+
" clip_inputs = processor(text=batch, images=images, return_tensors='np', padding='max_length', max_length=77, truncation=True).data\n",
|
278 |
" # each shard will have one prompt, images need to be reorganized to be associated to the correct shard\n",
|
279 |
" images_per_prompt_indices = np.asarray(range(0, len(images), batch_size))\n",
|
280 |
" clip_inputs['pixel_values'] = jnp.concatenate(list(clip_inputs['pixel_values'][images_per_prompt_indices + i] for i in range(batch_size)))\n",
|
|
|
286 |
"\n",
|
287 |
" # add to results table\n",
|
288 |
" for i, (idx, scores, sample) in enumerate(zip(top_scores, logits, batch)):\n",
|
289 |
+
" if sample == padding_item: continue\n",
|
290 |
" cur_images = [images[x] for x in images_per_prompt_indices + i]\n",
|
291 |
" top_images = [wandb.Image(cur_images[x]) for x in idx]\n",
|
292 |
" top_scores = [scores[x] for x in idx]\n",
|
293 |
+
" results.append([sample] + top_images + top_scores)\n",
|
294 |
"\n",
|
295 |
" # log results\n",
|
296 |
" table = wandb.Table(columns=columns, data=results)\n",
|