Readme
High quality image blending using Kandinsky 2.2 blending pipeline brought to you by FullJourney.AI
High quality image blending using Kandinsky 2.2 blending pipeline brought to you by your best friends at FullJourney.AI :)
Run this model in Node.js with one line of code:
npm install replicate
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import Replicate from "replicate";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});
Run charlesmccarthy/blend-images using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"charlesmccarthy/blend-images:1ed8aaaa04fa84f0c1191679e765d209b94866f6503038416dcbcb340fede892",
{
input: {
image1: "https://replicate.delivery/pbxt/JYUwgFNQbajoEQxvkJIPjDfIpE5Pj51iSfeU8CaYe1896Hyk/generatedimg_4_43531b30-b813-4226-9d5a-a80b5a29f613_1695126618.png",
image2: "https://replicate.delivery/pbxt/JYUwgCRwYFmMbVAK0RmU5ftY6zg86x6zjTrLG9Bq3abzB6Mw/generatedimg_2_466bb97d-67eb-406a-8a20-2aeb975ea45c_1695134551.png",
prompt: "A deer shaped clock"
}
}
);
console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import replicate
Run charlesmccarthy/blend-images using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"charlesmccarthy/blend-images:1ed8aaaa04fa84f0c1191679e765d209b94866f6503038416dcbcb340fede892",
input={
"image1": "https://replicate.delivery/pbxt/JYUwgFNQbajoEQxvkJIPjDfIpE5Pj51iSfeU8CaYe1896Hyk/generatedimg_4_43531b30-b813-4226-9d5a-a80b5a29f613_1695126618.png",
"image2": "https://replicate.delivery/pbxt/JYUwgCRwYFmMbVAK0RmU5ftY6zg86x6zjTrLG9Bq3abzB6Mw/generatedimg_2_466bb97d-67eb-406a-8a20-2aeb975ea45c_1695134551.png",
"prompt": "A deer shaped clock"
}
)
print(output)
To learn more, take a look at the guide on getting started with Python.
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run charlesmccarthy/blend-images using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \
-H "Authorization: Bearer $REPLICATE_API_TOKEN" \
-H "Content-Type: application/json" \
-H "Prefer: wait" \
-d $'{
"version": "1ed8aaaa04fa84f0c1191679e765d209b94866f6503038416dcbcb340fede892",
"input": {
"image1": "https://replicate.delivery/pbxt/JYUwgFNQbajoEQxvkJIPjDfIpE5Pj51iSfeU8CaYe1896Hyk/generatedimg_4_43531b30-b813-4226-9d5a-a80b5a29f613_1695126618.png",
"image2": "https://replicate.delivery/pbxt/JYUwgCRwYFmMbVAK0RmU5ftY6zg86x6zjTrLG9Bq3abzB6Mw/generatedimg_2_466bb97d-67eb-406a-8a20-2aeb975ea45c_1695134551.png",
"prompt": "A deer shaped clock"
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Add a payment method to run this model.
By signing in, you agree to our
terms of service and privacy policy
{
"completed_at": "2023-09-19T14:47:18.855162Z",
"created_at": "2023-09-19T14:44:58.838855Z",
"data_removed": false,
"error": null,
"id": "y6fgly3be4x5erx5sxj2ekhwxa",
"input": {
"image1": "https://replicate.delivery/pbxt/JYUwgFNQbajoEQxvkJIPjDfIpE5Pj51iSfeU8CaYe1896Hyk/generatedimg_4_43531b30-b813-4226-9d5a-a80b5a29f613_1695126618.png",
"image2": "https://replicate.delivery/pbxt/JYUwgCRwYFmMbVAK0RmU5ftY6zg86x6zjTrLG9Bq3abzB6Mw/generatedimg_2_466bb97d-67eb-406a-8a20-2aeb975ea45c_1695134551.png",
"prompt": "A deer shaped clock"
},
"logs": "0%| | 0/25 [00:00<?, ?it/s]\n 32%|███▏ | 8/25 [00:00<00:00, 74.16it/s]\n 64%|██████▍ | 16/25 [00:00<00:00, 76.68it/s]\n 96%|█████████▌| 24/25 [00:00<00:00, 77.78it/s]\n100%|██████████| 25/25 [00:00<00:00, 77.27it/s]\n 0%| | 0/25 [00:00<?, ?it/s]\n 32%|███▏ | 8/25 [00:00<00:00, 78.77it/s]\n 64%|██████▍ | 16/25 [00:00<00:00, 76.97it/s]\n 96%|█████████▌| 24/25 [00:00<00:00, 76.73it/s]\n100%|██████████| 25/25 [00:00<00:00, 77.01it/s]\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:12, 7.84it/s]\n 2%|▏ | 2/100 [00:00<00:12, 7.79it/s]\n 3%|▎ | 3/100 [00:00<00:12, 7.77it/s]\n 4%|▍ | 4/100 [00:00<00:12, 7.76it/s]\n 5%|▌ | 5/100 [00:00<00:12, 7.77it/s]\n 6%|▌ | 6/100 [00:00<00:12, 7.78it/s]\n 7%|▋ | 7/100 [00:00<00:11, 7.77it/s]\n 8%|▊ | 8/100 [00:01<00:11, 7.77it/s]\n 9%|▉ | 9/100 [00:01<00:11, 7.76it/s]\n 10%|█ | 10/100 [00:01<00:11, 7.76it/s]\n 11%|█ | 11/100 [00:01<00:11, 7.76it/s]\n 12%|█▏ | 12/100 [00:01<00:11, 7.76it/s]\n 13%|█▎ | 13/100 [00:01<00:11, 7.75it/s]\n 14%|█▍ | 14/100 [00:01<00:11, 7.75it/s]\n 15%|█▌ | 15/100 [00:01<00:10, 7.75it/s]\n 16%|█▌ | 16/100 [00:02<00:10, 7.76it/s]\n 17%|█▋ | 17/100 [00:02<00:10, 7.76it/s]\n 18%|█▊ | 18/100 [00:02<00:10, 7.75it/s]\n 19%|█▉ | 19/100 [00:02<00:10, 7.75it/s]\n 20%|██ | 20/100 [00:02<00:10, 7.75it/s]\n 21%|██ | 21/100 [00:02<00:10, 7.76it/s]\n 22%|██▏ | 22/100 [00:02<00:10, 7.75it/s]\n 23%|██▎ | 23/100 [00:02<00:09, 7.75it/s]\n 24%|██▍ | 24/100 [00:03<00:09, 7.76it/s]\n 25%|██▌ | 25/100 [00:03<00:09, 7.76it/s]\n 26%|██▌ | 26/100 [00:03<00:09, 7.76it/s]\n 27%|██▋ | 27/100 [00:03<00:09, 7.76it/s]\n 28%|██▊ | 28/100 [00:03<00:09, 7.75it/s]\n 29%|██▉ | 29/100 [00:03<00:09, 7.75it/s]\n 30%|███ | 30/100 [00:03<00:09, 7.75it/s]\n 31%|███ | 31/100 [00:03<00:08, 7.74it/s]\n 32%|███▏ | 32/100 [00:04<00:08, 7.74it/s]\n 33%|███▎ | 33/100 [00:04<00:08, 7.74it/s]\n 34%|███▍ | 34/100 [00:04<00:08, 7.75it/s]\n 35%|███▌ | 35/100 [00:04<00:08, 7.75it/s]\n 36%|███▌ | 36/100 [00:04<00:08, 7.75it/s]\n 37%|███▋ | 37/100 [00:04<00:08, 7.75it/s]\n 38%|███▊ | 38/100 [00:04<00:08, 7.75it/s]\n 39%|███▉ | 39/100 [00:05<00:07, 7.75it/s]\n 40%|████ | 40/100 [00:05<00:07, 7.75it/s]\n 41%|████ | 41/100 [00:05<00:07, 7.75it/s]\n 42%|████▏ | 42/100 [00:05<00:07, 7.74it/s]\n 43%|████▎ | 43/100 [00:05<00:07, 7.73it/s]\n 44%|████▍ | 44/100 [00:05<00:07, 7.73it/s]\n 45%|████▌ | 45/100 [00:05<00:07, 7.73it/s]\n 46%|████▌ | 46/100 [00:05<00:06, 7.73it/s]\n 47%|████▋ | 47/100 [00:06<00:06, 7.74it/s]\n 48%|████▊ | 48/100 [00:06<00:06, 7.74it/s]\n 49%|████▉ | 49/100 [00:06<00:06, 7.74it/s]\n 50%|█████ | 50/100 [00:06<00:06, 7.75it/s]\n 51%|█████ | 51/100 [00:06<00:06, 7.75it/s]\n 52%|█████▏ | 52/100 [00:06<00:06, 7.75it/s]\n 53%|█████▎ | 53/100 [00:06<00:06, 7.74it/s]\n 54%|█████▍ | 54/100 [00:06<00:05, 7.74it/s]\n 55%|█████▌ | 55/100 [00:07<00:05, 7.74it/s]\n 56%|█████▌ | 56/100 [00:07<00:05, 7.74it/s]\n 57%|█████▋ | 57/100 [00:07<00:05, 7.74it/s]\n 58%|█████▊ | 58/100 [00:07<00:05, 7.74it/s]\n 59%|█████▉ | 59/100 [00:07<00:05, 7.74it/s]\n 60%|██████ | 60/100 [00:07<00:05, 7.74it/s]\n 61%|██████ | 61/100 [00:07<00:05, 7.74it/s]\n 62%|██████▏ | 62/100 [00:08<00:04, 7.74it/s]\n 63%|██████▎ | 63/100 [00:08<00:04, 7.74it/s]\n 64%|██████▍ | 64/100 [00:08<00:04, 7.74it/s]\n 65%|██████▌ | 65/100 [00:08<00:04, 7.74it/s]\n 66%|██████▌ | 66/100 [00:08<00:04, 7.74it/s]\n 67%|██████▋ | 67/100 [00:08<00:04, 7.74it/s]\n 68%|██████▊ | 68/100 [00:08<00:04, 7.73it/s]\n 69%|██████▉ | 69/100 [00:08<00:04, 7.72it/s]\n 70%|███████ | 70/100 [00:09<00:03, 7.72it/s]\n 71%|███████ | 71/100 [00:09<00:03, 7.73it/s]\n 72%|███████▏ | 72/100 [00:09<00:03, 7.72it/s]\n 73%|███████▎ | 73/100 [00:09<00:03, 7.72it/s]\n 74%|███████▍ | 74/100 [00:09<00:03, 7.72it/s]\n 75%|███████▌ | 75/100 [00:09<00:03, 7.72it/s]\n 76%|███████▌ | 76/100 [00:09<00:03, 7.73it/s]\n 77%|███████▋ | 77/100 [00:09<00:02, 7.74it/s]\n 78%|███████▊ | 78/100 [00:10<00:02, 7.73it/s]\n 79%|███████▉ | 79/100 [00:10<00:02, 7.74it/s]\n 80%|████████ | 80/100 [00:10<00:02, 7.74it/s]\n 81%|████████ | 81/100 [00:10<00:02, 7.73it/s]\n 82%|████████▏ | 82/100 [00:10<00:02, 7.73it/s]\n 83%|████████▎ | 83/100 [00:10<00:02, 7.73it/s]\n 84%|████████▍ | 84/100 [00:10<00:02, 7.73it/s]\n 85%|████████▌ | 85/100 [00:10<00:01, 7.73it/s]\n 86%|████████▌ | 86/100 [00:11<00:01, 7.72it/s]\n 87%|████████▋ | 87/100 [00:11<00:01, 7.71it/s]\n 88%|████████▊ | 88/100 [00:11<00:01, 7.72it/s]\n 89%|████████▉ | 89/100 [00:11<00:01, 7.72it/s]\n 90%|█████████ | 90/100 [00:11<00:01, 7.72it/s]\n 91%|█████████ | 91/100 [00:11<00:01, 7.73it/s]\n 92%|█████████▏| 92/100 [00:11<00:01, 7.73it/s]\n 93%|█████████▎| 93/100 [00:12<00:00, 7.73it/s]\n 94%|█████████▍| 94/100 [00:12<00:00, 7.74it/s]\n 95%|█████████▌| 95/100 [00:12<00:00, 7.74it/s]\n 96%|█████████▌| 96/100 [00:12<00:00, 7.73it/s]\n 97%|█████████▋| 97/100 [00:12<00:00, 7.74it/s]\n 98%|█████████▊| 98/100 [00:12<00:00, 7.74it/s]\n 99%|█████████▉| 99/100 [00:12<00:00, 7.73it/s]\n100%|██████████| 100/100 [00:12<00:00, 7.73it/s]\n100%|██████████| 100/100 [00:12<00:00, 7.74it/s]",
"metrics": {
"predict_time": 17.637254,
"total_time": 140.016307
},
"output": "https://pbxt.replicate.delivery/jWpveeiis2oXLE4cmnTu5l2SpfKGdbqKBbLAHgBDFykqzsLjA/output.png",
"started_at": "2023-09-19T14:47:01.217908Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/y6fgly3be4x5erx5sxj2ekhwxa",
"cancel": "https://api.replicate.com/v1/predictions/y6fgly3be4x5erx5sxj2ekhwxa/cancel"
},
"version": "1ed8aaaa04fa84f0c1191679e765d209b94866f6503038416dcbcb340fede892"
}
0%| | 0/25 [00:00<?, ?it/s]
32%|███▏ | 8/25 [00:00<00:00, 74.16it/s]
64%|██████▍ | 16/25 [00:00<00:00, 76.68it/s]
96%|█████████▌| 24/25 [00:00<00:00, 77.78it/s]
100%|██████████| 25/25 [00:00<00:00, 77.27it/s]
0%| | 0/25 [00:00<?, ?it/s]
32%|███▏ | 8/25 [00:00<00:00, 78.77it/s]
64%|██████▍ | 16/25 [00:00<00:00, 76.97it/s]
96%|█████████▌| 24/25 [00:00<00:00, 76.73it/s]
100%|██████████| 25/25 [00:00<00:00, 77.01it/s]
0%| | 0/100 [00:00<?, ?it/s]
1%| | 1/100 [00:00<00:12, 7.84it/s]
2%|▏ | 2/100 [00:00<00:12, 7.79it/s]
3%|▎ | 3/100 [00:00<00:12, 7.77it/s]
4%|▍ | 4/100 [00:00<00:12, 7.76it/s]
5%|▌ | 5/100 [00:00<00:12, 7.77it/s]
6%|▌ | 6/100 [00:00<00:12, 7.78it/s]
7%|▋ | 7/100 [00:00<00:11, 7.77it/s]
8%|▊ | 8/100 [00:01<00:11, 7.77it/s]
9%|▉ | 9/100 [00:01<00:11, 7.76it/s]
10%|█ | 10/100 [00:01<00:11, 7.76it/s]
11%|█ | 11/100 [00:01<00:11, 7.76it/s]
12%|█▏ | 12/100 [00:01<00:11, 7.76it/s]
13%|█▎ | 13/100 [00:01<00:11, 7.75it/s]
14%|█▍ | 14/100 [00:01<00:11, 7.75it/s]
15%|█▌ | 15/100 [00:01<00:10, 7.75it/s]
16%|█▌ | 16/100 [00:02<00:10, 7.76it/s]
17%|█▋ | 17/100 [00:02<00:10, 7.76it/s]
18%|█▊ | 18/100 [00:02<00:10, 7.75it/s]
19%|█▉ | 19/100 [00:02<00:10, 7.75it/s]
20%|██ | 20/100 [00:02<00:10, 7.75it/s]
21%|██ | 21/100 [00:02<00:10, 7.76it/s]
22%|██▏ | 22/100 [00:02<00:10, 7.75it/s]
23%|██▎ | 23/100 [00:02<00:09, 7.75it/s]
24%|██▍ | 24/100 [00:03<00:09, 7.76it/s]
25%|██▌ | 25/100 [00:03<00:09, 7.76it/s]
26%|██▌ | 26/100 [00:03<00:09, 7.76it/s]
27%|██▋ | 27/100 [00:03<00:09, 7.76it/s]
28%|██▊ | 28/100 [00:03<00:09, 7.75it/s]
29%|██▉ | 29/100 [00:03<00:09, 7.75it/s]
30%|███ | 30/100 [00:03<00:09, 7.75it/s]
31%|███ | 31/100 [00:03<00:08, 7.74it/s]
32%|███▏ | 32/100 [00:04<00:08, 7.74it/s]
33%|███▎ | 33/100 [00:04<00:08, 7.74it/s]
34%|███▍ | 34/100 [00:04<00:08, 7.75it/s]
35%|███▌ | 35/100 [00:04<00:08, 7.75it/s]
36%|███▌ | 36/100 [00:04<00:08, 7.75it/s]
37%|███▋ | 37/100 [00:04<00:08, 7.75it/s]
38%|███▊ | 38/100 [00:04<00:08, 7.75it/s]
39%|███▉ | 39/100 [00:05<00:07, 7.75it/s]
40%|████ | 40/100 [00:05<00:07, 7.75it/s]
41%|████ | 41/100 [00:05<00:07, 7.75it/s]
42%|████▏ | 42/100 [00:05<00:07, 7.74it/s]
43%|████▎ | 43/100 [00:05<00:07, 7.73it/s]
44%|████▍ | 44/100 [00:05<00:07, 7.73it/s]
45%|████▌ | 45/100 [00:05<00:07, 7.73it/s]
46%|████▌ | 46/100 [00:05<00:06, 7.73it/s]
47%|████▋ | 47/100 [00:06<00:06, 7.74it/s]
48%|████▊ | 48/100 [00:06<00:06, 7.74it/s]
49%|████▉ | 49/100 [00:06<00:06, 7.74it/s]
50%|█████ | 50/100 [00:06<00:06, 7.75it/s]
51%|█████ | 51/100 [00:06<00:06, 7.75it/s]
52%|█████▏ | 52/100 [00:06<00:06, 7.75it/s]
53%|█████▎ | 53/100 [00:06<00:06, 7.74it/s]
54%|█████▍ | 54/100 [00:06<00:05, 7.74it/s]
55%|█████▌ | 55/100 [00:07<00:05, 7.74it/s]
56%|█████▌ | 56/100 [00:07<00:05, 7.74it/s]
57%|█████▋ | 57/100 [00:07<00:05, 7.74it/s]
58%|█████▊ | 58/100 [00:07<00:05, 7.74it/s]
59%|█████▉ | 59/100 [00:07<00:05, 7.74it/s]
60%|██████ | 60/100 [00:07<00:05, 7.74it/s]
61%|██████ | 61/100 [00:07<00:05, 7.74it/s]
62%|██████▏ | 62/100 [00:08<00:04, 7.74it/s]
63%|██████▎ | 63/100 [00:08<00:04, 7.74it/s]
64%|██████▍ | 64/100 [00:08<00:04, 7.74it/s]
65%|██████▌ | 65/100 [00:08<00:04, 7.74it/s]
66%|██████▌ | 66/100 [00:08<00:04, 7.74it/s]
67%|██████▋ | 67/100 [00:08<00:04, 7.74it/s]
68%|██████▊ | 68/100 [00:08<00:04, 7.73it/s]
69%|██████▉ | 69/100 [00:08<00:04, 7.72it/s]
70%|███████ | 70/100 [00:09<00:03, 7.72it/s]
71%|███████ | 71/100 [00:09<00:03, 7.73it/s]
72%|███████▏ | 72/100 [00:09<00:03, 7.72it/s]
73%|███████▎ | 73/100 [00:09<00:03, 7.72it/s]
74%|███████▍ | 74/100 [00:09<00:03, 7.72it/s]
75%|███████▌ | 75/100 [00:09<00:03, 7.72it/s]
76%|███████▌ | 76/100 [00:09<00:03, 7.73it/s]
77%|███████▋ | 77/100 [00:09<00:02, 7.74it/s]
78%|███████▊ | 78/100 [00:10<00:02, 7.73it/s]
79%|███████▉ | 79/100 [00:10<00:02, 7.74it/s]
80%|████████ | 80/100 [00:10<00:02, 7.74it/s]
81%|████████ | 81/100 [00:10<00:02, 7.73it/s]
82%|████████▏ | 82/100 [00:10<00:02, 7.73it/s]
83%|████████▎ | 83/100 [00:10<00:02, 7.73it/s]
84%|████████▍ | 84/100 [00:10<00:02, 7.73it/s]
85%|████████▌ | 85/100 [00:10<00:01, 7.73it/s]
86%|████████▌ | 86/100 [00:11<00:01, 7.72it/s]
87%|████████▋ | 87/100 [00:11<00:01, 7.71it/s]
88%|████████▊ | 88/100 [00:11<00:01, 7.72it/s]
89%|████████▉ | 89/100 [00:11<00:01, 7.72it/s]
90%|█████████ | 90/100 [00:11<00:01, 7.72it/s]
91%|█████████ | 91/100 [00:11<00:01, 7.73it/s]
92%|█████████▏| 92/100 [00:11<00:01, 7.73it/s]
93%|█████████▎| 93/100 [00:12<00:00, 7.73it/s]
94%|█████████▍| 94/100 [00:12<00:00, 7.74it/s]
95%|█████████▌| 95/100 [00:12<00:00, 7.74it/s]
96%|█████████▌| 96/100 [00:12<00:00, 7.73it/s]
97%|█████████▋| 97/100 [00:12<00:00, 7.74it/s]
98%|█████████▊| 98/100 [00:12<00:00, 7.74it/s]
99%|█████████▉| 99/100 [00:12<00:00, 7.73it/s]
100%|██████████| 100/100 [00:12<00:00, 7.73it/s]
100%|██████████| 100/100 [00:12<00:00, 7.74it/s]
This model costs approximately $0.049 to run on Replicate, or 20 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.
This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 51 seconds. The predict time for this model varies significantly based on the inputs.
High quality image blending using Kandinsky 2.2 blending pipeline brought to you by FullJourney.AI
This model is cold. You'll get a fast response if the model is warm and already running, and a slower response if the model is cold and starting up.
Choose a file from your machine
Hint: you can also drag files onto the input
Choose a file from your machine
Hint: you can also drag files onto the input
0%| | 0/25 [00:00<?, ?it/s]
32%|███▏ | 8/25 [00:00<00:00, 74.16it/s]
64%|██████▍ | 16/25 [00:00<00:00, 76.68it/s]
96%|█████████▌| 24/25 [00:00<00:00, 77.78it/s]
100%|██████████| 25/25 [00:00<00:00, 77.27it/s]
0%| | 0/25 [00:00<?, ?it/s]
32%|███▏ | 8/25 [00:00<00:00, 78.77it/s]
64%|██████▍ | 16/25 [00:00<00:00, 76.97it/s]
96%|█████████▌| 24/25 [00:00<00:00, 76.73it/s]
100%|██████████| 25/25 [00:00<00:00, 77.01it/s]
0%| | 0/100 [00:00<?, ?it/s]
1%| | 1/100 [00:00<00:12, 7.84it/s]
2%|▏ | 2/100 [00:00<00:12, 7.79it/s]
3%|▎ | 3/100 [00:00<00:12, 7.77it/s]
4%|▍ | 4/100 [00:00<00:12, 7.76it/s]
5%|▌ | 5/100 [00:00<00:12, 7.77it/s]
6%|▌ | 6/100 [00:00<00:12, 7.78it/s]
7%|▋ | 7/100 [00:00<00:11, 7.77it/s]
8%|▊ | 8/100 [00:01<00:11, 7.77it/s]
9%|▉ | 9/100 [00:01<00:11, 7.76it/s]
10%|█ | 10/100 [00:01<00:11, 7.76it/s]
11%|█ | 11/100 [00:01<00:11, 7.76it/s]
12%|█▏ | 12/100 [00:01<00:11, 7.76it/s]
13%|█▎ | 13/100 [00:01<00:11, 7.75it/s]
14%|█▍ | 14/100 [00:01<00:11, 7.75it/s]
15%|█▌ | 15/100 [00:01<00:10, 7.75it/s]
16%|█▌ | 16/100 [00:02<00:10, 7.76it/s]
17%|█▋ | 17/100 [00:02<00:10, 7.76it/s]
18%|█▊ | 18/100 [00:02<00:10, 7.75it/s]
19%|█▉ | 19/100 [00:02<00:10, 7.75it/s]
20%|██ | 20/100 [00:02<00:10, 7.75it/s]
21%|██ | 21/100 [00:02<00:10, 7.76it/s]
22%|██▏ | 22/100 [00:02<00:10, 7.75it/s]
23%|██▎ | 23/100 [00:02<00:09, 7.75it/s]
24%|██▍ | 24/100 [00:03<00:09, 7.76it/s]
25%|██▌ | 25/100 [00:03<00:09, 7.76it/s]
26%|██▌ | 26/100 [00:03<00:09, 7.76it/s]
27%|██▋ | 27/100 [00:03<00:09, 7.76it/s]
28%|██▊ | 28/100 [00:03<00:09, 7.75it/s]
29%|██▉ | 29/100 [00:03<00:09, 7.75it/s]
30%|███ | 30/100 [00:03<00:09, 7.75it/s]
31%|███ | 31/100 [00:03<00:08, 7.74it/s]
32%|███▏ | 32/100 [00:04<00:08, 7.74it/s]
33%|███▎ | 33/100 [00:04<00:08, 7.74it/s]
34%|███▍ | 34/100 [00:04<00:08, 7.75it/s]
35%|███▌ | 35/100 [00:04<00:08, 7.75it/s]
36%|███▌ | 36/100 [00:04<00:08, 7.75it/s]
37%|███▋ | 37/100 [00:04<00:08, 7.75it/s]
38%|███▊ | 38/100 [00:04<00:08, 7.75it/s]
39%|███▉ | 39/100 [00:05<00:07, 7.75it/s]
40%|████ | 40/100 [00:05<00:07, 7.75it/s]
41%|████ | 41/100 [00:05<00:07, 7.75it/s]
42%|████▏ | 42/100 [00:05<00:07, 7.74it/s]
43%|████▎ | 43/100 [00:05<00:07, 7.73it/s]
44%|████▍ | 44/100 [00:05<00:07, 7.73it/s]
45%|████▌ | 45/100 [00:05<00:07, 7.73it/s]
46%|████▌ | 46/100 [00:05<00:06, 7.73it/s]
47%|████▋ | 47/100 [00:06<00:06, 7.74it/s]
48%|████▊ | 48/100 [00:06<00:06, 7.74it/s]
49%|████▉ | 49/100 [00:06<00:06, 7.74it/s]
50%|█████ | 50/100 [00:06<00:06, 7.75it/s]
51%|█████ | 51/100 [00:06<00:06, 7.75it/s]
52%|█████▏ | 52/100 [00:06<00:06, 7.75it/s]
53%|█████▎ | 53/100 [00:06<00:06, 7.74it/s]
54%|█████▍ | 54/100 [00:06<00:05, 7.74it/s]
55%|█████▌ | 55/100 [00:07<00:05, 7.74it/s]
56%|█████▌ | 56/100 [00:07<00:05, 7.74it/s]
57%|█████▋ | 57/100 [00:07<00:05, 7.74it/s]
58%|█████▊ | 58/100 [00:07<00:05, 7.74it/s]
59%|█████▉ | 59/100 [00:07<00:05, 7.74it/s]
60%|██████ | 60/100 [00:07<00:05, 7.74it/s]
61%|██████ | 61/100 [00:07<00:05, 7.74it/s]
62%|██████▏ | 62/100 [00:08<00:04, 7.74it/s]
63%|██████▎ | 63/100 [00:08<00:04, 7.74it/s]
64%|██████▍ | 64/100 [00:08<00:04, 7.74it/s]
65%|██████▌ | 65/100 [00:08<00:04, 7.74it/s]
66%|██████▌ | 66/100 [00:08<00:04, 7.74it/s]
67%|██████▋ | 67/100 [00:08<00:04, 7.74it/s]
68%|██████▊ | 68/100 [00:08<00:04, 7.73it/s]
69%|██████▉ | 69/100 [00:08<00:04, 7.72it/s]
70%|███████ | 70/100 [00:09<00:03, 7.72it/s]
71%|███████ | 71/100 [00:09<00:03, 7.73it/s]
72%|███████▏ | 72/100 [00:09<00:03, 7.72it/s]
73%|███████▎ | 73/100 [00:09<00:03, 7.72it/s]
74%|███████▍ | 74/100 [00:09<00:03, 7.72it/s]
75%|███████▌ | 75/100 [00:09<00:03, 7.72it/s]
76%|███████▌ | 76/100 [00:09<00:03, 7.73it/s]
77%|███████▋ | 77/100 [00:09<00:02, 7.74it/s]
78%|███████▊ | 78/100 [00:10<00:02, 7.73it/s]
79%|███████▉ | 79/100 [00:10<00:02, 7.74it/s]
80%|████████ | 80/100 [00:10<00:02, 7.74it/s]
81%|████████ | 81/100 [00:10<00:02, 7.73it/s]
82%|████████▏ | 82/100 [00:10<00:02, 7.73it/s]
83%|████████▎ | 83/100 [00:10<00:02, 7.73it/s]
84%|████████▍ | 84/100 [00:10<00:02, 7.73it/s]
85%|████████▌ | 85/100 [00:10<00:01, 7.73it/s]
86%|████████▌ | 86/100 [00:11<00:01, 7.72it/s]
87%|████████▋ | 87/100 [00:11<00:01, 7.71it/s]
88%|████████▊ | 88/100 [00:11<00:01, 7.72it/s]
89%|████████▉ | 89/100 [00:11<00:01, 7.72it/s]
90%|█████████ | 90/100 [00:11<00:01, 7.72it/s]
91%|█████████ | 91/100 [00:11<00:01, 7.73it/s]
92%|█████████▏| 92/100 [00:11<00:01, 7.73it/s]
93%|█████████▎| 93/100 [00:12<00:00, 7.73it/s]
94%|█████████▍| 94/100 [00:12<00:00, 7.74it/s]
95%|█████████▌| 95/100 [00:12<00:00, 7.74it/s]
96%|█████████▌| 96/100 [00:12<00:00, 7.73it/s]
97%|█████████▋| 97/100 [00:12<00:00, 7.74it/s]
98%|█████████▊| 98/100 [00:12<00:00, 7.74it/s]
99%|█████████▉| 99/100 [00:12<00:00, 7.73it/s]
100%|██████████| 100/100 [00:12<00:00, 7.73it/s]
100%|██████████| 100/100 [00:12<00:00, 7.74it/s]