typetext
ID
p5xbnxbb5i4u6j7s5p644d6x2u
Status
Succeeded
Source
Web
Hardware
A100 (40GB)
Total duration
Created
Webhook
–
Input
- prompt
- Toy train colorful, macro photo. Toy airplane colorful, macro photo. Toy car colorful, macro photo. Toy boat colorful, macro photo.
- negative_prompt
- low-resolutiondefaultlow-resolutiontypetext
- width
- 768default768typenumeric-rangevalue typeintegerminimum256maximum2048
- height
- 768default768typenumeric-rangevalue typeintegerminimum256maximum2048
- num_inference_steps
- 50default50typenumeric-rangevalue typeintegerminimum1maximum150
- seed
- 62967typenumericvalue typeinteger
{
"height": 768,
"negative_prompt": "low-resolution",
"num_inference_steps": 50,
"prompt": "Toy train colorful, macro photo.\nToy airplane colorful, macro photo.\nToy car colorful, macro photo.\nToy boat colorful, macro photo.",
"seed": 62967,
"width": 768
}Install Replicate’s Node.js client library:
npm install replicate
Set the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_XNR**********************************
This is your API token. Keep it to yourself.
Import and set up the client:
import Replicate from "replicate";
import fs from "node:fs";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});
Run lucataco/style-aligned using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e",
{
input: {
height: 768,
negative_prompt: "low-resolution",
num_inference_steps: 50,
prompt: "Toy train colorful, macro photo.\nToy airplane colorful, macro photo.\nToy car colorful, macro photo.\nToy boat colorful, macro photo.",
seed: 62967,
width: 768
}
}
);
// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"
// To write the file to disk:
fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:
pip install replicate
Set the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_XNR**********************************
This is your API token. Keep it to yourself.
Import the client:
import replicate
Run lucataco/style-aligned using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e",
input={
"height": 768,
"negative_prompt": "low-resolution",
"num_inference_steps": 50,
"prompt": "Toy train colorful, macro photo.\nToy airplane colorful, macro photo.\nToy car colorful, macro photo.\nToy boat colorful, macro photo.",
"seed": 62967,
"width": 768
}
)
# To access the file URL:
print(output[0].url())
#=> "http://example.com"
# To write the file to disk:
with open("my-image.png", "wb") as file:
file.write(output[0].read())
To learn more, take a look at the guide on getting started with Python.
Set the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_XNR**********************************
This is your API token. Keep it to yourself.
Run lucataco/style-aligned 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": "lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e",
"input": {
"height": 768,
"negative_prompt": "low-resolution",
"num_inference_steps": 50,
"prompt": "Toy train colorful, macro photo.\\nToy airplane colorful, macro photo.\\nToy car colorful, macro photo.\\nToy boat colorful, macro photo.",
"seed": 62967,
"width": 768
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{
"id": "p5xbnxbb5i4u6j7s5p644d6x2u",
"model": "lucataco/style-aligned",
"version": "a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e",
"input": {
"height": 768,
"negative_prompt": "low-resolution",
"num_inference_steps": 50,
"prompt": "Toy train colorful, macro photo.\nToy airplane colorful, macro photo.\nToy car colorful, macro photo.\nToy boat colorful, macro photo.",
"seed": 62967,
"width": 768
},
"logs": "Using seed: 62967\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:05<04:43, 5.78s/it]\n 4%|▍ | 2/50 [00:06<02:02, 2.56s/it]\n 6%|▌ | 3/50 [00:06<01:11, 1.53s/it]\n 8%|▊ | 4/50 [00:06<00:47, 1.04s/it]\n 10%|█ | 5/50 [00:06<00:34, 1.29it/s]\n 12%|█▏ | 6/50 [00:07<00:27, 1.63it/s]\n 14%|█▍ | 7/50 [00:07<00:22, 1.95it/s]\n 16%|█▌ | 8/50 [00:07<00:18, 2.25it/s]\n 18%|█▊ | 9/50 [00:08<00:16, 2.50it/s]\n 20%|██ | 10/50 [00:08<00:14, 2.71it/s]\n 22%|██▏ | 11/50 [00:08<00:13, 2.87it/s]\n 24%|██▍ | 12/50 [00:09<00:12, 3.00it/s]\n 26%|██▌ | 13/50 [00:09<00:11, 3.09it/s]\n 28%|██▊ | 14/50 [00:09<00:11, 3.16it/s]\n 30%|███ | 15/50 [00:09<00:10, 3.21it/s]\n 32%|███▏ | 16/50 [00:10<00:10, 3.24it/s]\n 34%|███▍ | 17/50 [00:10<00:10, 3.27it/s]\n 36%|███▌ | 18/50 [00:10<00:09, 3.29it/s]\n 38%|███▊ | 19/50 [00:11<00:09, 3.30it/s]\n 40%|████ | 20/50 [00:11<00:09, 3.29it/s]\n 42%|████▏ | 21/50 [00:11<00:08, 3.30it/s]\n 44%|████▍ | 22/50 [00:12<00:08, 3.31it/s]\n 46%|████▌ | 23/50 [00:12<00:08, 3.31it/s]\n 48%|████▊ | 24/50 [00:12<00:07, 3.32it/s]\n 50%|█████ | 25/50 [00:13<00:07, 3.32it/s]\n 52%|█████▏ | 26/50 [00:13<00:07, 3.32it/s]\n 54%|█████▍ | 27/50 [00:13<00:06, 3.32it/s]\n 56%|█████▌ | 28/50 [00:13<00:06, 3.32it/s]\n 58%|█████▊ | 29/50 [00:14<00:06, 3.32it/s]\n 60%|██████ | 30/50 [00:14<00:06, 3.32it/s]\n 62%|██████▏ | 31/50 [00:14<00:05, 3.32it/s]\n 64%|██████▍ | 32/50 [00:15<00:05, 3.32it/s]\n 66%|██████▌ | 33/50 [00:15<00:05, 3.32it/s]\n 68%|██████▊ | 34/50 [00:15<00:04, 3.32it/s]\n 70%|███████ | 35/50 [00:16<00:04, 3.32it/s]\n 72%|███████▏ | 36/50 [00:16<00:04, 3.32it/s]\n 74%|███████▍ | 37/50 [00:16<00:03, 3.31it/s]\n 76%|███████▌ | 38/50 [00:16<00:03, 3.31it/s]\n 78%|███████▊ | 39/50 [00:17<00:03, 3.31it/s]\n 80%|████████ | 40/50 [00:17<00:03, 3.31it/s]\n 82%|████████▏ | 41/50 [00:17<00:02, 3.30it/s]\n 84%|████████▍ | 42/50 [00:18<00:02, 3.31it/s]\n 86%|████████▌ | 43/50 [00:18<00:02, 3.31it/s]\n 88%|████████▊ | 44/50 [00:18<00:01, 3.31it/s]\n 90%|█████████ | 45/50 [00:19<00:01, 3.31it/s]\n 92%|█████████▏| 46/50 [00:19<00:01, 3.31it/s]\n 94%|█████████▍| 47/50 [00:19<00:00, 3.32it/s]\n 96%|█████████▌| 48/50 [00:19<00:00, 3.32it/s]\n 98%|█████████▊| 49/50 [00:20<00:00, 3.32it/s]\n100%|██████████| 50/50 [00:20<00:00, 3.32it/s]\n100%|██████████| 50/50 [00:20<00:00, 2.43it/s]",
"output": [
"https://replicate.delivery/pbxt/BXpfqgS1ErTfRUmI9fxcxBY5inzWYayPXIpKNayZKnqDdqeHB/output-0.png",
"https://replicate.delivery/pbxt/E7e2jdEllnT7Z6hlXWuCIuj6fi5lrJh99kjeYNnwdw9FdqeHB/output-1.png",
"https://replicate.delivery/pbxt/oNpi9q3SY26cO1kzjqL7eJqv3G9aGUTMlhPPVOU8if8jOVfjA/output-2.png",
"https://replicate.delivery/pbxt/pkolzSOMs6JrBpdfaQUrGICCa9gYE2GsumJnFlf54tljOVfjA/output-3.png"
],
"data_removed": false,
"error": null,
"source": "web",
"status": "succeeded",
"created_at": "2023-12-05T22:14:05.156174Z",
"started_at": "2023-12-05T22:17:44.581344Z",
"completed_at": "2023-12-05T22:18:12.354683Z",
"urls": {
"cancel": "https://api.replicate.com/v1/predictions/p5xbnxbb5i4u6j7s5p644d6x2u/cancel",
"get": "https://api.replicate.com/v1/predictions/p5xbnxbb5i4u6j7s5p644d6x2u"
},
"metrics": {
"predict_time": 27.773339,
"total_time": 247.198509
}
}Generated in
Using seed: 62967
0%| | 0/50 [00:00<?, ?it/s]
2%|▏ | 1/50 [00:05<04:43, 5.78s/it]
4%|▍ | 2/50 [00:06<02:02, 2.56s/it]
6%|▌ | 3/50 [00:06<01:11, 1.53s/it]
8%|▊ | 4/50 [00:06<00:47, 1.04s/it]
10%|█ | 5/50 [00:06<00:34, 1.29it/s]
12%|█▏ | 6/50 [00:07<00:27, 1.63it/s]
14%|█▍ | 7/50 [00:07<00:22, 1.95it/s]
16%|█▌ | 8/50 [00:07<00:18, 2.25it/s]
18%|█▊ | 9/50 [00:08<00:16, 2.50it/s]
20%|██ | 10/50 [00:08<00:14, 2.71it/s]
22%|██▏ | 11/50 [00:08<00:13, 2.87it/s]
24%|██▍ | 12/50 [00:09<00:12, 3.00it/s]
26%|██▌ | 13/50 [00:09<00:11, 3.09it/s]
28%|██▊ | 14/50 [00:09<00:11, 3.16it/s]
30%|███ | 15/50 [00:09<00:10, 3.21it/s]
32%|███▏ | 16/50 [00:10<00:10, 3.24it/s]
34%|███▍ | 17/50 [00:10<00:10, 3.27it/s]
36%|███▌ | 18/50 [00:10<00:09, 3.29it/s]
38%|███▊ | 19/50 [00:11<00:09, 3.30it/s]
40%|████ | 20/50 [00:11<00:09, 3.29it/s]
42%|████▏ | 21/50 [00:11<00:08, 3.30it/s]
44%|████▍ | 22/50 [00:12<00:08, 3.31it/s]
46%|████▌ | 23/50 [00:12<00:08, 3.31it/s]
48%|████▊ | 24/50 [00:12<00:07, 3.32it/s]
50%|█████ | 25/50 [00:13<00:07, 3.32it/s]
52%|█████▏ | 26/50 [00:13<00:07, 3.32it/s]
54%|█████▍ | 27/50 [00:13<00:06, 3.32it/s]
56%|█████▌ | 28/50 [00:13<00:06, 3.32it/s]
58%|█████▊ | 29/50 [00:14<00:06, 3.32it/s]
60%|██████ | 30/50 [00:14<00:06, 3.32it/s]
62%|██████▏ | 31/50 [00:14<00:05, 3.32it/s]
64%|██████▍ | 32/50 [00:15<00:05, 3.32it/s]
66%|██████▌ | 33/50 [00:15<00:05, 3.32it/s]
68%|██████▊ | 34/50 [00:15<00:04, 3.32it/s]
70%|███████ | 35/50 [00:16<00:04, 3.32it/s]
72%|███████▏ | 36/50 [00:16<00:04, 3.32it/s]
74%|███████▍ | 37/50 [00:16<00:03, 3.31it/s]
76%|███████▌ | 38/50 [00:16<00:03, 3.31it/s]
78%|███████▊ | 39/50 [00:17<00:03, 3.31it/s]
80%|████████ | 40/50 [00:17<00:03, 3.31it/s]
82%|████████▏ | 41/50 [00:17<00:02, 3.30it/s]
84%|████████▍ | 42/50 [00:18<00:02, 3.31it/s]
86%|████████▌ | 43/50 [00:18<00:02, 3.31it/s]
88%|████████▊ | 44/50 [00:18<00:01, 3.31it/s]
90%|█████████ | 45/50 [00:19<00:01, 3.31it/s]
92%|█████████▏| 46/50 [00:19<00:01, 3.31it/s]
94%|█████████▍| 47/50 [00:19<00:00, 3.32it/s]
96%|█████████▌| 48/50 [00:19<00:00, 3.32it/s]
98%|█████████▊| 49/50 [00:20<00:00, 3.32it/s]
100%|██████████| 50/50 [00:20<00:00, 3.32it/s]
100%|██████████| 50/50 [00:20<00:00, 2.43it/s]