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Input
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 laion-ai/ongo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"laion-ai/ongo:1b3cd15121ec450baa71bbbdacddef9217519f12ca12ccfef36eeaa20ad89b9d",
{
input: {
seed: -1,
steps: 100,
width: 384,
height: 384,
prompt: "bunny rabbit in style of van gogh",
negative: "",
batch_size: 3,
guidance_scale: 5,
aesthetic_rating: 8,
aesthetic_weight: 0.1,
init_skip_fraction: 0,
intermediate_outputs: false
}
}
);
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 laion-ai/ongo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"laion-ai/ongo:1b3cd15121ec450baa71bbbdacddef9217519f12ca12ccfef36eeaa20ad89b9d",
input={
"seed": -1,
"steps": 100,
"width": 384,
"height": 384,
"prompt": "bunny rabbit in style of van gogh",
"negative": "",
"batch_size": 3,
"guidance_scale": 5,
"aesthetic_rating": 8,
"aesthetic_weight": 0.1,
"init_skip_fraction": 0,
"intermediate_outputs": False
}
)
# The laion-ai/ongo model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
# https://replicate.com/laion-ai/ongo/api#output-schema
print(item)
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 laion-ai/ongo 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": "1b3cd15121ec450baa71bbbdacddef9217519f12ca12ccfef36eeaa20ad89b9d",
"input": {
"seed": -1,
"steps": 100,
"width": 384,
"height": 384,
"prompt": "bunny rabbit in style of van gogh",
"negative": "",
"batch_size": 3,
"guidance_scale": 5,
"aesthetic_rating": 8,
"aesthetic_weight": 0.1,
"init_skip_fraction": 0,
"intermediate_outputs": false
}
}' \
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
Output
{
"completed_at": "2022-07-17T07:44:30.683816Z",
"created_at": "2022-07-17T07:43:35.219634Z",
"data_removed": false,
"error": null,
"id": "juxcjpeddzf23nxwhdcypwyd54",
"input": {
"seed": -1,
"steps": "100",
"width": "384",
"height": "384",
"prompt": "bunny rabbit in style of van gogh",
"batch_size": "3",
"guidance_scale": 5,
"aesthetic_rating": 8,
"aesthetic_weight": 0.1
},
"logs": "Using seed 3741438641\nRunning simulation for bunny rabbit in style of van gogh\nEncoding text embeddings with bunny rabbit in style of van gogh dimensions\nUsing aesthetic embedding 8 with weight 0.1\nRunning diffusion...\n\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:01<02:40, 1.62s/it]\n 2%|▏ | 2/100 [00:03<02:39, 1.63s/it]\n 3%|▎ | 3/100 [00:04<02:38, 1.63s/it]\n 4%|▍ | 4/100 [00:05<01:50, 1.15s/it]\n 5%|▌ | 5/100 [00:05<01:23, 1.14it/s]\n 6%|▌ | 6/100 [00:06<01:07, 1.39it/s]\n 7%|▋ | 7/100 [00:06<00:57, 1.62it/s]\n 8%|▊ | 8/100 [00:06<00:50, 1.82it/s]\n 9%|▉ | 9/100 [00:07<00:45, 1.98it/s]\n 10%|█ | 10/100 [00:07<00:42, 2.11it/s]\n 11%|█ | 11/100 [00:08<00:40, 2.20it/s]\n 12%|█▏ | 12/100 [00:08<00:38, 2.27it/s]\n 13%|█▎ | 13/100 [00:08<00:37, 2.32it/s]\n 14%|█▍ | 14/100 [00:09<00:36, 2.36it/s]\n 15%|█▌ | 15/100 [00:09<00:35, 2.39it/s]\n 16%|█▌ | 16/100 [00:10<00:34, 2.41it/s]\n 17%|█▋ | 17/100 [00:10<00:34, 2.42it/s]\n 18%|█▊ | 18/100 [00:10<00:33, 2.43it/s]\n 19%|█▉ | 19/100 [00:11<00:33, 2.44it/s]\n 20%|██ | 20/100 [00:11<00:32, 2.45it/s]\n 21%|██ | 21/100 [00:12<00:32, 2.45it/s]\n 22%|██▏ | 22/100 [00:12<00:31, 2.46it/s]\n 23%|██▎ | 23/100 [00:13<00:31, 2.46it/s]\n 24%|██▍ | 24/100 [00:13<00:30, 2.46it/s]\n 25%|██▌ | 25/100 [00:13<00:30, 2.46it/s]\n 26%|██▌ | 26/100 [00:14<00:30, 2.46it/s]\n 27%|██▋ | 27/100 [00:14<00:29, 2.46it/s]\n 28%|██▊ | 28/100 [00:15<00:29, 2.46it/s]\n 29%|██▉ | 29/100 [00:15<00:28, 2.46it/s]\n 30%|███ | 30/100 [00:15<00:28, 2.46it/s]\n 31%|███ | 31/100 [00:16<00:28, 2.46it/s]\n 32%|███▏ | 32/100 [00:16<00:27, 2.46it/s]\n 33%|███▎ | 33/100 [00:17<00:27, 2.47it/s]\n 34%|███▍ | 34/100 [00:17<00:26, 2.47it/s]\n 35%|███▌ | 35/100 [00:17<00:26, 2.47it/s]\n 36%|███▌ | 36/100 [00:18<00:25, 2.47it/s]\n 37%|███▋ | 37/100 [00:18<00:25, 2.47it/s]\n 38%|███▊ | 38/100 [00:19<00:25, 2.47it/s]\n 39%|███▉ | 39/100 [00:19<00:24, 2.46it/s]\n 40%|████ | 40/100 [00:19<00:24, 2.47it/s]\n 41%|████ | 41/100 [00:20<00:23, 2.46it/s]\n 42%|████▏ | 42/100 [00:20<00:23, 2.47it/s]\n 43%|████▎ | 43/100 [00:21<00:23, 2.47it/s]\n 44%|████▍ | 44/100 [00:21<00:22, 2.47it/s]\n 45%|████▌ | 45/100 [00:21<00:22, 2.47it/s]\n 46%|████▌ | 46/100 [00:22<00:21, 2.47it/s]\n 47%|████▋ | 47/100 [00:22<00:21, 2.47it/s]\n 48%|████▊ | 48/100 [00:23<00:21, 2.47it/s]\n 49%|████▉ | 49/100 [00:23<00:20, 2.47it/s]\n 50%|█████ | 50/100 [00:23<00:20, 2.47it/s]\n 51%|█████ | 51/100 [00:24<00:19, 2.47it/s]\n 52%|█████▏ | 52/100 [00:24<00:19, 2.47it/s]\n 53%|█████▎ | 53/100 [00:25<00:19, 2.47it/s]\n 54%|█████▍ | 54/100 [00:25<00:18, 2.47it/s]\n 55%|█████▌ | 55/100 [00:25<00:18, 2.47it/s]\n 56%|█████▌ | 56/100 [00:26<00:17, 2.48it/s]\n 57%|█████▋ | 57/100 [00:26<00:17, 2.48it/s]\n 58%|█████▊ | 58/100 [00:27<00:16, 2.48it/s]\n 59%|█████▉ | 59/100 [00:27<00:16, 2.48it/s]\n 60%|██████ | 60/100 [00:28<00:16, 2.48it/s]\n 61%|██████ | 61/100 [00:28<00:15, 2.48it/s]\n 62%|██████▏ | 62/100 [00:28<00:15, 2.48it/s]\n 63%|██████▎ | 63/100 [00:29<00:14, 2.48it/s]\n 64%|██████▍ | 64/100 [00:29<00:14, 2.49it/s]\n 65%|██████▌ | 65/100 [00:30<00:14, 2.49it/s]\n 66%|██████▌ | 66/100 [00:30<00:13, 2.48it/s]\n 67%|██████▋ | 67/100 [00:30<00:13, 2.49it/s]\n 68%|██████▊ | 68/100 [00:31<00:12, 2.49it/s]\n 69%|██████▉ | 69/100 [00:31<00:12, 2.49it/s]\n 70%|███████ | 70/100 [00:32<00:12, 2.49it/s]\n 71%|███████ | 71/100 [00:32<00:11, 2.48it/s]\n 72%|███████▏ | 72/100 [00:32<00:11, 2.49it/s]\n 73%|███████▎ | 73/100 [00:33<00:10, 2.49it/s]\n 74%|███████▍ | 74/100 [00:33<00:10, 2.50it/s]\n 75%|███████▌ | 75/100 [00:34<00:10, 2.49it/s]\n 76%|███████▌ | 76/100 [00:34<00:09, 2.49it/s]\n 77%|███████▋ | 77/100 [00:34<00:09, 2.49it/s]\n 78%|███████▊ | 78/100 [00:35<00:08, 2.49it/s]\n 79%|███████▉ | 79/100 [00:35<00:08, 2.49it/s]\n 80%|████████ | 80/100 [00:36<00:08, 2.49it/s]\n 81%|████████ | 81/100 [00:36<00:07, 2.48it/s]\n 82%|████████▏ | 82/100 [00:36<00:07, 2.49it/s]\n 83%|████████▎ | 83/100 [00:37<00:06, 2.49it/s]\n 84%|████████▍ | 84/100 [00:37<00:06, 2.49it/s]\n 85%|████████▌ | 85/100 [00:38<00:06, 2.49it/s]\n 86%|████████▌ | 86/100 [00:38<00:05, 2.49it/s]\n 87%|████████▋ | 87/100 [00:38<00:05, 2.49it/s]\n 88%|████████▊ | 88/100 [00:39<00:04, 2.50it/s]\n 89%|████████▉ | 89/100 [00:39<00:04, 2.49it/s]\n 90%|█████████ | 90/100 [00:40<00:04, 2.49it/s]\n 91%|█████████ | 91/100 [00:40<00:03, 2.49it/s]\n 92%|█████████▏| 92/100 [00:40<00:03, 2.49it/s]\n 93%|█████████▎| 93/100 [00:41<00:02, 2.49it/s]\n 94%|█████████▍| 94/100 [00:41<00:02, 2.49it/s]\n 95%|█████████▌| 95/100 [00:42<00:02, 2.50it/s]\n 96%|█████████▌| 96/100 [00:42<00:01, 2.50it/s]\n 97%|█████████▋| 97/100 [00:42<00:01, 2.49it/s]\n 98%|█████████▊| 98/100 [00:43<00:00, 2.50it/s]\n 99%|█████████▉| 99/100 [00:43<00:00, 2.50it/s]\n100%|██████████| 100/100 [00:44<00:00, 2.50it/s]\n100%|██████████| 100/100 [00:44<00:00, 2.27it/s]\nSaving final sample/s",
"metrics": {
"predict_time": 46.807893,
"total_time": 55.464182
},
"output": [
[
"https://replicate.delivery/mgxm/a33510a3-92b2-432e-a778-92753c78d936/current_0.png",
"https://replicate.delivery/mgxm/9ef7669c-4c7d-483d-b818-be78e5aa08ec/current_1.png",
"https://replicate.delivery/mgxm/9696c825-c8b3-4940-ab70-0717af035d9d/current_2.png"
]
],
"started_at": "2022-07-17T07:43:43.875923Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/juxcjpeddzf23nxwhdcypwyd54",
"cancel": "https://api.replicate.com/v1/predictions/juxcjpeddzf23nxwhdcypwyd54/cancel"
},
"version": "9ef304ed2499cfddc1ec4ff3bc1b657c03db722ac1e52577e3c42b97d176c040"
}
Using seed 3741438641
Running simulation for bunny rabbit in style of van gogh
Encoding text embeddings with bunny rabbit in style of van gogh dimensions
Using aesthetic embedding 8 with weight 0.1
Running diffusion...
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Saving final sample/s
This example was created by a different version, laion-ai/ongo:9ef304ed.