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Input
Run this model in Node.js with one line of code:
npx create-replicate --model=laion-ai/ongo
or set up a project from scratch
Install Replicate’s Node.js client library:
npm install replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:
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:818c3886693975754e0d8962fa51bd967c8a4a332e123a6099086f08d068c49f",
{
input: {
seed: -1,
steps: 100,
width: 256,
height: 256,
prompt: "star wars concept art",
negative: "",
batch_size: 6,
guidance_scale: 5,
aesthetic_rating: 9,
aesthetic_weight: 0.5,
init_skip_fraction: 0
}
}
);
console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:
pip install replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:
import replicateRun 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:818c3886693975754e0d8962fa51bd967c8a4a332e123a6099086f08d068c49f",
input={
"seed": -1,
"steps": 100,
"width": 256,
"height": 256,
"prompt": "star wars concept art",
"negative": "",
"batch_size": 6,
"guidance_scale": 5,
"aesthetic_rating": 9,
"aesthetic_weight": 0.5,
"init_skip_fraction": 0
}
)
# 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.
Set the
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": "818c3886693975754e0d8962fa51bd967c8a4a332e123a6099086f08d068c49f",
"input": {
"seed": -1,
"steps": 100,
"width": 256,
"height": 256,
"prompt": "star wars concept art",
"negative": "",
"batch_size": 6,
"guidance_scale": 5,
"aesthetic_rating": 9,
"aesthetic_weight": 0.5,
"init_skip_fraction": 0
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Add a payment method to run this model.
Sign in with GitHub
By signing in, you agree to our
terms of service and privacy policy
Output
{
"completed_at": "2022-06-05T03:33:57.873395Z",
"created_at": "2022-06-05T03:32:47.310397Z",
"data_removed": false,
"error": null,
"id": "memxvg44yjecrk6kcpgxhwqvfq",
"input": {
"seed": -1,
"steps": "100",
"width": 256,
"height": 256,
"prompt": "star wars concept art",
"batch_size": "6",
"guidance_scale": 5,
"aesthetic_rating": 9,
"aesthetic_weight": 0.5
},
"logs": "Encoding text with BERT\nEncoding text with CLIP\nUsing aesthetic embedding 9 with weight 0.5\nLoading image\nUsing inpaint model but no image is provided. Initializing with zeros.\nPacking CLIP and BERT embeddings into kwargs\nRunning diffusion...\n\n0it [00:00, ?it/s]\n\n 0%| | 0/100 [00:00<?, ?it/s]\u001b[A\n1it [00:04, 4.92s/it]\n\n 1%| | 1/100 [00:04<08:06, 4.92s/it]\u001b[A\n2it [00:06, 3.04s/it]\n\n 2%|▏ | 2/100 [00:06<04:58, 3.04s/it]\u001b[A\n3it [00:08, 2.45s/it]\n\n 3%|▎ | 3/100 [00:08<03:57, 2.45s/it]\u001b[A\n4it [00:08, 1.65s/it]\n\n 4%|▍ | 4/100 [00:08<02:38, 1.65s/it]\u001b[A\n5it [00:09, 1.21s/it]\n\n 5%|▌ | 5/100 [00:09<01:55, 1.21s/it]\u001b[A\n6it [00:10, 1.28s/it]\n\n 6%|▌ | 6/100 [00:10<02:00, 1.28s/it]\u001b[A\n7it [00:11, 1.01s/it]\n\n 7%|▋ | 7/100 [00:11<01:33, 1.01s/it]\u001b[A\n8it [00:11, 1.21it/s]\n\n 8%|▊ | 8/100 [00:11<01:15, 1.21it/s]\u001b[A\n9it [00:11, 1.42it/s]\n\n 9%|▉ | 9/100 [00:11<01:04, 1.42it/s]\u001b[A\n10it [00:12, 1.61it/s]\n\n 10%|█ | 10/100 [00:12<00:55, 1.61it/s]\u001b[A\n11it [00:13, 1.16it/s]\n\n 11%|█ | 11/100 [00:13<01:17, 1.16it/s]\u001b[A\n12it [00:14, 1.36it/s]\n\n 12%|█▏ | 12/100 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1.81it/s]\u001b[A\n100it [01:09, 1.93it/s]\n\n100%|██████████| 100/100 [01:09<00:00, 1.93it/s]\u001b[A\n100%|██████████| 100/100 [01:09<00:00, 1.44it/s]\n\n100it [01:09, 1.44it/s]\nFinished generating with seed 315760442",
"metrics": {
"predict_time": 70.412455,
"total_time": 70.562998
},
"output": [
"https://replicate.delivery/mgxm/06b6cdea-c75c-46e1-b919-99288bbc9549/current.png",
"https://replicate.delivery/mgxm/c1e130da-5edf-458a-9a42-737abc0eb913/current.png",
"https://replicate.delivery/mgxm/fc98a203-0e22-48f0-8248-a9467f860ae4/current.png",
"https://replicate.delivery/mgxm/8fb678c4-56b1-4d14-9938-99e4998df0d0/current.png",
"https://replicate.delivery/mgxm/2adb9f2b-60a8-409f-bc65-1e196253df40/current.png",
"https://replicate.delivery/mgxm/c1101c9a-9e2c-45f9-8b70-f98eff23860e/current.png",
"https://replicate.delivery/mgxm/a352d686-67c0-4ad7-ade7-bee2a50ddac9/current.png",
"https://replicate.delivery/mgxm/d122e760-611c-43b6-bc5c-619f0e87e8eb/current.png",
"https://replicate.delivery/mgxm/ba6d6934-dbb7-452e-b7cb-79eedab36c28/current.png",
"https://replicate.delivery/mgxm/e6b07409-1f09-4f68-b6a6-5a2b6f502cd9/current.png",
"https://replicate.delivery/mgxm/575ac2a4-2160-488d-a67c-1a4b5a1b1d93/current.png",
"https://replicate.delivery/mgxm/b0d563e8-f220-4fba-866d-cc65128a1ef4/current.png",
"https://replicate.delivery/mgxm/cb0ebe24-af22-4e3f-b462-07c2a420bd82/current.png",
"https://replicate.delivery/mgxm/ba0ca5ad-2ea8-4359-8287-bc18b92035f9/current.png",
"https://replicate.delivery/mgxm/c9be4118-e0be-42f3-bafa-74a6d65b6527/current.png",
"https://replicate.delivery/mgxm/0b4770fc-e513-4eb6-b283-aa07d5dc26e3/current.png",
"https://replicate.delivery/mgxm/ed1301cf-9f13-4dff-aba9-b3a61f44dc1d/current.png",
"https://replicate.delivery/mgxm/cb255924-d87b-42b1-a779-4c016341fbea/current.png",
"https://replicate.delivery/mgxm/70e53929-d01f-4c2d-b611-8dea1d535b9c/current.png",
"https://replicate.delivery/mgxm/eb4e1fdf-8f7f-4588-b631-dbf9069c168b/current.png"
],
"started_at": "2022-06-05T03:32:47.460940Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/memxvg44yjecrk6kcpgxhwqvfq",
"cancel": "https://api.replicate.com/v1/predictions/memxvg44yjecrk6kcpgxhwqvfq/cancel"
},
"version": "818c3886693975754e0d8962fa51bd967c8a4a332e123a6099086f08d068c49f"
}Generated in
Encoding text with BERT
Encoding text with CLIP
Using aesthetic embedding 9 with weight 0.5
Loading image
Using inpaint model but no image is provided. Initializing with zeros.
Packing CLIP and BERT embeddings into kwargs
Running diffusion...
0it [00:00, ?it/s]
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9it [00:11, 1.42it/s]
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10it [00:12, 1.61it/s]
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11it [00:13, 1.16it/s]
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12it [00:14, 1.36it/s]
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13it [00:14, 1.55it/s]
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14it [00:15, 1.71it/s]
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15it [00:15, 1.85it/s]
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16it [00:17, 1.25it/s]
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17it [00:17, 1.45it/s]
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18it [00:17, 1.62it/s]
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19it [00:18, 1.78it/s]
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20it [00:18, 1.90it/s]
20%|██ | 20/100 [00:18<00:42, 1.90it/s]
21it [00:20, 1.26it/s]
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22it [00:20, 1.46it/s]
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23it [00:21, 1.63it/s]
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24it [00:21, 1.78it/s]
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25it [00:21, 1.90it/s]
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27it [00:23, 1.46it/s]
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28it [00:24, 1.63it/s]
28%|██▊ | 28/100 [00:24<00:44, 1.63it/s]
29it [00:24, 1.78it/s]
29%|██▉ | 29/100 [00:24<00:39, 1.78it/s]
30it [00:25, 1.90it/s]
30%|███ | 30/100 [00:25<00:36, 1.90it/s]
31it [00:26, 1.26it/s]
31%|███ | 31/100 [00:26<00:54, 1.26it/s]
32it [00:26, 1.45it/s]
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33it [00:27, 1.63it/s]
33%|███▎ | 33/100 [00:27<00:41, 1.63it/s]
34it [00:27, 1.77it/s]
34%|███▍ | 34/100 [00:27<00:37, 1.77it/s]
35it [00:28, 1.89it/s]
35%|███▌ | 35/100 [00:28<00:34, 1.89it/s]
36it [00:29, 1.26it/s]
36%|███▌ | 36/100 [00:29<00:50, 1.26it/s]
37it [00:30, 1.46it/s]
37%|███▋ | 37/100 [00:30<00:43, 1.46it/s]
38it [00:30, 1.62it/s]
38%|███▊ | 38/100 [00:30<00:38, 1.62it/s]
39it [00:31, 1.76it/s]
39%|███▉ | 39/100 [00:31<00:34, 1.76it/s]
40it [00:31, 1.88it/s]
40%|████ | 40/100 [00:31<00:31, 1.88it/s]
41it [00:32, 1.25it/s]
41%|████ | 41/100 [00:32<00:47, 1.25it/s]
42it [00:33, 1.45it/s]
42%|████▏ | 42/100 [00:33<00:39, 1.45it/s]
43it [00:33, 1.62it/s]
43%|████▎ | 43/100 [00:33<00:35, 1.62it/s]
44it [00:34, 1.76it/s]
44%|████▍ | 44/100 [00:34<00:31, 1.76it/s]
45it [00:34, 1.88it/s]
45%|████▌ | 45/100 [00:34<00:29, 1.88it/s]
46it [00:36, 1.26it/s]
46%|████▌ | 46/100 [00:36<00:42, 1.26it/s]
47it [00:36, 1.46it/s]
47%|████▋ | 47/100 [00:36<00:36, 1.46it/s]
48it [00:37, 1.62it/s]
48%|████▊ | 48/100 [00:37<00:32, 1.62it/s]
49it [00:37, 1.76it/s]
49%|████▉ | 49/100 [00:37<00:28, 1.76it/s]
50it [00:37, 1.88it/s]
50%|█████ | 50/100 [00:37<00:26, 1.88it/s]
51it [00:39, 1.26it/s]
51%|█████ | 51/100 [00:39<00:38, 1.26it/s]
52it [00:39, 1.46it/s]
52%|█████▏ | 52/100 [00:39<00:32, 1.46it/s]
53it [00:40, 1.62it/s]
53%|█████▎ | 53/100 [00:40<00:28, 1.62it/s]
54it [00:40, 1.76it/s]
54%|█████▍ | 54/100 [00:40<00:26, 1.76it/s]
55it [00:41, 1.89it/s]
55%|█████▌ | 55/100 [00:41<00:23, 1.89it/s]
56it [00:42, 1.27it/s]
56%|█████▌ | 56/100 [00:42<00:34, 1.27it/s]
57it [00:42, 1.46it/s]
57%|█████▋ | 57/100 [00:42<00:29, 1.46it/s]
58it [00:43, 1.62it/s]
58%|█████▊ | 58/100 [00:43<00:25, 1.62it/s]
59it [00:43, 1.76it/s]
59%|█████▉ | 59/100 [00:43<00:23, 1.76it/s]
60it [00:44, 1.88it/s]
60%|██████ | 60/100 [00:44<00:21, 1.88it/s]
61it [00:45, 1.27it/s]
61%|██████ | 61/100 [00:45<00:30, 1.27it/s]
62it [00:46, 1.47it/s]
62%|██████▏ | 62/100 [00:46<00:25, 1.47it/s]
63it [00:46, 1.63it/s]
63%|██████▎ | 63/100 [00:46<00:22, 1.63it/s]
64it [00:47, 1.77it/s]
64%|██████▍ | 64/100 [00:47<00:20, 1.77it/s]
65it [00:47, 1.89it/s]
65%|██████▌ | 65/100 [00:47<00:18, 1.89it/s]
66it [00:48, 1.28it/s]
66%|██████▌ | 66/100 [00:48<00:26, 1.28it/s]
67it [00:49, 1.48it/s]
67%|██████▋ | 67/100 [00:49<00:22, 1.48it/s]
68it [00:49, 1.64it/s]
68%|██████▊ | 68/100 [00:49<00:19, 1.64it/s]
69it [00:50, 1.78it/s]
69%|██████▉ | 69/100 [00:50<00:17, 1.78it/s]
70it [00:50, 1.90it/s]
70%|███████ | 70/100 [00:50<00:15, 1.90it/s]
71it [00:51, 1.28it/s]
71%|███████ | 71/100 [00:51<00:22, 1.28it/s]
72it [00:52, 1.48it/s]
72%|███████▏ | 72/100 [00:52<00:18, 1.48it/s]
73it [00:52, 1.65it/s]
73%|███████▎ | 73/100 [00:52<00:16, 1.65it/s]
74it [00:53, 1.78it/s]
74%|███████▍ | 74/100 [00:53<00:14, 1.78it/s]
75it [00:53, 1.90it/s]
75%|███████▌ | 75/100 [00:53<00:13, 1.90it/s]
76it [00:55, 1.29it/s]
76%|███████▌ | 76/100 [00:55<00:18, 1.29it/s]
77it [00:55, 1.48it/s]
77%|███████▋ | 77/100 [00:55<00:15, 1.48it/s]
78it [00:56, 1.65it/s]
78%|███████▊ | 78/100 [00:56<00:13, 1.65it/s]
79it [00:56, 1.79it/s]
79%|███████▉ | 79/100 [00:56<00:11, 1.79it/s]
80it [00:56, 1.90it/s]
80%|████████ | 80/100 [00:56<00:10, 1.90it/s]
81it [00:58, 1.30it/s]
81%|████████ | 81/100 [00:58<00:14, 1.30it/s]
82it [00:58, 1.49it/s]
82%|████████▏ | 82/100 [00:58<00:12, 1.49it/s]
83it [00:59, 1.66it/s]
83%|████████▎ | 83/100 [00:59<00:10, 1.66it/s]
84it [00:59, 1.79it/s]
84%|████████▍ | 84/100 [00:59<00:08, 1.79it/s]
85it [01:00, 1.91it/s]
85%|████████▌ | 85/100 [01:00<00:07, 1.91it/s]
86it [01:01, 1.30it/s]
86%|████████▌ | 86/100 [01:01<00:10, 1.30it/s]
87it [01:01, 1.49it/s]
87%|████████▋ | 87/100 [01:01<00:08, 1.49it/s]
88it [01:02, 1.66it/s]
88%|████████▊ | 88/100 [01:02<00:07, 1.66it/s]
89it [01:02, 1.81it/s]
89%|████████▉ | 89/100 [01:02<00:06, 1.81it/s]
90it [01:03, 1.92it/s]
90%|█████████ | 90/100 [01:03<00:05, 1.92it/s]
91it [01:04, 1.30it/s]
91%|█████████ | 91/100 [01:04<00:06, 1.30it/s]
92it [01:04, 1.50it/s]
92%|█████████▏| 92/100 [01:04<00:05, 1.50it/s]
93it [01:05, 1.66it/s]
93%|█████████▎| 93/100 [01:05<00:04, 1.66it/s]
94it [01:05, 1.80it/s]
94%|█████████▍| 94/100 [01:05<00:03, 1.80it/s]
95it [01:06, 1.92it/s]
95%|█████████▌| 95/100 [01:06<00:02, 1.92it/s]
96it [01:07, 1.31it/s]
96%|█████████▌| 96/100 [01:07<00:03, 1.31it/s]
97it [01:08, 1.50it/s]
97%|█████████▋| 97/100 [01:08<00:01, 1.50it/s]
98it [01:08, 1.67it/s]
98%|█████████▊| 98/100 [01:08<00:01, 1.67it/s]
99it [01:08, 1.81it/s]
99%|█████████▉| 99/100 [01:08<00:00, 1.81it/s]
100it [01:09, 1.93it/s]
100%|██████████| 100/100 [01:09<00:00, 1.93it/s]
100%|██████████| 100/100 [01:09<00:00, 1.44it/s]
100it [01:09, 1.44it/s]
Finished generating with seed 315760442