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Input
Run this model in Node.js with one line of code:
npx create-replicate --model=laion-ai/laionide
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/laionide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"laion-ai/laionide:11a727b3c74f842f7e9d7db88df5da4d1b8389c14e186d6ceb1724abeebe4bc9",
{
input: {
seed: 0,
prompt: "trippy acid lsd psychedelic oil painting of a man with a suit",
side_x: 80,
side_y: 48,
batch_size: 3,
upsample_temp: 0.998,
guidance_scale: 12,
upsample_stage: true,
timestep_respacing: "27",
sr_timestep_respacing: "17"
}
}
);
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/laionide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"laion-ai/laionide:11a727b3c74f842f7e9d7db88df5da4d1b8389c14e186d6ceb1724abeebe4bc9",
input={
"seed": 0,
"prompt": "trippy acid lsd psychedelic oil painting of a man with a suit",
"side_x": 80,
"side_y": 48,
"batch_size": 3,
"upsample_temp": 0.998,
"guidance_scale": 12,
"upsample_stage": True,
"timestep_respacing": "27",
"sr_timestep_respacing": "17"
}
)
# The laion-ai/laionide 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/laionide/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/laionide 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": "laion-ai/laionide:11a727b3c74f842f7e9d7db88df5da4d1b8389c14e186d6ceb1724abeebe4bc9",
"input": {
"seed": 0,
"prompt": "trippy acid lsd psychedelic oil painting of a man with a suit",
"side_x": 80,
"side_y": 48,
"batch_size": 3,
"upsample_temp": 0.998,
"guidance_scale": 12,
"upsample_stage": true,
"timestep_respacing": "27",
"sr_timestep_respacing": "17"
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
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Output
{
"completed_at": "2022-02-16T00:19:30.609657Z",
"created_at": "2022-02-16T00:18:59.571967Z",
"data_removed": false,
"error": null,
"id": "4bcmbmh3lbapxnhjlftzomxis4",
"input": {
"seed": 0,
"prompt": "trippy acid lsd psychedelic oil painting of a man with a suit",
"side_x": "80",
"side_y": "48",
"batch_size": "3",
"upsample_temp": "0.998",
"guidance_scale": 12,
"upsample_stage": true,
"timestep_respacing": "27",
"sr_timestep_respacing": "17"
},
"logs": "Generating 80x48 samples with 27 timesteps using GLIDE-base-64px...\n\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:18, 1.31it/s]\n 8%|▊ | 2/25 [00:01<00:17, 1.33it/s]\n 12%|█▏ | 3/25 [00:02<00:16, 1.34it/s]\n 16%|█▌ | 4/25 [00:02<00:10, 1.91it/s]\n 20%|██ | 5/25 [00:02<00:08, 2.49it/s]\n 24%|██▍ | 6/25 [00:02<00:06, 3.06it/s]\n 28%|██▊ | 7/25 [00:02<00:05, 3.58it/s]\n 32%|███▏ | 8/25 [00:03<00:04, 4.02it/s]\n 36%|███▌ | 9/25 [00:03<00:03, 4.37it/s]\n 40%|████ | 10/25 [00:03<00:03, 4.64it/s]\n 44%|████▍ | 11/25 [00:03<00:02, 4.83it/s]\n 48%|████▊ | 12/25 [00:03<00:02, 4.99it/s]\n 52%|█████▏ | 13/25 [00:04<00:02, 5.11it/s]\n 56%|█████▌ | 14/25 [00:04<00:02, 5.21it/s]\n 60%|██████ | 15/25 [00:04<00:01, 5.28it/s]\n 64%|██████▍ | 16/25 [00:04<00:01, 5.32it/s]\n 68%|██████▊ | 17/25 [00:04<00:01, 5.35it/s]\n 72%|███████▏ | 18/25 [00:05<00:01, 5.36it/s]\n 76%|███████▌ | 19/25 [00:05<00:01, 5.36it/s]\n 80%|████████ | 20/25 [00:05<00:00, 5.36it/s]\n 84%|████████▍ | 21/25 [00:05<00:00, 5.38it/s]\n 88%|████████▊ | 22/25 [00:05<00:00, 5.38it/s]\n 92%|█████████▏| 23/25 [00:05<00:00, 5.39it/s]\n 96%|█████████▌| 24/25 [00:06<00:00, 5.37it/s]\n100%|██████████| 25/25 [00:06<00:00, 5.35it/s]\n100%|██████████| 25/25 [00:06<00:00, 3.96it/s]\nUpsampling outputs from GLIDE-base 80x48 to 320x192 using 17 timesteps...\n\n 0%| | 0/15 [00:00<?, ?it/s]\n 7%|▋ | 1/15 [00:01<00:26, 1.89s/it]\n 13%|█▎ | 2/15 [00:03<00:24, 1.89s/it]\n 20%|██ | 3/15 [00:05<00:22, 1.89s/it]\n 27%|██▋ | 4/15 [00:06<00:14, 1.33s/it]\n 33%|███▎ | 5/15 [00:06<00:10, 1.02s/it]\n 40%|████ | 6/15 [00:07<00:07, 1.19it/s]\n 47%|████▋ | 7/15 [00:07<00:05, 1.39it/s]\n 53%|█████▎ | 8/15 [00:08<00:04, 1.55it/s]\n 60%|██████ | 9/15 [00:08<00:03, 1.68it/s]\n 67%|██████▋ | 10/15 [00:09<00:02, 1.78it/s]\n 73%|███████▎ | 11/15 [00:09<00:02, 1.86it/s]\n 80%|████████ | 12/15 [00:10<00:01, 1.91it/s]\n 87%|████████▋ | 13/15 [00:10<00:01, 1.95it/s]\n 93%|█████████▎| 14/15 [00:10<00:00, 1.98it/s]\n100%|██████████| 15/15 [00:11<00:00, 2.00it/s]\n100%|██████████| 15/15 [00:11<00:00, 1.31it/s]",
"metrics": {
"predict_time": 30.742278,
"total_time": 31.03769
},
"output": [
{
"file": "https://replicate.delivery/mgxm/6e152613-c32a-438c-a7d6-56d8f1d9dc48/upsample_predictions.png"
}
],
"started_at": "2022-02-16T00:18:59.867379Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/4bcmbmh3lbapxnhjlftzomxis4",
"cancel": "https://api.replicate.com/v1/predictions/4bcmbmh3lbapxnhjlftzomxis4/cancel"
},
"version": "11a727b3c74f842f7e9d7db88df5da4d1b8389c14e186d6ceb1724abeebe4bc9"
}Generated in
Generating 80x48 samples with 27 timesteps using GLIDE-base-64px...
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28%|██▊ | 7/25 [00:02<00:05, 3.58it/s]
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36%|███▌ | 9/25 [00:03<00:03, 4.37it/s]
40%|████ | 10/25 [00:03<00:03, 4.64it/s]
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52%|█████▏ | 13/25 [00:04<00:02, 5.11it/s]
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68%|██████▊ | 17/25 [00:04<00:01, 5.35it/s]
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96%|█████████▌| 24/25 [00:06<00:00, 5.37it/s]
100%|██████████| 25/25 [00:06<00:00, 5.35it/s]
100%|██████████| 25/25 [00:06<00:00, 3.96it/s]
Upsampling outputs from GLIDE-base 80x48 to 320x192 using 17 timesteps...
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27%|██▋ | 4/15 [00:06<00:14, 1.33s/it]
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40%|████ | 6/15 [00:07<00:07, 1.19it/s]
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53%|█████▎ | 8/15 [00:08<00:04, 1.55it/s]
60%|██████ | 9/15 [00:08<00:03, 1.68it/s]
67%|██████▋ | 10/15 [00:09<00:02, 1.78it/s]
73%|███████▎ | 11/15 [00:09<00:02, 1.86it/s]
80%|████████ | 12/15 [00:10<00:01, 1.91it/s]
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100%|██████████| 15/15 [00:11<00:00, 1.31it/s]