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Input
Run this model in Node.js with one line of code:
npx create-replicate --model=datacte/mobius
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 datacte/mobius using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"datacte/mobius:197f2145583f80c7c3ec520d2a1080aa7986601e1612e417ccd6e4f50fe0624f",
{
input: {
width: 1024,
height: 1024,
prompt: "The image features an older man, a long white beard and mustache, He has a stern expression, giving the impression of a wise and experienced individual. The mans beard and mustache are prominent, adding to his distinguished appearance. The close-up shot of the mans face emphasizes his facial features and the intensity of his gaze.",
scheduler: "DPMSolverMultistep",
num_outputs: 1,
guidance_scale: 3,
num_inference_steps: 50
}
}
);
// 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 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 datacte/mobius using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"datacte/mobius:197f2145583f80c7c3ec520d2a1080aa7986601e1612e417ccd6e4f50fe0624f",
input={
"width": 1024,
"height": 1024,
"prompt": "The image features an older man, a long white beard and mustache, He has a stern expression, giving the impression of a wise and experienced individual. The mans beard and mustache are prominent, adding to his distinguished appearance. The close-up shot of the mans face emphasizes his facial features and the intensity of his gaze.",
"scheduler": "DPMSolverMultistep",
"num_outputs": 1,
"guidance_scale": 3,
"num_inference_steps": 50
}
)
print(output)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 datacte/mobius 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": "datacte/mobius:197f2145583f80c7c3ec520d2a1080aa7986601e1612e417ccd6e4f50fe0624f",
"input": {
"width": 1024,
"height": 1024,
"prompt": "The image features an older man, a long white beard and mustache, He has a stern expression, giving the impression of a wise and experienced individual. The mans beard and mustache are prominent, adding to his distinguished appearance. The close-up shot of the mans face emphasizes his facial features and the intensity of his gaze.",
"scheduler": "DPMSolverMultistep",
"num_outputs": 1,
"guidance_scale": 3,
"num_inference_steps": 50
}
}' \
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": "2024-10-07T18:54:23.926538Z",
"created_at": "2024-10-07T18:54:17.866000Z",
"data_removed": false,
"error": null,
"id": "s1kkmbgf19rj00cjd1ar3c1dg0",
"input": {
"width": 1024,
"height": 1024,
"prompt": "The image features an older man, a long white beard and mustache, He has a stern expression, giving the impression of a wise and experienced individual. The mans beard and mustache are prominent, adding to his distinguished appearance. The close-up shot of the mans face emphasizes his facial features and the intensity of his gaze.",
"scheduler": "DPMSolverMultistep",
"num_outputs": 1,
"guidance_scale": 3,
"num_inference_steps": 50
},
"logs": "Using seed: 21520\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:05, 8.95it/s]\n 6%|▌ | 3/50 [00:00<00:04, 10.84it/s]\n 10%|█ | 5/50 [00:00<00:04, 9.83it/s]\n 12%|█▏ | 6/50 [00:00<00:04, 9.61it/s]\n 14%|█▍ | 7/50 [00:00<00:04, 9.44it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 9.33it/s]\n 18%|█▊ | 9/50 [00:00<00:04, 9.24it/s]\n 20%|██ | 10/50 [00:01<00:04, 9.18it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 9.14it/s]\n 24%|██▍ | 12/50 [00:01<00:04, 9.11it/s]\n 26%|██▌ | 13/50 [00:01<00:04, 9.09it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 9.07it/s]\n 30%|███ | 15/50 [00:01<00:03, 9.06it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 9.06it/s]\n 34%|███▍ | 17/50 [00:01<00:03, 9.05it/s]\n 36%|███▌ | 18/50 [00:01<00:03, 9.05it/s]\n 38%|███▊ | 19/50 [00:02<00:03, 9.05it/s]\n 40%|████ | 20/50 [00:02<00:03, 9.04it/s]\n 42%|████▏ | 21/50 [00:02<00:03, 9.04it/s]\n 44%|████▍ | 22/50 [00:02<00:03, 9.04it/s]\n 46%|████▌ | 23/50 [00:02<00:02, 9.04it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 9.04it/s]\n 50%|█████ | 25/50 [00:02<00:02, 9.04it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 9.04it/s]\n 54%|█████▍ | 27/50 [00:02<00:02, 9.04it/s]\n 56%|█████▌ | 28/50 [00:03<00:02, 9.04it/s]\n 58%|█████▊ | 29/50 [00:03<00:02, 9.04it/s]\n 60%|██████ | 30/50 [00:03<00:02, 9.04it/s]\n 62%|██████▏ | 31/50 [00:03<00:02, 9.04it/s]\n 64%|██████▍ | 32/50 [00:03<00:01, 9.04it/s]\n 66%|██████▌ | 33/50 [00:03<00:01, 9.04it/s]\n 68%|██████▊ | 34/50 [00:03<00:01, 9.04it/s]\n 70%|███████ | 35/50 [00:03<00:01, 9.04it/s]\n 72%|███████▏ | 36/50 [00:03<00:01, 9.04it/s]\n 74%|███████▍ | 37/50 [00:04<00:01, 9.04it/s]\n 76%|███████▌ | 38/50 [00:04<00:01, 9.04it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 9.04it/s]\n 80%|████████ | 40/50 [00:04<00:01, 9.04it/s]\n 82%|████████▏ | 41/50 [00:04<00:00, 9.04it/s]\n 84%|████████▍ | 42/50 [00:04<00:00, 9.04it/s]\n 86%|████████▌ | 43/50 [00:04<00:00, 9.04it/s]\n 88%|████████▊ | 44/50 [00:04<00:00, 9.04it/s]\n 90%|█████████ | 45/50 [00:04<00:00, 9.04it/s]\n 92%|█████████▏| 46/50 [00:05<00:00, 9.04it/s]\n 94%|█████████▍| 47/50 [00:05<00:00, 9.04it/s]\n 96%|█████████▌| 48/50 [00:05<00:00, 9.04it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 9.04it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.04it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.12it/s]",
"metrics": {
"predict_time": 6.052366482,
"total_time": 6.060538
},
"output": [
"https://replicate.delivery/yhqm/rSV3LUK0laYpHhoMCvIoaFGHlOHkixw8ShLW2WpQ8fwvAPyJA/out-0.png"
],
"started_at": "2024-10-07T18:54:17.874172Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/s1kkmbgf19rj00cjd1ar3c1dg0",
"cancel": "https://api.replicate.com/v1/predictions/s1kkmbgf19rj00cjd1ar3c1dg0/cancel"
},
"version": "197f2145583f80c7c3ec520d2a1080aa7986601e1612e417ccd6e4f50fe0624f"
}Generated in
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