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hfailab /clip-gen:509cb1e4
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 hfailab/clip-gen using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"hfailab/clip-gen:509cb1e4144904a68b21ad72fb1a1b3fd9aa9a3a1125616a25bbb61ce1d515e0",
{
input: {
text: "A photo of a tower in front of a mountain",
num_samples: 8
}
}
);
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 hfailab/clip-gen using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"hfailab/clip-gen:509cb1e4144904a68b21ad72fb1a1b3fd9aa9a3a1125616a25bbb61ce1d515e0",
input={
"text": "A photo of a tower in front of a mountain",
"num_samples": 8
}
)
print(output)
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 hfailab/clip-gen 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": "509cb1e4144904a68b21ad72fb1a1b3fd9aa9a3a1125616a25bbb61ce1d515e0",
"input": {
"text": "A photo of a tower in front of a mountain",
"num_samples": 8
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
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Output
{
"completed_at": "2022-05-21T01:02:36.231744Z",
"created_at": "2022-05-21T01:01:26.253200Z",
"data_removed": false,
"error": null,
"id": "2eelkh7hejf57bu24j42anrisu",
"input": {
"text": "A photo of a tower in front of a mountain",
"num_samples": "8"
},
"logs": "CLIP similarity tensor([0.3148, 0.3105, 0.3086, 0.3075, 0.3037, 0.3021, 0.2997, 0.2991],\n device='cuda:0')",
"metrics": {
"predict_time": 69.772575,
"total_time": 69.978544
},
"output": "https://replicate.delivery/mgxm/aee9d2e3-d781-4d6f-b907-4b3272e484cf/output.png",
"started_at": "2022-05-21T01:01:26.459169Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/2eelkh7hejf57bu24j42anrisu",
"cancel": "https://api.replicate.com/v1/predictions/2eelkh7hejf57bu24j42anrisu/cancel"
},
"version": "509cb1e4144904a68b21ad72fb1a1b3fd9aa9a3a1125616a25bbb61ce1d515e0"
}
CLIP similarity tensor([0.3148, 0.3105, 0.3086, 0.3075, 0.3037, 0.3021, 0.2997, 0.2991],
device='cuda:0')