defaultCairo skyline at sunset.
typetext
ID
44klygz64ja5zd6hmtpzcpe2um
Status
Succeeded
Source
Web
Hardware
–
Total duration
Created
by danaakk
Webhook
–
Input
- prompts
- general AI after singularity
- drawer
- vqgandefaultvqgantypeenumvalue typestringoptionspixel, vqgan, vdiff, fft, fast_pixel, line_sketch, clipdraw
- settings
- defaulttypetext
{
"drawer": "vqgan",
"prompts": "general AI after singularity",
"settings": "\n"
}Install Replicate’s Node.js client library:
npm install replicate
Set the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_DYG**********************************
This is your API token. Keep it to yourself.
Import and set up the client:
import Replicate from "replicate";
import fs from "node:fs";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});
Run pixray/text2image using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"pixray/text2image:5c347a4bfa1d4523a58ae614c2194e15f2ae682b57e3797a5bb468920aa70ebf",
{
input: {
drawer: "vqgan",
prompts: "general AI after singularity",
settings: "\n"
}
}
);
// 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 replicate
Set the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_DYG**********************************
This is your API token. Keep it to yourself.
Import the client:
import replicate
Run pixray/text2image using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"pixray/text2image:5c347a4bfa1d4523a58ae614c2194e15f2ae682b57e3797a5bb468920aa70ebf",
input={
"drawer": "vqgan",
"prompts": "general AI after singularity",
"settings": "\n"
}
)
# The pixray/text2image 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/pixray/text2image/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=r8_DYG**********************************
This is your API token. Keep it to yourself.
Run pixray/text2image 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": "pixray/text2image:5c347a4bfa1d4523a58ae614c2194e15f2ae682b57e3797a5bb468920aa70ebf",
"input": {
"drawer": "vqgan",
"prompts": "general AI after singularity",
"settings": "\\n"
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
26 / 26
{
"id": "44klygz64ja5zd6hmtpzcpe2um",
"model": "pixray/text2image",
"version": "5c347a4bfa1d4523a58ae614c2194e15f2ae682b57e3797a5bb468920aa70ebf",
"input": {
"drawer": "vqgan",
"prompts": "general AI after singularity",
"settings": "\n"
},
"logs": "---> BasePixrayPredictor Predict\nUsing seed: 11031345554930866835\nreusing cached copy of model models/vqgan_imagenet_f16_16384.ckpt\nAll CLIP models already loaded: ['ViT-B/32', 'ViT-B/16']\nUsing device: cuda:0\nOptimising using: Adam\nUsing text prompts: ['general AI after singularity']\n0it [00:00, ?it/s]\n \niter: 0, loss: 1.9, losses: 0.898, 0.0604, 0.879, 0.0628 (-0=>1.9)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 10, loss: 1.83, losses: 0.86, 0.0607, 0.854, 0.0603 (-0=>1.835)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 20, loss: 1.8, losses: 0.843, 0.0623, 0.838, 0.0602 (-1=>1.795)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 30, loss: 1.75, losses: 0.817, 0.0658, 0.806, 0.0636 (-0=>1.752)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 40, loss: 1.72, losses: 0.796, 0.0671, 0.795, 0.0651 (-1=>1.714)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 50, loss: 1.69, losses: 0.786, 0.0674, 0.77, 0.0664 (-1=>1.686)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 60, loss: 1.68, losses: 0.78, 0.0684, 0.766, 0.0678 (-6=>1.681)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 70, loss: 1.66, losses: 0.773, 0.0687, 0.751, 0.0696 (-3=>1.659)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 80, loss: 1.65, losses: 0.764, 0.0698, 0.743, 0.0693 (-0=>1.646)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 90, loss: 1.66, losses: 0.769, 0.0691, 0.749, 0.0697 (-4=>1.645)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 100, loss: 1.65, losses: 0.765, 0.0692, 0.743, 0.0691 (-14=>1.645)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 110, loss: 1.64, losses: 0.766, 0.0698, 0.738, 0.07 (-5=>1.642)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 120, loss: 1.63, losses: 0.753, 0.0713, 0.735, 0.0696 (-0=>1.629)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 130, loss: 1.63, losses: 0.758, 0.0699, 0.737, 0.0706 (-10=>1.629)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 140, loss: 1.63, losses: 0.757, 0.0708, 0.733, 0.0698 (-7=>1.618)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 150, loss: 1.62, losses: 0.752, 0.069, 0.73, 0.0698 (-17=>1.618)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 160, loss: 1.63, losses: 0.755, 0.0707, 0.734, 0.0698 (-27=>1.618)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 170, loss: 1.62, losses: 0.755, 0.0704, 0.73, 0.0698 (-7=>1.616)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 180, loss: 1.63, losses: 0.755, 0.0731, 0.727, 0.0728 (-17=>1.616)\nDropping learning rate\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 190, loss: 1.61, losses: 0.749, 0.071, 0.723, 0.0718 (-2=>1.613)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 200, loss: 1.61, losses: 0.75, 0.071, 0.721, 0.0711 (-7=>1.606)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 210, loss: 1.62, losses: 0.751, 0.0721, 0.727, 0.0709 (-3=>1.602)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 220, loss: 1.61, losses: 0.746, 0.0708, 0.72, 0.0699 (-4=>1.602)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 230, loss: 1.61, losses: 0.741, 0.0722, 0.722, 0.0697 (-3=>1.598)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 240, loss: 1.61, losses: 0.744, 0.0716, 0.722, 0.0712 (-1=>1.596)\n0it [00:00, ?it/s]\n0it [00:06, ?it/s]\n0it [00:00, ?it/s]\niter: 250, finished (-11=>1.596)\n0it [00:00, ?it/s]\n0it [00:00, ?it/s]",
"output": [
"https://replicate.delivery/pbxt/PZtjS5lTSypVPx68deWbBVq8SzXO8DdLeZ2Qb5RchfM1sKfCB/tempfile.png",
"https://replicate.delivery/pbxt/3MQDjIVN2gaeD6McAGRRBknHN77GfSGsVyfAQmE3gu6DtKfCB/tempfile.png",
"https://replicate.delivery/pbxt/cT2UM5NURkouKlUMMP0MBGdxMW8revecaIfXFA1VgVcOtKfCB/tempfile.png",
"https://replicate.delivery/pbxt/WvffMKzTnImQTEjRxnq10ELqBKSsYamfPUIiyhfsXwA0aVeFC/tempfile.png",
"https://replicate.delivery/pbxt/60zzQ5ox0I7wA5GpWFP6cwnzMfaEKy87lAylwECorMXaryXIA/tempfile.png",
"https://replicate.delivery/pbxt/aL42TNL9TOICB9yvQbtqFLEjRO0KwPKRSfgeQRye8Jv1tKfCB/tempfile.png",
"https://replicate.delivery/pbxt/gj45baC6n9roLRUk3fD1ZCvROk2AWWEDPPAhEawiL6fBXlvQA/tempfile.png",
"https://replicate.delivery/pbxt/kWJ0KMnaep0CdCc3nEtML1tt7ohke1t8jpX3n2b2EU0HXlvQA/tempfile.png",
"https://replicate.delivery/pbxt/0eJbCn7y0ejG1UnLSHA4U03N5Dshevfpw9eS9MJvuIsr5q8FC/tempfile.png",
"https://replicate.delivery/pbxt/y9yCDuKwUjoEOxDeXSTUTpq0umFavazeJMX4mYpQqdxUXlvQA/tempfile.png",
"https://replicate.delivery/pbxt/Dqw83SnUfer1OkpBh6gbGfurewFU52efTAwG4OpEROeNtryXIA/tempfile.png",
"https://replicate.delivery/pbxt/jXleLMxt2WQRYyt9NqteXIgguANxfbV4FJtma6AhPlNBvKfCB/tempfile.png",
"https://replicate.delivery/pbxt/rDKRDaevtwXTfkzb6eP83c1kMmuYvuD2zHsosuiCMf0f8q8FC/tempfile.png",
"https://replicate.delivery/pbxt/fvjpHERGfNublERnupU9miUGyd3uZdfJKmeAmeypWUAo9q8FC/tempfile.png",
"https://replicate.delivery/pbxt/HzCDLU38ow50DBGydFkfqQjqEoWl6JO7IzbbUaffzfbOfq8FC/tempfile.png",
"https://replicate.delivery/pbxt/ivNg2YMHPFIqKRg7T0enauXwlg0jL3wryYvqPHnEhco8ryXIA/tempfile.png",
"https://replicate.delivery/pbxt/PctiudtZ1845LZ3foBfTiPq8D1htCuAUnuYooR40PURAYlvQA/tempfile.png",
"https://replicate.delivery/pbxt/X8uTSAYcoJYDEJYpWvS7MlcQ419eojiOM3F5rcEB0oGDsyXIA/tempfile.png",
"https://replicate.delivery/pbxt/XUMtGuXGwsYhIVDq00URi5DBfnkdEPVw6e4FMk5OJZjMYlvQA/tempfile.png",
"https://replicate.delivery/pbxt/3jgzuNca5g53IVKl8melGXCoZUyjsV9xnOyq0lXlidkJsyXIA/tempfile.png",
"https://replicate.delivery/pbxt/VIHjtd1t5FaQBJQSYyBpYNm6LkPSYtpj9lNjwfA8lwlMsyXIA/tempfile.png",
"https://replicate.delivery/pbxt/pN5vNaEqtkbCC5ZfepycfKF1ge96bY49VNTOSYjwNbZfDr8FC/tempfile.png",
"https://replicate.delivery/pbxt/fVD6Kn9PYY38Uqhp4mWJjXfujQNeuZ7Orvft0keKeUlvJW5LE/tempfile.png",
"https://replicate.delivery/pbxt/Jdn4lD6GOQakM5pFkW94P4oksZwclKJ9WvPdTk5PB5ALW5LE/tempfile.png",
"https://replicate.delivery/pbxt/DVvJmuk75nYoGx3uuutQG59gGfUdGQkDk3DSxu7CQ5ZZsyXIA/tempfile.png",
"https://replicate.delivery/pbxt/eKUqe5fxUiGvgJnMqapMlRIzMziuHSuFKrU8mhnfiEcMjVeFC/tempfile.png"
],
"data_removed": false,
"error": null,
"source": "web",
"status": "succeeded",
"created_at": "2023-04-07T23:13:23.673751Z",
"started_at": "2023-04-07T23:13:23.71393Z",
"completed_at": "2023-04-07T23:16:03.319036Z",
"urls": {
"cancel": "https://api.replicate.com/v1/predictions/44klygz64ja5zd6hmtpzcpe2um/cancel",
"get": "https://api.replicate.com/v1/predictions/44klygz64ja5zd6hmtpzcpe2um"
},
"metrics": {
"predict_time": 159.605106,
"total_time": 159.645285
}
}Generated in
---> BasePixrayPredictor Predict
Using seed: 11031345554930866835
reusing cached copy of model models/vqgan_imagenet_f16_16384.ckpt
All CLIP models already loaded: ['ViT-B/32', 'ViT-B/16']
Using device: cuda:0
Optimising using: Adam
Using text prompts: ['general AI after singularity']
0it [00:00, ?it/s]
iter: 0, loss: 1.9, losses: 0.898, 0.0604, 0.879, 0.0628 (-0=>1.9)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 10, loss: 1.83, losses: 0.86, 0.0607, 0.854, 0.0603 (-0=>1.835)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 20, loss: 1.8, losses: 0.843, 0.0623, 0.838, 0.0602 (-1=>1.795)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 30, loss: 1.75, losses: 0.817, 0.0658, 0.806, 0.0636 (-0=>1.752)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 40, loss: 1.72, losses: 0.796, 0.0671, 0.795, 0.0651 (-1=>1.714)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 50, loss: 1.69, losses: 0.786, 0.0674, 0.77, 0.0664 (-1=>1.686)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 60, loss: 1.68, losses: 0.78, 0.0684, 0.766, 0.0678 (-6=>1.681)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 70, loss: 1.66, losses: 0.773, 0.0687, 0.751, 0.0696 (-3=>1.659)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 80, loss: 1.65, losses: 0.764, 0.0698, 0.743, 0.0693 (-0=>1.646)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 90, loss: 1.66, losses: 0.769, 0.0691, 0.749, 0.0697 (-4=>1.645)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 100, loss: 1.65, losses: 0.765, 0.0692, 0.743, 0.0691 (-14=>1.645)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 110, loss: 1.64, losses: 0.766, 0.0698, 0.738, 0.07 (-5=>1.642)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 120, loss: 1.63, losses: 0.753, 0.0713, 0.735, 0.0696 (-0=>1.629)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 130, loss: 1.63, losses: 0.758, 0.0699, 0.737, 0.0706 (-10=>1.629)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 140, loss: 1.63, losses: 0.757, 0.0708, 0.733, 0.0698 (-7=>1.618)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 150, loss: 1.62, losses: 0.752, 0.069, 0.73, 0.0698 (-17=>1.618)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 160, loss: 1.63, losses: 0.755, 0.0707, 0.734, 0.0698 (-27=>1.618)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 170, loss: 1.62, losses: 0.755, 0.0704, 0.73, 0.0698 (-7=>1.616)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 180, loss: 1.63, losses: 0.755, 0.0731, 0.727, 0.0728 (-17=>1.616)
Dropping learning rate
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 190, loss: 1.61, losses: 0.749, 0.071, 0.723, 0.0718 (-2=>1.613)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 200, loss: 1.61, losses: 0.75, 0.071, 0.721, 0.0711 (-7=>1.606)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 210, loss: 1.62, losses: 0.751, 0.0721, 0.727, 0.0709 (-3=>1.602)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 220, loss: 1.61, losses: 0.746, 0.0708, 0.72, 0.0699 (-4=>1.602)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 230, loss: 1.61, losses: 0.741, 0.0722, 0.722, 0.0697 (-3=>1.598)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 240, loss: 1.61, losses: 0.744, 0.0716, 0.722, 0.0712 (-1=>1.596)
0it [00:00, ?it/s]
0it [00:06, ?it/s]
0it [00:00, ?it/s]
iter: 250, finished (-11=>1.596)
0it [00:00, ?it/s]
0it [00:00, ?it/s]