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dribnet /pixray-text2image:50f96fcd
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=dribnet/pixray-text2image
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 dribnet/pixray-text2image using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"dribnet/pixray-text2image:50f96fcd1980e7dcaba18e757acbac05e7f2ad4fbcb4a75f86a13c4086df26d0",
{
input: {
drawer: "vqgan",
prompts: "Robots skydiving high above the city",
settings: "\n"
}
}
);
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 dribnet/pixray-text2image using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"dribnet/pixray-text2image:50f96fcd1980e7dcaba18e757acbac05e7f2ad4fbcb4a75f86a13c4086df26d0",
input={
"drawer": "vqgan",
"prompts": "Robots skydiving high above the city",
"settings": "\n"
}
)
# The dribnet/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/dribnet/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=<paste-your-token-here>Find your API token in your account settings.
Run dribnet/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": "50f96fcd1980e7dcaba18e757acbac05e7f2ad4fbcb4a75f86a13c4086df26d0",
"input": {
"drawer": "vqgan",
"prompts": "Robots skydiving high above the city",
"settings": "\\n"
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:
brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/dribnet/pixray-text2image@sha256:50f96fcd1980e7dcaba18e757acbac05e7f2ad4fbcb4a75f86a13c4086df26d0 \
-i 'drawer="vqgan"' \
-i 'prompts="Robots skydiving high above the city"' \
-i $'settings="\\n"'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/dribnet/pixray-text2image@sha256:50f96fcd1980e7dcaba18e757acbac05e7f2ad4fbcb4a75f86a13c4086df26d0curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "drawer": "vqgan", "prompts": "Robots skydiving high above the city", "settings": "\\n" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Add a payment method to run this model.
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Output
{
"completed_at": "2021-12-01T01:31:24.192336Z",
"created_at": "2021-12-01T01:27:12.367827Z",
"data_removed": false,
"error": null,
"id": "2bce3gyk7ja7bc6el56h7marn4",
"input": {
"aspect": "widescreen",
"prompts": "Robots skydiving high above the city",
"quality": "normal"
},
"logs": "---> BasePixrayPredictor Predict\nUsing seed:\n7848817447306549499\nWorking with z of shape (1, 256, 16, 16) = 65536 dimensions.\nloaded pretrained LPIPS loss from taming/modules/autoencoder/lpips/vgg.pth\nVQLPIPSWithDiscriminator running with hinge loss.\nRestored from models/vqgan_imagenet_f16_16384.ckpt\nUsing device:\ncuda:0\nOptimising using:\nAdam\nUsing text prompts:\n['Robots skydiving high above the city']\n\n0it [00:00, ?it/s]\n/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:3609: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n warnings.warn(\niter: 0, loss: 2, losses: 0.965, 0.0457, 0.943, 0.0472 (-0=>2.001)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 10, loss: 1.81, losses: 0.862, 0.0465, 0.852, 0.046 (-0=>1.807)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 20, loss: 1.69, losses: 0.806, 0.052, 0.785, 0.0511 (-0=>1.694)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 30, loss: 1.66, losses: 0.782, 0.0555, 0.77, 0.0534 (-6=>1.655)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 40, loss: 1.62, losses: 0.764, 0.056, 0.748, 0.0536 (-2=>1.586)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 50, loss: 1.64, losses: 0.779, 0.0543, 0.755, 0.053 (-8=>1.583)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 60, loss: 1.6, losses: 0.759, 0.056, 0.733, 0.0546 (-4=>1.558)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 70, loss: 1.55, losses: 0.729, 0.0566, 0.706, 0.0551 (-0=>1.547)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 80, loss: 1.55, losses: 0.731, 0.0557, 0.705, 0.0539 (-0=>1.546)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 90, loss: 1.59, losses: 0.754, 0.0568, 0.723, 0.0555 (-6=>1.535)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 100, loss: 1.57, losses: 0.749, 0.057, 0.709, 0.0565 (-2=>1.532)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 110, loss: 1.57, losses: 0.744, 0.0581, 0.708, 0.057 (-2=>1.522)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 120, loss: 1.57, losses: 0.741, 0.0591, 0.709, 0.0576 (-12=>1.522)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 130, loss: 1.52, losses: 0.716, 0.0608, 0.682, 0.0593 (-4=>1.515)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 140, loss: 1.57, losses: 0.744, 0.0588, 0.71, 0.0593 (-14=>1.515)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 150, loss: 1.55, losses: 0.733, 0.0606, 0.698, 0.0588 (-24=>1.515)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 160, loss: 1.49, losses: 0.7, 0.0611, 0.668, 0.061 (-0=>1.491)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 170, loss: 1.53, losses: 0.719, 0.0616, 0.691, 0.0592 (-10=>1.491)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 180, loss: 1.55, losses: 0.728, 0.0591, 0.702, 0.0591 (-20=>1.491)\n\n0it [00:00, ?it/s]\nDropping learning rate\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 190, loss: 1.55, losses: 0.725, 0.062, 0.7, 0.0606 (-2=>1.503)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 200, loss: 1.53, losses: 0.721, 0.0601, 0.694, 0.0592 (-3=>1.495)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 210, loss: 1.48, losses: 0.695, 0.0616, 0.666, 0.0614 (-0=>1.484)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 220, loss: 1.54, losses: 0.723, 0.0609, 0.693, 0.0603 (-6=>1.482)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 230, loss: 1.49, losses: 0.697, 0.062, 0.665, 0.0624 (-16=>1.482)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 240, loss: 1.53, losses: 0.717, 0.0617, 0.692, 0.0604 (-26=>1.482)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 250, finished (-2=>1.481)\n\n0it [00:00, ?it/s]\n\n0it [00:00, ?it/s]",
"metrics": {
"predict_time": 250.596219,
"total_time": 251.824509
},
"output": [
{
"file": "https://replicate.delivery/mgxm/4992351b-d1db-4415-b25a-a51a3fe8f8fe/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/469032f7-7a0b-46c8-8c4c-36f70bb37b37/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/2a82daf7-731b-4641-b7de-661a31a49fae/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/a4ecc63b-222f-474f-8ade-a2d7d4464fd4/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/e5fa5aad-ca17-4451-8ed1-9ba5a6cfc6ab/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/d018ef02-8764-449c-baed-02db65187f2e/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/054e7679-b7a8-4fed-88c3-56fd42665954/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/e1f0b3f2-407a-43bf-a735-1e969fd4befd/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/c38e2952-bf2d-4faa-9a03-2ec4de3f3683/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/9f848682-6373-415e-9b6c-26f393f85c91/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/1df4cd14-a0c2-4a97-a5fa-bf22c07f18ea/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/17f4718e-0f3c-482d-8985-2f0338cf2468/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/9f360e10-d2f0-44c9-b413-6dbc157a15b8/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/8b3ca6b3-99a3-4a65-a3ef-e142b0980692/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/a5319666-d2d0-46a8-aebf-f015addbe856/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/772a808b-2d3c-41a0-acd1-1c30106c67ab/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/f0e788fd-cee9-4ba5-8216-e5274035551d/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/2338f84b-d5af-4afe-93ce-a888b97cda7c/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/b7f65950-c1d4-408f-bd53-ebeb0d0c7e72/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/9b500b52-622a-4678-bdd6-0a26f9f9033d/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/431af114-5799-4d87-bae3-92f87cdcd801/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/11f1bce4-9d95-4f5b-ade4-dd24916a6032/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/edf782f5-2fa2-43e9-8e34-bdf4c9933b85/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/cbd88190-7873-4fd9-8bfb-9a89451c0451/tempfile.png"
}
],
"started_at": "2021-12-01T01:27:13.596117Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/2bce3gyk7ja7bc6el56h7marn4",
"cancel": "https://api.replicate.com/v1/predictions/2bce3gyk7ja7bc6el56h7marn4/cancel"
},
"version": "7eeecc6f78234419555288d44cc7a5bc3eee80519da21c24efef6fbffe4cc3fe"
}Generated in
---> BasePixrayPredictor Predict
Using seed:
7848817447306549499
Working with z of shape (1, 256, 16, 16) = 65536 dimensions.
loaded pretrained LPIPS loss from taming/modules/autoencoder/lpips/vgg.pth
VQLPIPSWithDiscriminator running with hinge loss.
Restored from models/vqgan_imagenet_f16_16384.ckpt
Using device:
cuda:0
Optimising using:
Adam
Using text prompts:
['Robots skydiving high above the city']
0it [00:00, ?it/s]
/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:3609: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
warnings.warn(
iter: 0, loss: 2, losses: 0.965, 0.0457, 0.943, 0.0472 (-0=>2.001)
0it [00:00, ?it/s]
0it [00:08, ?it/s]
0it [00:00, ?it/s]
iter: 10, loss: 1.81, losses: 0.862, 0.0465, 0.852, 0.046 (-0=>1.807)
0it [00:00, ?it/s]
0it [00:08, ?it/s]
0it [00:00, ?it/s]
iter: 20, loss: 1.69, losses: 0.806, 0.052, 0.785, 0.0511 (-0=>1.694)
0it [00:00, ?it/s]
0it [00:08, ?it/s]
0it [00:00, ?it/s]
iter: 30, loss: 1.66, losses: 0.782, 0.0555, 0.77, 0.0534 (-6=>1.655)
0it [00:00, ?it/s]
0it [00:08, ?it/s]
0it [00:00, ?it/s]
iter: 40, loss: 1.62, losses: 0.764, 0.056, 0.748, 0.0536 (-2=>1.586)
0it [00:00, ?it/s]
0it [00:08, ?it/s]
0it [00:00, ?it/s]
iter: 50, loss: 1.64, losses: 0.779, 0.0543, 0.755, 0.053 (-8=>1.583)
0it [00:00, ?it/s]
0it [00:08, ?it/s]
0it [00:00, ?it/s]
iter: 60, loss: 1.6, losses: 0.759, 0.056, 0.733, 0.0546 (-4=>1.558)
0it [00:00, ?it/s]
0it [00:08, ?it/s]
0it [00:00, ?it/s]
iter: 70, loss: 1.55, losses: 0.729, 0.0566, 0.706, 0.0551 (-0=>1.547)
0it [00:00, ?it/s]
0it [00:08, ?it/s]
0it [00:00, ?it/s]
iter: 80, loss: 1.55, losses: 0.731, 0.0557, 0.705, 0.0539 (-0=>1.546)
0it [00:00, ?it/s]
0it [00:08, ?it/s]
0it [00:00, ?it/s]
iter: 90, loss: 1.59, losses: 0.754, 0.0568, 0.723, 0.0555 (-6=>1.535)
0it [00:00, ?it/s]
0it [00:08, ?it/s]
0it [00:00, ?it/s]
iter: 100, loss: 1.57, losses: 0.749, 0.057, 0.709, 0.0565 (-2=>1.532)
0it [00:00, ?it/s]
0it [00:08, ?it/s]
0it [00:00, ?it/s]
iter: 110, loss: 1.57, losses: 0.744, 0.0581, 0.708, 0.057 (-2=>1.522)
0it [00:00, ?it/s]
0it [00:08, ?it/s]
0it [00:00, ?it/s]
iter: 120, loss: 1.57, losses: 0.741, 0.0591, 0.709, 0.0576 (-12=>1.522)
0it [00:00, ?it/s]
0it [00:08, ?it/s]
0it [00:00, ?it/s]
iter: 130, loss: 1.52, losses: 0.716, 0.0608, 0.682, 0.0593 (-4=>1.515)
0it [00:00, ?it/s]
0it [00:08, ?it/s]
0it [00:00, ?it/s]
iter: 140, loss: 1.57, losses: 0.744, 0.0588, 0.71, 0.0593 (-14=>1.515)
0it [00:00, ?it/s]
0it [00:09, ?it/s]
0it [00:00, ?it/s]
iter: 150, loss: 1.55, losses: 0.733, 0.0606, 0.698, 0.0588 (-24=>1.515)
0it [00:00, ?it/s]
0it [00:09, ?it/s]
0it [00:00, ?it/s]
iter: 160, loss: 1.49, losses: 0.7, 0.0611, 0.668, 0.061 (-0=>1.491)
0it [00:00, ?it/s]
0it [00:09, ?it/s]
0it [00:00, ?it/s]
iter: 170, loss: 1.53, losses: 0.719, 0.0616, 0.691, 0.0592 (-10=>1.491)
0it [00:00, ?it/s]
0it [00:09, ?it/s]
0it [00:00, ?it/s]
iter: 180, loss: 1.55, losses: 0.728, 0.0591, 0.702, 0.0591 (-20=>1.491)
0it [00:00, ?it/s]
Dropping learning rate
0it [00:09, ?it/s]
0it [00:00, ?it/s]
iter: 190, loss: 1.55, losses: 0.725, 0.062, 0.7, 0.0606 (-2=>1.503)
0it [00:00, ?it/s]
0it [00:09, ?it/s]
0it [00:00, ?it/s]
iter: 200, loss: 1.53, losses: 0.721, 0.0601, 0.694, 0.0592 (-3=>1.495)
0it [00:00, ?it/s]
0it [00:09, ?it/s]
0it [00:00, ?it/s]
iter: 210, loss: 1.48, losses: 0.695, 0.0616, 0.666, 0.0614 (-0=>1.484)
0it [00:00, ?it/s]
0it [00:09, ?it/s]
0it [00:00, ?it/s]
iter: 220, loss: 1.54, losses: 0.723, 0.0609, 0.693, 0.0603 (-6=>1.482)
0it [00:00, ?it/s]
0it [00:09, ?it/s]
0it [00:00, ?it/s]
iter: 230, loss: 1.49, losses: 0.697, 0.062, 0.665, 0.0624 (-16=>1.482)
0it [00:00, ?it/s]
0it [00:09, ?it/s]
0it [00:00, ?it/s]
iter: 240, loss: 1.53, losses: 0.717, 0.0617, 0.692, 0.0604 (-26=>1.482)
0it [00:00, ?it/s]
0it [00:09, ?it/s]
0it [00:00, ?it/s]
iter: 250, finished (-2=>1.481)
0it [00:00, ?it/s]
0it [00:00, ?it/s]This example was created by a different version, dribnet/pixray-text2image:7eeecc6f.