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dribnet /pixray-text2pixel:d838a15c
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=dribnet/pixray-text2pixel
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-text2pixel using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"dribnet/pixray-text2pixel:d838a15c29f59f286c7f1caaf71db26f21f184419e80309c78a2689de319c6af",
{
input: {
aspect: "widescreen",
prompts: "Aliens destroying NYC skyline with lasers. #pixelart",
pixel_scale: 1
}
}
);
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-text2pixel using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"dribnet/pixray-text2pixel:d838a15c29f59f286c7f1caaf71db26f21f184419e80309c78a2689de319c6af",
input={
"aspect": "widescreen",
"prompts": "Aliens destroying NYC skyline with lasers. #pixelart",
"pixel_scale": 1
}
)
# The dribnet/pixray-text2pixel 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-text2pixel/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-text2pixel 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": "d838a15c29f59f286c7f1caaf71db26f21f184419e80309c78a2689de319c6af",
"input": {
"aspect": "widescreen",
"prompts": "Aliens destroying NYC skyline with lasers. #pixelart",
"pixel_scale": 1
}
}' \
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-text2pixel@sha256:d838a15c29f59f286c7f1caaf71db26f21f184419e80309c78a2689de319c6af \
-i 'aspect="widescreen"' \
-i 'prompts="Aliens destroying NYC skyline with lasers. #pixelart"' \
-i 'pixel_scale=1'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-text2pixel@sha256:d838a15c29f59f286c7f1caaf71db26f21f184419e80309c78a2689de319c6afcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "aspect": "widescreen", "prompts": "Aliens destroying NYC skyline with lasers. #pixelart", "pixel_scale": 1 } }' \ 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": "2021-10-18T14:04:46.052011Z",
"created_at": "2021-10-18T13:57:23.402890Z",
"data_removed": false,
"error": null,
"id": "2v3fn7tdivaxthvcihsv4qocmq",
"input": {
"aspect": "widescreen",
"prompts": "Aliens destroying NYC skyline with lasers. #pixelart",
"pixel_scale": 1
},
"logs": "---> BasePixrayPredictor Predict\nUsing seed:\n3244908265805850817\nRunning pixeldrawer with 80x45 grid\nUsing device:\ncuda:0\nOptimising using:\nAdam\nUsing text prompts:\n['Aliens destroying NYC skyline with lasers. #pixelart']\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: 2.98, losses: 0.98, 0.0797, 0.897, 0.0479, 0.925, 0.0489 (-0=>2.978)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 10, loss: 2.66, losses: 0.904, 0.0797, 0.791, 0.0474, 0.793, 0.0479 (-0=>2.663)\n\n0it [00:00, ?it/s]\n\n0it [00:12, ?it/s]\n\n0it [00:00, ?it/s]\niter: 20, loss: 2.55, losses: 0.861, 0.083, 0.75, 0.0482, 0.757, 0.0482 (-2=>2.536)\n\n0it [00:00, ?it/s]\n\n0it [00:12, ?it/s]\n\n0it [00:00, ?it/s]\niter: 30, loss: 2.41, losses: 0.812, 0.0844, 0.711, 0.0496, 0.705, 0.0487 (-2=>2.368)\n\n0it [00:00, ?it/s]\n\n0it [00:12, ?it/s]\n\n0it [00:00, ?it/s]\niter: 40, loss: 2.28, losses: 0.761, 0.0877, 0.662, 0.0538, 0.664, 0.0509 (-2=>2.276)\n\n0it [00:00, ?it/s]\n\n0it [00:12, ?it/s]\n\n0it [00:00, ?it/s]\niter: 50, loss: 2.33, losses: 0.793, 0.0868, 0.667, 0.0534, 0.68, 0.05 (-5=>2.23)\n\n0it [00:00, ?it/s]\n\n0it [00:12, ?it/s]\n\n0it [00:00, ?it/s]\niter: 60, loss: 2.16, losses: 0.725, 0.0875, 0.612, 0.0578, 0.626, 0.0523 (-0=>2.162)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 70, loss: 2.13, losses: 0.715, 0.0874, 0.601, 0.0588, 0.61, 0.0538 (-0=>2.126)\n\n0it [00:00, ?it/s]\n\n0it [00:12, ?it/s]\n\n0it [00:00, ?it/s]\niter: 80, loss: 2.12, losses: 0.71, 0.0884, 0.599, 0.0585, 0.607, 0.0546 (-2=>2.117)\n\n0it [00:00, ?it/s]\n\n0it [00:12, ?it/s]\n\n0it [00:00, ?it/s]\niter: 90, loss: 2.09, losses: 0.705, 0.0881, 0.592, 0.0599, 0.591, 0.0552 (-2=>2.089)\n\n0it [00:00, ?it/s]\n\n0it [00:12, ?it/s]\n\n0it [00:00, ?it/s]\niter: 100, loss: 2.09, losses: 0.702, 0.0885, 0.588, 0.0596, 0.594, 0.0547 (-0=>2.087)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 110, loss: 2.08, losses: 0.7, 0.0874, 0.584, 0.0601, 0.59, 0.0554 (-0=>2.076)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 120, loss: 2.19, losses: 0.744, 0.0869, 0.618, 0.0577, 0.628, 0.0533 (-10=>2.076)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 130, loss: 2.08, losses: 0.698, 0.0875, 0.588, 0.0592, 0.588, 0.055 (-0=>2.076)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 140, loss: 2.2, losses: 0.752, 0.0879, 0.618, 0.0573, 0.628, 0.0532 (-3=>2.065)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 150, loss: 2.17, losses: 0.74, 0.0879, 0.613, 0.0576, 0.621, 0.054 (-13=>2.065)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 160, loss: 2.05, losses: 0.687, 0.0876, 0.575, 0.0612, 0.582, 0.0566 (-0=>2.051)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 170, loss: 2.06, losses: 0.696, 0.0881, 0.575, 0.061, 0.58, 0.0562 (-2=>2.044)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 180, loss: 2.04, losses: 0.683, 0.0891, 0.571, 0.0618, 0.574, 0.0569 (-0=>2.036)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 190, loss: 2.05, losses: 0.687, 0.0883, 0.575, 0.0608, 0.579, 0.0561 (-10=>2.036)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 200, loss: 2.04, losses: 0.683, 0.0887, 0.579, 0.0608, 0.575, 0.0566 (-20=>2.036)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 210, loss: 2.15, losses: 0.731, 0.0867, 0.606, 0.0583, 0.611, 0.0543 (-2=>2.024)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 220, loss: 2.03, losses: 0.681, 0.0889, 0.568, 0.0617, 0.573, 0.0571 (-12=>2.024)\n\n0it [00:00, ?it/s]\nDropping learning rate\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 230, loss: 2.17, losses: 0.735, 0.087, 0.615, 0.0577, 0.621, 0.0537 (-4=>2.02)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 240, loss: 2.15, losses: 0.728, 0.0867, 0.61, 0.0584, 0.613, 0.0539 (-14=>2.02)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 250, loss: 2.14, losses: 0.725, 0.0876, 0.603, 0.059, 0.607, 0.0547 (-24=>2.02)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 260, loss: 2.02, losses: 0.679, 0.0877, 0.568, 0.0618, 0.566, 0.0576 (-7=>2.015)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 270, loss: 2.1, losses: 0.712, 0.0872, 0.592, 0.0607, 0.597, 0.0554 (-17=>2.015)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 280, loss: 2.15, losses: 0.729, 0.0866, 0.608, 0.0584, 0.618, 0.0542 (-27=>2.015)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 290, loss: 2.16, losses: 0.723, 0.0869, 0.614, 0.0591, 0.619, 0.0542 (-1=>2.013)\n\n0it [00:00, ?it/s]\n\n0it [00:13, ?it/s]\n\n0it [00:00, ?it/s]\niter: 300, finished (-6=>2.008)\n\n0it [00:00, ?it/s]\n\n0it [00:00, ?it/s]",
"metrics": {
"total_time": 442.649121
},
"output": [
{
"file": "https://replicate.delivery/mgxm/8ffcfae6-0de2-4899-aca7-d9a04e029039/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/738ed44f-eeb9-4b2b-be40-d2efddfa60c7/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/d8eee85c-650d-48ea-85c2-3c81819c9434/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/ef3f4cae-328a-49f1-a298-3404e48e2d58/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/c1d53c1c-7650-40a5-af60-e660d69552eb/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/9ad596f1-8082-43c5-b9f4-da401f5ff320/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/8805d912-3932-47e3-ad4e-795dda711204/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/4a7fc2ac-f9fa-482e-8d56-a0b71e48fe81/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/e7c04761-109f-45b1-a948-537f70f7d015/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/144e752f-1845-47cd-a52c-6175d232309b/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/c11c8442-d1c2-47e1-8589-5731d7a70871/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/0b3b5f5b-ebf4-4849-9604-1a1f6c2dcdc7/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/16f77eb7-0313-4a5b-8caf-24fc205b946f/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/243f275f-2344-4e3a-9c7e-afeca16f2bc6/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/082f35c0-b35c-4214-8708-dad689c00304/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/62411dfb-70b3-488a-874e-58be9e97a935/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/4899efbe-e0bb-4835-8072-92b8ba8a33b6/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/1c9fdeb0-d577-4009-86b3-8273b989dc87/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/041bc6c2-2317-4de7-acf5-bc7f5a16bbe6/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/eb851523-0099-4b1d-8548-830f90b377e4/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/36f01b5e-42aa-4b84-bd61-40ad872745ee/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/16eded9e-240a-4987-8e09-5b16dbb15201/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/65e47c77-187f-4a07-86c2-5561046d2bd8/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/03c870ac-6f45-46e9-94d0-c646cc5eaf3a/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/76a863dd-82c5-4236-996e-00db7ddffb57/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/3bff070f-9f03-4975-9cab-2244c1ba875c/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/f63b52f6-6f84-47a0-95b1-ab530761997c/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/4131dc96-4240-4335-99b4-0689a2f0c0cd/tempfile.png"
},
{
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}
],
"started_at": "2021-11-30T17:07:40.853064Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/2v3fn7tdivaxthvcihsv4qocmq",
"cancel": "https://api.replicate.com/v1/predictions/2v3fn7tdivaxthvcihsv4qocmq/cancel"
},
"version": "70efb5e487c7bd6bad2068ff465b9dc5a410336c539b316ac4eda6f0d4e56a54"
}---> BasePixrayPredictor Predict
Using seed:
3244908265805850817
Running pixeldrawer with 80x45 grid
Using device:
cuda:0
Optimising using:
Adam
Using text prompts:
['Aliens destroying NYC skyline with lasers. #pixelart']
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: 2.98, losses: 0.98, 0.0797, 0.897, 0.0479, 0.925, 0.0489 (-0=>2.978)
0it [00:00, ?it/s]
0it [00:13, ?it/s]
0it [00:00, ?it/s]
iter: 10, loss: 2.66, losses: 0.904, 0.0797, 0.791, 0.0474, 0.793, 0.0479 (-0=>2.663)
0it [00:00, ?it/s]
0it [00:12, ?it/s]
0it [00:00, ?it/s]
iter: 20, loss: 2.55, losses: 0.861, 0.083, 0.75, 0.0482, 0.757, 0.0482 (-2=>2.536)
0it [00:00, ?it/s]
0it [00:12, ?it/s]
0it [00:00, ?it/s]
iter: 30, loss: 2.41, losses: 0.812, 0.0844, 0.711, 0.0496, 0.705, 0.0487 (-2=>2.368)
0it [00:00, ?it/s]
0it [00:12, ?it/s]
0it [00:00, ?it/s]
iter: 40, loss: 2.28, losses: 0.761, 0.0877, 0.662, 0.0538, 0.664, 0.0509 (-2=>2.276)
0it [00:00, ?it/s]
0it [00:12, ?it/s]
0it [00:00, ?it/s]
iter: 50, loss: 2.33, losses: 0.793, 0.0868, 0.667, 0.0534, 0.68, 0.05 (-5=>2.23)
0it [00:00, ?it/s]
0it [00:12, ?it/s]
0it [00:00, ?it/s]
iter: 60, loss: 2.16, losses: 0.725, 0.0875, 0.612, 0.0578, 0.626, 0.0523 (-0=>2.162)
0it [00:00, ?it/s]
0it [00:13, ?it/s]
0it [00:00, ?it/s]
iter: 70, loss: 2.13, losses: 0.715, 0.0874, 0.601, 0.0588, 0.61, 0.0538 (-0=>2.126)
0it [00:00, ?it/s]
0it [00:12, ?it/s]
0it [00:00, ?it/s]
iter: 80, loss: 2.12, losses: 0.71, 0.0884, 0.599, 0.0585, 0.607, 0.0546 (-2=>2.117)
0it [00:00, ?it/s]
0it [00:12, ?it/s]
0it [00:00, ?it/s]
iter: 90, loss: 2.09, losses: 0.705, 0.0881, 0.592, 0.0599, 0.591, 0.0552 (-2=>2.089)
0it [00:00, ?it/s]
0it [00:12, ?it/s]
0it [00:00, ?it/s]
iter: 100, loss: 2.09, losses: 0.702, 0.0885, 0.588, 0.0596, 0.594, 0.0547 (-0=>2.087)
0it [00:00, ?it/s]
0it [00:13, ?it/s]
0it [00:00, ?it/s]
iter: 110, loss: 2.08, losses: 0.7, 0.0874, 0.584, 0.0601, 0.59, 0.0554 (-0=>2.076)
0it [00:00, ?it/s]
0it [00:13, ?it/s]
0it [00:00, ?it/s]
iter: 120, loss: 2.19, losses: 0.744, 0.0869, 0.618, 0.0577, 0.628, 0.0533 (-10=>2.076)
0it [00:00, ?it/s]
0it [00:13, ?it/s]
0it [00:00, ?it/s]
iter: 130, loss: 2.08, losses: 0.698, 0.0875, 0.588, 0.0592, 0.588, 0.055 (-0=>2.076)
0it [00:00, ?it/s]
0it [00:13, ?it/s]
0it [00:00, ?it/s]
iter: 140, loss: 2.2, losses: 0.752, 0.0879, 0.618, 0.0573, 0.628, 0.0532 (-3=>2.065)
0it [00:00, ?it/s]
0it [00:13, ?it/s]
0it [00:00, ?it/s]
iter: 150, loss: 2.17, losses: 0.74, 0.0879, 0.613, 0.0576, 0.621, 0.054 (-13=>2.065)
0it [00:00, ?it/s]
0it [00:13, ?it/s]
0it [00:00, ?it/s]
iter: 160, loss: 2.05, losses: 0.687, 0.0876, 0.575, 0.0612, 0.582, 0.0566 (-0=>2.051)
0it [00:00, ?it/s]
0it [00:13, ?it/s]
0it [00:00, ?it/s]
iter: 170, loss: 2.06, losses: 0.696, 0.0881, 0.575, 0.061, 0.58, 0.0562 (-2=>2.044)
0it [00:00, ?it/s]
0it [00:13, ?it/s]
0it [00:00, ?it/s]
iter: 180, loss: 2.04, losses: 0.683, 0.0891, 0.571, 0.0618, 0.574, 0.0569 (-0=>2.036)
0it [00:00, ?it/s]
0it [00:13, ?it/s]
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iter: 190, loss: 2.05, losses: 0.687, 0.0883, 0.575, 0.0608, 0.579, 0.0561 (-10=>2.036)
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0it [00:13, ?it/s]
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iter: 200, loss: 2.04, losses: 0.683, 0.0887, 0.579, 0.0608, 0.575, 0.0566 (-20=>2.036)
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iter: 210, loss: 2.15, losses: 0.731, 0.0867, 0.606, 0.0583, 0.611, 0.0543 (-2=>2.024)
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iter: 220, loss: 2.03, losses: 0.681, 0.0889, 0.568, 0.0617, 0.573, 0.0571 (-12=>2.024)
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Dropping learning rate
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iter: 230, loss: 2.17, losses: 0.735, 0.087, 0.615, 0.0577, 0.621, 0.0537 (-4=>2.02)
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iter: 240, loss: 2.15, losses: 0.728, 0.0867, 0.61, 0.0584, 0.613, 0.0539 (-14=>2.02)
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iter: 250, loss: 2.14, losses: 0.725, 0.0876, 0.603, 0.059, 0.607, 0.0547 (-24=>2.02)
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0it [00:13, ?it/s]
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iter: 260, loss: 2.02, losses: 0.679, 0.0877, 0.568, 0.0618, 0.566, 0.0576 (-7=>2.015)
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iter: 270, loss: 2.1, losses: 0.712, 0.0872, 0.592, 0.0607, 0.597, 0.0554 (-17=>2.015)
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iter: 280, loss: 2.15, losses: 0.729, 0.0866, 0.608, 0.0584, 0.618, 0.0542 (-27=>2.015)
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iter: 290, loss: 2.16, losses: 0.723, 0.0869, 0.614, 0.0591, 0.619, 0.0542 (-1=>2.013)
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iter: 300, finished (-6=>2.008)
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0it [00:00, ?it/s]This example was created by a different version, dribnet/pixray-text2pixel:70efb5e4.