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dribnet / pixray-api
Uses pixray with raw settings.
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=dribnet/pixray-api
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";
import fs from "node:fs";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});Run dribnet/pixray-api using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"dribnet/pixray-api:01c87158fa585a5924b31332dcffbb0e488466a5ca556ebdb2da130ccf3ddeb8",
{
input: {
settings: "# find palettes at https://lospec.com/palette-list\npalette: https://lospec.com/palette-list/cl8uds-32x.png\nfilters: lookup\nprompts: \"robots at sunset\"\nquality: better\nseed: god_mode\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 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-api using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"dribnet/pixray-api:01c87158fa585a5924b31332dcffbb0e488466a5ca556ebdb2da130ccf3ddeb8",
input={
"settings": "# find palettes at https://lospec.com/palette-list\npalette: https://lospec.com/palette-list/cl8uds-32x.png\nfilters: lookup\nprompts: \"robots at sunset\"\nquality: better\nseed: god_mode\n"
}
)
# The dribnet/pixray-api 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-api/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-api 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": "dribnet/pixray-api:01c87158fa585a5924b31332dcffbb0e488466a5ca556ebdb2da130ccf3ddeb8",
"input": {
"settings": "# find palettes at https://lospec.com/palette-list\\npalette: https://lospec.com/palette-list/cl8uds-32x.png\\nfilters: lookup\\nprompts: \\"robots at sunset\\"\\nquality: better\\nseed: god_mode\\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-api@sha256:01c87158fa585a5924b31332dcffbb0e488466a5ca556ebdb2da130ccf3ddeb8 \
-i $'settings="# find palettes at https://lospec.com/palette-list\\npalette: https://lospec.com/palette-list/cl8uds-32x.png\\nfilters: lookup\\nprompts: \\"robots at sunset\\"\\nquality: better\\nseed: god_mode\\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-api@sha256:01c87158fa585a5924b31332dcffbb0e488466a5ca556ebdb2da130ccf3ddeb8curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "settings": "# find palettes at https://lospec.com/palette-list\\npalette: https://lospec.com/palette-list/cl8uds-32x.png\\nfilters: lookup\\nprompts: \\"robots at sunset\\"\\nquality: better\\nseed: god_mode\\n" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Add a payment method or purchase credits to run this model.
Each run costs approximately $0.099. Alternatively, try out our featured models for free.
Sign in with GitHub
By signing in, you agree to our
terms of service and privacy policy
Output
{
"completed_at": "2021-11-28T10:06:55.200045Z",
"created_at": "2021-11-28T10:00:49.325906Z",
"data_removed": false,
"error": null,
"id": "le76voshdvfjzajwbzhavclkla",
"input": {
"settings": "# find palettes at https://lospec.com/palette-list\npalette: https://lospec.com/palette-list/cl8uds-32x.png\nfilters: lookup\nprompts: \"robots at sunset\"\nquality: better\nseed: god_mode\n"
},
"logs": "---> BasePixrayPredictor Predict\nFound 8 colors in https://lospec.com/palette-list/cl8uds-32x.png\nUsing seed:\n392990954\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\ncolor table has 8 entries like [[0.6470588235294118, 0.7176470588235294, 0.8313725490196079], [0.9882352941176471, 0.6901960784313725, 0.5490196078431373], [0.6039215686274509, 0.6705882352941176, 0.788235294117647], [0.5607843137254902, 0.6274509803921569, 0.7490196078431373], [0.9372549019607843, 0.615686274509804, 0.4980392156862745]]\nUsing device:\ncuda:0\nOptimising using:\nAdam\nUsing text prompts:\n['robots at sunset']\n\n0it [00:00, ?it/s]\n/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:3631: 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(\n/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)\n return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\niter: 0, loss: 3.4, losses: 0.395, 0.994, 0.0795, 0.915, 0.047, 0.916, 0.0481 (-0=>3.395)\n\n0it [00:00, ?it/s]\niter: 10, loss: 2.84, losses: 0.0314, 0.945, 0.0797, 0.85, 0.0455, 0.839, 0.047 (-0=>2.838)\n\n0it [00:11, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 20, loss: 2.8, losses: 0.0259, 0.935, 0.0811, 0.838, 0.0445, 0.831, 0.0458 (-0=>2.802)\n\n0it [00:00, ?it/s]\niter: 30, loss: 2.79, losses: 0.0302, 0.931, 0.0805, 0.835, 0.0446, 0.823, 0.0454 (-2=>2.789)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 40, loss: 2.78, losses: 0.031, 0.926, 0.081, 0.83, 0.0443, 0.82, 0.0458 (-3=>2.76)\n\n0it [00:00, ?it/s]\niter: 50, loss: 2.76, losses: 0.0377, 0.918, 0.0814, 0.825, 0.0437, 0.814, 0.0452 (-13=>2.76)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 60, loss: 2.75, losses: 0.0688, 0.9, 0.0821, 0.813, 0.0428, 0.801, 0.0451 (-6=>2.732)\n\n0it [00:00, ?it/s]\niter: 70, loss: 2.7, losses: 0.0738, 0.879, 0.0798, 0.798, 0.0439, 0.777, 0.0456 (-0=>2.697)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 80, loss: 2.75, losses: 0.138, 0.877, 0.0817, 0.791, 0.0444, 0.776, 0.0466 (-10=>2.697)\n\n0it [00:00, ?it/s]\niter: 90, loss: 2.76, losses: 0.128, 0.893, 0.0822, 0.795, 0.044, 0.772, 0.0458 (-20=>2.697)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 100, loss: 2.74, losses: 0.143, 0.88, 0.0824, 0.783, 0.0459, 0.759, 0.0481 (-6=>2.672)\n\n0it [00:00, ?it/s]\niter: 110, loss: 2.71, losses: 0.116, 0.874, 0.0821, 0.782, 0.0443, 0.762, 0.047 (-16=>2.672)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 120, loss: 2.72, losses: 0.147, 0.862, 0.0833, 0.778, 0.0459, 0.755, 0.0483 (-4=>2.661)\n\n0it [00:00, ?it/s]\niter: 130, loss: 2.65, losses: 0.152, 0.843, 0.0834, 0.744, 0.0468, 0.734, 0.0485 (-6=>2.652)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 140, loss: 2.69, losses: 0.145, 0.852, 0.0822, 0.767, 0.0457, 0.748, 0.0485 (-6=>2.633)\n\n0it [00:00, ?it/s]\niter: 150, loss: 2.68, losses: 0.173, 0.849, 0.0832, 0.749, 0.046, 0.738, 0.047 (-8=>2.628)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 160, loss: 2.73, losses: 0.17, 0.859, 0.0837, 0.773, 0.0461, 0.751, 0.0474 (-18=>2.628)\n\n0it [00:00, ?it/s]\niter: 170, loss: 2.73, losses: 0.166, 0.87, 0.0834, 0.765, 0.0466, 0.756, 0.0472 (-28=>2.628)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 180, loss: 2.68, losses: 0.164, 0.848, 0.0844, 0.744, 0.0476, 0.746, 0.0485 (-38=>2.628)\n\n0it [00:00, ?it/s]\niter: 190, loss: 2.6, losses: 0.156, 0.818, 0.0838, 0.727, 0.0475, 0.72, 0.0484 (-0=>2.6)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 200, loss: 2.7, losses: 0.177, 0.851, 0.0837, 0.752, 0.047, 0.739, 0.0484 (-10=>2.6)\n\n0it [00:00, ?it/s]\niter: 210, loss: 2.7, losses: 0.175, 0.85, 0.0843, 0.751, 0.0473, 0.746, 0.0487 (-20=>2.6)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 220, loss: 2.7, losses: 0.166, 0.858, 0.0853, 0.751, 0.0464, 0.743, 0.0481 (-30=>2.6)\n\n0it [00:00, ?it/s]\nDropping learning rate\niter: 230, loss: 2.6, losses: 0.153, 0.824, 0.0858, 0.722, 0.048, 0.717, 0.0488 (-0=>2.599)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 240, loss: 2.65, losses: 0.149, 0.843, 0.0851, 0.74, 0.0471, 0.738, 0.0493 (-1=>2.574)\n\n0it [00:00, ?it/s]\niter: 250, loss: 2.65, losses: 0.151, 0.842, 0.0842, 0.744, 0.0474, 0.736, 0.0486 (-11=>2.574)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 260, loss: 2.64, losses: 0.154, 0.836, 0.0851, 0.737, 0.0476, 0.731, 0.0481 (-21=>2.574)\n\n0it [00:00, ?it/s]\niter: 270, loss: 2.65, losses: 0.145, 0.843, 0.0853, 0.742, 0.047, 0.735, 0.0482 (-4=>2.562)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 280, loss: 2.61, losses: 0.16, 0.825, 0.0853, 0.721, 0.049, 0.716, 0.049 (-14=>2.562)\n\n0it [00:00, ?it/s]\niter: 290, loss: 2.64, losses: 0.155, 0.835, 0.0849, 0.735, 0.0479, 0.734, 0.0484 (-24=>2.562)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 300, finished (-34=>2.562)\n\n0it [00:00, ?it/s]\n\n0it [00:00, ?it/s]",
"metrics": {
"total_time": 365.874139
},
"output": [
{
"file": "https://replicate.delivery/mgxm/908a2b66-f39c-45b5-80e0-2c3bd1eb89e6/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/c1639b8a-8d93-47b3-b99a-154f64effefc/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/0c1f6a24-8065-4f0e-959c-39a33d9c1ac3/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/4ecde433-c878-47b9-8d34-2208a1fcddfe/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/ef7a3e70-62a2-4a97-965a-2c458d952f60/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/ec99a125-29cd-4bd1-ad8c-efd52cdb37bd/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/cb02501c-13a7-4886-a130-93893136bc0b/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/f37939e4-4f3e-4fe9-987a-655259370aac/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/194d00ed-685e-40a6-befc-b4e5634ea7a9/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/5d69d8ad-6c23-4217-8d6a-3e4d298b3e8a/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/556c728b-e8f0-45cc-abf1-593571e6fbf7/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/5d4ba414-7230-4344-aeff-380712b9ae0c/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/e86c93b8-644c-48ac-a047-bc586d664e5e/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/fd4faa26-6bee-4f32-aeb9-30ef070c854c/tempfile.png"
},
{
"file": "https://replicate.delivery/mgxm/d1649fbe-e5cb-4374-871b-1c5f51bbd8d2/tempfile.png"
}
],
"started_at": "2021-11-30T22:00:25.336971Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/le76voshdvfjzajwbzhavclkla",
"cancel": "https://api.replicate.com/v1/predictions/le76voshdvfjzajwbzhavclkla/cancel"
},
"version": "30a982e43193c50480874788bbddcbb3f58397b74c3a580b8f5d3786ab778ade"
}---> BasePixrayPredictor Predict
Found 8 colors in https://lospec.com/palette-list/cl8uds-32x.png
Using seed:
392990954
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
color table has 8 entries like [[0.6470588235294118, 0.7176470588235294, 0.8313725490196079], [0.9882352941176471, 0.6901960784313725, 0.5490196078431373], [0.6039215686274509, 0.6705882352941176, 0.788235294117647], [0.5607843137254902, 0.6274509803921569, 0.7490196078431373], [0.9372549019607843, 0.615686274509804, 0.4980392156862745]]
Using device:
cuda:0
Optimising using:
Adam
Using text prompts:
['robots at sunset']
0it [00:00, ?it/s]
/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:3631: 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(
/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
iter: 0, loss: 3.4, losses: 0.395, 0.994, 0.0795, 0.915, 0.047, 0.916, 0.0481 (-0=>3.395)
0it [00:00, ?it/s]
iter: 10, loss: 2.84, losses: 0.0314, 0.945, 0.0797, 0.85, 0.0455, 0.839, 0.047 (-0=>2.838)
0it [00:11, ?it/s]
0it [00:22, ?it/s]
0it [00:00, ?it/s]
iter: 20, loss: 2.8, losses: 0.0259, 0.935, 0.0811, 0.838, 0.0445, 0.831, 0.0458 (-0=>2.802)
0it [00:00, ?it/s]
iter: 30, loss: 2.79, losses: 0.0302, 0.931, 0.0805, 0.835, 0.0446, 0.823, 0.0454 (-2=>2.789)
0it [00:12, ?it/s]
0it [00:22, ?it/s]
0it [00:00, ?it/s]
iter: 40, loss: 2.78, losses: 0.031, 0.926, 0.081, 0.83, 0.0443, 0.82, 0.0458 (-3=>2.76)
0it [00:00, ?it/s]
iter: 50, loss: 2.76, losses: 0.0377, 0.918, 0.0814, 0.825, 0.0437, 0.814, 0.0452 (-13=>2.76)
0it [00:12, ?it/s]
0it [00:22, ?it/s]
0it [00:00, ?it/s]
iter: 60, loss: 2.75, losses: 0.0688, 0.9, 0.0821, 0.813, 0.0428, 0.801, 0.0451 (-6=>2.732)
0it [00:00, ?it/s]
iter: 70, loss: 2.7, losses: 0.0738, 0.879, 0.0798, 0.798, 0.0439, 0.777, 0.0456 (-0=>2.697)
0it [00:12, ?it/s]
0it [00:22, ?it/s]
0it [00:00, ?it/s]
iter: 80, loss: 2.75, losses: 0.138, 0.877, 0.0817, 0.791, 0.0444, 0.776, 0.0466 (-10=>2.697)
0it [00:00, ?it/s]
iter: 90, loss: 2.76, losses: 0.128, 0.893, 0.0822, 0.795, 0.044, 0.772, 0.0458 (-20=>2.697)
0it [00:12, ?it/s]
0it [00:22, ?it/s]
0it [00:00, ?it/s]
iter: 100, loss: 2.74, losses: 0.143, 0.88, 0.0824, 0.783, 0.0459, 0.759, 0.0481 (-6=>2.672)
0it [00:00, ?it/s]
iter: 110, loss: 2.71, losses: 0.116, 0.874, 0.0821, 0.782, 0.0443, 0.762, 0.047 (-16=>2.672)
0it [00:12, ?it/s]
0it [00:22, ?it/s]
0it [00:00, ?it/s]
iter: 120, loss: 2.72, losses: 0.147, 0.862, 0.0833, 0.778, 0.0459, 0.755, 0.0483 (-4=>2.661)
0it [00:00, ?it/s]
iter: 130, loss: 2.65, losses: 0.152, 0.843, 0.0834, 0.744, 0.0468, 0.734, 0.0485 (-6=>2.652)
0it [00:12, ?it/s]
0it [00:22, ?it/s]
0it [00:00, ?it/s]
iter: 140, loss: 2.69, losses: 0.145, 0.852, 0.0822, 0.767, 0.0457, 0.748, 0.0485 (-6=>2.633)
0it [00:00, ?it/s]
iter: 150, loss: 2.68, losses: 0.173, 0.849, 0.0832, 0.749, 0.046, 0.738, 0.047 (-8=>2.628)
0it [00:12, ?it/s]
0it [00:22, ?it/s]
0it [00:00, ?it/s]
iter: 160, loss: 2.73, losses: 0.17, 0.859, 0.0837, 0.773, 0.0461, 0.751, 0.0474 (-18=>2.628)
0it [00:00, ?it/s]
iter: 170, loss: 2.73, losses: 0.166, 0.87, 0.0834, 0.765, 0.0466, 0.756, 0.0472 (-28=>2.628)
0it [00:12, ?it/s]
0it [00:22, ?it/s]
0it [00:00, ?it/s]
iter: 180, loss: 2.68, losses: 0.164, 0.848, 0.0844, 0.744, 0.0476, 0.746, 0.0485 (-38=>2.628)
0it [00:00, ?it/s]
iter: 190, loss: 2.6, losses: 0.156, 0.818, 0.0838, 0.727, 0.0475, 0.72, 0.0484 (-0=>2.6)
0it [00:12, ?it/s]
0it [00:22, ?it/s]
0it [00:00, ?it/s]
iter: 200, loss: 2.7, losses: 0.177, 0.851, 0.0837, 0.752, 0.047, 0.739, 0.0484 (-10=>2.6)
0it [00:00, ?it/s]
iter: 210, loss: 2.7, losses: 0.175, 0.85, 0.0843, 0.751, 0.0473, 0.746, 0.0487 (-20=>2.6)
0it [00:12, ?it/s]
0it [00:22, ?it/s]
0it [00:00, ?it/s]
iter: 220, loss: 2.7, losses: 0.166, 0.858, 0.0853, 0.751, 0.0464, 0.743, 0.0481 (-30=>2.6)
0it [00:00, ?it/s]
Dropping learning rate
iter: 230, loss: 2.6, losses: 0.153, 0.824, 0.0858, 0.722, 0.048, 0.717, 0.0488 (-0=>2.599)
0it [00:12, ?it/s]
0it [00:22, ?it/s]
0it [00:00, ?it/s]
iter: 240, loss: 2.65, losses: 0.149, 0.843, 0.0851, 0.74, 0.0471, 0.738, 0.0493 (-1=>2.574)
0it [00:00, ?it/s]
iter: 250, loss: 2.65, losses: 0.151, 0.842, 0.0842, 0.744, 0.0474, 0.736, 0.0486 (-11=>2.574)
0it [00:12, ?it/s]
0it [00:22, ?it/s]
0it [00:00, ?it/s]
iter: 260, loss: 2.64, losses: 0.154, 0.836, 0.0851, 0.737, 0.0476, 0.731, 0.0481 (-21=>2.574)
0it [00:00, ?it/s]
iter: 270, loss: 2.65, losses: 0.145, 0.843, 0.0853, 0.742, 0.047, 0.735, 0.0482 (-4=>2.562)
0it [00:12, ?it/s]
0it [00:22, ?it/s]
0it [00:00, ?it/s]
iter: 280, loss: 2.61, losses: 0.16, 0.825, 0.0853, 0.721, 0.049, 0.716, 0.049 (-14=>2.562)
0it [00:00, ?it/s]
iter: 290, loss: 2.64, losses: 0.155, 0.835, 0.0849, 0.735, 0.0479, 0.734, 0.0484 (-24=>2.562)
0it [00:12, ?it/s]
0it [00:22, ?it/s]
0it [00:00, ?it/s]
iter: 300, finished (-34=>2.562)
0it [00:00, ?it/s]
0it [00:00, ?it/s]This output was created using a different version of the model, dribnet/pixray-api:30a982e4.
Examples
Run time and cost
This model costs approximately $0.099 to run on Replicate, or 10 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.
This model runs on Nvidia T4 GPU hardware. Predictions typically complete within 8 minutes. The predict time for this model varies significantly based on the inputs.
