dribnet / pixray-genesis

Cold

Public
160.7K runs
T4

Iterate in playground

Run with an API

Playground API Examples README Versions

Prediction

dribnet/pixray-genesis:2027157f7e0661133093a4dacda67607e5fe49647718300fe6403fe1960748e3

Model

dribnet/pixray-genesis:2027157f

swsda6i43zevhk3zca75bstrwa

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

over 3 years ago by @dribnet

Input

title: gradients
quality: draft
optional_settings

{
  "title": "gradients",
  "quality": "draft",
  "optional_settings": "\n"
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run dribnet/pixray-genesis using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "dribnet/pixray-genesis:2027157f7e0661133093a4dacda67607e5fe49647718300fe6403fe1960748e3",
  {
    input: {
      title: "gradients",
      quality: "draft",
      optional_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 replicate

Import the client:

import replicate

Run dribnet/pixray-genesis using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "dribnet/pixray-genesis:2027157f7e0661133093a4dacda67607e5fe49647718300fe6403fe1960748e3",
    input={
        "title": "gradients",
        "quality": "draft",
        "optional_settings": "\n"
    }
)

# The dribnet/pixray-genesis 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-genesis/api#output-schema
    print(item)

To learn more, take a look at the guide on getting started with Python.

Run dribnet/pixray-genesis 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-genesis:2027157f7e0661133093a4dacda67607e5fe49647718300fe6403fe1960748e3",
    "input": {
      "title": "gradients",
      "quality": "draft",
      "optional_settings": "\\n"
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2021-11-02T02:50:25.757826Z",
  "created_at": "2021-11-02T02:49:28.628053Z",
  "data_removed": false,
  "error": null,
  "id": "swsda6i43zevhk3zca75bstrwa",
  "input": {
    "title": "gradients",
    "quality": "draft",
    "optional_settings": "\n"
  },
  "logs": "---> Pixray Genesis Init\nUsing seed:\n5563025599011693809\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['gradients']\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: 0.906, losses: 0.86, 0.0459 (-0=>0.9061)\n\n0it [00:00, ?it/s]\niter: 10, loss: 0.859, losses: 0.815, 0.0443 (-1=>0.8542)\n\n0it [00:04, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 20, loss: 0.84, losses: 0.795, 0.0444 (-3=>0.8383)\n\n0it [00:00, ?it/s]\niter: 30, loss: 0.831, losses: 0.787, 0.044 (-1=>0.8276)\n\n0it [00:05, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 40, loss: 0.821, losses: 0.777, 0.0439 (-0=>0.8214)\n\n0it [00:00, ?it/s]\niter: 50, loss: 0.817, losses: 0.774, 0.0434 (-1=>0.8086)\n\n0it [00:05, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 60, loss: 0.806, losses: 0.762, 0.0437 (-5=>0.804)\n\n0it [00:00, ?it/s]\niter: 70, loss: 0.807, losses: 0.763, 0.044 (-1=>0.7982)\n\n0it [00:05, ?it/s]\nDropping learning rate\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 80, loss: 0.802, losses: 0.757, 0.0446 (-3=>0.7963)\n\n0it [00:00, ?it/s]\niter: 90, loss: 0.8, losses: 0.756, 0.0443 (-13=>0.7963)\n\n0it [00:05, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 100, finished (-1=>0.7956)\n\n0it [00:00, ?it/s]\n\n0it [00:00, ?it/s]",
  "metrics": {
    "total_time": 57.129773
  },
  "output": [
    {
      "file": "https://replicate.delivery/mgxm/fd99bff6-e0b9-4e27-b8c7-52bb3dfce43d/tempfile.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/da12d566-529d-46a2-82c0-680984fe522d/tempfile.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/42333e12-1c95-4348-b685-17d51ee553f4/tempfile.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/c6d39d49-2291-479b-98a3-f39e9b02c39d/tempfile.png"
    }
  ],
  "started_at": "2021-11-30T11:01:25.573479Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/swsda6i43zevhk3zca75bstrwa",
    "cancel": "https://api.replicate.com/v1/predictions/swsda6i43zevhk3zca75bstrwa/cancel"
  },
  "version": "2027157f7e0661133093a4dacda67607e5fe49647718300fe6403fe1960748e3"
}

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