paper11667/clipstyler:f2d6b24e – Run with an API on Replicate

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paper11667 /clipstyler:f2d6b24e

Playground API

Input

Run this model in Node.js with one line of code:

npx create-replicate --model=paper11667/clipstyler

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set 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 paper11667/clipstyler using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "paper11667/clipstyler:f2d6b24e6002f25f77ae89c2b0a5987daa6d0bf751b858b94b8416e8542434d1",
  {
    input: {
      text: "sketch with crayon",
      image: "https://replicate.delivery/mgxm/e4500aa0-f71b-42ff-a540-aadb44c8d1b2/face.jpg",
      iterations: 100
    }
  }
);
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

Set 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 replicate

Run paper11667/clipstyler using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "paper11667/clipstyler:f2d6b24e6002f25f77ae89c2b0a5987daa6d0bf751b858b94b8416e8542434d1",
    input={
        "text": "sketch with crayon",
        "image": "https://replicate.delivery/mgxm/e4500aa0-f71b-42ff-a540-aadb44c8d1b2/face.jpg",
        "iterations": 100
    }
)
print(output)

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 paper11667/clipstyler 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": "f2d6b24e6002f25f77ae89c2b0a5987daa6d0bf751b858b94b8416e8542434d1",
    "input": {
      "text": "sketch with crayon",
      "image": "https://replicate.delivery/mgxm/e4500aa0-f71b-42ff-a540-aadb44c8d1b2/face.jpg",
      "iterations": 100
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2021-12-05T15:00:38.792184Z",
  "created_at": "2021-12-05T14:59:30.323839Z",
  "data_removed": false,
  "error": null,
  "id": "qckvikgch5go7mhnqymnheheki",
  "input": {
    "text": "sketch with crayon",
    "image": "https://replicate.delivery/mgxm/e4500aa0-f71b-42ff-a540-aadb44c8d1b2/face.jpg",
    "iterations": "100"
  },
  "logs": "/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:3060: UserWarning: Default upsampling behavior when mode=bicubic 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(\"Default upsampling behavior when mode={} is changed \"\n/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:131: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:3060: 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(\"Default upsampling behavior when mode={} is changed \"\n/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torchvision/transforms/functional_tensor.py:876: UserWarning: Argument fill/fillcolor is not supported for Tensor input. Fill value is zero\n  warnings.warn(\"Argument fill/fillcolor is not supported for Tensor input. Fill value is zero\")\nAfter 0 iterations\nTotal loss: 9802.1005859375\nContent loss: 5.383391380310059\npatch loss: 0.9482421875\ndir loss: 0.91650390625\nTV loss: 0.3422693610191345\nAfter 20 iterations\nTotal loss: 8772.9951171875\nContent loss: 3.439800500869751\npatch loss: 0.87939453125\ndir loss: 0.681640625\nTV loss: 0.2753937542438507\nAfter 40 iterations\nTotal loss: 8309.794921875\nContent loss: 2.8816094398498535\npatch loss: 0.84228515625\ndir loss: 0.5947265625\nTV loss: 0.30363643169403076\nAfter 60 iterations\nTotal loss: 8068.50048828125\nContent loss: 2.774502754211426\npatch loss: 0.8193359375\ndir loss: 0.5517578125\nTV loss: 0.3253156840801239\nAfter 80 iterations\nTotal loss: 7695.32177734375\nContent loss: 2.5266687870025635\npatch loss: 0.78466796875\ndir loss: 0.50390625\nTV loss: 0.321286678314209\nAfter 100 iterations\nTotal loss: 7482.86083984375\nContent loss: 2.315229892730713\npatch loss: 0.7685546875\ndir loss: 0.4384765625\nTV loss: 0.32614612579345703",
  "metrics": {
    "predict_time": 42.086328,
    "total_time": 68.468345
  },
  "output": [
    {
      "file": "https://replicate.delivery/mgxm/bdcd9c0c-09df-4ea7-8ad8-0efa3fa48eda/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/ea95e311-13b1-41e3-bfe1-0d2c9e6dfc5d/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/d4e354f6-e22b-4b70-abc8-63e92b0de85d/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/9f274601-c587-4d54-b841-f7f3af3f9636/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/59d6234b-3662-429d-93ba-637d849f1383/out.png"
    }
  ],
  "started_at": "2021-12-05T14:59:56.705856Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/qckvikgch5go7mhnqymnheheki",
    "cancel": "https://api.replicate.com/v1/predictions/qckvikgch5go7mhnqymnheheki/cancel"
  },
  "version": "847f902b2e3b7d34dc7e500ec7b03eb574a28610fdddf87d527e26e7c4286016"
}

Generated in

42.1 seconds

Tweak itReport

/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:3060: UserWarning: Default upsampling behavior when mode=bicubic 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("Default upsampling behavior when mode={} is changed "
/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:131: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
  warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:3060: 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("Default upsampling behavior when mode={} is changed "
/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torchvision/transforms/functional_tensor.py:876: UserWarning: Argument fill/fillcolor is not supported for Tensor input. Fill value is zero
  warnings.warn("Argument fill/fillcolor is not supported for Tensor input. Fill value is zero")
After 0 iterations
Total loss: 9802.1005859375
Content loss: 5.383391380310059
patch loss: 0.9482421875
dir loss: 0.91650390625
TV loss: 0.3422693610191345
After 20 iterations
Total loss: 8772.9951171875
Content loss: 3.439800500869751
patch loss: 0.87939453125
dir loss: 0.681640625
TV loss: 0.2753937542438507
After 40 iterations
Total loss: 8309.794921875
Content loss: 2.8816094398498535
patch loss: 0.84228515625
dir loss: 0.5947265625
TV loss: 0.30363643169403076
After 60 iterations
Total loss: 8068.50048828125
Content loss: 2.774502754211426
patch loss: 0.8193359375
dir loss: 0.5517578125
TV loss: 0.3253156840801239
After 80 iterations
Total loss: 7695.32177734375
Content loss: 2.5266687870025635
patch loss: 0.78466796875
dir loss: 0.50390625
TV loss: 0.321286678314209
After 100 iterations
Total loss: 7482.86083984375
Content loss: 2.315229892730713
patch loss: 0.7685546875
dir loss: 0.4384765625
TV loss: 0.32614612579345703

This example was created by a different version, paper11667/clipstyler:847f902b.