Prediction

ID

yptuj4iqxvbjzgmgxm4pbs33km

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

over 3 years ago

Input

lr: "0.01"
text: Leopard
image
resize_flag
optimize_steps: 200
output_aug_flag
output_aug_time: 16
center_crop_flag
display_every_step: "10"
short_edge_target_len: "384"

{
  "lr": "0.01",
  "text": "Leopard",
  "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
  "resize_flag": true,
  "optimize_steps": 200,
  "output_aug_flag": true,
  "output_aug_time": 16,
  "center_crop_flag": false,
  "display_every_step": "10",
  "short_edge_target_len": "384"
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run mistake0316/style-transfer-clip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
  {
    input: {
      lr: "0.01",
      text: "Leopard",
      image: "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
      resize_flag: true,
      optimize_steps: 200,
      output_aug_flag: true,
      output_aug_time: 16,
      center_crop_flag: false,
      display_every_step: "10",
      short_edge_target_len: "384"
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

Run mistake0316/style-transfer-clip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
    input={
        "lr": "0.01",
        "text": "Leopard",
        "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
        "resize_flag": True,
        "optimize_steps": 200,
        "output_aug_flag": True,
        "output_aug_time": 16,
        "center_crop_flag": False,
        "display_every_step": "10",
        "short_edge_target_len": "384"
    }
)

# The mistake0316/style-transfer-clip 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/mistake0316/style-transfer-clip/api#output-schema
    print(item)

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

Run mistake0316/style-transfer-clip 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": "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
    "input": {
      "lr": "0.01",
      "text": "Leopard",
      "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
      "resize_flag": true,
      "optimize_steps": 200,
      "output_aug_flag": true,
      "output_aug_time": 16,
      "center_crop_flag": false,
      "display_every_step": "10",
      "short_edge_target_len": "384"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2021-11-25T04:48:29.808643Z",
  "created_at": "2021-11-25T04:46:19.466058Z",
  "data_removed": false,
  "error": null,
  "id": "yptuj4iqxvbjzgmgxm4pbs33km",
  "input": {
    "lr": "0.01",
    "text": "Leopard",
    "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
    "resize_flag": true,
    "optimize_steps": 200,
    "output_aug_flag": true,
    "output_aug_time": 16,
    "center_crop_flag": false,
    "display_every_step": "10",
    "short_edge_target_len": "384"
  },
  "logs": "loading content\nLeopard\ntensor([[49406, 15931, 49407,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0]], device='cuda:0')\n/root/.pyenv/versions/3.7.12/lib/python3.7/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\")\nstep : 0\n\tloss :  0.7587890625\nstep : 10\n\tloss :  0.6875\nstep : 20\n\tloss :  0.67822265625\nstep : 30\n\tloss :  0.67529296875\nstep : 40\n\tloss :  0.67138671875\nstep : 50\n\tloss :  0.67333984375\nstep : 60\n\tloss :  0.66796875\nstep : 70\n\tloss :  0.66748046875\nstep : 80\n\tloss :  0.6650390625\nstep : 90\n\tloss :  0.6640625\nstep : 100\n\tloss :  0.6640625\nstep : 110\n\tloss :  0.662109375\nstep : 120\n\tloss :  0.66650390625\nstep : 130\n\tloss :  0.66259765625\nstep : 140\n\tloss :  0.66162109375\nstep : 150\n\tloss :  0.66064453125\nstep : 160\n\tloss :  0.66064453125\nstep : 170\n\tloss :  0.658203125\nstep : 180\n\tloss :  0.65869140625\nstep : 190\n\tloss :  0.65966796875\nstep : 199\n\tloss :  0.65966796875",
  "metrics": {},
  "output": [
    {
      "file": "https://replicate.delivery/mgxm/d09798f5-1758-4565-8284-982f3a34b436/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/0178491c-01fd-48ce-83fb-3dd6e69dfafc/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/1b8d2a42-55a6-4e89-af74-4869cb83c4d2/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/ca2e66ae-fdae-4c6a-935e-f878db80ea48/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/14cfac63-1b2a-42dd-a5a1-48ba0e8b3f0c/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/e1a54a1a-6ec4-4a5c-8a05-7ab60a229872/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/0407cb67-95e6-40f1-a430-ac40e20c9a4d/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/e29b2aae-cb9e-42fb-8adc-0b7153cd847b/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/93322bc1-5eb9-4048-83fb-995a95bf1177/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/eb1a2f8e-f30a-46af-bae6-bb80c07bcc26/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/9081a5ad-098e-4c96-885a-ca99fd779884/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/584fd3e0-5812-4bff-a3f3-1e361eca0bd0/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/af7ba34f-d925-4605-bc94-33bc89c0adf9/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/cc8d731a-5afb-4e7a-8058-b268590a9e22/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/193e7991-3096-40ef-8af9-cd1b81e18e26/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/84981b8d-4247-4667-a0ed-1a0e0183f634/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/f9dbb5dd-661a-43d9-b553-a7d800c10514/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/f88951ed-0559-4371-ae3a-c68c62157deb/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/f629b205-5dc6-4b9a-8cf2-0afab7412f5d/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/e8ef20e6-8952-4d6d-aa53-39ecf5151735/out.png"
    }
  ],
  "started_at": null,
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/yptuj4iqxvbjzgmgxm4pbs33km",
    "cancel": "https://api.replicate.com/v1/predictions/yptuj4iqxvbjzgmgxm4pbs33km/cancel"
  },
  "version": "4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a"
}

mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a

Prediction

Model

ID

jcqnuj573bhghhrm4zwqacekhi

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

over 3 years ago

Input

lr: "0.01"
text: firework
image
resize_flag
optimize_steps: 100
output_aug_flag
output_aug_time: 16
center_crop_flag
display_every_step: "10"
short_edge_target_len: "384"

{
  "lr": "0.01",
  "text": "firework",
  "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
  "resize_flag": true,
  "optimize_steps": 100,
  "output_aug_flag": true,
  "output_aug_time": 16,
  "center_crop_flag": false,
  "display_every_step": "10",
  "short_edge_target_len": "384"
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run mistake0316/style-transfer-clip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
  {
    input: {
      lr: "0.01",
      text: "firework",
      image: "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
      resize_flag: true,
      optimize_steps: 100,
      output_aug_flag: true,
      output_aug_time: 16,
      center_crop_flag: false,
      display_every_step: "10",
      short_edge_target_len: "384"
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

Run mistake0316/style-transfer-clip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
    input={
        "lr": "0.01",
        "text": "firework",
        "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
        "resize_flag": True,
        "optimize_steps": 100,
        "output_aug_flag": True,
        "output_aug_time": 16,
        "center_crop_flag": False,
        "display_every_step": "10",
        "short_edge_target_len": "384"
    }
)

# The mistake0316/style-transfer-clip 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/mistake0316/style-transfer-clip/api#output-schema
    print(item)

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

Run mistake0316/style-transfer-clip 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": "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
    "input": {
      "lr": "0.01",
      "text": "firework",
      "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
      "resize_flag": true,
      "optimize_steps": 100,
      "output_aug_flag": true,
      "output_aug_time": 16,
      "center_crop_flag": false,
      "display_every_step": "10",
      "short_edge_target_len": "384"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2021-11-25T05:00:42.326675Z",
  "created_at": "2021-11-25T04:59:34.870724Z",
  "data_removed": false,
  "error": null,
  "id": "jcqnuj573bhghhrm4zwqacekhi",
  "input": {
    "lr": "0.01",
    "text": "firework",
    "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
    "resize_flag": true,
    "optimize_steps": 100,
    "output_aug_flag": true,
    "output_aug_time": 16,
    "center_crop_flag": false,
    "display_every_step": "10",
    "short_edge_target_len": "384"
  },
  "logs": "loading content\nfirework\ntensor([[49406, 40750, 49407,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0]], device='cuda:0')\n/root/.pyenv/versions/3.7.12/lib/python3.7/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\")\nstep : 0\n\tloss :  0.78466796875\nstep : 10\n\tloss :  0.720703125\nstep : 20\n\tloss :  0.693359375\nstep : 30\n\tloss :  0.6865234375\nstep : 40\n\tloss :  0.68017578125\nstep : 50\n\tloss :  0.67626953125\nstep : 60\n\tloss :  0.677734375\nstep : 70\n\tloss :  0.67529296875\nstep : 80\n\tloss :  0.67529296875\nstep : 90\n\tloss :  0.669921875\nstep : 99\n\tloss :  0.6689453125",
  "metrics": {
    "total_time": 67.455951
  },
  "output": [
    {
      "file": "https://replicate.delivery/mgxm/c103846a-1904-4fd4-9f41-c544cef17033/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/c4a0c918-32d9-453e-af0c-908b2caea859/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/651b0134-5ba3-4bea-afba-1742af12feb3/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/c2f19f57-8c9e-4f43-884f-f960a6cf4325/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/a307c5c0-3a9b-4ecf-8074-0897481d48d1/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/59557dd9-729a-4796-8b02-c4ea77fd0502/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/7a19121d-b6fb-4c65-bdd9-3a9a58f31298/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/022244c7-ad90-4c49-80f5-99f762c9281b/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/58be45c7-15fd-4030-800a-254c5e2d782f/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/4fb41829-6d3e-4bd2-9fac-4264b53e2d0e/out.png"
    }
  ],
  "started_at": "2022-01-18T11:14:25.494689Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/jcqnuj573bhghhrm4zwqacekhi",
    "cancel": "https://api.replicate.com/v1/predictions/jcqnuj573bhghhrm4zwqacekhi/cancel"
  },
  "version": "4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a"
}

mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a

Prediction

Model

ID

pylsrttc6zbx7guxfondk3rs6m

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

over 3 years ago

Input

lr: "0.01"
text: bush
image
resize_flag
optimize_steps: 200
output_aug_flag
output_aug_time: "8"
center_crop_flag
display_every_step: "10"
short_edge_target_len: "384"

{
  "lr": "0.01",
  "text": "bush",
  "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
  "resize_flag": true,
  "optimize_steps": 200,
  "output_aug_flag": true,
  "output_aug_time": "8",
  "center_crop_flag": true,
  "display_every_step": "10",
  "short_edge_target_len": "384"
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run mistake0316/style-transfer-clip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
  {
    input: {
      lr: "0.01",
      text: "bush",
      image: "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
      resize_flag: true,
      optimize_steps: 200,
      output_aug_flag: true,
      output_aug_time: "8",
      center_crop_flag: true,
      display_every_step: "10",
      short_edge_target_len: "384"
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

Run mistake0316/style-transfer-clip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
    input={
        "lr": "0.01",
        "text": "bush",
        "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
        "resize_flag": True,
        "optimize_steps": 200,
        "output_aug_flag": True,
        "output_aug_time": "8",
        "center_crop_flag": True,
        "display_every_step": "10",
        "short_edge_target_len": "384"
    }
)

# The mistake0316/style-transfer-clip 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/mistake0316/style-transfer-clip/api#output-schema
    print(item)

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

Run mistake0316/style-transfer-clip 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": "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
    "input": {
      "lr": "0.01",
      "text": "bush",
      "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
      "resize_flag": true,
      "optimize_steps": 200,
      "output_aug_flag": true,
      "output_aug_time": "8",
      "center_crop_flag": true,
      "display_every_step": "10",
      "short_edge_target_len": "384"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2021-11-25T06:35:48.051283Z",
  "created_at": "2021-11-25T06:34:35.226702Z",
  "data_removed": false,
  "error": null,
  "id": "pylsrttc6zbx7guxfondk3rs6m",
  "input": {
    "lr": "0.01",
    "text": "bush",
    "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
    "resize_flag": true,
    "optimize_steps": 200,
    "output_aug_flag": true,
    "output_aug_time": "8",
    "center_crop_flag": true,
    "display_every_step": "10",
    "short_edge_target_len": "384"
  },
  "logs": "loading content\nbush\ntensor([[49406,  6867, 49407,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0]], device='cuda:0')\nstep : 0\n\tloss :  0.76708984375\nstep : 10\n\tloss :  0.69970703125\nstep : 20\n\tloss :  0.68603515625\nstep : 30\n\tloss :  0.67578125\nstep : 40\n\tloss :  0.66943359375\nstep : 50\n\tloss :  0.66748046875\nstep : 60\n\tloss :  0.666015625\nstep : 70\n\tloss :  0.66064453125\nstep : 80\n\tloss :  0.66015625\nstep : 90\n\tloss :  0.66064453125\nstep : 100\n\tloss :  0.658203125\nstep : 110\n\tloss :  0.65673828125\nstep : 120\n\tloss :  0.65380859375\nstep : 130\n\tloss :  0.658203125\nstep : 140\n\tloss :  0.65576171875\nstep : 150\n\tloss :  0.654296875\nstep : 160\n\tloss :  0.65478515625\nstep : 170\n\tloss :  0.65234375\nstep : 180\n\tloss :  0.65185546875\nstep : 190\n\tloss :  0.65234375\nstep : 199\n\tloss :  0.65087890625",
  "metrics": {
    "total_time": 72.824581
  },
  "output": [
    {
      "file": "https://replicate.delivery/mgxm/8a4eea14-af09-4c69-8238-30d71e893a55/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/d22021a2-60a9-4fcf-a651-b6b35ea97a8a/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/2a034186-1f39-4af7-bed6-b4d5bb299fe3/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/1b5076f4-f885-4559-a36c-ed4d5a73337f/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/b1862fcd-2402-423c-b6c2-f35fff666c5d/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/4a7ec2a0-1fb8-4cd3-a29e-68d6151dba84/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/e9933c08-4399-4f8f-90e7-5cbf0d84541d/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/fc7f0054-ae0e-4c29-a235-9ea6084fdde2/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/7c75ac50-1fdb-487d-9266-ada857c67cf2/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/a3ef10ff-23d0-4928-9290-c8fa0702fbf6/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/a6dbde1f-a0a8-46ae-8ae0-95feae68d615/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/c97fd967-6c28-4c4c-a2a7-f6adef701e48/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/bc46523c-5f4b-4377-bee5-f951d647187b/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/8ccf92af-0fb8-44fe-8d94-f0212a6a5c77/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/5e4fedb3-9627-49c2-b481-717475ed8f0d/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/27ece1db-9b85-4770-b1de-1188c001511b/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/985bcf6a-562f-401c-88a9-f7c9dc841d18/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/fd59d8a8-b547-4707-bbbb-345c6bf4292e/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/ebe3324a-0a90-4465-9b15-ce488c9b70d3/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/30280404-ecbf-4297-a6ed-284c9b9d3cec/out.png"
    }
  ],
  "started_at": "2022-05-29T22:57:33.796957Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/pylsrttc6zbx7guxfondk3rs6m",
    "cancel": "https://api.replicate.com/v1/predictions/pylsrttc6zbx7guxfondk3rs6m/cancel"
  },
  "version": "4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a"
}

mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a

Prediction

Model

ID

r6pssh6kxbcrpf3yytchb6hfha

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

over 3 years ago

Input

lr: "0.01"
text: cheese cake
image
resize_flag
optimize_steps: "100"
output_aug_flag
output_aug_time: "8"
center_crop_flag
display_every_step: "10"
short_edge_target_len: "384"

{
  "lr": "0.01",
  "text": "cheese cake",
  "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
  "resize_flag": true,
  "optimize_steps": "100",
  "output_aug_flag": true,
  "output_aug_time": "8",
  "center_crop_flag": true,
  "display_every_step": "10",
  "short_edge_target_len": "384"
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run mistake0316/style-transfer-clip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
  {
    input: {
      lr: "0.01",
      text: "cheese cake",
      image: "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
      resize_flag: true,
      optimize_steps: "100",
      output_aug_flag: true,
      output_aug_time: "8",
      center_crop_flag: true,
      display_every_step: "10",
      short_edge_target_len: "384"
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

Run mistake0316/style-transfer-clip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
    input={
        "lr": "0.01",
        "text": "cheese cake",
        "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
        "resize_flag": True,
        "optimize_steps": "100",
        "output_aug_flag": True,
        "output_aug_time": "8",
        "center_crop_flag": True,
        "display_every_step": "10",
        "short_edge_target_len": "384"
    }
)

# The mistake0316/style-transfer-clip 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/mistake0316/style-transfer-clip/api#output-schema
    print(item)

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

Run mistake0316/style-transfer-clip 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": "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
    "input": {
      "lr": "0.01",
      "text": "cheese cake",
      "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
      "resize_flag": true,
      "optimize_steps": "100",
      "output_aug_flag": true,
      "output_aug_time": "8",
      "center_crop_flag": true,
      "display_every_step": "10",
      "short_edge_target_len": "384"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2021-11-25T04:09:01.518247Z",
  "created_at": "2021-11-25T04:06:53.426493Z",
  "data_removed": false,
  "error": null,
  "id": "r6pssh6kxbcrpf3yytchb6hfha",
  "input": {
    "lr": "0.01",
    "text": "cheese cake",
    "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
    "resize_flag": true,
    "optimize_steps": "100",
    "output_aug_flag": true,
    "output_aug_time": "8",
    "center_crop_flag": true,
    "display_every_step": "10",
    "short_edge_target_len": "384"
  },
  "logs": "loading content\ncheese cake\ntensor([[49406,  4108,  2972, 49407,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0]], device='cuda:0')\n/root/.pyenv/versions/3.7.12/lib/python3.7/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\")\nstep : 0\n\tloss :  0.79052734375\nstep : 10\n\tloss :  0.72021484375\nstep : 20\n\tloss :  0.7001953125\nstep : 30\n\tloss :  0.6884765625\nstep : 40\n\tloss :  0.6796875\nstep : 50\n\tloss :  0.66796875\nstep : 60\n\tloss :  0.671875\nstep : 70\n\tloss :  0.662109375\nstep : 80\n\tloss :  0.6689453125\nstep : 90\n\tloss :  0.66064453125\nstep : 99\n\tloss :  0.65966796875",
  "metrics": {
    "total_time": 128.091754
  },
  "output": [
    {
      "file": "https://replicate.delivery/mgxm/6d528b2d-e204-423c-8df7-1b83a1afd3a2/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/2faa21da-2ef5-45c3-b4ce-2fbc27d0946d/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/4015aafd-4773-4fed-ac7a-e1586d7c8333/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/52ee6647-c869-4bab-beea-80173fc85eac/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/28964bf5-0e41-4fb7-a332-de7d29ae50e0/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/f9d18057-8d9c-4ac3-a7c7-f92be00b7e43/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/ab014327-e1fc-4177-8181-acc562d7f2eb/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/437574f5-ad47-4708-8f05-347e6c5f7e8f/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/87e8ca5b-a313-4a74-83a8-e59fd8d444db/out.png"
    }
  ],
  "started_at": "2021-12-01T05:17:21.263747Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/r6pssh6kxbcrpf3yytchb6hfha",
    "cancel": "https://api.replicate.com/v1/predictions/r6pssh6kxbcrpf3yytchb6hfha/cancel"
  },
  "version": "4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a"
}

mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a

Prediction

Model

ID

tjjlhfjmbjgwthry34asedeipa

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

over 3 years ago

Input

lr: "0.01"
text: green and blue mosaic
image
resize_flag
optimize_steps: "100"
output_aug_flag
output_aug_time: "8"
center_crop_flag
display_every_step: "10"
short_edge_target_len: "384"

{
  "lr": "0.01",
  "text": "green and blue mosaic",
  "image": "https://replicate.delivery/mgxm/406d0b26-1840-4d52-9071-917df59ce07d/doge.jpeg",
  "resize_flag": true,
  "optimize_steps": "100",
  "output_aug_flag": true,
  "output_aug_time": "8",
  "center_crop_flag": true,
  "display_every_step": "10",
  "short_edge_target_len": "384"
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run mistake0316/style-transfer-clip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
  {
    input: {
      lr: "0.01",
      text: "green and blue mosaic",
      image: "https://replicate.delivery/mgxm/406d0b26-1840-4d52-9071-917df59ce07d/doge.jpeg",
      resize_flag: true,
      optimize_steps: "100",
      output_aug_flag: true,
      output_aug_time: "8",
      center_crop_flag: true,
      display_every_step: "10",
      short_edge_target_len: "384"
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

Run mistake0316/style-transfer-clip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
    input={
        "lr": "0.01",
        "text": "green and blue mosaic",
        "image": "https://replicate.delivery/mgxm/406d0b26-1840-4d52-9071-917df59ce07d/doge.jpeg",
        "resize_flag": True,
        "optimize_steps": "100",
        "output_aug_flag": True,
        "output_aug_time": "8",
        "center_crop_flag": True,
        "display_every_step": "10",
        "short_edge_target_len": "384"
    }
)

# The mistake0316/style-transfer-clip 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/mistake0316/style-transfer-clip/api#output-schema
    print(item)

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

Run mistake0316/style-transfer-clip 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": "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
    "input": {
      "lr": "0.01",
      "text": "green and blue mosaic",
      "image": "https://replicate.delivery/mgxm/406d0b26-1840-4d52-9071-917df59ce07d/doge.jpeg",
      "resize_flag": true,
      "optimize_steps": "100",
      "output_aug_flag": true,
      "output_aug_time": "8",
      "center_crop_flag": true,
      "display_every_step": "10",
      "short_edge_target_len": "384"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2021-11-25T04:35:19.455453Z",
  "created_at": "2021-11-25T04:31:47.006607Z",
  "data_removed": false,
  "error": null,
  "id": "tjjlhfjmbjgwthry34asedeipa",
  "input": {
    "lr": "0.01",
    "text": "green and blue mosaic",
    "image": "https://replicate.delivery/mgxm/406d0b26-1840-4d52-9071-917df59ce07d/doge.jpeg",
    "resize_flag": true,
    "optimize_steps": "100",
    "output_aug_flag": true,
    "output_aug_time": "8",
    "center_crop_flag": true,
    "display_every_step": "10",
    "short_edge_target_len": "384"
  },
  "logs": "loading content\ngreen and blue mosaic\ntensor([[49406,  1901,   537,  1746, 17506, 49407,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0]], device='cuda:0')\n/root/.pyenv/versions/3.7.12/lib/python3.7/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\")\nstep : 0\n\tloss :  0.794921875\nstep : 10\n\tloss :  0.69873046875\nstep : 20\n\tloss :  0.6689453125\nstep : 30\n\tloss :  0.6513671875\nstep : 40\n\tloss :  0.63818359375\nstep : 50\n\tloss :  0.62890625\nstep : 60\n\tloss :  0.62451171875\nstep : 70\n\tloss :  0.6201171875\nstep : 80\n\tloss :  0.61328125\nstep : 90\n\tloss :  0.6142578125\nstep : 99\n\tloss :  0.60791015625",
  "metrics": {
    "total_time": 212.448846
  },
  "output": [
    {
      "file": "https://replicate.delivery/mgxm/a6ad966f-4a8c-4a35-9f3a-cdb673a9660c/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/37584e4e-4419-4f49-87b8-365efa4e04d7/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/c563c06a-8b56-495f-a772-e5a7428bdc20/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/d826bd25-08d1-450a-a818-8b50b752babf/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/5e8fedc5-11e5-42b6-9c0e-544907f2b906/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/e8792a16-d6a9-4b9e-a5d6-459bc6827ccb/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/ed1c4f87-ae4a-4fd9-b6cd-aa57b740424d/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/b6ad0e9a-5388-4364-8acd-2d0a4f5cede1/out.png"
    }
  ],
  "started_at": "2022-03-21T02:13:07.586925Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/tjjlhfjmbjgwthry34asedeipa",
    "cancel": "https://api.replicate.com/v1/predictions/tjjlhfjmbjgwthry34asedeipa/cancel"
  },
  "version": "4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a"
}

mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a

Prediction

Model

ID

ty753xnswnc75og5xmlpsgc7ie

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

over 3 years ago

Input

lr: 0.001
text: bubble tea
image
resize_flag
optimize_steps: "100"
output_aug_flag
output_aug_time: 32
center_crop_flag
display_every_step: "10"
short_edge_target_len: "384"

{
  "lr": 0.001,
  "text": "bubble tea",
  "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
  "resize_flag": true,
  "optimize_steps": "100",
  "output_aug_flag": true,
  "output_aug_time": 32,
  "center_crop_flag": false,
  "display_every_step": "10",
  "short_edge_target_len": "384"
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run mistake0316/style-transfer-clip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
  {
    input: {
      lr: 0.001,
      text: "bubble tea",
      image: "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
      resize_flag: true,
      optimize_steps: "100",
      output_aug_flag: true,
      output_aug_time: 32,
      center_crop_flag: false,
      display_every_step: "10",
      short_edge_target_len: "384"
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

Run mistake0316/style-transfer-clip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
    input={
        "lr": 0.001,
        "text": "bubble tea",
        "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
        "resize_flag": True,
        "optimize_steps": "100",
        "output_aug_flag": True,
        "output_aug_time": 32,
        "center_crop_flag": False,
        "display_every_step": "10",
        "short_edge_target_len": "384"
    }
)

# The mistake0316/style-transfer-clip 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/mistake0316/style-transfer-clip/api#output-schema
    print(item)

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

Run mistake0316/style-transfer-clip 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": "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
    "input": {
      "lr": 0.001,
      "text": "bubble tea",
      "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
      "resize_flag": true,
      "optimize_steps": "100",
      "output_aug_flag": true,
      "output_aug_time": 32,
      "center_crop_flag": false,
      "display_every_step": "10",
      "short_edge_target_len": "384"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2021-11-25T10:21:29.235136Z",
  "created_at": "2021-11-25T10:19:53.336394Z",
  "data_removed": false,
  "error": null,
  "id": "ty753xnswnc75og5xmlpsgc7ie",
  "input": {
    "lr": 0.001,
    "text": "bubble tea",
    "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
    "resize_flag": true,
    "optimize_steps": "100",
    "output_aug_flag": true,
    "output_aug_time": 32,
    "center_crop_flag": false,
    "display_every_step": "10",
    "short_edge_target_len": "384"
  },
  "logs": "loading content\nbubble tea\ntensor([[49406, 10799,  3274, 49407,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0]], device='cuda:0')\nstep : 0\n\tloss :  0.7666015625\nstep : 10\n\tloss :  0.7421875\nstep : 20\n\tloss :  0.71875\nstep : 30\n\tloss :  0.7021484375\nstep : 40\n\tloss :  0.68310546875\nstep : 50\n\tloss :  0.67236328125\nstep : 60\n\tloss :  0.6669921875\nstep : 70\n\tloss :  0.66455078125\nstep : 80\n\tloss :  0.65283203125\nstep : 90\n\tloss :  0.6513671875\nstep : 99\n\tloss :  0.6474609375",
  "metrics": {
    "total_time": 95.898742
  },
  "output": [
    {
      "file": "https://replicate.delivery/mgxm/a31aabd9-cc6b-4f2d-bbc4-56877258e8da/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/03978353-4c65-4411-a16c-a87fde83be88/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/132d8157-ecb4-40fa-8ca4-6472d43c9b7d/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/a8ca7391-f89c-4b78-8836-411cd9a34d76/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/634b8879-0ecf-4b12-9de6-3f0ed253fc32/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/628d82fe-6f00-4557-b21e-f7eaf66795e7/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/4a997244-62ca-4fef-99a7-0458327dae4f/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/ce59df4a-b9a8-4683-8738-dc550902248b/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/a1a82ebe-5a64-4ecc-b7e1-9357420e9f7b/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/3f64353b-40b8-472b-a927-4d879ca402be/out.png"
    }
  ],
  "started_at": "2022-02-14T14:00:13.533018Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/ty753xnswnc75og5xmlpsgc7ie",
    "cancel": "https://api.replicate.com/v1/predictions/ty753xnswnc75og5xmlpsgc7ie/cancel"
  },
  "version": "4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a"
}

mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a

Prediction

Model

ID

inejaulchbfcrbzq3amdyxms6a

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

over 3 years ago

Input

lr: "0.01"
text: pancake
image
resize_flag
optimize_steps: "100"
output_aug_flag
output_aug_time: 32
center_crop_flag
display_every_step: "10"
short_edge_target_len: "384"

{
  "lr": "0.01",
  "text": "pancake",
  "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
  "resize_flag": true,
  "optimize_steps": "100",
  "output_aug_flag": true,
  "output_aug_time": 32,
  "center_crop_flag": true,
  "display_every_step": "10",
  "short_edge_target_len": "384"
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run mistake0316/style-transfer-clip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
  {
    input: {
      lr: "0.01",
      text: "pancake",
      image: "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
      resize_flag: true,
      optimize_steps: "100",
      output_aug_flag: true,
      output_aug_time: 32,
      center_crop_flag: true,
      display_every_step: "10",
      short_edge_target_len: "384"
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

Run mistake0316/style-transfer-clip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
    input={
        "lr": "0.01",
        "text": "pancake",
        "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
        "resize_flag": True,
        "optimize_steps": "100",
        "output_aug_flag": True,
        "output_aug_time": 32,
        "center_crop_flag": True,
        "display_every_step": "10",
        "short_edge_target_len": "384"
    }
)

# The mistake0316/style-transfer-clip 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/mistake0316/style-transfer-clip/api#output-schema
    print(item)

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

Run mistake0316/style-transfer-clip 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": "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
    "input": {
      "lr": "0.01",
      "text": "pancake",
      "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
      "resize_flag": true,
      "optimize_steps": "100",
      "output_aug_flag": true,
      "output_aug_time": 32,
      "center_crop_flag": true,
      "display_every_step": "10",
      "short_edge_target_len": "384"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2021-11-26T10:08:31.128908Z",
  "created_at": "2021-11-26T10:07:25.005697Z",
  "data_removed": false,
  "error": null,
  "id": "inejaulchbfcrbzq3amdyxms6a",
  "input": {
    "lr": "0.01",
    "text": "pancake",
    "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
    "resize_flag": true,
    "optimize_steps": "100",
    "output_aug_flag": true,
    "output_aug_time": 32,
    "center_crop_flag": true,
    "display_every_step": "10",
    "short_edge_target_len": "384"
  },
  "logs": "loading content\npancake\ntensor([[49406, 18723, 49407,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0]], device='cuda:0')\nstep : 0\n\tloss :  0.7568359375\nstep : 10\n\tloss :  0.6923828125\nstep : 20\n\tloss :  0.66748046875\nstep : 30\n\tloss :  0.6591796875\nstep : 40\n\tloss :  0.65283203125\nstep : 50\n\tloss :  0.6474609375\nstep : 60\n\tloss :  0.6455078125\nstep : 70\n\tloss :  0.64208984375\nstep : 80\n\tloss :  0.640625\nstep : 90\n\tloss :  0.6376953125\nstep : 99\n\tloss :  0.63916015625",
  "metrics": {
    "total_time": 66.123211
  },
  "output": [
    {
      "file": "https://replicate.delivery/mgxm/893819c5-8d9d-4780-9d93-e8708f88b727/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/fe8e52d8-15c2-4b82-94f5-69e6b7a60bd7/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/4693425e-0194-459d-b1af-0c5e48f9b346/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/a60358d9-f051-4373-9502-c3f47b5aba1b/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/ede0fe19-384f-4647-ba64-3a0d285f723a/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/72e309b4-a954-4dba-a3ef-0932730ff068/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/9ffa03b7-e86d-4b64-9a9e-bc80263a91ee/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/45ecf32d-b94f-4845-be6b-59749802f048/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/d2d4cdbc-4c89-411f-a4ff-016b79fa0a9d/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/28f04b3a-6e5e-4e39-8ff7-f5e2a24de149/out.png"
    }
  ],
  "started_at": "2022-01-16T20:17:08.585517Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/inejaulchbfcrbzq3amdyxms6a",
    "cancel": "https://api.replicate.com/v1/predictions/inejaulchbfcrbzq3amdyxms6a/cancel"
  },
  "version": "4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a"
}

mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a

Prediction

Model

ID

l44qbwjyxff2lovnzk7h72paoa

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

over 3 years ago

Input

lr: "0.01"
text: colorful pearls
image
resize_flag
optimize_steps: "100"
output_aug_flag
output_aug_time: "8"
center_crop_flag
display_every_step: "10"
short_edge_target_len: "384"

{
  "lr": "0.01",
  "text": "colorful pearls",
  "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
  "resize_flag": true,
  "optimize_steps": "100",
  "output_aug_flag": true,
  "output_aug_time": "8",
  "center_crop_flag": true,
  "display_every_step": "10",
  "short_edge_target_len": "384"
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run mistake0316/style-transfer-clip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
  {
    input: {
      lr: "0.01",
      text: "colorful pearls",
      image: "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
      resize_flag: true,
      optimize_steps: "100",
      output_aug_flag: true,
      output_aug_time: "8",
      center_crop_flag: true,
      display_every_step: "10",
      short_edge_target_len: "384"
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

Run mistake0316/style-transfer-clip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
    input={
        "lr": "0.01",
        "text": "colorful pearls",
        "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
        "resize_flag": True,
        "optimize_steps": "100",
        "output_aug_flag": True,
        "output_aug_time": "8",
        "center_crop_flag": True,
        "display_every_step": "10",
        "short_edge_target_len": "384"
    }
)

# The mistake0316/style-transfer-clip 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/mistake0316/style-transfer-clip/api#output-schema
    print(item)

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

Run mistake0316/style-transfer-clip 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": "mistake0316/style-transfer-clip:4f26fddbeddab99fdbd1f4a38884f7f1fa70e015a89071583af82aec8d39b50a",
    "input": {
      "lr": "0.01",
      "text": "colorful pearls",
      "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
      "resize_flag": true,
      "optimize_steps": "100",
      "output_aug_flag": true,
      "output_aug_time": "8",
      "center_crop_flag": true,
      "display_every_step": "10",
      "short_edge_target_len": "384"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2021-11-27T09:02:53.338825Z",
  "created_at": "2021-11-27T09:02:17.050293Z",
  "data_removed": false,
  "error": null,
  "id": "l44qbwjyxff2lovnzk7h72paoa",
  "input": {
    "lr": "0.01",
    "text": "colorful pearls",
    "image": "https://replicate.delivery/mgxm/0f51aae9-ef91-4ee6-a83f-065abbb7a65c/doge.jpeg",
    "resize_flag": true,
    "optimize_steps": "100",
    "output_aug_flag": true,
    "output_aug_time": "8",
    "center_crop_flag": true,
    "display_every_step": "10",
    "short_edge_target_len": "384"
  },
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