Prediction

ID

hva3rgqw65hrdddtrkyskq23qq

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

almost 4 years ago

Input

seed: "0"
image
gen_model: cc_icgan
num_samples: "4"
conditional_class: castle

{
  "seed": "0",
  "image": "https://replicate.delivery/mgxm/cd93a7fb-2dd7-41eb-80d5-b276159bd1fa/fantasy_drawing.jpeg",
  "gen_model": "cc_icgan",
  "num_samples": "4",
  "conditional_class": "castle"
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "meta/ic_gan:93381a1593b23062bcd95585b0f2b2c0b30c5684c8df904b881e7ad3b2e3227f",
  {
    input: {
      seed: "0",
      image: "https://replicate.delivery/mgxm/cd93a7fb-2dd7-41eb-80d5-b276159bd1fa/fantasy_drawing.jpeg",
      gen_model: "cc_icgan",
      num_samples: "4",
      conditional_class: "castle"
    }
  }
);

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

output = replicate.run(
    "meta/ic_gan:93381a1593b23062bcd95585b0f2b2c0b30c5684c8df904b881e7ad3b2e3227f",
    input={
        "seed": "0",
        "image": "https://replicate.delivery/mgxm/cd93a7fb-2dd7-41eb-80d5-b276159bd1fa/fantasy_drawing.jpeg",
        "gen_model": "cc_icgan",
        "num_samples": "4",
        "conditional_class": "castle"
    }
)

# The meta/ic_gan 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/meta/ic_gan/api#output-schema
    print(item)

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

Run meta/ic_gan 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": "meta/ic_gan:93381a1593b23062bcd95585b0f2b2c0b30c5684c8df904b881e7ad3b2e3227f",
    "input": {
      "seed": "0",
      "image": "https://replicate.delivery/mgxm/cd93a7fb-2dd7-41eb-80d5-b276159bd1fa/fantasy_drawing.jpeg",
      "gen_model": "cc_icgan",
      "num_samples": "4",
      "conditional_class": "castle"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2021-09-24T18:50:12.277405Z",
  "created_at": "2021-09-24T18:45:54.881050Z",
  "data_removed": false,
  "error": null,
  "id": "hva3rgqw65hrdddtrkyskq23qq",
  "input": {
    "seed": "0",
    "image": "https://replicate.delivery/mgxm/cd93a7fb-2dd7-41eb-80d5-b276159bd1fa/fantasy_drawing.jpeg",
    "gen_model": "cc_icgan",
    "num_samples": "4",
    "conditional_class": "castle"
  },
  "logs": "using resnet50 to extract features\nInside resnet function, using ImageNet pretrained from model url!\nDownloading: \"https://download.pytorch.org/models/resnet50-19c8e357.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-19c8e357.pth\n\n  0%|          | 0.00/97.8M [00:00<?, ?B/s]\n\n 10%|9         | 9.68M/97.8M [00:00<00:00, 101MB/s]\n\n 31%|###       | 30.0M/97.8M [00:00<00:00, 167MB/s]\n\n 47%|####7     | 46.3M/97.8M [00:00<00:00, 169MB/s]\n\n 69%|######9   | 67.7M/97.8M [00:00<00:00, 191MB/s]\n\n 95%|#########4| 92.7M/97.8M [00:00<00:00, 217MB/s]\n\n100%|##########| 97.8M/97.8M [00:00<00:00, 197MB/s]\nUsing pretrained weights on full ImageNet.\nObtaining instance features from input image!\nFor name best\nbest0\n we have an FID:\n8.461181640625\nCheckpoint with name\nbest1\n not in folder.\nFinal name selected is\nbest0\nLoading best0 weights from .//cc_icgan_biggan_imagenet_res256...\nExperiment name is cc_icgan_biggan_imagenet_res256\nAdding attention layer in G at resolution 64\nParam count for Gs initialized parameters: 92299715\nLoading weights...\nLoading best0 weights from .//cc_icgan_biggan_imagenet_res256...\nPutting G in eval mode..\n/src/predict.py:126: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n  instance_vector = torch.tensor(",
  "metrics": {},
  "output": [
    {
      "file": "https://replicate.delivery/mgxm/03b4e597-15d3-4118-94e2-4f28fdefaf2a/out.png"
    }
  ],
  "started_at": null,
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/hva3rgqw65hrdddtrkyskq23qq",
    "cancel": "https://api.replicate.com/v1/predictions/hva3rgqw65hrdddtrkyskq23qq/cancel"
  },
  "version": "93381a1593b23062bcd95585b0f2b2c0b30c5684c8df904b881e7ad3b2e3227f"
}

meta/ic_gan:93381a1593b23062bcd95585b0f2b2c0b30c5684c8df904b881e7ad3b2e3227f

Prediction

Model

ID

dmupo24nc5cj5jbt5da3ixauiy

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

almost 4 years ago

Input

seed: "0"
image
gen_model: icgan
num_samples: "4"

{
  "seed": "0",
  "image": "https://replicate.delivery/mgxm/6ad5b06a-a6c9-4b5f-bef7-fd6abdfeeee4/colorful_painting.jpeg",
  "gen_model": "icgan",
  "num_samples": "4"
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "meta/ic_gan:93381a1593b23062bcd95585b0f2b2c0b30c5684c8df904b881e7ad3b2e3227f",
  {
    input: {
      seed: "0",
      image: "https://replicate.delivery/mgxm/6ad5b06a-a6c9-4b5f-bef7-fd6abdfeeee4/colorful_painting.jpeg",
      gen_model: "icgan",
      num_samples: "4"
    }
  }
);

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

output = replicate.run(
    "meta/ic_gan:93381a1593b23062bcd95585b0f2b2c0b30c5684c8df904b881e7ad3b2e3227f",
    input={
        "seed": "0",
        "image": "https://replicate.delivery/mgxm/6ad5b06a-a6c9-4b5f-bef7-fd6abdfeeee4/colorful_painting.jpeg",
        "gen_model": "icgan",
        "num_samples": "4"
    }
)

# The meta/ic_gan 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/meta/ic_gan/api#output-schema
    print(item)

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

Run meta/ic_gan 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": "meta/ic_gan:93381a1593b23062bcd95585b0f2b2c0b30c5684c8df904b881e7ad3b2e3227f",
    "input": {
      "seed": "0",
      "image": "https://replicate.delivery/mgxm/6ad5b06a-a6c9-4b5f-bef7-fd6abdfeeee4/colorful_painting.jpeg",
      "gen_model": "icgan",
      "num_samples": "4"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2021-09-25T12:08:16.978020Z",
  "created_at": "2021-09-25T11:59:27.141515Z",
  "data_removed": false,
  "error": null,
  "id": "dmupo24nc5cj5jbt5da3ixauiy",
  "input": {
    "seed": "0",
    "image": "https://replicate.delivery/mgxm/6ad5b06a-a6c9-4b5f-bef7-fd6abdfeeee4/colorful_painting.jpeg",
    "gen_model": "icgan",
    "num_samples": "4"
  },
  "logs": "using resnet50 to extract features\nLoading pretrained weights from:\nswav_pretrained.pth.tar\nkey\nprojection_head.0.weight\n not in dict\nkey\nprojection_head.0.bias\n not in dict\nkey\nprojection_head.1.weight\n not in dict\nkey\nprojection_head.1.bias\n not in dict\nkey\nprojection_head.1.running_mean\n not in dict\nkey\nprojection_head.1.running_var\n not in dict\nkey\nprojection_head.1.num_batches_tracked\n not in dict\nkey\nprojection_head.3.weight\n not in dict\nkey\nprojection_head.3.bias\n not in dict\nkey\nprototypes.weight\n not in dict\nNetwork key\nfc.weight\n not in dict to load\nNetwork key\nfc.bias\n not in dict to load\nObtaining instance features from input image!\nFor name best\nbest0\n we have an FID:\n22.453704833984375\nCheckpoint with name\nbest1\n not in folder.\nFinal name selected is\nbest0\nLoading best0 weights from .//icgan_biggan_imagenet_res256...\nExperiment name is icgan_biggan_imagenet_res256\nAdding attention layer in G at resolution 64\nParam count for Gs initialized parameters: 90014147\nLoading weights...\nLoading best0 weights from .//icgan_biggan_imagenet_res256...\nPutting G in eval mode..\n/src/predict.py:126: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n  instance_vector = torch.tensor(",
  "metrics": {},
  "output": [
    {
      "file": "https://replicate.delivery/mgxm/efbc7068-9c32-4dbb-8823-3c9fed1adee5/out.png"
    }
  ],
  "started_at": null,
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/dmupo24nc5cj5jbt5da3ixauiy",
    "cancel": "https://api.replicate.com/v1/predictions/dmupo24nc5cj5jbt5da3ixauiy/cancel"
  },
  "version": "93381a1593b23062bcd95585b0f2b2c0b30c5684c8df904b881e7ad3b2e3227f"
}

meta/ic_gan:93381a1593b23062bcd95585b0f2b2c0b30c5684c8df904b881e7ad3b2e3227f

Prediction

Model

ID

p53qai47cnbxxl7wabn2j52ts4

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

almost 4 years ago

Input

seed: "0"
image
gen_model: cc_icgan
num_samples: "4"
conditional_class: Labrador_retriever

{
  "seed": "0",
  "image": "https://replicate.delivery/mgxm/2a02cdee-868e-4660-a702-124f76792c7a/dogimage.jpeg",
  "gen_model": "cc_icgan",
  "num_samples": "4",
  "conditional_class": "Labrador_retriever"
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "meta/ic_gan:93381a1593b23062bcd95585b0f2b2c0b30c5684c8df904b881e7ad3b2e3227f",
  {
    input: {
      seed: "0",
      image: "https://replicate.delivery/mgxm/2a02cdee-868e-4660-a702-124f76792c7a/dogimage.jpeg",
      gen_model: "cc_icgan",
      num_samples: "4",
      conditional_class: "Labrador_retriever"
    }
  }
);

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

output = replicate.run(
    "meta/ic_gan:93381a1593b23062bcd95585b0f2b2c0b30c5684c8df904b881e7ad3b2e3227f",
    input={
        "seed": "0",
        "image": "https://replicate.delivery/mgxm/2a02cdee-868e-4660-a702-124f76792c7a/dogimage.jpeg",
        "gen_model": "cc_icgan",
        "num_samples": "4",
        "conditional_class": "Labrador_retriever"
    }
)

# The meta/ic_gan 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/meta/ic_gan/api#output-schema
    print(item)

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

Run meta/ic_gan 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": "meta/ic_gan:93381a1593b23062bcd95585b0f2b2c0b30c5684c8df904b881e7ad3b2e3227f",
    "input": {
      "seed": "0",
      "image": "https://replicate.delivery/mgxm/2a02cdee-868e-4660-a702-124f76792c7a/dogimage.jpeg",
      "gen_model": "cc_icgan",
      "num_samples": "4",
      "conditional_class": "Labrador_retriever"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

meta/ic_gan:93381a1593b23062bcd95585b0f2b2c0b30c5684c8df904b881e7ad3b2e3227f

Prediction

Model

ID

e4zt2orolbgrjdyo2gf33lcjla

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

almost 4 years ago

Input

seed: "0"
image
gen_model: cc_icgan
num_samples: "4"
conditional_class: Sussex_spaniel

{
  "seed": "0",
  "image": "https://replicate.delivery/mgxm/3d4f4dc8-139d-4182-83e8-2cdb48cad2fb/dogimage.jpeg",
  "gen_model": "cc_icgan",
  "num_samples": "4",
  "conditional_class": "Sussex_spaniel"
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "meta/ic_gan:93381a1593b23062bcd95585b0f2b2c0b30c5684c8df904b881e7ad3b2e3227f",
  {
    input: {
      seed: "0",
      image: "https://replicate.delivery/mgxm/3d4f4dc8-139d-4182-83e8-2cdb48cad2fb/dogimage.jpeg",
      gen_model: "cc_icgan",
      num_samples: "4",
      conditional_class: "Sussex_spaniel"
    }
  }
);

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

output = replicate.run(
    "meta/ic_gan:93381a1593b23062bcd95585b0f2b2c0b30c5684c8df904b881e7ad3b2e3227f",
    input={
        "seed": "0",
        "image": "https://replicate.delivery/mgxm/3d4f4dc8-139d-4182-83e8-2cdb48cad2fb/dogimage.jpeg",
        "gen_model": "cc_icgan",
        "num_samples": "4",
        "conditional_class": "Sussex_spaniel"
    }
)

# The meta/ic_gan 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/meta/ic_gan/api#output-schema
    print(item)

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

Run meta/ic_gan 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": "meta/ic_gan:93381a1593b23062bcd95585b0f2b2c0b30c5684c8df904b881e7ad3b2e3227f",
    "input": {
      "seed": "0",
      "image": "https://replicate.delivery/mgxm/3d4f4dc8-139d-4182-83e8-2cdb48cad2fb/dogimage.jpeg",
      "gen_model": "cc_icgan",
      "num_samples": "4",
      "conditional_class": "Sussex_spaniel"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

meta/ic_gan:93381a1593b23062bcd95585b0f2b2c0b30c5684c8df904b881e7ad3b2e3227f

Prediction

Model

ID

jn7pgvowtfc67ksu4gicljqtvy

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

almost 4 years ago

Input

seed: 0
image
gen_model: icgan
num_samples: "4"

{
  "seed": 0,
  "image": "https://replicate.delivery/mgxm/86206479-6ce0-45da-98e7-514d9273484b/dogimage.jpeg",
  "gen_model": "icgan",
  "num_samples": "4"
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "meta/ic_gan:93381a1593b23062bcd95585b0f2b2c0b30c5684c8df904b881e7ad3b2e3227f",
  {
    input: {
      seed: 0,
      image: "https://replicate.delivery/mgxm/86206479-6ce0-45da-98e7-514d9273484b/dogimage.jpeg",
      gen_model: "icgan",
      num_samples: "4"
    }
  }
);

console.log(output);

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