bfirsh/bfirshbooth – Run with an API on Replicate

bfirsh / bfirshbooth

Generates bfirshes

Cold

Public
6.5K runs
T4

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

painting of bfirsh in the style of andy warholpainting of bfirsh in the style of andy warhol

Input prompt

Default: "a cjw cat in a bucket"

width

integer

Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits

Default: 512

height

integer

Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits

Default: 512

num_outputs

integer

Number of images to output

Default: 1

Including num_inference_steps and 2 more...

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

npx create-replicate --model=bfirsh/bfirshbooth

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

const output = await replicate.run(
  "bfirsh/bfirshbooth:891add47ca1ca0425ef5a5d5588f1db4d47ec7929336dabba20edb1fd8eeae59",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "painting of bfirsh in the style of andy warhol",
      num_outputs: 1,
      guidance_scale: 7.5,
      num_inference_steps: 50
    }
  }
);

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

output = replicate.run(
    "bfirsh/bfirshbooth:891add47ca1ca0425ef5a5d5588f1db4d47ec7929336dabba20edb1fd8eeae59",
    input={
        "width": 512,
        "height": 512,
        "prompt": "painting of bfirsh in the style of andy warhol",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "num_inference_steps": 50
    }
)
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 bfirsh/bfirshbooth 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": "891add47ca1ca0425ef5a5d5588f1db4d47ec7929336dabba20edb1fd8eeae59",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "painting of bfirsh in the style of andy warhol",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2022-11-21T00:18:03.001819Z",
  "created_at": "2022-11-21T00:17:31.809368Z",
  "data_removed": false,
  "error": null,
  "id": "yrgxjif2cbg3bfccbyl5iwfn64",
  "input": {
    "width": 512,
    "height": 512,
    "prompt": "painting of bfirsh in the style of andy warhol",
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "num_inference_steps": 50
  },
  "logs": "Loading pipeline...\nUsing seed: 52078\nGlobal seed set to 52078\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:13,  3.62it/s]\n  4%|▍         | 2/50 [00:00<00:13,  3.58it/s]\n  6%|▌         | 3/50 [00:00<00:13,  3.56it/s]\n  8%|▊         | 4/50 [00:01<00:13,  3.43it/s]\n 10%|█         | 5/50 [00:01<00:12,  3.48it/s]\n 12%|█▏        | 6/50 [00:01<00:12,  3.50it/s]\n 14%|█▍        | 7/50 [00:02<00:12,  3.42it/s]\n 16%|█▌        | 8/50 [00:02<00:12,  3.46it/s]\n 18%|█▊        | 9/50 [00:02<00:11,  3.49it/s]\n 20%|██        | 10/50 [00:02<00:11,  3.46it/s]\n 22%|██▏       | 11/50 [00:03<00:11,  3.45it/s]\n 24%|██▍       | 12/50 [00:03<00:10,  3.47it/s]\n 26%|██▌       | 13/50 [00:03<00:10,  3.49it/s]\n 28%|██▊       | 14/50 [00:04<00:10,  3.44it/s]\n 30%|███       | 15/50 [00:04<00:10,  3.45it/s]\n 32%|███▏      | 16/50 [00:04<00:09,  3.48it/s]\n 34%|███▍      | 17/50 [00:04<00:09,  3.46it/s]\n 36%|███▌      | 18/50 [00:05<00:09,  3.44it/s]\n 38%|███▊      | 19/50 [00:05<00:08,  3.47it/s]\n 40%|████      | 20/50 [00:05<00:08,  3.48it/s]\n 42%|████▏     | 21/50 [00:06<00:08,  3.45it/s]\n 44%|████▍     | 22/50 [00:06<00:08,  3.46it/s]\n 46%|████▌     | 23/50 [00:06<00:07,  3.48it/s]\n 48%|████▊     | 24/50 [00:06<00:07,  3.46it/s]\n 50%|█████     | 25/50 [00:07<00:07,  3.45it/s]\n 52%|█████▏    | 26/50 [00:07<00:06,  3.46it/s]\n 54%|█████▍    | 27/50 [00:07<00:06,  3.48it/s]\n 56%|█████▌    | 28/50 [00:08<00:06,  3.45it/s]\n 58%|█████▊    | 29/50 [00:08<00:06,  3.45it/s]\n 60%|██████    | 30/50 [00:08<00:05,  3.47it/s]\n 62%|██████▏   | 31/50 [00:08<00:05,  3.47it/s]\n 64%|██████▍   | 32/50 [00:09<00:05,  3.45it/s]\n 66%|██████▌   | 33/50 [00:09<00:04,  3.47it/s]\n 68%|██████▊   | 34/50 [00:09<00:04,  3.47it/s]\n 70%|███████   | 35/50 [00:10<00:04,  3.45it/s]\n 72%|███████▏  | 36/50 [00:10<00:04,  3.45it/s]\n 74%|███████▍  | 37/50 [00:10<00:03,  3.46it/s]\n 76%|███████▌  | 38/50 [00:10<00:03,  3.45it/s]\n 78%|███████▊  | 39/50 [00:11<00:03,  3.44it/s]\n 80%|████████  | 40/50 [00:11<00:02,  3.46it/s]\n 82%|████████▏ | 41/50 [00:11<00:02,  3.45it/s]\n 84%|████████▍ | 42/50 [00:12<00:02,  3.44it/s]\n 86%|████████▌ | 43/50 [00:12<00:02,  3.46it/s]\n 88%|████████▊ | 44/50 [00:12<00:01,  3.47it/s]\n 90%|█████████ | 45/50 [00:12<00:01,  3.46it/s]\n 92%|█████████▏| 46/50 [00:13<00:01,  3.45it/s]\n 94%|█████████▍| 47/50 [00:13<00:00,  3.47it/s]\n 96%|█████████▌| 48/50 [00:13<00:00,  3.48it/s]\n 98%|█████████▊| 49/50 [00:14<00:00,  3.46it/s]\n100%|██████████| 50/50 [00:14<00:00,  3.46it/s]\n100%|██████████| 50/50 [00:14<00:00,  3.46it/s]",
  "metrics": {
    "predict_time": 15.087912,
    "total_time": 31.192451
  },
  "output": [
    "https://replicate.delivery/pbxt/61tAUJMcbx4pPRknVmfxue9MgX24EKSXbfaHwa2O9cfobdIAB/out-0.png"
  ],
  "started_at": "2022-11-21T00:17:47.913907Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/yrgxjif2cbg3bfccbyl5iwfn64",
    "cancel": "https://api.replicate.com/v1/predictions/yrgxjif2cbg3bfccbyl5iwfn64/cancel"
  },
  "version": "452a652f79fb6704c1ac571a7c27ef849940c1893e59ebfbacfeac2fd34d790b"
}

Generated in

15.1 seconds

Tweak itReport

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This output was created using a different version of the model, bfirsh/bfirshbooth:452a652f.

Examples

View more examples

Run time and cost

This model costs approximately $0.013 to run on Replicate, or 76 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 56 seconds. The predict time for this model varies significantly based on the inputs.

Readme

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Model description

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Intended use

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Ethical considerations

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Caveats and recommendations

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