Prediction

Model

bfirsh/bfirshbooth:452a652f

yrgxjif2cbg3bfccbyl5iwfn64

Status

Succeeded

Source

Web

Hardware

–

Total duration

31.2s

Created

about 3 years ago by bfirsh

Webhook

–

Input

prompt: painting of bfirsh in the style of andy warhol
width: 512
height: 512
num_outputs: 1
num_inference_steps: 50
guidance_scale: 7.5

{
  "guidance_scale": 7.5,
  "height": 512,
  "num_inference_steps": 50,
  "num_outputs": 1,
  "prompt": "painting of bfirsh in the style of andy warhol",
  "width": 512
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_ZRw**********************************

This is your API token. Keep it to yourself.

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

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:452a652f79fb6704c1ac571a7c27ef849940c1893e59ebfbacfeac2fd34d790b",
  {
    input: {
      guidance_scale: 7.5,
      height: 512,
      num_inference_steps: 50,
      num_outputs: 1,
      prompt: "painting of bfirsh in the style of andy warhol",
      width: 512
    }
  }
);

// 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=r8_ZRw**********************************

This is your API token. Keep it to yourself.

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:452a652f79fb6704c1ac571a7c27ef849940c1893e59ebfbacfeac2fd34d790b",
    input={
        "guidance_scale": 7.5,
        "height": 512,
        "num_inference_steps": 50,
        "num_outputs": 1,
        "prompt": "painting of bfirsh in the style of andy warhol",
        "width": 512
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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=r8_ZRw**********************************

This is your API token. Keep it to yourself.

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": "bfirsh/bfirshbooth:452a652f79fb6704c1ac571a7c27ef849940c1893e59ebfbacfeac2fd34d790b",
    "input": {
      "guidance_scale": 7.5,
      "height": 512,
      "num_inference_steps": 50,
      "num_outputs": 1,
      "prompt": "painting of bfirsh in the style of andy warhol",
      "width": 512
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "yrgxjif2cbg3bfccbyl5iwfn64",
  "model": "bfirsh/bfirshbooth",
  "version": "452a652f79fb6704c1ac571a7c27ef849940c1893e59ebfbacfeac2fd34d790b",
  "input": {
    "guidance_scale": 7.5,
    "height": 512,
    "num_inference_steps": 50,
    "num_outputs": 1,
    "prompt": "painting of bfirsh in the style of andy warhol",
    "width": 512
  },
  "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]",
  "output": [
    "https://replicate.delivery/pbxt/61tAUJMcbx4pPRknVmfxue9MgX24EKSXbfaHwa2O9cfobdIAB/out-0.png"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2022-11-21T00:17:31.809368Z",
  "started_at": "2022-11-21T00:17:47.913907Z",
  "completed_at": "2022-11-21T00:18:03.001819Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/yrgxjif2cbg3bfccbyl5iwfn64/cancel",
    "get": "https://api.replicate.com/v1/predictions/yrgxjif2cbg3bfccbyl5iwfn64"
  },
  "metrics": {
    "predict_time": 15.087912,
    "total_time": 31.192451
  }
}

Generated in

15.1 seconds

Tweak it Report

Loading pipeline... Using seed: 52078 Global seed set to 52078 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.62it/s] 4%|▍ | 2/50 [00:00<00:13, 3.58it/s] 6%|▌ | 3/50 [00:00<00:13, 3.56it/s] 8%|▊ | 4/50 [00:01<00:13, 3.43it/s] 10%|█ | 5/50 [00:01<00:12, 3.48it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.50it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.42it/s] 16%|█▌ | 8/50 [00:02<00:12, 3.46it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.49it/s] 20%|██ | 10/50 [00:02<00:11, 3.46it/s] 22%|██▏ | 11/50 [00:03<00:11, 3.45it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.47it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.49it/s] 28%|██▊ | 14/50 [00:04<00:10, 3.44it/s] 30%|███ | 15/50 [00:04<00:10, 3.45it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.48it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.46it/s] 36%|███▌ | 18/50 [00:05<00:09, 3.44it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.47it/s] 40%|████ | 20/50 [00:05<00:08, 3.48it/s] 42%|████▏ | 21/50 [00:06<00:08, 3.45it/s] 44%|████▍ | 22/50 [00:06<00:08, 3.46it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.48it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.46it/s] 50%|█████ | 25/50 [00:07<00:07, 3.45it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.46it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.48it/s] 56%|█████▌ | 28/50 [00:08<00:06, 3.45it/s] 58%|█████▊ | 29/50 [00:08<00:06, 3.45it/s] 60%|██████ | 30/50 [00:08<00:05, 3.47it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.47it/s] 64%|██████▍ | 32/50 [00:09<00:05, 3.45it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.47it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.47it/s] 70%|███████ | 35/50 [00:10<00:04, 3.45it/s] 72%|███████▏ | 36/50 [00:10<00:04, 3.45it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.46it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.45it/s] 78%|███████▊ | 39/50 [00:11<00:03, 3.44it/s] 80%|████████ | 40/50 [00:11<00:02, 3.46it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.45it/s] 84%|████████▍ | 42/50 [00:12<00:02, 3.44it/s] 86%|████████▌ | 43/50 [00:12<00:02, 3.46it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.47it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.46it/s] 92%|█████████▏| 46/50 [00:13<00:01, 3.45it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.47it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.48it/s] 98%|█████████▊| 49/50 [00:14<00:00, 3.46it/s] 100%|██████████| 50/50 [00:14<00:00, 3.46it/s] 100%|██████████| 50/50 [00:14<00:00, 3.46it/s]