paappraiser / looneytunes

{
  "width": 1024,
  "height": 1024,
  "prompt": "a photo TOK , a simple portrate ",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.6,
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "paappraiser/looneytunes:8bb9ba7b0dc67e6573280705ac3f106955a36099d2e1511f633e75e738d0768e",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "a photo TOK , a simple portrate ",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      prompt_strength: 0.8,
      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

Import the client:

import replicate

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

output = replicate.run(
    "paappraiser/looneytunes:8bb9ba7b0dc67e6573280705ac3f106955a36099d2e1511f633e75e738d0768e",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "a photo TOK , a simple portrate ",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)
print(output)

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

Run paappraiser/looneytunes 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": "paappraiser/looneytunes:8bb9ba7b0dc67e6573280705ac3f106955a36099d2e1511f633e75e738d0768e",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "a photo TOK , a simple portrate ",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "prompt_strength": 0.8,
      "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": "2023-09-06T00:15:30.179503Z",
  "created_at": "2023-09-06T00:14:12.811749Z",
  "data_removed": false,
  "error": null,
  "id": "yguhcn3bede55m36p4duvmlf7u",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "a photo TOK , a simple portrate ",
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "lora_scale": 0.6,
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "apply_watermark": true,
    "high_noise_frac": 0.8,
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 25800\nPrompt: a photo <s0><s1> , a simple portrate\ntxt2img mode\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:38,  1.28it/s]\n  4%|▍         | 2/50 [00:01<00:23,  2.08it/s]\n  6%|▌         | 3/50 [00:01<00:18,  2.60it/s]\n  8%|▊         | 4/50 [00:01<00:15,  2.95it/s]\n 10%|█         | 5/50 [00:01<00:14,  3.18it/s]\n 12%|█▏        | 6/50 [00:02<00:13,  3.34it/s]\n 14%|█▍        | 7/50 [00:02<00:12,  3.45it/s]\n 16%|█▌        | 8/50 [00:02<00:11,  3.52it/s]\n 18%|█▊        | 9/50 [00:02<00:11,  3.58it/s]\n 20%|██        | 10/50 [00:03<00:11,  3.61it/s]\n 22%|██▏       | 11/50 [00:03<00:10,  3.64it/s]\n 24%|██▍       | 12/50 [00:03<00:10,  3.65it/s]\n 26%|██▌       | 13/50 [00:04<00:10,  3.66it/s]\n 28%|██▊       | 14/50 [00:04<00:09,  3.67it/s]\n 30%|███       | 15/50 [00:04<00:09,  3.68it/s]\n 32%|███▏      | 16/50 [00:04<00:09,  3.68it/s]\n 34%|███▍      | 17/50 [00:05<00:08,  3.68it/s]\n 36%|███▌      | 18/50 [00:05<00:08,  3.69it/s]\n 38%|███▊      | 19/50 [00:05<00:08,  3.68it/s]\n 40%|████      | 20/50 [00:05<00:08,  3.68it/s]\n 42%|████▏     | 21/50 [00:06<00:07,  3.68it/s]\n 44%|████▍     | 22/50 [00:06<00:07,  3.68it/s]\n 46%|████▌     | 23/50 [00:06<00:07,  3.68it/s]\n 48%|████▊     | 24/50 [00:07<00:07,  3.68it/s]\n 50%|█████     | 25/50 [00:07<00:06,  3.68it/s]\n 52%|█████▏    | 26/50 [00:07<00:06,  3.68it/s]\n 54%|█████▍    | 27/50 [00:07<00:06,  3.68it/s]\n 56%|█████▌    | 28/50 [00:08<00:05,  3.68it/s]\n 58%|█████▊    | 29/50 [00:08<00:05,  3.68it/s]\n 60%|██████    | 30/50 [00:08<00:05,  3.68it/s]\n 62%|██████▏   | 31/50 [00:08<00:05,  3.68it/s]\n 64%|██████▍   | 32/50 [00:09<00:04,  3.67it/s]\n 66%|██████▌   | 33/50 [00:09<00:04,  3.68it/s]\n 68%|██████▊   | 34/50 [00:09<00:04,  3.68it/s]\n 70%|███████   | 35/50 [00:10<00:04,  3.67it/s]\n 72%|███████▏  | 36/50 [00:10<00:03,  3.67it/s]\n 74%|███████▍  | 37/50 [00:10<00:03,  3.68it/s]\n 76%|███████▌  | 38/50 [00:10<00:03,  3.68it/s]\n 78%|███████▊  | 39/50 [00:11<00:02,  3.68it/s]\n 80%|████████  | 40/50 [00:11<00:02,  3.68it/s]\n 82%|████████▏ | 41/50 [00:11<00:02,  3.68it/s]\n 84%|████████▍ | 42/50 [00:11<00:02,  3.67it/s]\n 86%|████████▌ | 43/50 [00:12<00:01,  3.67it/s]\n 88%|████████▊ | 44/50 [00:12<00:01,  3.67it/s]\n 90%|█████████ | 45/50 [00:12<00:01,  3.67it/s]\n 92%|█████████▏| 46/50 [00:12<00:01,  3.67it/s]\n 94%|█████████▍| 47/50 [00:13<00:00,  3.67it/s]\n 96%|█████████▌| 48/50 [00:13<00:00,  3.68it/s]\n 98%|█████████▊| 49/50 [00:13<00:00,  3.68it/s]\n100%|██████████| 50/50 [00:14<00:00,  3.69it/s]\n100%|██████████| 50/50 [00:14<00:00,  3.55it/s]",
  "metrics": {
    "predict_time": 16.924537,
    "total_time": 77.367754
  },
  "output": [
    "https://replicate.delivery/pbxt/Hf9ORnEquEzaZSWH0i8NFQd3FTH0V5RspHOnvfdTzSXhaXhRA/out-0.png"
  ],
  "started_at": "2023-09-06T00:15:13.254966Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/yguhcn3bede55m36p4duvmlf7u",
    "cancel": "https://api.replicate.com/v1/predictions/yguhcn3bede55m36p4duvmlf7u/cancel"
  },
  "version": "8bb9ba7b0dc67e6573280705ac3f106955a36099d2e1511f633e75e738d0768e"
}

Generated in

16.9 seconds

Tweak it Share Report

Using seed: 25800
Prompt: a photo <s0><s1> , a simple portrate
txt2img mode
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