width: 768
height: 768
prompt: a portrait painting of a cat wearing a tuxedo and top hat in the style of TOK
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.6
num_outputs: 1
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt
prompt_strength: 0.8
num_inference_steps: 20

{
  "width": 768,
  "height": 768,
  "prompt": "a portrait painting of a cat wearing a tuxedo and top hat in the style of TOK",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.6,
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "apply_watermark": false,
  "high_noise_frac": 0.8,
  "negative_prompt": "",
  "prompt_strength": 0.8,
  "num_inference_steps": 20
}

Install Replicate’s Node.js client library:

npm install replicate

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 brinnaebent/sdxl-lora-monet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "brinnaebent/sdxl-lora-monet:7f5d28ab8ed4de56770de0402ac91f1d6ffe3caff39c4b979ed3ed0779a452ed",
  {
    input: {
      width: 768,
      height: 768,
      prompt: "a portrait painting of a cat wearing a tuxedo and top hat in the style of TOK",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: false,
      high_noise_frac: 0.8,
      negative_prompt: "",
      prompt_strength: 0.8,
      num_inference_steps: 20
    }
  }
);

// 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 brinnaebent/sdxl-lora-monet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "brinnaebent/sdxl-lora-monet:7f5d28ab8ed4de56770de0402ac91f1d6ffe3caff39c4b979ed3ed0779a452ed",
    input={
        "width": 768,
        "height": 768,
        "prompt": "a portrait painting of a cat wearing a tuxedo and top hat in the style of TOK",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": False,
        "high_noise_frac": 0.8,
        "negative_prompt": "",
        "prompt_strength": 0.8,
        "num_inference_steps": 20
    }
)

# 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.

Run brinnaebent/sdxl-lora-monet 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": "brinnaebent/sdxl-lora-monet:7f5d28ab8ed4de56770de0402ac91f1d6ffe3caff39c4b979ed3ed0779a452ed",
    "input": {
      "width": 768,
      "height": 768,
      "prompt": "a portrait painting of a cat wearing a tuxedo and top hat in the style of TOK",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": false,
      "high_noise_frac": 0.8,
      "negative_prompt": "",
      "prompt_strength": 0.8,
      "num_inference_steps": 20
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-03-14T13:11:29.534464Z",
  "created_at": "2024-03-14T13:11:10.388947Z",
  "data_removed": false,
  "error": null,
  "id": "qm6dqe3b3d5zqas4wqikkfusba",
  "input": {
    "width": 768,
    "height": 768,
    "prompt": "a portrait painting of a cat wearing a tuxedo and top hat in the style of TOK",
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "lora_scale": 0.6,
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "apply_watermark": false,
    "high_noise_frac": 0.8,
    "negative_prompt": "",
    "prompt_strength": 0.8,
    "num_inference_steps": 20
  },
  "logs": "Using seed: 30968\nEnsuring enough disk space...\nFree disk space: 2303544709120\nDownloading weights: https://replicate.delivery/pbxt/3EPC6UfisfjkNUVOovT6pQpi1Dj2QyesyrM07bjJXl5KBVAlA/trained_model.tar\n2024-03-14T13:11:18Z | INFO  | [ Initiating ] dest=/src/weights-cache/9d9ab83b7d6d6b38 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/3EPC6UfisfjkNUVOovT6pQpi1Dj2QyesyrM07bjJXl5KBVAlA/trained_model.tar\n2024-03-14T13:11:23Z | INFO  | [ Complete ] dest=/src/weights-cache/9d9ab83b7d6d6b38 size=\"186 MB\" total_elapsed=5.540s url=https://replicate.delivery/pbxt/3EPC6UfisfjkNUVOovT6pQpi1Dj2QyesyrM07bjJXl5KBVAlA/trained_model.tar\nb''\nDownloaded weights in 5.741840600967407 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a portrait painting of a cat wearing a tuxedo and top hat in the style of <s0><s1>\ntxt2img mode\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:00<00:03,  6.19it/s]\n 10%|█         | 2/20 [00:00<00:02,  6.14it/s]\n 15%|█▌        | 3/20 [00:00<00:02,  6.12it/s]\n 20%|██        | 4/20 [00:00<00:02,  6.11it/s]\n 25%|██▌       | 5/20 [00:00<00:02,  6.10it/s]\n 30%|███       | 6/20 [00:00<00:02,  6.09it/s]\n 35%|███▌      | 7/20 [00:01<00:02,  6.08it/s]\n 40%|████      | 8/20 [00:01<00:01,  6.09it/s]\n 45%|████▌     | 9/20 [00:01<00:01,  6.09it/s]\n 50%|█████     | 10/20 [00:01<00:01,  6.09it/s]\n 55%|█████▌    | 11/20 [00:01<00:01,  6.08it/s]\n 60%|██████    | 12/20 [00:01<00:01,  6.08it/s]\n 65%|██████▌   | 13/20 [00:02<00:01,  6.08it/s]\n 70%|███████   | 14/20 [00:02<00:00,  6.08it/s]\n 75%|███████▌  | 15/20 [00:02<00:00,  6.07it/s]\n 80%|████████  | 16/20 [00:02<00:00,  6.07it/s]\n 85%|████████▌ | 17/20 [00:02<00:00,  6.07it/s]\n 90%|█████████ | 18/20 [00:02<00:00,  6.07it/s]\n 95%|█████████▌| 19/20 [00:03<00:00,  6.07it/s]\n100%|██████████| 20/20 [00:03<00:00,  6.07it/s]\n100%|██████████| 20/20 [00:03<00:00,  6.08it/s]",
  "metrics": {
    "predict_time": 11.54859,
    "total_time": 19.145517
  },
  "output": [
    "https://replicate.delivery/pbxt/DfEb9F3hpnSLPSfynAbDPscCqEtAh4nNeP4LoNay9PMBMVAlA/out-0.png"
  ],
  "started_at": "2024-03-14T13:11:17.985874Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/qm6dqe3b3d5zqas4wqikkfusba",
    "cancel": "https://api.replicate.com/v1/predictions/qm6dqe3b3d5zqas4wqikkfusba/cancel"
  },
  "version": "7f5d28ab8ed4de56770de0402ac91f1d6ffe3caff39c4b979ed3ed0779a452ed"
}

Generated in

11.6 seconds

Tweak it Iterate in playground ShareReport

Using seed: 30968
Ensuring enough disk space...
Free disk space: 2303544709120
Downloading weights: https://replicate.delivery/pbxt/3EPC6UfisfjkNUVOovT6pQpi1Dj2QyesyrM07bjJXl5KBVAlA/trained_model.tar
2024-03-14T13:11:18Z | INFO  | [ Initiating ] dest=/src/weights-cache/9d9ab83b7d6d6b38 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/3EPC6UfisfjkNUVOovT6pQpi1Dj2QyesyrM07bjJXl5KBVAlA/trained_model.tar
2024-03-14T13:11:23Z | INFO  | [ Complete ] dest=/src/weights-cache/9d9ab83b7d6d6b38 size="186 MB" total_elapsed=5.540s url=https://replicate.delivery/pbxt/3EPC6UfisfjkNUVOovT6pQpi1Dj2QyesyrM07bjJXl5KBVAlA/trained_model.tar
b''
Downloaded weights in 5.741840600967407 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: a portrait painting of a cat wearing a tuxedo and top hat in the style of <s0><s1>
txt2img mode
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