Prediction

Model

hilongjw/sdxl-page:b8620bc9

qbcnqhlbizouqnc2k3bnpkxqjm

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

17.6s

Created

about 2 years ago

Webhook

–

Input

prompt: landing page screenshot of Nike
negative_prompt: Empty string
width: 512
height: 2048
num_outputs: 1
scheduler: K_EULER
num_inference_steps: 50
guidance_scale: 7.5
prompt_strength: 0.8
refine: no_refiner
high_noise_frac: 0.8
apply_watermark: true
lora_scale: 0.6

{
  "apply_watermark": true,
  "guidance_scale": 7.5,
  "height": 2048,
  "high_noise_frac": 0.8,
  "lora_scale": 0.6,
  "negative_prompt": "",
  "num_inference_steps": 50,
  "num_outputs": 1,
  "prompt": "landing page screenshot of Nike",
  "prompt_strength": 0.8,
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "width": 512
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_TQU**********************************

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

const output = await replicate.run(
  "hilongjw/sdxl-page:b8620bc9e357fa6bea42da19874b8fb97546bdaaef29c36fd8ce278848e478d9",
  {
    input: {
      apply_watermark: true,
      guidance_scale: 7.5,
      height: 2048,
      high_noise_frac: 0.8,
      lora_scale: 0.6,
      negative_prompt: "",
      num_inference_steps: 50,
      num_outputs: 1,
      prompt: "landing page screenshot of Nike",
      prompt_strength: 0.8,
      refine: "no_refiner",
      scheduler: "K_EULER",
      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_TQU**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run hilongjw/sdxl-page using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "hilongjw/sdxl-page:b8620bc9e357fa6bea42da19874b8fb97546bdaaef29c36fd8ce278848e478d9",
    input={
        "apply_watermark": True,
        "guidance_scale": 7.5,
        "height": 2048,
        "high_noise_frac": 0.8,
        "lora_scale": 0.6,
        "negative_prompt": "",
        "num_inference_steps": 50,
        "num_outputs": 1,
        "prompt": "landing page screenshot of Nike",
        "prompt_strength": 0.8,
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "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_TQU**********************************

This is your API token. Keep it to yourself.

Run hilongjw/sdxl-page 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": "hilongjw/sdxl-page:b8620bc9e357fa6bea42da19874b8fb97546bdaaef29c36fd8ce278848e478d9",
    "input": {
      "apply_watermark": true,
      "guidance_scale": 7.5,
      "height": 2048,
      "high_noise_frac": 0.8,
      "lora_scale": 0.6,
      "negative_prompt": "",
      "num_inference_steps": 50,
      "num_outputs": 1,
      "prompt": "landing page screenshot of Nike",
      "prompt_strength": 0.8,
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "width": 512
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "qbcnqhlbizouqnc2k3bnpkxqjm",
  "model": "hilongjw/sdxl-page",
  "version": "b8620bc9e357fa6bea42da19874b8fb97546bdaaef29c36fd8ce278848e478d9",
  "input": {
    "apply_watermark": true,
    "guidance_scale": 7.5,
    "height": 2048,
    "high_noise_frac": 0.8,
    "lora_scale": 0.6,
    "negative_prompt": "",
    "num_inference_steps": 50,
    "num_outputs": 1,
    "prompt": "landing page screenshot of Nike",
    "prompt_strength": 0.8,
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "width": 512
  },
  "logs": "Using seed: 48149\nEnsuring enough disk space...\nFree disk space: 2244497874944\nDownloading weights: https://replicate.delivery/pbxt/R5qB7lFtLNZxFJUFkIKX9ZDyjO6ejRA1vGE9hVffXbHkb1eHB/trained_model.tar\nb'Downloaded 186 MB bytes in 0.241s (773 MB/s)\\nExtracted 186 MB in 0.068s (2.7 GB/s)\\n'\nDownloaded weights in 0.456148624420166 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: landing page screenshot of Nike\ntxt2img mode\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:13,  3.51it/s]\n  4%|▍         | 2/50 [00:00<00:13,  3.62it/s]\n  6%|▌         | 3/50 [00:00<00:12,  3.66it/s]\n  8%|▊         | 4/50 [00:01<00:12,  3.68it/s]\n 10%|█         | 5/50 [00:01<00:12,  3.69it/s]\n 12%|█▏        | 6/50 [00:01<00:11,  3.70it/s]\n 14%|█▍        | 7/50 [00:01<00:11,  3.71it/s]\n 16%|█▌        | 8/50 [00:02<00:11,  3.71it/s]\n 18%|█▊        | 9/50 [00:02<00:11,  3.71it/s]\n 20%|██        | 10/50 [00:02<00:10,  3.71it/s]\n 22%|██▏       | 11/50 [00:02<00:10,  3.71it/s]\n 24%|██▍       | 12/50 [00:03<00:10,  3.71it/s]\n 26%|██▌       | 13/50 [00:03<00:09,  3.71it/s]\n 28%|██▊       | 14/50 [00:03<00:09,  3.71it/s]\n 30%|███       | 15/50 [00:04<00:09,  3.71it/s]\n 32%|███▏      | 16/50 [00:04<00:09,  3.71it/s]\n 34%|███▍      | 17/50 [00:04<00:08,  3.71it/s]\n 36%|███▌      | 18/50 [00:04<00:08,  3.71it/s]\n 38%|███▊      | 19/50 [00:05<00:08,  3.71it/s]\n 40%|████      | 20/50 [00:05<00:08,  3.71it/s]\n 42%|████▏     | 21/50 [00:05<00:07,  3.71it/s]\n 44%|████▍     | 22/50 [00:05<00:07,  3.69it/s]\n 46%|████▌     | 23/50 [00:06<00:07,  3.70it/s]\n 48%|████▊     | 24/50 [00:06<00:07,  3.70it/s]\n 50%|█████     | 25/50 [00:06<00:06,  3.70it/s]\n 52%|█████▏    | 26/50 [00:07<00:06,  3.70it/s]\n 54%|█████▍    | 27/50 [00:07<00:06,  3.71it/s]\n 56%|█████▌    | 28/50 [00:07<00:05,  3.70it/s]\n 58%|█████▊    | 29/50 [00:07<00:05,  3.70it/s]\n 60%|██████    | 30/50 [00:08<00:05,  3.70it/s]\n 62%|██████▏   | 31/50 [00:08<00:05,  3.70it/s]\n 64%|██████▍   | 32/50 [00:08<00:04,  3.70it/s]\n 66%|██████▌   | 33/50 [00:08<00:04,  3.70it/s]\n 68%|██████▊   | 34/50 [00:09<00:04,  3.70it/s]\n 70%|███████   | 35/50 [00:09<00:04,  3.70it/s]\n 72%|███████▏  | 36/50 [00:09<00:03,  3.70it/s]\n 74%|███████▍  | 37/50 [00:09<00:03,  3.70it/s]\n 76%|███████▌  | 38/50 [00:10<00:03,  3.70it/s]\n 78%|███████▊  | 39/50 [00:10<00:02,  3.70it/s]\n 80%|████████  | 40/50 [00:10<00:02,  3.69it/s]\n 82%|████████▏ | 41/50 [00:11<00:02,  3.69it/s]\n 84%|████████▍ | 42/50 [00:11<00:02,  3.69it/s]\n 86%|████████▌ | 43/50 [00:11<00:01,  3.69it/s]\n 88%|████████▊ | 44/50 [00:11<00:01,  3.69it/s]\n 90%|█████████ | 45/50 [00:12<00:01,  3.69it/s]\n 92%|█████████▏| 46/50 [00:12<00:01,  3.69it/s]\n 94%|█████████▍| 47/50 [00:12<00:00,  3.69it/s]\n 96%|█████████▌| 48/50 [00:12<00:00,  3.69it/s]\n 98%|█████████▊| 49/50 [00:13<00:00,  3.69it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.69it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.70it/s]",
  "output": [
    "https://replicate.delivery/pbxt/Qqafum31j4RlAiBRyQYuaq4rzQpArbeiblHlKmEWXwpp0bfjA/out-0.png"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2023-12-06T05:48:07.856962Z",
  "started_at": "2023-12-06T05:48:09.640047Z",
  "completed_at": "2023-12-06T05:48:25.485564Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/qbcnqhlbizouqnc2k3bnpkxqjm/cancel",
    "get": "https://api.replicate.com/v1/predictions/qbcnqhlbizouqnc2k3bnpkxqjm"
  },
  "metrics": {
    "predict_time": 15.845517,
    "total_time": 17.628602
  }
}

Generated in

15.8 seconds

Tweak it Report

Using seed: 48149 Ensuring enough disk space... Free disk space: 2244497874944 Downloading weights: https://replicate.delivery/pbxt/R5qB7lFtLNZxFJUFkIKX9ZDyjO6ejRA1vGE9hVffXbHkb1eHB/trained_model.tar b'Downloaded 186 MB bytes in 0.241s (773 MB/s)\nExtracted 186 MB in 0.068s (2.7 GB/s)\n' Downloaded weights in 0.456148624420166 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: landing page screenshot of Nike txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.51it/s] 4%|▍ | 2/50 [00:00<00:13, 3.62it/s] 6%|▌ | 3/50 [00:00<00:12, 3.66it/s] 8%|▊ | 4/50 [00:01<00:12, 3.68it/s] 10%|█ | 5/50 [00:01<00:12, 3.69it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.70it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.71it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.71it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.71it/s] 20%|██ | 10/50 [00:02<00:10, 3.71it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.71it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.71it/s] 26%|██▌ | 13/50 [00:03<00:09, 3.71it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.71it/s] 30%|███ | 15/50 [00:04<00:09, 3.71it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.71it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.71it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.71it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.71it/s] 40%|████ | 20/50 [00:05<00:08, 3.71it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.71it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.69it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.70it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.70it/s] 50%|█████ | 25/50 [00:06<00:06, 3.70it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.70it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.71it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.70it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.70it/s] 60%|██████ | 30/50 [00:08<00:05, 3.70it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.70it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.70it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.70it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.70it/s] 70%|███████ | 35/50 [00:09<00:04, 3.70it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.70it/s] 74%|███████▍ | 37/50 [00:09<00:03, 3.70it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.70it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.70it/s] 80%|████████ | 40/50 [00:10<00:02, 3.69it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.69it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.69it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.69it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.69it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.69it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.69it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.69it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.69it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.69it/s] 100%|██████████| 50/50 [00:13<00:00, 3.69it/s] 100%|██████████| 50/50 [00:13<00:00, 3.70it/s]