defaultAn astronaut riding a rainbow unicorn
typetext
{
"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
}npm install replicate
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_TQU**********************************
This is your API token. Keep it to yourself.
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.
pip install replicate
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_TQU**********************************
This is your API token. Keep it to yourself.
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.
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.
{
"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
}
}