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lightweight-ai / test_sk2ig
(Updated 2 months ago)
- Public
- 56 runs
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=lightweight-ai/test_sk2ig
or set up a project from scratch
Install Replicate’s Node.js client library:
npm install replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
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 lightweight-ai/test_sk2ig using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"lightweight-ai/test_sk2ig:0ef99511f3836ec50b98c33661d1cf495bc3e38c3635011ef2ad4310245b967d",
{
input: {
seed: 0,
image: "https://replicate.delivery/pbxt/Mq4t16RmMjs2zkyD0y2gFL7OZBlFvcjSRmoi2HqTyKfJlrzj/aa96dadef832b0af3ecbefea17fc8acc%20%28Copy%29.jpg",
prompt: "rose",
style_name: "3D Model",
hed_enabled: true,
canny_enabled: false,
guidance_scale: 5,
negative_prompt: "ugly, blurry, deformed",
num_inference_steps: 25,
controlnet_conditioning_scale: 0.5
}
}
);
// 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 replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:
import replicateRun lightweight-ai/test_sk2ig using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"lightweight-ai/test_sk2ig:0ef99511f3836ec50b98c33661d1cf495bc3e38c3635011ef2ad4310245b967d",
input={
"seed": 0,
"image": "https://replicate.delivery/pbxt/Mq4t16RmMjs2zkyD0y2gFL7OZBlFvcjSRmoi2HqTyKfJlrzj/aa96dadef832b0af3ecbefea17fc8acc%20%28Copy%29.jpg",
"prompt": "rose",
"style_name": "3D Model",
"hed_enabled": True,
"canny_enabled": False,
"guidance_scale": 5,
"negative_prompt": "ugly, blurry, deformed",
"num_inference_steps": 25,
"controlnet_conditioning_scale": 0.5
}
)
print(output)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=<paste-your-token-here>Find your API token in your account settings.
Run lightweight-ai/test_sk2ig 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": "lightweight-ai/test_sk2ig:0ef99511f3836ec50b98c33661d1cf495bc3e38c3635011ef2ad4310245b967d",
"input": {
"seed": 0,
"image": "https://replicate.delivery/pbxt/Mq4t16RmMjs2zkyD0y2gFL7OZBlFvcjSRmoi2HqTyKfJlrzj/aa96dadef832b0af3ecbefea17fc8acc%20%28Copy%29.jpg",
"prompt": "rose",
"style_name": "3D Model",
"hed_enabled": true,
"canny_enabled": false,
"guidance_scale": 5,
"negative_prompt": "ugly, blurry, deformed",
"num_inference_steps": 25,
"controlnet_conditioning_scale": 0.5
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:
brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/lightweight-ai/test_sk2ig@sha256:0ef99511f3836ec50b98c33661d1cf495bc3e38c3635011ef2ad4310245b967d \
-i 'seed=0' \
-i 'image="https://replicate.delivery/pbxt/Mq4t16RmMjs2zkyD0y2gFL7OZBlFvcjSRmoi2HqTyKfJlrzj/aa96dadef832b0af3ecbefea17fc8acc%20%28Copy%29.jpg"' \
-i 'prompt="rose"' \
-i 'style_name="3D Model"' \
-i 'hed_enabled=true' \
-i 'canny_enabled=false' \
-i 'guidance_scale=5' \
-i 'negative_prompt="ugly, blurry, deformed"' \
-i 'num_inference_steps=25' \
-i 'controlnet_conditioning_scale=0.5'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/lightweight-ai/test_sk2ig@sha256:0ef99511f3836ec50b98c33661d1cf495bc3e38c3635011ef2ad4310245b967dcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 0, "image": "https://replicate.delivery/pbxt/Mq4t16RmMjs2zkyD0y2gFL7OZBlFvcjSRmoi2HqTyKfJlrzj/aa96dadef832b0af3ecbefea17fc8acc%20%28Copy%29.jpg", "prompt": "rose", "style_name": "3D Model", "hed_enabled": true, "canny_enabled": false, "guidance_scale": 5, "negative_prompt": "ugly, blurry, deformed", "num_inference_steps": 25, "controlnet_conditioning_scale": 0.5 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Add a payment method to run this model.
Sign in with GitHub
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terms of service and privacy policy
Output
{
"completed_at": "2025-04-15T05:58:03.493255Z",
"created_at": "2025-04-15T05:55:29.084000Z",
"data_removed": false,
"error": null,
"id": "dhz6sztgqhrm80cp7028jy2ahg",
"input": {
"seed": 0,
"image": "https://replicate.delivery/pbxt/Mq4t16RmMjs2zkyD0y2gFL7OZBlFvcjSRmoi2HqTyKfJlrzj/aa96dadef832b0af3ecbefea17fc8acc%20%28Copy%29.jpg",
"prompt": "rose",
"style_name": "3D Model",
"hed_enabled": true,
"canny_enabled": false,
"guidance_scale": 5,
"negative_prompt": "ugly, blurry, deformed",
"num_inference_steps": 25,
"controlnet_conditioning_scale": 0.5
},
"logs": "0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:07, 3.11it/s]\n 8%|▊ | 2/25 [00:00<00:04, 5.02it/s]\n 12%|█▏ | 3/25 [00:00<00:04, 5.33it/s]\n 16%|█▌ | 4/25 [00:00<00:03, 5.48it/s]\n 20%|██ | 5/25 [00:00<00:03, 5.57it/s]\n 24%|██▍ | 6/25 [00:01<00:03, 5.62it/s]\n 28%|██▊ | 7/25 [00:01<00:03, 5.65it/s]\n 32%|███▏ | 8/25 [00:01<00:02, 5.68it/s]\n 36%|███▌ | 9/25 [00:01<00:02, 5.70it/s]\n 40%|████ | 10/25 [00:01<00:02, 5.70it/s]\n 44%|████▍ | 11/25 [00:02<00:02, 5.71it/s]\n 48%|████▊ | 12/25 [00:02<00:02, 5.71it/s]\n 52%|█████▏ | 13/25 [00:02<00:02, 5.71it/s]\n 56%|█████▌ | 14/25 [00:02<00:01, 5.71it/s]\n 60%|██████ | 15/25 [00:02<00:01, 5.71it/s]\n 64%|██████▍ | 16/25 [00:02<00:01, 5.70it/s]\n 68%|██████▊ | 17/25 [00:03<00:01, 5.70it/s]\n 72%|███████▏ | 18/25 [00:03<00:01, 5.70it/s]\n 76%|███████▌ | 19/25 [00:03<00:01, 5.70it/s]\n 80%|████████ | 20/25 [00:03<00:00, 5.70it/s]\n 84%|████████▍ | 21/25 [00:03<00:00, 5.70it/s]\n 88%|████████▊ | 22/25 [00:03<00:00, 5.70it/s]\n 92%|█████████▏| 23/25 [00:04<00:00, 5.70it/s]\n 96%|█████████▌| 24/25 [00:04<00:00, 5.69it/s]\n100%|██████████| 25/25 [00:04<00:00, 5.68it/s]\n100%|██████████| 25/25 [00:04<00:00, 5.60it/s]\n출력 이미지가 /tmp/outpaint_output.png에 저장되었습니다.",
"metrics": {
"predict_time": 6.049118312,
"total_time": 154.409255
},
"output": [
"https://replicate.delivery/xezq/QeAp5DKcSOU8QCJnecgXzCGzhca0hoW8mikGcYWcJ98rd6iUA/outpaint_output.png"
],
"started_at": "2025-04-15T05:57:57.444137Z",
"status": "succeeded",
"urls": {
"stream": "https://stream.replicate.com/v1/files/bcwr-t7f6wty2uetcmotro54xyu55ab23z7au6mxgpv2levkqjf3od7ra",
"get": "https://api.replicate.com/v1/predictions/dhz6sztgqhrm80cp7028jy2ahg",
"cancel": "https://api.replicate.com/v1/predictions/dhz6sztgqhrm80cp7028jy2ahg/cancel"
},
"version": "0ef99511f3836ec50b98c33661d1cf495bc3e38c3635011ef2ad4310245b967d"
}Generated in
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출력 이미지가 /tmp/outpaint_output.png에 저장되었습니다.Examples
Run time and cost
This model runs on Nvidia L40S GPU hardware. We don't yet have enough runs of this model to provide performance information.
