typefile
{
"apply_cc_preset": false,
"controlnet_conditioning_scale": 0.6,
"guidance_scale": 5,
"input_image": "https://replicate.delivery/pbxt/MgvTlU1iEWwXmQmO07LHRhkfDgdBe6uqXD6eSf5bryynQ5tO/cat-test.jpeg",
"num_inference_steps": 28,
"prompt": "",
"upscale_factor": 4
}npm install replicate
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_SIK**********************************
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 d3vshoaib/andro-upscaler using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"d3vshoaib/andro-upscaler:abd1b69ca014d61acaeec7a25c8589d48e19852a189fa2d52b275f447882d39b",
{
input: {
apply_cc_preset: false,
controlnet_conditioning_scale: 0.6,
guidance_scale: 5,
input_image: "https://replicate.delivery/pbxt/MgvTlU1iEWwXmQmO07LHRhkfDgdBe6uqXD6eSf5bryynQ5tO/cat-test.jpeg",
num_inference_steps: 28,
prompt: "",
upscale_factor: 4
}
}
);
// To access the file URL:
console.log(output.url()); //=> "http://example.com"
// To write the file to disk:
fs.writeFile("my-image.png", output);
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_SIK**********************************
This is your API token. Keep it to yourself.
import replicate
Run d3vshoaib/andro-upscaler using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"d3vshoaib/andro-upscaler:abd1b69ca014d61acaeec7a25c8589d48e19852a189fa2d52b275f447882d39b",
input={
"apply_cc_preset": False,
"controlnet_conditioning_scale": 0.6,
"guidance_scale": 5,
"input_image": "https://replicate.delivery/pbxt/MgvTlU1iEWwXmQmO07LHRhkfDgdBe6uqXD6eSf5bryynQ5tO/cat-test.jpeg",
"num_inference_steps": 28,
"prompt": "",
"upscale_factor": 4
}
)
# To access the file URL:
print(output.url())
#=> "http://example.com"
# To write the file to disk:
with open("my-image.png", "wb") as file:
file.write(output.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_SIK**********************************
This is your API token. Keep it to yourself.
Run d3vshoaib/andro-upscaler 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": "d3vshoaib/andro-upscaler:abd1b69ca014d61acaeec7a25c8589d48e19852a189fa2d52b275f447882d39b",
"input": {
"apply_cc_preset": false,
"controlnet_conditioning_scale": 0.6,
"guidance_scale": 5,
"input_image": "https://replicate.delivery/pbxt/MgvTlU1iEWwXmQmO07LHRhkfDgdBe6uqXD6eSf5bryynQ5tO/cat-test.jpeg",
"num_inference_steps": 28,
"prompt": "",
"upscale_factor": 4
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
{
"id": "74wg50vm3nrj00cnpayb9w69ew",
"model": "d3vshoaib/andro-upscaler",
"version": "abd1b69ca014d61acaeec7a25c8589d48e19852a189fa2d52b275f447882d39b",
"input": {
"apply_cc_preset": false,
"controlnet_conditioning_scale": 0.6,
"guidance_scale": 5,
"input_image": "https://replicate.delivery/pbxt/MgvTlU1iEWwXmQmO07LHRhkfDgdBe6uqXD6eSf5bryynQ5tO/cat-test.jpeg",
"num_inference_steps": 28,
"prompt": "",
"upscale_factor": 4
},
"logs": "Seed: 63947\nProcessing input image dimensions\nUpscaling Started, Starting memory monitoring...\n[08:47:30 AM] VRAM: 34.8GB/79.2GB (34.8GB cached) Free: 9.6GB, RAM: 71.5GB/2014.4GB, CPU: 6.5%\n[08:47:33 AM] VRAM: 35.2GB/79.2GB (42.0GB cached) Free: 2.0GB, RAM: 71.5GB/2014.4GB, CPU: 8.8%\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:02<01:14, 2.77s/it]\n[08:47:36 AM] VRAM: 35.2GB/79.2GB (42.0GB cached) Free: 2.0GB, RAM: 71.1GB/2014.4GB, CPU: 7.2%\n 7%|▋ | 2/28 [00:04<00:58, 2.24s/it]\n[08:47:39 AM] VRAM: 35.2GB/79.2GB (42.0GB cached) Free: 2.0GB, RAM: 71.0GB/2014.4GB, CPU: 6.1%\n 11%|█ | 3/28 [00:07<01:02, 2.49s/it]\n[08:47:42 AM] VRAM: 36.0GB/79.2GB (42.0GB cached) Free: 1.1GB, RAM: 70.9GB/2014.4GB, CPU: 5.4%\n 14%|█▍ | 4/28 [00:10<01:02, 2.61s/it]\n[08:47:45 AM] VRAM: 35.8GB/79.2GB (42.0GB cached) Free: 1.4GB, RAM: 71.0GB/2014.4GB, CPU: 9.2%\n 18%|█▊ | 5/28 [00:13<01:01, 2.67s/it]\n[08:47:48 AM] VRAM: 35.7GB/79.2GB (42.0GB cached) Free: 1.4GB, RAM: 71.8GB/2014.4GB, CPU: 13.6%\n 21%|██▏ | 6/28 [00:15<00:59, 2.71s/it]\n[08:47:51 AM] VRAM: 36.2GB/79.2GB (42.0GB cached) Free: 1.0GB, RAM: 72.1GB/2014.4GB, CPU: 5.9%\n 25%|██▌ | 7/28 [00:18<00:57, 2.74s/it]\n[08:47:54 AM] VRAM: 36.2GB/79.2GB (42.0GB cached) Free: 1.0GB, RAM: 72.1GB/2014.4GB, CPU: 6.2%\n 29%|██▊ | 8/28 [00:21<00:55, 2.76s/it]\n[08:47:57 AM] VRAM: 36.2GB/79.2GB (42.0GB cached) Free: 1.0GB, RAM: 72.3GB/2014.4GB, CPU: 7.1%\n 32%|███▏ | 9/28 [00:24<00:52, 2.77s/it]\n[08:48:00 AM] VRAM: 36.1GB/79.2GB (42.0GB cached) Free: 1.1GB, RAM: 71.8GB/2014.4GB, CPU: 7.7%\n 36%|███▌ | 10/28 [00:26<00:49, 2.77s/it]\n[08:48:03 AM] VRAM: 36.2GB/79.2GB (42.0GB cached) Free: 1.0GB, RAM: 71.7GB/2014.4GB, CPU: 5.5%\n 39%|███▉ | 11/28 [00:29<00:47, 2.78s/it]\n 43%|████▎ | 12/28 [00:32<00:44, 2.78s/it]\n[08:48:06 AM] VRAM: 35.2GB/79.2GB (42.0GB cached) Free: 2.0GB, RAM: 72.0GB/2014.4GB, CPU: 4.9%\n 46%|████▋ | 13/28 [00:35<00:41, 2.79s/it]\n[08:48:09 AM] VRAM: 35.2GB/79.2GB (42.0GB cached) Free: 2.0GB, RAM: 72.0GB/2014.4GB, CPU: 5.5%\n 50%|█████ | 14/28 [00:38<00:39, 2.79s/it]\n[08:48:12 AM] VRAM: 35.2GB/79.2GB (42.0GB cached) Free: 2.0GB, RAM: 72.2GB/2014.4GB, CPU: 5.8%\n 54%|█████▎ | 15/28 [00:40<00:36, 2.79s/it]\n[08:48:15 AM] VRAM: 35.2GB/79.2GB (42.0GB cached) Free: 2.0GB, RAM: 72.1GB/2014.4GB, CPU: 6.7%\n 57%|█████▋ | 16/28 [00:43<00:33, 2.79s/it]\n[08:48:18 AM] VRAM: 35.2GB/79.2GB (42.0GB cached) Free: 2.0GB, RAM: 71.7GB/2014.4GB, CPU: 6.1%\n 61%|██████ | 17/28 [00:46<00:30, 2.79s/it]\n[08:48:21 AM] VRAM: 35.8GB/79.2GB (42.0GB cached) Free: 1.4GB, RAM: 71.8GB/2014.4GB, CPU: 5.5%\n 64%|██████▍ | 18/28 [00:49<00:27, 2.79s/it]\n[08:48:24 AM] VRAM: 35.8GB/79.2GB (42.0GB cached) Free: 1.4GB, RAM: 71.9GB/2014.4GB, CPU: 6.1%\n 68%|██████▊ | 19/28 [00:52<00:25, 2.79s/it]\n[08:48:27 AM] VRAM: 35.9GB/79.2GB (42.0GB cached) Free: 1.2GB, RAM: 71.9GB/2014.4GB, CPU: 9.1%\n 71%|███████▏ | 20/28 [00:54<00:22, 2.80s/it]\n[08:48:30 AM] VRAM: 36.1GB/79.2GB (42.0GB cached) Free: 1.0GB, RAM: 71.6GB/2014.4GB, CPU: 13.9%\n 75%|███████▌ | 21/28 [00:57<00:19, 2.80s/it]\n[08:48:33 AM] VRAM: 36.2GB/79.2GB (42.0GB cached) Free: 1.0GB, RAM: 71.6GB/2014.4GB, CPU: 6.4%\n 79%|███████▊ | 22/28 [01:00<00:16, 2.79s/it]\n[08:48:36 AM] VRAM: 36.1GB/79.2GB (42.0GB cached) Free: 1.0GB, RAM: 71.9GB/2014.4GB, CPU: 6.5%\n 82%|████████▏ | 23/28 [01:03<00:13, 2.79s/it]\n[08:48:39 AM] VRAM: 36.2GB/79.2GB (42.0GB cached) Free: 1.0GB, RAM: 71.0GB/2014.4GB, CPU: 6.8%\n 86%|████████▌ | 24/28 [01:06<00:11, 2.80s/it]\n[08:48:42 AM] VRAM: 36.1GB/79.2GB (42.0GB cached) Free: 1.1GB, RAM: 71.2GB/2014.4GB, CPU: 11.0%\n 89%|████████▉ | 25/28 [01:08<00:08, 2.80s/it]\n 93%|█████████▎| 26/28 [01:11<00:05, 2.80s/it]\n[08:48:45 AM] VRAM: 35.2GB/79.2GB (42.0GB cached) Free: 2.0GB, RAM: 72.0GB/2014.4GB, CPU: 8.0%\n 96%|█████████▋| 27/28 [01:14<00:02, 2.80s/it]\n100%|██████████| 28/28 [01:17<00:00, 2.80s/it]\n100%|██████████| 28/28 [01:17<00:00, 2.76s/it]\n[08:48:48 AM] VRAM: 41.1GB/79.2GB (47.1GB cached) Free: -9.0GB, RAM: 72.3GB/2014.4GB, CPU: 6.8%\nStopping memory monitoring...\nSaving output image",
"output": "https://replicate.delivery/yhqm/ps26N2kfSKytbqpbLYheUJeUcAteVOXm9Dzo1dgYX6iOHipRB/output.jpg",
"data_removed": false,
"error": null,
"source": "web",
"status": "succeeded",
"created_at": "2025-03-20T08:47:29.821Z",
"started_at": "2025-03-20T08:47:29.827547Z",
"completed_at": "2025-03-20T08:48:51.287827Z",
"urls": {
"cancel": "https://api.replicate.com/v1/predictions/74wg50vm3nrj00cnpayb9w69ew/cancel",
"get": "https://api.replicate.com/v1/predictions/74wg50vm3nrj00cnpayb9w69ew",
"stream": "https://stream.replicate.com/v1/files/yswh-ndlx2qfcwxodoso3ltgbzms4g2lghmp5zd7yqckzu2drxvddwtza",
"web": "https://replicate.com/p/74wg50vm3nrj00cnpayb9w69ew"
},
"metrics": {
"predict_time": 81.460279686,
"total_time": 81.466827
}
}
