Prediction
wzesk/littoral_upsample:331109b1b1fea81550537ebc723bb9c9f1ea2fb7a6e2018e636e9d299dabd735ID
1ffnd8s5t1rj40cmejxt4y4a14
Status
Succeeded
Source
Web
Hardware
A100 (80GB)
Total duration
Created
Input
- image
{
"image": "https://raw.githubusercontent.com/Wzesk/Real-ESRGAN/7e18ad3530a145074d230497323d6ec012b2f811/test_images/20240121T052111_20240121T053340_T43NBG_nir.png"
}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 wzesk/littoral_upsample using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"wzesk/littoral_upsample:331109b1b1fea81550537ebc723bb9c9f1ea2fb7a6e2018e636e9d299dabd735",
{
input: {
image: "https://raw.githubusercontent.com/Wzesk/Real-ESRGAN/7e18ad3530a145074d230497323d6ec012b2f811/test_images/20240121T052111_20240121T053340_T43NBG_nir.png"
}
}
);
// 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.
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 wzesk/littoral_upsample using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"wzesk/littoral_upsample:331109b1b1fea81550537ebc723bb9c9f1ea2fb7a6e2018e636e9d299dabd735",
input={
"image": "https://raw.githubusercontent.com/Wzesk/Real-ESRGAN/7e18ad3530a145074d230497323d6ec012b2f811/test_images/20240121T052111_20240121T053340_T43NBG_nir.png"
}
)
# 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.
Set the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run wzesk/littoral_upsample 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": "wzesk/littoral_upsample:331109b1b1fea81550537ebc723bb9c9f1ea2fb7a6e2018e636e9d299dabd735",
"input": {
"image": "https://raw.githubusercontent.com/Wzesk/Real-ESRGAN/7e18ad3530a145074d230497323d6ec012b2f811/test_images/20240121T052111_20240121T053340_T43NBG_nir.png"
}
}' \
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/wzesk/littoral_upsample@sha256:331109b1b1fea81550537ebc723bb9c9f1ea2fb7a6e2018e636e9d299dabd735 \
-i 'image="https://raw.githubusercontent.com/Wzesk/Real-ESRGAN/7e18ad3530a145074d230497323d6ec012b2f811/test_images/20240121T052111_20240121T053340_T43NBG_nir.png"'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/wzesk/littoral_upsample@sha256:331109b1b1fea81550537ebc723bb9c9f1ea2fb7a6e2018e636e9d299dabd735curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://raw.githubusercontent.com/Wzesk/Real-ESRGAN/7e18ad3530a145074d230497323d6ec012b2f811/test_images/20240121T052111_20240121T053340_T43NBG_nir.png" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{
"completed_at": "2025-01-17T14:47:39.413626Z",
"created_at": "2025-01-17T14:46:49.552000Z",
"data_removed": false,
"error": null,
"id": "1ffnd8s5t1rj40cmejxt4y4a14",
"input": {
"image": "https://raw.githubusercontent.com/Wzesk/Real-ESRGAN/7e18ad3530a145074d230497323d6ec012b2f811/test_images/20240121T052111_20240121T053340_T43NBG_nir.png"
},
"logs": "/src/RealESRGAN/model.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\nloadnet = torch.load(model_path)",
"metrics": {
"predict_time": 1.024635476,
"total_time": 49.861626
},
"output": "https://replicate.delivery/yhqm/PWom14LDS442E18QqiBBz0WzgoZGfSazprOoyfIMPoVLeDMoA/upsampled_image.png",
"started_at": "2025-01-17T14:47:38.388991Z",
"status": "succeeded",
"urls": {
"stream": "https://stream.replicate.com/v1/files/qoxq-xtxtpyk2racnaxefp4lljr67tkysxsfdhcpwsg6ahxmb7e7wa4ya",
"get": "https://api.replicate.com/v1/predictions/1ffnd8s5t1rj40cmejxt4y4a14",
"cancel": "https://api.replicate.com/v1/predictions/1ffnd8s5t1rj40cmejxt4y4a14/cancel"
},
"version": "331109b1b1fea81550537ebc723bb9c9f1ea2fb7a6e2018e636e9d299dabd735"
}Generated in
/src/RealESRGAN/model.py:49: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
loadnet = torch.load(model_path)