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jerray /meinamix-v11-controlnet:af1741c3
Input
Run this model in Node.js with one line of code:
npm install replicate
REPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import Replicate from "replicate";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});
Run jerray/meinamix-v11-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"jerray/meinamix-v11-controlnet:af1741c3cd054cf6a28d2bc764c9be7e143cea2cb2c614d07ea6708f4bf0831c",
{
input: {
width: 768,
height: 768,
prompt: "a photo of an astronaut riding a horse on mars",
scheduler: "DPMSolverMultistep",
num_outputs: 1,
guidance_scale: 7.5,
canny_low_threshold: 100,
num_inference_steps: 50,
canny_high_threshold: 200,
control_guidance_end: 1,
control_guidance_start: 0,
controlnet_conditioning_scale: 1
}
}
);
console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
REPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import replicate
Run jerray/meinamix-v11-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"jerray/meinamix-v11-controlnet:af1741c3cd054cf6a28d2bc764c9be7e143cea2cb2c614d07ea6708f4bf0831c",
input={
"width": 768,
"height": 768,
"prompt": "a photo of an astronaut riding a horse on mars",
"scheduler": "DPMSolverMultistep",
"num_outputs": 1,
"guidance_scale": 7.5,
"canny_low_threshold": 100,
"num_inference_steps": 50,
"canny_high_threshold": 200,
"control_guidance_end": 1,
"control_guidance_start": 0,
"controlnet_conditioning_scale": 1
}
)
print(output)
To learn more, take a look at the guide on getting started with Python.
REPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run jerray/meinamix-v11-controlnet 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": "af1741c3cd054cf6a28d2bc764c9be7e143cea2cb2c614d07ea6708f4bf0831c",
"input": {
"width": 768,
"height": 768,
"prompt": "a photo of an astronaut riding a horse on mars",
"scheduler": "DPMSolverMultistep",
"num_outputs": 1,
"guidance_scale": 7.5,
"canny_low_threshold": 100,
"num_inference_steps": 50,
"canny_high_threshold": 200,
"control_guidance_end": 1,
"control_guidance_start": 0,
"controlnet_conditioning_scale": 1
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
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Output
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