Readme
This model doesn't have a readme.
Run this model in Node.js with one line of code:
npm install replicate
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import Replicate from "replicate";
import fs from "node:fs";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});
Run asronline/sdxl-mk1-stages using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"asronline/sdxl-mk1-stages:a8bfe4fa3d11094d66d2e199a73f2b6724a50235e9dd43c808c3ab326d08abb3",
{
input: {
width: 1920,
height: 1080,
prompt: "In the style of MK1, a Shaolin temple, with beautiful trees and lanterns",
refine: "no_refiner",
scheduler: "K_EULER",
lora_scale: 0.6,
num_outputs: 1,
guidance_scale: 7.5,
apply_watermark: true,
high_noise_frac: 0.8,
negative_prompt: "",
prompt_strength: 0.8,
num_inference_steps: 50
}
}
);
// 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=<paste-your-token-here>
Find your API token in your account settings.
import replicate
Run asronline/sdxl-mk1-stages using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"asronline/sdxl-mk1-stages:a8bfe4fa3d11094d66d2e199a73f2b6724a50235e9dd43c808c3ab326d08abb3",
input={
"width": 1920,
"height": 1080,
"prompt": "In the style of MK1, a Shaolin temple, with beautiful trees and lanterns",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": True,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 50
}
)
print(output)
To learn more, take a look at the guide on getting started with Python.
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run asronline/sdxl-mk1-stages 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": "asronline/sdxl-mk1-stages:a8bfe4fa3d11094d66d2e199a73f2b6724a50235e9dd43c808c3ab326d08abb3",
"input": {
"width": 1920,
"height": 1080,
"prompt": "In the style of MK1, a Shaolin temple, with beautiful trees and lanterns",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": true,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 50
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Add a payment method to run this model.
By signing in, you agree to our
terms of service and privacy policy
{
"completed_at": "2023-09-18T01:00:18.503566Z",
"created_at": "2023-09-18T00:59:46.511897Z",
"data_removed": false,
"error": null,
"id": "jde6ialbqcre5vkoqaam2cuovq",
"input": {
"width": 1920,
"height": 1080,
"prompt": "In the style of MK1, a Shaolin temple, with beautiful trees and lanterns",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": true,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 50
},
"logs": "Using seed: 42804\nPrompt: In the style of MK1, a Shaolin temple, with beautiful trees and lanterns\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:28, 1.71it/s]\n 4%|▍ | 2/50 [00:01<00:28, 1.71it/s]\n 6%|▌ | 3/50 [00:01<00:27, 1.71it/s]\n 8%|▊ | 4/50 [00:02<00:26, 1.71it/s]\n 10%|█ | 5/50 [00:02<00:26, 1.71it/s]\n 12%|█▏ | 6/50 [00:03<00:25, 1.71it/s]\n 14%|█▍ | 7/50 [00:04<00:25, 1.71it/s]\n 16%|█▌ | 8/50 [00:04<00:24, 1.71it/s]\n 18%|█▊ | 9/50 [00:05<00:23, 1.72it/s]\n 20%|██ | 10/50 [00:05<00:23, 1.72it/s]\n 22%|██▏ | 11/50 [00:06<00:22, 1.72it/s]\n 24%|██▍ | 12/50 [00:07<00:22, 1.72it/s]\n 26%|██▌ | 13/50 [00:07<00:21, 1.72it/s]\n 28%|██▊ | 14/50 [00:08<00:20, 1.72it/s]\n 30%|███ | 15/50 [00:08<00:20, 1.72it/s]\n 32%|███▏ | 16/50 [00:09<00:19, 1.72it/s]\n 34%|███▍ | 17/50 [00:09<00:19, 1.72it/s]\n 36%|███▌ | 18/50 [00:10<00:18, 1.72it/s]\n 38%|███▊ | 19/50 [00:11<00:18, 1.72it/s]\n 40%|████ | 20/50 [00:11<00:17, 1.72it/s]\n 42%|████▏ | 21/50 [00:12<00:16, 1.72it/s]\n 44%|████▍ | 22/50 [00:12<00:16, 1.71it/s]\n 46%|████▌ | 23/50 [00:13<00:15, 1.71it/s]\n 48%|████▊ | 24/50 [00:13<00:15, 1.71it/s]\n 50%|█████ | 25/50 [00:14<00:14, 1.71it/s]\n 52%|█████▏ | 26/50 [00:15<00:14, 1.71it/s]\n 54%|█████▍ | 27/50 [00:15<00:13, 1.71it/s]\n 56%|█████▌ | 28/50 [00:16<00:12, 1.71it/s]\n 58%|█████▊ | 29/50 [00:16<00:12, 1.71it/s]\n 60%|██████ | 30/50 [00:17<00:11, 1.71it/s]\n 62%|██████▏ | 31/50 [00:18<00:11, 1.71it/s]\n 64%|██████▍ | 32/50 [00:18<00:10, 1.71it/s]\n 66%|██████▌ | 33/50 [00:19<00:09, 1.71it/s]\n 68%|██████▊ | 34/50 [00:19<00:09, 1.71it/s]\n 70%|███████ | 35/50 [00:20<00:08, 1.71it/s]\n 72%|███████▏ | 36/50 [00:21<00:08, 1.71it/s]\n 74%|███████▍ | 37/50 [00:21<00:07, 1.71it/s]\n 76%|███████▌ | 38/50 [00:22<00:07, 1.71it/s]\n 78%|███████▊ | 39/50 [00:22<00:06, 1.71it/s]\n 80%|████████ | 40/50 [00:23<00:05, 1.70it/s]\n 82%|████████▏ | 41/50 [00:23<00:05, 1.71it/s]\n 84%|████████▍ | 42/50 [00:24<00:04, 1.71it/s]\n 86%|████████▌ | 43/50 [00:25<00:04, 1.70it/s]\n 88%|████████▊ | 44/50 [00:25<00:03, 1.70it/s]\n 90%|█████████ | 45/50 [00:26<00:02, 1.70it/s]\n 92%|█████████▏| 46/50 [00:26<00:02, 1.70it/s]\n 94%|█████████▍| 47/50 [00:27<00:01, 1.70it/s]\n 96%|█████████▌| 48/50 [00:28<00:01, 1.70it/s]\n 98%|█████████▊| 49/50 [00:28<00:00, 1.70it/s]\n100%|██████████| 50/50 [00:29<00:00, 1.70it/s]\n100%|██████████| 50/50 [00:29<00:00, 1.71it/s]",
"metrics": {
"predict_time": 31.99763,
"total_time": 31.991669
},
"output": [
"https://replicate.delivery/pbxt/rld4ni3fm13CNi94LWflcw8SIU8jbfKjj7a9o3fh8L6EyUVGB/out-0.png"
],
"started_at": "2023-09-18T00:59:46.505936Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/jde6ialbqcre5vkoqaam2cuovq",
"cancel": "https://api.replicate.com/v1/predictions/jde6ialbqcre5vkoqaam2cuovq/cancel"
},
"version": "a8bfe4fa3d11094d66d2e199a73f2b6724a50235e9dd43c808c3ab326d08abb3"
}
Using seed: 42804
Prompt: In the style of MK1, a Shaolin temple, with beautiful trees and lanterns
txt2img mode
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This model costs approximately $0.033 to run on Replicate, or 30 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.
This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 34 seconds. The predict time for this model varies significantly based on the inputs.
This model doesn't have a readme.
This model is warm. You'll get a fast response if the model is warm and already running, and a slower response if the model is cold and starting up.
This model costs approximately $0.033 to run on Replicate, but this varies depending on your inputs. View more.
Choose a file from your machine
Hint: you can also drag files onto the input
Choose a file from your machine
Hint: you can also drag files onto the input
Using seed: 42804
Prompt: In the style of MK1, a Shaolin temple, with beautiful trees and lanterns
txt2img mode
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