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twn39 /playground-v2-1024px-aesthetic:b52e6662
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=twn39/playground-v2-1024px-aesthetic
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";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});Run twn39/playground-v2-1024px-aesthetic using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"twn39/playground-v2-1024px-aesthetic:b52e666276fa44552baa4335b7069bd23e27086dd022bf811e1c11057b709fd2",
{
input: {
width: 1024,
height: 1024,
prompt: "Astronaut in black hole, cold color palette, muted colors, detailed, 8k",
scheduler: "KarrasDPM",
num_outputs: 2,
guidance_scale: 3,
negative_prompt: "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
num_inference_steps: 25
}
}
);
// 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 twn39/playground-v2-1024px-aesthetic using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"twn39/playground-v2-1024px-aesthetic:b52e666276fa44552baa4335b7069bd23e27086dd022bf811e1c11057b709fd2",
input={
"width": 1024,
"height": 1024,
"prompt": "Astronaut in black hole, cold color palette, muted colors, detailed, 8k",
"scheduler": "KarrasDPM",
"num_outputs": 2,
"guidance_scale": 3,
"negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
"num_inference_steps": 25
}
)
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 twn39/playground-v2-1024px-aesthetic 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": "twn39/playground-v2-1024px-aesthetic:b52e666276fa44552baa4335b7069bd23e27086dd022bf811e1c11057b709fd2",
"input": {
"width": 1024,
"height": 1024,
"prompt": "Astronaut in black hole, cold color palette, muted colors, detailed, 8k",
"scheduler": "KarrasDPM",
"num_outputs": 2,
"guidance_scale": 3,
"negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
"num_inference_steps": 25
}
}' \
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/twn39/playground-v2-1024px-aesthetic@sha256:b52e666276fa44552baa4335b7069bd23e27086dd022bf811e1c11057b709fd2 \
-i 'width=1024' \
-i 'height=1024' \
-i 'prompt="Astronaut in black hole, cold color palette, muted colors, detailed, 8k"' \
-i 'scheduler="KarrasDPM"' \
-i 'num_outputs=2' \
-i 'guidance_scale=3' \
-i 'negative_prompt="drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly"' \
-i 'num_inference_steps=25'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/twn39/playground-v2-1024px-aesthetic@sha256:b52e666276fa44552baa4335b7069bd23e27086dd022bf811e1c11057b709fd2curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "Astronaut in black hole, cold color palette, muted colors, detailed, 8k", "scheduler": "KarrasDPM", "num_outputs": 2, "guidance_scale": 3, "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly", "num_inference_steps": 25 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
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Output
{
"completed_at": "2023-12-08T02:22:17.899945Z",
"created_at": "2023-12-08T02:22:05.376817Z",
"data_removed": false,
"error": null,
"id": "2aqw55lbp7lrmsawkcck6ksw2e",
"input": {
"width": 1024,
"height": 1024,
"prompt": "Astronaut in black hole, cold color palette, muted colors, detailed, 8k",
"scheduler": "KarrasDPM",
"num_outputs": 2,
"guidance_scale": 3,
"negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
"num_inference_steps": 25
},
"logs": "0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:09, 2.61it/s]\n 8%|▊ | 2/25 [00:00<00:06, 3.65it/s]\n 12%|█▏ | 3/25 [00:00<00:07, 3.08it/s]\n 16%|█▌ | 4/25 [00:01<00:07, 2.88it/s]\n 20%|██ | 5/25 [00:01<00:07, 2.78it/s]\n 24%|██▍ | 6/25 [00:02<00:06, 2.71it/s]\n 28%|██▊ | 7/25 [00:02<00:06, 2.68it/s]\n 32%|███▏ | 8/25 [00:02<00:06, 2.65it/s]\n 36%|███▌ | 9/25 [00:03<00:06, 2.64it/s]\n 40%|████ | 10/25 [00:03<00:05, 2.63it/s]\n 44%|████▍ | 11/25 [00:04<00:05, 2.62it/s]\n 48%|████▊ | 12/25 [00:04<00:04, 2.61it/s]\n 52%|█████▏ | 13/25 [00:04<00:04, 2.61it/s]\n 56%|█████▌ | 14/25 [00:05<00:04, 2.61it/s]\n 60%|██████ | 15/25 [00:05<00:03, 2.60it/s]\n 64%|██████▍ | 16/25 [00:05<00:03, 2.60it/s]\n 68%|██████▊ | 17/25 [00:06<00:03, 2.60it/s]\n 72%|███████▏ | 18/25 [00:06<00:02, 2.60it/s]\n 76%|███████▌ | 19/25 [00:07<00:02, 2.59it/s]\n 80%|████████ | 20/25 [00:07<00:01, 2.59it/s]\n 84%|████████▍ | 21/25 [00:07<00:01, 2.59it/s]\n 88%|████████▊ | 22/25 [00:08<00:01, 2.59it/s]\n 92%|█████████▏| 23/25 [00:08<00:00, 2.59it/s]\n 96%|█████████▌| 24/25 [00:09<00:00, 2.59it/s]\n100%|██████████| 25/25 [00:09<00:00, 2.59it/s]\n100%|██████████| 25/25 [00:09<00:00, 2.65it/s]",
"metrics": {
"predict_time": 12.485813,
"total_time": 12.523128
},
"output": [
"https://replicate.delivery/pbxt/PpHYwKfk7NxDSqbcON2rI8foCuCiAzMmV2ftIZ1LhirxeLAIB/out-0.jpg",
"https://replicate.delivery/pbxt/fhBDK6iosxQ6UqCzXXPtGBZxXaDISVtsMjoFTSTymHusfCASA/out-1.jpg"
],
"started_at": "2023-12-08T02:22:05.414132Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/2aqw55lbp7lrmsawkcck6ksw2e",
"cancel": "https://api.replicate.com/v1/predictions/2aqw55lbp7lrmsawkcck6ksw2e/cancel"
},
"version": "b52e666276fa44552baa4335b7069bd23e27086dd022bf811e1c11057b709fd2"
}Generated in
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