Prediction

Model

lucataco/pixart-xl-2:816c9967

o23ccgdbvi46p2uvxuqrhoccry

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

2m 23s

Created

about 2 years ago

Webhook

–

Input

prompt: an astronaut sitting in a diner, eating fries, cinematic, analog film
style: None
width: 1024
height: 1024
num_outputs: 1
scheduler: DPMSolverMultistep
num_inference_steps: 14
guidance_scale: 4.5

{
  "guidance_scale": 4.5,
  "height": 1024,
  "num_inference_steps": 14,
  "num_outputs": 1,
  "prompt": "an astronaut sitting in a diner, eating fries, cinematic, analog film",
  "scheduler": "DPMSolverMultistep",
  "style": "None",
  "width": 1024
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_GiB**********************************

This is your API token. Keep it to yourself.

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 lucataco/pixart-xl-2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/pixart-xl-2:816c99673841b9448bc2539834c16d40e0315bbf92fef0317b57a226727409bb",
  {
    input: {
      guidance_scale: 4.5,
      height: 1024,
      num_inference_steps: 14,
      num_outputs: 1,
      prompt: "an astronaut sitting in a diner, eating fries, cinematic, analog film",
      scheduler: "DPMSolverMultistep",
      style: "None",
      width: 1024
    }
  }
);

// 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 replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_GiB**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run lucataco/pixart-xl-2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/pixart-xl-2:816c99673841b9448bc2539834c16d40e0315bbf92fef0317b57a226727409bb",
    input={
        "guidance_scale": 4.5,
        "height": 1024,
        "num_inference_steps": 14,
        "num_outputs": 1,
        "prompt": "an astronaut sitting in a diner, eating fries, cinematic, analog film",
        "scheduler": "DPMSolverMultistep",
        "style": "None",
        "width": 1024
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].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=r8_GiB**********************************

This is your API token. Keep it to yourself.

Run lucataco/pixart-xl-2 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": "lucataco/pixart-xl-2:816c99673841b9448bc2539834c16d40e0315bbf92fef0317b57a226727409bb",
    "input": {
      "guidance_scale": 4.5,
      "height": 1024,
      "num_inference_steps": 14,
      "num_outputs": 1,
      "prompt": "an astronaut sitting in a diner, eating fries, cinematic, analog film",
      "scheduler": "DPMSolverMultistep",
      "style": "None",
      "width": 1024
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "id": "o23ccgdbvi46p2uvxuqrhoccry",
  "model": "lucataco/pixart-xl-2",
  "version": "816c99673841b9448bc2539834c16d40e0315bbf92fef0317b57a226727409bb",
  "input": {
    "guidance_scale": 4.5,
    "height": 1024,
    "num_inference_steps": 14,
    "num_outputs": 1,
    "prompt": "an astronaut sitting in a diner, eating fries, cinematic, analog film",
    "scheduler": "DPMSolverMultistep",
    "style": "None",
    "width": 1024
  },
  "logs": "Using seed: 255397398\nPrompt: an astronaut sitting in a diner, eating fries, cinematic, analog film  Negative Prompt: None\n  0%|          | 0/14 [00:00<?, ?it/s]\n  7%|▋         | 1/14 [00:00<00:04,  2.86it/s]\n 14%|█▍        | 2/14 [00:00<00:03,  3.16it/s]\n 21%|██▏       | 3/14 [00:00<00:03,  3.26it/s]\n 29%|██▊       | 4/14 [00:01<00:03,  3.29it/s]\n 36%|███▌      | 5/14 [00:01<00:02,  3.32it/s]\n 43%|████▎     | 6/14 [00:01<00:02,  3.34it/s]\n 50%|█████     | 7/14 [00:02<00:02,  3.35it/s]\n 57%|█████▋    | 8/14 [00:02<00:01,  3.36it/s]\n 64%|██████▍   | 9/14 [00:02<00:01,  3.36it/s]\n 71%|███████▏  | 10/14 [00:03<00:01,  3.36it/s]\n 79%|███████▊  | 11/14 [00:03<00:00,  3.36it/s]\n 86%|████████▌ | 12/14 [00:03<00:00,  3.36it/s]\n 93%|█████████▎| 13/14 [00:03<00:00,  3.36it/s]\n100%|██████████| 14/14 [00:04<00:00,  3.36it/s]\n100%|██████████| 14/14 [00:04<00:00,  3.33it/s]",
  "output": [
    "https://replicate.delivery/pbxt/nSBVHLqeoD1KECVJ5OJSm90ihtI0zm4qeBvQ9ACZNMQUfg9jA/out-0.png"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2023-12-04T04:27:50.578331Z",
  "started_at": "2023-12-04T04:30:07.171124Z",
  "completed_at": "2023-12-04T04:30:13.212351Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/o23ccgdbvi46p2uvxuqrhoccry/cancel",
    "get": "https://api.replicate.com/v1/predictions/o23ccgdbvi46p2uvxuqrhoccry"
  },
  "metrics": {
    "predict_time": 6.041227,
    "total_time": 142.63402
  }
}

Generated in

6.0 seconds

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