lucataco/pixart-xl-2:816c9967 – Run with an API on Replicate

Version

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lucataco /pixart-xl-2:816c9967

Input

prompt

string

Shift + Return to add a new line

an astronaut sitting in a diner, eating fries, cinematic, analog filman astronaut sitting in a diner, eating fries, cinematic, analog film

Input prompt

Default: "A small cactus with a happy face in the Sahara desert"

negative_prompt

string

Shift + Return to add a new line

Negative prompt

style

string

Image style

Default: "None"

width

integer

Width of output image

Default: 1024

height

integer

Height of output image

Default: 1024

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

scheduler

string

scheduler

Default: "DPMSolverMultistep"

num_inference_steps

integer

(minimum: 1, maximum: 100)

Number of denoising steps

Default: 14

guidance_scale

number

(minimum: 1, maximum: 50)

Scale for classifier-free guidance

Default: 4.5

seed

integer

Random seed. Leave blank to randomize the seed

Run this model in Node.js with one line of code:

npx create-replicate --model=lucataco/pixart-xl-2

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set 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 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: {
      style: "None",
      width: 1024,
      height: 1024,
      prompt: "an astronaut sitting in a diner, eating fries, cinematic, analog film",
      scheduler: "DPMSolverMultistep",
      num_outputs: 1,
      guidance_scale: 4.5,
      num_inference_steps: 14
    }
  }
);
console.log(output);

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=<paste-your-token-here>

Find your API token in your account settings.

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

To 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 cog

If 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/lucataco/pixart-xl-2@sha256:816c99673841b9448bc2539834c16d40e0315bbf92fef0317b57a226727409bb \
  -i 'style="None"' \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="an astronaut sitting in a diner, eating fries, cinematic, analog film"' \
  -i 'scheduler="DPMSolverMultistep"' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=4.5' \
  -i 'num_inference_steps=14'

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/lucataco/pixart-xl-2@sha256:816c99673841b9448bc2539834c16d40e0315bbf92fef0317b57a226727409bb
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "style": "None",
      "width": 1024,
      "height": 1024,
      "prompt": "an astronaut sitting in a diner, eating fries, cinematic, analog film",
      "scheduler": "DPMSolverMultistep",
      "num_outputs": 1,
      "guidance_scale": 4.5,
      "num_inference_steps": 14
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

{
  "completed_at": "2023-12-04T04:30:13.212351Z",
  "created_at": "2023-12-04T04:27:50.578331Z",
  "data_removed": false,
  "error": null,
  "id": "o23ccgdbvi46p2uvxuqrhoccry",
  "input": {
    "style": "None",
    "width": 1024,
    "height": 1024,
    "prompt": "an astronaut sitting in a diner, eating fries, cinematic, analog film",
    "scheduler": "DPMSolverMultistep",
    "num_outputs": 1,
    "guidance_scale": 4.5,
    "num_inference_steps": 14
  },
  "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]",
  "metrics": {
    "predict_time": 6.041227,
    "total_time": 142.63402
  },
  "output": [
    "https://replicate.delivery/pbxt/nSBVHLqeoD1KECVJ5OJSm90ihtI0zm4qeBvQ9ACZNMQUfg9jA/out-0.png"
  ],
  "started_at": "2023-12-04T04:30:07.171124Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/o23ccgdbvi46p2uvxuqrhoccry",
    "cancel": "https://api.replicate.com/v1/predictions/o23ccgdbvi46p2uvxuqrhoccry/cancel"
  },
  "version": "816c99673841b9448bc2539834c16d40e0315bbf92fef0317b57a226727409bb"
}

Generated in

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