fofr/pulid-lightning – Run with an API on Replicate

fofr / pulid-lightning

Use a face to instantly make images. Uses SDXL Lightning checkpoints.

Cold

Public
128.7K runs
L40S
GitHub
Paper
License

Run with an API

Playground API Examples README Versions

Input

face_image

*file

The face image to use for the generation

prompt

string

Shift + Return to add a new line

A screenshot of a person in a video game, unreal engine, ps5A screenshot of a person in a video game, unreal engine, ps5

You might need to include a gender in the prompt to get the desired result

Default: "A photo of a person"

negative_prompt

string

Shift + Return to add a new line

Things you do not want to see in your image

Default: ""

width

integer

Width of the output image (ignored if structure image given)

Default: 1024

height

integer

Height of the output image (ignored if structure image given)

Default: 1024

checkpoint_model

string

Model to use for the generation

Default: "default"

face_style

string

Style of the face

Default: "high-fidelity"

number_of_images

integer

(minimum: 1, maximum: 10)

Number of images to generate

Default: 1

output_format

string

Format of the output images

Default: "webp"

output_quality

integer

(minimum: 0, maximum: 100)

Quality of the output images, from 0 to 100. 100 is best quality, 0 is lowest quality.

Default: 80

seed

integer

Set a seed for reproducibility. Random by default.

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

npx create-replicate --model=fofr/pulid-lightning

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 fofr/pulid-lightning using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fofr/pulid-lightning:a0321a0cef20aa9e0828f8a9873dc3fd019266bc929f2fb8d70b76d52e122382",
  {
    input: {
      width: 1152,
      height: 768,
      prompt: "A screenshot of a person in a video game, unreal engine, ps5",
      face_image: "https://replicate.delivery/pbxt/Kt8dp9R5JbK7ixrWbjM3fTXUiz4t0fi4JOSqyRkReHrMgYjm/guy.webp",
      face_style: "high-fidelity",
      output_format: "webp",
      output_quality: 80,
      negative_prompt: "photo, photography",
      checkpoint_model: "artistic",
      number_of_images: 1
    }
  }
);

// 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=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run fofr/pulid-lightning using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fofr/pulid-lightning:a0321a0cef20aa9e0828f8a9873dc3fd019266bc929f2fb8d70b76d52e122382",
    input={
        "width": 1152,
        "height": 768,
        "prompt": "A screenshot of a person in a video game, unreal engine, ps5",
        "face_image": "https://replicate.delivery/pbxt/Kt8dp9R5JbK7ixrWbjM3fTXUiz4t0fi4JOSqyRkReHrMgYjm/guy.webp",
        "face_style": "high-fidelity",
        "output_format": "webp",
        "output_quality": 80,
        "negative_prompt": "photo, photography",
        "checkpoint_model": "artistic",
        "number_of_images": 1
    }
)
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 fofr/pulid-lightning 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": "a0321a0cef20aa9e0828f8a9873dc3fd019266bc929f2fb8d70b76d52e122382",
    "input": {
      "width": 1152,
      "height": 768,
      "prompt": "A screenshot of a person in a video game, unreal engine, ps5",
      "face_image": "https://replicate.delivery/pbxt/Kt8dp9R5JbK7ixrWbjM3fTXUiz4t0fi4JOSqyRkReHrMgYjm/guy.webp",
      "face_style": "high-fidelity",
      "output_format": "webp",
      "output_quality": 80,
      "negative_prompt": "photo, photography",
      "checkpoint_model": "artistic",
      "number_of_images": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-05-09T10:23:02.845388Z",
  "created_at": "2024-05-09T10:22:58.502000Z",
  "data_removed": false,
  "error": null,
  "id": "fdjhdp59rsrgj0cfbkgb70mt94",
  "input": {
    "width": 1152,
    "height": 768,
    "prompt": "A screenshot of a person in a video game, unreal engine, ps5",
    "face_image": "https://replicate.delivery/pbxt/Kt8dp9R5JbK7ixrWbjM3fTXUiz4t0fi4JOSqyRkReHrMgYjm/guy.webp",
    "face_style": "high-fidelity",
    "output_format": "webp",
    "output_quality": 80,
    "negative_prompt": "photo, photography",
    "checkpoint_model": "artistic",
    "number_of_images": 1
  },
  "logs": "Random seed set to: 559882634\nChecking inputs\n✅ /tmp/inputs/image.png\n====================================\nChecking weights\n✅ Juggernaut_RunDiffusionPhoto2_Lightning_4Steps.safetensors\n✅ models/antelopev2\n✅ ProteusV0.4-Lighting.safetensors\n✅ ip-adapter_pulid_sdxl_fp16.safetensors\n✅ dreamshaperXL_lightningDPMSDE.safetensors\n====================================\nRunning workflow\ngot prompt\nExecuting node 3, title: KSampler, class type: KSampler\n  0%|          | 0/4 [00:00<?, ?it/s]\n 25%|██▌       | 1/4 [00:00<00:01,  2.18it/s]\n 50%|█████     | 2/4 [00:00<00:00,  2.09it/s]\n 75%|███████▌  | 3/4 [00:01<00:00,  2.14it/s]\n100%|██████████| 4/4 [00:01<00:00,  2.73it/s]\n100%|██████████| 4/4 [00:01<00:00,  2.47it/s]\nExecuting node 8, title: VAE Decode, class type: VAEDecode\nExecuting node 47, title: Save Image, class type: SaveImage\nPrompt executed in 1.95 seconds\noutputs:  {'47': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nComfyUI_00001_.png",
  "metrics": {
    "predict_time": 4.302588,
    "total_time": 4.343388
  },
  "output": [
    "https://replicate.delivery/pbxt/3nXWaSvDV9pFHtyOsWxScx9kfrZegzN4BbQ3z6LOie9MwKllA/ComfyUI_00001_.webp"
  ],
  "started_at": "2024-05-09T10:22:58.542800Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/fdjhdp59rsrgj0cfbkgb70mt94",
    "cancel": "https://api.replicate.com/v1/predictions/fdjhdp59rsrgj0cfbkgb70mt94/cancel"
  },
  "version": "a0321a0cef20aa9e0828f8a9873dc3fd019266bc929f2fb8d70b76d52e122382"
}

Generated in

4.3 seconds

Tweak it ShareReport

Examples

View more examples

Run time and cost

This model costs approximately $0.0047 to run on Replicate, or 212 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 5 seconds. The predict time for this model varies significantly based on the inputs.

Readme

Follow me on X @fofrAI

This model uses:

ComfyUI
PuLID_ComfyUI by @cubiq

It uses the following fine-tuned lightning weights:

dreamshaperXL_lightningDPMSDE.safetensors
ProteusV0.4-Lighting.safetensors
Juggernaut_RunDiffusionPhoto2_Lightning_4Steps.safetensors

As part of its face detection is uses InsightFace’s antelopev2 model, which cannot be used commercially.