lucataco/ip_adapter-face – Run with an API on Replicate

lucataco / ip_adapter-face

The image prompt adapter is designed to enable a pretrained text-to-image diffusion model to generate SDv1.5 images with an image prompt

Warm

Public
2.4K runs
L40S
GitHub
Paper
License

Run with an API

Playground API Examples README Versions

Input

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

npx create-replicate --model=lucataco/ip_adapter-face

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

const output = await replicate.run(
  "lucataco/ip_adapter-face:b6618893d3d0ee4a9b3a1e990a00be6bd6c3b6fddef83e0b68efc525de54580c",
  {
    input: {
      seed: 15251,
      image: "https://replicate.delivery/pbxt/Jqd79M3DQVfYYvYcZYURBMxc0kd3oeM9Z5jiYSUONnHEfhlW/ai_face2.png",
      prompt: "photo of a beautiful girl wearing casual shirt in a garden",
      num_outputs: 1,
      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.

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

output = replicate.run(
    "lucataco/ip_adapter-face:b6618893d3d0ee4a9b3a1e990a00be6bd6c3b6fddef83e0b68efc525de54580c",
    input={
        "seed": 15251,
        "image": "https://replicate.delivery/pbxt/Jqd79M3DQVfYYvYcZYURBMxc0kd3oeM9Z5jiYSUONnHEfhlW/ai_face2.png",
        "prompt": "photo of a beautiful girl wearing casual shirt in a garden",
        "num_outputs": 1,
        "num_inference_steps": 50
    }
)
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/ip_adapter-face 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/ip_adapter-face:b6618893d3d0ee4a9b3a1e990a00be6bd6c3b6fddef83e0b68efc525de54580c",
    "input": {
      "seed": 15251,
      "image": "https://replicate.delivery/pbxt/Jqd79M3DQVfYYvYcZYURBMxc0kd3oeM9Z5jiYSUONnHEfhlW/ai_face2.png",
      "prompt": "photo of a beautiful girl wearing casual shirt in a garden",
      "num_outputs": 1,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2023-11-09T16:42:36.834553Z",
  "created_at": "2023-11-09T16:42:29.064071Z",
  "data_removed": false,
  "error": null,
  "id": "vap6gtdbtrmhk2tlhdhnvz4yzq",
  "input": {
    "seed": 15251,
    "image": "https://replicate.delivery/pbxt/Jqd79M3DQVfYYvYcZYURBMxc0kd3oeM9Z5jiYSUONnHEfhlW/ai_face2.png",
    "prompt": "photo of a beautiful girl wearing casual shirt in a garden",
    "num_outputs": 1,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 15251\n  0%|          | 0/50 [00:00<?, ?it/s]\n  6%|▌         | 3/50 [00:00<00:01, 23.79it/s]\n 12%|█▏        | 6/50 [00:00<00:01, 23.63it/s]\n 18%|█▊        | 9/50 [00:00<00:01, 23.58it/s]\n 24%|██▍       | 12/50 [00:00<00:01, 23.56it/s]\n 30%|███       | 15/50 [00:00<00:01, 23.52it/s]\n 36%|███▌      | 18/50 [00:00<00:01, 23.49it/s]\n 42%|████▏     | 21/50 [00:00<00:01, 23.45it/s]\n 48%|████▊     | 24/50 [00:01<00:01, 23.44it/s]\n 54%|█████▍    | 27/50 [00:01<00:00, 23.41it/s]\n 60%|██████    | 30/50 [00:01<00:00, 23.39it/s]\n 66%|██████▌   | 33/50 [00:01<00:00, 23.39it/s]\n 72%|███████▏  | 36/50 [00:01<00:00, 23.38it/s]\n 78%|███████▊  | 39/50 [00:01<00:00, 23.40it/s]\n 84%|████████▍ | 42/50 [00:01<00:00, 23.38it/s]\n 90%|█████████ | 45/50 [00:01<00:00, 23.39it/s]\n 96%|█████████▌| 48/50 [00:02<00:00, 23.35it/s]\n100%|██████████| 50/50 [00:02<00:00, 23.42it/s]",
  "metrics": {
    "predict_time": 7.79416,
    "total_time": 7.770482
  },
  "output": [
    "https://replicate.delivery/pbxt/udBop2Tbe0zNaSqQEESSgmA5PDRuGJhxxnfLnlng5e24vXtjA/out-0.png"
  ],
  "started_at": "2023-11-09T16:42:29.040393Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/vap6gtdbtrmhk2tlhdhnvz4yzq",
    "cancel": "https://api.replicate.com/v1/predictions/vap6gtdbtrmhk2tlhdhnvz4yzq/cancel"
  },
  "version": "b6618893d3d0ee4a9b3a1e990a00be6bd6c3b6fddef83e0b68efc525de54580c"
}

Generated in

7.8 seconds

Tweak itReport

Examples

View more examples

Run time and cost

This model costs approximately $0.11 to run on Replicate, or 9 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 111 seconds. The predict time for this model varies significantly based on the inputs.

Readme

Implementation of ip_adapter-plus-face_demo For Stable Diffusion v1.5

@article{ye2023ip-adapter,
  title={IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models},
  author={Ye, Hu and Zhang, Jun and Liu, Sibo and Han, Xiao and Yang, Wei},
  booktitle={arXiv preprint arxiv:2308.06721},
  year={2023}
}