anhappdev/test – Run with an API on Replicate

Input

method

string

The method to use

Default: "remove"

prompt

string

Shift + Return to add a new line

The prompt to guide the image generation. Use ++ to emphasize and -- to de-emphasize parts of the sentence

Default: "a photo of an astronaut++ riding a horse on mars"

negative_prompt

string

Shift + Return to add a new line

Specify things to not see in the output

Default: ""

image

*file

Preview

The image which will be inpainted.Parts of the image will be masked out with `mask_image` and repainted according to `prompt`.

mask_image

*file

Preview

A black and white image to use as mask for inpainting over the image provided. White pixels in the mask will be repainted, while black pixels will be preserved

num_outputs

integer

(minimum: 1, maximum: 8)

Number of images to output. Higher number of outputs may cause OOM.

Default: 3

num_inference_steps

integer

(minimum: 1, maximum: 50)

The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference

Default: 30

guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance

Default: 5

seed

integer

Random seed. Leave blank to randomize the seed

preview_input_image

boolean

Include the input image with mask overlay in the output

Default: false

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

npx create-replicate --model=anhappdev/test

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

const output = await replicate.run(
  "anhappdev/test:cb08e051a7ca58867efff571ac440027a55979e49b2d97008dcdc63ee32687ac",
  {
    input: {
      seed: 12345,
      image: "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg",
      method: "replace",
      prompt: "a black leather couch",
      mask_image: "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg",
      num_outputs: 3,
      guidance_scale: 5,
      negative_prompt: "",
      num_inference_steps: 20,
      preview_input_image: true
    }
  }
);

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

output = replicate.run(
    "anhappdev/test:cb08e051a7ca58867efff571ac440027a55979e49b2d97008dcdc63ee32687ac",
    input={
        "seed": 12345,
        "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg",
        "method": "replace",
        "prompt": "a black leather couch",
        "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg",
        "num_outputs": 3,
        "guidance_scale": 5,
        "negative_prompt": "",
        "num_inference_steps": 20,
        "preview_input_image": True
    }
)
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 anhappdev/test 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": "anhappdev/test:cb08e051a7ca58867efff571ac440027a55979e49b2d97008dcdc63ee32687ac",
    "input": {
      "seed": 12345,
      "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg",
      "method": "replace",
      "prompt": "a black leather couch",
      "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg",
      "num_outputs": 3,
      "guidance_scale": 5,
      "negative_prompt": "",
      "num_inference_steps": 20,
      "preview_input_image": true
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2023-05-14T03:54:56.567211Z",
  "created_at": "2023-05-14T03:54:41.023753Z",
  "data_removed": false,
  "error": null,
  "id": "46u5gqzuynd4novpfvyvwllk34",
  "input": {
    "seed": 12345,
    "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg",
    "method": "replace",
    "prompt": "a black leather couch",
    "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg",
    "num_outputs": 3,
    "guidance_scale": 5,
    "num_inference_steps": 20,
    "preview_input_image": true
  },
  "logs": "2023-05-14 03:54:42,165 [INFO] :: Using seed: 12345\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:00<00:04,  4.11it/s]\n 10%|█         | 2/20 [00:00<00:03,  5.28it/s]\n 15%|█▌        | 3/20 [00:00<00:02,  5.81it/s]\n 20%|██        | 4/20 [00:00<00:02,  6.11it/s]\n 25%|██▌       | 5/20 [00:00<00:02,  6.27it/s]\n 30%|███       | 6/20 [00:01<00:02,  6.38it/s]\n 35%|███▌      | 7/20 [00:01<00:02,  6.45it/s]\n 40%|████      | 8/20 [00:01<00:01,  6.52it/s]\n 45%|████▌     | 9/20 [00:01<00:01,  6.53it/s]\n 50%|█████     | 10/20 [00:01<00:01,  6.56it/s]\n 55%|█████▌    | 11/20 [00:01<00:01,  6.58it/s]\n 60%|██████    | 12/20 [00:01<00:01,  6.59it/s]\n 65%|██████▌   | 13/20 [00:02<00:01,  6.59it/s]\n 70%|███████   | 14/20 [00:02<00:00,  6.59it/s]\n 75%|███████▌  | 15/20 [00:02<00:00,  6.59it/s]\n 80%|████████  | 16/20 [00:02<00:00,  6.58it/s]\n 85%|████████▌ | 17/20 [00:02<00:00,  6.59it/s]\n 90%|█████████ | 18/20 [00:02<00:00,  6.59it/s]\n 95%|█████████▌| 19/20 [00:02<00:00,  6.58it/s]\n100%|██████████| 20/20 [00:03<00:00,  6.60it/s]\n100%|██████████| 20/20 [00:03<00:00,  6.41it/s]",
  "metrics": {
    "predict_time": 15.460722,
    "total_time": 15.543458
  },
  "output": [
    "https://replicate.delivery/pbxt/fwlYIMKSiIzfoEcWeRsL6hbGMQJDnYvm43a7zXMpxkvbsB3hA/replace-masked-image.jpg",
    "https://replicate.delivery/pbxt/MPfJSJobh00vIaNRMnp27FgLSTr1BokKfJ5zpPZDFEEO2g7QA/replace-out-0.jpg",
    "https://replicate.delivery/pbxt/IYeL9GKP7Yy0eUp5jQS6RSiJmPR5OkJbM7Xvcb71hs9P2g7QA/replace-out-1.jpg",
    "https://replicate.delivery/pbxt/SjAZOny5wL57DtUQ64aL636sEcoK8LSedZm7jjU1DlpHbwdIA/replace-out-2.jpg"
  ],
  "started_at": "2023-05-14T03:54:41.106489Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/46u5gqzuynd4novpfvyvwllk34",
    "cancel": "https://api.replicate.com/v1/predictions/46u5gqzuynd4novpfvyvwllk34/cancel"
  },
  "version": "c72d8651fe7f8e534dca29c636156b681b976253673a1a7d6c397224b3c6cb8e"
}

Generated in

15.5 seconds

Tweak itReport

This output was created using a different version of the model, anhappdev/test:c72d8651.

Run time and cost

This model costs approximately $0.023 to run on Replicate, or 43 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 A100 (80GB) GPU hardware. Predictions typically complete within 17 seconds.

anhappdev / test

Input

Output

Examples

Run time and cost

Readme

Logs (46u5gqzuynd4novpfvyvwllk34)