anhappdev/test:1ae4f947 | Run with an API on Replicate

Version

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anhappdev /test:1ae4f947

Input

prompt

string

Shift + Return to add a new line

A big beautiful++ Christmas++ tree++++ in a living room--A big beautiful++ Christmas++ tree++++ in a living room--

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: 7.5

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=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";
import fs from "node:fs";

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:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e",
  {
    input: {
      seed: 123,
      image: "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg",
      prompt: "A big beautiful++ Christmas++ tree++++ in a living room--",
      mask_image: "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg",
      num_outputs: 3,
      guidance_scale: 7.5,
      negative_prompt: "",
      num_inference_steps: 40
    }
  }
);

// 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:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e",
    input={
        "seed": 123,
        "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg",
        "prompt": "A big beautiful++ Christmas++ tree++++ in a living room--",
        "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg",
        "num_outputs": 3,
        "guidance_scale": 7.5,
        "negative_prompt": "",
        "num_inference_steps": 40
    }
)
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:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e",
    "input": {
      "seed": 123,
      "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg",
      "prompt": "A big beautiful++ Christmas++ tree++++ in a living room--",
      "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg",
      "num_outputs": 3,
      "guidance_scale": 7.5,
      "negative_prompt": "",
      "num_inference_steps": 40
    }
  }' \
  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/anhappdev/test@sha256:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e \
  -i 'seed=123' \
  -i 'image="https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg"' \
  -i 'prompt="A big beautiful++ Christmas++ tree++++ in a living room--"' \
  -i 'mask_image="https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg"' \
  -i 'num_outputs=3' \
  -i 'guidance_scale=7.5' \
  -i 'negative_prompt=""' \
  -i 'num_inference_steps=40'

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/anhappdev/test@sha256:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 123,
      "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg",
      "prompt": "A big beautiful++ Christmas++ tree++++ in a living room--",
      "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg",
      "num_outputs": 3,
      "guidance_scale": 7.5,
      "negative_prompt": "",
      "num_inference_steps": 40
    }
  }' \
  http://localhost:5000/predictions

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

Output

{
  "completed_at": "2023-04-14T12:05:54.408925Z",
  "created_at": "2023-04-14T12:05:45.000517Z",
  "data_removed": false,
  "error": null,
  "id": "iqkruiv7vrccpbi5hnzaq7qnwy",
  "input": {
    "seed": 123,
    "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg",
    "prompt": "A big beautiful++ Christmas++ tree++++ in a living room--",
    "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg",
    "num_outputs": 3,
    "guidance_scale": 7.5,
    "num_inference_steps": 40
  },
  "logs": "2023-04-14 12:05:46,224 [INFO] :: Running pipeline using stabilityai/stable-diffusion-2-inpainting\n2023-04-14 12:05:46,224 [INFO] :: Using seed: 123\n  0%|          | 0/40 [00:00<?, ?it/s]\n  2%|▎         | 1/40 [00:00<00:09,  4.09it/s]\n  8%|▊         | 3/40 [00:00<00:05,  7.35it/s]\n 12%|█▎        | 5/40 [00:00<00:04,  8.61it/s]\n 18%|█▊        | 7/40 [00:00<00:03,  9.24it/s]\n 22%|██▎       | 9/40 [00:01<00:03,  9.60it/s]\n 28%|██▊       | 11/40 [00:01<00:02,  9.82it/s]\n 32%|███▎      | 13/40 [00:01<00:02,  9.96it/s]\n 38%|███▊      | 15/40 [00:01<00:02, 10.06it/s]\n 42%|████▎     | 17/40 [00:01<00:02, 10.13it/s]\n 48%|████▊     | 19/40 [00:01<00:02, 10.17it/s]\n 52%|█████▎    | 21/40 [00:02<00:01, 10.19it/s]\n 57%|█████▊    | 23/40 [00:02<00:01, 10.20it/s]\n 62%|██████▎   | 25/40 [00:02<00:01, 10.22it/s]\n 68%|██████▊   | 27/40 [00:02<00:01, 10.23it/s]\n 72%|███████▎  | 29/40 [00:02<00:01, 10.24it/s]\n 78%|███████▊  | 31/40 [00:03<00:00, 10.25it/s]\n 82%|████████▎ | 33/40 [00:03<00:00, 10.20it/s]\n 88%|████████▊ | 35/40 [00:03<00:00, 10.22it/s]\n 92%|█████████▎| 37/40 [00:03<00:00, 10.23it/s]\n 98%|█████████▊| 39/40 [00:03<00:00, 10.23it/s]\n100%|██████████| 40/40 [00:04<00:00,  9.87it/s]",
  "metrics": {
    "predict_time": 9.299582,
    "total_time": 9.408408
  },
  "output": [
    "https://replicate.delivery/pbxt/CGLr7LVy6WayPdxuHgJoCaMG8Ew9t2sMonu2nSA0IKTozbME/out-0.jpeg",
    "https://replicate.delivery/pbxt/NCMbRaT383bwBden1l9S98iTlfvIyC3cluwKWmPjXyGiOvxQA/out-1.jpeg",
    "https://replicate.delivery/pbxt/2liMFvmZegSRB6eeEruHYKKexllY2V2oPFeQzXHIBBcQ05NGC/out-2.jpeg"
  ],
  "started_at": "2023-04-14T12:05:45.109343Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/iqkruiv7vrccpbi5hnzaq7qnwy",
    "cancel": "https://api.replicate.com/v1/predictions/iqkruiv7vrccpbi5hnzaq7qnwy/cancel"
  },
  "version": "1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e"
}

Generated in

9.3 seconds

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