anhappdev/test:1e4a0597 | Run with an API on Replicate

Version

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anhappdev /test:1e4a0597

Input

prompt

string

Shift + Return to add a new line

a black++ leather++ couch in a living room-- in front of wall--a black++ leather++ couch in a living room-- in front of wall--

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

preprocessing

string

Max image size is 512x512. Bigger image will be resized.

Default: "pad"

num_outputs

integer

(minimum: 1, maximum: 4)

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

Default: 2

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:1e4a05970ece5b18cf3670cf2b5fe4a97bd595853ec19abced65d8fe9c98def9",
  {
    input: {
      image: "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg",
      prompt: "a black++ leather++ couch in a living room-- in front of wall--",
      mask_image: "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg",
      num_outputs: 3,
      preprocessing: "pad",
      guidance_scale: 7.5,
      negative_prompt: "",
      num_inference_steps: 30
    }
  }
);

// 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:1e4a05970ece5b18cf3670cf2b5fe4a97bd595853ec19abced65d8fe9c98def9",
    input={
        "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg",
        "prompt": "a black++ leather++ couch in a living room-- in front of wall--",
        "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg",
        "num_outputs": 3,
        "preprocessing": "pad",
        "guidance_scale": 7.5,
        "negative_prompt": "",
        "num_inference_steps": 30
    }
)
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:1e4a05970ece5b18cf3670cf2b5fe4a97bd595853ec19abced65d8fe9c98def9",
    "input": {
      "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg",
      "prompt": "a black++ leather++ couch in a living room-- in front of wall--",
      "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg",
      "num_outputs": 3,
      "preprocessing": "pad",
      "guidance_scale": 7.5,
      "negative_prompt": "",
      "num_inference_steps": 30
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2023-04-02T07:54:29.142719Z",
  "created_at": "2023-04-02T07:54:02.912224Z",
  "data_removed": false,
  "error": null,
  "id": "oyj2r4rnfbhlrnecq2vlo22s3q",
  "input": {
    "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg",
    "prompt": "a black++ leather++ couch in a living room-- in front of wall--",
    "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg",
    "num_outputs": 3,
    "preprocessing": "pad",
    "guidance_scale": 7.5,
    "num_inference_steps": 30
  },
  "logs": "2023-04-02 07:54:03,638 [DEBUG] main.py.predict():162 :: Running pipeline using stabilityai/stable-diffusion-2-inpainting\n2023-04-02 07:54:03,639 [INFO] main.py.predict():163 :: Using seed: 5245\n2023-04-02 07:54:03,639 [DEBUG] main.py.predict():164 :: Using preprocessing_choice: pad\n2023-04-02 07:54:03,908 [DEBUG] TiffImagePlugin.py.load():856 :: tag: Orientation (274) - type: short (3) - value: b'\\x00\\x01'\n2023-04-02 07:54:03,908 [DEBUG] TiffImagePlugin.py.load():856 :: tag: ExifIFD (34665) - type: long (4) - value: b'\\x00\\x00\\x00&'\n2023-04-02 07:54:05,398 [DEBUG] main.py.predict():178 :: Using prompt: RAW photo, a black++ leather++ couch in a living room-- in front of wall--, 8k uhd, dslr, high quality, high resolution\n2023-04-02 07:54:05,398 [DEBUG] main.py.predict():179 :: Using negative_prompt: , sketch, cartoon, drawing, anime, deformed, distorted, disfigured\n  0%|          | 0/30 [00:00<?, ?it/s]\n  3%|▎         | 1/30 [00:01<00:40,  1.39s/it]\n  7%|▋         | 2/30 [00:01<00:25,  1.08it/s]\n 10%|█         | 3/30 [00:02<00:21,  1.27it/s]\n 13%|█▎        | 4/30 [00:03<00:18,  1.38it/s]\n 17%|█▋        | 5/30 [00:03<00:17,  1.46it/s]\n 20%|██        | 6/30 [00:04<00:15,  1.50it/s]\n 23%|██▎       | 7/30 [00:05<00:15,  1.53it/s]\n 27%|██▋       | 8/30 [00:05<00:14,  1.55it/s]\n 30%|███       | 9/30 [00:06<00:13,  1.55it/s]\n 33%|███▎      | 10/30 [00:07<00:12,  1.57it/s]\n 37%|███▋      | 11/30 [00:07<00:12,  1.57it/s]\n 40%|████      | 12/30 [00:08<00:11,  1.57it/s]\n 43%|████▎     | 13/30 [00:08<00:10,  1.57it/s]\n 47%|████▋     | 14/30 [00:09<00:10,  1.56it/s]\n 50%|█████     | 15/30 [00:10<00:09,  1.56it/s]\n 53%|█████▎    | 16/30 [00:10<00:08,  1.56it/s]\n 57%|█████▋    | 17/30 [00:11<00:08,  1.55it/s]\n 60%|██████    | 18/30 [00:12<00:07,  1.55it/s]\n 63%|██████▎   | 19/30 [00:12<00:07,  1.54it/s]\n 67%|██████▋   | 20/30 [00:13<00:06,  1.54it/s]\n 70%|███████   | 21/30 [00:14<00:05,  1.54it/s]\n 73%|███████▎  | 22/30 [00:14<00:05,  1.54it/s]\n 77%|███████▋  | 23/30 [00:15<00:04,  1.53it/s]\n 80%|████████  | 24/30 [00:16<00:03,  1.53it/s]\n 83%|████████▎ | 25/30 [00:16<00:03,  1.53it/s]\n 87%|████████▋ | 26/30 [00:17<00:02,  1.53it/s]\n 90%|█████████ | 27/30 [00:18<00:01,  1.52it/s]\n 93%|█████████▎| 28/30 [00:18<00:01,  1.51it/s]\n 97%|█████████▋| 29/30 [00:19<00:00,  1.51it/s]\n100%|██████████| 30/30 [00:20<00:00,  1.51it/s]\n100%|██████████| 30/30 [00:20<00:00,  1.50it/s]\n2023-04-02 07:54:26,723 [DEBUG] main.py.predict():197 :: Saved to /content/drive/MyDrive/tmp/images/out/out-0.png\n2023-04-02 07:54:26,846 [DEBUG] main.py.predict():197 :: Saved to /content/drive/MyDrive/tmp/images/out/out-1.png\n2023-04-02 07:54:26,961 [DEBUG] main.py.predict():197 :: Saved to /content/drive/MyDrive/tmp/images/out/out-2.png",
  "metrics": {
    "predict_time": 26.070355,
    "total_time": 26.230495
  },
  "output": [
    "https://replicate.delivery/pbxt/pUH6mzB5ARYFMdKDWjd7vxXu2SeRtc36GkenL8fQLC1l1cbhA/out-0.png",
    "https://replicate.delivery/pbxt/6uOXSpdgDVLGJhhVVtV6lsitWTuXlHAvKrzPUeFPeoDzautQA/out-1.png",
    "https://replicate.delivery/pbxt/aQelQG63kSTcUqy9wBQkEDJT520ovmEv2gcwK6w6lDLaN3WIA/out-2.png"
  ],
  "started_at": "2023-04-02T07:54:03.072364Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/oyj2r4rnfbhlrnecq2vlo22s3q",
    "cancel": "https://api.replicate.com/v1/predictions/oyj2r4rnfbhlrnecq2vlo22s3q/cancel"
  },
  "version": "1e4a05970ece5b18cf3670cf2b5fe4a97bd595853ec19abced65d8fe9c98def9"
}

Generated in

26.1 seconds

Tweak it Report View full prediction

2023-04-02 07:54:03,638 [DEBUG] main.py.predict():162 :: Running pipeline using stabilityai/stable-diffusion-2-inpainting
2023-04-02 07:54:03,639 [INFO] main.py.predict():163 :: Using seed: 5245
2023-04-02 07:54:03,639 [DEBUG] main.py.predict():164 :: Using preprocessing_choice: pad
2023-04-02 07:54:03,908 [DEBUG] TiffImagePlugin.py.load():856 :: tag: Orientation (274) - type: short (3) - value: b'\x00\x01'
2023-04-02 07:54:03,908 [DEBUG] TiffImagePlugin.py.load():856 :: tag: ExifIFD (34665) - type: long (4) - value: b'\x00\x00\x00&'
2023-04-02 07:54:05,398 [DEBUG] main.py.predict():178 :: Using prompt: RAW photo, a black++ leather++ couch in a living room-- in front of wall--, 8k uhd, dslr, high quality, high resolution
2023-04-02 07:54:05,398 [DEBUG] main.py.predict():179 :: Using negative_prompt: , sketch, cartoon, drawing, anime, deformed, distorted, disfigured
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2023-04-02 07:54:26,723 [DEBUG] main.py.predict():197 :: Saved to /content/drive/MyDrive/tmp/images/out/out-0.png
2023-04-02 07:54:26,846 [DEBUG] main.py.predict():197 :: Saved to /content/drive/MyDrive/tmp/images/out/out-1.png
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