Prediction

Model

anhappdev/test:1e4a0597

oyj2r4rnfbhlrnecq2vlo22s3q

Status

Succeeded

Source

Web

Hardware

–

Total duration

26.2s

Created

over 2 years ago

Webhook

–

Input

prompt: a black++ leather++ couch in a living room-- in front of wall--
image: https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg
mask_image: https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg
preprocessing: pad
num_outputs: 3
num_inference_steps: 30
guidance_scale: 7.5

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

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_UPm**********************************

This is your API token. Keep it to yourself.

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

// 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=r8_UPm**********************************

This is your API token. Keep it to yourself.

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

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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=r8_UPm**********************************

This is your API token. Keep it to yourself.

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

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

Output

{
  "id": "oyj2r4rnfbhlrnecq2vlo22s3q",
  "model": "anhappdev/test",
  "version": "1e4a05970ece5b18cf3670cf2b5fe4a97bd595853ec19abced65d8fe9c98def9",
  "input": {
    "guidance_scale": 7.5,
    "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg",
    "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg",
    "num_inference_steps": 30,
    "num_outputs": 3,
    "preprocessing": "pad",
    "prompt": "a black++ leather++ couch in a living room-- in front of wall--"
  },
  "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",
  "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"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2023-04-02T07:54:02.912224Z",
  "started_at": "2023-04-02T07:54:03.072364Z",
  "completed_at": "2023-04-02T07:54:29.142719Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/oyj2r4rnfbhlrnecq2vlo22s3q/cancel",
    "get": "https://api.replicate.com/v1/predictions/oyj2r4rnfbhlrnecq2vlo22s3q"
  },
  "metrics": {
    "predict_time": 26.070355,
    "total_time": 26.230495
  }
}

Generated in

26.1 seconds

Tweak it Report

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 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:01<00:40, 1.39s/it] 7%|▋ | 2/30 [00:01<00:25, 1.08it/s] 10%|█ | 3/30 [00:02<00:21, 1.27it/s] 13%|█▎ | 4/30 [00:03<00:18, 1.38it/s] 17%|█▋ | 5/30 [00:03<00:17, 1.46it/s] 20%|██ | 6/30 [00:04<00:15, 1.50it/s] 23%|██▎ | 7/30 [00:05<00:15, 1.53it/s] 27%|██▋ | 8/30 [00:05<00:14, 1.55it/s] 30%|███ | 9/30 [00:06<00:13, 1.55it/s] 33%|███▎ | 10/30 [00:07<00:12, 1.57it/s] 37%|███▋ | 11/30 [00:07<00:12, 1.57it/s] 40%|████ | 12/30 [00:08<00:11, 1.57it/s] 43%|████▎ | 13/30 [00:08<00:10, 1.57it/s] 47%|████▋ | 14/30 [00:09<00:10, 1.56it/s] 50%|█████ | 15/30 [00:10<00:09, 1.56it/s] 53%|█████▎ | 16/30 [00:10<00:08, 1.56it/s] 57%|█████▋ | 17/30 [00:11<00:08, 1.55it/s] 60%|██████ | 18/30 [00:12<00:07, 1.55it/s] 63%|██████▎ | 19/30 [00:12<00:07, 1.54it/s] 67%|██████▋ | 20/30 [00:13<00:06, 1.54it/s] 70%|███████ | 21/30 [00:14<00:05, 1.54it/s] 73%|███████▎ | 22/30 [00:14<00:05, 1.54it/s] 77%|███████▋ | 23/30 [00:15<00:04, 1.53it/s] 80%|████████ | 24/30 [00:16<00:03, 1.53it/s] 83%|████████▎ | 25/30 [00:16<00:03, 1.53it/s] 87%|████████▋ | 26/30 [00:17<00:02, 1.53it/s] 90%|█████████ | 27/30 [00:18<00:01, 1.52it/s] 93%|█████████▎| 28/30 [00:18<00:01, 1.51it/s] 97%|█████████▋| 29/30 [00:19<00:00, 1.51it/s] 100%|██████████| 30/30 [00:20<00:00, 1.51it/s] 100%|██████████| 30/30 [00:20<00:00, 1.50it/s] 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 2023-04-02 07:54:26,961 [DEBUG] main.py.predict():197 :: Saved to /content/drive/MyDrive/tmp/images/out/out-2.png