Prediction

dc4dqllbowaiqmef53cpihtj5m

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

4m 47s

Created

over 1 year ago

Input

image
prompt: a perfect mirrored reflective chrome ball sphere
negative_prompt: matte, diffuse, flat, dull
num_inference_steps: 30
controlnet_conditioning_scale: 0.5

{
  "image": "https://replicate.delivery/pbxt/KJoE5o7FxdBpbyaooud9PQWSmzB44JjprzEQ32hiUaZN32Sr/bed.png",
  "prompt": "a perfect mirrored reflective chrome ball sphere",
  "negative_prompt": "matte, diffuse, flat, dull",
  "num_inference_steps": 30,
  "controlnet_conditioning_scale": 0.5
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "lucataco/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
  {
    input: {
      image: "https://replicate.delivery/pbxt/KJoE5o7FxdBpbyaooud9PQWSmzB44JjprzEQ32hiUaZN32Sr/bed.png",
      prompt: "a perfect mirrored reflective chrome ball sphere",
      negative_prompt: "matte, diffuse, flat, dull",
      num_inference_steps: 30,
      controlnet_conditioning_scale: 0.5
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "lucataco/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
    input={
        "image": "https://replicate.delivery/pbxt/KJoE5o7FxdBpbyaooud9PQWSmzB44JjprzEQ32hiUaZN32Sr/bed.png",
        "prompt": "a perfect mirrored reflective chrome ball sphere",
        "negative_prompt": "matte, diffuse, flat, dull",
        "num_inference_steps": 30,
        "controlnet_conditioning_scale": 0.5
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run lucataco/diffusionlight 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/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
    "input": {
      "image": "https://replicate.delivery/pbxt/KJoE5o7FxdBpbyaooud9PQWSmzB44JjprzEQ32hiUaZN32Sr/bed.png",
      "prompt": "a perfect mirrored reflective chrome ball sphere",
      "negative_prompt": "matte, diffuse, flat, dull",
      "num_inference_steps": 30,
      "controlnet_conditioning_scale": 0.5
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-01-30T21:42:37.656926Z",
  "created_at": "2024-01-30T21:37:50.625579Z",
  "data_removed": false,
  "error": null,
  "id": "dc4dqllbowaiqmef53cpihtj5m",
  "input": {
    "image": "https://replicate.delivery/pbxt/KJoE5o7FxdBpbyaooud9PQWSmzB44JjprzEQ32hiUaZN32Sr/bed.png",
    "prompt": "a perfect mirrored reflective chrome ball sphere",
    "negative_prompt": "matte, diffuse, flat, dull",
    "num_inference_steps": 30,
    "controlnet_conditioning_scale": 0.5
  },
  "logs": "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)\nreturn _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n  0%|          | 0/29 [00:00<?, ?it/s]\n  3%|▎         | 1/29 [00:00<00:09,  2.81it/s]\n  7%|▋         | 2/29 [00:00<00:08,  3.10it/s]\n 10%|█         | 3/29 [00:00<00:08,  3.21it/s]\n 14%|█▍        | 4/29 [00:01<00:07,  3.26it/s]\n 17%|█▋        | 5/29 [00:01<00:07,  3.30it/s]\n 21%|██        | 6/29 [00:01<00:06,  3.31it/s]\n 24%|██▍       | 7/29 [00:02<00:06,  3.32it/s]\n 28%|██▊       | 8/29 [00:02<00:06,  3.33it/s]\n 31%|███       | 9/29 [00:02<00:06,  3.33it/s]\n 34%|███▍      | 10/29 [00:03<00:05,  3.34it/s]\n 38%|███▊      | 11/29 [00:03<00:05,  3.34it/s]\n 41%|████▏     | 12/29 [00:03<00:05,  3.34it/s]\n 45%|████▍     | 13/29 [00:03<00:04,  3.34it/s]\n 48%|████▊     | 14/29 [00:04<00:04,  3.34it/s]\n 52%|█████▏    | 15/29 [00:04<00:04,  3.33it/s]\n 55%|█████▌    | 16/29 [00:04<00:03,  3.33it/s]\n 59%|█████▊    | 17/29 [00:05<00:03,  3.33it/s]\n 62%|██████▏   | 18/29 [00:05<00:03,  3.33it/s]\n 66%|██████▌   | 19/29 [00:05<00:03,  3.33it/s]\n 69%|██████▉   | 20/29 [00:06<00:02,  3.33it/s]\n 72%|███████▏  | 21/29 [00:06<00:02,  3.33it/s]\n 76%|███████▌  | 22/29 [00:06<00:02,  3.33it/s]\n 79%|███████▉  | 23/29 [00:06<00:01,  3.33it/s]\n 83%|████████▎ | 24/29 [00:07<00:01,  3.33it/s]\n 86%|████████▌ | 25/29 [00:07<00:01,  3.33it/s]\n 90%|████████▉ | 26/29 [00:07<00:00,  3.33it/s]\n 93%|█████████▎| 27/29 [00:08<00:00,  3.33it/s]\n 97%|█████████▋| 28/29 [00:08<00:00,  3.32it/s]\n100%|██████████| 29/29 [00:08<00:00,  3.32it/s]\n100%|██████████| 29/29 [00:08<00:00,  3.31it/s]",
  "metrics": {
    "predict_time": 13.74481,
    "total_time": 287.031347
  },
  "output": "https://replicate.delivery/pbxt/YqYtularwBrQP9e3foKhaQyeA21k5du04umfYA3eBqqnpPOSC/output.png",
  "started_at": "2024-01-30T21:42:23.912116Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/dc4dqllbowaiqmef53cpihtj5m",
    "cancel": "https://api.replicate.com/v1/predictions/dc4dqllbowaiqmef53cpihtj5m/cancel"
  },
  "version": "958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef"
}

Generated in

13.8 seconds

lucataco/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef

/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)
return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:00<00:09,  2.81it/s]
  7%|▋         | 2/29 [00:00<00:08,  3.10it/s]
 10%|█         | 3/29 [00:00<00:08,  3.21it/s]
 14%|█▍        | 4/29 [00:01<00:07,  3.26it/s]
 17%|█▋        | 5/29 [00:01<00:07,  3.30it/s]
 21%|██        | 6/29 [00:01<00:06,  3.31it/s]
 24%|██▍       | 7/29 [00:02<00:06,  3.32it/s]
 28%|██▊       | 8/29 [00:02<00:06,  3.33it/s]
 31%|███       | 9/29 [00:02<00:06,  3.33it/s]
 34%|███▍      | 10/29 [00:03<00:05,  3.34it/s]
 38%|███▊      | 11/29 [00:03<00:05,  3.34it/s]
 41%|████▏     | 12/29 [00:03<00:05,  3.34it/s]
 45%|████▍     | 13/29 [00:03<00:04,  3.34it/s]
 48%|████▊     | 14/29 [00:04<00:04,  3.34it/s]
 52%|█████▏    | 15/29 [00:04<00:04,  3.33it/s]
 55%|█████▌    | 16/29 [00:04<00:03,  3.33it/s]
 59%|█████▊    | 17/29 [00:05<00:03,  3.33it/s]
 62%|██████▏   | 18/29 [00:05<00:03,  3.33it/s]
 66%|██████▌   | 19/29 [00:05<00:03,  3.33it/s]
 69%|██████▉   | 20/29 [00:06<00:02,  3.33it/s]
 72%|███████▏  | 21/29 [00:06<00:02,  3.33it/s]
 76%|███████▌  | 22/29 [00:06<00:02,  3.33it/s]
 79%|███████▉  | 23/29 [00:06<00:01,  3.33it/s]
 83%|████████▎ | 24/29 [00:07<00:01,  3.33it/s]
 86%|████████▌ | 25/29 [00:07<00:01,  3.33it/s]
 90%|████████▉ | 26/29 [00:07<00:00,  3.33it/s]
 93%|█████████▎| 27/29 [00:08<00:00,  3.33it/s]
 97%|█████████▋| 28/29 [00:08<00:00,  3.32it/s]
100%|██████████| 29/29 [00:08<00:00,  3.32it/s]
100%|██████████| 29/29 [00:08<00:00,  3.31it/s]

Prediction

Model

c3twa5lb3dphdu5rhzrf5lqngm

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

2m 41s

Created

over 1 year ago

Input

image
prompt: a perfect mirrored reflective chrome ball sphere
negative_prompt: matte, diffuse, flat, dull
num_inference_steps: 30
controlnet_conditioning_scale: 0.5

{
  "image": "https://replicate.delivery/pbxt/KJoKQAifOmk7YFHsSVzSabOJdgjb6QXrgyjSCh3rskrUK32a/ski.jpeg",
  "prompt": "a perfect mirrored reflective chrome ball sphere",
  "negative_prompt": "matte, diffuse, flat, dull",
  "num_inference_steps": 30,
  "controlnet_conditioning_scale": 0.5
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "lucataco/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
  {
    input: {
      image: "https://replicate.delivery/pbxt/KJoKQAifOmk7YFHsSVzSabOJdgjb6QXrgyjSCh3rskrUK32a/ski.jpeg",
      prompt: "a perfect mirrored reflective chrome ball sphere",
      negative_prompt: "matte, diffuse, flat, dull",
      num_inference_steps: 30,
      controlnet_conditioning_scale: 0.5
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "lucataco/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
    input={
        "image": "https://replicate.delivery/pbxt/KJoKQAifOmk7YFHsSVzSabOJdgjb6QXrgyjSCh3rskrUK32a/ski.jpeg",
        "prompt": "a perfect mirrored reflective chrome ball sphere",
        "negative_prompt": "matte, diffuse, flat, dull",
        "num_inference_steps": 30,
        "controlnet_conditioning_scale": 0.5
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run lucataco/diffusionlight 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/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
    "input": {
      "image": "https://replicate.delivery/pbxt/KJoKQAifOmk7YFHsSVzSabOJdgjb6QXrgyjSCh3rskrUK32a/ski.jpeg",
      "prompt": "a perfect mirrored reflective chrome ball sphere",
      "negative_prompt": "matte, diffuse, flat, dull",
      "num_inference_steps": 30,
      "controlnet_conditioning_scale": 0.5
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-01-30T21:51:51.655582Z",
  "created_at": "2024-01-30T21:49:10.599328Z",
  "data_removed": false,
  "error": null,
  "id": "c3twa5lb3dphdu5rhzrf5lqngm",
  "input": {
    "image": "https://replicate.delivery/pbxt/KJoKQAifOmk7YFHsSVzSabOJdgjb6QXrgyjSCh3rskrUK32a/ski.jpeg",
    "prompt": "a perfect mirrored reflective chrome ball sphere",
    "negative_prompt": "matte, diffuse, flat, dull",
    "num_inference_steps": 30,
    "controlnet_conditioning_scale": 0.5
  },
  "logs": "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)\nreturn _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n  0%|          | 0/29 [00:00<?, ?it/s]\n  3%|▎         | 1/29 [00:00<00:10,  2.72it/s]\n  7%|▋         | 2/29 [00:00<00:08,  3.09it/s]\n 10%|█         | 3/29 [00:00<00:08,  3.19it/s]\n 14%|█▍        | 4/29 [00:01<00:07,  3.27it/s]\n 17%|█▋        | 5/29 [00:01<00:07,  3.32it/s]\n 21%|██        | 6/29 [00:01<00:06,  3.34it/s]\n 24%|██▍       | 7/29 [00:02<00:06,  3.36it/s]\n 28%|██▊       | 8/29 [00:02<00:06,  3.37it/s]\n 31%|███       | 9/29 [00:02<00:05,  3.38it/s]\n 34%|███▍      | 10/29 [00:03<00:05,  3.38it/s]\n 38%|███▊      | 11/29 [00:03<00:05,  3.38it/s]\n 41%|████▏     | 12/29 [00:03<00:05,  3.39it/s]\n 45%|████▍     | 13/29 [00:03<00:04,  3.39it/s]\n 48%|████▊     | 14/29 [00:04<00:04,  3.39it/s]\n 52%|█████▏    | 15/29 [00:04<00:04,  3.39it/s]\n 55%|█████▌    | 16/29 [00:04<00:03,  3.39it/s]\n 59%|█████▊    | 17/29 [00:05<00:03,  3.38it/s]\n 62%|██████▏   | 18/29 [00:05<00:03,  3.38it/s]\n 66%|██████▌   | 19/29 [00:05<00:02,  3.38it/s]\n 69%|██████▉   | 20/29 [00:05<00:02,  3.38it/s]\n 72%|███████▏  | 21/29 [00:06<00:02,  3.38it/s]\n 76%|███████▌  | 22/29 [00:06<00:02,  3.38it/s]\n 79%|███████▉  | 23/29 [00:06<00:01,  3.38it/s]\n 83%|████████▎ | 24/29 [00:07<00:01,  3.38it/s]\n 86%|████████▌ | 25/29 [00:07<00:01,  3.38it/s]\n 90%|████████▉ | 26/29 [00:07<00:00,  3.38it/s]\n 93%|█████████▎| 27/29 [00:08<00:00,  3.38it/s]\n 97%|█████████▋| 28/29 [00:08<00:00,  3.37it/s]\n100%|██████████| 29/29 [00:08<00:00,  3.37it/s]\n100%|██████████| 29/29 [00:08<00:00,  3.35it/s]",
  "metrics": {
    "predict_time": 12.652394,
    "total_time": 161.056254
  },
  "output": "https://replicate.delivery/pbxt/Vkrkqo4CIxqVGNHcZy6EGY11W5KaBuu6ggRYeFsJ5fN3FyRSA/output.png",
  "started_at": "2024-01-30T21:51:39.003188Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/c3twa5lb3dphdu5rhzrf5lqngm",
    "cancel": "https://api.replicate.com/v1/predictions/c3twa5lb3dphdu5rhzrf5lqngm/cancel"
  },
  "version": "958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef"
}

Generated in

12.7 seconds

lucataco/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef

/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)
return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:00<00:10,  2.72it/s]
  7%|▋         | 2/29 [00:00<00:08,  3.09it/s]
 10%|█         | 3/29 [00:00<00:08,  3.19it/s]
 14%|█▍        | 4/29 [00:01<00:07,  3.27it/s]
 17%|█▋        | 5/29 [00:01<00:07,  3.32it/s]
 21%|██        | 6/29 [00:01<00:06,  3.34it/s]
 24%|██▍       | 7/29 [00:02<00:06,  3.36it/s]
 28%|██▊       | 8/29 [00:02<00:06,  3.37it/s]
 31%|███       | 9/29 [00:02<00:05,  3.38it/s]
 34%|███▍      | 10/29 [00:03<00:05,  3.38it/s]
 38%|███▊      | 11/29 [00:03<00:05,  3.38it/s]
 41%|████▏     | 12/29 [00:03<00:05,  3.39it/s]
 45%|████▍     | 13/29 [00:03<00:04,  3.39it/s]
 48%|████▊     | 14/29 [00:04<00:04,  3.39it/s]
 52%|█████▏    | 15/29 [00:04<00:04,  3.39it/s]
 55%|█████▌    | 16/29 [00:04<00:03,  3.39it/s]
 59%|█████▊    | 17/29 [00:05<00:03,  3.38it/s]
 62%|██████▏   | 18/29 [00:05<00:03,  3.38it/s]
 66%|██████▌   | 19/29 [00:05<00:02,  3.38it/s]
 69%|██████▉   | 20/29 [00:05<00:02,  3.38it/s]
 72%|███████▏  | 21/29 [00:06<00:02,  3.38it/s]
 76%|███████▌  | 22/29 [00:06<00:02,  3.38it/s]
 79%|███████▉  | 23/29 [00:06<00:01,  3.38it/s]
 83%|████████▎ | 24/29 [00:07<00:01,  3.38it/s]
 86%|████████▌ | 25/29 [00:07<00:01,  3.38it/s]
 90%|████████▉ | 26/29 [00:07<00:00,  3.38it/s]
 93%|█████████▎| 27/29 [00:08<00:00,  3.38it/s]
 97%|█████████▋| 28/29 [00:08<00:00,  3.37it/s]
100%|██████████| 29/29 [00:08<00:00,  3.37it/s]
100%|██████████| 29/29 [00:08<00:00,  3.35it/s]

Prediction

Model

2unlkz3brcoxc6p5wh7riqccsq

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

12.4s

Created

over 1 year ago

Input

image
prompt: a perfect mirrored reflective chrome ball sphere
negative_prompt: matte, diffuse, flat, dull
num_inference_steps: 30
controlnet_conditioning_scale: 0.5

{
  "image": "https://replicate.delivery/pbxt/KJoRUsamLsqRyHxi3zsvimTpWPF4y2YNBp5G6qPuSgwinvaO/fountain.jpg",
  "prompt": "a perfect mirrored reflective chrome ball sphere",
  "negative_prompt": "matte, diffuse, flat, dull",
  "num_inference_steps": 30,
  "controlnet_conditioning_scale": 0.5
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "lucataco/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
  {
    input: {
      image: "https://replicate.delivery/pbxt/KJoRUsamLsqRyHxi3zsvimTpWPF4y2YNBp5G6qPuSgwinvaO/fountain.jpg",
      prompt: "a perfect mirrored reflective chrome ball sphere",
      negative_prompt: "matte, diffuse, flat, dull",
      num_inference_steps: 30,
      controlnet_conditioning_scale: 0.5
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "lucataco/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
    input={
        "image": "https://replicate.delivery/pbxt/KJoRUsamLsqRyHxi3zsvimTpWPF4y2YNBp5G6qPuSgwinvaO/fountain.jpg",
        "prompt": "a perfect mirrored reflective chrome ball sphere",
        "negative_prompt": "matte, diffuse, flat, dull",
        "num_inference_steps": 30,
        "controlnet_conditioning_scale": 0.5
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run lucataco/diffusionlight 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/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
    "input": {
      "image": "https://replicate.delivery/pbxt/KJoRUsamLsqRyHxi3zsvimTpWPF4y2YNBp5G6qPuSgwinvaO/fountain.jpg",
      "prompt": "a perfect mirrored reflective chrome ball sphere",
      "negative_prompt": "matte, diffuse, flat, dull",
      "num_inference_steps": 30,
      "controlnet_conditioning_scale": 0.5
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-01-30T21:52:11.810101Z",
  "created_at": "2024-01-30T21:51:59.412923Z",
  "data_removed": false,
  "error": null,
  "id": "2unlkz3brcoxc6p5wh7riqccsq",
  "input": {
    "image": "https://replicate.delivery/pbxt/KJoRUsamLsqRyHxi3zsvimTpWPF4y2YNBp5G6qPuSgwinvaO/fountain.jpg",
    "prompt": "a perfect mirrored reflective chrome ball sphere",
    "negative_prompt": "matte, diffuse, flat, dull",
    "num_inference_steps": 30,
    "controlnet_conditioning_scale": 0.5
  },
  "logs": "0%|          | 0/29 [00:00<?, ?it/s]\n  3%|▎         | 1/29 [00:00<00:08,  3.40it/s]\n  7%|▋         | 2/29 [00:00<00:07,  3.39it/s]\n 10%|█         | 3/29 [00:00<00:07,  3.38it/s]\n 14%|█▍        | 4/29 [00:01<00:07,  3.38it/s]\n 17%|█▋        | 5/29 [00:01<00:07,  3.38it/s]\n 21%|██        | 6/29 [00:01<00:06,  3.37it/s]\n 24%|██▍       | 7/29 [00:02<00:06,  3.37it/s]\n 28%|██▊       | 8/29 [00:02<00:06,  3.37it/s]\n 31%|███       | 9/29 [00:02<00:05,  3.37it/s]\n 34%|███▍      | 10/29 [00:02<00:05,  3.37it/s]\n 38%|███▊      | 11/29 [00:03<00:05,  3.37it/s]\n 41%|████▏     | 12/29 [00:03<00:05,  3.37it/s]\n 45%|████▍     | 13/29 [00:03<00:04,  3.37it/s]\n 48%|████▊     | 14/29 [00:04<00:04,  3.36it/s]\n 52%|█████▏    | 15/29 [00:04<00:04,  3.37it/s]\n 55%|█████▌    | 16/29 [00:04<00:03,  3.37it/s]\n 59%|█████▊    | 17/29 [00:05<00:03,  3.37it/s]\n 62%|██████▏   | 18/29 [00:05<00:03,  3.37it/s]\n 66%|██████▌   | 19/29 [00:05<00:02,  3.37it/s]\n 69%|██████▉   | 20/29 [00:05<00:02,  3.37it/s]\n 72%|███████▏  | 21/29 [00:06<00:02,  3.38it/s]\n 76%|███████▌  | 22/29 [00:06<00:02,  3.38it/s]\n 79%|███████▉  | 23/29 [00:06<00:01,  3.38it/s]\n 83%|████████▎ | 24/29 [00:07<00:01,  3.39it/s]\n 86%|████████▌ | 25/29 [00:07<00:01,  3.39it/s]\n 90%|████████▉ | 26/29 [00:07<00:00,  3.39it/s]\n 93%|█████████▎| 27/29 [00:07<00:00,  3.39it/s]\n 97%|█████████▋| 28/29 [00:08<00:00,  3.39it/s]\n100%|██████████| 29/29 [00:08<00:00,  3.39it/s]\n100%|██████████| 29/29 [00:08<00:00,  3.38it/s]",
  "metrics": {
    "predict_time": 12.36379,
    "total_time": 12.397178
  },
  "output": "https://replicate.delivery/pbxt/zeUOMvmigWz5CKmuVxXPerf6hQJ2LkdcUYrvvtipDDKXMkjkA/output.png",
  "started_at": "2024-01-30T21:51:59.446311Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/2unlkz3brcoxc6p5wh7riqccsq",
    "cancel": "https://api.replicate.com/v1/predictions/2unlkz3brcoxc6p5wh7riqccsq/cancel"
  },
  "version": "958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef"
}

Generated in

12.4 seconds

lucataco/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef

Prediction

Model

rmgafv3bwvpjrcyhsryzumunky

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

14.0s

Created

over 1 year ago

Input

image
prompt: a perfect mirrored reflective chrome ball sphere
negative_prompt: matte, diffuse, flat, dull
num_inference_steps: 30
controlnet_conditioning_scale: 0.5

{
  "image": "https://replicate.delivery/pbxt/KJoRu8KEOxkJq4rtVV3kEga53JZj7iXtActgz9Lfe36M6Xa0/temple.png",
  "prompt": "a perfect mirrored reflective chrome ball sphere",
  "negative_prompt": "matte, diffuse, flat, dull",
  "num_inference_steps": 30,
  "controlnet_conditioning_scale": 0.5
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "lucataco/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
  {
    input: {
      image: "https://replicate.delivery/pbxt/KJoRu8KEOxkJq4rtVV3kEga53JZj7iXtActgz9Lfe36M6Xa0/temple.png",
      prompt: "a perfect mirrored reflective chrome ball sphere",
      negative_prompt: "matte, diffuse, flat, dull",
      num_inference_steps: 30,
      controlnet_conditioning_scale: 0.5
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "lucataco/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
    input={
        "image": "https://replicate.delivery/pbxt/KJoRu8KEOxkJq4rtVV3kEga53JZj7iXtActgz9Lfe36M6Xa0/temple.png",
        "prompt": "a perfect mirrored reflective chrome ball sphere",
        "negative_prompt": "matte, diffuse, flat, dull",
        "num_inference_steps": 30,
        "controlnet_conditioning_scale": 0.5
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run lucataco/diffusionlight 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/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
    "input": {
      "image": "https://replicate.delivery/pbxt/KJoRu8KEOxkJq4rtVV3kEga53JZj7iXtActgz9Lfe36M6Xa0/temple.png",
      "prompt": "a perfect mirrored reflective chrome ball sphere",
      "negative_prompt": "matte, diffuse, flat, dull",
      "num_inference_steps": 30,
      "controlnet_conditioning_scale": 0.5
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-01-30T21:52:40.335140Z",
  "created_at": "2024-01-30T21:52:26.321345Z",
  "data_removed": false,
  "error": null,
  "id": "rmgafv3bwvpjrcyhsryzumunky",
  "input": {
    "image": "https://replicate.delivery/pbxt/KJoRu8KEOxkJq4rtVV3kEga53JZj7iXtActgz9Lfe36M6Xa0/temple.png",
    "prompt": "a perfect mirrored reflective chrome ball sphere",
    "negative_prompt": "matte, diffuse, flat, dull",
    "num_inference_steps": 30,
    "controlnet_conditioning_scale": 0.5
  },
  "logs": "0%|          | 0/29 [00:00<?, ?it/s]\n  3%|▎         | 1/29 [00:00<00:08,  3.39it/s]\n  7%|▋         | 2/29 [00:00<00:07,  3.38it/s]\n 10%|█         | 3/29 [00:00<00:07,  3.38it/s]\n 14%|█▍        | 4/29 [00:01<00:07,  3.38it/s]\n 17%|█▋        | 5/29 [00:01<00:07,  3.37it/s]\n 21%|██        | 6/29 [00:01<00:06,  3.37it/s]\n 24%|██▍       | 7/29 [00:02<00:06,  3.37it/s]\n 28%|██▊       | 8/29 [00:02<00:06,  3.37it/s]\n 31%|███       | 9/29 [00:02<00:05,  3.37it/s]\n 34%|███▍      | 10/29 [00:02<00:05,  3.37it/s]\n 38%|███▊      | 11/29 [00:03<00:05,  3.37it/s]\n 41%|████▏     | 12/29 [00:03<00:05,  3.37it/s]\n 45%|████▍     | 13/29 [00:03<00:04,  3.37it/s]\n 48%|████▊     | 14/29 [00:04<00:04,  3.38it/s]\n 52%|█████▏    | 15/29 [00:04<00:04,  3.38it/s]\n 55%|█████▌    | 16/29 [00:04<00:03,  3.38it/s]\n 59%|█████▊    | 17/29 [00:05<00:03,  3.38it/s]\n 62%|██████▏   | 18/29 [00:05<00:03,  3.39it/s]\n 66%|██████▌   | 19/29 [00:05<00:02,  3.39it/s]\n 69%|██████▉   | 20/29 [00:05<00:02,  3.39it/s]\n 72%|███████▏  | 21/29 [00:06<00:02,  3.39it/s]\n 76%|███████▌  | 22/29 [00:06<00:02,  3.39it/s]\n 79%|███████▉  | 23/29 [00:06<00:01,  3.38it/s]\n 83%|████████▎ | 24/29 [00:07<00:01,  3.38it/s]\n 86%|████████▌ | 25/29 [00:07<00:01,  3.38it/s]\n 90%|████████▉ | 26/29 [00:07<00:00,  3.39it/s]\n 93%|█████████▎| 27/29 [00:07<00:00,  3.38it/s]\n 97%|█████████▋| 28/29 [00:08<00:00,  3.38it/s]\n100%|██████████| 29/29 [00:08<00:00,  3.39it/s]\n100%|██████████| 29/29 [00:08<00:00,  3.38it/s]",
  "metrics": {
    "predict_time": 14.001297,
    "total_time": 14.013795
  },
  "output": "https://replicate.delivery/pbxt/weKcHtMwx5X9FCvkzkh7RnOW9yQ1DcgxxPXpIm7lKX7TD5IJA/output.png",
  "started_at": "2024-01-30T21:52:26.333843Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/rmgafv3bwvpjrcyhsryzumunky",
    "cancel": "https://api.replicate.com/v1/predictions/rmgafv3bwvpjrcyhsryzumunky/cancel"
  },
  "version": "958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef"
}

Generated in

14.0 seconds

lucataco/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef

Prediction

Model

33g3vddbsmz73wwsgu7adxmvdi

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

12.4s

Created

over 1 year ago

Input

image
prompt: a perfect mirrored reflective chrome ball sphere
negative_prompt: matte, diffuse, flat, dull
num_inference_steps: 30
controlnet_conditioning_scale: 0.5

{
  "image": "https://replicate.delivery/pbxt/KJoSORjE5eWmSivlqO28n5qI3ovSYubYzYR9nY2TDhCWaeDa/anime_on_wall%20%281%29.jpg",
  "prompt": "a perfect mirrored reflective chrome ball sphere",
  "negative_prompt": "matte, diffuse, flat, dull",
  "num_inference_steps": 30,
  "controlnet_conditioning_scale": 0.5
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "lucataco/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
  {
    input: {
      image: "https://replicate.delivery/pbxt/KJoSORjE5eWmSivlqO28n5qI3ovSYubYzYR9nY2TDhCWaeDa/anime_on_wall%20%281%29.jpg",
      prompt: "a perfect mirrored reflective chrome ball sphere",
      negative_prompt: "matte, diffuse, flat, dull",
      num_inference_steps: 30,
      controlnet_conditioning_scale: 0.5
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "lucataco/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
    input={
        "image": "https://replicate.delivery/pbxt/KJoSORjE5eWmSivlqO28n5qI3ovSYubYzYR9nY2TDhCWaeDa/anime_on_wall%20%281%29.jpg",
        "prompt": "a perfect mirrored reflective chrome ball sphere",
        "negative_prompt": "matte, diffuse, flat, dull",
        "num_inference_steps": 30,
        "controlnet_conditioning_scale": 0.5
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run lucataco/diffusionlight 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/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
    "input": {
      "image": "https://replicate.delivery/pbxt/KJoSORjE5eWmSivlqO28n5qI3ovSYubYzYR9nY2TDhCWaeDa/anime_on_wall%20%281%29.jpg",
      "prompt": "a perfect mirrored reflective chrome ball sphere",
      "negative_prompt": "matte, diffuse, flat, dull",
      "num_inference_steps": 30,
      "controlnet_conditioning_scale": 0.5
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-01-30T21:53:09.162373Z",
  "created_at": "2024-01-30T21:52:56.792138Z",
  "data_removed": false,
  "error": null,
  "id": "33g3vddbsmz73wwsgu7adxmvdi",
  "input": {
    "image": "https://replicate.delivery/pbxt/KJoSORjE5eWmSivlqO28n5qI3ovSYubYzYR9nY2TDhCWaeDa/anime_on_wall%20%281%29.jpg",
    "prompt": "a perfect mirrored reflective chrome ball sphere",
    "negative_prompt": "matte, diffuse, flat, dull",
    "num_inference_steps": 30,
    "controlnet_conditioning_scale": 0.5
  },
  "logs": "0%|          | 0/29 [00:00<?, ?it/s]\n  3%|▎         | 1/29 [00:00<00:08,  3.39it/s]\n  7%|▋         | 2/29 [00:00<00:07,  3.38it/s]\n 10%|█         | 3/29 [00:00<00:07,  3.38it/s]\n 14%|█▍        | 4/29 [00:01<00:07,  3.38it/s]\n 17%|█▋        | 5/29 [00:01<00:07,  3.37it/s]\n 21%|██        | 6/29 [00:01<00:06,  3.37it/s]\n 24%|██▍       | 7/29 [00:02<00:06,  3.37it/s]\n 28%|██▊       | 8/29 [00:02<00:06,  3.37it/s]\n 31%|███       | 9/29 [00:02<00:05,  3.37it/s]\n 34%|███▍      | 10/29 [00:02<00:05,  3.37it/s]\n 38%|███▊      | 11/29 [00:03<00:05,  3.37it/s]\n 41%|████▏     | 12/29 [00:03<00:05,  3.38it/s]\n 45%|████▍     | 13/29 [00:03<00:04,  3.38it/s]\n 48%|████▊     | 14/29 [00:04<00:04,  3.38it/s]\n 52%|█████▏    | 15/29 [00:04<00:04,  3.39it/s]\n 55%|█████▌    | 16/29 [00:04<00:03,  3.38it/s]\n 59%|█████▊    | 17/29 [00:05<00:03,  3.39it/s]\n 62%|██████▏   | 18/29 [00:05<00:03,  3.39it/s]\n 66%|██████▌   | 19/29 [00:05<00:02,  3.39it/s]\n 69%|██████▉   | 20/29 [00:05<00:02,  3.39it/s]\n 72%|███████▏  | 21/29 [00:06<00:02,  3.38it/s]\n 76%|███████▌  | 22/29 [00:06<00:02,  3.38it/s]\n 79%|███████▉  | 23/29 [00:06<00:01,  3.38it/s]\n 83%|████████▎ | 24/29 [00:07<00:01,  3.38it/s]\n 86%|████████▌ | 25/29 [00:07<00:01,  3.38it/s]\n 90%|████████▉ | 26/29 [00:07<00:00,  3.38it/s]\n 93%|█████████▎| 27/29 [00:07<00:00,  3.38it/s]\n 97%|█████████▋| 28/29 [00:08<00:00,  3.38it/s]\n100%|██████████| 29/29 [00:08<00:00,  3.38it/s]\n100%|██████████| 29/29 [00:08<00:00,  3.38it/s]",
  "metrics": {
    "predict_time": 12.352713,
    "total_time": 12.370235
  },
  "output": "https://replicate.delivery/pbxt/IetkYzUifEg9vU4LxWfiSLHufv4IwblMvYeDaj9zX33h4QOSC/output.png",
  "started_at": "2024-01-30T21:52:56.809660Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/33g3vddbsmz73wwsgu7adxmvdi",
    "cancel": "https://api.replicate.com/v1/predictions/33g3vddbsmz73wwsgu7adxmvdi/cancel"
  },
  "version": "958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef"
}

Generated in

12.4 seconds

lucataco/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef

Prediction

Model

wgkwsddbkt4bbzw5dnhhshivzy

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

13.4s

Created

over 1 year ago

Input

image
prompt: a perfect mirrored reflective chrome ball sphere
negative_prompt: matte, diffuse, flat, dull
num_inference_steps: 30
controlnet_conditioning_scale: 0.5

{
  "image": "https://replicate.delivery/pbxt/KJom4S0KWCYFLr3KxttipoAZGGfSd8NySbatwcyl7meDIKn5/oxaca.jpg",
  "prompt": "a perfect mirrored reflective chrome ball sphere",
  "negative_prompt": "matte, diffuse, flat, dull",
  "num_inference_steps": 30,
  "controlnet_conditioning_scale": 0.5
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "lucataco/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
  {
    input: {
      image: "https://replicate.delivery/pbxt/KJom4S0KWCYFLr3KxttipoAZGGfSd8NySbatwcyl7meDIKn5/oxaca.jpg",
      prompt: "a perfect mirrored reflective chrome ball sphere",
      negative_prompt: "matte, diffuse, flat, dull",
      num_inference_steps: 30,
      controlnet_conditioning_scale: 0.5
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "lucataco/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
    input={
        "image": "https://replicate.delivery/pbxt/KJom4S0KWCYFLr3KxttipoAZGGfSd8NySbatwcyl7meDIKn5/oxaca.jpg",
        "prompt": "a perfect mirrored reflective chrome ball sphere",
        "negative_prompt": "matte, diffuse, flat, dull",
        "num_inference_steps": 30,
        "controlnet_conditioning_scale": 0.5
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run lucataco/diffusionlight 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/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
    "input": {
      "image": "https://replicate.delivery/pbxt/KJom4S0KWCYFLr3KxttipoAZGGfSd8NySbatwcyl7meDIKn5/oxaca.jpg",
      "prompt": "a perfect mirrored reflective chrome ball sphere",
      "negative_prompt": "matte, diffuse, flat, dull",
      "num_inference_steps": 30,
      "controlnet_conditioning_scale": 0.5
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-01-30T22:13:56.337001Z",
  "created_at": "2024-01-30T22:13:42.891700Z",
  "data_removed": false,
  "error": null,
  "id": "wgkwsddbkt4bbzw5dnhhshivzy",
  "input": {
    "image": "https://replicate.delivery/pbxt/KJom4S0KWCYFLr3KxttipoAZGGfSd8NySbatwcyl7meDIKn5/oxaca.jpg",
    "prompt": "a perfect mirrored reflective chrome ball sphere",
    "negative_prompt": "matte, diffuse, flat, dull",
    "num_inference_steps": 30,
    "controlnet_conditioning_scale": 0.5
  },
  "logs": "0%|          | 0/29 [00:00<?, ?it/s]\n  3%|▎         | 1/29 [00:00<00:10,  2.71it/s]\n  7%|▋         | 2/29 [00:00<00:08,  3.06it/s]\n 10%|█         | 3/29 [00:00<00:08,  3.19it/s]\n 14%|█▍        | 4/29 [00:01<00:07,  3.25it/s]\n 17%|█▋        | 5/29 [00:01<00:07,  3.29it/s]\n 21%|██        | 6/29 [00:01<00:06,  3.31it/s]\n 24%|██▍       | 7/29 [00:02<00:06,  3.32it/s]\n 28%|██▊       | 8/29 [00:02<00:06,  3.33it/s]\n 31%|███       | 9/29 [00:02<00:05,  3.34it/s]\n 34%|███▍      | 10/29 [00:03<00:05,  3.34it/s]\n 38%|███▊      | 11/29 [00:03<00:05,  3.34it/s]\n 41%|████▏     | 12/29 [00:03<00:05,  3.35it/s]\n 45%|████▍     | 13/29 [00:03<00:04,  3.35it/s]\n 48%|████▊     | 14/29 [00:04<00:04,  3.36it/s]\n 52%|█████▏    | 15/29 [00:04<00:04,  3.36it/s]\n 55%|█████▌    | 16/29 [00:04<00:03,  3.37it/s]\n 59%|█████▊    | 17/29 [00:05<00:03,  3.37it/s]\n 62%|██████▏   | 18/29 [00:05<00:03,  3.37it/s]\n 66%|██████▌   | 19/29 [00:05<00:02,  3.37it/s]\n 69%|██████▉   | 20/29 [00:06<00:02,  3.37it/s]\n 72%|███████▏  | 21/29 [00:06<00:02,  3.37it/s]\n 76%|███████▌  | 22/29 [00:06<00:02,  3.37it/s]\n 79%|███████▉  | 23/29 [00:06<00:01,  3.37it/s]\n 83%|████████▎ | 24/29 [00:07<00:01,  3.37it/s]\n 86%|████████▌ | 25/29 [00:07<00:01,  3.37it/s]\n 90%|████████▉ | 26/29 [00:07<00:00,  3.37it/s]\n 93%|█████████▎| 27/29 [00:08<00:00,  3.37it/s]\n 97%|█████████▋| 28/29 [00:08<00:00,  3.37it/s]\n100%|██████████| 29/29 [00:08<00:00,  3.37it/s]\n100%|██████████| 29/29 [00:08<00:00,  3.34it/s]",
  "metrics": {
    "predict_time": 13.407797,
    "total_time": 13.445301
  },
  "output": "https://replicate.delivery/pbxt/PRHzwpG72XJ7MpfeFgEQSWxpT9gGRTm66GPPMsWV1hljayRSA/output.png",
  "started_at": "2024-01-30T22:13:42.929204Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/wgkwsddbkt4bbzw5dnhhshivzy",
    "cancel": "https://api.replicate.com/v1/predictions/wgkwsddbkt4bbzw5dnhhshivzy/cancel"
  },
  "version": "958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef"
}

Generated in

13.4 seconds