Prediction

q4jtomdbf3xolkbavbmqdsl77i

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

30.0s

Created

over 1 year ago

Input

model: RealESRGAN_x4plus
resolution: 2k
video_path

{
  "model": "RealESRGAN_x4plus",
  "resolution": "2k",
  "video_path": "https://replicate.delivery/pbxt/JvzIGnVgv9E2J0n2UzLaWhdXIEu13AbxpR3DjlwfS8KLUHLg/replicate-prediction-wkusuc3brp5hwd27zaymz5rgdi.mp4"
}

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/real-esrgan-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/real-esrgan-video:c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320",
  {
    input: {
      model: "RealESRGAN_x4plus",
      resolution: "2k",
      video_path: "https://replicate.delivery/pbxt/JvzIGnVgv9E2J0n2UzLaWhdXIEu13AbxpR3DjlwfS8KLUHLg/replicate-prediction-wkusuc3brp5hwd27zaymz5rgdi.mp4"
    }
  }
);

// 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/real-esrgan-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/real-esrgan-video:c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320",
    input={
        "model": "RealESRGAN_x4plus",
        "resolution": "2k",
        "video_path": "https://replicate.delivery/pbxt/JvzIGnVgv9E2J0n2UzLaWhdXIEu13AbxpR3DjlwfS8KLUHLg/replicate-prediction-wkusuc3brp5hwd27zaymz5rgdi.mp4"
    }
)
print(output)

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

Run lucataco/real-esrgan-video 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": "c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320",
    "input": {
      "model": "RealESRGAN_x4plus",
      "resolution": "2k",
      "video_path": "https://replicate.delivery/pbxt/JvzIGnVgv9E2J0n2UzLaWhdXIEu13AbxpR3DjlwfS8KLUHLg/replicate-prediction-wkusuc3brp5hwd27zaymz5rgdi.mp4"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2023-11-24T18:58:26.077925Z",
  "created_at": "2023-11-24T18:57:56.041075Z",
  "data_removed": false,
  "error": null,
  "id": "q4jtomdbf3xolkbavbmqdsl77i",
  "input": {
    "model": "RealESRGAN_x4plus",
    "resolution": "2k",
    "video_path": "https://replicate.delivery/pbxt/JvzIGnVgv9E2J0n2UzLaWhdXIEu13AbxpR3DjlwfS8KLUHLg/replicate-prediction-wkusuc3brp5hwd27zaymz5rgdi.mp4"
  },
  "logs": "Upscaling from 1024x512 to 2560x1440, crop_to_fit=True\n/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional.\nwarnings.warn(\nDownloading: \"https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth\" to /Real-ESRGAN/weights/RealESRGAN_x4plus.pth\n  0%|          | 0.00/63.9M [00:00<?, ?B/s]\n 68%|██████▊   | 43.5M/63.9M [00:00<00:00, 456MB/s]\n100%|██████████| 63.9M/63.9M [00:00<00:00, 472MB/s]\ninference:   0%|          | 0/25 [00:00<?, ?frame/s]\ninference:   4%|▍         | 1/25 [00:03<01:13,  3.06s/frame]\ninference:   8%|▊         | 2/25 [00:03<00:37,  1.63s/frame]\ninference:  12%|█▏        | 3/25 [00:04<00:25,  1.17s/frame]\ninference:  16%|█▌        | 4/25 [00:04<00:19,  1.05frame/s]\ninference:  20%|██        | 5/25 [00:05<00:16,  1.20frame/s]\ninference:  24%|██▍       | 6/25 [00:06<00:14,  1.31frame/s]\ninference:  28%|██▊       | 7/25 [00:06<00:12,  1.40frame/s]\ninference:  32%|███▏      | 8/25 [00:07<00:11,  1.44frame/s]\ninference:  36%|███▌      | 9/25 [00:08<00:10,  1.49frame/s]\ninference:  40%|████      | 10/25 [00:08<00:09,  1.52frame/s]\ninference:  44%|████▍     | 11/25 [00:09<00:09,  1.54frame/s]\ninference:  48%|████▊     | 12/25 [00:09<00:08,  1.56frame/s]\ninference:  52%|█████▏    | 13/25 [00:10<00:07,  1.57frame/s]\ninference:  56%|█████▌    | 14/25 [00:11<00:06,  1.58frame/s]\ninference:  60%|██████    | 15/25 [00:11<00:06,  1.59frame/s]\ninference:  64%|██████▍   | 16/25 [00:12<00:05,  1.60frame/s]\ninference:  68%|██████▊   | 17/25 [00:13<00:04,  1.60frame/s]\ninference:  72%|███████▏  | 18/25 [00:13<00:04,  1.61frame/s]\ninference:  76%|███████▌  | 19/25 [00:14<00:03,  1.61frame/s]\ninference:  80%|████████  | 20/25 [00:14<00:03,  1.61frame/s]\ninference:  84%|████████▍ | 21/25 [00:15<00:02,  1.61frame/s]\ninference:  88%|████████▊ | 22/25 [00:16<00:01,  1.61frame/s]\ninference:  92%|█████████▏| 23/25 [00:16<00:01,  1.62frame/s]\ninference:  96%|█████████▌| 24/25 [00:17<00:00,  1.62frame/s]\ninference: 100%|██████████| 25/25 [00:18<00:00,  1.62frame/s]\ninference: 100%|██████████| 25/25 [00:19<00:00,  1.29frame/s]\n[h264 @ 0x55bb5e205bc0] Cannot load libnvcuvid.so.1\n[h264 @ 0x55bb5e205bc0] Failed loading nvcuvid.\n[h264 @ 0x55bb5e205bc0] Failed setup for format cuda: hwaccel initialisation returned error.\nCropping to fit done..\nUpscaled video saved to: /src/ouput/tmpyt02kswkreplicate-prediction-wkusuc3brp5hwd27zaymz5rgdi_upscaled_2560_1440.mp4",
  "metrics": {
    "predict_time": 30.018293,
    "total_time": 30.03685
  },
  "output": "https://replicate.delivery/pbxt/GCO6AfW3f4qZpUKfJKAGexVymnB6e9bbcCmq5F26IAlOKSdPC/tmpyt02kswkreplicate-prediction-wkusuc3brp5hwd27zaymz5rgdi_out.mp4",
  "started_at": "2023-11-24T18:57:56.059632Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/q4jtomdbf3xolkbavbmqdsl77i",
    "cancel": "https://api.replicate.com/v1/predictions/q4jtomdbf3xolkbavbmqdsl77i/cancel"
  },
  "version": "c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320"
}

Generated in

30.0 seconds

lucataco/real-esrgan-video:c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320

Upscaling from 1024x512 to 2560x1440, crop_to_fit=True
/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional.
warnings.warn(
Downloading: "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth" to /Real-ESRGAN/weights/RealESRGAN_x4plus.pth
  0%|          | 0.00/63.9M [00:00<?, ?B/s]
 68%|██████▊   | 43.5M/63.9M [00:00<00:00, 456MB/s]
100%|██████████| 63.9M/63.9M [00:00<00:00, 472MB/s]
inference:   0%|          | 0/25 [00:00<?, ?frame/s]
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inference: 100%|██████████| 25/25 [00:18<00:00,  1.62frame/s]
inference: 100%|██████████| 25/25 [00:19<00:00,  1.29frame/s]
[h264 @ 0x55bb5e205bc0] Cannot load libnvcuvid.so.1
[h264 @ 0x55bb5e205bc0] Failed loading nvcuvid.
[h264 @ 0x55bb5e205bc0] Failed setup for format cuda: hwaccel initialisation returned error.
Cropping to fit done..
Upscaled video saved to: /src/ouput/tmpyt02kswkreplicate-prediction-wkusuc3brp5hwd27zaymz5rgdi_upscaled_2560_1440.mp4

Prediction

Model

b76kwvlbq3l5gumj7zdsa24e6i

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

25.0s

Created

over 1 year ago

Input

model: RealESRGAN_x4plus
resolution: FHD
video_path

{
  "model": "RealESRGAN_x4plus",
  "resolution": "FHD",
  "video_path": "https://replicate.delivery/pbxt/JvzJwGbiuwhpRVTBdOOt3L49WAsYlBmB2yNqcyxeIlTRQ5xl/replicate-prediction-wkusuc3brp5hwd27zaymz5rgdi.mp4"
}

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/real-esrgan-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/real-esrgan-video:c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320",
  {
    input: {
      model: "RealESRGAN_x4plus",
      resolution: "FHD",
      video_path: "https://replicate.delivery/pbxt/JvzJwGbiuwhpRVTBdOOt3L49WAsYlBmB2yNqcyxeIlTRQ5xl/replicate-prediction-wkusuc3brp5hwd27zaymz5rgdi.mp4"
    }
  }
);

// 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/real-esrgan-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/real-esrgan-video:c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320",
    input={
        "model": "RealESRGAN_x4plus",
        "resolution": "FHD",
        "video_path": "https://replicate.delivery/pbxt/JvzJwGbiuwhpRVTBdOOt3L49WAsYlBmB2yNqcyxeIlTRQ5xl/replicate-prediction-wkusuc3brp5hwd27zaymz5rgdi.mp4"
    }
)
print(output)

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

Run lucataco/real-esrgan-video 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": "c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320",
    "input": {
      "model": "RealESRGAN_x4plus",
      "resolution": "FHD",
      "video_path": "https://replicate.delivery/pbxt/JvzJwGbiuwhpRVTBdOOt3L49WAsYlBmB2yNqcyxeIlTRQ5xl/replicate-prediction-wkusuc3brp5hwd27zaymz5rgdi.mp4"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2023-11-24T19:02:19.363909Z",
  "created_at": "2023-11-24T19:01:54.394895Z",
  "data_removed": false,
  "error": null,
  "id": "b76kwvlbq3l5gumj7zdsa24e6i",
  "input": {
    "model": "RealESRGAN_x4plus",
    "resolution": "FHD",
    "video_path": "https://replicate.delivery/pbxt/JvzJwGbiuwhpRVTBdOOt3L49WAsYlBmB2yNqcyxeIlTRQ5xl/replicate-prediction-wkusuc3brp5hwd27zaymz5rgdi.mp4"
  },
  "logs": "Upscaling from 1024x512 to 1920x1080, crop_to_fit=True\n/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional.\nwarnings.warn(\ninference:   0%|          | 0/25 [00:00<?, ?frame/s]\ninference:   4%|▍         | 1/25 [00:01<00:47,  1.96s/frame]\ninference:   8%|▊         | 2/25 [00:02<00:26,  1.16s/frame]\ninference:  12%|█▏        | 3/25 [00:03<00:19,  1.10frame/s]\ninference:  16%|█▌        | 4/25 [00:03<00:16,  1.27frame/s]\ninference:  20%|██        | 5/25 [00:04<00:14,  1.39frame/s]\ninference:  24%|██▍       | 6/25 [00:04<00:12,  1.47frame/s]\ninference:  28%|██▊       | 7/25 [00:05<00:11,  1.53frame/s]\ninference:  32%|███▏      | 8/25 [00:06<00:10,  1.57frame/s]\ninference:  36%|███▌      | 9/25 [00:06<00:10,  1.60frame/s]\ninference:  40%|████      | 10/25 [00:07<00:09,  1.62frame/s]\ninference:  44%|████▍     | 11/25 [00:07<00:08,  1.64frame/s]\ninference:  48%|████▊     | 12/25 [00:08<00:07,  1.64frame/s]\ninference:  52%|█████▏    | 13/25 [00:09<00:07,  1.65frame/s]\ninference:  56%|█████▌    | 14/25 [00:09<00:06,  1.66frame/s]\ninference:  60%|██████    | 15/25 [00:10<00:06,  1.66frame/s]\ninference:  64%|██████▍   | 16/25 [00:10<00:05,  1.67frame/s]\ninference:  68%|██████▊   | 17/25 [00:11<00:04,  1.66frame/s]\ninference:  72%|███████▏  | 18/25 [00:12<00:04,  1.66frame/s]\ninference:  76%|███████▌  | 19/25 [00:12<00:03,  1.67frame/s]\ninference:  80%|████████  | 20/25 [00:13<00:03,  1.66frame/s]\ninference:  84%|████████▍ | 21/25 [00:13<00:02,  1.66frame/s]\ninference:  88%|████████▊ | 22/25 [00:14<00:01,  1.67frame/s]\ninference:  92%|█████████▏| 23/25 [00:15<00:01,  1.67frame/s]\ninference:  96%|█████████▌| 24/25 [00:15<00:00,  1.67frame/s]\ninference: 100%|██████████| 25/25 [00:16<00:00,  1.67frame/s]\ninference: 100%|██████████| 25/25 [00:17<00:00,  1.46frame/s]\n[h264 @ 0x564bdc2f31c0] Cannot load libnvcuvid.so.1\n[h264 @ 0x564bdc2f31c0] Failed loading nvcuvid.\n[h264 @ 0x564bdc2f31c0] Failed setup for format cuda: hwaccel initialisation returned error.\nCropping to fit done..\nUpscaled video saved to: /src/ouput/tmp84vybs1breplicate-prediction-wkusuc3brp5hwd27zaymz5rgdi_upscaled_1920_1080.mp4",
  "metrics": {
    "predict_time": 24.952834,
    "total_time": 24.969014
  },
  "output": "https://replicate.delivery/pbxt/PpWmE8IEmDppOZiqAOhr22BZfm7ZNNkC0Zq8iXohdKvdK19IA/tmp84vybs1breplicate-prediction-wkusuc3brp5hwd27zaymz5rgdi_out.mp4",
  "started_at": "2023-11-24T19:01:54.411075Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/b76kwvlbq3l5gumj7zdsa24e6i",
    "cancel": "https://api.replicate.com/v1/predictions/b76kwvlbq3l5gumj7zdsa24e6i/cancel"
  },
  "version": "c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320"
}

Generated in

25.0 seconds

lucataco/real-esrgan-video:c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320

Upscaling from 1024x512 to 1920x1080, crop_to_fit=True
/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional.
warnings.warn(
inference:   0%|          | 0/25 [00:00<?, ?frame/s]
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inference:  80%|████████  | 20/25 [00:13<00:03,  1.66frame/s]
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inference: 100%|██████████| 25/25 [00:16<00:00,  1.67frame/s]
inference: 100%|██████████| 25/25 [00:17<00:00,  1.46frame/s]
[h264 @ 0x564bdc2f31c0] Cannot load libnvcuvid.so.1
[h264 @ 0x564bdc2f31c0] Failed loading nvcuvid.
[h264 @ 0x564bdc2f31c0] Failed setup for format cuda: hwaccel initialisation returned error.
Cropping to fit done..
Upscaled video saved to: /src/ouput/tmp84vybs1breplicate-prediction-wkusuc3brp5hwd27zaymz5rgdi_upscaled_1920_1080.mp4

Prediction

Model

vj23pn3by7wkj74yne7uajai74

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

16.4s

Created

over 1 year ago

Input

model: realesr-animevideov3
resolution: FHD
video_path

{
  "model": "realesr-animevideov3",
  "resolution": "FHD",
  "video_path": "https://replicate.delivery/pbxt/JvzRdxkqq56Px4lWdJXculV0UVA4SBMFNapglxPSPUbiP7sE/out.mp4"
}

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/real-esrgan-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/real-esrgan-video:c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320",
  {
    input: {
      model: "realesr-animevideov3",
      resolution: "FHD",
      video_path: "https://replicate.delivery/pbxt/JvzRdxkqq56Px4lWdJXculV0UVA4SBMFNapglxPSPUbiP7sE/out.mp4"
    }
  }
);

// 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/real-esrgan-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/real-esrgan-video:c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320",
    input={
        "model": "realesr-animevideov3",
        "resolution": "FHD",
        "video_path": "https://replicate.delivery/pbxt/JvzRdxkqq56Px4lWdJXculV0UVA4SBMFNapglxPSPUbiP7sE/out.mp4"
    }
)
print(output)

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

Run lucataco/real-esrgan-video 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": "c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320",
    "input": {
      "model": "realesr-animevideov3",
      "resolution": "FHD",
      "video_path": "https://replicate.delivery/pbxt/JvzRdxkqq56Px4lWdJXculV0UVA4SBMFNapglxPSPUbiP7sE/out.mp4"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2023-11-24T19:08:04.868872Z",
  "created_at": "2023-11-24T19:07:48.499419Z",
  "data_removed": false,
  "error": null,
  "id": "vj23pn3by7wkj74yne7uajai74",
  "input": {
    "model": "realesr-animevideov3",
    "resolution": "FHD",
    "video_path": "https://replicate.delivery/pbxt/JvzRdxkqq56Px4lWdJXculV0UVA4SBMFNapglxPSPUbiP7sE/out.mp4"
  },
  "logs": "Upscaling from 512x512 to 1920x1080, crop_to_fit=True\n/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional.\nwarnings.warn(\nDownloading: \"https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth\" to /Real-ESRGAN/weights/realesr-animevideov3.pth\n  0%|          | 0.00/2.39M [00:00<?, ?B/s]\n100%|██████████| 2.39M/2.39M [00:00<00:00, 224MB/s]\ninference:   0%|          | 0/48 [00:00<?, ?frame/s]\ninference:   2%|▏         | 1/48 [00:01<01:11,  1.52s/frame]\ninference:   4%|▍         | 2/48 [00:01<00:32,  1.43frame/s]\ninference:   6%|▋         | 3/48 [00:01<00:19,  2.31frame/s]\ninference:   8%|▊         | 4/48 [00:01<00:13,  3.25frame/s]\ninference:  10%|█         | 5/48 [00:01<00:10,  4.21frame/s]\ninference:  12%|█▎        | 6/48 [00:02<00:08,  5.06frame/s]\ninference:  15%|█▍        | 7/48 [00:02<00:07,  5.85frame/s]\ninference:  17%|█▋        | 8/48 [00:02<00:06,  6.50frame/s]\ninference:  19%|█▉        | 9/48 [00:02<00:05,  7.06frame/s]\ninference:  21%|██        | 10/48 [00:02<00:05,  7.49frame/s]\ninference:  23%|██▎       | 11/48 [00:02<00:04,  7.82frame/s]\ninference:  25%|██▌       | 12/48 [00:02<00:04,  8.06frame/s]\ninference:  27%|██▋       | 13/48 [00:02<00:04,  8.20frame/s]\ninference:  29%|██▉       | 14/48 [00:03<00:04,  8.21frame/s]\ninference:  31%|███▏      | 15/48 [00:03<00:03,  8.26frame/s]\ninference:  33%|███▎      | 16/48 [00:03<00:03,  8.36frame/s]\ninference:  35%|███▌      | 17/48 [00:03<00:03,  8.37frame/s]\ninference:  38%|███▊      | 18/48 [00:03<00:03,  8.35frame/s]\ninference:  40%|███▉      | 19/48 [00:03<00:03,  8.37frame/s]\ninference:  42%|████▏     | 20/48 [00:03<00:03,  8.38frame/s]\ninference:  44%|████▍     | 21/48 [00:03<00:03,  8.36frame/s]\ninference:  46%|████▌     | 22/48 [00:03<00:03,  8.34frame/s]\ninference:  48%|████▊     | 23/48 [00:04<00:02,  8.47frame/s]\ninference:  50%|█████     | 24/48 [00:04<00:02,  8.53frame/s]\ninference:  52%|█████▏    | 25/48 [00:04<00:02,  8.54frame/s]\ninference:  54%|█████▍    | 26/48 [00:04<00:02,  8.52frame/s]\ninference:  56%|█████▋    | 27/48 [00:04<00:02,  8.59frame/s]\ninference:  58%|█████▊    | 28/48 [00:04<00:02,  8.63frame/s]\ninference:  60%|██████    | 29/48 [00:04<00:02,  8.66frame/s]\ninference:  62%|██████▎   | 30/48 [00:04<00:02,  8.70frame/s]\ninference:  65%|██████▍   | 31/48 [00:05<00:01,  8.73frame/s]\ninference:  67%|██████▋   | 32/48 [00:05<00:01,  8.68frame/s]\ninference:  69%|██████▉   | 33/48 [00:05<00:01,  8.58frame/s]\ninference:  71%|███████   | 34/48 [00:05<00:01,  8.56frame/s]\ninference:  73%|███████▎  | 35/48 [00:05<00:01,  8.49frame/s]\ninference:  75%|███████▌  | 36/48 [00:05<00:01,  8.53frame/s]\ninference:  77%|███████▋  | 37/48 [00:05<00:01,  8.52frame/s]\ninference:  79%|███████▉  | 38/48 [00:05<00:01,  8.48frame/s]\ninference:  81%|████████▏ | 39/48 [00:05<00:01,  8.50frame/s]\ninference:  83%|████████▎ | 40/48 [00:06<00:00,  8.43frame/s]\ninference:  85%|████████▌ | 41/48 [00:06<00:00,  8.36frame/s]\ninference:  88%|████████▊ | 42/48 [00:06<00:00,  8.31frame/s]\ninference:  90%|████████▉ | 43/48 [00:06<00:00,  8.05frame/s]\ninference:  92%|█████████▏| 44/48 [00:06<00:00,  8.17frame/s]\ninference:  94%|█████████▍| 45/48 [00:06<00:00,  8.13frame/s]\ninference:  96%|█████████▌| 46/48 [00:06<00:00,  8.12frame/s]\ninference:  98%|█████████▊| 47/48 [00:06<00:00,  8.15frame/s]\ninference: 100%|██████████| 48/48 [00:07<00:00,  8.09frame/s]\ninference: 100%|██████████| 48/48 [00:08<00:00,  5.66frame/s]\n[h264 @ 0x55bc5dd209c0] Cannot load libnvcuvid.so.1\n[h264 @ 0x55bc5dd209c0] Failed loading nvcuvid.\n[h264 @ 0x55bc5dd209c0] Failed setup for format cuda: hwaccel initialisation returned error.\nCropping to fit done..\nUpscaled video saved to: /src/ouput/tmpzqrp8f42out_upscaled_1920_1080.mp4",
  "metrics": {
    "predict_time": 16.356674,
    "total_time": 16.369453
  },
  "output": "https://replicate.delivery/pbxt/t8bbWpPayKqLJJYrZMTwDvfzAdmdoehTnpAvJpR0hrVUaq7RA/tmpzqrp8f42out_out.mp4",
  "started_at": "2023-11-24T19:07:48.512198Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/vj23pn3by7wkj74yne7uajai74",
    "cancel": "https://api.replicate.com/v1/predictions/vj23pn3by7wkj74yne7uajai74/cancel"
  },
  "version": "c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320"
}

Generated in

16.4 seconds

lucataco/real-esrgan-video:c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320

Upscaling from 512x512 to 1920x1080, crop_to_fit=True
/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional.
warnings.warn(
Downloading: "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth" to /Real-ESRGAN/weights/realesr-animevideov3.pth
  0%|          | 0.00/2.39M [00:00<?, ?B/s]
100%|██████████| 2.39M/2.39M [00:00<00:00, 224MB/s]
inference:   0%|          | 0/48 [00:00<?, ?frame/s]
inference:   2%|▏         | 1/48 [00:01<01:11,  1.52s/frame]
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inference: 100%|██████████| 48/48 [00:07<00:00,  8.09frame/s]
inference: 100%|██████████| 48/48 [00:08<00:00,  5.66frame/s]
[h264 @ 0x55bc5dd209c0] Cannot load libnvcuvid.so.1
[h264 @ 0x55bc5dd209c0] Failed loading nvcuvid.
[h264 @ 0x55bc5dd209c0] Failed setup for format cuda: hwaccel initialisation returned error.
Cropping to fit done..
Upscaled video saved to: /src/ouput/tmpzqrp8f42out_upscaled_1920_1080.mp4

Prediction

Model

34afjvjb6xjvrgmxskvph6clma

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

4m 45s

Created

about 1 year ago by @lucataco

Input

model: RealESRGAN_x4plus
resolution: 2k
video_path

{
  "model": "RealESRGAN_x4plus",
  "resolution": "2k",
  "video_path": "https://replicate.delivery/pbxt/KX1xsphkWK5eQ6WAU1P33xOvNNaoWWRcpxbv4Z0oX1Wn9bCH/sora-glass-short.mp4"
}

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/real-esrgan-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/real-esrgan-video:c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320",
  {
    input: {
      model: "RealESRGAN_x4plus",
      resolution: "2k",
      video_path: "https://replicate.delivery/pbxt/KX1xsphkWK5eQ6WAU1P33xOvNNaoWWRcpxbv4Z0oX1Wn9bCH/sora-glass-short.mp4"
    }
  }
);

// 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/real-esrgan-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/real-esrgan-video:c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320",
    input={
        "model": "RealESRGAN_x4plus",
        "resolution": "2k",
        "video_path": "https://replicate.delivery/pbxt/KX1xsphkWK5eQ6WAU1P33xOvNNaoWWRcpxbv4Z0oX1Wn9bCH/sora-glass-short.mp4"
    }
)
print(output)

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

Run lucataco/real-esrgan-video 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": "c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320",
    "input": {
      "model": "RealESRGAN_x4plus",
      "resolution": "2k",
      "video_path": "https://replicate.delivery/pbxt/KX1xsphkWK5eQ6WAU1P33xOvNNaoWWRcpxbv4Z0oX1Wn9bCH/sora-glass-short.mp4"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-03-08T03:40:25.373468Z",
  "created_at": "2024-03-08T03:35:40.672917Z",
  "data_removed": false,
  "error": null,
  "id": "34afjvjb6xjvrgmxskvph6clma",
  "input": {
    "model": "RealESRGAN_x4plus",
    "resolution": "2k",
    "video_path": "https://replicate.delivery/pbxt/KX1xsphkWK5eQ6WAU1P33xOvNNaoWWRcpxbv4Z0oX1Wn9bCH/sora-glass-short.mp4"
  },
  "logs": "Upscaling from 1024x576 to 2560x1440, crop_to_fit=True\n/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional.\nwarnings.warn(\nDownloading: \"https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth\" to /Real-ESRGAN/weights/RealESRGAN_x4plus.pth\n  0%|          | 0.00/63.9M [00:00<?, ?B/s]\n  9%|▉         | 5.77M/63.9M [00:00<00:01, 46.6MB/s]\n 47%|████▋     | 30.0M/63.9M [00:00<00:00, 156MB/s] \n 85%|████████▌ | 54.6M/63.9M [00:00<00:00, 199MB/s]\n100%|██████████| 63.9M/63.9M [00:00<00:00, 183MB/s]\ninference:   0%|          | 0/238 [00:00<?, ?frame/s]\ninference:   0%|          | 1/238 [00:01<04:28,  1.13s/frame]\ninference:   1%|          | 2/238 [00:01<03:46,  1.04frame/s]\ninference:   1%|▏         | 3/238 [00:02<03:30,  1.12frame/s]\ninference:   2%|▏         | 4/238 [00:03<03:22,  1.16frame/s]\ninference:   2%|▏         | 5/238 [00:04<03:18,  1.17frame/s]\ninference:   3%|▎         | 6/238 [00:05<03:17,  1.18frame/s]\ninference:   3%|▎         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[03:25<00:00,  1.21frame/s]\ninference: 100%|██████████| 238/238 [03:26<00:00,  1.15frame/s]\nCropping to fit done..\nUpscaled video saved to: /src/ouput/tmpelxlzoqmsora-glass-short_upscaled_2560_1440.mp4",
  "metrics": {
    "predict_time": 221.180025,
    "total_time": 284.700551
  },
  "output": "https://replicate.delivery/pbxt/qPXMkdXAuRrrO9jktd2iFdrlf51xC3qdBy3YUhYAEkgU1BPJA/tmpelxlzoqmsora-glass-short_out.mp4",
  "started_at": "2024-03-08T03:36:44.193443Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/34afjvjb6xjvrgmxskvph6clma",
    "cancel": "https://api.replicate.com/v1/predictions/34afjvjb6xjvrgmxskvph6clma/cancel"
  },
  "version": "c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320"
}

Generated in

3 minutes 41 seconds

lucataco/real-esrgan-video:c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320

Upscaling from 1024x576 to 2560x1440, crop_to_fit=True
/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional.
warnings.warn(
Downloading: "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth" to /Real-ESRGAN/weights/RealESRGAN_x4plus.pth
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Cropping to fit done..
Upscaled video saved to: /src/ouput/tmpelxlzoqmsora-glass-short_upscaled_2560_1440.mp4

Prediction

Model

d8n2pfdmyhrgj0cf27z97ezjrr

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

3m 6s

Created

about 1 year ago by @fofr

Input

model: RealESRGAN_x4plus
resolution: 4k
video_path

{
  "model": "RealESRGAN_x4plus",
  "resolution": "4k",
  "video_path": "https://replicate.delivery/pbxt/KnyddBznuzcs3dsfcxhDZ4lUtn5ZYSOB9vPOmQX7KBDIfLvC/replicate-prediction-7zmprcqn1drgj0cf27c9ab2410-1.mp4"
}

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/real-esrgan-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/real-esrgan-video:c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320",
  {
    input: {
      model: "RealESRGAN_x4plus",
      resolution: "4k",
      video_path: "https://replicate.delivery/pbxt/KnyddBznuzcs3dsfcxhDZ4lUtn5ZYSOB9vPOmQX7KBDIfLvC/replicate-prediction-7zmprcqn1drgj0cf27c9ab2410-1.mp4"
    }
  }
);

// 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/real-esrgan-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/real-esrgan-video:c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320",
    input={
        "model": "RealESRGAN_x4plus",
        "resolution": "4k",
        "video_path": "https://replicate.delivery/pbxt/KnyddBznuzcs3dsfcxhDZ4lUtn5ZYSOB9vPOmQX7KBDIfLvC/replicate-prediction-7zmprcqn1drgj0cf27c9ab2410-1.mp4"
    }
)
print(output)

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

Run lucataco/real-esrgan-video 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": "c23768236472c41b7a121ee735c8073e29080c01b32907740cfada61bff75320",
    "input": {
      "model": "RealESRGAN_x4plus",
      "resolution": "4k",
      "video_path": "https://replicate.delivery/pbxt/KnyddBznuzcs3dsfcxhDZ4lUtn5ZYSOB9vPOmQX7KBDIfLvC/replicate-prediction-7zmprcqn1drgj0cf27c9ab2410-1.mp4"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-04-24T21:27:21.863050Z",
  "created_at": "2024-04-24T21:24:16.244000Z",
  "data_removed": false,
  "error": null,
  "id": "d8n2pfdmyhrgj0cf27z97ezjrr",
  "input": {
    "model": "RealESRGAN_x4plus",
    "resolution": "4k",
    "video_path": "https://replicate.delivery/pbxt/KnyddBznuzcs3dsfcxhDZ4lUtn5ZYSOB9vPOmQX7KBDIfLvC/replicate-prediction-7zmprcqn1drgj0cf27c9ab2410-1.mp4"
  },
  "logs": "Upscaling from 488x656 to 2160x3840, crop_to_fit=True\n/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional.\nwarnings.warn(\nDownloading: \"https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth\" to /Real-ESRGAN/weights/RealESRGAN_x4plus.pth\n  0%|          | 0.00/63.9M [00:00<?, ?B/s]\n 58%|█████▊    | 36.9M/63.9M [00:00<00:00, 387MB/s]\n100%|██████████| 63.9M/63.9M [00:00<00:00, 445MB/s]\nYou are generating video that is larger than 4K, which will be very slow due to IO speed. We highly recommend to decrease the outscale(aka, -s).\ninference:   0%|          | 0/96 [00:00<?, ?frame/s]\ninference:   1%|          | 1/96 [00:01<03:06,  1.96s/frame]\ninference:   2%|▏         | 2/96 [00:02<01:42,  1.09s/frame]\ninference:   3%|▎         | 3/96 [00:02<01:14,  1.24frame/s]\ninference:   4%|▍         | 4/96 [00:03<01:00,  1.53frame/s]\ninference:   5%|▌         | 5/96 [00:03<00:52,  1.74frame/s]\ninference:   6%|▋         | 6/96 [00:04<00:48,  1.87frame/s]\ninference:   7%|▋         | 7/96 [00:04<00:44,  2.00frame/s]\ninference:   8%|▊         | 8/96 [00:05<00:42,  2.06frame/s]\ninference:   9%|▉         | 9/96 [00:05<00:40,  2.15frame/s]\ninference:  10%|█         | 10/96 [00:05<00:39,  2.17frame/s]\ninference:  11%|█▏        | 11/96 [00:06<00:38,  2.23frame/s]\ninference:  12%|█▎        | 12/96 [00:06<00:37,  2.23frame/s]\ninference:  14%|█▎        | 13/96 [00:07<00:36,  2.28frame/s]\ninference:  15%|█▍        | 14/96 [00:07<00:36,  2.26frame/s]\ninference:  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2.21frame/s]\ninference:  98%|█████████▊| 94/96 [00:43<00:00,  2.19frame/s]\ninference:  99%|█████████▉| 95/96 [00:43<00:00,  2.23frame/s]\ninference: 100%|██████████| 96/96 [00:44<00:00,  2.20frame/s]\ninference: 100%|██████████| 96/96 [00:50<00:00,  1.89frame/s]\n[h264 @ 0x559bcfafc800] Cannot load libnvcuvid.so.1\n[h264 @ 0x559bcfafc800] Failed loading nvcuvid.\n[h264 @ 0x559bcfafc800] Failed setup for format cuda: hwaccel initialisation returned error.\nCropping to fit done..\nUpscaled video saved to: /src/ouput/tmpxaoltl2treplicate-prediction-7zmprcqn1drgj0cf27c9ab2410-1_upscaled_2160_3840.mp4",
  "metrics": {
    "predict_time": 73.592536,
    "total_time": 185.61905
  },
  "output": "https://replicate.delivery/pbxt/VTbGXAPB7f2DbqhiMYROWYlqUqvVDnA8iWnyzHuQ1be4sytSA/tmpxaoltl2treplicate-prediction-7zmprcqn1drgj0cf27c9ab2410-1_out.mp4",
  "started_at": "2024-04-24T21:26:08.270514Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/d8n2pfdmyhrgj0cf27z97ezjrr",
    "cancel": "https://api.replicate.com/v1/predictions/d8n2pfdmyhrgj0cf27z97ezjrr/cancel"
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}

Generated in

73.6 seconds