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Input
Run this model in Node.js with one line of code:
npx create-replicate --model=lee101/fast-vfx
or set up a project from scratch
Install Replicate’s Node.js client library:
npm install replicateSet 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 lee101/fast-vfx using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"lee101/fast-vfx:bc40ad9478d509f254f95a53a0ff6092b2714764f44e55a890ae888f3842e68f",
{
input: {
video: "https://replicate.delivery/pbxt/LwiMx5pzYfD7KMQ7QTY2vKNZRAXdMMrowLWTchwFquCVpg7E/cant-keep-getting-away.mp4",
num_levels: 25,
output_codec: "h264_nvenc"
}
}
);
// 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.
Install Replicate’s Python client library:
pip install replicateSet 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 replicateRun lee101/fast-vfx using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"lee101/fast-vfx:bc40ad9478d509f254f95a53a0ff6092b2714764f44e55a890ae888f3842e68f",
input={
"video": "https://replicate.delivery/pbxt/LwiMx5pzYfD7KMQ7QTY2vKNZRAXdMMrowLWTchwFquCVpg7E/cant-keep-getting-away.mp4",
"num_levels": 25,
"output_codec": "h264_nvenc"
}
)
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 lee101/fast-vfx 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": "lee101/fast-vfx:bc40ad9478d509f254f95a53a0ff6092b2714764f44e55a890ae888f3842e68f",
"input": {
"video": "https://replicate.delivery/pbxt/LwiMx5pzYfD7KMQ7QTY2vKNZRAXdMMrowLWTchwFquCVpg7E/cant-keep-getting-away.mp4",
"num_levels": 25,
"output_codec": "h264_nvenc"
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Add a payment method to run this model.
Sign in with GitHub
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terms of service and privacy policy
Output
{
"completed_at": "2024-11-10T04:19:38.145388Z",
"created_at": "2024-11-10T04:15:11.877000Z",
"data_removed": false,
"error": null,
"id": "g5nfkckg0nrgy0ck2h4ac52m24",
"input": {
"video": "https://replicate.delivery/pbxt/LwiMx5pzYfD7KMQ7QTY2vKNZRAXdMMrowLWTchwFquCVpg7E/cant-keep-getting-away.mp4",
"num_levels": 25,
"output_codec": "h264_nvenc"
},
"logs": "[W1110 04:19:33.962606569 conversion.cpp:411] Warning: The output format NV12 is selected. This will be implicitly converted to YUV444P, in which all the color components Y, U, V have the same dimension. 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888])\ncuda:0\ntorch.float32\ntorch.Size([1, 3, 498, 888])\ntorch.Size([1, 3, 498, 888])\ncuda:0\ntorch.float32\ntorch.Size([1, 3, 498, 888])\ntorch.Size([1, 3, 498, 888])\ncuda:0\ntorch.float32\ntorch.Size([1, 3, 498, 888])\ntorch.Size([1, 3, 498, 888])\ncuda:0\ntorch.float32\ntorch.Size([1, 3, 498, 888])\ntorch.Size([1, 3, 498, 888])\ncuda:0\ntorch.float32\ntorch.Size([1, 3, 498, 888])\ntorch.Size([1, 3, 498, 888])\ncuda:0\ntorch.float32\ntorch.Size([1, 3, 498, 888])\ntorch.Size([1, 3, 498, 888])\ncuda:0\ntorch.float32\ntorch.Size([1, 3, 498, 888])\ntorch.Size([1, 3, 498, 888])\ncuda:0\ntorch.float32\ntorch.Size([1, 3, 498, 888])\ntorch.Size([1, 3, 498, 888])\ncuda:0\ntorch.float32\ntorch.Size([1, 3, 498, 888])\ntorch.Size([1, 3, 498, 888])\ncuda:0\ntorch.float32\ntorch.Size([1, 3, 498, 888])\ntorch.Size([1, 3, 498, 888])\ncuda:0\ntorch.float32\ntorch.Size([1, 3, 498, 888])\ntorch.Size([1, 3, 498, 888])\ncuda:0\ntorch.float32\ntorch.Size([1, 3, 498, 888])\ntorch.Size([1, 3, 498, 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888])\ncuda:0\ntorch.float32\ntorch.Size([1, 3, 498, 888])\ntorch.Size([1, 3, 498, 888])\ncuda:0\ntorch.float32\ntorch.Size([1, 3, 498, 888])\ntorch.Size([1, 3, 498, 888])\ncuda:0\ntorch.float32\ntorch.Size([1, 3, 498, 888])\ntorch.Size([1, 3, 498, 888])\ncuda:0\ntorch.float32\ntorch.Size([1, 3, 498, 888])\ntorch.Size([1, 3, 498, 888])\ncuda:0\ntorch.float32\n[W1110 04:19:37.684450582 chunked_buffer.cpp:98] Warning: The number of buffered frames exceeded the buffer size. Dropping the old frames. To avoid this, you can set a higher buffer_chunk_size value. (function operator())\ntorch.Size([1, 3, 498, 888])\ntorch.Size([1, 3, 498, 888])\ncuda:0\ntorch.float32\ntorch.Size([1, 3, 498, 888])\ntorch.Size([1, 3, 498, 888])\ncuda:0\ntorch.float32\ntorch.Size([1, 3, 498, 888])\ntorch.Size([1, 3, 498, 888])\ncuda:0\ntorch.float32",
"metrics": {
"predict_time": 5.108525454,
"total_time": 266.268388
},
"output": "https://replicate.delivery/xezq/IBNugfsCMb0LIyLD9DwGB2foPkMu6fQG7xV402blfE9rl59OB/tmpx5077nxg.mp4",
"started_at": "2024-11-10T04:19:33.036863Z",
"status": "succeeded",
"urls": {
"stream": "https://stream.replicate.com/v1/files/sqpt-ezofpzqforpfetkb35q3ibkqtypbweskfrx4wephgrwwqzgndtha",
"get": "https://api.replicate.com/v1/predictions/g5nfkckg0nrgy0ck2h4ac52m24",
"cancel": "https://api.replicate.com/v1/predictions/g5nfkckg0nrgy0ck2h4ac52m24/cancel"
},
"version": "bc40ad9478d509f254f95a53a0ff6092b2714764f44e55a890ae888f3842e68f"
}Generated in
[W1110 04:19:33.962606569 conversion.cpp:411] Warning: The output format NV12 is selected. This will be implicitly converted to YUV444P, in which all the color components Y, U, V have the same dimension. (function operator())
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torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
[W1110 04:19:37.684450582 chunked_buffer.cpp:98] Warning: The number of buffered frames exceeded the buffer size. Dropping the old frames. To avoid this, you can set a higher buffer_chunk_size value. (function operator())
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32
torch.Size([1, 3, 498, 888])
torch.Size([1, 3, 498, 888])
cuda:0
torch.float32