chenxwh/depthcrafter:1c9bfc62 – Run with an API on Replicate

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chenxwh /depthcrafter:1c9bfc62

Playground API

Input

video

*file

Input video

num_denoising_steps

integer

(minimum: 1, maximum: 25)

Number of denoising steps

Default: 10

guidance_scale

number

(minimum: 1, maximum: 1.2)

Scale for classifier-free guidance

Default: 1.2

max_res

integer

Maximum resolution

Default: 1024

process_length

integer

(minimum: 1, maximum: 280)

Number of frames to process

Default: 60

target_fps

integer

fps of the output video

Default: 15

window_size

integer

Window size

Default: 110

overlap

integer

Overlap size

Default: 15

save_npz

boolean

Save npz file

Default: false

datast

string

Assigned resolution for specific dataset evaluation

Default: "open"

seed

integer

Random seed. Leave blank to randomize the seed

Run this model in Node.js with one line of code:

npx create-replicate --model=chenxwh/depthcrafter

or set up a project from scratch

Install Replicate’s Node.js client library

npm install replicate

Set the REPLICATE_API_TOKEN environment variable

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "chenxwh/depthcrafter:1c9bfc62fd0750ddadeccf1af4cdef4d74a8a064debd8152ade87f3c9dc30d2b",
  {
    input: {
      video: "https://replicate.delivery/pbxt/LiSi8OkoMdHvnAJJu2xkrie4iykABkvxUOMXQ7ehixoOPF0e/example_02.mp4",
      datast: "open",
      max_res: 1024,
      overlap: 15,
      save_npz: true,
      target_fps: 15,
      window_size: 110,
      guidance_scale: 1.2,
      process_length: 60,
      num_denoising_steps: 10
    }
  }
);
console.log(output);

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

Install Replicate’s Python client library

pip install replicate

Set the REPLICATE_API_TOKEN environment variable

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client

import replicate

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

output = replicate.run(
    "chenxwh/depthcrafter:1c9bfc62fd0750ddadeccf1af4cdef4d74a8a064debd8152ade87f3c9dc30d2b",
    input={
        "video": "https://replicate.delivery/pbxt/LiSi8OkoMdHvnAJJu2xkrie4iykABkvxUOMXQ7ehixoOPF0e/example_02.mp4",
        "datast": "open",
        "max_res": 1024,
        "overlap": 15,
        "save_npz": True,
        "target_fps": 15,
        "window_size": 110,
        "guidance_scale": 1.2,
        "process_length": 60,
        "num_denoising_steps": 10
    }
)
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 chenxwh/depthcrafter 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": "1c9bfc62fd0750ddadeccf1af4cdef4d74a8a064debd8152ade87f3c9dc30d2b",
    "input": {
      "video": "https://replicate.delivery/pbxt/LiSi8OkoMdHvnAJJu2xkrie4iykABkvxUOMXQ7ehixoOPF0e/example_02.mp4",
      "datast": "open",
      "max_res": 1024,
      "overlap": 15,
      "save_npz": true,
      "target_fps": 15,
      "window_size": 110,
      "guidance_scale": 1.2,
      "process_length": 60,
      "num_denoising_steps": 10
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Install Cog

brew install cog

If you don’t have Homebrew, there are other installation options available.

Pull and run chenxwh/depthcrafter using Cog (this will download the full model and run it in your local environment):

cog predict r8.im/chenxwh/depthcrafter@sha256:1c9bfc62fd0750ddadeccf1af4cdef4d74a8a064debd8152ade87f3c9dc30d2b \
  -i 'video="https://replicate.delivery/pbxt/LiSi8OkoMdHvnAJJu2xkrie4iykABkvxUOMXQ7ehixoOPF0e/example_02.mp4"' \
  -i 'datast="open"' \
  -i 'max_res=1024' \
  -i 'overlap=15' \
  -i 'save_npz=true' \
  -i 'target_fps=15' \
  -i 'window_size=110' \
  -i 'guidance_scale=1.2' \
  -i 'process_length=60' \
  -i 'num_denoising_steps=10'

To learn more, take a look at the Cog documentation.

Pull and run chenxwh/depthcrafter using Docker (this will download the full model and run it in your local environment):

docker run -d -p 5000:5000 --gpus=all r8.im/chenxwh/depthcrafter@sha256:1c9bfc62fd0750ddadeccf1af4cdef4d74a8a064debd8152ade87f3c9dc30d2b
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "video": "https://replicate.delivery/pbxt/LiSi8OkoMdHvnAJJu2xkrie4iykABkvxUOMXQ7ehixoOPF0e/example_02.mp4",
      "datast": "open",
      "max_res": 1024,
      "overlap": 15,
      "save_npz": true,
      "target_fps": 15,
      "window_size": 110,
      "guidance_scale": 1.2,
      "process_length": 60,
      "num_denoising_steps": 10
    }
  }' \
  http://localhost:5000/predictions

Output

npz

out.npz

depth_video

{
  "completed_at": "2024-10-01T00:37:53.048401Z",
  "created_at": "2024-10-01T00:34:56.416000Z",
  "data_removed": false,
  "error": null,
  "id": "a2exdg3941rgp0cj8p0838863m",
  "input": {
    "video": "https://replicate.delivery/pbxt/LiSi8OkoMdHvnAJJu2xkrie4iykABkvxUOMXQ7ehixoOPF0e/example_02.mp4",
    "datast": "open",
    "max_res": 1024,
    "overlap": 15,
    "save_npz": true,
    "target_fps": 15,
    "window_size": 110,
    "guidance_scale": 1.2,
    "process_length": 60,
    "num_denoising_steps": 10
  },
  "logs": "Using seed: 56096\n==> frames shape: (60, 576, 1024, 3)\nElapsed time for encoding video: 8618.146484375 ms\n  0%|          | 0/10 [00:00<?, ?it/s]\n 10%|█         | 1/10 [00:05<00:47,  5.26s/it]\n 20%|██        | 2/10 [00:12<00:49,  6.16s/it]\n 30%|███       | 3/10 [00:18<00:45,  6.45s/it]\n 40%|████      | 4/10 [00:25<00:39,  6.59s/it]\n 50%|█████     | 5/10 [00:32<00:33,  6.67s/it]\n 60%|██████    | 6/10 [00:39<00:26,  6.72s/it]\n 70%|███████   | 7/10 [00:46<00:20,  6.76s/it]\n 80%|████████  | 8/10 [00:52<00:13,  6.78s/it]\n 90%|█████████ | 9/10 [00:59<00:06,  6.80s/it]\n100%|██████████| 10/10 [01:06<00:00,  6.81s/it]\n100%|██████████| 10/10 [01:06<00:00,  6.66s/it]\nElapsed time for denoising video: 68239.7265625 ms\nElapsed time for decoding video: 17792.451171875 ms",
  "metrics": {
    "predict_time": 103.510892014,
    "total_time": 176.632401
  },
  "output": {
    "npz": "https://replicate.delivery/pbxt/yceY2s25LETaGCXM0LydkrfQsTtNqO7vvXjvT9N7GVEeyeIOB/out.npz",
    "depth_video": "https://replicate.delivery/pbxt/A2D6foB4Jes7jE3X5tqnWBGTZmt9wp2lVguxf4LGB8sAzeIOB/out.mp4"
  },
  "started_at": "2024-10-01T00:36:09.537509Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/a2exdg3941rgp0cj8p0838863m",
    "cancel": "https://api.replicate.com/v1/predictions/a2exdg3941rgp0cj8p0838863m/cancel"
  },
  "version": "1c9bfc62fd0750ddadeccf1af4cdef4d74a8a064debd8152ade87f3c9dc30d2b"
}

Generated in

1 minute 44 seconds

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