camenduru/magictime – Run with an API on Replicate

camenduru / magictime

MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators

Cold

Public
366 runs
A100 (80GB)
GitHub
Paper
License

Run with an API

Playground API Examples README Versions

Input

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

npx create-replicate --model=camenduru/magictime

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

const output = await replicate.run(
  "camenduru/magictime:91e4bb80b45832b5bafdbc10d94fd1d364d0d6ad80f5b1498fcb25d217cb3a9c",
  {
    input: {
      seed: "1496541313",
      width: 512,
      height: 512,
      prompt: "Bean sprouts grow and mature from seeds.",
      dreambooth: "ToonYou_beta6.safetensors",
      negative_prompt: "worst quality, low quality, letterboxed"
    }
  }
);
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 camenduru/magictime using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "camenduru/magictime:91e4bb80b45832b5bafdbc10d94fd1d364d0d6ad80f5b1498fcb25d217cb3a9c",
    input={
        "seed": "1496541313",
        "width": 512,
        "height": 512,
        "prompt": "Bean sprouts grow and mature from seeds.",
        "dreambooth": "ToonYou_beta6.safetensors",
        "negative_prompt": "worst quality, low quality, letterboxed"
    }
)
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 camenduru/magictime 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": "91e4bb80b45832b5bafdbc10d94fd1d364d0d6ad80f5b1498fcb25d217cb3a9c",
    "input": {
      "seed": "1496541313",
      "width": 512,
      "height": 512,
      "prompt": "Bean sprouts grow and mature from seeds.",
      "dreambooth": "ToonYou_beta6.safetensors",
      "negative_prompt": "worst quality, low quality, letterboxed"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-04-13T11:51:20.211191Z",
  "created_at": "2024-04-13T11:46:00.610000Z",
  "data_removed": false,
  "error": null,
  "id": "xpjs97aq49rgm0cetx3tmxe0bg",
  "input": {
    "seed": "1496541313",
    "width": 512,
    "height": 512,
    "prompt": "Bean sprouts grow and mature from seeds.",
    "dreambooth": "ToonYou_beta6.safetensors",
    "motion_module": "motion_module.ckpt",
    "negative_prompt": "worst quality, low quality, letterboxed"
  },
  "logs": "### seed: 1496541313\n  0%|          | 0/25 [00:00<?, ?it/s]\n  4%|▍         | 1/25 [00:02<01:07,  2.82s/it]\n  8%|▊         | 2/25 [00:05<01:03,  2.76s/it]\n 12%|█▏        | 3/25 [00:08<01:00,  2.75s/it]\n 16%|█▌        | 4/25 [00:11<00:57,  2.75s/it]\n 20%|██        | 5/25 [00:13<00:55,  2.75s/it]\n 24%|██▍       | 6/25 [00:16<00:52,  2.75s/it]\n 28%|██▊       | 7/25 [00:19<00:49,  2.75s/it]\n 32%|███▏      | 8/25 [00:22<00:46,  2.75s/it]\n 36%|███▌      | 9/25 [00:24<00:43,  2.75s/it]\n 40%|████      | 10/25 [00:27<00:41,  2.74s/it]\n 44%|████▍     | 11/25 [00:30<00:38,  2.74s/it]\n 48%|████▊     | 12/25 [00:32<00:35,  2.75s/it]\n 52%|█████▏    | 13/25 [00:35<00:32,  2.75s/it]\n 56%|█████▌    | 14/25 [00:38<00:30,  2.75s/it]\n 60%|██████    | 15/25 [00:41<00:27,  2.75s/it]\n 64%|██████▍   | 16/25 [00:44<00:24,  2.75s/it]\n 68%|██████▊   | 17/25 [00:46<00:22,  2.75s/it]\n 72%|███████▏  | 18/25 [00:49<00:19,  2.75s/it]\n 76%|███████▌  | 19/25 [00:52<00:16,  2.75s/it]\n 80%|████████  | 20/25 [00:55<00:13,  2.76s/it]\n 84%|████████▍ | 21/25 [00:57<00:11,  2.76s/it]\n 88%|████████▊ | 22/25 [01:00<00:08,  2.76s/it]\n 92%|█████████▏| 23/25 [01:03<00:05,  2.76s/it]\n 96%|█████████▌| 24/25 [01:06<00:02,  2.76s/it]\n100%|██████████| 25/25 [01:08<00:00,  2.76s/it]\n100%|██████████| 25/25 [01:08<00:00,  2.75s/it]\n  0%|          | 0/16 [00:00<?, ?it/s]\n 31%|███▏      | 5/16 [00:00<00:00, 35.27it/s]\n 56%|█████▋    | 9/16 [00:00<00:00, 16.61it/s]\n 75%|███████▌  | 12/16 [00:00<00:00, 14.21it/s]\n 88%|████████▊ | 14/16 [00:00<00:00, 13.35it/s]\n100%|██████████| 16/16 [00:01<00:00, 12.74it/s]\n100%|██████████| 16/16 [00:01<00:00, 14.40it/s]",
  "metrics": {
    "predict_time": 74.898709,
    "total_time": 319.601191
  },
  "output": "https://replicate.delivery/pbxt/OtpFsf7A39zFYSxYkuAIieieA4uS7zqjtW1zvceR3gAe2RQVC/sample.mp4",
  "started_at": "2024-04-13T11:50:05.312482Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/xpjs97aq49rgm0cetx3tmxe0bg",
    "cancel": "https://api.replicate.com/v1/predictions/xpjs97aq49rgm0cetx3tmxe0bg/cancel"
  },
  "version": "07cfe8a58e98a111ead097bb8cd66bc6a8e2219a03eb024c4332fdcf46b94afc"
}

Generated in

74.9 seconds

Tweak it ShareReport

### seed: 1496541313
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100%|██████████| 16/16 [00:01<00:00, 12.74it/s]
100%|██████████| 16/16 [00:01<00:00, 14.40it/s]

This example was created by a different version, camenduru/magictime:07cfe8a5.

Examples

View more examples

Run time and cost

This model costs approximately $0.36 to run on Replicate, or 2 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia A100 (80GB) GPU hardware. Predictions typically complete within 5 minutes. The predict time for this model varies significantly based on the inputs.

Readme

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Input

Output

Examples

Run time and cost

Readme

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