Prediction

ID

sbwnhhrb3ihbvriz6jouig5wai

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

2m

Created

over 1 year ago

Input

image
prompt: Several statues made of porcelain chunks and gold mendings, the face of the statues have lips and eyes, the eyes are blinking, the lips are opening like the statues are talking, the head of the statues are turning towards the camera
max_frames: 16
guidance_scale: 9
num_inference_steps: 50

{
  "image": "https://replicate.delivery/pbxt/KAaJWyluKBrWzbe5EhQArYZcVXdpOvcLyF81menWifyusgCe/1.jpeg",
  "prompt": "Several statues made of porcelain chunks and gold mendings, the face of the statues have lips and eyes, the eyes are blinking, the lips are opening like the statues are talking, the head of the statues are turning towards the camera",
  "max_frames": 16,
  "guidance_scale": 9,
  "num_inference_steps": 50
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
  {
    input: {
      image: "https://replicate.delivery/pbxt/KAaJWyluKBrWzbe5EhQArYZcVXdpOvcLyF81menWifyusgCe/1.jpeg",
      prompt: "Several statues made of porcelain chunks and gold mendings, the face of the statues have lips and eyes, the eyes are blinking, the lips are opening like the statues are talking, the head of the statues are turning towards the camera",
      max_frames: 16,
      guidance_scale: 9,
      num_inference_steps: 50
    }
  }
);

// 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 ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    input={
        "image": "https://replicate.delivery/pbxt/KAaJWyluKBrWzbe5EhQArYZcVXdpOvcLyF81menWifyusgCe/1.jpeg",
        "prompt": "Several statues made of porcelain chunks and gold mendings, the face of the statues have lips and eyes, the eyes are blinking, the lips are opening like the statues are talking, the head of the statues are turning towards the camera",
        "max_frames": 16,
        "guidance_scale": 9,
        "num_inference_steps": 50
    }
)
print(output)

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

Run ali-vilab/i2vgen-xl 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": "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    "input": {
      "image": "https://replicate.delivery/pbxt/KAaJWyluKBrWzbe5EhQArYZcVXdpOvcLyF81menWifyusgCe/1.jpeg",
      "prompt": "Several statues made of porcelain chunks and gold mendings, the face of the statues have lips and eyes, the eyes are blinking, the lips are opening like the statues are talking, the head of the statues are turning towards the camera",
      "max_frames": 16,
      "guidance_scale": 9,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

1 minute 56 seconds

ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4

Prediction

Model

ID

7nmo2lrb6jtysf7hozhcw5ayqa

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

1m 56s

Created

over 1 year ago

Input

image
prompt: A blonde girl in jeans
max_frames: 16
guidance_scale: 9
num_inference_steps: 50

{
  "image": "https://replicate.delivery/pbxt/KA6KcZp2UhselAqryBuWaIV2w3KPKYJpVM9cQtqSctlhwdK5/img_0002.png",
  "prompt": "A blonde girl in jeans",
  "max_frames": 16,
  "guidance_scale": 9,
  "num_inference_steps": 50
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
  {
    input: {
      image: "https://replicate.delivery/pbxt/KA6KcZp2UhselAqryBuWaIV2w3KPKYJpVM9cQtqSctlhwdK5/img_0002.png",
      prompt: "A blonde girl in jeans",
      max_frames: 16,
      guidance_scale: 9,
      num_inference_steps: 50
    }
  }
);

// 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 ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    input={
        "image": "https://replicate.delivery/pbxt/KA6KcZp2UhselAqryBuWaIV2w3KPKYJpVM9cQtqSctlhwdK5/img_0002.png",
        "prompt": "A blonde girl in jeans",
        "max_frames": 16,
        "guidance_scale": 9,
        "num_inference_steps": 50
    }
)
print(output)

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

Run ali-vilab/i2vgen-xl 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": "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    "input": {
      "image": "https://replicate.delivery/pbxt/KA6KcZp2UhselAqryBuWaIV2w3KPKYJpVM9cQtqSctlhwdK5/img_0002.png",
      "prompt": "A blonde girl in jeans",
      "max_frames": 16,
      "guidance_scale": 9,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

1 minute 56 seconds

ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4

Prediction

Model

ID

2m3npajb3lzubejsnd46tfql6y

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

4m 43s

Created

over 1 year ago

Input

image
prompt: A girl with yellow hair and black clothes stood in front of the camera
max_frames: 16
guidance_scale: 9
num_inference_steps: 50

{
  "image": "https://replicate.delivery/pbxt/KAaakS0H8XnzXtfFCWvqUBjC759vUXdJ18Y5JgfeGFA2PTie/img_0004.png",
  "prompt": "A girl with yellow hair and black clothes stood in front of the camera",
  "max_frames": 16,
  "guidance_scale": 9,
  "num_inference_steps": 50
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
  {
    input: {
      image: "https://replicate.delivery/pbxt/KAaakS0H8XnzXtfFCWvqUBjC759vUXdJ18Y5JgfeGFA2PTie/img_0004.png",
      prompt: "A girl with yellow hair and black clothes stood in front of the camera",
      max_frames: 16,
      guidance_scale: 9,
      num_inference_steps: 50
    }
  }
);

// 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 ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    input={
        "image": "https://replicate.delivery/pbxt/KAaakS0H8XnzXtfFCWvqUBjC759vUXdJ18Y5JgfeGFA2PTie/img_0004.png",
        "prompt": "A girl with yellow hair and black clothes stood in front of the camera",
        "max_frames": 16,
        "guidance_scale": 9,
        "num_inference_steps": 50
    }
)
print(output)

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

Run ali-vilab/i2vgen-xl 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": "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    "input": {
      "image": "https://replicate.delivery/pbxt/KAaakS0H8XnzXtfFCWvqUBjC759vUXdJ18Y5JgfeGFA2PTie/img_0004.png",
      "prompt": "A girl with yellow hair and black clothes stood in front of the camera",
      "max_frames": 16,
      "guidance_scale": 9,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

1 minute 56 seconds

ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4

Prediction

Model

ID

nfxtm5bbxwwpu26nnthp6mfgqm

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

4m 10s

Created

over 1 year ago

Input

image
prompt: A bustling space habitat
max_frames: 32
guidance_scale: 9
num_inference_steps: 50

{
  "image": "https://replicate.delivery/pbxt/KAadCEQoo11IDW10ct9QrGEdi2CuAvfY9GgwTu6VCdd2ERNd/a.webp",
  "prompt": "A bustling space habitat",
  "max_frames": 32,
  "guidance_scale": 9,
  "num_inference_steps": 50
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
  {
    input: {
      image: "https://replicate.delivery/pbxt/KAadCEQoo11IDW10ct9QrGEdi2CuAvfY9GgwTu6VCdd2ERNd/a.webp",
      prompt: "A bustling space habitat",
      max_frames: 32,
      guidance_scale: 9,
      num_inference_steps: 50
    }
  }
);

// 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 ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    input={
        "image": "https://replicate.delivery/pbxt/KAadCEQoo11IDW10ct9QrGEdi2CuAvfY9GgwTu6VCdd2ERNd/a.webp",
        "prompt": "A bustling space habitat",
        "max_frames": 32,
        "guidance_scale": 9,
        "num_inference_steps": 50
    }
)
print(output)

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

Run ali-vilab/i2vgen-xl 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": "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    "input": {
      "image": "https://replicate.delivery/pbxt/KAadCEQoo11IDW10ct9QrGEdi2CuAvfY9GgwTu6VCdd2ERNd/a.webp",
      "prompt": "A bustling space habitat",
      "max_frames": 32,
      "guidance_scale": 9,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

3 minutes 54 seconds

ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4

Prediction

Model

ID

g7cgxfbbs2w7dlxjz6nyovoluu

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

2m 10s

Created

over 1 year ago

Input

image
prompt: a girl standing in a field of wheat under a storm cloud
max_frames: 16
guidance_scale: 9
num_inference_steps: 50

{
  "image": "https://replicate.delivery/pbxt/KAaiN6IY6R62sAwZUI1j20wRrce79ECMpCV8v1jhLqn66pk7/img_0010.png",
  "prompt": "a girl standing in a field of wheat under a storm cloud",
  "max_frames": 16,
  "guidance_scale": 9,
  "num_inference_steps": 50
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
  {
    input: {
      image: "https://replicate.delivery/pbxt/KAaiN6IY6R62sAwZUI1j20wRrce79ECMpCV8v1jhLqn66pk7/img_0010.png",
      prompt: "a girl standing in a field of wheat under a storm cloud",
      max_frames: 16,
      guidance_scale: 9,
      num_inference_steps: 50
    }
  }
);

// 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 ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    input={
        "image": "https://replicate.delivery/pbxt/KAaiN6IY6R62sAwZUI1j20wRrce79ECMpCV8v1jhLqn66pk7/img_0010.png",
        "prompt": "a girl standing in a field of wheat under a storm cloud",
        "max_frames": 16,
        "guidance_scale": 9,
        "num_inference_steps": 50
    }
)
print(output)

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

Run ali-vilab/i2vgen-xl 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": "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    "input": {
      "image": "https://replicate.delivery/pbxt/KAaiN6IY6R62sAwZUI1j20wRrce79ECMpCV8v1jhLqn66pk7/img_0010.png",
      "prompt": "a girl standing in a field of wheat under a storm cloud",
      "max_frames": 16,
      "guidance_scale": 9,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

1 minute 56 seconds

ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4

Prediction

Model

ID

qmtrv3rbp7xcqrvsog4zb3uc3e

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

2m 53s

Created

over 1 year ago

Input

image
prompt: a painting of a city street with a giant monster
max_frames: 24
guidance_scale: 9
num_inference_steps: 50

{
  "image": "https://replicate.delivery/pbxt/KAaj7PDEVxENh3Y4O1RYadsRJt3zOWscUiFjYzsFZUrqFKVs/img_0011.png",
  "prompt": "a painting of a city street with a giant monster",
  "max_frames": 24,
  "guidance_scale": 9,
  "num_inference_steps": 50
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
  {
    input: {
      image: "https://replicate.delivery/pbxt/KAaj7PDEVxENh3Y4O1RYadsRJt3zOWscUiFjYzsFZUrqFKVs/img_0011.png",
      prompt: "a painting of a city street with a giant monster",
      max_frames: 24,
      guidance_scale: 9,
      num_inference_steps: 50
    }
  }
);

// 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 ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    input={
        "image": "https://replicate.delivery/pbxt/KAaj7PDEVxENh3Y4O1RYadsRJt3zOWscUiFjYzsFZUrqFKVs/img_0011.png",
        "prompt": "a painting of a city street with a giant monster",
        "max_frames": 24,
        "guidance_scale": 9,
        "num_inference_steps": 50
    }
)
print(output)

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

Run ali-vilab/i2vgen-xl 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": "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    "input": {
      "image": "https://replicate.delivery/pbxt/KAaj7PDEVxENh3Y4O1RYadsRJt3zOWscUiFjYzsFZUrqFKVs/img_0011.png",
      "prompt": "a painting of a city street with a giant monster",
      "max_frames": 24,
      "guidance_scale": 9,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

2 minutes 53 seconds

ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4

Prediction

Model

ID

wc32s5rbb5ec2un3xly3eerulq

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

1m 56s

Created

over 1 year ago

Input

image
prompt: Papers were floating in the air on a table in the library
max_frames: 16
guidance_scale: 9
num_inference_steps: 50

{
  "image": "https://replicate.delivery/pbxt/KAanGj3w6ZzEY8LrpwIEJ5TADtm29iO5a6LJfGolWuouVxaZ/img_0009.png",
  "prompt": "Papers were floating in the air on a table in the library",
  "max_frames": 16,
  "guidance_scale": 9,
  "num_inference_steps": 50
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
  {
    input: {
      image: "https://replicate.delivery/pbxt/KAanGj3w6ZzEY8LrpwIEJ5TADtm29iO5a6LJfGolWuouVxaZ/img_0009.png",
      prompt: "Papers were floating in the air on a table in the library",
      max_frames: 16,
      guidance_scale: 9,
      num_inference_steps: 50
    }
  }
);

// 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 ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    input={
        "image": "https://replicate.delivery/pbxt/KAanGj3w6ZzEY8LrpwIEJ5TADtm29iO5a6LJfGolWuouVxaZ/img_0009.png",
        "prompt": "Papers were floating in the air on a table in the library",
        "max_frames": 16,
        "guidance_scale": 9,
        "num_inference_steps": 50
    }
)
print(output)

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

Run ali-vilab/i2vgen-xl 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": "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    "input": {
      "image": "https://replicate.delivery/pbxt/KAanGj3w6ZzEY8LrpwIEJ5TADtm29iO5a6LJfGolWuouVxaZ/img_0009.png",
      "prompt": "Papers were floating in the air on a table in the library",
      "max_frames": 16,
      "guidance_scale": 9,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

1 minute 56 seconds

ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4

Prediction

Model

ID

lb63s2zbjcanc7iiubicybgocm

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

4m 46s

Created

over 1 year ago

Input

image
prompt: A red woodcut bird
max_frames: 24
guidance_scale: 9
num_inference_steps: 50

{
  "image": "https://replicate.delivery/pbxt/KAarl3xLEJdskvaosegmeOt0fKixCbUAJGH9Ls5BllWOo88z/img_0006.png",
  "prompt": "A red woodcut bird",
  "max_frames": 24,
  "guidance_scale": 9,
  "num_inference_steps": 50
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
  {
    input: {
      image: "https://replicate.delivery/pbxt/KAarl3xLEJdskvaosegmeOt0fKixCbUAJGH9Ls5BllWOo88z/img_0006.png",
      prompt: "A red woodcut bird",
      max_frames: 24,
      guidance_scale: 9,
      num_inference_steps: 50
    }
  }
);

// 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 ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    input={
        "image": "https://replicate.delivery/pbxt/KAarl3xLEJdskvaosegmeOt0fKixCbUAJGH9Ls5BllWOo88z/img_0006.png",
        "prompt": "A red woodcut bird",
        "max_frames": 24,
        "guidance_scale": 9,
        "num_inference_steps": 50
    }
)
print(output)

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

Run ali-vilab/i2vgen-xl 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": "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    "input": {
      "image": "https://replicate.delivery/pbxt/KAarl3xLEJdskvaosegmeOt0fKixCbUAJGH9Ls5BllWOo88z/img_0006.png",
      "prompt": "A red woodcut bird",
      "max_frames": 24,
      "guidance_scale": 9,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

2 minutes 49 seconds

ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4

Prediction

Model

ID

xwhu2tzbrekcauusnec5ypicwa

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

3m 31s

Created

over 1 year ago

Input

image
prompt: A green frog floats on the surface of the water on green lotus leaves, with several pink lotus flowers, in a Chinese painting style.
max_frames: 16
guidance_scale: 9
num_inference_steps: 50

{
  "image": "https://replicate.delivery/pbxt/KAaqHGiebUQV40JOi5wcz5DsxSn1Hil78jbA0Tnou6VfFd8t/img_0001.jpg",
  "prompt": "A green frog floats on the surface of the water on green lotus leaves, with several pink lotus flowers, in a Chinese painting style.",
  "max_frames": 16,
  "guidance_scale": 9,
  "num_inference_steps": 50
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
  {
    input: {
      image: "https://replicate.delivery/pbxt/KAaqHGiebUQV40JOi5wcz5DsxSn1Hil78jbA0Tnou6VfFd8t/img_0001.jpg",
      prompt: "A green frog floats on the surface of the water on green lotus leaves, with several pink lotus flowers, in a Chinese painting style.",
      max_frames: 16,
      guidance_scale: 9,
      num_inference_steps: 50
    }
  }
);

// 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 ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    input={
        "image": "https://replicate.delivery/pbxt/KAaqHGiebUQV40JOi5wcz5DsxSn1Hil78jbA0Tnou6VfFd8t/img_0001.jpg",
        "prompt": "A green frog floats on the surface of the water on green lotus leaves, with several pink lotus flowers, in a Chinese painting style.",
        "max_frames": 16,
        "guidance_scale": 9,
        "num_inference_steps": 50
    }
)
print(output)

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

Run ali-vilab/i2vgen-xl 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": "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    "input": {
      "image": "https://replicate.delivery/pbxt/KAaqHGiebUQV40JOi5wcz5DsxSn1Hil78jbA0Tnou6VfFd8t/img_0001.jpg",
      "prompt": "A green frog floats on the surface of the water on green lotus leaves, with several pink lotus flowers, in a Chinese painting style.",
      "max_frames": 16,
      "guidance_scale": 9,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

1 minute 56 seconds

ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4

Prediction

Model

ID

h5nyeorbapvtn2a4mtmlxqluvq

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

1m 56s

Created

over 1 year ago

Input

image
prompt: Chinese ink painting, two boats and two coconut trees by the sea
max_frames: 16
guidance_scale: 9
num_inference_steps: 50

{
  "image": "https://replicate.delivery/pbxt/KAb3JpvqEpXcyd97aKWKiVZcSV4hLZP8raqAplfxmbYAWi94/img_0007.jpg",
  "prompt": "Chinese ink painting, two boats and two coconut trees by the sea",
  "max_frames": 16,
  "guidance_scale": 9,
  "num_inference_steps": 50
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
  {
    input: {
      image: "https://replicate.delivery/pbxt/KAb3JpvqEpXcyd97aKWKiVZcSV4hLZP8raqAplfxmbYAWi94/img_0007.jpg",
      prompt: "Chinese ink painting, two boats and two coconut trees by the sea",
      max_frames: 16,
      guidance_scale: 9,
      num_inference_steps: 50
    }
  }
);

// 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 ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    input={
        "image": "https://replicate.delivery/pbxt/KAb3JpvqEpXcyd97aKWKiVZcSV4hLZP8raqAplfxmbYAWi94/img_0007.jpg",
        "prompt": "Chinese ink painting, two boats and two coconut trees by the sea",
        "max_frames": 16,
        "guidance_scale": 9,
        "num_inference_steps": 50
    }
)
print(output)

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

Run ali-vilab/i2vgen-xl 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": "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    "input": {
      "image": "https://replicate.delivery/pbxt/KAb3JpvqEpXcyd97aKWKiVZcSV4hLZP8raqAplfxmbYAWi94/img_0007.jpg",
      "prompt": "Chinese ink painting, two boats and two coconut trees by the sea",
      "max_frames": 16,
      "guidance_scale": 9,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

1 minute 56 seconds

ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4

Prediction

Model

ID

s34qjvrbxt6a2hdorscqjbvxde

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

4m 42s

Created

over 1 year ago

Input

image
prompt: A dog in a suit and tie faces the camera
max_frames: 24
guidance_scale: 9
num_inference_steps: 50

{
  "image": "https://replicate.delivery/pbxt/KAb3LUmkyxE5UXeMXLZpZxFJ32RpKE3eXQpik3g2hUW277Ve/img_0008.png",
  "prompt": "A dog in a suit and tie faces the camera",
  "max_frames": 24,
  "guidance_scale": 9,
  "num_inference_steps": 50
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
  {
    input: {
      image: "https://replicate.delivery/pbxt/KAb3LUmkyxE5UXeMXLZpZxFJ32RpKE3eXQpik3g2hUW277Ve/img_0008.png",
      prompt: "A dog in a suit and tie faces the camera",
      max_frames: 24,
      guidance_scale: 9,
      num_inference_steps: 50
    }
  }
);

// 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 ali-vilab/i2vgen-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    input={
        "image": "https://replicate.delivery/pbxt/KAb3LUmkyxE5UXeMXLZpZxFJ32RpKE3eXQpik3g2hUW277Ve/img_0008.png",
        "prompt": "A dog in a suit and tie faces the camera",
        "max_frames": 24,
        "guidance_scale": 9,
        "num_inference_steps": 50
    }
)
print(output)

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

Run ali-vilab/i2vgen-xl 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": "ali-vilab/i2vgen-xl:5821a338d00033abaaba89080a17eb8783d9a17ed710a6b4246a18e0900ccad4",
    "input": {
      "image": "https://replicate.delivery/pbxt/KAb3LUmkyxE5UXeMXLZpZxFJ32RpKE3eXQpik3g2hUW277Ve/img_0008.png",
      "prompt": "A dog in a suit and tie faces the camera",
      "max_frames": 24,
      "guidance_scale": 9,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

2 minutes 48 seconds