zsxkib/wan2.1-with-lora-l40s:f5873940 – Run with an API on Replicate

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zsxkib /wan2.1-with-lora-l40s:f5873940

Playground API

Input

prompt

*string

Shift + Return to add a new line

Text prompt for video generation

negative_prompt

string

Shift + Return to add a new line

Things you do not want to see in your video

Default: ""

image

file

Image to use as a starting frame for image to video generation.

aspect_ratio

string

The aspect ratio of the video. 16:9, 9:16, 1:1, etc.

Default: "16:9"

frames

integer

The number of frames to generate (1 to 5 seconds)

Default: 81

model

string

The model to use. 1.3b is faster, but 14b is better quality. A LORA either works with 1.3b or 14b, depending on the version it was trained on.

Default: "14b"

resolution

string

The resolution of the video. 720p is not supported for 1.3b.

Default: "480p"

lora_url

string

Shift + Return to add a new line

Optional: The URL of a LORA to use

lora_strength_model

number

Strength of the LORA applied to the model. 0.0 is no LORA.

Default: 1

lora_strength_clip

number

Strength of the LORA applied to the CLIP model. 0.0 is no LORA.

Default: 1

fast_mode

string

Speed up generation with different levels of acceleration. Faster modes may degrade quality somewhat. The speedup is dependent on the content, so different videos may see different speedups.

Default: "Balanced"

sample_steps

integer

(minimum: 1, maximum: 60)

Number of generation steps. Fewer steps means faster generation, at the expensive of output quality. 30 steps is sufficient for most prompts

Default: 30

sample_guide_scale

number

(minimum: 0, maximum: 10)

Higher guide scale makes prompt adherence better, but can reduce variation

Default: 5

sample_shift

number

(minimum: 0, maximum: 10)

Sample shift factor

Default: 8

seed

integer

Set a seed for reproducibility. Random by default.

benchmark

boolean

Run generation multiple times and report timing statistics

Default: false

benchmark_runs

integer

(minimum: 1, maximum: 100)

Number of benchmark runs if benchmarking is enabled

Default: 10

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

npx create-replicate --model=zsxkib/wan2.1-with-lora-l40s

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 zsxkib/wan2.1-with-lora-l40s using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "zsxkib/wan2.1-with-lora-l40s:f5873940274bd26c50b6af0b58b2e9ea189fbc3def7afca346803e12c8984389",
  {
    input: {
      model: "14b",
      frames: 81,
      benchmark: false,
      fast_mode: "Balanced",
      resolution: "480p",
      aspect_ratio: "16:9",
      sample_shift: 8,
      sample_steps: 30,
      benchmark_runs: 10,
      negative_prompt: "",
      lora_strength_clip: 1,
      sample_guide_scale: 5,
      lora_strength_model: 1
    }
  }
);
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 zsxkib/wan2.1-with-lora-l40s using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "zsxkib/wan2.1-with-lora-l40s:f5873940274bd26c50b6af0b58b2e9ea189fbc3def7afca346803e12c8984389",
    input={
        "model": "14b",
        "frames": 81,
        "benchmark": False,
        "fast_mode": "Balanced",
        "resolution": "480p",
        "aspect_ratio": "16:9",
        "sample_shift": 8,
        "sample_steps": 30,
        "benchmark_runs": 10,
        "negative_prompt": "",
        "lora_strength_clip": 1,
        "sample_guide_scale": 5,
        "lora_strength_model": 1
    }
)
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 zsxkib/wan2.1-with-lora-l40s 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": "f5873940274bd26c50b6af0b58b2e9ea189fbc3def7afca346803e12c8984389",
    "input": {
      "model": "14b",
      "frames": 81,
      "benchmark": false,
      "fast_mode": "Balanced",
      "resolution": "480p",
      "aspect_ratio": "16:9",
      "sample_shift": 8,
      "sample_steps": 30,
      "benchmark_runs": 10,
      "negative_prompt": "",
      "lora_strength_clip": 1,
      "sample_guide_scale": 5,
      "lora_strength_model": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

No output yet! Press "Submit" to start a prediction.