zsxkib/wan2.1-with-lora-l40s | Run with an API on Replicate

zsxkib

wan2.1-with-lora-l40s

Public

12 runs

L40S

Run with an API

Pricing

Playground API Examples README Versions

Input

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";
import fs from "node:fs";

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
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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": "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
    }
  }' \
  https://api.replicate.com/v1/predictions

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

You can run this model locally using Cog. First, install Cog:

brew install cog

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

Run this to download the model and run it in your local environment:

cog predict r8.im/zsxkib/wan2.1-with-lora-l40s@sha256:f5873940274bd26c50b6af0b58b2e9ea189fbc3def7afca346803e12c8984389 \
  -i 'model="14b"' \
  -i 'frames=81' \
  -i 'benchmark=false' \
  -i 'fast_mode="Balanced"' \
  -i 'resolution="480p"' \
  -i 'aspect_ratio="16:9"' \
  -i 'sample_shift=8' \
  -i 'sample_steps=30' \
  -i 'benchmark_runs=10' \
  -i 'negative_prompt=""' \
  -i 'lora_strength_clip=1' \
  -i 'sample_guide_scale=5' \
  -i 'lora_strength_model=1'

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

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/zsxkib/wan2.1-with-lora-l40s@sha256:f5873940274bd26c50b6af0b58b2e9ea189fbc3def7afca346803e12c8984389
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "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
    }
  }' \
  http://localhost:5000/predictions

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

Output

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

Run time and cost

This model runs on Nvidia L40S GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

This model doesn't have a readme.