const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN
})
const model =
const input = {
prompt:
};
const [output] = await replicate.run(model, { input });
console.log(output);
With Replicate you can
google/imagen-4-fast
Use this fast version of Imagen 4 when speed and cost are more important than quality
925.3K runs
bytedance/seedream-4
Unified text-to-image generation and precise single-sentence editing at up to 4K resolution
1.1M runs
ideogram-ai/ideogram-v3-turbo
Turbo is the fastest and cheapest Ideogram v3. v3 creates images with stunning realism, creative designs, and consistent styles
2.2M runs
qwen/qwen-image
An image generation foundation model in the Qwen series that achieves significant advances in complex text rendering.
513.1K runs
black-forest-labs/flux-schnell
The fastest image generation model tailored for local development and personal use
494.5M runs
black-forest-labs/flux-1.1-pro
Faster, better FLUX Pro. Text-to-image model with excellent image quality, prompt adherence, and output diversity.
57.9M runs
prunaai/hidream-l1-dev
This is an optimised version of the hidream-l1-dev model using the pruna ai optimisation toolkit!
43K runs
stability-ai/stable-diffusion-3.5-large-turbo
A text-to-image model that generates high-resolution images with fine details. It supports various artistic styles and produces diverse outputs from the same prompt, with a focus on fewer inference steps
799.5K runs
All the latest models are on Replicate. They’re not just demos — they all actually work and have production-ready APIs.
AI shouldn’t be locked up inside academic papers and demos. Make it real by pushing it to Replicate.
openai/gpt-5-structured
GPT-5 with support for structured outputs, web search and custom tools
65.7K runs
kwaivgi/kling-v2.1
Use Kling v2.1 to generate 5s and 10s videos in 720p and 1080p resolution from a starting image (image-to-video)
1.8M runs
wan-video/wan-2.5-t2v
Alibaba Wan 2.5 text to video generation model
1.2K runs
pixverse/pixverse-v5
Create 5s-8s videos with enhanced character movement, visual effects, and exclusive 1080p-8s support. Optimized for anime characters and complex actions
30.3K runs
qwen/qwen-image-edit-plus
The latest Qwen-Image’s iteration with improved multi-image editing, single-image consistency, and native support for ControlNet
131.9K runs
google/nano-banana
Google's latest image editing model in Gemini 2.5
10.2M runs
leonardoai/lucid-origin
Artistic and high-quality visuals with improved prompt adherence, diversity, and definition
26.3K runs
bytedance/seedream-4
Unified text-to-image generation and precise single-sentence editing at up to 4K resolution
1.1M runs
qwen/qwen-image
An image generation foundation model in the Qwen series that achieves significant advances in complex text rendering.
513.1K runs
minimax/hailuo-02
Hailuo 2 is a text-to-video and image-to-video model that can make 6s or 10s videos at 768p (standard) or 1080p (pro). It excels at real world physics.
116.6K runs
deepseek-ai/deepseek-v3.1
Latest hybrid thinking model from Deepseek
4.6K runs
prunaai/wan-2.2-image
This model generates beautiful cinematic 2 megapixel images in 3-4 seconds and is derived from the Wan 2.2 model through optimisation techniques from the pruna package
388.3K runs
You can get started with any model with just one line of code. But as you do more complex things, you can fine-tune models or deploy your own custom code.
Our community has already published thousands of models that are ready to use in production. You can run these with one line of code.
import replicate
output = replicate.run(
"black-forest-labs/flux-dev",
input={
"aspect_ratio": "1:1",
"num_outputs": 1,
"output_format": "jpg",
"output_quality": 80,
"prompt": "An astronaut riding a rainbow unicorn, cinematic, dramatic",
}
)
print(output)
You can improve models with your own data to create new models that are better suited to specific tasks.
Image models like SDXL can generate images of a particular person, object, or style.
Train a model:
training = replicate.trainings.create(
destination="mattrothenberg/drone-art"
version="ostris/flux-dev-lora-trainer:e440909d3512c31646ee2e0c7d6f6f4923224863a6a10c494606e79fb5844497",
input={
"steps": 1000,
"input_images":
,
"trigger_word": "TOK",
},
)
This will result in a new model:
mattrothenberg/drone-art
Fantastical images of drones on land and in the sky
0 runs
mattrothenberg/drone-art
Fantastical images of drones on land and in the sky
0 runs
Then, you can run it with one line of code:
output = replicate.run(
"mattrothenberg/drone-art:abcde1234...",
input={"prompt": "a photo of TOK forming a rainbow in the sky"}),
)
You aren’t limited to the models on Replicate: you can deploy your own custom models using Cog, our open-source tool for packaging machine learning models.
Cog takes care of generating an API server and deploying it on a big cluster in the cloud. We scale up and down to handle demand, and you only pay for the compute that you use.
First, define the environment your model runs in with cog.yaml:
build:
gpu: true
system_packages:
- "libgl1-mesa-glx"
- "libglib2.0-0"
python_version: "3.10"
python_packages:
- "torch==1.13.1"
predict: "predict.py:Predictor"
Next, define how predictions are run on your model with predict.py:
from cog import BasePredictor, Input, Path
import torch
class Predictor(BasePredictor):
def setup(self):
"""Load the model into memory to make running multiple predictions efficient"""
self.model = torch.load("./weights.pth")
# The arguments and types the model takes as input
def predict(self,
image: Path = Input(description="Grayscale input image")
) -> Path:
"""Run a single prediction on the model"""
processed_image = preprocess(image)
output = self.model(processed_image)
return postprocess(output)
Thousands of businesses are building their AI products on Replicate. Your team can deploy an AI feature in a day and scale to millions of users, without having to be machine learning experts.
Learn more about our enterprise plansIf you get a ton of traffic, Replicate scales up automatically to handle the demand. If you don't get any traffic, we scale down to zero and don't charge you a thing.
Replicate only bills you for how long your code is running. You don't pay for expensive GPUs when you're not using them.
Deploying machine learning models at scale is hard. If you've tried, you know. API servers, weird dependencies, enormous model weights, CUDA, GPUs, batching.
Prediction throughput (requests per second)
Metrics let you keep an eye on how your models are performing, and logs let you zoom in on particular predictions to debug how your model is behaving.
With Replicate and tools like Next.js and Vercel, you can wake up with an idea and watch it hit the front page of Hacker News by the time you go to bed.