# replicate-python

**Repository Path**: KID_Codes/replicate-python

## Basic Information

- **Project Name**: replicate-python
- **Description**: Python client for Replicate
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-11-27
- **Last Updated**: 2026-03-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Replicate Python client

This is a Python client for [Replicate](https://replicate.com). It lets you run models from your Python code or Jupyter notebook, and do various other things on Replicate.

> **👋** Check out an interactive version of this tutorial on [Google Colab](https://colab.research.google.com/drive/1K91q4p-OhL96FHBAVLsv9FlwFdu6Pn3c).
>
>  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1K91q4p-OhL96FHBAVLsv9FlwFdu6Pn3c)

## Requirements

- Python 3.8+

## Install

```sh
pip install replicate
```

## Authenticate

Before running any Python scripts that use the API, you need to set your Replicate API token in your environment.

Grab your token from [replicate.com/account](https://replicate.com/account) and set it as an environment variable:

```
export REPLICATE_API_TOKEN=<your token>
```

We recommend not adding the token directly to your source code, because you don't want to put your credentials in source control. If anyone used your API key, their usage would be charged to your account.

## Run a model

Create a new Python file and add the following code:

```python
>>> import replicate
>>> replicate.run(
        "stability-ai/stable-diffusion:27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478",
        input={"prompt": "a 19th century portrait of a wombat gentleman"}
    )

['https://replicate.com/api/models/stability-ai/stable-diffusion/files/50fcac81-865d-499e-81ac-49de0cb79264/out-0.png']
```

Some models, like [methexis-inc/img2prompt](https://replicate.com/methexis-inc/img2prompt), receive images as inputs. To pass a file as an input, use a file handle or URL:

```python
>>> output = replicate.run(
        "salesforce/blip:2e1dddc8621f72155f24cf2e0adbde548458d3cab9f00c0139eea840d0ac4746",
        input={"image": open("path/to/mystery.jpg", "rb")},
    )

"an astronaut riding a horse"
```

## Run a model in the background

You can start a model and run it in the background:

```python
>>> model = replicate.models.get("kvfrans/clipdraw")
>>> version = model.versions.get("5797a99edc939ea0e9242d5e8c9cb3bc7d125b1eac21bda852e5cb79ede2cd9b")
>>> prediction = replicate.predictions.create(
    version=version,
    input={"prompt":"Watercolor painting of an underwater submarine"})

>>> prediction
Prediction(...)

>>> prediction.status
'starting'

>>> dict(prediction)
{"id": "...", "status": "starting", ...}

>>> prediction.reload()
>>> prediction.status
'processing'

>>> print(prediction.logs)
iteration: 0, render:loss: -0.6171875
iteration: 10, render:loss: -0.92236328125
iteration: 20, render:loss: -1.197265625
iteration: 30, render:loss: -1.3994140625

>>> prediction.wait()

>>> prediction.status
'succeeded'

>>> prediction.output
'https://.../output.png'
```

## Run a model in the background and get a webhook

You can run a model and get a webhook when it completes, instead of waiting for it to finish:

```python
model = replicate.models.get("kvfrans/clipdraw")
version = model.versions.get("5797a99edc939ea0e9242d5e8c9cb3bc7d125b1eac21bda852e5cb79ede2cd9b")
prediction = replicate.predictions.create(
    version=version,
    input={"prompt":"Watercolor painting of an underwater submarine"},
    webhook="https://example.com/your-webhook",
    webhook_events_filter=["completed"]
)
```

## Compose models into a pipeline

You can run a model and feed the output into another model:

```python
laionide = replicate.models.get("afiaka87/laionide-v4").versions.get("b21cbe271e65c1718f2999b038c18b45e21e4fba961181fbfae9342fc53b9e05")
swinir = replicate.models.get("jingyunliang/swinir").versions.get("660d922d33153019e8c263a3bba265de882e7f4f70396546b6c9c8f9d47a021a")
image = laionide.predict(prompt="avocado armchair")
upscaled_image = swinir.predict(image=image)
```

## Get output from a running model

Run a model and get its output while it's running:

```python
iterator = replicate.run(
    "pixray/text2image:5c347a4bfa1d4523a58ae614c2194e15f2ae682b57e3797a5bb468920aa70ebf",
    input={"prompts": "san francisco sunset"}
)

for image in iterator:
    display(image)
```

## Cancel a prediction

You can cancel a running prediction:

```python
>>> model = replicate.models.get("kvfrans/clipdraw")
>>> version = model.versions.get("5797a99edc939ea0e9242d5e8c9cb3bc7d125b1eac21bda852e5cb79ede2cd9b")
>>> prediction = replicate.predictions.create(
        version=version,
        input={"prompt":"Watercolor painting of an underwater submarine"}
    )

>>> prediction.status
'starting'

>>> prediction.cancel()

>>> prediction.reload()
>>> prediction.status
'canceled'
```

## List predictions

You can list all the predictions you've run:

```python
replicate.predictions.list()
# [<Prediction: 8b0ba5ab4d85>, <Prediction: 494900564e8c>]
```

## Load output files

Output files are returned as HTTPS URLs. You can load an output file as a buffer:

```python
import replicate
from urllib.request import urlretrieve

model = replicate.models.get("stability-ai/stable-diffusion")
version = model.versions.get("27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478")
out = version.predict(prompt="wavy colorful abstract patterns, cgsociety")
urlretrieve(out[0], "/tmp/out.png")
background = Image.open("/tmp/out.png")
```

## Development

See [CONTRIBUTING.md](CONTRIBUTING.md)