replicate 0.16.0

Python client for Replicate

Replicate Python client

This is a Python client for Replicate. 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.

Requirements

• Python 3.8+

Install

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 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:

>>> 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, receive images as inputs. To pass a file as an input, use a file handle or URL:

>>> 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:

>>> 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:

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:

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:

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:

>>> 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:

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:

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")

Create a model

You can create a model for a user or organization with a given name, visibility, and hardware SKU:

import replicate

model = replicate.models.create(
    owner="your-username",
    name="my-model",
    visibility="public",
    hardware="gpu-a40-large"
)

Here's how to list of all the available hardware for running models on Replicate:

>>> [hw.sku for hw in replicate.hardware.list()]
['cpu', 'gpu-t4', 'gpu-a40-small', 'gpu-a40-large']

Development

See CONTRIBUTING.md