Yanked

This release has been yanked by its maintainers, and will be ignored by installers, except when explicitly specified.
Consider using release 1.0.7 instead.
Reason given by maintainers: bug

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")

Development

See CONTRIBUTING.md

Metadata

Release files for replicate 0.15.6

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for replicate 0.15.6
File	Size	Uploaded
replicate-0.15.6.tar.gz	16.5 kB	Nov 2, 2023	Details

Built distribution (wheel)

Table of built distributions (wheels) for replicate 0.15.6
File	Interpreter	ABI	Platform	Reset
replicate-0.15.6-py3-none-any.whl 11.5 kB Nov 2, 2023	Python 3	none	any	Details

Total release size: 27.9 kB

Release files / replicate-0.15.6.tar.gz

Download URL	replicate-0.15.6.tar.gz
Size	16.5 kB
Tags	Source
SHA-256 checksum How to use checksums	`6e5ad12095eda5f18231fd9e4e22ad9807fb021acca9d05b7da85c0b7745870e`
BLAKE2b-256 checksum How to use checksums	`978a6a8896cf59bead8ebaf508ffe9823edd7bed1f51ab4199673ddaca1e24e8`
Upload date	Nov 2, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.11.6`

Release files / replicate-0.15.6-py3-none-any.whl

Download URL	replicate-0.15.6-py3-none-any.whl
Size	11.5 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`35207b48955c8288825608dafd7c0433b3fe6ad476c59830f65a3face3a39cc8`
BLAKE2b-256 checksum How to use checksums	`03630ca01fbecb8a033e970a592e7aebdc75d72b9cf104d6b08889d96024b69b`
Upload date	Nov 2, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.11.6`