Python client for Replicate
Project description
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
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file replicate-0.15.2.tar.gz.
File metadata
- Download URL: replicate-0.15.2.tar.gz
- Upload date:
- Size: 27.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.11.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | b99b7a7a0f9a5988a299d2070cf6157d2ea2a5504ea5ee68eaa41e484cd7fe9d |
|
| MD5 | 89809a401df72df5863b054391e265ae |
|
| BLAKE2b-256 | 74f66c4b955e28c730e12a2a921e01fd253b47fe264785cb6e6f2e8034cc5a0f |
Provenance
File details
Details for the file replicate-0.15.2-py3-none-any.whl.
File metadata
- Download URL: replicate-0.15.2-py3-none-any.whl
- Upload date:
- Size: 25.9 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.11.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | f73fa7055c01d83af33711209d0d9dfc0b7f887dc32074712034060e028b2a31 |
|
| MD5 | 2153129fa40170f7a05e88d2b6f2120d |
|
| BLAKE2b-256 | 657d5fd4330c43fd47a9766992cf5aeb3ed386b38acc0277abee707bb3a7390d |
