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"
[!NOTE] You can also use the Replicate client asynchronously by prepending
async_to the method name.Here's an example of how to run several predictions concurrently and wait for them all to complete:
import asyncio import replicate # https://replicate.com/stability-ai/sdxl model_version = "stability-ai/sdxl:39ed52f2a78e934b3ba6e2a89f5b1c712de7dfea535525255b1aa35c5565e08b" prompts = [ f"A chariot pulled by a team of {count} rainbow unicorns" for count in ["two", "four", "six", "eight"] ] async with asyncio.TaskGroup() as tg: tasks = [ tg.create_task(replicate.async_run(model_version, input={"prompt": prompt})) for prompt in prompts ] results = await asyncio.gather(*tasks) print(results)
Run a model and stream its output
Replicate’s API supports server-sent event streams (SSEs) for language models.
Use the stream method to consume tokens as they're produced by the model.
import replicate
# https://replicate.com/meta/llama-2-70b-chat
model_version = "meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3"
for event in replicate.stream(
model_version,
input={
"prompt": "Please write a haiku about llamas.",
},
):
print(str(event), end="")
For more information, see "Streaming output" in Replicate's docs.
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("ai-forever/kandinsky-2.2")
version = model.versions.get("ea1addaab376f4dc227f5368bbd8eff901820fd1cc14ed8cad63b29249e9d463")
prediction = replicate.predictions.create(
version=version,
input={"prompt":"Watercolor painting of an underwater submarine"},
webhook="https://example.com/your-webhook",
webhook_events_filter=["completed"]
)
For details on receiving webhooks, see replicate.com/docs/webhooks.
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>]
Lists of predictions are paginated. You can get the next page of predictions by passing the next property as an argument to the list method:
page1 = replicate.predictions.list()
if page1.next:
page2 = replicate.predictions.list(page1.next)
Load output files
Output files are returned as HTTPS URLs. You can load an output file as a buffer:
import replicate
from PIL import Image
from urllib.request import urlretrieve
out = replicate.run(
"stability-ai/stable-diffusion:27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478",
input={"prompt": "wavy colorful abstract patterns, oceans"}
)
urlretrieve(out[0], "/tmp/out.png")
background = Image.open("/tmp/out.png")
List models
You can the models you've created:
replicate.models.list()
Lists of models are paginated. You can get the next page of models by passing the next property as an argument to the list method, or you can use the paginate method to fetch pages automatically.
# Automatic pagination using `replicate.paginate` (recommended)
models = []
for page in replicate.paginate(replicate.models.list):
models.extend(page.results)
if len(models) > 100:
break
# Manual pagination using `next` cursors
page = replicate.models.list()
while page:
models.extend(page.results)
if len(models) > 100:
break
page = replicate.models.list(page.next) if page.next else None
You can also find collections of featured models on Replicate:
>>> collections = [collection for page in replicate.paginate(replicate.collections.list) for collection in page]
>>> collections[0].slug
"vision-models"
>>> collections[0].description
"Multimodal large language models with vision capabilities like object detection and optical character recognition (OCR)"
>>> replicate.collections.get("text-to-image").models
[<Model: stability-ai/sdxl>, ...]
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
Release history Release notifications | RSS feed
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