replicate 0.28.0

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, replacing the model identifier and input with your own:

>>> 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']

[!TIP] 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)

To run a model that takes a file input you can pass either a URL to a publicly accessible file on the Internet or a handle to a file on your local device.

>>> output = replicate.run(
        "andreasjansson/blip-2:f677695e5e89f8b236e52ecd1d3f01beb44c34606419bcc19345e046d8f786f9",
        input={ "image": open("path/to/mystery.jpg") }
    )

"an astronaut riding a horse"

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

for event in replicate.stream(
    "meta/meta-llama-3-70b-instruct",
    input={
        "prompt": "Please write a haiku about llamas.",
    },
):
    print(str(event), end="")

[!TIP] Some models, like meta/meta-llama-3-70b-instruct, don't require a version string. You can always refer to the API documentation on the model page for specifics.

You can also stream the output of a prediction you create. This is helpful when you want the ID of the prediction separate from its output.

prediction = replicate.predictions.create(
    model="meta/meta-llama-3-70b-instruct"
    input={"prompt": "Please write a haiku about llamas."},
    stream=True,
)

for event in prediction.stream():
    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 list 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']

Fine-tune a model

Use the training API to fine-tune models to make them better at a particular task. To see what language models currently support fine-tuning, check out Replicate's collection of trainable language models.

If you're looking to fine-tune image models, check out Replicate's guide to fine-tuning image models.

Here's how to fine-tune a model on Replicate:

training = replicate.trainings.create(
    model="stability-ai/sdxl",
    version="39ed52f2a78e934b3ba6e2a89f5b1c712de7dfea535525255b1aa35c5565e08b",
    input={
      "input_images": "https://my-domain/training-images.zip",
      "token_string": "TOK",
      "caption_prefix": "a photo of TOK",
      "max_train_steps": 1000,
      "use_face_detection_instead": False
    },
    # You need to create a model on Replicate that will be the destination for the trained version.
    destination="your-username/model-name"
)

Customize client behavior

The replicate package exports a default shared client. This client is initialized with an API token set by the REPLICATE_API_TOKEN environment variable.

You can create your own client instance to pass a different API token value, add custom headers to requests, or control the behavior of the underlying HTTPX client:

import os
from replicate.client import Client

replicate = Client(
  api_token=os.environ["SOME_OTHER_REPLICATE_API_TOKEN"]
  headers={
    "User-Agent": "my-app/1.0
  }
)

[!WARNING] Never hardcode authentication credentials like API tokens into your code. Instead, pass them as environment variables when running your program.

Development

See CONTRIBUTING.md