--- name: replicate-376f6c7b8edd description: | Run machine learning models in the cloud via API. Access thousands of open-source models for image generation, language, audio, and video. Fine-tune models on custom data and deploy custom models with Cog packaging format. license: Apache-2.0 compatibility: 'python 3.8+, node 18+, curl/HTTP' metadata: author: terminal-skills version: 1.1.0 category: data-ai repository: https://github.com/replicate/replicate-python tags: "ml-api, model-hosting, image-generation, fine-tuning, serverless" --- # Replicate ## Overview Replicate runs open-source and proprietary models behind one HTTP API: image, video, audio and language models, billed per second of compute (or per output for some official models). You call `owner/name` for official models, or `owner/name:version` to pin a community model to an exact version. You can also fine-tune supported models (such as FLUX) and publish your own models packaged with Cog. Clients: `replicate` on PyPI (1.0.x) and npm (1.x). Model IDs, versions and input fields vary per model, so open the model's API tab on replicate.com before copying inputs. ## Instructions ### Install and authenticate ```bash pip install replicate # Python 3.8+ npm install replicate # Node 18+ export REPLICATE_API_TOKEN="r8_your_token_from_replicate_com_account" ``` Create tokens at replicate.com/account/api-tokens and keep them in the environment, never in source. ### Run a model Since `replicate` 1.0.0, `run()` returns `FileOutput` objects instead of URL strings for models that output files. Use `.read()`, iterate the object, or `.url`; `use_file_output=False` restores plain URLs. ```python import replicate outputs = replicate.run( "black-forest-labs/flux-schnell", input={"prompt": "A futuristic cityscape at sunset, digital art", "num_outputs": 1}, ) for i, image in enumerate(outputs): with open(f"city_{i}.webp", "wb") as f: f.write(image.read()) print(image.url) ``` `run()` first asks the API to hold the request for up to 60 seconds (`wait=1..60`; `wait=False` skips this), then keeps polling until the prediction ends, and raises `replicate.exceptions.ModelError` if the prediction fails; `e.prediction.logs` shows why. ```typescript import Replicate from "replicate"; import { writeFile } from "node:fs/promises"; const replicate = new Replicate(); // reads REPLICATE_API_TOKEN const [image] = await replicate.run("black-forest-labs/flux-schnell", { input: { prompt: "A watercolor painting of a robot" }, }); await writeFile("robot.webp", image); ``` ### Language models and streaming ```python for event in replicate.stream( "meta/meta-llama-3-70b-instruct", input={"prompt": "Explain machine learning to a 5-year-old", "max_tokens": 500, "temperature": 0.7}, ): print(str(event), end="", flush=True) ``` Language-model input names differ between models (`max_tokens` vs `max_new_tokens`, `system_prompt`); read the model page. Older models such as Llama 2 may be unavailable. ### Background predictions and polling ```python import time, replicate prediction = replicate.predictions.create( model="black-forest-labs/flux-schnell", # official model: no version needed input={"prompt": "A cat in space"}, ) while prediction.status not in ("succeeded", "failed", "canceled"): time.sleep(2) prediction.reload() if prediction.status == "succeeded": print(prediction.output) # list of URLs when read from the prediction object else: print(prediction.error) ``` For community models pass `version="<64-character version id>"` instead of `model=`. Cancel with `prediction.cancel()`. ### Webhooks ```python prediction = replicate.predictions.create( model="black-forest-labs/flux-schnell", input={"prompt": "A mountain landscape"}, webhook="https://hooks.shopfront.dev/replicate", webhook_events_filter=["completed"], # start | output | logs | completed ) ``` Verify every delivery: Replicate signs it with `webhook-id`, `webhook-timestamp` and `webhook-signature` headers. Sign `{id}.{timestamp}.{raw body}` with the base64 part of the `whsec_...` key returned by `GET https://api.replicate.com/v1/webhooks/default/secret`, compare in constant time, and reject old timestamps. The Python client has `replicate.webhooks.validate`, and the Node client `validateWebhook`, for this. ### Fine-tuning FLUX SDXL fine-tuning inputs from older guides no longer apply; the supported route is the FLUX trainer. ```python import replicate trainer = replicate.models.get("replicate/fast-flux-trainer") training = replicate.trainings.create( model="replicate/fast-flux-trainer", version=trainer.latest_version.id, destination="jdoe/mug-style-flux", # model you own; create it first at replicate.com/create input={ "input_images": "https://assets.jdoe-studio.com/mug-photos.zip", # zip of training images (the guide uses about 20) "trigger_word": "MUGSTYLE", # made-up string, not a real word "lora_type": "style", # or "subject" "training_steps": 1000, }, ) training.reload() print(training.status) ``` When it succeeds, run `jdoe/mug-style-flux` with a prompt containing the trigger word. Training is billed per second on H100s, typically a couple of dollars. ### Deploy a custom model with Cog Install Cog with Homebrew (`brew install replicate/tap/cog`) or from the release page with its checksum verified, and have Docker running. ```yaml # cog.yaml build: python_version: "3.12" python_packages: - torch==2.3.1 - transformers==4.44.0 gpu: true predict: "predict.py:Predictor" ``` ```python # predict.py from cog import BasePredictor, Input class Predictor(BasePredictor): def setup(self): """Load weights once per container start.""" from transformers import pipeline self.pipe = pipeline("text-generation", model="./model", device=0) def predict( self, prompt: str = Input(description="Input text"), max_tokens: int = Input(description="Tokens to generate", default=100, ge=1, le=1000), ) -> str: return self.pipe(prompt, max_new_tokens=max_tokens)[0]["generated_text"] ``` ```bash cog predict -i prompt="Write a product tagline for a ceramic mug" # test locally cog login # paste your API token cog push r8.im/jdoe/mug-writer # create the model page first ``` ## Examples ### Example 1: Generate and save product shots Request: "Make 4 product photos of a ceramic mug and save them." ```python outputs = replicate.run("black-forest-labs/flux-schnell", input={"prompt": "Ceramic coffee mug on oak table, soft morning light", "num_outputs": 4}) for i, img in enumerate(outputs): open(f"mug_{i}.webp", "wb").write(img.read()) ``` Result: four `mug_0.webp` to `mug_3.webp` files in the working directory; the run is billed per image. ### Example 2: Get notified instead of polling Request: "Start a render and call my server when it finishes." Use the webhook snippet above. Result: `create()` returns at once with status `starting`; Replicate POSTs the final prediction JSON (status `succeeded`, `output` URLs) to your endpoint, which should verify the signature and return 2xx. ## Guidelines - Inputs, outputs and logs of API predictions are deleted after an hour by default; download what you need to keep. - Pin `owner/name:version` for community models so a new model version cannot change your output; official models change without a version pin. - Cold starts: a model that has not run recently can take tens of seconds to boot. Use deployments for always-warm capacity. - Check `prediction.error` and `logs` on failure; do not retry blindly, since failed input validation fails every time. - Token handling: keep it in `REPLICATE_API_TOKEN`; the Cog `predict.py` can read secrets from inputs, not from files in the image. - Do not send private or regulated data to public models without reading Replicate's data-retention page. --- Source attribution: TerminalSkills Original project: https://github.com/TerminalSkills/skills/blob/3bfbf7f76459d89422eddc0e5f572ad0e6c393c9/skills/replicate/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. 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