Replicate CLI

The Replicate CLI is a command-line tool for interacting with Replicate's AI model platform. It enables running predictions, managing models, creating deployments, and fine-tuning models directly from the terminal.

Authentication

Before using the Replicate CLI, set the API token:

bash

export REPLICATE_API_TOKEN=<token-from-replicate.com/account>

Alternatively, authenticate interactively:

bash

replicate auth login

Verify authentication:

bash

replicate account current

Core Commands

Running Predictions

The primary use case is running predictions against hosted models.

Basic prediction:

bash

replicate run <owner/model> input_key=value

Examples:

Image generation:

bash

replicate run stability-ai/sdxl prompt="a studio photo of a rainbow colored corgi"

Text generation with streaming:

bash

replicate run meta/llama-2-70b-chat --stream prompt="Tell me a joke"

Prediction flags:

```
--stream
```
- Stream output tokens in real-time (for text models)
```
--no-wait
```
- Submit prediction without waiting for completion
```
--web
```
- Open prediction in browser
```
--json
```
- Output result as JSON
```
--save
```
- Save outputs to local directory

--output-directory <dir>

- Specify output directory (default:

./{prediction-id}

)

Input Handling

File uploads: Prefix local file paths with

bash

replicate run nightmareai/real-esrgan image=@photo.jpg

Output chaining: Use

{{.output}}

template syntax to chain predictions:

bash

replicate run stability-ai/sdxl prompt="a corgi" | \
replicate run nightmareai/real-esrgan image={{.output[0]}}

Model Operations

View model schema (see required inputs and outputs):

bash

replicate model schema <owner/model>
replicate model schema stability-ai/sdxl --json

List models:

bash

replicate model list
replicate model list --json

Show model details:

bash

replicate model show <owner/model>

Create a new model:

bash

replicate model create <owner/name> \
  --hardware gpu-a100-large \
  --private \
  --description "Model description"

Model creation flags:

```
--hardware <sku>
```
- Hardware SKU (see
```
references/hardware.md
```
)
```
--private
```
/
```
--public
```
- Visibility setting
```
--description <text>
```
- Model description
```
--github-url <url>
```
- Link to source repository
```
--license-url <url>
```
- License information
```
--cover-image-url <url>
```
- Cover image for model page

Training (Fine-tuning)

Fine-tune models using the training command:

bash

replicate train <base-model> \
  --destination <owner/new-model> \
  input_key=value

Example - Fine-tune SDXL with DreamBooth:

bash

replicate train stability-ai/sdxl \
  --destination myuser/custom-sdxl \
  --web \
  input_images=@training-images.zip \
  use_face_detection_instead=true

List trainings:

bash

replicate training list

Show training details:

bash

replicate training show <training-id>

Deployments

Deployments provide dedicated, always-on inference endpoints with predictable performance.

Create deployment:

bash

replicate deployments create <name> \
  --model <owner/model> \
  --hardware <sku> \
  --min-instances 1 \
  --max-instances 3

Example:

bash

replicate deployments create text-to-image \
  --model stability-ai/sdxl \
  --hardware gpu-a100-large \
  --min-instances 1 \
  --max-instances 5

Update deployment:

bash

replicate deployments update <name> \
  --max-instances 10 \
  --version <version-id>

List deployments:

bash

replicate deployments list

Show deployment details and schema:

bash

replicate deployments show <name>
replicate deployments schema <name>

Hardware

List available hardware options:

bash

replicate hardware list

See

references/hardware.md

for detailed hardware information and selection guidelines.

Scaffolding

Create a local development environment from an existing prediction:

bash

replicate scaffold <prediction-id-or-url> --template=<node|python>

This generates a project with the prediction's model and inputs pre-configured.

Command Aliases

For convenience, these aliases are available:

Alias	Equivalent Command
`replicate run`	`replicate prediction create`
`replicate stream`	`replicate prediction create --stream`
`replicate train`	`replicate training create`

Short aliases for subcommands:

```
replicate m
```
=
```
replicate model
```
```
replicate p
```
=
```
replicate prediction
```
```
replicate t
```
=
```
replicate training
```
```
replicate d
```
=
```
replicate deployments
```
```
replicate hw
```
=
```
replicate hardware
```
```
replicate a
```
=
```
replicate account
```

Common Workflows

Image Generation Pipeline

Generate an image and upscale it:

bash

replicate run stability-ai/sdxl \
  prompt="professional photo of a sunset" \
  negative_prompt="blurry, low quality" | \
replicate run nightmareai/real-esrgan \
  image={{.output[0]}} \
  --save

Check Model Inputs Before Running

Always check the model schema to understand required inputs:

bash

replicate model schema owner/model-name

Batch Processing

Run predictions and save outputs:

bash

for prompt in "cat" "dog" "bird"; do
  replicate run stability-ai/sdxl prompt="$prompt" --save --output-directory "./outputs/$prompt"
done

Monitor Long-Running Tasks

Submit without waiting, then check status:

bash

# Submit
replicate run owner/model input=value --no-wait --json > prediction.json

# Check status later
replicate prediction show $(jq -r '.id' prediction.json)

Best Practices

Always check schema first - Run
```
replicate model schema <model>
```
to understand required and optional inputs before running predictions.
Use streaming for text models - Add
```
--stream
```
flag when running language models to see output in real-time.
Save outputs explicitly - Use
```
--save
```
and
```
--output-directory
```
to organize prediction outputs.
Use JSON output for automation - Add
```
--json
```
flag when parsing outputs programmatically.
Open in web for debugging - Add
```
--web
```
flag to view predictions in the Replicate dashboard for detailed logs.
Chain predictions efficiently - Use the
```
{{.output}}
```
syntax to pass outputs between models without intermediate saves.

Troubleshooting

Authentication errors:

Verify
```
REPLICATE_API_TOKEN
```
is set correctly
Run
```
replicate account current
```
to test authentication

Model not found:

Check model name format:
```
owner/model-name
```
Verify model exists at replicate.com

Input validation errors:

Run
```
replicate model schema <model>
```
to see required inputs
Check input types (string, number, file)

File upload issues:

Ensure
```
@
```
prefix is used for local files
Verify file path is correct and file exists

Additional Resources

Replicate documentation: https://replicate.com/docs
Model explorer: https://replicate.com/explore
API reference: https://replicate.com/docs/reference/http
GitHub repository: https://github.com/replicate/cli

replicate-cli

NPX Install

Tags

SKILL.md Content

Replicate CLI

Authentication

Core Commands

Running Predictions

Input Handling

Model Operations

Training (Fine-tuning)

Deployments

Hardware

Scaffolding

Command Aliases

Common Workflows

Image Generation Pipeline

Check Model Inputs Before Running

Batch Processing

Monitor Long-Running Tasks

Best Practices

Troubleshooting

Additional Resources