FLUX.1

FLUX.1 recipes: 2 variants (dev, schnell), diffusion-transformer architecture.

Text-to-image

Setup

Install the Baseten CLI and sign in, then install the Python requests library.

Install and sign in to Baseten

macOS or Linux

Terminal

brew tap basetenlabs/baseten
brew install baseten

Windows

Download and extract the binary, then move baseten.exe to a directory on your PATH:

Terminal

Invoke-WebRequest `
  https://github.com/basetenlabs/baseten-cli/releases/download/v0.4.0/baseten_0.4.0_windows_amd64.zip `
  -OutFile baseten.zip; Expand-Archive -Force baseten.zip .

For other platforms or a specific version, see the Baseten CLI install reference.

baseten auth login

Install requests

uv pip install requests

Prefer not to install? Sign in with uvx truss login --browser and deploy with uvx truss push.

Pick the model you want to deploy. Each tab is a self-contained recipe.

dev

black-forest-labs/FLUX.1-dev is a 12B-parameter diffusion transformer model.

This preset serves FLUX.1 dev on H100 40GB, tuned for text-to-image throughput.

Hardware

H100_40GB

Write the config

Create and move into the project directory:

mkdir flux1-dev-throughput && cd flux1-dev-throughput

Then create a file named config.yaml and paste the following:

config.yaml

external_package_dirs: []
model_metadata:
  output_media:
    - json_path: "data"
      media_type: "image/jpeg"
      encoding: "base64"
      label: "Generated Image"

  example_model_input: {"prompt": 'black forest gateau cake spelling out the words "FLUX DEV", tasty, food photography, dynamic shot'}
  repo_id: black-forest-labs/FLUX.1-dev
model_name: "model:flux1-dev preset:throughput"
python_version: py311
requirements:
  - git+https://github.com/huggingface/diffusers.git@fc6a91e3834c35e57b398ad1c0d99f6f83557e04
  - transformers>=4.0.0,<5.0.0
  - accelerate
  - sentencepiece
  - protobuf
weights:
  - source: "hf://black-forest-labs/FLUX.1-dev@main"
    mount_location: "/models/FLUX.1-dev"
    auth_secret_name: "hf_access_token"
resources:
  accelerator: H100_40GB
  use_gpu: true
secrets:
  hf_access_token: null
system_packages:
  - ffmpeg
  - libsm6
  - libxext6

Deploy

Push the config to Baseten with the Baseten CLI, or with the Truss CLI if you prefer it:

Baseten CLI

baseten model push

You should see output similar to:

Pushing model "flux1-dev-throughput"...
Uploading model...
Uploaded model in 0s
✨ Model flux1-dev-throughput was successfully pushed ✨

  Model:       flux1-dev-throughput (abc1d2ef)
  Deployment:  xyz123
  Environment: production

🪵 View logs:
   deployment:   baseten model deployment logs --model-id abc1d2ef --deployment-id xyz123
   environment:  baseten model environment logs --model-id abc1d2ef --environment production  (once deployed)
   app:          https://app.baseten.co/models/abc1d2ef/logs/xyz123

🚀 Invoke your model:
   URL:  https://model-abc1d2ef.api.baseten.co/deployment/xyz123/predict
   CLI:  baseten model predict --model-id abc1d2ef

baseten model push prints your model ID (abc1d2ef in the example). The examples below use it wherever you see {model_id}, and read your API key from the BASETEN_API_KEY environment variable.

Call the model

Use the /predict endpoint to generate your model’s images.

The deployment returns the generated image as base64-encoded bytes. Decode the response to write the image to disk.

Python

main.py

import base64
import os
import requests

response = requests.post(
    "https://model-{model_id}.api.baseten.co/environments/production/sync/predict",
    headers={"Authorization": f"Bearer {os.environ['BASETEN_API_KEY']}"},
    json={"prompt": "black forest gateau cake spelling out the words \"FLUX DEV\", tasty, food photography, dynamic shot"},
)

image_b64 = response.json()["data"]
with open("output.png", "wb") as f:
    f.write(base64.b64decode(image_b64))

cURL

curl -s https://model-{model_id}.api.baseten.co/environments/production/sync/predict \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $BASETEN_API_KEY" \
  -d '{"prompt": "black forest gateau cake spelling out the words \"FLUX DEV\", tasty, food photography, dynamic shot"}' \
  | jq -r '.data' | base64 --decode > output.png

schnell

black-forest-labs/FLUX.1-schnell is a 12B-parameter diffusion transformer model.

This preset serves FLUX.1 schnell on H100 40GB. The step-distilled model delivers the fastest FLUX image generation per dollar.

Hardware

H100_40GB

Write the config

Create and move into the project directory:

mkdir flux1-schnell-throughput && cd flux1-schnell-throughput

Then create a file named config.yaml and paste the following:

config.yaml

external_package_dirs: []
model_metadata:
  output_media:
    - json_path: "data"
      media_type: "image/jpeg"
      encoding: "base64"
      label: "Generated Image"

  example_model_input: {"prompt": 'black forest gateau cake spelling out the words "FLUX SCHNELL", tasty, food photography, dynamic shot'}
  repo_id: black-forest-labs/FLUX.1-schnell
model_name: "model:flux1-schnell preset:throughput"
python_version: py311
requirements:
  - git+https://github.com/huggingface/diffusers.git@fc6a91e3834c35e57b398ad1c0d99f6f83557e04
  - transformers>=4.0.0,<5.0.0
  - accelerate
  - sentencepiece
  - protobuf
  - b10-transfer
weights:
  - source: "hf://black-forest-labs/FLUX.1-schnell@main"
    mount_location: "/models/FLUX.1-schnell"
    auth_secret_name: "hf_access_token"
resources:
  accelerator: H100_40GB
  use_gpu: true
secrets:
  hf_access_token: null
system_packages:
  - ffmpeg
  - libsm6
  - libxext6

Deploy

Push the config to Baseten with the Baseten CLI, or with the Truss CLI if you prefer it:

Baseten CLI

baseten model push

You should see output similar to:

Pushing model "flux1-schnell-throughput"...
Uploading model...
Uploaded model in 0s
✨ Model flux1-schnell-throughput was successfully pushed ✨

  Model:       flux1-schnell-throughput (abc1d2ef)
  Deployment:  xyz123
  Environment: production

🪵 View logs:
   deployment:   baseten model deployment logs --model-id abc1d2ef --deployment-id xyz123
   environment:  baseten model environment logs --model-id abc1d2ef --environment production  (once deployed)
   app:          https://app.baseten.co/models/abc1d2ef/logs/xyz123

🚀 Invoke your model:
   URL:  https://model-abc1d2ef.api.baseten.co/deployment/xyz123/predict
   CLI:  baseten model predict --model-id abc1d2ef

baseten model push prints your model ID (abc1d2ef in the example). The examples below use it wherever you see {model_id}, and read your API key from the BASETEN_API_KEY environment variable.

Call the model

Use the /predict endpoint to generate your model’s images.

The deployment returns the generated image as base64-encoded bytes. Decode the response to write the image to disk.

Python

main.py

import base64
import os
import requests

response = requests.post(
    "https://model-{model_id}.api.baseten.co/environments/production/sync/predict",
    headers={"Authorization": f"Bearer {os.environ['BASETEN_API_KEY']}"},
    json={"prompt": "black forest gateau cake spelling out the words \"FLUX SCHNELL\", tasty, food photography, dynamic shot"},
)

image_b64 = response.json()["data"]
with open("output.png", "wb") as f:
    f.write(base64.b64decode(image_b64))

cURL

curl -s https://model-{model_id}.api.baseten.co/environments/production/sync/predict \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $BASETEN_API_KEY" \
  -d '{"prompt": "black forest gateau cake spelling out the words \"FLUX SCHNELL\", tasty, food photography, dynamic shot"}' \
  | jq -r '.data' | base64 --decode > output.png

Next steps

Call your model

Endpoint anatomy, authentication, and sync versus async inference

Autoscaling

Scale replicas with traffic, including scale to zero