FLUX.1

Setup

Sign in to Baseten

uvx truss login --browser

Install requests

uv pip install requests

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

dev
schnell

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:

uvx truss push

You should see output similar to:

✨ Model flux1-dev-throughput was successfully pushed ✨

   Model ID:      abc1d2ef
   Deployment ID: xyz123
   Endpoint:      model-abc1d2ef.api.baseten.co
   Logs:          https://app.baseten.co/models/abc1d2ef/logs/xyz123

truss 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
cURL

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 -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

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:

uvx truss push

You should see output similar to:

✨ Model flux1-schnell-throughput was successfully pushed ✨

   Model ID:      abc1d2ef
   Deployment ID: xyz123
   Endpoint:      model-abc1d2ef.api.baseten.co
   Logs:          https://app.baseten.co/models/abc1d2ef/logs/xyz123

truss 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
cURL

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 -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

Examples

Models

Engines

Custom Docker servers

Custom Python models

Chains

Setup

Hardware

Write the config

Deploy

Call the model

Hardware

Write the config

Deploy

Call the model

Next steps

Call your model

Autoscaling

​Setup

Hardware

​Write the config

​Deploy

​Call the model

Hardware

​Write the config

​Deploy

​Call the model

​Next steps

Call your model

Autoscaling

Setup

Write the config

Deploy

Call the model

Write the config

Deploy

Call the model

Next steps