Working with BFL FLUX in Pixeltable - Pixeltable Documentation

This documentation page is also available as an interactive notebook. You can launch the notebook in Kaggle or Colab, or download it for use with an IDE or local Jupyter installation, by clicking one of the above links.

This notebook demonstrates how to use Black Forest Labs (BFL) FLUX models for image generation and editing through Pixeltable. BFL FLUX offers state-of-the-art text-to-image generation with models like FLUX.2 and FLUX 1.1, featuring:

High-fidelity image generation with accurate hands, faces, and textures
Multi-reference image editing
Precise hex color control
Typography and text rendering

Prerequisites

A BFL API key from dashboard.bfl.ai
Pixeltable installed

Setup

First, install Pixeltable and set up your API key:

%pip install -qU pixeltable

import getpass
import os

# Set your BFL API key - get one from https://dashboard.bfl.ai
if 'BFL_API_KEY' not in os.environ:
    os.environ['BFL_API_KEY'] = getpass.getpass(
        'Enter your BFL API key: '
    )

import pixeltable as pxt
from pixeltable.functions import bfl

# Create a directory for our examples
pxt.drop_dir('bfl_demo', force=True)
pxt.create_dir('bfl_demo')

Connected to Pixeltable database at: postgresql+psycopg://postgres:@/pixeltable?host=/Users/pjlb/.pixeltable/pgdata
Created directory ‘bfl_demo’.
<pixeltable.catalog.dir.Dir at 0x1527b9d90>

Text-to-Image Generation

Generate images from text prompts using FLUX models. FLUX 1.1 [pro] offers fast, reliable results.

# Create a table for image generation
prompts_t = pxt.create_table(
    'bfl_demo.prompts',
    {'prompt': pxt.String, 'style': pxt.String},
    if_exists='replace',
)

# Add a computed column that generates images from prompts
prompts_t.add_computed_column(
    image=bfl.generate(
        prompts_t.prompt, model='flux-pro-1.1', width=1024, height=1024
    )
)

Created table ‘prompts’.
Added 0 column values with 0 errors.
No rows affected.

# Insert some prompts to generate images
prompts_t.insert(
    [
        {
            'prompt': 'A majestic mountain landscape at golden hour, photorealistic',
            'style': 'landscape',
        },
        {
            'prompt': 'A futuristic city with flying cars and neon lights, cyberpunk style',
            'style': 'sci-fi',
        },
    ]
)

Inserting rows into `prompts`: 2 rows [00:00, 442.18 rows/s]
Inserted 2 rows with 0 errors.
2 rows inserted, 6 values computed.

# View the generated images
prompts_t.select(prompts_t.prompt, prompts_t.image).show()

Image Editing

Edit existing images with text prompts using FLUX models. This is powerful for:

Changing backgrounds
Adding or removing objects
Style transfer
Multi-reference editing

# Create a table for image editing
edit_t = pxt.create_table(
    'bfl_demo.edits',
    {'original': pxt.Image, 'edit_prompt': pxt.String},
    if_exists='replace',
)

# Add computed column for edited images
edit_t.add_computed_column(
    edited=bfl.edit(
        edit_t.edit_prompt, edit_t.original, model='flux-2-pro'
    )
)

Created table ‘edits’.
Added 0 column values with 0 errors.
No rows affected.

# Insert an image and edit prompt
# Replace with your own image URL or path
edit_t.insert(
    original='https://images.unsplash.com/photo-1506744038136-46273834b3fb?w=1024&h=768&fit=crop',
    edit_prompt='Add a dramatic sunset with vibrant orange and purple colors',
)

Inserting rows into `edits`: 1 rows [00:00, 600.99 rows/s]
Inserted 1 row with 0 errors.
1 row inserted, 4 values computed.

# View original and edited images
edit_t.select(edit_t.original, edit_t.edited, edit_t.edit_prompt).show()

Using Seeds for Reproducibility

Use the seed parameter to get reproducible results:

# Create a table with seed support
seed_t = pxt.create_table(
    'bfl_demo.seeded',
    {'prompt': pxt.String, 'seed': pxt.Int},
    if_exists='replace',
)

seed_t.add_computed_column(
    image=bfl.generate(
        seed_t.prompt,
        model='flux-pro-1.1',
        width=512,
        height=512,
        seed=seed_t.seed,
    )
)

# Same seed = same image
seed_t.insert(
    [
        {'prompt': 'A red rose in a crystal vase', 'seed': 42},
        {
            'prompt': 'A red rose in a crystal vase',
            'seed': 42,
        },  # Same result
        {
            'prompt': 'A red rose in a crystal vase',
            'seed': 123,
        },  # Different result
    ]
)

Created table ‘seeded’.
Added 0 column values with 0 errors.
Inserting rows into `seeded`: 3 rows [00:00, 1211.18 rows/s]
Inserted 3 rows with 0 errors.
3 rows inserted, 6 values computed.

seed_t.collect()

Image Expansion (Outpainting)

Expand an image beyond its original boundaries using bfl.expand(). This is perfect for:

Making images wider or taller
Adapting content for different aspect ratios
Extending scenes naturally

# Create a table for image expansion
expand_t = pxt.create_table(
    'bfl_demo.expanded',
    {'image': pxt.Image, 'expand_prompt': pxt.String},
    if_exists='replace',
)

# Add computed column to expand images horizontally
expand_t.add_computed_column(
    wide=bfl.expand(
        expand_t.expand_prompt,
        expand_t.image,
        left=256,  # Add 256 pixels to left
        right=256,  # Add 256 pixels to right
    )
)

Created table ‘expanded’.
Added 0 column values with 0 errors.
No rows affected.

# Insert an image to expand - using a known small image
expand_t.insert(
    image='https://replicate.delivery/pbxt/HtGQBfA5TrqFYZBf0UL18NTqHrzt8UiSIsAkUuMHtjvFDO6p/overture-creations-5sI6fQgYIuo.png',
    expand_prompt='Continue the room interior with similar decor and lighting',
)

Inserting rows into `expanded`: 1 rows [00:00, 379.78 rows/s]
Inserted 1 row with 0 errors.
1 row inserted, 4 values computed.

# View original and expanded images
expand_t.select(expand_t.image, expand_t.wide).show()

Inpainting with Fill

bfl.fill() allows you to inpaint specific regions of an image using a mask:

Black areas in the mask are preserved
White areas in the mask are inpainted based on the prompt

Use cases: remove unwanted objects, replace backgrounds, edit text in images, restore damaged areas.

# Create a table for inpainting
fill_t = pxt.create_table(
    'bfl_demo.filled',
    {'image': pxt.Image, 'mask': pxt.Image, 'fill_prompt': pxt.String},
    if_exists='replace',
)

# Add computed column to fill masked regions
fill_t.add_computed_column(
    filled=bfl.fill(
        fill_t.fill_prompt,
        fill_t.image,
        fill_t.mask,
        steps=50,
        guidance=30,
    )
)

Created table ‘filled’.
Added 0 column values with 0 errors.
No rows affected.

# Insert image and mask - the white area in the mask will be inpainted
fill_t.insert(
    image='https://replicate.delivery/pbxt/HtGQBfA5TrqFYZBf0UL18NTqHrzt8UiSIsAkUuMHtjvFDO6p/overture-creations-5sI6fQgYIuo.png',
    mask='https://replicate.delivery/pbxt/HtGQBqO9MtVbPm0G0K43nsvvjBB0E0PaWOhuNRrRBBT4ttbf/mask.png',
    fill_prompt='A fluffy golden retriever sitting on the couch',
)

Inserting rows into `filled`: 1 rows [00:00, 531.87 rows/s]
Inserted 1 row with 0 errors.
1 row inserted, 6 values computed.

# View original image, mask, and filled result
fill_t.select(fill_t.image, fill_t.mask, fill_t.filled).show()

Best Practices

Use specific prompts: Include details about style, lighting, composition, and subject
Start with flux-pro-1.1: Fast and reliable for most use cases
Use seeds for reproducibility: When you need consistent results
Resolution: Minimum 64x64, max 4MP (2048x2048), dimensions must be multiples of 16
Input image limits: Max 20MP for editing/expand/fill operations
Safety tolerance: Default is 2; lower = stricter moderation (0-6 scale)

​Prerequisites

​Setup

​Text-to-Image Generation

​Image Editing

​Using Seeds for Reproducibility

​Image Expansion (Outpainting)

​Inpainting with Fill

​Best Practices

​Learn More

Prerequisites

Setup

Text-to-Image Generation

Image Editing

Using Seeds for Reproducibility

Image Expansion (Outpainting)

Inpainting with Fill

Best Practices

Learn More