FLUX.1 [dev]

FLUX.1 [dev] is a 12 billion parameter rectified flow transformer developed by Black Forest Labs. It uses a hybrid architecture combining MMDiT (Multi-Modal Diffusion Transformer) and SingleDiT blocks, with dual text encoders — CLIP ViT-L/14 (77 tokens) for global semantic alignment and T5-v1.1-XXL (up to 512 tokens) for rich, nuanced language understanding. A 16-channel VAE (4× more channels than SDXL) enables higher fidelity latent representations. The model uses Rotary Positional Encoding (RoPE) and a Flow Matching Euler Discrete scheduler, making it highly capable across varied resolutions and aspect ratios. It is guidance-distilled from FLUX.1 [pro], achieving near-pro quality at significantly lower inference cost.

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api_example.sh

curl -X POST "https://platform.qubrid.com/v1/images/generations" \
  -H "Authorization: Bearer QUBRID_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "black-forest-labs/FLUX.1-dev",
  "prompt": "cinematic shot of a lone astronaut standing on a desolate alien planet, glowing orange sunset sky, dust storms swirling, dramatic lighting, ultra-wide lens composition, movie still aesthetic, realistic space suit details, volumetric atmosphere, 8k sci-fi film scene",
  "num_inference_steps": 28,
  "guidance": 3.5,
  "seed": -1,
  "aspect_ratio": "1:1",
  "image_size": 1024,
  "output_format": "jpg",
  "output_quality": 80
}'

Technical Specifications

Model Architecture & Performance

Variant Dev (guidance-distilled from FLUX.1 [pro])
Model Size 12B parameters (~23GB in bf16)
Quantization None (fp8 / NF4 community variants available)
Architecture Rectified flow transformer — hybrid MMDiT + SingleDiT blocks with RoPE (Rotary Positional Encoding) and Flow Matching Euler Discrete scheduler
Precision bfloat16
License FLUX.1-dev Non-Commercial License
Release Date August 2024
Developers Black Forest Labs

Pricing

Pay-per-use, no commitments

Per Image $0.005/Image

API Reference

Complete parameter documentation

Parameter Type Default Description
num_inference_steps number 28 Number of denoising steps. More steps yield higher quality but slower generation.
guidance number 3.5 How closely the model follows the prompt. Higher values produce more literal interpretation of the text.
seed number -1 Random seed for reproducible generation. Use -1 for random.
aspect_ratio string 1:1 Aspect ratio of the output image. Options: 1:1, 16:9, 21:9, 3:2, 2:3, 4:5, 5:4, 3:4, 4:3, 9:16, 9:21.
image_size number 1024 Base size in pixels for the longest side of the output image.
output_format string jpg Format of the generated image. Options: png, jpg, webp.
output_quality number 80 Compression quality for jpg/webp output (1–100). Higher values retain more detail.

Explore the full request and response schema in our external API documentation

Performance

Strengths & considerations

Strengths Considerations
12B parameters with state-of-the-art output quality
Dual text encoders: CLIP L/14 + T5-v1.1-XXL for deep prompt understanding
16-channel VAE for high-fidelity image encoding
Hybrid MMDiT + SingleDiT transformer architecture
Supports wide range of aspect ratios and resolutions
Open weights — compatible with LoRA, ControlNet, and fine-tuning
Guidance distillation from FLUX.1 [pro] for efficient inference
Non-commercial license only — separate commercial license required from Black Forest Labs
12B parameters require significant VRAM (~24GB in fp16); quantized versions (fp8, NF4) needed for consumer hardware
May reflect societal biases present in training data
Not designed to produce factually accurate or grounded outputs
Generation quality sensitive to prompt length, style, and step count

Use cases

Recommended applications for this model

Photorealistic image generation
Digital illustration and concept art
Marketing and branding creatives
Photography and portrait generation
Research and fine-tuning (LoRA, ControlNet)

Enterprise
Platform Integration

Docker

Docker Support

Official Docker images for containerized deployments

Kubernetes

Kubernetes Ready

Production-grade KBS manifests and Helm charts

SDK

SDK Libraries

Official SDKs for Python, Javascript, Go, and Java

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