workflow-templates-broken-zap-2026-06-08/site/knowledge/models/flux.md

Flux

Flux is a family of state-of-the-art text-to-image and image editing models developed by Black Forest Labs (BFL).

Model Variants

Flux.1 Schnell

• Ultra-fast inference (1-4 steps)
• 12B parameter rectified flow transformer
• Apache 2.0 license (open source)
• Best for rapid prototyping and real-time applications

Flux.1 Dev

• High-quality 12B parameter development model
• 20-50 steps for best results
• Non-commercial license for research
• Guidance-distilled for efficient generation

Flux.1 Pro

• Highest quality Flux.1 outputs via commercial API
• Best prompt adherence and detail

Flux.2 Dev

• 32B parameter rectified flow transformer
• Unified text-to-image, single-reference editing, and multi-reference editing
• No fine-tuning needed for character/object/style reference
• Up to 4MP photorealistic output with improved autoencoder
• Non-commercial license; quantized versions available for consumer GPUs

Flux.2 Klein

• Fastest Flux model family — sub-second inference on modern hardware
• Klein 4B: ~8GB VRAM, Apache 2.0 license, ideal for edge deployment
• Klein 9B: Best quality-to-latency ratio, non-commercial license
• Base (undistilled) variants available for fine-tuning and LoRA training
• Supports text-to-image, single-reference editing, and multi-reference editing

Flux.1 Kontext

• In-context image generation and editing via text instructions
• Available as Kontext Max (premium), Pro (API), and Dev (open-weights, 12B)
• Character consistency across multiple scenes without fine-tuning
• Typography manipulation and local editing within images

Flux.1 Fill

• Dedicated inpainting and outpainting model
• Maintains consistency with surrounding image context
• Available as Fill Pro (API) and Fill Dev (open-weights)

Flux Redux / Canny / Depth

• Redux: Image variation generation from reference images
• Canny: Edge-detection-based structural conditioning
• Depth: Depth-map-based structural conditioning for pose/layout control

Key Features

• Excellent text rendering in images
• Strong prompt following and instruction adherence
• High resolution output (up to 4MP with Flux.2)
• Multi-reference editing: combine up to 6 reference images
• Consistent style and quality across generations

Hardware Requirements

• Flux.2 Klein 4B: ~8GB VRAM (consumer GPUs like RTX 4070)
• Flux.2 Klein 9B: ~20GB VRAM
• Flux.1 models: 12GB VRAM minimum (fp16), 24GB recommended
• Flux.2 Dev: 64GB+ VRAM native, FP8 quantized ~40GB
• Quantized and weight-streaming options available for lower VRAM cards

Common Use Cases

• Text-to-image generation
• Iterative image editing via text instructions
• Character-consistent multi-scene generation
• Inpainting and outpainting
• Style transfer and image variation
• Structural conditioning (canny, depth)

Key Parameters

• steps: 1-4 (Schnell/Klein distilled), 20-50 (Dev/Base)
• guidance_scale: 3.5-4.0 typical for Flux.2, 3.5 for Flux.1
• resolution: Up to 2048x2048 (Flux.1), up to 4MP (Flux.2)
• seed: For reproducible generation
• prompt_upsampling: Optional LLM-based prompt enhancement (Flux.2)

Blog References

• FLUX.2 Day-0 Support in Hanzo Studio — FLUX.2 with 4MP output, multi-reference consistency, professional text rendering
• FLUX.2 [klein] 4B & 9B — Fastest Flux models, sub-second inference, unified generation and editing
• The Complete AI Upscaling Handbook — Benchmarks for upscaling workflows