other
12B parameters
Commercial OK
Reviewed May 2026

FLUX.1 [schnell]

12B rectified-flow transformer, timestep-distilled to 1-4 sampling steps, released under Apache-2.0. Same architecture as FLUX.1 [dev] but trades a bit of fidelity for ~10x faster sampling and an unrestricted commercial license.

License: apache-2.0·Context: 0 tokens
BLK · VERDICT

Our verdict

OP · Fredoline Eruo|VERIFIED MAY 29, 2026
unrated

The pragmatic choice when you actually need to ship. Lose ~10-15% quality vs dev, gain a real commercial license and 10x faster inference. If you're building a product on top of FLUX, this is the one.

Overview

12B rectified-flow transformer, timestep-distilled to 1-4 sampling steps, released under Apache-2.0. Same architecture as FLUX.1 [dev] but trades a bit of fidelity for ~10x faster sampling and an unrestricted commercial license.

Strengths

  • Apache-2.0 — only frontier-class open image model with no commercial restrictions
  • 1-4 step sampling (~1-2s/image on RTX 4090) vs 20-50 for FLUX.1 [dev]
  • Same 12B MMDiT backbone — keeps most of dev's prompt adherence and typography
  • Drop-in replacement in diffusers / ComfyUI / FP8 / NF4 / GGUF tooling

Weaknesses

  • Distillation visibly degrades fine detail and hands vs FLUX.1 [dev]
  • Does not respond to CFG/guidance scale — locked to a single distilled trajectory
  • Still needs ~24GB VRAM at BF16; consumer cards require quantization
  • LoRA / fine-tuning ecosystem is thinner than dev's

Quantization variants

Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.

QuantizationFile sizeVRAM required
Q4_K_M6.6 GB9 GB

Get the model

HuggingFace

Original weights

huggingface.co/black-forest-labs/FLUX.1-schnell

Source repository — direct quantization required.

Hardware that runs this

Cards with enough VRAM for at least one quantization of FLUX.1 [schnell].

Compare alternatives

Models worth comparing

Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.

Frequently asked

What's the minimum VRAM to run FLUX.1 [schnell]?

9GB of VRAM is enough to run FLUX.1 [schnell] at the Q4_K_M quantization (file size 6.6 GB). Higher-quality quantizations need more.

Can I use FLUX.1 [schnell] commercially?

Yes — FLUX.1 [schnell] ships under the apache-2.0, which permits commercial use. Always read the license text before deployment.

What's the context length of FLUX.1 [schnell]?

FLUX.1 [schnell] supports a context window of 0 tokens (about 0K).

Source: huggingface.co/black-forest-labs/FLUX.1-schnell

Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.

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Before you buy

Verify FLUX.1 [schnell] runs on your specific hardware before committing money.