AuraFlow-v0.3
An open-source 6.8B parameter text-to-image diffusion transformer model by fal.ai, released under Apache 2.0 with linear attention and fast inference.
The brutal truth
A very clean, royalty-free Apache 2.0 DiT. If you dislike Stability's license terms and need an alternative to FLUX, AuraFlow is a real option.
Files and Versions
Direct inspected tree artifacts from Hugging Face. Verified filenames, exact byte sizes, model weight shards, and configuration definitions.
fal/AuraFlow-v0.3Files and versions are hosted on Hugging Face. You can view all branches, commits, model weights, and tokenizer artifacts directly.
Inspect fal/AuraFlow-v0.3 /tree/main↗Decision matrix
✓ RUN / USE THIS IF…
- •You demand pure Apache 2.0 for image generation
- •You have a 12GB or 16GB GPU
- •You want open model weights
- •Your 24GB GPU comfortably fits the model weights (~12GB VRAM) with dedicated headroom for inference.
✕ WALK AWAY IF…
- •You want the absolute photorealism leader (FLUX Schnell wins)
- •Your GPU has less than 12GB VRAM and you cannot tolerate slow CPU/system RAM offload paging.
- •You prefer a plug-and-play web app interface and don't want to manage local model checkpoints, drivers, or runtimes.
- •You need specialized frontier reasoning that exceeds this model's parameter envelope.
True cost of ownership
About
An open-source 6.8B parameter text-to-image diffusion transformer model by fal.ai, released under Apache 2.0 with linear attention and fast inference.
What it does
Download the weights from Hugging Face, pick a quantisation that matches your GPU, and run it locally with Ollama, llama.cpp, vLLM, or a compatible runtime. Permissive Apache 2.0 open diffusion transformer runs on your own hardware — no per-token bill, no third-party data handling.
Key features
Pure Apache 2.0 open-source license with zero royalties
Clean open research architecture by fal.ai
Strong adherence to complex artistic descriptors
Use cases
Explore visual directions before committing to production.
Place products in editorial scenes without a photo shoot.
Generate campaign imagery with consistent art direction.
Illustrate articles and decks with a consistent visual voice.
Quantization & Hardware Fit
VRAM Hardware Compatibility & Memory Allocation
| Quant | VRAM | Quality / Fit on 24GB | Install / Run Command (COMFYUI) | |
|---|---|---|---|---|
| FP8● ACTIVE | 12 GB | Standard · ✓ Fits (12GB spare) | comfy model download --url https://huggingface.co/fal/AuraFlow-v0.3 --relative-path models/diffusion_models | |
| FP16 | 20 GB | Standard · ✓ Fits (4GB spare) | comfy model download --url https://huggingface.co/fal/AuraFlow-v0.3 --relative-path models/diffusion_models |
Buyer doubts, cleared
Can I actually run AuraFlow-v0.3 on my 24GB GPU?
What's the fine print on the license?
Does the Text-to-image actually hold up in practice?
User reviews
We evaluated AuraFlow-v0.3 against two hosted alternatives. It won on cost-per-output but lost on latency. For batch work it is a clear yes.
The FP8 build is the sweet spot. Anything larger needs serious hardware for marginal quality gains.
Used AuraFlow-v0.3 for a client project. Documentation was solid, though the fine-tuning path is less polished than base inference.
AuraFlow-v0.3 handled our batch jobs without surprises. The API is well documented and the rate limits are reasonable.
