# FLUX.1
FLUX.1 is a family of text-to-image models released by [Black Forest Labs](https://www.edgepedia.org/black-forest-labs) on August 1, 2024, built as a 12-billion-parameter rectified flow transformer trained in the latent space of an image encoder.[1](https://arxiv.org/html/2507.09595) It shipped in three variants: Pro, available only through licensed API endpoints; Dev, an open-weight model licensed strictly for non-commercial use; and Schnell, an open-weight speed-optimized model released under Apache 2.0.[1](https://arxiv.org/html/2507.09595) • [2](https://indianexpress.com/article/technology/artificial-intelligence/what-is-flux-1-ai-image-generator-9510027/) Its combination of transformer and diffusion techniques at 12 billion parameters set it apart from the [Stable Diffusion 3](https://www.edgepedia.org/stable-diffusion-3) models it effectively competed against, which ranged from 800 million to 8 billion parameters.[2](https://indianexpress.com/article/technology/artificial-intelligence/what-is-flux-1-ai-image-generator-9510027/)
| Key fact | Detail |
|---|---|
| Release date | August 1, 2024, by Black Forest Labs[2](https://indianexpress.com/article/technology/artificial-intelligence/what-is-flux-1-ai-image-generator-9510027/) |
| Architecture | Rectified flow transformer, 12B parameters, all three variants share it[1](https://arxiv.org/html/2507.09595) |
| Variants | Pro (API only), Dev (open weights, non-commercial), Schnell (open weights, Apache 2.0)[1](https://arxiv.org/html/2507.09595) |
| Schnell speed | 1 to 4 generation steps via latent adversarial diffusion distillation[3](https://huggingface.co/black-forest-labs/FLUX.1-schnell) |
| Training disclosure | Dataset, scheduling strategy, and hyperparameters not publicly disclosed[1](https://arxiv.org/html/2507.09595) |
| Follow-ups | FLUX 1.1 [pro] (Ultra and Raw modes); Flux.1 Kontext image-editing model in mid 2025[1](https://arxiv.org/html/2507.09595) • [4](https://education.civitai.com/quickstart-guide-to-flux-1/) |
## Architecture and training as published
All three FLUX.1 variants share the same architecture of 12 billion parameters.[1](https://arxiv.org/html/2507.09595) According to the vendor, the models are rectified flow transformers for text-to-image generation, and the public models use a mix of multimodal and parallel diffusion transformer blocks trained with flow matching.[3](https://huggingface.co/black-forest-labs/FLUX.1-schnell) • [6](https://www.generativeaipub.com/p/flux1-an-open-weights-ai-image-model)
The variants differ mainly in how they were distilled from the base capability. Schnell was trained using latent [adversarial diffusion distillation](https://www.edgepedia.org/adversarial-diffusion-distillation) and can generate high-quality images in only 1 to 4 steps.[3](https://huggingface.co/black-forest-labs/FLUX.1-schnell) Dev is guidance-distilled from Pro, giving an open-weight approximation of the closed model's quality.[1](https://arxiv.org/html/2507.09595)
What Black Forest Labs did not publish is as notable as what it did. The exact training setup, including the dataset, scheduling strategy, and hyperparameters, has not been publicly disclosed; the architecture described above was reverse-engineered by researchers from public inference code.[1](https://arxiv.org/html/2507.09595) The company also did not identify the source of the data used to train the model.[2](https://indianexpress.com/article/technology/artificial-intelligence/what-is-flux-1-ai-image-generator-9510027/) No retrieved source states the training compute used, and none states a context length.
## Licensing and availability
The license split across the family determined what each variant could legally be used for:
- **Pro** is governed by the Flux API License and is accessible only through licensed API endpoints such as [Replicate](https://www.edgepedia.org/replicate) or Fal.ai; its weights were never publicly released.[1](https://arxiv.org/html/2507.09595) • [4](https://education.civitai.com/quickstart-guide-to-flux-1/)
- **Dev-tier models** (FLUX.1 [dev], Fill, Canny, Depth, Redux, Kontext [dev], Krea [dev]) are governed by the FLUX.1-dev Non-Commercial License. Dev can be downloaded and run locally, but strictly for non-commercial use.[1](https://arxiv.org/html/2507.09595) • [5](https://github.com/black-forest-labs/flux?tab=readme-ov-file) • [4](https://education.civitai.com/quickstart-guide-to-flux-1/)
- **Schnell** is released under Apache 2.0, permitting personal, scientific, and commercial purposes without restriction.[3](https://huggingface.co/black-forest-labs/FLUX.1-schnell)
The Apache 2.0 license on Schnell mattered because it was the only variant whose weights and outputs a commercial user could adopt outright. A business could run Schnell locally, fine-tune it, and build a product on it without negotiating a license, while Dev outputs may be used for personal, scientific, and commercial purposes; however, the model itself is licensed for non-commercial use, and Pro required API access.[7](https://github.com/black-forest-labs/flux/blob/main/model_cards/FLUX.1-dev.md) The dev-tier variants collectively covered text-to-image, in/out-painting, structural conditioning, image variation, and image editing tasks.[5](https://github.com/black-forest-labs/flux?tab=readme-ov-file)
## Benchmark results: vendor versus independent
At launch, Black Forest Labs claimed that FLUX.1 Pro and FLUX.1 Dev surpass [Midjourney v6](https://www.edgepedia.org/midjourney-v6).0, [DALL-E 3](https://www.edgepedia.org/dall-e-3), and Stable Diffusion 3 Ultra in visual quality, prompt coherence, size and aspect variability, typography, and output diversity.[6](https://www.generativeaipub.com/p/flux1-an-open-weights-ai-image-model) These are vendor-reported claims, relayed through secondary coverage rather than an independent evaluation.
No independent benchmark data was retrieved for this article. The sources available do not include third-party evaluations from [Artificial Analysis](https://www.edgepedia.org/artificial-analysis), Hugging Face leaderboards, or academic comparisons, so the vendor's superiority claims over Midjourney v6.0, DALL-E 3, and SD3 Ultra cannot be independently confirmed or contradicted here. Readers should treat the comparison as the maker's own assessment.
## Reception, adoption and ecosystem
The community praised Flux.1's image fidelity shortly after release, with commentary attributing the quality to the 12-billion-parameter transformer architecture.[4](https://education.civitai.com/quickstart-guide-to-flux-1/) Viral FLUX.1 images were often further enhanced using Low-Rank Adaptation (LoRA) fine-tuning, a lightweight method for steering a model toward particular styles or subjects, which became a significant part of the model's ecosystem.[2](https://indianexpress.com/article/technology/artificial-intelligence/what-is-flux-1-ai-image-generator-9510027/) Hosting through API endpoints such as Replicate and Fal.ai made even the closed Pro variant accessible to developers without self-hosting.[1](https://arxiv.org/html/2507.09595)
No retrieved source documents API pricing for Replicate, fal.ai, or other providers, or how it compared with rivals' pricing.
## What changed after 2024
Black Forest Labs extended the FLUX.1 line in two directions. FLUX 1.1 [pro], an enhanced version of the Pro model, generated images faster while improving image quality, prompt adherence and diversity, and introduced two modes: Ultra, enabling 4x higher resolution without compromising speed, and Raw, producing hyper-realistic candid-style images, plus LLM-based prompt upsampling.[1](https://arxiv.org/html/2507.09595)
In mid 2025 the company released [Flux](https://www.edgepedia.org/flux-text-to-image-model).1 Kontext, a multimodal model allowing edits to images using both text prompts and image inputs, described by Civitai's education team as state of the art for context-aware editing.[4](https://education.civitai.com/quickstart-guide-to-flux-1/) Kontext [dev] joined the non-commercial dev tier alongside the Fill, Canny, Depth, and Redux variants.[5](https://github.com/black-forest-labs/flux?tab=readme-ov-file) [FLUX.2](https://www.edgepedia.org/flux-2), the successor generation, is covered in its own article; the retrieved evidence for this article does not document its release or its effect on FLUX.1's standing.
## References
1. Demystifying Flux Architecture, arXiv. https://arxiv.org/html/2507.09595
2. What is FLUX.1, a new open-source AI image generator to take on Midjourney?, Indian Express. https://indianexpress.com/article/technology/artificial-intelligence/what-is-flux-1-ai-image-generator-9510027/
3. black-forest-labs/FLUX.1-schnell, Hugging Face model card. https://huggingface.co/black-forest-labs/FLUX.1-schnell
4. Quickstart Guide to Flux.1, Civitai Education. https://education.civitai.com/quickstart-guide-to-flux-1/
5. black-forest-labs/flux, GitHub README. https://github.com/black-forest-labs/flux?tab=readme-ov-file
6. Flux.1: An Open-Weights AI Image Model We Should Pay Attention To, Generative AI Publishing. https://www.generativeaipub.com/p/flux1-an-open-weights-ai-image-model
7. flux/model_cards/FLUX.1-dev.md at main · black-forest-labs/flux, https://github.com/black-forest-labs/flux/blob/main/model_cards/FLUX.1-dev.md
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*Topic: Encyclopedia › Technology and the built world › Computing and digital systems › Modern AI: foundation models, generative AI, and the AI industry › Model families and named models › Image generation models*
*Initially written Sep 17, 2026 · Edited: Oct 11, 2026; Oct 15, 2026*
*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*
License: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license