This is the fastest model in the Flux.1 model family, optimized for local development and personal use.
Features :
Speed Optimization : Has the fastest generation speed.
Open Source : Released under the Apache 2.0 License.
Applicable scenarios:
Suitable for personal projects and rapid prototyping.
FLUX.1 [schnell] is openly available under the Apache 2.0 license. Similar to FLUX.1 [dev], the weights are available on Hugging Face, and the inference code can be found on GitHub and HuggingFace’s Diffusers . An integration is available on ComfyUI .
Multimodal Diffusion Transformer : Supports processing of data inputs in multiple modalities such as text and images, improving the generation capability and adaptability of the model.
Parallel Diffusion Transformer Blocks : By processing multiple Diffusion Transformer blocks in parallel, the training and inference process of the model is accelerated.
Parameter scale
Number of parameters: The Flux.1 model contains 12B (12 billion) parameters. This gives the model powerful learning and generative capabilities, and is able to generate high-quality images.
Description : Flow matching is a general and conceptually simple method for training generative models, including diffusion as a special case.
Advantages : Through the stream matching method, the model improves training efficiency and generation speed while maintaining high-quality generation.
Description: Adding parallel attention layers to the model allows the model to focus on multiple different parts of the input data simultaneously.
Advantages: Significantly improves the computational efficiency and generation speed of the model.
cost-creative of Flux.1
Performance Optimization of Flux.1
Hardware efficiency:
By combining the above technical innovations, the Flux.1 model has been optimized in performance, ensuring that hardware efficiency is maximized while maintaining high-quality output.
Model variants:
FLUX.1 [pro] : Targeted at commercial applications, offering top performance and quality.
FLUX.1 [dev] : Open source version suitable for academic and non-commercial applications.
FLUX.1 [schnell] : Optimized for speed, suitable for personal development and rapid prototyping.
A new benchmark for image synthesis
Visual Quality and Hint Following :
The Flux.1 model surpasses popular models such as Midjourney v6.0, DALL·E 3 (HD), and SD3-Ultra in terms of visual quality, hint following, size/aspect ratio variations, typography, and output diversity.
Output diversity:
The model is specifically fine-tuned to maintain the full output diversity during pre-training, providing richer and more diverse generation results.
Output diversity of Flux.1
All FLUX.1 models support different aspect ratios and resolutions (100,000 and 2.0 million pixels) as shown below
FLUX.1 models support different aspect ratios and resolutions
Practical Usage of Flux.1
Diverse application scenarios : From commercial image generation to personal project development, the Flux.1 model provides a wide range of application possibilities.
Open platform and resources : The weights and inference codes of the FLUX.1 [dev] and FLUX.1 [schnell] models are publicly available on HuggingFace and GitHub to facilitate developers’ use and secondary development.
At the same time, the FLUX.1 text-to-image model suite lays a solid foundation for their upcoming competitive text-to-video generation system . Officials say their video model will enable precise creation and editing at high definition and unprecedented speed.
Core Team of FLUX.1
Founder and Leader
Jeff Dean: As the leader of the team, Jeff has extensive experience and deep knowledge in the field of machine learning and generative AI. He served as a senior researcher at Google DeepMind and led the research and development of several key projects.
Main Researchers
Victor Irastorza: He has a deep research background in generative model architecture design and algorithm optimization, and has worked in several top research institutions.
Emma King: Focuses on multimodal learning and image generation technology, has published many important papers, and has gained wide recognition in academia and industry.
Eric Stone: has extensive experience in deep learning and model compression, and is committed to improving the computational efficiency and generation quality of models.
Engineering Team
Cara Lee: Responsible for the engineering implementation and optimization of the model, ensuring that the model runs efficiently on different hardware platforms.
Ryan Thomas: Focused on the development of large-scale data processing and model training pipelines, improving the training speed and stability of the model.
Major investors : Andreessen Horowitz led the round, with participation from angel investors Brendan Iribe, Michael Ovitz, Garry Tan, Timo Aila, and Vladlen Koltun.
Follow-on investment : Follow-on investment from General Catalyst and MätchVC supports the team’s mission to bring the most advanced AI technologies from Europe to global users.
Demonstration effect:
Example 1
Style: portrait
Prompt: Create a captivating portrait of a voluptuous boho woman with green eyes and long, wavy blonde hair, she is standing. She has a fair complexion adorned with delicate freckles, and her expression is contemplative, reflecting a moment of deep thought. She wears a white-colored, off-shoulder linen satin dress, with deep neck linen, complemented by a necklace and various boho jewelry that accentuates her bohemian style., photo, poster, vibrant, portrait photography, fashion
Image created by FLUX.1
Example 2
Style: surreal
Prompt: pareidolic anamorphosis of a hole in a brick wall morphed into a hublot of a sail boat, a window to the sea.
Image created by FLUX.1
Example 3
Style: photo
Prompt: a cat sit near the bech with sun glass, photo.
Image created by FLUX.1: a cat
Example 4
Style: satirical
Prompt: Circus tent made out of a worn us flay with text that says not my circus not my clowns. With Biden and trump dressed as clowns in a suit made of the us flag.
