metadata

license: other
license_name: flux-1-dev-non-commercial-license
license_link: LICENSE.md
pipeline_tag: text-to-image

Flux-Mini

A 3.2B MMDiT distilled from Flux-dev for efficient text-to-image generation

github: https://github.com/TencentARC/FluxKits

Nowadays, text-to-image (T2I) models are growing stronger but larger, which limits their practical applicability, especially on consumer-level devices. To bridge this gap, we distilled the 12B Flux-dev model into a 3.2B Flux-mini model, trying to preserve its strong image generation capabilities. Specifically, we prune the original Flux-dev by reducing its depth from 19 + 38 (number of double blocks and single blocks) to 5 + 10. The pruned model is further tuned with denoising and feature alignment objectives on a curated image-text dataset.

We empirically found that different blocks have different impacts on the generation quality, thus we initialize the student model with several most important blocks. The distillation process consists of three objectives: the denoise loss, the output alignment loss as well as the feature alignment loss. The feature alignment loss is designed in a way such that the output of block-x in the student model is encouraged to match that of block-4x in the teacher model. The distillation process is performed with 512x512 Laion images recaptioned with Qwen-VL in the first stage for 90k steps, and 1024x1024 images generated by Flux using the prompts in JourneyDB with another 90k steps.

Limitations

With limited computing and data resources, the capability of our Flux-mini is still limited in certain domains. To facilitate the development of flux-based models, we open-sourced the codes to distill Flux in this link. We appeal people interested in this project to collaborate together to build a more applicable and powerful text-to-image model!

The current model is ok with generating common images such as human/animal faces, landscapes, fantasy and abstract scenes.
Unfortunately, it is still incompetent in many scenarios. Including but not limited to:

Fine-grained details, such as texts, human/animal structures,
Perspective and Geometric Structure
Dynamics and Motion
Commonsense knowledge, e.g., brand logo
Physical Plausibility
Cultural Diversity

Since our model is trained with prompts in JourneyDB, we encourage users to apply our model with similar prompt formats (compositions of nouns and adjectives) to achieve the best quality. For example: "profile of sad Socrates, full body, high detail, dramatic scene, Epic dynamic action, wide angle, cinematic, hyper-realistic, concept art, warm muted tones as painted by Bernie Wrightson, Frank Frazetta."

Thank you for your attention! We will continue to improve our model and release new versions in the future.