modelli/Shuttle 3 Diffusion - fp8

Shuttle 3 Diffusion - fp8

10/14/2025

9:16:50 AM

Parole Chiave e Tag Correlati

checkpoint,complex prompts,flux.1,flux.1 s,fp8,image quality,fotorealistico,resource efficiency,shuttle 3 diffusion,shuttleai,style,text-to-image ai model,typography

Close-up portrait of a female robot with smooth porcelain white skin and intricate golden radial markings on the face, featuring symmetrical patterns, striking yellow eyes, and a transparent glass body with gold details.

An elderly man with white hair and beard wearing a VR headset stands in the middle of a busy urban street, raising his hands with a confused expression under dramatic lighting.

A chubby old clown with a horn on his head sits frowning on a wooden chair on stage, holding a bottle of whisky, surrounded by red curtains and illuminated by yellow dramatic stage lights.

Suggerimenti

The model enters a refiner mode beyond 10 steps to enhance image details without altering composition.

Shuttle 3 Diffusion produces images similar to Flux Pro in just 4 steps.

It uses a special training method to improve details and colors beyond the Schnell-series models.

Sponsor del Creatore

You can try out the model through a website at https://chat.shuttleai.com/images

You can use Shuttle 3 Diffusion via API through ShuttleAI

Shuttle 3 Diffusion

Shuttle 3 Diffusion is a text-to-image AI model designed to create detailed and diverse images from textual prompts. It offers enhanced performance in image quality, typography, understanding complex prompts, and resource efficiency.

You can try out the model through a website at https://chat.shuttleai.com/images

Using the model via API

You can use Shuttle 3 Diffusion via API through ShuttleAI

- ShuttleAI

- Documentation

Shuttle 3 Diffusion uses Flux.1 Schnell as its base. It can produce images similar to Flux Pro in just 4 steps, and it is licensed under Apache 2. The model was partially de-distilled during training. When used beyond 10 steps, it enters "refiner mode," enhancing image details without altering the composition. We overcame the limitations of the Schnell-series models by employing a special training method, resulting in improved details and colors.