| | --- |
| | language: |
| | - en |
| | license: apache-2.0 |
| | library_name: diffusers |
| | pipeline_tag: text-to-image |
| | tags: |
| | - text-to-image |
| | - image-generation |
| | - shuttle |
| | widget: |
| | - text: >- |
| | Venus floating market at dawn, fantasy digital art, highly detailed, atmospheric lighting with film-like light leaks, impressive background, studio photo style, cinematic, intricate details. |
| | output: |
| | url: gallery/1.webp |
| | - text: >- |
| | Silent forest, sun barely piercing treetops, mysterious lake turns dark red at dawn, reflecting colorful sky. Lone tree on shore with diamond-like dewdrops, photorealistic. |
| | output: |
| | url: gallery/2.webp |
| | - text: >- |
| | A beautiful photo showcases a night waterfall in the jungle, illuminated with a subtle blue tint that adds an ethereal touch. Fireflies float delicately around, their gentle glow enhancing the magical ambiance of the scene. |
| | output: |
| | url: gallery/3.webp |
| | |
| | instance_prompt: null |
| | --- |
| | |
| | # Shuttle 3 Diffusion |
| |
|
| | Join our [Discord](https://discord.gg/shuttleai) to get the latest updates, news, and more. |
| |
|
| | <Gallery /> |
| |
|
| | ## Model Variants |
| | These model variants provide different precision levels and formats optimized for diverse hardware capabilities and use cases |
| | - [bfloat16](https://huggingface.co/shuttleai/shuttle-3-diffusion) |
| | - [GGUF](https://huggingface.co/shuttleai/shuttle-3-diffusion-GGUF) |
| | - [fp8](https://huggingface.co/shuttleai/shuttle-3-diffusion-fp8) |
| |
|
| | Shuttle 3 Diffusion is a text-to-image AI model designed to create detailed and diverse images from textual prompts in just 4 steps. 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](https://shuttleai.com/) |
| | - [ShuttleAI Docs](https://docs.shuttleai.com/) |
| |
|
| | ## Using the model with 🧨 Diffusers |
| | Install or upgrade diffusers |
| | ```shell |
| | pip install -U diffusers |
| | ``` |
| | Then you can use `DiffusionPipeline` to run the model |
| | ```python |
| | import torch |
| | from diffusers import DiffusionPipeline |
| | |
| | # Load the diffusion pipeline from a pretrained model, using bfloat16 for tensor types. |
| | pipe = DiffusionPipeline.from_pretrained( |
| | "shuttleai/shuttle-3-diffusion", torch_dtype=torch.bfloat16 |
| | ).to("cuda") |
| | |
| | # Uncomment the following line to save VRAM by offloading the model to CPU if needed. |
| | # pipe.enable_model_cpu_offload() |
| | |
| | # Uncomment the lines below to enable torch.compile for potential performance boosts on compatible GPUs. |
| | # Note that this can increase loading times considerably. |
| | # pipe.transformer.to(memory_format=torch.channels_last) |
| | # pipe.transformer = torch.compile( |
| | # pipe.transformer, mode="max-autotune", fullgraph=True |
| | # ) |
| | |
| | # Set your prompt for image generation. |
| | prompt = "A cat holding a sign that says hello world" |
| | |
| | # Generate the image using the diffusion pipeline. |
| | image = pipe( |
| | prompt, |
| | height=1024, |
| | width=1024, |
| | guidance_scale=3.5, |
| | num_inference_steps=4, |
| | max_sequence_length=256, |
| | # Uncomment the line below to use a manual seed for reproducible results. |
| | # generator=torch.Generator("cpu").manual_seed(0) |
| | ).images[0] |
| | |
| | # Save the generated image. |
| | image.save("shuttle.png") |
| | ``` |
| | To learn more check out the [diffusers](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux) documentation |
| |
|
| | ## Using the model with ComfyUI |
| |
|
| | To run local inference with Shuttle 3 Diffusion using [ComfyUI](https://github.com/comfyanonymous/ComfyUI), you can use this [safetensors file](https://huggingface.co/shuttleai/shuttle-3-diffusion/blob/main/shuttle-3-diffusion.safetensors). |
| |
|
| | ## Comparison to other models |
| | Shuttle 3 Diffusion can produce images better images than Flux Dev in just four steps, while being licensed under Apache 2. |
| |  |
| | [More examples](https://docs.shuttleai.com/getting-started/shuttle-diffusion) |
| |
|
| | ## Training Details |
| | Shuttle 3 Diffusion uses Flux.1 Schnell as its base. It can produce images similar to Flux Dev or 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. |
