Shuttle 3 Diffusion

🚀 Quick Start

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

✨ Features

Model Variants

These model variants provide different precision levels and formats optimized for diverse hardware capabilities and use cases:

• bfloat16
• GGUF
• fp8

Comparison to other models

Shuttle 3 Diffusion can produce better images than Flux Dev in just four steps, while being licensed under Apache 2. More examples

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.

📦 Installation

Using the model via API

You can use Shuttle 3 Diffusion via API through ShuttleAI:

• ShuttleAI
• ShuttleAI Docs

Using the model with 🧨 Diffusers

Install or upgrade diffusers:

pip install -U diffusers

Using the model with ComfyUI

To run local inference with Shuttle 3 Diffusion using ComfyUI, you can use this safetensors file.

💻 Usage Examples

Basic Usage

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 documentation

📄 License

This project is licensed under the Apache-2.0 license.