Shuttle 3.1 Aesthetic

🚀 Shuttle 3.1 Aesthetic

Shuttle 3.1 Aesthetic is a text - to - image AI model that can generate detailed and aesthetic images from text prompts in just 4 to 6 steps, offering enhanced performance in multiple aspects.

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🚀 Quick Start

Shuttle 3.1 Aesthetic is a text - to - image AI model designed to create detailed and aesthetic images from textual prompts in just 4 to 6 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://designer.shuttleai.com/

✨ Features

Model Variants

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

• bfloat16
• fp8
• GGUF (soon)

Training Details

Shuttle 3.1 Aesthetic uses Shuttle 3 Diffusion as its base. It can produce images similar to Flux Dev in just 4 steps, and it is licensed under Apache 2. The model was partially de - distilled during training. 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 3.1 Aesthetic via API through ShuttleAI:

• ShuttleAI
• ShuttleAI Docs

Using the model with 🧨 Diffusers

Install or upgrade diffusers:

pip install -U diffusers

💻 Usage Examples

Using the model with 🧨 Diffusers

Then you can use DiffusionPipeline to run the model:

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-3.1-aesthetic", 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.

Using the model with ComfyUI

To run local inference with Shuttle 3.1 Aesthetic using ComfyUI, you can use this safetensors file.

📄 License

This model is licensed under Apache 2.0.