Shuttle Jaguar

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

• bfloat16
• fp8
• GGUF

Shuttle Jaguar is a text-to-image AI model designed to generate highly aesthetic, cinematic, and realistic images from textual prompts in just four steps, all while being licensed under Apache 2.

Using the model via API

You can use Shuttle Jaguar via API through ShuttleAI

• ShuttleAI
• ShuttleAI Docs

Using the model with 🧨 Diffusers

Install or upgrade diffusers

pip install -U 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-jaguar", 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 Jaguar using ComfyUI, you can use this safetensors file.