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shuttleai
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Model Details
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shuttleai
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These model variants provide different precision levels and formats optimized for diverse hardware capabilities and use cases
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
You can use Shuttle 3 Diffusion via API through ShuttleAI
Install or upgrade diffusers
shell
pip install -U diffusersThen you can use DiffusionPipeline to run the model
python
import torchfrom 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
To run local inference with Shuttle 3 Diffusion using ComfyUI, you can use this safetensors file.
Shuttle 3 Diffusion can produce images better images than Flux Dev in just four steps, while being licensed under Apache 2.
More examples
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.
pip install -U diffusersimport torchfrom 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")