Last updated on October 29, 2024
Stable Diffusion 3.5, the latest in Stability AI's lineup, introduces several powerful models that cater to both high-end and consumer-grade hardware, offering versatile options for text-to-image generation. Two key variants of this version are Stable Diffusion 3.5 Large and Stable Diffusion 3.5 Large Turbo, each designed for different needs, from high-quality, detailed images to faster, more efficient output.
Stable Diffusion 3.5 Large is the most powerful model in the series, featuring 8 billion parameters. It excels in generating detailed, high-resolution images (up to 1 megapixel) and is particularly good at adhering closely to complex text prompts. This model is built using the Multimodal Diffusion Transformer (MMDiT) architecture, which enables precise text-to-image generation. It also incorporates Query-Key Normalization (QK Normalization) to stabilize the training process, ensuring consistent and reliable outputs.
Ideal for high-end digital art, concept design, or any scenario where image quality and detail are paramount. For example:
from diffusers import StableDiffusionPipeline
# Load the Stable Diffusion 3.5 Large model
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-3.5-large")
# Generate an image from a detailed prompt
prompt = "A hyper-realistic portrait of a lion in a jungle"
image = pipe(prompt).images[0]
# Save or display the image
image.save("realistic_lion.jpg")
Stable Diffusion 3.5 Large excels at creating intricate, detailed images but takes longer compared to faster models due to its higher parameter count. However, the results are superior in visual quality and prompt accuracy.
Metric | Stable Diffusion 3.5 Large |
---|---|
Inference Steps | More, slower |
Image Quality | Superior |
Resource Efficiency | High-end consumer hardware |
Prompt Adherence | Excellent |
Stable Diffusion 3.5 Large Turbo is designed for those who prioritize speed without compromising too much on image quality. While it shares the same 8 billion parameter architecture as the Large model, it uses Adversarial Diffusion Distillation (ADD) to reduce the number of inference stepsβproducing images in just four steps. This makes it ideal for scenarios where fast, high-quality image generation is needed, such as rapid prototyping, real-time applications, or batch processing.
This variant is perfect for use cases that require quick iterations and high-quality images without the need for the finest details. For example:
from diffusers import StableDiffusionPipeline
# Load the Stable Diffusion 3.5 Large Turbo model
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-3.5-large-turbo")
# Generate an image from a prompt
prompt = "A futuristic cityscape at sunset with flying cars"
image = pipe(prompt).images[0]
# Save or display the image
image.save("futuristic_city.jpg")
The Turbo variant is optimized for speed, generating images in just four steps, making it one of the fastest models in the series. While it may trade off some complexity in output, it still offers high-quality images with excellent prompt adherence.
Metric | Stable Diffusion 3.5 Large Turbo |
---|---|
Inference Steps | 4 steps (very fast) |
Image Quality | High |
Resource Efficiency | Excellent |
Prompt Adherence | Strong |
Model | Parameter Size | Inference Steps | Image Quality | Speed | Best Use Case |
---|---|---|---|---|---|
Stable Diffusion 3.5 Large | 8B parameters | More (slower) | Superior | Moderate | High-quality professional image creation |
Stable Diffusion 3.5 Turbo | 8B parameters | 4 steps | High | Very fast | Rapid high-quality image generation |
Both Stable Diffusion 3.5 Large and Large Turbo are free to use under the Stability AI Community License, which offers:
Stable Diffusion 3.5 Large is the best choice for those who need detailed, high-quality images with a focus on creativity and precision, while Stable Diffusion 3.5 Large Turbo is designed for users who need faster image generation without sacrificing much in terms of quality. Both models are accessible and versatile, making them suitable for a wide range of applications, from artistic creation to real-time generation in consumer hardware environments.