Learn Prompting🟢 Stable Diffusion 3.5
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
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.
Key Features:
- High Image Quality: Best for users who need precise, photorealistic images or complex art.
- Superior Prompt Adherence: Capable of accurately reflecting intricate text descriptions.
- Scalability: Designed to work with high-end consumer hardware and professional setups, making it suitable for artists, designers, and researchers.
How to Use:
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.webp")
Applications:
- Photorealistic image creation
- High-end artwork and concept design
- Detailed illustrations requiring nuanced prompt interpretation
Performance:
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
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 steps—producing 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.
Key Features:
- Fast Image Generation: Creates images in fewer steps, significantly cutting down on time.
- QK Normalization: Maintains high prompt adherence while speeding up the generation process.
- Optimized for Consumer Hardware: Despite its speed, it remains efficient enough to run on standard hardware.
How to Use:
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.webp")
Applications:
- Rapid image generation for prototyping
- Real-time image generation in apps or games
- Quick batch processing for creative content
Performance:
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 |
Comparison of Stable Diffusion 3.5 Large vs. Large Turbo
| 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 |
Licensing and Availability
Both Stable Diffusion 3.5 Large and Large Turbo are free to use under the Stability AI Community License, which offers:
- Free non-commercial use: Ideal for personal projects and research.
- Commercial use for businesses with revenue under $1M annually.
- Full ownership rights to generated media, allowing users to freely use and distribute their creations.
Conclusion
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.
Valeriia Kuka
Valeriia Kuka, Head of Content at Learn Prompting, is passionate about making AI and ML accessible. Valeriia previously grew a 60K+ follower AI-focused social media account, earning reposts from Stanford NLP, Amazon Research, Hugging Face, and AI researchers. She has also worked with AI/ML newsletters and global communities with 100K+ members and authored clear and concise explainers and historical articles.