workflow-templates-broken-zap-2026-06-08/site/knowledge/models/sd3-5.md

Stable Diffusion 3.5

Stable Diffusion 3.5 is Stability AI's text-to-image model family based on the Multimodal Diffusion Transformer (MMDiT) architecture with rectified flow matching.

Model Variants

Stable Diffusion 3.5 Large

• 8.1 billion parameter MMDiT model
• Highest quality and prompt adherence in the SD family
• 1 megapixel native resolution (1024×1024)
• 28-50 inference steps recommended

Stable Diffusion 3.5 Large Turbo

• Distilled version of SD 3.5 Large
• 4-step inference for fast generation
• Guidance scale of 0 (classifier-free guidance disabled)
• Comparable quality to full model at fraction of the time

Stable Diffusion 3.5 Medium

• 2.5 billion parameter MMDiT-X architecture
• Designed for consumer hardware (9.9GB VRAM for transformer)
• Dual attention blocks in first 12 transformer layers
• Multi-resolution generation from 0.25 to 2 megapixels
• Skip Layer Guidance recommended for better coherency

Key Features

• Three text encoders: CLIP ViT-L, OpenCLIP ViT-bigG (77 tokens each), T5-XXL (256 tokens)
• QK-normalization for stable training and easier fine-tuning
• Rectified flow matching replaces traditional DDPM/DDIM sampling
• Strong text rendering and typography in generated images
• Diverse output styles (photography, 3D, painting, line art)
• Highly customizable base for fine-tuning and LoRA training
• T5-XXL encoder optional (can be removed to save memory with minimal quality loss)
• Supports negative prompts for excluding unwanted elements

Hardware Requirements

• Large: 24GB+ VRAM recommended (fp16), quantizable to fit smaller GPUs
• Large Turbo: 16GB+ VRAM recommended
• Medium: 10GB VRAM minimum (excluding text encoders)
• NF4 quantization available via bitsandbytes for low-VRAM GPUs
• CPU offloading supported via diffusers pipeline

Common Use Cases

• Photorealistic image generation
• Artistic illustration and concept art
• Typography and text-heavy designs
• Product visualization
• Fine-tuning and LoRA development
• ControlNet-guided generation

Key Parameters

• steps: 28-50 for Large, 4 for Large Turbo, 20-40 for Medium
• guidance_scale: 4.5-7.5 for Large/Medium, 0 for Large Turbo
• max_sequence_length: T5 token limit (77 or 256, higher = better prompt understanding)
• resolution: 1024×1024 native, flexible aspect ratios around 1MP
• negative_prompt: Text describing elements to exclude (not supported by Turbo)