Our flagship 8B parameter model, setting new standards in image generation with superior quality and unmatched prompt adherence.
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Unleash the full potential of Stable Diffusion 3.5
8B parameters delivering exceptional image quality and detail with the most powerful model in the Stable Diffusion family.
Optimized for 1 megapixel resolution, perfect for professional content creation and commercial applications.
Market-leading prompt adherence with enhanced text understanding through multiple advanced encoders.
Technical details and capabilities
Implement SD 3.5 Large in your projects
import torch
from diffusers import StableDiffusion3Pipeline
pipe = StableDiffusion3Pipeline.from_pretrained(
"stabilityai/stable-diffusion-3.5-large",
torch_dtype=torch.bfloat16
)
pipe = pipe.to("cuda")
image = pipe(
"your prompt here",
num_inference_steps=28,
guidance_scale=3.5
).images[0]Prepare your environment with required GPU drivers and dependencies.
Download and configure the SD 3.5 Large model.
Ensure sufficient GPU memory for optimal performance.
Set up the generation pipeline with recommended parameters.
Optimize your results with SD 3.5 Large
Real-world performance metrics
| Configuration | Time (sec) | VRAM Usage |
|---|---|---|
| RTX 4090 | 2.8s | 12GB |
| RTX 4080 | 3.2s | 12GB |
| RTX 3090 | 3.5s | 12GB |
Explore the cutting-edge features of SD 3.5 Large
Triple encoder system combines OpenCLIP-ViT/G, CLIP-ViT/L, and T5-xxl for superior prompt comprehension and artistic interpretation.
Delivers exceptional detail preservation, accurate color reproduction, and sophisticated compositional understanding at 1MP resolution.
Advanced architecture supports efficient fine-tuning and customization for specific use cases and artistic styles.
Industry-specific solutions powered by SD 3.5 Large
Understanding the architecture and capabilities of SD 3.5 Large
Maximize performance and quality with SD 3.5 Large
Join the SD 3.5 Large community
Common questions about Stable Diffusion 3.5 Large
SD 3.5 Large features 8B parameters, making it the most powerful in the family. It offers superior image quality and prompt adherence compared to Medium (2.5B parameters), while maintaining better quality but slower speed compared to Large Turbo.
For optimal performance, we recommend a GPU with at least 12GB VRAM. The model can be run on lower VRAM configurations using optimization techniques like model quantization and CPU offloading, but this may impact generation speed.
Under the Stability AI Community License, SD 3.5 Large is free for commercial use by organizations with annual revenue under $1M. For larger organizations, an Enterprise License is required - contact Stability AI for details.
SD 3.5 Large is optimized for 1 megapixel resolution (e.g., 1024x1024 or equivalent dimensions). While it can generate other resolutions, this is the sweet spot for quality and performance.
Yes, SD 3.5 Large supports fine-tuning. The model features QK normalization which improves training stability. Detailed fine-tuning guides are available in the official documentation.
SD 3.5 Large typically requires 25-30 inference steps for optimal quality. While not as fast as Large Turbo (4 steps), it provides superior image quality and better prompt adherence for professional applications.
You can integrate through various methods: Hugging Face Diffusers, ComfyUI, direct API access via Stability AI API, or self-hosting. Each option offers different levels of control and convenience.
SD 3.5 Large supports extended context lengths: 77 tokens for CLIP encoders and up to 256 tokens for T5 encoder. This allows for detailed prompts and complex descriptions while maintaining coherence.
Key factors include using detailed prompts, maintaining consistent guidance scale (recommended 7.0-8.5), using appropriate number of inference steps, and properly formatting your inputs. Documentation provides detailed guidelines.
Yes, the model supports batch processing. However, batch size will depend on available VRAM. Using techniques like gradient checkpointing and attention optimization can help manage memory usage.
The model includes comprehensive safety measures including filtered training data and content guidelines. Additional safety features can be implemented through API integrations and custom pipelines.
Techniques include using model quantization (4-bit or 8-bit), enabling attention slicing, implementing gradient checkpointing, and utilizing CPU offloading. These can reduce VRAM requirements significantly.
Support is available through official documentation, community forums, GitHub discussions, and Discord channels. Enterprise users have access to additional support through Stability AI.
Yes, you can download and run the model locally using frameworks like Diffusers or ComfyUI. This requires appropriate hardware and setup but offers maximum control and privacy.
Stability AI regularly releases updates and improvements. Check the official repository and announcement channels for the latest versions and changelog information.
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