Stable Diffusion 3.5 Large Turbo
Stableby Stability AI
Distilled version of SD 3.5 Large requiring only 4 inference steps. Same 2.5B MMDiT architecture but ~7x faster. Good for rapid iteration and previewing.
VRAM requirements, GPU fit, and setup notes for Stable Diffusion 3.5 Large Turbo, including 8GB/12GB fit guidance where relevant. Recommended runtimes: ComfyUI and Diffusers support. Best download size: ~16.5 GB at FP16.
- Only 4 steps needed — ~7x faster than SD 3.5 Large
- Same 2.5B MMDiT architecture, distilled
- Good for rapid iteration and previewing
- Same VRAM requirement as SD 3.5 Large
Your hardware
Detecting...
Image Quality Benchmarks
Measured quality metrics for Stable Diffusion 3.5 Large Turbo outputs.
How often humans prefer this model's output (0-100%)
Visual quality and composition rating (5-9 scale)
VRAM Requirements by Resolution and Precision
Compare which GPUs can run Stable Diffusion 3.5 Large Turbo at different precisions. FP8 uses less memory than FP16 when available, and the grade shows how comfortably each GPU handles the workload.
FP16 (full precision)
| Resolution | VRAM Required | RTX 4090 24GB | RTX 3060 12GB | RTX 4060 8GB | MacBook Pro M4 Pro 24GB |
|---|---|---|---|---|---|
| 512×512 | 17.5 GB | S● | F● | F● | B● |
| 768×768 | 17.7 GB | S● | F● | F● | B● |
| 1024×1024 | 18.0 GB | S● | F● | F● | B● |
Optimization Tips
ControlNets available
Add guided generation with 1 adapter (+3.2 GB VRAM each)
Run with Python
from diffusers import StableDiffusion3Pipeline
import torch
pipe = StableDiffusion3Pipeline.from_pretrained(
"stabilityai/stable-diffusion-3.5-large-turbo",
torch_dtype=torch.float16
)
pipe.to("cuda")
image = pipe(
prompt="your prompt here",
num_inference_steps=4,
height=1024,
width=1024,
).images[0]
image.save("output.png")Get started
Setup instructions for running Stable Diffusion 3.5 Large Turbo locally
1. Download the model
Get the checkpoint from HuggingFace
2. Place in:
ComfyUI/models/checkpoints/3. Launch ComfyUI
python main.pyMemory Breakdown
VRAM allocation at 1024×1024 on RTX 4090 24GB (24 GB)
Estimated Generation Time
Time per image at 1024×1024, 28 steps, FP16.
Sample Outputs
Available Formats, Downloads & Setup
Download Stable Diffusion 3.5 Large Turbo in the precision that matches your GPU. Lower precision usually means less VRAM pressure, while higher precision keeps more quality.
| 格式 | 精度 | 大小 | 提供商 | |
|---|---|---|---|---|
| safetensors推荐 | FP16 | 16.5 GB | official | 下载 |
ControlNet Support
1 ControlNet available for Stable Diffusion 3.5 Large Turbo. ControlNets add guided image generation (edges, depth, pose) at the cost of extra VRAM.
Canny Edge
+3.2 GB VRAMEdge detection ControlNet inherited from SD 3.5 Large. Works with turbo but may need fewer steps.
LoRA Ecosystem
LimitedFew LoRAs available. SD 3.5 Turbo shares LoRA compatibility with SD 3.5 Large.
Related Workflows
You might also like
Frequently asked questions
FAQ — Stable Diffusion 3.5 Large Turbo VRAM, Runtimes & Fit
How much VRAM does Stable Diffusion 3.5 Large Turbo need?
Stable Diffusion 3.5 Large Turbo (2.5B parameters) requires approximately 18.0 GB of VRAM at FP16 precision for standard 1024×1024 image generation. If you want a lighter setup, lower precisions like FP8 can reduce memory use when available.
Can I run Stable Diffusion 3.5 Large Turbo on an 8GB GPU?
Stable Diffusion 3.5 Large Turbo usually needs more than 8GB for comfortable local use. Check the VRAM table above for the exact resolution and precision trade-off.
Does Stable Diffusion 3.5 Large Turbo work in ComfyUI and Diffusers?
Stable Diffusion 3.5 Large Turbo is marked for ComfyUI and Diffusers support in our catalog, so those are the runtimes we recommend first for local setup. If your workflow uses another front end, check the model's available formats and workflow notes above before downloading.
Can I run Stable Diffusion 3.5 Large Turbo on RTX 4090?
Yes, the RTX 4090 (24 GB VRAM) can run Stable Diffusion 3.5 Large Turbo comfortably at FP16. Expected generation time is around ~4s per image at 1024×1024.
Does Stable Diffusion 3.5 Large Turbo support ControlNet?
Yes, Stable Diffusion 3.5 Large Turbo has 1 ControlNet adapter available: Canny Edge. Each ControlNet adds roughly 3.2 GB of extra VRAM.
Does Stable Diffusion 3.5 Large Turbo have LoRA support?
Few LoRAs available. SD 3.5 Turbo shares LoRA compatibility with SD 3.5 Large. The LoRA ecosystem for Stable Diffusion 3.5 Large Turbo is rated as "minimal". Each LoRA adds roughly 0.2 GB of extra VRAM.
How fast is Stable Diffusion 3.5 Large Turbo?
On a reference GPU (RTX 4090 24GB), Stable Diffusion 3.5 Large Turbo generates a 1024×1024 image in approximately ~4s at FP16 with 28 inference steps. Faster GPUs with higher memory bandwidth will produce images more quickly.
About Stable Diffusion 3.5 Large Turbo
See also