Stable Diffusion 3.5 Medium
Stableby Stability AI
Lightweight 2.0B MMDiT-X model balancing quality and accessibility. Runs on consumer GPUs with 8GB+ VRAM. Good prompt adherence with triple text encoder (5.5B combined: T5-XXL + CLIP-L + OpenCLIP-G).
VRAM requirements, GPU fit, and setup notes for Stable Diffusion 3.5 Medium, including 8GB/12GB fit guidance where relevant. Recommended runtimes: ComfyUI and Diffusers support. Best download size: ~5.1 GB at FP16.
- Only 2.0B MMDiT-X params — runs on 8GB VRAM
- MMDiT architecture like SD 3.5 Large
- Good quality-to-VRAM ratio
- Triple text encoder for prompt adherence
Your hardware
Detecting...
Image Quality Benchmarks
Measured quality metrics for Stable Diffusion 3.5 Medium outputs.
How often humans prefer this model's output (0-100%)
Visual quality and composition rating (5-9 scale)
Text-image alignment accuracy (higher is better)
VRAM Requirements by Resolution and Precision
Compare which GPUs can run Stable Diffusion 3.5 Medium 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 | 9.7 GB | S● | A● | D● | S● |
| 768×768 | 9.8 GB | S● | A● | D● | S● |
| 1024×1024 | 10.0 GB | S● | A● | D● | S● |
Optimization Tips
Turbo / LCM distillation
Use distilled scheduler at 4-8 steps for faster iteration
ControlNets available
Add guided generation with 1 adapter (+1.5 GB VRAM each)
Run with Python
from diffusers import StableDiffusion3Pipeline
import torch
pipe = StableDiffusion3Pipeline.from_pretrained(
"stabilityai/stable-diffusion-3.5-medium",
torch_dtype=torch.float16
)
pipe.to("cuda")
image = pipe(
prompt="your prompt here",
num_inference_steps=28,
guidance_scale=7.0,
height=1024,
width=1024,
).images[0]
image.save("output.png")Get started
Setup instructions for running Stable Diffusion 3.5 Medium locally
1. Download the model
Get the checkpoint from HuggingFace
2. Place in:
ComfyUI/models/checkpoints/3. Launch ComfyUI
python main.pyComfyUI Workflow
Basic txt2img workflow for Stable Diffusion 3.5 Medium
Drag & drop into ComfyUI or use File → Import
Memory 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 Medium in the precision that matches your GPU. Lower precision usually means less VRAM pressure, while higher precision keeps more quality.
| Formato | Precisão | Tamanho | Provedor | |
|---|---|---|---|---|
| safetensorsRecomendado | FP16 | 5.1 GB | official | Baixar |
ControlNet Support
1 ControlNet available for Stable Diffusion 3.5 Medium. ControlNets add guided image generation (edges, depth, pose) at the cost of extra VRAM.
Canny Edge
+1.5 GB VRAMEdge-based structural guidance. Trained for SD 3.5 Large but partially compatible with SD 3.5 Medium. Results may vary — limited community testing.
LoRA Ecosystem
LimitedVery few LoRAs available for SD 3.5 Medium.
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Frequently asked questions
FAQ — Stable Diffusion 3.5 Medium VRAM, Runtimes & Fit
How much VRAM does Stable Diffusion 3.5 Medium need?
Stable Diffusion 3.5 Medium (2B parameters) requires approximately 10.0 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 Medium on an 8GB GPU?
Stable Diffusion 3.5 Medium 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 Medium work in ComfyUI and Diffusers?
Stable Diffusion 3.5 Medium 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 Medium on RTX 4090?
Yes, the RTX 4090 (24 GB VRAM) can run Stable Diffusion 3.5 Medium comfortably at FP16. Expected generation time is around ~7s per image at 1024×1024.
Does Stable Diffusion 3.5 Medium support ControlNet?
Yes, Stable Diffusion 3.5 Medium has 1 ControlNet adapter available: Canny Edge. Each ControlNet adds roughly 1.5 GB of extra VRAM.
Does Stable Diffusion 3.5 Medium have LoRA support?
Very few LoRAs available for SD 3.5 Medium. The LoRA ecosystem for Stable Diffusion 3.5 Medium is rated as "minimal". Each LoRA adds roughly 0.1 GB of extra VRAM.
How fast is Stable Diffusion 3.5 Medium?
On a reference GPU (RTX 4090 24GB), Stable Diffusion 3.5 Medium generates a 1024×1024 image in approximately ~7s at FP16 with 28 inference steps. Faster GPUs with higher memory bandwidth will produce images more quickly.
About Stable Diffusion 3.5 Medium
See also