stabilityai/stable-diffusion-3.5-medium

Overview

Stable Diffusion 3.5 text-to-image generation models, served via vLLM-Omni with optional Cache-DiT acceleration.

Supported variants:

• stabilityai/stable-diffusion-3.5-medium — 2.5B params
• stabilityai/stable-diffusion-3.5-large — 8.1B params
• stabilityai/stable-diffusion-3.5-large-turbo — 8.1B params (timestep-distilled for few-step inference)

Prerequisites

• vLLM-Omni on top of vLLM 0.12.0
• diffusers library

Installation

uv venv
source .venv/bin/activate
uv pip install vllm==0.12.0
uv pip install git+https://github.com/vllm-project/vllm-omni.git

Python Usage

from vllm_omni.entrypoints.omni import Omni

omni = Omni(model="stabilityai/stable-diffusion-3.5-medium")

images = omni.generate(
    prompt="a cat wearing sunglasses, cyberpunk style",
    negative_prompt="blurry, low quality",
    height=1024, width=1024,
    num_inference_steps=28,
    guidance_scale=7.5,
    num_outputs_per_prompt=2,
)

CLI Usage

python examples/offline_inference/text_to_image/text_to_image.py \
  --model stabilityai/stable-diffusion-3.5-medium \
  --prompt "a cat wearing sunglasses, cyberpunk style" \
  --negative-prompt "blurry, low quality" \
  --height 1024 --width 1024 \
  --num-inference-steps 28 \
  --guidance-scale 7.5

Cache-DiT Acceleration

Enable caching for significant speed-ups:

omni = Omni(
    model="stabilityai/stable-diffusion-3.5-medium",
    cache_backend="cache_dit",
    cache_config={
        "Fn_compute_blocks": 8,
        "Bn_compute_blocks": 0,
        "max_warmup_steps": 4,
        "residual_diff_threshold": 0.12,
    },
)

Key Parameters

Parameter	Default	Description
height	1024	Image height (multiples of 16)
width	1024	Image width (multiples of 16)
num_inference_steps	28	Denoising steps
guidance_scale	1.0	Classifier-free guidance scale

References

• SD3.5 Medium
• SD3.5 Large
• SD3.5 Large Turbo
• Cache-DiT Acceleration