stabilityai/stable-diffusion-3.5-medium

Stability AI's Stable Diffusion 3.5 text-to-image family (medium 2.5B, large 8.1B, large-turbo) via vLLM-Omni with Cache-DiT acceleration

View on HuggingFace

dense2.5B0 ctxvLLM 0.12.0+vLLM-Omninightlyomni

Default configuration: 8× H200 · BF16

vllm serve stabilityai/stable-diffusion-3.5-medium \
  --omni

The interactive builder loads with JavaScript. Append .md to this page's URL, query string included, for any configuration's command as Markdown — e.g. /stabilityai/stable-diffusion-3.5-medium.md.

Guide

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 0.33.0
diffusers library

Installation

uv venv
source .venv/bin/activate
uv pip install --torch-backend=cu130 vllm==0.33.0
uv pip install git+https://github.com/vllm-project/vllm-omni.git

AMD ROCm (MI300X, MI325X, MI355X)

The Dockerfile builds the frozen vLLM 0.33.0 source over its published bootstrap image. ROCm build and native model validation remain pending; this is not a claim that a final ROCm wheel is available.

uv venv --python 3.12
source .venv/bin/activate
git clone https://github.com/vllm-project/vllm-omni.git && cd vllm-omni && docker build -f docker/Dockerfile.rocm -t vllm-omni-rocm:v033-source .

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