Stable Diffusion 3.5 Medium (2.5B)
A 2.5B parameter consumer-friendly DiT designed to run on 8GB–10GB consumer GPUs with fast generation times and solid composition.
The brutal truth
The 8GB sweet spot for SD3.5. Fast and competent, though FLUX.1 Schnell at FP8 still edges it out in overall aesthetic appeal.
Files and Versions
Direct inspected tree artifacts from Hugging Face. Verified filenames, exact byte sizes, model weight shards, and configuration definitions.
stabilityai/stable-diffusion-3.5-mediumFiles and versions are hosted on Hugging Face. You can view all branches, commits, model weights, and tokenizer artifacts directly.
Inspect stabilityai/stable-diffusion-3.5-medium /tree/main↗Decision matrix
✓ RUN / USE THIS IF…
- •You have an 8GB VRAM GPU
- •You want fast ComfyUI generation
- •You prefer Stability AI tooling ecosystem
- •Your 24GB GPU comfortably fits the model weights (~7GB VRAM) with dedicated headroom for inference.
✕ WALK AWAY IF…
- •You have 16GB+ VRAM (use SD 3.5 Large or FLUX)
- •Your GPU has less than 7GB VRAM and you cannot tolerate slow CPU/system RAM offload paging.
- •You prefer a plug-and-play web app interface and don't want to manage local model checkpoints, drivers, or runtimes.
- •You need specialized frontier reasoning that exceeds this model's parameter envelope.
True cost of ownership
About
A 2.5B parameter consumer-friendly DiT designed to run on 8GB–10GB consumer GPUs with fast generation times and solid composition.
What it does
Download the weights from Hugging Face, pick a quantisation that matches your GPU, and run it locally with Ollama, llama.cpp, vLLM, or a compatible runtime. Fast 8GB consumer image generation runs on your own hardware — no per-token bill, no third-party data handling.
Key features
Fits comfortably on 8GB VRAM consumer cards
Fast generation times in ComfyUI and Automatic1111
Decent typography adherence
Use cases
Explore visual directions before committing to production.
Place products in editorial scenes without a photo shoot.
Generate campaign imagery with consistent art direction.
Illustrate articles and decks with a consistent visual voice.
Quantization & Hardware Fit
VRAM Hardware Compatibility & Memory Allocation
| Quant | VRAM | Quality / Fit on 24GB | Install / Run Command (COMFYUI) | |
|---|---|---|---|---|
| GGUF Q4● ACTIVE | 7 GB | Standard · ✓ Fits (17GB spare) | comfy model download --url https://huggingface.co/stabilityai/stable-diffusion-3.5-medium --relative-path models/diffusion_models | |
| FP8 | 9 GB | Standard · ✓ Fits (15GB spare) | comfy model download --url https://huggingface.co/stabilityai/stable-diffusion-3.5-medium --relative-path models/diffusion_models | |
| FP16 | 16 GB | Standard · ✓ Fits (8GB spare) | comfy model download --url https://huggingface.co/stabilityai/stable-diffusion-3.5-medium --relative-path models/diffusion_models |
Buyer doubts, cleared
Can I actually run Stable Diffusion 3.5 Medium (2.5B) on my 24GB GPU?
What's the fine print on the license?
Does the Text-to-image (DiT) actually hold up in practice?
User reviews
Switched our internal workflow to Stable Diffusion 3.5 Medium (2.5B) after the last pricing change. Nobody noticed a quality difference, and the bill dropped by half.
Used Stable Diffusion 3.5 Medium (2.5B) for a client project. Documentation was solid, though the fine-tuning path is less polished than base inference.
Reasoning quality is close to the bigger models for structured tasks. Open-ended planning still needs a frontier model.
Clean interface, consistent results once you land on a prompt you like. Wish there were more export presets for client handoffs.
