stable-diffusion-v1-5
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
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Deep dive
Notes, sources, and the full write-up
stable-diffusion-v1-5 is an open-weight image generation model from stable-diffusion-v1-5, published on Hugging Face in August 2024. It has 860M parameters and is released under the creativeml-openrail-m license.
HardwareLink to this section
At full FP16 precision the weights need about 2 GB of memory; 8-bit quantization brings that to roughly 1 GB. That is small enough for most consumer GPUs, Apple Silicon Macs, and many CPUs.
PopularityLink to this section
stable-diffusion-v1-5 had 1,019,121 downloads on Hugging Face in the last month and has 1,364 likes.
Run it locallyLink to this section
The weights are on Hugging Face and load with Hugging Face Diffusers (or ComfyUI). Follow the model card for the exact setup and license terms.
Frequently asked
Quick answers to common questions
How much VRAM does stable-diffusion-v1-5 need?
stable-diffusion-v1-5 with 0.86B parameters needs approximately 1 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is stable-diffusion-v1-5 better than other stable-diffusion-v1-5 models?
stable-diffusion-v1-5 has 0.86B parameters with 8,192 context - a strong choice for image-generation.
What license is stable-diffusion-v1-5 under?
stable-diffusion-v1-5 is released under the creativeml-openrail-m license, making it suitable for most commercial and personal projects.
What hardware runs stable-diffusion-v1-5 well?
With 0.86B parameters, stable-diffusion-v1-5 requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for stable-diffusion-v1-5?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~1 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
What models compete with stable-diffusion-v1-5?
stable-diffusion-v1-5 competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec