stable-video-diffusion-img2vid
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-video-diffusion-img2vid is an open-weight video generation model from stabilityai, published on Hugging Face in November 2023. It has 1.5B parameters and is released under a custom license (see the model card).
HardwareLink to this section
At full FP16 precision the weights need about 3 GB of memory; 8-bit quantization brings that to roughly 2 GB. That is small enough for most consumer GPUs, Apple Silicon Macs, and many CPUs.
PopularityLink to this section
stable-video-diffusion-img2vid had 40,268 downloads on Hugging Face in the last month and has 1,088 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-video-diffusion-img2vid need?
stable-video-diffusion-img2vid with 1.5B parameters needs approximately 1 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is stable-video-diffusion-img2vid better than other stabilityai models?
stable-video-diffusion-img2vid has 1.5B parameters with 8,192 context - a strong choice for video-generation, image-to-video.
What license is stable-video-diffusion-img2vid under?
stable-video-diffusion-img2vid is released under the Other license, making it suitable for most commercial and personal projects.
What hardware runs stable-video-diffusion-img2vid well?
With 1.5B parameters, stable-video-diffusion-img2vid 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-video-diffusion-img2vid?
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-video-diffusion-img2vid?
stable-video-diffusion-img2vid 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