Model Detail
Wan2.2-I2V-A14B-GGUF
—Wan2.2-I2V-A14B-GGUF is an image generation model with 14B parameters released by QuantStack. The model is registered under the image-to-video pipeline tag on Hugging Face, distributed under the permissive apache-2.0 license.
Wan2.2-I2V-A14B-GGUF ships with 14B parameters, distributed as a quantized weight variant for lower-VRAM inference. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
Wan2.2-I2V-A14B-GGUF is best fit for text-to-image generation and creative iteration. It is a less obvious choice for production photography pipelines that need exact reproducibility. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Collaborative Few-Step Distillation and Low-Bit Quantization for Wan2.2 Dual-Expert Video Diffusion Models
arXiv:2606.00658v1 Announce Type: cross Abstract: Large video diffusion models achieve strong visual quality but remain expensive to deploy because each sample requires many denoising steps and a large resident parameter footprint. This paper studies a deployment-oriented compression pipeline for Wa
Timestep-Aware SVDQuant-GPTQ for W4A4 Quantization of Wan2.2-I2V
arXiv:2605.27003v1 Announce Type: cross Abstract: W4A4 quantization of large video diffusion Transformers offers substantial memory savings but is hindered by two main challenges: sparse large-magnitude activation outliers, and strongly timestep-dependent activation distributions across the multi-st
Tail-Aware HiFloat4: W4A4 Post-Training Quantization for Wan2.2
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