Resource Optimization Programme
Three major solution paths for insufficient video memory:
- Memory Offload Technology: Enable the -offload_model parameter to dynamically move components such as the ViT visual encoder to CPU memory.
- distributed computing
- Cloud Service Solutions: Recommended use of AWS p4d.24xlarge instances (8×A100) or Lambda Labs' GPU Cluster Service
: For multi-GPU environments (e.g., 2×A100), use the torchrun command with the -ulysses_size parameter to achieve model parallelism
Tuning Tips
- Resolution Compromise: Reducing the -size parameter to 640*480 reduces the memory footprint by about 40%.
- Segment generation: Split long audio into two separate clips with -num_clip 2
- Precision Adjustment: Add -fp16 parameter to change to mixed-precision calculations (requires RTX 30-series graphics card or higher)
This answer comes from the articleWan2.2-S2V-14B: Video Generation Model for Speech-Driven Character Mouth SynchronizationThe
May not be reproduced without permission:AI productivity tools " How to overcome the problem of generation failure due to insufficient hardware?
English 
简体中文 
日本語 
Deutsch 
Português do Brasil