modelscope / modelscope/DiffSynth-Studio
VACE 1.3B inference speed does not benefit from multiple GPUs
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Description
Hi,
I'm trying to run VACE 1.3B model using the example script (after changing it to using the 1.3B model). However, I notice that the multi-GPU set-up does not show a speed-up compared to the single-GPU setting. Both takes around 5 minutes to generate a 480p 81-frame video over 50 timesteps. (I'm using 1xA100 vs. 8xA100).
In contrast, the inference script provided by the original VACE codebase does show significant speedup with --ring_size 8 using 8 GPUs (around 5 minutes on 1 GPU and around 1 minute GPU).
I tried to dig a bit on why this happens but I couldn't locate the reason or fix it. Can someone take a look? Thanks!
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Research direction
Start with examples/wanvideo/acceleration/unified_sequence_parallel.py and compare its multi-GPU configuration with the original VACE model-inference setup using --ring_size 8. Reproduce the reported 1-GPU and 8-GPU timings for the VACE 1.3B model at 480p and 81 frames, then trace why the configurations differ. Done means the DiffSynth-Studio example shows a measurable multi-GPU speedup comparable to the reference setup.
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Assessment
- Tech stack
- python
- Domain
- distributed-systems, machine-learning, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100