Practical solutions for low graphics memory GPUs
HunyuanWorld-1.0's official recommendation of 33GB of video memory is demanding, but it can be run on a regular GPU by doing the following:
- Reduced resolution: Modifying the output resolution parameter in the configuration file (e.g., reducing from 1024×512 to 512×256) reduces video memory consumption by 75%
- step by step: Run panorama generation (demo_panogen.py) and 3D scene generation (demo_scenegen.py) in two separate runs to avoid memory accumulation
- Enable CPU mode: Add to the command
--device cpuparameter, some computation tasks can be transferred to memory - Adaptation with LoRA: Download a lightweight version of the model provided by the community (e.g., the 4-bit quantized version), and the video memory requirement can be reduced to 8GB
Note: It is recommended to turn off semantic layering when running with low configurations (remove the--labels_fg1/2parameters) and prioritizes the generation of simple scenes (e.g., small indoor spaces).
This answer comes from the articleHunyuanWorld-1.0: Generating Interactive 360° 3D Worlds from Text or ImagesThe
May not be reproduced without permission:AI productivity tools " How to solve the problem that HunyuanWorld-1.0 fails to generate due to insufficient video memory on normal GPU?
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