Graphics Memory Optimization Strategies for High Resolution Image Processing
Multi-level solution for video memory problems that can be caused by 1344×1344 high-resolution images:
- basic program: force gradient_checkpointing to be enabled (set use_checkpointing=True in load())
- Intermediate Program: automatic image chunking (modify tile_size parameter of predict() method)
- Advanced Programs: Use model parallelism (requires 2 GPUs, configure device_map='auto')
Typical configuration code:
from cogvlm2 import CogVLM2
# Secure Load Mode
model = CogVLM2.load(
'image_model',
use_checkpointing=True, # Save 30% Memory
max_image_size=1024 # Limit input size
)
# Block Processing Big Picture
result = model.predict(
'big_image.jpg',
tile_size=512, # chunk size
overlap=64 # Overlapping pixels between blocks
)
Handling of extreme situations: When the image exceeds 2048×2048, it is recommended to 1) use the TiledVLM extension component 2) convert to cloud API calls 3) use LANCZOS resampling for quality reduction during preprocessing.
This answer comes from the articleCogVLM2: Open Source Multimodal Modeling with Support for Video Comprehension and Multi-Round DialogueThe
