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xinntao / Real-ESRGAN

Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
BSD 3-Clause "New" or "Revised" License
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Enhancement of Memory Management for Large-Scale Image Processing in Real-ESRGAN #735

Open yihong1120 opened 11 months ago

yihong1120 commented 11 months ago

Dear Real-ESRGAN Contributors,

I hope this message finds you well. I am reaching out to discuss a potential enhancement to the Real-ESRGAN project, specifically regarding the optimisation of memory usage during large-scale image processing tasks.

As you are aware, Real-ESRGAN has proven to be an invaluable tool for image and video restoration, offering remarkable improvements in visual quality. However, when dealing with high-resolution images or processing a substantial number of images in batch, users may encounter limitations due to memory constraints, particularly on systems with limited RAM.

To address this, I propose the following enhancements:

  1. Dynamic Memory Allocation: Implement a more dynamic memory management system that can adapt to the available system resources. This could involve the use of memory-mapped files for temporary storage of intermediate data, thereby reducing the RAM footprint.

  2. Efficient Batch Processing: Introduce a more memory-efficient batch processing mechanism that can handle large datasets by processing images in smaller, manageable batches and freeing up memory after each batch is completed.

  3. Memory Profiling Tools: Incorporate memory profiling tools within the Real-ESRGAN toolkit to allow users to monitor memory usage in real-time. This would enable users to optimise their workflows based on the insights provided by these tools.

  4. Optimisation Guidelines: Provide documentation and guidelines on best practices for memory optimisation when using Real-ESRGAN. This could include tips on image pre-processing, system configuration, and the use of specific command-line arguments to reduce memory usage.

  5. Enhanced Tiling Support: Improve the existing tiling feature to reduce memory consumption without compromising the consistency of the output images, especially when dealing with large images that require tiling to fit into memory.

I believe these enhancements will make Real-ESRGAN more accessible to users with varying system capabilities and will further solidify its position as a leading solution for image and video enhancement.

I look forward to your thoughts on this matter and am eager to contribute to the discussion and development of these features.

Best regards, yihong1120

TerryBrand231 commented 10 months ago

I agree and support this. And, the Real-ESRGAN repository does not seem to be actively maintained.