Image2Video Error On RTX 5090: Troubleshooting

by Alex Johnson 47 views

Introduction: The Frustration of Image2Video Errors

Image2video generation using models like Image2video Enhanced Lightning v2 14B can be a thrilling experience, allowing you to breathe life into static images. However, when you encounter errors, such as the one described by our user, it can be incredibly frustrating. This guide delves into the common causes of this issue, provides potential solutions, and offers a path toward successfully generating videos on your RTX 5090.

The Problem: A Deep Dive into the Error Message

The user reports a RuntimeError with the message: "Expected max_shared_mem > 0 to be true, but got false." This error points towards a problem within the CUDA environment. The CUDA environment is how your GPU, in this case, the RTX 5090, communicates with the software, allowing for the rapid computations needed for video generation. Specifically, the error is occurring during a process called gptq_marlin_repack, which suggests an issue with how the model weights are being handled or loaded onto the GPU. This is often associated with the memory allocation or utilization of shared memory within the GPU's cores. It's often related to a lack of shared memory resources on the GPU or potential conflicts in how the model is attempting to access this shared memory.

Understanding the Context: Wan2GP and Your Setup

The user is employing Wan2GP, a specific environment or framework, likely built on top of the PyTorch deep learning framework, designed to facilitate the generation of videos from images or other input data. The reported pip list reveals a complex environment with numerous packages. It's essential to ensure compatibility between these packages, particularly PyTorch, CUDA, and any libraries related to model optimization (like optimum-quanto), to address the current error.

Troubleshooting Steps: Solutions to the Error

Step 1: CUDA and Driver Verification

  • Driver Compatibility: Ensure your NVIDIA drivers are up-to-date. Visit the NVIDIA website to download the latest drivers compatible with your RTX 5090 and CUDA version. Outdated drivers can lead to compatibility issues with the libraries used in Wan2GP.
  • CUDA Toolkit: Confirm the CUDA toolkit version. The error message may be related to a specific CUDA version. Check the compatibility of your CUDA toolkit with your PyTorch installation. You may need to reinstall PyTorch with the correct CUDA version. This ensures that the deep learning framework is correctly configured to leverage the GPU.

Step 2: Library Dependencies and Conflicts

  • Package Compatibility: Examine the pip list to check for conflicting package versions. Specifically, look at the versions of libraries like torch, torchvision, torchaudio, and any related to CUDA, such as nvidia-cuda-runtime-cu12 or nvidia-cudnn-cu12. Incompatible versions can cause runtime errors.
  • Reinstall Key Libraries: Sometimes, a clean install of critical libraries resolves such conflicts. Try uninstalling and reinstalling torch, torchvision, and torchaudio. Make sure to install the versions that are compatible with your CUDA and Python versions. This process ensures the fundamental components are installed properly.

Step 3: Memory and Hardware Considerations

  • GPU Memory: The RTX 5090 has significant memory, but complex models like Image2video Enhanced Lightning v2 14B can still strain it, particularly with large batch sizes or high-resolution outputs. Try reducing the batch size or image resolution to see if the error disappears. This will reduce the memory demand of the video generation process.
  • Shared Memory: The error specifically mentions shared memory. While you don't directly control shared memory allocation in most cases, ensure other applications aren't consuming excessive GPU resources. Close unnecessary programs running in the background to free up resources.

Step 4: Environment and Software Issues

  • Virtual Environment: Ensure that you are running Wan2GP in a clean, activated virtual environment. This prevents conflicts with other Python packages installed on your system. A clean environment ensures a more stable and isolated working space.
  • Model Loading: It's possible the model isn't loading correctly. Try downloading the model again, or verify its integrity. Corruption during download can cause similar errors. Make sure the model files are complete and not corrupted.

Detailed Examination of the Error Traceback

Decoding the Error Message

The traceback provides a detailed view of the error's origin. The issue stems from within the optimum-quanto library, specifically when attempting to pack the FP8 data into a format suitable for the GPU. This suggests a problem in the quantization process, possibly due to a version mismatch or an issue with the CUDA kernels used for FP8 operations. The