ComfyUI Node: ApplyTeaCachePatch

ApplyTeaCachePatch:

The ApplyTeaCachePatch node is designed to enhance the performance of specific AI models by applying the TeaCache patch, which accelerates the model's processing capabilities. This node is particularly effective when used in conjunction with nodes that have the suffix ForwardOverrider. It is specifically tailored for models such as Flux, HunYuanVideo, LTXVideo, WanVideo, and MochiVideo. By optimizing the model's execution, the TeaCache patch helps in reducing computational overhead, thereby speeding up the processing time without compromising the quality of the output. This makes it an invaluable tool for AI artists looking to improve the efficiency of their workflows, especially when dealing with complex video models.

ApplyTeaCachePatch Input Parameters:

model

The model parameter represents the AI model to which the TeaCache patch will be applied. It is crucial as it determines the specific model that will benefit from the performance enhancements provided by the patch. This parameter does not have a default value as it requires the user to specify the model they are working with.

rel_l1_thresh

The rel_l1_thresh parameter is a threshold value that influences the patch's application by determining the level of relative L1 norm thresholding. This parameter helps in controlling the sensitivity of the patch application, impacting how aggressively the model's performance is optimized. The specific range and default value are not provided, but it is essential for fine-tuning the patch's effectiveness.

cache_device

The cache_device parameter specifies the device used for caching during the patch application. By default, it is set to "offload_device", which indicates that the caching process will be offloaded to a secondary device, potentially freeing up resources on the primary device and enhancing performance.

wan_coefficients

The wan_coefficients parameter is an option that affects the stability of the initial steps in the WanVideo model. When set to "disabled", it may lead to instability in the first few steps, but it can also optimize performance under certain conditions. This parameter allows users to balance between stability and performance based on their specific needs.

ApplyTeaCachePatch Output Parameters:

model

The output model is the same AI model provided as input, but with the TeaCache patch applied. This patched model is optimized for faster execution, making it more efficient for processing tasks. The output model retains its original functionality while benefiting from the performance enhancements introduced by the patch.

ApplyTeaCachePatch Usage Tips:

• Ensure that the model you are applying the TeaCache patch to is one of the supported types: Flux, HunYuanVideo, LTXVideo, WanVideo, or MochiVideo, as the patch is specifically designed for these models.
• Experiment with the rel_l1_thresh parameter to find the optimal threshold that balances performance improvement with the quality of the output. Adjusting this parameter can significantly impact the effectiveness of the patch.

ApplyTeaCachePatch Common Errors and Solutions:

TeaCache patch is not applied because the model is not supported.

• Explanation: This error occurs when the model provided is not one of the supported types for the TeaCache patch.
• Solution: Verify that the model you are using is either Flux, HunYuanVideo, LTXVideo, WanVideo, or MochiVideo. If not, consider using a supported model to apply the patch.

Unstable results in the first few steps when wan_coefficients is disabled.

• Explanation: Disabling wan_coefficients can lead to instability in the initial steps of the WanVideo model.
• Solution: If stability is a concern, consider enabling wan_coefficients or adjusting the rel_l1_thresh parameter to mitigate instability while maintaining performance improvements.