Great work! I can't wait to use TeaCache to accelerate diffusion models.

I'm currently trying to integrate TeaCache with Ruyi-Models. However, I think I might have made a mistake, as I'm not getting a good L1 difference visualization according to the paper. Here are the results I obtained.

• Time Embs is the original embeddings of the timestep.
• Time With Conditions is the timestep embeddings added with condition embeddings (such as text and image).
• Time Modulated Inputs is the values after transformer.block[0].norm1.
• Transformer Inputs is the input of transformer.
• Transformer Outputs is the output just after all transformer blocks, before final layer.
• Transformer Final Outputs is the final output of transformer.

I've tried several timestep embeddings and model outputs, but it seems that the timestep embeddings don't have a strong correlation with Ruyi's model output. Could you help me identify which timestep embedding and model output should be used to achieve better correlation? Thank you!