Insight for: 参考图加噪问题

This issue questions the rationale behind adding noise to the reference image `x0` during Stage 1 training in Helios, alongside noise application to history. This indicates a developer seeking deeper understanding of the model's training methodology and its impact on video generation quality. For B2B SaaS offering AI models, transparency in training procedures is vital for users to effectively fine-tune, debug, and optimize model performance for their specific applications. Unexplained design choices can create friction and reduce confidence. Providing clear explanations for such technical decisions enhances the perceived robustness and reliability of the model, fostering greater adoption and trust within the developer community.