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Continuous-time diffusion models have demonstrated strong capabilities for modeling complex data distributions across various domains. However, these models often encounter gradient conflicts, where parameter updates at different timesteps interfere with each other, hindering effective training. To provide theoretical insights into this phenomenon, we introduce Delta Loss Lower Bound (DELLBO), a tractable lower bound on the reduction in the integrated training loss. Through mathematical analysis of DELLBO, we derive the optimal parameter update direction as the opposite of the vector from the origin to the nearest point in the convex hull of per-timestep gradient vectors. Building on this insight, we propose Self-adaptive Gradient Conflict Mitigator (SGCM), a simple yet effective method that adjusts parameter update directions during training. The results confirm that SGCM behaves consistently with our theoretical analysis and demonstrate its effectiveness for speech enhancement.