Stable Fine-Grained Image Synthesis with Gradient-Constrained Discriminators and Hierarchical Adaptive Sampling

Recent advancements in conditional generative adversarial networks (GANs) have enabled fine-grained control over image attributes, yet critical challenges remain in long-tailed scenarios: unstable training dynamics from unbounded discriminator gradients and severe attribute entanglement caused by imbalanced distributions. A dual-mechanism framework integrating gradient-constrained discriminators and adaptive semantic balancing is proposed. First, a dynamic gradient penalty based on Lipschitz continuity constraints is developed to stabilize training by enforcing topological consistency between real and generated data manifolds. Second, a hierarchical conditional sampling strategy adaptively re-weights latent space distributions to mitigate local mode overfitting in imbalanced categories. Theoretical analysis reveals that unbounded discriminator gradients in traditional ACGANs lead to pattern degradation, while the proposed gradient constraints provably suppress this. Experiments on AnimeFace and CelebA-HQ demonstrate enhanced semantic controllability and visual consistency, particularly for rare attribute combinations. This work establishes a unified framework for stable fine-grained generation, advancing theoretical foundations in conditional GANs and practical applications in virtual content creation.