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Abstract Creating ultra‐high‐resolution spatially varying bidirectional reflectance functions (SVBRDFs) is critical for photorealistic 3D content creation, to faithfully represent fine‐scale surface details required for close‐up rendering. However, achieving 4K generation faces two key challenges: (1) the need to synthesize multiple reflectance maps at full resolution, which multiplies the pixel budget and imposes prohibitive memory and computational cost, and (2) the requirement to maintain strong pixellevel alignment across maps at 4K, which is particularly difficult when adapting pretrained models designed for the RGB image domain. We introduce HiMat, a diffusion‐based framework tailored for efficient and diverse 4K SVBRDF generation. To address the first challenge, HiMat generates in a high‐compression latent space via a DC‐AE and employs a pretrained diffusion transformer with linear attention to improve per‐map efficiency. To address the second challenge, we propose CrossStitch, a lightweight convolutional module that enforces cross‐map consistency without incurring the cost of global attention. Our experiments show that HiMat achieves high‐fidelity 4K SVBRDF generation with superior efficiency, structural consistency, and diversity compared to prior methods. Beyond materials, our framework also generalizes to related applications such as intrinsic decomposition.