Antibody-drug conjugates (ADCs) are innovative cancer therapies composed of three main components: a monoclonal antibody, payload, and linker. The linker plays a crucial role in ADC pharmacokinetics and payload release. Current challenges in linker design stem from the need to balance complex requirements, including stability, controlled release, toxicity profile, biological context, linker length, and steric hindrance. Leveraging computational tools, particularly generative models, offers a path to explore chemical space and design optimized linkers rapidly. This work introduces a diffusion model capable of producing novel linker structures. Critically, the model considers the context of the specific antibody and payload comprising the ADC, encoding these components to guide linker generation. Evaluation from ProTox-3.0 and SwissADME demonstrates that the generated linkers maintain favorable toxicity and drug-like properties, comparing favorably to a baseline generative model and existing linkers. These findings suggest that diffusion models can accelerate ADC linker design by integrating domain-specific constraints with conditional generation.