IgGM is a generative foundation model for antibody and nanobody design, implemented as a deep learning library with a command-line interface. The system performs tasks such as CDR loop design, framework region redesign (humanization), inverse design from complex structures, affinity maturation, and structure prediction. It uses a diffusion-based approach to co-design amino acid sequences and 3D backbone structures, leveraging an ESM2-based protein language model, Evoformer blocks with triangular attention, invariant point attention, and a structure module similar to AlphaFold2. The pipeline is orchestrated by the `AbDesigner` class and invoked via the `design.py` script, accepting PDB and FASTA inputs and generating output sequences and structures. The repository includes well-structured reusable components for rigid-body transformations (Rotation, Rigid), PDB parsing, coordinate conversion, and diffusion schedules, though some research artifacts (misspellings, commented code) and dependency pinning to older PyTorch/CUDA versions indicate moderate technical drift.