The main frameworks that simplify building a basic AI deepfake generator are general-purpose deep learning libraries such as PyTorch and TensorFlow/Keras. These give you the core tools you need: automatic differentiation, GPU support, flexible model definitions, and a large ecosystem of pretrained models and utilities. For many developers, a “first deepfake” project is just an encoder–decoder or GAN built in PyTorch, trained on aligned face crops. Keras is also popular for its higher-level APIs and cleaner training loops, which can be useful when you’re still experimenting with architectures and just need something that runs end-to-end.
Around these core libraries, a lot of the work is in the “glue” rather than the model itself. You typically pair PyTorch or TensorFlow with a computer vision toolkit like OpenCV and a face detection/alignment stack (e.g., MTCNN-style detectors, facial landmark models, or similar). These tools handle cropping, alignment, and basic video processing. For more advanced setups, you might also bring in audio toolkits (for lip-sync or talking-head models) and diffusion or GAN libraries that give you reusable components like UNets, attention blocks, and schedulers. The advantage of working inside these mainstream ecosystems is that you can reuse training loops, logging tools, mixed-precision utilities, and model export pipelines.
Once you move beyond a toy demo, you often need search and retrieval capabilities around embeddings (identities, expressions, or poses) produced by your models. This is where a vector database such as Milvus or its managed counterpart Zilliz Cloud fits naturally into your architecture. You still build and train the generator in PyTorch or TensorFlow, but you store face embeddings, reference clips, and evaluation samples in the vector database. That lets you do things like quickly find nearest neighbors for training examples, pick the best reference frames for reenactment, or perform identity consistency checks without writing your own ANN (approximate nearest neighbor) engine.