explainingai-code/StableDiffusion-PyTorch

Supports multiple conditioning mechanisms—class labels, text (via CLIP embeddings), and semantic masks—either independently or in combination, operating on latent space via VQVAE compression. Uses DDPM with linear scheduling for the diffusion process and provides modular training pipelines for autoencoders and diffusion models across MNIST and CelebHQ datasets, with configuration-driven setup for custom data.

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