2024Multi-Modal Diffusion Model for Generative Media

CascadeGen

A unified multi-modal diffusion architecture generating high-fidelity images, video clips, and synthesized audio from cross-modal conditioning.

Source

Generative models are typically isolated by modality (image, video, or audio). CascadeGen investigates unified cross-modal generation by leveraging cascaded latent diffusion representations conditioned on multimodal embeddings.

Built in Python using PyTorch and modern transformer backbones, CascadeGen processes cross-modal inputs through joint embedding projections. The system applies progressive latent upsampling to synthesize high-resolution images, short video sequences, and matching acoustic waveforms conditioned on input text or reference imagery.

highlights

Unified latent diffusion pipeline supporting cross-modal text, image, and audio conditioning
Cascaded upscaling architecture ensuring crisp high-frequency detail generation
Modular PyTorch inference engine with optimized GPU memory footprint

built with

Python
PyTorch
Diffusion Models
Transformers
OpenCV
JavaScript

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