Categories: Image

Training Diffusion Models with micro-budget (8xH100) in only 3 days!

🧵Scaling up generative models is crucial to unlock new capabilities. But scaling down is equally necessary to democratize the end-to-end development of generative models.

Excited to share our new work on scaling down diffusion generative models by drastically reducing the overhead of training them from scratch.

Now anyone can train a stable-diffusion quality model from scratch in just $2,000 (2.6 training days on a single 8xH100 node).

arxiv.org/abs/2407.15811

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Video generation models as world simulators

We explore large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of variable durations, resolutions and aspect ratios. We leverage a transformer architecture that operates on spacetime patches of video and image latent codes. Our largest model, Sora, is capable…

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Discrete diffusion models have emerged as strong alternatives to autoregressive language models, with recent work initializing and finetuning a base unimodal model for bi-modal generation. Diverging from previous approaches, we introduce the first tri-modal Masked Diffusion Models (MDM) pretrained from scratch on text, image-text, and audio-text data. We systematically analyze…

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