DiffusionBlocks: Blockwise Training for Generative Models via
  Score-Based Diffusion
Training large neural networks with end-to-end backpropagation creates
significant memory bottlenecks, limiting accessibility to state-of-the-art AI
research. We propose DiffusionBlocks, a novel training framework
that interprets neural network blocks as performing denoising operations in a
continuous-time diffusion process. By partitioning the network into
independently trainable blocks and optimizing noise level assignments based on
equal cumulative probability mass, our approach achieves significant memory
efficiency while maintaining competitive performance compared to traditional
backpropagation in generative tasks. Experiments on image generation and
language modeling tasks demonstrate memory reduction proportional to the number
of blocks while achieving superior performance. DiffusionBlocks provides a
promising pathway for democratizing access to large-scale neural network
training with limited computational resources.
685208ce0164cd131671042d
Makoto Shing
extracted_confirmed
2025-06-18T00:32:08.632Z
60c2e7747a42b2edc5d2ccf7
https://cdn-avatars.huggingface.co/v1/production/uploads/60c2e7747a42b2edc5d2ccf7/FGlJt3Zd6NEcE-DKC3-dL.jpeg
Makoto Shing
user
mkshing
685208ce0164cd131671042e
Takuya Akiba
extracted_confirmed
2025-06-18T00:44:22.068Z
6482810dba6c556892f6f257
https://cdn-avatars.huggingface.co/v1/production/uploads/6482810dba6c556892f6f257/c7-wiVKenXiRtwnRpnjZN.jpeg
Takuya Akiba
user
iwiwi
DiffusionBlocks: Blockwise Training for Generative Models via
  Score-Based Diffusion
Training large neural networks with end-to-end backpropagation creates
significant memory bottlenecks, limiting accessibility to state-of-the-art AI
research. We propose DiffusionBlocks, a novel training framework
that interprets neural network blocks as performing denoising operations in a
continuous-time diffusion process. By partitioning the network into
independently trainable blocks and optimizing noise level assignments based on
equal cumulative probability mass, our approach achieves significant memory
efficiency while maintaining competitive performance compared to traditional
backpropagation in generative tasks. Experiments on image generation and
language modeling tasks demonstrate memory reduction proportional to the number
of blocks while achieving superior performance. DiffusionBlocks provides a
promising pathway for democratizing access to large-scale neural network
training with limited computational resources.
A novel training framework called DiffusionBlocks optimizes neural network blocks as denoising operations in a diffusion process, achieving memory efficiency and competitive performance in generative tasks.
DiffusionBlocks denoising operations continuous-time diffusion process independently trainable blocks equal cumulative probability mass memory efficiency generative tasks image generation language modeling