Can someone give me the lowdown on pruned models vs full . . . Pruned models- smaller and faster, think of them like compressed music files, fine for generating images where the loss of detail is not noticeable Full size models are only needed for training purposes, where the added level of detail can make a bigger difference, you are unlikely to notice major differences in using them for image generation
[2506. 10035v1] FastFLUX: Pruning FLUX with Block-wise . . . Recent advancements in text-to-image (T2I) generation have led to the emergence of highly expressive models such as diffusion transformers (DiTs), exemplified by FLUX However, their massive parameter sizes lead to slow inference, high memory usage, and poor deployability Existing acceleration methods (e g , single-step distillation and attention pruning) often suffer from significant
Pruned vs Full Model: Understanding the Trade-offs in Machine . . . Studies have demonstrated that pruning can remove 90% or more of a model’s parameters while losing only 1-2% accuracy in many cases This phenomenon, known as the “lottery ticket hypothesis,” suggests that dense networks contain sparse subnetworks that can perform comparably to the full model
Neural Network Pruning: How to Accelerate Inference with . . . Introduction In this post, I will demonstrate how to use pruning to significantly reduce a model’s size and latency while maintaining minimal accuracy loss In the example, we achieve a 90% reduction in model size and 5 5x faster inference time, all while preserving the same level of accuracy
A comprehensive review of network pruning based on pruning . . . Most pruning algorithms require a trained original model, these pruning methods can only reduce the inference time of the model, but if you can prune the model before training, you can train the super large model that the existing GPU cannot compute, and you can also reduce the training cost by using sparse matrix operations