摘要：清华大学计算机系朱军教授带领的 TSAIL 团队提出 DPM-Solver（NeurIPS 2022 Oral，约前 1.7%）和 DPM-Solver++，将扩散模型的快速采样算法提升到了极致：无需额外训练，仅需 10 到 25 步就可以获得极高质量的采样。推荐：Stable Diffusion 采样速度翻倍！仅需 10 到 25 步的扩散模型采样算法。论文 6：AI...
摘要:本文提出了一种大批量训练算法 AGVM (Adaptive Gradient Variance Modulator),不仅可以适配于目标检测任务,同时可以适配各类分割任务。AGVM 可以把目标检测的训练批量大小扩大到 1536,帮助研究人员四分钟训练 Faster R-CNN,3.5 小时把 COCO 刷到 62.2 mAP,均打破了目标检测训练速度的世界纪录。论文被 NeurIPS 202...
第一个样图:魔法师在夜空中打出彩色『Stable Diffusion 3』 主要看点: 1,艺术字体的展示,STABLE中的字母A做了一个常见的艺术化处理; 2,魔法彩色能量效果; 提示词: Prompt:Epic anime artwork of a wizard atop a mountain at night casting a cosmic spell into the dark sky that says "Stable Diffusion 3...
https://developer.nvidia.com/blog/tensorrt-accelerates-stable-diffusion-nearly-2x-faster-with-8-bit-post-training-quantization/?=&linkId=100000248434902&fbclid=IwAR0tMbb8IzhKllj0r0PLp_do4W-P8ha0X_pxOkMxuk93Hcvgh-R-TiQ0cE4
PixArt系列的提出,在Sora和Stable Diffusion 3的基础上,进一步确认了Diffusion Transformer这一架构的有效性。但除此之外,笔者认为PixArt系列以efficiency作为其研究动机,很好地展示了一套从class-conditional models到text-to-image generation models的微调范式,对于后续研究有着不错的参考价值。 我是@叫我Alonzo就好了 ,...
diffusion_utils inflating: __MACOSX/paddlenlp/transformers/._skep inflating: __MACOSX/paddlenlp/transformers/._squeezebert inflating: __MACOSX/paddlenlp/transformers/._dallebart inflating: paddlenlp/transformers/tokenizer_utils_faster.py inflating: __MACOSX/paddlenlp/transformers/._tokenizer_utils_faster....
Faster DPM models (DPM-Solver and UniPC)更快的 DPM 模型( DPM-Solver 和 UniPC )Diffusion Probabilistic Models (DPM) are, as the name suggests, probabilistic. In each step, equations are not solved by deterministic numerical methods as in the case of Euler, Heun or LMS, but the problem ...
Reverse diffusion works by subtracting the predicted noise from the image successively.反向扩散的工作原理是连续从图像中减去预测的噪声。 You may notice we have no control over generating a cat or dog’s image. We will address this when we talk about conditioning. For now, image generation isun...
AGVM 可以把目标检测的训练批量大小扩大到 1536,帮助研究人员四分钟训练 Faster R-CNN,3.5 小时把 COCO 刷到 62.2 mAP,均打破了目标检测训练速度的世界纪录。论文被 NeurIPS 2022 接收。 详细对比 AGVM 和传统方法,体现出了本研究方法的优势。 推荐:四分钟内就能训练目标检测器,商汤基模型团队是怎么做到的?
In a previousblog post, we investigated how to make stable diffusion faster using TensorRT at inference time, here we will investigate how to make it even faster, using Memory Efficient Attention from thexformerslibrary. A few words about memory efficient attention ...