python main.py --base configs/latent-diffusion/<config_spec>.yaml -t --gpus 0 配置文件路径: https://github.com/CompVis/latent-diffusion/blob/main/models/ldm/bsr_sr/config.yaml 论文中测试数据集:ImageNet-Val 数据大小:64*64 -> 256*256 利用官方提供的预训练模型实现图像超分(inference部分):...
Super-Resolution with Latent Diffusion LDMs可以通过直接调节低分辨率的图像来有效地训练超分辨率任务。超分辨率实验遵循SR3,采用双三次插值退化将图像进行4倍下采样得到LR图,并按照SR3的数据处理管道在ImageNet上进行训练。采用在OpenImages(VQ-reg)上预训练的\{f=4\}自编码器得到LR的浅表示,并连接低分辨率条件y得...
By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) ac...
High-Resolution Image Synthesis with Latent Diffusion Models - GitHub - zhangxujinsh/latent-diffusion: High-Resolution Image Synthesis with Latent Diffusion Models
Real-world low-resolution (LR) videos have diverse and complex degradations, imposing great challenges on video super-resolution (VSR) algorithms to reproduce their high-resolution (HR) counterparts with high quality. Recently, the diffusion models have shown compelling performance in generating ...
The diffusion model-based MRI SR enhances the resolution of brain MR images, significantly improving diagnostic and prognostic accuracy for AD and MCI. Superresolved 3T* images closely matched actual 3T MRIs in quality and volumetric accuracy, and notably improved the prediction performance of ...
were generated by theDPT-Large depth estimation modelat a resolution of 384 × 384. These maps were converted into three-channel RGB-like arrays to match the input requirements of the Stable Diffusion model. This conversion involved unpacking the 16-bit depth data into three 8-bit...
Real-world low-resolution (LR) videos have diverse and complex degradations, imposing great challenges on video super-resolution (VSR) algorithms to reproduce their high-resolution (HR) counterparts with high quality. Recently, the diffusion models have shown compelling performance in generating realisti...
提出了Latent Diffusion Models(LDMs) 1、对比transformer-based的方法,该方法能够在压缩的空间(work on a compression level)对图像进行重建,生成比之前的方法更加可靠与详细的结果。并能应用于百万像素图像的高分辨率合成(high-resolution synthesis of megapixel images)。
LDM Super Resolution Pipeline Stable Diffusion SD v1 架构 SD v1.1 - v1.5 SD v2 SD 中的 Lora 相对于 DDIM, DDPM 以及 SDE,High-Resolution Image Synthesis with Latent Diffusion Models 一文重点在于 latent Space 和 Conditioning Cross Attention,而非 diffusion pipeline 流程。 以此不同于前几份笔记,...