两位专家,Dr. Pixel(一位著名的计算机视觉专家)和 Dr. Token(一位语言模型和变压器的专家),围坐在桌旁,讨论最近关于“Llama for Scalable Image Generation”的论文。 Dr. Pixel: (看着论文) Dr. Token,我一直在阅读这篇关于“Llama for Scalable Image Generation”的有趣论文。他
device) model_kwargs = dict(c=z) loss_dict = self.train_diffusion.training_losses( self.net, target, t, model_kwargs) loss = loss_dict["loss"] # 仅取 masked tokens 所对应的 loss if mask is not None: loss = (loss * mask).sum() / mask.sum() return loss.mean() 以上的 self...
Autoregressive Model Beats Diffusion: 🦙 Llama for Scalable Image Generation - FoundationVision/LlamaGen
denoising_diffusion_nullspace_model_ddnm__method_explained 6 0 10:46 App gligen_cvpr2023_openset_grounded_texttoimage_generation 930 0 06:37 App freeu__paper_explained 0 0 09:44 App vpd_iccv2023_unleashing_texttoimage_diffusion_models_for_visual_perception 2 0 11:39 App nulltext_inv...
Multiple experts diagnosed each image as plus, pre-plus, or normal, and a consensus diagnosis was formed. Plus disease images only accounted for ~10% of the dataset, so downsampling was used to prevent overfitting the model to classes with higher representation. The final dataset consisted of ...
After you download the pretrained checkpoints for T2I generation, opennotebooks/T2I_sampling.ipynband follows the instructions in the notebook file. We recommend to use a GPU such as NVIDIA V100 or A100, which has enough memory size over 32GB, considering the model size. ...
《Cascaded Diffusion Models for High Fidelity Image Generation》 回到Stable Diffusion本身,我们先看一下没有两种World划分的Diffusion Model是怎么训练的。 首先我们生成一堆的随机噪声,然后随机选择其中一种噪声,添加到图片上。这样就得到了一组数据集:
ImageNet-256 generation benchmark, our base model, xAR-B (172M), outperforms DiT-XL/SiT-XL (675M) while achieving 20×faster inference. Meanwhile, xAR-H sets a new state-of-the-art with an FID of 1.24, running 2.2×faster than the previous best-performing model without relying on ...
The autoregressive generation phase generates the remaining new tokens sequentially. At iteration 𝑡, the model takes one token 𝑥𝑛+𝑡 as input and computes the probability 𝑃 (𝑥𝑛+𝑡+1 | 𝑥1, . . . , 𝑥𝑛+𝑡) with the key vectors 𝑘1, . . . , 𝑘𝑛+𝑡...
Previous works studied image in-context learning, urging models to generate a single image guided by demonstrations. In this project, we propose and study video in-context learning, where the model starts from an existing video clip and generates diverse potential future sequences, each semantically...