DiT使用了标准的ViT设计，把编码后的图像分成多个patch token输入到transformer中建模。
SDXL最大的改变是架构变更为和Sora一样的Diffusion Transformer，可预见的是，在自然语言理解上肯定更好...
The resulting models are also quite large, which makes it challenging toidentify the source of biasor inaccurate results. "Their complexity can also make it difficult to interpret their inner workings, hindering their explainability and transparency," Masood said. Image of a transformer model ar...
Application-oriented comparison of VAE, GAN, Transformer, and Diffusion model is summarized based on the previous introduction [6–9,62] as shown in Table 2. Noted that for generative models, there is a common disadvantage, that is, difficult to evaluate empirically. Among the Pros and Cons,...
You may have noticed that most of the findings are generic and not tied to the ADM architecture specifically—or even diffusion, for that matter. This is indeed an interesting avenue for future investigation. The principles could also be directly applied to, for example, diffusion transformer trai...
It uses large transformer language models for text encoding and achieves high-fidelity image generation. Imagen has been noted for its high FID score, indicating its effectiveness in producing images that closely align with human-rated quality and text-image alignment. Omnigen Omnigen...
FORA introduces simple yet effective caching mechanism in Diffusion Transformer Architecture for faster inference sampling. - prathebaselva/FORA
Densely connected convolutional transformer for single image dehazing 2023, Journal of Visual Communication and Image Representation Citation Excerpt : Similarly, [14] is also unable to handle non-homogeneous haze conditions. The recent growth of deep learning techniques and large scale training datasets ...
For companies looking to accelerate their Transformer models inference, our new 🤗 Infinity product offers a plug-and-play containerized solution, achieving down to 1ms latency on GPU and 2ms on Intel Xeon Ice Lake CPUs. If you found this post interesting or useful to ...
如图2b所示,这相当于对每种模态有两个独立的transformer,但将两种模态的序列合并用于注意力操作,使得两种表示可以在自己的空间中工作,同时考虑到另一种。 \quad 模型规模参数化: 在规模实验中,通过设置隐藏尺寸为 64 · d(在MLP块中扩展到 4 · 64 · d 通道)和注意力头的数量等于 d,按照模型深度 d(即...