步骤3. 进入像素编码器: We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human ...
Paper: 《High-Resolution Image Synthesis with Latent Diffusion Models》 Code: GitHub - CompVis/latent-diffusion: High-Resolution Image Synthesis with Latent Diffusion Models Motivation 虽然Diffusion models取得了很好的生成效果,但是它的计算量非常大,训练和推理都非常耗时。Latent difussion model通过在latent ...
Figure 1. Overview of the LDM3D model architecture. LDM3D LDM3D is a state-of-the-art diffusion model with 1.6 billion parameters, derived from Stable Diffusion v1.4 but tailored to generate images and depth maps concurrently from textual input. It employs a variational autoencoder...
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...
Model architecture: Kandinsky 2.1 inherits best practicies from Dall-E 2 and Latent diffusion, while introducing some new ideas. As text and image encoder it uses CLIP model and diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach increases the visual performance...
So at the highest level, we can simply think of a diffusion model as a neural network that generates things. As we've come to be familiar with, the "things" that we all are most interested in using diffusion models for currently are images. We'll later cover the network architecture ...
In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and spatial downsampling, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn ...
First, using the exact same architecture as for our Video LDM, we apply our temporal finetuning strategy to a pre-trained pixel-space image diffusion model, which is clearly outperformed by ours. Further, we train an End-to-End LDM, whose entire ...
Method(Model) Architecture & Scale SDXL 用的 UNet 是之前的三倍大,模型参数的增长主要是由于更多的注意力block和更大的交叉注意力 图1 左边是SDXL的用户满意度超过了 SD1.5, SD2.1(2.1还不如1.5啊,难怪c站一大堆模型都是1.5的) 右边是SDXL的两阶段pipeline,在基础模型之后多了一个refiner。先用SDXL生成...
In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn ...