We propose a text-guided variational image generation method to address the challenge of getting clean data for anomaly detection in industrial manufacturing. Our method utilizes text information about the target object, learned from extensive text library documents, to generate non-defective data images...
Text generation is the process of generating text by an AI system that resembles human-written text patterns and styles. Image generation is the task of creating realistic images from scratch or based on an input dataset. They have become increasingly popular as these generators offer a novel way...
For translation between unseen domains, the latent generation is also done by forward DDPM [23] process as will be explained later. Figure 2: Overview of DiffusionCLIP. The input image is first converted to the latent via diffusion models. Then, guided by directional CLIP loss, the diffusion ...
CONFORM: Contrast is All You Need for High-Fidelity Text-to-Image Diffusion Models Contrastive Denoising Score for Text-guided Latent Diffusion Image Editing Residual Learning in Diffusion Models SwiftBrush: One-Step Text-to-Image Diffusion Model with Variational Score Distillation DreamMatcher: Appearanc...
VQGAN-CLIP [9, 10, 45] leverage CLIP for text-guided image generation. Concurrent work uses CLIP to fine-tune a pre-trained StyleGAN [12], and for image stylization [6]. Another concurrent work uses the ShapeNet dataset [5] and CLIP to perform unconditional 3D voxel generatio...
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models 尾巴 背景 在AI应用领域,图像是业界公认最内卷的方向之一,以至于出现很多硕博同学花了几年时光,刚基于当时的SOTA(State Of The Art,业内用于表示“效果最好的方法”),取得了一丢丢微弱的提升,论文都还没写完,某个大佬...
X-Dreamer 的关键组成部分是两种创新设计: Camera-Guided Low-Rank Adaptation (CG-LoRA) 和 Attention-Mask Alignment (AMA) 损失。 首先,现有方法 [7,8,9,10] 通常采用 2D 预训练扩散模型 [5,12] 来进行 text-to-3D 生成,缺乏与相机参数的固有联系。为了解决此限制并确保 X-Dreamer 产生直接受相机参数影...
本期内容将进行文生图(Text-to-Image)方向的主要论文解读。 变分自编码器 VAE (Variational Auto-Encoder) 论文解读 自编码器 (Auto-Encoder) 架构 自编码器(Auto-Encoder)是一种无监督学习的神经网络,用于学习输入数据的压缩表示。具体而言,可以将其分为两个部分: ...
所以鉴于此,在这里我准备先把这个图像生成这块之前的一些工作先大概介绍一下,非常简略的从刚开始的这个 Gan 模型,然后还有 auto encoder, variational auto encoder 就是 VAE 这一系列的工作。然后再到最新的这个 diffusion model 扩散模型以及它的一系列后续工作。
Hierarchical Text-Conditional Image Generation with CLIP Latents 是一种层级式的基于CLIP特征的根据文本生成图像模型。 层级式的意思是说在图像生成时,先生成64*64再生成256*256,最终生成令人叹为观止的1024*1024的高清大图。 DALLE·2模型根据CLIP的文本特征和图像特征最终生成图像,可以看做CLIP的反向过程,因此DALLE...