A method and device for image generation are provided. The method includes: obtaining a text describing a content of an image to be generated; extracting, using a text encoder, a text feature vector from the text; determining a semantic mask as spatial constraints of the image to be ...
The goal of text-based image generation is to create premium and authentic images conditioned on specific textual descriptions. The main challenges in this assignment include improving image quality and achieving a deep integration between the image and textual information. To address these issues, this...
InstaFlow通过ReFlow+蒸馏的方法,做到了将Stable Diffusion的N步扩散压缩到了一步,大幅提升了SD模型的生成速度。 InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation 这篇文章是作者ICLR2023:Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Fl...
Text-to-image generation is a comprehensive task that combines the fields of Computer Vision (CV) and Natural Language Processing (NLP). Research on the methods of text to image based on Generative Adversarial Networks (GANs) continues to grow in popular
Text-to-image generation: MidJourney can generate images based on textual prompts. Image captioning: MidJourney can add natural language captions to images. Describe feature: Users can upload an image to the MidJourney AI and receive four different textual descriptions of the image. ...
First, because text data is discrete, the image domain adversarial example generation methods cannot be directly applied to it. Second, while perturbations in images are small changes in pixel values that are difficult to detect with the human eye, small perturbations for textual adversarial examples...
ChenyangQiQi / FateZero Public Notifications You must be signed in to change notification settings Fork 108 Star 1.1k [ICCV 2023 Oral] "FateZero: Fusing Attentions for Zero-shot Text-based Video Editing" fate-zero-edit.github.io/ </...
Image generation、Gallium nitride、Generators、Visualization、Image resolution、Semantics、Feature extraction 三、为什么提出CookGAN? 生成性对抗网络(GAN)自在T2I领域应用以来,在解决照片真实感质量和语义一致性问题方面取得了许多进展。虽然这两个方面都强调图像质量,但忽略了:图像生成中的因果视觉场景。
[20] in the fusion module. The perceptual loss can guide the reconstruction of image style, while the texture loss can guide the generation of image textures. The perceptual loss is the L1 loss of the feature mapsϕiextracted from five ReLU layers of the VGG-19 model, whereϕi...
Nichol, A. et al. GLIDE: towards photorealistic image generation and editing with text-guided diffusion models. InInternational Conference on Machine LearningVol. 162, 16784–16804 (PMLR, 2022). Ramesh, A., Dhariwal, P., Nichol, A., Chu, C. & Chen, M. Hierarchical text-conditional image...