【1】High-Resolution Image Synthesis with Latent Diffusion Models. 【2】Code:github.com/CompVis/late 【3】更多的细节可参考:here 推荐阅读 wei12580:Text-to-Image图像生成系列之OpenAI的CLIP wei12580:Text-to-Image图像生成系列之ControlNet wei12580:Text-to-Image图像生成系列之LoRA理论篇 [ICLR2022] wei...
Text-to-Image图像生成系列之OpenAI的CLIP 具体论文参见:Learning Transferable Visual Models From Natural Language Supervision,codeCLIP的全称是Contrastive Language Image Pre-training 引言 直接从原始文本学习的预训练方法,已经在NLP领域大放光彩很多… 阅读全文 ...
(引自:Explaining the code of the popular text-to-image algorithm (VQGAN+CLIP in PyTorch) | by Alexa Steinbrück | Medium) z的解释: 图像生成(forward pass)和反向传播(backward pass)(二者均属于推理阶段,后面会细说): 注意“backpropagate through CLIP and VQGAN all the way back to latent vecto...
StackGAN简介 StackGAN具有两个GAN堆叠在一起形成了一个能够生成高分辨率图像的网络。它分为两个阶段,Stage-I和Stage-II。 Stage-I网络生成具有基本颜色和粗略草图的低分辨率图像,并以文本嵌入为条件,而Stage-II网络获取由Stage-I网络生成的图像并生成以文本嵌入为条件的高分辨率图像。基本上,第二个网络可以纠正缺陷并...
ImageReward 主要细节 数据集收集(Data Collection) RM 模型如何训练(RM Training) ImageReward 的实验结果 一些想法 简介 在RLHF 技术笔记我们介绍了在语言模型中训练 RM 模型来判断模型生成的结果如何。本文,ImageReward,作者主要对 text-to-image 领域提出了一个 reward model,从而可以对根据 text 生成的 Image ...
Recently, text-to-image synthesis has achieved great progresses with the advancement of the Generative Adversarial Network (GAN). However, training the GAN models requires a large amount of pairwise image-text data, which is extremely labor-intensive to collect. In this paper, we make the first...
ofa-sys/ofa • • 7 Feb 2022 In this work, we pursue a unified paradigm for multimodal pretraining to break the scaffolds of complex task/modality-specific customization. 4 Paper Code A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces dome...
image_sizeint128Batch size to use during training. gf_dimint64Number of conv filters in the first layer of the generator. df_dimint64Number of conv filters in the first layer of the discriminator. caption_vector_lengthint4800Length of the caption vector embedding (vector generated using skip-th...
efficient network architecture and improving step distillation. Specifically, we propose an efficient UNet by identifying the redundancy of the original model and reducing the computation of the image decoder via data distillation. Further, we enhance the step distillation by exploring training strategies ...
The pre-trained model tarballs have been pre-downloaded from Hugging Face and saved with the appropriate model signature in Amazon Simple Storage Service (Amazon S3) buckets, such that the training job runs in network isolation. See the following code: from sagemaker...