Method 首先作者团队分析了,text token embedding和LoRA weights在概念学习上的不同倾向。如下图所示: 图4 相关结论如下: 根据a,b列,可以发现Textual Inversion和P+的token embeding倾向于学习in-domain的concept,而对于没见过的concepts,则无能为力。 根据c,d列,可以发现toke
3)视觉分词器(Visual Tokenizer):一方面,将图像转换为一系列的token的一种简单方法是将每个图像分割成一系列的patch,并且然后将每个patch映射到一个连续的嵌入中,例如在Fuyu中采用的方法。另一方面,受语言模型启发,在每个单词由离散词典进行分词的情况下,也有一系列的工作将图像转换为离散的token。典型的视觉词典包括VQ...
First, we adopt a novel multi-word textual inversion technique to extract a detailed text description capturing the image's characteristics. Then, we use this description and the image to generate a 3D model with FlexiCubes. Additionally, MTFusion enhances FlexiCubes by employing a special decoder...
Textual Inver- sion optimizes a new V∗ token for each new concept. We also compare with the competitive baseline of Custom Diffusion (w/ fine-tune all), where we fine-tune all the parameters in the U-Net [58] diffusion model, along with the V∗ token em- bedding ...
The diffuser training code is modified from the followingDreamBooth,Textual Inversiontraining scripts. For more details on how to setup accelarate please referhere. Fine-tuning on human faces For fine-tuning on human faces, we recommendlearning_rate=5e-6andmax_train_steps=750in the above diffuser...
Finally, you can use the special token within !HIGHRES_PROMPT to reference the original/main prompt. Useful if you want to add to the original prompt in some way. !HIGHRES_PROMPT = <prompt>, highly detailed, 8k Set to nothing to clear it (if you don't set anything here and use ...
Transformers在自然语言处理任务中取得了先进的性能,这推动了大型语言模型(Large Language Models, LLMs)的发展,它们通过在大量token上预训练Transformer架构来学习语言的一般统计特性。 Dosovitskiy等人介绍了将Transformers应用于图像任务的Vision Transformer (ViT),它将图像转换为图像块的序列表示,可以由Transformers处理。
Awesome-Biomolecule-Language-Cross-Modeling: a curated list of resources for paper "Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey" - QizhiPei/Awesome-Biomolecule-Language-Cross-Modeling
We exploit the pre-trained Latent Diffusion Model (1.4B parameters) trained on the LAION-400M dataset [54] and follow the same procedure as the Textual Inversion. We set the model’s hyperparameters to an image resolution of (512,512), a batch size of 4, gradient accumulation steps of ...