CLIP的模型结构其实非常简单:包括两个部分,即文本编码器(Text Encoder)和图像编码器(Image Encoder)。Text Encoder选择的是Text Transformer模型;Image Encoder选择了两种模型,一是基于CNN的ResNet(对比了不同层数的ResNet),二是基于Transformer的ViT。 CLIP在文本-图像对数据上的训练过程训练过程 训练过程 CLIP在文本-...
Text Encoder是一个transformer结构; t是一个learnable parameter,初始化为nn.Parameter(torch.ones([])∗np.log(1/0.07)),作用是避免logits超过100,从而避免训练过程的不稳定。 图3、Zero-shot的CLIP在很多数据集上表现都超过完全supervised的baseline。 包括在zero-shot,supervised,representation learning,feature的...
虚线以下部分表示利用CLIP的text encoder生成图片的过程,在获取输入文本描述的text embedding之后,将其输入一个prior(autoregressive or diffusion),来获取image embedding,然后将image embedding送入diffusion model(decoder,改进版GLIDE)来生成图像。 prior网络的训练过程,对一个图片文本对 和已经训练好的CLIP模型(text enco...
Image Encoder 作者使用四个Stage的ResNet进行特征提取,其中Fv2∈RH8×W8×C2Fv2∈RH8×W8×C2,..,Fv4∈RH32×W32×C4Fv4∈RH32×W32×C4。 Text Encoder 对于输入T∈RLT∈RL,通过Transfomer提取到的特征为Ft∈RL×CFt∈RL×C。全局文本表示Fs∈RC′Fs∈RC′。其中C,C′C,C′是特征维度,LL是referring ...
UnCLIP的image/text embedding是使用CLIP encoder提取的,CLIP encoder是用(image, text)的对比学习训练出来的,两种embedding天然就在同一个latent space LDM是图像encoder是通过AE的图像重建Loss训练出来的,text embedding是通过外挂language model提取,DM Prior学习过程还得兼顾将两种embedding空间对齐。
an image encoder that will embed (smash) images into mathematical space. Whenever you fit a supervised learning model, you have to find some way to measure the "goodness" or the "badness" of that model – the goal is to fit a model that is as "most good" and "least bad" as possibl...
In the second stage, the ID-specific text tokens and their encoder become static, providing constraints for fine-tuning the image encoder. With the help of the designed loss in the downstream task, the image encoder is able to represent data as vectors in the feature embedding accurately. The...
Contrastive pretraining of image-text foundation models, such as CLIP, demonstrated excellent zero-shot performance and improved robustness on a wide range of downstream tasks. However, these models utilize large transformer-based encoders with significant memory and latency overhead which pose challenges...
return vae, unet, tokenizer, text_encoder, scheduler def load_image(p): ''' Function to load images from a defined path ''' return Image.open(p).convert('RGB').resize((512,512)) def pil_to_latents(image): ''' Function to convert image to latents ...
Then, given two quality-related antonym prompts, we fine-tune the CLIP image encoder by ranking the similarity between the prompts and the images, according to their corresponding level of degradation. At the same time, for each pair of equally distorted crops, we force the similarity between ...