tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=torch.float16) text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=torch.float16).to("cuda") scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedul...
针对这一问题,中科院、北大和字节豆包大模型团队发布了 DetailCaps-4870 数据集,并提出了一种有效的评估指标 CAPTURE,取得了开源评估指标中最高的专家评价一致性,并低成本实现了与GPT-Eval可比的效果。 论文:https://arxiv.org/abs/2405.19092 数据集:https://huggingface.co/datasets/foundation-multimodal-models/De...
所谓的2d 的postiion embedding,其实就是一张图片的width和height这两个轴当作x,y轴,然后分别搞一个position embedding,这样每个patch就有对应两个position embedding了,只不过这里两个position embedding的size都是256/2 =128,用的时候concat成256维就完事了。感觉这种2d的position embedding更make sense吧,不知道为啥...
数据集:https://huggingface.co/datasets/foundation-multimodal-models/DetailCaps-4870 代码:https://github.com/foundation-multimodal-models/CAPTURE 简介 当前的 LVLM(large vision-language model)评测存在以下问题: 现有的 LVLM 评测方案主要采用 VQA 形式,很大程度受到指令遵循(instruction following)能力的影响,且 ...
model_args will be passed as kwargs through to models on creation. See example at https://huggingface.co/gaunernst/vit_base_patch16_1024_128.audiomae_as2m_ft_as20k/blob/main/config.json Usage: huggingface#2035 Updated imagenet eval and test set csv files with latest models vision_...
More flexible pos embedding resize (non-square) for ViT and TnT. ThanksAlexander Soare Addefficientnetv2_rw_mmodel and weights (started training before official code). 84.8 top-1, 53M params. Add EfficientNet-V2 official model defs w/ ported weights from officialTensorflow/Kerasimpl. ...
from diffusers import AutoencoderKL, UNet2DConditionModel, DDIMScheduler ## Helper functions def load_artifacts(): ''' A function to load all diffusion artifacts ''' vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="vae", torch_dtype=torch.float16).to("cuda...
The SAT was trained for 70 epochs with the batch size of 16, embedding dimension of 100, attention and decoder dimension of 512, dropout value 0.1. The encoder and decoder learning rates were \(4\times 10^{-7}\) and \(3\times 10^{-7}\), respectively. The Cross Entropy loss was ...
from huggingface_hub import create_repo, upload_folder from packaging import version from peft import LoraConfig from peft.utils import get_peft_model_state_dict from torchvision import transforms from tqdm.auto import tqdm from transformers import CLIPTextModel, CLIPTokenizer import diffusers ...
pipeline(管道)是huggingface transformers库中一种极简方式使用大模型推理的抽象,将所有大模型分为音频(Audio)、计算机视觉(Computer vision)、自然语言处理(NLP)、多模态(Multimodal)等4大类,28小类任务(tasks)。共计覆盖32万个模型 今天介绍CV计算机视觉的第三篇,图像分割(image-segmentation),在huggingface库内有800...