Janus的图像生成编码器和解码器都是由若干ResnetBlock和AttnBlock堆叠而成的,编码器还有下采样层,缩小图像尺寸,使用的是kernel size=3,stride=2的二维卷积实现。解码器有上采样层,还原图像尺寸,使用torch的interpolate接口进行插值操作。 ResnetBlock的代码如下所示,为一堆二维卷积的堆叠操作: class ResnetBlock(nn....
c. 通过 ˆΨ 对 {Jj_t−1} 对应于 K 中关键帧的图像序列进行编辑,使用 ExtAttn 扩展的注意力机制。 d. 从序列 {Jj_t−1} 中提取关键帧的标记(tokens) Tbase。 e. 使用 TokenFlow 编辑技术对 Jt 进行编辑,其中 TokenFlow(Fγ(Tbase)) 表示应用 NN 场 Fγ 到 Tbase 上的结果。 最终输出编辑...
In 2021,Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet³ was published, presenting a methodology that would circumvent the heavy pre-training requirement of previous vistransformers. They achieved this by replacing thepatch tokenizationin the ViT model² with the a Toke...
assert 0.0 < attn._v_scale < 1.0 if not current_platform.is_rocm(): # NOTE: This code path requires validation on Non-CUDA platform # NOTE: it is valid for scales to be 1.0 (default value), but # we know these checkpoints have scales < 1.0 assert 0.0 < attn._k_scale < 1.0 ...
attn(self.norm1(x)) x = x + self.drop_path(self.mlp(self.norm2(x))) return x 整体结构很简单,输入经过一次LayerNorm,然后输入到Attention多头注意力模块。输出再次经过LayerNorm,最后设置一定比例的Dropout T2T Module 代码语言:javascript 代码运行次数:0 运行 AI代码解释 class T2T_module(nn.Module)...
Fusion Read-Write Head Fusion Token Cross Latent Linear Fusion Summary Attn. Attn. Attn. Erase 72.49 3.94 0.33 2.43 Add 75.86 76.26 76.17 76.26 Add-Erase 76.26 76.33 75.82 74.74 GFLOPs 1.86 2.92 3.07 2.62Table 2: Ablation over the latent embedding dimension, c for the read-write head on Vi...
If you don't use flash-attn, please modify the configs of weights, referring to this🚀 Quick Startimport os import torch from transformers import AutoTokenizer from internvl.model.internvl_chat import InternVLChatModel from utils import post_process, generate_similiarity_map, load_image ...
attn_implementation="flash_attention_2", trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(EMU_HUB, trust_remote_code=True, padding_side="left") image_processor = AutoImageProcessor.from_pretrained(VQ_HUB, trust_remote_code=True) ...
# test.py # test.py import torch from PIL import Image from modelscope import AutoModel, AutoTokenizer model = AutoModel.from_pretrained('OpenBMB/MiniCPM-V-2_6', trust_remote_code=True, attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager model...
(a) Input; (b) Affinity map (the generated affinity maps for the points marked by the green crosses); (c) V1-attn (the generated transformer attention maps from MCTformer-V1, where the red squares denote the original attention scores for the corresponding points in (b)); (d...