# os.system(cmd_str) # else: # cmd_str = 'python run_color_black.py -c '+str(images_num)+' -w 5 -f 64 -b 3 -t 8 -bl 1 -rbl -cs 5 -k 5 -rk -d 1 -ft fonts/cn/' + ttfname + '.ttf -id images -i out/number.txt --out
Here is my python source code for ASCII generator. With my code: Given input image, we could generate ASCII art stored under text format in different languages (.txt) Given input image, we could generate ASCII art stored under image formats in different languages (.png, .jpg, ...). In...
本项目在萤火二号集群上用 PyTorch 实现了论文 《CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP》。 CLIP-GEN 是一个 Language-Free 的文本生成图像的方法,它不依赖图文训练样本,通过预训练 CLIP 模型的强大表征能力,只需要图片数据就可以训练出一个文本生成图像的模型。该方法的基本...
Basic usage python train.py --data_set="flowers" Options z_dim: Noise Dimension. Default is 100. t_dim: Text feature dimension. Default is 256. batch_size: Batch Size. Default is 64. image_size: Image dimension. Default is 64. gf_dim: Number of conv in the first layer generator. ...
遍历一段文字,统计每个字后面出现的字和其次数,当前一个字确定的时候,计算后一个字出现的百分比,用这个百分比作为文字生成器中后一个字出现的概率进行文字生成 1fromrandomimportrandint234defmakeDict(text):5#替换换行符和引号6text = text.replace('\n','')7text = text.replace('\“','')8text = text...
Implement pointer-generator networks and coverage mechanisms How does the Attention Mechanism Work? Now, let’s talk about the inner workings of the attention mechanism. As I mentioned at the start of the article, this is a math-heavy section so consider this as optional learning. I still high...
squeeze(1), hidden import paddle import paddle.nn as nn # define the generator class Generator(nn.Layer): def __init__(self, noise_dim, projected_embed_dim, ngf): super(Generator, self).__init__() self.num_channels = 3 self.image_size = 64 self.noise_dim = noise_dim self.embed...
Caption Generator》https://arxiv.org/pdf/1411.4555.pdf 该论文中的Encoder结构,修改为CNN 以用于ImageCaption...Attention》https://arxiv.org/pdf/1502.03044v1.pdf 该论文又进一步引入了注意力机制。 Abstract:Inspired by recent work in Image Caption论文合辑2 ...
python main.py --validation --split=2 --pretrain_model=model/netG.pdparams 使用预训练模型预测 将需要测试的文件放在参数pretrain_model确定的目录下,运行下面指令,输出图片保存在image\目录中 python main.py --validation --split=2 --pretrain_model=model/netG.pdparams LICENSE 本项目所遵守的开源协...
Define if the generator will return masks for the text 定义生成器是否将返回文本的掩码 -d [DISTORSION], --distorsion [DISTORSION] Define a distorsion applied to the resulting image. 0:None (Default), 1: Sine wave, 2: Cosine wave, 3:Random ...