### Introduction ### Method #### One-Stage Text-to-Image Backbone

\begin{aligned} L_D=&-\mathbb E_{x\sim \mathbb P_r}[min(0,-1+D(x,e))]\\ &-(1/2)\mathbb E_{G(z)\sim \mathbb P_g}[min(0,-1-D(G(z),e))]\\ &-(1/2)\mathbb E_{x\sim \mathbb P_{mis}}[min(0,-1-D(x,e))]\\ L_G=&-\mathbb E_{G(z)\sim \mathbb P_{g}}[D(G(z),e)] \end{aligned}

#### Target-Aware Discriminator \begin{aligned} L_D=&-\mathbb E_{x\sim \mathbb P_r}[min(0,-1+D(x,e))]\\ &-(1/2)\mathbb E_{G(z)\sim \mathbb P_g}[min(0,-1-D(G(z),e))]\\ &-(1/2)\mathbb E_{x\sim \mathbb P_{mis}}[min(0,-1-D(x,e))]\\ &+k\mathbb E_{x\sim \mathbb P_r}[(||\nabla_x D(x,e)||+||\nabla_e D(x,e)||)^p]\\ L_G=&-\mathbb E_{G(z)\sim \mathbb P_{g}}[D(G(z),e)] \end{aligned}

$k,p$是用来平衡梯度影响的超参数 ##### One-Way Output Figure 4(a)中是过去方法常用的，左侧只使用图片特征计算非条件损失，右侧使用图片和文本的特征计算条件loss。如Figure 3(b)中所示，非条件损失将结果导向真图，条件损失将结果导向图片与文本相互匹配，而在训练时仅将两者简单相加，很可能会使结果并不能如预期的一样指向真图且匹配，所以该文设计了一个单向输出的模块，直接预测整个损失

One-Way Output (OW-O)的效果同样见Table 2，可见比没有时要强

#### Efficient Text-Image Fusion Figure 2中可见UPBlock叠了七个，每个UPBlock的结构如上图，放缩参数$\gamma$和偏移参数$\theta$分别由两个MLP网络预测，

$\gamma=MLP_1(e),\,\,\,\theta=MLP_2(e)$

$AFF(x_i|e)=\bold{\gamma}_i\cdot x_i+\bold{\theta}_i$

### Experiments
{"ft_lang_label":"__label__zh","ft_lang_prob":0.9684338,"math_prob":0.99980253,"size":1392,"snap":"2023-40-2023-50","text_gpt3_token_len":1193,"char_repetition_ratio":0.061959654,"word_repetition_ratio":0.0,"special_character_ratio":0.123563215,"punctuation_ratio":0.0,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.999959,"pos_list":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18],"im_url_duplicate_count":[null,1,null,1,null,1,null,2,null,1,null,1,null,1,null,1,null,2,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-12-04T03:35:09Z\",\"WARC-Record-ID\":\"<urn:uuid:e6dcdec0-2c40-4b8a-b983-de9732396cb8>\",\"Content-Length\":\"101630\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:44b58c84-9b5a-4e43-9af6-90d2597f3ef4>\",\"WARC-Concurrent-To\":\"<urn:uuid:0cc58076-87e6-4086-a3a2-1b4c9e2b9684>\",\"WARC-IP-Address\":\"81.68.225.110\",\"WARC-Target-URI\":\"http://www.yukinoo.site/archives/df-ganasimpleandeffectivebaselinefortext-to-imagesynthesis\",\"WARC-Payload-Digest\":\"sha1:6RU4Z5SMSY6QD257E3DHPCLPKDHMB2CS\",\"WARC-Block-Digest\":\"sha1:F3IK7JRXT622TEBKCLOD42H65RZNXCXT\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-50/CC-MAIN-2023-50_segments_1700679100523.4_warc_CC-MAIN-20231204020432-20231204050432-00121.warc.gz\"}"}