This type of cookie didn’t reach nationwide fame until 1939 when Betty Crocker popularized it in her radio show. However, the classic cookie, the ‘cocolate chip cookie’ was only invented in 1937 by Ruth Graves Wakefield (1905-1977), of Whitman, Massachusetts, who ran the Toll House Restaurant. The name itself derives from the Dutch word ‘koekje’ (small or round cake) which represents the small pieces of dough that Dutch bakers used to place in their ovens to test the temperature. In contrast, cookies are Dutch in origin. Traditionally, such biscuits are hard and dry in texture and they’re know (and commonplace) from recipe books going back at least to the Elizabethan era. The name of these comes from a corruption of the Latin bis cotum (baked twice) which became biscuit in English and biscotti in Italian. The modern biscuit, however, is a French invention, and by the 14th century it was possible to buy little fruit-filled wafers on the streets of paris. It wasn’t until the Moorish conquest of Spain and the crusades of the 12th and 13th centuries that Arabic cooking practices slowly came to Europe. After all, it still works stably.The history of the biscuit follows that of sugar and it seems that the first biscuits were baked in Persia during the 7th Century BCE. The training script train_v16.py is dirty, but I'm not going to refactor it.In order to ensure that this code is consistent with my original dirty code, please follow me to reproduce the results using this code step by step.If the training process is blocked when training with multi GPUs, please upgrade the tl2 via pip install -I tl2.train_ffhq_r32.sh -> train_ffhq_r64.sh -> train_ffhq_r128.sh -> train_ffhq_r256.shĮxp/cips3d/bash/finetuning_exp: (require pre-trained models from the above step).I will release all the pre-trained models when the reproducing is over. Please refer to the scripts in exp/cips3d/bash. dest=datasets/AFHQv2/AFHQv2_stylegan2.zip If you find any problems with this idea, please open an issue. Adding an auxiliary discriminator stably solves the mirror symmetry problem. In practice, progressive training is able to guarantee this. Otherwise, if the main discriminator dominates the generator, the mirror symmetry problem will still occur. Note that in the initial stage of training, the auxiliary discriminator must dominate the generator more than the main discriminator does. We propose to use an auxiliary discriminator to solve this problem (please see the paper). The problem of mirror symmetry refers to the sudden change of the direction of the bangs near the yaw angle of pi/2. Demo videos demo1.mp4 demo2.mp4 demo_animal_finetuned.mp4 demo3.mp4 demo4.mp4 demo5.mp4 Mirror symmetry problem But if the github star reaches two hundred, I will advance the date. ✔️ () We are planning to publish the training code here in December. I will open source the training code in the near future. If you find any problems, please open an issue. Now I have provided a GUI script and models to facilitate the experiment of network interpolation (see below). The configuration files (yaml files) for training will be released next. ✔️ () All the code files have been released. ✔️ () The configuration files (yaml files) for training are being released. Please upgrade the tl2 package with pip install -I tl2. This repository contains the code of the paper,ĬIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis.
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