text:
\section{Preprocessing ImageNet}
An initial challenge is the lack of high-resolution data; the mean resolution of ImageNet is $469\times387$.
Similar to the procedure used for generating CelebA-HQ\cite{Karras2018ICLR}, we preprocess the whole dataset with SwinIR-Large~\cite{Liang2021ICCV}, a recent model for real-world image super-resolution.
Of course, a trivial way of achieving good performance on this dataset would be to draw samples from a $256^2$ generative model and passing it through SwinIR. However, SwinIR adds significant computational overhead as it is $60$ times slower than our upsampling stack. Furthermore, this way, StyleGAN-XL's weights can be used for initialization when finetuning on other high-resolution datasets. Lastly, combining StyleGAN-XL and SwinIR would impair translation equivariance.

\section{Classes of unaligned Humans}
As mentioned in section \ref{sec:limitations}, we observe that ADM~\cite{Dhariwal2021NEURIPS} generates more convincing human faces than StyleGAN-XL and BigGAN. Both GANs can synthesize realistic faces; however, the main challenge in this setting is that the dataset is unstructured, and the humans are not aligned.
Brock et al.~\cite{Brock2019ICLR} remarked the particular challenge of classes containing details to which human observers are more sensitive. We show examples in~\figref{fig:aquamen}.
\aquamen

\section{Implementation details}

\boldparagraph{Inversion.}
Following \cite{Karras2020CVPR}, we use basic latent optimization in $\mathcal{W}$ for inversion. Given a target image, we first compute its average style code $\bar{\mathbf{w}}\newcommand{\bW}{\mathbf{W}}$ by running $10000$ random latent codes $\mathbf{z}}\newcommand{\bZ}{\mathbf{Z}$ and target specific class samples $\mathbf{c}}\newcommand{\bC}{\mathbf{C}$ through the mapping network. As the class label of the target image is unknown, we pass it to a pretrained classifier. We then use the classifier logits as a multinomial distribution to sample $\mathbf{c}}\newcommand{\bC}{\mathbf{C}$. In our experiments, we use Deit-M~\cite{Touvron2021ICML} as a classifier, but other choices are possible.
At the beginning of optimization , we initialize $\mathbf{w}}\newcommand{\bW}{\mathbf{W} = \bar{\mathbf{w}}\newcommand{\bW}{\mathbf{W}}$. The components of $\mathbf{w}}\newcommand{\bW}{\mathbf{W}$ are the only trainable parameters. The optimization runs for 1000 iterations using the Adam optimizer~\cite{Kingma2015ICLR} with default parameters.
We optimize the LPIPS~\cite{Zhang2018CVPR} distance between the target image and the generated image.
For StyleGAN-XL, the maximum learning rate is $\lambda_{max} = 0.05$. It is ramped up from zero linearly during the first 50 iterations and ramped down to zero using a cosine schedule during the last 250 iterations.
For BigGAN, we empirically found $\lambda_{max} = 0.001$ and a ramp-down over the last 750 iterations to yield the best results.
All inversion experiments are performed at resolution $512^2$ and computed on $5k$ images ($10$\% of the validation set). We report the results in \tabref{tab:invresults}.
\invresults

\boldparagraph{Training StyleGAN3 on ImageNet.}
For training StyleGAN3, we use the official PyTorch implementation\footnote{\url{https://github.com/NVlabs/stylegan3.git}}.
The results in \figref{fig:teaser} are computed with the StyleGAN3-R configuration on resolution $256^2$ until the discriminator has seen $10$ million images. We find that StyleGAN3-R and StyleGAN3-T converge to similar FID without any changes to their training paradigm. The run with the best FID score was selected from three runs with different random seeds. We use a channel base of $16384$ and train on $8$ GPUs with total batch size $256$, $\gamma=0.256$. The remaining settings are chosen according to the default configuration of the code release.
For the ablation study in \tabref{tab:ablation}, we use the StyleGAN3-T configuration as baseline since StyleGAN-XL builds upon the translational-equivariant layers of StyleGAN3.
We train on $4$ GPUs with total batch size $256$ and batch size $32$ per GPU, $\gamma=0.25$, and disable augmentation.

\boldparagraph{Training \& Evaluation.}
For all our training runs, we do not use data amplification via \textit{x-flips} following~\cite{Karras2019CVPR}. Furthermore, we evaluate all metrics using the official StyleGAN3 codebase. For the baseline values in ~\tabref{tab:sotapr}, we report the numbers of~\cite{Dhariwal2021NEURIPS}. The official codebase of ADM\footnote{\url{https://github.com/openai/guided-diffusion}} provides files containing $50$k samples for ADM and BigGAN. We utilize the provided samples to compute rFID. In addition, we recompute precision and recall for all baselines, as ~\cite{Dhariwal2021NEURIPS} compute these metrics between $10$k real samples and $50$k generated samples.
We follow the original formulation of~\cite{Kynknniemi2019NEURIPS} and use $50$k real and $50$k generated samples.

\boldparagraph{Layer configurations.}
We start progressive growing at resolution $16^2$ using $11$ layers. The layer specifications are computed according to \cite{Karras2021NEURIPS} and remain fixed for the remaining training. For the next stage, at resolution $32^2$, we discard the last $2$ layers and add $7$ new ones. The specifications for the new layers are computed according to \cite{Karras2021NEURIPS} for a model with resolution $32^2$ and $16$ layers. Continuing this strategy up to resolution $1024^2$ yields the flexible layer specification of StyleGAN-XL in \figref{fig:layerspecs}.
\layerspecs

\section{Introduction}
\label{sec:intro}

Computer graphics has long been concerned with generating photorealistic images at high resolution that allow for direct control over semantic attributes. Until recently, the primary paradigm was to create carefully designed 3D models which are then rendered using realistic camera and illumination models. A parallel line of research approaches the problem from a data-centric perspective.
In particular, probabilistic generative models ~\cite{Goodfellow2014NEURIPS,Oord2017NEURIPS,Kingma2018NEURIPS,Song2021ICLR} have shifted the paradigm from designing assets to designing training procedures and datasets. Style-based GANs (StyleGANs) are a specific instance of these models, and they exhibit many desirable properties. They achieve high image fidelity~\cite{Karras2019CVPR, Karras2020CVPR}, fine-grained semantic control~\cite{Haerkoenen2020NEURIPS, WU2021CVPRa,Ling2021ARXIV}, and recently alias-free generation enabling realistic animation~\cite{Karras2021NEURIPS}. Moreover, they reach impressive photorealism on carefully curated datasets, especially of human faces. However, when trained on large and unstructured datasets like ImageNet~\cite{Deng2009CVPR}, StyleGANs do not achieve satisfactory results yet. One other problem plaguing data-centric methods, in general, is that they become prohibitively more expensive when scaling to higher resolutions as bigger models are required.

Initially, StyleGAN~\cite{Karras2019CVPR} was proposed to explicitly disentangle factors of variations, allowing for better control and interpolation quality~\cite{Karras2019CVPR}.
However, its architecture is more restrictive than a standard generator network~\cite{Radford2016ICLR, Karras2018ICLR, Liu2021ICLR} which seems to come at a price when training on complex and diverse datasets such as ImageNet. Previous attempts at scaling StyleGAN and StyleGAN2 to ImageNet led to sub-par results~\cite{Gwern2020MISC, Grigoryev2022ICLR},  giving reason to believe it might be fundamentally limited for highly diverse datasets~\cite{Gwern2020MISC}.

BigGAN~\cite{Brock2019ICLR} is the state-of-the-art GAN model for image synthesis on ImageNet. The main factors for BigGANs success are larger batch and model sizes.
However, BigGAN has not reached a similar standing as StyleGAN as its performance varies significantly between training runs~\cite{Karras2020NeurIPS} and as it does not employ an intermediate latent space which is essential for GAN-based image editing~\cite{Abdal2021TOG, Patashnik2021ICCV, Collins2020CVPR, WU2021CVPRa}. Recently, BigGAN has been superseded in performance by diffusion models~\cite{Dhariwal2021NEURIPS}. Diffusion models achieve more diverse image synthesis than GANs but are significantly slower during inference and prior work on GAN-based editing is not directly applicable. Following these arguments, successfully training StyleGAN on ImageNet has several advantages over existing methods.

The previously failed attempts at scaling StyleGAN raise the question of whether architectural constraints fundamentally limit style-based generators or if the missing piece is the right training strategy.
Recent work by Sauer et al. \cite{Sauer2021NEURIPS} introduced \textit{Projected GANs} which project generated and real samples into a fixed, pretrained feature space. Rephrasing the GAN setup this way leads to significant improvements in training stability, training time, and data efficiency.
Leveraging the benefits of Projected GAN training might enable scaling StyleGAN to ImageNet.
However, as observed by~\cite{Sauer2021NEURIPS}, the advantages of Projected GANs only partially extend to StyleGAN on the unimodal datasets they investigated.
We study this issue and propose architectural changes to address it.
We then design a progressive growing strategy tailored to the latest StyleGAN3.
These changes in conjunction with Projected GAN already allow surpassing prior attempts of training StyleGAN on ImageNet.
To further improve results, we analyze the pretrained feature network used for Projected GANs and find that the two standard neural architectures for computer vision, CNNs and ViTs~\cite{Dosovitskiy2021ICLR}, significantly improve performance when used jointly. Lastly, we leverage \textit{classifier guidance}, a technique originally introduced for diffusion models to inject additional class-information~\cite{Dhariwal2021NEURIPS}.

Our contributions culminate in a new state-of-the-art on large-scale image synthesis, pushing the performance beyond existing GAN and diffusion models.
We showcase inversion and editing for ImageNet classes and find that Pivotal Tuning Inversion (PTI)~\cite{Roich2021ARXIV}, a powerful new inversion paradigm, combines well with our model and even embeds out-of-domain images smoothly into our learned latent space. Our efficient training strategy allows us to triple the parameters of the standard StyleGAN3 while reaching prior state-of-the-art performance of diffusion models~\cite{Dhariwal2021NEURIPS} in a fraction of their training time. It further enables us to be the first to demonstrate image synthesis on ImageNet-scale at a resolution of $1024^2$ pixels. We will open-source our code and models upon publication.
The supplementary video can be found at \url{https://sites.google.com/view/stylegan-xl/}.
\section{Limitations and Future Work}
\label{sec:limitations}
Our contributions allow StyleGAN to accomplish state-of-the-art high-resolution image synthesis on ImageNet.  Exploring new editing methods and dataset generation~\cite{Chai2021CVPR,Li2022ARXIV} using StyleGAN-XL are exciting future avenues. Furthermore, future work may tackle an even larger megapixel dataset. However, a larger yet diverse dataset is not available so far. Current large-scale, high-resolution datasets are of single object classes or contain many similar images~\cite{Zhang2020ECCV,Fregin2018ICRA,Perot2020NEURIPS}. In the following, we discuss some limitations of the current model which should be addressed in the future.

\boldparagraph{Architectural Limitations.}
First, StyleGAN-XL is three times larger than StyleGAN3, constituting a higher computational overhead when used as a starting point for finetuning. Therefore, it will be worth exploring GAN distillation methods~\cite{Chang2020ACCV} that trade-off performance for model size. Second, StyleGAN-XL uses translation-equivariant layers of StyleGAN3-T. As described above, StyleGAN3-R tends to produce overly symmetrical images and adds significant computational overhead. Finding a more efficient rotational-equivariant architecture is an important future direction.
Lastly, we find StyleGAN3, and consequently, StyleGAN-XL, harder to edit, e.g., high-quality edits via $\mathcal{W}$ are noticeably easier to achieve with StyleGAN2. As already observed in~\cite{Karras2021NEURIPS}, StyleGAN3's semantic controllability is reduced for the sake of equivariance.
However, techniques using the \textit{StyleSpace}~\cite{WU2021CVPRa}, e.g. StyleMC~\cite{Kocasari2021WACV}, tend to yield better results in our experiments confirming the findings of concurrent work by Alaluf et al.~\cite{Alaluf2022ARXIV}. Furthermore, we remark that for applications where equivariance is not essential, our framework can easily be used with StyleGAN2 layers instead.

\boldparagraph{Drawbacks Compared to Diffusion Models.}
Low data coverage is a known problem of GANs, and StyleGAN-XL makes notable headway on this issue. However, StyleGAN-XL is still outperformed by diffusion models regarding data coverage. Furthermore, similar to~\cite{Brock2019ICLR}, classes of unaligned humans appear harder to model for a GAN. For such classes, ADM~\cite{Dhariwal2021NEURIPS} generates more convincing human faces than BigGAN~\cite{Brock2019ICLR} or StyleGAN-XL, see supplementary. Whether the points above are a general limitation of GANs remains an interesting open question for future research.
\section{Scaling StyleGAN to ImageNet}
\label{sec:method}
As mentioned before, StyleGAN has several advantages over existing approaches that work well on ImageNet.
But a na\"{i}ve training strategy does not yield state-of-the-art performance~\cite{Gwern2020MISC, Grigoryev2022ICLR}.
Our experiments confirm that even the latest StyleGAN3 does not scale well, see \figref{fig:teaser}. Particularly at high resolutions, the training becomes unstable, resulting in a high FID.
Therefore, our goal is to train a StyleGAN3 generator on ImageNet successfully. Success is defined in terms of sample quality primarily measured by inception score (IS)~\cite{Salimans2016NEURIPS} and diversity measured by Fr\'echet Inception Distance (FID)~\cite{Heusel2017NEURIPS}.
Throughout this section, we gradually introduce changes to the StyleGAN3 baseline (\textbf{Config-A}) and track the improvements in \tabref{tab:ablation}. First, we modify the generator and its regularization losses, adapting the latent space to work well with Projected GAN (\textbf{Config-B}) and for the class-conditional setting (\textbf{Config-C}). We then revisit progressive growing to improve training speed and performance (\textbf{Config-D}). Next, we investigate the feature networks used for Projected GAN training to find a well-suited configuration (\textbf{Config-E}). Lastly, we propose classifier guidance for GANs to provide class information via a pretrained classifier (\textbf{Config-F}). Our contributions enable us to train a significantly larger model than previously possible while requiring less computation than prior art. Our model is three times larger in terms of depth and parameter count than a standard StyleGAN3. However, to match the prior state-of-the-art performance of ADM~\cite{Dhariwal2021NEURIPS} at a resolution of $512^2$ pixels, training the models on a single NVIDIA Tesla V100 takes $400$ days compared to the previously required $1914$ V100-days. We refer to our model as \textbf{StyleGAN-XL} (\figref{fig:system}).
\system
\ablation

\subsection{Adapting Regularization and Architectures}
Training on a diverse and class-conditional dataset makes it necessary to introduce several adjustments to the standard StyleGAN configuration. We construct our generator architecture using layers of StyleGAN3-T, the translational-equivariant configuration of StyleGAN3. In initial experiments, we found the rotational-equivariant StyleGAN3-R to generate overly symmetric images on more complex datasets, resulting in kaleidoscope-like patterns.

\boldparagraph{Regularization.}
In GAN training, it is common to use regularization for both, the generator and the discriminator. Regularization improves results on uni-modal datasets like FFHQ~\cite{Karras2019CVPR} or LSUN~\cite{Yu2015ARXIV}, whereas it can be detrimental on multi-modal datasets~\cite{Brock2019ICLR, Gwern2020MISC}. Therefore, we aim to avoid regularization when possible. Karras et al.~\cite{Karras2021NEURIPS} find style mixing to be unnecessary for the latest StyleGAN3; hence, we also disable it. Path length regularization can lead to poor results on complex datasets~\cite{Gwern2020MISC} and is, per default, disabled for StyleGAN3~\cite{Karras2021NEURIPS}. However, path length regularization is attractive as it enables high-quality inversion~\cite{Karras2020CVPR}.
We also observe unstable behavior and divergence when using path length regularization in practice. We found that this problem can be circumvented by only applying regularization after the model has been sufficiently trained, i.e., after 200k images. For the discriminator, following~\cite{Sauer2021NEURIPS}, we use spectral normalization without gradient penalties. In addition, we blur all images with a Gaussian filter with $\sigma=2$ pixels for the first $200k$ images. Discriminator blurring has been introduced in ~\cite{Karras2021NEURIPS} for StyleGAN3-R. It prevents the discriminator from focusing on high frequencies early on, which we found beneficial across all settings we investigated.

\boldparagraph{Low-Dimensional Latent Space.}
As observed in~\cite{Sauer2021NEURIPS}, Projected GANs work better with FastGAN~\cite{Liu2021ICLR} than with StyleGAN. One main difference between these generators is their latent space, StyleGAN's latent space is comparatively high dimensional
(FastGAN: $\mathbb{R}^{100}$, BigGAN: $\mathbb{R}^{128}$, StyleGAN: $\mathbb{R}^{512}$).
Recent findings indicate that the \textit{intrinsic dimension}  of natural image datasets is relatively low~\cite{Pope2021ICLR}, ImageNet's dimension estimate is around $40$. Accordingly, a latent code of size $512$ is highly redundant, making the mapping network's task harder at the beginning of training. Consequently, the generator is slow to adapt and cannot benefit from Projected GAN's speed up. We therefore reduce StyleGAN's latent code $\mathbf{z}}\newcommand{\bZ}{\mathbf{Z}$ to $64$ and now observe stable training in combination with Projected GAN, resulting in lower FID than the baseline (\textbf{Config-B}). We keep the original dimension of the \textit{style code} $\mathbf{w}}\newcommand{\bW}{\mathbf{W} \in \mathbb{R}^{512}$ to not restrict the model capacity of the mapping network $\bG_m$.

\boldparagraph{Pretrained Class Embeddings.}
Conditioning the model on class information is essential to control the sample class and improve overall performance. A class-conditional variant of StyleGAN was first proposed in~\cite{Karras2020NeurIPS} for CIFAR10~\cite{Krizhevsky2009CITESEER} where a one-hot encoded label is embedded into a 512-dimensional vector and concatenated with $\mathbf{z}}\newcommand{\bZ}{\mathbf{Z}$. For the discriminator, class information is projected onto the last discriminator layer~\cite{Miyato2018ICLR}. We observe that \textbf{Config-B} tends to generate similar samples per class.
To quantify mode coverage, we leverage the recall metric~\cite{Kynknniemi2019NEURIPS} and find that \textbf{Config-B} achieves a low recall of~$0.004$. We hypothesize that the class embeddings collapse when training with Projected GAN. Therefore, to prevent this collapse, we aim to ease optimization of the embeddings via pretraining. We extract and spatially pool the lowest resolution features of an Efficientnet-lite0~\cite{Tan2019ICML} and calculate the mean per ImageNet class.
The network has a low channel count to keep the embedding dimension small, following the arguments of the previous section. The embedding passes through a linear projection to match the size of $\mathbf{z}}\newcommand{\bZ}{\mathbf{Z}$ to avoid an imbalance. Both $\bG_m$ and $\bD_i$ are conditioned on the embedding.
During GAN training, the embedding and the linear projection are optimized to allow specialization. Using this configuration, we observe that the model generates diverse samples per class, and recall increases to $0.15$ (\textbf{Config-C}). Note that for all configurations in this ablation, we restrict the training time to $15\;V\text{-}100\;days$. Hence, the absolute recall is markedly lower compared to the fully trained models. Conditioning a GAN on pretrained features was also recently investigated by~\cite{Casanova2021NEURIPS}. In contrast to our approach, Casanova et al.~\cite{Casanova2021NEURIPS} condition on specific \textit{instances}, instead of learning a general class embedding.

\subsection{Reintroducing Progressive Growing}
Progressively growing the output resolution of a GAN was introduced by~\cite{Karras2018ICLR} for fast and more stable training. The original formulation adds layers during training to both $\bG$ and $\bD$ and gradually fades in their contribution. However, in a later work, it was discarded~\cite{Karras2020CVPR} as it can contribute to texture sticking artifacts. Recent work by Karras et al.~\cite{Karras2021NEURIPS} finds that the primary cause of these artifacts is aliasing, so they redesign each layer of StyleGAN to prevent it. This motivates us to reconsider progressive growing with a carefully crafted strategy that aims to suppress aliasing as best as possible. Training first on very low resolutions, as small as $16^2$ pixels, enables us to break down the daunting task of training on high-resolution ImageNet into smaller subtasks. This idea is in line with the latest work on diffusion models~\cite{Nichol2021ICML, Saharia2021ARXIV, Dhariwal2021NEURIPS, Ho2021ARXIV}. They observe considerable improvements in FID on ImageNet when using a two-stage model, i.e., stacking an independent low-resolution model and an upsampling model to generate the final image.

Commonly, GANs follow a rigid sampling rate progression, i.e., at each resolution, there is a fixed amount of layers followed by an upsampling operation using fixed filter parameters. StyleGAN3 does not follow such a progression. Instead, the layer count is set to $14$, independent of the output resolution, and the filter parameters of up- and downsampling operations are carefully designed for antialiasing under the given configuration. The last two layers are critically sampled to generate high-frequency details. When adding layers for the subsequent highest resolution, discarding the previously critically sampled layers is crucial as they would introduce aliasing when used as intermediate layers~\cite{Karras2020CVPR, Karras2021NEURIPS}. Furthermore, we adjust the filter parameters of the added layers to adhere to the flexible layer specification of~\cite{Karras2021NEURIPS}; we refer to the supplementary for details. In contrast to~\cite{Karras2018ICLR} we do not add layers to the discriminator. Instead, to fully utilize the pretrained feature network $\bF$, we upsample both data and synthesized images to $\bF$'s training resolution ($224^2$ pixels) when training on smaller images.

We start progressive growing at a resolution of $16^2$ using $11$ layers. Every time the resolution increases, we cut off $2$ layers and add $7$ new ones. Empirically, fewer layers result in worse performance; adding more leads to increased overhead and diminishing returns. For the final stage at $1024^2$, we add only $5$ layers as the last two are not discarded. This amounts to $39$ layers at the maximum resolution of $1024^2$. Instead of a fixed growing schedule, each stage is trained until FID stops decreasing. We find it beneficial to use a large batch size of $2048$ on lower resolution ($16^2$ to $64^2$), similar to~\cite{Brock2019ICLR}. On higher resolutions, smaller batch sizes suffice ($128^2$ to $256^2$: $256$, $512^2$ to $1024^2$: $128$). Once new layers are added, the lower resolution layers remain fixed to prevent mode collapse.

In our ablation study, FID improves only slightly (\textbf{Config-D}) compared to \textbf{Config-C}. However, the main advantage can be seen at high resolutions, where progressive growing drastically reduces training time. At resolution $512^2$, we reach the prior state-of-the-art (FID$\;=3.85$) after $2$ V100-days.  This reduction is in contrast to other methods such as ADM, where doubling the resolution from $256^2$ to $512^2$ pixels corresponds to increasing training time from $393$ to $1914$ V100-days to find the best performing model\footnote{Note that these settings are not directly comparable as the stem of our model is pretrained, but the values should give a general sense of the order of magnitude.}. As our aim is not to introduce texture sticking artifacts, we measure $EQ\text{-}T$, a metric for determining translation equivariance~\cite{Karras2021NEURIPS}, where higher is better.
\textbf{Config-C} yields $EQ\text{-}T=55$, while \textbf{Config-D} attains $EQ\text{-}T=48$.
This only slight reduction in equivariance shows that \textbf{Config-D} restricts aliasing almost as well as a configuration without growing. For context, architectures with aliasing yield $EQ\text{-}T\sim 15$.

\subsection{Exploiting Multiple Feature Networks}
An ablation study conducted in~\cite{Sauer2021NEURIPS} finds that most pretrained feature networks $\bF$ perform similarly in terms of FID when used for Projected GAN training regardless of training data, pretraining objective, or network architecture. However, the study does not answer if combining several $\bF$  is advantageous. Starting from the standard configuration, an EfficientNet-lite0, we add a second $\bF$ to inspect the influence of its pretraining objective (classification or self-supervision) and architecture (CNN or Vision Transformer (ViT)~\cite{Dosovitskiy2021ICLR}). The results in \tabref{tab:ablation} show that an additional CNN leads to slightly lower FID. Combining networks with different pretraining objectives does not offer benefits over using two classifier networks. However, combining an EfficientNet with a ViT improves performance significantly. This result corroborates recent results in neural architecture literature, which find that supervised and self-supervised representations are similar~\cite{Grigg2021ARXIV}, whereas ViTs and CNNs learn different representations~\cite{Raghu2021NEURIPS}. Combining both architectures appears to have complementary effects for Projected GANs. We do not see significant improvements when adding more networks; hence, \textbf{Config-E} uses the combination of EfficientNet~\cite{Tan2019ICML} and DeiT-M~\cite{Touvron2021ICML}.

\subsection{Classifier Guidance for GANs}
Dhariwal and Nichol~\cite{Dhariwal2021NEURIPS} introduced classifier guidance to inject class information into diffusion models. Classifier guidance modifies each diffusion step at time step $t$ by adding the gradients of a pretrained classifier $\nabla_{\mathbf{x}}\newcommand{\bX}{\mathbf{X}_t}\log p_{\phi}(\mathbf{c}}\newcommand{\bC}{\mathbf{C}|x_t, t)$. The best results are obtained when applying guidance on class-conditional models and scaling the classifier gradients by a constant $\lambda>1$.  This successful combination indicates that our model may also profit from classifier guidance, even though we already supply class information via embeddings.

We first pass the generated image $\mathbf{x}}\newcommand{\bX}{\mathbf{X}$ through a pretrained classifier CLF to predict the class label $c_i$. We then add a cross-entropy loss $\mathcal{L}_{CE} = -\sum_{i=0}^{C} c_i \log CLF({x}_i) $ as an additional term to the generator loss and scale this term by a constant $\lambda$. For the classifier, we use DeiT-S~\cite{Touvron2021ICML}, which exhibits strong classification performance while not adding much overhead to the training. Similar to ~\cite{Dhariwal2021NEURIPS}, we observe a significant improvement in IS, indicating an increase in sample quality (\textbf{Config-F}).
We find $\lambda=8$ to work well empirically.
Classifier guidance only works well on higher resolutions ($>32^2$); otherwise, it leads to mode collapse. This is in contrast to~\cite{Dhariwal2021NEURIPS} who exclusively guide their low-resolution model. The difference stems from how guidance is applied: we use it for model training, whereas~\cite{Dhariwal2021NEURIPS} guide the sampling process.
\section{Background}
\label{sec:background}
We first introduce the main building blocks of our system:
the StyleGAN3 generator~\cite{Karras2021NEURIPS} and Projected GAN's~\cite{Sauer2021NEURIPS} feature projectors and multi-scale discriminators.

\boldparagraph{StyleGAN.}
This section describes style-based generators in general with a focus on the latest StyleGAN3~\cite{Karras2021NEURIPS}. A StyleGAN generator consists of a mapping network $\bG_m$ and a synthesis network $\bG_s$. First, $\bG_m$ maps a normally distributed latent code $\mathbf{z}}\newcommand{\bZ}{\mathbf{Z}$ to a style code $\mathbf{w}}\newcommand{\bW}{\mathbf{W}$. This style code $\mathbf{w}}\newcommand{\bW}{\mathbf{W}$ is then used for modulating the convolution kernels of $\bG_s$ to control the synthesis process.
The synthesis network $\bG_s$ of StyleGAN3 starts from a spatial map defined by Fourier features~\cite{Tancik2020NEURIPS, Xu2021CVPR}. This input then passes through $N$ layers of convolutions, non-linearities, and upsampling to generate an image.
Each non-linearity is wrapped by an upsampling and downsampling operation to prevent aliasing.
The low-pass filters used for these operations are carefully designed to
balance image quality, antialiasing, and training speed.
Concretely, their cutoff and stopband frequencies grow geometrically with network depth, the transition band half-widths are as wide as possible within the limits of the layer sampling rate, and only the last two layers are critically sampled, i.e., the filter cutoff equals the bandlimit. The number of layers $N$ is $14$, independent of the final output resolution.

Style mixing and path length regularization are methods for regularizing style-based generators. In style mixing, an image is generated by feeding sampled style codes $\mathbf{w}}\newcommand{\bW}{\mathbf{W}$ into different layers of $\bG_s$ independently. Path length regularization encourages that a step of fixed size in latent space results in a corresponding fixed change in pixel intensity of the generated image~\cite{Karras2020CVPR}.
This inductive bias leads to a smoother generator mapping and has several advantages including fewer artifacts, more predictable training behavior, and better inversion.

Progressive growing was introduced by~\cite{Karras2018ICLR} for stable training at high resolutions but~\cite{Karras2020CVPR} found that it can impair shift-equivariance. Karras et al.~\cite{Karras2021NEURIPS} observe that texture sticking artifacts are caused by a lack of equivariance and carefully design StyleGAN3 to prevent texture sticking. Hence, in this paper, as we build on StyleGAN3, we can revisit the idea of progressive growing to improve convergence speed and synthesis quality.

\boldparagraph{Projected GAN.}
The original adversarial game between a generator $\bG$ and a discriminator $\bD$ can be extended by a set of feature projectors $\{\bP_l\}$~\cite{Sauer2021NEURIPS}. The projectors map real images $\mathbf{x}}\newcommand{\bX}{\mathbf{X}$ and images generated by $\bG$ to the discriminator's input space.
The Projected GAN objective is formulated as
\begin{equation}
\begin{aligned}
    \min_\bG \max_{\{\bD_l\}} &\sum_{l \in \mathcal{L}} \Big (
    \mathbb{E}_{\mathbf{x}}\newcommand{\bX}{\mathbf{X}} [\log \bD_l(\bP_l(\mathbf{x}}\newcommand{\bX}{\mathbf{X}))]\\
    &\quad \; + \mathbb{E}_{\mathbf{z}}\newcommand{\bZ}{\mathbf{Z}}[ \log( 1- \bD_l(\bP_l(\bG(\mathbf{z}}\newcommand{\bZ}{\mathbf{Z}))))] \Big)
\end{aligned}
\end{equation}
\label{eq:GANobjective2}

where $\{\bD_l\}$ is a set of independent discriminators operating on different feature projections.
The projectors consist of a pretrained feature network $\bF$, cross-channel mixing (CCM) and cross-scale mixing (CSM) layers.
The purpose of CCM and CSM is to prohibit the discriminators from focusing on only a subset of its input feature space which would result in mode collapse. Both modules employ differentiable random projections that are not optimized during GAN training. CCM mixes features across channels via random 1x1 convolutions, CSM mixes features across scales via residual random 3x3 convolution blocks and bilinear upsampling. The output of CSM is a feature pyramid consisting of four feature maps at different resolutions. Four discriminators operate independently on these feature maps. Each discriminator uses a simple convolutional architecture and spectral normalization~\cite{Miyato2018ICLR}. The depth of the discriminator varies depending on its input resolution, i.e., a spatially larger feature map corresponds to a deeper discriminator. Other than spectral normalization, Projected GANs do not use additional regularization such as gradient penalties~\cite{Mescheder2018ICML}. Lastly, ~\cite{Sauer2021NEURIPS} apply differentiable data-augmentation \cite{Zhao2020NeurIPS} before $\bF$ which improves Projected GAN's performance independent of the dataset size.

Sauer et al.~\cite{Sauer2021NEURIPS} evaluate several combinations of $\bF$ and $\bG$ and find an EfficientNet-Lite0~\cite{Tan2019ICML} and a FastGAN generator~\cite{Liu2021ICLR} to work especially well. When using a StyleGAN generator, they observe that the discriminators can quickly overpower the generator for suboptimal learning rates. The authors suspect that the generator might adapt too slowly due to its design which modulates feature maps with styles learned by a mapping network.

\section{Results}
\label{sec:results}
In this section, we first compare StyleGAN-XL to the state-of-the-art approaches for image synthesis on ImageNet. We then evaluate the inversion and editing capabilities of StyleGAN-XL. As described above, we scale our model to a resolution of $1024^2$ pixels, which no prior work has attempted so far on ImageNet. The resolution of most images in ImageNet is lower. We therefore preprocess the data with a super-resolution network~\cite{Liang2021ICCV}, see supplementary.

\subsection{Image Synthesis}
Both our work and ~\cite{Dhariwal2021NEURIPS} use classifier networks to guide the generator. To ensure the models are not inadvertently optimizing for FID and IS, which also utilize a classifier network, we propose random-FID (rFID). For rFID, we calculate the Fr\'echet distance in the \texttt{pool\_3} layer of a randomly initialized inception network~\cite{Szegedy2015CVPR}. The efficacy of random features for evaluating generative models has been demonstrated in~\cite{Naeem2020ICML}. Furthermore, we report sFID~\cite{Nash2021ICML} to assess the spatial structure of generated samples.
Lastly, sample fidelity and diversity are evaluated via precision and  recall~\cite{Kynknniemi2019NEURIPS} complementing FID and IS.

In \tabref{tab:sotapr}, we compare StyleGAN-XL to the currently strongest GAN model (BigGAN-deep~\cite{Brock2019ICLR}) and diffusion models (CDM~\cite{Ho2021ARXIV}, ADM~\cite{Dhariwal2021NEURIPS}) on ImageNet.
The values for ADM are calculated with and without additional methods (Upsampling \textbf{U} and Classifier Guidance \textbf{G}). Both BigGAN and StyleGAN-XL allow for the truncation trick, i.e., drawing a latent code from a truncated sampling space. A lower truncation $\psi$ increases sample quality while lowering sample diversity, resulting in an FID vs. IS tradeoff~\cite{Brock2019ICLR}.
We find that StyleGAN-XL substantially outperforms all baselines across all resolutions in FID, sFID, rFID, and IS.
An exception is precision and recall. According to recall, StyleGAN-XL's sample diversity lies between BigGAN and ADM, making progress in closing the gap between these model types.
StyleGAN-XL's increase in diversity comes at the price of individual sample quality measured by precision, where BigGAN is the best among all compared approaches.
Interestingly, StyleGAN-XL attains high diversity across all resolutions, which can be attributed to our progressive growing strategy. Furthermore, this strategy enables to scale to megapixel resolution successfully. Training at $1024^2$ for a single V100-day yields a noteworthy FID~of~$4.3$. At this resolution, we do not compare to baselines
because of resource constraints as they are prohibitively expensive to train. \figref{fig:highres} visualizes generated samples at increasing resolutions.
\sotapr
\highres

\subsection{Inversion and Manipulation}
GAN-editing methods first \textit{invert} a given image into latent space, i.e., find a style code $w$ that reconstructs the image as faithful as possible when passed through $\bG_s$. Then, $\mathbf{w}}\newcommand{\bW}{\mathbf{W}$ can be manipulated to achieve semantically meaningful edits.

\boldparagraph{Inversion.}
Standard approaches for inverting $\bG_s$ use either latent optimization~\cite{Abdal2019ICCV,Creswell2019NEURAL,Karras2020CVPR} or an encoder~\cite{Perarnau2016ARXIV,Alaluf2021ICCV,Tov2021TOG}. A common way to achieve low reconstruction error is to use an extended definition of the latent space: $\mathcal{W}+$. For $\mathcal{W}+$ a separate $\mathbf{w}}\newcommand{\bW}{\mathbf{W}$ is chosen for each layer of $\bG_s$. However, as highlighted by~\cite{Tov2021TOG}, this extended definition achieves higher reconstruction quality in exchange for lower editability. Therefore, ~\cite{Tov2021TOG} carefully design an encoder to maintain editability by mapping to regions of $\mathcal{W}+$ that are close to the original distribution of $\mathcal{W}$.
We follow~\cite{Karras2020CVPR} and use the original latent space $\mathcal{W}$.
We find that StyleGAN-XL already achieves satisfactory inversion results using basic latent optimization.
For inversion on the ImageNet validation set at $512^2$, StyleGAN-XL yields $\text{PSNR}=13.5$ on average, improving over BigGAN at $\text{PSNR}=10.8$.
Besides better pixel-wise reconstruction, StyleGAN-XL's inversions are
semantically closer to the target images.
We measure the FID between reconstructions and targets, and StyleGAN-XL attains $\text{FID}=21.7$ while BigGAN reaches $\text{FID}=47.5$.
\figref{fig:inversion} shows qualitative results. For implementation details and additional metrics, we refer to the supplementary.

Given the results above, it is also possible to further refine the obtained reconstructions. Roich et al.~\cite{Roich2021ARXIV} recently introduced pivotal tuning inversion (PTI). PTI uses an initial inverted style code as a pivot point around which the generator is finetuned. Additional regularization prevents altering the generator output far from the pivot. Combining PTI with StyleGAN-XL allows us to invert both in-domain (ImageNet validation set) and out-of-domain images almost precisely. At the same time, the generator output remains perceptually smooth, see~\figref{fig:interpolations}.
\inversion
\interpolations

\boldparagraph{Image Manipulation.}
Given the inverted images, we can leverage GAN-based editing methods to manipulate the style code $\mathbf{w}}\newcommand{\bW}{\mathbf{W}$. In \figref{fig:editing}~(Left), we first invert a given source image via latent space optimization. We then apply different manipulation directions obtained by GANspace~\cite{Haerkoenen2020NEURIPS} and StyleMC~\cite{Kocasari2021WACV}. Prior work~\cite{Jahanian2020ICLR} also investigates in-plane translation. This operation can be directly defined in the input grid of StyleGAN-XL. The input grid also allows to perform extrapolation by increasing the grid size.

An inherent property of StyleGAN is the ability of style mixing by supplying the style codes of two samples to different layers of $\bG_s$, generating a hybrid image. This hybrid takes on different semantic properties of both inputs. Style mixing is commonly employed for instances of a single domain, i.e., combining two human portraits. StyleGAN-XL inherits this ability and, to a certain extent, even generates out-of-domain combinations between different classes, akin to counterfactual images presented in~\cite{Sauer2021ICLR}. This technique works best for aligned samples, similar to StyleGAN's originally favored setting, FFHQ. Curated examples are shown in \figref{fig:editing}~(Right).
\editing

redpajama_set_name: RedPajamaArXiv


text:
Infant or infants are human children at the youngest stage of life.

Infant or Infants may also refer to:
 Cleveland Infants, a one-year baseball team in the Players' League
 Zanesville Infants, a short-lived baseball franchise affiliated with the Central League
 Infante, also anglicised as Infant, a title and rank in the Iberian kingdoms of Spain and Portugal
 a legal term referring to any child under the age of legal adulthood
 The Infant (Fonvizin play), 1872
 Infant school, a school for children up to age 7 in England and Wales
 Infant simulator, a lifelike electronic doll

See also

 Infant moth (disambiguation), several species of moth
 Infanta (disambiguation)
 Infante (disambiguation)
 Babyhood (disambiguation)
 Baby (disambiguation)

redpajama_set_name: RedPajamaWikipedia


text:
Laura Bond September 21, 2000 4:00AM
Things seem a bit peculiar over at nobody in particular presents, the local promotions house you no doubt recognize from all of those ticket stubs on the floor of your car. Last week, Russ Austin, who was then serving as NIPP's local booking agent, was informed that his services were no longer needed on a full- or even part-time basis, as his position was being, in Austin's words, "diminished." The announcement caused a reactionary ripple among a community of local musicians who'd come to appreciate Austin's accessibility and willingness to book heavier locals like Rogue and Throat Culture, bands that don't necessarily fit in with the larger NIPP operating aesthetic. The concern was, and continues to be, that in Austin's absence, NIPP will move away from booking and promoting local talent, particularly the kind with a metal bend. It's a concern Austin shares.
"[NIPP] want bands that are going to appease their image," he says. "A lot of the shows that I was booking, they didn't make a lot of money, but they at least broke even, and some of them did really well -- they'd bring in a lot of bar business. But I think part of it is that heavy metal is a word that evokes so much terror in people. It's almost like, even if it pays, its not something they are interested in pursuing."
Austin, who started with nobody in particular as an intern two and a half years ago, says that he may continue to work with the company as a freelance promoter. In the meantime, Peter Ore, who is already the company's national booking agent, will handle NIPP's national and local booking responsibilities. Ore says the volume of live music being presented by NIPP will not decrease in Austin's absence and that the decision to eliminate his position was based on simple economics, not a displeasure with Austin's heavy leanings. "There are maybe a few bands in town that can draw a big enough crowd to make giving them a headline gig worthwhile from a business standpoint," Ore says. "Russ did a great job, but at the end of the day, we cannot justify having a full-time position devoted to local bookings."
It's a good bet, though, that some players will make it their business to see how much of the NIPP calendar is occupied by locals. Ah, such is show business.
About a decade ago, as the fanzine movement was building momentum, many independent record stores took on a dual function: They became proletariat libraries of sorts, where stacks of crudely Xeroxed and stapled publications lined the counters, competing for space with, say, the cash register and the punk-rock clerk's unscrubbed elbows. Yet these days, the stacks are noticeably shorter. With the ease and accessibility of the Internet, self-publishing -- on paper, anyway -- seems to be on the wane. More and more DIY-minded folks have realized that ranting about capitalistic imperialism and how much Pearl Jam sucks in a limitless digital space is a tad less labor-intensive than spending all your time (and beer money) at Kinko's. Not to mention the way that making deliveries on your bike can kill an entire Saturday. Yet the zine is not dead, boys. Not entirely, at least.
Set to distribute bimonthly in Denver and Fort Collins beginning October 1, Royal Buzz is a new underground music mag led by Amy Parker, Joel Abell and Mike Freeman, the folks behind local indie label King Bee Records (now King Bee Music, Incorporated). According to Parker, the publication will differ from other zines in one important way: It will actually come out. On schedule.
"I think part of what happened with the movement was that zines got so lazy," she says. "Zines that were coming out once a month you now see maybe every three months. People would send stuff in and never see it published. We're kind of trying to change that perception by being reliable, having a little more integrity. We want magazine quality with a zine feel to it."
Parker, who quit a job as managing editor of the Fort Collins-based arts and entertainment magazine The Scene in order to edit Royal Buzz, says the magazine will feature articles by promoters, bandmembers (including Dan Fox from The Dropskots and Jim Chandler from The Down-N-Outs) and local underground scenesters. And though the Buzz's focus will be largely local, Parker says it will also cover national bands that don't always glean coverage in the pages of larger publications (like, um, the very one you are reading).
The first issue of Royal Buzz, a sixteen-page affair, will be officially launched at a party at the Starlight in Fort Collins on October 7 featuring Gina Go Faster, the first King Bee signees (who, incidentally, are set to release a new seven-inch with Durango-based outfit The Thirteens the same week). Soon after, the publication will launch a Web-based counterpart at geocities. com/kingbeerecords/. Beware, though: Reading this publication may cause you to adopt a hive mentality.
Coming soon to a theater (or classroom) near you this week are a couple of excellent shows: Le Tigre, the newest project from Bikini Kill matriarch Kathleen Hanna, will pounce on the Raven on Tuesday, September 26, with the Pin Downs and Rainbow Sugar. Should be a show to make Sarah McLachlan wet her Hanes Her Way briefs...The Meat Puppets, whose current incarnation includes only one original member, Curt Kirkwood, will open for Elastica on Sunday, September 24, at the Bluebird (see Critic's Choice, page 98). The Pups, a former SST Records fave and grunge forebear, release Golden Lies this week, the band's first full-length in five years. Fans may be surprised to learn that in addition to Kirkwood's signature country-punk guitar histrionics, the disc features rap, of all things. Somehow, though, it works...Local fusionist fave the Good Vibes Quartet releases The Bad Side of the Good Vibes Quartet in a performance at Trios Enoteca on Saturday, September 23. Feel it, man...It's not a concert, but it might be the thing to ensure that local musicians appear in front of audiences, not judges: Colorado Lawyers for the Arts presents a daylong seminar on legal issues facing musical and visual artists on Sunday, September 23, at Red Rocks Community College. The program, which will touch on grant- and contract-writing, marketing, the Internet and copyright issues, is probably worthwhile to independent artists who prefer to handle their own affairs rather than turn them over to record labels, as well as anyone looking to use the Internet as a marketing or distribution tool. 'Cause right now the music industry is sue-happy. Just ask MP3.com.
Laura Bond
Contact: Laura Bond
The 50 best rap lyrics of all time: Complete list
The 50 worst rap lyrics: The complete list
Country Singer Rudy Grant Is Retiring From Playing Venue Gigs
Ark Life Wants You to Sing Along
By Jon Solomon
Chive Fest Could Set the Tone for Future Events in Denver Parks
By Gina Tron
Thurston Moore Talks About His Four Summers Teaching in Boulder
By Tom Murphy

redpajama_set_name: RedPajamaCommonCrawl


text:
Spurs brand to be reviewed
By System Administrator August 30, 2001 12:00 am January 29, 2015 9:44 pm
Tottenham Hotspur Football Club is evaluating its brand as part of an overall examination of the business affairs of the club.
Management consultants have been looking at all areas of the club, as well as the brand identity, but a decision on whether the 'feasibility study is taken through to a detailed study' has yet to be made, says a club spokesman. It is not known when that decision will be taken.
'Tottenham has had consultants looking at the whole business area of the club, including internal and external communications, sponsorship and naming rights. Looking at the brand is part of that process,' the spokesman says.
30 August 2001 Projects
Viemeister takes Springtime offer
Sense and sustainability
Gutteridge creates pack for black


text:
Q: Batch parameter not getting correct The user input parameter is not getting set into checkoutdir variable.
@echo off
echo.
echo. [ SVN Updater ]
setlocal enableDelayedExpansion
set SOURCE=E:\Svncheckout\21MAY2016\
set SVN=C:\Program Files\TortoiseSVN\bin
set SVN_REPO_URL=https://192.168.1.35:8443/svn/
set projects=JPO/JPOMobile/branches/2016-05-18 JPO/JPOWEB/branches/20160518
(for %%i in (%projects%) do (
            echo. Going to repository - %%i
            echo. Please provide checkoutdir name
            set /p checkoutdir=
            echo. Checking out %SVN_REPO_URL%%%i from SVN...
            "%SVN%\TortoiseProc.exe" /command:checkout /path:"%SOURCE%%checkoutdir%" /url:"%SVN_REPO_URL%%%i" /closeonend:2
    )
)
echo.
echo. Operation complete.

Problem :
The checkoutdir is set to the projects directory name
For first iterarion, it is set to 2016-05-18 that results to path in E:\Svncheckout\21MAY2016\2016-05-18. For second iteration, it is set to 20160518 that results to path in E:\Svncheckout\21MAY2016\20160518

A: This code should work:
@echo off
echo.
echo. [ SVN Updater ]
setlocal EnableDelayedExpansion
set "SOURCE=E:\Svncheckout\21MAY2016\"
set "SVN=%ProgramFiles%\TortoiseSVN\bin"
set "SVN_REPO_URL=https://192.168.1.35:8443/svn/"
set "projects=JPO/JPOMobile/branches/2016-05-18 JPO/JPOWEB/branches/20160518"
for %%i in (%projects%) do (
    echo. Going to repository - %%i
    echo. Please provide checkoutdir name
    set /p "checkoutdir=Directory name: "
    echo. Checking out %SVN_REPO_URL%%%i from SVN...
    "%SVN%\TortoiseProc.exe" /command:checkout /path:"%SOURCE%!checkoutdir!" /url:"%SVN_REPO_URL%%%i" /closeonend:2
)
echo.
echo. Operation complete.
endlocal

It is necessary to reference a variable with delayed expansion using exclamation marks instead of percent signs after enabling delayed expansion.
Open a command prompt window, run set /? and read all output pages.
The environment variables referenced with percent signs are always expanded on parsing the command line respectively the command block defined with ( ... ).

A: It worked.I had to change %checkoutdir% to !checkoutdir!

redpajama_set_name: RedPajamaStackExchange


text:
De Corsellis-Sinaps hersencollectie is een verzameling van meer dan 3000 hersenen van mensen op sterk water. De collectie bevindt zich in het Psychiatrisch Ziekenhuis in het Belgische Duffel. De verzameling is eigendom van SINAPS, een samenwerkingsverband van het Psychiatrisch Ziekenhuis en de Universiteit Antwerpen.

De collectie is uitzonderlijk doordat wetgeving vaak in de weg staat om de hersenen te bewaren, evenals ethische reglementeringen.

Door het Fonds voor Wetenschappelijk Onderzoek - Vlaanderen is de collectie aangeduid als een van de zes verborgen parels van de wetenschap.

Geschiedenis
In 1951 begint de Britse neuropatholoog John Corsellis met het verzamelen van de hersenen van patiënten die overleden in Runwell Hospital, een psychiatrische ziekenhuis in Essex.

In 1994 overleed Corsellis en nam zijn collega Clive Bruton deze taak over.

In 1997 overleed deze collega en verhuisde de verzameling naar een ziekenhuis in Ealing.

In 2016 kampte het ziekenhuis met plaatsgebrek en stootte men de hersencollectie af die op dat moment uit 8500 hersenen bestond. In het Psychiatrisch Ziekenhuis van Duffel was er interesse in de hersenen die voor psychiatrisch onderzoek interessant zijn en zo verhuisden 3348 hersenen van het Verenigd Koninkrijk naar België.

Externe link
 Officiële website

Verzameling
Duffel
Hersenen


text:
Hepatitis C screening pilot project at a tertiary care addictions services in Liverpool
First published: 10/05/2019 | Last updated: May 20th, 2019
In England, data from the Unlinked Anonymous Monitoring (UAM) 2013 survey of people who inject drugs (PWID) found 50% tested positive for antibodies to HCV (anti-HCV).
After working closely with a Hepatitis Community Nurse, based at Brownlow Group Practice and Royal Liverpool Hospital, for many years on a client-by-client basis, we decided a year ago to set up a pilot Hepatitic C Mouth swab screening at community drug team site in the addictions service.
This was a retrospective cross sectional evaluation. We evaluated the data collected at this clinic to see how many mouth swab positive patient would go one to be PCR positive and eventually take up treatment
We screened a total of 144 patients, out of which 86 (60%) were Hepatitis C mouth swab positive. Out of the 86 positive patients 16% were completely new diagnosis, 17.4% had cirrhosis on fibroscan, 10.4% had been referred for assessment to specialist Hepatitis C clinic and were on the waiting list, whilst 19.7% were either currently on or awaiting start of treatment. The rate of treatment is way above the national rate for Hepatitis C treatment uptake.
It is apparent that the addictions services are one of the most important services to offer Hepatitis C screening, particularity PWID population. We have been running this pilot which shows really high uptake of treatment. The commissioners in the country should take note of the outcomes and consider funding full BBV services based and integrated in addictions services across the country.
Jayne Wilkie, Senior Hepatitis Nurse, Royal Liverpool University Hospital Jodi Goss, Physical Health Nurse, Addictions Services, Mersey Care NHS Foundation Trust
Qualified as a registered nurse in March 2003 at John Moores University. After qualifying she worked on medical gastroenterology ward at the Royal Liverpool Hospital. In 2009 she moved to primary health care and worked within a General practice to assess needs of patients with hepatitis c within the hard to reach marginalized groups. Her honorary contract enabled her to work within secondary health care at the Royal Liverpool hospital.
Jayne is now a Hepatology Nurse Specialist for the Royal Liverpool Hospital and continues to see hard-to-reach clients in the community with outreach clinics in the prison, GP Practice and local drug teams.
Conflicts of interest:
Funding Sources: Liverpool City Council
No conflict of interest
Download the full presentation here
Dr Yasir Abbasi
Yasir Abbasi is the Clinical Director for Addiction Services at Mersey Care NHS Trust and an Honorary Senior Lecturer at the University of Liverpool. He was a member of the Addictions to Medicine Task force set up by PHE, Yorkshire & Humber region. In 2013 he was awarded the doctor of the year award by Yorkshire Evening Post. He is currently the chair for the North British Association for Addiction Professionals and is also a founding trustee of P.A.I.N (Pain Killer Addiction Information Network) charity. He is the regional specialty representative for the Royal College of Psychiatrists. He has a special interest in prescription drug dependence and is involved in the all parties parliamentary group set up to address this issue. He has appeared at various media outlets and written in newspapers to raise awareness around issues related to substance misuse.


Now that you've invested in gold and silver bullion, don't forget about properly storing and protecting your investment. There are two main options in storing your gold purchase: you can have it stored by a third-party such as a depository or bank deposit box, or take personal possession.
Many investors prefer to store their gold at home because they worry about the difficulty of accessing their gold in the event of an economic crisis. They feel that physical possession is the only way to ensure true ownership. If you wish to access your gold, there is no need to contact a bank or depository and wait for your investment to be shipped to you. With home storage, you have exceptional liquidity because you can sell or trade your metal 24/7.
There are other good reasons to store your gold at home. Many people prefer to store gold at home because it is very private. Another benefit is that at-home storage of gold is free, while depositories charge a monthly or yearly fee.
On the other hand, keeping a significant amount of gold or silver in your home can be risky if you do not take the right precautions to protect your investment. Just as when storing any valuables or cash in your home, it is important to keep your precious metals safe from theft, fire, or other catastrophic events.
In order to sleep well at night, never hold more gold in your home than you feel comfortable. If you feel that it is too big of a risk to keep your entire investment stored in your house, consider keeping just a few gold coins so that you have them on hand in case of emergency.
If you do choose to store your gold and silver at home, use discretion. It can be tempting to pull out a gold bar to show off, but investment should be a private decision. Limit the number of people who have knowledge of your collection to reduce the chance of becoming a potential target.
For the best protection, store your precious metals in a safe. Fortunately, the relatively small area required to store gold makes home storage convenient. The high value and density of gold means that even a small home safe is large enough for most physical gold investments.
If you opt for a home safe, make sure the safe is physically secured to the building, to prevent a thief from taking the entire unopened safe. Other investors keep their gold in the walls or ceiling of their home, or bury it on their property.
Think about additional ways to keep your gold stash away from the view of a burglar. Consider installing a home alarm system for further protection. Finally, make sure that your homeowner's insurance is sufficient for covering your precious metal collection. If in doubt, contact your insurance agent to ensure that your investment is covered.
When it comes to home storage of gold, an important factor to consider is your security. Once you have taken the proper safety measures, you will feel more comfortable having physical possession of your coins and bullion. Precious metal investors agree that peace of mind is worth its weight in gold.


The climate and the organizational culture are two very important components for increasing the knowledge process and the culture. The organizational climate is a essential component in the socialization process. Culture socialization and of the knowledge in an organization is a fundamental premise for its success in times where collaboration is a source of competitive advantages.


The Peepal Grove School, Waldorf schools (or Steiner schools), Sands School, Summerhill School, Friends schools, Sudbury Valley School, and open classroom schools are all examples of such alternative education systems.
However there is ample of scope for these ideas to be included in mainstream education system. This can be seen in the changes made to the kindergarten approach. Kindergarten was the brainchild of Friedrich Frobel from Germany. Similarly many qualified and talented writers have paved the way for the modern education system to take its present form. Thinkers and writers such as Johann Heinrich Pestalozzi, Amos Bronson, Alcott, Ralf Waldo Emerson, Henry David Thoreau et al have all contributed to the education system in a big way.
Other notable names include Maria Montessori, Rudolf Steiner, John Caldwell Holt, Paul Goodman and others.


Lydia frontman and founder Leighton Antelman doesn't take his band for granted… anymore.
When the Arizona-based indie-rock project initially formed in 2003, a high school-aged Antelman was mostly just messing around and having fun writing songs with his friends. "I was winging it," he describes.
When high school ended, Antelman enrolled at Arizona State but, only a year in, realized higher education wasn't right for him. So, as an experiment (and, he notes, against every older adult's recommendation), he hit the road with Lydia. Now it's 14 years later, and he's been churning out albums and touring ever since.
Today, Lydia are seven albums into their career, with their latest output, Liquor, arriving on June 22 via Weekday Records. The trio, which now features core trio Antelman, Matt Keller, and Shawn Strader, all live in the Phoenix suburbs and continue to collaborate on new material.
When they weren't driving to each other's houses to share fresh material, the trio also came together at Big Bear Lake in California, where they recorded three tracks from Liquor. Renting a house together, the group holed up and recorded a mountain of material, which they later whittled down to form the bones of Liquor.
Moving forward, you count on Lydia to keep rolling out new music; writing is just something Antelman builds into his day, like any job. "I think a lot of musicians can get into trouble if they're waiting for something to inspire them," he says.
Dynamic melody-driven vocals and energetic performances.
Download our new Glass Key EP on iTunes, Amazon, Google Play, and anywhere else you download and listen to music!


text:
Q: Is there a way to access the source code of the DJI Pilot android application? I'm working on a flight planning application for use in automating drone flights for purposes of mapping(creating orthophotomosaics and 3D models). I've been able to use the DJI Mobile SDK and the DJI UX SDK for Android to create an app similar to DJI GO. However I'd like to have develop a mapping mission and oblique mission similar to the one in DJI Pilot. Instead of creating the UI elements and implementing the logic from scratch, I was wondering if there's an SDK for DJI Pilot from which I can build on?

A: There isn't anything better than the uxsdk I'm aware of.
And I think ux is going end of life soon.
The source code is proprietary code. The apk's is encrypted, and it would be illegal to use any source code from it anyway.
So I think you left to implement everything from scratch.


North Star-Bailey Company offers a complete line of energy saving vinyl replacement windows, wood clad replacement windows, and both vinyl and wood clad new construction windows. Our window product lines include: awning windows, double hung windows, casement windows, sliding windows, picture windows, basement windows, garden windows, bay windows, bow windows, special shape windows and custom box bay windows.
All of our window products are Energy Star qualified, which save you energy and money, increasing the comfort, warmth and value of your home.
Take steps today to make your windows and home more energy efficient, more beautiful, easy to clean and maintenance free as well, all saving you money on high heating bills and costly maintenance.


text:
Least green states include leaders in wind energy
The Huffington Post yesterday featured an article on the "10 least green states" in the U.S., based on an analysis by 24/7 Wall St. While some were no surprise, the list included a few that have been truly outstanding in their development of wind power.
Topping the group of wind turbines leaders on the list is Texas. The Lone Star State has moved out to a strong lead in recent years, and now has more wind farm installed than the next three states (Iowa, California, and Minnesota) combined–or to put it another way, more wind power than the 38 states with the least installed.
How did Texas achieve such a commanding leadership position? Chris Head, at Americans for Energy Leadership, has the story (The Curious Case of the Texas Wind Industry, February 9, 2011). Briefly, the state is blessed with extraordinary wind resources (enough to produce more electricity than the entire U.S. uses) and has taken advantage of them through a series of visionary policies.
Other wind leaders on the HuffPo list are Illinois (ranking 7th in the U.S., with enough installed wind farm generating capacity to power the equivalent of more than half a million average American homes) and Indiana (ranking 11th, with enough wind energy to power 344,000 homes). Both states have come on strong in recent years due to the discovery that they have stronger winds than previously estimated by wind researchers.
It's no wonder that wind energy has also made great strides nationally recently, accounting for more than a third (35%) of all new electric generating capacity installed in the past four years and installing enough capacity in the past three to generate more electricity than five new nuclear power plants.
From a utility's standpoint, adding wind farm to the generation portfolio provides excellent insurance against a range of possible regulations–wind has zero air and water emissions and produces no hazardous waste.
Wind power also generates electricity with virtually no water use, unlike all forms of thermal generation (coal, natural gas, nuclear)–the U.S. Department of Energy has calculated that if wind energy grows to supply 20% of U.S. electricity by 2030, some 4 trillion gallons of water will be saved–enough to fill a string of water bottles, laid end-to-end, stretching from the Earth to Saturn and back, twice.
At the same time, the economies of local communities, counties and states all benefit when new wind farm projects are installed, bringing rental payments to farmers and ranchers and local property tax revenue to counties. This year alone, wind turbines project operators will pay farmers an estimated $120 million for the use of a small fraction of their land.
So, hats off to Texas, Illinois, Indiana and the many other states that have powered ahead in recent years with wind, filling the leadership vacuum that has been left by the lack of a serious national energy policy.
By Tom Gray, www.awea.org/blog/
eólicaeólicoLatinoaméricaMéxicoZacatecas
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Post beautiful photos of different dance forms.
The Enchanting History of Ballet Dancing - A Magic in Making
It is believed that ballet dancing emerged as early as the Renaissance period (1400s) in Italy. The early ballet dance forms were of classical type, which after several reforms, developed the neoclassical style.
Ningthoujam Sandhyarani
"At the ballet, you really feel like you're in the presence of something outside the rest of your life. Higher than the rest of your life."
- Robert Caro
Ballet, an age-old form of dance, has always left the audience in awe of its beauty. The illusion of unbelievable movements--seemingly effortless, are choreographed either to tell a story, or simply express music. Every emotion of the story is expressed with unnatural body movements, perfected after years of practice. For those who cherish the dance form not just as performers but also as spectators, ballet is a religion.
15th Century: The Origin
The dominant presence of French words in ballet vocabulary is often misinterpreted as the underlying evidence of the origin. French courts did popularize ballet, but the first formal performance is accredited to an Italian, Domenico da Piacenza. He was a dance master famous for his proficiency in various Italian folk dances.
In 1416, he wrote a book, De arte saltandi et choreas ducendi (On the Art of Dancing and Conducting Dances), in which he explained four different dance forms and their movements. He named one of these forms as Balli or Balletti (singular: balletto).
It is believed that in 1490, Bergonzio di Botta (an Italian dance master) asked Leonardo da Vinci to design a balli pageant as an entertainment for the Duke of Milan. The dance, thus designed, was an artistic representation of the harmony of celestial bodies.
During the Italian Renaissance, balli was used as a tool to entertain and impress the neighboring nobility. Every aristocratic extravaganza was essentially accompanied by and often considered incomplete without balli. Men wore 'contemporary' court house costumes, like wigs, jackets, bloomers, and blouses. Women, on the other hand, were dressed in ornate gowns for these dances, which were entrammeling even to stand.
16th Century: The French Connection
In 16th century France, nobility enjoyed various celebrations with social dances, known as ballets de cour. These dances were generally performed by hired artists who, after their performance, asked the audience to join in. In 1533, Catherine de' Medici, an Italian belonging to the Medici family--de facto rulers of Florence, married Henry II, the heir to the French crown. Her Italian upbringing and her love for art became very evident in the magnificent and entertaining parties of her court.
Although these parties were arranged with an aim of informal politics, they did in fact have a huge influence on ballet as we know today. Catherine, being an art connoisseur, promoted ballet in her parties and also provided financial support. Balthasar de Beaujoyeulx, an Italian violinist, composer, and choreographer, had accompanied Catherine to France as her chief composer. In 1573, when Charles IX was King of France, Ballet des Polonais was performed for the Polish Ambassador. It was the first ballets de cour and was choreographed by Balthasar. It was performed on a temporary stage constructed in the Tuileries Palace by sixteen ladies of the court representing the sixteen provinces of France. The music was composed by Roland de Lassus and was performed by an orchestra of thirty violins.
In 1581, another of Balthasar's choreographed ballet was performed in the court of Catherine de' Medici. It was commissioned by Louise of Lorraine (wife of Henry III, later Queen consort of France) to celebrate the wedding of Anne de Batarnay de Joyeuse, Henry III's intimate friend and Marguerite de Lorraine (Sister of Queen Louise). Over twenty-four dancers performed the five-hour long ballet based on the story of mythological Greek goddess, Circe. It was the first ballet to have a printed account and was published in 1582 as The Ballet Comique de la Reine by Balthasar. It is well-known in the history of ballet for its dramatic enactment and elaborate costumes.
This particular performance had an overwhelming effect on entire Europe. The art of ballet began to be considered as worthy for every aristocratic celebration across the continent.
17th Century: Royal Academy of Dance
King Louis XIV of France was very close to his mother and shared her interest and admiration for art. As a young prince, he was attracted to ballet and debuted at the age of 14 as Apollo, the Sun God, in a ballets de cour - Le Ballet de la Nuit (The Ballet of the Night). He wore an ornate golden dress with golden high heels.
Louis Hesselin choreographed the twelve-hour ballet and parts of Louis XIV performance to flaunt his shoes, which laid the foundation for the first five positions of ballet as documented by Pierre Beauchamp.
The young Prince Louis' performances were not as strenuous as we see today. They were rather divided into smaller sections and performed at intervals. As he acquired the throne, he continued to support ballet and other art forms. He founded the Académie Royale de Danse in March 1661 and further added to it, the Paris Opera in 1669 (formerly known as Académie Royale de Musique).
Françoise Prévost debuted in the Paris Opera in a ballet called Atys. She was known for her expressive portrayal and light movements. Impressed by her talent, Louis XIV started training in formal ballet under her. The King's interest in ballet popularized the art form, and it became part of everyday life in the country. It influenced the court culture of Spain, Germany, Portugal, and many other European countries. Aristocracy in Europe was largely impressed and began active participation in ballet.
18th Century: Revolution in Ballet
During King Louis XIV's reign, ballet flourished in France and the neighboring countries. Professional dance troupes were formed, which traveled to increase awareness and entertain people.
The advent of 18th century brought some changes in the age-old ballet and set the wheels in motion to completely revolutionize the dance form. Until the late seventeenth century, male dancers performed the feminine roles, as the ballet costumes were heavy and required strength. However, in 1681, the first ballerina appeared in Le Triomphe de l'amour (triumph of love).
In 1700s, many other significant events took place that revived ballet. Raoul Auger Feuillet wrote Chorégraphie, ou l'art de décrire la danse which described in detail, the steps, positions, and techniques of ballet as prevalent at that time and are still followed.
Lettres sur la danse, et sur les ballets, one of the greatest works of Jean-Georges Noverre, emphasized that ballet was a dance of expressions and therefore must be performed by the dancer with absolute use of the entire body. This required giving up the heavy head dresses, costumes, and resorting to minimal clothing.
This century also brought a change in themes of ballet. Mythological stories were replaced by romantic themes, thus broadening the scope for female performers. Gradually, female performers known as ballerina assumed more important roles. Because of the lean and light bodies of ballerinas, choreographers expanded on the ideas of the dancers floating in air like a feather and landing perfectly on the toes. Soon enough, men, who once ruled the dance form, became supporters and were included in the ballets to aid the ballerinas.
Although France was considered home to ballet, Venice became the dance center of Europe, largely due to the Venice Carnival. Many dancers from around the world came to participate in the carnival, and thus several ballet performances took place in the carnival, making Italian ballet techniques a favorite among most of southern Europe.
19th Century: Ballerinas and Romantic Movement
By the 19th century, ballet became a female occupation. Most of the major works featured female dancers as predominant performers. Also, romantic ballets replaced the older ballets.
One of the first romantic ballets, La Sylphide, was choreographed by Filippo Taglioni in 1832. It was performed by his daughter Marie Taglioni, who after her performance in the above-mentioned ballet, was popularly known as the Christian Dancer.
In this ballet, Marie, who was adorning the role of a fairy, wore a sleeveless costume that ended just between her knee and ankle. The costume became an overnight sensation in Paris. Soon after, many other ballerinas became well-known for their performances and their innovation in pointe work. Geneviève Gosselin and Fanny Elssler were two very notable artists of this time.
The boxed toe shoe that every ballerina wears today was invented to support the leg movements as a result of development of new techniques in ballet.
Ballet in Russia
In the end of the nineteenth century, ballet lost its hold on Paris. During the same time, ballet was becoming popular in Russia and Denmark due to talented and dedicated dance masters, like August Bournonville, Jules Perrot, Arthur Saint-Léon, and Enrico Cecchetti.
The most notable work was done by Marius Petipa who choreographed many ballets, like The Pharaoh's Daughter and The Talisman, that had the orientalism effect. Petipa together with composer Tchaikovsky choreographed The Nutcracker and The Sleeping Beauty. He is also known for his revival of Giselle and Swan Lake.
Ballet Russes, a ballet company founded in 1909 by Sergei Diaghilev, brought back ballet to Paris. Most members of the company belonged to the exiled community. He fused the French and Russian ballet in Russian folklore--the Firebird and the Petrushka.
Meanwhile, back in Russia, ballet was developing under the Soviet rule, but at a very slow pace. Although, the era was marked by few of the greatest ballet performances, like the Romeo and Juliet by Prokofiev and Lavrovsky, the Flames of Paris--the ballet version of the Pushkin poem, The Fountain of Bakhchisarai--composed by Boris Asafiev and choreographed by Rostislav Zakharov, and the Cinderella by Prokofiev.
Ballet in the United States of America
Michel Fokine started his career in St. Petersburg and then moved to Paris and worked with Diaghilev on numerous ballets. After a misunderstanding, Fokine left Paris and settled in New York. In his own style and backed with historic cultural evidences, he choreographed Scheherazade and Cleopatra. He also developed his own versions of the Firebird and Petrushka. The Dying swan, which brought overnight fame to the Russian ballerina Anna Pavlova, is considered to be the best of Fokine's work.
Neoclassical Ballet: Prominent Works
Frederick Ashton
• Symphonic Variations, 1946
• Cinderella, 1948
• Sylvia, 1952
• Romeo and Juliet, 1956
• Ondine, 1958
• La Fille mal gardée, 1960
Roland Petit
• Le Jeune Homme et La Mort, 1946
• Carmen, 1949
• Notre Dame de Paris, 1965
• Proust, ou Les intermittences du coeur, 1974
• Clavigo, 1999
Serge Lifar
• Les Créatures de Prométhée, 1929
• Le Spectre de la rose, 1931
• L'Après-midi d'un faune, 1935
• Icare, 1935
• Istar, 1941
• Suite en Blanc, 1943
• Apollo, 1928
• Serenade, 1934
• Concerto Barocco, 1940
• Symphony in C, 1947
• Agon, 1957
• Jewels, 1967
Famous Ballets
• Swan Lake
Choreography by Julius Reisinger; Music by Pyotr Tchaikovsky
• Romeo and Juliet
Choreography by Leonid Lavrovsky; Music by Sergei Prokofiev
• Giselle
Choreography by Coralli and Perot; Music by Adolphe Adam
• The Nutcracker
Choreography by Petipa and Ivanov; Music by Pyotr Tchaikovsky
• La Bayadère (The Temple Dancer)
Choreography by Marius Petipa; Music by Ludwig Minkus
• Le Sacre du Printemps (The Rite of Spring)
Choreography by Vaslav Nijinsky; Music by Igor Stravinsky
• A Midsummer Night's Dream
Choreographed by Frederick Ashton; Music by Felix Mendelssohn
• The Sleeping Beauty
Choreography by Marius Petipa; Music by Pyotr Tchaikovsky
• Don Quixote
• Cinderella
Choreography by Rostislav Zakharov;Music by Sergei Prokofiev
Tips to Prepare for Pointe Work
Famous Ballerinas
Beginning Ballet as an Adult
Ballet Dance Moves
Dance Team Names
Types of Dances and Different Dancing Styles
Dance Group Names for Girls
Salsa Dance Costumes: Salsa Dancewear and Shoes
Tips for Buying a Dance Costume for a Competition
History of Jazz Dance
Jazz Dance Steps
African Dance Types
Inspirational Dance Quotes
Cool Dance Moves
Facts about Mexican Folk Dancing
Facts about Flamenco Dancing
How to Make a Tutu
Mexican Dances
Charleston Dance Steps: How to Dance the Charleston
Cool and Catchy Names for Your Dance Studio
List of Most Famous Tap Dancers Of All Time
Step-by-Step Dance Routines for Beginners
Disco Dance Moves
Line Dancing Steps for Beginners
Traditional Jewish Dance
Swing Dance Styles
History of Brazilian Dance
Famous Dancers
Street Dance Moves for Beginners
Two-step Dance Instructions
History of Cumbia Dance


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Home / Stories / Archive by category "Adventurous Journey"
Corey raises funds for the Australia Zoo Hospital and meets the Irwin family!
Through taking part in the readathon, Corey raised an amazing $1326, making him the fourth highest fundraiser for the Zoo Hospital. As a reward for his efforts, Corey had the incredible opportunity to tour the hospital with the zoo owners, the Irwin family!


Holidays are a fantastic time to get family and friends around, not only for great meals and to make memories. But it's also a great time to test out some new cocktail recipes.
Back when I was a kid in Casper, There seemed to be so many fun and kid-centric things to do between the swimming pools, movie theaters, and arcades.
What house would you sort your hometown into? Gryffindor for the brave? Slytherin for the ambitious?
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While the times are getting better, some of us are just destined to be broke.


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Q: How to create a heartbeat using MassTransit when consuming a message from AWS SQS Using MassTransit with AWS SQS (simple queue service), we would like to implement a heartbeat, as advised in the AWS docs:

If you don't know how long it takes to process a message, create a heartbeat for your consumer process.

This requires changing the visibility of the message from code. AWS provides the ChangeMessageVisibility API for this. MassTransit does not seem to support this native AWS interface. Which is still no problem by itself, as it boils down to calling a HTTP endpoint (or using the SQSClient SDK, C# in our case).
For this to work one needs the RequestHandle. It is part of the raw response when the AWS CLI is used to receive the message (aws sqs receive-message --queue-url http://localhost:4566/queue/my-qeueu).
{
    "MessageId": "878eb3d8-2d1e-406f-87b6-24a3d493f5a9",
    "ReceiptHandle": "878eb3d8-2d1e-406f-87b6-24a3d493f5a9#464f467d-82a9-421f-bc10-cddd63630b7d",
    ...left out for brevity
}

Now the message ID is known at run time when inspecting the ConsumeContext<TMyMessage>.ReceiveContext.TransportHeaders['MessageId']. The RequestHandle, however, is nowhere to be found, or so it seems.
So how can one change the visibility timeout of the message in a AWS SQS queue, consumed by a MassTransit consumer?

A: As this is a duplicate of the same question asked on GitHub Discussions, the same answer would be that MassTransit automatically extends the visibility timeout until the consumer completes.
Up to the maximum of 12 hours that SQS allows.


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text:
class AbstractReplicationMessage(object):
    pass

class ObjectCreateMessage(AbstractReplicationMessage):
    pass

class ObjectDeleteMessage(AbstractReplicationMessage):
    pass

class ObjectUpdateMessage(AbstractReplicationMessage):
    pass

class PingMessage(AbstractReplicationMessage):
    pass

class PongMessage(AbstractReplicationMessage):
    pass

class ReplicationClientService(AbstractService):
    """
    The ReplicationClientService is responsible to replicate certain
    game objects over a network to the server and/or other clients.
    TODO: implement
    """

    priority = 5

    def __init__(self, connection = ("localhost", 12345)):
        self.messages = []
        # TODO set up client

    def on_tick(self, dt):
        """
        Send contents of objects this client/server has authority over.
        Receive contents of other objects and update them.
        """

        # TODO receive from server
        # deserialize messages
        # loop messages
        # if msg.type == object update:
        # get object by ID and set attributes

        # check for dirty objects
        # serialize objects
        # append to messages
        # TODO send all messages to the server

        pass

    def on_object_added(self, obj):
        """
        Check if the object is to be replicated (i.e: has the 'local'
        attribute). If it is a locally created object (local=True) it
        is to be replicated to the server.
        """
        try:
            if obj.local:
                self.messages.append(('c', obj.id, obj.serialize()))
        except AttributeError: pass # not a replicable object

    def on_object_removed(self, obj):
        """
        Check if the object is replicated (see 'on_object_added') and
        whether or not it is a local object. If it is a local object
        it needs to be destroyed on the server and on other clients
        too, thus a destroy message needs to be sent.
        """
        try:
            if obj.local:
                self.messages.append(('d', obj.id))
        except AttributeError: pass # not a replicable object

redpajama_set_name: RedPajamaGithub