Latent Transsexual
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Latent Transsexual
News & discussion for transsexual men & women r/ Transsexual
The Difference Between a Non-GRS Seeking Transwoman and a Non-Op. MtF Transsexual
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The Difference Between Being Transgender & Being Transsexual
Hi, so as someone who wants to learn about the difference between transsexual and transgender, what is the difference?
How does this sub feel about nonbinary and genderfluid people?
Soon to be woman♡ (Preop transsexual)
A space for transsexual women and men to discuss their situations and opinions.
Here, ‘trans’ is always short for ‘transsexual’ — a man or woman born with a mind-body gender incongruity that causes dysphoria, for which the remedy is full socio-medico-legal transition.
transgender erasure of transsexuals
Censor usernames - Don't mock individuals
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A Non-GRS seeking Transwoman is a transgender person with the mind and personality of a woman who, in fact, lives her life as woman and who maintains every aspect of a female appearance and behavior; but, who does not necessarily suffer from gender dysphoria, and who may not even consider herself to have a medical condition that requires any sort of surgery or hormonal treatment.
A Non-Op MtF Transsexual is a person with the medical condition of gender dysphoria who has the mind and the personality of a woman who, for some reason, is totally unable to be happy in the body of a man and has determined for herself that she will transform it into the body of a woman; but, who also, for some other reason, stops short of making an attempt to transform her male sexual organs into female ones, even though she claims she genuinely desires them. A Non-Op MtF Transsexual maintains every aspect of a female appearance and behavior and lives her life as a woman because she has a complete inability to live as a man without eventually becoming severely depressed and running the risk of either commiting suicide or becoming a fatality of the morbid consequences of a pattern of poor judgements and neglect resulting from the distress of dysphoria. In order to prevent herself from becoming a fatality, her medical condition requires her to receive sexological therapy from a sexologist or a licensed social worker, follow a strict regimen of good health and best lifestyle practices, and be treated with hormone replacement therapy in concert with a series of anatomically altering surgeries to correct her physical sexual characteristics by transforming them into female ones. Her ability to forgo GRS is the only thing that differentiates her from any other MtF Transsexual, and she may cease being a Non-Op at any time by making an effort to obtain GRS.
Without making that effort, the survival rate of Non-Op MtF Transsexuals is low enough for a speculation to be made that Non-Ops are no more able to cope with the insufficient treatment of their gender dysphoria than GRS operation seeking or "Op" MtF Transsexuals who fail to obtain the sexual organ transforming operation. For this reason, when a Non-Op MtF Transsexual and an Op MtF Transsexual who has failed to obtain GRS soon enough are dead, the two MtF Transsexuals are virtually indistinguishable from each other. Therefore, although some MtF Transsexuals prefer to maintain a distinction between Non-Op MtF Transsexuals and themselves by emphasizing the pursuit of GRS as the mark of a True Transsexual and refusing a Non-Op MtF entry into the same social category, as if she were merely a Pseudo- Transsexual, the justification for this distinction is only theoretical, and has not been proven to exist in real life. To the contrary, when it is taken into consideration that sufficient medical treatment for gender dysphoria is not equally accessible or affordable to all MtF Transsexual Women, a Non-Op MtF Transsexual is much more likely to be a Transsexual who does not seek GRS because of some existing impediment to achieving it than a gender dysphoria suffering type of Transwoman.
A transgender person is anyone whose mind and whose personality crosses the traditional gender boundaries of their culture based upon their anatomical sex.
A transsexual is a person who has a medical condition of gender dysphoria that requires treatment through a combination of hormone replacement therapy, surgical correction of physical sexual characteristics, and, in the most severe cases, genital reassignment surgery which effectively transforms the sexual genitalia of the patient from those of one sex to the other. Due to a steep rise in morbidity and clinical depression among transsexual patients when the condition is left untreated, the lack of sufficient treatment can often be fatal.
Sometimes, a transgender person has an overwhelming desire to and/or derives an intense satisfaction from an increased ability to identify with or posses the physical sexual characteristics of a gender other than one that is traditionally assigned to the anatomical sex of the body at birth, socially referred to as gender euphoria. This is not always considered to be a medical condition or, if it is considered to be one, one for which similar treatment is medically necessary. Therefore, transgender persons who would like to obtain the same access to hormone replacement therapy or certain surgical procedures may not be permitted to do so by the medical establishment or may not be able to pay for these treatments because they aren't covered by health insurance. This refusal of access is routinely a source of an immense amount of frustration and anger at the medical establishment, as well as a deep distrust of all transsexuals who maintain a distinction between the condition of gender dysphoria and gender euphoria as it is perceived by non-dysphoria diagnosed or non-genital reassignment surgery seeking transgender persons. As a result, anybody who publicly identifies as a transsexual or refers to gender dysphoria as a medical condition is subject to social ostracism or banishment by the transgender community.
I have a transgender friend. They are in the process of transitioning, and I also believe I may be trans, which is why I am asking about it. I don’t understand what is different from transgender and transsexual, as a lot of people I know who are transgender have transitioned, thereby, from what I know, would make them transsexual? I don’t understand it too well.
I was curious if this sub is against gender non-conforming people, like nonbinary and genderfluid people. I was lurking for a little bit and I can't tell, so can anyone answer? I was also wondering how this sub feels about trans people who dont have bottom dysphoria, and trans people who define themselves being trans because of euphoria, not dysphoria.
Until about ten years ago there were several blogs by women who had undergone treatment decades ago and were experienced by both society and themselves as simply and unconditionally just women. The friend who helped me realize that for transsexuals transitioning is just taking a simple step across to the other side wrote one of them.
Many of these women tried to send a message to those like themselves that the purpose of treatment is to simply fix what is wrong. And that once it was the pain could be forgotten. And that since they no longer had no need to carry the diagnosis, transsexuals were distinct from transgenderists... who identified as transgender, were proud of it, and remained transgender for life.
Most of these women stopped writing around the same time. My friend included. Because they were doxxed by transgender activists who told them that unless they shut up or made their blogs private their information would be plastered across the internet.
And since transsexuals in general only wish to live anonymous lives as normal men and women, publishing their past would have destroyed the peace and joy they enjoyed in the real world.
I guess I'm an anachronism. When I joined forums to search for information I was terrified by what people told me was the right thing to do.
Accept myself as I the broken misfit I felt I was.
Realize that the way society and I have always viewed sex and gender is wrong.
View the abominable male thing that is the root of my suffering as a lovely pleasurable female organ
Understand that the surgery that was my hope would make no difference whatsoever to what I was
Comprehend that it didn't matter if I looked, sounded and dressed like a man because it was the duty of society to call me a girl if I just asked it to
Proudly love remaining transgender no matter how well I could "pass" (for the real thing)
I guess I was just obtuse because none of that made sense to me. And all I wanted was to fix what was wrong so I could be like my sisters.
TRANS GIRLS 90K (@transgirls8) | Твиттер
News & discussion for transsexual men & women
GitHub - NVlabs/stylegan: StyleGAN - Official TensorFlow Implementation
Transsexuality | Britannica
Transsexual Images, Stock Photos & Vectors | Shutterstock
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StyleGAN - Official TensorFlow Implementation
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Picture: These people are not real – they were produced by our generator that allows control over different aspects of the image.
This repository contains the official TensorFlow implementation of the following paper:
A Style-Based Generator Architecture for Generative Adversarial Networks
Tero Karras (NVIDIA), Samuli Laine (NVIDIA), Timo Aila (NVIDIA)
https://arxiv.org/abs/1812.04948
Abstract: We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces.
For business inquiries, please contact researchinquiries@nvidia.com
For press and other inquiries, please contact Hector Marinez at hmarinez@nvidia.com
★★★ NEW: StyleGAN2-ADA-PyTorch is now available; see the full list of versions here ★★★
Material related to our paper is available via the following links:
Additional material can be found on Google Drive:
All material, excluding the Flickr-Faces-HQ dataset, is made available under Creative Commons BY-NC 4.0 license by NVIDIA Corporation. You can use, redistribute, and adapt the material for non-commercial purposes , as long as you give appropriate credit by citing our paper and indicating any changes that you've made.
For license information regarding the FFHQ dataset, please refer to the Flickr-Faces-HQ repository .
inception_v3_features.pkl and inception_v3_softmax.pkl are derived from the pre-trained Inception-v3 network by Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. The network was originally shared under Apache 2.0 license on the TensorFlow Models repository.
vgg16.pkl and vgg16_zhang_perceptual.pkl are derived from the pre-trained VGG-16 network by Karen Simonyan and Andrew Zisserman. The network was originally shared under Creative Commons BY 4.0 license on the Very Deep Convolutional Networks for Large-Scale Visual Recognition project page.
vgg16_zhang_perceptual.pkl is further derived from the pre-trained LPIPS weights by Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The weights were originally shared under BSD 2-Clause "Simplified" License on the PerceptualSimilarity repository.
A minimal example of using a pre-trained StyleGAN generator is given in pretrained_example.py . When executed, the script downloads a pre-trained StyleGAN generator from Google Drive and uses it to generate an image:
A more advanced example is given in generate_figures.py . The script reproduces the figures from our paper in order to illustrate style mixing, noise inputs, and truncation:
The pre-trained networks are stored as standard pickle files on Google Drive:
The above code downloads the file and unpickles it to yield 3 instances of dnnlib.tflib.Network . To generate images, you will typically want to use Gs – the other two networks are provided for completeness. In order for pickle.load() to work, you will need to have the dnnlib source directory in your PYTHONPATH and a tf.Session set as default. The session can initialized by calling dnnlib.tflib.init_tf() .
There are three ways to use the pre-trained generator:
Use Gs.run() for immediate-mode operation where the inputs and outputs are numpy arrays:
The first argument is a batch of latent vectors of shape [num, 512] . The second argument is reserved for class labels (not used by StyleGAN). The remaining keyword arguments are optional and can be used to further modify the operation (see below). The output is a batch of images, whose format is dictated by the output_transform argument.
Use Gs.get_output_for() to incorporate the generator as a part of a larger TensorFlow expression:
The above code is from metrics/frechet_inception_distance.py . It generates a batch of random images and feeds them directly to the Inception-v3 network without having to convert the data to numpy arrays in between.
Look up Gs.components.mapping and Gs.components.synthesis to access individual sub-networks of the generator. Similar to Gs , the sub-networks are represented as independent instances of dnnlib.tflib.Network :
The above code is from generate_figures.py . It first transforms a batch of latent vectors into the intermediate W space using the mapping network and then turns these vectors into a batch of images using the synthesis network. The dlatents array stores a separate copy of the same w vector for each layer of the synthesis network to facilitate style mixing.
The exact details of the generator are defined in training/networks_stylegan.py (see G_style , G_mapping , and G_synthesis ). The following keyword arguments can be specified to modify the behavior when calling run() and get_output_for() :
truncation_psi and truncation_cutoff control the truncation trick that that is performed by default when using Gs (ψ=0.7, cutoff=8). It can be disabled by setting truncation_psi=1 or is_validation=True , and the image quality can be further improved at the cost of variation by setting e.g. truncation_psi=0.5 . Note that truncation is always disabled when using the sub-networks directly. The average w needed to manually perform the truncation trick can be looked up using Gs.get_var('dlatent_avg') .
randomize_noise determines whether to use re-randomize the noise inputs for each generated image ( True , default) or whether to use specific noise values for the entire minibatch ( False ). The specific values can be accessed via the tf.Variable instances that are found using [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')] .
When using the mapping network directly, you can specify dlatent_broadcast=None to disable the automatic duplication of dlatents over the layers of the synthesis network.
Runtime performance can be fine-tuned via structure='fixed' and dtype='float16' . The former disables support for progressive growing, which is not needed for a fully-trained generator, and the latter performs all computation using half-precision floating point arithmetic.
The training and evaluation scripts operate on datasets stored as multi-resolution TFRecords. Each dataset is represented by a directory containing the same image data in several resolutions to enable efficient streaming. There is a separate *.tfrecords file for each resolution, and if the dataset contains labels, they are stored in a separate file as well. By default, the scripts expect to find the datasets at datasets//-.tfrecords . The directory can be changed by editing config.py :
To obtain the FFHQ dataset ( datasets/ffhq ), please refer to the Flickr-Faces-HQ repository .
To obtain the CelebA-HQ dataset ( datasets/celebahq ), please refer to the Progressive GAN repository .
To obtain other datasets, including LSUN, please consult their corresponding project pages. The datasets can be converted to multi-resolution TFRecords using the provided dataset_tool.py :
Once the datasets are set up, you can train your own StyleGAN networks as follows:
By default, train.py is configured to train the highest-quality StyleGAN (configuration F in Table 1) for the FFHQ dataset at 1024×1024 resolution using 8 GPUs. Please note that we have used 8 GPUs in all of our experiments. Training with fewer GPUs may not produce identical results – if you wish to compare against our technique, we strongly recommend using the same number of GPUs.
Expected training times for the default configuration using Tesla V100 GPUs:
The quality and disentanglement metrics used in our paper can be evaluated using run_metrics.py . By default, the script will evaluate the Fréchet Inception Distance ( fid50k ) for the pre-trained FFHQ generator and write the results into a newly created directory under results . The exact behavior can be changed by uncommenting or editing specific lines in run_metrics.py .
Expected evaluation time and results for the pre-trained FFHQ generator using one Tesla V100 GPU:
Please note that the exact results may vary from run to run due to the non-deterministic nature of TensorFlow.
We thank Jaakko Lehtinen, David Luebke, and Tuomas Kynkäänniemi for in-depth discussions and helpful comments; Janne Hellsten, Tero Kuosmanen, and Pekka Jänis for compute infrastructure and help with the code release.
StyleGAN - Official TensorFlow Implementation
High-quality version of the paper PDF.
High-quality version of the result video.
Example images produced using our generator.
High-quality images to be used in articles, blog posts, etc.
100,000 generated images for different amounts of truncation.
Generated using Flickr-Faces-HQ dataset at 1024×1024.
Generated using LSUN Bedroom dataset at 256×256.
Generated using LSUN Car dataset at 512×384.
Generated using LSUN Cat dataset at 256×256.
Example videos produced using our generator.
Individual segments of the result video as high-quality MP4.
Pre-trained networks as pickled instances of dnnlib.tflib.Network .
StyleGAN trained with Flickr-Faces-HQ dataset at 1024×1024.
StyleGAN trained with CelebA-HQ dataset at 1024×1024.
StyleGAN trained with LSUN Bedroom dataset at 256×256.
StyleGAN trained with LSUN Car dataset at 512×384.
StyleGAN trained with LSUN Cat dataset at 256×256.
Auxiliary networks for the quality and disentanglement metrics.
Standard Inception-v3 classifier that outputs a raw feature vector.
Standard LPIPS metric to estimate perceptual similarity.
Binary classifier trained to detect a single attribute of CelebA-HQ.
Please see the file listing for remaining networks.
Fréchet Inception Distance using 50,000 images.
Perceptual Path Length for full paths in Z .
Perceptual Path Length for full paths in W .
Perceptual Path Length for path endpoints in Z .
Perceptual Path Length for path endpoints in W .
