{"id":101605,"date":"2021-10-12T16:22:58","date_gmt":"2021-10-12T14:23:00","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/nvidia-otkryla-kod-stylegan3-sistemy-mashinnogo-obucheniya-dlya-sinteza-licz"},"modified":"2021-10-12T16:22:58","modified_gmt":"2021-10-12T14:23:00","slug":"nvidia-otkryla-kod-stylegan3-sistemy-mashinnogo-obucheniya-dlya-sinteza-licz","status":"publish","type":"post","link":"https:\/\/prohoster.info\/en\/blog\/news\/nvidia-otkryla-kod-stylegan3-sistemy-mashinnogo-obucheniya-dlya-sinteza-licz","title":{"rendered":"NVIDIA Releases Source Code for StyleGAN3, a Machine Learning System for Face Synthesis","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

NVIDIA has released the source code for StyleGAN3, a machine learning system based on a generative adversarial network (GAN) aimed at synthesizing realistic images of human faces. The code is written in Python using the PyTorch framework and is distributed under the NVIDIA Source Code License, which imposes restrictions on commercial use. <\/p>\n

Ready-trained models are also available for download, trained on the Flickr-Faces-HQ (FFHQ) collection, which includes 70,000 high-quality (1024\u00d71024) PNG images of human faces. Additionally, models have been developed based on the AFHQv2 collections (animal face photos) and Metfaces (images of human faces from classic portrait paintings). While the focus during development is on faces, the system can also be trained to generate any objects, such as landscapes and cars. Tools are provided for self-training the neural network on personal image collections. One or more NVIDIA graphics cards are required for operation (Tesla V100 or A100 GPU recommended), at least 12 GB of RAM, PyTorch 1.9, and CUDA toolkit 11.1 or higher. A special detector is being developed to determine the artificial nature of the generated faces. <\/p>\n

The system allows for the synthesis of a new face image through the interpolation of features from multiple faces, combining their characteristic traits while adapting the final image to a desired age, gender, hair length, smile type, nose shape, skin color, glasses, and photo angle. The generator treats the image as a collection of styles, automatically separating distinctive details (freckles, hair, glasses) from common high-level attributes (pose, gender, aging changes) and allows for arbitrary combinations with dominant properties identified through weighting coefficients. As a result, images are generated that are indistinguishable from real photographs.

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The first version of the StyleGAN technology was published in 2019, followed by an improved edition, StyleGAN2, in 2020, which enhanced image quality and eliminated some artifacts. However, the system remained static, meaning it did not allow for realistic animation and facial movement. The main goal in developing StyleGAN3 was to adapt the technology for its application in animation and video. <\/p>\n

StyleGAN3 features a reworked image generation architecture that eliminates aliasing and introduces new training scenarios for the neural network. It includes new utilities for interactive visualization (visualizer.py), analysis (avg_spectra.py), and video generation (gen_video.py). The implementation also reduces memory consumption and accelerates the training process.

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A key feature of the StyleGAN3 architecture is the transition to interpreting all signals in the neural network as continuous processes, allowing manipulation of relative positions when forming details, which are not tied to absolute pixel coordinates in the image but are anchored to the surface of the depicted objects. In StyleGAN and StyleGAN2, the pixel binding during generation led to problems in dynamic visualization; for instance, when the image moved, there was a misalignment of small details, such as wrinkles and hairs, which moved separately from the rest of the face. In StyleGAN3, these issues are resolved, making the technology suitable for video generation.