SPADE
BigGAN-PyTorch
SPADE | BigGAN-PyTorch | |
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11 | 4 | |
7,533 | 2,804 | |
0.1% | - | |
0.0 | 0.0 | |
9 months ago | 10 months ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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SPADE
- T2i Segmentation Colors Reference - Work in progress v18
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I'm looking for an AI Art generator from images
GauGAN (https://github.com/NVlabs/SPADE) - This is a PyTorch implementation of the SPADE (SPatially-Adaptive (DE)normalization) algorithm, which can generate images from segmentation maps. You can use it to generate realistic images of objects, landscapes, and other scenes.
- MegaPortraits: High-Res Deepfakes Created From a Single Photo
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Can NVIDIA Canvas be used as an API?
It is open source, if that helps. Here is a GitHub link.
- Care sunt meseriile/profesiile care, in opinia voastra, nu vor fi inlocuite de AI in urmatorii 35-50 de ani si de ce?
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Where/How should I start?
Generating photorealistic landscapes from brush strokes: Semantic Image Synthesis with Spatially-Adaptive Normalization
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Blursed rock formation
This look like an AI image synthesized with Nvidia's SPADE.
- Nvidia Canvas
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Gaugan Nvidia Unity
Spade Nvidia
BigGAN-PyTorch
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[D] Pre-trained weights for GANs online?
The second link gives you the entire source code for training the model https://github.com/ajbrock/BigGAN-PyTorch/tree/master . Looks like BigGAN.py and BigGANdeep.py are the two files that define the architecture. Can you work with that?
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I'm looking for an AI Art generator from images
BigGAN (https://github.com/ajbrock/BigGAN-PyTorch) - This is a PyTorch implementation of the BigGAN model for generating high-resolution images. It is trained on a large dataset and can generate a wide range of images, including photographs of animals, objects, and landscapes.
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[D] Using activity regularization instead of batch norm.
Tangentially, theoretically you can't use BN in the discriminator for WGAN-GP anyway (assuming that you're using the Gulrajani work) because it breaks the sample independence assumptions of the GP. If you have a relatively structured dataset (eg all faces, all cars, all giraffes, etc.) and no class conditioning, look into StyleGAN2-ADA for the best results. If you have a dataset with a lot of variation and a lot of classes try using the BigGAN repo.
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Έφτιαξα ένα AI που παράγει εικόνες από μερικές λέξεις. Να τι έφτιαξε όταν του είπα να σκεφτεί ένα "Αφηρημένο Πορτρέτο"
BigGan και Deep Dream https://github.com/ajbrock/BigGAN-PyTorch https://github.com/google/deepdream
What are some alternatives?
gaugan - Photorealistic landscape drawings using the Nvidia SPADE model
stylegan2-ada-pytorch - StyleGAN2-ADA - Official PyTorch implementation
WaveFunctionCollapse - Bitmap & tilemap generation from a single example with the help of ideas from quantum mechanics
pytorch-tutorial - PyTorch Tutorial for Deep Learning Researchers
awesome-NeRF - A curated list of awesome neural radiance fields papers
PyTorch-StudioGAN - StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.
Parsec-Cloud-Preparation-Tool - Launch Parsec enabled cloud computers via your own cloud provider account.
transfer-learning-conv-ai - 🦄 State-of-the-Art Conversational AI with Transfer Learning
sketch-to-art - 🖼 Create artwork from your casual sketch with GAN and style transfer
pytorch-forecasting - Time series forecasting with PyTorch
pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch
code-generator - Web Application to generate your training scripts with PyTorch Ignite