gansformer
pytorch-CycleGAN-and-pix2pix
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| gansformer | pytorch-CycleGAN-and-pix2pix | |
|---|---|---|
| 7 | 10 | |
| 1,302 | 21,998 | |
| - | - | |
| 1.8 | 2.8 | |
| almost 2 years ago | 3 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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gansformer
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[D] GANs + Transformer = SOTA compositional generator? Compositional Transformers for Scene Generation explained (5-minute summary by Casual GAN Papers)
Code for https://arxiv.org/abs/2111.08960 found: https://github.com/dorarad/gansformer
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Generative Adversarial Transformers [R]
As for whether the Ys are shared across layers, check the code.
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[Project] These players does not exist
I tested the gansformer (https://github.com/dorarad/gansformer) to generate football player faces. Here are some selected results (actually some of the images are real players):
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GANsformers: Scene Generation with Generative Adversarial Transformers 🔥
References: Paperâ–º: https://arxiv.org/pdf/2103.01209.pdf Codeâ–º: https://github.com/dorarad/gansformer Complete referenceâ–º: Drew A. Hudson and C. Lawrence Zitnick, Generative Adversarial Transformers, (2021), Published on Arxiv.
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[R] Generative Adversarial Transformers (2103.01209)
https://github.com/dorarad/gansformer/blob/148f72964219f8ead2621204bc5cfa89200b6879/training/network.py#L461
pytorch-CycleGAN-and-pix2pix
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List of AI-Models
Click to Learn more...
- I want an A.I. to learn my art style so I can keep making art in my art style despite not having the time to do it.
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I'm looking for an AI Art generator from images
pix2pix (https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) - This is a PyTorch implementation of the pix2pix algorithm for image-to-image translation. Given a set of images, the model can learn to generate a new image from a different domain that is similar to the input image.
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Seamless textures with SD and PBR maps with a pix2pix cGAN
Using junyanz/pytorch-CycleGAN-and-pix2pix as a basis for pix2pix, I applied the same blending method to fix seams. It essentially takes an input image and generates an output. The results depend on the paired training data. In this case, each map (height, roughness, etc.) is a separate checkpoint and had to be trained on paired training data with the diffuse as the input and the respective map as the output.
- IA art
- Segmentation and clasification with UNET
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Trying to understand PatchGAN discriminator
Code for https://arxiv.org/abs/1611.07004 found: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
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I made a 3d topographic map based on my recent civ6 game
pix2pix algorithm is used for translating Civ6Maps to heightmaps. Synthesized terrain was rendered in blender.
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This Wojak Does Not Exist
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
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Training a neural net to generate Wojaks
I'm working on creating a face-to-wojak model using PyTorch CycleGan/Pix2Pix [0] and found some of my outputs to be outrageous yet somehow relatable. People are into it so thought I'd share on HN
[0] https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
What are some alternatives?
pytorch-generative - Easy generative modeling in PyTorch.
pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs
SteganoGAN - SteganoGAN is a tool for creating steganographic images using adversarial training.
generative-inpainting-pytorch - A PyTorch reimplementation for paper Generative Image Inpainting with Contextual Attention (https://arxiv.org/abs/1801.07892)
Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch - [ECCV 2022] Compositional Generation using Diffusion Models
pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
data-efficient-gans - [NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training
Deep-Fakes
long-range-arena - Long Range Arena for Benchmarking Efficient Transformers
AnimeGAN - Generating Anime Images by Implementing Deep Convolutional Generative Adversarial Networks paper
gnn-lspe - Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022
PaddleGAN - PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, Wav2Lip, picture repair, image editing, photo2cartoon, image style transfer, GPEN, and so on.