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Hello,
I was wondering how I can generate samples using the decoder network after training. In a VAE, I would just sample from the prior distribution z~N(0,1) and generate a data point using the de…
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## 扩散模型
## GAN模型
## 一致性模型(Consistency Model)
其一,无需对抗训练(adversarial training),就能直接生成高质量的图像样本。
其二,相比扩散模型可能需要几百甚至上千次迭代,一致性模型只需要**一两步**就能搞定多种图像任务——
包括上色、去噪、超分等,都可以在几步之内搞定,而不需要对这些任务进行明确训练。(当然,如果进行…
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Dear p0p4k,
I am currently engaged in research on Korean voice synthesis models and have been utilizing your well-crafted vits2_pytorch implementation for training a Korean model. It has been funct…
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Is there a plan to develop a memory-efficient back-propagation training mode? Perhaps a flag that by activating it, during back-propagation, the forward-pass network states get recomputed by inverting…
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Hi! Is there a corresponding paper published for this code?
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There are various ways this could be done:
* purely from samples. pros: model agnostic, cons: high variance, need to use a kernel method
* using mixture of gaussians: pros: lower variance, cons: ver…
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### 🚀 The feature, motivation and pitch
Dear experts,
I am attempting to export a normalizing flows model built using the Zuko libraries, which are based on PyTorch, to ONNX. However, I am encou…
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### Issue Description
Many normalizing flows require the use of transpositions during the flow, allowing different transformations to operate on different dimensions. Since transpositions don't chang…
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Hi Edvard,
I need to invert a MAF layer (using another flow function and not an affine one), however I am having issues grasping the concept of it.
So:
1. When training normalizing flows to us…