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# Welcome to JunYoung's blog | DDIM(Denoising Diffusion Implicit Models) 이해하기
Diffusion implicit modeling
[https://junia3.github.io/blog/ddim](https://junia3.github.io/blog/ddim)
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Thanks for your work. But I have a question while reading the training part of the paper. It said `Specifically,
we first add Gaussian noise 𝜖 ∼ N (0, 𝐼) into the given generative UV features plane 𝑥…
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### Checklist
- [ ] The issue exists after disabling all extensions
- [ ] The issue exists on a clean installation of webui
- [ ] The issue is caused by an extension, but I believe it is caused b…
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Hello author, I am a beginner in image restoration. May I ask if the repaint in generate mode is related to RePaint in "Repainting using Denoising Diffusion Probabilistic Models"? Looking forward to y…
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The denoising visualization in diffusion U-ViT
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Hi,
Thank you for sharing your code. As mentioned in your paper, there are 2 stages of training. First for the denoising diffusion model, and second focuses on the leapfrog initializer. It seems th…
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### Model/Pipeline/Scheduler description
Achieving faithful image-to-noise inversion with Denoising Diffusion models remains a challenge, particularly for more recent models trained to generate image…
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Hello, would it be possible for you to share the code for the two conference papers you've recently published? This would greatly facilitate replication and comparison efforts. Thank you.
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I am trying to run the 1D diffusion model with conditioning but getting the following error.
`x = self.init_conv(x)` in line https://github.com/lucidrains/denoising-diffusion-pytorch/blob/5ff2393c7…
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Hi diffusion developers,
Thank you for the open source development!
I have a naive question about the objective "pred_x_start". If we use this objective, after training we have a model that can…