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Reflected Diffusion Models are a new class of diffusion models that respect general boundary constraints and correctly train for thresholded sampling. They brid
by ttul 3y ago
Reflected Diffusion Models are a new class of diffusion models that respect general boundary constraints and correctly train for thresholded sampling. They bridge the gap between theoretical frameworks and practical implementation details, leading to improved performance and image quality in generative modeling tasks.
Diffusion models are generative models that work by perturbing data using a stochastic process, then reversing this process to generate new data. However, when discretized into discrete steps (as in most practical implementations), the sampling process can negatively affect data quality. To alleviate this issue, a technique called thresholding is employed, resulting in better samples but breaking the theoretical framework.
Reflected Diffusion Models use a reflected stochastic differential equation (rSDE) to correctly model the thresholding process. This allows the models to train appropriately while respecting boundary constraints, like images with pixel values in the [0, 255] range.
The benefits of Reflected Diffusion Models include improved perceptual quality, correct handling of boundary constraints, better image generation with guidance, and general applicability to different shapes of data domains. This has the potential to expand the applications of diffusion models in fields such as image, language, and molecule generation.
- ttul 3y agoAs is so often the case in neural network design, preventing numbers from blowing up is the cause of so many necessary, but poorly understood math hacks. It seems these researchers have found a potentially better way to control the sampling process so that diffusion models can generate outputs that are more correct. I often wonder if Midjourney, behind the scenes, has already discovered something like this in their v5 release, which is substantially better at modelling things like hands.
- toxik 3y agoI hate to ask but is this ChatGPT?
- dr_dshiv 3y agoOnly if you don’t find it useful
- visarga 3y agochatGPT "of the gaps", it is chatGPT only when it is not useful
- refulgentis 3y agoIt is ChatGPT and it's not useful. It purposefully hides while it attempts to illuminate. It masquerades as summarizing the paper, but it's a random grab of whatever OP decided to jam into the chatbox that fits in the context window, (if you check, its mostly regurgitating the abstract). However, it presents itself as a summary as if the entire paper was read. I'm _really_ into AI stuff but the flippant nature its used at on HN has me scared. People here are generally well-educated, considered in their decisions, and are well acquainted with tech. If HN can't avoid glaring issues with it, who can? The top example that came to mind was someone laboriously sharing a really nice data-driven comment, someone asks source? they say ChatGPT, and acted like we were trolling for saying it hallucinates.
- smrtinsert 3y ago> People here are generally well-educated This isn't a given (anymore?).
- ttul 3y agoThe entire blog post fit into the context window. I did not use GPT-4 to summarize the whole paper. Either way, I found the summary informative after reading the blog post end-to-end in the more human manner anyway. I don't think GPT-4 missed anything crucial from the blog.
- refulgentis 3y agoI stand corrected: only 4100 tokens! It missed a _ton_ for me, it seems to pick out unique phrases and elegantly create a "skeleton" of the original. > I did not use GPT-4 to summarize the whole paper. > I don't think GPT-4 missed anything crucial from the blog. ?
- 3y ago
- devxpy 3y agoIs there a way to run this locally?