High-Resolution Image Synthesis with Latent Diffusion Models · HackerPapers discussions

High-Resolution Image Synthesis with Latent Diffusion Models — mentioned by cookiengineer

Tue, 30 Dec 2025 20:37:17 GMT

> But no LLM used for that? Well, I mean, I self-hosted stable diffusion to be able to generate the teaser images and the award images (for the ranks in the Game Over Dialog). I only implemented the initial SD paper [1] back then so I have no clue whether the u-net architecture changed when it comes to the semantic mapping of the text/labels. Did that change and is Stable Diffusion 3 now an LLM, actually? [1] https://arxiv.org/pdf/2112.10752

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High-Resolution Image Synthesis with Latent Diffusion Models — mentioned by godelski

Wed, 24 Jul 2024 23:29:21 GMT

> I don't think the "model collapse" problem is particularly important these days. I think you might misunderstand what model collapse is. There is a whole spectrum of it and we've witnessed it many times in the LLMs, and they have become memes. A fairly recent example is the Golden Gate Claude[0]. This is mode{,l} collapse. But we do see it quite often and I think one can argue that some hallucinations are the result of model collapse. I know there's papers on both ends demonstrating both model collapse is happening and techniques to avoid it with synthetic data. But you have to always be…

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High-Resolution Image Synthesis with Latent Diffusion Models — mentioned by parlancex

Fri, 09 Feb 2024 16:51:37 GMT

Everything you've said is _intuitively_ correct, but empirically wrong. I've experimented with training VAEs for audio diffusion for the last few months and here's what I found: - Although the best results for a stand-alone VAE might require increasing the KL loss weight as high as you can to reach an isotropic gaussian latent space without compromising reconstruction quality, beyond a certain point this actually substantially decreases the ability of the diffusion model to properly interpret the latent space and degrades generation quality. The motivation behind constraining the KL loss…

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High-Resolution Image Synthesis with Latent Diffusion Models — mentioned by godelski

Sun, 02 Jul 2023 23:10:11 GMT

Yeah no problem, this is even closer to my area of focus! What do you know about physics and thermodynamics? I'd say a good intro for low background is from Tomczak[0]. He has a book, but the blog posts are nearly identical. He did a post doc with Max Welling (someone you should learn about if you want to get deep, like I was suggesting before). So I'd switch things up slightly. I'd go Intro -> Autoregressive -> Flow -> VAE -> Hierarchical VAE -> Energy Based Models -> Diffusion. It is worth learning about GANs btw, but this progression should be natural and build up. Continuing from there…

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High-Resolution Image Synthesis with Latent Diffusion Models — mentioned by capableweb

Sun, 23 Apr 2023 21:32:31 GMT

The model was initially released in August 22, 2022, and everything from the GitHub organization that hosted the code, to the actual licenses being around and all the communication made it very clear that there was a bunch of groups involved with the model; Runway, CompVis, and Stability AI Then Stability AI raised money from investors in October 2022. Are you really telling me these investors didn't even do any sort of due diligence (not even "barely any" but literally any at all), didn't realized that Stable Diffusion was the work of many, and checked the license of the model and code?…

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High-Resolution Image Synthesis with Latent Diffusion Models — mentioned by firechickenbird

Mon, 06 Mar 2023 06:50:18 GMT

Very interesting, although I was expecting the Stable Diffusion paper[1] to be one of the most cited in 2022 [1]: High-Resolution Image Synthesis With Latent Diffusion Models, https://arxiv.org/abs/2112.10752

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High-Resolution Image Synthesis with Latent Diffusion Models — mentioned by rassibassi

Sat, 10 Sep 2022 06:48:20 GMT

Yes, and another reason for the small model size and the novelty of the underlying paper [1], is that the diffusion model is not acting on the pixel space but rather on a latent space. This means that this 'latent diffusion model' does not only learn the task at hand (image synthesis) but in parallel also a powerful lossy compression model via an outer auto encoder structure. Now, the number of weights (model size) can be reduced drastically as the inner neural network layers act on a lower dimensional latent space rather than a high dimensional pixel space. It's fascinating because it shows…

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High-Resolution Image Synthesis with Latent Diffusion Models — mentioned by Dzugaru

Sat, 03 Sep 2022 09:29:51 GMT

It is fed using a fascinating mechanism called "cross-attention" that originated in the NN architecture called Transformer - which was used to achieve state-of-the-art translations. It uses something like associative memory, where a NN inside Stable Diffusion, that generates image (UNet, working in latent space), at almost each step "asks" the whole encoded prompt to provide data at various positions using query vector Q that is matched against key vectors K and value vectors V [0]. How Stable Diffusion works [1] as a whole is not really hard to comprehend at a high level - but you'll need…

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High-Resolution Image Synthesis with Latent Diffusion Models — mentioned by lucidrains

Wed, 17 Aug 2022 16:44:01 GMT

It is from the Heidelberg giants, Patrick Esser and Robin Rombach They continued building off their latent diffusion direction (encode with vqgan-vae and then diffusion in latent space) All roads lead to rome https://github.com/CompVis/stable-diffusion https://arxiv.org/abs/2112.10752

Paper and discussion history. A mention is not an endorsement.