Jan 12, 2021 · Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps in order to...

Feb 1, 2023 · Abstract: Our goal is to extend the denoising diffusion implicit model (DDIM) to general diffusion models~ (DMs) besides isotropic diffusions. Instead of constructing a non-Markov noising process as in the original DDIM, we examine the mechanism of DDIM from a numerical perspective.

Sep 3, 2022 · Skip to main content Comparison between different samplers in Stable Diffusion (warning, large resolution image) : r/StableDiffusion

Sep 26, 2024 · This paper describes an efficient algorithm for solving noisy linear inverse problems using pretrained diffusion models. Extending the paradigm of denoising diffusion implicit models (DDIM), we propose conditional diffusion implicit models (CDIM) that modify the diffusion updates to enforce a constraint upon the final output. For noiseless inverse problems, CDIM exactly satisfies the ...

Mar 11, 2023 · Thanks to u/Ateist who pointed out that UniPC uses DDIM for the Hires Fix step so the first image in my post won't be indicative of the quality of this sampler on its own.

Dec 9, 2022 · Can anyone explain differences between sampling methods and their uses to me in simple terms, because all the info I've found so far is either very contradicting or complex and goes over my head

Mar 13, 2024 · Welcome to the unofficial ComfyUI subreddit. Please share your tips, tricks, and workflows for using this software to create your AI art. Please keep posted images SFW. And above all, BE NICE. A lot of people are just discovering this technology, and want to show off what they created. Belittling their efforts will get you banned. Also, if this is new and exciting to you, feel free to post ...

Got very good results using DDIM, DPM++ SDE Karras, Euler Ancestral (apparently hated for inefficiency, but it works!). PLMS is the worst with the most distorted results.

Sep 11, 2022 · Has to do with how diffusion-based models work. Basically, they start with a random noise image and 'mine' the noisy image for a less noisy output. This process is defined by a differential equation that describes how much noise is removed in a step. Solving these equations is a bit tricky; there's different approaches with tradeoffs between speed and accuracy and occasionally some special ...

Dec 13, 2022 · DDIM, on the other hand, is a type of machine learning model that operates on a high-dimensional latent space, rather than the raw pixel values of the image. DDIMs use a diffusion-based approach to gradually refine the representation of the image in the latent space, removing noise and improving the quality of the image.