Fast constrained sampling in pre-trained diffusion models
- URL: http://arxiv.org/abs/2410.18804v1
- Date: Thu, 24 Oct 2024 14:52:38 GMT
- Title: Fast constrained sampling in pre-trained diffusion models
- Authors: Alexandros Graikos, Nebojsa Jojic, Dimitris Samaras,
- Abstract summary: Diffusion models have dominated the field of large, generative image models.
We propose an algorithm for fast-constrained sampling in large pre-trained diffusion models.
- Score: 77.21486516041391
- License:
- Abstract: Diffusion models have dominated the field of large, generative image models, with the prime examples of Stable Diffusion and DALL-E 3 being widely adopted. These models have been trained to perform text-conditioned generation on vast numbers of image-caption pairs and as a byproduct, have acquired general knowledge about natural image statistics. However, when confronted with the task of constrained sampling, e.g. generating the right half of an image conditioned on the known left half, applying these models is a delicate and slow process, with previously proposed algorithms relying on expensive iterative operations that are usually orders of magnitude slower than text-based inference. This is counter-intuitive, as image-conditioned generation should rely less on the difficult-to-learn semantic knowledge that links captions and imagery, and should instead be achievable by lower-level correlations among image pixels. In practice, inverse models are trained or tuned separately for each inverse problem, e.g. by providing parts of images during training as an additional condition, to allow their application in realistic settings. However, we argue that this is not necessary and propose an algorithm for fast-constrained sampling in large pre-trained diffusion models (Stable Diffusion) that requires no expensive backpropagation operations through the model and produces results comparable even to the state-of-the-art \emph{tuned} models. Our method is based on a novel optimization perspective to sampling under constraints and employs a numerical approximation to the expensive gradients, previously computed using backpropagation, incurring significant speed-ups.
Related papers
- An Ordinary Differential Equation Sampler with Stochastic Start for Diffusion Bridge Models [13.00429687431982]
Diffusion bridge models initialize the generative process from corrupted images instead of pure Gaussian noise.
Existing diffusion bridge models often rely on Differential Equation samplers, which result in slower inference speed.
We propose a high-order ODE sampler with a start for diffusion bridge models.
Our method is fully compatible with pretrained diffusion bridge models and requires no additional training.
arXiv Detail & Related papers (2024-12-28T03:32:26Z) - VIPaint: Image Inpainting with Pre-Trained Diffusion Models via Variational Inference [5.852077003870417]
We show that our VIPaint method significantly outperforms previous approaches in both the plausibility and diversity of imputations.
We show that our VIPaint method significantly outperforms previous approaches in both the plausibility and diversity of imputations.
arXiv Detail & Related papers (2024-11-28T05:35:36Z) - ReNoise: Real Image Inversion Through Iterative Noising [62.96073631599749]
We introduce an inversion method with a high quality-to-operation ratio, enhancing reconstruction accuracy without increasing the number of operations.
We evaluate the performance of our ReNoise technique using various sampling algorithms and models, including recent accelerated diffusion models.
arXiv Detail & Related papers (2024-03-21T17:52:08Z) - CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster
Image Generation [49.3016007471979]
Large generative diffusion models have revolutionized text-to-image generation and offer immense potential for conditional generation tasks.
However, their widespread adoption is hindered by the high computational cost, which limits their real-time application.
We introduce a novel method dubbed CoDi, that adapts a pre-trained latent diffusion model to accept additional image conditioning inputs.
arXiv Detail & Related papers (2023-10-02T17:59:18Z) - Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional
Image Synthesis [62.07413805483241]
Steered Diffusion is a framework for zero-shot conditional image generation using a diffusion model trained for unconditional generation.
We present experiments using steered diffusion on several tasks including inpainting, colorization, text-guided semantic editing, and image super-resolution.
arXiv Detail & Related papers (2023-09-30T02:03:22Z) - Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image Attenuation [53.04220377034574]
We propose incorporating an analytical image attenuation process into the forward diffusion process for high-quality (un)conditioned image generation.
Our method represents the forward image-to-noise mapping as simultaneous textitimage-to-zero mapping and textitzero-to-noise mapping.
We have conducted experiments on unconditioned image generation, textite.g., CIFAR-10 and CelebA-HQ-256, and image-conditioned downstream tasks such as super-resolution, saliency detection, edge detection, and image inpainting.
arXiv Detail & Related papers (2023-06-23T18:08:00Z) - On Distillation of Guided Diffusion Models [94.95228078141626]
We propose an approach to distilling classifier-free guided diffusion models into models that are fast to sample from.
For standard diffusion models trained on the pixelspace, our approach is able to generate images visually comparable to that of the original model.
For diffusion models trained on the latent-space (e.g., Stable Diffusion), our approach is able to generate high-fidelity images using as few as 1 to 4 denoising steps.
arXiv Detail & Related papers (2022-10-06T18:03:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.