EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models
(2401.11739)Abstract
Diffusion models have recently received increasing research attention for their remarkable transfer abilities in semantic segmentation tasks. However, generating fine-grained segmentation masks with diffusion models often requires additional training on annotated datasets, leaving it unclear to what extent pre-trained diffusion models alone understand the semantic relations of their generated images. To address this question, we leverage the semantic knowledge extracted from Stable Diffusion (SD) and aim to develop an image segmentor capable of generating fine-grained segmentation maps without any additional training. The primary difficulty stems from the fact that semantically meaningful feature maps typically exist only in the spatially lower-dimensional layers, which poses a challenge in directly extracting pixel-level semantic relations from these feature maps. To overcome this issue, our framework identifies semantic correspondences between image pixels and spatial locations of low-dimensional feature maps by exploiting SD's generation process and utilizes them for constructing image-resolution segmentation maps. In extensive experiments, the produced segmentation maps are demonstrated to be well delineated and capture detailed parts of the images, indicating the existence of highly accurate pixel-level semantic knowledge in diffusion models.
Overview
-
Diffusion models, specifically Stable Diffusion, contain substantial semantic information in their intermediate representations, which can be used for tasks like semantic segmentation.
-
EmerDiff is a new framework that uses the semantic knowledge in pre-trained diffusion models to create detailed segmentation maps without extra training or annotations.
-
EmerDiff generates low-resolution segmentation maps using k-means clustering on features from the diffusion model, then refines them to high-resolution by a modulated denoising process.
-
The framework was evaluated on multiple datasets and showed its ability to produce segmentation maps with significant alignment to the detailed elements of images.
-
The study demonstrates the potential of diffusion models to comprehend and represent pixel-level semantics, providing insights for further research in generative model applications.
Introduction
Diffusion models have carved a significant place in the AI landscape as a state-of-the-art generative method for synthesizing high-quality images. A remarkable aspect of these pre-trained models is their semantic enrichment; they encoded substantial semantic information in their intermediate representations. Notably, this property has been harnessed to achieve impressive transfer capabilities in tasks such as semantic segmentation. However, the majority of existing applications require additional knowledge inputs beyond the pre-trained models, like mask annotations or hand-crafted priors, for generating segmentation maps. This raises the question: to what extent can pre-trained diffusion models alone capture the semantic relations of the images they generate?
Semantic Knowledge Extraction
A recent study proposes EmerDiff, a framework that builds directly on the semantic knowledge encompassed within a pre-trained diffusion model—specifically Stable Diffusion—to generate fine-grained segmentation maps without the aid of supplementary training or external annotations. At the core of EmerDiff's methodology is the observation that semantically meaningful feature maps predominantly reside in lower-dimensional layers of diffusion models. While this localization of semantic features often leads to coarse outcomes when traditional segmentation approaches are applied, EmerDiff taps into the inherent capability of diffusion models to translate these low-resolution semantic blueprints into detailed high-resolution images.
Methodology
The framework first crafts low-resolution (16x16) segmentation maps by applying k-means clustering to feature maps extracted from key layers within the diffusion model. Then, it elegantly bridges the resolution gap by progressively mapping each pixel of the target high-resolution output to its corresponding semantic element within these maps. This is achieved through a modulated denoising process, where local perturbations in the values of low-resolution feature maps selectively influence pixels semantically linked to that location—a process that sheds light on the pixel-level semantic associations embedded within the diffusion model.
Results
EmerDiff underwent extensive qualitative and quantitative evaluations across multiple datasets, and the results reveal an insightful narrative. The framework generates segmentation maps that align notably well with the detailed parts of the images, revealing a profound understanding of the semantics at play. These outcomes are instrumental in not only proving the rich pixel-level semantic knowledge diffusion models possess but also setting a benchmark for future advancements in leveraging generative models.
Conclusion
In conclusion, EmerDiff marks a significant stride in unraveling the intrinsic semantic understanding of pre-trained diffusion models. The framework has demonstrated that it is feasible to capitalize on the latent knowledge embedded within diffusion models to yield detailed segmentation maps, all while negating the dependency on additional training or annotation. This exploration invites new perspectives on the discriminative capabilities of generative models and broadens the horizon for future research in this intriguing domain.
Create an account to read this summary for free: