AccDiffusion v2: Towards More Accurate Higher-Resolution Diffusion Extrapolation (2412.02099v2)

Abstract: Diffusion models suffer severe object repetition and local distortion when the inference resolution differs from its pre-trained resolution. We propose AccDiffusion v2, an accurate method for patch-wise higher-resolution diffusion extrapolation without training. Our in-depth analysis in this paper shows that using an identical text prompt for different patches leads to repetitive generation, while the absence of a prompt undermines image details. In response, our AccDiffusion v2 novelly decouples the vanilla image-content-aware prompt into a set of patch-content-aware prompts, each of which serves as a more precise description of a patch. Further analysis reveals that local distortion arises from inaccurate descriptions in prompts about the local structure of higher-resolution images. To address this issue, AccDiffusion v2, for the first time, introduces an auxiliary local structural information through ControlNet during higher-resolution diffusion extrapolation aiming to mitigate the local distortions. Finally, our analysis indicates that global semantic information is conducive to suppressing both repetitive generation and local distortion. Hence, our AccDiffusion v2 further proposes dilated sampling with window interaction for better global semantic information during higher-resolution diffusion extrapolation. We conduct extensive experiments, including both quantitative and qualitative comparisons, to demonstrate the efficacy of our AccDiffusion v2. The quantitative comparison shows that AccDiffusion v2 achieves state-of-the-art performance in image generation extrapolation without training. The qualitative comparison intuitively illustrates that AccDiffusion v2 effectively suppresses the issues of repetitive generation and local distortion in image generation extrapolation. Our code is available at https://github.com/lzhxmu/AccDiffusion_v2.

• The paper introduces patch-content-aware prompts, ControlNet integration, and dilated sampling to enhance high-resolution diffusion extrapolation.
• It overcomes object repetition and local distortion, achieving state-of-the-art results on metrics such as FID, IS, and CLIP.
• The framework enables training-free adaptation, offering practical benefits for fields like advertising, gaming, and digital content creation.

An Expert Overview of AccDiffusion v2: Enhanced High-Resolution Image Generation Through Diffusion Models

The paper "AccDiffusion v2: Towards More Accurate Higher-Resolution Diffusion Extrapolation" presents a significant exploration into improving the performance of diffusion models for high-resolution image generation. This paper addresses two key challenges faced by diffusion models: object repetition and local distortion during high-resolution inference, which arise when using a uniform prompt for patch-wise generation and lack of global consistency. The authors propose the AccDiffusion v2 framework as a training-free enhancement for stable diffusion models, utilizing a combination of patch-content-aware prompts, ControlNet integration, and dilated sampling with window interaction.

Critical Contributions

The AccDiffusion v2 framework demonstrates notable innovation in several areas:

1. Patch-Content-Aware Prompts: Recognizing that using a uniform prompt for all patches can lead to repetitive generation, AccDiffusion v2 introduces patch-content-aware prompts. This approach uses cross-attention maps to derive more accurate prompts for individual patches, ensuring that only relevant content is emphasized during generation. The adjustment minimizes repetition by tailoring textual inputs to match the content of each patch more precisely.
2. ControlNet-Assisted Generation: To address the persistent issue of local distortion, AccDiffusion v2 incorporates global structural guidance using ControlNet. This method involves integrating structure information extracted from low-resolution generations to guide the high-resolution patch-wise generation process. The canny edge detector provides structure information, which synergizes with textual prompts to produce high-fidelity local details and reduce structural inaccuracies.
3. Dilated Sampling with Window Interaction: Global semantic consistency was enhanced through a novel dilated sampling technique with window interaction, which mitigates the fragmentation and noise typically introduced by independent sampling processes. By allowing interaction between sub-patches within the sampling window, the framework enhances the coherence of the global semantic information.

Results and Implications

Extensive qualitative and quantitative analysis shows that AccDiffusion v2 achieves state-of-the-art performance in high-resolution image generation tasks. FID, IS, and CLIP scores confirm that this framework effectively bridges the gap between low-resolution input and high-resolution output without the need for additional training or extensive computational costs typical of current SD models.

The implications of this research not only enhance the potential for diffusion models in creating high-resolution imagery efficiently but also push forward the boundary of training-free adaptation in generative models. Practically, this approach can be pivotal for fields like advertising, gaming, and digital content creation, where high-resolution outputs are vital but data and computational resources for training are often limited.

Speculation on Future Directions

This research opens several avenues for further exploration. Notably, there is potential for integrating more advanced vision-LLMs to refine prompts dynamically, possibly enhancing patch accuracy further. Additionally, future studies might explore optimizing the computational efficiency of such frameworks to reduce inference times while maintaining, or even improving, image quality. The incorporation of additional control signals beyond structure—such as depth or motion vectors—could also yield more intricate and contextually rich outputs, broadening the applicability across different media types, including video and extended reality environments.

In conclusion, "AccDiffusion v2" presents a substantial step forward in the quest to achieve training-free, high-resolution image generation with reduced artifacts, marking a promising development in the evolution of diffusion models.