Diffusion models have enabled the generation of high-quality images with a strong focus on realism and textual fidelity. Yet, large-scale text-to-image models, such as Stable Diffusion, struggle to generate images where foreground objects are placed over a chroma key background, limiting their ability to separate foreground and background elements without fine-tuning. To address this limitation, we present a novel Training-Free Chroma Key Content Generation Diffusion Model (TKG-DM), which optimizes the initial random noise to produce images with foreground objects on a specifiable color background. Our proposed method is the first to explore the manipulation of the color aspects in initial noise for controlled background generation, enabling precise separation of foreground and background without fine-tuning. Extensive experiments demonstrate that our training-free method outperforms existing methods in both qualitative and quantitative evaluations, matching or surpassing fine-tuned models. Finally, we successfully extend it to other tasks (e.g., consistency models and text-to-video), highlighting its transformative potential across various generative applications where independent control of foreground and background is crucial.

Large-scale, pre-trained Text-to-Image (T2I) diffusion models have gained significant popularity in image synthesis and have shown unexpected potential in image Super-Resolution (SR). However, they are usually trained with a resolution limit of 512×512, making scaling beyond this resolution an unresolved but necessary challenge. To address this limitation, we propose a novel approach that enables them to generate 2K, 4K, and even 8K images without any additional training. Our method leverages MultiDiffusion, which distributes the generation across multiple diffusion paths, and local degradation-aware prompt extraction, which guides the T2I model according to its low-resolution input. As a result, we unlock higher resolutions, allowing T2I diffusion to be applied to image SR tasks without limitation on resolution.

Latent dataset distillation, which exploits pre-trained generative priors, has gained significant interest in recent years because it can be applied agnostic to any distillation algorithm and addresses two significant limitations of classical distillation algorithms: cross-architecture generalization and high-resolution synthesis. However, existing approaches typically distill from the entire dataset, potentially including non-beneficial samples. We introduce a novel "Prune First, Distill After" framework that systematically prunes datasets via loss-based sampling prior to latent distillation. By leveraging pruning before classical distillation techniques and generative priors, we create a representative coreset that leads to enhanced generalization for unseen architectures - a significant challenge of current distillation methods. More specifically, our proposed framework significantly boosts distilled quality, achieving up to a 5.2 percentage points accuracy increase even with substantial dataset pruning, i.e., removing 80% of the original dataset prior to distillation. Overall, our experimental results highlight the advantages of our easy-sample prioritization and cross-architecture robustness, paving the way for more effective and high-quality dataset distillation.

Dataset distillation seeks to condense datasets into smaller but highly representative synthetic samples. While diffusion models now lead all generative benchmarks, current distillation methods avoid them and rely instead on GANs or autoencoders, or, at best, sampling from a fixed diffusion prior. This trend arises because naive backpropagation through the long denoising chain leads to vanishing gradients, which prevents effective synthetic sample optimization. To address this limitation, we introduce Latent Dataset Distillation with Diffusion Models (LD3M), the first method to learn gradient-based distilled latents and class embeddings end-to-end through a pre-trained latent diffusion model. A linearly decaying skip connection, injected from the initial noisy state into every reverse step, preserves the gradient signal across dozens of timesteps without requiring diffusion weight fine-tuning. Across multiple ImageNet subsets at $128\times128$ and $256\times256$, LD3M improves downstream accuracy by up to 4.8 percentage points (1 IPC) and 4.2 points (10 IPC) over the prior state-of-the-art. The code for LD3M is provided at https://github.com/Brian-Moser/prune_and_distill.

Generating high-resolution images with generative models has recently been made widely accessible by leveraging diffusion models pre-trained on large-scale datasets. Various techniques, such as MultiDiffusion and SyncDiffusion, have further pushed image generation beyond training resolutions, i.e., from square images to panorama, by merging multiple overlapping diffusion paths or employing gradient descent to maintain perceptual coherence. However, these methods suffer from significant computational inefficiencies due to generating and averaging numerous predictions, which is required in practice to produce high-quality and seamless images. This work addresses this limitation and presents a novel approach that eliminates the need to generate and average numerous overlapping denoising predictions. Our method shifts non-overlapping denoising windows over time, ensuring that seams in one timestep are corrected in the next. This results in coherent, high-resolution images with fewer over-all steps. We demonstrate the effectiveness of our approach through qualitative and quantitative evaluations, comparing it with MultiDijfusion, SyncDiffusion, and StitchDijfusion. Our method offers several key benefits, including improved computational efficiency and faster inference times while producing comparable or better image quality.

Diffusion models in image Super-Resolution (SR) treat all image regions uniformly, which risks compromising the overall image quality by potentially introducing artifacts during denoising of less-complex regions. To address this, we propose “You Only Diffuse Areas” (YODA), a dynamic attention-guided diffusion process for image SR. YODA selectively focuses on spatial regions defined by attention maps derived from the low-resolution images and the current de-noising time step. This time-dependent targeting enables a more efficient conversion to high-resolution outputs by focusing on areas that benefit the most from the iterative refinement process, i.e., detail-rich objects. We empirically validate YODA by extending leading diffusion-based methods SR3, DiffBIR, and SRDiff. Our experiments demonstrate new state-of-the-art performances in face and general SR tasks across PSNR, SSIM, and LPIPS metrics. As a side effect, we find that YODA reduces color shift issues and stabilizes training with small batches.

Diffusion models (DMs) have disrupted the image super-resolution (SR) field and further closed the gap between image quality and human perceptual preferences. They are easy to train and can produce very high-quality samples that exceed the realism of those produced by previous generative methods. Despite their promising results, they also come with new challenges that need further research: high computational demands, comparability, lack of explainability, color shifts, and more. Unfortunately, entry into this field is overwhelming because of the abundance of publications. To address this, we provide a unified recount of the theoretical foundations underlying DMs applied to image SR and offer a detailed analysis that underscores the unique characteristics and methodologies within this domain, distinct from broader existing reviews in the field. This article articulates a cohesive understanding of DM principles and explores current research avenues, including alternative input domains, conditioning techniques, guidance mechanisms, corruption spaces, and zero-shot learning approaches. By offering a detailed examination of the evolution and current trends in image SR through the lens of DMs, this article sheds light on the existing challenges and charts potential future directions, aiming to inspire further innovation in this rapidly advancing area.

Image Super-Resolution (SR) remains challenging, particularly in achieving high-quality details without extensive computational cost. Existing methods often struggle to balance the trade-off between image quality, especially in high-frequency details, and computational efficiency. In this paper, we present a novel Diffusion-Wavelet (DiWa) approach for bridging this gap. It leverages the strengths of diffusion models and discrete wavelet transformation. By enabling the diffusion model to operate in the frequency domain, our models effectively hallucinate highfrequency information for SR images on the wavelet spectrum, resulting in high-quality and detailed reconstructions in image space. Quantitatively, our method outperforms other state-ofthe-art diffusion-based SR methods, namely SR3 and SRDiff, regarding PSNR, SSIM, and LPIPS on both face (8x scaling) and general (4x scaling) SR benchmarks. Meanwhile, using the frequency domain allows us to use fewer parameters than the compared models: 92M parameters instead of 550M compared to SR3 and 9.3M instead of 12M compared to SRDiff. Additionally, DiWa outperforms other state-of-the-art generative methods on general SR datasets while saving inference time (ca. 250 %).

With the advent of Deep Learning (DL), Super-Resolution (SR) has also become a thriving research area. However, despite promising results, the field still faces challenges that require further research, e.g., allowing flexible upsampling, more effective loss functions, and better evaluation metrics. We review the domain of SR in light of recent advances and examine state-of-the-art models such as diffusion (DDPM) and transformer-based SR models. We critically discuss contemporary strategies used in SR and identify promising yet unexplored research directions. We complement previous surveys by incorporating the latest developments in the field, such as uncertainty-driven losses, wavelet networks, neural architecture search, novel normalization methods, and the latest evaluation techniques. We also include several visualizations for the models and methods throughout each chapter to facilitate a global understanding of the trends in the field. This review ultimately aims at helping researchers to push the boundaries of DL applied to SR.