3 citations · 4 across the 8 of their papers we have counts for
10 papers
HiGS: History-Guided Sampling for Plug-and-Play Enhancement of Diffusion Models
Seyedmorteza Sadat, Farnood Salehi, Romann M. Weber
While diffusion models have made remarkable progress in image generation, their outputs can still appear unrealistic and lack fine details, especially when using fewer number of ne…
HiWave: Training-Free High-Resolution Image Generation via Wavelet-Based Diffusion Sampling
Tobias Vontobel, Seyedmorteza Sadat, Farnood Salehi +1
Diffusion models have emerged as the leading approach for image synthesis, demonstrating exceptional photorealism and diversity. However, training diffusion models at high resoluti…
Guidance in the Frequency Domain Enables High-Fidelity Sampling at Low CFG Scales
Seyedmorteza Sadat, Tobias Vontobel, Farnood Salehi +1
Classifier-free guidance (CFG) has become an essential component of modern conditional diffusion models. Although highly effective in practice, the underlying mechanisms by which C…
Token Perturbation Guidance for Diffusion Models
Javad Rajabi, Soroush Mehraban, Seyedmorteza Sadat +1
Classifier-free guidance (CFG) has become an essential component of modern diffusion models to enhance both generation quality and alignment with input conditions. However, CFG req…
Efficient Distillation of Classifier-Free Guidance using Adapters
Cristian Perez Jensen, Seyedmorteza Sadat
While classifier-free guidance (CFG) is essential for conditional diffusion models, it doubles the number of neural function evaluations (NFEs) per inference step. To mitigate this…
Regressor-Guided Image Editing Shifts Emotion and Disengagement Timing in Social Media
Christoph Gebhardt, Robin Willardt, Seyedmorteza Sadat +5
Internet overuse is a widespread phenomenon in today's digital society. Existing interventions, such as time limits or grayscaling, often rely on restrictive controls that provoke…
Eliminating Oversaturation and Artifacts of High Guidance Scales in Diffusion Models
Seyedmorteza Sadat, Otmar Hilliges, Romann M. Weber
Classifier-free guidance (CFG) is crucial for improving both generation quality and alignment between the input condition and final output in diffusion models. While a high guidanc…
No Training, No Problem: Rethinking Classifier-Free Guidance for Diffusion Models
Seyedmorteza Sadat, Manuel Kansy, Otmar Hilliges +1
Classifier-free guidance (CFG) has become the standard method for enhancing the quality of conditional diffusion models. However, employing CFG requires either training an uncondit…
LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models
Seyedmorteza Sadat, Jakob Buhmann, Derek Bradley +2
Advances in latent diffusion models (LDMs) have revolutionized high-resolution image generation, but the design space of the autoencoder that is central to these systems remains un…
CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling
Seyedmorteza Sadat, Jakob Buhmann, Derek Bradley +2
While conditional diffusion models are known to have good coverage of the data distribution, they still face limitations in output diversity, particularly when sampled with a high…