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Hila Chefer, Patrick Esser, Dominik Lorenz, Dustin Podell et autres
Strong semantic representations improve the convergence and generation quality of diffusion and flow models. Existing approaches largely rely on external models, which require separate training, operate on misaligned objectives, and exhibit unexpected scaling behavior. We argue that this dependence arises from the …
Hila Chefer, Patrick Esser, Dominik Lorenz, Dustin Podell et autres
Strong semantic representations improve the convergence and generation quality of diffusion and flow models. Existing approaches largely rely on external models, which require separate training, operate on misaligned objectives, and exhibit unexpected scaling behavior. We argue that this dependence arises from the …
Danielle Cohen, Hila Chefer, Lior Wolf
Deep neural networks (DNNs) have demonstrated remarkable success, yet their wide adoption is often hindered by their opaque decision-making. To address this, attribution methods have been proposed to assign relevance values to each part of the input. However, different methods often produce …
Hila Chefer, Uriel Singer, Amit Zohar, Yuval Kirstain et autres
Despite tremendous recent progress, generative video models still struggle to capture real-world motion, dynamics, and physics. We show that this limitation arises from the conventional pixel reconstruction objective, which biases models toward appearance fidelity at the expense of motion coherence. To address …
Danielle Cohen, Hila Chefer, Lior Wolf
il (code pays fourni par la source)
Yarden Bakish, Itamar Zimerman, Hila Chefer, Lior Wolf
The development of effective explainability tools for Transformers is a crucial pursuit in deep learning research. One of the most promising approaches in this domain is Layer-wise Relevance Propagation (LRP), which propagates relevance scores backward through the network to the input space …
il, us (code pays fourni par la source)
Ariel Shaulov, Itay Hazan, Lior Wolf, Hila Chefer
Text-to-video diffusion models are notoriously limited in their ability to model temporal aspects such as motion, physics, and dynamic interactions. Existing approaches address this limitation by retraining the model or introducing external conditioning signals to enforce temporal consistency. In this work, we …
il, us (code pays fourni par la source)
Omer Bar-Tal, Hila Chefer, Omer Tov, Charles Herrmann et autres
il, us (code pays fourni par la source)
Hila Chefer, Shiran Zada, Roni Paiss, Ariel Ephrat et autres
Customizing text-to-image (T2I) models has seen tremendous progress recently, particularly in areas such as personalization, stylization, and conditional generation. However, expanding this progress to video generation is still in its infancy, primarily due to the lack of customized video data. In this …
il, us (code pays fourni par la source)
Hila Chefer, Shiran Zada, Roni Paiss, Ariel Ephrat et autres
Customizing text-to-image (T2I) models has seen tremendous progress recently, particularly in areas such as personalization, stylization, and conditional generation. However, expanding this progress to video generation is still in its infancy, primarily due to the lack of customized video data. In this …
Omer Bar-Tal, Hila Chefer, Omer Tov, Charles Herrmann et autres
We introduce Lumiere -- a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion -- a pivotal challenge in video synthesis. To this end, we introduce a Space-Time U-Net architecture that generates the entire temporal duration of …
Idan Schwartz, Vésteinn Snæbjarnarson, Hila Chefer, Serge Belongie et autres
Recent advances in text-to-image diffusion models have enabled the generation of diverse and high-quality images. While impressive, the images often fall short of depicting subtle details and are susceptible to errors due to ambiguity in the input text. One way of alleviating …
il, dk (code pays fourni par la source)
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