579 research outputs found
HAPPEN TO BE FASHIONABLE? : NEW PRACTICE CREATION THROUGH THE SEQUENCE OF MULTIPLE ACTORS
In contrast to previous research, this paper illustrates a process in which institutional entrepreneurs play less significant roles in creating a new practice. We drew on a historical case study that deals with the emergence of a new practice of emphasizing fashionable design of a type of clothing known as meisen. In the historical case study, multiple actors played distinctive and essential roles, which, as a whole, led to the creation of a new practice.
Equilibrium Properties of a Trapped Dipolar Fermion at Finite Temperatures
We study the equilibrium properties of a dipolar Fermi gas at finite
temperatures. We introduce a variational ansatz for the phase-space
distribution function that can describe the deformation in both real and
momentum space. The effect of dipole--dipole interactions on the thermal
equilibrium is discussed with particular emphasis on the deformation in
momentum space. We examine the stability of the system by varying the
temperature, trap aspect ratio, and the dipole moment. In addition, we discuss
how the deformation in both real and momentum space can be observed in the
high-temperature regime.Comment: 7 pages, 5 figure
Masked-Attention Diffusion Guidance for Spatially Controlling Text-to-Image Generation
Text-to-image synthesis has achieved high-quality results with recent
advances in diffusion models. However, text input alone has high spatial
ambiguity and limited user controllability. Most existing methods allow spatial
control through additional visual guidance (e.g., sketches and semantic masks)
but require additional training with annotated images. In this paper, we
propose a method for spatially controlling text-to-image generation without
further training of diffusion models. Our method is based on the insight that
the cross-attention maps reflect the positional relationship between words and
pixels. Our aim is to control the attention maps according to given semantic
masks and text prompts. To this end, we first explore a simple approach of
directly swapping the cross-attention maps with constant maps computed from the
semantic regions. Some prior works also allow training-free spatial control of
text-to-image diffusion models by directly manipulating cross-attention maps.
However, these approaches still suffer from misalignment to given masks because
manipulated attention maps are far from actual ones learned by diffusion
models. To address this issue, we propose masked-attention guidance, which can
generate images more faithful to semantic masks via indirect control of
attention to each word and pixel by manipulating noise images fed to diffusion
models. Masked-attention guidance can be easily integrated into pre-trained
off-the-shelf diffusion models (e.g., Stable Diffusion) and applied to the
tasks of text-guided image editing. Experiments show that our method enables
more accurate spatial control than baselines qualitatively and quantitatively.Comment: Accepted to The Visual Computer, code:
https://github.com/endo-yuki-t/MA
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