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<metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/"><dc:title>Image editing with diffusion models in the domain of fashion</dc:title><dc:creator>Keserič,	Luka	(Avtor)
	</dc:creator><dc:creator>Peer,	Peter	(Mentor)
	</dc:creator><dc:creator>Štruc,	Vitomir	(Komentor)
	</dc:creator><dc:subject>diffusion models</dc:subject><dc:subject>generative models</dc:subject><dc:subject>computer vision</dc:subject><dc:subject>image editing</dc:subject><dc:subject>fashion editing</dc:subject><dc:description>Image editing, specifically appearance or style transfer, is an area of computer
vision that has seen significant growth due to recent advancements in
image diffusion models. There are many different approaches to appearance
transfer with fine-tuning, external network adapters and tuning-free methods.
In this work, we focus on the latter, where we leverage the existing
Stable Diffusion network with some modifications to the internal processing
of the attention maps and latent vectors, without modifying the model
weights. We propose an appearance transfer method based on partial masking
and timestep-controlled appearance modulation, controlling the area and
amount of appearance we transfer. The results on the benchmarks outperform
current baseline models, which shows the method’s potential for future
improvements.</dc:description><dc:date>2026</dc:date><dc:date>2026-03-02 16:10:00</dc:date><dc:type>Magistrsko delo/naloga</dc:type><dc:identifier>180098</dc:identifier><dc:identifier>VisID: 37938</dc:identifier><dc:identifier>COBISS_ID: 271564035</dc:identifier><dc:language>sl</dc:language></metadata>
