StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing

Li, Senmao; Weijer, Joost van de; Hu, Taihang; Khan, Fahad Shahbaz; Hou, Qibin; Wang, Yaxing; Yang, Jian

doi:10.48550/arxiv.2303.15649

Cited by 1 publication

(2 citation statements)

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“…Beyond conventional generative frameworks, some methods incorporate a custom network to better align with specific editing intentions. StyleDiffusion [179] introduces a Mapping Network that maps features of the input image to an embedding space aligned with the embedding space of textual prompts, effectively generating a prompt embedding. Cross-attention layers are Testing-Time Finetuning Approaches Denosing Model Finetuning UniTune [172], Custom-Edit [173], KV-Inversion [174] Embedding Finetuning…”

Section: Guidance With a Hypernetworkmentioning

confidence: 99%

“…Null-Text Inversion [175], DPL [176], DiffusionDisentanglement [177], Prompt Tuning Inversion [178] Guidance with a Hypernetwork StyleDiffusion [179], InST [180] Latent Variable Optimization DragonDiffusion [181], DragDiffusion [182], DDS [183], DiffuseIT [184], CDS [185], MagicRemover [186] Hybrid Finetuning Imagic [187], LayerDiffusion [188], Forgedit [189], SINE [190] Fig. 6: Taxonomy of testing-time finetuning approaches for image editing.…”

Section: Guidance With a Hypernetworkmentioning

confidence: 99%

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M3VSNET: Unsupervised Multi-Metric Multi-View Stereo Network

Huang

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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The present Multi-view stereo (MVS) methods with supervised learning-based networks have an impressive performance comparing with traditional MVS methods. However, the ground-truth depth maps for training are hard to be obtained and are within limited kinds of scenarios. In this paper, we propose a novel unsupervised multi-metric MVS network, named M 3 VSNet, for dense point cloud reconstruction without any supervision. To improve the robustness and completeness of point cloud reconstruction, we propose a novel multi-metric loss function that combines pixel-wise and feature-wise loss function to learn the inherent constraints from different perspectives of matching correspondences. Besides, we also incorporate the normal-depth consistency in the 3D point cloud format to improve the accuracy and continuity of the estimated depth maps. Experimental results show that M 3 VSNet establishes the state-of-the-arts unsupervised method and achieves better performance than previous supervised MVSNet on the DTU dataset and demonstrates the powerful generalization ability on the Tanks & Temples benchmark with effective improvement.

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Section: Guidance With a Hypernetworkmentioning

confidence: 99%