FEC: Three Finetuning-free Methods to Enhance Consistency for Real Image Editing-Reference-Cited by-同舟云学术

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FEC: Three Finetuning-free Methods to Enhance Consistency for Real Image Editing

Published:2023-11-03 Issue: Volume:32 Page:76-87
ISSN:
Container-title:2023 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML)
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Author:

Chen Songyan¹,Huang Jiancheng¹

Affiliation:

1. Chinese Academy of Sciences,Shenzhen Institute of Advanced Technology,Shenzhen,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10424714/10424727/10424833.pdf?arnumber=10424833

Reference34 articles.

1. Zero-shot text-to-image generation;Ramesh

2. Glide: Towards photorealistic image generation and editing with text-guided diffusion models;Nichol,2021

3. Scaling autoregressive models for content-rich text-to-image generation;Yu,2022

4. Hierarchical text-conditional image generation with clip latents;Ramesh,2022

5. High-Resolution Image Synthesis with Latent Diffusion Models

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Entwined Inversion: Tune-Free Inversion For Real Image Faithful Reconstruction and Editing;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

2. Image Clustering and Generation with HDGMVAE-I;Lecture Notes in Computer Science;2024

3. SpecRef: A Fast Training-free Baseline of Specific Reference-Condition Real Image Editing;2023 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML);2023-11-03

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