MegaPortrait: Revisiting Diffusion Control for High-fidelity Portrait Generation
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Abstract (arXiv)
We propose MegaPortrait. It's an innovative system for creating personalized portrait images in computer vision. It has three modules: Identity Net, Shading Net, and Harmonization Net. Identity Net generates learned identity using a customized model fine-tuned with source images. Shading Net re-renders portraits using extracted representations. Harmonization Net fuses pasted faces and the reference image's body for coherent results. Our approach with off-the-shelf Controlnets is better than state-of-the-art AI portrait products in identity preservation and image fidelity. MegaPortrait has a simple but effective design and we compare it with other methods and products to show its superiority.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 18 references that are in the map. First posted on arXiv 7 Nov 2024.
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References · 14
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2022Classifier-Free Diffusion Guidancecited by 5,847
- 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
- 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,152
- 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
- 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
- 2023IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Modelscited by 1,464
- 2022Compositional Visual Generation with Composable Diffusion Modelscited by 624
- 2022Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Modelscited by 513
- 2024InstantID: Zero-shot Identity-Preserving Generation in Secondscited by 436
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