Polaris retrieves and integrates relevant models from a large library of checkpoints and adapters to enable scalable instruction-guided image generation and editing without additional training.
Personalized image generation with deep generative models: A decade survey
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 4years
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MULTI uses two-stage textual inversion to disentangle camera lens, sensor, view, and domain factors for novel image generation, supporting dataset extension and ControlNet modifications on the new DF-RICO benchmark.
Gate-and-Merge enables zero-shot compositional personalization of VLMs by independently learning concept-specific LoRA adapters and merging them in weight space with cue-based gating to suppress interference.
HiFi-Inpaint delivers state-of-the-art detail-preserving human-product images by adding Shared Enhancement Attention and Detail-Aware Loss to reference-based inpainting on a new 40K dataset.
citing papers explorer
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Polaris: Scaling Up Instruction-Guided Image Generation Towards Millions of Personalized Style Needs
Polaris retrieves and integrates relevant models from a large library of checkpoints and adapters to enable scalable instruction-guided image generation and editing without additional training.
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MULTI: Disentangling Camera Lens, Sensor, View, and Domain for Novel Image Generation
MULTI uses two-stage textual inversion to disentangle camera lens, sensor, view, and domain factors for novel image generation, supporting dataset extension and ControlNet modifications on the new DF-RICO benchmark.
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Gate-and-Merge: Zero-shot Compositional Personalization of Vision Language Models
Gate-and-Merge enables zero-shot compositional personalization of VLMs by independently learning concept-specific LoRA adapters and merging them in weight space with cue-based gating to suppress interference.
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HiFi-Inpaint: Towards High-Fidelity Reference-Based Inpainting for Generating Detail-Preserving Human-Product Images
HiFi-Inpaint delivers state-of-the-art detail-preserving human-product images by adding Shared Enhancement Attention and Detail-Aware Loss to reference-based inpainting on a new 40K dataset.