T2I-Adapters are lightweight modules that enable fine-grained control over color and structure in text-to-image diffusion models by aligning external conditions with the frozen model's internal knowledge.
Parameter-efficient transfer learning for nlp
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
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LLaMA-Adapter V2 achieves open-ended visual instruction following in LLMs by unlocking more parameters, early fusion of visual tokens, and joint training on disjoint parameter groups with only 14M added parameters.
citing papers explorer
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T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models
T2I-Adapters are lightweight modules that enable fine-grained control over color and structure in text-to-image diffusion models by aligning external conditions with the frozen model's internal knowledge.
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LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
LLaMA-Adapter V2 achieves open-ended visual instruction following in LLMs by unlocking more parameters, early fusion of visual tokens, and joint training on disjoint parameter groups with only 14M added parameters.