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Found 3 papers, 2 papers with code
CTRLorALTer: Conditional LoRAdapter for Efficient 0-Shot Control & Altering of T2I Models
Text-to-image generative models have become a prominent and powerful tool that excels at generating high-resolution realistic images.
Continuous, Subject-Specific Attribute Control in T2I Models by Identifying Semantic Directions
We demonstrate that these directions can be used to augment the prompt text input with fine-grained control over attributes of specific subjects in a compositional manner (control over multiple attributes of a single subject) without having to adapt the diffusion model.
Boosting Latent Diffusion with Flow Matching
We demonstrate that introducing FM between the Diffusion model and the convolutional decoder offers high-resolution image synthesis with reduced computational cost and model size.
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