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Found 3 papers, 1 papers with code
MIST: Mitigating Intersectional Bias with Disentangled Cross-Attention Editing in Text-to-Image Diffusion Models
Addressing intersectional bias is crucial because it amplifies the negative effects of discrimination based on race, gender, and other identities.
GANTASTIC: GAN-based Transfer of Interpretable Directions for Disentangled Image Editing in Text-to-Image Diffusion Models
The rapid advancement in image generation models has predominantly been driven by diffusion models, which have demonstrated unparalleled success in generating high-fidelity, diverse images from textual prompts.
RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models
Recent advancements in diffusion-based models have demonstrated significant success in generating images from text.
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