λ-ECLIPSE is a novel, resource-efficient approach to personalized text-to-image (P-T2I) generation, leveraging the latent space of pre-trained CLIP models. Unlike traditional methods that rely on heavy computing resources and diffusion models, λ-ECLIPSE operates in the compressed latent space of UnCLIP models, such as DALL-E 2 and Kandinsky v2.2, without the need for explicit diffusion modeling. This approach significantly reduces training time and computational demands while maintaining competitive performance in concept and composition alignment. Key contributions of λ-ECLIPSE include: 1. **Resource Efficiency**: λ-ECLIPSE achieves multi-subject-driven P-T2I with only 34M parameters and 74 GPU hours of training, compared to heavy resource-intensive methods. 2. **Concept and Composition Alignment**: It outperforms existing baselines in both concept and composition alignment, even with lower resource utilization. 3. **Multi-Concept Interpolations**: λ-ECLIPSE can perform seamless transitions between multiple concepts, leveraging the smooth latent space of CLIP. The method involves: - **Image-Text Interleaved Pre-Training**: λ-ECLIPSE uses a dataset of 2 million high-quality image-text pairs, where text embeddings are replaced with image embeddings for subject-specific concepts. - **Contrastive Loss**: A contrastive loss function is used to balance concept and composition alignment, ensuring that the model does not overemphasize either aspect. - **Additional Control**: λ-ECLIPSE can incorporate additional controls, such as Canny edge maps, to refine image generation. Experiments on datasets like Dreambench, Multibench, and ConceptBed demonstrate that λ-ECLIPSE achieves superior performance in concept replication and composition fidelity, outperforming other methods with fewer resources. Qualitative results show that λ-ECLIPSE generates images that are more compositionally coherent and conceptually accurate compared to other baselines. λ-ECLIPSE represents a promising direction for improving the efficiency and effectiveness of P-T2I applications, particularly in multi-subject and multi-concept scenarios.λ-ECLIPSE is a novel, resource-efficient approach to personalized text-to-image (P-T2I) generation, leveraging the latent space of pre-trained CLIP models. Unlike traditional methods that rely on heavy computing resources and diffusion models, λ-ECLIPSE operates in the compressed latent space of UnCLIP models, such as DALL-E 2 and Kandinsky v2.2, without the need for explicit diffusion modeling. This approach significantly reduces training time and computational demands while maintaining competitive performance in concept and composition alignment. Key contributions of λ-ECLIPSE include: 1. **Resource Efficiency**: λ-ECLIPSE achieves multi-subject-driven P-T2I with only 34M parameters and 74 GPU hours of training, compared to heavy resource-intensive methods. 2. **Concept and Composition Alignment**: It outperforms existing baselines in both concept and composition alignment, even with lower resource utilization. 3. **Multi-Concept Interpolations**: λ-ECLIPSE can perform seamless transitions between multiple concepts, leveraging the smooth latent space of CLIP. The method involves: - **Image-Text Interleaved Pre-Training**: λ-ECLIPSE uses a dataset of 2 million high-quality image-text pairs, where text embeddings are replaced with image embeddings for subject-specific concepts. - **Contrastive Loss**: A contrastive loss function is used to balance concept and composition alignment, ensuring that the model does not overemphasize either aspect. - **Additional Control**: λ-ECLIPSE can incorporate additional controls, such as Canny edge maps, to refine image generation. Experiments on datasets like Dreambench, Multibench, and ConceptBed demonstrate that λ-ECLIPSE achieves superior performance in concept replication and composition fidelity, outperforming other methods with fewer resources. Qualitative results show that λ-ECLIPSE generates images that are more compositionally coherent and conceptually accurate compared to other baselines. λ-ECLIPSE represents a promising direction for improving the efficiency and effectiveness of P-T2I applications, particularly in multi-subject and multi-concept scenarios.