Text-to-Image Generation and Customization

Analyze the impact of different text encoders (e.g., CLIP, T5) on generation quality and prompt following

Implement text conditioning using cross-attention mechanisms

Implement negative prompting and prompt re-weighting to refine generation control

Analyze cross-attention maps to debug prompt-to-image alignment

Implement Low-Rank Adaptation (LoRA) to efficiently fine-tune a text-to-image model on a custom dataset

Implement a concept-learning technique (e.g., DreamBooth) to inject a new subject into a model