Główne pojęcia
Enhancing image editing through the Ground-A-Score methodology by incorporating grounding during score distillation.
Streszczenie
The article introduces Ground-A-Score, a model-agnostic image editing method that incorporates grounding during score distillation to ensure precise reflection of complex editing prompts. The approach breaks down prompts into subtasks, optimizing image editing outcomes while preserving original attributes. The method outperforms conventional approaches in handling multifaceted prompts and maintaining high-quality outcomes.
Introduction
Recent advances in generative models for diverse data domains.
Text-to-image diffusion models facilitate various image editing techniques.
Challenges arise from complex text prompts leading to oversight in requests.
Method: Ground-A-Score
Breaks down complex editing prompts into multiple modification subtasks.
Selectively aggregates gradients with grounding information for precise edits.
Introduces null-text penalty to prevent undesired object distortion.
Experimental Results
Qualitative comparison with other baseline models like CDS, DDS, InstructPix2Pix, and GLIGEN.
Quantitative evaluation using CLIP scores and LPIPS perceptual loss.
User study results show higher scores for fidelity, preservation, and quality with Ground-A-Score.
Additional Results
Detailed editing prompts generated by GPT4-vision for synthetic scenarios.
Chain-of-Thought prompt structure for scheduling subtasks in image editing queries.
Statystyki
"Ground-A-Score achieved a better image quality with small LPIPS conceptual loss compared to other methods."
"Ground-A-Score had the highest agreement between prompt and output regions when measured separately."
Cytaty
"Noise timestep and weight function play crucial roles in optimizing the image latent."
"Null-text penalty prevents objects from being deleted during optimization."