Introduces CLIP guidance and prior-preservation loss to constrain point-based diffusion image edits within the model's sampling distribution for more consistent results.
Compare with SOTA methods To validate the effectiveness of our proposed method, we design a set of comparative experiments focusing on Prior-Preserving Reg- ularization (PPR)
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Drag within Prior Distribution: Text-Conditioned Point-Based Image Editing within Distribution Constraints
Introduces CLIP guidance and prior-preservation loss to constrain point-based diffusion image edits within the model's sampling distribution for more consistent results.