Drag-guided diffusion models for vehicle image generation
Denoising diffusion models trained at web-scale have revolutionized image generation. The application of these tools to engineering design is an intriguing possibility, but is currently limited by their inability to parse and enforce concrete engineering constraints. In this paper, we take a step towards this goal by proposing physics-based guidance, which enables optimization of a performance metric (as predicted by a surrogate model) during the generation process. As a proof-of-concept, we add drag guidance to Stable Diffusion, which allows this tool to generate images of novel vehicles while simultaneously minimizing their predicted drag coefficients.
Code (0)
등록된 구현이 없습니다.
Tasks
DenoisingImage GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
DirectDrag: High-Fidelity, Mask-Free, Prompt-Free Drag-based Image Editing via Readout-Guided Feature Alignment
Drag-based image editing using generative models provides intuitive control over image structures. However, existing methods rely heavily on manually provided masks and textual prompts to preserve semantic fidelity and m…
Image ManipulationImage EditingOptimization and Generation in Aerodynamics Inverse Design
Aerodynamic inverse design can improve vehicle and aircraft efficiency, but practical design rarely seeks performance alone: vehicle refinement must reduce drag while preserving visual features linked to design language,…
DragEntity: Trajectory Guided Video Generation using Entity and Positional Relationships
In recent years, diffusion models have achieved tremendous success in the field of video generation, with controllable video generation receiving significant attention. However, existing control methods still face two li…
Video GenerationTDEdit: A Unified Diffusion Framework for Text-Drag Guided Image Manipulation
This paper explores image editing under the joint control of text and drag interactions. While recent advances in text-driven and drag-driven editing have achieved remarkable progress, they suffer from complementary limi…
Image ManipulationImage EditingAdaptiveDrag: Semantic-Driven Dragging on Diffusion-Based Image Editing
Recently, several point-based image editing methods (e.g., DragDiffusion, FreeDrag, DragNoise) have emerged, yielding precise and high-quality results based on user instructions. However, these methods often make insuffi…