Cultural Structural Fusion Motorcycle Prompt

Different prompt structure works pretty much the same way. Asked GPT to dress up 4 motorcycles with clothing items from their countries. 2x2 grid, 16:9, do this for 4 unusual motorcycles and 4 countries top clothing items class CulturalStructuralFusion: def __init__(self, SUBJECT): s = infer(SUBJECT) # country, theme, geometry, mapping, translate, material self.s = s def skeleton(self): # F₁: hard points preserved return hard_points_and_aero_lines(self.s.geometry) def skin(self): # F₃: topological mapping (theme follows aero) return map_theme_to_curvature(self.s.theme, self.s.geometry) def translate_features(self): # F₄: functional translation return {k: self.s.translate[k] for k in [vent, grille, headlight, seam]} def render(self): return render_3d( skeleton=self.skeleton(), skin=self. skin(), # F₂: cultural overwrite features=self.translate_features(), # F₄: vents become theme anatomy material=self.s.material, # F₅: coherent material world lighting=studio_macro() ) def verify(self, img): assert geometry_preserved(img, self.s.geometry), "F₁: subject shape lost" assert original_livery_gone(img) and theme_pure(img), "F₂: decal wrap / mixed themes" assert patterns_follow_curvature(img, self.s.geometry),"F₃: flat decals on curved aero" assert all(v == self.s.translate[v] for v in apertures(img)), "F₄: modern parts untouched" assert single_material_world(img, self.s.material), "F₅: mixed materials" CulturalStructuralFusion(SUBJECT).render()