SudoAI用文字生成3D模型体验

import numpy as np def simulate_text_encoding(text, vocab_size=1000, embed_dim=64):    np.random.seed(hash(text) % (2**31))    tokens = text.lower().split()    token_ids = [hash(t) % vocab_size for t in tokens]    embedding = np.zeros(embed_dim, dtype=np.float32)    for tid in token_ids:        np.random.seed(tid)        embedding += np.random.randn(embed_dim).astype(np.float32)    embedding = embedding / (np.linalg.norm(embedding) + 1e-8)    return embedding def simulate_voxel_generation(text_feat, grid_size=16, threshold=0.5):    np.random.seed(int(np.sum(text_feat * 1000)) % (2**31))    voxels = np.random.rand(grid_size, grid_size, grid_size).astype(np.float32)    center = grid_size // 2    for x in range(grid_size):        for y in range(grid_size):            for z in range(grid_size):                dist = np.sqrt((x-center)**2 + (y-center)**2 + (z-center)**2)                shape_bias = np.exp(-dist / (grid_size * 0.4))                voxels[x, y, z] = voxels[x, y, z] * 0.3 + shape_bias * 0.7    binary_voxels = (voxels > threshold).astype(np.float32)    return binary_voxels def count_surface_voxels(voxels):    grid_size = voxels.shape[0]    surface_count = 0    for x in range(grid_size):        for y in range(grid_size):            for z in range(grid_size):                if voxels[x, y, z] == 0:                    continue                is_surface = False                for dx, dy, dz in [(-1,0,0),(1,0,0),(0,-1,0),(0,1,0),(0,0,-1),(0,0,1)]:                    nx, ny, nz = x+dx, y+dy, z+dz                    if nx<0 or nx>=grid_size or ny<0 or ny>=grid_size or nz<0 or nz>=grid_size:                        is_surface = True                        break                    if voxels[nx, ny, nz] == 0:                        is_surface = True                        break                if is_surface:                    surface_count += 1    return surface_count descriptions = ["a simple wooden chest", "a medieval iron chest", "a gothic cathedral"]for desc in descriptions:    feat = simulate_text_encoding(desc)    voxels = simulate_voxel_generation(feat)    filled = int(np.sum(voxels))    surface = count_surface_voxels(voxels)    print(f"{desc}: filled={filled}, surface={surface}, faces={surface*6}")