pip install torch torchvision torchaudio

pip install gsplat # Fast CUDA rasterization

pip install hf_transfer # For faster model downloads

import torch

from gsplat import GaussianModel, Camera

def initialize_3d_ai_scene(num_points=100000):

"""

Initializes a 3D AI scene using Gaussian Splatting logic.

"""

# 1. Define the 3D spatial points (means)

means = torch.randn((num_points, 3), device="cuda")

# 2. Define the 'Instance' attributes: Scale, Rotation, Opacity, and Color

# We use log-space for scales and quaternions for rotation

scales = torch.randn((num_points, 3), device="cuda")

quats = torch.randn((num_points, 4), device="cuda")

opacities = torch.sigmoid(torch.randn((num_points, 1), device="cuda"))

colors = torch.rand((num_points, 3), device="cuda") # RGB

print(f"Initialized 3D AI model with {num_points} Gaussian instances.")

return {

"means": means,

"scales": scales,

"quats": quats,

"opacities": opacities,

"colors": colors

}

# HUD: Processing Visualization Simulation

def display_hud_stats(scene):

print("--- 3D GENERATOR HUD ---")

print(f"VRAM Usage: {torch.cuda.memory_allocated() / 1024**2:.2f} MB")

print(f"Active Gaussians: {scene['means'].shape[0]}")

print(f"Spatial Variance: {torch.var(scene['means']).item():.4f}")

print("------------------------")

# Execution

if __name__ == "__main__":

if torch.cuda.is_available():

my_3d_scene = initialize_3d_ai_scene()

display_hud_stats(my_3d_scene)

else:

print("CUDA not detected. A GPU is required for 3D AI generation.")

import torch

import torch.nn as nn

from gsplat.project_gaussians import project_gaussians

from gsplat.rasterize_gaussians import rasterize_gaussians

class Advanced3DGenerator(nn.Module):

def __init__(self, num_instances=50000):

super().__init__()

self.num_instances = num_instances

# Advanced Feature Encoder (Pre-trained ViT or ResNet)

self.encoder = nn.Sequential(

nn.Conv2d(3, 64, 3, padding=1),

nn.ReLU(),

nn.AdaptiveAvgPool2d((16, 16))

)

# Parameter Head: Predicts Position, Rotation (Quat), Scale, Opacity, and Color

# Each instance is a 3D Gaussian 'blob'

self.param_head = nn.Linear(64 * 16 * 16, num_instances * 14)

def forward(self, x):

features = self.encoder(x).view(x.shape[0], -1)

raw_params = self.param_head(features).view(-1, self.num_instances, 14)

# Instance Parameter Splitting

means3D = raw_params[..., 0:3] # (x, y, z)

scales = torch.exp(raw_params[..., 3:6]) # (sx, sy, sz) in log-space

quats = nn.functional.normalize(raw_params[..., 6:10], dim=-1) # (w, x, y, z)

opacities = torch.sigmoid(raw_params[..., 10:11])

colors = torch.sigmoid(raw_params[..., 11:14]) # RGB

return {

"means3D": means3D,

"scales": scales,

"quats": quats,

"opacities": opacities,

"colors": colors

}

# HUD: Advanced Telemetry for AI Training

def print_ai_hud(step, loss, vram):

print(f"\n[HUD] STEP: {step:04d} | LOSS: {loss:.6f} | VRAM: {vram:.2f}GB")

print(f"[HUD] INSTANCE COUNT: 50,000 Gaussians | STATUS: OPTIMIZING")

print("-" * 50)

def train_step(model, optimizer, target_image, camera_params):

optimizer.zero_grad()

# 1. Generate 3D Instances

scene_data = model(target_image)

# 2. Differentiable Rasterization (Projecting 3D to 2D)

# Using gsplat for high-speed CUDA rendering

rendered_image, _, _ = rasterize_gaussians(

scene_data["means3D"],

scene_data["quats"],

scene_data["scales"],

scene_data["opacities"],

scene_data["colors"],

camera_params

)

# 3. Calculate Scientific Loss (L1 + SSIM)

l1_loss = torch.abs(rendered_image - target_image).mean()

loss = l1_loss # Simplified for this example

loss.backward()

optimizer.step()

return loss.item()

# Execution Setup

device = "cuda" if torch.cuda.is_available() else "cpu"

model = Advanced3DGenerator().to(device)

optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)

# HUD Notification

print_ai_hud(1, 0.8421, torch.cuda.memory_reserved() / 1e9 if device == "cuda" else 0)