Real-Time Video Generation: Sora за мілісекунди

Sora генерує хвилину відео за годину. Це вражає... для офлайн production. Але що, якщо тобі потрібне відео зараз? Для стріму? Для відеоконференції? Для гри?

Real-time video generation — це коли AI створює відео з затримкою в мілісекунди. Не post-production, а live. Зміни обличчя, віртуальні персонажі, динамічний контент — все в реальному часі.

StreamDiffusion. NeRF streaming. Neural video synthesis. Це вже працює. І це змінює правила гри для всіх — від стрімерів на Twitch до голлівудських студій.

Чому real-time — принципово інша задача

Офлайн generation (Sora, Runway):

• Часу необмежено (години на хвилину відео)
• Якість максимальна (ітеративне уточнення)
• Багаторазова постобробка
• Compute-intensive процеси допустимі

Real-time вимоги:

• Latency < 100ms (ideally < 33ms для 30 FPS)
• Consistent framerate без dropped frames
• Streaming architecture з мінімальним буфером
• Hardware constraints жорсткі

Різниця в складності порівняльна з рендерингом Pixar проти реального часу в відеогрі.

Порівняння computational budget:

Sora (1 хвилина відео):
  - 1800 frames × ~10 seconds/frame = 5 годин compute
  - Quality: максимальна
  - Use case: production content

StreamDiffusion (1 хвилина real-time):
  - 1800 frames × 0.011 seconds/frame = 20 секунд compute
  - Quality: прийнятна для live
  - Use case: interactive applications

Анатомія bottlenecks у Diffusion моделях

Стандартний Stable Diffusion inference:

import torch
from diffusers import StableDiffusionPipeline
import time

def profile_standard_diffusion():
    """Профілювання стандартного diffusion inference."""

    pipe = StableDiffusionPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5",
        torch_dtype=torch.float16
    ).to("cuda")

    # Standard settings
    num_inference_steps = 50
    image_size = 512

    timings = {}

    # Profile each step
    start = time.time()

    # 1. Text encoding
    prompt_embeds = pipe.encode_prompt("a photo of a cat", "cuda", 1, True)
    timings['text_encoding'] = time.time() - start

    # 2. Latent initialization
    start = time.time()
    latents = torch.randn(1, 4, 64, 64, device="cuda", dtype=torch.float16)
    timings['latent_init'] = time.time() - start

    # 3. Denoising loop (the bottleneck!)
    start = time.time()
    for i, t in enumerate(pipe.scheduler.timesteps):
        # Кожен крок — повний forward pass через U-Net
        noise_pred = pipe.unet(latents, t, prompt_embeds[0]).sample
        latents = pipe.scheduler.step(noise_pred, t, latents).prev_sample
    timings['denoising_loop'] = time.time() - start
    timings['per_step'] = timings['denoising_loop'] / num_inference_steps

    # 4. VAE decoding
    start = time.time()
    image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor).sample
    timings['vae_decode'] = time.time() - start

    return timings

# Типові результати на RTX 4090:
# {
#     'text_encoding': 0.015,      # 15ms
#     'latent_init': 0.001,        # 1ms
#     'denoising_loop': 2.5,       # 2500ms (50 steps × 50ms)
#     'per_step': 0.05,            # 50ms per step
#     'vae_decode': 0.025          # 25ms
# }
# Total: ~2.5 секунди = 0.4 FPS (не real-time)

Головні bottlenecks:

1. Many iterative steps — 50 sequential U-Net passes
2. Heavy U-Net computation — 1.5B+ parameters per forward pass
3. Sequential dependency — кожен step залежить від попереднього
4. Memory bandwidth — великі тензори постійно переміщуються

Техніки прискорення: від 50 кроків до 4

1. Latent Consistency Models (LCM)

LCM distills знання з багатокрокової моделі в модель, яка працює за 4 кроки:

import torch
from diffusers import DiffusionPipeline, LCMScheduler

class LCMAccelerator:
    """Прискорення diffusion через Latent Consistency Models."""

    def __init__(self, base_model: str = "stabilityai/stable-diffusion-xl-base-1.0"):
        self.pipe = DiffusionPipeline.from_pretrained(
            base_model,
            torch_dtype=torch.float16,
            variant="fp16"
        ).to("cuda")

        # Load LCM LoRA adapter
        self.pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")

        # Switch to LCM scheduler
        self.pipe.scheduler = LCMScheduler.from_config(self.pipe.scheduler.config)

        # Enable optimizations
        self.pipe.enable_xformers_memory_efficient_attention()

    def generate_fast(
        self,
        prompt: str,
        num_inference_steps: int = 4,  # Замість 50!
        guidance_scale: float = 1.5    # LCM працює з низьким guidance
    ):
        """Генерація за 4 кроки замість 50."""
        return self.pipe(
            prompt=prompt,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            height=1024,
            width=1024
        ).images[0]

    def benchmark(self, prompt: str, num_runs: int = 10):
        """Бенчмарк швидкості."""
        import time

        # Warmup
        _ = self.generate_fast(prompt)

        # Benchmark
        times = []
        for _ in range(num_runs):
            start = time.time()
            _ = self.generate_fast(prompt)
            torch.cuda.synchronize()
            times.append(time.time() - start)

        return {
            'mean_time': sum(times) / len(times),
            'fps': 1.0 / (sum(times) / len(times)),
            'min_time': min(times),
            'max_time': max(times)
        }

# Результат: ~200ms per image = 5 FPS
# Порівняння: стандартний SD = 2500ms = 0.4 FPS
# Прискорення: 12.5x

2. SD-Turbo / SDXL-Turbo

Distillation моделей для single-step generation:

from diffusers import AutoPipelineForImage2Image
import torch

class TurboGenerator:
    """Single-step generation з SD-Turbo."""

    def __init__(self):
        self.pipe = AutoPipelineForImage2Image.from_pretrained(
            "stabilityai/sdxl-turbo",
            torch_dtype=torch.float16,
            variant="fp16"
        ).to("cuda")

    def generate_single_step(
        self,
        image,
        prompt: str,
        strength: float = 0.5
    ):
        """Генерація за 1 крок."""
        return self.pipe(
            prompt=prompt,
            image=image,
            num_inference_steps=1,  # Single step!
            guidance_scale=0.0,     # No CFG для speed
            strength=strength
        ).images[0]

3. Model Distillation

class DistilledUNet(torch.nn.Module):
    """Distilled версія U-Net для швидшого inference."""

    def __init__(self, teacher_unet, compression_ratio: float = 0.25):
        super().__init__()
        self.compression_ratio = compression_ratio

        # Reduced channel dimensions
        self.channels = [
            int(c * compression_ratio)
            for c in [320, 640, 1280, 1280]
        ]

        # Simplified architecture
        self.down_blocks = self._build_down_blocks()
        self.mid_block = self._build_mid_block()
        self.up_blocks = self._build_up_blocks()

    def _build_down_blocks(self):
        """Спрощені down blocks."""
        blocks = torch.nn.ModuleList()
        for i, ch in enumerate(self.channels[:-1]):
            blocks.append(
                torch.nn.Sequential(
                    torch.nn.Conv2d(ch if i > 0 else 4, self.channels[i+1], 3, 2, 1),
                    torch.nn.GroupNorm(8, self.channels[i+1]),
                    torch.nn.SiLU()
                )
            )
        return blocks

    @classmethod
    def distill_from_teacher(
        cls,
        teacher,
        train_dataloader,
        num_epochs: int = 10
    ):
        """Distillation з teacher моделі."""
        student = cls(teacher)
        optimizer = torch.optim.AdamW(student.parameters(), lr=1e-4)

        for epoch in range(num_epochs):
            for batch in train_dataloader:
                # Teacher forward
                with torch.no_grad():
                    teacher_output = teacher(batch['latents'], batch['timesteps'])

                # Student forward
                student_output = student(batch['latents'], batch['timesteps'])

                # Distillation loss
                loss = torch.nn.functional.mse_loss(student_output, teacher_output)

                optimizer.zero_grad()
                loss.backward()
                optimizer.step()

        return student

StreamDiffusion: архітектура для real-time

Ключова ідея (2024): Batch denoising з pipelining

Традиційний підхід:
  Frame 1: [step 1] → [step 2] → [step 3] → [step 4] → output
  Frame 2:                                              [step 1] → [step 2] → ...
  (Sequential, slow)

StreamDiffusion:
  Frame 1: [step 4] ─────────────────────────────────► output
  Frame 2: [step 3]
  Frame 3: [step 2]
  Frame 4: [step 1]
  Frame 5: [noise] ─► start

  Всі 5 frames обробляються в ОДНОМУ batch!
  Latency = 1 batch forward pass ≈ 11ms

from streamdiffusion import StreamDiffusion
from streamdiffusion.image_utils import postprocess_image
import torch
import cv2
import numpy as np
from typing import Optional

class RealTimeStreamDiffusion:
    """Real-time video transformation з StreamDiffusion."""

    def __init__(
        self,
        model_id: str = "stabilityai/sd-turbo",
        t_index_list: list = [32, 45],
        frame_buffer_size: int = 1,
        width: int = 512,
        height: int = 512,
        device: str = "cuda"
    ):
        self.device = device
        self.width = width
        self.height = height

        # Initialize StreamDiffusion pipeline
        self.stream = StreamDiffusion(
            pipe=None,  # Will be loaded
            t_index_list=t_index_list,
            torch_dtype=torch.float16,
            frame_buffer_size=frame_buffer_size,
            width=width,
            height=height,
            use_lcm_lora=True,
            output_type="pt",
            use_tiny_vae=True,  # Faster VAE
            cfg_type="none"     # Disable CFG for speed
        )

        # Load model
        self.stream.load_model(model_id)

        # Compile for maximum speed
        self.stream.prepare(
            prompt="",
            num_inference_steps=50,  # Internal parameter
            guidance_scale=1.0
        )

        # Optional: TensorRT acceleration
        self._compile_tensorrt()

    def _compile_tensorrt(self):
        """Compile to TensorRT для ще більшого прискорення."""
        try:
            self.stream.unet = torch.compile(
                self.stream.unet,
                mode="reduce-overhead",
                fullgraph=True
            )
            self.stream.vae = torch.compile(
                self.stream.vae,
                mode="reduce-overhead"
            )
        except Exception as e:
            print(f"TensorRT compilation failed: {e}")

    def set_prompt(self, prompt: str, negative_prompt: str = ""):
        """Зміна prompt (можна змінювати в real-time)."""
        self.stream.prepare(
            prompt=prompt,
            negative_prompt=negative_prompt,
            num_inference_steps=50,
            guidance_scale=1.2
        )

    def process_frame(self, frame: np.ndarray) -> np.ndarray:
        """Обробка одного кадру."""
        # Resize to model input size
        input_frame = cv2.resize(frame, (self.width, self.height))

        # Convert to tensor
        input_tensor = (
            torch.from_numpy(input_frame)
            .permute(2, 0, 1)
            .float()
            .unsqueeze(0)
            .to(self.device)
            / 255.0
        )

        # StreamDiffusion inference
        output_tensor = self.stream(input_tensor)

        # Convert back to numpy
        output_frame = (
            output_tensor.squeeze(0)
            .permute(1, 2, 0)
            .cpu()
            .numpy()
            * 255
        ).astype(np.uint8)

        return output_frame

    def run_webcam_loop(self, style_prompt: str):
        """Real-time webcam transformation."""
        cap = cv2.VideoCapture(0)
        cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
        cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)

        self.set_prompt(style_prompt)

        frame_times = []

        while True:
            ret, frame = cap.read()
            if not ret:
                break

            start_time = time.time()

            # Process frame
            output = self.process_frame(frame)

            # Calculate FPS
            frame_time = time.time() - start_time
            frame_times.append(frame_time)
            if len(frame_times) > 30:
                frame_times.pop(0)
            fps = 1.0 / (sum(frame_times) / len(frame_times))

            # Display
            cv2.putText(
                output,
                f"FPS: {fps:.1f}",
                (10, 30),
                cv2.FONT_HERSHEY_SIMPLEX,
                1, (0, 255, 0), 2
            )

            cv2.imshow('StreamDiffusion Real-Time', output)

            if cv2.waitKey(1) & 0xFF == ord('q'):
                break

        cap.release()
        cv2.destroyAllWindows()


# Приклад використання
if __name__ == "__main__":
    stream = RealTimeStreamDiffusion()

    # Style prompts для різних ефектів
    styles = [
        "cyberpunk style, neon lights, futuristic city",
        "oil painting, impressionist style, vibrant colors",
        "anime style, studio ghibli, detailed illustration",
        "pixel art, retro game aesthetic, 8-bit"
    ]

    stream.run_webcam_loop(styles[0])

Face Animation та LivePortrait

Real-time face reenactment з нейронними мережами:

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from typing import Tuple, Dict

class FaceAnimator:
    """Real-time face animation з source image + driver video."""

    def __init__(self, device: str = "cuda"):
        self.device = device

        # Face detection та landmark extraction
        self.face_detector = self._load_face_detector()
        self.landmark_extractor = self._load_landmark_model()

        # 3D face reconstruction
        self.face_3dmm = Face3DMM()

        # Neural renderer
        self.renderer = NeuralFaceRenderer()

        # Temporal smoothing
        self.prev_params = None
        self.smoothing_factor = 0.3

    def _extract_motion(self, driver_frame: np.ndarray) -> Dict:
        """Витягування параметрів руху з driver frame."""
        # Detect face
        face_bbox = self.face_detector(driver_frame)

        # Extract landmarks
        landmarks = self.landmark_extractor(driver_frame, face_bbox)

        # Fit 3DMM to get pose and expression
        params = self.face_3dmm.fit(landmarks)

        return {
            'rotation': params['rotation'],      # [pitch, yaw, roll]
            'translation': params['translation'], # [x, y, z]
            'expression': params['expression'],   # 52D expression vector
            'jaw_open': params['jaw_open']        # Mouth opening
        }

    def transfer_motion(
        self,
        source_image: np.ndarray,
        driver_frame: np.ndarray
    ) -> np.ndarray:
        """Transfer motion from driver to source."""

        # Extract source identity (one-time)
        source_params = self.face_3dmm.extract_identity(source_image)

        # Extract driver motion
        motion_params = self._extract_motion(driver_frame)

        # Apply temporal smoothing
        if self.prev_params is not None:
            for key in motion_params:
                motion_params[key] = (
                    self.smoothing_factor * self.prev_params[key] +
                    (1 - self.smoothing_factor) * motion_params[key]
                )
        self.prev_params = motion_params

        # Combine identity + motion
        combined_params = {
            'identity': source_params['identity'],
            'texture': source_params['texture'],
            **motion_params
        }

        # Neural rendering
        output_image = self.renderer.render(combined_params)

        return output_image


class Face3DMM(nn.Module):
    """3D Morphable Model для face reconstruction."""

    def __init__(
        self,
        identity_dim: int = 80,
        expression_dim: int = 64,
        texture_dim: int = 80
    ):
        super().__init__()
        self.identity_dim = identity_dim
        self.expression_dim = expression_dim
        self.texture_dim = texture_dim

        # Load pretrained basis
        self.register_buffer('identity_basis', self._load_basis('identity'))
        self.register_buffer('expression_basis', self._load_basis('expression'))
        self.register_buffer('mean_shape', self._load_mean_shape())

        # Encoder network
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 64, 7, 2, 3),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            # ... more layers
            nn.AdaptiveAvgPool2d(1),
            nn.Flatten(),
            nn.Linear(512, identity_dim + expression_dim + 6 + 3)  # params + pose
        )

    def encode(self, face_image: torch.Tensor) -> Dict[str, torch.Tensor]:
        """Encode face image to 3DMM parameters."""
        features = self.encoder(face_image)

        return {
            'identity': features[:, :self.identity_dim],
            'expression': features[:, self.identity_dim:self.identity_dim + self.expression_dim],
            'rotation': features[:, -9:-6],
            'translation': features[:, -6:-3],
            'scale': features[:, -3:]
        }

    def decode(self, params: Dict[str, torch.Tensor]) -> torch.Tensor:
        """Decode parameters to 3D face mesh."""
        # Shape = mean + identity_basis @ identity + expression_basis @ expression
        shape = (
            self.mean_shape +
            torch.einsum('bi,ijk->bjk', params['identity'], self.identity_basis) +
            torch.einsum('bi,ijk->bjk', params['expression'], self.expression_basis)
        )
        return shape


class NeuralFaceRenderer(nn.Module):
    """Neural renderer для photo-realistic face generation."""

    def __init__(self, feature_dim: int = 256, output_size: int = 512):
        super().__init__()
        self.output_size = output_size

        # StyleGAN-like generator
        self.mapping = MappingNetwork(input_dim=512, output_dim=512, num_layers=8)

        self.synthesis = nn.ModuleList([
            SynthesisBlock(512, 512, 4),    # 4x4
            SynthesisBlock(512, 512, 8),    # 8x8
            SynthesisBlock(512, 512, 16),   # 16x16
            SynthesisBlock(512, 256, 32),   # 32x32
            SynthesisBlock(256, 128, 64),   # 64x64
            SynthesisBlock(128, 64, 128),   # 128x128
            SynthesisBlock(64, 32, 256),    # 256x256
            SynthesisBlock(32, 16, 512),    # 512x512
        ])

        self.to_rgb = nn.Conv2d(16, 3, 1)

    def render(self, params: Dict) -> torch.Tensor:
        """Render face from parameters."""
        # Encode parameters to latent
        latent = self._params_to_latent(params)

        # Map to w space
        w = self.mapping(latent)

        # Progressive synthesis
        x = None
        for block in self.synthesis:
            x = block(x, w)

        # Final RGB output
        image = self.to_rgb(x)
        image = torch.sigmoid(image)

        return image

    def _params_to_latent(self, params: Dict) -> torch.Tensor:
        """Convert 3DMM params to latent vector."""
        return torch.cat([
            params['identity'],
            params['expression'],
            params['rotation'],
            params['translation']
        ], dim=1)

3D Gaussian Splatting для real-time NeRF

Революція в real-time 3D rendering:

import torch
import torch.nn as nn
import numpy as np
from dataclasses import dataclass
from typing import List, Tuple

@dataclass
class Gaussian3D:
    """Представлення одного 3D Gaussian."""
    position: torch.Tensor      # [3] - xyz
    covariance: torch.Tensor    # [3, 3] - 3D covariance matrix
    color: torch.Tensor         # [3] або [48] для SH coefficients
    opacity: torch.Tensor       # [1]
    scale: torch.Tensor         # [3]
    rotation: torch.Tensor      # [4] - quaternion


class GaussianSplatRenderer:
    """Real-time renderer з 3D Gaussian Splatting."""

    def __init__(
        self,
        num_gaussians: int = 1000000,
        sh_degree: int = 3,
        device: str = "cuda"
    ):
        self.device = device
        self.num_gaussians = num_gaussians
        self.sh_degree = sh_degree

        # Initialize gaussian parameters
        self.positions = torch.zeros(num_gaussians, 3, device=device)
        self.scales = torch.ones(num_gaussians, 3, device=device) * 0.01
        self.rotations = torch.zeros(num_gaussians, 4, device=device)
        self.rotations[:, 0] = 1  # Identity quaternion

        # Spherical harmonics for view-dependent color
        num_sh_coeffs = (sh_degree + 1) ** 2
        self.sh_coefficients = torch.zeros(
            num_gaussians, num_sh_coeffs, 3, device=device
        )

        self.opacities = torch.zeros(num_gaussians, 1, device=device)

    def project_to_2d(
        self,
        camera_matrix: torch.Tensor,
        view_matrix: torch.Tensor,
        image_size: Tuple[int, int]
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Project 3D gaussians to 2D screen space."""

        # Transform positions to camera space
        positions_cam = (
            view_matrix[:3, :3] @ self.positions.T +
            view_matrix[:3, 3:4]
        ).T

        # Project to screen
        positions_proj = (
            camera_matrix[:3, :3] @ positions_cam.T
        ).T
        positions_2d = positions_proj[:, :2] / positions_proj[:, 2:3]

        # Scale to image coordinates
        positions_2d[:, 0] = (positions_2d[:, 0] + 1) * image_size[1] / 2
        positions_2d[:, 1] = (positions_2d[:, 1] + 1) * image_size[0] / 2

        # Compute 2D covariance from 3D covariance + camera projection
        cov_2d = self._compute_2d_covariance(
            camera_matrix, view_matrix, positions_cam
        )

        return positions_2d, cov_2d

    def render(
        self,
        camera_matrix: torch.Tensor,
        view_matrix: torch.Tensor,
        image_size: Tuple[int, int] = (512, 512)
    ) -> torch.Tensor:
        """Render scene from given camera viewpoint."""

        # Project gaussians
        positions_2d, cov_2d = self.project_to_2d(
            camera_matrix, view_matrix, image_size
        )

        # Sort by depth for correct blending
        depths = (view_matrix[:3, :3] @ self.positions.T + view_matrix[:3, 3:4])[2]
        sorted_indices = torch.argsort(depths)

        # Evaluate spherical harmonics for view-dependent color
        view_dir = self._compute_view_directions(view_matrix)
        colors = self._evaluate_sh(view_dir)

        # Rasterize gaussians
        image = self._rasterize(
            positions_2d[sorted_indices],
            cov_2d[sorted_indices],
            colors[sorted_indices],
            self.opacities[sorted_indices],
            image_size
        )

        return image

    def _rasterize(
        self,
        positions: torch.Tensor,
        covariances: torch.Tensor,
        colors: torch.Tensor,
        opacities: torch.Tensor,
        image_size: Tuple[int, int]
    ) -> torch.Tensor:
        """Rasterize gaussians using tile-based rendering."""

        # Initialize output image
        image = torch.zeros(
            image_size[0], image_size[1], 3,
            device=self.device
        )
        accumulated_alpha = torch.zeros(
            image_size[0], image_size[1],
            device=self.device
        )

        # Tile-based rendering for efficiency
        tile_size = 16
        num_tiles_x = (image_size[1] + tile_size - 1) // tile_size
        num_tiles_y = (image_size[0] + tile_size - 1) // tile_size

        for ty in range(num_tiles_y):
            for tx in range(num_tiles_x):
                # Find gaussians overlapping this tile
                tile_min = torch.tensor([tx * tile_size, ty * tile_size])
                tile_max = tile_min + tile_size

                # Filter gaussians
                mask = self._gaussians_in_tile(positions, covariances, tile_min, tile_max)
                if not mask.any():
                    continue

                # Render tile
                tile = self._render_tile(
                    positions[mask],
                    covariances[mask],
                    colors[mask],
                    opacities[mask],
                    tile_min, tile_max
                )

                # Write to output
                image[
                    tile_min[1]:tile_max[1],
                    tile_min[0]:tile_max[0]
                ] = tile

        return image

    def _evaluate_sh(self, view_directions: torch.Tensor) -> torch.Tensor:
        """Evaluate spherical harmonics для view-dependent color."""
        # Simplified SH evaluation (degree 0 only for speed)
        return torch.sigmoid(self.sh_coefficients[:, 0, :])

    @torch.compile
    def forward_optimized(
        self,
        camera_matrix: torch.Tensor,
        view_matrix: torch.Tensor,
        image_size: Tuple[int, int]
    ) -> torch.Tensor:
        """Optimized forward pass з torch.compile."""
        return self.render(camera_matrix, view_matrix, image_size)


class InstantNGP:
    """Instant Neural Graphics Primitives для швидкого NeRF."""

    def __init__(
        self,
        base_resolution: int = 16,
        num_levels: int = 16,
        features_per_level: int = 2,
        log2_hashmap_size: int = 19
    ):
        self.base_resolution = base_resolution
        self.num_levels = num_levels
        self.features_per_level = features_per_level
        self.hashmap_size = 2 ** log2_hashmap_size

        # Multi-resolution hash encoding
        self.hash_tables = nn.ParameterList([
            nn.Parameter(torch.randn(self.hashmap_size, features_per_level) * 0.001)
            for _ in range(num_levels)
        ])

        # Small MLP (just 2 layers!)
        total_features = num_levels * features_per_level
        self.density_net = nn.Sequential(
            nn.Linear(total_features, 64),
            nn.ReLU(),
            nn.Linear(64, 16)
        )

        self.color_net = nn.Sequential(
            nn.Linear(16 + 3, 64),  # +3 for view direction
            nn.ReLU(),
            nn.Linear(64, 3),
            nn.Sigmoid()
        )

    def hash_encode(self, positions: torch.Tensor) -> torch.Tensor:
        """Multi-resolution hash encoding."""
        encoded = []

        for level in range(self.num_levels):
            # Resolution for this level
            resolution = self.base_resolution * (2 ** level)

            # Grid coordinates
            grid_pos = positions * resolution
            grid_floor = torch.floor(grid_pos).long()

            # Trilinear interpolation weights
            weights = grid_pos - grid_floor.float()

            # Hash lookup for 8 corners
            features = self._trilinear_interpolate(
                self.hash_tables[level],
                grid_floor,
                weights
            )

            encoded.append(features)

        return torch.cat(encoded, dim=-1)

TensorRT оптимізація для production

import tensorrt as trt
import torch
import numpy as np

class TensorRTOptimizer:
    """Оптимізація моделей через TensorRT."""

    def __init__(self, max_batch_size: int = 1, precision: str = "fp16"):
        self.max_batch_size = max_batch_size
        self.precision = precision
        self.logger = trt.Logger(trt.Logger.WARNING)

    def optimize_unet(
        self,
        unet: torch.nn.Module,
        sample_input: torch.Tensor,
        output_path: str = "unet.engine"
    ):
        """Convert U-Net to TensorRT engine."""

        # Export to ONNX
        onnx_path = output_path.replace('.engine', '.onnx')
        torch.onnx.export(
            unet,
            sample_input,
            onnx_path,
            opset_version=17,
            input_names=['latent', 'timestep', 'encoder_hidden_states'],
            output_names=['noise_pred'],
            dynamic_axes={
                'latent': {0: 'batch'},
                'encoder_hidden_states': {0: 'batch'}
            }
        )

        # Build TensorRT engine
        builder = trt.Builder(self.logger)
        network = builder.create_network(
            1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
        )
        parser = trt.OnnxParser(network, self.logger)

        with open(onnx_path, 'rb') as f:
            parser.parse(f.read())

        config = builder.create_builder_config()
        config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 8 << 30)  # 8GB

        if self.precision == "fp16":
            config.set_flag(trt.BuilderFlag.FP16)
        elif self.precision == "int8":
            config.set_flag(trt.BuilderFlag.INT8)

        # Build engine
        engine = builder.build_serialized_network(network, config)

        with open(output_path, 'wb') as f:
            f.write(engine)

        return output_path

    def create_inference_session(self, engine_path: str):
        """Create inference session from engine."""
        with open(engine_path, 'rb') as f:
            engine_data = f.read()

        runtime = trt.Runtime(self.logger)
        engine = runtime.deserialize_cuda_engine(engine_data)
        context = engine.create_execution_context()

        return TensorRTSession(engine, context)


class TensorRTSession:
    """TensorRT inference session."""

    def __init__(self, engine, context):
        self.engine = engine
        self.context = context
        self.bindings = []
        self.outputs = {}

        # Allocate buffers
        for i in range(engine.num_io_tensors):
            name = engine.get_tensor_name(i)
            shape = engine.get_tensor_shape(name)
            dtype = trt.nptype(engine.get_tensor_dtype(name))

            buffer = torch.empty(
                tuple(shape),
                dtype=torch.float16 if dtype == np.float16 else torch.float32,
                device='cuda'
            )
            self.bindings.append(buffer.data_ptr())

            if engine.get_tensor_mode(name) == trt.TensorIOMode.OUTPUT:
                self.outputs[name] = buffer

    def infer(self, inputs: dict) -> dict:
        """Run inference."""
        # Copy inputs
        for name, tensor in inputs.items():
            idx = self.engine.get_binding_index(name)
            self.bindings[idx] = tensor.data_ptr()

        # Execute
        self.context.execute_v2(self.bindings)

        return self.outputs

Streaming архітектура для production

import asyncio
import cv2
import numpy as np
from typing import Callable, Optional
from dataclasses import dataclass
from concurrent.futures import ThreadPoolExecutor

@dataclass
class StreamConfig:
    """Конфігурація streaming pipeline."""
    input_width: int = 640
    input_height: int = 480
    output_width: int = 512
    output_height: int = 512
    target_fps: int = 30
    buffer_size: int = 3
    enable_temporal_smoothing: bool = True


class RealTimeVideoPipeline:
    """Production-ready real-time video generation pipeline."""

    def __init__(
        self,
        generator,
        config: StreamConfig
    ):
        self.generator = generator
        self.config = config

        # Frame buffers
        self.input_buffer = asyncio.Queue(maxsize=config.buffer_size)
        self.output_buffer = asyncio.Queue(maxsize=config.buffer_size)

        # Temporal consistency
        self.prev_output = None
        self.temporal_weight = 0.2 if config.enable_temporal_smoothing else 0.0

        # Stats
        self.frame_times = []
        self.dropped_frames = 0

        # Thread pool for I/O
        self.executor = ThreadPoolExecutor(max_workers=4)

    async def capture_loop(self, source):
        """Async capture loop."""
        cap = cv2.VideoCapture(source)
        cap.set(cv2.CAP_PROP_FRAME_WIDTH, self.config.input_width)
        cap.set(cv2.CAP_PROP_FRAME_HEIGHT, self.config.input_height)
        cap.set(cv2.CAP_PROP_FPS, self.config.target_fps)

        while True:
            ret, frame = await asyncio.get_event_loop().run_in_executor(
                self.executor, cap.read
            )

            if not ret:
                break

            try:
                self.input_buffer.put_nowait(frame)
            except asyncio.QueueFull:
                self.dropped_frames += 1
                # Skip frame to maintain real-time

        cap.release()

    async def process_loop(self):
        """Main processing loop."""
        while True:
            frame = await self.input_buffer.get()

            start_time = time.time()

            # Preprocess
            processed = cv2.resize(
                frame,
                (self.config.output_width, self.config.output_height)
            )

            # Generate
            output = self.generator.process_frame(processed)

            # Temporal smoothing
            if self.prev_output is not None and self.temporal_weight > 0:
                output = (
                    self.temporal_weight * self.prev_output +
                    (1 - self.temporal_weight) * output
                ).astype(np.uint8)
            self.prev_output = output.astype(np.float32)

            # Track timing
            frame_time = time.time() - start_time
            self.frame_times.append(frame_time)
            if len(self.frame_times) > 100:
                self.frame_times.pop(0)

            try:
                self.output_buffer.put_nowait(output)
            except asyncio.QueueFull:
                # Replace oldest frame
                try:
                    self.output_buffer.get_nowait()
                except:
                    pass
                self.output_buffer.put_nowait(output)

    async def display_loop(self, window_name: str = "Real-Time Generation"):
        """Display output frames."""
        while True:
            output = await self.output_buffer.get()

            # Add stats overlay
            fps = 1.0 / (sum(self.frame_times) / len(self.frame_times))
            cv2.putText(
                output,
                f"FPS: {fps:.1f} | Dropped: {self.dropped_frames}",
                (10, 30),
                cv2.FONT_HERSHEY_SIMPLEX,
                0.7, (0, 255, 0), 2
            )

            cv2.imshow(window_name, output)

            if cv2.waitKey(1) & 0xFF == ord('q'):
                break

        cv2.destroyAllWindows()

    async def run(self, source: int = 0):
        """Run full pipeline."""
        await asyncio.gather(
            self.capture_loop(source),
            self.process_loop(),
            self.display_loop()
        )

    def get_stats(self) -> dict:
        """Get pipeline statistics."""
        return {
            'avg_fps': 1.0 / (sum(self.frame_times) / len(self.frame_times)) if self.frame_times else 0,
            'avg_latency_ms': sum(self.frame_times) / len(self.frame_times) * 1000 if self.frame_times else 0,
            'dropped_frames': self.dropped_frames,
            'buffer_usage': self.input_buffer.qsize() / self.config.buffer_size
        }

Benchmark: порівняння методів

| Метод | Resolution | FPS (RTX 4090) | Latency | Quality |

|-------|------------|----------------|---------|---------|

| Stable Diffusion (50 steps) | 512×512 | 0.4 | 2500ms | High |

| SD + LCM (4 steps) | 512×512 | 5 | 200ms | Good |

| SD-Turbo (1 step) | 512×512 | 15 | 67ms | Medium |

| StreamDiffusion | 512×512 | 91 | 11ms | Medium |

| 3D Gaussian Splatting | 1080p | 130+ | 8ms | High |

| Instant-NGP | 800×800 | 60 | 16ms | High |

Практичні застосування

1. Live Streaming & Content Creation

• Real-time style transfer для стрімерів
• AI-powered background replacement
• Virtual avatar animation
• Live filters без post-processing

2. Gaming & Interactive Media

• AI-generated environments
• Dynamic NPC face animation
• Procedural texture generation
• Real-time upscaling (DLSS-style)

3. Video Conferencing

• Privacy-preserving avatars
• Automatic lighting normalization
• Bandwidth reduction через neural compression
• Real-time translation з lip sync

4. Virtual Production

• LED wall content generation
• Real-time previsualization
• Motion capture face replacement
• Virtual set extension

Ідеї для наукових досліджень

Для бакалаврської роботи:

• StreamDiffusion demo з webcam та різними стилями
• Benchmark різних LCM моделей на різному hardware
• Simple real-time video style transfer application

Для магістерської дисертації:

• Custom temporal consistency methods для video
• Hardware-specific optimization (Jetson, Apple Silicon)
• Real-time NeRF viewer з інтерактивним редагуванням

Для PhD досліджень:

• Novel architectures для real-time generation
• Theoretical latency bounds для diffusion
• Quality-latency trade-offs: optimal scheduling

Висновок

Real-time video generation — це демократизація відеовиробництва. Традиційно live production потребував дорогих камер, професійного освітлення, green screens і цілих команд постпродакшну.

AI-based real-time generation:

• Webcam достатньо
• Будь-яке освітлення
• Будь-який background
• Миттєвий результат

Від StreamDiffusion (91 FPS) до 3D Gaussian Splatting (130+ FPS) — технології вже досягли production-ready рівня. І це лише початок.

Якщо ви плануєте дослідження в галузі оптимізації inference, real-time rendering чи neural video synthesis — команда SKP-Degree допоможе з формулюванням наукової новизни, реалізацією прототипу та оформленням роботи. Звертайтесь на skp-degree.com.ua або пишіть у Telegram: @kursovi_diplomy — від концепції до успішного захисту.

Ключові слова: real-time video generation, StreamDiffusion, LCM, Latent Consistency Models, 3D Gaussian Splatting, Instant-NGP, NeRF, TensorRT, low-latency inference, neural rendering, face animation, streaming architecture, дипломна робота, магістерська, AI-дослідження.