Text-to-Video Generation

Video Generation Approaches

from diffusers import DiffusionPipeline
import torch

def generate_video(prompt, num_frames=16):
    pipe = DiffusionPipeline.from_pretrained(
        "ali-vilab/text-to-video-ms-1.7b",
        torch_dtype=torch.float16
    )
    pipe = pipe.to("cuda")

    video_frames = pipe(
        prompt,
        num_inference_steps=50,
        num_frames=num_frames
    ).frames

    return video_frames

Temporal Consistency

class TemporalConsistencyModule:
    def __init__(self, model):
        self.model = model
        self.prev_frames = []

    def maintain_consistency(self, current_frame):
        if self.prev_frames:
            # Apply temporal attention
            temporal_context = self.model.temporal_attn(
                current_frame,
                self.prev_frames[-3:]  # Use last 3 frames
            )
            current_frame = self.blend(current_frame, temporal_context)

        self.prev_frames.append(current_frame)
        return current_frame

    def blend(self, frame, context, alpha=0.3):
        return alpha * frame + (1 - alpha) * context

Video Models Comparison

Summary

Text-to-video generation combines spatial and temporal modeling to create coherent video content. The field is rapidly advancing with new architectures and training techniques.

Next: We'll explore speech and audio generation.