2026 年影片生成前景。文字轉影片：Sora、Runway Gen-3、Kling、Pika Labs。圖像到影片。使用 AI 進行影片編輯。時間一致性挑戰。 API 整合模式。
生成式 AI：使用 AI 創建圖像和視頻
第 5 部分：視訊生成和多模式
亞洲開發網
簡介
2024-2026 年是 AI 視訊世代 的巨大飛躍——從 Sora (OpenAI)、Runway Gen-3、Kling (快手) 到 Pika Labs。對於許多用例來說，「玩具演示」的品質已成為「生產就緒」。
1. 2026 年影片生成格局
平台
開發商
優勢
最長持續時間
索拉
開放人工智慧
照相寫實主義，物理學
60 年代
跑道 Gen-3
跑道
創意控制、編輯
10-40 秒
克林
快手
運動品質，價格實惠
10 秒
皮卡
皮卡實驗室
速度快，易用性
10 秒
盧瑪夢想機
魯瑪人工智慧
3D 一致性
5-10 秒
視訊穩定
穩定性人工智慧
開源
4s
2.OpenAI Sora API
from openai import OpenAI
client = OpenAI()
# Text-to-Video
response = client.videos.generate(
model="sora",
prompt="""
A golden retriever playing fetch on a sunny beach.
The waves gently crash in the background.
The dog runs towards the camera with a ball in its mouth.
Shot on a cinematic camera, shallow depth of field.
""",
duration=10, # seconds
resolution="1080p",
aspect_ratio="16:9",
)
video_url = response.data[0].url
3. Runway Gen-3 API
import requests
RUNWAY_API_KEY = "your_api_key"
# Text-to-video
response = requests.post(
"https://api.runwayml.com/v1/generate/video",
headers={"Authorization": f"Bearer {RUNWAY_API_KEY}"},
json={
"prompt": "A timelapse of a flower blooming",
"model": "gen3",
"duration": 10,
"resolution": "1280x768",
}
)
task_id = response.json()["task_id"]
# Poll for completion
import time
while True:
status = requests.get(
f"https://api.runwayml.com/v1/tasks/{task_id}",
headers={"Authorization": f"Bearer {RUNWAY_API_KEY}"},
).json()
if status["status"] == "completed":
video_url = status["output"]["video_url"]
break
time.sleep(5)
4. 影像到視頻
# Animate a still image into a video
import base64
with open("landscape.png", "rb") as f:
image_b64 = base64.b64encode(f.read()).decode()
response = requests.post(
"https://api.runwayml.com/v1/generate/video",
headers={"Authorization": f"Bearer {RUNWAY_API_KEY}"},
json={
"prompt": "camera slowly pans right, clouds moving",
"image": image_b64,
"model": "gen3",
"duration": 5,
}
)
5.穩定的視訊擴散（開源）
from diffusers import StableVideoDiffusionPipeline
from diffusers.utils import load_image, export_to_video
import torch
pipe = StableVideoDiffusionPipeline.from_pretrained(
"stabilityai/stable-video-diffusion-img2vid-xt",
torch_dtype=torch.float16,
)
pipe.to("cuda")
# Image-to-video
image = load_image("input_frame.png").resize((1024, 576))
frames = pipe(
image,
num_frames=25, # number of frames
decode_chunk_size=4,
motion_bucket_id=127, # amount of motion (0-255)
fps=7,
num_inference_steps=25,
).frames[0]
export_to_video(frames, "output.mp4", fps=7)
6. 時間一致性
Challenge: giữ cho video nhất quán qua các frame
Vấn đề thường gặp:
- Flickering: brightness/color thay đổi giữa frames
- Morphing: objects thay đổi shape
- Disappearing: objects xuất hiện/biến mất
- Physics: vật thể di chuyển không tự nhiên
Giải pháp:
- Temporal attention layers (Sora)
- Frame interpolation (FILM, RIFE)
- Optical flow guidance
- Longer context windows
7. 影片編輯管道
# Complete video creation workflow
class VideoCreationPipeline:
def __init__(self, sora_client, runway_key):
self.sora = sora_client
self.runway_key = runway_key
async def create_video(self, script):
"""Full pipeline: script → storyboard → video → edit"""
# 1. Generate storyboard (images for each scene)
scenes = self.parse_script(script)
storyboard = []
for scene in scenes:
img = self.sora.images.generate(
model="dall-e-3",
prompt=scene["visual_description"],
)
storyboard.append(img)
# 2. Animate each scene
clips = []
for img, scene in zip(storyboard, scenes):
clip = await self.animate_scene(img, scene["motion"])
clips.append(clip)
# 3. Concatenate clips
final = self.concatenate_clips(clips)
return final
def parse_script(self, script):
"""Parse script into scenes"""
# Use LLM to break down script
response = self.sora.chat.completions.create(
model="gpt-4",
messages=[{
"role": "user",
"content": f"Break this into video scenes: {script}"
}]
)
return eval(response.choices[0].message.content)