Seedance 2

Seedance 2 is a community skill for generating videos using the Seedance AI video generation model, covering text-to-video generation, image-to-video animation, motion control, style transfer, and video editing through natural language prompts.

What Is This?

Overview

Seedance 2 provides tools for creating AI-generated videos from text descriptions or reference images. It covers text-to-video generation that creates video clips from written scene descriptions with specified motion and camera angles, image-to-video animation that transforms static images into dynamic video sequences with natural movement, motion control that specifies camera movements, subject trajectories, and transition effects, style transfer that applies visual aesthetics from reference content to generated videos, and video editing that modifies existing clips through descriptive prompts. The skill helps creators produce video content with AI.

Who Should Use This

This skill serves content creators producing social media videos, marketing teams generating promotional video content, and designers creating animated visual concepts from static mockups.

Why Use It?

Problems It Solves

Traditional video production requires expensive equipment, filming locations, and editing expertise. Creating animated content from static designs demands motion graphics skills and specialized software. Iterating on video concepts is slow when each revision requires re-filming or manual editing. Generating consistent visual styles across multiple video clips requires careful coordination.

Core Highlights

Text-to-video generates clips from written scene descriptions. Image animator transforms static images into smooth video sequences. Motion controller specifies camera paths and subject movements. Style applicator maintains consistent visual aesthetics across clips.

How to Use It?

Basic Usage

import requests
import time
import json

class SeedanceClient:
  def __init__(
    self, api_key: str,
    base_url: str
  ):
    self.api_key = api_key
    self.base = base_url
    self.headers = {
      'Authorization':
        f'Bearer {api_key}',
      'Content-Type':
        'application/json'}

  def generate(
    self,
    prompt: str,
    duration: int = 4,
    resolution: str
      = '1080p'
  ) -> str:
    resp = requests.post(
      f'{self.base}'
      f'/generate',
      headers=self.headers,
      json={
        'prompt': prompt,
        'duration':
          duration,
        'resolution':
          resolution})
    return resp.json()[
      'task_id']

  def poll(
    self, task_id: str,
    interval: int = 10
  ) -> dict:
    while True:
      resp = requests.get(
        f'{self.base}'
        f'/status/'
        f'{task_id}',
        headers=
          self.headers)
      data = resp.json()
      if data['status'] in (
        'completed',
        'failed'
      ):
        return data
      time.sleep(interval)

client = SeedanceClient(
  'sk-key',
  'https://api.example'
  '.com/v2')
task = client.generate(
  'A cat walking through '
  'a garden at sunset')
result = client.poll(task)
print(result['video_url'])

Real-World Examples

import requests
import json
from pathlib import Path

class VideoBatch:
  def __init__(
    self,
    client,
    output_dir: str
  ):
    self.client = client
    self.output = Path(
      output_dir)
    self.output.mkdir(
      exist_ok=True)

  def generate_series(
    self,
    prompts: list[dict]
  ) -> list:
    tasks = []
    for p in prompts:
      task_id = (
        self.client
        .generate(
          p['prompt'],
          p.get(
            'duration', 4),
          p.get(
            'resolution',
            '1080p')))
      tasks.append({
        'id': task_id,
        'name':
          p['name']})
    results = []
    for t in tasks:
      result = (
        self.client
        .poll(t['id']))
      if result[
        'status'
      ] == 'completed':
        video = requests.get(
          result[
            'video_url'])
        path = (
          self.output /
          f'{t["name"]}.mp4')
        path.write_bytes(
          video.content)
        results.append({
          'name':
            t['name'],
          'path':
            str(path)})
    return results

batch = VideoBatch(
  client, 'output')
clips = [
  {'name': 'intro',
   'prompt':
     'Logo reveal '
     'with particles'},
  {'name': 'scene1',
   'prompt':
     'Mountain landscape '
     'time lapse'}]
results = (
  batch.generate_series(
    clips))
for r in results:
  print(
    f'{r["name"]}: '
    f'{r["path"]}')

Advanced Tips

Write detailed prompts specifying camera angles, lighting conditions, and motion direction for more predictable results. Use image-to-video mode with a reference frame to maintain visual consistency across a series of clips. Generate multiple variations of the same prompt and select the best output for final use.

When to Use It?

Use Cases

Generate a product showcase video from product photographs with smooth camera orbits. Create social media content with animated text overlays and dynamic transitions. Produce concept videos for client presentations without filming or stock footage.

Related Topics

AI video generation, text-to-video, image animation, motion control, content creation, generative AI, and video production.

Important Notes

Requirements

API access to the Seedance 2 model endpoint with valid credentials. Clear text prompts describing the desired scene, motion, and visual style for generation. Sufficient API quota for video generation tasks which consume more resources than image generation.

Usage Recommendations

Do: use specific, descriptive prompts that include camera movement, lighting, and subject details for consistent results. Generate test clips at lower resolution before producing final high-resolution outputs. Save successful prompt templates for reuse across similar video projects.

Don't: expect frame-perfect control over every element in generated video since AI models produce approximate results. Generate videos with identifiable real people without proper consent and usage rights. Rely on single generation attempts for production use since multiple iterations improve quality.

Limitations

Generated videos have limited duration typically measured in seconds rather than minutes. Fine-grained control over specific frame details is not possible with current generation technology. Output quality varies across prompts, and complex scenes with multiple subjects may produce artifacts or inconsistencies.