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const main = async () => {
const response = await fetch('https://api.ai.cc/v2/generate/video/kling/generation', {
method: 'POST',
headers: {
Authorization: 'Bearer ',
'Content-Type': 'application/json',
},
body: JSON.stringify({
model: 'kling-video/v1.5/standard/image-to-video',
prompt: 'Mona Lisa puts on glasses with her hands.',
image_url: 'https://s2-111386.kwimgs.com/bs2/mmu-aiplatform-temp/kling/20240620/1.jpeg',
duration: '5',
}),
}).then((res) => res.json());
console.log('Generation:', response);
};
main()
import requests
def main():
url = "https://api.ai.cc/v2/generate/video/kling/generation"
payload = {
"model": "kling-video/v1.5/standard/image-to-video",
"prompt": "Mona Lisa puts on glasses with her hands.",
"image_url": "https://s2-111386.kwimgs.com/bs2/mmu-aiplatform-temp/kling/20240620/1.jpeg",
"duration": "5",
}
headers = {"Authorization": "Bearer ", "Content-Type": "application/json"}
response = requests.post(url, json=payload, headers=headers)
print("Generation:", response.json())
if __name__ == "__main__":
main()
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Product Detail
✨ The Kling V1.5 Standard Image-to-Video model marks a pivotal evolution in the Kling AI family, uniquely specializing in converting static and sequential images into vibrant, high-fidelity videos. Building on the sophisticated design principles and multimodal expertise of Kling V1.5 Standard, this variant introduces robust image-to-video synthesis capabilities, enabling a seamless transition from still visuals to fluid motion content. This model is tailored for a broad spectrum of professional applications ranging from creative storytelling and digital marketing to immersive educational tools and realistic simulations, providing versatile outputs that merge visual richness with contextual depth.
⚙️ Technical Specifications
Input Modalities: Accepts single images or short image sequences, optionally paired with text prompts to refine narrative direction and style interpretation.
Video Quality: Produces videos with remarkable temporal coherence, preserving spatial details while rendering naturalistic motion, setting a new standard for image-to-video realism.
Duration: Generates clips up to 8 seconds long, optimized specifically for dynamic short-form content compatible with social platforms and promotional clips.
Resolution & Frame Rate: Outputs HD-quality video with frame rates fine-tuned to deliver smooth visual flow balanced against computational efficiency for prompt rendering.
Motion Effects: Implements subtle but effective camera maneuvers—including pans, zooms, and simulated depth-of-field adjustments—enriching narrative impact without sacrificing processing speed.
🧠 Technical Details
Architecture: Engineered on an advanced transformer backbone integrated with temporal convolutional networks, translating static spatial features from input images into coherent, temporally consistent video frames.
Training Corpus: Developed on an extensive and proprietary multimodal dataset combining diverse high-quality images coupled with their corresponding video sequences, augmented through synthetic transformations and real-world variability to enhance robustness and reduce biases.
Performance: Carefully optimized to balance high-fidelity visual output and computational demand, ensuring wide accessibility and efficient operation for both enterprise-scale and independent developers.
💲 API Pricing
Only $0.0588 per second of generated video!
✨ Key Features
✔️ Direct Image-to-Video Generation: Converts individual images or sequences directly into full-motion video without intermediary manual steps, streamlining complex content creation workflows.
💬 Narrative Enhancement via Text Prompts: Optionally incorporates textual descriptions to tailor emotional tone, thematic elements, and stylistic nuances, ensuring personalized storytelling alignment.
🎬 Enhanced Motion Realism: Utilizes advanced algorithms to simulate natural camera movements and object dynamics, producing visually engaging videos with an authentic cinematic feel.
✅ Consistency Across Frames: Maintains spatial and temporal coherence throughout video duration, minimizing flickering, artifacting, and discontinuities for a smooth viewing experience.
💡 Use Cases
- ➡️ Creative storytelling and digital art animation
- ➡️ Social media video content generation
- ➡️ Marketing and promotional video creation
- ➡️ Educational and training video synthesis
- ➡️ Simulation and visualization in industries such as gaming and virtual reality
- ➡️ Rapid prototyping of dynamic visual content from static images
- ➡️ Enhancing video production workflows through AI-assisted animation
💻 Code Sample
⚖️ Comparison with Other Models
Vs Kling V1.5 Standard (Text-to-Video): This variant expands modality support by adding robust image-based inputs, augmenting creative possibilities while preserving video generation speed and output fidelity.
Vs Previous Image-to-Video Models: Delivers significant advancements in motion continuity, visual realism, and prompt-conditioned customization, thanks to cutting-edge architectural improvements and enriched training data.
🔒 Security and Compliance
- 🛡️ Rigorous data privacy measures and secure image processing pipelines.
- 🕵️ Real-time content moderation, bias detection, and ethical safeguards aligned with responsible AI frameworks.
- ⚙️ Customizable compliance controls suitable for regulated industries such as healthcare, finance, and legal domains.
- 🌐 Adherence to global privacy laws and industry standards, ensuring trustworthiness and safe deployment in sensitive environments.
These embedded security protocols, combined with technical excellence, equip organizations to confidently integrate Kling V1.5 Standard Image-to-Video into mission-critical video production workflows.
❓ Frequently Asked Questions (FAQ)
Q: What specialized architecture enables Kling V1.5 Standard I2V's image-to-video transformation?
A: Kling V1.5 Standard I2V employs a motion-aware conditional diffusion architecture specifically optimized for animating static images while preserving original content fidelity. It features appearance-flow disentanglement networks, temporal coherence encoders, and adaptive motion priors.
Q: How does the model infer and generate plausible motion from single images?
A: The architecture incorporates sophisticated motion inference engines that analyze image content to identify potential movement vectors, understand physical constraints, and generate biologically/physically plausible animations. It employs category-specific motion priors for diverse image types.
Q: What types of image-to-video transformations does Kling V1.5 Standard I2V handle most effectively?
A: The model excels at bringing portrait photos to life with subtle expressions, animating landscape and nature scenes, creating dynamic product visualizations, generating architectural walkthroughs, and transforming artistic illustrations into animated sequences.
Q: What level of creative control does the I2V model provide for different applications?
A: The system offers adjustable motion parameters including intensity control, direction specification, animation style selection, and duration adjustment. Users can guide the type of motion applied to different image elements and control the balance between subtle and dramatic transformation.
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