Longcat Video Prompt Guide: AI Video Generation

Open-Source Video Generation Gets Serious

Meituan released Longcat Video in September 2025 under an MIT license, bringing a 13.6 billion parameter Dense Transformer architecture to the open-source video generation space¹. The model generates up to 961 frames, supports both text-to-video and image-to-video workflows, and outputs at 480p or 720p resolution.

What distinguishes Longcat Video from earlier open-source models is temporal coherence across extended sequences. Most video models struggle to maintain consistent subject appearance and logical motion progression beyond a few seconds. Longcat Video addresses this through its Dense Transformer architecture, though you'll still need careful prompt engineering to get reliable results. Note that Longcat Video is separate from Longcat-Flash, which is a 560-billion-parameter language model for text reasoning.

Prompt Structure That Works

Longcat Video responds to detailed, structured prompts. Minimal descriptions produce minimal results. Your prompt needs five components:

Scene Description: Visual elements, setting, atmosphere
Motion Direction: How objects or characters move within the frame
Cinematographic Elements: Camera movement, lighting, perspective
Style References: Visual aesthetics (photorealistic, anime, documentary)
Technical Qualifiers: Resolution and quality indicators

Compare these two prompts:

Weak: "a car driving down a road"

Strong: "A sleek red sports car driving down a winding coastal highway at sunset. The camera follows alongside the vehicle, capturing reflections of the golden sun on its polished surface. The scene transitions from close-up details of the wheels to a wide aerial shot revealing the dramatic coastline below. Cinematic lighting, photorealistic, 4K quality."

The second prompt gives the model concrete visual targets and motion choreography.

Negative Prompts Matter

Longcat Video accepts negative prompts to filter unwanted elements. The default negative prompt includes:

"Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background"

Add specific exclusions for your use case: "camera shake," "color distortion," or "abrupt scene changes" to improve output quality.

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Text-to-Video Techniques

Temporal Sequencing

Video generation requires sequential thinking. Structure prompts with temporal markers:

"A butterfly emerges from its chrysalis, slowly unfurling its vibrant wings. Initially, the wings appear damp and crumpled. Then, they gradually expand as fluid pumps through their veins. Finally, the butterfly rests momentarily before taking its first flight into a sunlit garden."

This sequential structure guides the model toward coherent narrative progression rather than static scenes with minimal movement.

Motion Vocabulary

Use specific motion terminology:

Verbs: floating, accelerating, dissolving, emerging, circling
Adverbs: smoothly, gradually, rapidly, rhythmically, gently
Transitions: transforming into, fading to, zooming out to reveal

Example: "A small seed planted in rich soil gradually sprouts, with delicate green shoots slowly emerging from the earth and steadily growing upward toward the sunlight."

Image-to-Video Strategy

Source Image Selection

Not all images convert well to video. Effective source images have:

Clear focal points: Distinct subjects that can be animated
Depth cues: Visual information suggesting foreground, midground, background
Directional elements: Components implying motion (winding paths, flowing water)
Dynamic potential: Subjects that naturally suggest movement (clouds, trees, fabric)

Complementary Prompting

Your prompt should extend what's in the image, not contradict it. For a mountain landscape image:

"The majestic mountain landscape comes alive as clouds drift slowly across the peaks. A gentle breeze causes the foreground pine trees to sway slightly, while a distant eagle soars across the valley. The afternoon light gradually shifts to golden sunset tones, casting increasingly long shadows across the terrain."

Parameter Configuration

Parameter	Range	Recommended Settings
Resolution	480p / 720p	480p for testing; 720p at 30fps for final output
num_frames	17-961	60-120 for concepts; 150-300 for complete scenes; 300+ for extended sequences
num_inference_steps	8-50	15-20 for drafts; 30-40 for balanced quality; 40-50 for maximum quality
guidance_scale	1-10	4-6 for balanced results; 7-10 for strict prompt adherence
fps	1-60	15fps for 480p; 30fps for 720p

Output Format Options

X264 (.mp4): Universal compatibility
VP9 (.webm): Web-optimized
PRORES4444 (.mov): Professional editing workflows
GIF (.gif): Social media sharing

Common Issues and Fixes

Static or Minimal Movement

If your video appears too static:

Add motion-specific language to your prompt
Increase frame count
Use dynamic verbs and transition descriptions

Inconsistent Subject Appearance

If subjects change appearance throughout the video:

Add "consistent" to your prompt
Strengthen the description of defining features
Use negative prompt to specify "no changing appearance"

Unnatural Motion

If movement feels robotic:

Use organic motion terms ("flowing," "natural," "smooth")
Avoid contradictory motion directions
Add "realistic physics" to your prompt

API Implementation

Basic integration requires minimal setup with the Queue API:

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/longcat-video/text-to-video/720p", {
  input: {
    prompt: "realistic filming style, a person wearing a dark helmet...",
    num_frames: 300,
    num_inference_steps: 30,
    guidance_scale: 5,
  },
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      console.log(`Processing: ${update.logs}`);
    }
  },
});

The subscribe method handles request queuing and status updates automatically. Generation times vary based on queue depth and system load. For production implementations, review the Model Endpoints API documentation for webhook integration and advanced queue management.

Deployment Considerations

For local deployment, Longcat Video requires approximately 80GB of VRAM on an NVIDIA GPU system². This hardware requirement makes cloud deployment the practical choice for most production scenarios.

Running on fal eliminates infrastructure management while providing optimized generation. The platform handles backend requirements including model loading, GPU allocation, and queue management through fal Serverless.

Rate limits and quotas vary by account tier. Check your fal dashboard for current limits applicable to your subscription level.

Open-Source Alternative to Proprietary Models

While Sora 2 from OpenAI has dominated headlines in 2025, Longcat Video represents a viable open-source alternative². The key difference: you control the entire generation pipeline. No subscription fees, no content restrictions, no black-box processing.

The trade-off is prompt complexity. Proprietary models often include additional guardrails and prompt optimization layers. With Longcat Video, you control every parameter, which means more flexibility but also more responsibility for prompt engineering and tuning.

For teams that need generation transparency, model customization, or freedom from vendor lock-in, Longcat Video delivers production-grade results with complete operational control. If you need additional text-to-video options, explore models like Kling 1.6 Pro or Pixverse for comparison.