🎬 Text-to-Video Workflow Explained

Text-to-video is an AI process that generates video content from text descriptions. At JollyAI, this workflow uses advanced deep learning models (primarily Wan 2.2 and LTX2) to understand your written prompt and create corresponding video frames that form a cohesive motion sequence.

The process involves several complex AI components working together: a text encoder that understands your description, a diffusion model that generates visual content, and a temporal module that ensures smooth motion between frames.

1. Prompt Processing

When you enter your text description, the AI first processes your prompt through a CLIP (Contrastive Language-Image Pre-training) model. This converts your words into numerical representations (embeddings) that the AI can understand and work with.

2. Noise Initialization

The video generation starts with random noise - essentially "static" that contains no visual information. This serves as the starting point for the AI to build upon.

3. Denoising Process

The core of text-to-video generation is diffusion. Through multiple iterations (typically 20-50 steps), the AI gradually transforms the noise into meaningful visual content:

• Step 1-10: Major shapes and composition emerge
• Step 10-20: Details and textures start forming
• Step 20-30: Refinement of subject and background
• Step 30+: Final polish and detail enhancement

4. Temporal Coherence

Unlike static images, videos require temporal consistency - making sure each frame flows naturally into the next. AI models use special techniques to maintain:

• Subject consistency (character/object stays recognizable)
• Motion fluidity (movements look natural)
• Background stability (environment doesn't jump randomly)
• Lighting remains consistent) coherence (lighting

5. Video Encoding

Finally, the generated frames are compiled into a video file (MP4 format) using appropriate encoding settings for quality and file size balance.

JollyAI offers multiple text-to-video models, each with different strengths:

Wan 2.2 Text-to-Video

• Speed: Fast (3-5 minutes)
• Quality: Very good 680P output
• Best for: Quick video generation, experimentation
• Learn more about Wan 2.2 →

LTX2 Text-to-Video

• Speed: Moderate (5-10 minutes)
• Quality: Excellent, high-fidelity output
• Best for: Professional-quality videos
• Learn more about LTX2 →

The quality of your video largely depends on how well you describe what you want. Follow this structure for best results:

Recommended Prompt Structure:

Essential Elements to Include:

• Subject: What or who is in the video
• Action: What should happen or move
• Setting: Where does it take place
• Style: Art style, mood, or aesthetic
• Camera: Any specific camera movements

Motion Keywords to Use:

Aspect Ratio

Choose the right aspect ratio for your needs:

• 16:9 - Widescreen, ideal for YouTube and presentations
• 9:16 - Vertical, perfect for TikTok and Instagram Reels
• 1:1 - Square, good for general social media

Duration

Current video generation produces 3-5 second clips. This is optimal for:

• Social media content
• Loop animations
• Short scenes and transitions
• Concept previews

✅ Do:

• Start with simple, clear prompts
• Add specific motion keywords
• Include lighting and atmosphere details
• Try multiple variations to find what works
• Use reference images when possible

❌ Don't:

• Don't use overly complex, multi-scene prompts
• Avoid vague descriptions like "make it cool"
• Don't expect narrative storytelling in one clip
• Avoid contradictory elements in prompts