Having recently undertaken a project to produce a series of fifteen-second promotional video adverts, I opted to utilize Leonardo AI's Motion feature for initial storyboard generation. The primary objective was to assess its efficacy in translating written scene descriptions into coherent visual sequences, with a secondary but critical analysis of the cost-to-output ratio compared to traditional manual storyboarding or alternative generative platforms. My workflow involved the generation of approximately 40 individual motion clips, which were then edited externally into final storyboard sequences.
The technical configuration for the Motion generations was as follows, adhering to a consistent template to maintain stylistic coherence across clips:
```
{
"model": "Leonardo Diffusion XL",
"motion_intensity": "Medium",
"duration_frames": 24,
"prompt_template": "Cinematic storyboard frame, scene: {scene_description}, style: clean modern advertising, shot type: {shot_type}, color grade: vibrant",
"negative_prompt": "text, watermark, deformed, blurry, low contrast"
}
```
A breakdown of the resource expenditure and output quality is necessary for a proper evaluation:
* **Cost Allocation:** The project consumed 17,850 Leonardo tokens. Based on the current pricing of the Premium plan at $30 for 30,000 tokens, the direct financial outlay was approximately $17.85. This does not account for the time investment in prompt engineering and subsequent editing.
* **Success Rate:** Of the 40 clips generated, 28 were deemed immediately usable (70% success rate). 7 required a single regeneration with adjusted prompts, and 5 were discarded due to irreparable artifacts or misaligned motion.
* **Performance Metrics:**
* **Consistency:** Character and object consistency across sequential frames for a given scene was moderate. The system struggled with maintaining exact wardrobe details but performed adequately on broader elements like setting and color palette.
* **Motion Fidelity:** The "Medium" intensity setting provided plausible subtle motion (e.g., leaves rustling, a slight camera pan). Attempts at higher intensity often resulted in undesirable warping or loss of compositional integrity.
* **Prompt Adherence:** The system demonstrated high fidelity to described shot types (e.g., "close-up," "wide establishing shot") and mood descriptors. Specific object placement within the frame remained unpredictable.
When compared to the hourly rate of a freelance storyboard artist or the cumulative credits required on other AI video platforms for a similar volume of 2-3 second clips, Leonardo's Motion presents a compelling case for rapid ideation and low-fidelity prototyping. However, for client-ready, precise boards requiring strict adherence to brand guidelines, the technology is not yet a direct replacement. The cost savings are tangible, but they are currently offset by a need for rigorous quality control and the inability to guarantee specific compositional details without extensive iterative spending. The feature is best classified as a high-efficiency idea generator rather than a precision production tool.
Spreadsheets or it didn't happen.