Flova AI Produces 1-Minute Summary Video in 30 Minutes

The Code Factory channel introduced the video production process using Flova AI, emphasizing that this tool enables consistent work from video planning to production. It was explained that Flova AI provides a complete guideline on what steps users should follow when producing videos. This allows users to create videos efficiently based on systematic planning.

Typically, when using AI engines like Sidens with general prompts, it's easy for the consistency of faces or atmosphere to decline. The Code Factory channel explained that this video demonstrates how Flova AI effectively maintains such consistency. This is considered a significant challenge in AI-powered video production.

Code Factory recommended conducting preliminary research and planning through metaprompting using AI such as GPT or Claude Code. For example, by providing AI with context on a specific topic like the Battle of Myeongnyang to research necessary information, and then creating optimized prompts based on that, video production can begin. This process enables more efficient and accurate work, it was explained. This is a crucial step in understanding and optimizing AI utilization.

When Flova AI was requested, via metaprompting, for a prompt to produce a 1-minute video about the Battle of Myeongnyang, the AI provided a detailed, second-by-second 60-second screen composition plan. For example, it specifically outlined what content would appear from 0 to 6 seconds, from 6 to 14 seconds, and so on. This process optimizes the prompts to suit Flova AI's characteristics, ensuring that the necessary content for each part of the video is clearly reflected.

In the Flova AI generator, users can change the production rules as desired. Code Factory configured it to proceed with their approval at each step. They recommended the Sidens 2.5 engine for model selection, stating that it shows the best performance and should be used for video production.

The Flova AI storyboard is largely composed of key elements, shots, and audio layers. Key elements are used to generate assets that will be commonly used throughout the video, while shots create scene-specific videos based on these assets. Finally, the audio layer allows background music to be inserted, enabling systematic management of all video elements.

The process of pre-generating images for key elements required for the video, such as Panokseon (turtle ship), the Myeongnyang Strait, and the Japanese fleet, was demonstrated using Flova AI. It was explained that creating assets in advance and then producing the video based on them significantly enhances video consistency. Users can modify prompts and reset elements before generation to achieve desired results, and can also check estimated credit consumption beforehand. Flova AI is efficient as it can generate multiple assets simultaneously through parallel processing.

The 'Unsoted Media' tab in Flova AI allows users to view all generated media at a glance. In this tab, users can select and review individual generated media to utilize necessary assets in the storyboard. It was also explained that the 2K high-definition generation function based on Nanovanna 2 can produce clearer results.

Flova AI generated video shots step-by-step according to the second-by-second plan entered in the prompt beforehand. Each shot, such as the scenery of the Myeongnyang Strait and the entry of the Japanese fleet, contains detailed content and is a crucial element in forming the overall flow of the video. A total of 8 shots were generated in parallel, enhancing the efficiency of video production.

Generating a video all at once is time-consuming and difficult to modify, but Flova AI uses a stepwise approach of first generating images and then creating the video based on them. This method allows users to quickly obtain results similar to their intent and provides an efficient workflow that progresses from image generation to automatic video and audio creation. This significantly reduces the time and effort spent on video modifications.

Flova AI automatically generated video and sound based on approved images. The demonstrated Myeongnyang Battle video consisted of over 6 scenes, including the first and second scenes, depicting the battle process. All scenes' video and sound were automatically generated, showcasing the level of video production automation.

Code Factory noted that in Flova AI tests, technical limitations were found in videos requiring complex movements that heavily rely on a physics engine. They pointed out that this area specifically needs improvement for such video types and expressed anticipation for better expressiveness through future updates.

Flova AI demonstrated excellent capability in realistically implementing environmental details, such as the swaying of waves. However, the cannon firing scenes appeared somewhat unnatural, leading to an assessment that it needs to be confirmed whether this was an intentional effect or a technical limitation. This suggests that AI still has room for development in implementing realistic movements.

Flova AI automatically generated narration based on the historical facts of the Jeongyujaeran War. Additionally, various background music options suited to the video's atmosphere were provided, allowing users to choose their preferred music. This method of individually selecting and combining each element to complete the final video offers users a high degree of freedom.

Flova AI provides a timeline function, similar to general video editing tools, allowing users to directly modify generated videos. The timeline enables individual review and modification of background music and narration for each scene, increasing flexibility for post-production work on completed videos. This means that users can further refine the drafts generated by AI.

Code Factory assessed that Flova AI can produce high-quality results with a single prompt, making it immediately usable for single video productions, such as history channels. They emphasized that even if there are unnatural parts in the video, immediate adjustments like image replacement are possible, making it highly valuable for practical use. This shows that AI video production can contribute to actual content creation beyond mere demonstrations.

While modifying a video generated all at once is very difficult, Flova AI significantly increases editing efficiency by managing it in separate sections per shot and providing a partial rendering function. Since each section can be rendered and inserted separately, editing is much faster and easier. This prevents the inefficiency of having to regenerate the entire video from start to finish every time and complements the workflow.

The production of the 1-minute Battle of Myeongnyang video using Flova AI took approximately 30 minutes from planning to completion. Furthermore, it was explained that this entire process was completed at a low cost of less than 1 credit. This suggests that AI video production is highly efficient in terms of time and cost, and is an accessible solution for small-scale creators and production companies.