Kling O1 Update: Video Editing Through Prompts and Reference Images

The Kling O1 update introduces advanced prompt-based video editing capabilities that allow creators to modify existing videos using text descriptions combined with reference images for unprecedented creative control. Core Innovation: This update represents a significant shift in AI video editing by enabling users to describe desired changes in natural language while providing reference images to guide the transformation. Industry analysis indicates that generative AI video tools have seen 280% adoption growth in 2025, with prompt-based editing emerging as the most requested feature among creative professionals. Practical Application: Users can now upload a video clip, describe modifications through text prompts like "change the background to a sunset beach" or "adjust the lighting to golden hour," and provide reference images showing the exact aesthetic they want. The AI interprets both the textual instructions and visual references to apply precise edits. Real-World Impact: Early reports from content creators indicate this approach reduces video editing time by allowing direct communication of creative intent without manual frame-by-frame adjustments, though results vary based on prompt specificity and reference image quality.

The Technical Process: Kling O1's system analyzes your source video, interprets your text prompt, and examines the reference image to understand the desired visual transformation. The AI then applies changes while attempting to maintain temporal consistency across frames. Input Requirements: Users provide three elements: the original video footage, a detailed text prompt describing the intended changes, and one or more reference images that illustrate the target style, lighting, color palette, or composition. The more specific your prompt and the more relevant your reference image, the more accurate the output. Processing Workflow: The system first segments the video to identify key elements mentioned in your prompt. It then extracts visual characteristics from your reference images—such as color grading, atmospheric effects, or compositional elements—and maps these onto the corresponding elements in your video footage. The AI maintains motion continuity and object relationships from the original video while applying the stylistic and content modifications specified through your combined prompt and reference inputs.

Style and Atmosphere Changes: You can transform the overall aesthetic of footage by providing reference images of different times of day, weather conditions, or artistic styles. For example, converting a daytime scene to night, adding fog or rain effects, or applying cinematic color grading based on reference stills from films. Background Modifications: The system allows replacement or alteration of backgrounds while keeping foreground subjects intact. Provide a reference image of your desired background environment—whether a different location, time period, or entirely fantastical setting—and describe the change in your prompt. Lighting and Color Adjustments: Reference images excel at communicating specific lighting setups or color palettes. You can match the golden hour warmth from a reference photo, replicate studio lighting configurations, or apply specific color grading schemes that would traditionally require extensive manual color correction. Object and Element Changes: Current capabilities include modifying specific objects within scenes, changing clothing colors or styles, adjusting architectural elements, or adding atmospheric effects based on reference imagery. It's important to note that complex modifications involving significant motion changes or complete scene reconstructions may produce inconsistent results, as the technology works best with stylistic and atmospheric transformations rather than fundamental structural changes.

Prompt Specificity: Be precise about which elements you want changed and which should remain untouched. Instead of "make it look better," use "apply the warm sunset lighting from the reference image to the building facades while keeping the people unchanged." Reference Image Selection: Choose reference images that clearly demonstrate the specific quality you want to transfer. If you're targeting a lighting effect, use a reference image where that lighting is prominent and unambiguous. Avoid cluttered references that contain multiple competing visual elements. Layered Instructions: Structure prompts to address different aspects separately: first describe what should change, then specify what should remain constant, and finally reference which aspects of the reference image should guide the transformation. For example: "Change the sky to match the dramatic clouds in the reference image, keep all foreground elements unchanged, and apply the reference's color temperature to the entire scene." Iterative Refinement: Start with broader prompts and reference images, evaluate the results, then provide more specific follow-up instructions. This approach helps you understand how the system interprets your inputs and allows progressive refinement toward your vision. Common Mistakes to Avoid: Don't use ambiguous language like "similar to" without specifying what aspect should be similar. Avoid reference images that contradict your text prompt. Don't expect the system to infer intentions—explicit instructions produce more reliable results.

Speed and Accessibility: Prompt-based editing with reference images significantly reduces the technical barrier to achieving complex visual effects. Tasks that traditionally required expertise in color grading software, compositing tools, or VFX applications can now be attempted through natural language descriptions. Workflow Differences: Traditional editing demands frame-by-frame precision, masking, keyframing, and deep software knowledge. The Kling O1 approach allows creators to communicate intent at a conceptual level—describing the desired outcome rather than executing technical steps. This makes certain stylistic transformations accessible to creators without extensive post-production experience. Control and Precision Trade-offs: Conventional editing software provides granular control over every parameter and pixel. Prompt-based systems offer less fine-tuned control but faster exploration of creative directions. Professional editors might use Kling O1 for rapid prototyping or mood boards, then refine in traditional tools when precise control is needed. Complementary Rather Than Replacement: Current AI video editing tools, including Kling O1, work best as complements to traditional workflows rather than complete replacements. They excel at specific transformation types—atmospheric changes, style transfers, and conceptual explorations—while traditional tools maintain advantages in precision editing, complex compositing, and final quality control. For creators and businesses looking to explore AI-enhanced workflows efficiently, platforms like Aimensa offer streamlined access to multiple AI video tools, though the optimal workflow often involves combining AI-assisted editing with traditional techniques based on project requirements.

Temporal Consistency Challenges: One of the most significant limitations involves maintaining consistency across frames, particularly in longer videos or scenes with complex motion. You may notice flickering effects, style inconsistencies between frames, or elements that don't maintain their transformations smoothly throughout the clip. Interpretation Variability: The system's understanding of prompts and reference images can vary. Ambiguous language or reference images with multiple prominent features may lead to unexpected interpretations. What seems obvious to you as a creator might be processed differently by the AI, requiring multiple iterations to achieve the intended result. Complex Scene Limitations: Scenes with intricate compositions, multiple overlapping subjects, or significant depth complexity may not process as cleanly. The technology currently works most reliably with relatively straightforward compositions where the elements to be modified are clearly distinguishable from those that should remain unchanged. Quality and Resolution Constraints: Processing time increases substantially with higher resolution footage, and quality may degrade depending on the complexity of transformations. Some edits may introduce artifacts, especially in areas with fine detail or rapid motion. Creative Control Boundaries: While prompt-based editing offers intuitive interaction, it provides less granular control than traditional tools. If the initial results aren't quite right, you may need to completely rephrase your approach rather than making small incremental adjustments, which can be less efficient than direct parameter manipulation in conventional software. These limitations are characteristic of emerging technology and will likely improve as AI video editing systems continue to develop throughout 2025 and beyond.

Content Creator Workflows: Social media creators and YouTubers can rapidly test different aesthetic approaches for their content without investing hours in manual color grading or effects work. The ability to apply consistent visual styles across multiple videos using reference images helps maintain brand consistency efficiently. Marketing and Advertising: Marketing teams can quickly generate multiple visual variations of the same footage to test different emotional tones or seasonal themes. For instance, transforming summer product footage to winter aesthetics using reference images and prompts, or adjusting the mood of testimonial videos to match different campaign themes. Pre-visualization and Client Presentations: Video production professionals can use prompt-based editing to create quick mood boards and style explorations for client presentations. Instead of describing a vision verbally, they can demonstrate it by applying reference-based transformations to test footage, facilitating clearer creative communication. Stock Footage Customization: Creators working with stock video can adapt generic footage to specific project needs by changing backgrounds, adjusting atmospheric conditions, or applying stylistic treatments that make stock content feel more original and tailored to their specific requirements. Educational and Tutorial Content: Educators creating video content can demonstrate visual concepts by showing before-and-after transformations, making it easier to teach cinematography, color theory, or visual storytelling principles through practical examples generated via prompts and reference images. The approach proves most valuable when speed and creative exploration matter more than absolute precision, or when creators need to achieve professional-looking results without extensive technical training in traditional video editing software.