happyhorse-1.0-video-edit is the video editing model of HappyHorse 1.0, designed for style transfer, outfit changes, and element replacement in existing clips. Based on the original video and text editing intent, it can incorporate reference images to describe the target appearance and optionally preserve the original audio. It is suitable for workflows that already have footage and want to create visual variations rather than generate new videos from scratch.
Clarify capacity, inputs and outputs, and invocation methods before selecting a model.
Resolution, duration, frame rate, and format are native specifications; reference images, audio options, and task delivery are used according to this platform's video editing operations.
Learn what happyhorse-1.0-video-edit can bring to your work.
Use an existing video as the editing foundation and describe in text the style, clothing, or visual elements that need to change. Prompts should specify both what to modify and what to preserve, for example, changing the clothing material while retaining the original camera movement, so the task focuses on transforming the footage rather than redesigning the entire shot.
When text cannot accurately describe textures, color schemes, or clothing details, reference images can help convey the intent. Video editing supports up to five images, making them suitable for providing examples of target clothing, materials, or styles; reference images guide the direction of changes and should not be understood as pixel-by-pixel copying or strict local locking.
Visual transformation does not necessarily require replacing the sound. For clips with existing narration, music, or ambient audio, use origin to preserve the original video audio; auto can also be selected. Before delivery, check visual edits and audio results separately, and avoid assuming that sound will change according to the same intent simply because the visual style changes.
Start with specific tasks to find where the model can be effective.
Input an already filmed product clip, then provide images of the target material or visual style and describe the desired color palette and decorative direction in text. The output can be used to compare short-video variations across different creative approaches, making it suitable for marketing previews with existing footage; product branding and key visual details still need frame-by-frame review.
Use a person clip as input, pair it with target clothing images, and describe the clothing to replace and the shot content to preserve. The generated result is suitable for discussing styling directions or producing concept samples; pay close attention to turns, occlusions, and rapid movement, and do not treat the preview directly as precise clothing reproduction.
Submit visual editing instructions for clips with existing narration or ambient sound, and select origin to preserve the original audio. Applications can receive a task ID asynchronously, obtain the video link through queries or callbacks, and then proceed to review and editing workflows, making this suitable for footage where the audio content is already finalized and only the visual style needs adjustment.
Choose based on task complexity, input materials, and expected results.
The starting point for this model is a video to be edited. If you only have a script, choose happyhorse-1.1-t2v; if you only have a first-frame image, consider happyhorse-1.1-i2v; if you want to generate new shots using reference images, consider happyhorse-1.1-r2v. The 1.1 generation models do not automatically replace 1.0 video editing; your choice should be based on the input materials and editing objective.
When the goal is style transfer, outfit changes, or element replacement, this model directly matches the task. If the core requirement is to replicate the effects or camera motion from another video, consider wan2.7-videoedit. The two should not be judged solely by version numbers; preserving an existing shot while changing its appearance and transferring motion effects from another video are different editing objectives.
From a small-scale task to production integration.
Define the objective, required inputs, and output requirements, using real business examples as a starting point.
Open the trial page, confirm the parameters supported by this endpoint, then submit a small-scale task to review the results.
Keep the complete model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
Before formal use, understand the output quality and capability scope.
Answers to common questions about using happyhorse-1.0-video-edit.
You cannot submit only text for video editing. This model requires the video to be edited, video_url, and an editing instruction, prompt; reference images are optional supporting materials. To generate a clip directly from text, use a t2v model; if you only have a static first-frame image, choose an i2v model.
This model's editing operation supports zero to five reference images, submitted through image_urls. Nine reference images fall within the scope of reference-image-to-video operations and do not apply here. It is recommended to select images directly relevant to the modification goal and explain their purpose in the prompt, avoiding mixing conflicting styles.
Set audio_setting to origin to preserve the original video audio; the other optional value is auto. If the task only changes the visual appearance and the original narration or background music still needs to be retained, origin better matches this goal. After completion, you should still listen to the delivered file to confirm that the sound and visuals suit the intended use.
Submit the video_edit operation, full model ID, video URL, and editing instruction to POST /happyhorse/videos. Using async lets you receive a task_id first, then query through /happyhorse/tasks; you can also provide callback_url to receive the result and obtain the video URL after successful completion.
The duration in video editing results represents billable video duration, recorded as the combined length of the input and output videos, so it cannot be directly treated as the final video's playback length. Editing duration follows the source video, and actual usage is subject to the statistics after task completion; see the pricing page for specific fees.
Model information · Updated: 2026-10-01. See the API and pricing sections for invocation parameters and billing rules.
Start with a clear goal and evaluate from real results whether it suits your work.
