happyhorse-1.0-t2v is the text-to-video model of HappyHorse 1.0, designed for the creative workflow from written concepts to dynamic short films. It focuses on understanding scenes, actions, and visual styles, producing realistic visuals that are natural, smooth, and rich in detail. On this platform, you can submit generation tasks using prompts, select duration, resolution, and aspect ratio, making it suitable for creative storyboards, short-film prototypes, and visual direction exploration.
Submit requests to the public API at api.acedata.cloud using the documented parameters, then use the results in your application.
Input parameters and result formats vary by service. Use the public API for this model and follow its guide for generation, task retrieval and editing operations.
Clarify capacity, inputs and outputs, and invocation methods before selecting a model.
Text-to-video generation and realistic dynamic performance are capabilities of this model; duration, aspect ratio, and task delivery methods follow the instructions for this platform's invocation endpoint.
Learn what happyhorse-1.0-t2v can bring to your work.
The core of this model is understanding text descriptions and generating videos. When creating, you can organize the subject, environment, actions, and camera movement into a clear sequence—for example, describing a white horse raising its head in the morning light, its mane swaying in the wind, while the camera slowly pushes in—so the visual focus develops around a single intent rather than stacking unrelated requirements.
HappyHorse 1.0's text-to-video capability emphasizes realistic dynamic rendering and natural, fluid, richly detailed expression. It is suitable for exploring human or animal movements, environmental changes, and camera atmosphere; prompts can specify motion direction and lighting conditions, then compare visual approaches through different descriptions without needing to create a first-frame image first.
Generation tasks can be submitted asynchronously, and completion callbacks can also be configured. After saving the task_id, an application can query the task or receive the completed result, then retrieve the video_url, duration, and resolution. This delivery method is suitable for embedding into an asset workspace, allowing users to continue working on other content after submitting an idea.
Start with specific tasks to find where the model can be most effective.
Enter descriptions of the scene, subject action, lighting, and camera movement for an advertising shot to generate a short video for discussing visual direction. For example, first validate the environmental atmosphere around the product and the camera rhythm, then decide on the final shooting plan. The deliverable is dynamic proposal material rather than static storyboard notes that rely on textual explanation.
Write prompts around a clear action, select a 9:16 aspect ratio, and generate short-video assets suited to vertical composition. You can try different scenes, lighting, or camera movements separately, then select assets for editing. Complete videos involving subtitles, brand marks, or voice-over can be further produced in post-production workflows.
Organize a single scene in a story into subject, environment, action, and visual style, then generate a watchable clip to support discussion of mood and narrative pacing. This is suitable for early-stage creation when image assets are not yet available; when multiple shots are needed, it is recommended to generate and review them section by section before combining them into a complete previsualization sequence.
Choose based on task complexity, input materials, and expected results.
If you already have prompts tailored for happyhorse-1.0-t2v, or want to compare creative options under a fixed model, you can explicitly select this version. happyhorse-1.1-t2v is another text-to-video model in the same series and is also the default generation option; when 1.0 is needed, you should specify model, and must not treat requests with the model omitted as 1.0 calls.
If you only have a text concept and want to freely explore composition, choose t2v for a more direct approach. If you must start from a specified first frame, choose i2v; if you need to use images to constrain characters or style, choose r2v; to modify an existing video, choose video-edit. These are different creation modes, and you cannot replace them by simply adding image or video fields to t2v.
From a small-scale task to full integration.
Define the goal, required inputs, and output requirements, and use real business examples as a starting point.
Open the trial page, confirm the parameters supported by this entry point, 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.
Understand output quality and capability scope before formal use.
Answers to common questions about using happyhorse-1.0-t2v.
No. Text-to-video uses action=generate; simply provide a prompt describing the subject, scene, actions, and style. If you want to generate strictly from a single image, choose the corresponding i2v model instead of adding a first-frame requirement to this text-generation model.
Explicitly specify model=happyhorse-1.0-t2v in the request, together with action=generate. The default text-to-video option is 1.1, so if you need to consistently use the 1.0 workflow, save the complete model configuration rather than saving only the prompt or reusing examples that omit the model.
This platform's text-to-video entry supports 3–15 seconds, with 5 seconds by default; available aspect ratios are 16:9, 9:16, 1:1, 4:3, and 3:4. It is recommended to determine the aspect ratio based on the final display location before writing composition descriptions; content exceeding the single-generation range can be split, generated separately, and then edited together.
You can submit an asynchronous task using async=true, save the task_id, and query it through the task API; you can also provide callback_url to receive the result after the task is complete. Before obtaining the video, check the task status and distinguish between pending, succeeded, and error before arranging download and subsequent processing.
It is suitable for generating dynamic visual clips from text, but dialogue, background music, or lip-sync should not be regarded as guaranteed delivery capabilities of this model. When producing short films with sound, you can first generate and review the visuals, then add voice-over, music, and sound editing so that audio and video each meet the project requirements.
Model information · Updated: 2026-10-01. For call parameters and billing rules, see the API and pricing sections.
Choose a development approach or application suited to this model and follow its setup guide.
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SDK installation, authentication and examples
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$0.10–$0.18· per second
You pay$13,584for 142,800 credits · $0.0951 / credits46% OFF
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This is only a basic call example. See the full docs for more parameters and advanced usage.
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