happyhorse-1.1-r2v is the reference-image-to-video model of HappyHorse 1.1, suited for tasks with existing character, clothing, or prop assets where you want to continue creating short videos in new scenes. It combines ordered reference images with textual shot descriptions, using visual assets to constrain subjects and prompts to arrange actions, environments, and camera work, with a focus on short-video creation that requires high character consistency.
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.
Native specifications include audio capabilities and 480P output; this platform entry provides reference-image generation calls at 720P and 1080P.
Learn what happyhorse-1.1-r2v can bring to your work.
Unlike using text alone to describe appearance, reference images provide clear visual guidance for characters, clothing, and props. You can change environments and actions in prompts while continuing to use the same character assets, making it suitable for creating short shots in different scenes around one character; consistency is a creative goal, not a guarantee of being completely unchanged frame by frame.
Images are not unordered attachments: use character1 and character2 to point to the corresponding assets, and specify which image provides the subject and which provides the clothing or visual style. For example, have character1 walk through grass while using the leather and gold-trim style of character2, establishing a clear relationship between assets and shot descriptions.
Submit tasks through POST /happyhorse/videos, explicitly select reference_to_video and this model, then set duration, resolution, and aspect ratio. When persistent connections are inconvenient, use async or callback_url, save the task_id to query results or receive completion notifications, and obtain the video link for download and subsequent editing.
Start with specific tasks to find where the model can be effective.
Provide reference images of the same character, describe the scene, action, and filming approach for each shot, and produce editable short clips. For example, create shots of a character walking on grass or pausing on a street, first verify appearance consistency, then select results that meet narrative requirements to form a series of content.
Prepare character, clothing, or prop assets, clearly specify the purpose of each reference image in the prompt, and generate dynamic previews of characters carrying props or presenting looks. The deliverables are suitable for discussing visual direction, action arrangements, and scene atmosphere; when product details are involved, check whether textures, logos, and structures match the original design.
Using the same set of character reference assets, create landscape, portrait, or square short clips for creative test videos in different display placements. When providing input, adjust both the aspect ratio and composition description rather than only changing the ratio; after output, check subject placement and action space, then pass it to the editing workflow to add subtitles and brand information.
Choose based on task complexity, input materials, and expected results.
If you want a character to continue appearing in a new environment and do not require the reference image itself to become the opening frame, choose happyhorse-1.1-r2v. If the task focuses on animating a finalized image as the first frame, choose happyhorse-1.1-i2v; if there are no image assets and you mainly rely on script and shot text, use happyhorse-1.1-t2v.
Both 1.1-r2v and 1.0-r2v generate from reference images. The stated difference in public specifications is that 1.1 adds a native 480P option, while this entry still provides 720P and 1080P. Do not interpret version numbers as a fixed quality increase. If you already have a video that needs wardrobe changes, style transfer, or local replacement, choose happyhorse-1.0-video-edit.
From a small-scale task to formal integration.
Define the goal, required inputs, and output requirements, using real business examples as a starting point.
Open the trial page, confirm the parameters supported by this entry, 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 official use, understand the output quality and capability scope.
Answers to common questions about using happyhorse-1.1-r2v.
This model uses reference images to guide characters and visual elements, and should not be used as a fixed first-frame workflow. If you need an image with a specific composition as the opening frame, choose happyhorse-1.1-i2v. r2v is better suited for preserving the reference subject while rearranging the environment, actions, and camera through text.
Put 1–9 image URLs in image_urls, and reference them in order in the prompt using character1, character2, and so on. In addition to identifying the corresponding assets, clearly describe the subject's actions, scene, and the purpose of each asset, rather than merely listing names without explaining their relationships.
Set action to reference_to_video, model to happyhorse-1.1-r2v, and submit prompt and image_urls. Do not rely on the default text-to-video action. Then choose resolution, ratio, and duration according to your needs to form a complete reference-image generation request.
This model has native audio capabilities, but reference-image generation does not mean preserving the original video's sound, nor does it mean specifying voice-over or lip synchronization. The origin usage for preserving original audio belongs to video editing tasks; if audio has specific delivery requirements, review the generated result and arrange any necessary audio post-production.
After submitting with async, save task_id and query the task through /happyhorse/tasks; you can also provide callback_url to receive completion notifications. The result includes the task status and video_url; statuses may be pending, succeeded, or error. Download the video for review only after it has completed successfully.
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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