veo3-fast is a video generation model in the Google Veo 3 series designed for speed and creative iteration, suitable for turning advertising concepts, product images, and animation scripts into dynamic clips. On this platform, you can start creating from text, or use first-frame or first-and-last-frame images to structure shots, combined with landscape and portrait formats, 1080p retrieval, and asynchronous task processing to form a workflow from creative testing to asset delivery.
Submit requests to the public API at api.acedata.cloud using the documented parameters, then use the results in your application.
api.acedata.cloudveo3-fastInput 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.
Fast's speed-first positioning is a native model characteristic; the number of images, aspect ratios, and HD retrieval method are calling specifications of this platform.
The core value of veo3-fast is rapidly exploring different approaches. For the same product selling point, you can separately describe the scene, subject actions, camera movement, and visual style to generate comparable clips. It is ideal for selecting advertising directions before formal production, rather than packing multiple complete stories into a single request.
Image-to-video can start from one image, or use two images to define the opening and ending. Product stills, character concept art, or scene design images can all become starting points for creation; prompts describe the action and camera changes in between, moving assets from static displays toward purposeful dynamic expression.
Video creation can use asynchronous submission, so your application does not need to keep waiting for a connection. After submission, save the task_id, receive results through queries or callbacks, and then retrieve the video_url based on status. When an HD version is needed, use the video ID from the generation result to retrieve 1080p, making it easier to integrate with asset libraries and review workflows.
Enter product selling points, audience, and shot descriptions, then try lifestyle scenes, studio presentations, or animated expressions respectively, delivering a set of dynamic samples for team review. It is recommended to adjust only one creative variable at a time, such as the background, action, or camera distance, to make it easier to compare which opening is more suitable for subsequent production.
Use a product image as the first frame and describe the desired presentation action, such as the camera slowly moving closer, lighting changes, or the product entering a usage scenario. If a specific final composition already exists, you can also provide an end frame to deliver clips suitable for product introductions and social content editing, rather than using multiple product images as source material for blending.
Break an explanatory script into independent visual segments, clearly specify the subject, environment, and action for each segment, then generate animation mockups or operational scenarios. The deliverables are suitable for discussing pacing, supplementing training materials, or validating storyboards; the final course still requires organized editing, subtitles, and reviewed explanatory content.
When the task is to test multiple ad concepts, prepare dynamic samples for a product catalog, or repeatedly revise shot expressions, prioritize veo3-fast. If the main goal shifts to image quality, compare actual results with the veo3 standard version from the same series: the former emphasizes iteration speed, while the latter is a quality-first mode; do not substitute fixed processing times or uniform image-quality differences for footage review.
For text creation, a single-image first frame, or two-image first and last frames, veo3-fast already provides corresponding workflows. If the task is to blend multiple independent reference images, choose veo31-fast-ingredients; if 4k output is explicitly required, consider veo31, which supports that option. Two first and last frames define temporal endpoints and are not equivalent to multi-image content blending.
For text only, use text2video; for image-driven generation, use image2video. One image in image_urls is the first frame, and two images are the first and last frames; describe the action that occurs in between.
Specify model=veo3-fast at /veo/videos, choose an action based on text or images, and fill in prompt. Start with aspect_ratio=16:9 and resolution=720p, then increase the output tier according to this model's supported range.
After setting async=true, save the task_id, and retrieve the completed video through /veo/tasks or a callback; verify the visuals and audio, and save the video ID and downloaded file for subsequent production.
A glass of lemon soda on the table slowly rotates in the light, bubbles rise upward, the camera gently pushes in, and the only sounds are ice cubes and bubbles.
First compare visuals and sound effects to choose a direction, using text or a single first frame; Fast does not promise a fixed speed improvement for every task.
Both belong to the Veo 3 series. veo3-fast is better suited for rapid iteration, ad variations, and product samples; veo3 is the quality-first mode. You can first use Fast to screen ideas, then compare standard-version results for selected shots, rather than assuming there is a fixed difference in speed or image quality between the two.
Use image2video and pass image links in image_urls. One image serves as the first frame; two images serve as the first and last frames respectively. The prompt describes the actions and camera changes in between. This method is not multi-image blending, so it is recommended to prepare start and end images around the same subject or a clearly defined transition relationship.
For vertical video, set aspect_ratio to 9:16; use 16:9 for horizontal video. veo3-fast does not support 4k; the default output is 720p, and you can also choose 1080p or gif. Aspect ratio determines composition proportions, while resolution determines output clarity, so they should be set separately.
Submit the get1080p operation to /veo/videos and use video_id to specify the generated video. This value comes from the id in the completed result data, not task_id. When saving generation results, it is recommended to retain the task ID, video ID, and video link at the same time for easier subsequent processing.
No. After setting async to true, you can first obtain task_id and then query the task result; you can also provide callback_url to receive a result push upon completion. After obtaining the completed result, check state and read video_url to download the video or incorporate it into your own review and asset-management workflow.
Choose a development approach or application suited to this model and follow its setup guide.
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