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generate_video

Animate an input image into a video using a text prompt. Control duration, size, and motion intensity for custom results.

Instructions

Generate a video using WaveSpeed AI.

Args:
    image (str): Required. URL, base64 string, or local file path of the input image to animate.
    prompt (str): Required. Text description of the video to generate. MUST BE IN ENGLISH. Non-English prompts will be rejected or result in poor quality outputs.
    model (str, optional): Model to use for video generation.
    negative_prompt (str, optional): Text description of what to avoid in the video. Default: "".
    loras (list, optional): List of LoRA models to use, each with a path and scale. Format: [{"path": "model_path", "scale": weight_value}]. Default: [].
    size (str, optional): Size of the output video in format "width*height". Default: "832*480".
    num_inference_steps (int, optional): Number of denoising steps. Higher values improve quality but increase generation time. Default: 30.
    duration (int, optional): Duration of the video in seconds. Must be either 5 or 10. Default: 5.
    guidance_scale (float, optional): Guidance scale for text adherence. Controls how closely the video matches the text description. Default: 5.
    flow_shift (int, optional): Shift of the flow in the video. Affects motion intensity. Default: 3.
    seed (int, optional): Random seed for reproducible results. Set to -1 for random. Default: -1.
    enable_safety_checker (bool, optional): Whether to enable safety filtering. Default: True.
    output_directory (str, optional): Directory to save the generated video. Uses a temporary directory if not provided.
    request_id (str, optional): Request correlation ID for tracing the entire request chain. Strongly recommended to provide a unique ID (e.g., UUID) to correlate logs across the request lifecycle.

Returns:
    WaveSpeedResult object with the result of the video generation, containing:
    - status: "success" or "error"
    - urls: List of video URLs if successful
    - base64: List of base64 encoded videos if resource_mode is set to base64
    - local_files: List of local file paths if resource_mode is set to local
    - error: Error message if status is "error"
    - processing_time: Time taken to generate the video(s)
    
Examples:
    Basic usage: generate_video(image="https://example.com/image.jpg", prompt="The dog running through a forest")
    Advanced usage: generate_video(
        image="/path/to/local/image.jpg", 
        prompt="The dog running through a forest", 
        duration=10,
        negative_prompt="blurry, low quality"
    )
    
Note: 
    IMPORTANT: Prompts MUST be in English. The system only processes English prompts properly.
    Non-English prompts will be rejected or produce low-quality results. If user input is not in English,
    you MUST translate it to English before passing to this tool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYes
promptYes
modelNo
negative_promptNo
lorasNo
sizeNo832*480
num_inference_stepsNo
durationNo
guidance_scaleNo
flow_shiftNo
seedNo
enable_safety_checkerNo
output_directoryNo
request_idNo
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It discloses the generation process, safety checker, and return format (WaveSpeedResult). However, it does not mention any potential side effects, authentication needs, or rate limits, but these are standard for generation tools.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is well-structured with Args, Returns, Examples, and Note sections. It is front-loaded with the main purpose. However, it is somewhat verbose, especially with all parameter descriptions, but efficiency is reasonable for a complex tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, the description fully explains the return value structure (status, urls, base64, local_files, error, processing_time). It includes examples and a note about English prompts. The 14 parameters are all documented, making the tool easy to use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so description fully compensates. Every parameter is explained with type, default, and usage notes (e.g., duration must be 5 or 10, guidance_scale controls text adherence, loras format). This adds significant value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Generate a video using WaveSpeed AI' and lists required inputs (image and prompt). Sibling tools are image_to_image and text_to_image, which are for still image tasks, making this tool's purpose distinct.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit guidance that prompts must be in English and non-English prompts must be translated. Also notes constraints on duration (5 or 10 seconds). However, it does not explicitly contrast with siblings or state when not to use this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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