WaveSpeedAI APIHunyuan T2V

Hunyuan T2V

Hunyuan Video is an Open video generation model with high visual quality, motion diversity, text-video alignment, and generation stability. This endpoint generates videos from text descriptions.

Features

Hunyuan-Video/T2V is a state-of-the-art text-to-video generation model developed by Tencent, now available in collaboration with WaveSpeedAI. It transforms natural language prompts into high-quality videos with realistic motion and visual fidelity.

Key Features

  • High-Quality Video Generation: Produces videos with realistic motion and visual fidelity.
  • Flexible Aspect Ratios and Resolutions: Supports various video dimensions to suit different needs.
  • Advanced Prompt Handling: Utilizes a built-in prompt rewrite system for improved text interpretation.
  • Stable Motion and Temporal Consistency: Ensures coherent motion and scene transitions throughout the video.

ComfyUI

hunyuan-video/t2v is also available on ComfyUI, providing local inference capabilities through a node-based workflow. This ensures flexible and efficient video generation on your system, catering to various creative workflows.

Limitations

  • Increased Generation Time: Longer videos or higher resolutions may result in longer processing times.
  • High GPU Memory Requirements: Generating high-resolution videos may require substantial GPU memory.
  • Complex Motion Challenges: Some intricate motions may necessitate careful prompt engineering for optimal results.

Out-of-Scope Use

The model and its derivatives may not be used in any way that violates applicable national, federal, state, local, or international law or regulation, including but not limited to:

  • Exploiting, harming, or attempting to exploit or harm minors, including solicitation, creation, acquisition, or dissemination of child exploitative content.
  • Generating or disseminating verifiably false information with the intent to harm others.
  • Creating or distributing personal identifiable information that could be used to harm an individual.
  • Harassing, abusing, threatening, stalking, or bullying individuals or groups.
  • Producing non-consensual nudity or illegal pornographic content.
  • Making fully automated decisions that adversely affect an individual’s legal rights or create binding obligations.
  • Facilitating large-scale disinformation campaigns.

Accelerated Inference

Our accelerated inference approach leverages advanced optimization technology from WavespeedAI. This innovative fusion technique significantly reduces computational overhead and latency, enabling rapid image generation without compromising quality. The entire system is designed to efficiently handle large-scale inference tasks while ensuring that real-time applications achieve an optimal balance between speed and accuracy. For further details, please refer to the blog post.

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result


# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/hunyuan-video/t2v" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "prompt": "A playful, fluffy orange kitten wearing sunglasses skateboarding smoothly through a neon-lit futuristic cityscape at night, passing robots, flying cars, and holographic advertisements.",
    "size": "1280*720",
    "seed": -1,
    "num_inference_steps": 30,
    "enable_safety_checker": true
}'

# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"

Parameters

Task Submission Parameters

Request Parameters

ParameterTypeRequiredDefaultRangeDescription
promptstringYesA playful, fluffy orange kitten wearing sunglasses skateboarding smoothly through a neon-lit futuristic cityscape at night, passing robots, flying cars, and holographic advertisements.-The prompt to generate the video from.
sizestringNo1280*7201280*720, 720*1280The size of the output.
seedintegerNo-1-1 ~ 9999999999The seed to use for generating the video.
num_inference_stepsintegerNo302 ~ 30The number of inference steps to run. Lower gets faster results, higher gets better results.
enable_safety_checkerbooleanNotrue-If set to true, the safety checker will be enabled.

Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
data.idstringUnique identifier for the prediction, Task Id
data.modelstringModel ID used for the prediction
data.outputsarrayArray of URLs to the generated content (empty when status is not completed)
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.has_nsfw_contentsarrayArray of boolean values indicating NSFW detection for each output
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds

Result Query Parameters

Result Request Parameters

ParameterTypeRequiredDefaultDescription
idstringYes-Task ID

Result Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
dataobjectThe prediction data object containing all details
data.idstringUnique identifier for the prediction
data.modelstringModel ID used for the prediction
data.outputsarrayArray of URLs to the generated content (empty when status is not completed)
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.has_nsfw_contentsarrayArray of boolean values indicating NSFW detection for each output
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds
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