Hunyuan 3D
Generate 3D models from your images using Hunyuan 3D. A native 3D generative model enabling versatile and high-quality 3D asset creation.
Features
Hunyuan3D-V2-Multi-View is a state-of-the-art image-to-3D generative model developed by Tencent and now available on WaveSpeedAI. This model transforms single images into high-quality 3D assets by generating multi-view RGB images and reconstructing detailed 3D structures with realistic textures.
Key Features
- High-Fidelity 3D Generation: Produces detailed and accurate 3D models from single images.
- Multi-View Diffusion Framework: Utilizes a two-stage process involving multi-view image generation and 3D reconstruction.
- Rapid Processing: Generates multi-view images in approximately 4 seconds and reconstructs 3D assets in about 7 seconds.
- Versatile Applications: Suitable for various use cases, including game development, animation, and virtual reality.
- Open-Source Accessibility: Available for use and modification, fostering community collaboration and innovation.
ComfyUI
Hunyuan3D-V2-Multi-View 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
- Creative Focus: Designed primarily for creative 3D asset generation; not intended for precise scientific or engineering applications.
- Inherent Biases: Outputs may reflect biases present in the training data.
- Input Sensitivity: The quality and consistency of generated 3D models depend significantly on the quality of the input image; subtle variations may lead to output variability.
- Resource Requirements: High-resolution outputs may require substantial computational resources.
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/v2/wavespeed-ai/hunyuan3d-v2-multi-view" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
"front_image_url": "https://d2g64w682n9w0w.cloudfront.net/media/images/1744448061406038945_mD5ACGR3.jpg",
"back_image_url": "https://d2g64w682n9w0w.cloudfront.net/media/images/1744448063989972538_psGEBxuq.jpg",
"left_image_url": "https://d2g64w682n9w0w.cloudfront.net/media/images/1744448066831654669_1hCzuqnj.jpg",
"num_inference_steps": 50,
"guidance_scale": 7.5,
"octree_resolution": 256,
"textured_mesh": false
}'
# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v2/wavespeed-ai/hunyuan3d-v2-multi-view/requests/${requestId}" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"
Parameters
Task Submission Parameters
Request Parameters
Parameter | Type | Required | Default | Range | Description |
---|---|---|---|---|---|
front_image_url | string | Yes | https://d2g64w682n9w0w.cloudfront.net/media/images/1744448061406038945_mD5ACGR3.jpg | - | URL of image to use while generating the 3D model. |
back_image_url | string | Yes | https://d2g64w682n9w0w.cloudfront.net/media/images/1744448063989972538_psGEBxuq.jpg | - | URL of image to use while generating the 3D model. |
left_image_url | string | Yes | https://d2g64w682n9w0w.cloudfront.net/media/images/1744448066831654669_1hCzuqnj.jpg | - | URL of image to use while generating the 3D model. |
seed | integer | No | - | -1 ~ 9999999999 | The same seed and the same prompt given to the same version of the model will output the same image every time. |
num_inference_steps | integer | No | 50 | 1 ~ 50 | Number of inference steps to perform. |
guidance_scale | number | No | 7.5 | 0.0 ~ 20.0 | Guidance scale for the model. |
octree_resolution | integer | No | 256 | 1 ~ 1024 | Octree resolution for the model. |
textured_mesh | boolean | No | false | - | If set true, textured mesh will be generated and the price charged would be 3 times that of white mesh. |
Response Parameters
Parameter | Type | Description |
---|---|---|
code | integer | HTTP status code (e.g., 200 for success) |
message | string | Status message (e.g., “success”) |
data.id | string | Unique identifier for the prediction, Task Id |
data.model | string | Model ID used for the prediction |
data.outputs | array | Array of URLs to the generated content (empty when status is not completed ) |
data.urls | object | Object containing related API endpoints |
data.urls.get | string | URL to retrieve the prediction result |
data.has_nsfw_contents | array | Array of boolean values indicating NSFW detection for each output |
data.status | string | Status of the task: created , processing , completed , or failed |
data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
data.error | string | Error message (empty if no error occurred) |
data.timings | object | Object containing timing details |
data.timings.inference | integer | Inference time in milliseconds |
Result Query Parameters
Result Request Parameters
Parameter | Type | Required | Default | Description |
---|---|---|---|---|
id | string | Yes | - | Task ID |
Result Response Parameters
Parameter | Type | Description |
---|---|---|
code | integer | HTTP status code (e.g., 200 for success) |
message | string | Status message (e.g., “success”) |
data | object | The prediction data object containing all details |
data.id | string | Unique identifier for the prediction |
data.model | string | Model ID used for the prediction |
data.outputs | array | Array of URLs to the generated content (empty when status is not completed ) |
data.urls | object | Object containing related API endpoints |
data.urls.get | string | URL to retrieve the prediction result |
data.has_nsfw_contents | array | Array of boolean values indicating NSFW detection for each output |
data.status | string | Status of the task: created , processing , completed , or failed |
data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
data.error | string | Error message (empty if no error occurred) |
data.timings | object | Object containing timing details |
data.timings.inference | integer | Inference time in milliseconds |