Hunyuan3d V2 Mini
Playground
Try it on WavespeedAI!Hunyuan3D-V2-Mini is a Tencent image-to-3D generative model available on WaveSpeedAI. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Features
Hunyuan3D V2 Mini — Image-to-3D
Hunyuan3D V2 Mini is part of Tencent’s open-source Hunyuan3D-2 series — a state-of-the-art 3D generation system that transforms 2D images into high-fidelity 3D models with detailed textures. This lightweight version delivers fast, affordable 3D generation without sacrificing quality.
Why It Stands Out
- Single image input: Generate complete 3D models from just one 2D image.
- Fast generation: Completes model generation in as fast as 30 seconds.
- High-fidelity output: Produces detailed 3D models with accurate geometry and high-resolution textures.
- Decoupled architecture: Separates geometry generation and texture synthesis for improved quality.
- Simple workflow: Just upload an image — no 3D expertise required.
Technical Highlights
The Hunyuan3D-2 system adopts a separated process of geometry generation + texture synthesis:
-
Geometry Generation (Hunyuan3D-DiT): Based on a flow diffusion model that generates untextured 3D geometric models, with 2.6B parameters, capable of precisely extracting geometric information from input images.
-
Texture Synthesis (Hunyuan3D-Paint): Adds high-resolution (4K) textures to geometric models, with 1.3B parameters, supporting multi-view diffusion generation technology to ensure realistic textures and consistent lighting.
By decoupling shape and texture generation, the system effectively reduces complexity and improves generation quality.
Performance and Efficiency
- Fast Generation: Completes model generation in as fast as 30 seconds.
- Accelerated Inference: The optimized version shortens inference time by 50% through guidance distillation techniques.
- Multi-modal Support: Compatible with various input methods and integrations including Blender plugins and Gradio applications.
Parameters
| Parameter | Required | Description |
|---|---|---|
| image | Yes | Source image to convert to 3D (upload or public URL). |
How to Use
- Upload your image — drag and drop a file or paste a public URL.
- Click Run and wait for the 3D model to generate.
- Preview and download the result.
Best Use Cases
- Game Development — Quickly generate 3D assets from concept art or icons.
- App & UI Design — Create 3D icons and UI elements from 2D designs.
- E-commerce — Generate 3D product models for interactive displays.
- 3D Printing — Convert 2D designs into printable 3D models.
- AR/VR Content — Generate 3D objects for augmented and virtual reality.
- Rapid Prototyping — Visualize designs in 3D without manual modeling.
Pricing
| Output | Price |
|---|---|
| Per 3D model | $0.10 |
Pro Tips for Best Quality
- Use images with clear subjects against simple or transparent backgrounds.
- Front-facing views typically produce the best results.
- Ensure the subject is well-lit and clearly visible in the image.
- Icons and objects with distinct shapes work particularly well.
- Higher resolution source images generally yield more detailed 3D models.
Notes
- Ensure uploaded image URLs are publicly accessible.
- Processing time varies based on image complexity and current queue load.
- For higher quality output, consider using the full Hunyuan3D V2 model.
- Please ensure your content complies with usage guidelines.
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/hunyuan3d/v2-mini" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{}'
# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"
Parameters
Task Submission Parameters
Request Parameters
| Parameter | Type | Required | Default | Range | Description |
|---|---|---|---|---|---|
| image | string | Yes | - | URL of image to use while generating the 3D model. |
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 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, the ID of the prediction to get |
| data.model | string | Model ID used for the prediction |
| data.outputs | string | 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.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 |