Wan T2V 720p Ultra Fast
Turbo-charged inference for Wan 2.1 14B. Unleashing high-res text-to-video prowess with cutting-edge suite of video foundation models
Features
wan-2.1/t2v-720p-ultra-fast is an open-source AI video generation model developed by Alibaba Cloud, designed for text-to-video tasks.The 14-billion-parameter professional version excels in generating complex motions and simulating physical dynamics, delivering exceptional performance.
Built upon a causal 3D Variational Autoencoder (VAE) and Video Diffusion Transformer architecture, wan-2.1/t2v-720p-ultra-fast efficiently models spatiotemporal dependencies. In the authoritative VBench evaluation, the 14B version achieved a leading score of 86.22%, surpassing models like Sora, Luma, and Pika, and securing the top position. The model is available on Wavespeed AI, providing convenient access for developers.Leveraging cutting-edge acceleration techniques, wan-2.1/t2v-720p-ultra-fast pushes the limits of rapid video synthesis for creative and practical applications.
Key Features
- Ultra-Fast Inference: Optimized with advanced acceleration techniques, this model offers significantly faster generation times compared to the standard 720p variant—making it ideal for rapid iteration and real-time content creation.
- High-Resolution 720p Output: Produces sharp, vivid, and consistent 720p video content with dynamic motion and scene composition.
- Multilingual Text Generation: Supports the embedding of Chinese and English text in generated videos, broadening its versatility across international applications.
- State-of-the-Art Model Performance: Consistently outperforms leading open-source and commercial solutions in benchmarks for video quality, coherence, and responsiveness to text prompts.
ComfyUI
wan-2.1/t2v-720p-ultra-fast 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 Only: This model is built for imaginative video generation and should not be used for factually accurate or verifiable content.
- Training Bias: As with all data-driven systems, outputs may reflect biases present in the training datasets.
- Prompt Sensitivity: The quality of results may vary based on the specificity and clarity of the text prompt.
- Narrow Task Scope: Exclusively designed for text-to-video generation at 720p resolution. It does not support image-to-video tasks or video editing.
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/wan-2.1/t2v-720p-ultra-fast" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
"prompt": "4K Mars colony panorama: Dome cities under dust storm, rover convoys with realistic tread marks, sci-fi realism",
"negative_prompt": "",
"size": "1280*720",
"num_inference_steps": 30,
"duration": 5,
"guidance_scale": 5,
"flow_shift": 5,
"seed": -1,
"enable_safety_checker": true
}'
# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v2/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"
Parameters
Task Submission Parameters
Request Parameters
Parameter | Type | Required | Default | Range | Description |
---|---|---|---|---|---|
prompt | string | Yes | 4K Mars colony panorama: Dome cities under dust storm, rover convoys with realistic tread marks, sci-fi realism | - | The prompt for generating the output. |
negative_prompt | string | No | - | - | The negative prompt for generating the output. |
size | string | No | 1280*720 | 1280*720, 720*1280 | The size of the output. |
num_inference_steps | integer | No | 30 | 0 ~ 40 | The number of inference steps. |
duration | integer | No | 5 | 5 ~ 10 | Generate video duration length seconds. |
guidance_scale | number | No | 5 | 0.0 ~ 10.0 | The guidance scale for generation. |
flow_shift | number | No | 5 | 1.0 ~ 10.0 | The shift value for the timestep schedule for flow matching. |
seed | integer | No | -1 | -1 ~ 9999999999 | The seed for random number generation. |
enable_safety_checker | boolean | No | true | - | Whether to enable the safety checker. |
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 |