Wan2.1-VACE-14B-FusionX-Diffusers

10
license:apache-2.0
by
Alrott
Image Model
OTHER
14B params
New
10 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
32GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
14GB+ RAM

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by Wan2.1-VACE-14B-FusionX-Diffusers with quality assessment

Specialized For

general
multilingual

Training Datasets (1)

c4
🔵 6/10
general
multilingual
Key Strengths
  • •Scale and Accessibility: 750GB of publicly available, filtered text
  • •Systematic Filtering: Documented heuristics enable reproducibility
  • •Language Diversity: Despite English-only, captures diverse writing styles
Considerations
  • •English-Only: Limits multilingual applications
  • •Filtering Limitations: Offensive content and low-quality text remain despite filtering

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Quickstartbash
git clone https://github.com/Wan-Video/Wan2.1.git
cd Wan2.1
Quickstartbash
# Ensure torch >= 2.4.0
pip install -r requirements.txt
text
Download models using modelscope-cli:
text
#### Run Text-to-Video Generation

This repository supports two Text-to-Video models (1.3B and 14B) and two resolutions (480P and 720P). The parameters and configurations for these models are as follows:

<table>
    <thead>
        <tr>
            <th rowspan="2">Task</th>
            <th colspan="2">Resolution</th>
            <th rowspan="2">Model</th>
        </tr>
        <tr>
            <th>480P</th>
            <th>720P</th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td>t2v-14B</td>
            <td style="color: green;">✔️</td>
            <td style="color: green;">✔️</td>
            <td>Wan2.1-T2V-14B</td>
        </tr>
        <tr>
            <td>t2v-1.3B</td>
            <td style="color: green;">✔️</td>
            <td style="color: red;">❌</td>
            <td>Wan2.1-T2V-1.3B</td>
        </tr>
    </tbody>
</table>


##### (1) Without Prompt Extension

To facilitate implementation, we will start with a basic version of the inference process that skips the [prompt extension](#2-using-prompt-extention) step.

- Single-GPU inference

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