| # Models Overview |
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| WanGP supports multiple video generation models, each optimized for different use cases and hardware configurations. |
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| Most models can combined with Loras Accelerators (check the Lora guide) to accelerate the generation of a video x2 or x3 with little quality loss |
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| ## Wan 2.1 Text2Video Models |
| Please note that that the term *Text2Video* refers to the underlying Wan architecture but as it has been greatly improved overtime many derived Text2Video models can now generate videos using images. |
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| #### Wan 2.1 Text2Video 1.3B |
| - **Size**: 1.3 billion parameters |
| - **VRAM**: 6GB minimum |
| - **Speed**: Fast generation |
| - **Quality**: Good quality for the size |
| - **Best for**: Quick iterations, lower-end hardware |
| - **Command**: `python wgp.py --t2v-1-3B` |
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| #### Wan 2.1 Text2Video 14B |
| - **Size**: 14 billion parameters |
| - **VRAM**: 12GB+ recommended |
| - **Speed**: Slower but higher quality |
| - **Quality**: Excellent detail and coherence |
| - **Best for**: Final production videos |
| - **Command**: `python wgp.py --t2v-14B` |
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| #### Wan Vace 1.3B |
| - **Type**: ControlNet for advanced video control |
| - **VRAM**: 6GB minimum |
| - **Features**: Motion transfer, object injection, inpainting |
| - **Best for**: Advanced video manipulation |
| - **Command**: `python wgp.py --vace-1.3B` |
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| #### Wan Vace 14B |
| - **Type**: Large ControlNet model |
| - **VRAM**: 12GB+ recommended |
| - **Features**: All Vace features with higher quality |
| - **Best for**: Professional video editing workflows |
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| #### MoviiGen (Experimental) |
| - **Resolution**: Claims 1080p capability |
| - **VRAM**: 20GB+ required |
| - **Speed**: Very slow generation |
| - **Features**: Should generate cinema like video, specialized for 2.1 / 1 ratios |
| - **Status**: Experimental, feedback welcome |
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| ## Wan 2.1 Image-to-Video Models |
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| #### Wan 2.1 Image2Video 14B |
| - **Size**: 14 billion parameters |
| - **VRAM**: 12GB+ recommended |
| - **Speed**: Slower but higher quality |
| - **Quality**: Excellent detail and coherence |
| - **Best for**: Most Loras available work with this model |
| - **Command**: `python wgp.py --i2v-14B` |
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| #### FLF2V |
| - **Type**: Start/end frame specialist |
| - **Resolution**: Optimized for 720p |
| - **Official**: Wan team supported |
| - **Use case**: Image-to-video with specific endpoints |
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| ## Wan 2.1 Specialized Models |
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| #### Multitalk |
| - **Type**: Multi Talking head animation |
| - **Input**: Voice track + image |
| - **Works on**: People |
| - **Use case**: Lip-sync and voice-driven animation for up to two people |
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| #### FantasySpeaking |
| - **Type**: Talking head animation |
| - **Input**: Voice track + image |
| - **Works on**: People and objects |
| - **Use case**: Lip-sync and voice-driven animation |
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| #### Phantom |
| - **Type**: Person/object transfer |
| - **Resolution**: Works well at 720p |
| - **Requirements**: 30+ steps for good results |
| - **Best for**: Transferring subjects between videos |
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| #### Recam Master |
| - **Type**: Viewpoint change |
| - **Requirements**: 81+ frame input videos, 15+ denoising steps |
| - **Use case**: View same scene from different angles |
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| #### Sky Reels v2 Diffusion |
| - **Type**: Diffusion Forcing model |
| - **Specialty**: "Infinite length" videos |
| - **Features**: High quality continuous generation |
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| ## Wan Fun InP Models |
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| #### Wan Fun InP 1.3B |
| - **Size**: 1.3 billion parameters |
| - **VRAM**: 6GB minimum |
| - **Quality**: Good for the size, accessible to lower hardware |
| - **Best for**: Entry-level image animation |
| - **Command**: `python wgp.py --i2v-1-3B` |
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| #### Wan Fun InP 14B |
| - **Size**: 14 billion parameters |
| - **VRAM**: 12GB+ recommended |
| - **Quality**: Better end image support |
| - **Limitation**: Existing loras don't work as well |
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| ## Hunyuan Video Models |
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| #### Hunyuan Video Text2Video |
| - **Quality**: Among the best open source t2v models |
| - **VRAM**: 12GB+ recommended |
| - **Speed**: Slower generation but excellent results |
| - **Features**: Superior text adherence and video quality, up to 10s of video |
| - **Best for**: High-quality text-to-video generation |
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| #### Hunyuan Video Custom |
| - **Specialty**: Identity preservation |
| - **Use case**: Injecting specific people into videos |
| - **Quality**: Excellent for character consistency |
| - **Best for**: Character-focused video generation |
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| #### Hunyuan Video Avater |
| - **Specialty**: Generate up to 15s of high quality speech / song driven Video . |
| - **Use case**: Injecting specific people into videos |
| - **Quality**: Excellent for character consistency |
| - **Best for**: Character-focused video generation, Video synchronized with voice |
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| ## LTX Video Models |
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| #### LTX Video 13B |
| - **Specialty**: Long video generation |
| - **Resolution**: Fast 720p generation |
| - **VRAM**: Optimized by WanGP (4x reduction in requirements) |
| - **Best for**: Longer duration videos |
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| #### LTX Video 13B Distilled |
| - **Speed**: Generate in less than one minute |
| - **Quality**: Very high quality despite speed |
| - **Best for**: Rapid prototyping and quick results |
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| ## Model Selection Guide |
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| ### By Hardware (VRAM) |
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| #### 6-8GB VRAM |
| - Wan 2.1 T2V 1.3B |
| - Wan Fun InP 1.3B |
| - Wan Vace 1.3B |
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| #### 10-12GB VRAM |
| - Wan 2.1 T2V 14B |
| - Wan Fun InP 14B |
| - Hunyuan Video (with optimizations) |
| - LTX Video 13B |
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| #### 16GB+ VRAM |
| - All models supported |
| - Longer videos possible |
| - Higher resolutions |
| - Multiple simultaneous Loras |
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| #### 20GB+ VRAM |
| - MoviiGen (experimental 1080p) |
| - Very long videos |
| - Maximum quality settings |
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| ### By Use Case |
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| #### Quick Prototyping |
| 1. **LTX Video 13B Distilled** - Fastest, high quality |
| 2. **Wan 2.1 T2V 1.3B** - Fast, good quality |
| 3. **CausVid Lora** - 4-12 steps, very fast |
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| #### Best Quality |
| 1. **Hunyuan Video** - Overall best t2v quality |
| 2. **Wan 2.1 T2V 14B** - Excellent Wan quality |
| 3. **Wan Vace 14B** - Best for controlled generation |
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| #### Advanced Control |
| 1. **Wan Vace 14B/1.3B** - Motion transfer, object injection |
| 2. **Phantom** - Person/object transfer |
| 3. **FantasySpeaking** - Voice-driven animation |
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| #### Long Videos |
| 1. **LTX Video 13B** - Specialized for length |
| 2. **Sky Reels v2** - Infinite length videos |
| 3. **Wan Vace + Sliding Windows** - Up to 1 minute |
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| #### Lower Hardware |
| 1. **Wan Fun InP 1.3B** - Image-to-video |
| 2. **Wan 2.1 T2V 1.3B** - Text-to-video |
| 3. **Wan Vace 1.3B** - Advanced control |
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| ## Performance Comparison |
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| ### Speed (Relative) |
| 1. **CausVid Lora** (4-12 steps) - Fastest |
| 2. **LTX Video Distilled** - Very fast |
| 3. **Wan 1.3B models** - Fast |
| 4. **Wan 14B models** - Medium |
| 5. **Hunyuan Video** - Slower |
| 6. **MoviiGen** - Slowest |
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| ### Quality (Subjective) |
| 1. **Hunyuan Video** - Highest overall |
| 2. **Wan 14B models** - Excellent |
| 3. **LTX Video models** - Very good |
| 4. **Wan 1.3B models** - Good |
| 5. **CausVid** - Good (varies with steps) |
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| ### VRAM Efficiency |
| 1. **Wan 1.3B models** - Most efficient |
| 2. **LTX Video** (with WanGP optimizations) |
| 3. **Wan 14B models** |
| 4. **Hunyuan Video** |
| 5. **MoviiGen** - Least efficient |
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| ## Model Switching |
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| WanGP allows switching between models without restarting: |
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| 1. Use the dropdown menu in the web interface |
| 2. Models are loaded on-demand |
| 3. Previous model is unloaded to save VRAM |
| 4. Settings are preserved when possible |
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| ## Tips for Model Selection |
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| ### First Time Users |
| Start with **Wan 2.1 T2V 1.3B** to learn the interface and test your hardware. |
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| ### Production Work |
| Use **Hunyuan Video** or **Wan 14B** models for final output quality. |
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| ### Experimentation |
| **CausVid Lora** or **LTX Distilled** for rapid iteration and testing. |
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| ### Specialized Tasks |
| - **VACE** for advanced control |
| - **FantasySpeaking** for talking heads |
| - **LTX Video** for long sequences |
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| ### Hardware Optimization |
| Always start with the largest model your VRAM can handle, then optimize settings for speed vs quality based on your needs. |
