> This document showcases the performance test results of LightX2V across different hardware environments, including detailed comparison data for H200 and RTX 4090 platforms.
| **Wan2.1 Official** | Based on [Wan2.1 official repository](https://github.com/Wan-Video/Wan2.1) original implementation |
| **FastVideo** | Based on [FastVideo official repository](https://github.com/hao-ai-lab/FastVideo), using SageAttention2 backend optimization |
| **LightX2V_1** | Uses SageAttention2 to replace native attention mechanism, adopts DIT BF16+FP32 (partial sensitive layers) mixed precision computation, improving computational efficiency while maintaining precision |
| **LightX2V_2** | Unified BF16 precision computation, further reducing memory usage and computational overhead while maintaining generation quality |
| **LightX2V_3** | Introduces FP8 quantization technology to significantly reduce computational precision requirements, combined with Tiling VAE technology to optimize memory usage |
| **LightX2V_3-Distill** | Based on LightX2V_3 using 4-step distillation model(`infer_steps=4`, `enable_cfg=False`), further reducing inference steps while maintaining generation quality |
| **LightX2V_4** | Based on LightX2V_3 with TeaCache(teacache_thresh=0.2) caching reuse technology, achieving acceleration through intelligent redundant computation skipping |
| **Wan2GP(profile=3)** | Implementation based on [Wan2GP repository](https://github.com/deepbeepmeep/Wan2GP), using MMGP optimization technology. Profile=3 configuration is suitable for RTX 3090/4090 environments with at least 32GB RAM and 24GB VRAM, adapting to limited memory resources by sacrificing VRAM. Uses quantized models: [480P model](https://huggingface.co/DeepBeepMeep/Wan2.1/blob/main/wan2.1_image2video_480p_14B_quanto_mbf16_int8.safetensors) and [720P model](https://huggingface.co/DeepBeepMeep/Wan2.1/blob/main/wan2.1_image2video_720p_14B_quanto_mbf16_int8.safetensors) |
| **LightX2V_5** | Uses SageAttention2 to replace native attention mechanism, adopts DIT FP8+FP32 (partial sensitive layers) mixed precision computation, enables CPU offload technology, executes partial sensitive layers with FP32 precision, asynchronously offloads DIT inference process data to CPU, saves VRAM, with block-level offload granularity |
| **LightX2V_5-Distill** | Based on LightX2V_5 using 4-step distillation model(`infer_steps=4`, `enable_cfg=False`), further reducing inference steps while maintaining generation quality |
| **LightX2V_6** | Based on LightX2V_3 with CPU offload technology enabled, executes partial sensitive layers with FP32 precision, asynchronously offloads DIT inference process data to CPU, saves VRAM, with block-level offload granularity |
| **LightX2V_6-Distill** | Based on LightX2V_6 using 4-step distillation model(`infer_steps=4`, `enable_cfg=False`), further reducing inference steps while maintaining generation quality |
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*Coming soon...*
## 📁 Configuration Files Reference
### 720P 5s Video
Benchmark-related configuration files and execution scripts are available at:
*Coming soon...*
| Type | Link | Description |
|:-----|:-----|:------------|
| **Configuration Files** | [configs/bench](https://github.com/ModelTC/LightX2V/tree/main/configs/bench) | Contains JSON files with various optimization configurations |
-**Wan2.1 Official**: Based on [Wan2.1 official repository](https://github.com/Wan-Video/Wan2.1)
-**FastVideo**: Based on [FastVideo official repository](https://github.com/hao-ai-lab/FastVideo), using SageAttention backend
-**LightX2V_1**: Uses SageAttention2 to replace native attention mechanism, adopts DIT BF16+FP32 (partial sensitive layers) mixed precision computation, improving computational efficiency while maintaining precision
-**LightX2V_2**: Unified BF16 precision computation, further reducing memory usage and computational overhead while maintaining generation quality
-**LightX2V_3**: Introduces FP8 quantization technology to significantly reduce computational precision requirements, combined with Tiling VAE technology to optimize memory usage
-**LightX2V_3-Distill**: Based on LightX2V_3 using 4-step distillation model(`infer_step=4`, `enable_cfg=False`), further reducing inference steps while maintaining generation quality.
-**LightX2V_4**: Based on LightX2V_3 with TeaCache(teacache_thresh=0.2) caching reuse technology, achieving acceleration through intelligent redundant computation skipping
-**Configuration Files Reference**: Benchmark-related configuration files and execution scripts are available at:
-[Configuration Files](https://github.com/ModelTC/LightX2V/tree/main/configs/bench) - Contains JSON files with various optimization configurations
--prompt"A close-up cinematic view of a person cooking in a warm,sunlit kitchen, using a wooden spatula to stir-fry a colorful mix of freshvegetables—carrots, broccoli, and bell peppers—in a black frying pan on amodern induction stove. The scene captures the glistening texture of thevegetables, steam gently rising, and subtle reflections on the stove surface.In the background, soft-focus jars, fruits, and a window with natural daylightcreate a cozy atmosphere. The hand motions are smooth and rhythmic, with a realisticsense of motion blur and lighting."\
--prompt"A close-up cinematic view of a person cooking in a warm,sunlit kitchen, using a wooden spatula to stir-fry a colorful mix of freshvegetables—carrots, broccoli, and bell peppers—in a black frying pan on amodern induction stove. The scene captures the glistening texture of thevegetables, steam gently rising, and subtle reflections on the stove surface.In the background, soft-focus jars, fruits, and a window with natural daylightcreate a cozy atmosphere. The hand motions are smooth and rhythmic, with a realisticsense of motion blur and lighting."\
--prompt"A close-up cinematic view of a person cooking in a warm,sunlit kitchen, using a wooden spatula to stir-fry a colorful mix of freshvegetables—carrots, broccoli, and bell peppers—in a black frying pan on amodern induction stove. The scene captures the glistening texture of thevegetables, steam gently rising, and subtle reflections on the stove surface.In the background, soft-focus jars, fruits, and a window with natural daylightcreate a cozy atmosphere. The hand motions are smooth and rhythmic, with a realisticsense of motion blur and lighting."\
--prompt"A close-up cinematic view of a person cooking in a warm,sunlit kitchen, using a wooden spatula to stir-fry a colorful mix of freshvegetables—carrots, broccoli, and bell peppers—in a black frying pan on amodern induction stove. The scene captures the glistening texture of thevegetables, steam gently rising, and subtle reflections on the stove surface.In the background, soft-focus jars, fruits, and a window with natural daylightcreate a cozy atmosphere. The hand motions are smooth and rhythmic, with a realisticsense of motion blur and lighting."\
