Hy1.5-Distill-Models

🎬 Hy1.5-Distill-Models

<img src="https://raw.githubusercontent.com/ModelTC/LightX2V/main/assets/img_lightx2v.png" width="75%" />

---

🤗 HuggingFace | GitHub | License

---

This repository contains 4-step distilled models for HunyuanVideo-1.5 optimized for use with LightX2V. These distilled models enable ultra-fast 4-step inference without CFG (Classifier-Free Guidance), significantly reducing generation time while maintaining high-quality video output.

📋 Model List

4-Step Distilled Models

• hy1.5_t2v_480p_lightx2v_4step.safetensors - 480p Text-to-Video 4-step distilled model (16.7 GB)
• hy1.5_t2v_480p_scaled_fp8_e4m3_lightx2v_4step.safetensors - 480p Text-to-Video 4-step distilled model with FP8 quantization (8.85 GB)

🚀 Quick Start

Installation

First, install LightX2V:

pip install -v git+https://github.com/ModelTC/LightX2V.git

Or build from source:

git clone https://github.com/ModelTC/LightX2V.git
cd LightX2V
pip install -v -e .

Download Models

Download the distilled models from this repository:

# Using git-lfs
git lfs install
git clone https://huggingface.co/lightx2v/Hy1.5-Distill-Models

# Or download individual files using huggingface-hub
pip install huggingface-hub
python -c "from huggingface_hub import hf_hub_download; hf_hub_download(repo_id='lightx2v/Hy1.5-Distill-Models', filename='hy1.5_t2v_480p_lightx2v_4step.safetensors', local_dir='./models')"

💻 Usage in LightX2V

4-Step Distilled Model (Base Version)

"""
HunyuanVideo-1.5 text-to-video generation example.
This example demonstrates how to use LightX2V with HunyuanVideo-1.5 4-step distilled model for T2V generation.
"""

from lightx2v import LightX2VPipeline

# Initialize pipeline for HunyuanVideo-1.5
pipe = LightX2VPipeline(
    model_path="/path/to/hunyuanvideo-1.5/",  # Original model path
    model_cls="hunyuan_video_1.5",
    transformer_model_name="480p_t2v",
    task="t2v",
    # 4-step distilled model ckpt
    dit_original_ckpt="/path/to/hy1.5_t2v_480p_lightx2v_4step.safetensors"
)

# Alternative: create generator from config JSON file
# pipe.create_generator(config_json="../configs/hunyuan_video_15/hunyuan_video_t2v_480p.json")

# Enable offloading to significantly reduce VRAM usage with minimal speed impact
# Suitable for RTX 30/40/50 consumer GPUs
pipe.enable_offload(
    cpu_offload=True,
    offload_granularity="block",  # For HunyuanVideo-1.5, only "block" is supported
    text_encoder_offload=True,
    image_encoder_offload=False,
    vae_offload=False,
)

# Optional: Use lighttae
# pipe.enable_lightvae(
#     use_tae=True,
#     tae_path="/path/to/lighttaehy1_5.safetensors",
#     use_lightvae=False,
#     vae_path=None,
# )

# Create generator with specified parameters
# Note: 4-step distillation requires infer_steps=4, guidance_scale=1, and denoising_step_list
pipe.create_generator(
    attn_mode="sage_attn2",
    infer_steps=4,  # 4-step inference
    num_frames=81,
    guidance_scale=1,  # No CFG needed for distilled models
    sample_shift=9.0,
    aspect_ratio="16:9",
    fps=16,
    denoising_step_list=[1000, 750, 500, 250]  # Required for 4-step distillation
)

# Generation parameters
seed = 123
prompt = "A close-up shot captures a scene on a polished, light-colored granite kitchen counter, illuminated by soft natural light from an unseen window. Initially, the frame focuses on a tall, clear glass filled with golden, translucent apple juice standing next to a single, shiny red apple with a green leaf still attached to its stem. The camera moves horizontally to the right. As the shot progresses, a white ceramic plate smoothly enters the frame, revealing a fresh arrangement of about seven or eight more apples, a mix of vibrant reds and greens, piled neatly upon it. A shallow depth of field keeps the focus sharply on the fruit and glass, while the kitchen backsplash in the background remains softly blurred. The scene is in a realistic style."
negative_prompt = ""
save_result_path = "/path/to/save_results/output.mp4"

# Generate video
pipe.generate(
    seed=seed,
    prompt=prompt,
    negative_prompt=negative_prompt,
    save_result_path=save_result_path,
)

4-Step Distilled Model with FP8 Quantization

For even lower memory usage, use the FP8 quantized version:

from lightx2v import LightX2VPipeline

# Initialize pipeline
pipe = LightX2VPipeline(
    model_path="/path/to/hunyuanvideo-1.5/",  # Original model path
    model_cls="hunyuan_video_1.5",
    transformer_model_name="480p_t2v",
    task="t2v",
    # 4-step distilled model ckpt
    dit_original_ckpt="/path/to/hy1.5_t2v_480p_lightx2v_4step.safetensors"
)

# Enable FP8 quantization for the distilled model
pipe.enable_quantize(
    quant_scheme='fp8-sgl',
    dit_quantized=True,
    dit_quantized_ckpt="/path/to/hy1.5_t2v_480p_scaled_fp8_e4m3_lightx2v_4step.safetensors",
    text_encoder_quantized=False,  # Optional: can also quantize text encoder
    text_encoder_quantized_ckpt="/path/to/hy15_qwen25vl_llm_encoder_fp8_e4m3_lightx2v.safetensors",  # Optional
    image_encoder_quantized=False,
)

# Enable offloading for lower VRAM usage
pipe.enable_offload(
    cpu_offload=True,
    offload_granularity="block",
    text_encoder_offload=True,
    image_encoder_offload=False,
    vae_offload=False,
)

# Create generator
pipe.create_generator(
    attn_mode="sage_attn2",
    infer_steps=4,
    num_frames=81,
    guidance_scale=1,
    sample_shift=9.0,
    aspect_ratio="16:9",
    fps=16,
    denoising_step_list=[1000, 750, 500, 250]
)

# Generate video
pipe.generate(
    seed=123,
    prompt="Your prompt here",
    negative_prompt="",
    save_result_path="/path/to/output.mp4",
)

⚙️ Key Features

4-Step Distillation

These models use step distillation technology to compress the original 50-step inference process into just 4 steps, providing:

• 🚀 Ultra-Fast Inference: Generate videos in a fraction of the time
• 💡 No CFG Required: Set guidance_scale=1 (no classifier-free guidance needed)
• 📊 Quality Preservation: Maintains high visual quality despite fewer steps
• 💾 Lower Memory: Reduced computational requirements

FP8 Quantization (Optional)

The FP8 quantized version (hy1.5_t2v_480p_scaled_fp8_e4m3_lightx2v_4step.safetensors) provides additional benefits:

• 50% Memory Reduction: Further reduces VRAM usage
• Faster Computation: Optimized quantized kernels
• Maintained Quality: FP8 quantization preserves visual quality

Requirements

For FP8 quantized models, you need to install the SGL kernel:

# Requires torch == 2.8.0
pip install sgl-kernel --upgrade

Alternatively, you can use VLLM kernels:

pip install vllm

📊 Performance Benefits

Using 4-step distilled models provides:

• ~25x Speedup: Compared to standard 50-step inference
• Lower VRAM Requirements: Enables running on GPUs with less memory
• No CFG Overhead: Eliminates the need for classifier-free guidance computation
• Production Ready: Fast enough for real-time or near-real-time applications

🔗 Related Resources

• LightX2V GitHub Repository
• LightX2V Documentation
• HunyuanVideo-1.5 Original Model
• Hy1.5-Quantized-Models - For quantized inference without distillation
• LightX2V Examples
• Step Distillation Documentation

📝 Important Notes

• Critical Configuration:

  • Must set infer_steps=4 (not the default 50)
  • Must set guidance_scale=1 (CFG is not used in distilled models)
  • Must provide denoising_step_list=[1000, 750, 500, 250]

• Model Loading: All advanced configurations (including enable_quantize() and enable_offload()) must be called before create_generator(), otherwise they will not take effect.

• Original Model Required: The original HunyuanVideo-1.5 model weights are still required. The distilled model is used in conjunction with the original model structure.

• Attention Mode: For best performance, we recommend using SageAttention 2 (sage_attn2) as the attention mode.

• Resolution: Currently supports 480p resolution. Higher resolutions may be available in future releases.

🤝 Citation

If you use these distilled models in your research, please cite:

@misc{lightx2v,
  author = {LightX2V Contributors},
  title = {LightX2V: Light Video Generation Inference Framework},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/ModelTC/lightx2v}},
}

📄 License

This model is released under the Apache 2.0 License, same as the original HunyuanVideo-1.5 model.