Add hy distilled model infer example

README.md

@@ -20,6 +20,8 @@
 
 ## :fire: Latest News
 
+- **November 24, 2025:** 🚀 We released 4-step distilled models for HunyuanVideo-1.5! These models enable **ultra-fast 4-step inference** without CFG requirements, achieving approximately **25x speedup** compared to standard 50-step inference. Both base and FP8 quantized versions are now available: [Hy1.5-Distill-Models](https://huggingface.co/lightx2v/Hy1.5-Distill-Models).
+
 - **November 21, 2025:** 🚀 We support the [HunyuanVideo-1.5](https://huggingface.co/tencent/HunyuanVideo-1.5) video generation model since Day 0. With the same number of GPUs, LightX2V can achieve a speed improvement of over 2 times and supports deployment on GPUs with lower memory (such as the 24GB RTX 4090). It also supports CFG/Ulysses parallelism, efficient offloading, TeaCache/MagCache technologies, and more. We will soon update more models on our [HuggingFace page](https://huggingface.co/lightx2v), including step distillation, VAE distillation, and other related models. Quantized models and lightweight VAE models are now available: [Hy1.5-Quantized-Models](https://huggingface.co/lightx2v/Hy1.5-Quantized-Models) for quantized inference, and [LightTAE for HunyuanVideo-1.5](https://huggingface.co/lightx2v/Autoencoders/blob/main/lighttaehy1_5.safetensors) for fast VAE decoding. Refer to [this](https://github.com/ModelTC/LightX2V/tree/main/scripts/hunyuan_video_15) for usage tutorials, or check out the [examples directory](https://github.com/ModelTC/LightX2V/tree/main/examples) for code examples.
 
 ## 💡 Quick Start

README_zh.md

@@ -20,6 +20,8 @@
 
 ## :fire: 最新动态
 
+- **2025年11月24日:** 🚀 我们发布了HunyuanVideo-1.5的4步蒸馏模型！这些模型支持**超快速4步推理**，无需CFG配置，相比标准50步推理可实现约**25倍加速**。现已提供基础版本和FP8量化版本：[Hy1.5-Distill-Models](https://huggingface.co/lightx2v/Hy1.5-Distill-Models)。
+
 - **2025年11月21日:** 🚀 我们Day0支持了[HunyuanVideo-1.5](https://huggingface.co/tencent/HunyuanVideo-1.5)的视频生成模型，同样GPU数量，LightX2V可带来约2倍以上的速度提升，并支持更低显存GPU部署(如24G RTX4090)。支持CFG并行/Ulysses并行，高效Offload，TeaCache/MagCache等技术。同时支持沐曦，寒武纪等国产芯片部署。我们很快将在我们的[HuggingFace主页](https://huggingface.co/lightx2v)更新更多模型，包括步数蒸馏，VAE蒸馏等相关模型。量化模型和轻量VAE模型现已可用：[Hy1.5-Quantized-Models](https://huggingface.co/lightx2v/Hy1.5-Quantized-Models)用于量化推理，[HunyuanVideo-1.5轻量TAE](https://huggingface.co/lightx2v/Autoencoders/blob/main/lighttaehy1_5.safetensors)用于快速VAE解码。使用教程参考[这里](https://github.com/ModelTC/LightX2V/tree/main/scripts/hunyuan_video_15)，或查看[示例目录](https://github.com/ModelTC/LightX2V/tree/main/examples)获取代码示例。
 
 

examples/hunyuan_video/hunyuan_t2v_distill.py

+"""
+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/ckpts/hunyuanvideo-1.5/",
+    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_720p.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,
+)
+
+# 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
+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])
+
+
+# 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 = "/data/nvme0/gushiqiao/LightX2V/save_results/output.mp4"
+
+# Generate video
+pipe.generate(
+    seed=seed,
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    save_result_path=save_result_path,
+)