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HunyuanVideo-1.5

🎬 HunyuanVideo-1.5: A leading lightweight video generation model

HunyuanVideo-1.5 is a video generation model that delivers top-tier quality with only 8.3B parameters, significantly lowering the barrier to usage. It runs smoothly on consumer-grade GPUs, making it accessible for every developer and creator. This repository provides the implementation and tools needed to generate creative videos.

👏 Join our WeChat and Discord | 💻 Official website Try our model!

🔥🔥🔥 News

👋 Nov 20, 2025: We release the inference code and model weights of HunyuanVideo.

🎥 Demo

🧩 Community Contributions

If you develop/use HunyuanVideo in your projects, welcome to let us know.

ComfyUI - ComfyUI: A powerful and modular diffusion model GUI with a graph/nodes interface. ComfyUI supports HunyuanVideo-1.5 with various engineering optimizations for fast inference.
LightX2V - LightX2V: A lightweight and efficient video generation framework that integrates HunyuanVideo-1.5, supporting multiple engineering acceleration techniques for fast inference.

📑 Open-source Plan

HunyuanVideo-1.5 (T2V/I2V)
- Inference Code and checkpoints
- Diffusers Support
- Release all model weights (Sparse attention, distill model, and SR models)

📋 Table of Contents

🔥🔥🔥 News
🎥 Demo
🧩 Community Contributions
📑 Open-source Plan
📖 Introduction
✨ Key Features
📜 System Requirements
🛠️ Dependencies and Installation
🧱 Download Pretrained Models
📝 Prompt Guide
🔑 Usage
🧱 Models Cards
🎬 More Examples
📊 Evaluation
📚 Citation
🙏 Acknowledgements
🌟 Github Star History

📖 Introduction

We present HunyuanVideo 1.5, a lightweight yet powerful video generation model that achieves state-of-the-art visual quality and motion coherence with only 8.3 billion parameters, enabling efficient inference on consumer-grade GPUs. This achievement is built upon several key components, including meticulous data curation, an advanced DiT architecture with selective and sliding tile attention(SSTA), enhanced bilingual understanding through glyph-aware text encoding , progressive pre-training and post-training, and an efficient video super-resolution network. Leveraging these designs, we developed a unified framework capable of high-quality text-to-video and image-to-video generation across multiple durations and resolutions. Extensive experiments demonstrate that this compact and proficient model establishes a new state-of-the-art among open-source models. By releasing the code and weights of HunyuanVideo 1.5, we provide the community with a high-performance foundation that significantly lowers the cost of video creation and research, making advanced video generation more accessible to all.

✨ Key Features

Lightweight High-Performance Architecture: We propose an efficient architecture that integrates an 8.3B-parameter Diffusion Transformer (DiT) with a 3D causal VAE, achieving compression ratios of 16× in spatial dimensions and 4× along the temporal axis. Additionally, the innovative SSTA (Selective and Sliding Tile Attention) mechanism prunes redundant spatiotemporal kv blocks, significantly reduces computational overhead for long video sequences and accelerates inference, achieving an end-to-end speedup of $1.87 \times$ in 10-second 720p video synthesis compared to FlashAttention-3.

Video Super-Resolution Enhancement: We develop an efficient few-step super-resolution network that upscales outputs to 1080p. It enhances sharpness while correcting distortions, thereby refining details and overall visual texture.

End-to-End Training Optimization: This work employs a multi-stage, progressive training strategy covering the entire pipeline from pre-training to post-training. Combined with the Muon optimizer to accelerate convergence, this approach holistically refines motion coherence, aesthetic quality, and human preference alignment, achieving professional-grade content generation.

📜 System Requirements

Hardware Requirements

GPU: NVIDIA GPU with CUDA support
Minimum GPU Memory: 14 GB (with model offloading enabled)

Note: The memory requirements above are measured with model offloading enabled. If your GPU has sufficient memory, you may disable offloading for improved inference speed.

Software Requirements

Operating System: Linux
Python: Python 3.10 or higher
CUDA: Compatible CUDA version for your PyTorch installation

🛠️ Dependencies and Installation

Step 1: Clone the Repository

git clone https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5.git
cd HunyuanVideo-1.5

Step 2: Install Basic Dependencies

pip install -r requirements.txt
pip install -i https://mirrors.tencent.com/pypi/simple/ --upgrade tencentcloud-sdk-python

Step 3: Install Attention Libraries

Flash Attention It's recommended to install Flash Attention for faster inference and reduced GPU memory consumption. Detailed installation instructions are available at Flash Attention.
Flex-Block-Attention flex-block-attn is only required for sparse attention to achieve faster inference and can be installed by the following command:
```
git clone https://github.com/Tencent-Hunyuan/flex-block-attn.git
cd flex-block-attn
python3 setup.py install
```

SageAttention

git clone https://github.com/cooper1637/SageAttention.git
cd SageAttention 
export EXT_PARALLEL=4 NVCC_APPEND_FLAGS="--threads 8" MAX_JOBS=32 # Optional
python3 setup.py install

🧱 Download Pretrained Models

Download the pretrained models before generating videos. Detailed instructions are available at checkpoints-download.md.

📝 Prompt Guide

Prompt Writing Handbook

Prompt enhancement plays a crucial role in enabling our model to generate high-quality videos. By writing longer and more detailed prompts, the generated video will be significantly improved. We encourage you to craft comprehensive and descriptive prompts to achieve the best possible video quality. we recommend community partners consulting our official guide on how to write effective prompts.

Reference: HunyuanVideo 1.5 Prompt Handbook

System Prompts for Automatic Prompt Enhancement

For users seeking to optimize prompts for other large models, it is recommended to consult the definition of t2v_rewrite_system_prompt in the file hyvideo/utils/rewrite/t2v_prompt.py to guide text-to-video rewriting. Similarly, for image-to-video rewriting, refer to the definition of i2v_rewrite_system_prompt in hyvideo/utils/rewrite/i2v_prompt.py.

🔑 Usage

Video Generation

For prompt rewriting, we recommend using Gemini or models deployed via vLLM. This codebase currently only supports models compatible with the vLLM API. If you wish to use Gemini, you will need to implement your own interface calls.

For models with a vLLM API, note that T2V (text-to-video) and I2V (image-to-video) have different recommended models and environment variables:

T2V: use Qwen3-235B-A22B-Thinking-2507, configure T2V_REWRITE_BASE_URL and T2V_REWRITE_MODEL_NAME
I2V: use Qwen3-VL-235B-A22B-Instruct, configure I2V_REWRITE_BASE_URL and I2V_REWRITE_MODEL_NAME

You may set the above model names to any other vLLM-compatible models you have deployed (including HuggingFace models).
Rewriting is enabled by default; to disable it explicitly, use the --disable_rewrite flag. If no vLLM endpoint is configured, the pipeline runs without remote rewriting.

Example: Generate a video (works for both T2V and I2V; set IMAGE_PATH=none for T2V or provide an image path for I2V)

export T2V_REWRITE_BASE_URL="<your_vllm_server_base_url>"
export T2V_REWRITE_MODEL_NAME="<your_model_name>"
export I2V_REWRITE_BASE_URL="<your_vllm_server_base_url>"
export I2V_REWRITE_MODEL_NAME="<your_model_name>"

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."

IMAGE_PATH=./data/reference_image.png # Optional, 'none' or <image path>
SEED=1
ASPECT_RATIO=16:9
RESOLUTION=480p
OUTPUT_PATH=./outputs/output.mp4

# Configuration
N_INFERENCE_GPU=8 # Parallel inference GPU count
CFG_DISTILLED=true # Inference with CFG distilled model, 2x speedup
SPARSE_ATTN=true # Inference with sparse attention
SAGE_ATTN=false # Inference with SageAttention
MODEL_PATH=ckpts # Path to pretrained model

torchrun --nproc_per_node=$N_INFERENCE_GPU generate.py \
  --prompt "$PROMPT" \
  --image_path $IMAGE_PATH \
  --resolution $RESOLUTION \
  --aspect_ratio $ASPECT_RATIO \
  --seed $SEED \
  --cfg_distilled $CFG_DISTILLED \
  --sparse_attn $SPARSE_ATTN \
  --use_sageattn $SAGE_ATTN \
  --output_path $OUTPUT_PATH \
  --save_pre_sr_video \
  --model_path $MODEL_PATH

Command Line Arguments

Argument	Type	Required	Default	Description
`--prompt`	str	Yes	-	Text prompt for video generation
`--negative_prompt`	str	No	`''`	Negative prompt for video generation
`--resolution`	str	Yes	-	Video resolution: `480p` or `720p`
`--model_path`	str	Yes	-	Path to pretrained model directory
`--aspect_ratio`	str	No	`16:9`	Aspect ratio of the output video
`--num_inference_steps`	int	No	`50`	Number of inference steps
`--video_length`	int	No	`121`	Number of frames to generate
`--seed`	int	No	`123`	Random seed for reproducibility
`--image_path`	str	No	`None`	Path to reference image (enables i2v mode). Use `none` or `None` to explicitly use text-to-video mode
`--output_path`	str	No	`None`	Output file path (if not provided, saves to `./outputs/output_{transformer_version}_{timestamp}.mp4`)
`--sr`	bool	No	`true`	Enable super resolution (use `--sr false` or `--sr 0` to disable)
`--save_pre_sr_video`	bool	No	`false`	Save original video before super resolution (use `--save_pre_sr_video` or `--save_pre_sr_video true` to enable, only effective when super resolution is enabled)
`--rewrite`	bool	No	`true`	Enable prompt rewriting (use `--rewrite false` or `--rewrite 0` to disable, may result in lower quality video generation)
`--cfg_distilled`	bool	No	`false`	Enable CFG distilled model for faster inference (~2x speedup, use `--cfg_distilled` or `--cfg_distilled true` to enable)
`--sparse_attn`	bool	No	`false`	Enable sparse attention for faster inference (~1.5-2x speedup, requires H-series GPUs, auto-enables CFG distilled, use `--sparse_attn` or `--sparse_attn true` to enable)
`--offloading`	bool	No	`true`	Enable CPU offloading (use `--offloading false` or `--offloading 0` to disable for faster inference if GPU memory allows)
`--group_offloading`	bool	No	`None`	Enable group offloading (default: None, automatically enabled if offloading is enabled. Use `--group_offloading` or `--group_offloading true/1` to enable, `--group_offloading false/0` to disable)
`--dtype`	str	No	`bf16`	Data type for transformer: `bf16` (faster, lower memory) or `fp32` (better quality, slower, higher memory)
`--use_sageattn`	bool	No	`false`	Enable SageAttention (use `--use_sageattn` or `--use_sageattn true/1` to enable, `--use_sageattn false/0` to disable)
`--sage_blocks_range`	str	No	`0-53`	SageAttention blocks range (e.g., `0-5` or `0,1,2,3,4,5`)
`--enable_torch_compile`	bool	No	`false`	Enable torch compile for transformer (use `--enable_torch_compile` or `--enable_torch_compile true/1` to enable, `--enable_torch_compile false/0` to disable)

Note: Use --nproc_per_node to specify the number of GPUs. For example, --nproc_per_node=8 uses 8 GPUs.

🧱 Models Cards

ModelName	Download
HunyuanVideo 1.5-480P-T2V	480P-T2V
HunyuanVideo 1.5-480p-I2V	480p-I2V
HunyuanVideo 1.5-480p-T2V-distill	480p-T2V-distill
HunyuanVideo 1.5-480p-I2V-distill	480p-I2V-distill
HunyuanVideo 1.5-720P-T2V	720P-T2V
HunyuanVideo 1.5-720P-I2V	720P-I2V
HunyuanVideo 1.5-720P-T2V-distiill	Comming soon
HunyuanVideo 1.5-720P-I2V-distiill	720P-I2V-distiill
HunyuanVideo 1.5-720P-T2V-sparse-distiill	Comming soon
HunyuanVideo 1.5-720P-I2V-sparse-distiill	720P-I2V-sparse-distiill
HunyuanVideo 1.5-720p-sr	720p-sr
HunyuanVideo 1.5-1080p-sr	1080p-sr

🎬 More Examples

Features	Demo1	Demo2
Strong Instruction Following	📋 Show input prompt `一名哀伤的黑发中国女子凝望天空，复古胶片风格烘托出怀旧戏剧氛围` 📋 Show rewrite prompt 俯视角度，一位有着深色，略带凌乱的长卷发的年轻中国女性，佩戴着闪耀的珍珠项链和圆形金色耳环，她凌乱的头发被风吹散，她微微抬头，望向天空，神情十分哀伤，眼中含着泪水。嘴唇涂着红色口红。背景是带有华丽红色花纹的图案。画面呈现复古电影风格，色调低饱和，带着轻微柔焦，烘托情绪氛围，质感仿佛20世纪90年代的经典胶片风格，营造出怀旧且富有戏剧性的感觉。	📋 Show input prompt `建筑蓝图上的线条化为实体，瞬间生长出一个完整的复古工业风办公空间。` 📋 Show rewrite prompt 一座空旷的现代阁楼里，有一张铺展在地板中央的建筑蓝图。忽然间，图纸上的线条泛起微光，仿佛被某种无形的力量唤醒。紧接着，那些发光的线条开始向上延伸，从平面中挣脱，勾勒出立体的轮廓——就像在空中进行一场无声的3D打印。随后，奇迹在加速发生：极简的橡木办公桌、优雅的伊姆斯风格皮质椅、高挑的工业风金属书架，还有几盏爱迪生灯泡，以光纹为骨架迅速“生长”出来。转瞬间，线条被真实的材质填充——木材的温润、皮革的质感、金属的冷静，都在眨眼间完整呈现。最终，所有家具稳固落地，蓝图的光芒悄然褪去。一个完整的办公空间，就这样从二维的图纸中诞生。
Smooth Motion Generation	📋 Show input prompt `A cosmic loaf of bread, with a volcanic black crust, is precisely sliced open to reveal a swirling nebula interior.` 📋 Show rewrite prompt Cinematic 8K footage, with a stark, moody aesthetic. Under a dramatic top-down spotlight, a loaf of what appears to be bread rests on a slab of polished marble, which is flecked with silver that glitters like a starfield. The loaf's crust is a deep, matte black, cracked like cooled volcanic rock. A sleek, modern santoku knife, its sharp edge gleaming under the single light source, begins a series of clean, rhythmic cuts. With each precise, repetitive slice that falls away, the loaf’s impossible interior is revealed: not dough, but a compressed, swirling nebula of deep purples and blues, alive with pinpricks of glittering light. As the knife continues its precise motion, a fine, shimmering dust of cosmic particles settles on the marble. The extreme macro view focuses on the mesmerizing contrast between the blade’s cold steel and the ethereal, galaxy-filled substance of the bread. This is hyper-realistic macro videography at its finest.	📋 Show input prompt `A figure skater performs a rapid, graceful Biellmann spin, captured from all angles.` 📋 Show rewrite prompt The video captures a figure skater performing a Biellmann spin on ice. The subject is a female skater in a glittering costume. Initially, she spins on one leg. Then, she reaches back and pulls her free leg up. Next, she spins rapidly, becoming a blur of motion, with ice shavings spraying from her skate blade. The background is an ice rink with blurred advertising boards. The camera circles around the subject to capture the spin from all angles. The lighting is spotlit, creating lens flares and sparkles on her costume. The overall video presents a graceful artistic sports style.
Cinematic Aesthetics	📋 Show input prompt `固定镜头,焦点在图片里的挂钟上，镜头轻微摇晃营造手持摄影感，wjw,filmphotos,Film Grain,Reversal film photography，Wong Kar-wai movies,cinematic photography, HK film style,neon lighting, in the style of Wong Kar Wai film` 📋 Show rewrite prompt `Handheld lens shooting, the camera focuses on the wall clock hanging on the green-toned wall, shaking slightly. The second hand sweeps steadily across the clock face, and the shadow of the clock cast on the wall shifts subtly with the movement of the lens.`	📋 Show input prompt `The leaves of calamus shine in the sunlight, dotted with dewdrops that trickle down to the ground with the breeze.` 📋 Show rewrite prompt A macro shot focuses on long, slender calamus leaves, rendered in a cinematic photography realistic style. The main leaf, a vibrant, deep green, is positioned diagonally across the frame. Its surface is covered in tiny, glistening spherical dewdrops that catch and refract the bright morning sunlight, creating sparkling highlights. Initially, a larger, perfectly round dewdrop clings to the upper section of the leaf, its surface tension holding it in place. Then, as the leaf sways almost imperceptibly, the dewdrop begins to slowly dislodge. Next, it starts to trickle down the central vein of the leaf, its shape elongating slightly as it moves, leaving a subtle, glistening wet trail in its path. Finally, it reaches the pointed tip of the leaf, hangs for a brief moment, and falls out of the bottom of the frame. In the background, other leaves and blades of grass are softly blurred, creating a beautiful bokeh effect with soft, out-of-focus circles of light. The environment is bathed in the warm, golden glow of early morning sunlight, which streams in from behind the leaves, backlighting them and causing their wet edges to shine brilliantly. The overall impression is one of serene, natural beauty, captured in a highly realistic and detailed manner. This is a macro shot. The camera tilts down very slowly, following the path of the main dewdrop as it travels down the leaf. The lighting is soft and natural, with strong backlighting to create a radiant, glowing effect on the dewdrops and leaf edges, characteristic of professional nature photography. The atmosphere is peaceful and serene. The overall video presents a cinematic photography realistic style.
Text Rendering	📋 Show input prompt `赛博朋克风格的夜晚街角，一个巨大的招牌上， “Hunyuan Video 1.5”的霓虹灯管轮廓已经安装好。镜头推进，霓虹灯从“H”开始，伴随着‘滋滋’的电流声，每个字母依次亮起粉紫色的光芒，直到全部点亮，照亮了潮湿的街道。赛博朋克，城市美学` 📋 Show rewrite prompt On a wet street corner in a cyberpunk city at night, a large neon sign reading "Hunyuan Video 1.5" lights up sequentially, illuminating the dark, rainy environment with a pinkish-purple glow. he scene is a dark, rain-slicked street corner in a futuristic, cinematic cyberpunk city. Mounted on the metallic, weathered facade of a building is a massive, unlit neon sign. The sign's glass tube framework clearly spells out the words "Hunyuan Video 1.5". Initially, the street is dimly lit, with ambient light from distant skyscrapers creating shimmering reflections on the wet asphalt below. Then, the camera zooms in slowly toward the sign. As it moves, a low electrical sizzling sound begins. In the background, the dense urban landscape of the cyberpunk metropolis is visible through a light atmospheric haze, with towering structures adorned with their own flickering advertisements. A complex web of cables and pipes crisscrosses between the buildings. The shot is at a low angle, looking up at the sign to emphasize its grand scale. The lighting is high-contrast and dramatic, dominated by the neon glow which creates sharp, specular reflections and deep shadows. The atmosphere is moody and tech-noir. The overall video presents a cinematic photography realistic style.,	📋 Show input prompt `黑色背景上展示着艺术字体"Hunyuan Video 1.5"，每个字母都由不同的流体构成，持续缓慢流动。多种不同质地、不互溶的彩色液体（如金属、牛奶、透明凝胶）在无重力环境中漂浮、碰撞` 📋 Show rewrite prompt The artistic words "Hunyuan Video 1.5" are rendered in the center of the screen, with each character composed of a unique, slowly moving fluid, set against a deep black background, while colorful, immiscible liquid blobs float and collide around them in a zero-gravity environment. The main subject is the text "Hunyuan Video 1.5". The characters for "Hunyuan" are filled with a lustrous, molten gold liquid that swirls slowly. The letters for "Video" are composed of a creamy, opaque white fluid resembling milk, with gentle currents visible beneath its surface. The numbers "1.5" are made from a viscous, transparent blue gel that subtly undulates. Each fluid moves independently within the confines of its character's shape, creating a mesmerizing internal motion. This high-quality 3D CGI animation presents the fluids with photorealistic textures. In the surrounding space, several immiscible liquid blobs drift in zero gravity. A large, spherical blob of pearlescent liquid slowly floats from the upper left. A smaller, amorphous blob of shimmering, metallic silver drifts from the lower right, and a translucent, pink gelatinous mass wobbles nearby. Initially, these blobs drift aimlessly. Then, the silver blob slowly collides with the larger pearlescent one. As they make contact, their surfaces deform and ripple dynamically, but the liquids do not mix, pushing against each other before gently bouncing off and continuing their slow, separate paths in the pristine black void. The shot is at an eye-level angle, presenting a front view of the text. The camera remains static, ensuring the entire text "Hunyuan Video 1.5" is fully visible throughout the shot. The scene is lit by a soft, diffused light that highlights the brilliant reflections on the metallic fluids and the inner glow of the translucent gels, enhancing the high-quality 3D CGI animation. The atmosphere is quiet, abstract, and mesmerizing. The overall video has the polished look of a high-quality 3D CGI animation with a focus on abstract fluid dynamics.
Physics Compliance	📋 Show input prompt `The wind blows through the shabby bookshelf, and the pages flutter on it.` 📋 Show rewrite prompt In a dimly lit, dusty room, a gentle wind causes the pages of old books on a shabby wooden bookshelf to flutter. The bookshelf, made of dark, weathered wood, shows signs of age with peeling varnish, scratches, and a fine layer of settled dust on its surfaces. Several old books with faded, worn covers are arranged on the shelves; some stand upright while others lie on their sides. Initially, the scene is quiet. Then, a soft breeze enters the frame from the left, disturbing the dust on the shelves. Next, the yellowed, brittle pages of an open book lying flat begin to lift and ripple delicately. As the breeze continues, the pages of other books also start to flutter, some turning over slowly and gracefully, revealing aged text and faint illustrations within. In the background, the wall has faded, peeling wallpaper, and the overall atmosphere is one of quiet neglect and the passage of time. The shot is at an eye-level angle with the main subject. The camera pans to the left slowly. Soft, diffused sunlight filters through a dusty, off-camera window, creating distinct beams of light that cut through the dimness. This lighting highlights the texture of the old wood and the floating dust particles in the air, enhancing the photorealistic detail of the scene. The mood is melancholic and peaceful. The overall video presents a cinematic photography realistic style.	📋 Show input prompt `An intact soda can is slowly crushed by a hand.` 📋 Show rewrite prompt In a medium close-up, a hand slowly crushes an intact red and white soda can on a wooden table. A male hand with visible, realistic skin texture is wrapped firmly around the middle of an intact, pristine red and white aluminum soda can. The can, covered in glistening condensation droplets, rests on a dark, polished wooden surface. The cinematic realism captures every minute detail of the scene. Initially, the hand's grip is steady, with the can's cylindrical shape perfectly preserved. Then, the fingers begin to tighten slowly, the knuckles whitening slightly from the exertion. Next, the smooth aluminum surface starts to buckle under the controlled pressure, a sharp crease forming vertically down its side as the metallic sheen distorts. As the hand continues its deliberate squeeze, the can collapses inward progressively, the vibrant red paint wrinkling as the metal structure crumples. Finally, the can is left significantly crushed, its form now an irregular, crumpled shape held tightly in the fist. The scene takes place on a dark, polished wooden tabletop that catches soft, diffuse reflections. The grain of the wood is faintly discernible, adding a layer of texture to the foreground. The background is completely out of focus, rendered as a soft, dark, and non-descript blur, which isolates the main action and enhances the photorealistic quality of the shot. The shot is a medium close-up, presented in a cinematic photography realistic style. The camera remains static at a slightly high angle, looking down to provide a clear and unobstructed view of the can's deformation. Soft side lighting creates high contrast, sculpting the muscles and tendons of the hand while casting specular highlights on the metallic can and the water droplets. The atmosphere is focused and intense. The overall video presents a cinematic photography realistic style.
Camera Movement	📋 Show input prompt `圣诞节的家中，小女孩靠着妈妈听妈妈读书，背景是下着雪的窗外，镜头缓慢下移，一只可爱的长毛小白猫戴着圣诞帽趴在温暖的地摊上` 📋 Show rewrite prompt In a cozy home on Christmas, a young girl leans against her mother as they read a book, and the camera moves down to reveal a fluffy white cat in a Santa hat resting on a warm rug. In a warmly lit living room on a snowy Christmas evening, a young mother and her little daughter are sitting together on a comfortable sofa. The mother, with a gentle expression and wearing a cream-colored knitted sweater, holds an open storybook with colorful illustrations. Her daughter, a small girl with brown hair in pigtails and a red pajama set, leans her head affectionately on her mother's shoulder, her eyes fixed on the book. On the floor below them, a fluffy, long-haired white cat is curled up on a plush, beige wool rug. The cat wears a tiny red and white Santa hat perched between its ears. Initially, the shot focuses on the mother and daughter, capturing their quiet, shared moment. The mother’s finger gently rests on the page of the book. Then, the camera slowly moves downward, gliding past the book and their laps. Finally, the camera settles at a low angle, bringing the adorable white cat into sharp focus as the primary subject. The cat's chest gently rises and falls with each breath, its eyes peacefully closed. Through a large window in the background, large, soft snowflakes can be seen falling silently against the dark blue twilight sky, creating a peaceful and serene backdrop. Faint, out-of-focus golden Christmas lights twinkle in the corner of the room, adding to the warm, festive atmosphere. The scene is imbued with a sense of comfort and holiday warmth, creating a beautiful cinematic photography realistic image. The camera slowly moves downward. The shot uses soft, warm interior lighting that casts gentle shadows, creating a high-contrast, cinematic look. A shallow depth of field keeps the focus on the subjects while beautifully blurring the background elements. The mood is heartwarming, peaceful, and festive. The overall video presents a cinematic photography realistic style.	📋 Show input prompt `The hiker begins walking forward along the trail, causing the water bottle to swing rhythmically with each step. The camera gradually pulls back and rises to reveal a vast desert landscape stretching out ahead.` 📋 Show rewrite prompt `The hiker begins walking forward along the trail, causing the water bottle to swing rhythmically with each step. The camera gradually pulls back and rises to reveal a vast desert landscape stretching out ahead, while the sun position shifts from afternoon to dusk, casting increasingly longer shadows across the terrain as the figure becomes smaller in the frame.`
Multi-Style Support	📋 Show input prompt `Have the cake man begin to take chunks out of himself and eat it.` 📋 Show rewrite prompt `The cake man sits on the chair, with his hands resting on his knees. Then, he slowly raises his right hand and breaks off a piece of cake from his left shoulder. Next, he brings the piece of cake to his mouth and begins to chew. At the same time, his eyes widen slightly, and his mouth parts gently. After that, he raises his right hand again, breaks off another piece of cake from his right arm, and repeats the action of bringing it to his mouth to chew.`	📋 Show input prompt `A little girl, carrying a colorful handbag, skips through the garden. The video uses claymation style.` 📋 Show rewrite prompt A little girl with a colorful handbag skips through a whimsical claymation garden. In a vibrant garden constructed entirely from clay, a young girl, meticulously crafted in a claymation style, skips joyfully. She has chunky, sculpted yellow clay hair tied in pigtails that bounce with a slight stiffness, simple black button eyes, and a wide, permanently etched smile. She wears a simple pink clay dress with a white collar. In her left hand, she carries a small handbag molded from bright red and blue clay, which swings in a slightly jerky arc as she moves. Initially, the girl lifts her right leg high, her body momentarily suspended in a classic stop-motion pose. Then, she hops forward, landing lightly as her left leg swings through for the next skip. Her arms move in an exaggerated, back-and-forth rhythm, characteristic of stop-motion animation. Her movements are intentionally not perfectly fluid, highlighting the frame-by-frame nature of the claymation technique. The garden around her is a whimsical, textured world. In the foreground and mid-ground, oversized flowers with swirled purple and orange petals stand on thick green stems. The ground is a textured mat of green clay, showing subtle fingerprints and tool marks that add to the handmade charm. In the background, a pale blue clay backdrop features a simplified, smiling sun molded from yellow clay. The shot is at an eye-level angle with the main subject. The camera follows the subject, moving smoothly to the right to keep her in the frame. The lighting is bright and even, casting soft shadows that emphasize the rounded, three-dimensional forms of the clay models. The overall video presents a charming and detailed claymation style.
High Image-Video Consistency	📋 Show input prompt `女孩放下书，站起身，转身向屋内走去。镜头拉远。` 📋 Show rewrite prompt `女孩合上手中的书，将书放在身侧的窗台上。随后，她缓缓站起身，转身向屋内走去，身影逐渐没入门后的阴影中。镜头缓缓拉远，露出更多被绿植覆盖的屋檐和墙体。`	📋 Show input prompt `女人手上的鸟亲了女人一口` 📋 Show rewrite prompt `女人手臂上的白色鹦鹉缓缓转过头，将喙轻轻触碰女人的脸颊，随后收回头部。女人嘴角微微上扬，目光温柔地注视着鹦鹉。背景中的绿植保持静止。`

📊 Evaluation

Rating

We assess text-to-video generation using a comprehensive rating methodology that considers five key dimensions: text-video consistency, visual quality, structural stability, motion effects, and the aesthetic quality of individual frames. For image-to-video generation, the evaluation encompasses image-video consistency, instruction responsiveness, visual quality, structural stability, and motion effects.

GSB

The GSB(Good/Same/Bad) approach is widely used to evaluate the relative performance of two models based on overall video perception quality.We carefully construct 300 diverse text prompts and 300 image samples to cover balanced application scenarios for both text-to-video and image-to-video tasks. For each prompt or image input, an equal number of video samples are generated by each model in a single run to ensure comparability. To maintain fairness, inference is performed only once per input without any cherry-picking of results. All competing models are evaluated using their default configurations. The evaluation is conducted by over 100 professional assessors

📚 Citation

@misc{hunyuanvideo2025，
      title={HunyuanVideo 1.5 Technical Report},
      author={Tencent Hunyuan Foundation Model Team},
      year={2025},
      publisher = {GitHub},
      howpublished = {\url{https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5}},
}

🙏 Acknowledgements

We would like to thank the contributors to the Transformers, Diffusers , HuggingFace and Qwen-VL, for their open research and exploration.