--- license: apache-2.0 ---

HPSD: Hybrid-Policy Self-Distillation for Text-Image-to-Video Diffusion Models

Shanghai Jiao Tong University, Nanyang Technological University, Shanghai AI Laboratory,
University of Science and Technology of China, Fudan University, Shanghai Innovation Institute
The Chinese University of Hong Kong, CPII under InnoHK, JD.com, Adobe Research

(*Equal Contribution)(^§Project Leader)(^†Corresponding Author)

This model is presented in the paper [HPSD: Hybrid-Policy Self-Distillation for Text-Image-to-Video Diffusion Models](https://arxiv.org/abs/2608.13205). Project page: [https://bujiazi.github.io/hpsd.github.io/](https://bujiazi.github.io/hpsd.github.io/) ## Abstract Text-Image-to-Video (TI2V) models are an emerging unified architecture, where a single model simultaneously supports text-to-video (T2V) and image-to-video (I2V) generation. Given a high-quality first frame or a detailed textual prompt, TI2V models unlock substantially better visual quality than their T2V mode, raising a natural question: _can the capability elicited by such privileged conditions be internalized into the model's own base generation ability?_ A common approach toward this goal is model self-distillation. However, the most straightforward solution, supervised fine-tuning, follows an off-policy strategy: its supervision is confined to teacher-generated endpoints from a fixed offline distribution rather than student-visited states, lacking precise correction tailored to the evolving policy. Recent on-policy distillation methods instead suffer from condition-state mismatch, where supervision is steered toward the given first frame instead of the student's actual content, misleading the correction. To achieve self-distillation that absorbs the teacher's privileged prior while retaining precise policy correction, in this work, we propose **H**ybrid-**P**olicy **S**elf-**D**istillation (**HPSD**), a novel self-distillation framework where a single TI2V model acts as both teacher and student under different conditions: the teacher operates in TI2V mode with a high-quality first frame and an enhanced prompt, while the student runs in the base T2V mode with only the vanilla prompt. Specifically, the student inherits off-policy teacher trajectory points as anchors, locally refines them toward its own policy, and finally receives velocity-level supervision on these self-generated roll-outs. Extensive experiments demonstrate that HPSD significantly improves T2V performance while also delivering notable TI2V gains, effectively strengthening the model's base generation ability. ## 🌟 Model The `adapter_model.safetensors` is based on [WAN-2.2-TI2V](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers) with our HPSD training. ## 🔧 Github Link [https://github.com/Bujiazi/HPSD](https://github.com/Bujiazi/HPSD) ## 🚀 Inference ```python import torch from huggingface_hub import snapshot_download from diffusers import WanPipeline, AutoencoderKLWan from diffusers.utils import export_to_video from peft import PeftModel dtype = torch.bfloat16 device = "cuda" model_id = "Wan-AI/Wan2.2-TI2V-5B-Diffusers" checkpoint = snapshot_download(repo_id="Bujiazi/HPSD", repo_type="model") vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32) pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=dtype) pipe.to(device) pipe.transformer = PeftModel.from_pretrained(pipe.transformer, checkpoint, torch_dtype=dtype).to(device) height = 704 width = 1280 num_frames = 81 num_inference_steps = 50 guidance_scale = 5.0 base_seed = 42 prompt = "A sports car speeding along a winding coastal road at sunset. Dynamic low-angle tracking shot, realistic reflections, dramatic clouds, subtle motion blur, cinematic composition." negative_prompt = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" generator = torch.Generator(device=device).manual_seed(base_seed) output = pipe( prompt=prompt, negative_prompt=negative_prompt, height=height, width=width, num_frames=num_frames, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, generator=generator, ).frames[0] export_to_video(output, "hpsd_test.mp4", fps=16) ``` ## 📎 Citation If you find our work helpful, please consider giving a star ⭐ and citation 📝 ```bibtex @article{bu2026hpsd, title={HPSD: Hybrid-Policy Self-Distillation for Text-Image-to-Video Diffusion Models}, author={Bu, Jiazi and Ling, Pengyang and Zhou, Yujie and Wang, Yibin and Zang, Yuhang and Dai, Xuanlang and Ding, Shengyuan and Wei, Tianyi and Zhan, Xiaohang and Wang, Jiaqi and others}, journal={arXiv preprint arXiv:2608.13205}, year={2026} } ``` ## 💞 Acknowledgements The code is built upon the below repositories, we thank all the contributors for open-sourcing. * [Diffusers](https://github.com/huggingface/diffusers) * [WAN-2.2](https://github.com/Wan-Video/Wan2.2) * [LTX-2.3](https://github.com/Lightricks/LTX-2) * [Z-Image](https://github.com/Tongyi-MAI/Z-Image) * [Qwen3.6-27B](https://github.com/QwenLM/Qwen3.6) * [D-OPSD](https://github.com/vvvvvjdy/D-OPSD)