#6060th of 75 in Image and videoFor image, video and design, video and animation

hunyuanvideo-1.5

8.3B text-to-video and image-to-video diffusion model with training code

Stars
4.6k
License
custom license
Last commit
Apr 2026
Language
Python

Missed the latest GitHub check; these facts may be older.

Overview

HunyuanVideo-1.5 is Tencent's video generation model: an 8.3B-parameter DiT with a 3D causal VAE, producing text-to-video and image-to-video at 480p and 720p, with super-resolution models to 1080p. The repo includes generate.py inference (multi-GPU via torchrun), step- and CFG-distilled checkpoints, caching options, and a train.py with FSDP and LoRA support using the Muon optimizer.

Who it is for: Developers and researchers generating or fine-tuning video models on NVIDIA GPUs

Strengths

  • Runs with as little as 14 GB GPU memory when model offloading is enabled
  • Distilled variants: 480p I2V step-distilled in 8 or 12 steps, plus CFG-distilled checkpoints
  • Includes training code (FSDP, context parallel) and LoRA tuning, not just inference
  • Integrates with ComfyUI, Diffusers and LightX2V

Weaknesses

  • Requires an NVIDIA CUDA GPU on Linux; no CPU or Mac path described
  • Some checkpoints still marked coming soon, e.g. 720p T2V CFG-distill and sparse T2V
  • Prompt rewriting expects a separate vLLM endpoint serving a large Qwen3 model
  • Optional speedups need extra builds: Flash Attention, SageAttention, flex-block-attn, sgl-kernel

What it needs

  • GPU required
  • Needs PyTorch, CUDA, Flash Attention, SageAttention, flex-block-attn, sgl-kernel, vLLM (optional, prompt rewrite)
  • Models: HunyuanVideo-1.5 480p/720p T2V, HunyuanVideo-1.5 480p/720p I2V, 720p and 1080p super-resolution, Diffusers

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