HunyuanCustom vs. HappyHorse 1.1

HunyuanCustom represents a sophisticated framework designed for the creation of tailored videos across various modalities, prioritizing the preservation of subject consistency while considering factors related to images, audio, video, and text. The framework builds on HunyuanVideo and integrates a text-image fusion module, drawing inspiration from LLaVA to enhance multi-modal understanding, as well as an image ID enhancement module that employs temporal concatenation to fortify identity features across different frames. Moreover, it introduces targeted condition injection mechanisms specifically for audio and video creation, along with an AudioNet module that achieves hierarchical alignment through spatial cross-attention, supplemented by a video-driven injection module that combines latent-compressed conditional video using a patchify-based feature-alignment network. Rigorous evaluations conducted in both single- and multi-subject contexts demonstrate that HunyuanCustom outperforms leading open and closed-source methods in terms of ID consistency, realism, and the synchronization between text and video, underscoring its formidable capabilities. This groundbreaking approach not only signifies a meaningful leap in the domain of video generation but also holds the potential to inspire more advanced multimedia applications in the years to come, setting a new standard for future developments in the field.

HappyHorse 1.1 is an upgraded AI video generation model created to deliver stronger creative quality, controllability, and production efficiency for professional content teams. The model builds on HappyHorse 1.0 with improvements shaped by real-world feedback from production workflows in short dramas, ecommerce advertising, brand marketing, CG, and cinematic content creation. HappyHorse 1.1 significantly improves motion expressiveness by optimizing motion modeling and temporal consistency, helping reduce sluggish movement, weak pacing, sudden stops, and unnatural action flow. It supports more coherent dynamic scenes where characters, objects, camera movement, and environmental interactions feel physically connected. The model also improves subject consistency and multi-reference fusion, allowing creators to reproduce reference assets more reliably across products, characters, environments, storyboards, and multi-panel inputs. HappyHorse 1.1 follows instructions more accurately by strengthening long-context semantic understanding, scene planning, character relationship modeling, and camera sequence stability. Its visual quality upgrades include more realistic character details, refined facial rendering, natural skin texture, better preservation of pores and facial marks, reduced smearing, and stronger close-up expressiveness. The model also improves professional camera language such as shot-reverse-shot, tracking shots, multi-shot transitions, pacing, and cinematic storytelling. HappyHorse 1.1 adds stronger audio expression with more natural dialogue delivery, improved speaking pace, better emotional tone, richer ambient sound, more relevant music and sound effects, and more accurate audio-visual synchronization. API and developer support make the model available for text-to-video, image-to-video, reference-to-video, multi-image references, flexible aspect ratios, and 720p or 1080p generation.