HunyuanVideo

We generated a 10-second clip of a coastal city at night, rain falling on neon-lit streets, wide establishing shot pulling back from a rooftop. HunyuanVideo returned 720p footage with coherent camera motion, realistic rain particle behavior, and no obvious flickering between frames at the 5-second mark. At 8 seconds, temporal consistency started softening at the edges of moving objects. By 10 seconds, the background bokeh had drifted. That tells you almost everything you need to know about where HunyuanVideo sits in April 2026: the ceiling is genuinely impressive, the floor is hardware-gated, and the duration limit is real.

HunyuanVideo is Tencent's open-source video generation model family, first released December 3, 2024 as the largest open-weight video model at the time with 13 billion parameters. The ecosystem has since grown to include HunyuanVideo-1.5 (8.3B params, released November 2025, targeting consumer GPUs), HunyuanVideo-I2V (image-to-video, March 2026), and HunyuanVideo-Avatar (audio-driven portrait animation, May 2025). The model weights are free on Hugging Face under a custom Tencent community license. It runs locally through ComfyUI, supports LoRA fine-tuning, and has no per-clip fees from Tencent. As of April 2026, the GitHub repository has over 12,000 stars and the CivitAI LoRA community has produced more than 500 trained adaptations.

What HunyuanVideo's output actually looks like in April 2026

The original 13B model generates video at 540p to 720p resolutions with up to 129 frames per clip. Motion coherence is its headline strength: multi-person scenes hold character consistency across cuts where competing models lose identity, and camera movements like pans and pulls maintain spatial logic across the full clip length. The model uses a 3D VAE with CausalConv3D compression and a dual-stream to single-stream Diffusion Transformer architecture, which underpins its temporal consistency at shorter durations.

HunyuanVideo-1.5 (8.3B parameters, SSTA architecture) achieves roughly the same output quality as the original at nearly twice the inference speed, while dropping minimum VRAM requirements to 14GB with offloading enabled. The quality delta between 1.5 and the original is smaller than the hardware requirement difference, which is why most practitioners in April 2026 start with 1.5.

Known output limitations: text overlays produce illegible scribbles, consistent with all current video diffusion models. Fine facial details become "challenging" in close-up shots. Temporal consistency degrades past 8-10 seconds of clip length. Doubling output resolution roughly quadruples memory requirements, which creates a practical ceiling on 24GB consumer cards.

"I've ran hunyuanvideoai from their GitHub and it seems to generate realistic videos" - natvert, Hacker News, December 2024 (noting ~30-60 minute generation times on an A6000 at original release)

"Seriously go check it out as it easily beats cog and ltx video generation imo" - sdimg, r/StableDiffusion, December 2024

Best use cases, and the disasters to avoid

HunyuanVideo produces its best results on:

• Cinematic establishing shots and B-roll: Wide shots with environmental motion (weather, crowds, urban scenes) where temporal consistency across the full frame matters more than facial fidelity.

• Short promotional clips under 8 seconds: Product showcases, abstract brand visuals, nature footage where the duration stays within the model's temporal consistency window.

• LoRA-fine-tuned character work: The CivitAI community has produced style and character LoRAs that extend the base model for specific aesthetics. "Video Cloning" LoRAs trained on 16-22 reference images per subject are an active use pattern.

• HunyuanVideo-Avatar for digital presenters: Upload one portrait and an audio clip. The model handles lip sync, facial expression, and body movement for e-commerce presenters, virtual streamers, and cultural/educational content.

Avoid HunyuanVideo for anything requiring on-screen readable text, clips longer than 10 seconds without cuts, high-resolution close-up talking head footage, or rapid iteration cycles. At 5-12 minutes per clip on an RTX 4090, you cannot afford to test many variations in a session.

Motion and camera control: how well it holds up

HunyuanVideo handles implied camera motion reasonably well through prompt engineering (describing "pull back," "pan left," "handheld") but does not offer explicit ControlNet-style camera trajectory control in the base workflow. The model interprets camera movement semantically from text rather than accepting keyframe-level camera paths. For fixed or slow camera setups, results are reliable. For complex multi-axis camera work, results are inconsistent.

Multi-person scene handling is a specific strength. The architecture maintains character identity across 3-5 distinct subjects where comparable models at smaller parameter counts lose consistency. This matters for dialogue scenes, crowd shots, and any clip where the same person appears across multiple frames.

The cross-frame text guidance modules in the Diffusion Transformer architecture contribute to motion coherence, specifically reducing the flickering artifacts that plague shorter-parameter models on background elements. The 3D VAE compresses temporal information efficiently, which is why clips under 8 seconds hold up well even on compressed generation settings.

Export limits: resolution, length, watermarks

HunyuanVideo applies no watermarks. The model is self-hosted; output belongs entirely to the operator. Resolution options via ComfyUI span from 544p to 720p depending on VRAM headroom. The standard output format is MP4.

Practical limits in April 2026:

• 129 frames is the standard clip length setting (~5 seconds at 24fps, ~4.3 seconds at 30fps)

• Longer clips require significantly more VRAM and generate noticeably faster quality degradation in temporal consistency

• 720p generation on the original 13B model requires 60GB VRAM; on HunyuanVideo-1.5 with SSTA and offloading, 14-16GB is functional but slow

• FP8 quantization saves approximately 10GB memory vs the default precision setting

The license excludes use in the EU, UK, and South Korea. This is a hard territorial restriction in the Tencent Hunyuan Community License, not a technical limit, but it affects whether commercial projects in those regions can legally deploy outputs.

A real workflow: text-to-video filmmaking with HunyuanVideo

A typical ComfyUI workflow for HunyuanVideo-1.5 in April 2026 runs as follows. Install the kijai/ComfyUI-HunyuanVideoWrapper custom node. Load the 1.5 model weights from Hugging Face (tencent/HunyuanVideo-1.5). Enable VAE tiling to save 4-6GB of VRAM at the cost of 10-15% added decoding time. Set sampling steps to 20-30 for draft iterations (reduces memory usage by roughly 40% vs the default 50 steps with minimal visible quality loss). Confirm settings before launching full decode, since VRAM out-of-memory errors only surface at the VAE decode stage after the full generation has already run.

For HunyuanVideo-Avatar, the workflow is simpler: upload one portrait image and one audio clip, select generation style (photorealistic, animated, etc.), and run. The model handles lip sync and expression transfer automatically. Single-character mode is fully open-sourced; multi-character mode was announced for open-source release in late 2025.

Practitioners running production work on RTX 3090 hardware report 5-6 minutes per 5-second clip. RTX 4090 users see 8-12 minutes. An A6000 (48-80GB VRAM) drops to 4-8 minutes while enabling full 720p without quantization compromises.

The true cost per finished video

The model weights are free. Inference costs come from electricity and hardware depreciation. At a typical GPU compute cost of $0.50-$1.50/hour on cloud providers (RunPod, Replicate), a 5-second 720p clip taking 8-12 minutes costs roughly $0.07-$0.30 per clip depending on GPU tier and provider. That is substantially cheaper than Runway Gen-3 ($0.05/second via subscription, but with a monthly subscription gate) or Luma's credit-based pricing.

Self-hosted costs depend entirely on hardware. An RTX 4090 purchase amortized over 3 years and running 4 hours/day produces clips at effectively under $0.01 each at high volume. The true cost is setup time and the learning curve for ComfyUI workflow configuration.

For EU, UK, and South Korean teams, the license restriction means HunyuanVideo's "free" label comes with legal risk for commercial use, and cloud-based alternatives without territorial exclusions may be preferable regardless of per-clip cost.

HunyuanVideo vs. Mochi 1 vs. LTX-Video

HunyuanVideo leads on output quality, parameter scale (13B original, 8.3B in 1.5), multi-person scene consistency, and community ecosystem (500+ LoRAs, HunyuanVideo-Avatar, HunyuanVideo-I2V). Weak on speed, VRAM accessibility for the original model, and has a more restrictive custom license with geo-exclusions.

Mochi 1 (Genmo, 10B parameters, Apache 2.0 license) is the nearest quality rival and the more permissively licensed option. Its Asymmetric Diffusion Transformer with Asymmetric VAE (8x8 spatial, 6x temporal compression) produces native 30fps output, which gives it a genuine frame rate advantage over HunyuanVideo's configurable but lower-default fps. Mochi 1 excels at physics-accurate motion and character animation. It requires less VRAM than HunyuanVideo's original model, and its Apache 2.0 license allows commercial use globally without territorial exclusions. The tradeoff is a smaller community ecosystem and lower ceiling on photorealistic detail at equivalent settings compared to HunyuanVideo's 13B model.

LTX-Video (Lightricks, DiT architecture) is built for a completely different tradeoff: speed over maximum quality. LTX-Video generates 5 seconds of 24fps footage at 768x512 in approximately 4 seconds on an RTX 4090, compared to HunyuanVideo's 8-12 minutes for similar-length clips. For live demonstrations, rapid prototyping, or iterating through many prompt variations, LTX-Video is the rational choice. Its quality ceiling is meaningfully lower than HunyuanVideo, particularly for detail retention and multi-person scenes. LTX-Video caps at 1216x704 resolution.

The practical decision tree: choose HunyuanVideo when output quality is the priority and you have the hardware and patience. Choose Mochi 1 when you need native 30fps, physics-accurate motion, and a cleaner license. Choose LTX-Video when iteration speed matters more than maximum quality.

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