Hunyuan Video Prompt Guide: Mastering Tencent's Open-Source AI Model (2026)

Tencent’s HunyuanVideo broke a real bottleneck in AI video: the open-source models that were free to run locally produced obvious-AI clips, while the closed-source models (Sora, Veo, Kling) that produced clean clips locked you into expensive APIs. HunyuanVideo-1.5 cleared that gap — 8.3B parameters, comparable quality to mid-tier closed models, free weights you can run on a single high-end GPU.

But the prompt language is different. Hunyuan responds to motion descriptions and shot composition the way Veo does, but it has its own ideal phrasing — and the official prompt-rewrite model (yes, Tencent shipped a separate model just for rewriting your prompts) reveals exactly what it’s tuned for. This guide covers how to write Hunyuan prompts that produce production-quality output, with examples and the structural patterns that work.

What Hunyuan Is Good At (and What It Isn’t)

Before writing prompts, calibrate expectations. Hunyuan’s strengths in 2026:

Camera motion — handoffs between dolly, push-in, pan, and tilt are clean
Physics simulation — water, hair, fabric movement look natural
Long shot continuity — 5-10 second clips don’t degrade as badly as earlier models
Lighting transitions — sunrise → daylight, day → dusk shifts work
Image-to-video — the I2V variant produces some of the best image animation in any open model

Where it struggles:

Hands and fine fingers — still the AI-video weak point everywhere
Text in scenes — readable text rarely renders correctly
Multiple speaking characters — lip-sync stays rough; one character is the safe bet
Specific celebrity faces — guardrails plus training data make these unreliable
Very fast cuts — 1-2 second clips work better as longer single shots

Match the prompt to the strengths. A 6-second cinematic dolly shot with one subject in dramatic light is in Hunyuan’s wheelhouse. A 2-second cut of three people having a conversation isn’t.

The Prompt Structure That Works

Hunyuan’s official prompt-rewrite model gives away the ideal structure. It rewrites short user prompts into ~5 components in a specific order:

Subject — what is in the frame
Action — what is happening
Setting — where, time of day, weather
Style / camera — shot type, lens, motion, lighting
Mood / quality — atmosphere descriptors and quality tags

In English prose, that means:

[Subject doing action] [in setting], [shot description with camera and light],
[mood, quality tags]

Compare a weak prompt:

A woman walking through a forest

To a structured Hunyuan prompt:

A young woman in a wool coat walking slowly through a misty pine forest at golden hour, low handheld tracking shot with soft volumetric light filtering through the trees, cinematic, melancholy, 4K detail

The second prompt fills all five slots. The first only fills slots 1-3 and Hunyuan has to guess the rest — which is when you get generic output.

Motion Language That Hunyuan Responds To

Hunyuan was trained heavily on cinematic shot descriptions. The motion vocabulary that consistently works:

Camera moves:

“slow dolly in” / “slow dolly out” — push the camera toward or away from subject
“tracking shot” — camera follows the subject’s movement
“crane up” / “crane down” — vertical camera movement
“handheld” — adds organic shake; great for documentary feel
“static shot” / “locked off” — camera doesn’t move
“whip pan” — fast horizontal pan, works for transitions

Subject motion:

“walks slowly” / “runs at full speed” — Hunyuan respects motion intensity adverbs
“turns toward camera” — works reliably for direction changes
“raises [object] / sets [object] down” — short controlled actions land well
“the wind picks up, fabric flutters” — semantic motion descriptions Hunyuan handles unusually well

Lighting transitions:

“soft golden hour light” / “harsh midday sun” / “blue hour twilight”
“single key light from left” / “rim lighting from behind”
“fluorescent overhead” / “neon signs reflecting off wet pavement”

The pattern: be specific. “A man walking” leaves Hunyuan to invent the camera, light, and pace. “A man in a beige trench coat walking briskly down a wet city street at night, low tracking shot, neon signs reflecting in puddles” gives the model a complete frame to compose.

10 Hunyuan Prompts That Work (Copy-Paste Ready)

1. Cinematic establishing shot:

Aerial drone shot pulling up over a foggy mountain range at sunrise,
revealing layers of pine forest below, soft pink and orange light
breaking through clouds, cinematic IMAX quality, slow majestic motion

2. Product close-up:

Macro shot of a vintage gold watch laying on weathered leather,
slow rotation around the watch face, warm tungsten light from a desk lamp,
shallow depth of field, particles of dust visible in the light beam,
photo-realistic, advertising quality

3. Character moment:

A young woman with red hair sitting alone at a window seat in a coffee
shop, slowly stirring a cup, looking thoughtfully outside as rain falls,
soft natural light, slight handheld camera, intimate close-up framing,
melancholy mood, film grain

4. Action shot:

A black motorcycle accelerating down an empty desert highway at golden hour,
low tracking shot from the side, dust kicking up behind the rear wheel,
mountains in the background, cinematic, anamorphic lens, dynamic motion blur

5. Fantasy / stylized:

A glowing blue jellyfish drifting through a dark abyss with bioluminescent
particles floating around it, slow underwater camera tracking with the
creature, deep blacks, electric blue glow, dreamlike, otherworldly

6. Food video:

Top-down shot of melted chocolate being poured slowly over a stack of
pancakes, steam rising from the pancakes, warm morning light from the
side, the chocolate spreads and drips off the edge, food commercial style,
sharp focus on the chocolate flow, shallow depth elsewhere

7. Architectural / interior:

Slow dolly through an empty modernist living room with floor-to-ceiling
windows overlooking a sunset sea, dust particles visible in shafts of
warm orange light, minimalist furniture, peaceful and serene, architectural
photography style

8. Sci-fi:

Wide shot of a lone astronaut standing on a red Martian plain looking up
at a distant Earth in the sky, dust devils swirling in the background,
soft red ambient light from the surface, slow drift camera movement,
epic cinematic scale, hopeful mood

9. Crowd / urban:

A bustling Tokyo street at night seen from a low angle, neon signs and
giant LED screens reflecting off rain-soaked pavement, people walking
with umbrellas, slow steady-cam push forward, cyberpunk atmosphere,
saturated colors, film noir composition

10. Nature time-lapse style:

A blooming flower in a meadow opening over the course of a few seconds,
golden hour soft light from the side, gentle breeze causing slight motion
in surrounding grass, macro shot with shallow depth of field, peaceful,
National Geographic style, vivid natural colors

Each of these fills the 5-slot structure: subject, action, setting, camera + light, mood + quality. Copy them, swap parts, and adapt to your scene.

Image-to-Video: The Hunyuan-I2V Workflow

Hunyuan’s I2V variant takes a still image plus a prompt and animates the image. This is where Hunyuan often outperforms more expensive models. The trick is that the prompt should describe the animation, not the image itself.

Wrong (describes the image):

A woman in a red dress standing in a wheat field at sunset

Right (describes the motion):

The woman slowly turns her head toward the camera and smiles, a gentle
breeze moves the wheat and her hair, the sunlight stays warm and golden

The image already shows what’s there. Your prompt should describe what changes. Three patterns work consistently:

Subject motion — “the [subject] turns / walks / blinks / raises [object]”
Environmental motion — “wind moves the [grass / hair / fabric], leaves rustle, water ripples”
Light change — “the light slowly shifts from warm to cool, clouds pass and dim the scene”

Combine all three for the most dynamic results: “She raises her cup and takes a sip, steam rises gently, light flickers as if from a fireplace off-camera.”

Negative Prompts: What Hunyuan Listens To

Like Stable Diffusion, Hunyuan supports negative prompts — descriptions of what not to include. The standard quality negatives:

blurry, low quality, distorted, watermark, text, logo, low resolution,
deformed, ugly, bad anatomy, extra limbs, missing limbs, mutated hands,
flickering, frame jumps

Add specific negatives based on what your prompt is producing:

Faces too smooth → airbrushed, plastic skin, doll-like
Generic-looking output → generic, stock footage, cliché
Wrong era for a period piece → modern clothing, smartphones, contemporary

Don’t over-stuff negatives. Hunyuan responds to focused negative prompts (5-10 terms) better than to long lists.

Resolution and Length Trade-offs

HunyuanVideo-1.5 supports multiple resolutions and durations, but quality isn’t uniform across them:

Resolution	Duration	Best for
720p	3-5s	Most reliable quality, fastest generation
720p	6-10s	Good for cinematic scenes; slight degradation past 8s
1080p	3-5s	Production quality; longer generation time
1080p	6-10s	Highest quality but expensive; some flicker risk

For social media content (TikTok, Reels, Shorts), 720p × 5-7 seconds is the sweet spot. For ad spots or higher-stakes content, generate at 1080p × 5 seconds and use multiple clips edited together rather than one long generation.

If you need to compare against other models, see our breakdown of Sora 2 vs Veo 3 vs Kling 3 — Hunyuan slots in below those for absolute quality but ahead of every other open-source option.

The Prompt-Rewrite Trick

Tencent shipped a companion model called HunyuanVideo-PromptRewrite. It takes your short prompt and rewrites it into Hunyuan’s preferred long form. You can run it locally or use the rewrite logic manually.

What it does:

Adds the missing slots (camera, light, mood) when you only specify subject and action
Standardizes terminology (your “fast” becomes “rapid motion”)
Adds quality tags consistently (“cinematic, 4K, photo-realistic”)

Two modes: Normal (faithful expansion) and Master (creative liberties). Master often produces the most cinematic output but can drift from your original intent. Use Normal if you have a specific scene in mind; use Master for “make this look cool” prompts where you don’t care about exact details.

If you don’t want to run the rewrite model, you can prompt a generic LLM to do it: “Rewrite this video prompt in the Hunyuan structure: [subject doing action] [in setting], [camera and light], [mood, quality]: my original prompt here”. You’ll get 80% of the rewrite-model quality.

Where Hunyuan Fits in a 2026 Workflow

For most creators in 2026, the practical workflow is:

Hunyuan for bulk content where you control the GPU. Free, unlimited, decent quality. Good for content marketing, social posts, B-roll.
Veo 3 / Sora for hero shots where quality matters more than budget. See our Veo 3 prompt guide for more on that side.
Kling / Runway for specific stylistic looks they handle better.
Pika / Luma for cheap quick iterations and style tests.

The ability to write good prompts transfers across all of them. Master the 5-slot structure and you can move between models without restarting your prompt engineering. LzyPrompt generates prompts in the right structure for each tool — pick Hunyuan and you get the right formatting; switch to Veo and you get Veo’s preferred phrasing.

Common Mistakes

Underspecifying camera motion. “A car driving” without camera direction puts the car in the center of the frame, locked off. Add “low tracking shot from the side” or “front-on dolly back” to control framing.
Too many subjects. Hunyuan handles one subject well, two okay, three or more poorly. Crowds work as background; named subjects should stay singular.
Modern + period mixing. “A medieval knight checking his iPhone” produces incoherent output. Keep your time period and aesthetic consistent.
Forgetting quality tags. “Cinematic, 4K, photo-realistic” at the end of the prompt measurably improves output. They’re not magic words but they push the model toward higher-quality reference frames.
Trying to fix bad output with longer prompts. If a prompt isn’t working, simplify and rebuild from the 5-slot structure. Adding more adjectives to a broken prompt rarely helps.

FAQ

What’s the difference between HunyuanVideo and HunyuanVideo-I2V?

HunyuanVideo is the text-to-video model — you give it a prompt, it generates a clip. HunyuanVideo-I2V is the image-to-video variant — you give it a starting image plus a prompt that describes the motion, and it animates that image. I2V is generally easier to control because you’ve already locked the subject and composition in the input image.

Do I need a high-end GPU to run Hunyuan locally?

The full HunyuanVideo model needs around 60GB of VRAM, which is high-end workstation territory. HunyuanVideo-1.5 reduced that to roughly 24-32GB depending on resolution and quantization, which fits a single RTX 4090 or similar. Cloud GPU services (RunPod, Replicate, fal.ai) host the model if you don’t have local hardware.

How does Hunyuan compare to Sora and Veo for prompt sensitivity?

Hunyuan is more prompt-sensitive than Sora and slightly less so than Veo. Sora is forgiving — even a vague prompt produces something usable. Hunyuan rewards specificity, especially in camera and lighting language. Veo is somewhere in between but more responsive to cinematic terminology than either.

Can Hunyuan do realistic human faces?

For static or slow-moving close-ups, yes — Hunyuan-1.5 handles faces about as well as mid-tier closed models. For dramatic facial expressions or speaking characters, it still falls short of Sora and Veo. Hands remain a weak spot across all current AI video models, including Hunyuan.

Should I use Master mode or Normal mode for the prompt rewriter?

Normal mode if you have a specific scene in mind and want the model to stay faithful to your description. Master mode if your prompt is loose (“a cool space scene”) and you want the rewriter to invent strong cinematic details. For production work where you’re matching a storyboard, always use Normal. For exploration and idea-finding, Master often produces more impressive output.

Is the open-source license safe for commercial use?

HunyuanVideo’s license is permissive but not completely unrestricted — Tencent reserves rights for users with very large user bases and applies usage limits to the official API. For most independent creators, freelancers, and small studios, commercial use is fine. Check the current license on the Hugging Face page before integrating into a product, since terms have updated since the initial release.