Wan 2.7 Prompt Guide: Get Better Results Every Time

Most people treat Wan 2.7 prompts the same way they treat prompts for every other AI model: describe what you want, hope for the best, re-roll when it fails.

That approach works badly here — not because Wan 2.7 is weaker, but because it operates differently. The thinking mode means the model is processing your prompt as a brief, not a description. That changes what you put in it.

This is a practical guide to writing prompts that actually work across Wan 2.7's generation modes.

Wan 2.7 prompt guide: a creative director reviewing AI-generated storyboards, demonstrating how detailed prompts produce cinematic results

What Changes With Thinking Mode

Standard AI image and video models take your text and start generating. Wan 2.7's thinking mode inserts a reasoning step first — the model analyzes what you wrote for spatial relationships, compositional logic, and motion intent before it begins rendering.

The practical consequence: vague prompts get amplified differently here. In a standard model, a short prompt produces an average result. In Wan 2.7, a vague prompt gets interpreted through the model's internal logic — which may or may not match your intent. You get something, but it might not be something useful.

The upside: detailed prompts with clear creative intent produce output that matches them more accurately than in previous generations. The model is reasoning toward your description, not just pattern-matching to it.

Prompt Structure That Works

Think of a Wan 2.7 prompt as a creative brief with these components:

Subject → what or who is in the scene Action → what is happening or moving Environment → where, with what light and atmosphere Camera → angle, movement, focal length if relevant Style → aesthetic direction, if specific

You do not need all five for every generation. But the more of them you supply with specificity, the less the model has to infer — and inference is where drift happens.

Weak prompt:

A woman walking in a city at night

Strong prompt:

A young woman in a dark coat walks slowly through a rain-slicked city street at night, neon signs reflecting in puddles, medium shot, tracking camera following from behind, cinematic color grading, shallow depth of field

The second prompt does not just describe more — it resolves the questions the model would otherwise guess at: distance, pacing, camera behavior, lighting character, visual treatment.

Wan 2.7 prompt comparison: vague prompt vs detailed brief, showing the difference in output quality and cinematic control

Prompting for Text-to-Video (T2V)

For T2V, your prompt needs to carry the motion logic — not just the scene.

Always specify:

What moves (subject, camera, or both)
The direction and quality of that movement (slowly, sharply, drifting, cutting)
The tempo and pacing of the scene

What not to do: describe a static image and expect the model to invent interesting motion. It will, but you will not control what it invents.

Examples that work well:

A drone shot slowly descending through morning fog over a mountain forest, the canopy breaking open to reveal a mountain lake below, golden hour light filtering through the mist

A chef's hands carefully plate a dish in a Michelin-star kitchen, extreme close-up, shallow depth of field, the plate rotating slightly as garnishes are placed, warm overhead spot lighting

Examples that underperform:

A person in a room (no motion direction, no camera behavior, no environmental detail)

A zombie prowling in a prison cell doing a simple slow action (from a real community test — vague physical instructions tend to produce inconsistent results even with simple actions)

Prompting for Image-to-Video (I2V)

When you supply a reference image, the model locks the visual identity from that image and generates motion. Your prompt's job shifts — it is now about what changes, not what is there.

Focus your prompt on:

What moves in the scene
Camera behavior
Atmospheric changes (light shifting, wind, weather)

If your prompt describes things already visible in the image (the subject's appearance, the background setting), you are wasting prompt budget. The model already has that information from the reference.

Good I2V prompt pattern:

[Atmospheric change or camera move] + [subject motion] + [pacing and quality]

The camera slowly pushes in as the woman turns to look over her shoulder, her coat moving slightly in the wind, golden light warming the scene

Prompting for Text-to-Image (T2I)

For Wan 2.7's image generation, the community has found that highly specific cinematic or photographic prompts outperform generic descriptions. The thinking mode and text rendering capability both reward detailed intent.

A prompt from the community that produced strong results versus competing models:

Weathered elderly male, wire-rimmed spectacles, drenched skin and hair, moisture-slicked jacket, macro texture, intense gaze, tight close-up, clinging water droplets, heavy downpour, blurred rain streaks, pitch-black atmosphere, high-contrast monochrome, chiaroscuro lighting, specular highlights, Leica M11 Monochrom, 50mm f/1.4, Ilford HP5 grain, pushed +2 stops, high micro-contrast, Sebastiao Salgado style, gritty realism, 1:1

This level of specificity — camera body, lens, film stock, photographer style — is exactly what the thinking mode is designed to process and act on.

For text rendering inside images, be explicit:

A clean infographic poster with the headline "MOVE FAST" in bold Helvetica at the top, three bullet points below in 24pt, dark blue background, white text, minimal design

Prompting for Editing Modes

When using Wan 2.7's instruction-based video editing or image editing, treat your prompt as a targeted instruction, not a scene description.

Scope it to one change at a time:

Good: "Remove the car from the background and fill with the existing street texture" Good: "Change the jacket color from beige to deep navy" Good: "Slow the camera pan in the second half of the clip"

Bad: "Make the whole scene look more cinematic and change the lighting and add some people in the background" — too many changes in one instruction leads to inconsistent application.

Common Mistakes

Treating it like a keyword list. Commas between disconnected words ("sunset, dramatic, cinematic, 4K, award-winning") tell the model very little about what you actually want. Connect your descriptors into a coherent scene.

No motion logic in video prompts. If you do not describe what moves and how, the model infers it. That is fine for casual output, not fine for production work.

Neglecting camera behavior. Camera direction (push in, track right, crane up, handheld) dramatically affects the feel of the output. Omitting it is not neutral — the model defaults to something, and you may not want that default.

Over-prompting the reference in I2V. If you have supplied an image, do not re-describe the image in the prompt. Describe the motion and change instead.

Under-prompting and expecting the Prompt Enhancer to fix it. The built-in enhancer can expand a simple description — but it can only work with what you give it. If your seed prompt is directionless, the enhancement will be directionless.

Prompts From Other Models Often Work

One practical shortcut: Wan 2.7 is generally compatible with prompts written for Kling and Seedance. If you have prompts from other workflows that produced results you liked, import them and adjust for Wan 2.7's motion and camera logic. The community has confirmed this works without major translation needed.

Try your prompts at wan27.org.