Wan 2.1 14B Vace | Fast Image-to-Video API

Wan 2.1 14B VACE — wavespeed-ai/wan-2.1-14b-vace

Wan 2.1 14B VACE is a versatile, production-oriented video generation and editing model that supports multi-input workflows. You can provide a text prompt plus up to 5 reference images, and optionally add a source video, masks, or start/end frames to guide motion, structure, and edits. It also includes multiple task modes (e.g., depth) for more controlled video understanding and generation.

Key capabilities

Prompt-driven video generation with multi-modal controls
Up to 5 reference images to guide identity, style, wardrobe, or scene details
Optional video input for video-to-video transformation workflows
Mask support (mask_video / mask_image) for region-based edits
First/last frame guidance (first_image / last_image) for better continuity
Task modes (e.g., depth) for structured control and more predictable results

Use cases

Reference-guided video generation (character/style consistency across shots)
Video editing with masks (replace background, remove objects, localized changes)
Start-to-end guided storytelling using first_image + last_image
Video-to-video restyling (apply a new look while keeping motion)
Controlled motion and composition using task settings (e.g., depth)

Pricing

Mode	Size	Price per 5s video
Standard	832×480	$0.30
Fast Mode	832×480	$0.15
Standard	1280×720 / 720×1280	$0.40
Fast Mode	1280×720 / 720×1280	$0.25

Longer durations are billed in steps based on duration.

Inputs

prompt (required): what should happen in the video
images (optional): up to 5 reference images
video (optional): source video for video-to-video workflows
mask_video (optional): video mask for localized video edits
mask_image (optional): image mask for localized edits
first_image (optional): starting frame guidance
last_image (optional): ending frame guidance
negative_prompt (optional): what to avoid

Parameters

task: control mode selector (e.g., depth)
duration: video length (e.g., 5s)
size: output resolution (e.g., 832×480, 1280×720)
num_inference_steps: sampling steps
guidance_scale: prompt adherence strength
flow_shift: motion/flow behavior tuning
context_scale: reference/context strength tuning
seed: random seed (-1 for random; fixed for reproducibility)
enable_fast_mode: speed-optimized mode (if available in your UI)

Prompting guide (multi-reference + optional masks)

A reliable structure:

Define the main subject and action
Specify environment and camera beats
Assign roles to references (identity/style/outfit/background)
If using masks, clearly state what changes inside vs. outside the mask
If using first/last frames, describe how the motion should transition between them

Template: Use image 1 for identity, image 2 for outfit, image 3 for style. Generate a 5-second clip where [action]. Keep identity consistent. If mask is provided, change only the masked region to [edit], keep everything else unchanged.

Example prompts

An elegant lady carefully selects bags in a boutique. Soft natural lighting, shallow depth of field, subtle camera push-in, gentle hand movements, realistic fabric and leather textures.
Use the reference images for the same character and outfit. Walk through a luxury store aisle, turn to examine a handbag, warm highlights on leather, calm cinematic pacing.
If mask is provided: Replace only the masked background with a modern boutique interior, keep the subject unchanged, match lighting and shadows.

Wan 2.1 14b Vace API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1-14b-vace with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Wan 2.1 14b Vace below.

HTTP example

# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1-14b-vace" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d '{
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "video": "https://example.com/your-input.mp4",
    "task": "depth",
    "negative_prompt": "blurry, low quality, distorted",
    "duration": 5,
    "size": "832*480",
    "num_inference_steps": 30,
    "guidance_scale": 5,
    "flow_shift": 16,
    "context_scale": 1,
    "seed": -1
}'

# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY"

# When status is "completed", read the output from data.outputs[0].

Node.js example

// npm install wavespeed
const WaveSpeed = require('wavespeed');

const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env

const result = await client.run("wavespeed-ai/wan-2.1-14b-vace", {
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "video": "https://example.com/your-input.mp4",
        "task": "depth",
        "negative_prompt": "blurry, low quality, distorted",
        "duration": 5,
        "size": "832*480",
        "num_inference_steps": 30,
        "guidance_scale": 5,
        "flow_shift": 16,
        "context_scale": 1,
        "seed": -1
});

console.log(result.outputs[0]); // → URL of the generated output

Python example

# pip install wavespeed
import wavespeed

output = wavespeed.run(
    "wavespeed-ai/wan-2.1-14b-vace",
    {
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "video": "https://example.com/your-input.mp4",
    "task": "depth",
    "negative_prompt": "blurry, low quality, distorted",
    "duration": 5,
    "size": "832*480",
    "num_inference_steps": 30,
    "guidance_scale": 5,
    "flow_shift": 16,
    "context_scale": 1,
    "seed": -1
}
)

print(output["outputs"][0])  # → URL of the generated output

Wan 2.1 14b Vace API — Frequently asked questions

What is the Wan 2.1 14b Vace API?

Wan 2.1 14b Vace is a WaveSpeedAI model for video generation from images, exposed as a REST API on WaveSpeedAI. WAN 2.1 VACE is an all-in-one video model supporting Reference-to-Video (Image-to-Video), V2V, Masked V2V and Move/Swap/Animate capabilities. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Wan 2.1 14b Vace API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID; poll the prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/wan-2.1-14b-vace.

How much does Wan 2.1 14b Vace cost per run?

Wan 2.1 14b Vace starts at $0.30 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Wan 2.1 14b Vace accept?

Key inputs: `prompt`, `images`, `video`, `duration`, `size`, `seed`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/wavespeed-ai/wan-2.1-14b-vace.

How long does Wan 2.1 14b Vace take to generate?

Average end-to-end generation time on WaveSpeedAI is around 118 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.

Can I use Wan 2.1 14b Vace outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

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