Wan Flf2v

Wan Flf2v

Playground

Wan-2.1 FLF2V converts a start and end frame into a smooth, coherent video sequence, bridging frames with realistic motion transitions. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

Wan FLF2V — wavespeed-ai/wan-flf2v

Wan FLF2V is a first-last-frame-to-video model that generates a short video by interpolating a coherent motion path between a first_image and a last_image, guided by an optional text prompt. Provide the starting frame and the ending frame, then describe what happens in between (e.g., a transformation, assembly, reveal, or scene change). The model produces a smooth transition clip while keeping the beginning and ending states aligned with your provided frames.

Key capabilities

• First-last-frame guided video generation (first_image → last_image)
• Prompt-guided in-between action and transformation logic
• Supports negative_prompt to reduce artifacts and unwanted motion
• Size selection for different output resolutions
• Seed control for reproducible results

Use cases

• Transformation sequences (frame → completed object, sketch → final render)
• Product assembly and reveal animations (parts → finished product)
• Before/after transitions (makeover, restoration, environment change)
• Visual storytelling between two keyframes for ads and social
• Storyboard “in-betweening” from a first and last shot

Pricing

Inputs

• first_image (required): starting frame
• last_image (required): ending frame
• prompt (optional): describe the transition and what happens in between
• negative_prompt (optional): what to avoid (blur, jitter, distortion, artifacts)

Parameters

• duration: video length in seconds
• size: output resolution selection (e.g., 832×480)
• num_inference_steps: sampling steps
• guidance_scale: prompt adherence strength
• seed: random seed (-1 for random; fixed value for reproducible results)

Prompting guide (FLF2V)

A good prompt explains how the first becomes the last:

Template: Fixed camera. Start exactly from the first image and end exactly at the last image. In between, show [mechanism] smoothly and coherently. No flicker, no warping.

Example prompts

• Transform the car frame into a complete vehicle. Parts assemble step-by-step, bolts tighten, panels slide into place, paint and reflections appear gradually, cinematic lighting, smooth motion, fixed camera.
• A rough clay sculpture becomes a polished ceramic statue, cracks heal, glaze forms, subtle dust motes, steady camera, seamless transition.
• A bare room becomes fully furnished, objects slide into place naturally, lighting stays consistent, no jitter.

# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-flf2v" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "duration": 5,
    "size": "832*480",
    "num_inference_steps": 30,
    "guidance_scale": 5,
    "seed": -1
}'

# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"