Browse ModelsWavespeed AIWan 2.1 I2V 480p LoRA Ultra Fast

Wan 2.1 I2V 480p LoRA Ultra Fast

Wan 2.1 I2V 480p LoRA Ultra Fast

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Wan 2.1 i2v 480p Ultra-Fast generates unlimited image-to-video content at 480p, supporting custom LoRAs for style personalization. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

Wan 2.1 I2V 480p LoRA Ultra Fast — wavespeed-ai/wan-2.1/i2v-480p-lora-ultra-fast

Wan 2.1 I2V 480p LoRA Ultra Fast is a fast, cost-efficient image-to-video model that animates a single reference image into a short clip guided by your prompt. It supports LoRA injection so you can enforce a specific motion/style/character look while keeping generation lightweight—great for rapid iteration, previews, and high-volume variants.

Key capabilities

  • Image-to-video (I2V) generation at 480p
  • Prompt-driven motion with coherent, image-anchored composition
  • LoRA support for consistent style, motion patterns, or character look
  • Tunable quality/speed via inference steps
  • Great for quick drafts, storyboards, and large-scale A/B testing

Use cases

  • Quick animation passes for key art, avatars, and character cards
  • Motion experiments (e.g., 360° rotation, walk cycles, subtle facial motion) using LoRAs
  • Fast concept validation for ads and social loops
  • Batch generation of multiple motion variants from the same image
  • Previews before re-running at higher resolution (720p/1080p)

Pricing

ResolutionDurationPrice per runEffective price per second
480p5s$0.125$0.025/s
480p10s$0.188$0.019/s

Inputs

  • image (required): reference image that anchors subject and composition
  • prompt (required): action + camera + motion intent
  • loras (optional): LoRA items to apply (path + scale)

Parameters

  • duration: clip length (commonly 5s or 10s)
  • num_inference_steps: sampling steps (higher can improve motion coherence)
  • guidance_scale: prompt adherence strength
  • flow_shift: motion behavior tuning
  • negative_prompt: what to avoid (blur, jitter, artifacts, low quality)
  • seed: set for reproducible results (-1 for random)
  • size: output size preset (e.g., 832×480)
  • loras[].path: LoRA identifier (e.g., creator/name) or a safetensors URL
  • loras[].scale: LoRA strength (start around 0.8–1.0)

Prompting guide (I2V)

Keep the prompt like a director note:

  • Subject: what’s on screen
  • Motion: what changes over time (spin, turn, step forward, hair movement)
  • Camera: locked-off / slow push-in / orbit / handheld, etc.
  • Constraints: “smooth motion”, “no camera shake”, “keep character consistent”

Example prompt

Felt doll standing on a grassy field. The doll slowly rotates for a full 360-degree turn with smooth motion, stable framing, no jitter. Soft daylight, shallow depth of field, gentle background bokeh.

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result


# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/i2v-480p-lora-ultra-fast" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "loras": [
        {
            "path": "Remade-AI/Rotate",
            "scale": 1
        }
    ],
    "size": "832*480",
    "num_inference_steps": 30,
    "duration": 5,
    "guidance_scale": 5,
    "flow_shift": 3,
    "seed": -1
}'

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

Parameters

Task Submission Parameters

Request Parameters

ParameterTypeRequiredDefaultRangeDescription
imagestringYes-The image for generating the output.
promptstringYes-The positive prompt for the generation.
negative_promptstringNo-The negative prompt for the generation.
lorasarrayNomax 3 itemsList of LoRAs to apply (max 3).
loras[].pathstringYes-Path to the LoRA model
loras[].scalefloatYes-0.0 ~ 4.0Scale of the LoRA model
sizestringNo832*480832*480, 480*832The size of the generated media in pixels (width*height).
num_inference_stepsintegerNo301 ~ 40The number of inference steps to perform.
durationintegerNo55 ~ 10The duration of the generated media in seconds.
guidance_scalenumberNo50.00 ~ 20.00The guidance scale to use for the generation.
flow_shiftnumberNo31.0 ~ 10.0The shift value for the timestep schedule for flow matching.
seedintegerNo-1-1 ~ 2147483647The random seed to use for the generation. -1 means a random seed will be used.

Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
data.idstringUnique identifier for the prediction, Task Id
data.modelstringModel ID used for the prediction
data.outputsarrayArray of URLs to the generated content (empty when status is not completed)
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.has_nsfw_contentsarrayArray of boolean values indicating NSFW detection for each output
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds

Result Request Parameters

ParameterTypeRequiredDefaultDescription
idstringYes-Task ID

Result Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
dataobjectThe prediction data object containing all details
data.idstringUnique identifier for the prediction, the ID of the prediction to get
data.modelstringModel ID used for the prediction
data.outputsstringArray of URLs to the generated content (empty when status is not completed).
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds
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