Browse ModelsWavespeed AIWan 2.1 I2V 720p LoRA

Wan 2.1 I2V 720p LoRA

Wan 2.1 I2V 720p LoRA

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Wan 2.1 i2v-720p generates image-to-video outputs at 720p and supports custom LoRA adapters for personalized styles and fine-tuning. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

Wan 2.1 Image-to-Video 720p LoRA

Wan 2.1 Image-to-Video 720p LoRA is a powerful image-to-video generation model that transforms static images into dynamic 720p HD videos. With full LoRA support, apply custom styles, artistic effects, or consistent character appearances to create unique animated content.


Why It Stands Out

  • Image-driven generation: Animate any image while preserving its original style and composition.
  • LoRA support: Apply custom LoRA models for specific styles, characters, or aesthetics.
  • Prompt-guided motion: Describe camera movements, actions, and atmospheric effects.
  • Prompt Enhancer: Built-in AI-powered prompt optimization for better results.
  • Negative prompt support: Exclude unwanted elements for cleaner outputs.
  • HD 720p output: Generate crisp 1280×720 videos with rich detail.
  • Fine-tuned control: Adjust guidance scale and flow shift for precise results.
  • Reproducibility: Use the seed parameter to recreate exact results.

Parameters

ParameterRequiredDescription
imageYesSource image to animate (upload or public URL).
promptYesText description of desired motion and style.
negative_promptNoElements to avoid in the output.
lorasNoLoRA models to apply (path and scale).
sizeNoOutput resolution (default: 1280×720).
num_inference_stepsNoQuality/speed trade-off (default: 30).
durationNoVideo length: 5 or 10 seconds (default: 5).
guidance_scaleNoPrompt adherence strength (default: 5).
flow_shiftNoMotion flow control (default: 5).
seedNoSet for reproducibility; -1 for random.

How to Use

  1. Upload your source image — drag and drop a file or paste a public URL.
  2. Write a prompt describing the motion and action you want. Use the Prompt Enhancer for AI-assisted optimization.
  3. Add LoRAs (optional) — select LoRA models and adjust their scale.
  4. Add a negative prompt (optional) — specify elements to exclude.
  5. Adjust parameters — set duration, guidance scale, and other settings as needed.
  6. Click Run and wait for your video to generate.
  7. Preview and download the result.

How to Use LoRA

LoRA (Low-Rank Adaptation) lets you apply custom styles without retraining the full model.

  • Add LoRA: Enter the LoRA path and adjust the scale (0.0–1.0).
  • Recommended LoRAs: Check the interface for suggested LoRAs with preview images.
  • Scale adjustment: Higher scale means stronger style effect.

Best Use Cases

  • Style Transfer — Convert images to anime, cartoon, or artistic video styles.
  • Creative Animation — Apply unique visual effects like crush, melt, or transform.
  • Social Media Content — Turn photos into engaging video posts.
  • Marketing & Advertising — Animate product images with custom brand styles.
  • Artistic Projects — Create unique animated content with specific aesthetics.

Pricing

DurationPrice
5 seconds$0.30
10 seconds$0.45

Pro Tips for Best Quality

  • Use high-resolution, well-lit source images for optimal results.
  • Be specific in your prompt — describe the action, motion, and effects you want.
  • Start with LoRA scale around 0.7–1.0 and adjust based on results.
  • Use negative prompts to reduce artifacts like blur, distortion, or unwanted motion.
  • Check recommended LoRAs for inspiration and proven style effects.
  • Fix the seed when iterating to compare different parameter settings.

Notes

  • Ensure uploaded image URLs are publicly accessible.
  • Higher num_inference_steps produces better quality but increases generation time.
  • Processing time varies based on parameters and current queue load.
  • Please ensure your content complies with usage guidelines.

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-720p-lora" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "loras": [
        {
            "path": "Remade-AI/Crush",
            "scale": 1
        }
    ],
    "size": "1280*720",
    "num_inference_steps": 30,
    "duration": 5,
    "guidance_scale": 5,
    "flow_shift": 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}"

Parameters

Task Submission Parameters

Request Parameters

ParameterTypeRequiredDefaultRangeDescription
imagestringYes-The image for generating the output.
promptstringYes-
negative_promptstringNo-The negative prompt for the generation.
lorasarrayNomax 3 itemsThe LoRA weights for generating the output.
loras[].pathstringYes-Path to the LoRA model
loras[].scalefloatYes-0.0 ~ 4.0Scale of the LoRA model
sizestringNo1280*7201280*720, 720*1280The 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_shiftnumberNo51.0 ~ 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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