Wan 2.6 Developer Guide: Next-Generation Video Generation

What Changed in Wan 2.6

The Wan 2.6 model on fal introduces multi-modal video generation with three specialized endpoints: text-to-video (T2V), reference-to-video (R2V), and image-to-video (I2V). Each pathway handles distinct production requirements, from generating videos from text descriptions to maintaining character consistency across multiple shots. The model supports resolutions up to 1080p, durations from 5 to 15 seconds depending on the mode, and native audio integration.

This guide covers implementation patterns, API specifications, and production considerations for developers building video generation into applications.

Core Capabilities and Constraints

Wan 2.6 provides three generation pathways with different technical specifications:

Text-to-Video (T2V)

Creates video from text prompts with the following parameters:

Resolutions: 720p, 1080p (no 480p support)
Aspect Ratios: 16:9, 9:16, 1:1, 4:3, 3:4
Duration: 5, 10, or 15 seconds
Multi-shot: Intelligent scene segmentation for narrative content
Audio: Optional background audio integration

T2V works for storyboarding, concept visualization, and creating video content from scratch. The multi-shot capability segments longer prompts into coherent scenes rather than generating a single continuous shot.

Reference-to-Video (R2V)

Maintains subject consistency from reference videos:

Resolutions: 720p, 1080p
Aspect Ratios: 16:9, 9:16, 1:1, 4:3, 3:4
Duration: 5 or 10 seconds (15 seconds not supported)
References: 1 to 3 videos, tagged as @Video1, @Video2, @Video3

R2V preserves visual characteristics of people, animals, or objects across generated videos. This enables character continuity in multi-video projects but requires clean, well-lit reference footage.

Image-to-Video (I2V)

Animates static images into video sequences:

Resolutions: 480p, 720p, 1080p
Duration: 5, 10, or 15 seconds
Image Requirements: 360px minimum dimensions, 2000px maximum, 100MB file size limit
Audio: Optional background audio
Multi-shot: Defaults to false (single continuous shot)[^3]

I2V animates product showcases, illustrations, or existing imagery. Source image quality directly impacts output fidelity; higher resolution source images recommended for production use.

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API Setup and Authentication

Install the fal client library:

pip install fal-client

Initialize with your API key:

import fal_client

fal = fal_client.FalClient(api_key="your_api_key")

All three endpoints require authentication via the fal API key in request headers. See the fal.ai documentation for complete authentication details.

Text-to-Video Implementation

Basic T2V request structure:

result = fal.subscribe("wan/v2.6/text-to-video", {
    "prompt": "A serene mountain landscape with flowing rivers and changing seasons",
    "duration": "10",
    "resolution": "1080p",
    "aspect_ratio": "16:9"
})

video_url = result["video"]["url"]

Prompt Structure for Multi-Shot Videos

For coherent multi-shot sequences, use temporal markers:

prompt = """
Overall scene description.
Shot 1 [0-3s] First scene details, camera movement, lighting.
Shot 2 [3-6s] Second scene details, transition elements.
Shot 3 [6-10s] Final scene details, resolution.
"""

Include visual specifics: lighting conditions, camera angles, movement direction, and style references. The enable_prompt_expansion parameter uses an LLM to enhance short prompts, adding context and detail automatically.

Reference-to-Video for Character Consistency

R2V maintains subject identity across generated videos:

result = fal.subscribe("wan/v2.6/reference-to-video", {
    "prompt": "Dance battle between @Video1 and @Video2 in a futuristic neon cityscape",
    "video_urls": [
        "https://example.com/reference_video1.mp4",
        "https://example.com/reference_video2.mp4"
    ],
    "duration": "10",
    "resolution": "1080p"
})

R2V Requirements

Reference videos must be publicly accessible URLs
Subjects should be clearly visible and well-lit
Use 1 to 3 references maximum
Tag references consistently in prompts (@Video1, @Video2, @Video3)

The model extracts subject characteristics from reference videos and applies them to the generated content. Performance degrades with poor lighting, occlusion, or low-resolution references.

Image-to-Video Animation

The Wan 2.6 image-to-video endpoint animates static images:

result = fal.subscribe("wan/v2.6/image-to-video", {
    "prompt": "The car drives along a coastal highway at sunset",
    "image_url": "https://example.com/car_image.jpg",
    "duration": "5",
    "resolution": "1080p"
})

I2V Optimization

Start with high-resolution source images (minimum 360px, maximum 2000px per dimension)
Describe physically plausible motion for the image content
Consider composition; leave space for motion paths
Multi-shot defaults to false; enable explicitly if needed

I2V performs best when the prompt describes motion that naturally extends from the static image rather than introducing entirely new elements.

Advanced Configuration

Audio Integration

Add background audio to generated videos:

result = fal.subscribe("wan/v2.6/text-to-video", {
    "prompt": "Orchestra performing in a grand concert hall",
    "audio_url": "https://example.com/orchestral_music.mp3",
    "duration": "15"
})

Audio handling works as follows:

If audio exceeds video duration, it's truncated
If audio is shorter, remaining video is silent
Supported formats: WAV, MP3
Duration: 3 to 30 seconds
File size: up to 15MB

Prompt Expansion

The enable_prompt_expansion parameter enhances prompts via LLM:

result = fal.subscribe("wan/v2.6/text-to-video", {
    "prompt": "Car racing through desert",
    "enable_prompt_expansion": True
})

expanded_prompt = result["actual_prompt"]

This adds cinematographic detail and context. Increases processing time slightly but improves output quality for brief prompts.

Reproducible Generation

Specify a seed for consistent outputs:

result = fal.subscribe("wan/v2.6/text-to-video", {
    "prompt": "Futuristic cityscape with flying vehicles",
    "seed": 42
})

Identical parameters with the same seed produce deterministic results, useful for A/B testing or iterative refinement.

Production Patterns

Error Handling

Implement error handling for API failures:

try:
    result = fal.subscribe("wan/v2.6/text-to-video", {...})
except fal_client.ServerError as e:
    # Handle 5xx errors
    print(f"Server error: {e}")
except fal_client.ClientError as e:
    # Handle 4xx errors
    print(f"Client error: {e}")

Common failure modes include content moderation rejections, invalid parameters, and timeout errors for complex generations.

Progress Monitoring

Track generation progress for longer videos using the Queue API:

def on_progress(update):
    if isinstance(update, fal_client.InProgress):
        print(f"Processing: {update.progress}%")

result = fal.subscribe(
    "wan/v2.6/text-to-video",
    {...},
    on_update=on_progress
)

Generation times vary by resolution, duration, and current system load.

Scaling Considerations

fal infrastructure provides sub-second cold start times, automatic concurrent request scaling, and multi-region availability¹. This handles traffic spikes without manual intervention.

Troubleshooting

Content Moderation

If generation fails due to content moderation:

Review prompt for potentially unsafe content
Verify enable_safety_checker is set appropriately (defaults to true)
Use more specific language that clearly defines intended output

Performance Optimization

To reduce generation time:

Use 720p instead of 1080p for faster processing
Start with 5-second durations and extend as needed
Run concurrent API calls for multiple variations rather than sequential requests

Capability Comparison Table

Feature	T2V	R2V	I2V
480p Support	No	No	Yes
720p Support	Yes	Yes	Yes
1080p Support	Yes	Yes	Yes
15s Duration	Yes	No	Yes
Multi-shot Default	True	True	False
Reference Videos	No	1-3	No
Source Image	No	No	Required

Implementation Priorities

For production deployment:

Start with T2V for prototyping and understanding prompt patterns
Add R2V if character consistency across videos is required
Integrate I2V for animating existing assets or product imagery
Implement error handling before scaling beyond development
Monitor generation patterns by resolution and duration choices

Wan 2.6 on fal provides the infrastructure for production video generation with minimal development overhead. The three pathways address distinct use cases; choosing the appropriate mode for each application requirement determines both output quality and operational costs.