Seedance 1.5 Pro is an advanced AI video generation model developed by ByteDance that specializes in creating high-quality videos with synchronized audio from static images or text prompts. The model represents a significant breakthrough in audio-visual generation by treating sound and picture as a unified system rather than separate outputs, enabling native audio-visual synthesis in a single inference pass. This approach ensures that speech rhythm, lip movements, character motion, and camera dynamics remain perfectly aligned within the same temporal reference.

The model distinguishes itself through precise multilingual and dialect lip-syncing capabilities across six languages (English, Chinese, Japanese, Korean, Spanish, and Indonesian), dynamic cinematic camera control, and enhanced narrative coherence. Seedance 1.5 Pro has been optimized for professional-grade content creation with inference speeds boosted by over 10 times through advanced acceleration frameworks, making it practical for production workflows. The model was developed using meticulous post-training optimizations including Supervised Fine-Tuning on high-quality datasets and Reinforcement Learning from Human Feedback with multi-dimensional reward models.

What makes Seedance 1.5 Pro unique is its native audio-visual generation paradigm, which consistently outperforms traditional "video plus text-to-speech" stitching pipelines, especially in long dialogue sequences and rapid scene transitions. The model demonstrates leading performance in comprehensive benchmarks designed with input from film directors and technical experts, emphasizing both visual metrics (prompt following, motion stability and vividness, aesthetic quality) and audio metrics (audio prompt following, audio-visual synchronization, audio quality, and audio expressiveness).

Architecture

Native audio-visual joint generation model with distillation-based acceleration framework

Parameters

Not publicly specified

Resolution

1080p maximum output resolution, with 720p also supported

Input/Output formats

Image-to-video (I2V) and image-to-video-audio (I2VA) generation; text-to-video with audio synthesis

Video duration

4-12 seconds (automatic or manual selection); supports automatic duration adaptation with -1 parameter setting

Frame rate

24/30 fps standard

Processing speed

10x faster inference compared to baseline through quantization and parallel processing optimization

Inference hardware

Optimized for NVIDIA H100 graphics cards and other GPU/TPU configurations

• Audio-visual synchronization is the model's primary strength; leverage this for dialogue-heavy content, voice-overs, and multilingual projects where lip-sync accuracy is critical

• The model excels at understanding complex multi-layered prompts that specify actions, camera movements, and audio elements simultaneously

• Motion stability in extremely high-intensity action sequences may require iterative refinement or prompt simplification

• For character consistency across multiple shots, consider using reference images (up to 3) to guide generation and maintain visual continuity

• The automatic duration adaptation feature (setting length to -1) evaluates narrative rhythm and motion completeness to select natural endpoints, reducing wasted generations

• Prompt engineering should include specific cinematographic language such as camera movement types (tracking shots, Hitchcock zoom, pans, tilts) for optimal results

• The model handles complex prompts with multiple simultaneous elements (camera angles, lighting, emotions, audio) effectively in single inference passes

• For professional applications requiring brand consistency, plan your prompts to leverage the model's strength in narrative coherence and emotional expressiveness

• Use cinematographic terminology in prompts to trigger advanced camera work: specify "long-take tracking shot," "dramatic reveal with Hitchcock zoom," or "professional pan and tilt" for film-grade transitions

• Structure dialogue prompts with clear speaker identification and emotional context to maximize lip-sync accuracy across multiple languages

• Set video duration to -1 to allow the model to automatically select the most appropriate length between 4-12 seconds based on narrative rhythm

• For multilingual projects, explicitly specify the language and dialect in your prompt to leverage the model's six-language lip-sync capabilities

• Combine detailed action descriptions with specific audio requirements in a single prompt rather than generating video and audio separately

• When working with reference images, provide up to 3 images to guide generation and maintain character consistency across sequential clips

• Break down extremely complex motion sequences into shorter segments if motion stability issues arise, then extend scenes using sequential clip connection

• Include emotional and tonal descriptors in prompts (e.g., "emotionally expressive dialogue," "dramatic tension") to enhance audio character quality

• Test prompts with varying complexity levels to understand the model's performance boundaries for your specific use case

• Use the model's understanding of narrative pacing by including story progression cues in prompts for more coherent multi-scene generation

• Native audio-visual synthesis with exceptional synchronization between speech, lip movements, and character motion in a single generation pass

• Multilingual lip-sync across six languages with accurate dialect support

• Advanced cinematic camera control including long-take tracking shots, Hitchcock zoom effects, professional pans, tilts, zooms, and film-grade transitions

• Complex prompt understanding that processes multiple simultaneous elements including actions, camera movements, lighting, emotions, and audio

• Emotionally expressive audio generation that adapts to narrative context and character requirements

• Automatic video duration adaptation that evaluates narrative rhythm and motion completeness to select natural endpoints

• High-fidelity 1080p video output with professional-grade quality suitable for commercial applications

• Strong performance in dialogue-driven content where lip-sync accuracy and audio-visual coherence are essential

• Image-to-video generation with smooth motion and cinematic visuals from static images

• Narrative coherence across generated sequences with consistent character behavior and scene continuity

• Rapid inference speed enabling practical production workflows through 10x acceleration optimization

• Dialogue-driven video content including interviews, conversations, and character-focused narratives where lip-sync accuracy is critical

• Multilingual content creation for global audiences, leveraging accurate lip-sync across English, Chinese, Japanese, Korean, Spanish, and Indonesian

• Music videos and audio-visual storytelling projects where synchronized sound and picture are essential

• Commercial advertising and brand content requiring professional-grade quality and character consistency across multiple shots

• Content marketing and social media campaigns emphasizing engaging audio-visual storytelling

• Film and television production workflows where cinematic camera control and narrative coherence are required

• Voice-over projects and narrated content with precise audio-visual alignment

• Long-form video generation through sequential clip connection and scene extension capabilities

• Professional animation and motion graphics from static images with synchronized audio

• Educational and instructional video content with clear dialogue and visual demonstrations

• Motion stability in extremely high-intensity action scenarios may require iterative refinement or prompt simplification based on user feedback

• The model's motion expressiveness is dynamic and bold, which may require adjustment for projects requiring subtle or restrained movement

• Complex sequences with multiple simultaneous motion elements may benefit from breaking generation into shorter segments

• Audio generation is integrated with video, meaning audio characteristics are determined by the same inference pass as visual elements

• The model evaluates narrative rhythm and motion completeness when using automatic duration adaptation, which may produce unexpected lengths if narrative cues are ambiguous

• User feedback indicates strong performance in dialogue-heavy content and professional cinematography, with particular praise for audio-visual synchronization quality

• Community discussions highlight the model's effectiveness for multilingual projects and its ability to handle complex prompt specifications

• Professional evaluations using film and television production standards show leading performance in audio-visual synchronization, motion expressiveness, and narrative consistency

• Users report that the model's understanding of cinematic language enables reliable delivery of sophisticated camera movements when prompted with film terminology

• The 10x speed improvement has been widely noted as making professional-grade content creation more accessible for production workflows

• Motion stability requires improvement in extremely complex sequences with high-intensity action, potentially necessitating iterative refinement or prompt simplification

• Video generation is limited to 4-12 second durations, requiring sequential clip connection for longer-form content

• The model's inference speed and memory footprint on consumer hardware and edge devices have not been fully documented, with specifications primarily available for standard GPU/TPU configurations