Vidu Q1 Reference to Video is an advanced AI image generator designed to transform reference photos into realistic and consistent video scenes. Developed as part of the Vidu Q-series, this model specializes in maintaining character and scene consistency across multiple images, making it ideal for applications where visual continuity is critical. The model leverages multimodal AI capabilities, allowing users to input up to seven reference images to guide the generation of video clips that preserve identity, style, and key visual elements throughout the sequence.

A unique aspect of Vidu Q1 is its integration of audio generation, including background music and sound effects, which enables the creation of immersive multimedia experiences. The model also features intelligent directorial assistance, offering automated cinematography suggestions and narrative structure guidance to help users craft compelling video content regardless of their filmmaking expertise. Vidu Q1 stands out for its ability to produce dynamic anime-style videos, high-fidelity character consistency, and strong prompt adherence, making it a versatile tool for both professional and creative projects.

• Architecture: Multimodal AI (Q-series architecture)
• Parameters: Not publicly specified
• Resolution: Supports 720p and 1080p outputs
• Input/Output formats: Accepts 1–7 reference images (JPG, PNG); outputs video clips (MP4)
• Performance metrics: High fidelity in character/scene consistency, strong prompt adherence, supports durations of 4s/8s per clip

• Reference image quality and diversity directly impact output consistency and realism
• Best results are achieved with 3–7 well-lit, varied reference images showing key poses or angles
• Prompt specificity (subject, action, style, mood) improves adherence and output quality
• Longer clips may require more reference images for stable identity and scene continuity
• Balancing resolution and duration can affect generation speed and resource usage
• Overly complex prompts or mismatched references may reduce output fidelity
• Iterative refinement (preview, tweak, regenerate) is recommended for optimal results

• Use high-resolution, clear reference images with consistent lighting and background
• Structure prompts to clearly describe subject, desired action, camera movement, and style
• For character-driven scenes, include reference images showing different facial expressions and poses
• Start with shorter durations (4s) for faster iterations, then scale up as needed
• Experiment with prompt wording to fine-tune style and mood (e.g., “cinematic,” “anime,” “soft lighting”)
• Use the start/end frame control for smoother transitions in image-to-video mode
• Preview and adjust settings iteratively to achieve the desired narrative and visual consistency
• Leverage the model’s audio generation for enhanced immersion by specifying mood or genre in the prompt

• Generates realistic and consistent video scenes from multiple reference images
• Maintains character and scene identity across frames and clips
• Supports multimodal generation, including background music and sound effects
• Offers automated cinematography and narrative guidance for improved storytelling
• Excels at anime-style video generation with strong prompt adherence
• Produces high-fidelity outputs with smooth camera motion and stable parallax effects
• Adaptable to various creative and professional video production needs

• Professional content creation for marketing, advertising, and branding videos
• Storyboarding and previsualization for film and animation projects
• Anime and animated short production with consistent character design
• Social media content, including viral video clips and immersive narratives
• Educational and explainer videos with custom visuals and audio
• Personal creative projects such as fan animations, music videos, and visual storytelling
• Industry-specific applications in gaming, virtual production, and digital media

• Some experimental features, such as advanced audio generation, may behave unpredictably in edge cases
• Users report occasional prompt drift if reference images are too dissimilar or poorly lit
• Performance benchmarks indicate high resource requirements for longer clips and higher resolutions
• Consistency across frames is generally strong, but complex scenes may require more references for stability
• Positive feedback highlights ease of use, high-quality outputs, and strong character consistency
• Negative feedback patterns include occasional artifacts, slow generation for high-res clips, and limited control over fine details
• Community discussions recommend iterative refinement and careful prompt engineering for best results

• Requires multiple high-quality reference images for optimal consistency; single-image mode may yield less stable results
• May not be suitable for highly complex scenes or rapid motion without sufficient reference diversity
• Generation speed and resource usage can be limiting for longer or high-resolution video clips