Luma Labs API
APIFreeDream Machine API for photorealistic video generation.
Capabilities16 decomposed
text-to-video generation with physics-aware motion synthesis
Medium confidenceConverts natural language text prompts into photorealistic videos by leveraging Ray3.14 or Ray2 models that synthesize physically plausible motion, object interactions, and spatial relationships. The system processes text descriptions through a diffusion-based video generation pipeline that maintains temporal coherence across frames while respecting physics constraints for object movement, gravity, and collision dynamics. Supports multiple resolution tiers (Draft to 1080p) with optional HDR rendering for enhanced color depth and dynamic range.
Implements physics-aware motion synthesis where the diffusion model is constrained by physics priors during generation, preventing physically impossible motion sequences that competitors often produce. Ray3.14 uses multi-resolution hierarchical generation (Draft→1080p) with optional HDR variant, enabling cost-efficient iteration before high-quality rendering.
Produces more physically plausible motion than Runway or Pika Labs by incorporating physics constraints during generation rather than post-processing, reducing artifacts in object interactions and gravity-dependent motion.
image-to-video generation with temporal consistency
Medium confidenceExtends a static image into a multi-second video by synthesizing natural motion and scene evolution while maintaining visual consistency with the source image. The system uses the image as a spatial anchor and generates temporally coherent frames that respect the original composition, lighting, and object positions. Supports the same resolution tiers as text-to-video (Draft to 1080p) with optional HDR, and can incorporate optional text prompts to guide motion direction.
Uses optical flow and spatial anchoring to maintain pixel-level consistency with the source image while synthesizing plausible motion, preventing the 'drift' problem where generated videos diverge from the original composition. Supports optional text guidance as a secondary control signal without overriding image fidelity.
Maintains tighter visual fidelity to source images than Runway's image-to-video by using spatial constraint layers in the diffusion process, reducing hallucination of new objects or major composition shifts.
image background removal with semantic segmentation
Medium confidenceRemoves image backgrounds using semantic segmentation to identify and isolate foreground subjects. The system analyzes image content to distinguish subject from background, then removes the background while preserving subject edges and transparency. Operates at 1 credit per image, enabling batch background removal at scale.
Uses semantic segmentation rather than simple color-based keying, enabling accurate background removal even with complex or similar-colored backgrounds. Per-image pricing (1 credit) enables cost-efficient batch processing of large image catalogs.
Provides semantic segmentation-based background removal (more accurate than color-keying) integrated into a unified image/video platform, whereas competitors like Remove.bg use similar approaches but lack integration with video generation and other creative tools.
image blending and composition with multi-image fusion
Medium confidenceBlends multiple images together using generative inpainting to create seamless compositions. The system accepts multiple source images and a text prompt describing desired composition, then generates a blended result that incorporates elements from all sources while maintaining visual coherence. Operates at 1 credit per blend, enabling rapid composition exploration.
Uses generative inpainting to blend multiple images rather than simple alpha compositing, enabling intelligent fusion that respects content semantics and creates coherent compositions even when source images have different lighting, perspective, or scale. Per-blend pricing (1 credit) enables rapid composition exploration.
Provides intelligent multi-image blending using generative inpainting, whereas traditional compositing tools require manual masking and blending, reducing friction for rapid composition exploration and prototyping.
image reframing and aspect ratio adjustment
Medium confidenceReframes images to different aspect ratios or compositions using generative outpainting and inpainting. The system accepts an image and target aspect ratio, then intelligently extends or crops the image while maintaining subject focus and visual coherence. Operates at 2 credits per reframe, enabling rapid layout adaptation for different platforms or print formats.
Uses generative outpainting with subject-aware focus detection to intelligently extend or crop images for different aspect ratios, maintaining subject prominence and composition balance. Per-reframe pricing (2 credits) enables cost-efficient generation of multiple aspect ratio versions.
Provides intelligent aspect ratio adaptation using generative outpainting (maintaining subject focus), whereas simple cropping or scaling tools lose content or distort subjects, enabling rapid multi-platform content adaptation without manual composition.
video reframing and aspect ratio adjustment with motion preservation
Medium confidenceReframes videos to different aspect ratios using generative outpainting while preserving original motion and temporal structure. The system accepts a video and target aspect ratio, then extends or crops frames intelligently while maintaining motion coherence across the sequence. Operates at 32 credits per second of video, enabling aspect ratio adaptation for different platforms.
Applies generative outpainting frame-by-frame while maintaining optical flow consistency across the sequence, preventing temporal flickering and motion discontinuities that occur when reframing is applied independently to each frame. Per-second pricing enables cost-predictable video adaptation.
Preserves motion coherence across reframed video sequences using optical flow constraints, whereas simple cropping or scaling introduces temporal artifacts, enabling high-quality aspect ratio adaptation for multi-platform distribution.
credit-based usage tracking and cost estimation
Medium confidenceProvides transparent credit-based pricing model where each operation consumes a specific number of credits based on model, resolution, and duration. The system enables users to estimate costs before generation and track cumulative usage across operations. Credits are purchased through subscription tiers (Plus $30/mo, Pro $90/mo, Ultra $300/mo) or consumed from free trial allocations.
Implements transparent credit-based pricing where costs are predictable and documented per operation (e.g., Ray3.14 1080p = 80 credits), enabling cost-aware API usage and budget planning. Subscription tiers provide monthly credit allocations with 20% discount for annual billing.
Provides transparent per-operation credit costs (unlike competitors with opaque per-API-call pricing), enabling accurate cost estimation and budget planning for large-scale projects.
subscription tier management with usage scaling
Medium confidenceOffers tiered subscription plans (Plus, Pro, Ultra) with increasing monthly credit allocations and feature access. The system maps subscription tier to usage limits and feature availability (e.g., Plus includes commercial use, Pro includes 4x usage with Luma Agents, Ultra includes 15x usage). Enables users to select tier based on projected usage and feature requirements.
Implements tiered subscription model with explicit usage scaling (Pro = 4x, Ultra = 15x) and feature gating (commercial use in Plus+, Luma Agents in Pro+), enabling users to select tier based on both budget and feature requirements. Annual billing provides 20% discount vs. monthly.
Provides transparent tiered pricing with clear feature differentiation (commercial use, Luma Agents access), whereas competitors often use opaque per-API-call pricing without clear tier benefits, enabling easier subscription selection and budget planning.
video-to-video transformation with motion preservation
Medium confidenceAccepts an input video and applies style transfer, motion enhancement, or quality upscaling while preserving the original motion trajectories and temporal structure. The system analyzes optical flow from the input video to extract motion patterns, then regenerates frames with enhanced visual quality, different artistic styles, or improved physics simulation. Operates at the same resolution tiers as other generation modes but with higher credit costs (12-768 credits) due to per-frame processing complexity.
Decouples motion analysis (optical flow extraction) from visual synthesis, allowing independent control over motion preservation vs. style transformation. Uses hierarchical flow estimation to handle multi-scale motion patterns, preventing temporal flickering that occurs when motion is not properly aligned across frames.
Preserves motion more accurately than Runway's video-to-video by explicitly extracting and re-applying optical flow constraints, reducing the temporal jitter and motion drift common in style-transfer-only approaches.
cinematic camera control with preset motion patterns
Medium confidenceProvides predefined camera movement templates (pan, tilt, zoom, dolly, crane) that can be applied to text-to-video or image-to-video generations to create professional cinematography effects. The system interpolates camera parameters across the video duration using smooth spline curves, ensuring natural-looking motion without jarring transitions. Camera movements are constrained to physically plausible trajectories and interact correctly with scene geometry and object occlusion.
Implements camera movements as differentiable constraints in the video generation pipeline rather than post-processing effects, allowing the diffusion model to generate content that anticipates camera motion (e.g., objects moving into frame before camera pans). Preset patterns use spline interpolation with automatic ease-in/ease-out to avoid temporal discontinuities.
Integrates camera control into generation rather than applying it post-hoc, producing more natural-looking results where scene content and camera motion are temporally synchronized, unlike competitors that apply camera effects after generation.
multi-model video generation with provider abstraction
Medium confidenceAbstracts access to multiple third-party video generation models (Kling 2.6, Veo 3/3.1) alongside proprietary Ray models through a unified API interface. The system routes requests to the appropriate model backend based on user selection, handling model-specific input/output format translation and credit cost mapping. Enables users to compare output quality across models or select models based on cost-performance tradeoffs without managing separate API integrations.
Implements a provider abstraction layer that normalizes request/response formats across heterogeneous video generation backends (proprietary Ray models + third-party Kling/Veo), allowing single-API access to models with different input constraints and output characteristics. Credit cost mapping is transparent per model, enabling cost-aware selection.
Provides unified access to multiple state-of-the-art models (Ray, Kling, Veo) without requiring separate API keys or integrations, unlike competitors that typically support only their own models or require manual switching between platforms.
image generation with character and style reference control
Medium confidenceGenerates images using Luma Photon model with optional character reference and visual style blending inputs. The system uses reference images as spatial and stylistic anchors, allowing users to maintain consistent character appearance across multiple generations or blend visual styles from multiple reference images. Supports multiple quality tiers (1080p, 1080p fast, 720p coming soon) with fast variants enabling rapid iteration.
Implements character and style reference as separate control channels in the diffusion model, allowing independent adjustment of character consistency vs. style influence. Uses CLIP-based embedding alignment to match character appearance while preserving style diversity, preventing the 'style collapse' problem where strong style references override character identity.
Provides explicit character reference control (separate from style) that competitors like DALL-E or Midjourney lack, enabling consistent character generation across variations without requiring complex prompt engineering or LoRA fine-tuning.
image modification with inpainting and outpainting
Medium confidenceModifies existing images through inpainting (editing masked regions) or outpainting (extending image boundaries) using multiple proprietary models (Uni-1, Seedream, Nano Banana). The system accepts a base image, optional mask defining regions to modify, and text prompt describing desired changes. Supports multiple resolution tiers (1K to 4K for Seedream/Nano Banana Pro) with model-specific quality/speed tradeoffs.
Offers multiple model options with different cost-quality profiles (Seedream for budget-conscious edits, Nano Banana Pro for high-resolution, Uni-1 for complex modifications), allowing users to select based on edit complexity and resolution requirements. Mask-based control enables precise region targeting without affecting surrounding content.
Provides multiple model options for different use cases (unlike single-model competitors), with explicit mask support for precise control, enabling both quick edits (Seedream at 1-3 credits) and high-quality modifications (Nano Banana Pro at up to 53 credits).
text-to-speech synthesis with voice cloning and character selection
Medium confidenceConverts text to natural-sounding speech using ElevenLabs v3 model integrated through Luma's API. The system supports voice selection from a library of pre-defined voices or voice cloning from reference audio samples. Pricing is based on character count (21 credits per 1000 characters), enabling cost-predictable audio generation at scale. Supports multiple languages and accents through the underlying ElevenLabs model.
Integrates ElevenLabs v3 TTS with voice cloning capability, allowing users to maintain consistent voice identity across multiple generations or create branded voices without manual voice actor hiring. Character-based pricing (21 credits/1000 chars) enables predictable cost scaling for large-scale audio generation.
Provides voice cloning integrated into a unified video/audio generation platform, whereas competitors typically require separate TTS services or lack voice cloning entirely, reducing integration complexity for video creators.
sound effects and music generation with duration-based pricing
Medium confidenceGenerates sound effects and background music using ElevenLabs SFX v2 and Music v1 models integrated through Luma's API. The system accepts text descriptions of desired audio and generates corresponding sound effects or music tracks with duration-based pricing (25 credits/min for SFX, 98 credits/min for music). Enables audio-visual content creation without external music licensing or sound design.
Integrates both sound effects (SFX v2) and music generation (Music v1) through a unified API with duration-based pricing, enabling end-to-end audio-visual content creation without external dependencies. Text-to-audio synthesis allows generative audio creation without manual composition or licensing.
Provides integrated sound effects and music generation within a video creation platform, whereas competitors typically require separate music licensing services or lack generative audio capabilities, reducing friction for creators producing complete audio-visual content.
audio isolation and vocal separation
Medium confidenceSeparates vocal tracks from background audio using audio isolation model (4 credits/min). The system accepts a mixed audio file and extracts vocal components while suppressing instrumental or ambient background. Enables remixing, voiceover replacement, or vocal enhancement without access to original multitrack recordings.
Implements source separation using neural audio processing that isolates vocals while preserving spatial characteristics and timing, enabling clean vocal extraction without the phase artifacts common in traditional EQ-based approaches. Per-minute pricing enables cost-predictable processing for variable-length audio.
Provides integrated audio isolation within a video creation platform, whereas competitors typically require separate audio processing tools or plugins, reducing workflow friction for video creators needing vocal extraction.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with Luma Labs API, ranked by overlap. Discovered automatically through the match graph.
Pika
An idea-to-video platform that brings your creativity to motion.
Sora
An AI model that can create realistic and imaginative scenes from text instructions.
Kling AI
AI video generation with realistic motion and physics simulation.
Luma Dream Machine
AI video generation with physically accurate motion from text and images.
Vidu
AI video generation with consistent characters and multi-scene narratives.
KLING AI
Tools for creating imaginative images and videos.
Best For
- ✓Content creators and marketers building video assets at scale
- ✓VFX studios prototyping motion sequences before detailed animation
- ✓E-commerce teams generating product videos programmatically
- ✓Indie filmmakers with limited production budgets
- ✓E-commerce platforms converting product photography to video
- ✓Social media content creators extending static assets into video
- ✓Documentary filmmakers adding motion to archival photographs
- ✓Real estate agents creating virtual property tours from still images
Known Limitations
- ⚠Physics simulation is constrained to common scenarios; complex multi-body interactions may produce artifacts
- ⚠Maximum video duration not documented; pricing per-second suggests variable output length with unknown upper bound
- ⚠Text prompt length limit unknown; overly complex descriptions may degrade coherence
- ⚠Camera movements are pre-defined cinematic patterns rather than fully custom trajectories
- ⚠Temporal consistency degrades with complex scenes containing multiple independent moving objects
- ⚠Image resolution and quality directly impact output quality; low-resolution inputs produce soft, blurry videos
Requirements
Input / Output
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About
Dream Machine video generation API creating photorealistic videos from text and image prompts with natural motion, physics-aware generation, and cinematic camera control for creative and commercial applications.
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