AI Frameworks · OpenVGLab

OmniLottie

OmniLottie is a multimodal generative model that creates Lottie vector animations (JSON format) from text, images, or video inputs. It combines Vision-Language Models with a specialized token-based approach to produce complex, parameterized animations suitable for UI, design, and content creation workflows.

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Source: GitHub — github.com/OpenVGLab/OmniLottie

719

GitHub stars

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	OpenVGLab/OmniLottie
Owner	OpenVGLab
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	719
Forks	37
Open issues	8
Latest release	Unknown
Last updated	2026-04-06
Source	https://github.com/OpenVGLab/OmniLottie

What OmniLottie is

OmniLottie uses pre-trained VLMs and introduces parameterized Lottie tokens to generate animation specifications as structured JSON. The 4B-parameter model runs inference in 8-134 seconds depending on output token count, requires 15.2GB GPU memory, and is available in both original PyTorch format and HuggingFace safetensors format with full API support.

Quickstart

Get the OmniLottie source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/OpenVGLab/OmniLottie.git2cd OmniLottie3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Automated UI/UX Animation Generation

Generate production-ready Lottie animations from design briefs or reference images, reducing manual animation design time for mobile and web applications.

Content Creation at Scale

Batch-generate diverse animated assets from text descriptions, supporting rapid prototyping, marketing campaigns, and design iterations without specialized animation skills.

Video-to-Animation Conversion

Extract animation concepts from video footage and convert to Lottie format, enabling repurposing of video content into lightweight, scalable vector animations.

Implementation considerations

GPU requirement: minimum 15.2GB VRAM (tested on CUDA 12.1); production deployments should target T4/V100+ or equivalent inference hardware.
Dual model format support (original PyTorch + HuggingFace safetensors) means two separate inference code paths; standardize on HuggingFace format for new projects.
Output is raw Lottie JSON; validate generated animations against Lottie specification before deployment, as no output correctness guarantees are documented.
Batch processing supported via text file input; optimize throughput by batching requests and managing GPU queue to amortize inference overhead.
Example scripts provided for text/image/video inputs; extend inference pipeline by wrapping inference_hf.py or app_hf.py with additional preprocessing/validation layers.

When to avoid it — and what to weigh

Training Code Not Available — The repository does not include training code (marked incomplete in open-source plan). Fine-tuning on custom datasets or retraining from scratch is not currently supported.
High Production Inference Latency Requirements — Inference takes 8-134 seconds per request depending on complexity. Real-time or sub-second animation generation is not feasible without batching optimizations.
Complex Multi-Part Animation Orchestration — While the model handles complex animations, coordinating precise timing or state synchronization across multiple interdependent animation elements may require post-processing.
Strict Deterministic Output Control — Generation includes sampling and temperature parameters, making outputs non-deterministic. Use cases requiring bit-exact reproducibility may require custom output validation.

License & commercial use

Apache License 2.0 (Apache-2.0). Permissive OSI-approved license allowing commercial use, modification, and distribution with minimal restrictions (requires license and copyright notice attribution; no liability or warranty provided).

Apache-2.0 explicitly permits commercial use of the software. However, the model weights do not include stated commercial licensing terms or guarantees. Verify compliance with OpenVGLab's commercial terms; production deployments should review trademark/attribution policies with the maintainers.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	High

Security considerations

Apache-2.0 code is open-source and auditable. Model weights are downloaded from HuggingFace; verify integrity via hash checks. No documented security vulnerabilities, authentication, or sandboxing in inference pipeline. GPU inference environment inherits security posture of host system; isolate untrusted input processing with container/VM boundaries if needed.

Alternatives to consider

Runway ML / Gen-3

Closed-source, subscription-based video-to-animation platform; higher latency but established production track record and commercial support contracts.

Stable Diffusion (ControlNet + Custom LoRA)

Open-source image generation with animation frame control; lower cost but requires custom training and post-processing to generate valid Lottie JSON.

Adobe Firefly / Generative Fill

Commercial, integrated into design workflows (Adobe XD, After Effects); limited to specific use cases but no infrastructure management required.

Software development agency

Build on OmniLottie with DEV.co software developers

Deploy OmniLottie for your UI, design, or content workflows. Our team can help integrate it into your pipeline, optimize inference, and validate production quality.

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OmniLottie FAQ

Can I use OmniLottie without a GPU?

Theoretically yes (CPU inference supported), but 15.2GB model requires inference time in minutes to hours on CPU. GPU (CUDA 12.1, 15.2GB+ VRAM) is strongly recommended for practical use.

What is the difference between the original format and HuggingFace format?

Original uses pytorch_model.bin (inference.py); HuggingFace uses safetensors + config.json (inference_hf.py). HuggingFace format is recommended for new deployments and supports automatic downloading via from_pretrained() API.

Can I fine-tune OmniLottie on my own data?

Not currently. Training code is not released. MMLottie-2M dataset is available for reference, but fine-tuning is not officially supported; contact maintainers for research collaboration inquiries.

How accurate are the generated Lottie animations?

Accuracy varies by complexity. MMLottieBench benchmark available on HuggingFace for evaluation; no official accuracy metrics provided in documentation. Recommend validating outputs against Lottie specification before production deployment.

Software development & web development with DEV.co

From first prototype to production, DEV.co delivers software development services around tools like OmniLottie. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across ai frameworks and beyond.

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