IrmoAI

Converts natural language prompts into digital images using a diffusion-based generative model architecture. The system processes text embeddings through a latent diffusion pipeline, applying style parameters and conditioning vectors to guide image synthesis. Supports iterative refinement through prompt modification and parameter adjustment without requiring manual editing tools.

Generates short-form video content by synthesizing motion and temporal coherence from static images or text descriptions. Likely uses frame interpolation, optical flow, or video diffusion models to create smooth transitions and animated sequences. The system may support keyframe-based editing where users specify visual states at different timestamps and the model fills intermediate frames.

Provides AI-powered image editing capabilities such as background removal, object inpainting, upscaling, or style application through a web-based editor interface. The system likely uses segmentation models for object detection, inpainting diffusion models for content-aware fill, and super-resolution networks for upscaling. Users interact through a visual canvas with brush-based selection or automatic detection of regions to modify.

Enables bulk creation or transformation of multiple assets (images, videos) in a single workflow, likely through CSV/JSON input with template-based parameterization. The system queues batch jobs, processes them asynchronously, and returns results as downloadable archives or via API. Supports variable substitution in prompts (e.g., product name, color, style) to generate variations without manual re-entry.

Orchestrates workflows that combine image, video, and text generation in a single project context, allowing outputs from one modality to feed into another (e.g., generate image → animate to video → add voiceover). The system maintains project state and asset relationships, enabling users to iterate on individual components while preserving dependencies. May include timeline-based editing for synchronizing audio, video, and text elements.

Implements a freemium monetization model where users receive a monthly or daily allowance of generation credits that are consumed based on asset type, resolution, and processing complexity. The system tracks credit usage per user, enforces quota limits, and offers paid tiers or credit top-ups to increase capacity. Free tier likely includes watermarks, lower resolution outputs, or longer processing queues; premium tiers unlock higher quality and priority processing.