Omost

Features

Code-Driven Layout Generation - Uses an LLM to generate executable code for precise control over image bounding boxes and composition.
Regional - Manipulates attention scores to ensure specific text prompts only affect designated image areas.
Diffusion Layout Controllers - Defines regional bounding boxes and attention scores to guide the generation process of diffusion models.
Graph-Based Prompt Organization - Structures independent descriptive concepts into a graph to merge them into cohesive prompts.
Image Composition Controls - Uses large language models to generate executable code for precise control over image layout and composition.
Conversational Editing Interfaces - Refines image composition and details through a chat interface instead of rewriting prompts.
Regional Prompting - Controls the placement and appearance of specific objects using bounding boxes and regional attention.
Semantic Embedding Merging - Implements a greedy merging strategy for sub-prompts to prevent semantic truncation during text encoding.
Precise Visual Layout Control - Ensures visual elements appear in exact intended positions using a grid system of global and local descriptions.
Embedding Optimization Processes - Organizes sub-prompts into tree graphs to prevent semantic truncation during text encoding.
Embedding Optimizers - Provides a greedy merging strategy for sub-prompts to ensure coherent text encoding and prevent semantic truncation.
Grid-Based Image Layouts - Uses a discretized grid system to assign global and local descriptions to specific bounding boxes.
Conversational Image Editors - Provides a chat interface for iteratively refining visual content through natural language and automated code execution.
Generative - Provides a grid-based coordinate system to map global and local descriptions to specific image areas.
Latent Layout Mappings - Organizes image components by depth and color to create initial latent maps for diffusion models.
Image Editing - Allows users to refine generated visual content through iterative, chat-based adjustments to the image composition.
Prompt Optimizers - Optimizes descriptive concepts using structured graphs and embedding merges to prevent semantic truncation.
Code-to-Image Composition - Converts natural language prompts into executable code for a virtual canvas agent to arrange complex visual content.
Prompt Graph Organizers - Implements a prefix tree system to organize independent descriptive concepts into cohesive prompts via specific traversal paths.
Latent Element Composition - Organizes image components by relative depth and color to create layout maps for use as initial latents.
Image Composition - Arranges multiple visual elements on a virtual canvas using code and layout maps to create structured scenes.

Open-source alternatives to Omost

Similar open-source projects, ranked by how many features they share with Omost.

comfyanonymous/comfyui
comfyanonymous/ComfyUI
117,322View on GitHub
ComfyUI is a modular generative AI workflow orchestrator and node-based GUI for designing and executing complex diffusion model pipelines. It functions as both a visual interface for building generative logic graphs and a programmable backend API that exposes diffusion model operations for external integration. The system distinguishes itself through a graph-based execution model that supports differential workflow execution, re-running only modified nodes to reduce computation. It features dynamic model offloading to manage memory between system RAM and GPU VRAM and utilizes metadata-embedde
Python
View on GitHub117,322
acly/krita-ai-diffusion
Acly/krita-ai-diffusion
9,755View on GitHub
This project is a plugin for Krita that integrates Stable Diffusion image generation and editing tools directly into the painting interface. It functions as a remote diffusion backend client, bridging the digital canvas to local or remote servers to handle the computation required for AI image generation. The system distinguishes itself through a real-time painting interface that translates brushstrokes into generated imagery as the artist works. It acts as a structural orchestrator, using sketches, depth maps, and poses to maintain precise composition, and provides a generative inpainting to
Pythongenerative-aikrita-pluginstable-diffusion
View on GitHub9,755
black-forest-labs/flux
black-forest-labs/flux
25,637View on GitHub
Flux is a diffusion model inference engine designed for text-to-image generation and image-to-image manipulation. It provides a system for executing open-weight models to transform natural language descriptions into visual imagery or to modify existing images. The project distinguishes itself through a flow-matching framework for image generation and a structural image controller. This controller allows for guided synthesis by using depth maps and Canny edge detection to constrain the geometry and composition of the output. The toolkit covers a broad range of image editing capabilities, incl
Python
View on GitHub25,637
hlky/stable-diffusion-webui
hlky/stable-diffusion-webui
7,880View on GitHub
Stable Diffusion Web UI is a browser-based interface for generating, editing, and upscaling images and videos using latent diffusion models. It functions as a text-to-image generator, an AI image editor, and a tool for increasing image resolution and clarity. The system includes capabilities for custom model training, specifically allowing the creation of textual inversion embeddings to teach a model new concepts and visual styles from user photos. It also provides tools for AI video production, generating short clips from text prompts. The software covers image-to-image transformation, imag
Python
View on GitHub7,880

See all 30 alternatives to Omost

lllyasvielOmost

Features

Open-source alternatives to Omost

comfyanonymous/ComfyUI

Acly/krita-ai-diffusion

black-forest-labs/flux

hlky/stable-diffusion-webui

Star history

Open-source alternatives to Omost

comfyanonymous/ComfyUI

Acly/krita-ai-diffusion

black-forest-labs/flux

hlky/stable-diffusion-webui