2 repositorios
Awesome GitHub RepositoriesVisual Reference Guidance
Techniques for using external images as direct visual guides for diffusion process attention layers.
Distinct from Diffusion Models: Focuses on direct image-to-attention guiding without pre-trained control models, whereas Diffusion Models are the core architectures.
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Awesome Visual Reference Guidance GitHub Repositories
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- mikubill/sd-webui-controlnet
Mikubill/sd-webui-controlnet
17,853Ver en GitHubThis project is an extension for Stable Diffusion that provides an image-to-image control framework. It serves as a multi-control constraint manager and structural data preprocessor, allowing users to guide the layout and composition of generated images through spatial maps and structural constraints. The system enables multi-constraint image generation by combining several different control inputs to enforce multiple stylistic or spatial rules within a single generation pass. It provides tools for visual image referencing and precise geometric or anatomical templating to ensure generated ima
Provides a way to guide image generation using an image as a direct visual reference via attention layer linking.
Python - brycedrennan/imaginairy
brycedrennan/imaginAIry
8,155Ver en GitHubimaginAIry is a system for generating and refining images and videos using diffusion models. It operates as a web-based server that triggers generation requests through standard API calls, allowing for the creation of visuals and video sequences from text prompts or existing files. The project provides a suite for AI image editing and upscaling, enabling the modification of visuals through natural language instructions and super-resolution tools to increase detail and image size. The system includes capabilities for structural image control using depth maps, edge maps, and body poses to main
Uses external references such as depth maps, edge maps, and body poses to guide visual output and maintain structural consistency.
Python
