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7 रिपॉजिटरी

Awesome GitHub RepositoriesComposition-Controlled Generators

Image generators that incorporate spatial and structural constraints to manage subject placement and pose.

Distinct from Image Generation: Image Generation is a broad capability; this specifies the addition of composition control.

Explore 7 awesome GitHub repositories matching artificial intelligence & ml · Composition-Controlled Generators. Refine with filters or upvote what's useful.

Awesome Composition-Controlled Generators GitHub Repositories

AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
  • black-forest-labs/fluxblack-forest-labs का अवतार

    black-forest-labs/flux

    25,637GitHub पर देखें↗

    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

    Incorporates spatial and structural constraints like Canny edges and depth maps to preserve image composition.

    Python
    GitHub पर देखें↗25,637
  • apple/ml-stable-diffusionapple का अवतार

    apple/ml-stable-diffusion

    17,901GitHub पर देखें↗

    This project is a framework for running Stable Diffusion image generation models on Apple Silicon using Core ML hardware acceleration. It provides a local generative AI pipeline for producing images from text prompts using Swift and Python without relying on external cloud APIs. The system includes a model converter to transform deep learning checkpoints into Core ML formats and a model optimizer to quantize weights and activations. It features a ControlNet integration layer to guide image generation using external signals such as edge and depth maps. Capabilities cover text-to-image generat

    Guides and constrains visual output using ControlNet with edge and depth maps.

    Python
    GitHub पर देखें↗17,901
  • cs231n/cs231n.github.iocs231n का अवतार

    cs231n/cs231n.github.io

    10,923GitHub पर देखें↗

    This project is a static educational website and comprehensive curriculum focused on computer vision and deep learning. It serves as a public repository of instructional materials, lecture notes, and technical guides specifically detailing convolutional neural networks and visual recognition. The site is developed using static-site generation to host course documentation and student project directories. It provides structured academic resources that guide learners through image classification, generative modeling, and the implementation of various neural network architectures. The curriculum

    Explains the use of mapping networks and noise to control the style and variety of generated images.

    Jupyter Notebook
    GitHub पर देखें↗10,923
  • tencentarc/photomakerTencentARC का अवतार

    TencentARC/PhotoMaker

    10,122GitHub पर देखें↗

    PhotoMaker is a diffusion-based identity generator designed for person-specific image synthesis. It creates high-fidelity photos and avatars of specific individuals using stacked embeddings, which allows for the generation of consistent human identities without the need for custom model training or fine-tuning. The system utilizes zero-shot identity synthesis and identity adapters to maintain recognizable facial features across various visual contexts. It supports artistic style transfer by combining identity information with specialized model weights and integrates external control framework

    Combines identity preservation with external frameworks to manage subject poses and image layouts.

    Jupyter Notebook
    GitHub पर देखें↗10,122
  • qwenlm/qwen-imageQwenLM का अवतार

    QwenLM/Qwen-Image

    7,379GitHub पर देखें↗

    Qwen-Image is a text-to-image model and large language model image generation framework. It functions as an AI image editing suite and a personalized image trainer, capable of producing high-fidelity visuals and accurate typography from natural language descriptions. The system is distinguished by its precision text rendering engine, which integrates multi-script calligraphy and layout-coherent alphabetic text into images. It provides specialized capabilities for subject identity preservation and consistent subject generation across different poses and viewpoints, alongside a training pipelin

    Implements image generation with spatial and structural constraints using depth maps, sketches, and keypoints to manage subject pose.

    Python
    GitHub पर देखें↗7,379
  • pkuliyi2015/multidiffusion-upscaler-for-automatic1111pkuliyi2015 का अवतार

    pkuliyi2015/multidiffusion-upscaler-for-automatic1111

    5,002GitHub पर देखें↗

    This project is an AI image upscaling and high-resolution generation tool. It uses tiled diffusion to create ultra-large images by processing them in smaller, overlapping regions to prevent memory crashes on limited hardware. The system manages spatial composition through regional prompting, which routes specific text prompts to designated areas of an image. It maintains visual stability and global coherence during the upscaling process using noise inversion and structural guidance. Additional capabilities include tiled detail upscaling and memory optimization for the variational autoencoder

    Employs structural guides and tiled generation to manage the precise layout and composition of high-resolution outputs.

    Pythonimage-generationlarge-imagemultidiffusion
    GitHub पर देखें↗5,002
  • nunchaku-ai/comfyui-nunchakununchaku-ai का अवतार

    nunchaku-ai/ComfyUI-nunchaku

    2,901GitHub पर देखें↗

    ComfyUI-nunchaku is a 4-bit diffusion inference engine and a set of nodes for running low-precision quantized diffusion models within ComfyUI visual workflows. It provides a backend that reduces memory overhead and increases generation speed for transformer models. The project includes specialized tools for identity-preserving generation and an image-to-image guidance toolkit that uses depth maps and reference images. It also features a multimodal visual question answering implementation and a utility for merging multiple quantized model files into single unified files. The engine covers a b

    Directs the image generation process based on spatial or structural input to ensure precise composition.

    Pythoncomfyuidiffusionflux
    GitHub पर देखें↗2,901
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