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2 Repos

Awesome GitHub RepositoriesCascading Decoders

Multi-stage decoding architectures that iteratively increase image resolution through sequential model passes.

Distinct from Diffusion Model Training: Focuses on the structural decoding process for high-resolution synthesis, whereas Diffusion Model Training covers the general iterative noise prediction training.

Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Cascading Decoders. Refine with filters or upvote what's useful.

Awesome Cascading Decoders GitHub Repositories

Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
  • lucidrains/dalle2-pytorchAvatar von lucidrains

    lucidrains/DALLE2-pytorch

    11,310Auf GitHub ansehen↗

    This is a PyTorch implementation of a text-to-image model designed for synthesizing high-fidelity images from natural language descriptions. It utilizes a diffusion image generator to transform latent embeddings into visual data through an iterative denoising process. The system employs a two-stage latent mapping process, using a CLIP-based latent prior to map text embeddings to image embeddings before decoding them into pixels. It features a cascading diffusion decoder that produces high-resolution imagery by passing low-resolution outputs through a sequence of models at increasing scales.

    Implements a cascading diffusion decoder to produce high-resolution imagery by passing outputs through multiple models at increasing scales.

    Pythonartificial-intelligencedeep-learningtext-to-image
    Auf GitHub ansehen↗11,310
  • stability-ai/stablecascadeAvatar von Stability-AI

    Stability-AI/StableCascade

    6,548Auf GitHub ansehen↗

    StableCascade is a generative AI system and latent diffusion framework designed for text-to-image synthesis and image-to-image transformations. It utilizes a multi-stage cascade architecture that encodes and decodes images via a latent space to produce high-fidelity visual imagery. The system includes a cascade diffusion pipeline for controlling image structure through inpainting, outpainting, and super-resolution. It also provides a toolkit for image-to-image generation and the creation of image variations using embeddings. The framework supports model optimization through low-rank adaptati

    Uses cascading decoders to progressively increase image resolution through sequential model passes.

    Jupyter Notebook
    Auf GitHub ansehen↗6,548
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