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Stable-Diffusion-Latent-Space-Explorer

alen-smajic/Stable-Diffusion-Latent-Space-Explorer

Codebase for experiments with Stable Diffusion using diffusers library

GraphCanon updated 3w · GitHub synced 3w

227 stars12 forksLast push 3y Python MIT

Decision brief

Repo for experimenting with Stable Diffusion model using diffusers library

Good fit when

  • When you aim to explore latent space editing techniques specifically with Stable Diffusion models.
  • For image generation tasks where customization of the Stable Diffusion process is required.

Avoid when

  • If your project relies on a different diffusion model that does not benefit from diffusers library integrations.
  • When working under strict resource constraints, as setup requires significant system resources for deep learning operations.

Observed Jul 17, 2026 · Source: enrich:decision_facts

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Maintenance and security

Full trust report
Maintenance
Dormant (1111d since push)
As of 3w
Provenance
Not a fork · Personal account
As of 3w
Security (OSV)
No lockfile
As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install Stable-Diffusion-Latent-Space-Explorer
PyPI

How it fits your stack(1)

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Relationship graph

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Similar tools

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

This repository contains code for experimenting with the Stable Diffusion model and its variations in image editing and generation tasks.

Capability facts

Languages
python

Source: github.language · Aug 1, 2026

Categories

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Aug 1, 2026)

python -m venv venv
Source link

Tags

README

Installation

  1. Clone this repository.

    git clone https://github.com/alen-smajic/Stable-Diffusion-Latent-Space-Explorer
    
  2. Create a virtual environment and activate it.

    python -m venv venv
    
  3. Install PyTorch with CUDA (follow this PyTorch installation).

  4. Install diffusers and transformers libraries.

    pip install diffusers["torch"] transformers
    
  5. Optional: Install xFormers for efficient attention.

    pip install xformers
    

For agents

This page has a .md twin and JSON over the API.

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