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Stable Diffusion Latent Space Explorer

Stable Diffusion Latent Space Explorer is a Python research codebase that uses Hugging Face Diffusers to run configurable Stable Diffusion image-generation and image-space experiments. Its documented workflows include text-to-image, image-to-image, inpainting, diffusion visualization, interpolation, random walks and outpaint walks. Runs are launched from YAML configuration files and save experiment settings and gener

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Stable Diffusion Latent Space Explorer — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want to run and compare configurable Stable Diffusion experiments in a local Python environment, rather than use a hosted image-generation service.

Who it is for

  • Researchers exploring Stable Diffusion behavior and latent-space workflows.
  • Developers who want a configurable local codebase for image generation, image editing or inpainting experiments.

Documented uses

  • Generate images from text prompts with configurable experiment parameters.
  • Create image-to-image variations or run inpainting experiments.
  • Explore diffusion processes, latent interpolation and random walks.
  • Keep experiment configurations and generated images, embeddings or GIFs together in result folders.
Before you choose

Requirements & limits

The project repository is public and lists an MIT license. The documented workflow requires local setup and model weights; no hosted service or API is described.

  • The README requires PyTorch installed with CUDA and names Diffusers and Transformers as dependencies.
  • Model weights are obtained separately using Git LFS.
  • The README identifies specific tested Stable Diffusion models and lists torch 2.0.0, CUDA 11.8, Diffusers 0.14.0 and xFormers 0.0.16 as versions to try if issues arise.
  • xFormers is optional and described as supporting efficient attention.
  • The README cautions that generated results may vary even with identical configurations and random seeds.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the Stable Diffusion experiment codebase

Prepare the repository, Python environment, dependencies and a Stable Diffusion 2.1 model folder for the documented experiment workflow.

Before you start

  • Python
  • PyTorch installed with CUDA
  • Diffusers and Transformers
  • Git LFS for downloading model weights

Steps

  1. Clone the repository and create and activate a Python virtual environment.

  2. Install PyTorch with CUDA, then install Diffusers with its torch extra and Transformers.

  3. Install Git LFS and clone the Stable Diffusion 2.1 model repository to obtain local weights.

  4. Optionally install xFormers for efficient attention.

Expected outcome

A local repository environment with the documented dependencies and Stable Diffusion 2.1 weights available.

Guide limitations

  • The README gives tested model examples and suggested package versions, not a compatibility guarantee.
  • This guide covers setup only; experiment execution requires selecting and configuring an experiment file.
Documentation 1 ↗
Source-based practical guide

Run a configured Stable Diffusion experiment

Launch an experiment using a YAML configuration and inspect its saved outputs.

Before you start

  • The repository and dependencies set up locally
  • A local model-weights folder
  • An experiment configuration file

Steps

  1. Choose an experiment configuration file from the repository's experiment configs.

  2. Set the experiment parameters in that configuration file.

  3. Run the command below with the path to the chosen configuration file.

    Documented command
    python run_sd_experiment.py --exp_config

  4. Inspect the generated results folder, which contains the configuration and may include images, embeddings and GIFs.

Expected outcome

A timestamped experiment results folder associated with the model and experiment, containing the recorded configuration and generated outputs.

Guide limitations

  • The README notes that results may differ even with identical configurations and random seeds.
  • The specific parameters and outputs depend on the selected experiment configuration.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
alen-smajic/Stable-Diffusion-Latent-Space-Explorer · Official homepage title · retained in its source language
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