GraphCanon updated 3w · GitHub synced 3w
Decision brief
Stable-diffusion is a state-of-the-art latent text-to-image diffusion model underpinning image generation from textual inputs.
Good fit when
- For generating images based on text prompts with high fidelity and artistic detail.
- When you need reproducibility in outputs by leveraging the latent space for stable transformations.
Avoid when
- If the computational resources are limited, as it requires significant GPU power to train or fine-tune models.
- In cases where real-time generation performance is critical, due to its computation-intensive process.
Observed Jul 15, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (774d since push)
- As of 3w
- Provenance
- Not a fork · Organization 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
git clone https://github.com/CompVis/stable-diffusionHow it fits your stack(5)
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Evidence and technical details
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Overview
CompVis/stable-diffusion provides a state-of-the-art text-to-image diffusion model for generating images from textual descriptions.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Aug 1, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 1, 2026)
pip install transformers==4.19.2 diffusers invisible-watermarkSource link
Tags
README
Requirements
A suitable conda environment named ldm can be created
and activated with:
conda env create -f environment.yaml
conda activate ldm
You can also update an existing latent diffusion environment by running
conda install pytorch torchvision -c pytorch
pip install transformers==4.19.2 diffusers invisible-watermark
pip install -e .
For agents
This page has a .md twin and JSON over the API.