# PRIV-Creation/Awesome-Controllable-T2I-Diffusion-Models

A collection of resources on controllable generation with text-to-image diffusion models.

Repository: https://github.com/PRIV-Creation/Awesome-Controllable-T2I-Diffusion-Models
Canonical: https://ross.abutalabs.com/products/awesome-controllable-t2i-diffusion-models
License: MIT
License Family: permissive
Topics: awesome, awesome-list, diffusion-models, personalization, controllable-generation, multi-concept, spatial-controls, text-to-image
Last push: 2024-12-31T03:55:16+00:00

## Health v2 (maintenance only)
Score: 30/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 88
- inputs: {"age_days": 1238, "days_push": 610, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1110, forks 32 (observed 2026-08-28T04:03:37.556283+00:00)

## What it is
A curated awesome-list of research papers and resources on controllable generation with text-to-image diffusion models, accompanying an arXiv survey paper. It organizes works by condition types such as personalization, spatial control, and multi-condition generation.

## Use cases
- find papers on controllable text-to-image diffusion models
- research personalization methods for stable diffusion
- survey spatial control techniques for image generation
- keep up with new controllable generation papers
- find subject-driven and style-driven generation methods
- prepare a literature review on diffusion model conditioning

## When to choose
- you need a structured, categorized index of T2I diffusion control research
- you are writing a survey or literature review on controllable generation
- you want to track new papers via an actively maintained database

## When to avoid
- you need runnable code or a library rather than a paper list
- you want tutorials or beginner guides instead of research papers
- you need non-diffusion generative model resources

## Facets
- artifact type: learning-resource
- maturity: active
- function: stable-diffusion, machine-learning, deep-learning
- domain: artificial-intelligence, image-processing, awesome-lists, tutorials
- platform: cross-platform
- tags: awesome-list, diffusion-models, text-to-image, controllable-generation, personalization, survey-paper, research-papers

## Member repositories
- PRIV-Creation/Awesome-Controllable-T2I-Diffusion-Models (main) score 30

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:37.556283+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:43:25.673360+00:00, confidence not recorded.
  - readme: https://github.com/PRIV-Creation/Awesome-Controllable-T2I-Diffusion-Models (fetched 2026-08-28T04:03:37.556283+00:00, sha 8ab245150194)
- Data as of 2026-08-30T08:39:29.467469+00:00.