Pith Number

pith:BCOTYJIR

pith:2025:BCOTYJIRQGIEITWX52DGXJCWDS

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Causal-Adapter: Taming Text-to-Image Diffusion for Faithful Counterfactual Generation

Chaochao Lu, Chen Jin, Dino Oglic, Lei Tong, Philip Teare, Sotirios A. Tsaftaris, Tom Diethe, Zhihua Liu

Causal-Adapter adapts frozen text-to-image diffusion models for faithful counterfactual generation by enforcing causal attribute relationships.

arxiv:2509.24798 v6 · 2025-09-29 · cs.CV · cs.AI

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Claims

C1strongest claim

Causal-Adapter achieves state-of-the-art performance on both synthetic and real-world datasets, including up to a 91% reduction in MAE on Pendulum for accurate attribute control and up to an 87% reduction in FID on ADNI for high-fidelity MRI generation.

C2weakest assumption

The method assumes that causal relationships among image attributes can be sufficiently captured and enforced using only prompt-aligned injection and a conditioned token contrastive loss applied to a frozen diffusion backbone, without needing an explicit causal graph or additional supervised causal annotations.

C3one line summary

Causal-Adapter introduces a modular adapter for diffusion models that uses structural causal modeling, prompt-aligned injection, and conditioned token contrastive loss to enable faithful counterfactual image generation with strong attribute control and identity preservation.

Formal links

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Receipt and verification

First computed	2026-05-20T00:00:24.929373Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

089d3c25118190444ed7ee866ba4561cb0649009e17edb411ffefae86fed88b3

Aliases

arxiv: 2509.24798 · arxiv_version: 2509.24798v6 · doi: 10.48550/arxiv.2509.24798 · pith_short_12: BCOTYJIRQGIE · pith_short_16: BCOTYJIRQGIEITWX · pith_short_8: BCOTYJIR

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/BCOTYJIRQGIEITWX52DGXJCWDS \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 089d3c25118190444ed7ee866ba4561cb0649009e17edb411ffefae86fed88b3

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "58a8004939d4bc4cc8b23c0591eb68ee45b1211bd92229dda609cb789b0e7a09",
    "cross_cats_sorted": [
      "cs.AI"
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    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2025-09-29T13:49:28Z",
    "title_canon_sha256": "d41851b612436f5ffab09e7005f849d8612436d9782a434903b5080a022dad32"
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  "source": {
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    "kind": "arxiv",
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}