Pith Number

pith:UTRDYT6C

pith:2023:UTRDYT6CJUBBSHFVA7YYXGCZKC

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T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models

Chong Mou, Jian Zhang, Liangbin Xie, Xiaohu Qie, Xintao Wang, Yanze Wu, Ying Shan, Zhongang Qi

Lightweight adapters align external signals with the internal knowledge of frozen text-to-image diffusion models.

arxiv:2302.08453 v2 · 2023-02-16 · cs.CV · cs.AI · cs.LG · cs.MM

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

we propose to learn simple and lightweight T2I-Adapters to align internal knowledge in T2I models with external control signals, while freezing the original large T2I models.

C2weakest assumption

That the internal knowledge implicitly learned by large T2I models can be effectively aligned with external control signals using simple lightweight adapters without degrading generative quality or requiring full model retraining.

C3one line summary

T2I-Adapters are lightweight modules that enable fine-grained control over color and structure in text-to-image diffusion models by aligning external conditions with the frozen model's internal knowledge.

References

47 extracted · 47 resolved · 7 Pith anchors

[1] eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers 2022 · arXiv:2211.01324

[2] Coco- stuff: Thing and stuff classes in context 2018

[3] Vision transformer adapter for dense predictions 2022

[4] Openmmlab pose estimation toolbox and benchmark 2020

[5] Gen- erative adversarial networks: An overview 2018

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Receipt and verification

First computed	2026-05-17T23:38:46.339664Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a4e23c4fc24d02191cb507f18b9859509c58f3dc6a9de13d2dce55948f469b3c

Aliases

arxiv: 2302.08453 · arxiv_version: 2302.08453v2 · doi: 10.48550/arxiv.2302.08453 · pith_short_12: UTRDYT6CJUBB · pith_short_16: UTRDYT6CJUBBSHFV · pith_short_8: UTRDYT6C

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/UTRDYT6CJUBBSHFVA7YYXGCZKC \
  | 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: a4e23c4fc24d02191cb507f18b9859509c58f3dc6a9de13d2dce55948f469b3c

Canonical record JSON

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  "metadata": {
    "abstract_canon_sha256": "e545ed690231d735387208fe8386e4305dfae265b4b9b2599e1aabb9efe9bccf",
    "cross_cats_sorted": [
      "cs.AI",
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      "cs.MM"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2023-02-16T17:56:08Z",
    "title_canon_sha256": "3493f9a6a3b749cc00c722a39ae608f90bd5448d955798810d630ed3d7ddd30a"
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