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pith:2025:DOFPGUK7VYF3IGXNK2SCIVNV24
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In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer

Ji Xie, Yi Yang, Yu Lu, Zechuan Zhang, Zongxin Yang

Large Diffusion Transformers perform precise instructional image editing via in-context generation without major retraining.

arxiv:2504.20690 v3 · 2025-04-29 · cs.CV

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4 Citations open
5 Replications open
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Claims

C1strongest claim

ICEdit achieves state-of-the-art editing performance with only 0.1% of the training data and 1% trainable parameters compared to previous methods.

C2weakest assumption

That the inherent comprehension and generation abilities of large-scale Diffusion Transformers can be effectively leveraged for precise instructional editing through an in-context paradigm without any architectural modifications.

C3one line summary

ICEdit achieves state-of-the-art instructional image editing in Diffusion Transformers via in-context generation, requiring only 0.1% of prior training data and 1% trainable parameters.

References

60 extracted · 60 resolved · 13 Pith anchors

[1] Instructpix2pix: Learning to follow image editing instructions 2023
[2] Magicbrush: A manually annotated dataset for instruction-guided image editing.Advances in Neural Information Processing Systems, 36, 2024 2024
[3] Emu edit: Precise image editing via recognition and generation tasks 2023
[4] Ultraedit: Instruction-based fine-grained image editing at scale.Advances in Neural Information Processing Systems, 37:3058–3093, 2025 2025
[5] arXiv preprint arXiv:2309.17102 (2023) 2023

Formal links

1 machine-checked theorem link

Cited by

23 papers in Pith

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First computed 2026-05-17T23:38:47.348564Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

1b8af3515fae0bb41aed56a42455b5d715a7b16f51b8b63ee579750d58984a0d

Aliases

arxiv: 2504.20690 · arxiv_version: 2504.20690v3 · doi: 10.48550/arxiv.2504.20690 · pith_short_12: DOFPGUK7VYF3 · pith_short_16: DOFPGUK7VYF3IGXN · pith_short_8: DOFPGUK7
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/DOFPGUK7VYF3IGXNK2SCIVNV24 \
  | 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: 1b8af3515fae0bb41aed56a42455b5d715a7b16f51b8b63ee579750d58984a0d
Canonical record JSON
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