Paper Citation Record · LEDGER

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation

As of 18 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 20 inbound Pith citation observations for arXiv:2502.01572.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.01572 v2

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:57:58.156327Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:46:46.801330Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T21:00:08.910100Z

Reference resolution

46 of 46 outbound references displayed

verified exact2
verified fuzzy14
unresolved30
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6ed27e3c-55f7-469c-85c2-4c87668648d7 · outbound

This paper cites write newline.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:57.988902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc11ca15-d72e-4199-b1b6-885d09cf2cee · outbound

This paper cites Flux.1 ai, 2024.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Flux.1 ai, 2024

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:57:58.604078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 967a6a00-4b04-433f-801f-06c86d308947 · outbound

This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.000060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b58efa6-e432-4d7c-8a19-d21b6a666b93 · outbound

This paper cites an unresolved cited work.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.004667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8481abd6-d524-4a23-a9fd-928b7e97c8d8 · outbound

This paper cites an unresolved cited work.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-09T14:57:58.586239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 81e2a627-fb26-4bb9-8261-3fa36e7e6232 · outbound

This paper cites Civitai website.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Civitai website

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:57:58.575417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation acb46ef1-9320-4c13-9171-0df2d654a0d5 · outbound

This paper cites Scaling rectified flow transformers for high-resolution image synthesis.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Scaling rectified flow transformers for high-resolution image synthesis

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.015856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e15c2e3-47c8-48d2-a546-57682fe78bae · outbound

This paper cites Clipdraw: Exploring text-to-drawing synthesis through language-image encoders.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Clipdraw: Exploring text-to-drawing synthesis through language-image encoders

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.019882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80996edd-4386-4e98-a497-5f986ead27e7 · outbound

This paper cites AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.023421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38c31ad1-2d63-4f12-933e-aaf3e0bf6814 · outbound

This paper cites A Neural Representation of Sketch Drawings.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation A Neural Representation of Sketch Drawings

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.027450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 299516d9-8c10-4bd6-b0b8-90862536e6b2 · outbound

This paper cites Paint by numbers: Abstract image representations.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Paint by numbers: Abstract image representations

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:57:58.550776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 25c90448-1742-4ee5-bfea-bb6a907dc033 · outbound

This paper cites Prompt-to-Prompt Image Editing with Cross Attention Control.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Prompt-to-Prompt Image Editing with Cross Attention Control

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.035334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ab4c696-1fb0-44c3-8ebb-73d29dc703f1 · outbound

This paper cites A survey of stroke-based rendering.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation A survey of stroke-based rendering

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:57:58.539916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ece8bcf7-9dca-4ebf-8c74-9aa2b1e60a0a · outbound

This paper cites Denoising diffusion probabilistic models.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Denoising diffusion probabilistic models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.042415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0db9db6c-249e-495b-a5a2-25820d370ba3 · outbound

This paper cites J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.045617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31e41bd4-0bb2-48fe-bf7a-5df89af22e44 · outbound

This paper cites Ideogram ai, 2023.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Ideogram ai, 2023

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:57:58.516645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 61324d8e-628b-4ab4-9ed4-b7f7891b8e98 · outbound

This paper cites Rethinking style transfer: From pixels to parameterized brushstrokes.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Rethinking style transfer: From pixels to parameterized brushstrokes

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:57:58.505513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ef03b348-84aa-4c4a-9b84-830d8d31e2e7 · outbound

This paper cites Multi-concept customization of text-to-image diffusion.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Multi-concept customization of text-to-image diffusion

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.055186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76b7851e-1838-4126-abd3-28caba2d5720 · outbound

This paper cites Processing images and video for an impressionist effect.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Processing images and video for an impressionist effect

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:57:58.487684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6c17d20d-1ecd-4fab-b06f-7436c801c50f · outbound

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

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.062017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed4d4351-6757-4553-a80d-147b9b7fe592 · outbound

This paper cites Neural Painters: A learned differentiable constraint for generating brushstroke paintings.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Neural Painters: A learned differentiable constraint for generating brushstroke paintings

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-09T14:57:58.318771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7c6409e1-6aa2-4a5b-be23-50389245a262 · outbound

This paper cites Multi-modal Attention for Speech Emotion Recognition.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Multi-modal Attention for Speech Emotion Recognition

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.069148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7eb2123e-6bfd-4cc7-8b10-4dc0e9f9210f · outbound

This paper cites and Xie, S.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation and Xie, S

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.072539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6047b3a-39ab-4ad9-950c-230962d20e45 · outbound

This paper cites W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.076014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12edf0bf-f2c4-416b-9a0f-012a01d00a8f · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation High-resolution image synthesis with latent diffusion models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.079239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae651089-c66f-4721-87a1-bd861f3c4998 · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.082516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bf57cfb-9b2d-46a4-bfc4-80b46b2e894a · outbound

This paper cites Denoising Diffusion Implicit Models.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Denoising Diffusion Implicit Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.085800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a3380dc-6b68-42dd-a6ac-17a074365652 · outbound

This paper cites Cliptexture: Text-driven texture synthesis.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Cliptexture: Text-driven texture synthesis

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:57:58.451337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d7ca27cc-ad10-4fca-b39e-48c7bbb7f91e · outbound

This paper cites and Zhang, Y.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation and Zhang, Y

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:57:58.440371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 19bb9016-1b6b-4d49-804d-d5017cad067c · outbound

This paper cites Clipvg: Text-guided image manipulation using differentiable vector graphics.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Clipvg: Text-guided image manipulation using differentiable vector graphics

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:57:58.429433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 88ce3f48-f97a-42c6-ad5b-1d1c4ef834d3 · outbound

This paper cites ProcessPainter: Learn Painting Process from Sequence Data.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation ProcessPainter: Learn Painting Process from Sequence Data

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.102941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f535af2e-85fb-4ee1-a22c-131bac3f06c0 · outbound

This paper cites DiffSim: Taming Diffusion Models for Evaluating Visual Similarity.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation DiffSim: Taming Diffusion Models for Evaluating Visual Similarity

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.106513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5309b183-e9e4-4938-9e3f-50064fae9e25 · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Roformer: Enhanced transformer with rotary position embedding

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.110101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67323dc3-a4b1-434e-be71-d9be14f87e76 · outbound

This paper cites OminiControl: Minimal and Universal Control for Diffusion Transformer.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation OminiControl: Minimal and Universal Control for Diffusion Transformer

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.113457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34a37c1c-8454-4240-8d89-774469540e6a · outbound

This paper cites Paints-undo github page, 2024.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Paints-undo github page, 2024

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:57:58.411702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a10e90a1-63ff-47d2-8c60-a7ff45f02df5 · outbound

This paper cites Grid: Omni Visual Generation.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Grid: Omni Visual Generation

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1ac490e-afa9-417c-a990-1bfbedf15a0e · outbound

This paper cites Artist agent: A reinforcement learning approach to automatic stroke generation in oriental ink painting.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Artist agent: A reinforcement learning approach to automatic stroke generation in oriental ink painting

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:57:58.400616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 273926e2-47b9-4156-897f-2c4adc7a357d · outbound

This paper cites IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.127544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9534e1e-acd4-411a-a547-f07f9b1a2109 · outbound

This paper cites Adding Conditional Control to Text-to-Image Diffusion Models.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Adding Conditional Control to Text-to-Image Diffusion Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.131170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e71dcd27-f69d-4a46-93f0-a6185da8bfad · outbound

This paper cites Ssr-encoder: Encoding selective subject representation for subject-driven generation.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Ssr-encoder: Encoding selective subject representation for subject-driven generation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:57:58.389419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 64a4109a-07f8-4d9d-b2c3-62868f6a90d9 · outbound

This paper cites Fast personalized text to image synthesis with attention injection.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Fast personalized text to image synthesis with attention injection

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:57:58.377175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2d7ee338-ef0c-47fb-86c5-923bf2923bb0 · outbound

This paper cites Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Model.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Model

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.141487Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5c72d21-2129-45cc-8ac4-d27f581a566c · outbound

This paper cites Stable-Hair: Real-World Hair Transfer via Diffusion Model.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Stable-Hair: Real-World Hair Transfer via Diffusion Model

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.145032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb1d8669-99ec-4fa1-bf53-74bd44a3205e · outbound

This paper cites Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.149110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03ce2a9e-2bd1-4011-80a8-5f453fa1911e · outbound

This paper cites Learning to Sketch with Deep Q Networks and Demonstrated Strokes.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Learning to Sketch with Deep Q Networks and Demonstrated Strokes

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-09T14:57:58.201363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation eaa5234f-991c-48cc-86bd-876407d1bdc6 · outbound

This paper cites Asymmetry in Low-Rank Adapters of Foundation Models.

MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation Asymmetry in Low-Rank Adapters of Foundation Models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:58.156327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 039d8667-d1a3-41fd-9afe-7bca0471645e · inbound

FonTS: Text Rendering with Typography and Style Controls cites this paper.

FonTS: Text Rendering with Typography and Style Controls MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-12T10:26:51.096797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 374737ec-16f5-40c0-b49a-e0c6f6ecceb1 · inbound

LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion Transformer cites this paper.

LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion Transformer MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-09T16:37:25.874358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8ec263a-0240-437d-905e-534b3b71a9be · inbound

CineVerse: Consistent Keyframe Synthesis for Cinematic Scene Composition cites this paper.

CineVerse: Consistent Keyframe Synthesis for Cinematic Scene Composition MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:46.801330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26bdb86f-125e-4a6e-a8e5-d1089ff65177 · inbound

OmniConsistency: Learning Style-Agnostic Consistency from Paired Stylization Data cites this paper.

OmniConsistency: Learning Style-Agnostic Consistency from Paired Stylization Data MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:09.275472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d6b19c1-8468-4d46-90eb-7cf261940b9c · inbound

DiffDecompose: Layer-Wise Decomposition of Alpha-Composited Images via Diffusion Transformers cites this paper.

DiffDecompose: Layer-Wise Decomposition of Alpha-Composited Images via Diffusion Transformers MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T14:29:18.374730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bdbdaad-4883-4a05-a982-36cedb85592c · inbound

Autoregressive Images Watermarking through Lexical Biasing: An Approach Resistant to Regeneration Attack cites this paper.

Autoregressive Images Watermarking through Lexical Biasing: An Approach Resistant to Regeneration Attack MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T11:57:35.512200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b82dea6b-4752-40b1-9b8d-7f4016464b36 · inbound

RelationAdapter: Learning and Transferring Visual Relation with Diffusion Transformers cites this paper.

RelationAdapter: Learning and Transferring Visual Relation with Diffusion Transformers MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T11:26:28.950281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9daa639e-1679-4377-a5cf-b135dfdf5524 · inbound

TanDiT: Tangent-Plane Diffusion Transformer for High-Quality 360{\deg} Panorama Generation cites this paper.

TanDiT: Tangent-Plane Diffusion Transformer for High-Quality 360{\deg} Panorama Generation MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T22:29:28.515245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5584b48b-59b3-4299-9abc-2b5772edbb69 · inbound

FeRA: Frequency-Energy Constrained Routing for Effective Diffusion Adaptation Fine-Tuning cites this paper.

FeRA: Frequency-Energy Constrained Routing for Effective Diffusion Adaptation Fine-Tuning MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-03T20:54:30.382188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c48d2df-6219-4d34-961b-48c58467b5e9 · inbound

WorldWander: Bridging Egocentric and Exocentric Worlds in Video Generation cites this paper.

WorldWander: Bridging Egocentric and Exocentric Worlds in Video Generation MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-03T19:54:50.070424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 884e91e2-fc72-4291-9d66-89a6a73f1b86 · inbound

EditTransfer++: Toward Faithful and Efficient Visual-Prompt-Guided Image Editing cites this paper.

EditTransfer++: Toward Faithful and Efficient Visual-Prompt-Guided Image Editing MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:05:57.812352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0229818d-37de-4389-82ea-c0bb07a5de2d · inbound

OmniHumanoid: Streaming Cross-Embodiment Video Generation with Paired-Free Adaptation cites this paper.

OmniHumanoid: Streaming Cross-Embodiment Video Generation with Paired-Free Adaptation MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:57:27.947168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d3135517-97c4-45a3-87c0-b1fece647bf3 · inbound

Image-to-Video Diffusion: From Foundations to Open Frontiers cites this paper.

Image-to-Video Diffusion: From Foundations to Open Frontiers MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation

Reference 144

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:08:24.936744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 243503ef-a2f1-4584-8130-ffa7c93bdd52 · inbound

VISTA: Triplet-Supervised Video Style Transfer with Diffusion Transformers cites this paper.

VISTA: Triplet-Supervised Video Style Transfer with Diffusion Transformers MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-20T14:18:21.403107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 60aa761a-87bd-4d84-abc8-a2b9fd974172 · inbound

SWEET: Sparse World Modeling with Image Editing for Embodied Task Execution cites this paper.

SWEET: Sparse World Modeling with Image Editing for Embodied Task Execution MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-20T07:08:07.235554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 750429f9-242b-464e-b298-08b65dbb7ebc · inbound

EasyVFX: Frequency-Driven Decoupling for Resource-Efficient VFX Generation cites this paper.

EasyVFX: Frequency-Driven Decoupling for Resource-Efficient VFX Generation MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:31:09.841652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 71e8e947-b14b-498d-b180-e51565d28807 · inbound

PAI-Studio: Cinematic Video Background Replacement with Camera-Aware Motion cites this paper.

PAI-Studio: Cinematic Video Background Replacement with Camera-Aware Motion MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-07-01T21:16:14.776414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 184f5782-62c2-44df-ae36-c36b97546f76 · inbound

TryOnCrafter: Unleashing Camera Trajectories for Realistic Video Virtual Try-on via a Renderable 4D Try-on Proxy cites this paper.

TryOnCrafter: Unleashing Camera Trajectories for Realistic Video Virtual Try-on via a Renderable 4D Try-on Proxy MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation

Reference 35

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T21:00:08.911840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 259fd55a-5b7f-4a69-a4d4-2788c280fab9 · inbound

TryOnCrafter: Unleashing Camera Trajectories for Realistic Video Virtual Try-on via a Renderable 4D Try-on Proxy cites this paper.

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-12T12:05:10.800662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36625f80-31f9-4d13-9351-4ce90de08b3c · inbound

InstructionCrafter: Generating Consistent and High-Fidelity Visual Instructions cites this paper.

InstructionCrafter: Generating Consistent and High-Fidelity Visual Instructions MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-14T04:38:38.201575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.