Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation | Paper Citation Record

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This paper cites Denoising diffu- sion probabilistic models.Advances in neural information processing systems, 33:6840–6851.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Denoising diffu- sion probabilistic models.Advances in neural information processing systems, 33:6840–6851

Reference 1

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This paper cites Denoising Diffusion Implicit Models.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Denoising Diffusion Implicit Models

Reference 2

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Observation 84666e2e-9a9a-416c-a63c-deca3cafdb49 · outbound

This paper cites Flow matching for generative modeling.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Flow matching for generative modeling

Reference 3

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Observation 1cddfd30-dd49-4775-a2a2-c12d6db12cd0 · outbound

This paper cites Scaling rectified flowtransformersforhigh-resolutionimagesynthesis.InForty- first international conference on machine learning.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Scaling rectified flowtransformersforhigh-resolutionimagesynthesis.InForty- first international conference on machine learning

Reference 4

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Observation c4274da3-2d76-428b-81e7-0c8d734bd983 · outbound

This paper cites Consistency models.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Consistency models

Reference 5

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Observation 24eaa755-7c05-4d82-ba2f-06434762e7f7 · outbound

This paper cites Multistep Consistency Models.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Multistep Consistency Models

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:28:39.551210Z

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Observation 9f57c5a8-4651-4b9d-951c-ce1580a72fed · outbound

This paper cites Align Your Flow: Scaling Continuous-Time Flow Map Distillation.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Align Your Flow: Scaling Continuous-Time Flow Map Distillation

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:33:41.601618Z

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Observation 7e20a4bd-f721-42c0-a456-4ee2efadd77e · outbound

This paper cites Flow map matching with stochastic interpolants: A mathematical framework for consistency models.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Flow map matching with stochastic interpolants: A mathematical framework for consistency models

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T09:33:41.639906Z

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Observation ad82b1cf-94e0-4ffb-ad8b-3a6c5ee9c73a · outbound

This paper cites Mean Flows for One-step Generative Modeling.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Mean Flows for One-step Generative Modeling

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:29:30.500037Z

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Observation 6f52eed5-9cb1-4842-b34f-587929729f21 · outbound

This paper cites Zhang, A.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Zhang, A

Reference 10

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Observation 9e595388-e436-4c85-9af0-a437fa6a15d4 · outbound

This paper cites SplitMeanFlow: Interval Splitting Consistency in Few-Step Generative Modeling.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation SplitMeanFlow: Interval Splitting Consistency in Few-Step Generative Modeling

Reference 11

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Observation 17af12ea-0515-4483-8499-01b4a9404d26 · outbound

This paper cites Decoupled meanflow: Turning flow models into flow maps for acceler- ated sampling.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Decoupled meanflow: Turning flow models into flow maps for acceler- ated sampling

Reference 12

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Observation 1e62e046-6323-4fb5-b462-afe71a51de37 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Imagenet: A large-scale hierarchical image database

Reference 13

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Observation e6b7663d-ebe9-40fd-8242-62a962224a52 · outbound

This paper cites SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-15T00:56:50.121098Z

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Observation a93b0b49-62d6-4197-9e08-242ae8bc0634 · outbound

This paper cites Learning trans- ferable visual models from natural language supervision.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Learning trans- ferable visual models from natural language supervision

Reference 15

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Observation 925e388f-5715-4952-9da6-db60f64a966b · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research, 21(140):1–67.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research, 21(140):1–67

Reference 16

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Observation 06fd4b26-6939-450e-9e7d-10cc1e1e34c1 · outbound

This paper cites Qwen2 Technical Report.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Qwen2 Technical Report

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-10T14:09:09.439499Z

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Observation 9f3d9ad2-e725-4af6-8c6f-89c817520d60 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Gemma 2: Improving Open Language Models at a Practical Size

Reference 18

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Observation b7087298-efb4-45ea-a3e3-146de5a712e2 · outbound

This paper cites 8 Sana-sprint: One-step diffusion with continuous-time consis- tency distillation.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation 8 Sana-sprint: One-step diffusion with continuous-time consis- tency distillation

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:28:39.567279Z

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Observation 731fe970-5be0-4ab0-94e3-72c68ccf09b2 · outbound

This paper cites Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models

Reference 20

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Observation 9a1f6ecc-891f-4aa5-a9e4-3389e29e495a · outbound

This paper cites Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency

Reference 21

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Observation 4d3247bb-44c9-4e2b-a96b-409d69860435 · outbound

This paper cites Consistency Models Made Easy.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Consistency Models Made Easy

Reference 22

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Observation 5b328d3b-7d01-40e1-9061-6387feb5f2f2 · outbound

This paper cites Improved Techniques for Training Consistency Models.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Improved Techniques for Training Consistency Models

Reference 23

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Observation 005ca170-0da8-418e-a4cf-2ed10216f322 · outbound

This paper cites Advancing end- to-end pixel space generative modeling via self-supervised pre-training.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Advancing end- to-end pixel space generative modeling via self-supervised pre-training

Reference 24

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Observation 86826d24-af32-40d4-88f5-d78d49c3f718 · outbound

This paper cites Diffusion Transformers with Representation Autoencoders.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Diffusion Transformers with Representation Autoencoders

Reference 25

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Observation 57afe4cc-3a5d-4048-883f-4800c8f2cdf1 · outbound

This paper cites Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Reference 26

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Observation 2ecd1cf9-7c91-493c-901f-faa7fd1f330f · outbound

This paper cites Latent diffusion model without variational autoencoder.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Latent diffusion model without variational autoencoder

Reference 27

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Observation 9379771f-a636-438e-9704-b97d8decaeb4 · outbound

This paper cites Representation entanglement for generation: Training diffusion transformers is much easier than you think.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Representation entanglement for generation: Training diffusion transformers is much easier than you think

Reference 28

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Observation c9434ea0-c1c9-489f-a857-17cbdcd8881d · outbound

This paper cites InICCV.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation InICCV

Reference 29

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Observation 18624687-ba25-4118-8323-2cfbc76445ae · outbound

This paper cites Reconstruction Alignment Improves Unified Multimodal Models.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Reconstruction Alignment Improves Unified Multimodal Models

Reference 30

Resolution: metadata mismatch; arxiv_id, observed 2026-06-26T02:15:36.738364Z

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Observation 279719c3-82a0-4c51-9335-39830488559e · outbound

This paper cites Sana 1.5: Efficient scaling of training-time and inference-time compute in linear diffusion transformer.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Sana 1.5: Efficient scaling of training-time and inference-time compute in linear diffusion transformer

Reference 31

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Observation 47aa68e4-9830-48f4-8b0f-9a521601fa38 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation U-net: Convolutional networks for biomedical image segmentation

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Observation 0a5a0c6d-5950-4ef3-8a27-04a4b904802d · outbound

This paper cites Scalable diffusion models with transformers.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Scalable diffusion models with transformers

Reference 33

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Observation 7b5bd5ae-0276-43cb-ab47-0d9e54fd6a31 · outbound

This paper cites S2-guidance: Stochastic self guidance for training-free enhancement of diffusion models.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation S2-guidance: Stochastic self guidance for training-free enhancement of diffusion models

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Observation 907c7825-2665-4e8a-8525-c595ff44a7b9 · outbound

This paper cites Taming preference mode collapse via directional decoupling alignment in diffusion reinforcement learning.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Taming preference mode collapse via directional decoupling alignment in diffusion reinforcement learning

Reference 35

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Observation b9562984-82e3-4676-9a48-12165c14b8b5 · outbound

This paper cites Qwen Technical Report.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Qwen Technical Report

Reference 36

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Observation 9ec2f277-279c-4a71-8972-e8a26a78daae · outbound

This paper cites Qwen3 Technical Report.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Qwen3 Technical Report

Reference 37

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Observation d7f0a1c9-04c7-4d72-a98e-7ad5651a712f · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Gemma: Open Models Based on Gemini Research and Technology

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Observation a9db719e-fcfe-49bd-baa4-863eb986598f · outbound

This paper cites Gemma 3 Technical Report.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Gemma 3 Technical Report

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Resolution: verified exact; local_arxiv, observed 2026-05-10T09:28:39.617811Z

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Observation d6fe364b-5a3c-4b71-9499-4cff217e2ffc · outbound

This paper cites High-resolution image synthesis withlatentdiffusionmodels.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation High-resolution image synthesis withlatentdiffusionmodels

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:44:23.727838Z

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Observation b0e129c9-4d86-4b9a-8437-5b2c6eca2117 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

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Resolution: verified exact; arxiv_id, observed 2026-05-10T15:22:03.751402Z

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Observation 7700f5a4-e79f-493e-89ba-dee488f2652b · outbound

This paper cites Pixart-𝛼: Fast training of diffusion transformer for photorealistic text-to-image synthesis.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Pixart-𝛼: Fast training of diffusion transformer for photorealistic text-to-image synthesis

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:44:23.737243Z

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Observation 70510194-090f-412e-a1e4-b72b7d4fe14e · outbound

This paper cites PIXART-{\delta}: Fast and Controllable Image Generation with Latent Consistency Models.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation PIXART-{\delta}: Fast and Controllable Image Generation with Latent Consistency Models

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Resolution: verified exact; arxiv_id, observed 2026-05-10T09:28:39.623281Z

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Observation 4be4a9e5-2ab3-4348-b20b-f962c88e7451 · outbound

This paper cites Pixart-𝜎: Weak-to-strong training of diffusion transformer for 4k text-to-image generation.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Pixart-𝜎: Weak-to-strong training of diffusion transformer for 4k text-to-image generation

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:44:23.696510Z

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Observation 8f4cafac-6826-4145-b41e-161e1732a5a6 · outbound

This paper cites an unresolved cited work.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-05-21T21:44:23.706886Z

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Observation 6e216859-95ac-48c4-a414-5aefe50849b0 · outbound

This paper cites Flux-text: A simple and advanced diffusion transformer baseline for scene text editing.arXiv preprint arXiv:2505.03329.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Flux-text: A simple and advanced diffusion transformer baseline for scene text editing.arXiv preprint arXiv:2505.03329

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Resolution: verified exact; arxiv_id, observed 2026-05-10T09:33:41.605722Z

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Observation 63ba8b72-ade2-480c-b0a9-bfc0a78dd088 · outbound

This paper cites Nano banana.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Nano banana

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:44:23.680937Z

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Observation 126a2e29-ad8a-48f0-b1d4-c23345e402aa · outbound

This paper cites Qwen-Image Technical Report.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Qwen-Image Technical Report

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Resolution: verified exact; arxiv_id, observed 2026-05-10T14:29:07.137498Z

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Observation ce4cba4c-d39b-4163-9ddb-75a2738fb6e5 · outbound

This paper cites HunyuanImage 3.0 Technical Report.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation HunyuanImage 3.0 Technical Report

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Resolution: verified exact; arxiv_id, observed 2026-05-16T02:02:32.945705Z

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Observation 6cdca740-2263-4059-b665-c36a3b372e93 · outbound

This paper cites Playground v3: Improving Text-to-Image Alignment with Deep-Fusion Large Language Models.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Playground v3: Improving Text-to-Image Alignment with Deep-Fusion Large Language Models

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:28:39.561935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e982e22b-dde6-42c6-a6b4-63ffc68ace9d · outbound

This paper cites Blip3o-next: Next frontier of native image generation.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Blip3o-next: Next frontier of native image generation

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:28:39.607020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 928552b2-61f6-4326-8b44-9b687fad2b42 · outbound

This paper cites Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-13T04:15:55.027720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bb3cf0db-bcb1-4d7f-81f0-e83f2a3e504c · outbound

This paper cites Consistency Flow Matching: Defining Straight Flows with Velocity Consistency.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Consistency Flow Matching: Defining Straight Flows with Velocity Consistency

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:28:39.583244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5780362e-d0f8-4a4c-9ee6-4fd357f51749 · outbound

This paper cites Transition Models: Rethinking the Generative Learning Objective.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Transition Models: Rethinking the Generative Learning Objective

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:28:39.594127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 13a56e94-eb59-4ce1-a164-21801ea2336d · outbound

This paper cites Microsoft coco: Common objects in context.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Microsoft coco: Common objects in context

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:44:23.702133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e6ec3b3e-d11f-4e28-b7c5-891f75cd5523 · outbound

This paper cites Saco loss: Sample-wise affinity consistency for vision-language pre-training.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Saco loss: Sample-wise affinity consistency for vision-language pre-training

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:44:23.734872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a0232557-c79b-42fc-b4c8-b829612a6ede · outbound

This paper cites DINOv3.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation DINOv3

Reference 57

Resolution: metadata mismatch; local_arxiv, observed 2026-05-10T09:28:39.628421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 00aac801-13ad-4aec-ad65-958dad157038 · outbound

This paper cites ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:43:03.755490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b390162c-fe32-420d-9480-e024d708fc4c · outbound

This paper cites BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:28:39.609965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 09230663-92aa-4ccf-a58c-26893973f84b · outbound

This paper cites ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image Generation.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image Generation

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:28:39.596721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3d432ef3-d8fb-47df-b223-a5d3b74ead1e · outbound

This paper cites Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved Image Generation.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved Image Generation

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:28:39.636695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e65cb7a1-7749-40a6-9ab3-322a91769353 · outbound

This paper cites Geneval: An object-focused framework for evaluating text-to- imagealignment.AdvancesinNeuralInformationProcessing Systems, 36:52132–52152.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Geneval: An object-focused framework for evaluating text-to- imagealignment.AdvancesinNeuralInformationProcessing Systems, 36:52132–52152

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:44:23.686655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 434df2f4-984e-46fd-8521-074b35dcca44 · outbound

This paper cites Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:30:48.951760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1eb8ff30-d822-4b2b-9755-93090830d1da · outbound

This paper cites Cosmos world foundation model platform for physical ai.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Cosmos world foundation model platform for physical ai

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:44:23.677327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2a99ff9d-d136-4719-b385-a0e61630601d · outbound

This paper cites Lumina-Image 2.0: A Unified and Efficient Image Generative Framework.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Lumina-Image 2.0: A Unified and Efficient Image Generative Framework

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:28:39.604186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8d27b40e-ee67-4959-bf6b-d2e7504946e1 · outbound

This paper cites HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-05-16T17:13:39.137651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c0ffe458-cd3f-4b7b-8de6-d3db4160dcbe · outbound

This paper cites Seedream 3.0 Technical Report.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Seedream 3.0 Technical Report

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:55:38.865721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 98e17ba2-832a-433d-90f4-01c9902bf7cf · outbound

This paper cites Gpt-image-1.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Gpt-image-1

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:44:23.674569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6d0ff3b8-ab01-43ce-a8d0-b03f0767452c · outbound

This paper cites Transfer between Modalities with MetaQueries.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Transfer between Modalities with MetaQueries

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:49:23.311693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c96aa05d-13c8-4ef0-88af-bcbcf5319edf · outbound

This paper cites OpenUni: A Simple Baseline for Unified Multimodal Understanding and Generation.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation OpenUni: A Simple Baseline for Unified Multimodal Understanding and Generation

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:28:39.580506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3f09bd54-8d22-4e48-9d5f-b6c7e89f3d47 · outbound

This paper cites Vision as a Dialect: Unifying Visual Understanding and Generation via Text-Aligned Representations.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Vision as a Dialect: Unifying Visual Understanding and Generation via Text-Aligned Representations

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:28:39.564612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b0403c62-fbe1-4434-ad2a-a777f90716fa · outbound

This paper cites TBAC-UniImage: Unified Understanding and Generation by Ladder-Side Diffusion Tuning.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation TBAC-UniImage: Unified Understanding and Generation by Ladder-Side Diffusion Tuning

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:28:39.569797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ef46f3d8-b6e0-4506-8923-8125b0f47ab7 · outbound

This paper cites SDXL-Lightning: Progressive Adversarial Diffusion Distillation.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation SDXL-Lightning: Progressive Adversarial Diffusion Distillation

Reference 73

Resolution: verified exact; arxiv_id, observed 2026-05-17T05:08:49.084339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f4495ff5-71c5-4c97-867b-8d6acdbdbb2e · outbound

This paper cites Hyper-sd: Trajectory segmented consistency model for efficient image synthesis.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Hyper-sd: Trajectory segmented consistency model for efficient image synthesis

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:44:23.722845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f025159d-474c-47ea-94fd-77bce48e82ac · outbound

This paper cites Improved distribution matching distillation for fast image synthesis.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Improved distribution matching distillation for fast image synthesis

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:44:23.684809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c076c545-6e37-47bf-adaa-3921a74077b5 · outbound

This paper cites Lumina-next: Making lumina-t2x stronger and faster with next-dit.Advances in Neural Information Processing Systems, 37:131278–131315.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Lumina-next: Making lumina-t2x stronger and faster with next-dit.Advances in Neural Information Processing Systems, 37:131278–131315

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:44:23.749725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8b50e645-0356-47d0-8008-17bc23d6465a · outbound

This paper cites Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-05-15T16:40:06.446550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ce416278-a20e-4dcf-9845-66106728773b · outbound

This paper cites Hunyuan- dit: A powerful multi-resolution diffusion transformer with fine-grained chinese understanding.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Hunyuan- dit: A powerful multi-resolution diffusion transformer with fine-grained chinese understanding

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:44:23.752388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cbec2dbf-7924-4b89-8e6f-4e0e08778e87 · outbound

This paper cites DALL·E 3.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation DALL·E 3

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:44:23.701263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 341d00f8-d39e-41db-bde1-86461a66d5fa · outbound

This paper cites Fast high- resolution image synthesis with latent adversarial diffusion distillation.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Fast high- resolution image synthesis with latent adversarial diffusion distillation

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:44:23.747289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9620d42e-0206-4e69-8043-fa0282e5285b · outbound

This paper cites Text Conditions 𝑡−𝑟𝑢(𝑧,𝑟,𝑡)𝑢(𝑧,𝑟,𝑡)𝑣 𝑡−𝑟𝑢(𝑧,𝑟,𝑡)𝑣𝑢(𝑧,𝑟,𝑡) Figure 7.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Text Conditions 𝑡−𝑟𝑢(𝑧,𝑟,𝑡)𝑢(𝑧,𝑟,𝑡)𝑣 𝑡−𝑟𝑢(𝑧,𝑟,𝑡)𝑣𝑢(𝑧,𝑟,𝑡) Figure 7

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:44:23.722527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0d375dbb-8fc1-4b4a-bc37-ddcace2b52e7 · outbound

This paper cites We chose OpenUni because it shares the SANA- 1.5 diffusion backbone, but uses a InternVL3–based text encoder.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation We chose OpenUni because it shares the SANA- 1.5 diffusion backbone, but uses a InternVL3–based text encoder

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:44:23.712717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 294b7956-d89b-4bac-a097-25d02377dd9c · outbound

This paper cites When generating images from the same prompt and timing diffusion sampling only, BLIP3o-NEXT on H200 takes 1.24 s with 30 steps, while ours takes 0.22/0.12/0.08 s (4/2/1 steps).

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation When generating images from the same prompt and timing diffusion sampling only, BLIP3o-NEXT on H200 takes 1.24 s with 30 steps, while ours takes 0.22/0.12/0.08 s (4/2/1 steps)

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:44:23.739780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 42b38891-4185-4ebb-ae84-1bf28f468edc · outbound

This paper cites Which result best matches the prompt?.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation Which result best matches the prompt?

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:44:23.742049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f49fa23d-6c3e-4654-bef9-6cd23e3c56a7 · outbound

This paper cites DPG-Bench evaluation.Generatinghigh-fidelityimages from complex and detail-rich textual prompts in a limited number of denoising iterations is a highly challenging task.

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation DPG-Bench evaluation.Generatinghigh-fidelityimages from complex and detail-rich textual prompts in a limited number of denoising iterations is a highly challenging task

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T21:44:23.744879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Reference resolution

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