Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T17:40:18.263473Z
Paper Citation Record · LEDGER
Text Embedding Knows How to Quantize Text-Guided Diffusion Models
As of 10 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2507.10340.
A citation records a reference. It does not transfer a finding from one paper to another.
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One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
63 of 63 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 060bcccb-be3b-4955-abf7-57e8144f59e1 · outbound
This paper cites an unresolved cited work.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Unresolved cited work
Reference 1
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This paper cites an unresolved cited work.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Unresolved cited work
Reference 2
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Observation 16c16034-acc9-42f5-babd-616579a769d2 · outbound
This paper cites eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
Reference 3
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Observation 155272a7-720d-45d1-9fcf-ef21a645cf26 · outbound
This paper cites Analytic- DPM: An analytic estimate of the optimal reverse variance in diffusion probabilistic models.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Analytic- DPM: An analytic estimate of the optimal reverse variance in diffusion probabilistic models
Reference 4
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Observation 1606b075-803f-481c-aa47-c1c544cd01a9 · outbound
This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
Reference 5
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Observation 2293eed1-7868-4f16-80c7-da89fdc3aa75 · outbound
This paper cites Improving image generation with better captions.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Improving image generation with better captions
Reference 6
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Observation 8fe93053-723f-4665-a7f4-8ac4589110a3 · outbound
This paper cites PACT: Parameterized Clipping Activation for Quantized Neural Networks.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models PACT: Parameterized Clipping Activation for Quantized Neural Networks
Reference 7
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Source-reported events for the cited work
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Observation 179d6b59-509d-4b47-94cf-47e9192deb59 · outbound
This paper cites Perception pri- oritized training of diffusion models.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Perception pri- oritized training of diffusion models
Reference 8
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Observation 77476e07-5360-403c-8f1d-732de5657285 · outbound
This paper cites QNCD: Quantization Noise Correction for Diffusion Models.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models QNCD: Quantization Noise Correction for Diffusion Models
Reference 9
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Source-reported events for the cited work
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Observation bccf50b9-3ceb-46a5-8852-6338f0765db5 · outbound
This paper cites Mix- Path: A unified approach for one-shot neural architecture search.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Mix- Path: A unified approach for one-shot neural architecture search
Reference 10
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Observation 4bbac8a2-d061-470d-b1c1-bfce64125017 · outbound
This paper cites HAWQ: Hessian aware quantiza- tion of neural networks with mixed-precision.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models HAWQ: Hessian aware quantiza- tion of neural networks with mixed-precision
Reference 11
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Source-reported events for the cited work
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Observation d237404f-23f9-4e3d-adcb-bb3027352a89 · outbound
This paper cites Generative adversarial nets.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Generative adversarial nets
Reference 12
- Resolution
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Source-reported events for the cited work
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Observation 2a2f5249-4497-41d9-9927-0f51e27a0951 · outbound
This paper cites GIQA: Generated image quality assessment.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models GIQA: Generated image quality assessment
Reference 13
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Observation 9dba14df-1df8-4a42-8153-9f05a54854fb · outbound
This paper cites Learning both weights and connections for efficient neural network.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Learning both weights and connections for efficient neural network
Reference 14
- Resolution
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Observation 9621181b-7094-49a2-b10e-f5ff616eeab0 · outbound
This paper cites PTQD: Accurate post-training quantization for diffusion models.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models PTQD: Accurate post-training quantization for diffusion models
Reference 15
- Resolution
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Observation ae73b4b8-6d4c-4792-b9c1-9210f337ea09 · outbound
This paper cites CLIPScore: A reference-free evaluation metric for image captioning.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models CLIPScore: A reference-free evaluation metric for image captioning
Reference 16
- Resolution
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Observation d600dbcc-38d0-4083-85ff-11818eef8f20 · outbound
This paper cites GANs trained by a two time-scale update rule converge to a local nash equilib- rium.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models GANs trained by a two time-scale update rule converge to a local nash equilib- rium
Reference 17
- Resolution
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Source-reported events for the cited work
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Observation 2712aee2-85ba-4571-882a-2a8ad47d2ad3 · outbound
This paper cites Denoising diffu- sion probabilistic models.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Denoising diffu- sion probabilistic models
Reference 18
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 78bb9e20-1ffb-4983-b744-75d9c9d20a92 · outbound
This paper cites AdaBM: On-the-fly adaptive bit mapping for image super-resolution.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models AdaBM: On-the-fly adaptive bit mapping for image super-resolution
Reference 19
- Resolution
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Source-reported events for the cited work
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Observation 9d081323-e372-4627-894e-85e6fc7d2f66 · outbound
This paper cites CADyQ: Content-aware dynamic quantization for image super-resolution.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models CADyQ: Content-aware dynamic quantization for image super-resolution
Reference 20
- Resolution
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Source-reported events for the cited work
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Observation f8a21641-4b5d-4ee9-b3ad-3cf54ea1ddb5 · outbound
This paper cites BK-SDM: A lightweight, fast, and cheap version of stable diffusion.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models BK-SDM: A lightweight, fast, and cheap version of stable diffusion
Reference 21
- Resolution
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Observation da97c0eb-cd58-4527-83b4-b43e773640df · outbound
This paper cites Adam: A method for stochastic optimization.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Adam: A method for stochastic optimization
Reference 22
- Resolution
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Source-reported events for the cited work
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Observation feda5933-b1d3-46a5-b48b-815f01fddbf8 · outbound
This paper cites Pick-a-Pic: An open dataset of user preferences for text-to-image generation.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Pick-a-Pic: An open dataset of user preferences for text-to-image generation
Reference 23
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Observation 00acbf8f-6085-4321-8698-4a5e44f2b201 · outbound
This paper cites Improved precision and recall met- ric for assessing generative models.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Improved precision and recall met- ric for assessing generative models
Reference 24
- Resolution
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Observation 83f823bf-9662-4607-a017-3a9c3aa537f5 · outbound
This paper cites RefQSR: Reference-based quantization for image super-resolution networks.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models RefQSR: Reference-based quantization for image super-resolution networks
Reference 25
- Resolution
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Source-reported events for the cited work
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Observation 1171589e-b1e9-4c8f-8c0d-5a7c644d738f · outbound
This paper cites Auto- mated knowledge distillation via monte carlo tree search.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Auto- mated knowledge distillation via monte carlo tree search
Reference 26
- Resolution
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Source-reported events for the cited work
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Observation 74e1c134-50bc-400f-bf2a-5ebdde918c60 · outbound
This paper cites 9 Q-diffusion: Quantizing diffusion models.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models 9 Q-diffusion: Quantizing diffusion models
Reference 27
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation e771f4f7-61cb-4fab-9276-e6a7c8c95dda · outbound
This paper cites BRECQ: Push- ing the limit of post-training quantization by block recon- struction.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models BRECQ: Push- ing the limit of post-training quantization by block recon- struction
Reference 28
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 0f6105a8-6909-4013-88e4-0949a35baf3d · outbound
This paper cites Snap- Fusion: Text-to-image diffusion model on mobile devices within two seconds.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Snap- Fusion: Text-to-image diffusion model on mobile devices within two seconds
Reference 29
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Observation 7ad1520f-c584-45bb-bcb8-2c90122cb519 · outbound
This paper cites Microsoft COCO: Common objects in context.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Microsoft COCO: Common objects in context
Reference 30
- Resolution
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Observation a9f4984e-d339-4a32-a9e9-502b2dbbddd0 · outbound
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Text Embedding Knows How to Quantize Text-Guided Diffusion Models Progressive neural architecture search
Reference 31
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Source-reported events for the cited work
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Observation 87e90a76-304a-44ec-95ab-787293c2da21 · outbound
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Text Embedding Knows How to Quantize Text-Guided Diffusion Models Pseudo nu- merical methods for diffusion models on manifolds
Reference 32
- Resolution
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Source-reported events for the cited work
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Observation 7b3a6144-c743-437d-8910-061f34d42307 · outbound
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Text Embedding Knows How to Quantize Text-Guided Diffusion Models Instance-aware dynamic neural network quantization
Reference 33
- Resolution
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Source-reported events for the cited work
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Observation 41a165e2-fb78-4b33-b3cd-5701fd57e8f8 · outbound
This paper cites DPM-Solver: A fast ODE solver for dif- fusion probabilistic model sampling in around 10 steps.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models DPM-Solver: A fast ODE solver for dif- fusion probabilistic model sampling in around 10 steps
Reference 34
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Observation f3c0f54b-ffbc-4f99-aca3-ecda1373f496 · outbound
This paper cites On distillation of guided diffusion models.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models On distillation of guided diffusion models
Reference 35
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Source-reported events for the cited work
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Observation cacfc6a8-47f0-43fb-9598-f958f74d904a · outbound
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Text Embedding Knows How to Quantize Text-Guided Diffusion Models Improved denoising diffusion probabilistic models
Reference 36
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Observation 59b7abe0-d61c-4406-a90e-ed551aa3d77b · outbound
This paper cites CUTLASS, 2025.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models CUTLASS, 2025
Reference 37
- Resolution
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Source-reported events for the cited work
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Observation b13c3a4b-83ce-4241-9ed6-efc4338d634a · outbound
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Text Embedding Knows How to Quantize Text-Guided Diffusion Models Softmax bias correction for quantized generative models
Reference 38
- Resolution
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Source-reported events for the cited work
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Observation 4942ad4c-7654-4c28-9f2f-b967a1a66d97 · outbound
This paper cites Notes on regression and inheritance in the case of two parents.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Notes on regression and inheritance in the case of two parents
Reference 39
- Resolution
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Source-reported events for the cited work
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Observation bc6e60ee-e296-4dfc-811c-944690eb9951 · outbound
This paper cites SDXL: Improving latent diffusion mod- els for high-resolution image synthesis.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models SDXL: Improving latent diffusion mod- els for high-resolution image synthesis
Reference 40
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation cf62d1b3-fba3-48ef-96a8-e40f35b34401 · outbound
This paper cites Learn- ing transferable visual models from natural language super- vision.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Learn- ing transferable visual models from natural language super- vision
Reference 41
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 5cd6ebfd-43d6-41d9-8f2d-20522d72e8c5 · outbound
This paper cites High-resolution image synthesis with latent diffusion models.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models High-resolution image synthesis with latent diffusion models
Reference 42
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 5c3fac90-e1a0-4e62-8f91-25ec76dc695a · outbound
This paper cites DreamBooth: Fine tuning text-to-image diffusion models for subject-driven generation.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models DreamBooth: Fine tuning text-to-image diffusion models for subject-driven generation
Reference 43
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 32dc09dd-ab58-4139-95fb-2504ce8d4eb3 · outbound
This paper cites Memory- efficient personalization using quantized diffusion model.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Memory- efficient personalization using quantized diffusion model
Reference 44
- Resolution
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Source-reported events for the cited work
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Observation c90a20ff-1e94-4c5d-9147-9849141c0bed · outbound
This paper cites Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
Reference 45
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 15097128-8dd9-477a-a28f-562eca9b6bdc · outbound
This paper cites Progressive distillation for fast sampling of diffusion models.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Progressive distillation for fast sampling of diffusion models
Reference 46
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 2d253f76-ab38-4254-90a7-a41303bc6409 · outbound
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Text Embedding Knows How to Quantize Text-Guided Diffusion Models Improved techniques for training gans
Reference 47
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 8da39939-1139-4280-a8fa-120a8f091a3d · outbound
This paper cites Post-training quantization on diffusion models.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Post-training quantization on diffusion models
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:40:18.617409Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 6feeca65-e7c8-4b12-95ee-186342426aee · outbound
This paper cites Conceptual captions: A cleaned, hypernymed, im- age alt-text dataset for automatic image captioning.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Conceptual captions: A cleaned, hypernymed, im- age alt-text dataset for automatic image captioning
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:40:18.602197Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 119d9fc9-403b-4362-85bf-81b3d754019c · outbound
This paper cites Temporal dynamic quantization for dif- fusion models.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Temporal dynamic quantization for dif- fusion models
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:40:18.586316Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 8da7fceb-e2d7-4fdd-abb7-df865932591b · outbound
This paper cites Denois- ing diffusion implicit models.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Denois- ing diffusion implicit models
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:40:18.571347Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation ac7e1816-0580-4e89-a12a-f95c495cfd1a · outbound
This paper cites The proof and measurement of associa- tion between two things.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models The proof and measurement of associa- tion between two things
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:40:18.554038Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 2e5312b3-aabb-4f02-972e-c43782fe5f4a · outbound
This paper cites CHIP: Channel independence- based pruning for compact neural networks.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models CHIP: Channel independence- based pruning for compact neural networks
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:40:18.537095Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 7403a570-272f-43ed-b07f-40649c539821 · outbound
This paper cites Post-training quan- tization with progressive calibration and activation relaxing for text-to-image diffusion models.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Post-training quan- tization with progressive calibration and activation relaxing for text-to-image diffusion models
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:40:18.520529Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 8ea44325-bf06-4c62-ae84-580b9e4d2cc2 · outbound
This paper cites Post-training quan- tization with progressive calibration and activation relaxing for text-to-image diffusion models.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Post-training quan- tization with progressive calibration and activation relaxing for text-to-image diffusion models
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:40:18.505550Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 7c668848-8bed-449c-a1bf-ad0dddc1fa89 · outbound
This paper cites CABM: Content-aware bit map- ping for single image super-resolution network with large input.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models CABM: Content-aware bit map- ping for single image super-resolution network with large input
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:40:18.490045Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation d0c04ab3-489d-442d-9044-4e4b70ca7c8b · outbound
This paper cites Bayesian bits: Unifying quantization and pruning.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Bayesian bits: Unifying quantization and pruning
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:40:18.475020Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation f7bf8cdb-c51f-493d-a82b-63864e15c483 · outbound
This paper cites Ex- ploring clip for assessing the look and feel of images.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Ex- ploring clip for assessing the look and feel of images
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:40:18.458893Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 717dde72-6bb4-49e1-839c-977a35324c3e · outbound
This paper cites HAQ: Hardware-aware automated quantization with mixed precision.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models HAQ: Hardware-aware automated quantization with mixed precision
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:40:18.441427Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation e4f56b2a-f58c-4dab-adbb-df842be49e39 · outbound
This paper cites ImageRe- ward: Learning and evaluating human preferences for text- to-image generation.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models ImageRe- ward: Learning and evaluating human preferences for text- to-image generation
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:40:18.424164Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 669ce22b-dd38-4d2f-9c28-58f0df567642 · outbound
This paper cites Online knowledge distillation via mutual contrastive learning for visual recog- nition.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Online knowledge distillation via mutual contrastive learning for visual recog- nition
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:40:18.406940Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation bea83efc-14c3-4912-bbe7-9f708293e45d · outbound
This paper cites Adding conditional control to text-to-image diffusion models.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models Adding conditional control to text-to-image diffusion models
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:40:18.389789Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 20b497c2-47ce-4ffd-a69c-d4cc179bde05 · outbound
This paper cites proj in” which leverages the denoised image latent passed into the cross-attention block, (2) “at2.to v.
Text Embedding Knows How to Quantize Text-Guided Diffusion Models proj in” which leverages the denoised image latent passed into the cross-attention block, (2) “at2.to v
Reference 63
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-06T17:40:18.372423Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Pith citing papers
No inbound Pith citation observations are available.