SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices | Paper Citation Record

Observation e97814fc-2d53-45ee-b043-46d7264f553f · outbound

This paper cites Stable diffusion 3.5.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Stable diffusion 3.5

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Observation 3c04d5d7-09cb-419e-8ba4-e73faa4517aa · outbound

This paper cites GQA: Training generalized multi-query transformer models from multi-head checkpoints.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices GQA: Training generalized multi-query transformer models from multi-head checkpoints

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Observation a978448a-83ad-4930-9b7e-68a1b0805090 · outbound

This paper cites Sd3.5- flash: Distribution-guided distillation of generative flows,.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Sd3.5- flash: Distribution-guided distillation of generative flows,

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Observation 531885fc-5da1-4efc-b67f-d957cf63c934 · outbound

This paper cites All are worth words: A vit backbone for diffusion models.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices All are worth words: A vit backbone for diffusion models

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Observation 2a9ff00b-8056-493d-832b-ad53890ea9c2 · outbound

This paper cites All are worth words: A vit backbone for diffusion models.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices All are worth words: A vit backbone for diffusion models

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Observation 5e7a5cfd-5d54-41f1-9332-a5b9e62e5c85 · outbound

This paper cites Large scale gan training for high fidelity natural image synthe- sis.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Large scale gan training for high fidelity natural image synthe- sis

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Observation f0cf8a1f-ffdf-4b55-9dca-509e08e826a6 · outbound

This paper cites Once-for-all: Train one network and specialize it for efficient deployment.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Once-for-all: Train one network and specialize it for efficient deployment

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Observation 25fe78e3-36bc-4bbf-90ce-80ced33227f9 · outbound

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

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer

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Observation fec04d08-9a6b-4289-a498-0e4ef6b55ca2 · outbound

This paper cites EdgeFusion: On-Device Text-to-Image Generation.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices EdgeFusion: On-Device Text-to-Image Generation

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Observation f5421e55-af37-428a-8e1a-7ea89fc9132b · outbound

This paper cites PixArt-\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices PixArt-\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation

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Observation f61f53a6-57af-4cac-827d-07746378352d · outbound

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

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Pixart-α: Fast training of dif- fusion transformer for photorealistic text-to-image synthesis

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Observation a54e030b-a9d7-4c61-a8bf-798bd1f3550d · outbound

This paper cites Sana-sprint: One-step diffusion with continuous-time con- sistency distillation, 2025.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Sana-sprint: One-step diffusion with continuous-time con- sistency distillation, 2025

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Observation f91b811b-e907-4297-8d12-82adf11533f6 · outbound

This paper cites Scalable high-resolution pixel-space image syn- thesis with hourglass diffusion transformers.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Scalable high-resolution pixel-space image syn- thesis with hourglass diffusion transformers

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Observation 27562a3e-4609-4ef0-aa32-00bf91d9a5c6 · outbound

This paper cites Transformers are SSMs: Gen- eralized models and efficient algorithms through structured state space duality.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Transformers are SSMs: Gen- eralized models and efficient algorithms through structured state space duality

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Observation 80dd09cf-6cb1-4ac8-92d4-2b69f7a0b725 · outbound

This paper cites Deepfloyd.https://github.com/deep-floyd/IF,.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Deepfloyd.https://github.com/deep-floyd/IF,

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Observation 601e65fd-c153-4774-8dbc-a26dab476976 · outbound

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

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Imagenet: A large-scale hierarchical image database

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Observation afb08000-f073-45b3-b6c3-ff3af2003188 · outbound

This paper cites Kakade, Ali Farhadi, and Prateek Jain.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Kakade, Ali Farhadi, and Prateek Jain

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Observation 9c7e2b69-e7b3-4ebd-867e-5387a0efad6a · outbound

This paper cites Scaling rectified flow trans- formers for high-resolution image synthesis, 2024.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Scaling rectified flow trans- formers for high-resolution image synthesis, 2024

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Observation d31673e2-9eb7-468f-bf62-e95271aadcec · outbound

This paper cites Geneval: An object-focused framework for evaluating text- to-image alignment.Advances in Neural Information Pro- cessing Systems, 36, 2024.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Geneval: An object-focused framework for evaluating text- to-image alignment.Advances in Neural Information Pro- cessing Systems, 36, 2024

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Observation 9d8056fe-98b8-4688-9cc5-8d57e265e236 · outbound

This paper cites Generative adversarial nets.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Generative adversarial nets

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Observation 8ee94792-7bf9-42e3-8ba9-100838b22ada · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Mamba: Linear-Time Sequence Modeling with Selective State Spaces

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Observation 9efae607-6b48-414c-a57b-c0177c9a57e7 · outbound

This paper cites Generalized Neighborhood Attention: Multi-dimensional Sparse Attention at the Speed of Light.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Generalized Neighborhood Attention: Multi-dimensional Sparse Attention at the Speed of Light

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Observation 7b36c497-9ba4-4823-9fcd-1f858210b13c · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in neural information processing systems, 30, 2017.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in neural information processing systems, 30, 2017

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Observation ed8b0c87-364b-46ae-8524-b1ae087f41b3 · outbound

This paper cites Denoising diffu- sion probabilistic models.Advances in Neural Information Processing Systems (NeurIPS), 2020.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Denoising diffu- sion probabilistic models.Advances in Neural Information Processing Systems (NeurIPS), 2020

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Observation 7bce88a7-f12e-4308-ad12-edeea00237bf · outbound

This paper cites sim- ple diffusion: End-to-end diffusion for high resolution im- ages.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices sim- ple diffusion: End-to-end diffusion for high resolution im- ages

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Observation 3307d102-66d0-4921-9204-53e31c9cad5e · outbound

This paper cites Simpler diffu- sion: 1.5 fid on imagenet512 with pixel-space diffusion.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Simpler diffu- sion: 1.5 fid on imagenet512 with pixel-space diffusion

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Observation cf57ca3e-51c0-4bc6-b884-4e49ac9d31dc · outbound

This paper cites Dynabert: Dynamic bert with adaptive width and depth.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Dynabert: Dynamic bert with adaptive width and depth

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Observation c0213083-55ab-4ce9-ab32-548b70625845 · outbound

This paper cites SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices with Efficient Architectures and Training.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices with Efficient Architectures and Training

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Observation b9e1f8a4-4a45-459a-a0e6-8676889e2d93 · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices LoRA: Low-rank adaptation of large language models

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

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment

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This paper cites T2I-CompBench++: An Enhanced and Comprehensive Benchmark for Compositional Text-to- Image Generation .IEEE Transactions on Pattern Analysis Machine Intelligence, (01):1–17, 5555.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices T2I-CompBench++: An Enhanced and Comprehensive Benchmark for Compositional Text-to- Image Generation .IEEE Transactions on Pattern Analysis Machine Intelligence, (01):1–17, 5555

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This paper cites Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion

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This paper cites TP-blend: Textual-prompt attention pairing for precise object-style blending in diffusion models.Transactions on Machine Learning Research, 2025.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices TP-blend: Textual-prompt attention pairing for precise object-style blending in diffusion models.Transactions on Machine Learning Research, 2025

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Observation c0c87f4c-f57c-47c4-9a49-eecbdfd6e5f9 · outbound

This paper cites AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation

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Observation 03d3d03c-b063-4026-bb48-5e92f74b0715 · outbound

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Bk-sdm: Architecturally Compressed Sta- ble Diffusion for Efficient Text-to-Image Generation

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This paper cites Flux: A generative model by black for- est labs.https://github.com/black- forest- labs/flux, 2024.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Flux: A generative model by black for- est labs.https://github.com/black- forest- labs/flux, 2024

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Observation 22d20332-4802-417c-af0b-90511cb3f046 · outbound

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Playground v1,

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Observation 852ad940-c5b5-4191-a1ba-8ba83c13d50b · outbound

This paper cites Playground v2,.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Playground v2,

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Observation 1dbe7838-95e9-4aa1-ad9d-ca904a6a2fc5 · outbound

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Svdquant: Absorbing outliers by low-rank components for 4-bit diffusion models

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This paper cites Snap- fusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds.Advances in Neural Information Pro- cessing Systems, 36, 2024.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Snap- fusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds.Advances in Neural Information Pro- cessing Systems, 36, 2024

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices SDXL- Lightning: Progressive Adversarial Diffusion Distillation,

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Microsoft coco: Common objects in context

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Flow Matching for Generative Modeling

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Playground v3: Improving Text-to-Image Alignment with Deep-Fusion Large Language Models

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Observation 62e9a878-8b25-404e-b363-e3d7941dfe88 · outbound

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Measurement of the branching fraction and $\it CP$ asymmetry of $B^{0} \rightarrow \pi^{0} \pi^{0}$ decays using $198 \times 10^6$ $B\overline{B}$ pairs in Belle II data

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices LinFusion: 1 GPU, 1 Minute, 16K Image

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

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Observation 44070e9d-b485-43ac-bcda-bb2cdda49ebe · outbound

This paper cites On distillation of guided diffusion models.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices On distillation of guided diffusion models

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices SDEdit: Guided image synthesis and editing with stochastic differential equa- tions

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Observation 633bdcb1-2786-4cb5-b402-29cf6429a9f4 · outbound

This paper cites Qwen-image-lightning: Distilled qwen-image models for fast, high-fidelity text-to-image generation.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Qwen-image-lightning: Distilled qwen-image models for fast, high-fidelity text-to-image generation

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This paper cites Kim, Aliaksandr Siarohin, 10 and Anil Kag.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Kim, Aliaksandr Siarohin, 10 and Anil Kag

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Scalable Diffusion Models with Transformers

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Learning transferable visual models from natural language supervi- sion

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices High-resolution image syn- thesis with latent diffusion models

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Photorealistic text-to-image diffusion models with deep lan- guage understanding

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Progressive distillation for fast sampling of diffusion models

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Fast Transformer Decoding: One Write-Head is All You Need

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Consis- tency models.International Conference on Machine Learn- ing (ICML), 2023

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Bitsfusion: 1.99 bits weight quantization of diffusion model

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Gemma 3n

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Observation d92d55b5-af21-4786-95e7-cbdfc58b8d51 · outbound

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Qwen-image technical report,

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices SANA: Efficient high-resolution text-to-image synthesis with linear diffusion transformers

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Improved Distribution Matching Distillation for Fast Image Synthesis

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices One-step diffusion with distribution matching distillation

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices From slow bidirectional to fast autoregressive video diffusion mod- els

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Slimmable neural networks

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Scaling autoregressive models for content-rich text-to-image generation.Transactions on Machine Learn- ing Research, 2022

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices Fast Video Generation with Sliding Tile Attention

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Observation 4cc6e522-81ed-4a76-baec-ef4369aff215 · outbound

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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices MobileDiffusion: Instant Text-to-Image Generation on Mobile Devices

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Observation b970f782-34d1-4a5d-b8de-a2e041ab0961 · outbound

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