Text-to-Image Generation
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- 평가표
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- 보존된 평가 기록
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The development of the brain's blood supply in an embryo involves a complex process with several stages. Initially, there is a network of connections between the carotid and basilar artery systems that usually disappear during development. However, in some cases, these connections remain, leading to variations in the adult cerebral circulation. Around the 24th day of embryonic life (at the 3 mm embryonic stage), the internal carotid arteries (ICA) emerge. They are formed from a combination of the 3rd branchial arch arteries and the distal segments of the paired dorsal aortae. The ICA initially provides all the blood needed by the developing brain. As the brain grows, particularly the occipital region, brainstem, and cerebellum, the blood supply from the ICA becomes insufficient, prompting the development of the posterior circulation. At this stage (4-5 mm embryonic stage), the posterior circulation, specifically the hindbrain, is supplied by two longitudinal neural arteries. These arteries receive blood from several temporary connections (anastomoses) with the ICA, including: Trigeminal artery (TA), Otic artery (OA), Hypoglossal artery (HA), Proatlantal artery (ProA). These carotid-vertebrobasilar anastomoses act as temporary bridges, providing blood flow to the developing posterior circulation until the vertebral arteries (VA) and the basilar artery (BA) are fully formed.6
The BA forms between the 5-8 mm stage by the fusion of the longitudinal neural arteries. As the posterior communicating artery develops and connects with the distal BA, the TA, OA, and HA typically regress. Unlike the TA, OA, and HA, the ProA persists until the VA are fully developed. A segment of the ProA becomes incorporated into the V3 segment of the VA and the distal portions of the occipital artery. The VA develop between the 7-12 mm stage from transverse connections between cervical intersegmental arteries, starting with the ProA and progressing downwards to the 6th intersegmental artery. This 6th intersegmental artery eventually forms the origin of the adult VA from the subclavian artery. 7-8
Persistence of the HA into adulthood, the focus of this discussion, is a rare occurrence. It is the second most common persistent carotid-vertebrobasilar anastomosis, following the persistent trigeminal artery. The failure of the HA to regress during embryological development leads to PPHA. This condition can impact the normal blood flow dynamics in the brain and, in certain cases, can be associated with cerebrovascular issues like aneurysms or ischemic events.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/segmind/distill-sd
공식 코드BK-SDM: A Lightweight, Fast, and Cheap Version of Stable Diffusion
https://github.com/seilna/CNN-Units-in-NLP
공식 코드Discovery of Natural Language Concepts in Individual Units of CNNs
https://github.com/sen-mao/StyleDiffusion
공식 코드StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing
https://github.com/senmaoy/RAT-Diffusion
공식 코드Data Extrapolation for Text-to-image Generation on Small Datasets
https://github.com/senmaoy/recurrent-affine-transformation-for-text-to-image-synthesis
공식 코드https://github.com/seungjucha/verbdiff
공식 코드VerbDiff: Text-Only Diffusion Models with Enhanced Interaction Awareness
https://github.com/shahariar-shibli/adversarial-attack-on-pos-tags
공식 코드Adversarial Attacks on Parts of Speech: An Empirical Study in Text-to-Image Generation
https://github.com/share424/indonesian-text-to-image-synthesis-with-sentence-bert-and-fastgan
공식 코드Indonesian Text-to-Image Synthesis with Sentence-BERT and FastGAN
https://github.com/shengcheng/captions4t2i
공식 코드Precision or Recall? An Analysis of Image Captions for Training Text-to-Image Generation Model
https://github.com/shi-labs/forget-me-not
공식 코드Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models
https://github.com/shi-labs/prompt-free-diffusion
공식 코드Prompt-Free Diffusion: Taking "Text" out of Text-to-Image Diffusion Models
https://github.com/shi-labs/versatile-diffusion
공식 코드Versatile Diffusion: Text, Images and Variations All in One Diffusion Model
https://github.com/shihaozhaozsh/lavi-bridge
공식 코드Bridging Different Language Models and Generative Vision Models for Text-to-Image Generation
https://github.com/shihongl1998/llm-as-a-blackbox-optimizer
공식 코드Language Models as Black-Box Optimizers for Vision-Language Models
https://github.com/shilin-lu/mace
공식 코드https://github.com/shiml20/flowturbo
공식 코드FlowTurbo: Towards Real-time Flow-Based Image Generation with Velocity Refiner
https://github.com/shizhediao/davinci
공식 코드Write and Paint: Generative Vision-Language Models are Unified Modal Learners
https://github.com/showlab/boxdiff
공식 코드BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion
https://github.com/showlab/evolvedirector
공식 코드EvolveDirector: Approaching Advanced Text-to-Image Generation with Large Vision-Language Models
https://github.com/shuangquanfeng/fergi
공식 코드https://github.com/siatmmlab/awesome-diffusion-model-based-image-editing-methods
공식 코드https://github.com/sierkinhane/boxdiff
공식 코드BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion
https://github.com/simeonallmendinger/syntheticimagegeneration
공식 코드https://github.com/sincostanx/lusd
공식 코드LUSD: Localized Update Score Distillation for Text-Guided Image Editing
https://github.com/sixiaozheng/contextualstory
공식 코드ContextualStory: Consistent Visual Storytelling with Spatially-Enhanced and Storyline Context
https://github.com/slark-prime/maai_optimizer
공식 코드https://github.com/sled-group/InfEdit
공식 코드https://github.com/smatsumori/lattegan
공식 코드LatteGAN: Visually Guided Language Attention for Multi-Turn Text-Conditioned Image Manipulation
https://github.com/smile365317/abp
공식 코드Align Beyond Prompts: Evaluating World Knowledge Alignment in Text-to-Image Generation
https://github.com/snap-research/stable-flow
공식 코드https://github.com/sofeeyaj/poetry-in-pixels-coling2025
공식 코드Poetry in Pixels: Prompt Tuning for Poem Image Generation via Diffusion Models
https://github.com/softrepa/SoftREPA
공식 코드Aligning Text to Image in Diffusion Models is Easier Than You Think
https://github.com/songrise/Artist
공식 코드DiffArtist: Towards Structure and Appearance Controllable Image Stylization
https://github.com/songweige/rich-text-to-image
공식 코드https://github.com/soon-yau/upgpt
공식 코드UPGPT: Universal Diffusion Model for Person Image Generation, Editing and Pose Transfer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.