Baidu에서 공개한 오픈 웨이트 8B Diffusion Transformer 텍스트‑투‑이미지 모델로, 텍스트 렌더링, 복잡한 명령 수행, 구조화된 시각 구성에 뛰어납니다.

FLUX 및 Qwen Image와 같은 Diffusion Transformer를 위해 학습 없이 모델 패칭(model patching)과 캐스케이드 정제(cascade refinement)를 사용하여 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4K+ 생성 기능을 제공하는 ComfyUI 커스텀 노드 팩입니다. 4K+ 생성 기능을 제공하는 ComFLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLX, FLUX, FLUX, content_content_content_content_content_content_content_content_content_content_content_content_content__content_content_content_content_content_content_content_content_content_content_content_content_content_content_content_content_content_content_content_content_content_content_content_content_content_content_content_content_content_content_}^{\ even more detailed instructions for instructions-based instruction-tuning-tuning-tuning-tuning-tuning-tuning-tuning-tuning-tuning-tuning-tuning-tuning-tuning-tuning-tuning-tuning-tuning-content_content_content_content_content_________________________________                               _content_content_content_rectify-flow models, 使用하여 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4K+ 생성 기능을 제공하는 ComfyUI 커스텀 노드 팩입니다. 4K+ 생성 기능을 제공하는 ComFLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FLUX, FL_content_content_content_content_content_content_content_content_content_content______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________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4K+ 생성 기능을 제공하는 ComfyUI 커스텀 노드 팩입니다. FLUX, Qwen Image, Z-Image, Anima/Cosmos와 같은 Diffusion Transformer 모델을 사용하여 초고해상도 4K+ 이미지를 생성하거나 기존의 latents를 고해상도로 업스케일링하고 싶은 ComfyUI 사용자들을 위한 것입니다. ## Highlights * **Multi-Architecture Support:** FLUX, Nunchaku, Qwen Image, Krea-2, Z-Image, Anima/Cosmos와 호환됩니다. * **Training-Free:** 재학습이나 미세 조정이 필요하지 않습니다. 로더(loader) 이후에 모델을 패칭하는 방식으로 작동합니다. * **Ultra-High Resolution:** 4096×4096 이상의 해상도로 이미지를 생성할 수 있습니다. * **Performance Boosts:** 추론 시간을 단축하기 위해 희소 어텐션(sparse-attention) 가속을 위한 HAP를 포함합니다. * **Seamless Integration:** 기존 샘플러와 최적화 노드와 호환성을 유지하는 단일 노드 통합 방식입니다.

Z-Image Turbo 모델에 최적화된 ComfyUI 커스텀 노드 모음으로, 스타일 제어 강화, 저단계 일관성, 구성 다양성을 제공합니다.

Image MetaHub는 AI 생성 이미지, 비디오, 오디오를 인덱싱하고 ComfyUI, Automatic1111, InvokeAI, Midjourney 등의 도구에서 생성 메타데이터를 추출하며, 빠른 패싯 검색, 시각적 유사성 검색, 계보 추적, 내장 ComfyUI 워크스페이스를 제공하는 로컬 우선 데스크톱 앱(Electron + React)입니다. 코어 앱은 오픈소스(MPL 2.0)이며, $39 오프라인 Pro 라이선스로 고급 통합, 비교 보기, 무제한 클러스터링, 분석 기능을 이용할 수 있습니다.

CV‑CUDA는 NVIDIA의 오픈소스 GPU 가속 컴퓨터 비전 라이브러리입니다. GPU에서 완전히 실행되는 빠르고 확장 가능한 이미지/비디오 연산자(크기 조정, 필터, 색상 변환 등)를 제공하며 C/C++ 및 Python AI 프레임워크와 통합됩니다. Linux용 사전 빌드된 wheel(`cvcuda-cu12` 또는 `cvcuda-cu13`)을 통해 설치하고, `nvimgcodec`으로 이미지를 GPU로 직접 디코딩하며, 딥러닝 모델에 데이터를 공급하기 전에 CV‑CUDA 연산자를 연결할 수 있습니다. 이 라이브러리는 최신 NVIDIA GPU(Turing 이상)를 대상으로 하며 Microsoft Bing 시각적 검색 및 Tencent Cloud 비디오 처리와 같은 프로덕션 시스템에서 사용됩니다.

Stable Diffusion을 통합하여 3D 워크스페이스 내에서 시ーム리스 텍스처, 컨셉 아트, 이미지 에셋을 직접 생성하는 Blender 플러그인.

transformers형 텍스트 임베딩 시스템을 위한 텍스트 프롬프트 가중치 및 블렌딩 라이브러리로, 이미지 생성 파이프라인에서 프롬프트의 영향력을 정밀하게 제어할 수 있습니다.

waifu2x, Real-CUGAN, Real-ESRGAN 등의 여러 AI 이미지 확대 도구를 위한 GUI 쉘로, 이미지 향상을 위한 드래그 앤 드롭 인터페이스를 제공합니다.

개발자가 브라우저 또는 Node.js에서 AI를 사용하여 이미지의 해상도를 높이고, 노이즈를 제거하며, 흐림을 복원할 수 있는 자바스크립트 라이브러리입니다.

CFG를 자동으로 리스케일링하여 이미지 아티팩트와 과포화를 방지하고, 생성 속도를 가속화하는 도구를 제공하는 ComfyUI 확장 프로그램입니다.

PathML은 오픈소스 Python 라이브러리(또는 Docker 이미지)로, 전체 슬라이드 병리 워크플로 전체를 간소화합니다: 다양한 슬라이드 포맷 로드, 확장 가능한 전처리(염색 정규화, 타일링, GPU 변환), 데이터를 PyTorch 텐서로 변환, 세포 수준 그래프 구축, 그리고 딥러닝 또는 ONNX 모델 실행. AI 기반 계산 병리학의 진입 장벽을 낮추는 것이 목표이며, 이미 다수의 고임팩트 바이오메디컬 논문에서 사용되고 있습니다.

몇 장의 이미지만으로 새로운 개념을 학습하고 저장 공간 오버헤드를 최소화하면서 텍스트‑투‑이미지 확산 모델을 빠르고 효율적으로 파인튜닝하는 방법.

Stable Diffusion LoRA 모델 훈련 및 데이터셋 준비를 위한 Google Colab 노트북 모음으로, SDXL 지원도 포함됩니다.

SITS는 위성 이미지 시계열 데이터 큐브의 구축, 정규화, 분류를 위한 오픈소스 R 패키지(파이썬 래퍼 포함)입니다. 다양한 공개 EO 컬렉션에 연결 가능하며, GPU 가속된 CNN 및 어텐션 인코더를 포함한 다양한 ML/DL 모델을 제공하고, 학습, 분류, 평활화, 불확실성 추정, 액티브 러닝까지 엔드투엔드 도구를 제공합니다.

CUDA 및 ROCm과 같은 다양한 하드웨어 가속기에서 쉽게 실행할 수 있는, FaceFusion 얼굴 조작 플랫폼의 Docker 기반 배포.

이미지 합성부터 애니메이션 제작까지, 애니메이션 및 카툰 생성에 초점을 맞춘 연구 논문 및 데이터셋 큐레이션 컬렉션.

PIX는 JAX 기반의 이미지 처리 라이브러리로, GPU/TPU 가속 함수(예: flip, resize)를 제공하며, `jax.jit`으로 컴파일하거나 `jax.vmap`으로 벡터화하거나 `jax.pmap`으로 여러 장치에서 실행할 수 있습니다. 먼저 JAX를 설치한 후 `pip install dm-pix`를 실행하세요. NumPy 이미지 코드와 동일하게 사용할 수 있지만, JAX의 전체 성능과 자동 미분 기능을 제공합니다.

픽셀 기반 확산 모델을 사용하여 회전, 뒤집기 또는 재배열 시 정체성이 변하는 다중 뷰 착시 이미지를 생성하는 도구입니다.

학습이 필요 없는 반복 프레임워크로, 전역 레퍼런스 교차 주의 모듈을 사용해 최대 100 프레임에 걸친 긴 스토리 시각화에서 의미 일관성을 유지합니다.

MACE는 확산 모델을 위한 파인튜닝 프레임워크로, 유명인이나 노골적인 콘텐츠와 같은 최대 100개의 원치 않는 개념을 대량으로 삭제하면서 무관한 모델 지식은 보존합니다.

동적 보상 최적화를 사용하여 이미지 생성 모델이 다양한 유형의 여러 참조 이미지를 정확하게 결합할 수 있도록 하는 2단계 훈련 프레임워크입니다.

Nunchaku Qwen‑Image, Z‑Image‑Turbo, Krea2 ControlNet 모델용 LoRA 가중치를 로드하고 스택하는 ComfyUI 커스텀 노드 세트. UI 컨트롤, 실험적 AWQ 조정 지원, DiffSynth ControlNet 통합 포함.

HistomicsTK는 전체 슬라이드 병리 이미지를 분석하는 오픈소스 Python 툴킷입니다. 염색 정규화, 핵 세그멘테이션, 특징 추출 기능을 제공하며, 독립형 라이브러리 또는 Digital Slide Archive 웹 플랫폼의 플러그인으로 사용할 수 있습니다.

ResShift는 이미지 초해상도 및 복원을 위한 효율적인 확산 모델로, 샘플링 단계를 줄여 이미지 품질을 손상시키지 않으면서 추론 속도를 향상시킵니다.

무한 캔버스와 에이전트 기반 계획을 결합하여 다단계 프롬프트-이미지 생성을 지원하는 로컬 우선 AI 이미지 작업 공간입니다.

현실적인 객체 삽입을 위한 통합 도구 세트를 제공하는 이미지 합성 도구 상자. 조화, 그림자 생성, 배치 평가를 포함.

Civitai 및 Prompthero와 호환되는 생성 메타데이터를 포함한 이미지를 저장하는 ComfyUI 커스텀 노드 세트입니다.

FlowEdit 은 ICCV 2025 논문의 오픈소스 PyTorch 구현으로, Stable Diffusion 3, Flux 등 사전 학습된 확산 모델과 흐름 네트워크를 재사용하여 고비용 잠재 공간 역변환 없이 빠르게 텍스트 기반 이미지 편집을 가능하게 합니다. 사용자는 원본 이미지, 원본 설명문, 목표 설명문을 제공하면, 비용이 적은 편집 코드를 계산하고 편집된 이미지를 생성합니다. 리포지토리에는 실행 가능한 스크립트, YAML 기반 설정, ComfyUI 노드 링크가 포함되어 있어 광범위한 통합이 가능합니다.

A PyTorch implementation of Mean Flows and Improved Mean Flows for high-quality, one-step image generation.

텍스트 기반 객체 삽입, 객체 제거, 아웃페인팅을 지원하는 유연한 이미지 인페인팅 모델입니다.

Panel Cleaner는 기계학습 모델을 사용하여 만화의 대화 박스 내 텍스트를 탐지하고 마스크 처리하는 오픈소스 파이썬 도구입니다. 이후 텍스트를 제거(선택적 LaMa 인페인팅 포함)하거나 OCR을 통해 추출할 수 있습니다. 명령줄 또는 Qt GUI에서 사용 가능하며, 배치 처리, CUDA 가속, 사용자 정의 프로필을 지원합니다. GPL-v3 라이선스로 제공되며, pip 패키지, 사전 빌드된 바이너리, Flatpak, AUR, Docker 이미지 형태로 이용 가능합니다.

kraken은 역사적 및 비라틴 문자용으로 설계된 오픈소스 Python 기반 OCR 툴킷입니다. 트레이너블한 레이아웃 분석, 읽기 순서 탐지, 신경망 기반 문자 인식을 제공하며, 오른쪽에서 왼쪽, 양방향, 세로 텍스트를 지원합니다. 표준 XML 형식(ALTO, PageXML, hOCR)을 출력하고, 단어 수준의 경계 상자 제공, 공개 모델 리포지토리 보유. Linux 또는 macOS에서 pip/pipx로 설치 가능. `kraken get …`로 모델을 가져와 1줄 명령어로 이미지의 이진화, 세그멘테이션, OCR을 실행할 수 있습니다.

Claude Code용 프롬프트 컴파일 키트로 모호한 이미지 요청을 gpt-image-2용 전문적이고 검증된 프롬프트로 변환

ObjectClear는 타겟 객체와 그와 관련된 효과(그림자 등)를 동시에 제거하면서 배경의 일관성을 유지하는 객체 제거 모델입니다.

Noise2Noise 논문의 비공식 PyTorch 구현으로, 가우시안, 푸아송, 텍스트 오버레이 또는 몬테카를로 렌더링 노이즈를 사용하여 노이즈만 있는 데이터로 U‑Net 노이즈 제거기를 학습하고 테스트하는 스크립트를 제공합니다.

2D GANs를 Multiplane Images로 변환하여 3D 뷰 합성 및 메쉬 추출을 가능하게 하는 3D 인식형 생성 모델입니다.

마트료시카 확산 모델을 위한 엔드투엔드 프레임워크로, 최대 1024x1024 픽셀의 고해상도 텍스트-이미지 합성 모델을 효율적으로 학습할 수 있게 합니다.

영화, 광고, 게임 분야에서 사용할 수 있는 고충실도, 독특한 가상 인간의 얼굴을 생성하는 StyleGAN2 기반의 얼굴 생성기 모음입니다.

Qwen‑Image 텍스트-이미지 모델의 증류된 더 빠른 버전(4 또는 8단계 확산)으로 fp32/bf16/fp8 체크포인트, LoRA 어댑터, 바로 사용할 수 있는 ComfyUI 워크플로우를 제공합니다. 약간의 품질 손실로 12–25배 속도 향상을 제공하며 Diffusers, ComfyUI, Nunchaku, Cache‑dit와 통합됩니다.

AUTOMATIC1111의 Stable Diffusion WebUI용 모델 병합 확장 프로그램으로, 메모리 내에서 모델을 병합하고 디스크에 저장하지 않고 블록 단위 가중치 조정을 수행할 수 있습니다.

확산(diffusion) 기반 접근 방식을 사용하여 벡터 양자화(vector quantization)의 필요성을 제거한 PyTorch 기반의 자기회귀 이미지 생성 구현체입니다.

딥러닝과 미분 가능한 텐서를 사용하여 벡터 그래픽스(SVG)의 생성 및 애니메이션을 위한 계층적 생성 네트워크 및 라이브러리.

고해상도 현실적인 이미지를 생성하기 위한 원본 StyleGAN 아키텍처의 역사적 PyTorch 구현입니다.

Flux 및 SDXL 모델에 특히 최적화된 ComfyUI 노드 세트로, 샘플링 중 노이즈 스케줄(시그마)을 조정하여 이미지의 세부 사항을 강화하고 배경 흐림을 줄입니다.

실제 이미지를 예술적인 일러스트레이션으로 변환하도록 설계된 생성적 적대 신경망인 GANILLA의 PyTorch 구현체입니다.

FaceFusion Pinokio는 Pinokio를 통해 간편한 설치 프로세스를 제공하는 얼굴 조작 플랫폼으로, 이미지 및 동영상 내 얼굴을 교체하고 수정할 수 있습니다.

ColorFlow은 프레임 간에 캐릭터와 객체의 정체성을 일관되게 유지하면서 흑백 이미지 시퀀스를 색칠하는 리트리ieval-증강 확산 프레임워크입니다.

CaptchaSolver는 Node.js를 통해 실행되는 YOLO/Darknet 신경망 모델을 사용하여, 몇몇 파일 호스팅 사이트의 6자리 및 기하학적 캡차를 자동으로 해결하는 JDownloader 2 플러그인입니다. Windows/Linux(amd64)용 사전 컴파일된 바이너리와 다른 플랫폼용 빌드 지침을 제공하여, 서버나 NAS 장치에서 JDownloader를 완전히 자동화된 상태로 실행할 수 있도록 합니다.