'generation' 검색 결과

Add an animation that makes a finished interface look as though an AI agent is building it. It traces the element’s actual layout with dots, then reveals the interface, optionally alongside generation logs and CSS text.

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• Reveal a result card when AI output arrives
• Show a component being built in a product demo
• Add generation logs to a landing page reveal

Manage ComfyUI image generation from the command line. Validate workflows, which define generation steps, submit them for execution, monitor queued jobs, inspect available models, and download images from completed runs.

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• Validating and submitting image generation workflows
• Checking or interrupting queued generation jobs
• Listing available models and node types

Run the PAIDF Orchestration Event Video Generation DAG on Kubernetes - image-to-video anomaly generation, auto-labeling, and anomaly dataset generation. Select for requests about event video generation, anomaly video generation, image-to-video synthesis, Cosmos3 image2video, anomaly dataset creation, safety/surveillance SDG, or generating person-falling, person-climbing, person-running, fighting, smoking/vaping, fire/smoke, or shoplifting video clips from a seed image. Runs environment setup first when controller readiness is unknown. Not for person-crop clothing/attribute augmentation (that is image-attribute-augmentation-workflow) and not for video style transfer.

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Replicate의 API로 이미지·오디오·영상 생성용 AI 모델을 검색하고 비교해 실행합니다. 여러 모델을 시험하고 용도에 맞게 바꾸는 작업에 적합합니다.

• 이미지 생성 모델 찾기
• 오디오 생성 모델 비교하기
• 영상 생성 모델 시험하기

Run diffusers generation pipelines from a terminal and save their outputs. Inspect accepted inputs without downloading model weights, apply LoRA adapters during generation, run remotely through HF Jobs, and inspect software and GPU environment details.

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• Running one-off generation or batch jobs from a terminal
• Checking a new model's basic operation
• Inspecting accepted inputs before running a pipeline

Build image generation workflows in Python with Stable Diffusion and HuggingFace Diffusers. The guide covers text prompts, transforming existing images, replacing selected regions, controlling composition, applying learned styles, and adjusting memory use during generation.

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• Generating images from text descriptions
• Changing the style of an existing image
• Replacing a selected region of an image

Build image generation workflows in Python with Stable Diffusion and HuggingFace Diffusers. The guide covers text prompts, transforming existing images, replacing selected regions, controlling composition, applying learned styles, and adjusting memory use during generation.

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• Generating images from text descriptions
• Changing the style of an existing image
• Replacing a selected region of an image

Evaluate code generation models with HumanEval, MBPP, and multilingual benchmarks. The skill guides test execution and model comparisons using pass@k, an estimate of whether at least one of k generated solutions passes the tests.

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• Compare code generation models on shared benchmarks
• Measure code generation performance across languages
• Evaluate instruction-tuned coding models

Recover document and PDF generation tasks when sandbox errors provide little detail. Checks Python libraries, runs generation through the shell, captures errors, and verifies output files, with alternative PDF tools and text output as fallback options.

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• Recovering PDF generation after unclear sandbox errors
• Checking required PDF libraries before generation
• Capturing detailed document generation errors

Help implement Azure OpenAI features in C# and .NET applications using SDK examples. Cover authentication, chat, document-backed answers, image generation, and audio processing, including transcription and speech generation.

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• Adding streaming chat to a .NET application
• Generating JSON responses in a defined format
• Building answers backed by Azure AI Search

Build Python workflows that constrain language model output using text patterns and grammar rules. Generate structured JSON, restrict classifications to allowed labels, extract information, and combine reusable generation steps with loops and tool selection.

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• Generate JSON with a defined structure
• Constrain date, email, and identifier formats
• Classify text using a fixed set of labels

Generate music and sound effects from written descriptions with AudioCraft. Covers MusicGen and AudioGen, using reference melodies or musical styles, producing stereo audio, adjusting generation settings, and saving generated sounds as audio files.

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• Generate music from a written description
• Create sound effects and environmental audio
• Generate music using a reference melody or style

Evaluate code generation models with HumanEval, MBPP, and other benchmarks. Test generated programs, compare models across programming languages, and record pass@k scores, which estimate the chance that at least one of k solutions passes the tests.

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• Comparing code generation models on shared benchmarks
• Measuring correctness across programming languages
• Evaluating models trained to follow instructions

Generate text and images through a reverse-engineered Gemini Web API, an interface reconstructed from the web service. Use reference images for descriptions or variations, and continue conversations across requests with saved sessions.

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• Generating images from written prompts
• Describing the contents of reference images
• Creating variations from an existing image

Generate music and sound effects from written descriptions with AudioCraft. Covers MusicGen and AudioGen, using reference melodies or musical styles, producing stereo audio, adjusting generation settings, and saving generated sounds as audio files.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate music from a written description
• Create sound effects and environmental audio
• Generate music using a reference melody or style

Build Python workflows that constrain language model output using text patterns and grammar rules. Generate structured JSON, restrict classifications to allowed labels, extract information, and combine reusable generation steps with loops and tool selection.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate JSON with a defined structure
• Constrain date, email, and identifier formats
• Classify text using a fixed set of labels

Use when the user wants to orchestrate defect image generation with NVIDIA Cosmos AnomalyGen (Cosmos-Predict2-derived) on OSMO for PCBA, metal surface, and glass inspection. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment. Trigger keywords: defect image generation, dig workflow, dig pipeline, defect image detection workflow, aoi pipeline, aoi anomalygen, usd2roi anomalygen, day 0 pcba, day 1 pcba, day 1 real-photo alignment, day 1 manual roi, metal surface anomaly, glass defect, anomalygen finetune, setup_pcb, setup_metal, setup_glass, setup_pretrained, dig setup, dig datasets, dig pretrained checkpoint, dig image-edit endpoint, cosmos defect generation, cosmos-predict2 defect, cosmos-anomalygen, cosmos predict2 finetune.

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Generates images and text via reverse-engineered Gemini Web API. Supports text generation, image generation from prompts, reference images for vision input, and multi-turn conversations. Use when other skills need image generation backend, or when user requests "generate image with Gemini", "Gemini text generation", or needs vision-capable AI generation.

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Proteina-Complexa flow-based protein backbone generation with fold-conditioned sampling guidance. Use this skill when: (1) Generating de novo protein backbones with hierarchical fold conditioning, (2) Exploring long-chain backbone generation beyond standard diffusion baselines, (3) Using NVIDIA Proteina-style flow matching workflows for controllable backbone design, (4) Comparing flow-based backbone generation against RFdiffusion or BoltzGen, (5) Prototyping fold-guided backbone campaigns before sequence design. This skill is based on the public NVIDIA Digital Bio Proteina project and uses "Proteina-Complexa" as the BioClaw-facing skill label. For sequence design after backbone generation, use proteinmpnn or solublempnn. For QC thresholds, use protein-design-qc.

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Build image generation pipelines with Stable Diffusion, FLUX, ControlNet, LoRA, and ComfyUI workflows. Activate on: image generation pipeline, ComfyUI workflow, ControlNet, LoRA training, diffusion model. NOT for: video generation (ai-video-production-master), image classification (computer-vision-pipeline).

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Generate AI music and songs with Diffrythm, Tencent Song Generation via inference.sh CLI. Models: Diffrythm (fast song generation), Tencent Song Generation (full songs with vocals). Capabilities: text-to-music, song generation, instrumental, lyrics to song, soundtrack creation. Use for: ba...

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Generates images and text via reverse-engineered Gemini Web API. Supports text generation, image generation from prompts, reference images for vision input, and multi-turn conversations. Use when other skills need image generation backend, or when user requests "generate image with Gemini", "Gemini text generation", or needs vision-capable AI generation.

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Apple의 FoundationModels로 iOS 26 이상 앱에 기기 내 언어 모델을 통합합니다. 글 생성·요약, 데이터 추출, 앱 기능 호출과 생성 중 화면 갱신을 다룹니다.

• 오프라인으로 글을 생성하고 요약합니다
• 입력 문장에서 필요한 항목을 추출합니다
• AI가 앱의 자체 기능을 호출하게 합니다

주제나 대본으로 내레이션, 자막, 배경 음악이 있는 영상을 만듭니다. MoneyPrinterTurbo 설치와 설정, 생성 오류 복구부터 완성된 MP4 전달까지 진행합니다.

• 주제로 소셜 미디어용 짧은 영상 제작
• 해설 대본을 내레이션 영상으로 변환
• 스톡 영상을 활용한 홍보 영상 제작