Results for “imagen-4”

Process and manipulate images using ImageMagick: resize, convert formats, batch process, and retrieve metadata.

Build applications with Gemini API hosted models, including multimodal content, function calling, and structured outputs, using the latest SDKs and model specifications.

Mosaic Augmentation for Detection and Segmentation

Create C4 architecture diagrams at context, container, and component levels with Mermaid syntax, including templates and styling tips.

Upscale and enhance images using Real-ESRGAN, Thera, FLUX Upscaler, and Topaz via the inference.sh CLI.

Generate and edit images using Alibaba Qwen-Image-2.0 models via the inference.sh CLI, with support for text-to-image, multi-image editing, and text rendering.

Layer 4 of the keyword research pipeline. Spawns a "skeptical SEO" adversarial sub-agent to argue against every survivor of Layers 1-3. Catches mechanical-classifier blind spots — wrong SERP intent, hidden link-graph gauntlets, AIO trajectory shifts, vanity-rank metrics. Same pattern as quality-check's adversarial draft read, applied to keyword selection.

画像を理解（被写体・テキストOCR・構図・色・UI構造の分析）し、結果を構造化して返すスキル。CC CLI は GLM-5.3 等の vision 非対応モデルで稼働中のため画像を直接視認できず、主ルート Gemini 2.5 Flash（scripts/api/gemini_vision.py・無料枠）と副ルート 4_5v MCP（analyze_image・Readが返すCDN URL）の2経路で分析し、CCは結果の構造化・比較・保存に専任する。 ユーザーが「画像見て」「この画像何が写ってる」「画像比較して」「スクショ見て」「画像分析して」「画像理解」「vision-analyze」と言った時、または /vision-analyze を呼んだ時にトリガー。 ※画像生成（image generation）は対象外（make-song / video-prompt-spec / demo-site-sales参照）。ピクセル修正（花鈿除去等）は remove-huadian の役割。楽曲分析は analyze-song / reverse-engineer-song。

Apply a text watermark to a photo using layer-based image composition for brand protection and copyright.

Convert an image between PNG, JPEG, and WebP formats with quality control for web optimization.

封装阶段3的Copy Spec为可批量执行的提示词包，生成JSONL请求并调用APIMart API出图。

Batch-generate images via OpenAI Images API. Random prompt sampler + `index.html` gallery.

Build applications with Gemini API hosted models, including Gemini and Gemma 4, using multimodal content, function calling, structured outputs, and current SDKs for Python, JavaScript, Go, and Java.

Audit images across AEM Edge Delivery Services pages for SEO and performance, checking alt text quality, dimensions, lazy loading, fetch priority, file naming, and decorative image handling, and generate per-page reports with fix instructions.

Embeds target and source image parquets, then mines nearest-neighbour source images for augmentation in VCN AOI workflows.

Enhances image quality by upscaling resolution, sharpening details, and reducing compression artifacts, especially for screenshots used in presentations, documentation, or social media.

Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.

Convert user photos into 4 animated GIF stickers in Funko Pop / Pop Mart style with customizable captions.

Simulator.Company files & attachments specialist. Use when the user wants to upload a file, attach a document/image to an actor or comment, list/rename/detach workspace files. Activate when the user says "upload a file", "attach this document", "add an attachment", "rename the file", "detach the file", "list workspace files", "завантаж файл", "прикріпи файл/документ", "відкріпи вкладення", "загрузи файл", "прикрепи документ". For setting an actor's picture/avatar use the `uploadActorPicture` engine tool; for comments that carry files use `simulator-reactions`.

Generate images using Google Gemini 3.1 Flash Image Preview via the inference.sh CLI, with support for text-to-image, image editing, multi-image input, and Google Search grounding.

Resize, sharpen, and compress an image to fit email platform size limits in a single pipeline.

Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.

Create C4 software-architecture diagrams using Mermaid or Structurizr. Use when teams need clear system context, container, component, or code-level views.

Generate image captions, answer visual questions, and perform image-text retrieval using BLIP-2's Q-Former architecture with frozen vision encoders and LLMs.

Generate images with Flux 2 Klein (Black Forest Labs' distilled fast variant of Flux 2) on RunComfy — bundled with the model's documented prompting patterns so the skill gets sharper output than naive prompting against the same model. Documents Flux 2 Klein's strengths (sub-second latency, multi-reference brand styling, declarative subject-first prompts), the step-count strategy (4–8 for fast iteration, ~25 for polish), the 9B vs 4B variant trade-off, and when to route to Flux 2 Pro / Seedream 5 / GPT Image 2 instead. Calls `runcomfy run blackforestlabs/flux-2-klein/9b/text-to-image` (or `/4b/`) through the local RunComfy CLI. Triggers on "flux 2 klein", "flux-2-klein", "flux klein", "BFL flux 2", or any explicit ask to generate with this model.

Flamingo: A Visual Language Model for Few-Shot Learning

Generate and edit images using OpenAI's GPT-Image-2 model via the inference.sh CLI, supporting text-to-image, image editing, inpainting, and batch generation.

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Generate images using FLUX models via the inference.sh CLI, supporting text-to-image, image-to-image, and LoRA fine-tuning.

A container image is an attack surface you chose.

Generates and edits images and videos via fal.ai's queue-based API, supporting models like Flux, Gemini image, and Kling video-to-video, with automatic polling.

Generate and edit images on RunComfy via the `runcomfy` CLI — a smart router across the full image-model catalog: FLUX 2 (Klein 9B/4B, Pro, Dev, Flash, Turbo, Max), Google Nano Banana 2 / Pro, OpenAI GPT Image 2, ByteDance Seedream 5 / 4-5 / 4-0 and Dreamina 4-0, Alibaba Qwen Image and Z-Image Turbo, Wan 2-7. Covers both text-to-image (t2i) and image-to-image / edit (i2i) endpoints — the skill picks the right model for the user's actual intent (typography precision, photoreal portraits, sub-second iteration, multi-reference brand styling, open-weights workflow) and ships each model's documented prompting patterns plus the minimal `runcomfy run` invoke. Triggers on "generate image", "make a picture", "text to image", "AI image", "make an image of …", "image to image", "i2i", or any explicit ask to create or restyle an image.

Generates and edits images using GPT Image 2 across three modes: direct generation via OpenAI-compatible API, prompt engineering for host-native image tools, or pure prompt advisory. Includes 80+ structured templates for posters, UI mockups, product visuals, maps, slides, and more.

Generates images using Google Gemini's image generation model for UI placeholders, documentation, and design assets.

Generate images with Google Nano Banana 2 (Gemini-family flash-tier text-to-image) on RunComfy — bundled with the model's documented prompting patterns so the skill gets sharper output than naive prompting against the same model. Documents Nano Banana 2's strengths (rapid iteration, in-image typography rendering, predictable framing, optional web-grounded context), the resolution-tier pricing, the safety-tolerance dial, and when to route to Nano Banana Pro / GPT Image 2 / Flux 2 / Seedream instead. Calls `runcomfy run google/nano-banana-2/text-to-image` through the local RunComfy CLI. Triggers on "nano banana", "nano-banana-2", "nano banana 2", "google image gen", "gemini image", or any explicit ask to generate with this model.