# ComfyUI Prompt Manager ## A comprehensive prompt and recipe toolkit for [ComfyUI](https://github.com/comfyanonymous/ComfyUI) ComfyUI Prompt Manager is a prompt toolkit for ComfyUI focused on writing, generating, extracting, organizing, and reusing prompts with LoRA-aware workflows. It includes local LLM-powered prompt generation (llama.cpp or Ollama) with automatic download of Qwen3.5 models, metadata extraction from images/videos/JSON, prompt browser tooling, and advanced prompt save/load flows. For users who want full pipeline reuse, it also includes an optional Recipe toolset to build, edit, render, load, relay, and save complete generation configs from extracted or hand-authored data. Metadata extraction supports media generated in ComfyUI, A1111, and Forge, including Wan workflows with dual model/stack support. ## What This Provides - **Prompt authoring and management**: Prompt Manager (Advanced + Basic) for prompt save/load workflows, LoRA stacks or Multi Lora Stacks, trigger words, and reusable prompt libraries. - **Prompt generation (local LLM)**: Prompt Generator + Prompt Generator Options using llama.cpp or Ollama, with support for text enhancement and image-based analysis (Qwen3.5 vision models). - **Prompt and metadata extraction**: Prompt Extractor reads metadata from images/videos/JSON and can output prompt, LoRA, and recipe context. - **Prompt browsing utilities**: Browser tools to quickly find and load saved prompts or recipes. - **LoRA workflow support**: Multi-LoRA stack tooling and editable LoRA data when reusing saved entries. - **Optional full Recipe toolkit**: Recipe Builder, Recipe Extractor, Recipe Relay, Recipe Renderer, Recipe Model Loader/Picker, and Recipe Manager for complete generation pipelines. - **Model-aware recipe rendering**: Recipe Renderer supports Flux 1/2, Ernie, SDXL, Wan, Qwen, Z-Image, Wan Image, and Wan Video (I2V/T2V). - **Lora Preview integration**: When [Lora-Manager](https://github.com/willmiao/ComfyUI-Lora-Manager) is installed, LoRAs can be previewed on hover. - **Advanced media loading**: Extractor nodes can also act as advanced image loaders from Input and Output folders. ---

Prompt Creation With Lora and Trigger Words

Generating Prompts based on Images

Advanced Prompt Generation using Multiple Images

Recipe extraction and modifcation (Adding a Style Lora)

Easily find saved Prompts & Recipe using the Built-in File Browser

Use Extractor Nodes as an Image Loader, allowing browsing from Input and Output folder.

## v2.0 Introduces an Experimental Recipe Toolset Version 2.x introduces an optional Recipe toolset that can be used on its own, or in combination with the Prompt toolset. - Use the Recipe toolset when you want repeatable generation pipelines from extracted or hand-edited recipe data. - Recipe Data includes prompts (pos & neg), LoRA stack, mode used, KSampler settings, resolution, seed, and more. - This data can be created using Recipe Builder or extracted from existing media with Recipe Extractor. - Recipe Renderer provides an easy way to render this recipe. - Recipe data can also be used traditionally with the Recipe Relay node, which can access all of it. - Use the Prompt toolset when you want fast prompt library management and local LLM-assisted prompt creation. - Prompt Generator allows creation of new prompts using the LLM model of your choice. - Prompt Manager can save these to be reusable later. - Prompt Extractor is similar to Recipe Extractor, but can individually output prompts or LoRAs. - Use both together when you want to create Recipes, but use different prompts. ## Toolset Overview ### Prompt Toolset - Prompt Manager: dual LoRA stacks, trigger words, thumbnail workflows, and advanced save flows. - Prompt Extractor: reads metadata from images/videos/JSON and outputs prompt + LoRA + recipe context. - Prompt Generator + Options: local LLM prompt creation and enhancement using llama.cpp or Ollama. - Prompt Manager (Basic): category-based prompt save/load. A no-frills basic version (The OG). ### Recipe Toolset - Recipe Extractor: normalizes extracted metadata into reusable recipe_data. - Recipe Builder: edit and validate recipe_data in a cleaner authoring surface. - Recipe Renderer: execute recipe_data through built-in generation templates. - Recipe Hub: merge/append model blocks into one `recipe_data` and expose each model block as its own output. - Recipe Relay: edit one model block (`model_data`) with seed and LoRA stack overrides. - Recipe Model Loader: resolve and load models from recipe_data. - Recipe Manager: save and reuse recipe entries. ## Common Pipelines ### Prompt-first Pipeline 1. Write or generate prompts and connect LoRA stacks into Prompt Manager. 2. Generate with your normal ComfyUI graph. 3. Save reusable prompt entries when satisfied. ### Recipe-first Pipeline 1. Create preset in Recipe Builder - By extracting recipe_data from media or JSON, using Recipe Extractor - Or by directly modifying Recipe Builder to the values you want. 2. Render in Recipe Renderer. 3. Save reusable recipe entries with Recipe Manager. - To include Thumbnail, add after the Renderer node. ### Hybrid Pipeline 1. Extract or build recipe_data. 2. Add Prompt Generator or Save prompts into Builder. 3. Add LoRAs, either using LoRA stackers or saved LoRA stacks in Prompt Manager. 4. Render through Recipe Renderer. 5. Save final reusable entries in Recipe Manager. ## Preferences And Settings - Addon settings are available in ComfyUI Preferences (Settings) under Prompt Manager. - This is where you set model/backend defaults, NSFW visibility defaults, view preferences, and related addon behavior. - Prompt Generator backend choices (llama.cpp or Ollama) and related options are configured there. ## Workflow Examples Workflow examples are provided to help understand the basics. ## Documentation - Detailed node and feature reference: [docs/feature-reference.md](docs/feature-reference.md) - Full changelog history: [docs/changelog.md](docs/changelog.md) ## Installation 1. Navigate to your ComfyUI custom nodes directory: ``` cd ComfyUI/custom_nodes/ ``` 2. Clone this repository: ```bash git clone https://github.com/FranckyB/ComfyUI-Prompt-Manager.git ``` 3. Install dependencies: ```bash cd ComfyUI-Prompt-Manager pip install -r requirements.txt ``` 4. Install [llama.cpp](https://github.com/ggml-org/llama.cpp/tree/master): - Windows: ```bash winget install llama.cpp ``` - Linux: ```bash brew install llama.cpp ``` 5. Place custom `.gguf` models in models/gguf. - Or use preferences to set a custom path. 6. Restart ComfyUI. ## Requirements - ComfyUI - Python 3.8+ - requests - huggingface_hub - psutil - tqdm - Pillow - colorama - llama-server (from llama.cpp)

Name	Type	Description
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model_bopt	STRING	—
model_copt	STRING	—
model_dopt	STRING	—

Multi Prompts

Inputs (4)

Outputs (1)