# comfydv

**Quality-of-life nodes for ComfyUI, built to disappear into your workflow.**

`comfydv` fills the gaps ComfyUI's built-in library leaves on the table: string templates that build their own sockets as you type, seed-controlled randomisation, graceful mid-queue interruption, and a local-LLM integration that doesn't care whether you're running Ollama or llama.cpp. No Python required — install it, drop the nodes on your canvas, wire them up.

![Chat Completion in action](assets/ollama_chat.png)

## What is comfydv?

A small, focused ComfyUI utility pack. It exists because:

- **It reads your intent, not just your syntax.** Format String detects `{variables}` in a template and adds/removes input sockets live, as you type — no manual socket wrangling.
- **One LLM integration, any local backend.** Wire a Chat Completion node once; swap between Ollama and llama.cpp by changing a single upstream client node. Structured output, multi-turn history, and model load/unload work identically on both.
- **It fails politely.** Circuit Breaker halts a queue run cleanly instead of throwing a stack trace at you; a disconnected LLM server gets a specific, actionable error instead of a silent empty dropdown.
- **Small, tested, boring in the best way.** Every node is unit-tested and the local-LLM nodes are verified against real running servers, not just mocks.

## What's inside

| Node | What it does |
|------|---------------|
| **Format String** | Renders a Python f-string or Jinja2 template. Sockets appear and disappear automatically as you type variables. |
| **Random Choice** | Accepts any number of typed inputs and returns one at random, with a seed for reproducibility. |
| **Circuit Breaker** | Halts the current queue run gracefully — no crash, just a clean stop — when a condition isn't met. |
| **Ollama Client** / **LlamaCpp Client** | Configure a connection to a local Ollama or llama.cpp server. Both emit the same `LLM_CLIENT` socket — every node below works with either. |
| **LLM Model Selector** | Live dropdown of models available on the connected server. |
| **LLM Load Model** / **LLM Unload Model** | Explicit VRAM management — pin a model in memory before inference, evict it after. |
| **Chat Completion** | Send a prompt (optionally with history) to the connected server; response shown inline and as an output socket. |
| **Ollama Option — \*** | Seven composable parameter nodes (Temperature, Seed, Max Tokens, Top P, Top K, Repeat Penalty, Extra Body) that merge into Chat Completion's `options` input. |
| **Ollama Debug History** / **Ollama History Length** | Inspect an `OLLAMA_HISTORY` conversation list — pretty-print it or count its messages. |

## Install

**Via ComfyUI Manager** (recommended): search for `comfydv`, click Install.

**Manual:**

```bash
cd /path/to/ComfyUI/custom_nodes
git clone https://github.com/darth-veitcher/comfydv.git
```

Restart ComfyUI. Nodes appear under **dv/**, **dv/ollama**, and **dv/llamacpp** in the node menu. Runtime dependencies (`jinja2`, `aiohttp`, `pydantic-ai`) install automatically via `requirements.txt`.

For local-LLM nodes, bring your own backend:

- **Ollama** — [install Ollama](https://ollama.com/download), pull a model (`ollama pull qwen2.5:latest`).
- **llama.cpp** — [build/install `llama-server`](https://github.com/ggml-org/llama.cpp), launch it in [router mode](#llamacpp).

## Quickstart

1. Right-click the canvas → Add Node → **dv/** for Format String, Random Choice, and Circuit Breaker.
2. For local LLM nodes: start Ollama (`ollama serve`) or `llama-server` (router mode), then add nodes from **dv/ollama/** or **dv/llamacpp/** — the chat/model-management nodes are shared between both backends.

---

## Format String

Type a template, get sockets. `{variable_name}` in f-string mode, `{{ variable_name }}` in Jinja2 mode — either way, comfydv watches what you type and keeps the node's inputs in sync automatically.

![Format String — f-string mode](assets/fstring.png)

| Output | Content |
|--------|---------|
| `formatted_string` | The rendered result |
| `saved_file_path` | Where it was written, if `save_path` is set |
| `<var>` … | Each input passed through unchanged, for easy chaining |

Switch `template_type` to **Jinja2** to unlock filters (`| upper`, `| int`), conditionals, and loops:

![Format String — Jinja2 mode](assets/jinja2.png)

---

## Random Choice

Wire in any number of same-typed inputs — images, strings, conditioning, anything ComfyUI can carry over a socket — and get one back at random.

![Random Choice](assets/random.png)

`seed = 0` randomises every run; any other value locks the selection. Unused input slots vanish automatically when you disconnect them.

---

## Circuit Breaker

Stop a queue run cleanly when a condition isn't met, instead of letting a downstream node crash on bad input.

![Circuit Breaker](assets/circuit_breaker.png)

Wire a trigger (an image, or anything) into `trigger` and a boolean into `status`. `status = false` raises `InterruptProcessingException` — ComfyUI stops the run without an error dialog. `status = true` passes the trigger straight through.

Typical use: skip an expensive upscale pass when an upstream quality-check node says the draft's already good enough.

---

## Local LLMs

One set of nodes, two interchangeable backends. Configure a connection once with **Ollama Client** or **LlamaCpp Client** — both output the same `LLM_CLIENT` socket — and every downstream node (model selection, load/unload, chat, structured output, multi-turn history) works exactly the same way regardless of which one you picked. Swapping backends means rewiring one node, not rebuilding your graph.

### Connect and chat

![Ollama Client](assets/ollama_client.png)

1. **Ollama Client** (default `http://localhost:11434`) or **LlamaCpp Client** (default `http://localhost:8080`) — set the host.
2. **LLM Model Selector** — pick a model from the live dropdown, or wire a model name straight into Chat Completion.
3. **Chat Completion** — wire in client, model, and prompt. The response renders inline in the node and is also available as an output socket.

![Chat Completion](assets/ollama_chat.png)

A complete graph looks like this:

![Full LLM workflow](assets/ollama_workflow.png)

### Manual memory management

Single-GPU and memory-constrained setups need explicit control over what's resident in VRAM. **LLM Load Model** pins a model into memory; **LLM Unload Model** evicts it immediately, freeing room for the next model or the rest of your image pipeline.

![Load / Unload lifecycle](assets/ollama_lifecycle.png)

The **Load → Chat → Unload** chain is enforced by data dependencies, not by convention:

1. `LLMLoadModel.model_name` → `ChatCompletion.model` — guarantees Load runs before Chat, and feeds the model name straight in.
2. `ChatCompletion.model_name` → `LLMUnloadModel.model` — guarantees Unload runs after Chat completes.
3. *(Optional)* `ChatCompletion.response` → `LLMUnloadModel.passthrough` — Unload returns the response unchanged, so the rest of your workflow can still consume it.

### Tuning generation

Chain any combination of **Ollama Option —** nodes ahead of Chat Completion to override inference parameters:

![Ollama Option nodes](assets/ollama_options.png)

| Option node | Parameter |
|-------------|-----------|
| Temperature | `temperature` |
| Seed | `seed` |
| Max Tokens | `num_predict` |
| Top P | `top_p` |
| Top K | `top_k` |
| Repeat Penalty | `repeat_penalty` |
| Extra Body | arbitrary JSON, merged into `options` |

### Multi-turn conversations

`OLLAMA_HISTORY` flows out of Chat Completion as a `{"role", "content"}` list. Feed it back into the next Chat Completion call for multi-turn context, or inspect it with **Ollama Debug History** / **Ollama History Length**.

### llama.cpp

Everything above works unchanged against llama.cpp — swap in a **LlamaCpp Client** and the rest of the graph doesn't know the difference. The one thing llama.cpp needs that Ollama doesn't: **router mode**, a directory of models rather than a single `-m model.gguf`:

```bash
llama-server --models-dir ./models -c 8192
```

In exchange, router mode gives comfydv a richer live status than Ollama can report — `loading` and `downloading`, not just loaded/unloaded — plus the same explicit load/unload primitives Ollama's nodes already use.

### Upgrading a workflow saved before this rename

Nodes were renamed once, to make them backend-generic (`OllamaChatCompletion` → `ChatCompletion`, etc.). If ComfyUI reports old node types as missing when you reopen a saved workflow, reconnect using this table — behavior is unchanged, only the names are:

| Old | New |
|-----|-----|
| `OllamaChatCompletion` | `ChatCompletion` |
| `OllamaModelSelector` | `LLMModelSelector` |
| `OllamaLoadModel` | `LLMLoadModel` |
| `OllamaUnloadModel` | `LLMUnloadModel` |
| `OLLAMA_CLIENT` socket | `LLM_CLIENT` socket |

`OllamaClient` kept its name — delete and re-add any node showing as missing, then rewire it to the same `OllamaClient` node.