Generate Text | Ollama Nodes

This is the whole reason the pack exists. GenerateOllama sends a prompt to a local Ollama model and gets text back - and because it takes an IMAGE input, it can look at your images and describe them. That makes it the best captioning / image2prompt shortcut in the pack, and the one you'll actually keep in a workflow. The name undersells it: "generate text" is the mechanism, "describe this render so I can use it as training data" is the job.

Why you'd reach for it

Three big uses, in order of popularity:

1. Captioning for training. Feed it a batch of images and get detailed descriptions you can paste into LoRA training captions. This is the workflow the pack's README screenshot shows - batch image in, text out.
2. Vision feedback in a pipeline. Ask it what's in the frame - lighting, mood, subject - and route that text into a prompt template for the next stage.
3. Prompt enrichment. A handful of nodes in the ecosystem (like the Ollama-based enhancer that ships in ComfyUI-BerniniStudio) do this: plain idea in, detailed prompt out. Here you just change the system prompt to "expand this into a detailed image prompt."

The nice part, which r/comfyui posters consistently point out: it's fully local. No API key, no monthly bill, and Ollama unloads the model from VRAM after each call so your KSampler gets the memory back. That's a real advantage over calling a hosted vision API mid-workflow.

How it works

It calls ollama.generate on the Ollama server running at 127.0.0.1:11434. If you feed an images input, the node converts the tensor batch to PNG bytes and hands them to the model - which only works with a vision model. The default llava:latest is the classic choice; MiniCPM-V via Ollama is a popular alternative if you want more detail. The node always re-executes (it ignores caching), which is what you want when the input image changes.

The inputs that matter

• model_name - which Ollama model to use. Default llava:latest. Text-only models work fine too; just leave images unplugged.
• prompt - what you ask. Multiline. Default "Describe the images".
• system - the system prompt, multiline. The default is a decent detailed-description prompt (with a typo, "assitant", which you can ignore or fix). Tweak this to steer the style of your captions.
• images (optional) - an IMAGE tensor. Plug any image output here.
• format (optional) - set json to force structured JSON output, handy for feeding results into other nodes.
• context (optional) - the list of token IDs from a previous response output. Wire context from the last call back into context for multi-turn chat.
• options (optional) - a dictionary of Ollama generation options (temperature, num_predict, etc.), passed through to the server.
• keep_alive (optional) - 0 (default) unloads the model right after generation to free VRAM; -1 keeps it loaded for faster repeats. On a tight-VRAM card, leave it at 0.

The response output is the text you want. The other ten outputs - model, created_at, done, done_reason, context, total_duration, load_duration, prompt_eval_duration, eval_count, eval_duration - are Ollama's metadata (durations in nanoseconds, eval_count in tokens). Ignore them until you're benchmarking.

Getting it running

Two prerequisites. First, the pack itself: ComfyUI Manager → search "comfyui-ollama-nodes" → Install → restart, or:

cd ComfyUI/custom_nodes
git clone https://github.com/slyt/comfyui-ollama-nodes
cd comfyui-ollama-nodes
pip install -r requirements.txt

Second - the one people forget - Ollama must be running on the machine ComfyUI runs on (install from ollama.com). The pack assumes a server at 127.0.0.1:11434. Then pull a vision model once:

ollama pull llava

Troubleshooting

• "Connection refused" / nothing happens - Ollama isn't running or isn't on the same host as ComfyUI. If ComfyUI runs on a remote box, set OLLAMA_HOST and make sure the port is reachable.
• Vision model ignores the image - you're using a text-only model like llama3. Vision needs llava, minicpm-v, or similar.
• Node executes instantly with empty output - check the console log; the pack logs each generate call, and the error will be there. Stream mode also drives a progress bar (the README notes ShowText can't do true token-by-token streaming yet).

Worth the install? Yes. If you train LoRAs or want a self-contained local "explain this image" node in your graph, this is the pack's flagship and it does the job.