Image-to-Video Prompt Expander

Image-to-video is the workflow most local video people actually run - anchor the first frame to a real image and the consistency problems of pure text-to-video mostly evaporate. The catch is that I2V models still need a prompt that describes what's in the frame and what should move. This node does both halves for you: a vision model reads your starting image, then a second LLM pass turns that description plus your motion direction into a prompt the video model can follow.

It's the pack's dedicated I2V node (Eric's Prompt Enhancers), and it's a genuinely nice fit for the Wan 2.2 and LTX workflows people are running - those models reward detailed, physical descriptions, and punishing short prompts is a known failure mode in the ecosystem.

How it works

Two stages, two LLM calls. First the vision pass: your image gets captioned by a vision-capable model - either a VL model loaded in LM Studio or Ollama (vision_backend), or the pack's local Qwen3-VL backend. Then the expansion pass takes that caption, your motion_description, and the aesthetic dropdowns, and writes a video prompt that keeps the subject recognizable while adding camera work and movement.

The one setting that changes the whole character of the output is reference_mode:

• recreate_exact - keep the exact character, outfit and lighting; just animate this scene. The default, and what you want 80% of the time.
• style_transfer - match lighting and mood only, invent a new pose or scene.
• character_remix - keep the character, change the outfit and setting.
• face_only - preserve the face, ignore everything else.

motion_description is your plain-language direction ("slowly turns head and smiles at camera"), and camera_movement, shot_size, motion_speed are the usual options, all defaulting to auto so you can ignore them.

The outputs

• positive_prompt - wire this into your I2V model's conditioning. It's the whole point.
• negative_prompt - auto-generated, usually worth keeping.
• image_description - the raw caption from the vision pass. Inspect this when results feel off; it's also handy on its own as a detailed prompt for other things.
• status - tells you whether the vision pass actually ran (more on that below).

The gotcha: vision is the fragile part

use_vision_model defaults to true, but the vision pass only works if your backend can actually see. With the default lm_studio backend, the model currently loaded in LM Studio must be a vision model - a plain Llama 3 will caption nothing, and you'll get an empty image_description and a weak prompt. Fix it either by loading a VL model in LM Studio, or switch vision_backend to qwen3_vl. That last one is the pack's self-contained path: it loads Qwen3-VL locally via transformers and needs no API server. Just know that the first run downloads a multi-gigabyte model from Hugging Face, and it needs the optional deps first:

pip install transformers>=4.42.0 accelerate>=0.30.0 huggingface_hub>=0.23.0 bitsandbytes>=0.43.0

The expansion pass can also use its own backend (expansion_backend), so you can pair a small vision model for captioning with a bigger LLM for writing.

Install

Shared with the pack: ComfyUI Manager → search "Local LLM Prompt Enhancer", or clone https://github.com/EricRollei/Local_LLM_Prompt_Enhancer.git into ComfyUI/custom_nodes/, pip install -r requirements.txt, restart. The core requirements file is just requests; the transformers stack above is only needed if you use the qwen3_vl backend.

One workflow note: if you're already on the Advanced video node, you don't need this one - the Advanced node's reference_image input does the same image-to-video pass with auto mode detection and more reference modes. This node is the simpler, more focused version. For most people, that's the right one.