A comprehensive suite of nodes for ComfyUI featuring multi-provider LLM support (OpenAI, Gemini, Claude, Grok, Groq, QwenVL), local model inference (Phi, MiniCPM, Ollama), professional image effects, and advanced prompt generation tools.

📦 Installation

Method 1: ComfyUI Manager (Recommended)

Search for "comfyui_dagthomas" in ComfyUI Manager and click Install.

Method 2: Manual Installation

cd ComfyUI/custom_nodes
git clone https://github.com/dagthomas/comfyui_dagthomas
cd comfyui_dagthomas
pip install -r requirements.txt

🔑 API Key Configuration

Set your API keys as environment variables:

# OpenAI GPT
set OPENAI_API_KEY=sk-your-key-here

# Google Gemini
set GEMINI_API_KEY=your-key-here

# Anthropic Claude
set ANTHROPIC_API_KEY=your-key-here
# or
set CLAUDE_API_KEY=your-key-here

# xAI Grok
set XAI_API_KEY=your-key-here
# or
set GROK_API_KEY=your-key-here

# Groq
set GROQ_API_KEY=your-key-here

🧩 Node Categories

📝 Universal Nodes (Model-Agnostic)

APNext Universal Generator

Display Name: APNext Universal Generator

A model-agnostic prompt generator that automatically detects available API keys and supports all major LLM providers.

| Input | Description | |-------|-------------| | input_text | Base text to enhance | | model | Select provider:model or "auto-detect" | | generation_mode | Creative, Balanced, Focused, or Custom | | seed | Seed for reproducible variations | | style_preference | Cinematic, Photorealistic, Artistic, etc. | | detail_level | Brief to Very Detailed output |

Supported Models:

gpt:gpt-4o, gpt:gpt-4o-mini, gpt:gpt-4-turbo
gemini:gemini-2.5-flash, gemini:gemini-2.5-pro
claude:claude-sonnet-4.5, claude:claude-3-5-sonnet
grok:grok-beta, grok:grok-2-vision
groq:llama-3.3-70b-versatile

Returns: (generated_prompt, model_used, seed_used)

APNext Universal Vision Cloner

Display Name: APNext Universal Vision Cloner

Analyze images with any supported vision model to generate detailed descriptions or clone image styles.

| Input | Description | |-------|-------------| | images | One or more images to analyze | | model | Vision model to use (auto-detect available) | | fade_percentage | Blend percentage for multiple images | | analysis_mode | Detailed Analysis, Style Cloning, Scene Description, Creative Interpretation | | output_format | Text Only, JSON Structure, or Formatted Prompt |

Returns: (formatted_output, raw_response, faded_image, model_used)

🤖 Google Gemini Nodes

Gemini Prompt Enhancer

Display Name: APNext Gemini Prompt Enhancer

Enhances prompts with cinematic terminology and LLM refinement for video/image generation.

| Input | Description | |-------|-------------| | base_prompt | Original prompt to enhance | | enhancement_mode | Random Mix, Cinematic/Lighting/Camera/Motion/Style Focus, Full Enhancement, or LLM Only | | use_llm | Enable Gemini LLM enhancement | | intensity | Enhancement intensity (0.1-2.0) | | Optional dropdowns | visual_style, lighting_type, camera_angle, shot_size, lens_type, color_tone, etc. |

Returns: (enhanced_prompt, random_enhanced, llm_enhanced)

Gemini Custom Vision

Display Name: APNext Gemini Custom Vision

Analyze multiple images with custom prompts. Supports dynamic prompt templates with variable substitution.

| Input | Description | |-------|-------------| | images | Input images | | custom_prompt | Custom analysis prompt | | dynamic_prompt | Enable ##TAG##, ##SEX##, ##PRONOUNS##, ##WORDS## substitution | | fade_percentage | Blend multiple images together |

Returns: (output, clip_l, faded_image)

Gemini Text Only

Display Name: APNext Gemini Text Only

Pure text generation with Gemini models. Supports dynamic prompt templates.

Returns: (output, clip_l)

Gemini Next Scene

Display Name: APNext Gemini Next Scene

Generate cinematic transitions for visual narratives. Creates the "next scene" based on a previous prompt and current frame.

| Input | Description | |-------|-------------| | image | Current frame image | | original_prompt | Previous scene description | | focus_on | Camera Movement, Framing Evolution, Environmental Reveals, Atmospheric Shifts | | transition_intensity | Subtle, Moderate, or Dramatic |

Returns: (next_scene_prompt, short_description)

💬 OpenAI GPT Nodes

GPT Mini Generator

Display Name: APNext GPT Mini Generator

Efficient text generation using GPT-4o-mini.

| Input | Description | |-------|-------------| | input_text | Text to enhance | | happy_talk | Enthusiastic vs professional tone | | compress | Enable output compression | | poster | Movie poster style formatting |

GPT Vision Cloner

Display Name: APNext GPT Vision Cloner

Clone image styles using GPT-4o vision capabilities with custom prompts.

GPT Custom Vision

Display Name: APNext GPT Custom Vision

Full custom vision analysis with GPT-4o.

🧠 Anthropic Claude Nodes

Claude Text Generator

Display Name: APNext Claude Text Generator

Text generation with Claude models (Claude 3.5 Sonnet, Claude Sonnet 4.5).

| Input | Description | |-------|-------------| | input_text | Text to process | | claude_model | Model selection | | happy_talk, compress, poster | Output style controls | | variation_instruction | Custom instruction for creative variations |

Claude Vision Analyzer

Display Name: APNext Claude Vision Analyzer

Image analysis with Claude's multimodal capabilities.

⚡ xAI Grok Nodes

Grok Text Generator

Display Name: APNext Grok Text Generator

Text generation using xAI's Grok models.

Grok Vision Analyzer

Display Name: APNext Grok Vision Analyzer

Image analysis with Grok vision models.

🚀 Groq Nodes (Ultra-Fast Inference)

Groq Text Generator

Display Name: APNext Groq Text Generator

Lightning-fast text generation using Groq's optimized infrastructure with Llama and Mixtral models.

| Input | Description | |-------|-------------| | groq_model | llama-3.3-70b-versatile, llama-3.1-8b-instant, etc. | | Other standard LLM inputs |

Groq Vision Analyzer

Display Name: APNext Groq Vision Analyzer

Fast image analysis with Groq vision models.

🔍 QwenVL Nodes (Local Vision)

QwenVL Vision Analyzer

Display Name: APNext QwenVL Vision Analyzer

Local vision analysis using Qwen-VL models. Downloads models automatically.

| Input | Description | |-------|-------------| | images | Input images | | qwen_model | Qwen3-VL-4B-Instruct, etc. | | max_tokens | Maximum response length | | keep_model_loaded | Cache model in memory |

QwenVL Vision Cloner

Display Name: APNext QwenVL Vision Cloner

Clone image styles locally without API calls.

QwenVL Video Analyzer

Display Name: APNext QwenVL Video Analyzer

Analyze video content frame-by-frame.

QwenVL Next Scene

Display Name: APNext QwenVL Next Scene

Generate cinematic scene transitions locally using QwenVL models. Takes a previous scene description and 1-5 frame images, then creates natural camera movements, framing evolution, and atmospheric shifts. Multiple frames help the model understand motion/progression.

| Input | Description | |-------|-------------| | images | 1-5 frame images (batch) | | original_prompt | Previous scene description | | qwen_model | QwenVL model to use | | prompt_file | Custom prompt template file | | custom_prompt | Override with inline prompt (optional) | | max_frames | Max frames to use from batch (1-5) | | focus_on | Camera Movement, Framing Evolution, Environmental Reveals, Atmospheric Shifts | | transition_intensity | Subtle, Moderate, or Dramatic | | keep_model_loaded | Cache model in memory |

Returns: (next_scene_prompt, short_description)

Custom Prompts: Create your own prompt templates in data/custom_prompts/. Use ##ORIGINAL_PROMPT## as placeholder for the previous scene description. Included templates:

next_scene.txt - Default detailed cinematography prompt
qwen_next_scene_simple.txt - Simplified version
qwen_next_scene_video.txt - Optimized for AI video generation

QwenVL Frame Prep

Display Name: APNext QwenVL Frame Prep

Utility node to prepare multiple images for QwenVL Next Scene. Accepts up to 5 individual images or a batch, scales them to max dimensions, and outputs a batched tensor.

| Input | Description | |-------|-------------| | max_width | Maximum width (default 1024) | | max_height | Maximum height (default 1024) | | image_1 - image_5 | Individual image inputs | | image_batch | Pre-batched images (optional) |

Returns: (images, frame_count)

QwenVL Z-Image Vision

Display Name: APNext QwenVL Z-Image Vision

Analyzes images and outputs in Z-Image TurnBuilder chat format with <|im_start|>/<|im_end|> tokens.

🦙 Ollama Nodes (Local LLM)

Ollama Node

Display Name: APNext OllamaNode

Local LLM inference using Ollama. Supports any model installed in your Ollama instance.

| Input | Description | |-------|-------------| | input_text | Text to process | | model_name | Any Ollama model (llama3, mistral, etc.) | | happy_talk, compress | Output controls |

Ollama Vision

Display Name: APNext OllamaVision

Local vision analysis with Ollama multimodal models (llava, bakllava, etc.).

📸 MiniCPM Nodes (Local Vision)

MiniCPM Image Node

Display Name: APNext MiniCPM Image

Image understanding with MiniCPM-V 4.5 (OpenBMB). Supports thinking mode for complex reasoning.

| Input | Description | |-------|-------------| | images | Input images | | question | Question about the image | | enable_thinking | Deep reasoning mode | | precision | bfloat16 or float16 | | unload_after_inference | Free memory after use |

MiniCPM Video Node

Display Name: APNext MiniCPM Video

Video understanding and analysis.

🔬 Phi Nodes (Microsoft Vision)

Phi Model Loader

Display Name: APNext Phi Model Loader

Load Microsoft Phi-3.5-vision-instruct model.

| Input | Description | |-------|-------------| | model_version | Phi-3.5-vision-instruct | | image_crops | 4 or 16 crops for detail | | attention_mechanism | flash_attention_2, sdpa, or eager |

Phi Model Inference / Custom Inference

Display Name: APNext Phi Model Inference

Run inference with loaded Phi model.

🎨 Image FX Nodes

Professional image effects using optimized tensor operations.

APNext Bloom FX

Creates a bloom/glow effect on bright areas.

| Input | Description | |-------|-------------| | intensity | Bloom strength (0-5) | | threshold | Brightness threshold (0-1) | | blur_radius | Glow spread (1-50) | | blend_mode | additive, screen, or overlay |

APNext Color Grading FX

Professional color grading with LUT support or manual controls.

| Input | Description | |-------|-------------| | method | manual or lut_file | | lut_file | .cube, .3dl, or image LUT | | exposure | -3 to +3 stops | | contrast, saturation | Standard adjustments | | highlights, shadows | Tone controls | | temperature, tint | White balance |

Supported LUT Formats: .cube (Adobe/Blackmagic), .3dl (Autodesk/Flame), Image LUTs (.png, .jpg)

APNext Sharpen FX

Intelligent image sharpening.

APNext Noise FX

Add film grain and noise effects.

APNext Rough FX

Add texture and roughness.

APNext Cross Processing FX

Film cross-processing color effects.

APNext Split Toning FX

Separate color toning for highlights and shadows.

APNext HDR Tone Mapping FX

HDR-style tone mapping.

APNext Glitch Art FX

Digital glitch and databending effects.

APNext Film Halation FX

Classic film halation (light bleeding) effect.

📐 Latent Generators

APNext Latent Generator

Display Name: APNext Latent Generator

Generate latent tensors with intelligent dimension calculation.

| Input | Description | |-------|-------------| | width, height | Base dimensions (0 = auto-calculate) | | megapixel_scale | Target megapixels (0.1-2.0) | | aspect_ratio | 1:1, 3:2, 4:3, 16:9, 21:9 | | is_portrait | Portrait orientation |

Returns: (LATENT, width, height)

PGSD3 Latent Generator

Display Name: APNext PGSD3LatentGenerator

Optimized latent generation for Stable Diffusion 3 pipelines.

🎲 Prompt Generators

Auto Prompter

Display Name: Auto Prompter

Generate random prompts from extensive category databases.

| Input | Description | |-------|-------------| | subject | Main subject (can include LoRA triggers) | | custom | Prefix text for styling | | artform | Photography, digital art, etc. | | Various category selections | Random or specific choices |

APNext Node

Display Name: APNext Node

Advanced prompt building with category-based enhancements.

Overview

Node Family Overview

The system includes numerous nodes that can be chained together to create complex workflows:

Node Chaining Example

Supports 24 main categories with subcategories:

Architecture: styles, buildings, interiors, materials
Art: painting, sculpture, techniques, palettes
Artist: concept artists, illustrators, painters
Character: anime, fantasy, sci-fi, superheroes
Cinematic: directors, genres, effects, color grading
Fashion: designers, outfits, accessories
Feelings: emotional modifiers
Geography: countries, nationalities
Human: jobs, hobbies, groups
Interaction: individual, couple, group, crowd interactions
Keywords: modifiers, genres, trending terms
People: archetypes, body types, expressions
Photography: cameras, lenses, lighting, film types
Plots: action, romance, horror, sci-fi scenarios
Poses: portrait and action poses
Scene: weather, textures, environments
Science: astronomy, mathematics, medical
Stuff: seasonal objects, gadgets, fantasy items
Time: eras, decades, centuries
Typography: fonts, word art styles
Vehicle: cars, classic cars, vehicle types
Video Game: games, engines, actions

🔧 Utility Nodes

String Merger

Display Name: APNext String Merger

Combine multiple strings with separators.

Flexible String Merger

Display Name: APNext Flexible String Merger

Advanced string combining with custom formatting.

Sentence Mixer

Display Name: APNext Sentence Mixer

Shuffle and mix sentences from multiple inputs for creative variations.

Custom Prompt Loader

Display Name: APNext Custom Prompts

Load prompt templates from the data/custom_prompts/ directory.

Included templates:

promptcreator.txt - Full creative prompt generation
image_analyze.txt - Image analysis prompts
gemini_video.txt - Video generation prompts
cloner.txt - Style cloning prompts
Various LoRA-specific templates (ohwx, t5xxl, etc.)

Local Random Prompt

Display Name: APNext Local random prompt

Load random prompts from local text files.

Random Integer Generator

Display Name: APNext Random Integer Generator

Generate random integers with min/max range.

📁 Adding Custom Categories

Create your own categories for APNextNode:

Create a folder in data/next/ (e.g., data/next/mycategory/)
Add JSON files for each field

Simple Format

["item1", "item2", "item3"]

Advanced Format

{
  "preprompt": "with",
  "separator": " and ",
  "endprompt": "visual effects",
  "items": ["motion blur", "lens flare", "particle effects"],
  "attributes": {
    "motion blur": ["dynamic", "cinematic"],
    "lens flare": ["bright", "atmospheric"]
  }
}

📝 Custom Prompt Templates

Create your own prompt templates for use with the Custom Prompt Loader node.

Location

Place .txt files in: data/custom_prompts/

Creating a Template

Templates are plain text files containing instructions for LLM nodes. They support dynamic variable substitution:

| Variable | Description | |----------|-------------| | ##TAG## | Replaced with the tag input (e.g., "ohwx man") | | ##SEX## | Replaced with the sex input (e.g., "male", "female") | | ##PRONOUNS## | Replaced with pronouns (e.g., "him, his") | | ##WORDS## | Replaced with target word count |

Example Template

Create a file data/custom_prompts/my_style.txt:

As a professional art critic, describe the provided image in detail.
Focus on creating a cohesive scene as if describing a movie still.

If the subject is ##TAG##, use ##PRONOUNS## pronouns appropriately.
The subject is ##SEX##.

Include:
- Main subject description with clothing, accessories, position
- Setting and environment details
- Lighting type, direction, and atmosphere
- Color palette and emotional tone
- Camera angle and composition

Output approximately ##WORDS## words.
Do not use JSON format. Provide a single cohesive paragraph.

Included Templates

| Template | Purpose | |----------|---------| | promptcreator.txt | Detailed image analysis (~150 words) | | promptcreator_small.txt | Concise image analysis | | image_analyze.txt | General image description | | cloner.txt | Style cloning prompts | | gemini_video.txt | Video generation prompts | | gemini_ohwx.txt | LoRA trigger-aware prompts | | t5xxl.txt | T5-XXL optimized prompts | | ltxv.txt | LTX Video model prompts | | next_scene.txt | Cinematic scene transitions |

⚙️ Configuring LLM Models

Customize available models by editing JSON configuration files in the data/ folder.

Model Configuration Files

| File | Provider | Description | |------|----------|-------------| | gemini_models.json | Google Gemini | Gemini model list | | gpt_models.json | OpenAI | GPT model list | | claude_models.json | Anthropic | Claude model list | | grok_models.json | xAI | Grok model list | | groq_models.json | Groq | Groq model list (text + vision) | | qwenvl_models.json | QwenVL | Local Qwen vision models |

QwenVL Models - Adding Private/Custom Models

QwenVL nodes support loading additional models from private configuration files. This allows you to add custom or uncensored models without modifying the main configuration.

How to add private models:

Create a JSON file in data/ with a name matching private_*qwenvl*.json
- Examples: private_qwenvl_models.json, private_uncensored.qwenvl_models.json
Use the same format as qwenvl_models.json:

{
    "models": [
        "huihui-ai/Huihui-Qwen3-VL-4B-Instruct-abliterated",
        "huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated",
        "another-namespace/custom-model"
    ]
}

Restart ComfyUI - the models will appear in the QwenVL node dropdowns

Notes:

Private files are loaded in addition to the main qwenvl_models.json
Duplicate models are automatically filtered out
Supports full HuggingFace repo paths (namespace/model-name)
Models are downloaded to ComfyUI/models/LLM/Qwen-VL/ on first use

Basic Format

Most model files use a simple array format:

{
    "models": [
        "model-name-1",
        "model-name-2",
        "model-name-3"
    ]
}

Example: Adding New Gemini Models

Edit data/gemini_models.json:

{
    "models": [
        "gemini-2.5-pro",
        "gemini-2.5-flash",
        "gemini-flash-latest",
        "gemini-flash-lite-latest",
        "gemini-2.5-flash-lite",
        "gemini-exp-1206"
    ]
}

Example: Adding New Claude Models

Edit data/claude_models.json:

{
    "models": [
        "claude-sonnet-4.5",
        "claude-sonnet-4",
        "claude-sonnet-3.7",
        "claude-opus-4.1",
        "claude-opus-4",
        "claude-haiku-3.5",
        "claude-haiku-3"
    ]
}

Groq Models (Advanced Format)

Groq supports separate text and vision model lists:

{
    "text_models": [
        "llama-3.3-70b-versatile",
        "llama-3.1-8b-instant",
        "groq/compound",
        "qwen/qwen3-32b"
    ],
    "vision_models": [
        "meta-llama/llama-4-scout-17b-16e-instruct",
        "meta-llama/llama-4-maverick-17b-128e-instruct"
    ],
    "note": "Edit this file to add/remove models"
}

Notes

Restart ComfyUI after editing model configuration files
For Groq, the system will first try to fetch models from the API, then fall back to the JSON file
Model names must match exactly what the provider's API expects
Invalid model names will cause API errors at runtime

🖼️ Example Workflows

Example workflows are available in the examples/ directory:

APNext workflows: examples/flux/apnext/
Florence2 local: examples/flux/florence2/
GPT-4o Vision: examples/flux/gpt-4o_vision/
Ollama local: examples/flux/ollama_local_llm/
MiniCPM: examples/minicpm/

📋 Requirements

Pillow>=10.4.0
requests>=2.32.5
openai>=1.44.0
blend-modes>=2.1.0
huggingface_hub>=0.34.0
color_matcher>=0.5.0
chardet>=5.2.0
google-generativeai>=0.7.2
anthropic
transformers>=4.40.0
decord>=0.6.0
scipy>=1.10.0
tqdm>=4.67.1

🔄 Model Support Matrix

| Provider | Text | Vision | Video | Local | |----------|------|--------|-------|-------| | OpenAI GPT | ✅ | ✅ | ❌ | ❌ | | Google Gemini | ✅ | ✅ | ✅ | ❌ | | Anthropic Claude | ✅ | ✅ | ❌ | ❌ | | xAI Grok | ✅ | ✅ | ❌ | ❌ | | Groq | ✅ | ✅ | ❌ | ❌ | | QwenVL | ✅ | ✅ | ✅ | ✅ | | Ollama | ✅ | ✅ | ❌ | ✅ | | MiniCPM | ✅ | ✅ | ✅ | ✅ | | Phi-3.5 | ✅ | ✅ | ❌ | ✅ |

📝 License

MIT License

🙏 Acknowledgments

Built for the ComfyUI community. Special thanks to all contributors and users providing feedback.