youyegit-tdxh_node_comfyui/README.md

Introduction

Some nodes for stable diffusion comfyui.Sometimes it helps conveniently to use less nodes for doing the same things.

If you use workflow in my "blogs" repo, you need to dowmload these nodes.I don't guarantee that the nodes will stay the same always. Some nodes maybe have been changed if you update the new version.

How to install

The repo

The same with others custom nodes. Just cd custom_nodes and then git clone.

Translator model

If you use prompt translator to translate Chinese to English offline, you need download some models. Download the translator models from https://huggingface.co/facebook/mbart-large-50-many-to-one-mmt/tree/main into folder named "model" of this repo. The model folder tree of this repo: model/ └── mbart-large-50-many-to-many-mmt__only_to_English/ ├── pytorch_model.bin ├── config.json ├── sentencepiece.bpe.model ├── special_tokens_map.json ├── tmp2l0rt359 └── tokenizer_config.json

Environments

cd (this repo) pip install -r requirements.txt

Nodes Introductions

tdxh_image

• "TdxhImageToSize": TdxhImageToSize can Convert an image to size.
• "TdxhImageToSizeAdvanced":TdxhImageToSizeAdvanced can Convert an image to size and it will let you choose what to follow:"only_width", "only_height", "both_width_and_height","width * height", "only_ratio","only_image","get_SDXL_best_size".

tdxh_model

• "TdxhLoraLoader": TdxhLoraLoader adds a switch to the "LoraLoader", which shows as "bool_int" (0 -> OFF, 1 -> ON), and lets you choose "only_strength_both" or "strength_model_and_strength_clip".

tdxh_data

• "TdxhIntInput": TdxhIntInput returns the "INT" type.
• "TdxhFloatInput": TdxhFloatInput returns the "FLOAT" type.
• "TdxhStringInput": TdxhStringInput returns the "STRING" type.
• "TdxhSaveText": TdxhSaveText saves input text to an output text file and returns both the input text and the saved path as "STRING" outputs.
• "TdxhStringInputTranslator": TdxhStringInputTranslator returns the translated "STRING" type. (You need to download the translator model.)

tdxh_bool

• "TdxhOnOrOff": TdxhOnOrOff returns the "NUMBER" and "INT" type. When switching to ON, it returns 1; when switching to OFF, it returns 0.
• "TdxhBoolNumber": TdxhBoolNumber is similar to TdxhOnOrOff but lets you choose what to follow. "control_by_master" is the main control: if OFF, it returns "bool_int"; if ON, then only when both "bool_int_from_master" and "bool_int" are 1 does it return 1.

tdxh_efficiency

• "TdxhClipVison": TdxhClipVison adds a switch to the "CLIPVisionLoader" and the "clip_vision".
• "TdxhControlNetProcessor": TdxhControlNetProcessor adds a switch to ControlNet nodes and lets you preprocess the image. (It needs AUX preprocessor nodes.)
• "TdxhControlNetApply": TdxhControlNetApply adds a switch to ControlNet nodes and makes them more efficient to use.
• "TdxhReference": TdxhReference makes the "reference_only" node more efficient.
• "TdxhImg2ImgLatent": TdxhImg2ImgLatent can switch between original(main) latent and image latent (OFF -> main latent, ON -> image latent).

tdxh_ltx23

• "TdxhLtx23MultimodalDirector": TdxhLtx23MultimodalDirector is a multimodal control hub for LTX 2.3 style workflows. It accepts text, a dynamic image sequence, optional reference audio, and optional reference video frames, then outputs a ready-to-use positive prompt, negative prompt, modality-specific guidance strings, and a structured JSON control plan. The node is meant to sit upstream of LTX prompt/conditioning workflows so users can keep character consistency, scene continuity, voice or ambience guidance, and video style/camera/rhythm guidance in one place.

API nodes

DeepSeek API nodes

This repo now includes:

• TdxhDeepSeekChat

Config priority:

1. environment variable DEEPSEEK_API_KEY
2. local file deepseek_config.json

You can create deepseek_config.json from deepseek_config.example.json:

{
  "api_key": "sk-your-deepseek-api-key",
  "base_url": "https://api.deepseek.com",
  "timeout_seconds": 120
}

Notes:

• TdxhDeepSeekChat outputs answer, reasoning, status
• TdxhDeepSeekChat now has a thinking_enabled toggle, switching between deepseek-chat and deepseek-reasoner
• TdxhDeepSeekChat auto-enables JSON mode when the prompt clearly asks for JSON output
• both nodes support optional multi-round history with keep_history
• clear_history clears the stored conversation state inside the node instance
• config files are stored under api_nodes/configs/

Kimi API nodes

This repo now also includes:

• TdxhKimiChat
• TdxhKimiDynamicVisionChat
• TdxhLocalQwenVLDynamicVisionChat

Config priority:

1. environment variable MOONSHOT_API_KEY
2. environment variable KIMI_API_KEY
3. local file kimi_config.json

You can create kimi_config.json from kimi_config.example.json:

{
  "api_key": "sk-your-moonshot-api-key",
  "base_url": "https://api.moonshot.ai/v1",
  "chat_model": "kimi-k2-turbo-preview",
  "thinking_model": "kimi-k2.5",
  "vision_model": "moonshot-v1-8k-vision-preview",
  "timeout_seconds": 120
}

Notes:

• TdxhKimiChat defaults to a faster Moonshot chat model for non-thinking text requests and keeps kimi-k2.5 for thinking requests
• TdxhKimiChat can disable thinking by sending thinking: {"type":"disabled"}
• TdxhKimiChat already has a thinking_enabled toggle in the node UI
• TdxhKimiDynamicVisionChat supports a dynamic number of image inputs with an Update inputs button
• dynamic image inputs allow trailing image inputs to be empty, but do not allow gaps in the middle; if image_4 is connected then image_1 to image_3 must also be connected
• TdxhKimiDynamicVisionChat requires Moonshot Open Platform endpoints, not the Kimi Code endpoint
• TdxhLocalQwenVLDynamicVisionChat wraps the local ComfyUI-QwenVL GGUF backend under the tdxh naming convention
• TdxhLocalQwenVLDynamicVisionChat supports dynamic image inputs with an Update inputs button and also accepts one fixed optional video input
• TdxhLocalQwenVLDynamicVisionChat allows images and the fixed video input to be used together in one local multimodal request
• TdxhLocalQwenVLDynamicVisionChat does not add extra pip dependencies inside this repo, but it requires the external ComfyUI-QwenVL plugin to be installed
• TdxhLocalQwenVLDynamicVisionChat also depends on that plugin's GGUF vision runtime, such as a vision-capable llama-cpp-python build and matching QwenVL GGUF/mmproj model files
• ComfyUI placeholder images coming from LoadImage(example.png) are treated as empty image inputs, including common resize-like preprocessing results
• TdxhKimiDynamicVisionChat uploads valid images to Moonshot Files and reuses them via ms://file_id for lower request overhead on repeated calls
• both nodes output reasoning_content when the model returns it
• if keep_history is enabled, the node stores reasoning_content in assistant history to follow Moonshot's thinking-model guidance
• config files are stored under api_nodes/configs/

Multi-platform fallback node

This repo also includes:

• TdxhMultiPlatformChat
• TdxhMultiPlatformDynamicVisionChat

Features:

• supports provider priority ordering with provider_1, provider_2, and provider_3
• provider_3 defaults to disabled so the node structure can stay stable for future expansion
• current providers: deepseek, kimi, LocalQwen3VL-8B-Instruct-Q4_K_M, LocalQwen3VL-8B-Thinking-Q8_0
• in TdxhMultiPlatformChat, the two LocalQwen3VL entries are exposed for consistent provider lists but return a clear vision-only unsupported status
• reserved for future extension by adding more providers
• automatically falls back to the next provider when the previous one returns a non-OK status
• outputs:
  • answer
  • reasoning
  • status
  • used_provider
  • attempt_log

Vision notes:

• TdxhMultiPlatformDynamicVisionChat supports dynamic image inputs with an Update inputs button
• TdxhMultiPlatformDynamicVisionChat supports kimi, LocalQwen3VL-8B-Instruct-Q4_K_M, and LocalQwen3VL-8B-Thinking-Q8_0 in the same fallback chain, and also accepts one fixed optional video input
• if all connected images are placeholders such as LoadImage(example.png), the node treats them as empty and falls back to text chat
• deepseek is kept as a reserved provider slot but returns an explicit unsupported error until DeepSeek publishes official public vision API documentation

Thanks

Some codes are from The official ComfyUI and other custom nodes like The was-node-suite-comfyui. The translator's main code is from prompt_translator.