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).

API nodes

DeepSeek API nodes

This repo now includes:

TdxhDeepSeekChat

Config priority:

environment variable DEEPSEEK_API_KEY
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": 60
}

Notes:

TdxhDeepSeekChat outputs answer, reasoning, status
TdxhDeepSeekChat now has a thinking_enabled toggle, switching between deepseek-chat and deepseek-reasoner
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

Config priority:

environment variable MOONSHOT_API_KEY
environment variable KIMI_API_KEY
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",
  "timeout_seconds": 60
}

Notes:

TdxhKimiChat uses kimi-k2.5
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
ComfyUI placeholder images coming from LoadImage(example.png) are treated as empty image inputs, including common resize-like preprocessing results
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
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
if all connected images are placeholders such as LoadImage(example.png), the node treats them as empty and falls back to text chat
TdxhMultiPlatformDynamicVisionChat currently works with kimi; 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.