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ComfyDeploy: How ComfyUI GLM-4 Wrapper works in ComfyUI?

What is ComfyUI GLM-4 Wrapper?

ComfyUI GLM-4 Wrapper. This powerful tool enhances your prompt engineering process by allowing users to easily construct detailed, high-quality prompts for image/video generation based on user image and/or user prompts.

How to install it in ComfyDeploy?

Head over to the machine page

  1. Click on the "Create a new machine" button
  2. Select the Edit build steps
  3. Add a new step -> Custom Node
  4. Search for ComfyUI GLM-4 Wrapper and select it
  5. Close the build step dialig and then click on the "Save" button to rebuild the machine

ComfyUI GLM-4 Wrapper

This repository contains custom nodes for ComfyUI, specifically designed to enhance and infer prompts using the GLM-4 model on local hardware.

The nodes leverage the GLM-4 model to generate detailed and descriptive image/video captions or enhance user-provided prompts, among regular inference.

Prompts and inference can be combined with image if THUDM/glm-4v-9b model is used.

All models will be downloaded automatically through HuggingFace.co. THUDM/glm-4v-9b will hold ~26 GB of hdd space and THUDM/glm-4-9b will hold ~18 GB of hdd space.

The nodes containes an "unload_model" option which frees up VRAM space and makes it suitable for workflows that requires larger VRAM space, like FLUX.1-dev and CogVideoX-5b(-I2V).

The prompt enhancer is based on this example from THUDM convert_demo.py. Thier demo is only for usage through OpenAI API and I wanted to build something local.

Hope you will enjoy your enhanced prompts and inference capabilities of these models. They are great!

Update 2024-10-03

Added support for quantized models. They are performing exceptionally well. Check metrics below.

Model alexwww94/glm-4v-9b-gptq-4bit is significatly more lightweight than the original and will hold ~8.5 GB of hdd space.

Model alexwww94/glm-4v-9b-gptq-3bit is even more lightweight and will hold ~7.6 GB of hdd space.

Link to metrics

Features

  • GLM-4 Model Loader: Load various GLM-4 models with different precision and quantization settings.
  • GLM-4 Prompt Enhancer: Enhances base prompts using the GLM-4 model.
  • GLM-4 Inferencing: Performs inference using various GLM-4 models.

Installation

  1. Navigate to ComfyUI custom nodes:
cd /your/path/to/ComfyUI/ComfyUI/custom_nodes/
  1. Clone the repository:
git clone https://github.com/Nojahhh/ComfyUI_GLM4_Wrapper.git
  1. Navigate to the cloned directory:
cd ComfyUI_GLM4_Wrapper
  1. Install the required dependencies:
../../python_embeded python -m pip install -r requirements.txt

Usage

GLM-4 Model Loader

The GLM4ModelLoader class is responsible for loading GLM-4 models. It supports various models and precision settings.

Input Types

  • model: Choose the GLM-4 model to load. Model will download automatically from HuggingFace.co
  • precision: Precision type (fp16, fp32, bf16). THUDM/glm-4v-9b requires bf16 and is set to run in 4-bit by default based on it's size. alexwww94/glm-4v-9b-gptq-4bit requires bf16 and is set to run in 4-bit by default. alexwww94/glm-4v-9b-gptq-3bit requires bf16 and is set to run in 3-bit by default.
  • quantization: Set the number of bits for quantization (4, 8, 16). Default value of 4. (This option is bypassed when using the GPTQ-models).

Output

  • GLM4Pipeline: The GLM-4 pipeline.

GLM-4 Prompt Enhancer

Enhances a given prompt using the GLM-4 model.

Input Parameters

  • GLMPipeline: Provide a GLM-4 pipeline.
  • prompt: Base prompt to enhance.
  • max_tokens: Maximum number of output tokens.
  • temperature: Temperature parameter for sampling.
  • top_k: Top-k parameter for sampling.
  • top_p: Top-p parameter for sampling.
  • repetition_penalty: Repetition penalty for sampling.
  • image (optional): Image to enhance the prompt. Only works with THUDM/glm-4v-9b, alexwww94/glm-4v-9b-gptq-4bit and alexwww94/glm-4v-9b-gptq-3bit.
  • unload_model: Unload the model after use.

Output

  • enhanced_prompt: The enhanced prompt.

GLM-4 Inferencing

Performs inference using the GLM-4 model.

Input Parameters

  • GLMPipeline: Provide a GLM-4 pipeline.
  • system_prompt: System prompt for inferencing.
  • user_prompt: User prompt for inferencing.
  • max_tokens: Maximum number of output tokens.
  • temperature: Temperature parameter for sampling.
  • top_k: Top-k parameter for sampling.
  • top_p: Top-p parameter for sampling.
  • repetition_penalty: Repetition penalty for sampling.
  • image (optional): Image to use as input for inferencing. Only works with THUDM/glm-4v-9b, alexwww94/glm-4v-9b-gptq-4bit and alexwww94/glm-4v-9b-gptq-3bit.
  • unload_model: Unload the model after use.

Output

  • output_text: The generated text from the model.

Node Class Mappings

  • GLM-4 Model Loader: GLM4ModelLoader
  • GLM-4 Prompt Enhancer: GLM4PromptEnhancer
  • GLM-4 Inferencing: GLM4Inference

Node Display Name Mappings

  • GLM-4ModelLoader: "GLM-4 Model Loader"
  • GLM-4PromptEnhancer: "GLM-4 Prompt Enhancer"
  • GLM-4Inference: "GLM-4 Inferencing"

Supported Models

The following GLM-4 models are supported by this wrapper:

| Model Name | Size | Recommended Precision | |---------------------------------|-------|------------------------| | alexwww94/glm-4v-9b-gptq-4bit | 9B | bf16 (4-bit quant) | | alexwww94/glm-4v-9b-gptq-3bit | 9B | bf16 (3-bit quant) | | THUDM/glm-4v-9b | 9B | bf16 (4/8-bit quant) | | THUDM/glm-4-9b | 9B | fp16, fp32, bf16 | | THUDM/glm-4-9b-chat | 9B | fp16, fp32, bf16 | | THUDM/glm-4-9b-chat-1m | 9B | fp16, fp32, bf16 | | THUDM/LongCite-glm4-9b | 9B | fp16, fp32, bf16 | | THUDM/LongWriter-glm4-9b | 9B | fp16, fp32, bf16 |

Notes:

  • THUDM/glm-4v-9b requires bf16 precision and is default 4-bit quantization due to its size and the typical VRAM limitations of consumer-grade GPUs (often 24GB or less).
  • alexwww94/glm-4v-9b-gptq-4bit requires bf16 and is default 4-bit.
  • alexwww94/glm-4v-9b-gptq-3bit requires bf16 and is default 3-bit.
  • Only THUDM/glm-4v-9b, alexwww94/glm-4v-9b-gptq-4bit and alexwww94/glm-4v-9b-gptq-3bit models are able to handle image input.

Example Usage

Below is an example of how to use the GLM-4 Prompt Enhancer and GLM-4 Inferencing nodes in your code:

GLM-4 Prompt Enhancer

from comfyui_glm4_wrapper import GLM4ModelLoader, GLM4PromptEnhancer, GLM4Inference

# Load the model
model_loader = GLM4ModelLoader()
pipeline = model_loader.gen(model="THUDM/glm-4v-9b", precision="bf16", quantization="8")[0]

# Enhance the prompt
enhancer = GLM4PromptEnhancer()
enhanced_prompt = enhancer.enhance_prompt(
  GLMPipeline=pipeline,
  prompt="A beautiful sunrise over the mountains",
  max_tokens=200,
  temperature=0.1,
  top_k=40,
  top_p=0.7,
  repetition_penalty=1.1,
  image=None,  # PIL Image
  unload_model=True
)
print(enhanced_prompt)

GLM-4 Inferencing

from comfyui_glm4_wrapper import GLM4ModelLoader, GLM4PromptEnhancer, GLM4Inference

# Load the model
model_loader = GLM4ModelLoader()
pipeline = model_loader.gen(model="THUDM/glm-4v-9b", precision="bf16", quantization="8")[0]

# Perform inference
inference = GLM4Inference()
output_text = inference.infer(
  GLMPipeline=pipeline,
  system_prompt="Describe the scene in detail:",
  user_prompt="A bustling city street at night",
  max_tokens=250,
  temperature=0.7,
  top_k=50,
  top_p=1,
  repetition_penalty=1.0,
  image=None,
  unload_model=True
)
print(output_text)

For more detailed examples and advanced usage, please refer to the documentation or the example scripts provided in the repository.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgements

Contact

For any questions or feedback, please open an issue on GitHub or contact me at mellin.johan@gmail.com.