### Install QuantFunc for ComfyUI

Source: https://quantfunc.com

Commands to clone the repository into the ComfyUI custom nodes directory and install required dependencies.

```bash
cd ComfyUI/custom_nodes
git clone https://github.com/RealJonathanYip/ComfyUI-QuantFunc.git
pip install -r ComfyUI-QuantFunc/requirements.txt
```

--------------------------------

### Install QuantFunc ComfyUI Plugin

Source: https://quantfunc.com

Use this command to clone the QuantFunc repository into your ComfyUI custom nodes directory.

```bash
cd ComfyUI/custom_nodes && git clone https://github.com/RealJonathanYip/ComfyUI-QuantFunc.git
```

--------------------------------

### Initialize and run a QuantFunc pipeline

Source: https://quantfunc.com

Uses the QuantFuncPipeline to load a model with a specified VRAM budget and generate an image from a text prompt.

```python
1import os2import quantfunc as qf3 4with qf.QuantFuncPipeline.from_pretrained(5        diffusers_model_path="/models/Z-Image-Turbo",6        api_key=os.environ["QUANTFUNC_API_KEY"],7        vram_budget=0.8,        # 80% of the card; "8g"/"12288mb" too8) as pipe:9    out = pipe("a neon city at night", width=1024, height=1024, step=8)10    out.save("out.png")11    print("seed", out.seed, round(out.elapsed_seconds, 2), "s")
```

--------------------------------

### QuantFuncPipeline.from_pretrained

Source: https://quantfunc.com

Initializes a quantized model pipeline from a specified path and generates an image based on a text prompt.

```APIDOC
## Python SDK: QuantFuncPipeline

### Description
Initializes a model pipeline using the `from_pretrained` method and executes image generation with specified dimensions and steps.

### Method
Python SDK Method

### Parameters
- **diffusers_model_path** (string) - Required - The local path to the pre-trained model.
- **api_key** (string) - Required - The QuantFunc API key for authentication.
- **vram_budget** (float/string) - Optional - The VRAM allocation limit (e.g., 0.8, "8g", or "12288mb").

### Generation Parameters
- **prompt** (string) - Required - The text description for image generation.
- **width** (int) - Optional - The width of the output image.
- **height** (int) - Optional - The height of the output image.
- **step** (int) - Optional - The number of inference steps.

### Example
```python
import os
import quantfunc as qf

with qf.QuantFuncPipeline.from_pretrained(
        diffusers_model_path="/models/Z-Image-Turbo",
        api_key=os.environ["QUANTFUNC_API_KEY"],
        vram_budget=0.8
) as pipe:
    out = pipe("a neon city at night", width=1024, height=1024, step=8)
    out.save("out.png")
```
```

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