### Install Diffusers Library from Source

Source: https://huggingface.co/THUDM/CogView4-6B

Install the diffusers library directly from its GitHub repository and set it up as an editable package. This is required before using the CogView4-6B model.

```bash
pip install git+https://github.com/huggingface/diffusers.git
cd diffusers
pip install -e .
```

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

### Run CogView4-6B Text-to-Image Inference

Source: https://huggingface.co/THUDM/CogView4-6B

Load the CogView4-6B pipeline with BF16 precision, enable model CPU offloading and VAE slicing/tiling for reduced memory usage, and generate an image from a text prompt. The generated image is saved as 'cogview4.png'. Ensure you have `torch` installed.

```python
from diffusers import CogView4Pipeline
import torch

pipe = CogView4Pipeline.from_pretrained("THUDM/CogView4-6B", torch_dtype=torch.bfloat16)

# Open it for reduce GPU memory usage
pipe.enable_model_cpu_offload()
pipe.vae.enable_slicing()
pipe.vae.enable_tiling()

prompt = "A vibrant cherry red sports car sits proudly under the gleaming sun, its polished exterior smooth and flawless, casting a mirror-like reflection. The car features a low, aerodynamic body, angular headlights that gaze forward like predatory eyes, and a set of black, high-gloss racing rims that contrast starkly with the red. A subtle hint of chrome embellishes the grille and exhaust, while the tinted windows suggest a luxurious and private interior. The scene conveys a sense of speed and elegance, the car appearing as if it's about to burst into a sprint along a coastal road, with the ocean's azure waves crashing in the background."
image = pipe(
    prompt=prompt,
    guidance_scale=3.5,
    num_images_per_prompt=1,
    num_inference_steps=50,
    width=1024,
    height=1024,
).images[0]

image.save("cogview4.png")

```

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