---
pipeline_tag: text-to-image
library_name: diffusers
---
## Setup

Install the latest version of `diffusers`

```shell
pip install git+https://github.com/huggingface/diffusers.git
```

Login to your Hugging Face account

```shell
hf auth login
```

## How to use

The following code snippet demonstrates how to use the [Flux2](https://huggingface.co/black-forest-labs/FLUX.2-dev) modular pipeline with a remote text encoder and group offloading. It requires approximately 8GB of VRAM and 64GB of CPU RAM to generate an image.

```python
import torch
from diffusers.modular_pipelines.flux2 import ALL_BLOCKS
from diffusers.modular_pipelines import SequentialPipelineBlocks

blocks = SequentialPipelineBlocks.from_blocks_dict(ALL_BLOCKS['remote'])
pipe = blocks.init_pipeline("diffusers/flux2-modular")
pipe.load_components(torch_dtype=torch.bfloat16, device_map="cpu")
pipe.vae.to("cuda")
pipe.transformer.enable_group_offload(
    offload_type="leaf_level",
    onload_device=torch.device("cuda"),
    offload_device=torch.device("cpu"),
    use_stream=True,
    low_cpu_mem_usage=True,
)

prompt = "a photo of a cat"
output = pipe(prompt=prompt, num_inference_steps=28, output="images")
output[0].save("flux2-modular.png")
```