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README.md

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+## 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")
+```