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flux2-modular

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Setup

Install the latest version of diffusers

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

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How to use

The following code snippet demonstrates how to use the Flux2 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.

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")