#
GGUF

The GGUF file format is typically used to store models for inference with GGML and supports a variety of block wise quantization options. Diffusers supports loading checkpoints prequantized and saved in the GGUF format via `from_single_file` loading with Model classes. Loading GGUF checkpoints via Pipelines is currently not supported.

The following example will load the FLUX.1 DEV transformer model using the GGUF Q2_K quantization variant.

Before starting please install gguf in your environment

    pip install -U gguf

Since GGUF is a single file format, use `~FromSingleFileMixin.from_single_file` to load the model and pass in the GGUFQuantizationConfig.

When using GGUF checkpoints, the quantized weights remain in a low memory `dtype`(typically `torch.uint8`) and are dynamically dequantized and cast to the configured `compute_dtype` during each module’s forward pass through the model. The `GGUFQuantizationConfig` allows you to set the `compute_dtype`.

The functions used for dynamic dequantizatation are based on the great work done by city96, who created the Pytorch ports of the original `numpy` implementation by compilade.

    import torch

    from diffusers import FluxPipeline, FluxTransformer2DModel, GGUFQuantizationConfig

    ckpt_path = (
        "https://huggingface.co/city96/FLUX.1-dev-gguf/blob/main/flux1-dev-Q2_K.gguf"
    )
    transformer = FluxTransformer2DModel.from_single_file(
        ckpt_path,
        quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
        torch_dtype=torch.bfloat16,
    )
    pipe = FluxPipeline.from_pretrained(
        "black-forest-labs/FLUX.1-dev",
        transformer=transformer,
        generator=torch.manual_seed(0),
        torch_dtype=torch.bfloat16,
    )
    pipe.enable_model_cpu_offload()
    prompt = "A cat holding a sign that says hello world"
    image = pipe(prompt).images[0]
    image.save("flux-gguf.png")

##
Supported Quantization Types

- BF16
- Q4_0
- Q4_1
- Q5_0
- Q5_1
- Q8_0
- Q2_K
- Q3_K
- Q4_K
- Q5_K
- Q6_K

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