#
FreeU

FreeU improves image details by rebalancing the UNet’s backbone and skip connection weights. The skip connections can cause the model to overlook some of the backbone semantics which may lead to unnatural image details in the generated image. This technique does not require any additional training and can be applied on the fly during inference for tasks like image-to-image and text-to-video.

Use the enable_freeu() method on your pipeline and configure the scaling factors for the backbone (`b1` and `b2`) and skip connections (`s1` and `s2`). The number after each scaling factor corresponds to the stage in the UNet where the factor is applied. Take a look at the FreeU repository for reference hyperparameters for different models.

Stable Diffusion v1-5
Stable Diffusion v2-1
Stable Diffusion XL
Zeroscope

    import torch
    from diffusers import DiffusionPipeline

    pipeline = DiffusionPipeline.from_pretrained(
        "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, safety_checker=None
    ).to("cuda")
    pipeline.enable_freeu(s1=0.9, s2=0.2, b1=1.5, b2=1.6)
    generator = torch.Generator(device="cpu").manual_seed(33)
    prompt = ""
    image = pipeline(prompt, generator=generator).images[0]
    image

FreeU disabled
FreeU enabled

Call the disable_freeu() method to disable FreeU.

    pipeline.disable_freeu()

Update on GitHub