S-Lab, Nanyang Technological University
Paper | Project Page | Video | Demo
CVPR2024 Oral
We propose FreeU, a method that substantially improves diffusion model sample quality at no cost: no training, no additional parameter introduced, and no increase in memory or sampling time.
📖 For more visual results, go checkout our Project Page
- A demo is also available on the
(huge thanks to AK and all the HF team for their support).
- You can use the gradio demo locally by running
python demos/app.py.
def Fourier_filter(x, threshold, scale):
# FFT
x_freq = fft.fftn(x, dim=(-2, -1))
x_freq = fft.fftshift(x_freq, dim=(-2, -1))
B, C, H, W = x_freq.shape
mask = torch.ones((B, C, H, W)).cuda()
crow, ccol = H // 2, W //2
mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale
x_freq = x_freq * mask
# IFFT
x_freq = fft.ifftshift(x_freq, dim=(-2, -1))
x_filtered = fft.ifftn(x_freq, dim=(-2, -1)).real
return x_filtered
class Free_UNetModel(UNetModel):
"""
:param b1: backbone factor of the first stage block of decoder.
:param b2: backbone factor of the second stage block of decoder.
:param s1: skip factor of the first stage block of decoder.
:param s2: skip factor of the second stage block of decoder.
"""
def __init__(
self,
b1,
b2,
s1,
s2,
*args,
**kwargs
):
super().__init__(*args, **kwargs)
self.b1 = b1
self.b2 = b2
self.s1 = s1
self.s2 = s2
def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
"""
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:param context: conditioning plugged in via crossattn
:param y: an [N] Tensor of labels, if class-conditional.
:return: an [N x C x ...] Tensor of outputs.
"""
assert (y is not None) == (
self.num_classes is not None
), "must specify y if and only if the model is class-conditional"
hs = []
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
emb = self.time_embed(t_emb)
if self.num_classes is not None:
assert y.shape[0] == x.shape[0]
emb = emb + self.label_emb(y)
h = x.type(self.dtype)
for module in self.input_blocks:
h = module(h, emb, context)
hs.append(h)
h = self.middle_block(h, emb, context)
for module in self.output_blocks:
hs_ = hs.pop()
# --------------- FreeU code -----------------------
# Only operate on the first two stages
if h.shape[1] == 1280:
hidden_mean = h.mean(1).unsqueeze(1)
B = hidden_mean.shape[0]
hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True)
hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True)
hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3)
h[:,:640] = h[:,:640] * ((self.b1 - 1 ) * hidden_mean + 1)
hs_ = Fourier_filter(hs_, threshold=1, scale=self.s1)
if h.shape[1] == 640:
hidden_mean = h.mean(1).unsqueeze(1)
B = hidden_mean.shape[0]
hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True)
hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True)
hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3)
h[:,:320] = h[:,:320] * ((self.b2 - 1 ) * hidden_mean + 1)
hs_ = Fourier_filter(hs_, threshold=1, scale=self.s2)
# ---------------------------------------------------------
h = th.cat([h, hs_], dim=1)
h = module(h, emb, context)
h = h.type(x.dtype)
if self.predict_codebook_ids:
return self.id_predictor(h)
else:
return self.out(h)You can adjust these parameters based on your models, image/video style, or tasks. You can look over the following parameters.
b1: 1.3, b2: 1.4, s1: 0.9, s2: 0.2
b1: 1.5, b2: 1.6, s1: 0.9, s2: 0.2
b1: 1.1, b2: 1.2, s1: 0.9, s2: 0.2
b1: 1.4, b2: 1.6, s1: 0.9, s2: 0.2
b1: 1.3, b2: 1.4, s1: 0.9, s2: 0.2 SDXL results
When trying additional parameters, consider the following ranges:
- b1: 1 ≤ b1 ≤ 1.2
- b2: 1.2 ≤ b2 ≤ 1.6
- s1: s1 ≤ 1
- s2: s2 ≤ 1
If you tried FreeU and want to share your results, let me know and we can put up the link here.
- SDXL from Nasir Khalid
- comfyUI from Abraham
- SD2.1 from Justin DuJardin
- SDXL from Sebastian
- SDXL from tintwotin
- ComfyUI-FreeU (YouTube)
- ComfyUI-FreeU (中文)
- Rerender
- Collaborative-Diffusion
@inproceedings{si2023freeu,
title={FreeU: Free Lunch in Diffusion U-Net},
author={Si, Chenyang and Huang, Ziqi and Jiang, Yuming and Liu, Ziwei},
booktitle={CVPR},
year={2024}
}
Distributed under the MIT License. See LICENSE for more information.