How do you create the figures in your research paper?

[GoldenFishes]

Could you please show me how figures 2 and 3 in the paper were created? Such as,the figure of “The denoising process（figure 2）” and “Relative log amplitudes of Fourier for diffusion inter-mediate steps（figure 3）”

[GoldenFishes]

Never mind，I got it.

[ewrfcas]

Could you please show me how figures 2 and 3 in the paper were created? Such as,the figure of “The denoising process（figure 2）” and “Relative log amplitudes of Fourier for diffusion inter-mediate steps（figure 3）”

I have the same question. How do you draw these figures?

[GoldenFishes]

Could you please show me how figures 2 and 3 in the paper were created? Such as,the figure of “The denoising process（figure 2）” and “Relative log amplitudes of Fourier for diffusion inter-mediate steps（figure 3）”

I have the same question. How do you draw these figures?

Well, since you've asked, I'd like to know your research direction first.

[ewrfcas]

Could you please show me how figures 2 and 3 in the paper were created? Such as,the figure of “The denoising process（figure 2）” and “Relative log amplitudes of Fourier for diffusion inter-mediate steps（figure 3）”

I have the same question. How do you draw these figures?

Well, since you've asked, I'd like to know your research direction first.

Now, I am interested in image synthesis.
I just know the way to draw the figure of amplitude/sampling frequency as follows:

[GoldenFishes]

Now, I am interested in image synthesis. I just know the way to draw the figure of amplitude/sampling frequency as follows:

So, you're not involved in research related to diffusion models?

[GoldenFishes]

Could you please show me how figures 2 and 3 in the paper were created? Such as,the figure of “The denoising process（figure 2）” and “Relative log amplitudes of Fourier for diffusion inter-mediate steps（figure 3）”

I have the same question. How do you draw these figures?

import cv2
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.cm import get_cmap

def calculate_relative_log_frequency(image):
# Perform Fourier transform
fourier_transform = np.fft.fft2(image)
# Calculate the amplitude of the Fourier transform
amplitude = np.abs(fourier_transform)
# Calculate the relative log frequency
relative_log_frequency = np.log(amplitude + 1) - np.log(amplitude[0, 0] + 1) # Relative to the log frequency at 0 point
# Get the width of the image
image_width = image.shape[1]
# Calculate frequency values on the axis
frequency_values = np.fft.fftfreq(image_width)

# Keep only the part in the range 0.0 to 1.0
valid_indices = (frequency_values >= 0.0) & (frequency_values <= 1.0 / np.pi)
frequency_values = frequency_values[valid_indices]
relative_log_frequency = relative_log_frequency[:, valid_indices]

# Reduce data points to reduce oscillation
# Here, we add a step to take every 4th value
frequency_values = frequency_values[::4]
relative_log_frequency = relative_log_frequency[:, ::4]

# Rescale frequency values to the range 0.0 to 1.0
frequency_values = (frequency_values - min(frequency_values)) / (max(frequency_values) - min(frequency_values))
return relative_log_frequency[0], frequency_values

def plot_multiple_relative_log_frequencies(image_filenames, alpha=0.8):
plt.figure(figsize=(12, 6))
cmap = get_cmap('Blues')

new_labels = ["step1", "step100", "step200",
              "step300", "step400", "step500",
              "step600", "step700", "step800",
              "step900", "step1000"]

for i, filename in enumerate(image_filenames):
    image = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
    relative_log_frequency, frequency_values = calculate_relative_log_frequency(image)

    k = i / len(image_filenames)
    color = cmap(k + 0.2)

    # Use the new labels
    label = new_labels[i]

    # Adjust the alpha parameter to control the transparency of the curve
    plt.plot(frequency_values, relative_log_frequency, color=color, label=label, linewidth=2.5, alpha=alpha)

plt.xlabel('Frequency')
plt.ylabel('Relative Log Amplitude')
plt.xlim(0.0, 1.0)
plt.grid(True)
plt.legend()

# Modify x-axis labels
x_ticks = np.linspace(0, 1, 6)
x_tick_labels = [f'{x:.1f}π' for x in x_ticks]
plt.xticks(x_ticks, x_tick_labels)

plt.show()

Example usage

image_filenames = ["0-eps1.0.png", "0-eps0.9.png", "0-eps0.8.png",
"0-eps0.7.png", "0-eps0.6.png", "0-eps0.5.png",
"0-eps0.4.png", "0-eps0.3.png", "0-eps0.2.png",
"0-eps0.1.png", "0-eps0.0.png"]

plot_multiple_relative_log_frequencies(image_filenames)

[GoldenFishes]

I apologize for the formatting issue in the code. I hope it can help you.

[ewrfcas]

I apologize for the formatting issue in the code. I hope it can help you.

Thanks for your reply!

[FatLong666]

I apologize for the formatting issue in the code. I hope it can help you.

The code you apply above is about how to draw the Fig3? Do you konw how to draw the Fig2？ Hope you can help me.

[GoldenFishes]

The code you apply above is about how to draw the Fig3? Do you konw how to draw the Fig2？ Hope you can help me.

I can do Fig2 in other model such as U-ViT in UniDiffuser, I haven't try it in StableDiffusion U-Net.

[FatLong666]

The code you apply above is about how to draw the Fig3? Do you konw how to draw the Fig2？ Hope you can help me.

I can do Fig2 in other model such as U-ViT in UniDiffuser, I haven't try it in StableDiffusion U-Net.

Could you share the code with me about how to draw the Fig2 in U-ViT? Is it use the low or high filters to achieve it?

[GoldenFishes]

Could you share the code with me about how to draw the Fig2 in U-ViT? Is it use the low or high filters to achieve it?

The method for generating intermediate results in the denoising process can be found in my open-source code. Once you obtain these intermediate results, separating the high and low frequencies becomes much easier.
GoldenFishes/FreeV#1