xinyun/ComfyUI-KJNodes
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Update curve_nodes.py

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kijai 2024-05-02 10:01:44 +03:00
parent e914839605
commit 500f2cc20b
1 changed files with 26 additions and 25 deletions
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51
nodes/curve_nodes.py
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@ -582,8 +582,26 @@ bounding boxes.
bg_color = '#353535'
matplotlib.pyplot.rcParams['text.color'] = text_color
fig, ax = matplotlib.pyplot.subplots(figsize=(width/100, height/100), dpi=100)
fig.patch.set_facecolor(bg_color)
ax.set_facecolor(bg_color)
ax.grid(color=text_color, linestyle='-', linewidth=0.5)
ax.set_xlabel('x', color=text_color)
ax.set_ylabel('y', color=text_color)
for text in ax.get_xticklabels() + ax.get_yticklabels():
text.set_color(text_color)
ax.set_title('Gligen pos for: ' + prompt)
ax.set_xlabel('X Coordinate')
ax.set_ylabel('Y Coordinate')
#ax.legend().remove()
ax.set_xlim(0, width) # Set the x-axis to match the input latent width
ax.set_ylim(height, 0) # Set the y-axis to match the input latent height, with (0,0) at top-left
# Adjust the margins of the subplot
matplotlib.pyplot.subplots_adjust(left=0.08, right=0.95, bottom=0.05, top=0.95, wspace=0.2, hspace=0.2)
cmap = matplotlib.pyplot.get_cmap('rainbow')
image_batch = []
canvas = FigureCanvas(fig)
width, height = fig.get_size_inches() * fig.get_dpi()
# Draw a box at each coordinate
for i, ((x, y), size) in enumerate(zip(coordinates, size_multiplier)):
color_index = i / (len(coordinates) - 1)
@ -603,30 +621,13 @@ bounding boxes.
lw=1,
color=color,
mutation_scale=10))
fig.patch.set_facecolor(bg_color)
ax.set_facecolor(bg_color)
ax.grid(color=text_color, linestyle='-', linewidth=0.5)
ax.set_xlabel('x', color=text_color)
ax.set_ylabel('y', color=text_color)
for text in ax.get_xticklabels() + ax.get_yticklabels():
text.set_color(text_color)
ax.set_title('Gligen pos for: ' + prompt)
ax.set_xlabel('X Coordinate')
ax.set_ylabel('Y Coordinate')
ax.legend().remove()
ax.set_xlim(0, width) # Set the x-axis to match the input latent width
ax.set_ylim(height, 0) # Set the y-axis to match the input latent height, with (0,0) at top-left
# Adjust the margins of the subplot
matplotlib.pyplot.subplots_adjust(left=0.08, right=0.95, bottom=0.05, top=0.95, wspace=0.2, hspace=0.2)
canvas = FigureCanvas(fig)
canvas.draw()
canvas.draw()
image_np = np.frombuffer(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3).copy()
image_tensor = torch.from_numpy(image_np).float() / 255.0
image_tensor = image_tensor.unsqueeze(0)
image_batch.append(image_tensor)
matplotlib.pyplot.close(fig)
image_batch_tensor = torch.cat(image_batch, dim=0)
width, height = fig.get_size_inches() * fig.get_dpi()
image_np = np.frombuffer(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)
image_tensor = torch.from_numpy(image_np).float() / 255.0
image_tensor = image_tensor.unsqueeze(0)
return image_tensor
return image_batch_tensor