XY Plot¶
Documentation¶
- Class name:
XY Plot - Category:
Efficiency Nodes/Scripts - Output node:
False
The XY Plot node is designed to generate and manipulate XY plots based on various parameters and conditions. It supports operations such as flipping axes, adjusting label orientations, caching models, and handling unique identifiers for plot generation. This node is versatile in processing and visualizing data in a structured XY plot format, accommodating different types of plot inputs and configurations.
Input types¶
Required¶
grid_spacing- Specifies the spacing between grid lines in the plot, affecting the plot's granularity and visual density.
- Comfy dtype:
INT - Python dtype:
int
XY_flip- A boolean parameter that determines whether the X and Y axes should be flipped, altering the plot's orientation.
- Comfy dtype:
COMBO[STRING] - Python dtype:
bool
Y_label_orientation- Controls the orientation of Y-axis labels, enhancing readability based on the plot's layout and presentation.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
cache_models- Indicates whether to cache model data for efficiency, reducing load times for repeated plot generations.
- Comfy dtype:
COMBO[STRING] - Python dtype:
bool
ksampler_output_image- The output image from the ksampler, which is used as a reference or base for the XY plot.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
Optional¶
dependencies- Optional dependencies required for the plot generation, such as additional data sources or configurations.
- Comfy dtype:
DEPENDENCIES - Python dtype:
Dict[str, Any]
X- The X-axis values or data points for the plot, which can be a tuple unpacked if connected.
- Comfy dtype:
XY - Python dtype:
Tuple[float, ...]
Y- The Y-axis values or data points for the plot, similar to X, can be a tuple unpacked if connected.
- Comfy dtype:
XY - Python dtype:
Tuple[float, ...]
Output types¶
SCRIPT- Comfy dtype:
SCRIPT - Returns a dictionary containing the script configuration for the XY plot, including plot parameters and data points.
- Python dtype:
Dict[str, Any]
- Comfy dtype:
Usage tips¶
- Infra type:
CPU - Common nodes:
Source code¶
class TSC_XYplot:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"grid_spacing": ("INT", {"default": 0, "min": 0, "max": 500, "step": 5}),
"XY_flip": (["False","True"],),
"Y_label_orientation": (["Horizontal", "Vertical"],),
"cache_models": (["True", "False"],),
"ksampler_output_image": (["Images","Plot"],),},
"optional": {
"dependencies": ("DEPENDENCIES", ),
"X": ("XY", ),
"Y": ("XY", ),},
"hidden": {"my_unique_id": "UNIQUE_ID"},
}
RETURN_TYPES = ("SCRIPT",)
RETURN_NAMES = ("SCRIPT",)
FUNCTION = "XYplot"
CATEGORY = "Efficiency Nodes/Scripts"
def XYplot(self, grid_spacing, XY_flip, Y_label_orientation, cache_models, ksampler_output_image, my_unique_id,
dependencies=None, X=None, Y=None):
# Unpack X & Y Tuples if connected
if X != None:
X_type, X_value = X
else:
X_type = "Nothing"
X_value = [""]
if Y != None:
Y_type, Y_value = Y
else:
Y_type = "Nothing"
Y_value = [""]
# If types are the same exit. If one isn't "Nothing", print error
if X_type != "XY_Capsule" and (X_type == Y_type) and X_type not in ["Positive Prompt S/R", "Negative Prompt S/R"]:
if X_type != "Nothing":
print(f"{error('XY Plot Error:')} X and Y input types must be different.")
return (None,)
# Check that dependencies are connected for specific plot types
encode_types = {
"Checkpoint", "Refiner",
"LoRA", "LoRA Batch", "LoRA Wt", "LoRA MStr", "LoRA CStr",
"Positive Prompt S/R", "Negative Prompt S/R",
"AScore+", "AScore-",
"Clip Skip", "Clip Skip (Refiner)",
"ControlNetStrength", "ControlNetStart%", "ControlNetEnd%"
}
if X_type in encode_types or Y_type in encode_types:
if dependencies is None: # Not connected
print(f"{error('XY Plot Error:')} The dependencies input must be connected for certain plot types.")
# Return None
return (None,)
# Check if both X_type and Y_type are special lora_types
lora_types = {"LoRA Batch", "LoRA Wt", "LoRA MStr", "LoRA CStr"}
if (X_type in lora_types and Y_type not in lora_types) or (Y_type in lora_types and X_type not in lora_types):
print(
f"{error('XY Plot Error:')} Both X and Y must be connected to use the 'LoRA Plot' node.")
return (None,)
# Clean Schedulers from Sampler data (if other type is Scheduler)
if X_type == "Sampler" and Y_type == "Scheduler":
# Clear X_value Scheduler's
X_value = [(x[0], "") for x in X_value]
elif Y_type == "Sampler" and X_type == "Scheduler":
# Clear Y_value Scheduler's
Y_value = [(y[0], "") for y in Y_value]
# Embed information into "Scheduler" X/Y_values for text label
if X_type == "Scheduler" and Y_type != "Sampler":
# X_value second tuple value of each array entry = None
X_value = [(x, None) for x in X_value]
if Y_type == "Scheduler" and X_type != "Sampler":
# Y_value second tuple value of each array entry = None
Y_value = [(y, None) for y in Y_value]
# Clean VAEs from Checkpoint data if other type is VAE
if X_type == "Checkpoint" and Y_type == "VAE":
# Clear X_value VAE's
X_value = [(t[0], t[1], None) for t in X_value]
elif Y_type == "VAE" and X_type == "Checkpoint":
# Clear Y_value VAE's
Y_value = [(t[0], t[1], None) for t in Y_value]
# Flip X and Y
if XY_flip == "True":
X_type, Y_type = Y_type, X_type
X_value, Y_value = Y_value, X_value
# Define Ksampler output image behavior
xyplot_as_output_image = ksampler_output_image == "Plot"
# Pack xyplot tuple into its dictionary item under script
script = {"xyplot": (X_type, X_value, Y_type, Y_value, grid_spacing, Y_label_orientation, cache_models,
xyplot_as_output_image, my_unique_id, dependencies)}
return (script,)