XY Input: LoRA Plot¶
Documentation¶
- Class name:
XY Input: LoRA Plot - Category:
Efficiency Nodes/XY Inputs - Output node:
False
This node is designed to generate XY plot data specifically for LoRA (Low-Rank Adaptation) parameters, facilitating the visualization of relationships between different LoRA configurations and their impacts. It dynamically handles various LoRA-related inputs to produce coordinated X and Y values for plotting, accommodating a range of LoRA parameters such as batch, weight, model strength, and clip strength.
Input types¶
Required¶
input_mode- Specifies the mode of input, determining which LoRA parameters (e.g., batch, weight, model strength, clip strength) are considered for generating the plot. This affects how the node processes and interprets the provided data.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
lora_name- The name of the LoRA configuration being visualized, which is crucial for identifying and differentiating between various LoRA setups in the plot.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
model_strength- The strength of the model adaptation applied, relevant when visualizing the impact of model strength on LoRA's effectiveness.
- Comfy dtype:
FLOAT - Python dtype:
float
clip_strength- The strength of the clipping applied, relevant when assessing the impact of clip strength on LoRA's performance.
- Comfy dtype:
FLOAT - Python dtype:
float
X_batch_count- Specifies the number of batches to be considered for the X-axis values, influencing the granularity of the plot.
- Comfy dtype:
INT - Python dtype:
int
X_batch_path- The file path to the batch data for the X-axis, essential for locating and processing the specific LoRA data to be visualized.
- Comfy dtype:
STRING - Python dtype:
str
X_subdirectories- Indicates whether subdirectories should be included when searching for batch data, affecting the scope of data considered for the X-axis.
- Comfy dtype:
BOOLEAN - Python dtype:
bool
X_batch_sort- Determines the sorting order of batch data for the X-axis, which can influence the plot's representation of temporal or ordinal relationships.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
X_first_value- The starting value for generating a range of model strengths or clip strengths for the X-axis, setting the lower bound of the plot.
- Comfy dtype:
FLOAT - Python dtype:
float
X_last_value- The ending value for generating a range of model strengths or clip strengths for the X-axis, setting the upper bound of the plot.
- Comfy dtype:
FLOAT - Python dtype:
float
Y_batch_count- Specifies the number of batches to be considered for the Y-axis values, influencing the granularity of the plot.
- Comfy dtype:
INT - Python dtype:
int
Y_first_value- The starting value for generating a range of model strengths or clip strengths for the Y-axis, setting the lower bound of the plot.
- Comfy dtype:
FLOAT - Python dtype:
float
Y_last_value- The ending value for generating a range of model strengths or clip strengths for the Y-axis, setting the upper bound of the plot.
- Comfy dtype:
FLOAT - Python dtype:
float
Optional¶
lora_stack- An optional stack of LoRA configurations that can be included in the plot, allowing for the visualization of multiple LoRA setups within a single plot.
- Comfy dtype:
LORA_STACK - Python dtype:
list
Output types¶
X- Comfy dtype:
XY - The data represented on the X-axis, including the type and values derived from the specified LoRA parameters.
- Python dtype:
tuple
- Comfy dtype:
Y- Comfy dtype:
XY - The data represented on the Y-axis, including the type and values derived from the specified LoRA parameters.
- Python dtype:
tuple
- Comfy dtype:
Usage tips¶
- Infra type:
CPU - Common nodes: unknown
Source code¶
class TSC_XYplot_LoRA_Plot:
modes = ["X: LoRA Batch, Y: LoRA Weight",
"X: LoRA Batch, Y: Model Strength",
"X: LoRA Batch, Y: Clip Strength",
"X: Model Strength, Y: Clip Strength",
]
@classmethod
def INPUT_TYPES(cls):
loras = ["None"] + folder_paths.get_filename_list("loras")
return {"required": {
"input_mode": (cls.modes,),
"lora_name": (loras,),
"model_strength": ("FLOAT", {"default": 1.0, "min": -10.00, "max": 10.0, "step": 0.01}),
"clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"X_batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
"X_batch_path": ("STRING", {"default": xy_batch_default_path, "multiline": False}),
"X_subdirectories": ("BOOLEAN", {"default": False}),
"X_batch_sort": (["ascending", "descending"],),
"X_first_value": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 10.0, "step": 0.01}),
"X_last_value": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}),
"Y_batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
"Y_first_value": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 10.0, "step": 0.01}),
"Y_last_value": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}),},
"optional": {"lora_stack": ("LORA_STACK",)}
}
RETURN_TYPES = ("XY","XY",)
RETURN_NAMES = ("X","Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def __init__(self):
self.lora_batch = TSC_XYplot_LoRA_Batch()
def generate_values(self, mode, X_or_Y, *args, **kwargs):
result = self.lora_batch.xy_value(*args, **kwargs)
if result and result[0]:
xy_type, xy_value_list = result[0]
# Adjust type based on the mode
if "LoRA Weight" in mode:
xy_type = "LoRA Wt"
elif "Model Strength" in mode:
xy_type = "LoRA MStr"
elif "Clip Strength" in mode:
xy_type = "LoRA CStr"
# Check whether the value is for X or Y
if "LoRA Batch" in mode: # Changed condition
return self.generate_batch_values(*args, **kwargs)
else:
return ((xy_type, xy_value_list),)
return (None,)
def xy_value(self, input_mode, lora_name, model_strength, clip_strength, X_batch_count, X_batch_path, X_subdirectories,
X_batch_sort, X_first_value, X_last_value, Y_batch_count, Y_first_value, Y_last_value, lora_stack=None):
x_value, y_value = [], []
lora_stack = lora_stack if lora_stack else []
if "Model Strength" in input_mode and "Clip Strength" in input_mode:
if lora_name == 'None':
return (None,None,)
if "LoRA Batch" in input_mode:
lora_name = None
if "LoRA Weight" in input_mode:
model_strength = None
clip_strength = None
if "Model Strength" in input_mode:
model_strength = None
if "Clip Strength" in input_mode:
clip_strength = None
# Handling X values
if "X: LoRA Batch" in input_mode:
try:
x_value = self.lora_batch.xy_value(X_batch_path, X_subdirectories, X_batch_sort,
model_strength, clip_strength, X_batch_count, lora_stack)[0][1]
except Exception as e:
print(f"{error('XY Plot Error:')} {e}")
return (None,)
x_type = "LoRA Batch"
elif "X: Model Strength" in input_mode:
x_floats = generate_floats(X_batch_count, X_first_value, X_last_value)
x_type = "LoRA MStr"
x_value = [[(lora_name, x, clip_strength)] + lora_stack for x in x_floats]
# Handling Y values
y_floats = generate_floats(Y_batch_count, Y_first_value, Y_last_value)
if "Y: LoRA Weight" in input_mode:
y_type = "LoRA Wt"
y_value = [[(lora_name, y, y)] + lora_stack for y in y_floats]
elif "Y: Model Strength" in input_mode:
y_type = "LoRA MStr"
y_value = [[(lora_name, y, clip_strength)] + lora_stack for y in y_floats]
elif "Y: Clip Strength" in input_mode:
y_type = "LoRA CStr"
y_value = [[(lora_name, model_strength, y)] + lora_stack for y in y_floats]
return ((x_type, x_value), (y_type, y_value))