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efficiency-nodes-comfyui/efficiency_nodes.py
TSC 8ec22e9e1e Updated minimum Val for XYPlot Seeds++
2023-07-18 18:04:18 -05:00

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# Efficiency Nodes - A collection of my ComfyUI custom nodes to help streamline workflows and reduce total node count.
# by Luciano Cirino (Discord: TSC#9184) - April 2023
from comfy.sd import ModelPatcher, CLIP, VAE
from nodes import common_ksampler, CLIPSetLastLayer
from torch import Tensor
from PIL import Image, ImageOps, ImageDraw, ImageFont
from PIL.PngImagePlugin import PngInfo
import numpy as np
import torch
import ast
from pathlib import Path
import os
import sys
import subprocess
import json
import folder_paths
import psutil
# Get the absolute path of the parent directory of the current script
my_dir = os.path.dirname(os.path.abspath(__file__))
# Add the My directory path to the sys.path list
sys.path.append(my_dir)
# Construct the absolute path to the ComfyUI directory
comfy_dir = os.path.abspath(os.path.join(my_dir, '..', '..'))
# Add the ComfyUI directory path to the sys.path list
sys.path.append(comfy_dir)
# Construct the path to the font file
font_path = os.path.join(my_dir, 'arial.ttf')
# Import functions from ComfyUI
import comfy.samplers
import comfy.sd
import comfy.utils
# Import my util functions
from tsc_utils import *
MAX_RESOLUTION=8192
########################################################################################################################
# TSC Efficient Loader
class TSC_EfficientLoader:
@classmethod
def INPUT_TYPES(cls):
return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
"vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),),
"clip_skip": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
"lora_name": (["None"] + folder_paths.get_filename_list("loras"),),
"lora_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"positive": ("STRING", {"default": "Positive","multiline": True}),
"negative": ("STRING", {"default": "Negative", "multiline": True}),
"empty_latent_width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"empty_latent_height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 64})},
"optional": {"lora_stack": ("LORA_STACK", )},
"hidden": { "prompt": "PROMPT",
"my_unique_id": "UNIQUE_ID",},
}
RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "CLIP", "DEPENDENCIES",)
RETURN_NAMES = ("MODEL", "CONDITIONING+", "CONDITIONING-", "LATENT", "VAE", "CLIP", "DEPENDENCIES", )
FUNCTION = "efficientloader"
CATEGORY = "Efficiency Nodes/Loaders"
def efficientloader(self, ckpt_name, vae_name, clip_skip, lora_name, lora_model_strength, lora_clip_strength,
positive, negative, empty_latent_width, empty_latent_height, batch_size, lora_stack=None,
prompt=None, my_unique_id=None):
model: ModelPatcher | None = None
clip: CLIP | None = None
vae: VAE | None = None
# Create Empty Latent
latent = torch.zeros([batch_size, 4, empty_latent_height // 8, empty_latent_width // 8]).cpu()
# Clean globally stored objects
globals_cleanup(prompt)
# Retrieve cache numbers
vae_cache, ckpt_cache, lora_cache = get_cache_numbers("Efficient Loader")
if lora_name != "None":
lora_params = [(lora_name, lora_model_strength, lora_clip_strength)]
if lora_stack is not None:
lora_params.extend(lora_stack)
model, clip = load_lora(lora_params, ckpt_name, my_unique_id, cache=lora_cache, ckpt_cache=ckpt_cache, cache_overwrite=True)
if vae_name == "Baked VAE":
vae = get_bvae_by_ckpt_name(ckpt_name)
else:
model, clip, vae = load_checkpoint(ckpt_name, my_unique_id, cache=ckpt_cache, cache_overwrite=True)
lora_params = None
# Check for custom VAE
if vae_name != "Baked VAE":
vae = load_vae(vae_name, my_unique_id, cache=vae_cache, cache_overwrite=True)
# Debugging
###print_loaded_objects_entries()
# CLIP skip
if not clip:
raise Exception("No CLIP found")
clip = clip.clone()
clip.clip_layer(clip_skip)
# Data for XY Plot
dependencies = (vae_name, ckpt_name, clip, clip_skip, positive, negative, lora_params)
return (model, [[clip.encode(positive), {}]], [[clip.encode(negative), {}]], {"samples":latent}, vae, clip, dependencies, )
########################################################################################################################
# TSC LoRA Stacker
class TSC_LoRA_Stacker:
loras = ["None"] + folder_paths.get_filename_list("loras")
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"lora_name_1": (cls.loras,),
"lora_wt_1": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora_name_2": (cls.loras,),
"lora_wt_2": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora_name_3": (cls.loras,),
"lora_wt_3": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})},
"optional": {"lora_stack": ("LORA_STACK",)},
}
RETURN_TYPES = ("LORA_STACK",)
RETURN_NAMES = ("LORA_STACK",)
FUNCTION = "lora_stacker"
CATEGORY = "Efficiency Nodes/Misc"
def lora_stacker(self, lora_name_1, lora_wt_1, lora_name_2, lora_wt_2, lora_name_3, lora_wt_3, lora_stack=None):
# Create a list of tuples using provided parameters, exclude tuples with lora_name as "None"
loras = [(lora_name, lora_wt, lora_wt) for lora_name, lora_wt, lora_wt in
[(lora_name_1, lora_wt_1, lora_wt_1),
(lora_name_2, lora_wt_2, lora_wt_2),
(lora_name_3, lora_wt_3, lora_wt_3)]
if lora_name != "None"]
# If lora_stack is not None, extend the loras list with lora_stack
if lora_stack is not None:
loras.extend([l for l in lora_stack if l[0] != "None"])
return (loras,)
# TSC LoRA Stacker Advanced
class TSC_LoRA_Stacker_Adv:
loras = ["None"] + folder_paths.get_filename_list("loras")
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"lora_name_1": (cls.loras,),
"model_str_1": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"clip_str_1": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora_name_2": (cls.loras,),
"model_str_2": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"clip_str_2": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora_name_3": (cls.loras,),
"model_str_3": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"clip_str_3": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})},
"optional": {"lora_stack": ("LORA_STACK",)},
}
RETURN_TYPES = ("LORA_STACK",)
RETURN_NAMES = ("LORA_STACK",)
FUNCTION = "lora_stacker"
CATEGORY = "Efficiency Nodes/Misc"
def lora_stacker(self, lora_name_1, model_str_1, clip_str_1, lora_name_2, model_str_2, clip_str_2,
lora_name_3, model_str_3, clip_str_3, lora_stack=None):
# Create a list of tuples using provided parameters, exclude tuples with lora_name as "None"
loras = [(lora_name, model_str, clip_str) for lora_name, model_str, clip_str in
[(lora_name_1, model_str_1, clip_str_1),
(lora_name_2, model_str_2, clip_str_2),
(lora_name_3, model_str_3, clip_str_3)]
if lora_name != "None"]
# If lora_stack is not None, extend the loras list with lora_stack
if lora_stack is not None:
loras.extend([l for l in lora_stack if l[0] != "None"])
return (loras,)
########################################################################################################################
# TSC KSampler (Efficient)
class TSC_KSampler:
empty_image = pil2tensor(Image.new('RGBA', (1, 1), (0, 0, 0, 0)))
def __init__(self):
self.output_dir = os.path.join(comfy_dir, 'temp')
self.type = "temp"
@classmethod
def INPUT_TYPES(cls):
return {"required":
{"sampler_state": (["Sample", "Hold", "Script"], ),
"model": ("MODEL",),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"latent_image": ("LATENT",),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"preview_image": (["Disabled", "Enabled", "Output Only"],),
},
"optional": { "optional_vae": ("VAE",),
"script": ("SCRIPT",),},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID",},
}
RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "IMAGE", )
RETURN_NAMES = ("MODEL", "CONDITIONING+", "CONDITIONING-", "LATENT", "VAE", "IMAGE", )
OUTPUT_NODE = True
FUNCTION = "sample"
CATEGORY = "Efficiency Nodes/Sampling"
def sample(self, sampler_state, model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
latent_image, preview_image, denoise=1.0, prompt=None, extra_pnginfo=None, my_unique_id=None,
optional_vae=(None,), script=None):
# Extract node_settings from json
def get_settings():
# Get the directory path of the current file
my_dir = os.path.dirname(os.path.abspath(__file__))
# Construct the file path for node_settings.json
settings_file = os.path.join(my_dir, 'node_settings.json')
# Load the settings from the JSON file
with open(settings_file, 'r') as file:
node_settings = json.load(file)
# Retrieve the settings
kse_vae_tiled = node_settings.get("KSampler (Efficient)", {}).get('vae_tiled', False)
xy_vae_tiled = node_settings.get("XY Plot", {}).get('vae_tiled', False)
return kse_vae_tiled, xy_vae_tiled
kse_vae_tiled, xy_vae_tiled = get_settings()
# Functions for previewing images in Ksampler
def map_filename(filename):
prefix_len = len(os.path.basename(filename_prefix))
prefix = filename[:prefix_len + 1]
try:
digits = int(filename[prefix_len + 1:].split('_')[0])
except:
digits = 0
return (digits, prefix)
def compute_vars(input):
input = input.replace("%width%", str(images[0].shape[1]))
input = input.replace("%height%", str(images[0].shape[0]))
return input
def preview_images(images, filename_prefix):
filename_prefix = compute_vars(filename_prefix)
subfolder = os.path.dirname(os.path.normpath(filename_prefix))
filename = os.path.basename(os.path.normpath(filename_prefix))
full_output_folder = os.path.join(self.output_dir, subfolder)
try:
counter = max(filter(lambda a: a[1][:-1] == filename and a[1][-1] == "_",
map(map_filename, os.listdir(full_output_folder))))[0] + 1
except ValueError:
counter = 1
except FileNotFoundError:
os.makedirs(full_output_folder, exist_ok=True)
counter = 1
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
results = list()
for image in images:
i = 255. * image.cpu().numpy()
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
metadata = PngInfo()
if prompt is not None:
metadata.add_text("prompt", json.dumps(prompt))
if extra_pnginfo is not None:
for x in extra_pnginfo:
metadata.add_text(x, json.dumps(extra_pnginfo[x]))
file = f"{filename}_{counter:05}_.png"
img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4)
results.append({
"filename": file,
"subfolder": subfolder,
"type": self.type
});
counter += 1
return results
def get_value_by_id(key: str, my_unique_id):
global last_helds
for value, id_ in last_helds[key]:
if id_ == my_unique_id:
return value
return None
def update_value_by_id(key: str, my_unique_id, new_value):
global last_helds
for i, (value, id_) in enumerate(last_helds[key]):
if id_ == my_unique_id:
last_helds[key][i] = (new_value, id_)
return True
last_helds[key].append((new_value, my_unique_id))
return True
# Clean globally stored objects of non-existant nodes
globals_cleanup(prompt)
# Convert ID string to an integer
my_unique_id = int(my_unique_id)
# Vae input check
vae = optional_vae
if vae == (None,):
print('\033[33mKSampler(Efficient) Warning:\033[0m No vae input detected, preview and output image disabled.\n')
preview_image = "Disabled"
# Init last_results
if get_value_by_id("results", my_unique_id) is None:
last_results = list()
else:
last_results = get_value_by_id("results", my_unique_id)
# Init last_latent
if get_value_by_id("latent", my_unique_id) is None:
last_latent = latent_image
else:
last_latent = {"samples": None}
last_latent["samples"] = get_value_by_id("latent", my_unique_id)
# Init last_images
if get_value_by_id("images", my_unique_id) == None:
last_images = TSC_KSampler.empty_image
else:
last_images = get_value_by_id("images", my_unique_id)
# Initialize latent
latent: Tensor|None = None
# Define filename_prefix
filename_prefix = "KSeff_{:02d}".format(my_unique_id)
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Check the current sampler state
if sampler_state == "Sample":
# Sample using the common KSampler function and store the samples
samples = common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
latent_image, denoise=denoise)
# Extract the latent samples from the returned samples dictionary
latent = samples[0]["samples"]
# Store the latent samples in the 'last_helds' dictionary with a unique ID
update_value_by_id("latent", my_unique_id, latent)
# If not in preview mode, return the results in the specified format
if preview_image == "Disabled":
# Enable vae decode on next Hold
update_value_by_id("vae_decode", my_unique_id, True)
return {"ui": {"images": list()},
"result": (model, positive, negative, {"samples": latent}, vae, TSC_KSampler.empty_image,)}
else:
# Decode images and store
if kse_vae_tiled == False:
images = vae.decode(latent).cpu()
else:
images = vae.decode_tiled(latent).cpu()
update_value_by_id("images", my_unique_id, images)
# Disable vae decode on next Hold
update_value_by_id("vae_decode", my_unique_id, False)
# Generate image results and store
results = preview_images(images, filename_prefix)
update_value_by_id("results", my_unique_id, results)
# Determine what the 'images' value should be
images_value = list() if preview_image == "Output Only" else results
# Output image results to ui and node outputs
return {"ui": {"images": images_value},
"result": (model, positive, negative, {"samples": latent}, vae, images,)}
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# If the sampler state is "Hold"
elif sampler_state == "Hold":
# If not in preview mode, return the results in the specified format
if preview_image == "Disabled":
return {"ui": {"images": list()},
"result": (model, positive, negative, last_latent, vae, TSC_KSampler.empty_image,)}
else:
latent = last_latent["samples"]
if get_value_by_id("vae_decode", my_unique_id) == True:
# Decode images and store
if kse_vae_tiled == False:
images = vae.decode(latent).cpu()
else:
images = vae.decode_tiled(latent).cpu()
update_value_by_id("images", my_unique_id, images)
# Disable vae decode on next Hold
update_value_by_id("vae_decode", my_unique_id, False)
# Generate image results and store
results = preview_images(images, filename_prefix)
update_value_by_id("results", my_unique_id, results)
else:
images = last_images
results = last_results
# Determine what the 'images' value should be
images_value = list() if preview_image == "Output Only" else results
# Output image results to ui and node outputs
return {"ui": {"images": images_value},
"result": (model, positive, negative, {"samples": latent}, vae, images,)}
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
elif sampler_state == "Script":
# Store name of connected node to script input
script_node_name, script_node_id = extract_node_info(prompt, my_unique_id, 'script')
# If no valid script input connected, error out
if script == None or script == (None,) or script_node_name!="XY Plot":
if script_node_name!="XY Plot":
print('\033[31mKSampler(Efficient) Error:\033[0m No valid script input detected')
return {"ui": {"images": list()},
"result": (model, positive, negative, last_latent, vae, last_images,)}
# If no vae connected, throw errors
if vae == (None,):
print('\033[31mKSampler(Efficient) Error:\033[0m VAE must be connected to use Script mode.')
return {"ui": {"images": list()},
"result": (model, positive, negative, last_latent, vae, last_images,)}
# If preview_image set to disabled, run script anyways with message
if preview_image == "Disabled":
print('\033[33mKSampler(Efficient) Warning:\033[0m The preview image cannot be disabled when running'
' the XY Plot script, proceeding as if it was enabled.\n')
# Extract the 'samples' tensor and split it into individual image tensors
image_tensors = torch.split(latent_image['samples'], 1, dim=0)
# Get the shape of the first image tensor
shape = image_tensors[0].shape
# Extract the original height and width
latent_height, latent_width = shape[2] * 8, shape[3] * 8
# Set latent only to the first latent of batch
latent_image = {'samples': image_tensors[0]}
#___________________________________________________________________________________________________________
# Initialize, unpack, and clean variables for the XY Plot script
if script_node_name == "XY Plot":
# Initialize variables
vae_name = None
ckpt_name = None
clip = None
lora_params = None
positive_prompt = None
negative_prompt = None
clip_skip = None
# Unpack script Tuple (X_type, X_value, Y_type, Y_value, grid_spacing, Y_label_orientation, dependencies)
X_type, X_value, Y_type, Y_value, grid_spacing, Y_label_orientation, cache_models, xyplot_as_output_image,\
flip_xy, dependencies = script
# Unpack Effficient Loader dependencies
if dependencies is not None:
vae_name, ckpt_name, clip, clip_skip, positive_prompt, negative_prompt, lora_params = dependencies
# Helper function to process printout values
def process_xy_for_print(value, replacement, type_):
if isinstance(value, tuple) and type_ == "Scheduler":
return value[0] # Return only the first entry of the tuple
elif isinstance(value, tuple):
return tuple(replacement if v is None else v for v in value)
else:
return replacement if value is None else value
# Determine the replacements based on X_type and Y_type
replacement_X = scheduler if X_type == 'Sampler' else clip_skip if X_type == 'Checkpoint' else None
replacement_Y = scheduler if Y_type == 'Sampler' else clip_skip if Y_type == 'Checkpoint' else None
# Process X_value and Y_value
X_value_processed = [process_xy_for_print(v, replacement_X, X_type) for v in X_value]
Y_value_processed = [process_xy_for_print(v, replacement_Y, Y_type) for v in Y_value]
# Print XY Plot Inputs
print("-" * 40)
print("XY Plot Script Inputs:")
print(f"(X) {X_type}: {X_value_processed}")
print(f"(Y) {Y_type}: {Y_value_processed}")
print("-" * 40)
# If not caching models, set to 1.
if cache_models == "False":
vae_cache = ckpt_cache = lora_cache = 1
else:
# Retrieve cache numbers
vae_cache, ckpt_cache, lora_cache = get_cache_numbers("XY Plot")
# Pack cache numbers in a tuple
cache = (vae_cache, ckpt_cache, lora_cache)
# Embedd original prompts into prompt variables
positive_prompt = (positive_prompt, positive_prompt)
negative_prompt = (negative_prompt, negative_prompt)
#_______________________________________________________________________________________________________
#The below code will clean from the cache any ckpt/vae/lora models it will not be reusing.
# Map the type names to the dictionaries
dict_map = {"VAE": [], "Checkpoint": [], "LoRA": []}
# Create a list of tuples with types and values
type_value_pairs = [(X_type, X_value), (Y_type, Y_value)]
# Iterate over type-value pairs
for t, v in type_value_pairs:
if t in dict_map:
# Flatten the list of lists of tuples if the type is "LoRA"
if t == "LoRA":
dict_map[t] = [item for sublist in v for item in sublist]
else:
dict_map[t] = v
ckpt_dict = [t[0] for t in dict_map.get("Checkpoint", [])] if dict_map.get("Checkpoint", []) else []
lora_dict = [[t,] for t in dict_map.get("LoRA", [])] if dict_map.get("LoRA", []) else []
# If both ckpt_dict and lora_dict are not empty, manipulate lora_dict as described
if ckpt_dict and lora_dict:
lora_dict = [(lora_params, ckpt) for ckpt in ckpt_dict for lora_params in lora_dict]
# If lora_dict is not empty and ckpt_dict is empty, insert ckpt_name into each tuple in lora_dict
elif lora_dict:
lora_dict = [(lora_params, ckpt_name) for lora_params in lora_dict]
vae_dict = dict_map.get("VAE", [])
# prioritize Caching Checkpoints over LoRAs but not both.
if X_type == "LoRA":
ckpt_dict = []
if X_type == "Checkpoint":
lora_dict = []
# Print dict_arrays for debugging
###print(f"vae_dict={vae_dict}\nckpt_dict={ckpt_dict}\nlora_dict={lora_dict}")
# Clean values that won't be reused
clear_cache_by_exception(script_node_id, vae_dict=vae_dict, ckpt_dict=ckpt_dict, lora_dict=lora_dict)
# Print loaded_objects for debugging
###print_loaded_objects_entries()
#_______________________________________________________________________________________________________
# Function that changes appropiate variables for next processed generations (also generates XY_labels)
def define_variable(var_type, var, seed, steps, cfg, sampler_name, scheduler, denoise, vae_name, ckpt_name,
clip_skip, positive_prompt, negative_prompt, lora_params, var_label, num_label):
# Define default max label size limit
max_label_len = 36
# If var_type is "Seeds++ Batch", update var and seed, and generate labels
if var_type == "Seeds++ Batch":
text = f"Seed: {seed}"
# If var_type is "Steps", update steps and generate labels
elif var_type == "Steps":
steps = var
text = f"steps: {steps}"
# If var_type is "CFG Scale", update cfg and generate labels
elif var_type == "CFG Scale":
cfg = var
text = f"CFG: {round(cfg,2)}"
# If var_type is "Sampler", update sampler_name, scheduler, and generate labels
elif var_type == "Sampler":
sampler_name = var[0]
if var[1] == "":
text = f"{sampler_name}"
else:
if var[1] != None:
scheduler = (var[1], scheduler[1])
else:
scheduler = (scheduler[1], scheduler[1])
text = f"{sampler_name} ({scheduler[0]})"
text = text.replace("ancestral", "a").replace("uniform", "u").replace("exponential","exp")
# If var_type is "Scheduler", update scheduler and generate labels
elif var_type == "Scheduler":
if len(var) == 2:
scheduler = (var[0], scheduler[1])
text = f"{sampler_name} ({scheduler[0]})"
else:
scheduler = (var, scheduler[1])
text = f"{scheduler[0]}"
text = text.replace("ancestral", "a").replace("uniform", "u").replace("exponential","exp")
# If var_type is "Denoise", update denoise and generate labels
elif var_type == "Denoise":
denoise = var
text = f"denoise: {round(denoise, 2)}"
# If var_type is "VAE", update vae_name and generate labels
elif var_type == "VAE":
vae_name = var
vae_filename = os.path.splitext(os.path.basename(vae_name))[0]
text = f"VAE: {vae_filename}"
# If var_type is "Positive Prompt S/R", update positive_prompt and generate labels
elif var_type == "Positive Prompt S/R":
search_txt, replace_txt = var
if replace_txt != None:
positive_prompt = (positive_prompt[1].replace(search_txt, replace_txt, 1), positive_prompt[1])
else:
positive_prompt = (positive_prompt[1], positive_prompt[1])
replace_txt = search_txt
text = f"{replace_txt}"
# If var_type is "Negative Prompt S/R", update negative_prompt and generate labels
elif var_type == "Negative Prompt S/R":
search_txt, replace_txt = var
if replace_txt:
negative_prompt = (negative_prompt[1].replace(search_txt, replace_txt, 1), negative_prompt[1])
else:
negative_prompt = (negative_prompt[1], negative_prompt[1])
replace_txt = search_txt
text = f"(-) {replace_txt}"
# If var_type is "Checkpoint", update model and clip (if needed) and generate labels
elif var_type == "Checkpoint":
ckpt_name = var[0]
if var[1] == None:
clip_skip = (clip_skip[1],clip_skip[1])
else:
clip_skip = (var[1],clip_skip[1])
ckpt_filename = os.path.splitext(os.path.basename(ckpt_name))[0]
text = f"{ckpt_filename}"
elif var_type == "Clip Skip":
clip_skip = (var, clip_skip[1])
text = f"Clip Skip ({clip_skip[0]})"
elif var_type == "LoRA":
lora_params = var
max_label_len = 30 + (12 * (len(lora_params)-1))
if len(lora_params) == 1:
lora_name, lora_model_wt, lora_clip_wt = lora_params[0]
lora_filename = os.path.splitext(os.path.basename(lora_name))[0]
lora_model_wt = format(float(lora_model_wt), ".2f").rstrip('0').rstrip('.')
lora_clip_wt = format(float(lora_clip_wt), ".2f").rstrip('0').rstrip('.')
lora_filename = lora_filename[:max_label_len - len(f"LoRA: ({lora_model_wt})")]
if lora_model_wt == lora_clip_wt:
text = f"LoRA: {lora_filename}({lora_model_wt})"
else:
text = f"LoRA: {lora_filename}({lora_model_wt},{lora_clip_wt})"
elif len(lora_params) > 1:
lora_filenames = [os.path.splitext(os.path.basename(lora_name))[0] for lora_name, _, _ in lora_params]
lora_details = [(format(float(lora_model_wt), ".2f").rstrip('0').rstrip('.'),
format(float(lora_clip_wt), ".2f").rstrip('0').rstrip('.')) for _, lora_model_wt, lora_clip_wt in lora_params]
non_name_length = sum(len(f"({lora_details[i][0]},{lora_details[i][1]})") + 2 for i in range(len(lora_params)))
available_space = max_label_len - non_name_length
max_name_length = available_space // len(lora_params)
lora_filenames = [filename[:max_name_length] for filename in lora_filenames]
text_elements = [f"{lora_filename}({lora_details[i][0]})" if lora_details[i][0] == lora_details[i][1] else f"{lora_filename}({lora_details[i][0]},{lora_details[i][1]})" for i, lora_filename in enumerate(lora_filenames)]
text = " ".join(text_elements)
def truncate_texts(texts, num_label, max_label_len):
truncate_length = max(min(max(len(text) for text in texts), max_label_len), 24)
return [text if len(text) <= truncate_length else text[:truncate_length] + "..." for text in
texts]
# Add the generated text to var_label if it's not full
if len(var_label) < num_label:
var_label.append(text)
# If var_type VAE , truncate entries in the var_label list when it's full
if len(var_label) == num_label and (var_type == "VAE" or var_type == "Checkpoint" or var_type == "LoRA"):
var_label = truncate_texts(var_label, num_label, max_label_len)
# Return the modified variables
return steps, cfg, sampler_name, scheduler, denoise, vae_name, ckpt_name, clip_skip, \
positive_prompt, negative_prompt, lora_params, var_label
# _______________________________________________________________________________________________________
# The function below is used to smartly load Checkpoint/LoRA/VAE models between generations.
def define_model(model, clip, positive, negative, positive_prompt, negative_prompt, clip_skip, vae,
vae_name, ckpt_name, lora_params, index, types, script_node_id, cache):
# Encode prompt and apply clip_skip. Return new conditioning.
def encode_prompt(positive_prompt, negative_prompt, clip, clip_skip):
clip = CLIPSetLastLayer().set_last_layer(clip, clip_skip)[0]
return [[clip.encode(positive_prompt), {}]], [[clip.encode(negative_prompt), {}]]
# Variable to track wether to encode prompt or not
encode = False
# Unpack types tuple
X_type, Y_type = types
# Note: Index is held at 0 when Y_type == "Nothing"
# Load VAE if required
if (X_type == "VAE" and index == 0) or Y_type == "VAE":
vae = load_vae(vae_name, script_node_id, cache=cache[0])
# Load Checkpoint if required. If Y_type is LoRA, required models will be loaded by load_lora func.
if (X_type == "Checkpoint" and index == 0 and Y_type != "LoRA"):
if lora_params is None:
model, clip, _ = load_checkpoint(ckpt_name, script_node_id, output_vae=False, cache=cache[1])
else: # Load Efficient Loader LoRA
model, clip = load_lora(lora_params, ckpt_name, script_node_id,
cache=None, ckpt_cache=cache[1])
encode = True
# Load LoRA if required
elif (X_type == "LoRA" and index == 0):
# Don't cache Checkpoints
model, clip = load_lora(lora_params, ckpt_name, script_node_id, cache=cache[2])
encode = True
elif Y_type == "LoRA": # X_type must be Checkpoint, so cache those as defined
model, clip = load_lora(lora_params, ckpt_name, script_node_id,
cache=None, ckpt_cache=cache[1])
encode = True
# Encode Prompt if required
prompt_types = ["Positive Prompt S/R", "Negative Prompt S/R", "Clip Skip"]
if (X_type in prompt_types and index == 0) or Y_type in prompt_types:
encode = True
# Encode prompt if needed
if encode == True:
positive, negative = encode_prompt(positive_prompt[0], negative_prompt[0], clip, clip_skip)
return model, positive, negative, vae
# ______________________________________________________________________________________________________
# The below function is used to generate the results based on all the processed variables
def process_values(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
denoise, vae, latent_list=[], image_tensor_list=[], image_pil_list=[]):
# Sample
samples = common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
latent_image, denoise=denoise)
# Decode images and store
latent = samples[0]["samples"]
# Add the latent tensor to the tensors list
latent_list.append(latent)
# Decode the latent tensor
if xy_vae_tiled == False:
image = vae.decode(latent).cpu()
else:
image = vae.decode_tiled(latent).cpu()
# Add the resulting image tensor to image_tensor_list
image_tensor_list.append(image)
# Convert the image from tensor to PIL Image and add it to the image_pil_list
image_pil_list.append(tensor2pil(image))
# Return the touched variables
return latent_list, image_tensor_list, image_pil_list
# ______________________________________________________________________________________________________
# The below section is the heart of the XY Plot image generation
# Initiate Plot label text variables X/Y_label
X_label = []
Y_label = []
# Seed_updated for "Seeds++ Batch" incremental seeds
seed_updated = seed
# Store the KSamplers original scheduler inside the same scheduler variable
scheduler = (scheduler, scheduler)
# Store the Eff Loaders original clip_skip inside the same clip_skip variable
clip_skip = (clip_skip, clip_skip)
# Store types in a Tuple for easy function passing
types = (X_type, Y_type)
# Fill Plot Rows (X)
for X_index, X in enumerate(X_value):
# Seed control based on loop index during Batch
if X_type == "Seeds++ Batch":
# Update seed based on the inner loop index
seed_updated = seed + X_index
# Define X parameters and generate labels
steps, cfg, sampler_name, scheduler, denoise, vae_name, ckpt_name, clip_skip, positive_prompt, negative_prompt, \
lora_params, X_label = \
define_variable(X_type, X, seed_updated, steps, cfg, sampler_name, scheduler, denoise, vae_name, ckpt_name,
clip_skip, positive_prompt, negative_prompt, lora_params, X_label, len(X_value))
if X_type != "Nothing" and Y_type == "Nothing":
# Models & Conditionings
model, positive, negative , vae = \
define_model(model, clip, positive, negative, positive_prompt, negative_prompt, clip_skip[0], vae,
vae_name, ckpt_name, lora_params, 0, types, script_node_id, cache)
# Generate Results
latent_list, image_tensor_list, image_pil_list = \
process_values(model, seed_updated, steps, cfg, sampler_name, scheduler[0],
positive, negative, latent_image, denoise, vae)
elif X_type != "Nothing" and Y_type != "Nothing":
# Seed control based on loop index during Batch
for Y_index, Y in enumerate(Y_value):
if Y_type == "Seeds++ Batch":
# Update seed based on the inner loop index
seed_updated = seed + Y_index
# Define Y parameters and generate labels
steps, cfg, sampler_name, scheduler, denoise, vae_name, ckpt_name, clip_skip, positive_prompt, negative_prompt, lora_params, Y_label = \
define_variable(Y_type, Y, seed_updated, steps, cfg, sampler_name, scheduler, denoise, vae_name, ckpt_name,
clip_skip, positive_prompt, negative_prompt, lora_params, Y_label, len(Y_value))
# Models & Conditionings
model, positive, negative, vae = \
define_model(model, clip, positive, negative, positive_prompt, negative_prompt, clip_skip[0], vae,
vae_name, ckpt_name, lora_params, Y_index, types, script_node_id, cache)
# Generate Results
latent_list, image_tensor_list, image_pil_list = \
process_values(model, seed_updated, steps, cfg, sampler_name, scheduler[0],
positive, negative, latent_image, denoise, vae)
# Clean up cache
if cache_models == "False":
clear_cache_by_exception(script_node_id, vae_dict=[], ckpt_dict=[], lora_dict=[])
#
else:
# Prioritrize Caching Checkpoints over LoRAs.
if X_type == "LoRA":
clear_cache_by_exception(script_node_id, ckpt_dict=[])
elif X_type == "Checkpoint":
clear_cache_by_exception(script_node_id, lora_dict=[])
# ______________________________________________________________________________________________________
def print_plot_variables(X_type, Y_type, X_value, Y_value, seed, ckpt_name, lora_params,
vae_name, clip_skip, steps, cfg, sampler_name, scheduler, denoise,
num_rows, num_cols, latent_height, latent_width):
print("-" * 40) # Print an empty line followed by a separator line
print("\033[32mXY Plot Results:\033[0m")
def get_vae_name(X_type, Y_type, X_value, Y_value, vae_name):
if X_type == "VAE":
vae_name = ", ".join(map(lambda x: os.path.splitext(os.path.basename(str(x)))[0], X_value))
elif Y_type == "VAE":
vae_name = ", ".join(map(lambda y: os.path.splitext(os.path.basename(str(y)))[0], Y_value))
else:
vae_name = os.path.splitext(os.path.basename(str(vae_name)))[0]
return vae_name
def get_clip_skip(X_type, Y_type, X_value, Y_value, clip_skip):
if X_type == "Clip Skip":
clip_skip = ", ".join(map(str, X_value))
elif Y_type == "Clip Skip":
clip_skip = ", ".join(map(str, Y_value))
else:
clip_skip = clip_skip[1]
return clip_skip
def get_checkpoint_name(ckpt_type, ckpt_values, clip_skip_type, clip_skip_values, ckpt_name, clip_skip):
if ckpt_type == "Checkpoint":
if clip_skip_type == "Clip Skip":
ckpt_name = ", ".join([os.path.splitext(os.path.basename(str(ckpt[0])))[0] for ckpt in ckpt_values])
else:
ckpt_name = ", ".join([f"{os.path.splitext(os.path.basename(str(ckpt[0])))[0]}({str(ckpt[1]) if ckpt[1] is not None else str(clip_skip_values)})"
for ckpt in ckpt_values])
clip_skip = "_"
else:
ckpt_name = os.path.splitext(os.path.basename(str(ckpt_name)))[0]
return ckpt_name, clip_skip
def get_lora_name(X_type, Y_type, X_value, Y_value, lora_params=None):
if X_type != "LoRA" and Y_type != "LoRA":
if lora_params:
return f"[{', '.join([f'{os.path.splitext(os.path.basename(name))[0]}({round(model_wt, 3)},{round(clip_wt, 3)})' for name, model_wt, clip_wt in lora_params])}]"
else:
return None
else:
return get_lora_sublist_name(X_type,
X_value) if X_type == "LoRA" else get_lora_sublist_name(Y_type, Y_value) if Y_type == "LoRA" else None
def get_lora_sublist_name(lora_type, lora_value):
return ", ".join([
f"[{', '.join([f'{os.path.splitext(os.path.basename(str(x[0])))[0]}({round(x[1], 3)},{round(x[2], 3)})' for x in sublist])}]"
for sublist in lora_value])
# use these functions:
ckpt_type, clip_skip_type = (X_type, Y_type) if X_type in ["Checkpoint", "Clip Skip"] else (Y_type, X_type)
ckpt_values, clip_skip_values = (X_value, Y_value) if X_type in ["Checkpoint", "Clip Skip"] else (Y_value, X_value)
clip_skip = get_clip_skip(X_type, Y_type, X_value, Y_value, clip_skip)
ckpt_name, clip_skip = get_checkpoint_name(ckpt_type, ckpt_values, clip_skip_type, clip_skip_values, ckpt_name, clip_skip)
vae_name = get_vae_name(X_type, Y_type, X_value, Y_value, vae_name)
lora_name = get_lora_name(X_type, Y_type, X_value, Y_value, lora_params)
seed_list = [seed + x for x in X_value] if X_type == "Seeds++ Batch" else\
[seed + y for y in Y_value] if Y_type == "Seeds++ Batch" else [seed]
seed = ", ".join(map(str, seed_list))
steps = ", ".join(map(str, X_value)) if X_type == "Steps" else ", ".join(
map(str, Y_value)) if Y_type == "Steps" else steps
cfg = ", ".join(map(str, X_value)) if X_type == "CFG Scale" else ", ".join(
map(str, Y_value)) if Y_type == "CFG Scale" else cfg
if X_type == "Sampler":
if Y_type == "Scheduler":
sampler_name = ", ".join([f"{x[0]}" for x in X_value])
scheduler = ", ".join([f"{y}" for y in Y_value])
else:
sampler_name = ", ".join(
[f"{x[0]}({x[1] if x[1] != '' and x[1] is not None else scheduler[1]})" for x in X_value])
scheduler = "_"
elif Y_type == "Sampler":
if X_type == "Scheduler":
sampler_name = ", ".join([f"{y[0]}" for y in Y_value])
scheduler = ", ".join([f"{x}" for x in X_value])
else:
sampler_name = ", ".join(
[f"{y[0]}({y[1] if y[1] != '' and y[1] is not None else scheduler[1]})" for y in Y_value])
scheduler = "_"
else:
scheduler = ", ".join([str(x[0]) if isinstance(x, tuple) else str(x) for x in X_value]) if X_type == "Scheduler" else \
", ".join([str(y[0]) if isinstance(y, tuple) else str(y) for y in Y_value]) if Y_type == "Scheduler" else scheduler[0]
denoise = ", ".join(map(str, X_value)) if X_type == "Denoise" else ", ".join(
map(str, Y_value)) if Y_type == "Denoise" else denoise
# Printouts
print(f"img_count: {len(X_value)*len(Y_value)}")
print(f"img_dims: {latent_height} x {latent_width}")
print(f"plot_dim: {num_cols} x {num_rows}")
if clip_skip == "_":
print(f"ckpt(clipskip): {ckpt_name if ckpt_name is not None else ''}")
else:
print(f"ckpt: {ckpt_name if ckpt_name is not None else ''}")
print(f"clip_skip: {clip_skip if clip_skip is not None else ''}")
if lora_name:
print(f"lora(mod,clip): {lora_name if lora_name is not None else ''}")
print(f"vae: {vae_name if vae_name is not None else ''}")
print(f"seed: {seed}")
print(f"steps: {steps}")
print(f"cfg: {cfg}")
if scheduler == "_":
print(f"sampler(schr): {sampler_name}")
else:
print(f"sampler: {sampler_name}")
print(f"scheduler: {scheduler}")
print(f"denoise: {denoise}")
if X_type == "Positive Prompt S/R" or Y_type == "Positive Prompt S/R":
positive_prompt = ", ".join([str(x[0]) if i == 0 else str(x[1]) for i, x in enumerate(
X_value)]) if X_type == "Positive Prompt S/R" else ", ".join(
[str(y[0]) if i == 0 else str(y[1]) for i, y in
enumerate(Y_value)]) if Y_type == "Positive Prompt S/R" else positive_prompt
print(f"+prompt_s/r: {positive_prompt}")
if X_type == "Negative Prompt S/R" or Y_type == "Negative Prompt S/R":
negative_prompt = ", ".join([str(x[0]) if i == 0 else str(x[1]) for i, x in enumerate(
X_value)]) if X_type == "Negative Prompt S/R" else ", ".join(
[str(y[0]) if i == 0 else str(y[1]) for i, y in
enumerate(Y_value)]) if Y_type == "Negative Prompt S/R" else negative_prompt
print(f"-prompt_s/r: {negative_prompt}")
# ______________________________________________________________________________________________________
def adjusted_font_size(text, initial_font_size, latent_width):
font = ImageFont.truetype(str(Path(font_path)), initial_font_size)
text_width = font.getlength(text)
if text_width > (latent_width * 0.9):
scaling_factor = 0.9 # A value less than 1 to shrink the font size more aggressively
new_font_size = int(initial_font_size * (latent_width / text_width) * scaling_factor)
else:
new_font_size = initial_font_size
return new_font_size
# ______________________________________________________________________________________________________
# Disable vae decode on next Hold
update_value_by_id("vae_decode", my_unique_id, False)
def rearrange_list_A(arr, num_cols, num_rows):
new_list = []
for i in range(num_rows):
for j in range(num_cols):
index = j * num_rows + i
new_list.append(arr[index])
return new_list
def rearrange_list_B(arr, num_rows, num_cols):
new_list = []
for i in range(num_rows):
for j in range(num_cols):
index = i * num_cols + j
new_list.append(arr[index])
return new_list
# Extract plot dimensions
num_rows = max(len(Y_value) if Y_value is not None else 0, 1)
num_cols = max(len(X_value) if X_value is not None else 0, 1)
# Flip X & Y results back if flipped earlier (for Checkpoint/LoRA For loop optimizations)
if flip_xy == True:
X_type, Y_type = Y_type, X_type
X_value, Y_value = Y_value, X_value
X_label, Y_label = Y_label, X_label
num_rows, num_cols = num_cols, num_rows
image_pil_list = rearrange_list_A(image_pil_list, num_rows, num_cols)
else:
image_pil_list = rearrange_list_B(image_pil_list, num_rows, num_cols)
image_tensor_list = rearrange_list_A(image_tensor_list, num_cols, num_rows)
latent_list = rearrange_list_A(latent_list, num_cols, num_rows)
# Print XY Plot Results
print_plot_variables(X_type, Y_type, X_value, Y_value, seed, ckpt_name, lora_params, vae_name,
clip_skip, steps, cfg, sampler_name, scheduler, denoise,
num_rows, num_cols, latent_height, latent_width)
# Concatenate the tensors along the first dimension (dim=0)
latent_list = torch.cat(latent_list, dim=0)
# Store latent_list as last latent
update_value_by_id("latent", my_unique_id, latent_list)
# Calculate the dimensions of the white background image
border_size_top = latent_width // 15
# Longest Y-label length
if len(Y_label) > 0:
Y_label_longest = max(len(s) for s in Y_label)
else:
# Handle the case when the sequence is empty
Y_label_longest = 0 # or any other appropriate value
Y_label_scale = min(Y_label_longest + 4,24) / 24
if Y_label_orientation == "Vertical":
border_size_left = border_size_top
else: # Assuming Y_label_orientation is "Horizontal"
# border_size_left is now min(latent_width, latent_height) plus 20% of the difference between the two
border_size_left = min(latent_width, latent_height) + int(0.2 * abs(latent_width - latent_height))
border_size_left = int(border_size_left * Y_label_scale)
# Modify the border size, background width and x_offset initialization based on Y_type and Y_label_orientation
if Y_type == "Nothing":
bg_width = num_cols * latent_width + (num_cols - 1) * grid_spacing
x_offset_initial = 0
else:
if Y_label_orientation == "Vertical":
bg_width = num_cols * latent_width + (num_cols - 1) * grid_spacing + 3 * border_size_left
x_offset_initial = border_size_left * 3
else: # Assuming Y_label_orientation is "Horizontal"
bg_width = num_cols * latent_width + (num_cols - 1) * grid_spacing + border_size_left
x_offset_initial = border_size_left
# Modify the background height based on X_type
if X_type == "Nothing":
bg_height = num_rows * latent_height + (num_rows - 1) * grid_spacing
y_offset = 0
else:
bg_height = num_rows * latent_height + (num_rows - 1) * grid_spacing + 3 * border_size_top
y_offset = border_size_top * 3
# Create the white background image
background = Image.new('RGBA', (int(bg_width), int(bg_height)), color=(255, 255, 255, 255))
for row in range(num_rows):
# Initialize the X_offset
x_offset = x_offset_initial
for col in range(num_cols):
# Calculate the index for image_pil_list
index = col * num_rows + row
img = image_pil_list[index]
# Paste the image
background.paste(img, (x_offset, y_offset))
if row == 0 and X_type != "Nothing":
# Assign text
text = X_label[col]
# Add the corresponding X_value as a label above the image
initial_font_size = int(48 * img.width / 512)
font_size = adjusted_font_size(text, initial_font_size, img.width)
label_height = int(font_size*1.5)
# Create a white background label image
label_bg = Image.new('RGBA', (img.width, label_height), color=(255, 255, 255, 0))
d = ImageDraw.Draw(label_bg)
# Create the font object
font = ImageFont.truetype(str(Path(font_path)), font_size)
# Calculate the text size and the starting position
_, _, text_width, text_height = d.textbbox([0,0], text, font=font)
text_x = (img.width - text_width) // 2
text_y = (label_height - text_height) // 2
# Add the text to the label image
d.text((text_x, text_y), text, fill='black', font=font)
# Calculate the available space between the top of the background and the top of the image
available_space = y_offset - label_height
# Calculate the new Y position for the label image
label_y = available_space // 2
# Paste the label image above the image on the background using alpha_composite()
background.alpha_composite(label_bg, (x_offset, label_y))
if col == 0 and Y_type != "Nothing":
# Assign text
text = Y_label[row]
# Add the corresponding Y_value as a label to the left of the image
if Y_label_orientation == "Vertical":
initial_font_size = int(48 * latent_width / 512) # Adjusting this to be same as X_label size
font_size = adjusted_font_size(text, initial_font_size, latent_width)
else: # Assuming Y_label_orientation is "Horizontal"
initial_font_size = int(48 * (border_size_left/Y_label_scale) / 512) # Adjusting this to be same as X_label size
font_size = adjusted_font_size(text, initial_font_size, int(border_size_left/Y_label_scale))
# Create a white background label image
label_bg = Image.new('RGBA', (img.height, int(font_size*1.2)), color=(255, 255, 255, 0))
d = ImageDraw.Draw(label_bg)
# Create the font object
font = ImageFont.truetype(str(Path(font_path)), font_size)
# Calculate the text size and the starting position
_, _, text_width, text_height = d.textbbox([0,0], text, font=font)
text_x = (img.height - text_width) // 2
text_y = (font_size - text_height) // 2
# Add the text to the label image
d.text((text_x, text_y), text, fill='black', font=font)
# Rotate the label_bg 90 degrees counter-clockwise only if Y_label_orientation is "Vertical"
if Y_label_orientation == "Vertical":
label_bg = label_bg.rotate(90, expand=True)
# Calculate the available space between the left of the background and the left of the image
available_space = x_offset - label_bg.width
# Calculate the new X position for the label image
label_x = available_space // 2
# Calculate the Y position for the label image based on its orientation
if Y_label_orientation == "Vertical":
label_y = y_offset + (img.height - label_bg.height) // 2
else: # Assuming Y_label_orientation is "Horizontal"
label_y = y_offset + img.height - (img.height - label_bg.height) // 2
# Paste the label image to the left of the image on the background using alpha_composite()
background.alpha_composite(label_bg, (label_x, label_y))
# Update the x_offset
x_offset += img.width + grid_spacing
# Update the y_offset
y_offset += img.height + grid_spacing
images = pil2tensor(background)
# Generate image results and store
results = preview_images(images, filename_prefix)
update_value_by_id("results", my_unique_id, results)
# Squeeze and Stack the tensors, and store results
if xyplot_as_output_image == False:
image_tensor_list = torch.stack([tensor.squeeze() for tensor in image_tensor_list])
else:
image_tensor_list = images
update_value_by_id("images", my_unique_id, image_tensor_list)
# Print cache if set to true
if cache_models == "True":
print_loaded_objects_entries(script_node_id, prompt)
print("-" * 40) # Print an empty line followed by a separator line
images = list() if preview_image == "Output Only" else results
return {
"ui": {"images": images},
"result": (model, positive, negative, {"samples": latent_list}, vae, image_tensor_list,)
}
########################################################################################################################
# TSC XY Plot
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": (["Plot", "Images"],),},
"optional": {
"dependencies": ("DEPENDENCIES", ),
"X": ("XY", ),
"Y": ("XY", ),},
}
RETURN_TYPES = ("SCRIPT",)
RETURN_NAMES = ("SCRIPT",)
FUNCTION = "XYplot"
CATEGORY = "Efficiency Nodes/XY Plot"
def XYplot(self, grid_spacing, XY_flip, Y_label_orientation, cache_models, ksampler_output_image, 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 == Y_type):
if X_type != "Nothing":
print(f"\033[31mXY Plot Error:\033[0m X and Y input types must be different.")
return (None,)
# Check that dependencies is connected for Checkpoint and LoRA plots
types = ("Checkpoint", "LoRA", "Positive Prompt S/R", "Negative Prompt S/R")
if X_type in types or Y_type in types:
if dependencies == None: # Not connected
print(f"\033[31mXY Plot Error:\033[0m The dependencies input must be connected for certain plot types.")
# Return None
return (None,)
# Define X/Y_values for "Seeds++ Batch"
if X_type == "Seeds++ Batch":
X_value = [i for i in range(X_value[0])]
if Y_type == "Seeds++ Batch":
Y_value = [i for i in range(Y_value[0])]
# 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]
# Optimize image generation by prioritizing Checkpoint>LoRA>VAE>PromptSR as X in For Loop. Flip back when done.
if Y_type == "Checkpoint" or \
Y_type == "LoRA" and X_type not in {"Checkpoint"} or \
Y_type == "VAE" and X_type not in {"Checkpoint", "LoRA"} or \
Y_type == "Positive Prompt S/R" and X_type not in {"Checkpoint", "LoRA", "VAE",
"Negative Prompt S/R"} or \
Y_type == "Negative Prompt S/R" and X_type not in {"Checkpoint", "LoRA", "VAE",
"Positive Prompt S/R"} or \
X_type == "Nothing" and Y_type != "Nothing":
flip_xy = True
X_type, Y_type = Y_type, X_type
X_value, Y_value = Y_value, X_value
else:
flip_xy = False
# 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"
return ((X_type, X_value, Y_type, Y_value, grid_spacing, Y_label_orientation, cache_models,
xyplot_as_output_image, flip_xy, dependencies),)
# TSC XY Plot: Seeds Values
class TSC_XYplot_SeedsBatch:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"batch_count": ("INT", {"default": 1, "min": 0, "max": 50}),},
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Plot/XY Inputs"
def xy_value(self, batch_count):
if batch_count == 0:
return (None,)
xy_type = "Seeds++ Batch"
xy_value = [batch_count]
return ((xy_type, xy_value),)
# TSC XY Plot: Step Values
class TSC_XYplot_Steps:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"select_count": ("INT", {"default": 0, "min": 0, "max": 5}),
"steps_1": ("INT", {"default": 20, "min": 1, "max": 10000}),
"steps_2": ("INT", {"default": 20, "min": 1, "max": 10000}),
"steps_3": ("INT", {"default": 20, "min": 1, "max": 10000}),
"steps_4": ("INT", {"default": 20, "min": 1, "max": 10000}),
"steps_5": ("INT", {"default": 20, "min": 1, "max": 10000}),},
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Plot/XY Inputs"
def xy_value(self, select_count, steps_1, steps_2, steps_3, steps_4, steps_5):
xy_type = "Steps"
xy_value = [step for idx, step in enumerate([steps_1, steps_2, steps_3, steps_4, steps_5], start=1) if
idx <= select_count]
if not xy_value: # Check if the list is empty
return (None,)
return ((xy_type, xy_value),)
# TSC XY Plot: CFG Values
class TSC_XYplot_CFG:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"select_count": ("INT", {"default": 0, "min": 0, "max": 5}),
"cfg_1": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0}),
"cfg_2": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0}),
"cfg_3": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0}),
"cfg_4": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0}),
"cfg_5": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0}),},
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Plot/XY Inputs"
def xy_value(self, select_count, cfg_1, cfg_2, cfg_3, cfg_4, cfg_5):
xy_type = "CFG Scale"
xy_value = [cfg for idx, cfg in enumerate([cfg_1, cfg_2, cfg_3, cfg_4, cfg_5], start=1) if idx <= select_count]
if not xy_value: # Check if the list is empty
return (None,)
return ((xy_type, xy_value),)
# TSC XY Plot: Sampler Values
class TSC_XYplot_Sampler:
samplers = ["None"] + comfy.samplers.KSampler.SAMPLERS
schedulers = ["None"] + comfy.samplers.KSampler.SCHEDULERS
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"sampler_1": (cls.samplers,),
"scheduler_1": (cls.schedulers,),
"sampler_2": (cls.samplers,),
"scheduler_2": (cls.schedulers,),
"sampler_3": (cls.samplers,),
"scheduler_3": (cls.schedulers,),
"sampler_4": (cls.samplers,),
"scheduler_4": (cls.schedulers,),
"sampler_5": (cls.samplers,),
"scheduler_5": (cls.schedulers,),},
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Plot/XY Inputs"
def xy_value(self, sampler_1, scheduler_1, sampler_2, scheduler_2, sampler_3, scheduler_3,
sampler_4, scheduler_4, sampler_5, scheduler_5):
samplers = [sampler_1, sampler_2, sampler_3, sampler_4, sampler_5]
schedulers = [scheduler_1, scheduler_2, scheduler_3, scheduler_4, scheduler_5]
pairs = []
for sampler, scheduler in zip(samplers, schedulers):
if sampler != "None":
if scheduler != "None":
pairs.append((sampler, scheduler))
else:
pairs.append((sampler,None))
xy_type = "Sampler"
xy_value = pairs
if not xy_value: # Check if the list is empty
return (None,)
return ((xy_type, xy_value),)
# TSC XY Plot: Scheduler Values
class TSC_XYplot_Scheduler:
schedulers = ["None"] + comfy.samplers.KSampler.SCHEDULERS
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"scheduler_1": (cls.schedulers,),
"scheduler_2": (cls.schedulers,),
"scheduler_3": (cls.schedulers,),
"scheduler_4": (cls.schedulers,),
"scheduler_5": (cls.schedulers,),},
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Plot/XY Inputs"
def xy_value(self, scheduler_1, scheduler_2, scheduler_3, scheduler_4, scheduler_5):
xy_type = "Scheduler"
xy_value = [scheduler for scheduler in [scheduler_1, scheduler_2, scheduler_3, scheduler_4, scheduler_5] if
scheduler != "None"]
if not xy_value: # Check if the list is empty
return (None,)
return ((xy_type, xy_value),)
# TSC XY Plot: Denoise Values
class TSC_XYplot_Denoise:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"select_count": ("INT", {"default": 0, "min": 0, "max": 5}),
"denoise_1": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
"denoise_2": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
"denoise_3": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
"denoise_4": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
"denoise_5": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),},
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Plot/XY Inputs"
def xy_value(self, select_count, denoise_1, denoise_2, denoise_3, denoise_4, denoise_5):
xy_type = "Denoise"
xy_value = [denoise for idx, denoise in
enumerate([denoise_1, denoise_2, denoise_3, denoise_4, denoise_5], start=1) if idx <= select_count]
if not xy_value: # Check if the list is empty
return (None,)
return ((xy_type, xy_value),)
# TSC XY Plot: VAE Values
class TSC_XYplot_VAE:
vaes = ["None"] + folder_paths.get_filename_list("vae")
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"vae_name_1": (cls.vaes,),
"vae_name_2": (cls.vaes,),
"vae_name_3": (cls.vaes,),
"vae_name_4": (cls.vaes,),
"vae_name_5": (cls.vaes,),},
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Plot/XY Inputs"
def xy_value(self, vae_name_1, vae_name_2, vae_name_3, vae_name_4, vae_name_5):
xy_type = "VAE"
xy_value = [vae for vae in [vae_name_1, vae_name_2, vae_name_3, vae_name_4, vae_name_5] if vae != "None"]
if not xy_value: # Check if the list is empty
return (None,)
return ((xy_type, xy_value),)
# TSC XY Plot: Prompt S/R Positive
class TSC_XYplot_PromptSR_Positive:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"search_txt": ("STRING", {"default": "", "multiline": False}),
"replace_count": ("INT", {"default": 0, "min": 0, "max": 4}),
"replace_1":("STRING", {"default": "", "multiline": False}),
"replace_2": ("STRING", {"default": "", "multiline": False}),
"replace_3": ("STRING", {"default": "", "multiline": False}),
"replace_4": ("STRING", {"default": "", "multiline": False}),},
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Plot/XY Inputs"
def xy_value(self, search_txt, replace_count, replace_1, replace_2, replace_3, replace_4):
# If search_txt is empty, return (None,)
if search_txt == "":
return (None,)
xy_type = "Positive Prompt S/R"
# Create a list of replacement arguments
replacements = [replace_1, replace_2, replace_3, replace_4]
# Create base entry
xy_values = [(search_txt, None)]
if replace_count > 0:
# Append additional entries based on replace_count
xy_values.extend([(search_txt, replacements[i]) for i in range(replace_count)])
return ((xy_type, xy_values),)
# TSC XY Plot: Prompt S/R Negative
class TSC_XYplot_PromptSR_Negative:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"search_txt": ("STRING", {"default": "", "multiline": False}),
"replace_count": ("INT", {"default": 0, "min": 0, "max": 4}),
"replace_1":("STRING", {"default": "", "multiline": False}),
"replace_2": ("STRING", {"default": "", "multiline": False}),
"replace_3": ("STRING", {"default": "", "multiline": False}),
"replace_4": ("STRING", {"default": "", "multiline": False}),},
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Plot/XY Inputs"
def xy_value(self, search_txt, replace_count, replace_1, replace_2, replace_3, replace_4):
# If search_txt is empty, return (None,)
if search_txt == "":
return (None,)
xy_type = "Negative Prompt S/R"
# Create a list of replacement arguments
replacements = [replace_1, replace_2, replace_3, replace_4]
# Create base entry
xy_values = [(search_txt, None)]
if replace_count > 0:
# Append additional entries based on replace_count
xy_values.extend([(search_txt, replacements[i]) for i in range(replace_count)])
return ((xy_type, xy_values),)
# TSC XY Plot: Checkpoint Values
class TSC_XYplot_Checkpoint:
checkpoints = ["None"] + folder_paths.get_filename_list("checkpoints")
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"ckpt_name_1": (cls.checkpoints,),
"clip_skip1": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
"ckpt_name_2": (cls.checkpoints,),
"clip_skip2": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
"ckpt_name_3": (cls.checkpoints,),
"clip_skip3": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
"ckpt_name_4": (cls.checkpoints,),
"clip_skip4": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
"ckpt_name_5": (cls.checkpoints,),
"clip_skip5": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),},
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Plot/XY Inputs"
def xy_value(self, ckpt_name_1, clip_skip1, ckpt_name_2, clip_skip2, ckpt_name_3, clip_skip3,
ckpt_name_4, clip_skip4, ckpt_name_5, clip_skip5):
xy_type = "Checkpoint"
checkpoints = [ckpt_name_1, ckpt_name_2, ckpt_name_3, ckpt_name_4, ckpt_name_5]
clip_skips = [clip_skip1, clip_skip2, clip_skip3, clip_skip4, clip_skip5]
xy_value = [(checkpoint, clip_skip) for checkpoint, clip_skip in zip(checkpoints, clip_skips) if
checkpoint != "None"]
if not xy_value: # Check if the list is empty
return (None,)
return ((xy_type, xy_value),)
# TSC XY Plot: Clip Skip
class TSC_XYplot_ClipSkip:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"select_count": ("INT", {"default": 0, "min": 0, "max": 5}),
"clip_skip_1": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
"clip_skip_2": ("INT", {"default": -2, "min": -24, "max": -1, "step": 1}),
"clip_skip_3": ("INT", {"default": -3, "min": -24, "max": -1, "step": 1}),
"clip_skip_4": ("INT", {"default": -4, "min": -24, "max": -1, "step": 1}),
"clip_skip_5": ("INT", {"default": -5, "min": -24, "max": -1, "step": 1}),},
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Plot/XY Inputs"
def xy_value(self, select_count, clip_skip_1, clip_skip_2, clip_skip_3, clip_skip_4, clip_skip_5):
xy_type = "Clip Skip"
xy_value = [clip_skip for idx, clip_skip in
enumerate([clip_skip_1, clip_skip_2, clip_skip_3, clip_skip_4, clip_skip_5], start=1) if idx <= select_count]
if not xy_value: # Check if the list is empty
return (None,)
return ((xy_type, xy_value),)
# TSC XY Plot: LoRA Values
class TSC_XYplot_LoRA:
loras = ["None"] + folder_paths.get_filename_list("loras")
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"model_strengths": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"clip_strengths": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora_name_1": (cls.loras,),
"lora_name_2": (cls.loras,),
"lora_name_3": (cls.loras,),
"lora_name_4": (cls.loras,),
"lora_name_5": (cls.loras,)},
"optional": {"lora_stack": ("LORA_STACK", )}
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Plot/XY Inputs"
def xy_value(self, model_strengths, clip_strengths, lora_name_1, lora_name_2, lora_name_3, lora_name_4, lora_name_5,
lora_stack=None):
xy_type = "LoRA"
loras = [lora_name_1, lora_name_2, lora_name_3, lora_name_4, lora_name_5]
# Extend each sub-array with lora_stack if it's not None
xy_value = [[(lora, model_strengths, clip_strengths)] + (lora_stack if lora_stack else []) for lora in loras if
lora != "None"]
if not xy_value: # Check if the list is empty
return (None,)
return ((xy_type, xy_value),)
# TSC XY Plot: LoRA Advanced
class TSC_XYplot_LoRA_Adv:
loras = ["None"] + folder_paths.get_filename_list("loras")
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"lora_name_1": (cls.loras,),
"model_str_1": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"clip_str_1": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora_name_2": (cls.loras,),
"model_str_2": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"clip_str_2": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora_name_3": (cls.loras,),
"model_str_3": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"clip_str_3": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora_name_4": (cls.loras,),
"model_str_4": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"clip_str_4": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora_name_5": (cls.loras,),
"model_str_5": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"clip_str_5": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),},
"optional": {"lora_stack": ("LORA_STACK",)}
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Plot/XY Inputs"
def xy_value(self, lora_name_1, model_str_1, clip_str_1, lora_name_2, model_str_2, clip_str_2, lora_name_3,
model_str_3,
clip_str_3, lora_name_4, model_str_4, clip_str_4, lora_name_5, model_str_5, clip_str_5,
lora_stack=None):
xy_type = "LoRA"
loras = [lora_name_1, lora_name_2, lora_name_3, lora_name_4, lora_name_5]
model_strs = [model_str_1, model_str_2, model_str_3, model_str_4, model_str_5]
clip_strs = [clip_str_1, clip_str_2, clip_str_3, clip_str_4, clip_str_5]
# Extend each sub-array with lora_stack if it's not None
xy_value = [[(lora, model_str, clip_str)] + (lora_stack if lora_stack else []) for lora, model_str, clip_str in
zip(loras, model_strs, clip_strs) if lora != "None"]
if not xy_value: # Check if the list is empty
return (None,)
return ((xy_type, xy_value),)
# TSC XY Plot: LoRA Stacks
class TSC_XYplot_LoRA_Stacks:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"node_state": (["Enabled", "Disabled"],)},
"optional": {
"lora_stack_1": ("LORA_STACK",),
"lora_stack_2": ("LORA_STACK",),
"lora_stack_3": ("LORA_STACK",),
"lora_stack_4": ("LORA_STACK",),
"lora_stack_5": ("LORA_STACK",),},
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Plot/XY Inputs"
def xy_value(self, node_state, lora_stack_1=None, lora_stack_2=None, lora_stack_3=None, lora_stack_4=None, lora_stack_5=None):
xy_type = "LoRA"
xy_value = [stack for stack in [lora_stack_1, lora_stack_2, lora_stack_3, lora_stack_4, lora_stack_5] if stack is not None]
if not xy_value or not any(xy_value) or node_state == "Disabled":
return (None,)
else:
return ((xy_type, xy_value),)
# TSC XY Plot: Manual Entry Notes
class TSC_XYplot_Manual_XY_Entry_Info:
syntax = "(X/Y_types) (X/Y_values)\n" \
"Seeds++ Batch batch_count\n" \
"Steps steps_1;steps_2;...\n" \
"CFG Scale cfg_1;cfg_2;...\n" \
"Sampler(1) sampler_1;sampler_2;...\n" \
"Sampler(2) sampler_1,scheduler_1;...\n" \
"Sampler(3) sampler_1;...;,default_scheduler\n" \
"Scheduler scheduler_1;scheduler_2;...\n" \
"Denoise denoise_1;denoise_2;...\n" \
"VAE vae_1;vae_2;vae_3;...\n" \
"+Prompt S/R search_txt;replace_1;replace_2;...\n" \
"-Prompt S/R search_txt;replace_1;replace_2;...\n" \
"Checkpoint(1) ckpt_1;ckpt_2;ckpt_3;...\n" \
"Checkpoint(2) ckpt_1,clip_skip_1;...\n" \
"Checkpoint(3) ckpt_1;ckpt_2;...;,default_clip_skip\n" \
"Clip Skip clip_skip_1;clip_skip_2;...\n" \
"LoRA(1) lora_1;lora_2;lora_3;...\n" \
"LoRA(2) lora_1;...;,default_model_str,default_clip_str\n" \
"LoRA(3) lora_1,model_str_1,clip_str_1;..."
samplers = ";\n".join(comfy.samplers.KSampler.SAMPLERS)
schedulers = ";\n".join(comfy.samplers.KSampler.SCHEDULERS)
vaes = ";\n".join(folder_paths.get_filename_list("vae"))
ckpts = ";\n".join(folder_paths.get_filename_list("checkpoints"))
loras = ";\n".join(folder_paths.get_filename_list("loras"))
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"notes": ("STRING", {"default":
f"_____________SYNTAX_____________\n{cls.syntax}\n\n"
f"____________SAMPLERS____________\n{cls.samplers}\n\n"
f"___________SCHEDULERS___________\n{cls.schedulers}\n\n"
f"_____________VAES_______________\n{cls.vaes}\n\n"
f"___________CHECKPOINTS__________\n{cls.ckpts}\n\n"
f"_____________LORAS______________\n{cls.loras}\n","multiline": True}),},}
RETURN_TYPES = ()
CATEGORY = "Efficiency Nodes/XY Plot/XY Inputs"
# TSC XY Plot: Manual Entry
class TSC_XYplot_Manual_XY_Entry:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"X_type": (["Nothing", "Seeds++ Batch", "Steps", "CFG Scale", "Sampler", "Scheduler", "Denoise", "VAE",
"Positive Prompt S/R", "Negative Prompt S/R", "Checkpoint", "Clip Skip", "LoRA"],),
"X_value": ("STRING", {"default": "", "multiline": True}),
"Y_type": (["Nothing", "Seeds++ Batch", "Steps", "CFG Scale", "Sampler", "Scheduler", "Denoise", "VAE",
"Positive Prompt S/R", "Negative Prompt S/R", "Checkpoint", "Clip Skip", "LoRA"],),
"Y_value": ("STRING", {"default": "", "multiline": True}),},}
RETURN_TYPES = ("XY", "XY",)
RETURN_NAMES = ("X", "Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Plot/XY Inputs"
def xy_value(self, X_type, X_value, Y_type, Y_value, prompt=None, my_unique_id=None):
# Store X values as arrays
if X_type not in {"Positive Prompt S/R", "Negative Prompt S/R", "VAE", "Checkpoint", "LoRA"}:
X_value = X_value.replace(" ", "") # Remove spaces
X_value = X_value.replace("\n", "") # Remove newline characters
X_value = X_value.rstrip(";") # Remove trailing semicolon
X_value = X_value.split(";") # Turn to array
# Store Y values as arrays
if Y_type not in {"Positive Prompt S/R", "Negative Prompt S/R", "VAE", "Checkpoint", "LoRA"}:
Y_value = Y_value.replace(" ", "") # Remove spaces
Y_value = Y_value.replace("\n", "") # Remove newline characters
Y_value = Y_value.rstrip(";") # Remove trailing semicolon
Y_value = Y_value.split(";") # Turn to array
# Define the valid bounds for each type
bounds = {
"Seeds++ Batch": {"min": 1, "max": 50},
"Steps": {"min": 1, "max": 10000},
"CFG Scale": {"min": 0, "max": 100},
"Sampler": {"options": comfy.samplers.KSampler.SAMPLERS},
"Scheduler": {"options": comfy.samplers.KSampler.SCHEDULERS},
"Denoise": {"min": 0, "max": 1},
"VAE": {"options": folder_paths.get_filename_list("vae")},
"Checkpoint": {"options": folder_paths.get_filename_list("checkpoints")},
"Clip Skip": {"min": -24, "max": -1},
"LoRA": {"options": folder_paths.get_filename_list("loras"),
"model_str": {"min": 0, "max": 10},"clip_str": {"min": 0, "max": 10},},
}
# Validates a value based on its corresponding value_type and bounds.
def validate_value(value, value_type, bounds):
# ________________________________________________________________________
# Seeds++ Batch
if value_type == "Seeds++ Batch":
try:
x = int(float(value))
if x < bounds["Seeds++ Batch"]["min"]:
x = bounds["Seeds++ Batch"]["min"]
elif x > bounds["Seeds++ Batch"]["max"]:
x = bounds["Seeds++ Batch"]["max"]
except ValueError:
print(f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid batch count.")
return None
if float(value) != x:
print(f"\033[31mmXY Plot Error:\033[0m '{value}' is not a valid batch count.")
return None
return x
# ________________________________________________________________________
# Steps
elif value_type == "Steps":
try:
x = int(value)
if x < bounds["Steps"]["min"]:
x = bounds["Steps"]["min"]
elif x > bounds["Steps"]["max"]:
x = bounds["Steps"]["max"]
return x
except ValueError:
print(
f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid Step count.")
return None
# ________________________________________________________________________
# CFG Scale
elif value_type == "CFG Scale":
try:
x = float(value)
if x < bounds["CFG Scale"]["min"]:
x = bounds["CFG Scale"]["min"]
elif x > bounds["CFG Scale"]["max"]:
x = bounds["CFG Scale"]["max"]
return x
except ValueError:
print(
f"\033[31mXY Plot Error:\033[0m '{value}' is not a number between {bounds['CFG Scale']['min']}"
f" and {bounds['CFG Scale']['max']} for CFG Scale.")
return None
# ________________________________________________________________________
# Sampler
elif value_type == "Sampler":
if isinstance(value, str) and ',' in value:
value = tuple(map(str.strip, value.split(',')))
if isinstance(value, tuple):
if len(value) >= 2:
value = value[:2] # Slice the value tuple to keep only the first two elements
sampler, scheduler = value
scheduler = scheduler.lower() # Convert the scheduler name to lowercase
if sampler not in bounds["Sampler"]["options"]:
valid_samplers = '\n'.join(bounds["Sampler"]["options"])
print(
f"\033[31mXY Plot Error:\033[0m '{sampler}' is not a valid sampler. Valid samplers are:\n{valid_samplers}")
sampler = None
if scheduler not in bounds["Scheduler"]["options"]:
valid_schedulers = '\n'.join(bounds["Scheduler"]["options"])
print(
f"\033[31mXY Plot Error:\033[0m '{scheduler}' is not a valid scheduler. Valid schedulers are:\n{valid_schedulers}")
scheduler = None
if sampler is None or scheduler is None:
return None
else:
return sampler, scheduler
else:
print(
f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid sampler.'")
return None
else:
if value not in bounds["Sampler"]["options"]:
valid_samplers = '\n'.join(bounds["Sampler"]["options"])
print(
f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid sampler. Valid samplers are:\n{valid_samplers}")
return None
else:
return value, None
# ________________________________________________________________________
# Scheduler
elif value_type == "Scheduler":
if value not in bounds["Scheduler"]["options"]:
valid_schedulers = '\n'.join(bounds["Scheduler"]["options"])
print(
f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid Scheduler. Valid Schedulers are:\n{valid_schedulers}")
return None
else:
return value
# ________________________________________________________________________
# Denoise
elif value_type == "Denoise":
try:
x = float(value)
if x < bounds["Denoise"]["min"]:
x = bounds["Denoise"]["min"]
elif x > bounds["Denoise"]["max"]:
x = bounds["Denoise"]["max"]
return x
except ValueError:
print(
f"\033[31mXY Plot Error:\033[0m '{value}' is not a number between {bounds['Denoise']['min']} "
f"and {bounds['Denoise']['max']} for Denoise.")
return None
# ________________________________________________________________________
# VAE
elif value_type == "VAE":
if value not in bounds["VAE"]["options"]:
valid_vaes = '\n'.join(bounds["VAE"]["options"])
print(f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid VAE. Valid VAEs are:\n{valid_vaes}")
return None
else:
return value
# ________________________________________________________________________
# Checkpoint
elif value_type == "Checkpoint":
if isinstance(value, str) and ',' in value:
value = tuple(map(str.strip, value.split(',')))
if isinstance(value, tuple):
if len(value) >= 2:
value = value[:2] # Slice the value tuple to keep only the first two elements
checkpoint, clip_skip = value
try:
clip_skip = int(clip_skip) # Convert the clip_skip to integer
except ValueError:
print(f"\033[31mXY Plot Error:\033[0m '{clip_skip}' is not a valid clip_skip. "
f"Valid clip skip values are integers between {bounds['Clip Skip']['min']} and {bounds['Clip Skip']['max']}.")
return None
if checkpoint not in bounds["Checkpoint"]["options"]:
valid_checkpoints = '\n'.join(bounds["Checkpoint"]["options"])
print(
f"\033[31mXY Plot Error:\033[0m '{checkpoint}' is not a valid checkpoint. Valid checkpoints are:\n{valid_checkpoints}")
checkpoint = None
if clip_skip < bounds["Clip Skip"]["min"] or clip_skip > bounds["Clip Skip"]["max"]:
print(f"\033[31mXY Plot Error:\033[0m '{clip_skip}' is not a valid clip skip. "
f"Valid clip skip values are integers between {bounds['Clip Skip']['min']} and {bounds['Clip Skip']['max']}.")
clip_skip = None
if checkpoint is None or clip_skip is None:
return None
else:
return checkpoint, clip_skip
else:
print(
f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid checkpoint.'")
return None
else:
if value not in bounds["Checkpoint"]["options"]:
valid_checkpoints = '\n'.join(bounds["Checkpoint"]["options"])
print(
f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid checkpoint. Valid checkpoints are:\n{valid_checkpoints}")
return None
else:
return value, None
# ________________________________________________________________________
# Clip Skip
elif value_type == "Clip Skip":
try:
x = int(value)
if x < bounds["Clip Skip"]["min"]:
x = bounds["Clip Skip"]["min"]
elif x > bounds["Clip Skip"]["max"]:
x = bounds["Clip Skip"]["max"]
return x
except ValueError:
print(f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid Clip Skip.")
return None
# ________________________________________________________________________
# LoRA
elif value_type == "LoRA":
if isinstance(value, str) and ',' in value:
value = tuple(map(str.strip, value.split(',')))
if isinstance(value, tuple):
lora_name, model_str, clip_str = (value + (1.0, 1.0))[:3] # Defaults model_str and clip_str to 1 if not provided
if lora_name not in bounds["LoRA"]["options"]:
valid_loras = '\n'.join(bounds["LoRA"]["options"])
print(f"\033[31mXY Plot Error:\033[0m '{lora_name}' is not a valid LoRA. Valid LoRAs are:\n{valid_loras}")
lora_name = None
try:
model_str = float(model_str)
clip_str = float(clip_str)
except ValueError:
print(f"\033[31mXY Plot Error:\033[0m The LoRA model strength and clip strength values should be numbers"
f" between {bounds['LoRA']['model_str']['min']} and {bounds['LoRA']['model_str']['max']}.")
return None
if model_str < bounds["LoRA"]["model_str"]["min"] or model_str > bounds["LoRA"]["model_str"]["max"]:
print(f"\033[31mXY Plot Error:\033[0m '{model_str}' is not a valid LoRA model strength value. "
f"Valid lora model strength values are between {bounds['LoRA']['model_str']['min']} and {bounds['LoRA']['model_str']['max']}.")
model_str = None
if clip_str < bounds["LoRA"]["clip_str"]["min"] or clip_str > bounds["LoRA"]["clip_str"]["max"]:
print(f"\033[31mXY Plot Error:\033[0m '{clip_str}' is not a valid LoRA clip strength value. "
f"Valid lora clip strength values are between {bounds['LoRA']['clip_str']['min']} and {bounds['LoRA']['clip_str']['max']}.")
clip_str = None
if lora_name is None or model_str is None or clip_str is None:
return None
else:
return lora_name, model_str, clip_str
else:
if value not in bounds["LoRA"]["options"]:
valid_loras = '\n'.join(bounds["LoRA"]["options"])
print(
f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid LoRA. Valid LoRAs are:\n{valid_loras}")
return None
else:
return value, 1.0, 1.0
# ________________________________________________________________________
else:
return None
# Validate X_value array length is 1 if doing a "Seeds++ Batch"
if len(X_value) != 1 and X_type == "Seeds++ Batch":
print(f"\033[31mXY Plot Error:\033[0m '{';'.join(X_value)}' is not a valid batch count.")
return (None,None,)
# Validate Y_value array length is 1 if doing a "Seeds++ Batch"
if len(Y_value) != 1 and Y_type == "Seeds++ Batch":
print(f"\033[31mXY Plot Error:\033[0m '{';'.join(Y_value)}' is not a valid batch count.")
return (None,None,)
# Apply allowed shortcut syntax to certain input types
if X_type in ["Sampler", "Checkpoint", "LoRA"]:
if X_value[-1].startswith(','):
# Remove the leading comma from the last entry and store it as suffixes
suffixes = X_value.pop().lstrip(',').split(',')
# Split all preceding entries into subentries
X_value = [entry.split(',') for entry in X_value]
# Make all entries the same length as suffixes by appending missing elements
for entry in X_value:
entry += suffixes[len(entry) - 1:]
# Join subentries back into strings
X_value = [','.join(entry) for entry in X_value]
# Apply allowed shortcut syntax to certain input types
if Y_type in ["Sampler", "Checkpoint", "LoRA"]:
if Y_value[-1].startswith(','):
# Remove the leading comma from the last entry and store it as suffixes
suffixes = Y_value.pop().lstrip(',').split(',')
# Split all preceding entries into subentries
Y_value = [entry.split(',') for entry in Y_value]
# Make all entries the same length as suffixes by appending missing elements
for entry in Y_value:
entry += suffixes[len(entry) - 1:]
# Join subentries back into strings
Y_value = [','.join(entry) for entry in Y_value]
# Prompt S/R X Cleanup
if X_type in {"Positive Prompt S/R", "Negative Prompt S/R"}:
if X_value[0] == '':
print(f"\033[31mXY Plot Error:\033[0m Prompt S/R value can not be empty.")
return (None, None,)
else:
X_value = [(X_value[0], None) if i == 0 else (X_value[0], x) for i, x in enumerate(X_value)]
# Prompt S/R X Cleanup
if Y_type in {"Positive Prompt S/R", "Negative Prompt S/R"}:
if Y_value[0] == '':
print(f"\033[31mXY Plot Error:\033[0m Prompt S/R value can not be empty.")
return (None, None,)
else:
Y_value = [(Y_value[0], None) if i == 0 else (Y_value[0], y) for i, y in enumerate(Y_value)]
# Loop over each entry in X_value and check if it's valid
if X_type not in {"Nothing", "Positive Prompt S/R", "Negative Prompt S/R"}:
for i in range(len(X_value)):
X_value[i] = validate_value(X_value[i], X_type, bounds)
if X_value[i] == None:
return (None,None,)
# Loop over each entry in Y_value and check if it's valid
if Y_type not in {"Nothing", "Positive Prompt S/R", "Negative Prompt S/R"}:
for i in range(len(Y_value)):
Y_value[i] = validate_value(Y_value[i], Y_type, bounds)
if Y_value[i] == None:
return (None,None,)
# Nest LoRA value in another array to reflect LoRA stack changes
if X_type == "LoRA":
X_value = [[x] for x in X_value]
if Y_type == "LoRA":
Y_value = [[y] for y in Y_value]
# Clean X/Y_values
if X_type == "Nothing":
X_value = [""]
if Y_type == "Nothing":
Y_value = [""]
return ((X_type, X_value), (Y_type, Y_value),)
# TSC XY Plot: Seeds Values
class TSC_XYplot_JoinInputs:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"XY_1": ("XY",),
"XY_2": ("XY",),},
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Plot/XY Inputs"
def xy_value(self, XY_1, XY_2):
xy_type_1, xy_value_1 = XY_1
xy_type_2, xy_value_2 = XY_2
if xy_type_1 != xy_type_2:
print(f"\033[31mJoin XY Inputs Error:\033[0m Input types must match")
return (None,)
elif xy_type_1 == "Seeds++ Batch":
xy_type = xy_type_1
xy_value = [xy_value_1[0] + xy_value_2[0]]
elif xy_type_1 == "Positive Prompt S/R" or xy_type_1 == "Negative Prompt S/R":
xy_type = xy_type_1
xy_value = xy_value_1 + [(xy_value_1[0][0], t[1]) for t in xy_value_2[1:]]
else:
xy_type = xy_type_1
xy_value = xy_value_1 + xy_value_2
return ((xy_type, xy_value),)
########################################################################################################################
# TSC Image Overlay
class TSC_ImageOverlay:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"base_image": ("IMAGE",),
"overlay_image": ("IMAGE",),
"overlay_resize": (["None", "Fit", "Resize by rescale_factor", "Resize to width & heigth"],),
"resize_method": (["nearest-exact", "bilinear", "area"],),
"rescale_factor": ("FLOAT", {"default": 1, "min": 0.01, "max": 16.0, "step": 0.1}),
"width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
"height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
"x_offset": ("INT", {"default": 0, "min": -48000, "max": 48000, "step": 10}),
"y_offset": ("INT", {"default": 0, "min": -48000, "max": 48000, "step": 10}),
"rotation": ("INT", {"default": 0, "min": -180, "max": 180, "step": 5}),
"opacity": ("FLOAT", {"default": 0, "min": 0, "max": 100, "step": 5}),
},
"optional": {"optional_mask": ("MASK",),}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "apply_overlay_image"
CATEGORY = "Efficiency Nodes/Image"
def apply_overlay_image(self, base_image, overlay_image, overlay_resize, resize_method, rescale_factor,
width, height, x_offset, y_offset, rotation, opacity, optional_mask=None):
# Pack tuples and assign variables
size = width, height
location = x_offset, y_offset
mask = optional_mask
# Check for different sizing options
if overlay_resize != "None":
#Extract overlay_image size and store in Tuple "overlay_image_size" (WxH)
overlay_image_size = overlay_image.size()
overlay_image_size = (overlay_image_size[2], overlay_image_size[1])
if overlay_resize == "Fit":
overlay_image_size = (base_image.size[0],base_image.size[1])
elif overlay_resize == "Resize by rescale_factor":
overlay_image_size = tuple(int(dimension * rescale_factor) for dimension in overlay_image_size)
elif overlay_resize == "Resize to width & heigth":
overlay_image_size = (size[0], size[1])
samples = overlay_image.movedim(-1, 1)
overlay_image = comfy.utils.common_upscale(samples, overlay_image_size[0], overlay_image_size[1], resize_method, False)
overlay_image = overlay_image.movedim(1, -1)
overlay_image = tensor2pil(overlay_image)
# Add Alpha channel to overlay
overlay_image = overlay_image.convert('RGBA')
overlay_image.putalpha(Image.new("L", overlay_image.size, 255))
# If mask connected, check if the overlay_image image has an alpha channel
if mask is not None:
# Convert mask to pil and resize
mask = tensor2pil(mask)
mask = mask.resize(overlay_image.size)
# Apply mask as overlay's alpha
overlay_image.putalpha(ImageOps.invert(mask))
# Rotate the overlay image
overlay_image = overlay_image.rotate(rotation, expand=True)
# Apply opacity on overlay image
r, g, b, a = overlay_image.split()
a = a.point(lambda x: max(0, int(x * (1 - opacity / 100))))
overlay_image.putalpha(a)
# Split the base_image tensor along the first dimension to get a list of tensors
base_image_list = torch.unbind(base_image, dim=0)
# Convert each tensor to a PIL image, apply the overlay, and then convert it back to a tensor
processed_base_image_list = []
for tensor in base_image_list:
# Convert tensor to PIL Image
image = tensor2pil(tensor)
# Paste the overlay image onto the base image
if mask is None:
image.paste(overlay_image, location)
else:
image.paste(overlay_image, location, overlay_image)
# Convert PIL Image back to tensor
processed_tensor = pil2tensor(image)
# Append to list
processed_base_image_list.append(processed_tensor)
# Combine the processed images back into a single tensor
base_image = torch.stack([tensor.squeeze() for tensor in processed_base_image_list])
# Return the edited base image
return (base_image,)
########################################################################################################################
# Install simple_eval if missing from packages
def install_simpleeval():
if 'simpleeval' not in packages():
print("\033[32mEfficiency Nodes:\033[0m")
subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'simpleeval'])
def packages(versions=False):
return [(r.decode().split('==')[0] if not versions else r.decode()) for r in subprocess.check_output([sys.executable, '-m', 'pip', 'freeze']).split()]
install_simpleeval()
from simpleeval import simple_eval
# TSC Evaluate Integers (https://github.com/danthedeckie/simpleeval)
class TSC_EvaluateInts:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"python_expression": ("STRING", {"default": "((a + b) - c) / 2", "multiline": False}),
"print_to_console": (["False", "True"],),},
"optional": {
"a": ("INT", {"default": 0, "min": -48000, "max": 48000, "step": 1}),
"b": ("INT", {"default": 0, "min": -48000, "max": 48000, "step": 1}),
"c": ("INT", {"default": 0, "min": -48000, "max": 48000, "step": 1}),},
}
RETURN_TYPES = ("INT", "FLOAT", "STRING",)
OUTPUT_NODE = True
FUNCTION = "evaluate"
CATEGORY = "Efficiency Nodes/Simple Eval"
def evaluate(self, python_expression, print_to_console, a=0, b=0, c=0):
# simple_eval doesn't require the result to be converted to a string
result = simple_eval(python_expression, names={'a': a, 'b': b, 'c': c})
int_result = int(result)
float_result = float(result)
string_result = str(result)
if print_to_console == "True":
print("\n\033[31mEvaluate Integers:\033[0m")
print(f"\033[90m{{a = {a} , b = {b} , c = {c}}} \033[0m")
print(f"{python_expression} = \033[92m INT: " + str(int_result) + " , FLOAT: " + str(
float_result) + ", STRING: " + string_result + "\033[0m")
return (int_result, float_result, string_result,)
# TSC Evaluate Floats (https://github.com/danthedeckie/simpleeval)
class TSC_EvaluateFloats:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"python_expression": ("STRING", {"default": "((a + b) - c) / 2", "multiline": False}),
"print_to_console": (["False", "True"],),},
"optional": {
"a": ("FLOAT", {"default": 0, "min": -sys.float_info.max, "max": sys.float_info.max, "step": 1}),
"b": ("FLOAT", {"default": 0, "min": -sys.float_info.max, "max": sys.float_info.max, "step": 1}),
"c": ("FLOAT", {"default": 0, "min": -sys.float_info.max, "max": sys.float_info.max, "step": 1}),},
}
RETURN_TYPES = ("INT", "FLOAT", "STRING",)
OUTPUT_NODE = True
FUNCTION = "evaluate"
CATEGORY = "Efficiency Nodes/Simple Eval"
def evaluate(self, python_expression, print_to_console, a=0, b=0, c=0):
# simple_eval doesn't require the result to be converted to a string
result = simple_eval(python_expression, names={'a': a, 'b': b, 'c': c})
int_result = int(result)
float_result = float(result)
string_result = str(result)
if print_to_console == "True":
print("\n\033[31mEvaluate Floats:\033[0m")
print(f"\033[90m{{a = {a} , b = {b} , c = {c}}} \033[0m")
print(f"{python_expression} = \033[92m INT: " + str(int_result) + " , FLOAT: " + str(
float_result) + ", STRING: " + string_result + "\033[0m")
return (int_result, float_result, string_result,)
# TSC Evaluate Strings (https://github.com/danthedeckie/simpleeval)
class TSC_EvaluateStrs:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"python_expression": ("STRING", {"default": "a + b + c", "multiline": False}),
"print_to_console": (["False", "True"],)},
"optional": {
"a": ("STRING", {"default": "Hello", "multiline": False}),
"b": ("STRING", {"default": " World", "multiline": False}),
"c": ("STRING", {"default": "!", "multiline": False}),}
}
RETURN_TYPES = ("STRING",)
OUTPUT_NODE = True
FUNCTION = "evaluate"
CATEGORY = "Efficiency Nodes/Simple Eval"
def evaluate(self, python_expression, print_to_console, a="", b="", c=""):
variables = {'a': a, 'b': b, 'c': c} # Define the variables for the expression
functions = {"len": len} # Define the functions for the expression
result = simple_eval(python_expression, names=variables, functions=functions)
if print_to_console == "True":
print("\n\033[31mEvaluate Strings:\033[0m")
print(f"\033[90ma = {a} \nb = {b} \nc = {c}\033[0m")
print(f"{python_expression} = \033[92m" + str(result) + "\033[0m")
return (str(result),) # Convert result to a string before returning
# TSC Simple Eval Examples (https://github.com/danthedeckie/simpleeval)
class TSC_EvalExamples:
filepath = os.path.join(my_dir, 'workflows', 'SimpleEval_Node_Examples.txt')
with open(filepath, 'r') as file:
examples = file.read()
@classmethod
def INPUT_TYPES(cls):
return {"required": { "models_text": ("STRING", {"default": cls.examples ,"multiline": True}),},}
RETURN_TYPES = ()
CATEGORY = "Efficiency Nodes/Simple Eval"
# NODE MAPPING
NODE_CLASS_MAPPINGS = {
"KSampler (Efficient)": TSC_KSampler,
"Efficient Loader": TSC_EfficientLoader,
"LoRA Stacker": TSC_LoRA_Stacker,
"LoRA Stacker Adv.": TSC_LoRA_Stacker_Adv,
"XY Plot": TSC_XYplot,
"XY Input: Seeds++ Batch": TSC_XYplot_SeedsBatch,
"XY Input: Steps": TSC_XYplot_Steps,
"XY Input: CFG Scale": TSC_XYplot_CFG,
"XY Input: Sampler": TSC_XYplot_Sampler,
"XY Input: Scheduler": TSC_XYplot_Scheduler,
"XY Input: Denoise": TSC_XYplot_Denoise,
"XY Input: VAE": TSC_XYplot_VAE,
"XY Input: Positive Prompt S/R": TSC_XYplot_PromptSR_Positive,
"XY Input: Negative Prompt S/R": TSC_XYplot_PromptSR_Negative,
"XY Input: Checkpoint": TSC_XYplot_Checkpoint,
"XY Input: Clip Skip": TSC_XYplot_ClipSkip,
"XY Input: LoRA": TSC_XYplot_LoRA,
"XY Input: LoRA Adv.": TSC_XYplot_LoRA_Adv,
"XY Input: LoRA Stacks": TSC_XYplot_LoRA_Stacks,
"XY Input: Manual XY Entry": TSC_XYplot_Manual_XY_Entry,
"Manual XY Entry Info": TSC_XYplot_Manual_XY_Entry_Info,
"Join XY Inputs of Same Type": TSC_XYplot_JoinInputs,
"Image Overlay": TSC_ImageOverlay,
"Evaluate Integers": TSC_EvaluateInts,
"Evaluate Floats": TSC_EvaluateFloats,
"Evaluate Strings": TSC_EvaluateStrs,
"Simple Eval Examples": TSC_EvalExamples
}
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