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efficiency-nodes-comfyui/efficiency_nodes.py
TSC 92ff8fd8c3 V1.92 Efficiency Nodes MEGA Update (SDXL + MORE) 
This PR is a huge update, I added/fixed so many things that I will explain all the updates later on the release notes.
2023-08-30 20:06:15 -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 - August 2023
# https://github.com/LucianoCirino/efficiency-nodes-comfyui
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
from importlib import import_module
import os
import sys
import copy
import subprocess
import json
import psutil
# Get the absolute path of various directories
my_dir = os.path.dirname(os.path.abspath(__file__))
custom_nodes_dir = os.path.abspath(os.path.join(my_dir, '..'))
comfy_dir = os.path.abspath(os.path.join(my_dir, '..', '..'))
# Construct the path to the font file
font_path = os.path.join(my_dir, 'arial.ttf')
# Append comfy_dir to sys.path & import files
sys.path.append(comfy_dir)
from nodes import LatentUpscaleBy, KSampler, KSamplerAdvanced, VAEDecode, VAEDecodeTiled, CLIPSetLastLayer, CLIPTextEncode, ControlNetApplyAdvanced
from comfy_extras.nodes_clip_sdxl import CLIPTextEncodeSDXL, CLIPTextEncodeSDXLRefiner
import comfy.samplers
import comfy.sd
import comfy.utils
import comfy.latent_formats
sys.path.remove(comfy_dir)
# Append my_dir to sys.path & import files
sys.path.append(my_dir)
from tsc_utils import *
sys.path.remove(my_dir)
# Append custom_nodes_dir to sys.path
sys.path.append(custom_nodes_dir)
# GLOBALS
MAX_RESOLUTION=8192
REFINER_CFG_OFFSET = 0 #Refiner CFG Offset
########################################################################################################################
# Common function for encoding prompts
def encode_prompts(positive_prompt, negative_prompt, clip, clip_skip, refiner_clip, refiner_clip_skip, ascore, is_sdxl,
empty_latent_width, empty_latent_height, return_type="both"):
positive_encoded = negative_encoded = refiner_positive_encoded = refiner_negative_encoded = None
# Process base encodings if needed
if return_type in ["base", "both"]:
# Base clip skip
clip = CLIPSetLastLayer().set_last_layer(clip, clip_skip)[0]
if not is_sdxl:
positive_encoded = CLIPTextEncode().encode(clip, positive_prompt)[0]
negative_encoded = CLIPTextEncode().encode(clip, negative_prompt)[0]
else:
# Encode prompt for base
positive_encoded = CLIPTextEncodeSDXL().encode(clip, empty_latent_width, empty_latent_height, 0, 0,
empty_latent_width, empty_latent_height, positive_prompt,
positive_prompt)[0]
negative_encoded = CLIPTextEncodeSDXL().encode(clip, empty_latent_width, empty_latent_height, 0, 0,
empty_latent_width, empty_latent_height, negative_prompt,
negative_prompt)[0]
# Process refiner encodings if needed
if return_type in ["refiner", "both"] and is_sdxl and refiner_clip and refiner_clip_skip and ascore:
# Refiner clip skip
refiner_clip = CLIPSetLastLayer().set_last_layer(refiner_clip, refiner_clip_skip)[0]
# Encode prompt for refiner
refiner_positive_encoded = CLIPTextEncodeSDXLRefiner().encode(refiner_clip, ascore[0], empty_latent_width,
empty_latent_height, positive_prompt)[0]
refiner_negative_encoded = CLIPTextEncodeSDXLRefiner().encode(refiner_clip, ascore[1], empty_latent_width,
empty_latent_height, negative_prompt)[0]
# Return results based on return_type
if return_type == "base":
return positive_encoded, negative_encoded
elif return_type == "refiner":
return refiner_positive_encoded, refiner_negative_encoded
elif return_type == "both":
return positive_encoded, negative_encoded, refiner_positive_encoded, refiner_negative_encoded
########################################################################################################################
# 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", ),
"cnet_stack": ("CONTROL_NET_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,
cnet_stack=None, refiner_name="None", ascore=None, prompt=None, my_unique_id=None,
loader_type="regular"):
# Clean globally stored objects
globals_cleanup(prompt)
# Create Empty Latent
latent = torch.zeros([batch_size, 4, empty_latent_height // 8, empty_latent_width // 8]).cpu()
# Retrieve cache numbers
vae_cache, ckpt_cache, lora_cache, refn_cache = get_cache_numbers("Efficient Loader")
if lora_name != "None" or lora_stack:
# Initialize an empty list to store LoRa parameters.
lora_params = []
# Check if lora_name is not the string "None" and if so, add its parameters.
if lora_name != "None":
lora_params.append((lora_name, lora_model_strength, lora_clip_strength))
# If lora_stack is not None or an empty list, extend lora_params with its items.
if lora_stack:
lora_params.extend(lora_stack)
# Load LoRa(s)
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
# Load Refiner Checkpoint if given
if refiner_name != "None":
refiner_model, refiner_clip, _ = load_checkpoint(refiner_name, my_unique_id, output_vae=False,
cache=refn_cache, cache_overwrite=True, ckpt_type="refn")
else:
refiner_model = refiner_clip = None
# Extract clip_skips
refiner_clip_skip = clip_skip[1] if loader_type == "sdxl" else None
clip_skip = clip_skip[0] if loader_type == "sdxl" else clip_skip
# Encode prompt based on loader_type
positive_encoded, negative_encoded, refiner_positive_encoded, refiner_negative_encoded = \
encode_prompts(positive, negative, clip, clip_skip, refiner_clip, refiner_clip_skip, ascore,
loader_type == "sdxl", empty_latent_width, empty_latent_height)
# Apply ControlNet Stack if given
if cnet_stack:
positive_encoded = TSC_Apply_ControlNet_Stack().apply_cnet_stack(positive_encoded,cnet_stack)[0]
# Check for custom VAE
if vae_name != "Baked VAE":
vae = load_vae(vae_name, my_unique_id, cache=vae_cache, cache_overwrite=True)
# Data for XY Plot
dependencies = (vae_name, ckpt_name, clip, clip_skip, refiner_name, refiner_clip, refiner_clip_skip,
positive, negative, ascore, empty_latent_width, empty_latent_height, lora_params, cnet_stack)
### Debugging
###print_loaded_objects_entries()
print_loaded_objects_entries(my_unique_id, prompt)
if loader_type == "regular":
return (model, positive_encoded, negative_encoded, {"samples":latent}, vae, clip, dependencies,)
elif loader_type == "sdxl":
return ((model, clip, positive_encoded, negative_encoded, refiner_model, refiner_clip,
refiner_positive_encoded, refiner_negative_encoded), {"samples":latent}, vae, dependencies,)
#=======================================================================================================================
# TSC Efficient Loader SDXL
class TSC_EfficientLoaderSDXL(TSC_EfficientLoader):
@classmethod
def INPUT_TYPES(cls):
return {"required": { "base_ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
"base_clip_skip": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
"refiner_ckpt_name": (["None"] + folder_paths.get_filename_list("checkpoints"),),
"refiner_clip_skip": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
"positive_ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
"negative_ascore": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
"vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),),
"positive": ("STRING", {"default": "Positive","multiline": True}),
"negative": ("STRING", {"default": "Negative", "multiline": True}),
"empty_latent_width": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 128}),
"empty_latent_height": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 128}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 64})},
"optional": {"lora_stack": ("LORA_STACK", ), "cnet_stack": ("CONTROL_NET_STACK",),},
"hidden": { "prompt": "PROMPT", "my_unique_id": "UNIQUE_ID",},
}
RETURN_TYPES = ("SDXL_TUPLE", "LATENT", "VAE", "DEPENDENCIES",)
RETURN_NAMES = ("SDXL_TUPLE", "LATENT", "VAE", "DEPENDENCIES", )
FUNCTION = "efficientloaderSDXL"
CATEGORY = "Efficiency Nodes/Loaders"
def efficientloaderSDXL(self, base_ckpt_name, base_clip_skip, refiner_ckpt_name, refiner_clip_skip, positive_ascore,
negative_ascore, vae_name, positive, negative, empty_latent_width, empty_latent_height,
batch_size, lora_stack=None, cnet_stack=None, prompt=None, my_unique_id=None):
clip_skip = (base_clip_skip, refiner_clip_skip)
lora_name = "None"
lora_model_strength = lora_clip_strength = 0
return super().efficientloader(base_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=lora_stack,
cnet_stack=cnet_stack, refiner_name=refiner_ckpt_name, ascore=(positive_ascore, negative_ascore),
prompt=prompt, my_unique_id=my_unique_id, loader_type="sdxl")
#=======================================================================================================================
# TSC Unpack SDXL Tuple
class TSC_Unpack_SDXL_Tuple:
@classmethod
def INPUT_TYPES(cls):
return {"required": {"sdxl_tuple": ("SDXL_TUPLE",)},}
RETURN_TYPES = ("MODEL", "CLIP", "MODEL", "CLIP", "CONDITIONING","CONDITIONING", "CONDITIONING", "CONDITIONING",)
RETURN_NAMES = ("BASE_MODEL", "BASE_CLIP", "REFINER_MODEL", "REFINER_CLIP",
"BASE_CONDITIONING+", "BASE_CONDITIONING-","REFINER_CONDITIONING+","REFINER_CONDITIONING-",)
FUNCTION = "unpack_sdxl_tuple"
CATEGORY = "Efficiency Nodes/Misc"
def unpack_sdxl_tuple(self, sdxl_tuple):
return (sdxl_tuple[0], sdxl_tuple[1],sdxl_tuple[2],sdxl_tuple[3],
sdxl_tuple[4],sdxl_tuple[5],sdxl_tuple[6],sdxl_tuple[7],)
# =======================================================================================================================
# TSC Pack SDXL Tuple
class TSC_Pack_SDXL_Tuple:
@classmethod
def INPUT_TYPES(cls):
return {"required": {"base_model": ("MODEL",),
"base_clip": ("CLIP",),
"base_positive": ("CONDITIONING",),
"base_negative": ("CONDITIONING",),
"refiner_model": ("MODEL",),
"refiner_clip": ("CLIP",),
"refiner_positive": ("CONDITIONING",),
"refiner_negative": ("CONDITIONING",),},}
RETURN_TYPES = ("SDXL_TUPLE",)
RETURN_NAMES = ("SDXL_TUPLE",)
FUNCTION = "pack_sdxl_tuple"
CATEGORY = "Efficiency Nodes/Misc"
def pack_sdxl_tuple(self, base_model, base_clip, base_positive, base_negative,
refiner_model, refiner_clip, refiner_positive, refiner_negative):
return ((base_model, base_clip, base_positive, base_negative,
refiner_model, refiner_clip, refiner_positive, refiner_negative),)
########################################################################################################################
# TSC LoRA Stacker
class TSC_LoRA_Stacker:
modes = ["simple", "advanced"]
@classmethod
def INPUT_TYPES(cls):
loras = ["None"] + folder_paths.get_filename_list("loras")
inputs = {
"required": {
"input_mode": (cls.modes,),
"lora_count": ("INT", {"default": 3, "min": 0, "max": 50, "step": 1}),
}
}
for i in range(1, 50):
inputs["required"][f"lora_name_{i}"] = (loras,)
inputs["required"][f"lora_wt_{i}"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})
inputs["required"][f"model_str_{i}"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})
inputs["required"][f"clip_str_{i}"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})
inputs["optional"] = {
"lora_stack": ("LORA_STACK",)
}
return inputs
RETURN_TYPES = ("LORA_STACK",)
RETURN_NAMES = ("LORA_STACK",)
FUNCTION = "lora_stacker"
CATEGORY = "Efficiency Nodes/Stackers"
def lora_stacker(self, input_mode, lora_count, lora_stack=None, **kwargs):
# Extract values from kwargs
loras = [kwargs.get(f"lora_name_{i}") for i in range(1, lora_count + 1)]
# Create a list of tuples using provided parameters, exclude tuples with lora_name as "None"
if input_mode == "simple":
weights = [kwargs.get(f"lora_wt_{i}") for i in range(1, lora_count + 1)]
loras = [(lora_name, lora_weight, lora_weight) for lora_name, lora_weight in zip(loras, weights) if
lora_name != "None"]
else:
model_strs = [kwargs.get(f"model_str_{i}", model_strength) for i in range(1, lora_count + 1)]
clip_strs = [kwargs.get(f"clip_str_{i}", clip_strength) for i in range(1, lora_count + 1)]
loras = [(lora_name, model_str, clip_str) for lora_name, model_str, clip_str in
zip(loras, model_strs, clip_strs) 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 Control Net Stacker
class TSC_Control_Net_Stacker:
@classmethod
def INPUT_TYPES(cls):
return {"required": {"control_net": ("CONTROL_NET",),
"image": ("IMAGE",),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})},
"optional": {"cnet_stack": ("CONTROL_NET_STACK",)},
}
RETURN_TYPES = ("CONTROL_NET_STACK",)
RETURN_NAMES = ("CNET_STACK",)
FUNCTION = "control_net_stacker"
CATEGORY = "Efficiency Nodes/Stackers"
def control_net_stacker(self, control_net, image, strength, start_percent, end_percent, cnet_stack=None):
# If control_net_stack is None, initialize as an empty list
cnet_stack = [] if cnet_stack is None else cnet_stack
# Extend the control_net_stack with the new tuple
cnet_stack.extend([(control_net, image, strength, start_percent, end_percent)])
return (cnet_stack,)
#=======================================================================================================================
# TSC Apply ControlNet Stack
class TSC_Apply_ControlNet_Stack:
@classmethod
def INPUT_TYPES(cls):
return {"required": {"conditioning": ("CONDITIONING",),
"cnet_stack": ("CONTROL_NET_STACK",)},
}
RETURN_TYPES = ("CONDITIONING",)
RETURN_NAMES = ("CONDITIONING",)
FUNCTION = "apply_cnet_stack"
CATEGORY = "Efficiency Nodes/Stackers"
def apply_cnet_stack(self, conditioning, cnet_stack):
for control_net_tuple in cnet_stack:
control_net, image, strength, start_percent, end_percent = control_net_tuple
conditioning_new = ControlNetApplyAdvanced().apply_controlnet(conditioning, conditioning,
control_net, image, strength,
start_percent, end_percent)[0]
return (conditioning_new,)
########################################################################################################################
# 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_method": (["auto", "latent2rgb", "taesd", "none"],),
"vae_decode": (["true", "true (tiled)", "false", "output only", "output only (tiled)"],),
},
"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_method, vae_decode, denoise=1.0, prompt=None, extra_pnginfo=None, my_unique_id=None,
optional_vae=(None,), script=None, add_noise=None, start_at_step=None, end_at_step=None,
return_with_leftover_noise=None, sampler_type="regular"):
# Rename the vae variable
vae = optional_vae
# If vae is not connected, disable vae decoding
if vae == (None,) and vae_decode != "false":
print(f"{warning('KSampler(Efficient) Warning:')} No vae input detected, proceeding as if vae_decode was false.\n")
vae_decode = "false"
#---------------------------------------------------------------------------------------------------------------
# Unpack SDXL Tuple embedded in the 'model' channel
if sampler_type == "sdxl":
sdxl_tuple = model
model, _, positive, negative, refiner_model, _, refiner_positive, refiner_negative = sdxl_tuple
else:
refiner_model = refiner_positive = refiner_negative = None
#---------------------------------------------------------------------------------------------------------------
def keys_exist_in_script(*keys):
return any(key in script for key in keys) if script else False
# If no valid script input connected, error out
if not keys_exist_in_script("xyplot", "hiresfix", "tile") and sampler_state == "Script":
print(f"{error('KSampler(Efficient) Error:')} No valid script input detected")
if sampler_type == "sdxl":
result = (sdxl_tuple, latent_image, vae, TSC_KSampler.empty_image,)
else:
result = (model, positive, negative, latent_image, vae, TSC_KSampler.empty_image,)
return {"ui": {"images": list()}, "result": result}
#---------------------------------------------------------------------------------------------------------------
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(images,input):
input = input.replace("%width%", str(images[0].shape[1]))
input = input.replace("%height%", str(images[0].shape[0]))
return input
def preview_image(images, filename_prefix):
if images == list():
return list()
filename_prefix = compute_vars(images,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
#---------------------------------------------------------------------------------------------------------------
def vae_decode_latent(vae, samples, vae_decode):
return VAEDecodeTiled.decode_tiled(vae,samples)[0] if "tiled" in vae_decode else VAEDecode().decode(vae,samples)[0]
# ---------------------------------------------------------------------------------------------------------------
def sample_latent_image(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
denoise, sampler_type, add_noise, start_at_step, end_at_step, return_with_leftover_noise,
refiner_model=None, refiner_positive=None, refiner_negative=None):
if keys_exist_in_script("tile"):
tile_width, tile_height, tiling_strategy, blenderneko_tiled_nodes = script["tile"]
TSampler = blenderneko_tiled_nodes.TiledKSampler
TSamplerAdvanced = blenderneko_tiled_nodes.TiledKSamplerAdvanced
# Sample the latent_image(s) using the Comfy KSampler nodes
if sampler_type == "regular":
if keys_exist_in_script("tile"):
samples = TSampler().sample(model, seed, tile_width, tile_height, tiling_strategy, steps, cfg,
sampler_name, scheduler, positive, negative,
latent_image, denoise=denoise)[0]
else:
samples = KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
latent_image, denoise=denoise)[0]
elif sampler_type == "advanced":
if keys_exist_in_script("tile"):
samples = TSamplerAdvanced().sample(model, add_noise, seed, tile_width, tile_height, tiling_strategy,
steps, cfg, sampler_name, scheduler,
positive, negative, latent_image, start_at_step, end_at_step,
return_with_leftover_noise, "disabled", denoise=1.0)[0]
else:
samples = KSamplerAdvanced().sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler,
positive, negative, latent_image, start_at_step, end_at_step,
return_with_leftover_noise, denoise=1.0)[0]
elif sampler_type == "sdxl":
# Disable refiner if refine_at_step is -1
if end_at_step == -1:
end_at_step = steps
# Perform base model sampling
add_noise = return_with_leftover_noise = True
samples = KSamplerAdvanced().sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler,
positive, negative, latent_image, start_at_step, end_at_step,
return_with_leftover_noise, denoise=1.0)[0]
# Perform refiner model sampling
if refiner_model and end_at_step < steps:
add_noise = return_with_leftover_noise = False
samples = KSamplerAdvanced().sample(refiner_model, add_noise, seed, steps, cfg + REFINER_CFG_OFFSET,
sampler_name, scheduler, refiner_positive, refiner_negative,
samples, end_at_step, steps,
return_with_leftover_noise, denoise=1.0)[0]
# Check if "hiresfix" exists in the script after main sampling has taken place
if keys_exist_in_script("hiresfix"):
# Unpack the tuple from the script's "hiresfix" key
latent_upscale_method, upscale_by, hires_steps, hires_denoise, iterations, upscale_function = script["hiresfix"]
# Iterate for the given number of iterations
for _ in range(iterations):
upscaled_latent_image = upscale_function().upscale(samples, latent_upscale_method, upscale_by)[0]
# Use the regular KSampler for each iteration
if False: #if keys_exist_in_script("tile"): # Disabled for HiResFix
samples = TSampler().sample(model, seed, tile_width, tile_height, tiling_strategy, steps, cfg,
sampler_name, scheduler,
positive, negative, upscaled_latent_image, denoise=hires_denoise)[0]
else:
samples = KSampler().sample(model, seed, hires_steps, cfg, sampler_name, scheduler,
positive, negative, upscaled_latent_image, denoise=hires_denoise)[0]
return samples
# ---------------------------------------------------------------------------------------------------------------
# Clean globally stored objects of non-existant nodes
globals_cleanup(prompt)
# Init last_preview_images
if get_value_by_id("preview_images", my_unique_id) is None:
last_preview_images = list()
else:
last_preview_images = get_value_by_id("preview_images", 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 = get_value_by_id("latent", my_unique_id)
# Init last_output_images
if get_value_by_id("output_images", my_unique_id) == None:
last_output_images = TSC_KSampler.empty_image
else:
last_output_images = get_value_by_id("output_images", my_unique_id)
# Define filename_prefix
filename_prefix = "KSeff_{}".format(my_unique_id)
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# If the sampler state is "Sample" or "Script" without XY Plot
if sampler_state == "Sample" or (sampler_state == "Script" and not keys_exist_in_script("xyplot")):
# Store the global preview method
previous_preview_method = global_preview_method()
# Change the global preview method temporarily during sampling
set_preview_method(preview_method)
# Define commands arguments to send to front-end via websocket
if preview_method != "none" and "true" in vae_decode:
send_command_to_frontend(startListening=True, maxCount=steps-1, sendBlob=False)
samples = sample_latent_image(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
latent_image, denoise, sampler_type, add_noise, start_at_step, end_at_step,
return_with_leftover_noise, refiner_model, refiner_positive, refiner_negative)
# Cache samples in the 'last_helds' dictionary "latent"
update_value_by_id("latent", my_unique_id, samples)
# Define node output images & next Hold's vae_decode behavior
output_images = node_images = get_latest_image() ###
if vae_decode == "false":
update_value_by_id("vae_decode_flag", my_unique_id, True)
if preview_method == "none" or output_images == list():
output_images = TSC_KSampler.empty_image
else:
update_value_by_id("vae_decode_flag", my_unique_id, False)
decoded_image = vae_decode_latent(vae, samples, vae_decode)
output_images = node_images = decoded_image
# Cache output images to global 'last_helds' dictionary "output_images"
update_value_by_id("output_images", my_unique_id, output_images)
# Generate preview_images (PIL)
preview_images = preview_image(node_images, filename_prefix)
# Cache node preview images to global 'last_helds' dictionary "preview_images"
update_value_by_id("preview_images", my_unique_id, preview_images)
# Set xy_plot_flag to 'False' and set the stored (if any) XY Plot image tensor to 'None'
update_value_by_id("xy_plot_flag", my_unique_id, False)
update_value_by_id("xy_plot_image", my_unique_id, None)
if "output only" in vae_decode:
preview_images = list()
if preview_method != "none":
# Send message to front-end to revoke the last blob image from browser's memory (fixes preview duplication bug)
send_command_to_frontend(startListening=False)
if sampler_type == "sdxl":
result = (sdxl_tuple, samples, vae, output_images,)
else:
result = (model, positive, negative, samples, vae, output_images,)
return result if not preview_images and preview_method != "none" else {"ui": {"images": preview_images}, "result": result}
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# If the sampler state is "Hold"
elif sampler_state == "Hold":
output_images = last_output_images
preview_images = last_preview_images if "true" in vae_decode else list()
if get_value_by_id("vae_decode_flag", my_unique_id):
if "true" in vae_decode or "output only" in vae_decode:
output_images = node_images = vae_decode_latent(vae, last_latent, vae_decode)
update_value_by_id("vae_decode_flag", my_unique_id, False)
update_value_by_id("output_images", my_unique_id, output_images)
preview_images = preview_image(node_images, filename_prefix)
update_value_by_id("preview_images", my_unique_id, preview_images)
if "output only" in vae_decode:
preview_images = list()
# Check if holding an XY Plot image
elif get_value_by_id("xy_plot_flag", my_unique_id):
# Check if XY Plot node is connected
if keys_exist_in_script("xyplot"):
# Extract the 'xyplot_as_output_image' input parameter from the connected xy_plot
_, _, _, _, _, _, _, xyplot_as_output_image, _, _ = script["xyplot"]
if xyplot_as_output_image == True:
output_images = get_value_by_id("xy_plot_image", my_unique_id)
else:
output_images = get_value_by_id("output_images", my_unique_id)
preview_images = last_preview_images
else:
output_images = last_output_images
preview_images = last_preview_images if "true" in vae_decode else list()
if sampler_type == "sdxl":
result = (sdxl_tuple, last_latent, vae, output_images,)
else:
result = (model, positive, negative, last_latent, vae, output_images,)
return {"ui": {"images": preview_images}, "result": result}
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# If the sampler state is "Script" with XY Plot
elif sampler_state == "Script" and keys_exist_in_script("xyplot"):
# If no vae connected, throw errors
if vae == (None,):
print(f"{error('KSampler(Efficient) Error:')} VAE input must be connected in order to use the XY Plot script.")
return {"ui": {"images": list()},
"result": (model, positive, negative, last_latent, vae, TSC_KSampler.empty_image,)}
# If vae_decode is not set to true, print message that changing it to true
if "true" not in vae_decode:
print(f"{warning('KSampler(Efficient) Warning:')} VAE decoding must be set to \'true\'"
" for the XY Plot script, proceeding as if \'true\'.\n")
#___________________________________________________________________________________________________________
# Initialize, unpack, and clean variables for the XY Plot script
vae_name = None
ckpt_name = None
clip = None
clip_skip = None
refiner_name = None
refiner_clip = None
refiner_clip_skip = None
positive_prompt = None
negative_prompt = None
ascore = None
empty_latent_width = None
empty_latent_height = None
lora_stack = None
cnet_stack = None
# Split the 'samples' tensor
samples_tensors = torch.split(latent_image['samples'], 1, dim=0)
# Check if 'noise_mask' exists and split if it does
if 'noise_mask' in latent_image:
noise_mask_tensors = torch.split(latent_image['noise_mask'], 1, dim=0)
latent_tensors = [{'samples': img, 'noise_mask': mask} for img, mask in
zip(samples_tensors, noise_mask_tensors)]
else:
latent_tensors = [{'samples': img} for img in samples_tensors]
# Set latent only to the first of the batch
latent_image = latent_tensors[0]
# 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,\
xyplot_id, dependencies = script["xyplot"]
#_______________________________________________________________________________________________________
# The below section is used to check wether the XY_type is allowed for the Ksampler instance being used.
# If not the correct type, this section will abort the xy plot script.
samplers = {
"regular": {
"disallowed": ["AddNoise", "ReturnNoise", "StartStep", "EndStep", "RefineStep",
"Refiner", "Refiner On/Off", "AScore+", "AScore-"],
"name": "KSampler (Efficient)"
},
"advanced": {
"disallowed": ["RefineStep", "Denoise", "RefineStep", "Refiner", "Refiner On/Off",
"AScore+", "AScore-"],
"name": "KSampler Adv. (Efficient)"
},
"sdxl": {
"disallowed": ["AddNoise", "EndStep", "Denoise"],
"name": "KSampler SDXL (Eff.)"
}
}
# Define disallowed XY_types for each ksampler type
def get_ksampler_details(sampler_type):
return samplers.get(sampler_type, {"disallowed": [], "name": ""})
def suggest_ksampler(X_type, Y_type, current_sampler):
for sampler, details in samplers.items():
if sampler != current_sampler and X_type not in details["disallowed"] and Y_type not in details["disallowed"]:
return details["name"]
return "a different KSampler"
# In your main function or code segment:
details = get_ksampler_details(sampler_type)
disallowed_XY_types = details["disallowed"]
ksampler_name = details["name"]
if X_type in disallowed_XY_types or Y_type in disallowed_XY_types:
error_prefix = f"{error(f'{ksampler_name} Error:')}"
failed_type = []
if X_type in disallowed_XY_types:
failed_type.append(f"X_type: '{X_type}'")
if Y_type in disallowed_XY_types:
failed_type.append(f"Y_type: '{Y_type}'")
suggested_ksampler = suggest_ksampler(X_type, Y_type, sampler_type)
print(f"{error_prefix} Invalid value for {' and '.join(failed_type)}. "
f"Use {suggested_ksampler} for this XY Plot type."
f"\nDisallowed XY_types for this KSampler are: {', '.join(disallowed_XY_types)}.")
return {"ui": {"images": list()},
"result": (model, positive, negative, last_latent, vae, TSC_KSampler.empty_image,)}
#_______________________________________________________________________________________________________
# Unpack Effficient Loader dependencies
if dependencies is not None:
vae_name, ckpt_name, clip, clip_skip, refiner_name, refiner_clip, refiner_clip_skip,\
positive_prompt, negative_prompt, ascore, empty_latent_width, empty_latent_height,\
lora_stack, cnet_stack = dependencies
#_______________________________________________________________________________________________________
# Printout XY Plot values to be processed
def process_xy_for_print(value, replacement, type_):
if type_ == "Seeds++ Batch" and isinstance(value, list):
return [v + seed for v in value] # Add seed to every entry in the list
elif type_ == "Scheduler" and isinstance(value, tuple):
return value[0] # Return only the first entry of the tuple
elif type_ == "VAE" and isinstance(value, list):
# For each string in the list, extract the filename from the path
return [os.path.basename(v) for v in value]
elif (type_ == "Checkpoint" or type_ == "Refiner") and isinstance(value, list):
# For each tuple in the list, return only the first value if the second or third value is None
return [(os.path.basename(v[0]),) + v[1:] if v[1] is None or v[2] is None
else (os.path.basename(v[0]), v[1]) if v[2] is None
else (os.path.basename(v[0]),) + v[1:] for v in value]
elif type_ == "LoRA" and isinstance(value, list):
# Return only the first Tuple of each inner array
return [[(os.path.basename(v[0][0]),) + v[0][1:], "..."] if len(v) > 1
else [(os.path.basename(v[0][0]),) + v[0][1:]] for v in value]
elif type_ == "LoRA Batch" and isinstance(value, list):
# Extract the basename of the first value of the first tuple from each sublist
return [os.path.basename(v[0][0]) for v in value if v and isinstance(v[0], tuple) and v[0][0]]
elif (type_ == "LoRA Wt" or type_ == "LoRA MStr") and isinstance(value, list):
# Extract the first value of the first tuple from each sublist
return [v[0][1] for v in value if v and isinstance(v[0], tuple)]
elif type_ == "LoRA CStr" and isinstance(value, list):
# Extract the first value of the first tuple from each sublist
return [v[0][2] for v in value if v and isinstance(v[0], tuple)]
elif type_ == "ControlNetStrength" and isinstance(value, list):
# Extract the third entry of the first tuple from each inner list
return [round(inner_list[0][2], 3) for inner_list in value]
elif type_ == "ControlNetStart%" and isinstance(value, list):
# Extract the third entry of the first tuple from each inner list
return [round(inner_list[0][3], 3) for inner_list in value]
elif type_ == "ControlNetEnd%" and isinstance(value, list):
# Extract the third entry of the first tuple from each inner list
return [round(inner_list[0][4], 3) for inner_list in value]
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(X_value, replacement_X, X_type)
Y_value_processed = process_xy_for_print(Y_value, replacement_Y, Y_type)
print(info("-" * 40))
print(info('XY Plot Script Inputs:'))
print(info(f"(X) {X_type}:"))
for item in X_value_processed:
print(info(f" {item}"))
print(info(f"(Y) {Y_type}:"))
for item in Y_value_processed:
print(info(f" {item}"))
print(info("-" * 40))
#_______________________________________________________________________________________________________
# Perform various initializations in this section
# If not caching models, set to 1.
if cache_models == "False":
vae_cache = ckpt_cache = lora_cache = refn_cache = 1
else:
# Retrieve cache numbers
vae_cache, ckpt_cache, lora_cache, refn_cache = get_cache_numbers("XY Plot")
# Pack cache numbers in a tuple
cache = (vae_cache, ckpt_cache, lora_cache, refn_cache)
# Add seed to every entry in the list
X_value = [v + seed for v in X_value] if "Seeds++ Batch" == X_type else X_value
Y_value = [v + seed for v in Y_value] if "Seeds++ Batch" == Y_type else Y_value
# Embedd original prompts into prompt variables
positive_prompt = (positive_prompt, positive_prompt)
negative_prompt = (negative_prompt, negative_prompt)
# Set lora_stack to None if one of types are LoRA
if "LoRA" in X_type or "LoRA" in Y_type:
lora_stack = None
# Define the manipulated and static Control Net Variables with a tuple with shape (cn_1, cn_2, cn_3).
# The information in this tuple will be used by the plotter to properly plot Control Net XY input types.
cn_1, cn_2, cn_3 = None, None, None
# If X_type has "ControlNet" or both X_type and Y_type have "ControlNet"
if "ControlNet" in X_type:
cn_1, cn_2, cn_3 = X_value[0][0][2], X_value[0][0][3], X_value[0][0][4]
# If only Y_type has "ControlNet" and not X_type
elif "ControlNet" in Y_type:
cn_1, cn_2, cn_3 = Y_value[0][0][2], Y_value[0][0][3], Y_value[0][0][4]
# Additional checks for other substrings
if "ControlNetStrength" in X_type or "ControlNetStrength" in Y_type:
cn_1 = None
if "ControlNetStart%" in X_type or "ControlNetStart%" in Y_type:
cn_2 = None
if "ControlNetEnd%" in X_type or "ControlNetEnd%" in Y_type:
cn_3 = None
# Embed the information in cnet_stack
cnet_stack = (cnet_stack, (cn_1, cn_2, cn_3))
# Optimize image generation by prioritization:
priority = [
"Checkpoint",
"Refiner",
"LoRA",
"VAE",
]
conditioners = {
"Positive Prompt S/R",
"Negative Prompt S/R",
"AScore+",
"AScore-",
"Clip Skip",
"Clip Skip (Refiner)",
"ControlNetStrength",
"ControlNetStart%",
"ControlNetEnd%"
}
# Get priority values; return a high number if the type is not in priority list
x_priority = priority.index(X_type) if X_type in priority else 999
y_priority = priority.index(Y_type) if Y_type in priority else 999
# Check if both are conditioners
are_both_conditioners = X_type in conditioners and Y_type in conditioners
# Special cases
is_special_case = (
(X_type == "Refiner On/Off" and Y_type in ["RefineStep", "Steps"]) or
(X_type == "Nothing" and Y_type != "Nothing")
)
# Determine whether to flip
flip_xy = (y_priority < x_priority and not are_both_conditioners) or is_special_case
# Perform the flip if necessary
if flip_xy:
X_type, Y_type = Y_type, X_type
X_value, Y_value = Y_value, X_value
#_______________________________________________________________________________________________________
# The below code will clean from the cache any ckpt/vae/lora models it will not be reusing.
# Note: Special LoRA types will not trigger cache: "LoRA Batch", "LoRA Wt", "LoRA MStr", "LoRA CStr"
# Map the type names to the dictionaries
dict_map = {"VAE": [], "Checkpoint": [], "LoRA": [], "Refiner": []}
# Create a list of tuples with types and values
type_value_pairs = [(X_type, X_value.copy()), (Y_type, Y_value.copy())]
# 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
vae_dict = dict_map.get("VAE", [])
# Construct ckpt_dict and also update vae_dict based on the third entry of the tuples in dict_map["Checkpoint"]
if dict_map.get("Checkpoint", []):
ckpt_dict = [t[0] for t in dict_map["Checkpoint"]]
for t in dict_map["Checkpoint"]:
if t[2] is not None and t[2] != "Baked VAE":
vae_dict.append(t[2])
else:
ckpt_dict = []
lora_dict = [[t,] for t in dict_map.get("LoRA", [])] if dict_map.get("LoRA", []) else []
# Construct refn_dict
if dict_map.get("Refiner", []):
refn_dict = [t[0] for t in dict_map["Refiner"]]
else:
refn_dict = []
# If both ckpt_dict and lora_dict are not empty, manipulate lora_dict as described
if ckpt_dict and lora_dict:
lora_dict = [(lora_stack, ckpt) for ckpt in ckpt_dict for lora_stack 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_stack, ckpt_name) for lora_stack in lora_dict]
# Avoid caching models accross both X and Y
if X_type == "Checkpoint":
lora_dict = []
refn_dict = []
elif X_type == "Refiner":
ckpt_dict = []
lora_dict = []
elif X_type == "LoRA":
ckpt_dict = []
refn_dict = []
### Print dict_arrays for debugging
###print(f"vae_dict={vae_dict}\nckpt_dict={ckpt_dict}\nlora_dict={lora_dict}\nrefn_dict={refn_dict}")
# Clean values that won't be reused
clear_cache_by_exception(xyplot_id, vae_dict=vae_dict, ckpt_dict=ckpt_dict, lora_dict=lora_dict, refn_dict=refn_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, add_noise, seed, steps, start_at_step, end_at_step,
return_with_leftover_noise, cfg, sampler_name, scheduler, denoise, vae_name, ckpt_name,
clip_skip, refiner_name, refiner_clip_skip, positive_prompt, negative_prompt, ascore,
lora_stack, cnet_stack, var_label, num_label):
# Define default max label size limit
max_label_len = 36
# If var_type is "AddNoise", update 'add_noise' with 'var', and generate text label
if var_type == "AddNoise":
add_noise = var
text = f"AddNoise: {add_noise}"
# If var_type is "Seeds++ Batch", generate text label
elif var_type == "Seeds++ Batch":
seed = var
text = f"Seed: {seed}"
# If var_type is "Steps", update 'steps' with 'var' and generate text label
elif var_type == "Steps":
steps = var
text = f"Steps: {steps}"
# If var_type is "StartStep", update 'start_at_step' with 'var' and generate text label
elif var_type == "StartStep":
start_at_step = var
text = f"StartStep: {start_at_step}"
# If var_type is "EndStep", update 'end_at_step' with 'var' and generate text label
elif var_type == "EndStep":
end_at_step = var
text = f"EndStep: {end_at_step}"
# If var_type is "RefineStep", update 'end_at_step' with 'var' and generate text label
elif var_type == "RefineStep":
end_at_step = var
text = f"RefineStep: {end_at_step}"
# If var_type is "ReturnNoise", update 'return_with_leftover_noise' with 'var', and generate text label
elif var_type == "ReturnNoise":
return_with_leftover_noise = var
text = f"ReturnNoise: {return_with_leftover_noise}"
# If var_type is "CFG Scale", update cfg with var and generate text label
elif var_type == "CFG Scale":
cfg = var
text = f"CFG: {round(cfg,2)}"
# If var_type is "Sampler", update sampler_name and scheduler with var, and generate text label
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 "AScore+", update positive ascore and generate labels
elif var_type == "AScore+":
ascore = (var,ascore[1])
text = f"+AScore: {ascore[0]}"
# If var_type is "AScore-", update negative ascore and generate labels
elif var_type == "AScore-":
ascore = (ascore[0],var)
text = f"-AScore: {ascore[1]}"
# 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])
if var[2] != None:
vae_name = var[2]
ckpt_filename = os.path.splitext(os.path.basename(ckpt_name))[0]
text = f"{ckpt_filename}"
# If var_type is "Refiner", update model and clip (if needed) and generate labels
elif var_type == "Refiner":
refiner_name = var[0]
if var[1] == None:
refiner_clip_skip = (refiner_clip_skip[1],refiner_clip_skip[1])
else:
refiner_clip_skip = (var[1],refiner_clip_skip[1])
ckpt_filename = os.path.splitext(os.path.basename(refiner_name))[0]
text = f"{ckpt_filename}"
# If var_type is "Refiner On/Off", set end_at_step = max steps and generate labels
elif var_type == "Refiner On/Off":
end_at_step = int(var * steps)
text = f"Refiner: {'On' if var < 1 else 'Off'}"
elif var_type == "Clip Skip":
clip_skip = (var, clip_skip[1])
text = f"ClipSkip ({clip_skip[0]})"
elif var_type == "Clip Skip (Refiner)":
refiner_clip_skip = (var, refiner_clip_skip[1])
text = f"RefClipSkip ({refiner_clip_skip[0]})"
elif "LoRA" in var_type:
if not lora_stack:
lora_stack = var.copy()
else:
# Updating the first tuple of lora_stack
lora_stack[0] = tuple(v if v is not None else lora_stack[0][i] for i, v in enumerate(var[0]))
max_label_len = 50 + (12 * (len(lora_stack) - 1))
lora_name, lora_model_wt, lora_clip_wt = lora_stack[0]
lora_filename = os.path.splitext(os.path.basename(lora_name))[0]
if var_type == "LoRA":
if len(lora_stack) == 1:
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_stack) > 1:
lora_filenames = [os.path.splitext(os.path.basename(lora_name))[0] for lora_name, _, _ in
lora_stack]
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_stack]
non_name_length = sum(
len(f"({lora_details[i][0]},{lora_details[i][1]})") + 2 for i in range(len(lora_stack)))
available_space = max_label_len - non_name_length
max_name_length = available_space // len(lora_stack)
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)
elif var_type == "LoRA Batch":
text = f"LoRA: {lora_filename}"
elif var_type == "LoRA Wt":
lora_model_wt = format(float(lora_model_wt), ".2f").rstrip('0').rstrip('.')
text = f"LoRA[Wt]: {lora_model_wt}"
elif var_type == "LoRA MStr":
lora_model_wt = format(float(lora_model_wt), ".2f").rstrip('0').rstrip('.')
text = f"LoRA[M]: {lora_model_wt}"
elif var_type == "LoRA CStr":
lora_clip_wt = format(float(lora_clip_wt), ".2f").rstrip('0').rstrip('.')
text = f"LoRA[C]: {lora_clip_wt}"
elif var_type in ["ControlNetStrength", "ControlNetStart%", "ControlNetEnd%"]:
if "Strength" in var_type:
entry_index = 2
elif "Start%" in var_type:
entry_index = 3
elif "End%" in var_type:
entry_index = 4
# If the first entry of cnet_stack is None, set it to var
if cnet_stack[0] is None:
cnet_stack = (var, cnet_stack[1])
else:
# Extract the desired entry from var's first tuple
entry_from_var = var[0][entry_index]
# Extract the first tuple from cnet_stack[0][0] and make it mutable
first_cn_entry = list(cnet_stack[0][0])
# Replace the appropriate entry
first_cn_entry[entry_index] = entry_from_var
# Further update first_cn_entry based on cnet_stack[1]
for i, value in enumerate(cnet_stack[1][-3:]): # Considering last 3 entries
if value is not None:
first_cn_entry[i + 2] = value # "+2" to offset for the first 2 entries of the tuple
# Convert back to tuple for the updated values
updated_first_entry = tuple(first_cn_entry)
# Construct the updated cnet_stack[0] using the updated_first_entry and the rest of the values from cnet_stack[0]
updated_cnet_stack_0 = [updated_first_entry] + list(cnet_stack[0][1:])
# Update cnet_stack
cnet_stack = (updated_cnet_stack_0, cnet_stack[1])
# Print the desired value
text = f'{var_type}: {round(cnet_stack[0][0][entry_index], 3)}'
else: # No matching type found
text=""
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 == "Refiner" or "LoRA" in var_type):
var_label = truncate_texts(var_label, num_label, max_label_len)
# Return the modified variables
return add_noise, seed, steps, start_at_step, end_at_step, return_with_leftover_noise, cfg,\
sampler_name, scheduler, denoise, vae_name, ckpt_name, clip_skip, \
refiner_name, refiner_clip_skip, positive_prompt, negative_prompt, ascore,\
lora_stack, cnet_stack, var_label
#_______________________________________________________________________________________________________
# The function below is used to optimally load Checkpoint/LoRA/VAE models between generations.
def define_model(model, clip, clip_skip, refiner_model, refiner_clip, refiner_clip_skip,
ckpt_name, refiner_name, positive, negative, refiner_positive, refiner_negative,
positive_prompt, negative_prompt, ascore, vae, vae_name, lora_stack, cnet_stack, index,
types, xyplot_id, cache, sampler_type, empty_latent_width, empty_latent_height):
# Variable to track wether to encode prompt or not
encode = False
encode_refiner = False
# Unpack types tuple
X_type, Y_type = types
# Note: Index is held at 0 when Y_type == "Nothing"
# 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_stack is None:
model, clip, _ = load_checkpoint(ckpt_name, xyplot_id, cache=cache[1])
else: # Load Efficient Loader LoRA
model, clip = load_lora(lora_stack, ckpt_name, xyplot_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_stack, ckpt_name, xyplot_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_stack, ckpt_name, xyplot_id,
cache=None, ckpt_cache=cache[1])
encode = True
elif X_type == "LoRA Batch" or X_type == "LoRA Wt" or X_type == "LoRA MStr" or X_type == "LoRA CStr":
# Don't cache Checkpoints or LoRAs
model, clip = load_lora(lora_stack, ckpt_name, xyplot_id, cache=0)
encode = True
if (X_type == "Refiner" and index == 0) or Y_type == "Refiner":
refiner_model, refiner_clip, _ = \
load_checkpoint(refiner_name, xyplot_id, output_vae=False, cache=cache[3], ckpt_type="refn")
encode_refiner = True
# Encode base prompt if required
encode_types = ["Positive Prompt S/R", "Negative Prompt S/R", "Clip Skip", "ControlNetStrength",
"ControlNetStart%", "ControlNetEnd%"]
if (X_type in encode_types and index == 0) or Y_type in encode_types:
encode = True
# Encode refiner prompt if required
encode_refiner_types = ["Positive Prompt S/R", "Negative Prompt S/R", "AScore+", "AScore-",
"Clip Skip (Refiner)"]
if (X_type in encode_refiner_types and index == 0) or Y_type in encode_refiner_types:
encode_refiner = True
# Encode base prompt
if encode == True:
positive, negative = \
encode_prompts(positive_prompt, negative_prompt, clip, clip_skip, refiner_clip,
refiner_clip_skip, ascore, sampler_type == "sdxl", empty_latent_width,
empty_latent_height, return_type="base")
# Apply ControlNet Stack if given
if cnet_stack:
positive = TSC_Apply_ControlNet_Stack().apply_cnet_stack(positive, cnet_stack)
if encode_refiner == True:
refiner_positive, refiner_negative = \
encode_prompts(positive_prompt, negative_prompt, clip, clip_skip, refiner_clip,
refiner_clip_skip, ascore, sampler_type == "sdxl", empty_latent_width,
empty_latent_height, return_type="refiner")
# Load VAE if required
if (X_type == "VAE" and index == 0) or Y_type == "VAE":
#vae = load_vae(vae_name, xyplot_id, cache=cache[0])
vae = get_bvae_by_ckpt_name(ckpt_name) if vae_name == "Baked VAE" \
else load_vae(vae_name, xyplot_id, cache=cache[0])
elif X_type == "Checkpoint" and index == 0 and vae_name:
vae = get_bvae_by_ckpt_name(ckpt_name) if vae_name == "Baked VAE" \
else load_vae(vae_name, xyplot_id, cache=cache[0])
return model, positive, negative, refiner_model, refiner_positive, refiner_negative, vae
# ______________________________________________________________________________________________________
# The below function is used to generate the results based on all the processed variables
def process_values(model, refiner_model, add_noise, seed, steps, start_at_step, end_at_step,
return_with_leftover_noise, cfg, sampler_name, scheduler, positive, negative,
refiner_positive, refiner_negative, latent_image, denoise, vae, vae_decode,
sampler_type, latent_list=[], image_tensor_list=[], image_pil_list=[]):
if preview_method != "none":
send_command_to_frontend(startListening=True, maxCount=steps - 1, sendBlob=False)
samples = sample_latent_image(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
latent_image, denoise, sampler_type, add_noise, start_at_step, end_at_step,
return_with_leftover_noise, refiner_model, refiner_positive, refiner_negative)
# Add the latent tensor to the tensors list
latent_list.append(samples)
# Decode the latent tensor
image = vae_decode_latent(vae, samples, vae_decode)
# 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 = []
# Store the KSamplers original scheduler inside the same scheduler variable
scheduler = (scheduler, scheduler)
# Store the Eff Loaders original clip_skips inside the same clip_skip variables
clip_skip = (clip_skip, clip_skip)
refiner_clip_skip = (refiner_clip_skip, refiner_clip_skip)
# Store types in a Tuple for easy function passing
types = (X_type, Y_type)
# Store the global preview method
previous_preview_method = global_preview_method()
# Change the global preview method temporarily during this node's execution
set_preview_method(preview_method)
# Fill Plot Rows (X)
for X_index, X in enumerate(X_value):
# Define X parameters and generate labels
add_noise, seed, steps, start_at_step, end_at_step, return_with_leftover_noise, cfg,\
sampler_name, scheduler, denoise, vae_name, ckpt_name, clip_skip,\
refiner_name, refiner_clip_skip, positive_prompt, negative_prompt, ascore,\
lora_stack, cnet_stack, X_label = \
define_variable(X_type, X, add_noise, seed, steps, start_at_step, end_at_step,
return_with_leftover_noise, cfg, sampler_name, scheduler, denoise, vae_name,
ckpt_name, clip_skip, refiner_name, refiner_clip_skip, positive_prompt,
negative_prompt, ascore, lora_stack, cnet_stack, X_label, len(X_value))
if X_type != "Nothing" and Y_type == "Nothing":
# Models & Conditionings
model, positive, negative, refiner_model, refiner_positive, refiner_negative, vae = \
define_model(model, clip, clip_skip[0], refiner_model, refiner_clip, refiner_clip_skip[0],
ckpt_name, refiner_name, positive, negative, refiner_positive, refiner_negative,
positive_prompt[0], negative_prompt[0], ascore, vae, vae_name, lora_stack, cnet_stack[0],
0, types, xyplot_id, cache, sampler_type, empty_latent_width, empty_latent_height)
# Generate Results
latent_list, image_tensor_list, image_pil_list = \
process_values(model, refiner_model, add_noise, seed, steps, start_at_step, end_at_step,
return_with_leftover_noise, cfg, sampler_name, scheduler[0], positive, negative,
refiner_positive, refiner_negative, latent_image, denoise, vae, vae_decode, sampler_type)
elif X_type != "Nothing" and Y_type != "Nothing":
# Seed control based on loop index during Batch
for Y_index, Y in enumerate(Y_value):
# Define Y parameters and generate labels
add_noise, seed, steps, start_at_step, end_at_step, return_with_leftover_noise, cfg,\
sampler_name, scheduler, denoise, vae_name, ckpt_name, clip_skip,\
refiner_name, refiner_clip_skip, positive_prompt, negative_prompt, ascore,\
lora_stack, cnet_stack, Y_label = \
define_variable(Y_type, Y, add_noise, seed, steps, start_at_step, end_at_step,
return_with_leftover_noise, cfg, sampler_name, scheduler, denoise, vae_name,
ckpt_name, clip_skip, refiner_name, refiner_clip_skip, positive_prompt,
negative_prompt, ascore, lora_stack, cnet_stack, Y_label, len(Y_value))
# Models & Conditionings
model, positive, negative, refiner_model, refiner_positive, refiner_negative, vae = \
define_model(model, clip, clip_skip[0], refiner_model, refiner_clip, refiner_clip_skip[0],
ckpt_name, refiner_name, positive, negative, refiner_positive, refiner_negative,
positive_prompt[0], negative_prompt[0], ascore, vae, vae_name, lora_stack, cnet_stack[0],
Y_index, types, xyplot_id, cache, sampler_type, empty_latent_width,
empty_latent_height)
# Generate Results
latent_list, image_tensor_list, image_pil_list = \
process_values(model, refiner_model, add_noise, seed, steps, start_at_step, end_at_step,
return_with_leftover_noise, cfg, sampler_name, scheduler[0],
positive, negative, refiner_positive, refiner_negative, latent_image,
denoise, vae, vae_decode, sampler_type)
# Clean up cache
if cache_models == "False":
clear_cache_by_exception(xyplot_id, vae_dict=[], ckpt_dict=[], lora_dict=[], refn_dict=[])
else:
# Avoid caching models accross both X and Y
if X_type == "Checkpoint":
clear_cache_by_exception(xyplot_id, lora_dict=[], refn_dict=[])
elif X_type == "Refiner":
clear_cache_by_exception(xyplot_id, ckpt_dict=[], lora_dict=[])
elif X_type == "LoRA":
clear_cache_by_exception(xyplot_id, ckpt_dict=[], refn_dict=[])
# __________________________________________________________________________________________________________
# Function for printing all plot variables (WARNING: This function is an absolute mess)
def print_plot_variables(X_type, Y_type, X_value, Y_value, add_noise, seed, steps, start_at_step, end_at_step,
return_with_leftover_noise, cfg, sampler_name, scheduler, denoise, vae_name, ckpt_name,
clip_skip, refiner_name, refiner_clip_skip, ascore, lora_stack, cnet_stack, sampler_type,
num_rows, num_cols, latent_height, latent_width):
print("-" * 40) # Print an empty line followed by a separator line
print(f"{xyplot_message('XY Plot Results:')}")
def get_vae_name(X_type, Y_type, X_value, Y_value, vae_name):
if X_type == "VAE":
vae_name = "\n ".join(map(lambda x: os.path.splitext(os.path.basename(str(x)))[0], X_value))
elif Y_type == "VAE":
vae_name = "\n ".join(map(lambda y: os.path.splitext(os.path.basename(str(y)))[0], Y_value))
elif vae_name:
vae_name = os.path.splitext(os.path.basename(str(vae_name)))[0]
else:
vae_name = ""
return vae_name
def get_clip_skip(X_type, Y_type, X_value, Y_value, cskip):
if "Clip Skip" in X_type:
cskip = ", ".join(map(str, X_value))
elif "Clip Skip" in Y_type:
cskip = ", ".join(map(str, Y_value))
elif cskip[1] != None:
cskip = cskip[1]
else:
cskip = ""
return cskip
def get_checkpoint_name(X_type, Y_type, X_value, Y_value, ckpt_name, clip_skip, mode, vae_name=None):
# Define types based on mode
primary_type = "Checkpoint" if mode == "ckpt" else "Refiner"
clip_type = "Clip Skip" if mode == "ckpt" else "Clip Skip (Refiner)"
# Determine ckpt and othr based on primary type
if X_type == primary_type:
ckpt_type, ckpt_value = X_type, X_value.copy()
othr_type, othr_value = Y_type, Y_value.copy()
elif Y_type == primary_type:
ckpt_type, ckpt_value = Y_type, Y_value.copy()
othr_type, othr_value = X_type, X_value.copy()
else:
# Process as per original function if mode is "ckpt"
if mode == "ckpt":
if vae_name:
vae_name = get_vae_name(X_type, Y_type, X_value, Y_value, vae_name)
clip_skip = get_clip_skip(X_type, Y_type, X_value, Y_value, clip_skip)
ckpt_name = os.path.basename(ckpt_name)
return os.path.basename(ckpt_name), clip_skip, vae_name
else:
# For refn mode
return os.path.basename(ckpt_name), clip_skip
# Process clip skip based on mode
if othr_type == clip_type:
clip_skip = ", ".join(map(str, othr_value))
elif ckpt_value[0][1] != None:
clip_skip = None
# Process vae_name based on mode
if mode == "ckpt":
if othr_type == "VAE":
vae_name = get_vae_name(X_type, Y_type, X_value, Y_value, vae_name)
elif ckpt_value[0][2] != None:
vae_name = None
def format_name(v, _type):
base = os.path.basename(v[0])
if _type == clip_type and v[1] is not None:
return base
elif _type == "VAE" and v[1] is not None and v[2] is not None:
return f"{base}({v[1]})"
elif v[1] is not None and v[2] is not None:
return f"{base}({v[1]}) + vae:{v[2]}"
elif v[1] is not None:
return f"{base}({v[1]})"
else:
return base
ckpt_name = "\n ".join([format_name(v, othr_type) for v in ckpt_value])
if mode == "ckpt":
return ckpt_name, clip_skip, vae_name
else:
return ckpt_name, clip_skip
def get_lora_name(X_type, Y_type, X_value, Y_value, lora_stack=None):
lora_name = lora_wt = lora_model_str = lora_clip_str = None
# Check for all possible LoRA types
lora_types = ["LoRA", "LoRA Batch", "LoRA Wt", "LoRA MStr", "LoRA CStr"]
if X_type not in lora_types and Y_type not in lora_types:
if lora_stack:
names_list = []
for name, model_wt, clip_wt in lora_stack:
base_name = os.path.splitext(os.path.basename(name))[0]
formatted_str = f"{base_name}({round(model_wt, 3)},{round(clip_wt, 3)})"
names_list.append(formatted_str)
lora_name = f"[{', '.join(names_list)}]"
else:
if X_type in lora_types:
value = get_lora_sublist_name(X_type, X_value)
if X_type == "LoRA" or X_type == "LoRA Batch":
lora_name = value
elif X_type == "LoRA MStr":
lora_model_str = value
elif X_type == "LoRA CStr":
lora_clip_str = value
elif X_type == "LoRA Wt":
lora_wt = value
if Y_type in lora_types:
value = get_lora_sublist_name(Y_type, Y_value)
if Y_type == "LoRA" or Y_type == "LoRA Batch":
lora_name = value
elif Y_type == "LoRA MStr":
lora_model_str = value
elif Y_type == "LoRA CStr":
lora_clip_str = value
elif Y_type == "LoRA Wt":
lora_wt = value
return lora_name, lora_wt, lora_model_str, lora_clip_str
def get_lora_sublist_name(lora_type, lora_value):
if lora_type == "LoRA" or lora_type == "LoRA Batch":
formatted_sublists = []
for sublist in lora_value:
formatted_entries = []
for x in sublist:
base_name = os.path.splitext(os.path.basename(str(x[0])))[0]
formatted_str = f"{base_name}({round(x[1], 3)},{round(x[2], 3)})" if lora_type == "LoRA" else f"{base_name}"
formatted_entries.append(formatted_str)
formatted_sublists.append(f"{', '.join(formatted_entries)}")
return "\n ".join(formatted_sublists)
elif lora_type == "LoRA MStr":
return ", ".join([str(round(x[0][1], 3)) for x in lora_value])
elif lora_type == "LoRA CStr":
return ", ".join([str(round(x[0][2], 3)) for x in lora_value])
elif lora_type == "LoRA Wt":
return ", ".join([str(round(x[0][1], 3)) for x in lora_value]) # assuming LoRA Wt uses the second value
else:
return ""
# VAE, Checkpoint, Clip Skip, LoRA
ckpt_name, clip_skip, vae_name = get_checkpoint_name(X_type, Y_type, X_value, Y_value, ckpt_name, clip_skip, "ckpt", vae_name)
lora_name, lora_wt, lora_model_str, lora_clip_str = get_lora_name(X_type, Y_type, X_value, Y_value, lora_stack)
refiner_name, refiner_clip_skip = get_checkpoint_name(X_type, Y_type, X_value, Y_value, refiner_name, refiner_clip_skip, "refn")
# AddNoise
add_noise = ", ".join(map(str, X_value)) if X_type == "AddNoise" else ", ".join(
map(str, Y_value)) if Y_type == "AddNoise" else add_noise
# Seeds++ Batch
seed = "\n ".join(map(str, X_value)) if X_type == "Seeds++ Batch" else "\n ".join(
map(str, Y_value)) if Y_type == "Seeds++ Batch" else seed
# Steps
steps = ", ".join(map(str, X_value)) if X_type == "Steps" else ", ".join(
map(str, Y_value)) if Y_type == "Steps" else steps
# StartStep
start_at_step = ", ".join(map(str, X_value)) if X_type == "StartStep" else ", ".join(
map(str, Y_value)) if Y_type == "StartStep" else start_at_step
# EndStep/RefineStep
end_at_step = ", ".join(map(str, X_value)) if X_type in ["EndStep", "RefineStep"] else ", ".join(
map(str, Y_value)) if Y_type in ["EndStep", "RefineStep"] else end_at_step
# ReturnNoise
return_with_leftover_noise = ", ".join(map(str, X_value)) if X_type == "ReturnNoise" else ", ".join(
map(str, Y_value)) if Y_type == "ReturnNoise" else return_with_leftover_noise
# CFG
cfg = ", ".join(map(str, X_value)) if X_type == "CFG Scale" else ", ".join(
map(str, Y_value)) if Y_type == "CFG Scale" else round(cfg,3)
# Sampler/Scheduler
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
denoise = ", ".join(map(str, X_value)) if X_type == "Denoise" else ", ".join(
map(str, Y_value)) if Y_type == "Denoise" else round(denoise,3)
# Check if ascore is None
if ascore is None:
pos_ascore = neg_ascore = None
else:
# Ascore+
pos_ascore = (", ".join(map(str, X_value)) if X_type == "Ascore+"
else ", ".join(map(str, Y_value)) if Y_type == "Ascore+" else round(ascore[0],3))
# Ascore-
neg_ascore = (", ".join(map(str, X_value)) if X_type == "Ascore-"
else ", ".join(map(str, Y_value)) if Y_type == "Ascore-" else round(ascore[1],3))
#..........................................PRINTOUTS....................................................
print(f"(X) {X_type}")
print(f"(Y) {Y_type}")
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}")
print(f"ckpt: {ckpt_name if ckpt_name is not None else ''}")
if clip_skip:
print(f"clip_skip: {clip_skip}")
if sampler_type == "sdxl":
if refiner_clip_skip == "_":
print(f"refiner(clipskip): {refiner_name if refiner_name is not None else ''}")
else:
print(f"refiner: {refiner_name if refiner_name is not None else ''}")
print(f"refiner_clip_skip: {refiner_clip_skip if refiner_clip_skip is not None else ''}")
print(f"+ascore: {pos_ascore if pos_ascore is not None else ''}")
print(f"-ascore: {neg_ascore if neg_ascore is not None else ''}")
if lora_name:
print(f"lora: {lora_name if lora_name is not None else ''}")
if lora_wt:
print(f"lora_wt: {lora_wt}")
if lora_model_str:
print(f"lora_model_str: {lora_model_str}")
if lora_clip_str:
print(f"lora_clip_str: {lora_clip_str}")
if vae_name:
print(f"vae: {vae_name}")
if sampler_type == "advanced":
print(f"add_noise: {add_noise}")
print(f"seed: {seed}")
print(f"steps: {steps}")
if sampler_type == "advanced":
print(f"start_at_step: {start_at_step}")
print(f"end_at_step: {end_at_step}")
print(f"return_noise: {return_with_leftover_noise}")
if sampler_type == "sdxl":
print(f"start_at_step: {start_at_step}")
if X_type == "Refiner On/Off":
print(f"refine_at_percent: {X_value[0]}")
elif Y_type == "Refiner On/Off":
print(f"refine_at_percent: {Y_value[0]}")
else:
print(f"refine_at_step: {end_at_step}")
print(f"cfg: {cfg}")
if scheduler == "_":
print(f"sampler(scheduler): {sampler_name}")
else:
print(f"sampler: {sampler_name}")
print(f"scheduler: {scheduler}")
if sampler_type == "regular":
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}")
if "ControlNet" in X_type or "ControlNet" in Y_type:
cnet_strength, cnet_start_pct, cnet_end_pct = cnet_stack[1]
if "ControlNet" in X_type:
if "Strength" in X_type:
cnet_strength = [str(round(inner_list[0][2], 3)) for inner_list in X_value if
isinstance(inner_list, list) and
inner_list and isinstance(inner_list[0], tuple) and len(inner_list[0]) >= 3]
if "Start%" in X_type:
cnet_start_pct = [str(round(inner_list[0][3], 3)) for inner_list in X_value if
isinstance(inner_list, list) and
inner_list and isinstance(inner_list[0], tuple) and len(inner_list[0]) >= 3]
if "End%" in X_type:
cnet_end_pct = [str(round(inner_list[0][4], 3)) for inner_list in X_value if
isinstance(inner_list, list) and
inner_list and isinstance(inner_list[0], tuple) and len(inner_list[0]) >= 3]
if "ControlNet" in Y_type:
if "Strength" in Y_type:
cnet_strength = [str(round(inner_list[0][2], 3)) for inner_list in Y_value if
isinstance(inner_list, list) and
inner_list and isinstance(inner_list[0], tuple) and len(
inner_list[0]) >= 3]
if "Start%" in Y_type:
cnet_start_pct = [str(round(inner_list[0][3], 3)) for inner_list in Y_value if
isinstance(inner_list, list) and
inner_list and isinstance(inner_list[0], tuple) and len(
inner_list[0]) >= 3]
if "End%" in Y_type:
cnet_end_pct = [str(round(inner_list[0][4], 3)) for inner_list in Y_value if
isinstance(inner_list, list) and
inner_list and isinstance(inner_list[0], tuple) and len(
inner_list[0]) >= 3]
if "ControlNet" in X_type or "ControlNet" in Y_type:
print(f"cnet_strength: {', '.join(cnet_strength) if isinstance(cnet_strength, list) else cnet_strength}")
print(f"cnet_start%: {', '.join(cnet_start_pct) if isinstance(cnet_start_pct, list) else cnet_start_pct}")
print(f"cnet_end%: {', '.join(cnet_end_pct) if isinstance(cnet_end_pct, list) else cnet_end_pct}")
# ______________________________________________________________________________________________________
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_flag", 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)
# Extract final image dimensions
latent_height, latent_width = latent_list[0]['samples'].shape[2] * 8, latent_list[0]['samples'].shape[3] * 8
# Print XY Plot Results
print_plot_variables(X_type, Y_type, X_value, Y_value, add_noise, seed, steps, start_at_step, end_at_step,
return_with_leftover_noise, cfg, sampler_name, scheduler, denoise, vae_name, ckpt_name,
clip_skip, refiner_name, refiner_clip_skip, ascore, lora_stack, cnet_stack,
sampler_type, num_rows, num_cols, latent_height, latent_width)
# Concatenate the 'samples' and 'noise_mask' tensors along the first dimension (dim=0)
keys = latent_list[0].keys()
result = {}
for key in keys:
tensors = [d[key] for d in latent_list]
result[key] = torch.cat(tensors, dim=0)
latent_list = result
# 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
xy_plot_image = pil2tensor(background)
# Set xy_plot_flag to 'True' and cache the xy_plot_image tensor
update_value_by_id("xy_plot_image", my_unique_id, xy_plot_image)
update_value_by_id("xy_plot_flag", my_unique_id, True)
# Generate the preview_images and cache results
preview_images = preview_image(xy_plot_image, filename_prefix)
update_value_by_id("preview_images", my_unique_id, preview_images)
# Generate output_images and cache results
output_images = torch.stack([tensor.squeeze() for tensor in image_tensor_list])
update_value_by_id("output_images", my_unique_id, output_images)
# Set the output_image the same as plot image defined by 'xyplot_as_output_image'
if xyplot_as_output_image == True:
output_images = xy_plot_image
# Print cache if set to true
if cache_models == "True":
print_loaded_objects_entries(xyplot_id, prompt)
print("-" * 40) # Print an empty line followed by a separator line
# Set the preview method back to its original state
set_preview_method(previous_preview_method)
if preview_method != "none":
# Send message to front-end to revoke the last blob image from browser's memory (fixes preview duplication bug)
send_command_to_frontend(startListening=False)
if sampler_type == "sdxl":
result = (sdxl_tuple, {"samples": latent_list}, vae, output_images,)
else:
result = (model, positive, negative, {"samples": latent_list}, vae, output_images,)
return {"ui": {"images": preview_images}, "result": result}
#=======================================================================================================================
# TSC KSampler Adv (Efficient)
class TSC_KSamplerAdvanced(TSC_KSampler):
@classmethod
def INPUT_TYPES(cls):
return {"required":
{"sampler_state": (["Sample", "Hold", "Script"],),
"model": ("MODEL",),
"add_noise": (["enable", "disable"],),
"noise_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",),
"start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
"end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
"return_with_leftover_noise": (["disable", "enable"],),
"preview_method": (["auto", "latent2rgb", "taesd", "none"],),
"vae_decode": (["true", "true (tiled)", "false", "output only", "output only (tiled)"],),
},
"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_adv"
CATEGORY = "Efficiency Nodes/Sampling"
def sample_adv(self, sampler_state, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative,
latent_image, start_at_step, end_at_step, return_with_leftover_noise, preview_method, vae_decode,
prompt=None, extra_pnginfo=None, my_unique_id=None, optional_vae=(None,), script=None):
return super().sample(sampler_state, model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative,
latent_image, preview_method, vae_decode, denoise=1.0, prompt=prompt, extra_pnginfo=extra_pnginfo, my_unique_id=my_unique_id,
optional_vae=optional_vae, script=script, add_noise=add_noise, start_at_step=start_at_step,end_at_step=end_at_step,
return_with_leftover_noise=return_with_leftover_noise,sampler_type="advanced")
#=======================================================================================================================
# TSC KSampler SDXL (Efficient)
class TSC_KSamplerSDXL(TSC_KSampler):
@classmethod
def INPUT_TYPES(cls):
return {"required":
{"sampler_state": (["Sample", "Hold", "Script"],),
"sdxl_tuple": ("SDXL_TUPLE",),
"noise_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,),
"latent_image": ("LATENT",),
"start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
"refine_at_step": ("INT", {"default": -1, "min": -1, "max": 10000}),
"preview_method": (["auto", "latent2rgb", "taesd", "none"],),
"vae_decode": (["true", "true (tiled)", "false", "output only", "output only (tiled)"],),
},
"optional": {"optional_vae": ("VAE",),# "refiner_extras": ("REFINER_EXTRAS",),
"script": ("SCRIPT",),},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID",},
}
RETURN_TYPES = ("SDXL_TUPLE", "LATENT", "VAE", "IMAGE",)
RETURN_NAMES = ("SDXL_TUPLE", "LATENT", "VAE", "IMAGE",)
OUTPUT_NODE = True
FUNCTION = "sample_sdxl"
CATEGORY = "Efficiency Nodes/Sampling"
def sample_sdxl(self, sampler_state, sdxl_tuple, noise_seed, steps, cfg, sampler_name, scheduler, latent_image,
start_at_step, refine_at_step, preview_method, vae_decode, prompt=None, extra_pnginfo=None,
my_unique_id=None, optional_vae=(None,), refiner_extras=None, script=None):
# sdxl_tuple sent through the 'model' channel
# refine_extras sent through the 'positive' channel
negative = None
return super().sample(sampler_state, sdxl_tuple, noise_seed, steps, cfg, sampler_name, scheduler,
refiner_extras, negative, latent_image, preview_method, vae_decode, denoise=1.0,
prompt=prompt, extra_pnginfo=extra_pnginfo, my_unique_id=my_unique_id, optional_vae=optional_vae,
script=script, add_noise=None, start_at_step=start_at_step, end_at_step=refine_at_step,
return_with_leftover_noise=None,sampler_type="sdxl")
#=======================================================================================================================
# TSC KSampler SDXL Refiner Extras (DISABLED)
'''
class TSC_SDXL_Refiner_Extras:
@classmethod
def INPUT_TYPES(cls):
return {"required": {"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS,)}}
RETURN_TYPES = ("REFINER_EXTRAS",)
FUNCTION = "pack_refiner_extras"
CATEGORY = "Efficiency Nodes/Misc"
def pack_refiner_extras(self, seed, cfg, sampler_name, scheduler):
return ((seed, cfg, sampler_name, scheduler),)
'''
########################################################################################################################
# Common XY Plot Functions/Variables
XYPLOT_LIM = 50 #XY Plot default axis size limit
XYPLOT_DEF = 3 #XY Plot default batch count
CKPT_EXTENSIONS = LORA_EXTENSIONS = ['.safetensors', '.ckpt']
VAE_EXTENSIONS = ['.safetensors', '.ckpt', '.pt']
try:
xy_batch_default_path = os.path.abspath(os.sep) + "example_folder"
except Exception:
xy_batch_default_path = ""
def generate_floats(batch_count, first_float, last_float):
if batch_count > 1:
interval = (last_float - first_float) / (batch_count - 1)
return [round(first_float + i * interval, 3) for i in range(batch_count)]
else:
return [first_float] if batch_count == 1 else []
def generate_ints(batch_count, first_int, last_int):
if batch_count > 1:
interval = (last_int - first_int) / (batch_count - 1)
values = [int(first_int + i * interval) for i in range(batch_count)]
else:
values = [first_int] if batch_count == 1 else []
values = list(set(values)) # Remove duplicates
values.sort() # Sort in ascending order
return values
def get_batch_files(directory_path, valid_extensions, include_subdirs=False):
batch_files = []
try:
if include_subdirs:
# Using os.walk to get files from subdirectories
for dirpath, dirnames, filenames in os.walk(directory_path):
for file in filenames:
if any(file.endswith(ext) for ext in valid_extensions):
batch_files.append(os.path.join(dirpath, file))
else:
# Previous code for just the given directory
batch_files = [os.path.join(directory_path, f) for f in os.listdir(directory_path) if
os.path.isfile(os.path.join(directory_path, f)) and any(
f.endswith(ext) for ext in valid_extensions)]
except Exception as e:
print(f"Error while listing files in {directory_path}: {e}")
return batch_files
def print_xy_values(xy_type, xy_value, xy_name):
print("===== XY Value Returns =====")
print(f"{xy_name} Values:")
print("- Type:", xy_type)
print("- Entries:", xy_value)
print("============================")
#=======================================================================================================================
# 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": (["Images","Plot"],),},
"optional": {
"dependencies": ("DEPENDENCIES", ),
"X": ("XY", ),
"Y": ("XY", ),},
"hidden": {"my_unique_id": "UNIQUE_ID"},
}
RETURN_TYPES = ("SCRIPT",)
RETURN_NAMES = ("SCRIPT",)
FUNCTION = "XYplot"
CATEGORY = "Efficiency Nodes/Scripts"
def XYplot(self, grid_spacing, XY_flip, Y_label_orientation, cache_models, ksampler_output_image, my_unique_id,
dependencies=None, X=None, Y=None):
# Unpack X & Y Tuples if connected
if X != None:
X_type, X_value = X
else:
X_type = "Nothing"
X_value = [""]
if Y != None:
Y_type, Y_value = Y
else:
Y_type = "Nothing"
Y_value = [""]
# If types are the same exit. If one isn't "Nothing", print error
if (X_type == Y_type):
if X_type != "Nothing":
print(f"{error('XY Plot Error:')} X and Y input types must be different.")
return (None,)
# Check that dependencies are connected for specific plot types
encode_types = {
"Checkpoint", "Refiner",
"LoRA", "LoRA Batch", "LoRA Wt", "LoRA MStr", "LoRA CStr",
"Positive Prompt S/R", "Negative Prompt S/R",
"AScore+", "AScore-",
"Clip Skip", "Clip Skip (Refiner)",
"ControlNetStrength", "ControlNetStart%", "ControlNetEnd%"
}
if X_type in encode_types or Y_type in encode_types:
if dependencies is None: # Not connected
print(f"{error('XY Plot Error:')} The dependencies input must be connected for certain plot types.")
# Return None
return (None,)
# Check if both X_type and Y_type are special lora_types
lora_types = {"LoRA Batch", "LoRA Wt", "LoRA MStr", "LoRA CStr"}
if (X_type in lora_types and Y_type not in lora_types) or (Y_type in lora_types and X_type not in lora_types):
print(
f"{error('XY Plot Error:')} Both X and Y must be connected to use the 'LoRA Plot' node.")
return (None,)
# Clean Schedulers from Sampler data (if other type is Scheduler)
if X_type == "Sampler" and Y_type == "Scheduler":
# Clear X_value Scheduler's
X_value = [(x[0], "") for x in X_value]
elif Y_type == "Sampler" and X_type == "Scheduler":
# Clear Y_value Scheduler's
Y_value = [(y[0], "") for y in Y_value]
# Embed information into "Scheduler" X/Y_values for text label
if X_type == "Scheduler" and Y_type != "Sampler":
# X_value second tuple value of each array entry = None
X_value = [(x, None) for x in X_value]
if Y_type == "Scheduler" and X_type != "Sampler":
# Y_value second tuple value of each array entry = None
Y_value = [(y, None) for y in Y_value]
# Clean VAEs from Checkpoint data if other type is VAE
if X_type == "Checkpoint" and Y_type == "VAE":
# Clear X_value VAE's
X_value = [(t[0], t[1], None) for t in X_value]
elif Y_type == "VAE" and X_type == "Checkpoint":
# Clear Y_value VAE's
Y_value = [(t[0], t[1], None) for t in Y_value]
# Flip X and Y
if XY_flip == "True":
X_type, Y_type = Y_type, X_type
X_value, Y_value = Y_value, X_value
# Define Ksampler output image behavior
xyplot_as_output_image = ksampler_output_image == "Plot"
# Pack xyplot tuple into its dictionary item under script
script = {"xyplot": (X_type, X_value, Y_type, Y_value, grid_spacing, Y_label_orientation, cache_models,
xyplot_as_output_image, my_unique_id, dependencies)}
return (script,)
#=======================================================================================================================
# TSC XY Plot: Seeds Values
class TSC_XYplot_SeedsBatch:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),},
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def xy_value(self, batch_count):
if batch_count == 0:
return (None,)
xy_type = "Seeds++ Batch"
xy_value = list(range(batch_count))
return ((xy_type, xy_value),)
#=======================================================================================================================
# TSC XY Plot: Add/Return Noise
class TSC_XYplot_AddReturnNoise:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"XY_type": (["add_noise", "return_with_leftover_noise"],)}
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def xy_value(self, XY_type):
type_mapping = {
"add_noise": "AddNoise",
"return_with_leftover_noise": "ReturnNoise"
}
xy_type = type_mapping[XY_type]
xy_value = ["enable", "disable"]
return ((xy_type, xy_value),)
#=======================================================================================================================
# TSC XY Plot: Step Values
class TSC_XYplot_Steps:
parameters = ["steps","start_at_step", "end_at_step", "refine_at_step"]
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"target_parameter": (cls.parameters,),
"batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
"first_step": ("INT", {"default": 10, "min": 1, "max": 10000}),
"last_step": ("INT", {"default": 20, "min": 1, "max": 10000}),
"first_start_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
"last_start_step": ("INT", {"default": 10, "min": 0, "max": 10000}),
"first_end_step": ("INT", {"default": 10, "min": 0, "max": 10000}),
"last_end_step": ("INT", {"default": 20, "min": 0, "max": 10000}),
"first_refine_step": ("INT", {"default": 10, "min": 0, "max": 10000}),
"last_refine_step": ("INT", {"default": 20, "min": 0, "max": 10000}),
}
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def xy_value(self, target_parameter, batch_count, first_step, last_step, first_start_step, last_start_step,
first_end_step, last_end_step, first_refine_step, last_refine_step):
if target_parameter == "steps":
xy_type = "Steps"
xy_first = first_step
xy_last = last_step
elif target_parameter == "start at step":
xy_type = "StartStep"
xy_first = first_start_step
xy_last = last_start_step
elif target_parameter == "end_at_step":
xy_type = "EndStep"
xy_first = first_end_step
xy_last = last_end_step
elif target_parameter == "refine_at_step":
xy_type = "RefineStep"
xy_first = first_refine_step
xy_last = last_refine_step
xy_value = generate_ints(batch_count, xy_first, xy_last)
return ((xy_type, xy_value),) if xy_value else (None,)
#=======================================================================================================================
# TSC XY Plot: CFG Values
class TSC_XYplot_CFG:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
"first_cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0}),
"last_cfg": ("FLOAT", {"default": 9.0, "min": 0.0, "max": 100.0}),
}
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def xy_value(self, batch_count, first_cfg, last_cfg):
xy_type = "CFG Scale"
xy_value = generate_floats(batch_count, first_cfg, last_cfg)
return ((xy_type, xy_value),) if xy_value else (None,)
#=======================================================================================================================
# TSC XY Plot: Sampler & Scheduler Values
class TSC_XYplot_Sampler_Scheduler:
parameters = ["sampler", "scheduler", "sampler & scheduler"]
@classmethod
def INPUT_TYPES(cls):
samplers = ["None"] + comfy.samplers.KSampler.SAMPLERS
schedulers = ["None"] + comfy.samplers.KSampler.SCHEDULERS
inputs = {
"required": {
"target_parameter": (cls.parameters,),
"input_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM, "step": 1})
}
}
for i in range(1, XYPLOT_LIM+1):
inputs["required"][f"sampler_{i}"] = (samplers,)
inputs["required"][f"scheduler_{i}"] = (schedulers,)
return inputs
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def xy_value(self, target_parameter, input_count, **kwargs):
if target_parameter == "scheduler":
xy_type = "Scheduler"
schedulers = [kwargs.get(f"scheduler_{i}") for i in range(1, input_count + 1)]
xy_value = [scheduler for scheduler in schedulers if scheduler != "None"]
elif target_parameter == "sampler":
xy_type = "Sampler"
samplers = [kwargs.get(f"sampler_{i}") for i in range(1, input_count + 1)]
xy_value = [(sampler, None) for sampler in samplers if sampler != "None"]
else:
xy_type = "Sampler"
samplers = [kwargs.get(f"sampler_{i}") for i in range(1, input_count + 1)]
schedulers = [kwargs.get(f"scheduler_{i}") for i in range(1, input_count + 1)]
xy_value = [(sampler, scheduler if scheduler != "None" else None) for sampler,
scheduler in zip(samplers, schedulers) if sampler != "None"]
return ((xy_type, xy_value),) if xy_value else (None,)
#=======================================================================================================================
# TSC XY Plot: Denoise Values
class TSC_XYplot_Denoise:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
"first_denoise": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"last_denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def xy_value(self, batch_count, first_denoise, last_denoise):
xy_type = "Denoise"
xy_value = generate_floats(batch_count, first_denoise, last_denoise)
return ((xy_type, xy_value),) if xy_value else (None,)
#=======================================================================================================================
# TSC XY Plot: VAE Values
class TSC_XYplot_VAE:
modes = ["VAE Names", "VAE Batch"]
@classmethod
def INPUT_TYPES(cls):
vaes = ["None", "Baked VAE"] + folder_paths.get_filename_list("vae")
inputs = {
"required": {
"input_mode": (cls.modes,),
"batch_path": ("STRING", {"default": xy_batch_default_path, "multiline": False}),
"subdirectories": ("BOOLEAN", {"default": False}),
"batch_sort": (["ascending", "descending"],),
"batch_max": ("INT", {"default": -1, "min": -1, "max": XYPLOT_LIM, "step": 1}),
"vae_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM, "step": 1})
}
}
for i in range(1, XYPLOT_LIM+1):
inputs["required"][f"vae_name_{i}"] = (vaes,)
return inputs
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def xy_value(self, input_mode, batch_path, subdirectories, batch_sort, batch_max, vae_count, **kwargs):
xy_type = "VAE"
if "Batch" not in input_mode:
# Extract values from kwargs
vaes = [kwargs.get(f"vae_name_{i}") for i in range(1, vae_count + 1)]
xy_value = [vae for vae in vaes if vae != "None"]
else:
if batch_max == 0:
return (None,)
try:
vaes = get_batch_files(batch_path, VAE_EXTENSIONS, include_subdirs=subdirectories)
if not vaes:
print(f"{error('XY Plot Error:')} No VAE files found.")
return (None,)
if batch_sort == "ascending":
vaes.sort()
elif batch_sort == "descending":
vaes.sort(reverse=True)
# Construct the xy_value using the obtained vaes
xy_value = [vae for vae in vaes]
if batch_max != -1: # If there's a limit
xy_value = xy_value[:batch_max]
except Exception as e:
print(f"{error('XY Plot Error:')} {e}")
return (None,)
return ((xy_type, xy_value),) if xy_value else (None,)
#=======================================================================================================================
# TSC XY Plot: Prompt S/R
class TSC_XYplot_PromptSR:
@classmethod
def INPUT_TYPES(cls):
inputs = {
"required": {
"target_prompt": (["positive", "negative"],),
"search_txt": ("STRING", {"default": "", "multiline": False}),
"replace_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM-1}),
}
}
# Dynamically add replace_X inputs
for i in range(1, XYPLOT_LIM):
replace_key = f"replace_{i}"
inputs["required"][replace_key] = ("STRING", {"default": "", "multiline": False})
return inputs
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def xy_value(self, target_prompt, search_txt, replace_count, **kwargs):
if search_txt == "":
return (None,)
if target_prompt == "positive":
xy_type = "Positive Prompt S/R"
elif target_prompt == "negative":
xy_type = "Negative Prompt S/R"
# Create base entry
xy_values = [(search_txt, None)]
if replace_count > 0:
# Append additional entries based on replace_count
xy_values.extend([(search_txt, kwargs.get(f"replace_{i+1}")) for i in range(replace_count)])
return ((xy_type, xy_values),)
#=======================================================================================================================
# TSC XY Plot: Aesthetic Score
class TSC_XYplot_AScore:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"target_ascore": (["positive", "negative"],),
"batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
"first_ascore": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
"last_ascore": ("FLOAT", {"default": 10.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
}
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def xy_value(self, target_ascore, batch_count, first_ascore, last_ascore):
if target_ascore == "positive":
xy_type = "AScore+"
else:
xy_type = "AScore-"
xy_value = generate_floats(batch_count, first_ascore, last_ascore)
return ((xy_type, xy_value),) if xy_value else (None,)
#=======================================================================================================================
# TSC XY Plot: Refiner On/Off
class TSC_XYplot_Refiner_OnOff:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"refine_at_percent": ("FLOAT",{"default": 0.80, "min": 0.00, "max": 1.00, "step": 0.01})},
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def xy_value(self, refine_at_percent):
xy_type = "Refiner On/Off"
xy_value = [refine_at_percent, 1]
return ((xy_type, xy_value),)
#=======================================================================================================================
# TSC XY Plot: Clip Skip
class TSC_XYplot_ClipSkip:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"target_ckpt": (["Base","Refiner"],),
"batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
"first_clip_skip": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
"last_clip_skip": ("INT", {"default": -3, "min": -24, "max": -1, "step": 1}),
},
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def xy_value(self, target_ckpt, batch_count, first_clip_skip, last_clip_skip):
if target_ckpt == "Base":
xy_type = "Clip Skip"
else:
xy_type = "Clip Skip (Refiner)"
xy_value = generate_ints(batch_count, first_clip_skip, last_clip_skip)
return ((xy_type, xy_value),) if xy_value else (None,)
#=======================================================================================================================
# TSC XY Plot: Checkpoint Values
class TSC_XYplot_Checkpoint:
modes = ["Ckpt Names", "Ckpt Names+ClipSkip", "Ckpt Names+ClipSkip+VAE", "Checkpoint Batch"]
@classmethod
def INPUT_TYPES(cls):
checkpoints = ["None"] + folder_paths.get_filename_list("checkpoints")
vaes = ["Baked VAE"] + folder_paths.get_filename_list("vae")
inputs = {
"required": {
"target_ckpt": (["Base", "Refiner"],),
"input_mode": (cls.modes,),
"batch_path": ("STRING", {"default": xy_batch_default_path, "multiline": False}),
"subdirectories": ("BOOLEAN", {"default": False}),
"batch_sort": (["ascending", "descending"],),
"batch_max": ("INT", {"default": -1, "min": -1, "max": 50, "step": 1}),
"ckpt_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM, "step": 1})
}
}
for i in range(1, XYPLOT_LIM+1):
inputs["required"][f"ckpt_name_{i}"] = (checkpoints,)
inputs["required"][f"clip_skip_{i}"] = ("INT", {"default": -1, "min": -24, "max": -1, "step": 1})
inputs["required"][f"vae_name_{i}"] = (vaes,)
return inputs
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def xy_value(self, target_ckpt, input_mode, batch_path, subdirectories, batch_sort, batch_max, ckpt_count, **kwargs):
# Define XY type
xy_type = "Checkpoint" if target_ckpt == "Base" else "Refiner"
if "Batch" not in input_mode:
# Extract values from kwargs
checkpoints = [kwargs.get(f"ckpt_name_{i}") for i in range(1, ckpt_count + 1)]
clip_skips = [kwargs.get(f"clip_skip_{i}") for i in range(1, ckpt_count + 1)]
vaes = [kwargs.get(f"vae_name_{i}") for i in range(1, ckpt_count + 1)]
# Set None for Clip Skip and/or VAE if not correct modes
for i in range(ckpt_count):
if "ClipSkip" not in input_mode:
clip_skips[i] = None
if "VAE" not in input_mode:
vaes[i] = None
xy_value = [(checkpoint, clip_skip, vae) for checkpoint, clip_skip, vae in zip(checkpoints, clip_skips, vaes) if
checkpoint != "None"]
else:
if batch_max == 0:
return (None,)
try:
ckpts = get_batch_files(batch_path, CKPT_EXTENSIONS, include_subdirs=subdirectories)
if not ckpts:
print(f"{error('XY Plot Error:')} No Checkpoint files found.")
return (None,)
if batch_sort == "ascending":
ckpts.sort()
elif batch_sort == "descending":
ckpts.sort(reverse=True)
# Construct the xy_value using the obtained ckpts
xy_value = [(ckpt, None, None) for ckpt in ckpts]
if batch_max != -1: # If there's a limit
xy_value = xy_value[:batch_max]
except Exception as e:
print(f"{error('XY Plot Error:')} {e}")
return (None,)
return ((xy_type, xy_value),) if xy_value else (None,)
#=======================================================================================================================
# TSC XY Plot: LoRA Batch (DISABLED)
class TSC_XYplot_LoRA_Batch:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"batch_path": ("STRING", {"default": xy_batch_default_path, "multiline": False}),
"subdirectories": ("BOOLEAN", {"default": False}),
"batch_sort": (["ascending", "descending"],),
"batch_max": ("INT",{"default": -1, "min": -1, "max": XYPLOT_LIM, "step": 1}),
"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})},
"optional": {"lora_stack": ("LORA_STACK",)}
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def xy_value(self, batch_path, subdirectories, batch_sort, model_strength, clip_strength, batch_max, lora_stack=None):
if batch_max == 0:
return (None,)
xy_type = "LoRA"
loras = get_batch_files(batch_path, LORA_EXTENSIONS, include_subdirs=subdirectories)
if not loras:
print(f"{error('XY Plot Error:')} No LoRA files found.")
return (None,)
if batch_sort == "ascending":
loras.sort()
elif batch_sort == "descending":
loras.sort(reverse=True)
# Construct the xy_value using the obtained loras
xy_value = [[(lora, model_strength, clip_strength)] + (lora_stack if lora_stack else []) for lora in loras]
if batch_max != -1: # If there's a limit
xy_value = xy_value[:batch_max]
return ((xy_type, xy_value),) if xy_value else (None,)
#=======================================================================================================================
# TSC XY Plot: LoRA Values
class TSC_XYplot_LoRA:
modes = ["LoRA Names", "LoRA Names+Weights", "LoRA Batch"]
@classmethod
def INPUT_TYPES(cls):
loras = ["None"] + folder_paths.get_filename_list("loras")
inputs = {
"required": {
"input_mode": (cls.modes,),
"batch_path": ("STRING", {"default": xy_batch_default_path, "multiline": False}),
"subdirectories": ("BOOLEAN", {"default": False}),
"batch_sort": (["ascending", "descending"],),
"batch_max": ("INT", {"default": -1, "min": -1, "max": XYPLOT_LIM, "step": 1}),
"lora_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM, "step": 1}),
"model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
}
}
for i in range(1, XYPLOT_LIM+1):
inputs["required"][f"lora_name_{i}"] = (loras,)
inputs["required"][f"model_str_{i}"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})
inputs["required"][f"clip_str_{i}"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})
inputs["optional"] = {
"lora_stack": ("LORA_STACK",)
}
return inputs
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def __init__(self):
self.lora_batch = TSC_XYplot_LoRA_Batch()
def xy_value(self, input_mode, batch_path, subdirectories, batch_sort, batch_max, lora_count, model_strength,
clip_strength, lora_stack=None, **kwargs):
xy_type = "LoRA"
result = (None,)
lora_stack = lora_stack if lora_stack else []
if "Batch" not in input_mode:
# Extract values from kwargs
loras = [kwargs.get(f"lora_name_{i}") for i in range(1, lora_count + 1)]
model_strs = [kwargs.get(f"model_str_{i}", model_strength) for i in range(1, lora_count + 1)]
clip_strs = [kwargs.get(f"clip_str_{i}", clip_strength) for i in range(1, lora_count + 1)]
# Use model_strength and clip_strength for the loras where values are not provided
if "Weights" not in input_mode:
for i in range(lora_count):
model_strs[i] = model_strength
clip_strs[i] = clip_strength
# Extend each sub-array with lora_stack if it's not None
xy_value = [[(lora, model_str, clip_str)] + lora_stack for lora, model_str, clip_str
in zip(loras, model_strs, clip_strs) if lora != "None"]
result = ((xy_type, xy_value),)
else:
try:
result = self.lora_batch.xy_value(batch_path, subdirectories, batch_sort, model_strength,
clip_strength, batch_max, lora_stack)
except Exception as e:
print(f"{error('XY Plot Error:')} {e}")
return result
#=======================================================================================================================
# TSC XY Plot: LoRA Plot
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))
#=======================================================================================================================
# TSC XY Plot: LoRA Stacks
class TSC_XYplot_LoRA_Stacks:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"node_state": (["Enabled"],)},
"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 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: Control Net Strength (DISABLED)
class TSC_XYplot_Control_Net_Strength:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"control_net": ("CONTROL_NET",),
"image": ("IMAGE",),
"batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
"first_strength": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 10.0, "step": 0.01}),
"last_strength": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}),
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}),
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
},
"optional": {"cnet_stack": ("CONTROL_NET_STACK",)},
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def xy_value(self, control_net, image, batch_count, first_strength, last_strength,
start_percent, end_percent, cnet_stack=None):
if batch_count == 0:
return (None,)
xy_type = "ControlNetStrength"
strength_increment = (last_strength - first_strength) / (batch_count - 1) if batch_count > 1 else 0
xy_value = []
# Always add the first strength.
xy_value.append([(control_net, image, first_strength, start_percent, end_percent)])
# Add intermediate strengths only if batch_count is more than 2.
for i in range(1, batch_count - 1):
xy_value.append([(control_net, image, first_strength + i * strength_increment, start_percent,
end_percent)])
# Always add the last strength if batch_count is more than 1.
if batch_count > 1:
xy_value.append([(control_net, image, last_strength, start_percent, end_percent)])
# If cnet_stack is provided, extend each inner array with its content
if cnet_stack:
for inner_list in xy_value:
inner_list.extend(cnet_stack)
return ((xy_type, xy_value),)
#=======================================================================================================================
# TSC XY Plot: Control Net Start % (DISABLED)
class TSC_XYplot_Control_Net_Start:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"control_net": ("CONTROL_NET",),
"image": ("IMAGE",),
"batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
"first_start_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}),
"last_start_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
"strength": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}),
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
},
"optional": {"cnet_stack": ("CONTROL_NET_STACK",)},
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def xy_value(self, control_net, image, batch_count, first_start_percent, last_start_percent,
strength, end_percent, cnet_stack=None):
if batch_count == 0:
return (None,)
xy_type = "ControlNetStart%"
percent_increment = (last_start_percent - first_start_percent) / (batch_count - 1) if batch_count > 1 else 0
xy_value = []
# Always add the first start_percent.
xy_value.append([(control_net, image, strength, first_start_percent, end_percent)])
# Add intermediate start percents only if batch_count is more than 2.
for i in range(1, batch_count - 1):
xy_value.append([(control_net, image, strength, first_start_percent + i * percent_increment,
end_percent)])
# Always add the last start_percent if batch_count is more than 1.
if batch_count > 1:
xy_value.append([(control_net, image, strength, last_start_percent, end_percent)])
# If cnet_stack is provided, extend each inner array with its content
if cnet_stack:
for inner_list in xy_value:
inner_list.extend(cnet_stack)
return ((xy_type, xy_value),)
#=======================================================================================================================
# TSC XY Plot: Control Net End % (DISABLED)
class TSC_XYplot_Control_Net_End:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"control_net": ("CONTROL_NET",),
"image": ("IMAGE",),
"batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
"first_end_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}),
"last_end_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
"strength": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}),
"start_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
},
"optional": {"cnet_stack": ("CONTROL_NET_STACK",)},
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def xy_value(self, control_net, image, batch_count, first_end_percent, last_end_percent,
strength, start_percent, cnet_stack=None):
if batch_count == 0:
return (None,)
xy_type = "ControlNetEnd%"
percent_increment = (last_end_percent - first_end_percent) / (batch_count - 1) if batch_count > 1 else 0
xy_value = []
# Always add the first end_percent.
xy_value.append([(control_net, image, strength, start_percent, first_end_percent)])
# Add intermediate end percents only if batch_count is more than 2.
for i in range(1, batch_count - 1):
xy_value.append([(control_net, image, strength, start_percent,
first_end_percent + i * percent_increment)])
# Always add the last end_percent if batch_count is more than 1.
if batch_count > 1:
xy_value.append([(control_net, image, strength, start_percent, last_end_percent)])
# If cnet_stack is provided, extend each inner array with its content
if cnet_stack:
for inner_list in xy_value:
inner_list.extend(cnet_stack)
return ((xy_type, xy_value),)
# =======================================================================================================================
# TSC XY Plot: Control Net
class TSC_XYplot_Control_Net:
parameters = ["strength", "start_percent", "end_percent"]
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"control_net": ("CONTROL_NET",),
"image": ("IMAGE",),
"target_parameter": (cls.parameters,),
"batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
"first_strength": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 10.0, "step": 0.01}),
"last_strength": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}),
"first_start_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}),
"last_start_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
"first_end_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}),
"last_end_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
"strength": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}),
"start_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
},
"optional": {"cnet_stack": ("CONTROL_NET_STACK",)},
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def xy_value(self, control_net, image, target_parameter, batch_count, first_strength, last_strength, first_start_percent,
last_start_percent, first_end_percent, last_end_percent, strength, start_percent, cnet_stack=None):
return ((xy_type, xy_value),)
#=======================================================================================================================
# TSC XY Plot: Control Net Plot
class TSC_XYplot_Control_Net_Plot:
plot_types = ["X: Strength, Y: Start%",
"X: Strength, Y: End%",
"X: Start%, Y: Strength",
"X: Start%, Y: End%",
"X: End%, Y: Strength",
"X: End%, Y: Start%"]
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"control_net": ("CONTROL_NET",),
"image": ("IMAGE",),
"plot_type": (cls.plot_types,),
"strength": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}),
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
"X_batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
"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": {"cnet_stack": ("CONTROL_NET_STACK",)},
}
RETURN_TYPES = ("XY","XY",)
RETURN_NAMES = ("X","Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def get_value(self, axis, control_net, image, strength, start_percent, end_percent,
batch_count, first_value, last_value):
# Adjust upper bound for Start% and End% type
if axis in ["Start%", "End%"]:
first_value = min(1, first_value)
last_value = min(1, last_value)
increment = (last_value - first_value) / (batch_count - 1) if batch_count > 1 else 0
values = []
# Always add the first value.
if axis == "Strength":
values.append([(control_net, image, first_value, start_percent, end_percent)])
elif axis == "Start%":
values.append([(control_net, image, strength, first_value, end_percent)])
elif axis == "End%":
values.append([(control_net, image, strength, start_percent, first_value)])
# Add intermediate values only if batch_count is more than 2.
for i in range(1, batch_count - 1):
if axis == "Strength":
values.append(
[(control_net, image, first_value + i * increment, start_percent, end_percent)])
elif axis == "Start%":
values.append(
[(control_net, image, strength, first_value + i * increment, end_percent)])
elif axis == "End%":
values.append(
[(control_net, image, strength, start_percent, first_value + i * increment)])
# Always add the last value if batch_count is more than 1.
if batch_count > 1:
if axis == "Strength":
values.append([(control_net, image, last_value, start_percent, end_percent)])
elif axis == "Start%":
values.append([(control_net, image, strength, last_value, end_percent)])
elif axis == "End%":
values.append([(control_net, image, strength, start_percent, last_value)])
return values
def xy_value(self, control_net, image, strength, start_percent, end_percent, plot_type,
X_batch_count, X_first_value, X_last_value, Y_batch_count, Y_first_value, Y_last_value,
cnet_stack=None):
x_type, y_type = plot_type.split(", ")
# Now split each type by ": "
x_type = x_type.split(": ")[1].strip()
y_type = y_type.split(": ")[1].strip()
x_entry = None
y_entry = None
if X_batch_count > 0:
x_value = self.get_value(x_type, control_net, image, strength, start_percent,
end_percent, X_batch_count, X_first_value, X_last_value)
# If cnet_stack is provided, extend each inner array with its content
if cnet_stack:
for inner_list in x_value:
inner_list.extend(cnet_stack)
x_entry = ("ControlNet" + x_type, x_value)
if Y_batch_count > 0:
y_value = self.get_value(y_type, control_net, image, strength, start_percent,
end_percent, Y_batch_count, Y_first_value, Y_last_value)
# If cnet_stack is provided, extend each inner array with its content
if cnet_stack:
for inner_list in y_value:
inner_list.extend(cnet_stack)
y_entry = ("ControlNet" + y_type, y_value)
return (x_entry, y_entry,)
#=======================================================================================================================
# 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" \
"StartStep start_step_1;start_step_2;...\n" \
"EndStep end_step_1;end_step_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;..."
@classmethod
def INPUT_TYPES(cls):
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"))
return {"required": {
"notes": ("STRING", {"default":
f"_____________SYNTAX_____________\n{cls.syntax}\n\n"
f"____________SAMPLERS____________\n{samplers}\n\n"
f"___________SCHEDULERS___________\n{schedulers}\n\n"
f"_____________VAES_______________\n{vaes}\n\n"
f"___________CHECKPOINTS__________\n{ckpts}\n\n"
f"_____________LORAS______________\n{loras}\n","multiline": True}),},}
RETURN_TYPES = ()
CATEGORY = "Efficiency Nodes/XY Inputs"
#=======================================================================================================================
# TSC XY Plot: Manual Entry
class TSC_XYplot_Manual_XY_Entry:
plot_types = ["Nothing", "Seeds++ Batch", "Steps", "StartStep", "EndStep", "CFG Scale", "Sampler", "Scheduler",
"Denoise", "VAE", "Positive Prompt S/R", "Negative Prompt S/R", "Checkpoint", "Clip Skip", "LoRA"]
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"plot_type": (cls.plot_types,),
"plot_value": ("STRING", {"default": "", "multiline": True}),}
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def xy_value(self, plot_type, plot_value):
# Store X values as arrays
if plot_type not in {"Positive Prompt S/R", "Negative Prompt S/R", "VAE", "Checkpoint", "LoRA"}:
plot_value = plot_value.replace(" ", "") # Remove spaces
plot_value = plot_value.replace("\n", "") # Remove newline characters
plot_value = plot_value.rstrip(";") # Remove trailing semicolon
plot_value = plot_value.split(";") # Turn to array
# Define the valid bounds for each type
bounds = {
"Seeds++ Batch": {"min": 1, "max": 50},
"Steps": {"min": 1, "max": 10000},
"StartStep": {"min": 0, "max": 10000},
"EndStep": {"min": 0, "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": -10, "max": 10},"clip_str": {"min": -10, "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
# __________________________________________________________________________________________________________
# Start at Step
elif value_type == "StartStep":
try:
x = int(value)
if x < bounds["StartStep"]["min"]:
x = bounds["StartStep"]["min"]
elif x > bounds["StartStep"]["max"]:
x = bounds["StartStep"]["max"]
return x
except ValueError:
print(
f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid Start Step.")
return None
# __________________________________________________________________________________________________________
# End at Step
elif value_type == "EndStep":
try:
x = int(value)
if x < bounds["EndStep"]["min"]:
x = bounds["EndStep"]["min"]
elif x > bounds["EndStep"]["max"]:
x = bounds["EndStep"]["max"]
return x
except ValueError:
print(
f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid End Step.")
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"{error('XY Plot Error:')} '{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"{error('XY Plot Error:')} 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"{error('XY Plot Error:')} '{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"{error('XY Plot Error:')} '{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"{error('XY Plot Error:')} '{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 plot_value array length is 1 if doing a "Seeds++ Batch"
if len(plot_value) != 1 and plot_type == "Seeds++ Batch":
print(f"{error('XY Plot Error:')} '{';'.join(plot_value)}' is not a valid batch count.")
return (None,)
# Apply allowed shortcut syntax to certain input types
if plot_type in ["Sampler", "Checkpoint", "LoRA"]:
if plot_value[-1].startswith(','):
# Remove the leading comma from the last entry and store it as suffixes
suffixes = plot_value.pop().lstrip(',').split(',')
# Split all preceding entries into subentries
plot_value = [entry.split(',') for entry in plot_value]
# Make all entries the same length as suffixes by appending missing elements
for entry in plot_value:
entry += suffixes[len(entry) - 1:]
# Join subentries back into strings
plot_value = [','.join(entry) for entry in plot_value]
# Prompt S/R X Cleanup
if plot_type in {"Positive Prompt S/R", "Negative Prompt S/R"}:
if plot_value[0] == '':
print(f"{error('XY Plot Error:')} Prompt S/R value can not be empty.")
return (None,)
else:
plot_value = [(plot_value[0], None) if i == 0 else (plot_value[0], x) for i, x in enumerate(plot_value)]
# Loop over each entry in plot_value and check if it's valid
if plot_type not in {"Nothing", "Positive Prompt S/R", "Negative Prompt S/R"}:
for i in range(len(plot_value)):
plot_value[i] = validate_value(plot_value[i], plot_type, bounds)
if plot_value[i] == None:
return (None,)
# Nest LoRA value in another array to reflect LoRA stack changes
if plot_type == "LoRA":
plot_value = [[x] for x in plot_value]
# Clean X/Y_values
if plot_type == "Nothing":
plot_value = [""]
return ((plot_type, plot_value),)
#=======================================================================================================================
# TSC XY Plot: Join Inputs
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 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"{error('Join XY Inputs Error:')} Input types must match")
return (None,)
elif xy_type_1 == "Seeds++ Batch":
xy_type = xy_type_1
combined_length = len(xy_value_1) + len(xy_value_2)
xy_value = list(range(combined_length))
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,)
########################################################################################################################
# NODE MAPPING
NODE_CLASS_MAPPINGS = {
"KSampler (Efficient)": TSC_KSampler,
"KSampler Adv. (Efficient)":TSC_KSamplerAdvanced,
"KSampler SDXL (Eff.)": TSC_KSamplerSDXL,
"Efficient Loader": TSC_EfficientLoader,
"Eff. Loader SDXL": TSC_EfficientLoaderSDXL,
"LoRA Stacker": TSC_LoRA_Stacker,
"Control Net Stacker": TSC_Control_Net_Stacker,
"Apply ControlNet Stack": TSC_Apply_ControlNet_Stack,
"Unpack SDXL Tuple": TSC_Unpack_SDXL_Tuple,
"Pack SDXL Tuple": TSC_Pack_SDXL_Tuple,
#"Refiner Extras": TSC_SDXL_Refiner_Extras, # MAYBE FUTURE
"XY Plot": TSC_XYplot,
"XY Input: Seeds++ Batch": TSC_XYplot_SeedsBatch,
"XY Input: Add/Return Noise": TSC_XYplot_AddReturnNoise,
"XY Input: Steps": TSC_XYplot_Steps,
"XY Input: CFG Scale": TSC_XYplot_CFG,
"XY Input: Sampler/Scheduler": TSC_XYplot_Sampler_Scheduler,
"XY Input: Denoise": TSC_XYplot_Denoise,
"XY Input: VAE": TSC_XYplot_VAE,
"XY Input: Prompt S/R": TSC_XYplot_PromptSR,
"XY Input: Aesthetic Score": TSC_XYplot_AScore,
"XY Input: Refiner On/Off": TSC_XYplot_Refiner_OnOff,
"XY Input: Checkpoint": TSC_XYplot_Checkpoint,
"XY Input: Clip Skip": TSC_XYplot_ClipSkip,
"XY Input: LoRA": TSC_XYplot_LoRA,
"XY Input: LoRA Plot": TSC_XYplot_LoRA_Plot,
"XY Input: LoRA Stacks": TSC_XYplot_LoRA_Stacks,
"XY Input: Control Net": TSC_XYplot_Control_Net,
"XY Input: Control Net Plot": TSC_XYplot_Control_Net_Plot,
#"XY Input: Manual XY Entry": TSC_XYplot_Manual_XY_Entry, # DISABLED, NEEDS UPDATE
#"Manual XY Entry Info": TSC_XYplot_Manual_XY_Entry_Info, # DISABLED, NEEDS UPDATE
"Join XY Inputs of Same Type": TSC_XYplot_JoinInputs,
"Image Overlay": TSC_ImageOverlay
}
########################################################################################################################
# HirRes Fix Script with model latent upscaler (https://github.com/city96/SD-Latent-Upscaler)
comfy_latent_upscaler = None
sd_latent_upscaler_path = os.path.join(custom_nodes_dir, "SD-Latent-Upscaler")
if os.path.exists(sd_latent_upscaler_path):
printout = "Adding City96's 'SD-Latent-Upscaler' selections to the 'HighRes-Fix' node..."
print(f"{message('Efficiency Nodes:')} {printout}", end="")
try:
comfy_latent_upscaler = import_module("SD-Latent-Upscaler.comfy_latent_upscaler")
print(f"\r{message('Efficiency Nodes:')} {printout}{success('Success!')}")
except ImportError:
print(f"\r{message('Efficiency Nodes:')} {printout}{error('Failed!')}")
# TSC HighRes-Fix
class TSC_HighRes_Fix:
default_upscale_methods = LatentUpscaleBy.INPUT_TYPES()["required"]["upscale_method"][0]
latent_versions = []
if comfy_latent_upscaler:
latent_versions = comfy_latent_upscaler.LatentUpscaler.INPUT_TYPES()["required"]["latent_ver"][0]
latent_versions_updated = ["SD-Latent-Upscaler." + ver for ver in latent_versions]
allowed_scalings_raw = comfy_latent_upscaler.LatentUpscaler.INPUT_TYPES()["required"]["scale_factor"][0]
allowed_scalings = [float(scale) for scale in allowed_scalings_raw]
upscale_methods = default_upscale_methods + latent_versions_updated
@classmethod
def INPUT_TYPES(cls):
return {"required": {"latent_upscale_method": (cls.upscale_methods,),
"upscale_by": ("FLOAT", {"default": 1.25, "min": 0.01, "max": 8.0, "step": 0.25}),
"hires_steps": ("INT", {"default": 12, "min": 1, "max": 10000}),
"denoise": ("FLOAT", {"default": .56, "min": 0.0, "max": 1.0, "step": 0.01}),
"iterations": ("INT", {"default": 1, "min": 0, "max": 5, "step": 1}),
},
"optional": {"script": ("SCRIPT",)}}
RETURN_TYPES = ("SCRIPT",)
FUNCTION = "hires_fix_script"
CATEGORY = "Efficiency Nodes/Scripts"
def hires_fix_script(self, latent_upscale_method, upscale_by, hires_steps, denoise, iterations, script=None):
upscale_function = None
def float_to_string(num):
if num == int(num):
return "{:.1f}".format(num)
else:
return str(num)
if iterations > 0:
# For latent methods from SD-Latent-Upscaler
if latent_upscale_method in self.latent_versions_updated:
# Remove extra characters added
latent_upscale_method = latent_upscale_method.replace("SD-Latent-Upscaler.", "")
# Set function to comfy_latent_upscaler.LatentUpscaler.upscale()
upscale_function = comfy_latent_upscaler.LatentUpscaler
# Find the nearest valid scaling in allowed_scalings
nearest_scaling = min(self.allowed_scalings, key=lambda x: abs(x - upscale_by))
# Retrieve the index of the nearest scaling
nearest_scaling_index = self.allowed_scalings.index(nearest_scaling)
# Use the index to get the raw string representation
nearest_scaling_raw = self.allowed_scalings_raw[nearest_scaling_index]
upscale_by = float_to_string(upscale_by)
# Check if the input upscale_by value was different from the nearest valid value
if upscale_by != nearest_scaling_raw:
print(f"{warning('HighRes-Fix Warning:')} "
f"When using 'SD-Latent-Upscaler.{latent_upscale_method}', 'upscale_by' must be one of {self.allowed_scalings_raw}.\n"
f"Rounding to the nearest valid value ({nearest_scaling_raw}).\033[0m")
upscale_by = nearest_scaling_raw
# For default upscale methods
elif latent_upscale_method in self.default_upscale_methods:
# Set function to LatentUpscaleBy.upscale()
upscale_function = LatentUpscaleBy
# Construct the script output
script = script or {}
script["hiresfix"] = (latent_upscale_method, upscale_by, hires_steps, denoise, iterations, upscale_function)
return (script,)
NODE_CLASS_MAPPINGS.update({"HighRes-Fix Script": TSC_HighRes_Fix})
########################################################################################################################
'''# FUTURE
# Tiled Sampling KSamplers (https://github.com/BlenderNeko/ComfyUI_TiledKSampler)
blenderneko_tiled_ksampler_path = os.path.join(custom_nodes_dir, "ComfyUI_TiledKSampler")
if os.path.exists(blenderneko_tiled_ksampler_path):
printout = "Importing BlenderNeko's 'ComfyUI_TiledKSampler' to enable the 'Tiled Sampling' node..."
print(f"{message('Efficiency Nodes:')} {printout}", end="")
try:
blenderneko_tiled_nodes = import_module("ComfyUI_TiledKSampler.nodes")
print(f"\r{message('Efficiency Nodes:')} {printout}{success('Success!')}")
# TSC Tiled Sampling
class TSC_Tiled_Sampling:
@classmethod
def INPUT_TYPES(cls):
return {"required": {"tile_width": ("INT", {"default": 512, "min": 256, "max": MAX_RESOLUTION, "step": 64}),
"tile_height": ("INT", {"default": 512, "min": 256, "max": MAX_RESOLUTION, "step": 64}),
"tiling_strategy": (["random", "random strict", "padded", 'simple', 'none'],),
},
"optional": {"script": ("SCRIPT",)}}
RETURN_TYPES = ("SCRIPT",)
FUNCTION = "tiled_sampling"
CATEGORY = "Efficiency Nodes/Scripts"
def tiled_sampling(self, tile_width, tile_height, tiling_strategy, script=None):
if tiling_strategy != 'none':
script = script or {}
script["tile"] = (tile_width, tile_height, tiling_strategy, blenderneko_tiled_nodes)
return (script,)
NODE_CLASS_MAPPINGS.update({"Tiled Sampling Script": TSC_Tiled_Sampling})
except ImportError:
print(f"\r{message('Efficiency Nodes:')} {printout}{error('Failed!')}")
'''
########################################################################################################################
# Simpleeval Nodes (https://github.com/danthedeckie/simpleeval)
try:
import simpleeval
# TSC Evaluate Integers
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 = simpleeval.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(f"\n{error('Evaluate Integers:')}")
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
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 = simpleeval.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(f"\n{error('Evaluate Floats:')}")
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
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 = simpleeval.DEFAULT_FUNCTIONS.copy()
functions.update({"len": len}) # Add the functions for the expression
result = simpleeval.simple_eval(python_expression, names=variables, functions=functions)
if print_to_console == "True":
print(f"\n{error('Evaluate Strings:')}")
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
class TSC_EvalExamples:
@classmethod
def INPUT_TYPES(cls):
filepath = os.path.join(my_dir, 'workflows', 'SimpleEval_Node_Examples.txt')
with open(filepath, 'r') as file:
examples = file.read()
return {"required": {"models_text": ("STRING", {"default": examples, "multiline": True}), }, }
RETURN_TYPES = ()
CATEGORY = "Efficiency Nodes/Simple Eval"
# ==================================================================================================================
NODE_CLASS_MAPPINGS.update({"Evaluate Integers": TSC_EvaluateInts})
NODE_CLASS_MAPPINGS.update({"Evaluate Floats": TSC_EvaluateFloats})
NODE_CLASS_MAPPINGS.update({"Evaluate Strings": TSC_EvaluateStrs})
NODE_CLASS_MAPPINGS.update({"Simple Eval Examples": TSC_EvalExamples})
except ImportError:
print(f"{warning('Efficiency Nodes Warning:')} Failed to import python package 'simpleeval'; related nodes disabled.\n")
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