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tinyterraNodes.py
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tinyterraNodes.py
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#---------------------------------------------------------------------------------------------------------------------------------------------------#
# tinyterraNodes developed in 2023 by tinyterra https://github.com/TinyTerra #
# for ComfyUI https://github.com/comfyanonymous/ComfyUI #
#---------------------------------------------------------------------------------------------------------------------------------------------------#
import os
import re
import json
import copy
import torch
import random
import datetime
import comfy.sd
import comfy.utils
import numpy as np
import folder_paths
import comfy.samplers
import latent_preview
from torch import Tensor
from pathlib import Path
import comfy.model_management
from PIL.PngImagePlugin import PngInfo
from PIL import Image, ImageDraw, ImageFont
from comfy.sd import ModelPatcher, CLIP, VAE
from typing import Dict, List, Optional, Tuple, Union
from comfy_extras.chainner_models import model_loading
from .adv_encode import advanced_encode, advanced_encode_XL
class CC:
CLEAN = '\33[0m'
BOLD = '\33[1m'
ITALIC = '\33[3m'
UNDERLINE = '\33[4m'
BLINK = '\33[5m'
BLINK2 = '\33[6m'
SELECTED = '\33[7m'
BLACK = '\33[30m'
RED = '\33[31m'
GREEN = '\33[32m'
YELLOW = '\33[33m'
BLUE = '\33[34m'
VIOLET = '\33[35m'
BEIGE = '\33[36m'
WHITE = '\33[37m'
GREY = '\33[90m'
LIGHTRED = '\33[91m'
LIGHTGREEN = '\33[92m'
LIGHTYELLOW = '\33[93m'
LIGHTBLUE = '\33[94m'
LIGHTVIOLET = '\33[95m'
LIGHTBEIGE = '\33[96m'
LIGHTWHITE = '\33[97m'
class ttNl:
def __init__(self, input_string):
self.header_value = f'{CC.LIGHTGREEN}[ttN] {CC.GREEN}'
self.label_value = ''
self.title_value = ''
self.input_string = f'{input_string}{CC.CLEAN}'
def h(self, header_value):
self.header_value = f'{CC.LIGHTGREEN}[{header_value}] {CC.GREEN}'
return self
def full(self):
self.h('tinyterraNodes')
return self
def success(self):
self.label_value = f'Success: '
return self
def warn(self):
self.label_value = f'{CC.RED}Warning:{CC.LIGHTRED} '
return self
def error(self):
self.label_value = f'{CC.LIGHTRED}ERROR:{CC.RED} '
return self
def t(self, title_value):
self.title_value = f'{title_value}:{CC.CLEAN} '
return self
def p(self):
print(self.header_value + self.label_value + self.title_value + self.input_string)
class ttNpaths:
ComfyUI = folder_paths.base_path
tinyterraNodes = Path(__file__).parent
font_path = os.path.join(tinyterraNodes, 'arial.ttf')
# Globals
ttN_version = '1.0.6'
MAX_RESOLUTION=8192
loaded_objects = {
"ckpt": [], # (ckpt_name, model)
"clip": [], # (ckpt_name, clip)
"bvae": [], # (ckpt_name, vae)
"vae": [], # (vae_name, vae)
"lora": {}, # {lora_name: {uid: (model_lora, clip_lora)}}
}
last_helds: dict[str, list] = {
"results": [],
"pipe_line": [],
}
def clean_values(values):
original_values = values.split("; ")
cleaned_values = []
for value in original_values:
# Strip the semi-colon
cleaned_value = value.strip(';').strip()
if cleaned_value == "":
continue
# Try to convert the cleaned_value back to int or float if possible
try:
cleaned_value = int(cleaned_value)
except ValueError:
try:
cleaned_value = float(cleaned_value)
except ValueError:
pass
# Append the cleaned_value to the list
cleaned_values.append(cleaned_value)
return cleaned_values
# Loader Functions
def update_loaded_objects(prompt):
global loaded_objects
# Extract all Loader class type entries
ttN_pipeLoader_entries = [entry for entry in prompt.values() if entry["class_type"] == "ttN pipeLoader"]
ttN_pipeKSampler_entries = [entry for entry in prompt.values() if entry["class_type"] == "ttN pipeKSampler"]
ttN_XYPlot_entries = [entry for entry in prompt.values() if entry["class_type"] == "ttN xyPlot"]
# Collect all desired model, vae, and lora names
desired_ckpt_names = set()
desired_vae_names = set()
desired_lora_names = set()
desired_lora_settings = set()
for entry in ttN_pipeLoader_entries:
desired_ckpt_names.add(entry["inputs"]["ckpt_name"])
desired_vae_names.add(entry["inputs"]["vae_name"])
desired_lora_names.add(entry["inputs"]["lora1_name"])
desired_lora_settings.add(f'{entry["inputs"]["lora1_name"]};{entry["inputs"]["lora1_model_strength"]};{entry["inputs"]["lora1_clip_strength"]}')
desired_lora_names.add(entry["inputs"]["lora2_name"])
desired_lora_settings.add(f'{entry["inputs"]["lora2_name"]};{entry["inputs"]["lora2_model_strength"]};{entry["inputs"]["lora2_clip_strength"]}')
desired_lora_names.add(entry["inputs"]["lora3_name"])
desired_lora_settings.add(f'{entry["inputs"]["lora3_name"]};{entry["inputs"]["lora3_model_strength"]};{entry["inputs"]["lora3_clip_strength"]}')
for entry in ttN_pipeKSampler_entries:
desired_lora_names.add(entry["inputs"]["lora_name"])
desired_lora_settings.add(f'{entry["inputs"]["lora_name"]};{entry["inputs"]["lora_model_strength"]};{entry["inputs"]["lora_clip_strength"]}')
for entry in ttN_XYPlot_entries:
x_entry = entry["inputs"]["x_axis"].split(": ")[1] if entry["inputs"]["x_axis"] not in ttN_XYPlot.rejected else "None"
y_entry = entry["inputs"]["y_axis"].split(": ")[1] if entry["inputs"]["y_axis"] not in ttN_XYPlot.rejected else "None"
def add_desired_plot_values(axis_entry, axis_values):
vals = clean_values(entry["inputs"][axis_values])
if axis_entry == "vae_name":
for v in vals:
desired_vae_names.add(v)
elif axis_entry == "ckpt_name":
for v in vals:
desired_ckpt_names.add(v)
elif axis_entry in ["lora1_name", "lora2_name", "lora3_name"]:
for v in vals:
desired_lora_names.add(v)
add_desired_plot_values(x_entry, "x_values")
add_desired_plot_values(y_entry, "y_values")
# Check and clear unused ckpt, clip, and bvae entries
for list_key in ["ckpt", "clip", "bvae"]:
unused_indices = [i for i, entry in enumerate(loaded_objects[list_key]) if entry[0] not in desired_ckpt_names]
for index in sorted(unused_indices, reverse=True):
loaded_objects[list_key].pop(index)
# Check and clear unused vae entries
unused_vae_indices = [i for i, entry in enumerate(loaded_objects["vae"]) if entry[0] not in desired_vae_names]
for index in sorted(unused_vae_indices, reverse=True):
loaded_objects["vae"].pop(index)
loaded_ckpt_hashes = set()
for ckpt in loaded_objects["ckpt"]:
loaded_ckpt_hashes.add(str(ckpt[1])[33:-1])
# Check and clear unused lora entries
for lora_name, lora_models in dict(loaded_objects["lora"]).items():
if lora_name not in desired_lora_names:
loaded_objects["lora"].pop(lora_name)
else:
for UID in list(lora_models.keys()):
used_model_hash, lora_settings= UID.split(";", 1)
if used_model_hash not in loaded_ckpt_hashes or lora_settings not in desired_lora_settings:
loaded_objects["lora"][lora_name].pop(UID)
def load_checkpoint(ckpt_name, output_vae=True, output_clip=True):
"""
Searches for tuple index that contains ckpt_name in "ckpt" array of loaded_objects.
If found, extracts the model, clip, and vae from the loaded_objects.
If not found, loads the checkpoint, extracts the model, clip, and vae, and adds them to the loaded_objects.
Returns the model, clip, and vae.
"""
global loaded_objects
# Search for tuple index that contains ckpt_name in "ckpt" array of loaded_objects
checkpoint_found = False
for i, entry in enumerate(loaded_objects["ckpt"]):
if entry[0] == ckpt_name:
# Extract the second element of the tuple at 'i' in the "ckpt", "clip", "bvae" arrays
model = loaded_objects["ckpt"][i][1]
clip = loaded_objects["clip"][i][1]
vae = loaded_objects["bvae"][i][1]
checkpoint_found = True
break
# If not found, load ckpt
if checkpoint_found == False:
# Load Checkpoint
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True,
embedding_directory=folder_paths.get_folder_paths("embeddings"))
model = out[0]
clip = out[1]
vae = out[2]
# Update loaded_objects[] array
loaded_objects["ckpt"].append((ckpt_name, out[0]))
loaded_objects["clip"].append((ckpt_name, out[1]))
loaded_objects["bvae"].append((ckpt_name, out[2]))
return model, clip, vae
def load_vae(vae_name):
"""
Extracts the vae with a given name from the "vae" array in loaded_objects.
If the vae is not found, creates a new VAE object with the given name and adds it to the "vae" array.
"""
global loaded_objects
# Check if vae_name exists in "vae" array
if any(entry[0] == vae_name for entry in loaded_objects["vae"]):
# Extract the second tuple entry of the checkpoint
vae = [entry[1] for entry in loaded_objects["vae"] if entry[0] == vae_name][0]
else:
vae_path = folder_paths.get_full_path("vae", vae_name)
vae = comfy.sd.VAE(ckpt_path=vae_path)
# Update loaded_objects[] array
loaded_objects["vae"].append((vae_name, vae))
return vae
def load_lora(lora_name, model, clip, strength_model, strength_clip):
"""
Extracts the Lora model with a given name from the "lora" array in loaded_objects.
If the Lora model is not found or the strength values change or the original model has changed, creates a new Lora object with the given name and adds it to the "lora" array.
"""
global loaded_objects
# Get the model_hash as string
input_model_hash = str(model)[33:-1]
# Assign UID to model/lora/strengths combo
unique_id = f'{input_model_hash};{lora_name};{strength_model};{strength_clip}'
# Check if Lora model already exists
existing_lora_models = loaded_objects.get("lora", {}).get(lora_name, None)
if existing_lora_models and unique_id in existing_lora_models:
model_lora, clip_lora = existing_lora_models[unique_id]
return model_lora, clip_lora
# If Lora model not found or strength values changed or model changed, generate new Lora models
lora_path = folder_paths.get_full_path("loras", lora_name)
lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
if lora_name not in loaded_objects["lora"]:
loaded_objects["lora"][lora_name] = {}
loaded_objects["lora"][lora_name][unique_id] = (model_lora, clip_lora)
return model_lora, clip_lora
# Sampler Functions
def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, preview_latent=True, disable_pbar=False):
device = comfy.model_management.get_torch_device()
latent_image = latent["samples"]
if disable_noise:
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
else:
batch_inds = latent["batch_index"] if "batch_index" in latent else None
noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
noise_mask = None
if "noise_mask" in latent:
noise_mask = latent["noise_mask"]
preview_format = "JPEG"
if preview_format not in ["JPEG", "PNG"]:
preview_format = "JPEG"
previewer = False
if preview_latent:
previewer = latent_preview.get_previewer(device, model.model.latent_format)
pbar = comfy.utils.ProgressBar(steps)
def callback(step, x0, x, total_steps):
preview_bytes = None
if previewer:
preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0)
pbar.update_absolute(step + 1, total_steps, preview_bytes)
samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
out = latent.copy()
out["samples"] = samples
return out
def enforce_mul_of_64(d):
d = int(d)
if d<=7:
d = 8
leftover = d % 8 # 8 is the number of pixels per byte
if leftover != 0: # if the number of pixels is not a multiple of 8
if (leftover < 4): # if the number of pixels is less than 4
d -= leftover # remove the leftover pixels
else: # if the number of pixels is more than 4
d += 8 - leftover # add the leftover pixels
return int(d)
def upscale(samples, upscale_method, scale_by, crop):
s = samples.copy()
width = enforce_mul_of_64(round(samples["samples"].shape[3] * scale_by))
height = enforce_mul_of_64(round(samples["samples"].shape[2] * scale_by))
if (width > MAX_RESOLUTION):
width = MAX_RESOLUTION
if (height > MAX_RESOLUTION):
height = MAX_RESOLUTION
s["samples"] = comfy.utils.common_upscale(samples["samples"], width, height, upscale_method, crop)
return (s,)
def tensor2pil(image: torch.Tensor) -> Image.Image:
return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
def pil2tensor(image: Image.Image) -> torch.Tensor:
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
# Functions for saving
def get_save_image_path(filename_prefix: str, output_dir: str, image_width: int = 0, image_height: int = 0, output_folder: str = "Default") -> Tuple[str, str, int, str, str]:
def map_filename(filename: str) -> Tuple[int, str]:
prefix_len = len(os.path.basename(filename_prefix))
prefix = filename[:prefix_len]
digits = re.search('\d+', filename[prefix_len:])
return (int(digits.group()) if digits else 0, prefix)
filename_prefix = filename_prefix.replace("%width%", str(image_width)).replace("%height%", str(image_height))
subfolder = os.path.dirname(os.path.normpath(filename_prefix))
filename = os.path.basename(os.path.normpath(filename_prefix))
full_output_folder = output_folder if os.path.isdir(output_folder) else os.path.join(output_dir, subfolder)
try:
counter = max(filter(lambda a: a[1] == filename, map(map_filename, os.listdir(full_output_folder))))[0] + 1
except (ValueError, FileNotFoundError):
os.makedirs(full_output_folder, exist_ok=True)
counter = 1
return full_output_folder, filename, counter, subfolder, filename_prefix
def format_date(text: str, date: datetime.datetime) -> str:
date_formats = {
'd': lambda d: d.day,
'M': lambda d: d.month,
'h': lambda d: d.hour,
'm': lambda d: d.minute,
's': lambda d: d.second,
'yyyy': lambda d: d.year,
'yyy': lambda d: str(d.year)[1:],
'yy': lambda d: str(d.year)[2:]
}
for format_str, format_func in date_formats.items():
if format_str in text:
text = text.replace(format_str, '{:02d}'.format(format_func(date)))
return text
def gather_all_inputs(prompt: Dict[str, dict], unique_id: str, linkInput: str = '', collected_inputs: Optional[Dict[str, Union[str, List[str]]]] = None) -> Dict[str, Union[str, List[str]]]:
collected_inputs = collected_inputs or {}
prompt_inputs = prompt[str(unique_id)]["inputs"]
for pInput, pInputValue in prompt_inputs.items():
aInput = f"{linkInput}>{pInput}" if linkInput else pInput
if isinstance(pInputValue, list):
gather_all_inputs(prompt, pInputValue[0], aInput, collected_inputs)
else:
existing_value = collected_inputs.get(aInput)
if existing_value is None:
collected_inputs[aInput] = pInputValue
elif pInputValue not in existing_value:
collected_inputs[aInput] = existing_value + "; " + pInputValue
return collected_inputs
def filename_parser(filename_prefix: str, prompt: Dict[str, dict], my_unique_id: str) -> str:
filename_prefix = re.sub(r'%date:(.*?)%', lambda m: format_date(m.group(1), datetime.datetime.now()), filename_prefix)
all_inputs = gather_all_inputs(prompt, my_unique_id)
filename_prefix = re.sub(r'%(.*?)%', lambda m: str(all_inputs[m.group(1)]), filename_prefix)
filename_prefix = re.sub(r'[/\\]+', '-', filename_prefix)
return filename_prefix
def save_images(self, images, preview_prefix, save_prefix, image_output, prompt=None, extra_pnginfo=None, my_unique_id=None, embed_workflow=True, output_folder="Default", number_padding=5, overwrite_existing="False"):
if output_folder != "Default" and not os.path.exists(output_folder):
ttNl(f"Folder {output_folder} does not exist. Attempting to create...").warn().p()
try:
os.makedirs(output_folder)
ttNl(f"{output_folder} Created Successfully").success().p()
except:
ttNl(f"Failed to create folder {output_folder}").error().p()
if image_output in ("Hide"):
return []
elif image_output in ("Save", "Hide/Save"):
output_dir = output_folder if os.path.exists(output_folder) else folder_paths.get_output_directory()
filename_prefix = save_prefix
type = "output"
elif image_output in ("Preview"):
output_dir = folder_paths.get_temp_directory()
filename_prefix = preview_prefix
type = "temp"
filename_prefix = filename_parser(filename_prefix, prompt, my_unique_id)
full_output_folder, filename, counter, subfolder, filename_prefix = get_save_image_path(filename_prefix, output_dir, images[0].shape[1], images[0].shape[0], output_dir)
results = []
for image in images:
img = Image.fromarray(np.clip(255. * image.cpu().numpy(), 0, 255).astype(np.uint8))
metadata = PngInfo()
if embed_workflow in (True, "True"):
if prompt is not None:
metadata.add_text("prompt", json.dumps(prompt))
if extra_pnginfo is not None:
for key, value in extra_pnginfo.items():
metadata.add_text(key, json.dumps(value))
def filename_padding(number_padding, filename, counter):
return f"{filename}.png" if number_padding is None else f"{filename}_{counter:0{number_padding}}.png"
number_padding = None if number_padding == "None" else int(number_padding)
overwrite_existing = True if overwrite_existing == "True" else False
file = os.path.join(full_output_folder, filename_padding(number_padding, filename, counter))
if overwrite_existing or not os.path.isfile(file):
img.save(file, pnginfo=metadata, compress_level=4)
else:
if number_padding is None:
number_padding = 1
while os.path.isfile(file):
number_padding += 1
file = os.path.join(full_output_folder, filename_padding(number_padding, filename, counter))
img.save(file, pnginfo=metadata, compress_level=4)
results.append({"filename": file, "subfolder": subfolder, "type": type})
counter += 1
return results
#---------------------------------------------------------------ttN/pipe START----------------------------------------------------------------------#
class ttN_TSC_pipeLoader:
version = '1.0.1'
@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": 0, "step": 1}),
"lora1_name": (["None"] + folder_paths.get_filename_list("loras"),),
"lora1_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora1_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora2_name": (["None"] + folder_paths.get_filename_list("loras"),),
"lora2_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora2_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora3_name": (["None"] + folder_paths.get_filename_list("loras"),),
"lora3_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora3_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"positive": ("STRING", {"default": "Positive","multiline": True}),
"positive_token_normalization": (["none", "mean", "length", "length+mean"],),
"positive_weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"],),
"negative": ("STRING", {"default": "Negative", "multiline": True}),
"negative_token_normalization": (["none", "mean", "length", "length+mean"],),
"negative_weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"],),
"empty_latent_width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"empty_latent_height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
},
"hidden": {"prompt": "PROMPT", "ttNnodeVersion": ttN_TSC_pipeLoader.version}}
RETURN_TYPES = ("PIPE_LINE" ,"MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "CLIP", "INT",)
RETURN_NAMES = ("pipe","model", "positive", "negative", "latent", "vae", "clip", "seed",)
FUNCTION = "adv_pipeloader"
CATEGORY = "ttN/pipe"
def adv_pipeloader(self, ckpt_name, vae_name, clip_skip,
lora1_name, lora1_model_strength, lora1_clip_strength,
lora2_name, lora2_model_strength, lora2_clip_strength,
lora3_name, lora3_model_strength, lora3_clip_strength,
positive, positive_token_normalization, positive_weight_interpretation,
negative, negative_token_normalization, negative_weight_interpretation,
empty_latent_width, empty_latent_height, batch_size, seed, prompt=None):
model: ModelPatcher | None = None
clip: CLIP | None = None
vae: VAE | None = None
# Create Empty Latent
latent = torch.zeros([batch_size, 4, empty_latent_height // 8, empty_latent_width // 8]).cpu()
samples = {"samples":latent}
# Clean models from loaded_objects
update_loaded_objects(prompt)
# Load models
model, clip, vae = load_checkpoint(ckpt_name)
if lora1_name != "None":
model, clip = load_lora(lora1_name, model, clip, lora1_model_strength, lora1_clip_strength)
if lora2_name != "None":
model, clip = load_lora(lora2_name, model, clip, lora2_model_strength, lora2_clip_strength)
if lora3_name != "None":
model, clip = load_lora(lora3_name, model, clip, lora3_model_strength, lora3_clip_strength)
# Check for custom VAE
if vae_name != "Baked VAE":
vae = load_vae(vae_name)
# CLIP skip
if not clip:
raise Exception("No CLIP found")
clip = clip.clone()
if clip_skip != 0:
clip.clip_layer(clip_skip)
positive_embeddings_final, positive_pooled = advanced_encode(clip, positive, positive_token_normalization, positive_weight_interpretation, w_max=1.0, apply_to_pooled='enable')
positive_embeddings_final = [[positive_embeddings_final, {"pooled_output": positive_pooled}]]
negative_embeddings_final, negative_pooled = advanced_encode(clip, negative, negative_token_normalization, negative_weight_interpretation, w_max=1.0, apply_to_pooled='enable')
negative_embeddings_final = [[negative_embeddings_final, {"pooled_output": negative_pooled}]]
image = pil2tensor(Image.new('RGB', (1, 1), (0, 0, 0)))
pipe = {"model": model,
"positive": positive_embeddings_final,
"negative": negative_embeddings_final,
"samples": samples,
"vae": vae,
"clip": clip,
"images": image,
"seed": seed,
"loader_settings": {"ckpt_name": ckpt_name,
"vae_name": vae_name,
"clip_skip": clip_skip,
"lora1_name": lora1_name,
"lora1_model_strength": lora1_model_strength,
"lora1_clip_strength": lora1_clip_strength,
"lora2_name": lora2_name,
"lora2_model_strength": lora2_model_strength,
"lora2_clip_strength": lora2_clip_strength,
"lora3_name": lora3_name,
"lora3_model_strength": lora3_model_strength,
"lora3_clip_strength": lora3_clip_strength,
"positive": positive,
"positive_l": None,
"positive_g": None,
"positive_token_normalization": positive_token_normalization,
"positive_weight_interpretation": positive_weight_interpretation,
"positive_balance": None,
"negative": negative,
"negative_l": None,
"negative_g": None,
"negative_token_normalization": negative_token_normalization,
"negative_weight_interpretation": negative_weight_interpretation,
"negative_balance": None,
"empty_latent_width": empty_latent_width,
"empty_latent_height": empty_latent_height,
"batch_size": batch_size,
"seed": seed,
"empty_samples": samples,
"empty_image": image,}
}
return (pipe, model, positive_embeddings_final, negative_embeddings_final, samples, vae, clip, seed)
class ttN_TSC_pipeLoaderSDXL:
version = '1.0.0'
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"base_ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
"base_vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),),
"base_lora1_name": (["None"] + folder_paths.get_filename_list("loras"),),
"base_lora1_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"base_lora1_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"base_lora2_name": (["None"] + folder_paths.get_filename_list("loras"),),
"base_lora2_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"base_lora2_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"refiner_ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
"refiner_vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),),
"refiner_lora1_name": (["None"] + folder_paths.get_filename_list("loras"),),
"refiner_lora1_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"refiner_lora1_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"refiner_lora2_name": (["None"] + folder_paths.get_filename_list("loras"),),
"refiner_lora2_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"refiner_lora2_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"clip_skip": ("INT", {"default": -1, "min": -24, "max": 0, "step": 1}),
"positive": ("STRING", {"default": "Positive","multiline": True}),
"positive_token_normalization": (["none", "mean", "length", "length+mean"],),
"positive_weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"],),
"negative": ("STRING", {"default": "Negative", "multiline": True}),
"negative_token_normalization": (["none", "mean", "length", "length+mean"],),
"negative_weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"],),
"empty_latent_width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"empty_latent_height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
},
"hidden": {"prompt": "PROMPT", "ttNnodeVersion": ttN_TSC_pipeLoader.version}}
RETURN_TYPES = ("SDXL_PIPE_LINE" ,"MODEL", "CONDITIONING", "CONDITIONING", "VAE", "MODEL", "CONDITIONING", "CONDITIONING", "VAE", "LATENT", "CLIP", "INT",)
RETURN_NAMES = ("sdxl_pipe","base_model", "base_positive", "base_negative", "base_vae", "refiner_model", "refiner_positive", "refiner_negative", "refiner_vae", "latent", "clip", "seed",)
FUNCTION = "adv_pipeloader"
CATEGORY = "ttN/pipe"
def adv_pipeloader(self, base_ckpt_name, base_vae_name,
base_lora1_name, base_lora1_model_strength, base_lora1_clip_strength,
base_lora2_name, base_lora2_model_strength, base_lora2_clip_strength,
refiner_ckpt_name, refiner_vae_name,
refiner_lora1_name, refiner_lora1_model_strength, refiner_lora1_clip_strength,
refiner_lora2_name, refiner_lora2_model_strength, refiner_lora2_clip_strength,
clip_skip,
positive, positive_token_normalization, positive_weight_interpretation,
negative, negative_token_normalization, negative_weight_interpretation,
empty_latent_width, empty_latent_height, batch_size, seed, prompt=None):
base_model: ModelPatcher | None = None
base_clip: CLIP | None = None
base_vae: VAE | None = None
refiner_model: ModelPatcher | None = None
refiner_clip: CLIP | None = None
refiner_vae: VAE | None = None
def SDXL_loader(ckpt_name, vae_name,
lora1_name, lora1_model_strength, lora1_clip_strength,
lora2_name, lora2_model_strength, lora2_clip_strength,
positive, positive_token_normalization, positive_weight_interpretation,
negative, negative_token_normalization, negative_weight_interpretation,):
# Load models
model, clip, vae = load_checkpoint(ckpt_name)
if lora1_name != "None":
model, clip = load_lora(lora1_name, model, clip, lora1_model_strength, lora1_clip_strength)
if lora2_name != "None":
model, clip = load_lora(lora2_name, model, clip, lora2_model_strength, lora2_clip_strength)
# Check for custom VAE
if vae_name != "Baked VAE":
vae = load_vae(vae_name)
# CLIP skip
if not clip:
raise Exception("No CLIP found")
clip = clip.clone()
if clip_skip != 0:
clip.clip_layer(clip_skip)
positive_embeddings_final, positive_pooled = advanced_encode(clip, positive, positive_token_normalization, positive_weight_interpretation, w_max=1.0, apply_to_pooled='enable')
positive_embeddings_final = [[positive_embeddings_final, {"pooled_output": positive_pooled}]]
negative_embeddings_final, negative_pooled = advanced_encode(clip, negative, negative_token_normalization, negative_weight_interpretation, w_max=1.0, apply_to_pooled='enable')
negative_embeddings_final = [[negative_embeddings_final, {"pooled_output": negative_pooled}]]
return model, positive_embeddings_final, negative_embeddings_final, vae, clip
# Create Empty Latent
latent = torch.zeros([batch_size, 4, empty_latent_height // 8, empty_latent_width // 8]).cpu()
samples = {"samples":latent}
base_model, base_positive_embeddings, base_negative_embeddings, base_vae, base_clip = SDXL_loader(base_ckpt_name, base_vae_name,
base_lora1_name, base_lora1_model_strength, base_lora1_clip_strength,
base_lora2_name, base_lora2_model_strength, base_lora2_clip_strength,
positive, positive_token_normalization, positive_weight_interpretation,
negative, negative_token_normalization, negative_weight_interpretation)
refiner_model, refiner_positive_embeddings, refiner_negative_embeddings, refiner_vae, refiner_clip = SDXL_loader(refiner_ckpt_name, refiner_vae_name,
refiner_lora1_name, refiner_lora1_model_strength, refiner_lora1_clip_strength,
refiner_lora2_name, refiner_lora2_model_strength, refiner_lora2_clip_strength,
positive, positive_token_normalization, positive_weight_interpretation,
negative, negative_token_normalization, negative_weight_interpretation)
# Clean models from loaded_objects
update_loaded_objects(prompt)
image = pil2tensor(Image.new('RGB', (1, 1), (0, 0, 0)))
pipe = {"vars": {"base_model": base_model,
"base_positive_embeddings": base_positive_embeddings,
"base_negative_embeddings": base_negative_embeddings,
"base_vae": base_vae,
"base_clip": base_clip,
"refiner_model": refiner_model,
"refiner_positive_embeddings": refiner_positive_embeddings,
"refiner_negative_embeddings": refiner_negative_embeddings,
"refiner_vae": refiner_vae,
"refiner_clip": refiner_clip,
"samples": samples,
"images": image,
"seed": seed},
"orig": {"base_model": base_model,
"base_positive_embeddings": base_positive_embeddings,
"base_negative_embeddings": base_negative_embeddings,
"base_vae": base_vae,
"base_clip": base_clip,
"refiner_model": refiner_model,
"refiner_positive_embeddings": refiner_positive_embeddings,
"refiner_negative_embeddings": refiner_negative_embeddings,
"refiner_vae": refiner_vae,
"refiner_clip": refiner_clip,
"samples": samples,
"images": image,
"seed": seed},
"loader_settings": {"base_ckpt_name": base_ckpt_name,
"base_vae_name": base_vae_name,
"base_lora1_name": base_lora1_name,
"base_lora1_model_strength": base_lora1_model_strength,
"base_lora1_clip_strength": base_lora1_clip_strength,
"base_lora2_name": base_lora2_name,
"base_lora2_model_strength": base_lora2_model_strength,
"base_lora2_clip_strength": base_lora2_clip_strength,
"refiner_ckpt_name": refiner_ckpt_name,
"refiner_vae_name": refiner_vae_name,
"refiner_lora1_name": refiner_lora1_name,
"refiner_lora1_model_strength": refiner_lora1_model_strength,
"refiner_lora1_clip_strength": refiner_lora1_clip_strength,
"refiner_lora2_name": refiner_lora2_name,
"refiner_lora2_model_strength": refiner_lora2_model_strength,
"refiner_lora2_clip_strength": refiner_lora2_clip_strength,
"clip_skip": clip_skip,
"positive": positive,
"positive_token_normalization": positive_token_normalization,
"positive_weight_interpretation": positive_weight_interpretation,
"negative": negative,
"negative_token_normalization": negative_token_normalization,
"negative_weight_interpretation": negative_weight_interpretation,
"empty_latent_width": empty_latent_width,
"empty_latent_height": empty_latent_height,
"batch_size": batch_size,
"seed": seed,
"empty_samples": samples,
"empty_image": image,}
}
return (pipe, base_model, base_positive_embeddings, base_negative_embeddings, base_vae, refiner_model, refiner_positive_embeddings, refiner_negative_embeddings, refiner_vae, refiner_clip, samples, seed)
class ttN_TSC_pipeKSampler:
version = '1.0.3'
empty_image = pil2tensor(Image.new('RGBA', (1, 1), (0, 0, 0, 0)))
upscale_methods = ["None", "nearest-exact", "bilinear", "area", "bicubic", "bislerp"]
crop_methods = ["disabled", "center"]
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {"required":
{"pipe": ("PIPE_LINE",),
"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}),
"upscale_method": (cls.upscale_methods,),
"factor": ("FLOAT", {"default": 2, "min": 0.0, "max": 10.0, "step": 0.25}),
"crop": (cls.crop_methods,),
"sampler_state": (["Sample", "Hold"], ),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"image_output": (["Hide", "Preview", "Save", "Hide/Save"],),
"save_prefix": ("STRING", {"default": "ComfyUI"})
},
"optional":
{"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"optional_model": ("MODEL",),
"optional_positive": ("CONDITIONING",),
"optional_negative": ("CONDITIONING",),
"optional_latent": ("LATENT",),
"optional_vae": ("VAE",),
"optional_clip": ("CLIP",),
"xyPlot": ("XYPLOT",),
},
"hidden":
{"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID",
"embeddingsList": (folder_paths.get_filename_list("embeddings"),),
"ttNnodeVersion": ttN_TSC_pipeKSampler.version},
}
RETURN_TYPES = ("PIPE_LINE", "MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "CLIP", "IMAGE", "INT",)
RETURN_NAMES = ("pipe", "model", "positive", "negative", "latent","vae", "clip", "image", "seed", )
OUTPUT_NODE = True
FUNCTION = "sample"
CATEGORY = "ttN/pipe"
def sample(self, pipe, lora_name, lora_model_strength, lora_clip_strength, sampler_state, steps, cfg, sampler_name, scheduler, image_output, save_prefix, denoise=1.0,
optional_model=None, optional_positive=None, optional_negative=None, optional_latent=None, optional_vae=None, optional_clip=None, seed=None, xyPlot=None, upscale_method=None, factor=None, crop=None, prompt=None, extra_pnginfo=None, my_unique_id=None, start_step=None, last_step=None, force_full_denoise=False, disable_noise=False):
global last_helds
pipe = {**pipe}
# Clean Loader Models from Global
update_loaded_objects(prompt)
my_unique_id = int(my_unique_id)
preview_prefix = f"KSpipe_{my_unique_id:02d}"
pipe["model"] = optional_model if optional_model is not None else pipe["model"]
pipe["positive"] = optional_positive if optional_positive is not None else pipe["positive"]
pipe["negative"] = optional_negative if optional_negative is not None else pipe["negative"]
pipe["samples"] = optional_latent if optional_latent is not None else pipe["samples"]
pipe["vae"] = optional_vae if optional_vae is not None else pipe["vae"]
pipe["clip"] = optional_clip if optional_clip is not None else pipe["clip"]
if seed in (None, 'undefined'):
seed = pipe["seed"]
else:
pipe["seed"] = seed
def get_value_by_id(key: str, my_unique_id):
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):
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 handle_upscale(samples, upscale_method, factor, crop):
if upscale_method != "None":
samples = upscale(samples, upscale_method, factor, crop)[0]
return samples
def init_state(my_unique_id, key, default):
value = get_value_by_id(key, my_unique_id)
if value is not None:
return value
return default
def safe_split(s, delimiter):
parts = s.split(delimiter)
for part in parts:
if part in ('', ' ', ' '):
parts.remove(part)
while len(parts) < 2:
parts.append('None')
return parts
def get_output(pipe):
return (pipe,
pipe.get("model"),
pipe.get("positive"),
pipe.get("negative"),
pipe.get("samples"),
pipe.get("vae"),
pipe.get("clip"),
pipe.get("images"),
pipe.get("seed"))
def process_sample_state(self, pipe, lora_name, lora_model_strength, lora_clip_strength,
steps, cfg, sampler_name, scheduler, denoise,
image_output, preview_prefix, save_prefix, prompt, extra_pnginfo, my_unique_id, preview_latent, disable_noise=disable_noise):
# Load Lora
if lora_name not in (None, "None"):
pipe["model"], pipe["clip"] = load_lora(lora_name, pipe["model"], pipe["clip"], lora_model_strength, lora_clip_strength)
# Upscale samples if enabled
pipe["samples"] = handle_upscale(pipe["samples"], upscale_method, factor, crop)
pipe["samples"] = common_ksampler(pipe["model"], pipe["seed"], steps, cfg, sampler_name, scheduler, pipe["positive"], pipe["negative"], pipe["samples"], denoise=denoise, preview_latent=preview_latent, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, disable_noise=disable_noise)
latent = pipe["samples"]["samples"]
pipe["images"] = pipe["vae"].decode(latent).cpu()
results = save_images(self, pipe["images"], preview_prefix, save_prefix, image_output, prompt, extra_pnginfo, my_unique_id)
update_value_by_id("results", my_unique_id, results)
# Clean loaded_objects
update_loaded_objects(prompt)
new_pipe = {**pipe}
update_value_by_id("pipe_line", my_unique_id, new_pipe)
if image_output in ("Hide", "Hide/Save"):
return get_output(new_pipe)
return {"ui": {"images": results},
"result": get_output(new_pipe)}
def process_hold_state(self, pipe, image_output, preview_prefix, save_prefix, prompt, extra_pnginfo, my_unique_id):
ttNl('Held').t(f'pipeKSampler[{my_unique_id}]').p()
last_pipe = init_state(my_unique_id, "pipe_line", pipe)
last_results = init_state(my_unique_id, "results", list())
if image_output in ("Hide", "Hide/Save"):
return get_output(last_pipe)
return {"ui": {"images": last_results}, "result": get_output(last_pipe)}
def process_xyPlot(self, pipe, lora_name, lora_model_strength, lora_clip_strength,
steps, cfg, sampler_name, scheduler, denoise,
image_output, preview_prefix, save_prefix, prompt, extra_pnginfo, my_unique_id, preview_latent, xyPlot):
x_node_type, x_type = safe_split(xyPlot[0], ': ')
x_values = xyPlot[1]
if x_type == 'None':
x_values = []
y_node_type, y_type = safe_split(xyPlot[2], ': ')
y_values = xyPlot[3]
if y_type == 'None':
y_values = []
grid_spacing = xyPlot[4]
latent_id = xyPlot[5]
if x_type == 'None' and y_type == 'None':
ttNl('No Valid Plot Types - Reverting to default sampling...').t(f'pipeKSampler[{my_unique_id}]').warn().p()