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tinyterraNodes.py
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tinyterraNodes.py
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"""
@author: tinyterra
@title: tinyterraNodes
@nickname: ttNodes
@description: This extension offers various pipe nodes, fullscreen image viewer based on node history, dynamic widgets, interface customization, and more.
"""
#---------------------------------------------------------------------------------------------------------------------------------------------------#
# tinyterraNodes developed in 2023 by tinyterra https://github.com/TinyTerra #
# for ComfyUI https://github.com/comfyanonymous/ComfyUI #
# Like the pack and want to support me? https://www.buymeacoffee.com/tinyterra #
#---------------------------------------------------------------------------------------------------------------------------------------------------#
ttN_version = '1.2.0'
MAX_RESOLUTION=8192
import os
import re
import json
import time
import torch
import psutil
import random
import datetime
import comfy.sd
import comfy.utils
import numpy as np
import folder_paths
import comfy.samplers
import latent_preview
import comfy.model_base
from pathlib import Path
import comfy.model_management
from comfy.sd import CLIP, VAE
from comfy.cli_args import args
from urllib.request import urlopen
from collections import defaultdict
from PIL.PngImagePlugin import PngInfo
from PIL import Image, ImageDraw, ImageFont
from comfy.model_patcher import ModelPatcher
from comfy_extras.chainner_models import model_loading
from typing import Dict, List, Optional, Tuple, Union, Any
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)
return self
def interrupt(self, msg):
raise Exception(msg)
class ttNpaths:
ComfyUI = folder_paths.base_path
tinyterraNodes = Path(__file__).parent
font_path = os.path.join(tinyterraNodes, 'arial.ttf')
class ttNloader:
def __init__(self):
self.loaded_objects = {
"ckpt": defaultdict(tuple), # {ckpt_name: (model, ...)}
"clip": defaultdict(tuple),
"bvae": defaultdict(tuple),
"vae": defaultdict(object),
"lora": defaultdict(dict), # {lora_name: {UID: (model_lora, clip_lora)}}
}
self.memory_threshold = self.determine_memory_threshold(0.7)
def clean_values(self, values: str):
original_values = values.split("; ")
cleaned_values = []
for value in original_values:
cleaned_value = value.strip(';').strip()
if cleaned_value == "":
continue
try:
cleaned_value = int(cleaned_value)
except ValueError:
try:
cleaned_value = float(cleaned_value)
except ValueError:
pass
cleaned_values.append(cleaned_value)
return cleaned_values
def clear_unused_objects(self, desired_names: set, object_type: str):
keys = set(self.loaded_objects[object_type].keys())
for key in keys - desired_names:
del self.loaded_objects[object_type][key]
def get_input_value(self, entry, key):
val = entry["inputs"][key]
return val if isinstance(val, str) else val[0]
def process_pipe_loader(self, entry,
desired_ckpt_names, desired_vae_names,
desired_lora_names, desired_lora_settings, num_loras=3, suffix=""):
for idx in range(1, num_loras + 1):
lora_name_key = f"{suffix}lora{idx}_name"
desired_lora_names.add(self.get_input_value(entry, lora_name_key))
setting = f'{self.get_input_value(entry, lora_name_key)};{entry["inputs"][f"{suffix}lora{idx}_model_strength"]};{entry["inputs"][f"{suffix}lora{idx}_clip_strength"]}'
desired_lora_settings.add(setting)
desired_ckpt_names.add(self.get_input_value(entry, f"{suffix}ckpt_name"))
desired_vae_names.add(self.get_input_value(entry, f"{suffix}vae_name"))
def update_loaded_objects(self, prompt):
desired_ckpt_names = set()
desired_vae_names = set()
desired_lora_names = set()
desired_lora_settings = set()
for entry in prompt.values():
class_type = entry["class_type"]
if class_type == "ttN pipeLoader":
self.process_pipe_loader(entry, desired_ckpt_names=desired_ckpt_names,
desired_vae_names=desired_vae_names,
desired_lora_names=desired_lora_names,
desired_lora_settings=desired_lora_settings)
elif class_type == "ttN pipeLoaderSDXL":
self.process_pipe_loader(entry, num_loras=2, suffix="refiner_", desired_ckpt_names=desired_ckpt_names, desired_vae_names=desired_vae_names, desired_lora_names=desired_lora_names, desired_lora_settings=desired_lora_settings)
self.process_pipe_loader(entry, num_loras=2, desired_ckpt_names=desired_ckpt_names, desired_vae_names=desired_vae_names, desired_lora_names=desired_lora_names, desired_lora_settings=desired_lora_settings)
elif class_type == "ttN pipeKSampler" or class_type == "ttN pipeKSamplerAdvanced":
lora_name = self.get_input_value(entry, "lora_name")
desired_lora_names.add(lora_name)
setting = f'{lora_name};{entry["inputs"]["lora_model_strength"]};{entry["inputs"]["lora_clip_strength"]}'
desired_lora_settings.add(setting)
elif class_type == "ttN xyPlot":
for axis in ["x", "y"]:
axis_key = f"{axis}_axis"
if entry["inputs"][axis_key] != "None":
axis_entry = entry["inputs"][axis_key].split(": ")[1]
vals = self.clean_values(entry["inputs"][f"{axis}_values"])
desired_names_set = {
"vae_name": desired_vae_names,
"ckpt_name": desired_ckpt_names,
"lora_name": desired_lora_names,
"lora1_name": desired_lora_names,
"lora2_name": desired_lora_names,
"lora3_name": desired_lora_names,
}
if desired_names_set.get(axis_entry) is not None:
desired_names_set[axis_entry].update(vals)
elif class_type == "ttN multiModelMerge":
for letter in "ABC":
desired_ckpt_names.add(self.get_input_value(entry, f"ckpt_{letter}_name"))
object_types = ["ckpt", "clip", "bvae", "vae", "lora"]
for object_type in object_types:
desired_names = desired_ckpt_names if object_type in ["ckpt", "clip", "bvae"] else desired_vae_names if object_type == "vae" else desired_lora_names
self.clear_unused_objects(desired_names, object_type)
def add_to_cache(self, obj_type, key, value):
"""
Add an item to the cache with the current timestamp.
"""
timestamped_value = (value, time.time())
self.loaded_objects[obj_type][key] = timestamped_value
def determine_memory_threshold(self, percentage=0.8):
"""
Determines the memory threshold as a percentage of the total available memory.
Args:
- percentage (float): The fraction of total memory to use as the threshold.
Should be a value between 0 and 1. Default is 0.8 (80%).
Returns:
- memory_threshold (int): Memory threshold in bytes.
"""
total_memory = psutil.virtual_memory().total
memory_threshold = total_memory * percentage
return memory_threshold
def get_memory_usage(self):
"""
Returns the memory usage of the current process in bytes.
"""
process = psutil.Process(os.getpid())
return process.memory_info().rss
def eviction_based_on_memory(self):
"""
Evicts objects from cache based on memory usage and priority.
"""
current_memory = self.get_memory_usage()
if current_memory < self.memory_threshold:
return
eviction_order = ["vae", "lora", "bvae", "clip", "ckpt"]
for obj_type in eviction_order:
if current_memory < self.memory_threshold:
break
# Sort items based on age (using the timestamp)
items = list(self.loaded_objects[obj_type].items())
items.sort(key=lambda x: x[1][1]) # Sorting by timestamp
for item in items:
if current_memory < self.memory_threshold:
break
del self.loaded_objects[obj_type][item[0]]
current_memory = self.get_memory_usage()
def load_checkpoint(self, ckpt_name, config_name=None):
cache_name = ckpt_name
if config_name not in [None, "Default"]:
cache_name = ckpt_name + "_" + config_name
if cache_name in self.loaded_objects["ckpt"]:
return self.loaded_objects["ckpt"][cache_name][0], self.loaded_objects["clip"][cache_name][0], self.loaded_objects["bvae"][cache_name][0]
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
if config_name not in [None, "Default"]:
config_path = folder_paths.get_full_path("configs", config_name)
loaded_ckpt = comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
else:
loaded_ckpt = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
self.add_to_cache("ckpt", cache_name, loaded_ckpt[0])
self.add_to_cache("clip", cache_name, loaded_ckpt[1])
self.add_to_cache("bvae", cache_name, loaded_ckpt[2])
self.eviction_based_on_memory()
return loaded_ckpt[0], loaded_ckpt[1], loaded_ckpt[2]
def load_vae(self, vae_name):
if vae_name in self.loaded_objects["vae"]:
return self.loaded_objects["vae"][vae_name][0]
vae_path = folder_paths.get_full_path("vae", vae_name)
loaded_vae = comfy.sd.VAE(ckpt_path=vae_path)
self.add_to_cache("vae", vae_name, loaded_vae)
self.eviction_based_on_memory()
return loaded_vae
def load_lora(self, lora_name, model, clip, strength_model, strength_clip):
model_hash = str(model)[44:-1]
clip_hash = str(clip)[25:-1]
unique_id = f'{model_hash};{clip_hash};{lora_name};{strength_model};{strength_clip}'
if unique_id in self.loaded_objects["lora"] and unique_id in self.loaded_objects["lora"][lora_name]:
return self.loaded_objects["lora"][unique_id][0]
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)
self.add_to_cache("lora", unique_id, (model_lora, clip_lora))
self.eviction_based_on_memory()
return model_lora, clip_lora
class ttNsampler:
def __init__(self):
self.last_helds: dict[str, list] = {
"results": [],
"pipe_line": [],
}
@staticmethod
def tensor2pil(image: torch.Tensor) -> Image.Image:
"""Convert a torch tensor to a PIL image."""
return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
@staticmethod
def pil2tensor(image: Image.Image) -> torch.Tensor:
"""Convert a PIL image to a torch tensor."""
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
@staticmethod
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)
@staticmethod
def safe_split(to_split: str, delimiter: str) -> List[str]:
"""Split the input string and return a list of non-empty parts."""
parts = to_split.split(delimiter)
parts = [part for part in parts if part not in ('', ' ', ' ')]
while len(parts) < 2:
parts.append('None')
return parts
def common_ksampler(self, 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 get_value_by_id(self, key: str, my_unique_id: Any) -> Optional[Any]:
"""Retrieve value by its associated ID."""
try:
for value, id_ in self.last_helds[key]:
if id_ == my_unique_id:
return value
except KeyError:
return None
def update_value_by_id(self, key: str, my_unique_id: Any, new_value: Any) -> Union[bool, None]:
"""Update the value associated with a given ID. Return True if updated, False if appended, None if key doesn't exist."""
try:
for i, (value, id_) in enumerate(self.last_helds[key]):
if id_ == my_unique_id:
self.last_helds[key][i] = (new_value, id_)
return True
self.last_helds[key].append((new_value, my_unique_id))
return False
except KeyError:
return False
def upscale(self, samples, upscale_method, scale_by, crop):
s = samples.copy()
width = self.enforce_mul_of_64(round(samples["samples"].shape[3] * scale_by))
height = self.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 handle_upscale(self, samples: dict, upscale_method: str, factor: float, crop: bool) -> dict:
"""Upscale the samples if the upscale_method is not set to 'None'."""
if upscale_method != "None":
samples = self.upscale(samples, upscale_method, factor, crop)[0]
return samples
def init_state(self, my_unique_id: Any, key: str, default: Any) -> Any:
"""Initialize the state by either fetching the stored value or setting a default."""
value = self.get_value_by_id(key, my_unique_id)
if value is not None:
return value
return default
def get_output(self, pipe: dict) -> Tuple:
"""Return a tuple of various elements fetched from the input pipe dictionary."""
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 get_output_sdxl(self, sdxl_pipe: dict) -> Tuple:
"""Return a tuple of various elements fetched from the input sdxl_pipe dictionary."""
return (
sdxl_pipe,
sdxl_pipe.get("model"),
sdxl_pipe.get("positive"),
sdxl_pipe.get("negative"),
sdxl_pipe.get("vae"),
sdxl_pipe.get("refiner_model"),
sdxl_pipe.get("refiner_positive"),
sdxl_pipe.get("refiner_negative"),
sdxl_pipe.get("refiner_vae"),
sdxl_pipe.get("samples"),
sdxl_pipe.get("clip"),
sdxl_pipe.get("images"),
sdxl_pipe.get("seed")
)
class ttNxyPlot:
def __init__(self, xyPlotData, save_prefix, image_output, prompt, extra_pnginfo, my_unique_id):
self.x_node_type, self.x_type = ttNsampler.safe_split(xyPlotData.get("x_axis"), ': ')
self.y_node_type, self.y_type = ttNsampler.safe_split(xyPlotData.get("y_axis"), ': ')
self.x_values = xyPlotData.get("x_vals") if self.x_type != "None" else []
self.y_values = xyPlotData.get("y_vals") if self.y_type != "None" else []
self.grid_spacing = xyPlotData.get("grid_spacing")
self.latent_id = xyPlotData.get("latent_id")
self.output_individuals = xyPlotData.get("output_individuals")
self.x_label, self.y_label = [], []
self.max_width, self.max_height = 0, 0
self.latents_plot = []
self.image_list = []
self.num_cols = len(self.x_values) if len(self.x_values) > 0 else 1
self.num_rows = len(self.y_values) if len(self.y_values) > 0 else 1
self.total = self.num_cols * self.num_rows
self.num = 0
self.save_prefix = save_prefix
self.image_output = image_output
self.prompt = prompt
self.extra_pnginfo = extra_pnginfo
self.my_unique_id = my_unique_id
# Helper Functions
@staticmethod
def define_variable(plot_image_vars, value_type, value, index):
value_label = f"{value}"
if value_type == "seed":
seed = int(plot_image_vars["seed"])
if index != 0:
index = 1
if value == 'increment':
plot_image_vars["seed"] = seed + index
value_label = f"{plot_image_vars['seed']}"
elif value == 'decrement':
plot_image_vars["seed"] = seed - index
value_label = f"{plot_image_vars['seed']}"
elif value == 'randomize':
plot_image_vars["seed"] = random.randint(0, 0xffffffffffffffff)
value_label = f"{plot_image_vars['seed']}"
else:
plot_image_vars[value_type] = value
if value_type in ["steps", "cfg", "denoise", "clip_skip",
"lora1_model_strength", "lora1_clip_strength",
"lora2_model_strength", "lora2_clip_strength",
"lora3_model_strength", "lora3_clip_strength"]:
value_label = f"{value_type}: {value}"
if value_type in ["lora_model&clip_strength", "lora1_model&clip_strength", "lora2_model&clip_strength", "lora3_model&clip_strength"]:
loraNum = value_type.split("_")[0]
plot_image_vars[loraNum + "_model_strength"] = value
plot_image_vars[loraNum + "_clip_strength"] = value
type_label = value_type.replace("_model&clip", "")
value_label = f"{type_label}: {value}"
elif value_type == "positive_token_normalization":
value_label = f'(+) token norm.: {value}'
elif value_type == "positive_weight_interpretation":
value_label = f'(+) weight interp.: {value}'
elif value_type == "negative_token_normalization":
value_label = f'(-) token norm.: {value}'
elif value_type == "negative_weight_interpretation":
value_label = f'(-) weight interp.: {value}'
elif value_type == "positive":
value_label = f"pos prompt {index + 1}"
elif value_type == "negative":
value_label = f"neg prompt {index + 1}"
return plot_image_vars, value_label
@staticmethod
def get_font(font_size):
return ImageFont.truetype(str(Path(ttNpaths.font_path)), font_size)
@staticmethod
def update_label(label, value, num_items):
if len(label) < num_items:
return [*label, value]
return label
@staticmethod
def rearrange_tensors(latent, num_cols, num_rows):
new_latent = []
for i in range(num_rows):
for j in range(num_cols):
index = j * num_rows + i
new_latent.append(latent[index])
return new_latent
def calculate_background_dimensions(self):
border_size = int((self.max_width//8)*1.5) if self.y_type != "None" or self.x_type != "None" else 0
bg_width = self.num_cols * (self.max_width + self.grid_spacing) - self.grid_spacing + border_size * (self.y_type != "None")
bg_height = self.num_rows * (self.max_height + self.grid_spacing) - self.grid_spacing + border_size * (self.x_type != "None")
x_offset_initial = border_size if self.y_type != "None" else 0
y_offset = border_size if self.x_type != "None" else 0
return bg_width, bg_height, x_offset_initial, y_offset
def adjust_font_size(self, text, initial_font_size, label_width):
font = self.get_font(initial_font_size)
text_width, _ = font.getsize(text)
scaling_factor = 0.9
if text_width > (label_width * scaling_factor):
return int(initial_font_size * (label_width / text_width) * scaling_factor)
else:
return initial_font_size
def create_label(self, img, text, initial_font_size, is_x_label=True, max_font_size=70, min_font_size=10):
label_width = img.width if is_x_label else img.height
# Adjust font size
font_size = self.adjust_font_size(text, initial_font_size, label_width)
font_size = min(max_font_size, font_size) # Ensure font isn't too large
font_size = max(min_font_size, font_size) # Ensure font isn't too small
label_height = int(font_size * 1.5) if is_x_label else font_size
label_bg = Image.new('RGBA', (label_width, label_height), color=(255, 255, 255, 0))
d = ImageDraw.Draw(label_bg)
font = self.get_font(font_size)
# Check if text will fit, if not insert ellipsis and reduce text
if d.textsize(text, font=font)[0] > label_width:
while d.textsize(text+'...', font=font)[0] > label_width and len(text) > 0:
text = text[:-1]
text = text + '...'
# Compute text width and height for multi-line text
text_lines = text.split('\n')
text_widths, text_heights = zip(*[d.textsize(line, font=font) for line in text_lines])
max_text_width = max(text_widths)
total_text_height = sum(text_heights)
# Compute position for each line of text
lines_positions = []
current_y = 0
for line, line_width, line_height in zip(text_lines, text_widths, text_heights):
text_x = (label_width - line_width) // 2
text_y = current_y + (label_height - total_text_height) // 2
current_y += line_height
lines_positions.append((line, (text_x, text_y)))
# Draw each line of text
for line, (text_x, text_y) in lines_positions:
d.text((text_x, text_y), line, fill='black', font=font)
return label_bg
def sample_plot_image(self, plot_image_vars, samples, preview_latent, latents_plot, image_list, disable_noise, start_step, last_step, force_full_denoise):
model, clip, vae, positive, negative = None, None, None, None, None
if plot_image_vars["x_node_type"] == "loader" or plot_image_vars["y_node_type"] == "loader":
model, clip, vae = ttNcache.load_checkpoint(plot_image_vars['ckpt_name'])
if plot_image_vars['lora1_name'] != "None":
model, clip = ttNcache.load_lora(plot_image_vars['lora1_name'], model, clip, plot_image_vars['lora1_model_strength'], plot_image_vars['lora1_clip_strength'])
if plot_image_vars['lora2_name'] != "None":
model, clip = ttNcache.load_lora(plot_image_vars['lora2_name'], model, clip, plot_image_vars['lora2_model_strength'], plot_image_vars['lora2_clip_strength'])
if plot_image_vars['lora3_name'] != "None":
model, clip = ttNcache.load_lora(plot_image_vars['lora3_name'], model, clip, plot_image_vars['lora3_model_strength'], plot_image_vars['lora3_clip_strength'])
# Check for custom VAE
if plot_image_vars['vae_name'] not in ["Baked-VAE", "Baked VAE"]:
vae = ttNcache.load_vae(plot_image_vars['vae_name'])
# CLIP skip
if not clip:
raise Exception("No CLIP found")
clip = clip.clone()
clip.clip_layer(plot_image_vars['clip_skip'])
positive, positive_pooled = advanced_encode(clip, plot_image_vars['positive'], plot_image_vars['positive_token_normalization'], plot_image_vars['positive_weight_interpretation'], w_max=1.0, apply_to_pooled="enable")
positive = [[positive, {"pooled_output": positive_pooled}]]
negative, negative_pooled = advanced_encode(clip, plot_image_vars['negative'], plot_image_vars['negative_token_normalization'], plot_image_vars['negative_weight_interpretation'], w_max=1.0, apply_to_pooled="enable")
negative = [[negative, {"pooled_output": negative_pooled}]]
model = model if model is not None else plot_image_vars["model"]
clip = clip if clip is not None else plot_image_vars["clip"]
vae = vae if vae is not None else plot_image_vars["vae"]
positive = positive if positive is not None else plot_image_vars["positive_cond"]
negative = negative if negative is not None else plot_image_vars["negative_cond"]
seed = plot_image_vars["seed"]
steps = plot_image_vars["steps"]
cfg = plot_image_vars["cfg"]
sampler_name = plot_image_vars["sampler_name"]
scheduler = plot_image_vars["scheduler"]
denoise = plot_image_vars["denoise"]
if plot_image_vars["lora_name"] not in ('None', None):
model, clip = ttNcache.load_lora(plot_image_vars["lora_name"], model, clip, plot_image_vars["lora_model_strength"], plot_image_vars["lora_clip_strength"])
# Sample
samples = sampler.common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, samples, denoise=denoise, disable_noise=disable_noise, preview_latent=preview_latent, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise)
# Decode images and store
latent = samples["samples"]
# Add the latent tensor to the tensors list
latents_plot.append(latent)
# Decode the image
image = vae.decode(latent).cpu()
if self.output_individuals in [True, "True"]:
ttN_save = ttNsave(self.my_unique_id, self.prompt, self.extra_pnginfo)
ttN_save.images(image, self.save_prefix, self.image_output, group_id=self.num)
# Convert the image from tensor to PIL Image and add it to the list
pil_image = ttNsampler.tensor2pil(image)
image_list.append(pil_image)
# Update max dimensions
self.max_width = max(self.max_width, pil_image.width)
self.max_height = max(self.max_height, pil_image.height)
# Return the touched variables
return image_list, self.max_width, self.max_height, latents_plot
# Process Functions
def validate_xy_plot(self):
if self.x_type == 'None' and self.y_type == 'None':
ttNl('No Valid Plot Types - Reverting to default sampling...').t(f'pipeKSampler[{self.my_unique_id}]').warn().p()
return False
else:
return True
def get_latent(self, samples):
# Extract the 'samples' tensor from the dictionary
latent_image_tensor = samples["samples"]
# Split the tensor into individual image tensors
image_tensors = torch.split(latent_image_tensor, 1, dim=0)
# Create a list of dictionaries containing the individual image tensors
latent_list = [{'samples': image} for image in image_tensors]
# Set latent only to the first latent of batch
if self.latent_id >= len(latent_list):
ttNl(f'The selected latent_id ({self.latent_id}) is out of range.').t(f'pipeKSampler[{self.my_unique_id}]').warn().p()
ttNl(f'Automatically setting the latent_id to the last image in the list (index: {len(latent_list) - 1}).').t(f'pipeKSampler[{self.my_unique_id}]').warn().p()
self.latent_id = len(latent_list) - 1
return latent_list[self.latent_id]
def get_labels_and_sample(self, plot_image_vars, latent_image, preview_latent, start_step, last_step, force_full_denoise, disable_noise):
for x_index, x_value in enumerate(self.x_values):
plot_image_vars, x_value_label = self.define_variable(plot_image_vars, self.x_type, x_value, x_index)
self.x_label = self.update_label(self.x_label, x_value_label, len(self.x_values))
if self.y_type != 'None':
for y_index, y_value in enumerate(self.y_values):
self.num += 1
plot_image_vars, y_value_label = self.define_variable(plot_image_vars, self.y_type, y_value, y_index)
self.y_label = self.update_label(self.y_label, y_value_label, len(self.y_values))
ttNl(f'{CC.GREY}X: {x_value_label}, Y: {y_value_label}').t(f'Plot Values {self.num}/{self.total} ->').p()
self.image_list, self.max_width, self.max_height, self.latents_plot = self.sample_plot_image(plot_image_vars, latent_image, preview_latent, self.latents_plot, self.image_list, disable_noise, start_step, last_step, force_full_denoise)
else:
self.num += 1
ttNl(f'{CC.GREY}X: {x_value_label}').t(f'Plot Values {self.num}/{self.total} ->').p()
self.image_list, self.max_width, self.max_height, self.latents_plot = self.sample_plot_image(plot_image_vars, latent_image, preview_latent, self.latents_plot, self.image_list, disable_noise, start_step, last_step, force_full_denoise)
# Rearrange latent array to match preview image grid
self.latents_plot = self.rearrange_tensors(self.latents_plot, self.num_cols, self.num_rows)
# Concatenate the tensors along the first dimension (dim=0)
self.latents_plot = torch.cat(self.latents_plot, dim=0)
return self.latents_plot
def plot_images_and_labels(self):
# Calculate the background dimensions
bg_width, bg_height, x_offset_initial, y_offset = self.calculate_background_dimensions()
# Create the white background image
background = Image.new('RGBA', (int(bg_width), int(bg_height)), color=(255, 255, 255, 255))
for row_index in range(self.num_rows):
x_offset = x_offset_initial
for col_index in range(self.num_cols):
index = col_index * self.num_rows + row_index
img = self.image_list[index]
background.paste(img, (x_offset, y_offset))
# Handle X label
if row_index == 0 and self.x_type != "None":
label_bg = self.create_label(img, self.x_label[col_index], int(48 * img.width / 512))
label_y = (y_offset - label_bg.height) // 2
background.alpha_composite(label_bg, (x_offset, label_y))
# Handle Y label
if col_index == 0 and self.y_type != "None":
label_bg = self.create_label(img, self.y_label[row_index], int(48 * img.height / 512), False)
label_bg = label_bg.rotate(90, expand=True)
label_x = (x_offset - label_bg.width) // 2
label_y = y_offset + (img.height - label_bg.height) // 2
background.alpha_composite(label_bg, (label_x, label_y))
x_offset += img.width + self.grid_spacing
y_offset += img.height + self.grid_spacing
return sampler.pil2tensor(background)
class ttNsave:
def __init__(self, my_unique_id=0, prompt=None, extra_pnginfo=None, number_padding=5, overwrite_existing=False, output_dir=folder_paths.get_temp_directory()):
self.number_padding = int(number_padding) if number_padding not in [None, "None", 0] else None
self.overwrite_existing = overwrite_existing
self.my_unique_id = my_unique_id
self.prompt = prompt
self.extra_pnginfo = extra_pnginfo
self.type = 'temp'
self.output_dir = output_dir
if self.output_dir != folder_paths.get_temp_directory():
self.output_dir = self.folder_parser(self.output_dir, self.prompt, self.my_unique_id)
if not os.path.exists(self.output_dir):
self._create_directory(self.output_dir)
@staticmethod
def _create_directory(folder: str):
"""Try to create the directory and log the status."""
ttNl(f"Folder {folder} does not exist. Attempting to create...").warn().p()
if not os.path.exists(folder):
try:
os.makedirs(folder)
ttNl(f"{folder} Created Successfully").success().p()
except OSError:
ttNl(f"Failed to create folder {folder}").error().p()
pass
@staticmethod
def _map_filename(filename: str, filename_prefix: str) -> Tuple[int, str, Optional[int]]:
"""Utility function to map filename to its parts."""
# Get the prefix length and extract the prefix
prefix_len = len(os.path.basename(filename_prefix))
prefix = filename[:prefix_len]
# Search for the primary digits
digits = re.search(r'(\d+)', filename[prefix_len:])
# Search for the number in brackets after the primary digits
group_id = re.search(r'\((\d+)\)', filename[prefix_len:])
return (int(digits.group()) if digits else 0, prefix, int(group_id.group(1)) if group_id else 0)
@staticmethod
def _format_date(text: str, date: datetime.datetime) -> str:
"""Format the date according to specific patterns."""
date_formats = {
'd': lambda d: d.day,
'dd': lambda d: '{:02d}'.format(d.day),
'M': lambda d: d.month,
'MM': lambda d: '{:02d}'.format(d.month),
'h': lambda d: d.hour,
'hh': lambda d: '{:02d}'.format(d.hour),
'm': lambda d: d.minute,
'mm': lambda d: '{:02d}'.format(d.minute),
's': lambda d: d.second,
'ss': lambda d: '{:02d}'.format(d.second),
'y': lambda d: d.year,
'yy': lambda d: str(d.year)[2:],
'yyy': lambda d: str(d.year)[1:],
'yyyy': lambda d: d.year,
}
# We need to sort the keys in reverse order to ensure we match the longest formats first
for format_str in sorted(date_formats.keys(), key=len, reverse=True):
if format_str in text:
text = text.replace(format_str, str(date_formats[format_str](date)))
return text
@staticmethod
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]]]:
"""Recursively gather all inputs from the prompt dictionary."""
if prompt == None:
return None
collected_inputs = collected_inputs or {}
prompt_inputs = prompt[str(unique_id)]["inputs"]
for p_input, p_input_value in prompt_inputs.items():
a_input = f"{linkInput}>{p_input}" if linkInput else p_input
if isinstance(p_input_value, list):
ttNsave._gather_all_inputs(prompt, p_input_value[0], a_input, collected_inputs)
else:
existing_value = collected_inputs.get(a_input)
if existing_value is None:
collected_inputs[a_input] = p_input_value
elif p_input_value not in existing_value:
collected_inputs[a_input] = existing_value + "; " + p_input_value
return collected_inputs
@staticmethod
def _get_filename_with_padding(output_dir, filename, number_padding, group_id, ext):
"""Return filename with proper padding."""
try:
filtered = list(filter(lambda a: a[1] == filename, map(lambda x: ttNsave._map_filename(x, filename), os.listdir(output_dir))))
last = max(filtered)[0]
for f in filtered:
if f[0] == last:
if f[2] == 0 or f[2] == group_id:
last += 1
counter = last
except (ValueError, FileNotFoundError):
os.makedirs(output_dir, exist_ok=True)
counter = 1
if group_id == 0:
return f"{filename}.{ext}" if number_padding is None else f"{filename}_{counter:0{number_padding}}.{ext}"
else:
return f"{filename}_({group_id}).{ext}" if number_padding is None else f"{filename}_{counter:0{number_padding}}_({group_id}).{ext}"
@staticmethod
def filename_parser(output_dir: str, filename_prefix: str, prompt: Dict[str, dict], my_unique_id: str, number_padding: int, group_id: int, ext: str) -> str:
"""Parse the filename using provided patterns and replace them with actual values."""
subfolder = os.path.dirname(os.path.normpath(filename_prefix))
filename = os.path.basename(os.path.normpath(filename_prefix))
filename = re.sub(r'%date:(.*?)%', lambda m: ttNsave._format_date(m.group(1), datetime.datetime.now()), filename_prefix)
all_inputs = ttNsave._gather_all_inputs(prompt, my_unique_id)
filename = re.sub(r'%(.*?)%', lambda m: str(all_inputs.get(m.group(1), '')), filename)
filename = re.sub(r'[/\\]+', '-', filename)
filename = ttNsave._get_filename_with_padding(output_dir, filename, number_padding, group_id, ext)
return filename, subfolder
@staticmethod
def folder_parser(output_dir: str, prompt: Dict[str, dict], my_unique_id: str):
output_dir = re.sub(r'%date:(.*?)%', lambda m: ttNsave._format_date(m.group(1), datetime.datetime.now()), output_dir)
all_inputs = ttNsave._gather_all_inputs(prompt, my_unique_id)
return re.sub(r'%(.*?)%', lambda m: str(all_inputs.get(m.group(1), '')), output_dir)
def images(self, images, filename_prefix, output_type, embed_workflow=True, ext="png", group_id=0):
FORMAT_MAP = {
"png": "PNG",
"jpg": "JPEG",
"jpeg": "JPEG",
"bmp": "BMP",
"tif": "TIFF",
"tiff": "TIFF"
}
if ext not in FORMAT_MAP:
raise ValueError(f"Unsupported file extension {ext}")
if output_type == "Hide":
return list()
if output_type in ("Save", "Hide/Save"):
output_dir = self.output_dir if self.output_dir != folder_paths.get_temp_directory() else folder_paths.get_output_directory()
self.type = "output"
if output_type == "Preview":
output_dir = self.output_dir
filename_prefix = 'ttNpreview'
results = list()
for image in images:
img = Image.fromarray(np.clip(255. * image.cpu().numpy(), 0, 255).astype(np.uint8))
filename = filename_prefix.replace("%width%", str(img.size[0])).replace("%height%", str(img.size[1]))
filename, subfolder = ttNsave.filename_parser(output_dir, filename, self.prompt, self.my_unique_id, self.number_padding, group_id, ext)
file_path = os.path.join(output_dir, filename)
if ext == "png" and embed_workflow in (True, "True"):
metadata = PngInfo()
if self.prompt is not None:
metadata.add_text("prompt", json.dumps(self.prompt))
if hasattr(self, 'extra_pnginfo') and self.extra_pnginfo is not None:
for key, value in self.extra_pnginfo.items():
metadata.add_text(key, json.dumps(value))
if self.overwrite_existing or not os.path.isfile(file_path):
img.save(file_path, pnginfo=metadata, format=FORMAT_MAP[ext])
else:
if self.overwrite_existing or not os.path.isfile(file_path):
img.save(file_path, format=FORMAT_MAP[ext])
else:
ttNl(f"File {file_path} already exists... Skipping").error().p()
results.append({
"filename": file_path,
"subfolder": subfolder,
"type": self.type
})