nodes/KLL535-ComfyUI_SimpleButcher
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v1.0.3

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KLL535
2025-02-01 11:22:04 +03:00
parent cb72b5c349
commit 1c80ac0d39
5 changed files with 152 additions and 34 deletions
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CHANGELOG.md
+5 -1
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@@ -1,6 +1,10 @@
# nightly
# v1.0.3
- fix random
- add node Simple Load Image With Metadata
# v1.0.2
- fastest load LoRA hash
- add button **Update LoRA dictionary** in `Simple Lora Loader` for update lora_name.json
- add button **Update LoRA dictionary** in `Simple Lora Loader` for update **lora_name.json**
- fixed skipping execute of `Simple Load Line From Text File` node
# v1.0.1
- fixed LoRA search in any path specified with **extra_model_paths.yaml**
js/SimpleLoadImageWithMetadata.js
+9
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@@ -0,0 +1,9 @@
import { app } from "/scripts/app.js";
app.registerExtension({
name: "Simple Load Image With Metadata",
async beforeRegisterNodeDef(nodeType, nodeData, app) {
if (nodeData.name === "Simple Load Image With Metadata") {
nodeData.input.required.upload = ["IMAGEUPLOAD"];
}
},
});
nodes/nodes.py
+136 -31
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@@ -12,12 +12,14 @@ import folder_paths
import requests
import piexif
import time
import secrets
from safetensors import safe_open
from collections import deque
from datetime import datetime
from PIL import Image
from PIL.PngImagePlugin import PngInfo
from PIL import ExifTags
from typing import Dict
class AnyType(str):
@@ -49,6 +51,10 @@ def get_sha256(self, file_path: str):
return sha256_hash.hexdigest()
def generate_unique_seed():
return time.time_ns() ^ secrets.randbits(64)
#return time.time_ns()
################################
class SimpleLoadLineFromTextFile:
@@ -57,6 +63,7 @@ class SimpleLoadLineFromTextFile:
self.batch_counter = 0
self.line_counter = 0
self.random_list = []
self.my_random = random.Random() #isolated generator
@classmethod
def IS_CHANGED(s, **kwargs):
@@ -65,11 +72,14 @@ class SimpleLoadLineFromTextFile:
@classmethod
def INPUT_TYPES(cls):
#input_dir = folder_paths.get_input_directory()
#files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
return {
"required": {
"required": {
"name": ("STRING", {"default": "", "multiline": False, "tooltip": "Node name"}),
"start": ("INT", {"default": 0, "min": 0, "step": 1, "tooltip": "Start position for increment or decrement methods"}),
"load_file": ("BOOLEAN", {"default": True, "tooltip": "if True, load center of prompt from file"}),
"file_path": ("STRING", {"default": "", "tooltip": "Path to the file from which the lines for the central part of the prompt will be taken"}),
"file_path": ("STRING", {"default": "", "multiline": False, "tooltip": "Path to the file from which the lines for the central part of the prompt will be taken"}),
"next": (["increment", "decrement", "random", "random no repetitions"], {"default":"increment", "tooltip": "Option for enumerating lines, in case of randomness the start parameter is ignored"}),
},
"optional": {
@@ -85,7 +95,7 @@ class SimpleLoadLineFromTextFile:
CATEGORY = "📚 SimpleButcher"
DESCRIPTION = "Simple prompt loader from a text file to automate the batch process. Can be combined with each other and create total randomness text and lora that is taken from your text and lora templates"
def read_text_file(self, start = 0, load_file = False, file_path = "", next = "increment", prefix = "", postfix = "", count = 0):
def read_text_file(self, name = "", start = 0, load_file = False, file_path = "", next = "increment", prefix = "", postfix = "", count = 0):
if count == 0:
self.batch_counter = 0
@@ -102,7 +112,7 @@ class SimpleLoadLineFromTextFile:
file_path = os.path.expandvars(file_path)
if not os.path.isfile(file_path):
raise FileNotFoundError(f"The file at path '{file_path}' does not exist.")
with open(file_path, 'r', encoding='utf-8') as file:
with open(file_path, 'r', encoding='ISO-8859-1') as file: #utf-8
lines = file.readlines()
for line in lines:
@@ -122,19 +132,28 @@ class SimpleLoadLineFromTextFile:
if next == "random no repetitions":
if count == 0:
# generate random numbers
print(f"\033[93mRandomize\033[0m")
# save random numbers
random_seed = generate_unique_seed()
#print(f"\033[93m{random_seed}\033[0m")
self.my_random.seed(random_seed)
self.random_list = list(range(0, line_count))
random.shuffle(self.random_list)
self.line_counter = self.random_list[self.batch_counter % len(self.random_list)]
self.my_random.shuffle(self.random_list)
rnd = self.batch_counter % len(self.random_list)
self.line_counter = self.random_list[rnd]
elif next == "random":
self.line_counter = random.randint(0, line_count-1)
if count == 0:
# randomize
random_seed = generate_unique_seed()
#print(f"\033[93m{random_seed}\033[0m")
self.my_random.seed(random_seed)
rnd = self.my_random.randint(0, line_count-1)
self.line_counter = rnd
self.line_counter = self.line_counter % line_count
text = text + lines2[self.line_counter]
current_line = self.line_counter
console_text = f"[\033[95mBatch: {self.batch_counter}\033[0m -> \033[92mLine: {str(current_line)}/{str(line_count)}\033[0m]"
console_text = f"[\033[95mBatch {name}: {self.batch_counter}\033[0m -> \033[92mLine: {str(current_line)}/{str(line_count)}\033[0m]"
print(f"{console_text}")
if next == "increment":
@@ -144,7 +163,7 @@ class SimpleLoadLineFromTextFile:
if self.line_counter < 0:
self.line_counter = line_count-1
else:
console_text = f"[\033[95mBatch: {self.batch_counter}\033[0m]"
console_text = f"[\033[95mBatch {name}: {self.batch_counter}\033[0m -> Skip"
print(f"{console_text}")
if postfix != "":
@@ -153,7 +172,6 @@ class SimpleLoadLineFromTextFile:
text = text.replace("\r","")
text = text.replace("\n"," ")
text = text.replace("<"," <")
text = text.replace(" "," ")
return (text,self.batch_counter,current_line,line_count)
@@ -181,11 +199,7 @@ class SimpleExtractLoraFromText:
text_loras = re.findall(r'<lora:[^>]*>', text)
text_lora = ' '.join(text_loras)
text_lora = text_lora.strip()
text_lora = text_lora.replace(" "," ")
text_lora2 = text_lora.replace('\n',' ')
#text_prompt = ' '.join(s for s in text.split() if not (s.startswith("<") and s.endswith(">")))
#text_prompt = ' '.join(word for word in text.split() if not re.search('<.*?>', word))
@@ -329,6 +343,8 @@ class SimpleLoraLoader:
for item in self.loaded_lora:
item[2] = False
unique_elements = set()
for lora in loras:
lora = lora.replace("<lora:","")
lora = lora.replace(">","")
@@ -336,23 +352,36 @@ class SimpleLoraLoader:
lora_prompt_name = parts[0]
lora_path = ""
lora_hash = ""
for item in dic: # find filename
#find by filename
for item in dic:
if item[1] == lora_prompt_name:
lora_path = item[0]
lora_hash = item[3]
break
#or find by internal lora name
if lora_path == "":
for item in dic: # or find lora name
for item in dic:
if item[2] == lora_prompt_name:
lora_path = item[0]
lora_hash = item[3]
break
#not found
if lora_path == "":
console_text = "-Lora: "+lora_prompt_name
console_text = f"[\033[91m{console_text}\033[0m]"
console_text = f"[\033[91m-Lora: {lora_prompt_name}\033[0m] not found"
print(f"{console_text}")
continue
#skip duplicate
if lora_path in unique_elements:
console_text = f"[\033[91m-Lora: {lora_path}\033[0m] skip duplicate"
print(f"{console_text}")
continue
unique_elements.add(lora_path)
##########################
##### parce strength #####
@@ -471,7 +500,11 @@ class SimpleImageSaver:
"civitai_lora": ("STRING", {"default": '', "multiline": False, "tooltip": "STRING, Loras name from Simple Lora Loader node", "forceInput": True}),
"civitai_lora_hash": ("STRING", {"default": '', "multiline": False, "tooltip": "STRING, Loras hash from Simple Lora Loader node", "forceInput": True}),
"negative": ("STRING", {"default": '', "multiline": False, "tooltip": "STRING, Negative prompt", "forceInput": True}),
}
"save_workflow": ("BOOLEAN", {"default": False, "tooltip": "if True, saves workflow in the image"}),
},
"hidden": {
"extra_pnginfo": "EXTRA_PNGINFO",
},
}
RETURN_TYPES = ("STRING",)
@@ -495,7 +528,7 @@ class SimpleImageSaver:
i = new
return i+1
def image_saver(self, images, prompt, output_path, SEED, modelname, steps=0, sampler=None, schedule=None, CFG_scale=0.0, distilled_CFG_scale=0.0, width=0, height=0, beta_schedule_alpha=0.0, beta_schedule_beta=0.0, civitai_lora="", civitai_lora_hash="",negative=""):
def image_saver(self, images, prompt, output_path, SEED, modelname, steps=0, sampler=None, schedule=None, CFG_scale=0.0, distilled_CFG_scale=0.0, width=0, height=0, beta_schedule_alpha=0.0, beta_schedule_beta=0.0, civitai_lora="", civitai_lora_hash="",negative="",save_workflow=False,extra_pnginfo=None):
metadata_text = ""
@@ -525,11 +558,11 @@ class SimpleImageSaver:
####################
##### metadata #####
metadata_text = prompt
prompt_and_lora = prompt
if civitai_lora != "":
metadata_text = metadata_text + ' ' + civitai_lora
prompt_and_lora = prompt_and_lora + ' ' + civitai_lora
metadata_text = metadata_text + '\n'
metadata_text = prompt_and_lora + '\n'
if negative != "":
metadata_text = metadata_text + "Negative prompt: "+ negative + '\n'
@@ -556,10 +589,12 @@ class SimpleImageSaver:
modelhash = get_sha256(self,ckpt_path)[:10]
else:
modelhash = ""
metadata_text = metadata_text + "Model hash: "+modelhash+", "
if modelhash != "":
metadata_text = metadata_text + "Model hash: "+modelhash+", "
modelname = os.path.basename(modelname)
modelname = os.path.splitext(modelname)[0]
metadata_text = metadata_text + "Model: "+modelname+", "
if modelname != "":
metadata_text = metadata_text + "Model: "+modelname+", "
if beta_schedule_alpha != 0.0:
metadata_text = metadata_text + "Beta schedule alpha: "+str(beta_schedule_alpha)+", "
@@ -571,12 +606,20 @@ class SimpleImageSaver:
metadata_text = metadata_text + "Version: ComfyUI"
metadata = PngInfo()
metadata.add_text("parameters", metadata_text)
if save_workflow:
if prompt_and_lora != "":
metadata.add_text("prompt", prompt_and_lora)
if extra_pnginfo is not None:
for x in extra_pnginfo:
print(f"Write {x}")
metadata.add_text(x, json.dumps(extra_pnginfo[x]))
##### metadata #####
####################
metadata = PngInfo()
metadata.add_text("parameters", metadata_text)
filename = f"{free_number:05d}-{seed_text}.png"
img.save(os.path.join(output_path, filename), pnginfo=metadata, optimize=False)
@@ -585,11 +628,72 @@ class SimpleImageSaver:
################################
class SimpleLoadImageWithMetadataString:
@classmethod
def INPUT_TYPES(s):
input_dir = folder_paths.get_input_directory()
files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
return {"required":
{"image": (sorted(files), )},
}
CATEGORY = "📚 SimpleButcher"
RETURN_TYPES = ("IMAGE", "MASK", "STRING", "STRING")
RETURN_NAMES = ("image", "mask", "metadata_forge", "prompt")
FUNCTION = "read_image"
DESCRIPTION = "Load Image with metadata in simple string!"
def read_image(self, image):
image_path = folder_paths.get_annotated_filepath(image)
i = Image.open(image_path)
image = i.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
if 'A' in i.getbands():
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
mask = 1. - torch.from_numpy(mask)
else:
mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
width = i.width
height = i.height
format = i.format
metadata = i.info
#exif_workflow = ""
#if "workflow" in metadata:
# exif_workflow = metadata["workflow"]
exif_prompt = ""
if "prompt" in metadata:
exif_prompt = metadata["prompt"]
exif_metadata = ""
if "parameters" in metadata:
exif_metadata = metadata["parameters"]
return (image, mask, exif_metadata, exif_prompt)
@classmethod
def IS_CHANGED(s, image):
image_path = folder_paths.get_annotated_filepath(image)
m = hashlib.sha256()
with open(image_path, 'rb') as f:
m.update(f.read())
return m.digest().hex()
@classmethod
def VALIDATE_INPUTS(s, image):
if not folder_paths.exists_annotated_filepath(image):
return "Invalid image file: {}".format(image)
return True
################################
NODE_CLASS_MAPPINGS = {
"Simple Load Line From Text File": SimpleLoadLineFromTextFile,
"Simple Extract Lora From Text": SimpleExtractLoraFromText,
"Simple Lora Loader": SimpleLoraLoader,
"Simple Image Saver (as Forge)": SimpleImageSaver
"Simple Image Saver (as Forge)": SimpleImageSaver,
"Simple Load Image With Metadata": SimpleLoadImageWithMetadataString,
}
NODE_DISPLAY_NAME_MAPPINGS = {
@@ -597,5 +701,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"Simple Extract Lora From Text": "Simple Extract Lora From Text 📚",
"Simple Lora Loader": "Simple Lora Loader 📚",
"Simple Image Saver (as Forge)": "Simple Image Saver (as Forge) 📚",
"Simple Load Image With Metadata": "Simple Load Image With Metadata 📚",
}
pyproject.toml
+2 -2
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@@ -1,7 +1,7 @@
[project]
name = "simplebutcher"
description = "Node to automate batch generation with randomize prompts from text files. It mimics Forge's functionality, allowing you to combine text elements and LoRA. The node supports writing LoRA in any order within a text file using formats like <lora:name:1.0> or <lora:name:unet=1.0:te=0.75>, without needing separate nodes. The node understands LoRA names in Forge's style, when the name is not the filename, but the internal name from the metadata."
version = "1.0.2"
description = "Node to automate batch generation with randomize prompts from text files. It mimics Forge's functionality, allowing you to combine text elements and LoRA. The node supports writing LoRA in any order within a text file using formats like lora:name:1.0 or lora:name:unet=1.0:te=0.75, without needing separate nodes. The node understands LoRA names in Forge's style, when the name is not the filename, but the internal name from the metadata."
version = "1.0.3"
license = {file = "LICENSE"}
dependencies = ["piexif"]
test.txt
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