nodes/bash-j-mikey_nodes
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

modified: mikey_nodes.py

Browse Source
This commit is contained in:
bash-j
2023-07-29 12:45:05 +09:30
parent ef35a4546f
commit 97a99bc841
1 changed files with 225 additions and 6 deletions
Download Patch File Download Diff File Expand all files Collapse all files
mikey_nodes.py
+225 -6
View File
@@ -11,6 +11,7 @@ import numpy as np
from PIL import Image
from PIL.PngImagePlugin import PngInfo
import torch
import torch.nn.functional as F
import folder_paths
file_path = os.path.join(folder_paths.base_path, 'comfy_extras/nodes_clip_sdxl.py')
@@ -21,6 +22,7 @@ sys.modules[module_name] = module
spec.loader.exec_module(module)
from nodes_clip_sdxl import CLIPTextEncodeSDXL, CLIPTextEncodeSDXLRefiner
from comfy.model_management import unload_model, soft_empty_cache
from nodes import LoraLoader, ConditioningAverage, common_ksampler
import comfy.utils
@@ -451,6 +453,225 @@ class PromptWithSDXL:
refiner_width,
refiner_height,)
class PromptWithStyleV3:
def __init__(self):
self.loaded_lora = None
@classmethod
def INPUT_TYPES(s):
s.ratio_sizes, s.ratio_dict = read_ratios()
s.styles, s.pos_style, s.neg_style = read_styles()
s.fit = ['true','false']
s.custom_size = ['true', 'false']
return {"required": {"positive_prompt": ("STRING", {"multiline": True, 'default': 'Positive Prompt'}),
"negative_prompt": ("STRING", {"multiline": True, 'default': 'Negative Prompt'}),
"ratio_selected": (s.ratio_sizes,),
"custom_size": (s.custom_size,),
"fit_custom_size": (s.fit,),
"custom_width": ("INT", {"default": 1024, "min": 1, "max": 8192, "step": 1}),
"custom_height": ("INT", {"default": 1024, "min": 1, "max": 8192, "step": 1}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"base_model": ("MODEL",), "clip_base": ("CLIP",), "clip_refiner": ("CLIP",),
}
}
RETURN_TYPES = ('MODEL','LATENT',
'CONDITIONING','CONDITIONING','CONDITIONING','CONDITIONING',
'STRING','STRING')
RETURN_NAMES = ('base_model','samples',
'base_pos_cond','base_neg_cond','refiner_pos_cond','refiner_neg_cond',
'positive_prompt','negative_prompt')
FUNCTION = 'start'
CATEGORY = 'Mikey'
def extract_and_load_loras(self, text, model, clip):
# load loras detected in the prompt text
# The text for adding LoRA to the prompt, <lora:filename:multiplier>, is only used to enable LoRA, and is erased from prompt afterwards
# The multiplier is optional, and defaults to 1.0
# We update the model and clip, and return the new model and clip with the lora prompt stripped from the text
# If multiple lora prompts are detected we chain them together like: original clip > clip_with_lora1 > clip_with_lora2 > clip_with_lora3 > etc
lora_re = r'<lora:(.*?)(?::(.*?))?>'
# find all lora prompts
lora_prompts = re.findall(lora_re, text)
stripped_text = text
# if we found any lora prompts
if len(lora_prompts) > 0:
# loop through each lora prompt
for lora_prompt in lora_prompts:
# get the lora filename
lora_filename = lora_prompt[0]
# check for file extension in filename
if '.safetensors' not in lora_filename:
lora_filename += '.safetensors'
# get the lora multiplier
lora_multiplier = float(lora_prompt[1]) if lora_prompt[1] != '' else 1.0
# apply the lora to the clip using the LoraLoader.load_lora function
# def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
# ...
# return (model_lora, clip_lora)
# apply the lora to the clip
model, clip_lora = LoraLoader.load_lora(self, model, clip, lora_filename, lora_multiplier, lora_multiplier)
stripped_text = stripped_text.replace(f'<lora:{lora_filename}:{lora_multiplier}>', '')
return model, clip, stripped_text
def parse_prompts(self, positive_prompt, negative_prompt, style, seed):
positive_prompt = find_and_replace_wildcards(positive_prompt, seed)
negative_prompt = find_and_replace_wildcards(negative_prompt, seed)
if '{prompt}' in self.pos_style[style]:
positive_prompt = self.pos_style[style].replace('{prompt}', positive_prompt)
if positive_prompt == '' or positive_prompt == 'Positive Prompt' or positive_prompt is None:
pos_prompt = self.pos_style[style]
else:
pos_prompt = positive_prompt + ', ' + self.pos_style[style]
if negative_prompt == '' or negative_prompt == 'Negative Prompt' or negative_prompt is None:
neg_prompt = self.neg_style[style]
else:
neg_prompt = negative_prompt + ', ' + self.neg_style[style]
return pos_prompt, neg_prompt
def start(self, base_model, clip_base, clip_refiner, positive_prompt, negative_prompt, ratio_selected, batch_size, seed,
custom_size='false', fit_custom_size='false', custom_width=1024, custom_height=1024):
if custom_size == 'true':
if fit_custom_size == 'true':
if custom_width == 1 and custom_height == 1:
width, height = 1024, 1024
if custom_width == custom_height:
width, height = 1024, 1024
if f'{custom_width}:{custom_height}' in self.ratio_dict:
width, height = self.ratio_dict[f'{custom_width}:{custom_height}']
else:
width, height = sdxl_size(custom_width, custom_height)
else:
width, height = custom_width, custom_height
else:
width = self.ratio_dict[ratio_selected]["width"]
height = self.ratio_dict[ratio_selected]["height"]
latent = torch.zeros([batch_size, 4, height // 8, width // 8])
print(batch_size, 4, height // 8, width // 8)
refiner_width = width * 4
refiner_height = height * 4
# extract and load loras
base_model, clip_base_pos, pos_prompt = self.extract_and_load_loras(positive_prompt, base_model, clip_base)
base_model, clip_base_neg, neg_prompt = self.extract_and_load_loras(negative_prompt, base_model, clip_base)
# find and replace style syntax
# <style:style_name> will update the selected style
style_re = r'<style:(.*?)>'
pos_style_prompts = re.findall(style_re, pos_prompt)
neg_style_prompts = re.findall(style_re, neg_prompt)
# concat style prompts
style_prompts = pos_style_prompts + neg_style_prompts
print(style_prompts)
base_pos_conds = []
base_neg_conds = []
refiner_pos_conds = []
refiner_neg_conds = []
if len(style_prompts) == 0:
style_ = 'none'
pos_prompt_, neg_prompt_ = self.parse_prompts(positive_prompt, negative_prompt, style_, seed)
pos_style_, neg_style_ = '', ''
# encode text
sdxl_pos_cond = CLIPTextEncodeSDXL.encode(self, clip_base_pos, width, height, 0, 0, width, height, pos_prompt, pos_style_)[0]
sdxl_neg_cond = CLIPTextEncodeSDXL.encode(self, clip_base_neg, width, height, 0, 0, width, height, neg_prompt, neg_style_)[0]
refiner_pos_cond = CLIPTextEncodeSDXLRefiner.encode(self, clip_refiner, 6, refiner_width, refiner_height, pos_prompt)[0]
refiner_neg_cond = CLIPTextEncodeSDXLRefiner.encode(self, clip_refiner, 2.5, refiner_width, refiner_height, neg_prompt)[0]
return (base_model, {"samples":latent},
sdxl_pos_cond, sdxl_neg_cond,
refiner_pos_cond, refiner_neg_cond,
pos_prompt, neg_prompt)
for style_prompt in style_prompts:
""" get output from PromptWithStyle.start """
# strip all style syntax from prompt
style_ = style_prompt
print(style_ in self.styles)
if style_ not in self.styles:
style_ = 'none'
continue
pos_prompt_ = re.sub(style_re, '', pos_prompt)
neg_prompt_ = re.sub(style_re, '', neg_prompt)
pos_prompt_, neg_prompt_ = self.parse_prompts(pos_prompt_, neg_prompt_, style_, seed)
pos_style_, neg_style_ = str(self.pos_style[style_]), str(self.neg_style[style_])
width_, height_ = width, height
refiner_width_, refiner_height_ = refiner_width, refiner_height
# encode text
base_pos_conds.append(CLIPTextEncodeSDXL.encode(self, clip_base_pos, width_, height_, 0, 0, width_, height_, pos_prompt_, pos_style_)[0])
base_neg_conds.append(CLIPTextEncodeSDXL.encode(self, clip_base_neg, width_, height_, 0, 0, width_, height_, neg_prompt_, neg_style_)[0])
refiner_pos_conds.append(CLIPTextEncodeSDXLRefiner.encode(self, clip_refiner, 6, refiner_width_, refiner_height_, pos_prompt_)[0])
refiner_neg_conds.append(CLIPTextEncodeSDXLRefiner.encode(self, clip_refiner, 2.5, refiner_width_, refiner_height_, neg_prompt_)[0])
# loop through conds and add them together
sdxl_pos_cond = base_pos_conds[0]
weight = 1
if len(base_pos_conds) > 1:
for i in range(1, len(base_pos_conds)):
weight += 1
sdxl_pos_cond = ConditioningAverage.addWeighted(self, base_pos_conds[i], sdxl_pos_cond, 1 / weight)[0]
sdxl_neg_cond = base_neg_conds[0]
weight = 1
if len(base_neg_conds) > 1:
for i in range(1, len(base_neg_conds)):
weight += 1
sdxl_neg_cond = ConditioningAverage.addWeighted(self, base_neg_conds[i], sdxl_neg_cond, 1 / weight)[0]
refiner_pos_cond = refiner_pos_conds[0]
weight = 1
if len(refiner_pos_conds) > 1:
for i in range(1, len(refiner_pos_conds)):
weight += 1
refiner_pos_cond = ConditioningAverage.addWeighted(self, refiner_pos_conds[i], refiner_pos_cond, 1 / weight)[0]
refiner_neg_cond = refiner_neg_conds[0]
weight = 1
if len(refiner_neg_conds) > 1:
for i in range(1, len(refiner_neg_conds)):
weight += 1
refiner_neg_cond = ConditioningAverage.addWeighted(self, refiner_neg_conds[i], refiner_neg_cond, 1 / weight)[0]
# return
return (base_model, {"samples":latent},
sdxl_pos_cond, sdxl_neg_cond,
refiner_pos_cond, refiner_neg_cond,
pos_prompt, neg_prompt)
class PromptWithSDXL:
@classmethod
def INPUT_TYPES(s):
s.ratio_sizes, s.ratio_dict = read_ratios()
return {"required": {"positive_prompt": ("STRING", {"multiline": True, 'default': 'Positive Prompt'}),
"negative_prompt": ("STRING", {"multiline": True, 'default': 'Negative Prompt'}),
"positive_style": ("STRING", {"multiline": True, 'default': 'Positive Style'}),
"negative_style": ("STRING", {"multiline": True, 'default': 'Negative Style'}),
"ratio_selected": (s.ratio_sizes,),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff})
}
}
RETURN_TYPES = ('LATENT','STRING','STRING','STRING','STRING','INT','INT','INT','INT',)
RETURN_NAMES = ('samples','positive_prompt_text_g','negative_prompt_text_g','positive_style_text_l',
'negative_style_text_l','width','height','refiner_width','refiner_height',)
FUNCTION = 'start'
CATEGORY = 'Mikey'
def start(self, positive_prompt, negative_prompt, positive_style, negative_style, ratio_selected, batch_size, seed):
positive_prompt = find_and_replace_wildcards(positive_prompt, seed)
negative_prompt = find_and_replace_wildcards(negative_prompt, seed)
width = self.ratio_dict[ratio_selected]["width"]
height = self.ratio_dict[ratio_selected]["height"]
latent = torch.zeros([batch_size, 4, height // 8, width // 8])
refiner_width = width * 4
refiner_height = height * 4
return ({"samples":latent},
str(positive_prompt),
str(negative_prompt),
str(positive_style),
str(negative_style),
width,
height,
refiner_width,
refiner_height,)
class VAEDecode6GB:
""" deprecated. update comfy to fix issue. """
@classmethod
@@ -459,7 +680,7 @@ class VAEDecode6GB:
'samples': ('LATENT',)}}
RETURN_TYPES = ('IMAGE',)
FUNCTION = 'decode'
CATEGORY = 'Mikey/Latent'
#CATEGORY = 'Mikey/Latent'
def decode(self, vae, samples):
unload_model()
@@ -472,11 +693,9 @@ NODE_CLASS_MAPPINGS = {
'Save Image With Prompt Data': SaveImagesMikey,
'Resize Image for SDXL': ResizeImageSDXL,
'Prompt With Style': PromptWithStyle,
'Prompt With Style V2': PromptWithStyleV2, # 'Prompt With Style V2
'Prompt With Style V2': PromptWithStyleV2,
'Prompt With SDXL': PromptWithSDXL,
'Prompt With Style V3': PromptWithStyleV3,
'HaldCLUT': HaldCLUT,
'VAE Decode 6GB SDXL (deprecated)': VAEDecode6GB,
}
## TODO
# Resize Image and return the new width and height
# SDXL Ultimate Upscaler?
}