Prompt With Style (Mikey)¶
Documentation¶
- Class name:
Prompt With Style V3 - Category:
Mikey - Output node:
False
The Prompt With Style V3 node is designed to enhance text prompts with predefined styles, allowing for the dynamic integration of style elements into both positive and negative prompts. It processes input prompts by stripping existing style syntax, matching them with available styles, and then reformatting them according to the selected style, including handling of special syntax and wildcards. This node aims to provide a versatile tool for generating styled prompts that can be further customized or used directly in various text-based applications.
Input types¶
Required¶
positive_prompt- unknown
- Comfy dtype:
STRING - Python dtype:
unknown
negative_prompt- unknown
- Comfy dtype:
STRING - Python dtype:
unknown
ratio_selected- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
custom_size- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
fit_custom_size- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
custom_width- unknown
- Comfy dtype:
INT - Python dtype:
unknown
custom_height- unknown
- Comfy dtype:
INT - Python dtype:
unknown
batch_size- unknown
- Comfy dtype:
INT - Python dtype:
unknown
seed- unknown
- Comfy dtype:
INT - Python dtype:
unknown
target_mode- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
base_model- unknown
- Comfy dtype:
MODEL - Python dtype:
unknown
clip_base- unknown
- Comfy dtype:
CLIP - Python dtype:
unknown
clip_refiner- unknown
- Comfy dtype:
CLIP - Python dtype:
unknown
Output types¶
base_model- Comfy dtype:
MODEL - unknown
- Python dtype:
unknown
- Comfy dtype:
samples- Comfy dtype:
LATENT - unknown
- Python dtype:
unknown
- Comfy dtype:
base_pos_cond- Comfy dtype:
CONDITIONING - unknown
- Python dtype:
unknown
- Comfy dtype:
base_neg_cond- Comfy dtype:
CONDITIONING - unknown
- Python dtype:
unknown
- Comfy dtype:
refiner_pos_cond- Comfy dtype:
CONDITIONING - unknown
- Python dtype:
unknown
- Comfy dtype:
refiner_neg_cond- Comfy dtype:
CONDITIONING - unknown
- Python dtype:
unknown
- Comfy dtype:
positive_prompt- Comfy dtype:
STRING - unknown
- Python dtype:
unknown
- Comfy dtype:
negative_prompt- Comfy dtype:
STRING - unknown
- Python dtype:
unknown
- Comfy dtype:
Usage tips¶
- Infra type:
CPU - Common nodes:
Source code¶
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, {"default": "false"}),
"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}),
"target_mode": (["match", "2x", "4x", "2x90", "4x90",
"2048","2048-90","4096", "4096-90"], {"default": "4x"}),
"base_model": ("MODEL",), "clip_base": ("CLIP",), "clip_refiner": ("CLIP",),
},
"hidden": {"unique_id": "UNIQUE_ID", "extra_pnginfo": "EXTRA_PNGINFO", "prompt": "PROMPT"},
}
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
try:
lora_multiplier = float(lora_prompt[1]) if lora_prompt[1] != '' else 1.0
except:
lora_multiplier = 1.0
# apply the lora to the clip using the LoraLoader.load_lora function
# apply the lora to the clip
model, clip = load_lora(model, clip, lora_filename, lora_multiplier, lora_multiplier)
stripped_text = stripped_text.replace(f'<lora:{lora_filename}:{lora_multiplier}>', '')
stripped_text = stripped_text.replace(f'<lora:{lora_filename}>', '')
return model, clip, stripped_text
def parse_prompts(self, positive_prompt, negative_prompt, style, seed):
positive_prompt = find_and_replace_wildcards(positive_prompt, seed, debug=True)
negative_prompt = find_and_replace_wildcards(negative_prompt, seed, debug=True)
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, target_mode='match',
unique_id=None, extra_pnginfo=None, prompt=None):
if extra_pnginfo is None:
extra_pnginfo = {'PromptWithStyle': {}}
prompt_with_style = extra_pnginfo.get('PromptWithStyle', {})
add_metadata_to_dict(prompt_with_style, positive_prompt=positive_prompt, negative_prompt=negative_prompt,
ratio_selected=ratio_selected, batch_size=batch_size, seed=seed, custom_size=custom_size,
fit_custom_size=fit_custom_size, custom_width=custom_width, custom_height=custom_height,
target_mode=target_mode)
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 = find_latent_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)
# calculate dimensions for target_width, target height (base) and refiner_width, refiner_height (refiner)
ratio = min([width, height]) / max([width, height])
if target_mode == 'match':
target_width, target_height = width, height
refiner_width, refiner_height = width * 4, height * 4
#refiner_width, refiner_height = (4096, 4096 * ratio // 8 * 8) if width > height else (4096 * ratio // 8 * 8, 4096)
elif target_mode == '2x':
target_width, target_height = width * 2, height * 2
refiner_width, refiner_height = width * 4, height * 4
#refiner_width, refiner_height = (4096, 4096 * ratio // 8 * 8) if width > height else (4096 * ratio // 8 * 8, 4096)
elif target_mode == '4x':
target_width, target_height = width * 4, height * 4
refiner_width, refiner_height = width * 4, height * 4
#refiner_width, refiner_height = (4096, 4096 * ratio // 8 * 8) if width > height else (4096 * ratio // 8 * 8, 4096)
elif target_mode == '2x90':
target_width, target_height = height * 2, width * 2
refiner_width, refiner_height = width * 4, height * 4
#refiner_width, refiner_height = (4096, 4096 * ratio // 8 * 8) if width > height else (4096 * ratio // 8 * 8, 4096)
elif target_mode == '4x90':
target_width, target_height = height * 4, width * 4
refiner_width, refiner_height = width * 4, height * 4
#refiner_width, refiner_height = (4096, 4096 * ratio // 8 * 8) if width > height else (4096 * ratio // 8 * 8, 4096)
elif target_mode == '4096':
target_width, target_height = (4096, 4096 * ratio // 8 * 8) if width > height else (4096 * ratio // 8 * 8, 4096)
refiner_width, refiner_height = width * 4, height * 4
#refiner_width, refiner_height = (4096, 4096 * ratio // 8 * 8) if width > height else (4096 * ratio // 8 * 8, 4096)
elif target_mode == '4096-90':
target_width, target_height = (4096, 4096 * ratio // 8 * 8) if width < height else (4096 * ratio // 8 * 8, 4096)
refiner_width, refiner_height = width * 4, height * 4
#refiner_width, refiner_height = (4096, 4096 * ratio // 8 * 8) if width > height else (4096 * ratio // 8 * 8, 4096)
elif target_mode == '2048':
target_width, target_height = (2048, 2048 * ratio // 8 * 8) if width > height else (2048 * ratio // 8 * 8, 2048)
refiner_width, refiner_height = width * 4, height * 4
#refiner_width, refiner_height = (4096, 4096 * ratio // 8 * 8) if width > height else (4096 * ratio // 8 * 8, 4096)
elif target_mode == '2048-90':
target_width, target_height = (2048, 2048 * ratio // 8 * 8) if width < height else (2048 * ratio // 8 * 8, 2048)
refiner_width, refiner_height = width * 4, height * 4
#refiner_width, refiner_height = (4096, 4096 * ratio // 8 * 8) if width > height else (4096 * ratio // 8 * 8, 4096)
#print('Width:', width, 'Height:', height,
# 'Target Width:', target_width, 'Target Height:', target_height,
# 'Refiner Width:', refiner_width, 'Refiner Height:', refiner_height)
add_metadata_to_dict(prompt_with_style, width=width, height=height, target_width=target_width, target_height=target_height,
refiner_width=refiner_width, refiner_height=refiner_height, crop_w=0, crop_h=0)
# search and replace
positive_prompt = search_and_replace(positive_prompt, extra_pnginfo, prompt)
negative_prompt = search_and_replace(negative_prompt, extra_pnginfo, prompt)
# process random syntax
positive_prompt = process_random_syntax(positive_prompt, seed)
negative_prompt = process_random_syntax(negative_prompt, seed)
# check for $style in prompt, split the prompt into prompt and style
user_added_style = False
if '$style' in positive_prompt:
self.styles.append('user_added_style')
self.pos_style['user_added_style'] = positive_prompt.split('$style')[1].strip()
self.neg_style['user_added_style'] = ''
user_added_style = True
if '$style' in negative_prompt:
if 'user_added_style' not in self.styles:
self.styles.append('user_added_style')
self.neg_style['user_added_style'] = negative_prompt.split('$style')[1].strip()
user_added_style = True
if user_added_style:
positive_prompt = positive_prompt.split('$style')[0].strip()
if '$style' in negative_prompt:
negative_prompt = negative_prompt.split('$style')[0].strip()
positive_prompt = positive_prompt + '<style:user_added_style>'
# first process wildcards
positive_prompt_ = find_and_replace_wildcards(positive_prompt, seed, True)
negative_prompt_ = find_and_replace_wildcards(negative_prompt, seed, True)
add_metadata_to_dict(prompt_with_style, positive_prompt=positive_prompt_, negative_prompt=negative_prompt_)
if len(positive_prompt_) != len(positive_prompt) or len(negative_prompt_) != len(negative_prompt):
seed += random.randint(0, 1000000)
positive_prompt = positive_prompt_
negative_prompt = negative_prompt_
# 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_ = pos_prompt_, neg_prompt_
pos_prompt_, neg_prompt_ = strip_all_syntax(pos_prompt_), strip_all_syntax(neg_prompt_)
pos_style_, neg_style_ = strip_all_syntax(pos_style_), strip_all_syntax(neg_style_)
#print("pos_prompt_", pos_prompt_)
#print("neg_prompt_", neg_prompt_)
#print("pos_style_", pos_style_)
#print("neg_style_", neg_style_)
# encode text
add_metadata_to_dict(prompt_with_style, style=style_, clip_g_positive=pos_prompt, clip_l_positive=pos_style_)
add_metadata_to_dict(prompt_with_style, clip_g_negative=neg_prompt, clip_l_negative=neg_style_)
sdxl_pos_cond = CLIPTextEncodeSDXL.encode(self, clip_base_pos, width, height, 0, 0, target_width, target_height, pos_prompt_, pos_style_)[0]
sdxl_neg_cond = CLIPTextEncodeSDXL.encode(self, clip_base_neg, width, height, 0, 0, target_width, target_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]
#prompt.get(str(unique_id))['inputs']['output_positive_prompt'] = pos_prompt_
#prompt.get(str(unique_id))['inputs']['output_negative_prompt'] = neg_prompt_
#prompt.get(str(unique_id))['inputs']['output_latent_width'] = width
#prompt.get(str(unique_id))['inputs']['output_latent_height'] = height
#prompt.get(str(unique_id))['inputs']['output_target_width'] = target_width
#prompt.get(str(unique_id))['inputs']['output_target_height'] = target_height
#prompt.get(str(unique_id))['inputs']['output_refiner_width'] = refiner_width
#prompt.get(str(unique_id))['inputs']['output_refiner_height'] = refiner_height
#prompt.get(str(unique_id))['inputs']['output_crop_w'] = 0
#prompt.get(str(unique_id))['inputs']['output_crop_h'] = 0
return (base_model, {"samples":latent},
sdxl_pos_cond, sdxl_neg_cond,
refiner_pos_cond, refiner_neg_cond,
pos_prompt_, neg_prompt_, {'extra_pnginfo': extra_pnginfo})
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:
# try to match a key without being case sensitive
style_search = next((x for x in self.styles if x.lower() == style_.lower()), None)
# if there are still no matches
if style_search is None:
#print(f'Could not find style: {style_}')
style_ = 'none'
continue
else:
style_ = style_search
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_])
pos_prompt_, neg_prompt_ = strip_all_syntax(pos_prompt_), strip_all_syntax(neg_prompt_)
pos_style_, neg_style_ = strip_all_syntax(pos_style_), strip_all_syntax(neg_style_)
add_metadata_to_dict(prompt_with_style, style=style_, positive_prompt=pos_prompt_, negative_prompt=neg_prompt_,
positive_style=pos_style_, negative_style=neg_style_)
#base_model, clip_base_pos, pos_prompt_ = self.extract_and_load_loras(pos_prompt_, base_model, clip_base)
#base_model, clip_base_neg, neg_prompt_ = self.extract_and_load_loras(neg_prompt_, base_model, clip_base)
width_, height_ = width, height
refiner_width_, refiner_height_ = refiner_width, refiner_height
# encode text
add_metadata_to_dict(prompt_with_style, style=style_, clip_g_positive=pos_prompt_, clip_l_positive=pos_style_)
add_metadata_to_dict(prompt_with_style, clip_g_negative=neg_prompt_, clip_l_negative=neg_style_)
base_pos_conds.append(CLIPTextEncodeSDXL.encode(self, clip_base_pos, width_, height_, 0, 0, target_width, target_height, pos_prompt_, pos_style_)[0])
base_neg_conds.append(CLIPTextEncodeSDXL.encode(self, clip_base_neg, width_, height_, 0, 0, target_width, target_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])
# if none of the styles matched we will get an empty list so we need to check for that again
if len(base_pos_conds) == 0:
style_ = 'none'
pos_prompt_, neg_prompt_ = self.parse_prompts(positive_prompt, negative_prompt, style_, seed)
pos_style_, neg_style_ = pos_prompt_, neg_prompt_
pos_prompt_, neg_prompt_ = strip_all_syntax(pos_prompt_), strip_all_syntax(neg_prompt_)
pos_style_, neg_style_ = strip_all_syntax(pos_style_), strip_all_syntax(neg_style_)
# encode text
add_metadata_to_dict(prompt_with_style, style=style_, clip_g_positive=pos_prompt_, clip_l_positive=pos_style_)
add_metadata_to_dict(prompt_with_style, clip_g_negative=neg_prompt_, clip_l_negative=neg_style_)
sdxl_pos_cond = CLIPTextEncodeSDXL.encode(self, clip_base_pos, width, height, 0, 0, target_width, target_height, pos_prompt_, pos_style_)[0]
sdxl_neg_cond = CLIPTextEncodeSDXL.encode(self, clip_base_neg, width, height, 0, 0, target_width, target_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]
#prompt.get(str(unique_id))['inputs']['output_positive_prompt'] = pos_prompt_
#prompt.get(str(unique_id))['inputs']['output_negative_prompt'] = neg_prompt_
#prompt.get(str(unique_id))['inputs']['output_latent_width'] = width
#prompt.get(str(unique_id))['inputs']['output_latent_height'] = height
#prompt.get(str(unique_id))['inputs']['output_target_width'] = target_width
#prompt.get(str(unique_id))['inputs']['output_target_height'] = target_height
#prompt.get(str(unique_id))['inputs']['output_refiner_width'] = refiner_width
#prompt.get(str(unique_id))['inputs']['output_refiner_height'] = refiner_height
#prompt.get(str(unique_id))['inputs']['output_crop_w'] = 0
#prompt.get(str(unique_id))['inputs']['output_crop_h'] = 0
return (base_model, {"samples":latent},
sdxl_pos_cond, sdxl_neg_cond,
refiner_pos_cond, refiner_neg_cond,
pos_prompt_, neg_prompt_, {'extra_pnginfo': extra_pnginfo})
# 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
extra_pnginfo['PromptWithStyle'] = prompt_with_style
#prompt.get(str(unique_id))['inputs']['output_positive_prompt'] = pos_prompt_
#prompt.get(str(unique_id))['inputs']['output_negative_prompt'] = neg_prompt_
#prompt.get(str(unique_id))['inputs']['output_latent_width'] = width
#prompt.get(str(unique_id))['inputs']['output_latent_height'] = height
#prompt.get(str(unique_id))['inputs']['output_target_width'] = target_width
#prompt.get(str(unique_id))['inputs']['output_target_height'] = target_height
#prompt.get(str(unique_id))['inputs']['output_refiner_width'] = refiner_width
#prompt.get(str(unique_id))['inputs']['output_refiner_height'] = refiner_height
#prompt.get(str(unique_id))['inputs']['output_crop_w'] = 0
#prompt.get(str(unique_id))['inputs']['output_crop_h'] = 0
return (base_model, {"samples":latent},
sdxl_pos_cond, sdxl_neg_cond,
refiner_pos_cond, refiner_neg_cond,
pos_prompt_, neg_prompt_, {'extra_pnginfo': extra_pnginfo})