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Zero
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import os
import re
from nodes import MAX_RESOLUTION
from comfy_extras.nodes_clip_sdxl import CLIPTextEncodeSDXL
from .log import log_node_warn, log_node_info, log_node_success
from .constants import get_category, get_name
from .power_prompt_utils import get_and_strip_loras
from nodes import LoraLoader, CLIPTextEncode
import folder_paths
NODE_NAME = get_name('SDXL Power Prompt - Positive')
class RgthreeSDXLPowerPromptPositive:
"""The Power Prompt for positive conditioning."""
NAME = NODE_NAME
CATEGORY = get_category()
@classmethod
def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring
SAVED_PROMPTS_FILES = folder_paths.get_filename_list('saved_prompts')
SAVED_PROMPTS_CONTENT = []
for filename in SAVED_PROMPTS_FILES:
with open(folder_paths.get_full_path('saved_prompts', filename), 'r') as f:
SAVED_PROMPTS_CONTENT.append(f.read())
return {
'required': {
'prompt_g': ('STRING', {
'multiline': True
}),
'prompt_l': ('STRING', {
'multiline': True
}),
},
'optional': {
"opt_model": ("MODEL",),
"opt_clip": ("CLIP",),
"opt_clip_width": ("INT", {
"forceInput": True,
"default": 1024.0,
"min": 0,
"max": MAX_RESOLUTION
}),
"opt_clip_height": ("INT", {
"forceInput": True,
"default": 1024.0,
"min": 0,
"max": MAX_RESOLUTION
}),
'insert_lora': (['CHOOSE', 'DISABLE LORAS'] +
[os.path.splitext(x)[0] for x in folder_paths.get_filename_list('loras')],),
'insert_embedding': ([
'CHOOSE',
] + [os.path.splitext(x)[0] for x in folder_paths.get_filename_list('embeddings')],),
'insert_saved': ([
'CHOOSE',
] + SAVED_PROMPTS_FILES,),
# We'll hide these in the UI for now.
"target_width": ("INT", {
"default": -1,
"min": -1,
"max": MAX_RESOLUTION
}),
"target_height": ("INT", {
"default": -1,
"min": -1,
"max": MAX_RESOLUTION
}),
"crop_width": ("INT", {
"default": -1,
"min": -1,
"max": MAX_RESOLUTION
}),
"crop_height": ("INT", {
"default": -1,
"min": -1,
"max": MAX_RESOLUTION
}),
},
'hidden': {
'values_insert_saved': (['CHOOSE'] + SAVED_PROMPTS_CONTENT,),
}
}
RETURN_TYPES = ('CONDITIONING', 'MODEL', 'CLIP', 'STRING', 'STRING')
RETURN_NAMES = ('CONDITIONING', 'MODEL', 'CLIP', 'TEXT_G', 'TEXT_L')
FUNCTION = 'main'
def main(self,
prompt_g,
prompt_l,
opt_model=None,
opt_clip=None,
opt_clip_width=None,
opt_clip_height=None,
insert_lora=None,
insert_embedding=None,
insert_saved=None,
target_width=-1,
target_height=-1,
crop_width=-1,
crop_height=-1,
values_insert_saved=None):
if insert_lora == 'DISABLE LORAS':
prompt_g, loras_g, _skipped, _unfound = get_and_strip_loras(prompt_g,
True,
log_node=self.NAME)
prompt_l, loras_l, _skipped, _unfound = get_and_strip_loras(prompt_l,
True,
log_node=self.NAME)
loras = loras_g + loras_l
log_node_info(
NODE_NAME,
f'Disabling all found loras ({len(loras)}) and stripping lora tags for TEXT output.')
elif opt_model is not None and opt_clip is not None:
prompt_g, loras_g, _skipped, _unfound = get_and_strip_loras(prompt_g, log_node=self.NAME)
prompt_l, loras_l, _skipped, _unfound = get_and_strip_loras(prompt_l, log_node=self.NAME)
loras = loras_g + loras_l
if len(loras) > 0:
for lora in loras:
opt_model, opt_clip = LoraLoader().load_lora(opt_model, opt_clip, lora['lora'],
lora['strength'], lora['strength'])
log_node_success(NODE_NAME, f'Loaded "{lora["lora"]}" from prompt')
log_node_info(NODE_NAME, f'{len(loras)} Loras processed; stripping tags for TEXT output.')
elif '<lora:' in prompt_g or '<lora:' in prompt_l:
_prompt_g, loras_g, _skipped, _unfound = get_and_strip_loras(prompt_g,
True,
log_node=self.NAME)
_prompt_l, loras_l, _skipped, _unfound = get_and_strip_loras(prompt_l,
True,
log_node=self.NAME)
loras = loras_g + loras_l
if len(loras):
log_node_warn(
NODE_NAME, f'Found {len(loras)} lora tags in prompt but model & clip were not supplied!')
log_node_info(NODE_NAME, 'Loras not processed, keeping for TEXT output.')
conditioning = self.get_conditioning(prompt_g, prompt_l, opt_clip, opt_clip_width,
opt_clip_height, target_width, target_height, crop_width,
crop_height)
return (conditioning, opt_model, opt_clip, prompt_g, prompt_l)
def get_conditioning(self, prompt_g, prompt_l, opt_clip, opt_clip_width, opt_clip_height,
target_width, target_height, crop_width, crop_height):
"""Checks the inputs and gets the conditioning."""
conditioning = None
if opt_clip is not None:
do_regular_clip_text_encode = opt_clip_width and opt_clip_height
if do_regular_clip_text_encode:
target_width = target_width if target_width and target_width > 0 else opt_clip_width
target_height = target_height if target_height and target_height > 0 else opt_clip_height
crop_width = crop_width if crop_width and crop_width > 0 else 0
crop_height = crop_height if crop_height and crop_height > 0 else 0
try:
conditioning = CLIPTextEncodeSDXL().encode(opt_clip, opt_clip_width, opt_clip_height,
crop_width, crop_height, target_width,
target_height, prompt_g, prompt_l)[0]
except Exception:
do_regular_clip_text_encode = True
log_node_info(
self.NAME,
'Exception while attempting to CLIPTextEncodeSDXL, will fall back to standard encoding.'
)
else:
log_node_info(
self.NAME,
'CLIP supplied, but not CLIP_WIDTH and CLIP_HEIGHT. Text encoding will use standard ' +
'encoding with prompt_g and prompt_l concatenated.')
if not do_regular_clip_text_encode:
conditioning = CLIPTextEncode().encode(
opt_clip, f'{prompt_g if prompt_g else ""}\n{prompt_l if prompt_l else ""}')[0]
return conditioning
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