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from .utils import find_wildcards_seed, find_nearest_steps, is_linked_styles_selector
from .log import log_node_warn
from .translate import zh_to_en, has_chinese
from .wildcards import process_with_loras
from .adv_encode import advanced_encode
from nodes import ConditioningConcat, ConditioningCombine, ConditioningAverage, ConditioningSetTimestepRange, CLIPTextEncode
def prompt_to_cond(type, model, clip, clip_skip, lora_stack, text, prompt_token_normalization, prompt_weight_interpretation, a1111_prompt_style ,my_unique_id, prompt, easyCache, can_load_lora=True, steps=None, model_type=None):
styles_selector = is_linked_styles_selector(prompt, my_unique_id, type)
title = "Positive encoding" if type == 'positive' else "Negative encoding"
# Translate cn to en
if model_type not in ['hydit'] and text is not None and has_chinese(text):
text = zh_to_en([text])[0]
if model_type in ['hydit', 'flux', 'mochi']:
log_node_warn(title + "...")
embeddings_final, = CLIPTextEncode().encode(clip, text) if text is not None else (None,)
return (embeddings_final, "", model, clip)
log_node_warn(title + "...")
positive_seed = find_wildcards_seed(my_unique_id, text, prompt)
model, clip, text, cond_decode, show_prompt, pipe_lora_stack = process_with_loras(
text, model, clip, type, positive_seed, can_load_lora, lora_stack, easyCache)
wildcard_prompt = cond_decode if show_prompt or styles_selector else ""
clipped = clip.clone()
# 当clip模型不存在t5xxl时，可执行跳过层
if not hasattr(clip.cond_stage_model, 't5xxl'):
if clip_skip != 0:
clipped.clip_layer(clip_skip)
steps = steps if steps is not None else find_nearest_steps(my_unique_id, prompt)
return (advanced_encode(clipped, text, prompt_token_normalization,
prompt_weight_interpretation, w_max=1.0,
apply_to_pooled='enable',
a1111_prompt_style=a1111_prompt_style, steps=steps) if text is not None else None, wildcard_prompt, model, clipped)
def set_cond(old_cond, new_cond, mode, average_strength, old_cond_start, old_cond_end, new_cond_start, new_cond_end):
if not old_cond:
return new_cond
else:
if mode == "replace":
return new_cond
elif mode == "concat":
return ConditioningConcat().concat(new_cond, old_cond)[0]
elif mode == "combine":
return ConditioningCombine().combine(old_cond, new_cond)[0]
elif mode == 'average':
return ConditioningAverage().addWeighted(new_cond, old_cond, average_strength)[0]
elif mode == 'timestep':
cond_1 = ConditioningSetTimestepRange().set_range(old_cond, old_cond_start, old_cond_end)[0]
cond_2 = ConditioningSetTimestepRange().set_range(new_cond, new_cond_start, new_cond_end)[0]
return ConditioningCombine().combine(cond_1, cond_2)[0] |