| 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"
|
|
|
|
|
| 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()
|
|
|
| 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] |
