| 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] |
