From eee48b3207e9aa37085dfc164e55cb2ee291e44e Mon Sep 17 00:00:00 2001 From: silveroxides Date: Mon, 16 Jun 2025 16:18:24 +0200 Subject: [PATCH] revert main --- __init__.py | 214 ++++++++++++++++++++++++++-------------------------- 1 file changed, 109 insertions(+), 105 deletions(-) diff --git a/__init__.py b/__init__.py index 4c07f5b..93fb1d7 100644 --- a/__init__.py +++ b/__init__.py @@ -9,8 +9,6 @@ class SaveTokenEmbeddings: if not self.output_dir_list: print("SaveTokenEmbeddings: Warning: 'embeddings' folder type not found. Falling back to main output directory.") self.output_dir_list = [folder_paths.get_output_directory()] - os.makedirs(self.output_dir_list[0], exist_ok=True) - @classmethod def INPUT_TYPES(cls): @@ -93,28 +91,12 @@ class SaveTokenEmbeddings: for s in range(num_segments): current_segment_embeds = embeds_out_full[s] current_segment_mask = attention_mask_full[s] - actual_embeddings_this_segment_masked = current_segment_embeds[current_segment_mask == 1] - - processed_segment = actual_embeddings_this_segment_masked - num_tokens_in_segment_after_mask = processed_segment.shape[0] - - if num_tokens_in_segment_after_mask > 0: - if key_suffix == 'l' or key_suffix == 'g': # CLIP models (BOS and EOS) - if num_tokens_in_segment_after_mask >= 2: - processed_segment = processed_segment[1:-1] - else: - processed_segment = processed_segment[0:0] - elif key_suffix.startswith('t5') or key_suffix.startswith('umt5') or key_suffix.startswith('pile'): # T5 models (EOS only) - if num_tokens_in_segment_after_mask >= 1: - processed_segment = processed_segment[:-1] - else: - processed_segment = processed_segment[0:0] - - if processed_segment.shape[0] > 0: - all_actual_embeddings_for_this_clip_part.append(processed_segment) + actual_embeddings_this_segment = current_segment_embeds[current_segment_mask == 1] + if actual_embeddings_this_segment.shape[0] > 0: + all_actual_embeddings_for_this_clip_part.append(actual_embeddings_this_segment) if not all_actual_embeddings_for_this_clip_part: - print(f"SaveTokenEmbeddings: Warning: No actual tokens for '{key_suffix}' after special token removal. Skipping.") + print(f"SaveTokenEmbeddings: Warning: No actual tokens for '{key_suffix}'. Skipping.") continue final_embeddings_for_this_clip_part = torch.cat(all_actual_embeddings_for_this_clip_part, dim=0) @@ -134,8 +116,8 @@ class SaveTokenEmbeddings: subfolder_in_prefix, filename_base = os.path.split(filename_prefix) primary_output_dir = self.output_dir_list[0] full_output_folder = os.path.join(primary_output_dir, subfolder_in_prefix) - - os.makedirs(full_output_folder, exist_ok=True) # Ensures the target folder exists + if subfolder_in_prefix: + os.makedirs(full_output_folder, exist_ok=True) metadata = {} counter = 1 @@ -161,7 +143,6 @@ class SaveWeightedEmbeddings: if not self.output_dir_list: print("SaveWeightedEmbeddings: Warning: 'embeddings' folder type not found. Falling back to main output directory.") self.output_dir_list = [folder_paths.get_output_directory()] - os.makedirs(self.output_dir_list[0], exist_ok=True) @classmethod def INPUT_TYPES(cls): @@ -179,7 +160,8 @@ class SaveWeightedEmbeddings: CATEGORY = "EmbeddingToolkit" def save_weighted_embeddings(self, clip, text, filename_prefix): - if clip is None: raise RuntimeError("CLIP input is None.") + if clip is None: + raise RuntimeError("CLIP input is None.") if clip.cond_stage_model is None: print("SaveWeightedEmbeddings: Error: clip.cond_stage_model is None.") return {"ui": {"text": ["Error: CLIP's cond_stage_model is None."]}} @@ -195,8 +177,14 @@ class SaveWeightedEmbeddings: model_instance = getattr(actual_clip_model_wrapper, attr_name) if model_instance is not None and hasattr(model_instance, "process_tokens") and hasattr(model_instance, "transformer"): sd_clip_instances[key_suffix] = model_instance + else: + if model_instance is None: + print(f"SaveWeightedEmbeddings: Warning: Attribute '{attr_name}' is None. Skipping '{key_suffix}'.") + else: + print(f"SaveWeightedEmbeddings: Warning: Attribute '{attr_name}' (type {type(model_instance)}) is not a valid SDClipModel. Skipping '{key_suffix}'.") if not sd_clip_instances and hasattr(actual_clip_model_wrapper, "process_tokens") and hasattr(actual_clip_model_wrapper, "transformer"): + print(f"SaveWeightedEmbeddings: Warning: No standard attributes. Fallback: cond_stage_model as 'l'.") sd_clip_instances['l'] = actual_clip_model_wrapper if not sd_clip_instances: @@ -215,22 +203,34 @@ class SaveWeightedEmbeddings: print(f"SaveWeightedEmbeddings: Warning: No token data for '{key_suffix}'. Skipping.") continue + if not hasattr(sd_clip_model_inst, 'transformer') or sd_clip_model_inst.transformer is None: + print(f"SaveWeightedEmbeddings: Error: SDClipModel for '{key_suffix}' has invalid 'transformer'. Skipping.") + continue device = sd_clip_model_inst.transformer.get_input_embeddings().weight.device - prompt_token_id_segments = [[item[0] for item in seg] for seg in current_prompt_segments_with_weights] - if not prompt_token_id_segments: continue - max_len_for_empty = getattr(sd_clip_model_inst, 'max_length', 77) + prompt_token_id_segments = [] + max_segment_len = 0 + for segment_w_weights in current_prompt_segments_with_weights: + prompt_token_id_segments.append([item[0] for item in segment_w_weights]) + max_segment_len = max(max_segment_len, len(segment_w_weights)) + + if not prompt_token_id_segments: + print(f"SaveWeightedEmbeddings: Warning: No token ID segments for '{key_suffix}'. Skipping.") + continue + empty_token_sequence = [] - if hasattr(sd_clip_model_inst, "gen_empty_tokens"): - empty_token_sequence = sd_clip_model_inst.gen_empty_tokens(max_len_for_empty) + if hasattr(sd_clip_model_inst, "gen_empty_tokens") and hasattr(sd_clip_model_inst, "special_tokens"): + empty_token_sequence = sd_clip_model_inst.gen_empty_tokens(sd_clip_model_inst.special_tokens, max_segment_len) else: - pad_token_id = getattr(sd_clip_model_inst, 'pad_token_id', None) - if pad_token_id is None and hasattr(sd_clip_model_inst, "special_tokens") and "pad" in sd_clip_model_inst.special_tokens: + pad_token_id = None + if hasattr(sd_clip_model_inst, "special_tokens") and "pad" in sd_clip_model_inst.special_tokens: pad_token_id = sd_clip_model_inst.special_tokens["pad"] + if pad_token_id is not None: - empty_token_sequence = [pad_token_id] * max_len_for_empty + print(f"SaveWeightedEmbeddings: Warning: '{key_suffix}' SDClipModel instance missing 'gen_empty_tokens' or 'special_tokens'. Using simple pad token sequence for empty.") + empty_token_sequence = [pad_token_id] * max_segment_len else: - print(f"SaveWeightedEmbeddings: Error: Cannot determine pad/empty token for '{key_suffix}'. Skipping weighting for this part.") + print(f"SaveWeightedEmbeddings: Error: Cannot determine pad token for '{key_suffix}'. Skipping weighting for this part.") continue all_token_sequences_for_process_tokens = prompt_token_id_segments + [empty_token_sequence] @@ -248,50 +248,39 @@ class SaveWeightedEmbeddings: current_segment_raw_embeds = prompt_embeds_full_segments[s_idx] segment_data_from_tokenizer = current_prompt_segments_with_weights[s_idx] current_segment_mask_from_process_tokens = prompt_attention_mask_full_segments[s_idx] - weighted_segment_embeds = torch.zeros_like(current_segment_raw_embeds) - content_token_offset = 1 if (key_suffix == 'l' or key_suffix == 'g') else 0 - for token_idx_in_padded_seq in range(current_segment_raw_embeds.shape[0]): - is_active_token_in_mask = current_segment_mask_from_process_tokens[token_idx_in_padded_seq] == 1 - original_embedding_at_pos = current_segment_raw_embeds[token_idx_in_padded_seq] - embed_empty_for_this_pos = embeds_empty_segment_full[0, token_idx_in_padded_seq] - - if is_active_token_in_mask: - weight = 1.0 - original_token_idx_for_weight = token_idx_in_padded_seq - content_token_offset - if original_token_idx_for_weight >= 0 and original_token_idx_for_weight < len(segment_data_from_tokenizer): - weight = segment_data_from_tokenizer[original_token_idx_for_weight][1] + for token_idx in range(current_segment_raw_embeds.shape[0]): + is_active_token = current_segment_mask_from_process_tokens[token_idx] == 1 + original_embedding = current_segment_raw_embeds[token_idx] + embed_empty_for_this_pos = embeds_empty_segment_full[0, token_idx] + if is_active_token: + weight = segment_data_from_tokenizer[token_idx][1] if token_idx < len(segment_data_from_tokenizer) else 1.0 if weight == 1.0: - weighted_segment_embeds[token_idx_in_padded_seq] = original_embedding_at_pos + weighted_segment_embeds[token_idx] = original_embedding else: - weighted_segment_embeds[token_idx_in_padded_seq] = (original_embedding_at_pos - embed_empty_for_this_pos) * weight + embed_empty_for_this_pos + weighted_segment_embeds[token_idx] = (original_embedding - embed_empty_for_this_pos) * weight + embed_empty_for_this_pos else: - weighted_segment_embeds[token_idx_in_padded_seq] = original_embedding_at_pos + weighted_segment_embeds[token_idx] = original_embedding - actual_weighted_embeddings_this_segment_masked = weighted_segment_embeds[current_segment_mask_from_process_tokens == 1] - - processed_segment = actual_weighted_embeddings_this_segment_masked - num_tokens_in_segment_after_mask = processed_segment.shape[0] - - if num_tokens_in_segment_after_mask > 0: - if key_suffix == 'l' or key_suffix == 'g': - if num_tokens_in_segment_after_mask >= 2: processed_segment = processed_segment[1:-1] - else: processed_segment = processed_segment[0:0] - elif key_suffix.startswith('t5') or key_suffix.startswith('umt5') or key_suffix.startswith('pile'): - if num_tokens_in_segment_after_mask >= 1: processed_segment = processed_segment[:-1] - else: processed_segment = processed_segment[0:0] - - if processed_segment.shape[0] > 0: - all_weighted_actual_embeddings_for_this_clip_part.append(processed_segment) + actual_weighted_embeddings_this_segment = weighted_segment_embeds[current_segment_mask_from_process_tokens == 1] + if actual_weighted_embeddings_this_segment.shape[0] > 0: + all_weighted_actual_embeddings_for_this_clip_part.append(actual_weighted_embeddings_this_segment) if not all_weighted_actual_embeddings_for_this_clip_part: - print(f"SaveWeightedEmbeddings: Warning: No actual weighted tokens for '{key_suffix}' after special token removal. Skipping.") + print(f"SaveWeightedEmbeddings: Warning: No actual weighted tokens for '{key_suffix}'. Skipping.") continue final_embeddings_for_this_clip_part = torch.cat(all_weighted_actual_embeddings_for_this_clip_part, dim=0) - save_key_in_file = key_suffix if (key_suffix.startswith('t5') or key_suffix.startswith('umt5') or key_suffix.startswith('pile')) else f"clip_{key_suffix}" + + # --- CORRECTED KEY NAMING --- + if key_suffix.startswith('t5') or key_suffix.startswith('umt5') or key_suffix.startswith('pile'): + save_key_in_file = key_suffix # Use 't5xxl' directly + else: + save_key_in_file = f"clip_{key_suffix}" # Use 'clip_l', 'clip_g' + # --- END CORRECTION --- + tensors_to_save[save_key_in_file] = final_embeddings_for_this_clip_part.cpu() print(f"SaveWeightedEmbeddings: Processed weighted for '{save_key_in_file}': shape {final_embeddings_for_this_clip_part.shape}") @@ -302,7 +291,8 @@ class SaveWeightedEmbeddings: subfolder_in_prefix, filename_base = os.path.split(filename_prefix) primary_output_dir = self.output_dir_list[0] full_output_folder = os.path.join(primary_output_dir, subfolder_in_prefix) - os.makedirs(full_output_folder, exist_ok=True) + if subfolder_in_prefix: + os.makedirs(full_output_folder, exist_ok=True) metadata = {} counter = 1 @@ -321,13 +311,12 @@ class SaveWeightedEmbeddings: comfy.utils.save_torch_file(tensors_to_save, save_path, metadata=metadata) return {"ui": {"text": [f"Saved weighted to {save_path}"]}} - class SaveA1111WeightedEmbeddings: def __init__(self): self.output_dir_list = folder_paths.get_folder_paths("embeddings") if not self.output_dir_list: + print("SaveA1111WeightedEmbeddings: Warning: 'embeddings' folder type not found. Falling back to main output directory.") self.output_dir_list = [folder_paths.get_output_directory()] - os.makedirs(self.output_dir_list[0], exist_ok=True) @classmethod def INPUT_TYPES(cls): @@ -345,7 +334,8 @@ class SaveA1111WeightedEmbeddings: CATEGORY = "EmbeddingToolkit" def save_a1111_weighted_embeddings(self, clip, text, filename_prefix): - if clip is None: raise RuntimeError("CLIP input is None.") + if clip is None: + raise RuntimeError("CLIP input is None.") if clip.cond_stage_model is None: print("SaveA1111WeightedEmbeddings: Error: clip.cond_stage_model is None.") return {"ui": {"text": ["Error: CLIP's cond_stage_model is None."]}} @@ -355,13 +345,21 @@ class SaveA1111WeightedEmbeddings: sd_clip_instances = {} potential_clip_parts = {'l': 'clip_l', 'g': 'clip_g', 'pile_t5xl': 'pile_t5xl', 't5xl': 't5xl', 't5xxl': 't5xxl', 'umt5xxl': 'umt5xxl', 't5base': 't5base'} + for key_suffix, attr_name in potential_clip_parts.items(): if hasattr(actual_clip_model_wrapper, attr_name): model_instance = getattr(actual_clip_model_wrapper, attr_name) if model_instance is not None and hasattr(model_instance, "process_tokens") and hasattr(model_instance, "transformer"): sd_clip_instances[key_suffix] = model_instance + else: + if model_instance is None: + print(f"SaveA1111WeightedEmbeddings: Warning: Attribute '{attr_name}' is None. Skipping '{key_suffix}'.") + else: print(f"SaveA1111WeightedEmbeddings: Warning: Attribute '{attr_name}' (type {type(model_instance)}) is not a valid SDClipModel. Skipping '{key_suffix}'.") + if not sd_clip_instances and hasattr(actual_clip_model_wrapper, "process_tokens") and hasattr(actual_clip_model_wrapper, "transformer"): - sd_clip_instances['l'] = actual_clip_model_wrapper + print(f"SaveA1111WeightedEmbeddings: Warning: No standard attributes. Fallback: cond_stage_model as 'l'.") + sd_clip_instances['l'] = actual_clip_model_wrapper + if not sd_clip_instances: print(f"SaveA1111WeightedEmbeddings: Error: No valid SDClipModel instances found.") return {"ui": {"text": ["Error: No valid SDClipModel instances found."]}} @@ -374,11 +372,22 @@ class SaveA1111WeightedEmbeddings: elif key_suffix == 'l' and not isinstance(tokenized_text_with_weights, dict): current_prompt_segments_with_weights = tokenized_text_with_weights - if not current_prompt_segments_with_weights: continue + if not current_prompt_segments_with_weights: + print(f"SaveA1111WeightedEmbeddings: Warning: No token data for '{key_suffix}'. Skipping.") + continue + if not hasattr(sd_clip_model_inst, 'transformer') or sd_clip_model_inst.transformer is None: + print(f"SaveA1111WeightedEmbeddings: Error: SDClipModel for '{key_suffix}' has invalid 'transformer'. Skipping.") + continue device = sd_clip_model_inst.transformer.get_input_embeddings().weight.device - prompt_token_id_segments = [[item[0] for item in seg] for seg in current_prompt_segments_with_weights] - if not prompt_token_id_segments: continue + + prompt_token_id_segments = [] + for segment_w_weights in current_prompt_segments_with_weights: + prompt_token_id_segments.append([item[0] for item in segment_w_weights]) + + if not prompt_token_id_segments: + print(f"SaveA1111WeightedEmbeddings: Warning: No token ID segments for '{key_suffix}'. Skipping.") + continue embeds_out_full, attention_mask_full, _ = sd_clip_model_inst.process_tokens( prompt_token_id_segments, device @@ -393,42 +402,36 @@ class SaveA1111WeightedEmbeddings: current_segment_mask_from_process_tokens = attention_mask_full[s_idx] scaled_segment_embeds = torch.zeros_like(current_segment_raw_embeds) - content_token_offset = 1 if (key_suffix == 'l' or key_suffix == 'g') else 0 - for token_idx_in_padded_seq in range(current_segment_raw_embeds.shape[0]): - is_active_token_in_mask = current_segment_mask_from_process_tokens[token_idx_in_padded_seq] == 1 - original_embedding_at_pos = current_segment_raw_embeds[token_idx_in_padded_seq] + for token_idx in range(current_segment_raw_embeds.shape[0]): + is_active_token = current_segment_mask_from_process_tokens[token_idx] == 1 + original_embedding = current_segment_raw_embeds[token_idx] - if is_active_token_in_mask: - weight = 1.0 - original_token_idx_for_weight = token_idx_in_padded_seq - content_token_offset - if original_token_idx_for_weight >= 0 and original_token_idx_for_weight < len(segment_data_from_tokenizer): - weight = segment_data_from_tokenizer[original_token_idx_for_weight][1] - scaled_segment_embeds[token_idx_in_padded_seq] = original_embedding_at_pos * weight + if is_active_token: + weight = segment_data_from_tokenizer[token_idx][1] if token_idx < len(segment_data_from_tokenizer) else 1.0 + + scaled_segment_embeds[token_idx] = original_embedding * weight else: - scaled_segment_embeds[token_idx_in_padded_seq] = original_embedding_at_pos + scaled_segment_embeds[token_idx] = original_embedding - actual_scaled_embeddings_this_segment_masked = scaled_segment_embeds[current_segment_mask_from_process_tokens == 1] + actual_scaled_embeddings_this_segment = scaled_segment_embeds[current_segment_mask_from_process_tokens == 1] - processed_segment = actual_scaled_embeddings_this_segment_masked - num_tokens_in_segment_after_mask = processed_segment.shape[0] - if num_tokens_in_segment_after_mask > 0: - if key_suffix == 'l' or key_suffix == 'g': - if num_tokens_in_segment_after_mask >= 2: processed_segment = processed_segment[1:-1] - else: processed_segment = processed_segment[0:0] - elif key_suffix.startswith('t5') or key_suffix.startswith('umt5') or key_suffix.startswith('pile'): - if num_tokens_in_segment_after_mask >= 1: processed_segment = processed_segment[:-1] - else: processed_segment = processed_segment[0:0] + if actual_scaled_embeddings_this_segment.shape[0] > 0: + all_scaled_actual_embeddings_for_this_clip_part.append(actual_scaled_embeddings_this_segment) - if processed_segment.shape[0] > 0: - all_scaled_actual_embeddings_for_this_clip_part.append(processed_segment) - - if not all_scaled_actual_embeddings_for_this_clip_part: continue + if not all_scaled_actual_embeddings_for_this_clip_part: + print(f"SaveA1111WeightedEmbeddings: Warning: No actual scaled tokens found for '{key_suffix}' after processing. Skipping.") + continue final_embeddings_for_this_clip_part = torch.cat(all_scaled_actual_embeddings_for_this_clip_part, dim=0) - save_key_in_file = key_suffix if (key_suffix.startswith('t5') or key_suffix.startswith('umt5') or key_suffix.startswith('pile')) else f"clip_{key_suffix}" + + if key_suffix.startswith('t5') or key_suffix.startswith('umt5') or key_suffix.startswith('pile'): + save_key_in_file = key_suffix + else: + save_key_in_file = f"clip_{key_suffix}" + tensors_to_save[save_key_in_file] = final_embeddings_for_this_clip_part.cpu() - print(f"SaveA1111WeightedEmbeddings: Processed A1111-style for '{save_key_in_file}': shape {final_embeddings_for_this_clip_part.shape}") + print(f"SaveA1111WeightedEmbeddings: Processed A1111-style weighted for '{save_key_in_file}': shape {final_embeddings_for_this_clip_part.shape}") if not tensors_to_save: print("SaveA1111WeightedEmbeddings: Error: No A1111-style weighted embeddings generated.") @@ -437,7 +440,8 @@ class SaveA1111WeightedEmbeddings: subfolder_in_prefix, filename_base = os.path.split(filename_prefix) primary_output_dir = self.output_dir_list[0] full_output_folder = os.path.join(primary_output_dir, subfolder_in_prefix) - os.makedirs(full_output_folder, exist_ok=True) + if subfolder_in_prefix: + os.makedirs(full_output_folder, exist_ok=True) metadata = {} counter = 1 @@ -477,7 +481,7 @@ class SliceExistingEmbedding: print(f"SliceExistingEmbedding: Error listing embedding files: {e}") embedding_files = ["None"] - if not embedding_files: # Ensure there's at least one option + if not embedding_files: embedding_files = ["None"] return { @@ -548,7 +552,7 @@ class SliceExistingEmbedding: if num_tokens >= 2: processed_tensor = tensor[1:-1] any_sliced = True - elif num_tokens > 0 : # e.g. only 1 token + elif num_tokens > 0 : processed_tensor = tensor[0:0] any_sliced = True print(f" Resulting shape: {processed_tensor.shape}")