Fix caption dropout so it now works identically when caching text embeddings.
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@@ -2090,6 +2090,9 @@ class TextEmbeddingFileItemDTOMixin:
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# diff output preservation embeds (caption with trigger word replaced by class)
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self.dop_prompt_embeds: Union[PromptEmbeds, None] = None
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self._dop_text_embedding_path: Union[str, None] = None
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# blank caption embeds used for caption dropout when caching text embeddings
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self._blank_text_embedding_path: Union[str, None] = None
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self._loaded_text_embedding_path: Union[str, None] = None
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self.is_text_embedding_cached = False
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self.text_embedding_load_device = 'cpu'
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self.text_embedding_version = 1
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@@ -2151,6 +2154,25 @@ class TextEmbeddingFileItemDTOMixin:
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return self._dop_text_embedding_path
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def get_dropout_caption(self: 'FileItemDTO'):
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# when encoding live, dropped captions still get the trigger word injected
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# downstream (add_if_not_present when not a reg image), so match that here
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if self.trigger_word is not None and not self.is_reg:
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return inject_trigger_into_prompt('', trigger=self.trigger_word, add_if_not_present=True)
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return ''
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def get_blank_text_embedding_path(self: 'FileItemDTO', recalculate=False):
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if self._blank_text_embedding_path is not None and not recalculate:
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return self._blank_text_embedding_path
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else:
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# if the dropout caption matches the normal caption, this hashes to the
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# same path as the normal embedding and the cache file is shared
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self._blank_text_embedding_path = self._build_text_embedding_path(
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caption_override=self.get_dropout_caption()
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)
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return self._blank_text_embedding_path
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def cleanup_text_embedding(self):
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if self.prompt_embeds is not None:
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# we are caching on disk, don't save in memory
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@@ -2162,11 +2184,19 @@ class TextEmbeddingFileItemDTOMixin:
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if not self.is_text_embedding_cached:
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return
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if self.prompt_embeds is None:
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te_path = self.get_text_embedding_path()
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if self.dataset_config.caption_dropout_rate > 0:
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# get a random float form 0 to 1
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rand = random.random()
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if rand < self.dataset_config.caption_dropout_rate:
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# drop the caption by using the cached blank embedding
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te_path = self.get_blank_text_embedding_path()
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# load it from disk
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self.prompt_embeds = PromptEmbeds.load(self.get_text_embedding_path())
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self.prompt_embeds = PromptEmbeds.load(te_path)
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self._loaded_text_embedding_path = te_path
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if self.dataset_config.diff_output_preservation and self.dop_prompt_embeds is None:
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dop_path = self.get_dop_text_embedding_path()
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if dop_path == self.get_text_embedding_path():
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if dop_path == self._loaded_text_embedding_path:
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# no trigger word in caption, same embedding
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self.dop_prompt_embeds = self.prompt_embeds
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else:
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@@ -2199,6 +2229,11 @@ class TextEmbeddingCachingMixin:
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if dop_path != text_embedding_path:
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# trigger word was in the caption, cache the DOP version too
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encode_targets.append((dop_path, file_item.caption_dop))
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if self.dataset_config.caption_dropout_rate > 0:
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blank_path = file_item.get_blank_text_embedding_path(recalculate=True)
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if blank_path != text_embedding_path:
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# cache the dropout caption embedding (blank, or trigger word only)
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encode_targets.append((blank_path, file_item.get_dropout_caption()))
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# only process if not saved to disk
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encode_targets = [t for t in encode_targets if not os.path.exists(t[0])]
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if len(encode_targets) > 0:
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