diff --git a/ldm/generate.py b/ldm/generate.py index aab6ca33e2..f5e02ef96c 100644 --- a/ldm/generate.py +++ b/ldm/generate.py @@ -975,7 +975,7 @@ class Generate: ti_path, defer_injecting_tokens=True ) print( - f'>> Textual inversion triggers: {", ".join(self.model.textual_inversion_manager.get_all_trigger_strings())}' + f'>> Textual inversion triggers: {", ".join(sorted(self.model.textual_inversion_manager.get_all_trigger_strings()))}' ) self.model_name = model_name diff --git a/ldm/modules/textual_inversion_manager.py b/ldm/modules/textual_inversion_manager.py index 5360ed6bca..c3ca69e992 100644 --- a/ldm/modules/textual_inversion_manager.py +++ b/ldm/modules/textual_inversion_manager.py @@ -35,6 +35,7 @@ class TextualInversionManager(BaseTextualInversionManager): self.text_encoder = text_encoder self.full_precision = full_precision self.hf_concepts_library = HuggingFaceConceptsLibrary() + self.trigger_to_sourcefile = dict() default_textual_inversions: list[TextualInversion] = [] self.textual_inversions = default_textual_inversions @@ -60,15 +61,17 @@ class TextualInversionManager(BaseTextualInversionManager): def get_all_trigger_strings(self) -> list[str]: return [ti.trigger_string for ti in self.textual_inversions] - def load_textual_inversion(self, ckpt_path: Union[str,Path], defer_injecting_tokens: bool = False): + def load_textual_inversion( + self, ckpt_path: Union[str, Path], defer_injecting_tokens: bool = False + ): ckpt_path = Path(ckpt_path) if not ckpt_path.is_file(): return - + if str(ckpt_path).endswith(".DS_Store"): return - + try: scan_result = scan_file_path(str(ckpt_path)) if scan_result.infected_files == 1: @@ -90,27 +93,45 @@ class TextualInversionManager(BaseTextualInversionManager): return elif ( self.text_encoder.get_input_embeddings().weight.data[0].shape[0] - != embedding_info['token_dim'] + != embedding_info["token_dim"] ): print( f"** Notice: {ckpt_path.parents[0].name}/{ckpt_path.name} was trained on a model with an incompatible token dimension: {self.text_encoder.get_input_embeddings().weight.data[0].shape[0]} vs {embedding_info['token_dim']}." ) return - if embedding_info: - try: - self._add_textual_inversion( - embedding_info["name"], - embedding_info["embedding"], - defer_injecting_tokens=defer_injecting_tokens, - ) - except ValueError as e: - print(f' | Ignoring incompatible embedding {embedding_info["name"]}') - print(f" | The error was {str(e)}") - else: - print( - f">> Failed to load embedding located at {str(ckpt_path)}. Unsupported file." + # Resolve the situation in which an earlier embedding has claimed the same + # trigger string. We replace the trigger with '', as we used to. + trigger_str = embedding_info["name"] + sourcefile = ( + f"{ckpt_path.parent.name}/{ckpt_path.name}" + if ckpt_path.name == "learned_embeds.bin" + else ckpt_path.name + ) + + if trigger_str in self.trigger_to_sourcefile: + replacement_trigger_str = ( + f"<{ckpt_path.parent.name}>" + if ckpt_path.name == "learned_embeds.bin" + else f"<{ckpt_path.stem}>" ) + print( + f">> {sourcefile}: Trigger token '{trigger_str}' is already claimed by '{self.trigger_to_sourcefile[trigger_str]}'. Trigger this concept with {replacement_trigger_str}" + ) + trigger_str = replacement_trigger_str + + try: + self._add_textual_inversion( + trigger_str, + embedding_info["embedding"], + defer_injecting_tokens=defer_injecting_tokens, + ) + # remember which source file claims this trigger + self.trigger_to_sourcefile[trigger_str] = sourcefile + + except ValueError as e: + print(f' | Ignoring incompatible embedding {embedding_info["name"]}') + print(f" | The error was {str(e)}") def _add_textual_inversion( self, trigger_str, embedding, defer_injecting_tokens=False @@ -123,7 +144,7 @@ class TextualInversionManager(BaseTextualInversionManager): """ if trigger_str in [ti.trigger_string for ti in self.textual_inversions]: print( - f">> TextualInversionManager refusing to overwrite already-loaded token '{trigger_str}'" + f"** TextualInversionManager refusing to overwrite already-loaded token '{trigger_str}'" ) return if not self.full_precision: @@ -132,7 +153,7 @@ class TextualInversionManager(BaseTextualInversionManager): embedding = embedding.unsqueeze(0) elif len(embedding.shape) > 2: raise ValueError( - f"TextualInversionManager cannot add {trigger_str} because the embedding shape {embedding.shape} is incorrect. The embedding must have shape [token_dim] or [V, token_dim] where V is vector length and token_dim is 768 for SD1 or 1280 for SD2." + f"** TextualInversionManager cannot add {trigger_str} because the embedding shape {embedding.shape} is incorrect. The embedding must have shape [token_dim] or [V, token_dim] where V is vector length and token_dim is 768 for SD1 or 1280 for SD2." ) try: @@ -148,7 +169,7 @@ class TextualInversionManager(BaseTextualInversionManager): else: traceback.print_exc() print( - f">> TextualInversionManager was unable to add a textual inversion with trigger string {trigger_str}." + f"** TextualInversionManager was unable to add a textual inversion with trigger string {trigger_str}." ) raise @@ -295,7 +316,7 @@ class TextualInversionManager(BaseTextualInversionManager): elif file_type == "bin": return self._parse_embedding_bin(embedding_file) else: - print(f">> Not a recognized embedding file: {embedding_file}") + print(f"** Notice: unrecognized embedding file format: {embedding_file}") return None def _parse_embedding_pt(self, embedding_file): @@ -356,8 +377,9 @@ class TextualInversionManager(BaseTextualInversionManager): embedding_info = None else: for token in list(embedding_ckpt.keys()): - embedding_info["name"] = token or os.path.basename( - os.path.splitext(embedding_file)[0] + embedding_info["name"] = ( + token + or f"<{os.path.basename(os.path.splitext(embedding_file)[0])}>" ) embedding_info["embedding"] = embedding_ckpt[token] embedding_info[ @@ -381,7 +403,7 @@ class TextualInversionManager(BaseTextualInversionManager): embedding_info["name"] = ( token if token != "*" - else os.path.basename(os.path.splitext(embedding_file)[0]) + else f"<{os.path.basename(os.path.splitext(embedding_file)[0])}>" ) embedding_info["embedding"] = embedding_ckpt[ "string_to_param"