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Recognize and load diffusers-style LoRAs (.bin)
Prevent double-reporting of autoimported models - closes #3636 Allow autoimport of diffusers-style LoRA models - closes #3637
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@ -193,7 +193,10 @@ class ModelInstall(object):
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models_installed.update(self._install_path(path))
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# folders style or similar
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elif path.is_dir() and any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin'}]):
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elif path.is_dir() and any([(path/x).exists() for x in \
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{'config.json','model_index.json','learned_embeds.bin','pytorch_lora_weights.bin'}
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]
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):
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models_installed.update(self._install_path(path))
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# recursive scan
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@ -785,7 +785,7 @@ class ModelManager(object):
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if path in known_paths or path.parent in scanned_dirs:
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scanned_dirs.add(path)
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continue
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if any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin'}]):
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if any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin','pytorch_lora_weights.bin'}]):
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new_models_found.update(installer.heuristic_import(path))
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scanned_dirs.add(path)
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@ -794,7 +794,8 @@ class ModelManager(object):
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if path in known_paths or path.parent in scanned_dirs:
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continue
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if path.suffix in {'.ckpt','.bin','.pth','.safetensors','.pt'}:
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new_models_found.update(installer.heuristic_import(path))
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import_result = installer.heuristic_import(path)
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new_models_found.update(import_result)
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self.logger.info(f'Scanned {items_scanned} files and directories, imported {len(new_models_found)} models')
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installed.update(new_models_found)
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@ -126,6 +126,8 @@ class ModelProbe(object):
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return ModelType.Vae
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elif any(key.startswith(v) for v in {"lora_te_", "lora_unet_"}):
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return ModelType.Lora
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elif any(key.endswith(v) for v in {"to_k_lora.up.weight", "to_q_lora.down.weight"}):
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return ModelType.Lora
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elif any(key.startswith(v) for v in {"control_model", "input_blocks"}):
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return ModelType.ControlNet
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elif key in {"emb_params", "string_to_param"}:
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@ -136,7 +138,7 @@ class ModelProbe(object):
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if len(ckpt) < 10 and all(isinstance(v, torch.Tensor) for v in ckpt.values()):
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return ModelType.TextualInversion
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raise ValueError("Unable to determine model type")
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raise ValueError(f"Unable to determine model type for {model_path}")
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@classmethod
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def get_model_type_from_folder(cls, folder_path: Path, model: ModelMixin)->ModelType:
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@ -166,7 +168,7 @@ class ModelProbe(object):
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return type
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# give up
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raise ValueError("Unable to determine model type")
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raise ValueError("Unable to determine model type for {folder_path}")
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@classmethod
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def _scan_and_load_checkpoint(cls,model_path: Path)->dict:
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