add router API support for model manager heuristic_import()`

invokeai/app/api/routers/models.py

@@ -1,13 +1,13 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654) and 2023 Kent Keirsey (https://github.com/hipsterusername)
 
-from typing import Annotated, Literal, Optional, Union, Dict
+from typing import Literal, Optional, Union
 
 from fastapi import Query
 from fastapi.routing import APIRouter, HTTPException
 from pydantic import BaseModel, Field, parse_obj_as
 from ..dependencies import ApiDependencies
 from invokeai.backend import BaseModelType, ModelType
-from invokeai.backend.model_management.models import OPENAPI_MODEL_CONFIGS
+from invokeai.backend.model_management.models import OPENAPI_MODEL_CONFIGS, SchedulerPredictionType
 MODEL_CONFIGS = Union[tuple(OPENAPI_MODEL_CONFIGS)]
 
 models_router = APIRouter(prefix="/v1/models", tags=["models"])
@@ -51,11 +51,14 @@ class CreateModelResponse(BaseModel):
     info: Union[CkptModelInfo, DiffusersModelInfo] = Field(discriminator="format", description="The model info")
     status: str = Field(description="The status of the API response")
 
+class ImportModelRequest(BaseModel):
+    name: str = Field(description="A model path, repo_id or URL to import")
+    prediction_type: Optional[Literal['epsilon','v_prediction','sample']] = Field(description='Prediction type for SDv2 checkpoint files')
+
 class ConversionRequest(BaseModel):
     name: str = Field(description="The name of the new model")
     info: CkptModelInfo = Field(description="The converted model info")
     save_location: str = Field(description="The path to save the converted model weights")
-    
 
 class ConvertedModelResponse(BaseModel):
     name: str = Field(description="The name of the new model")
@@ -105,6 +108,28 @@ async def update_model(
 
     return model_response
 
+@models_router.post(
+    "/",
+    operation_id="import_model",
+    responses={200: {"status": "success"}},
+)
+async def import_model(
+    model_request: ImportModelRequest
+) -> None:
+    """ Add Model """
+    items_to_import = set([model_request.name])
+    prediction_types = { x.value: x for x in SchedulerPredictionType }
+    logger = ApiDependencies.invoker.services.logger
+    
+    installed_models = ApiDependencies.invoker.services.model_manager.heuristic_import(
+        items_to_import = items_to_import,
+        prediction_type_helper = lambda x: prediction_types.get(model_request.prediction_type)
+    )
+    if len(installed_models) > 0:
+        logger.info(f'Successfully imported {model_request.name}')
+    else:
+        logger.error(f'Model {model_request.name} not imported')
+        raise HTTPException(status_code=500, detail=f'Model {model_request.name} not imported')
 
 @models_router.delete(
     "/{model_name}",

invokeai/backend/install/model_install_backend.py

@@ -93,9 +93,10 @@ class ModelInstall(object):
     def __init__(self,
                  config:InvokeAIAppConfig,
                  prediction_type_helper: Callable[[Path],SchedulerPredictionType]=None,
+                 model_manager: ModelManager = None,
                  access_token:str = None):
         self.config = config
-        self.mgr = ModelManager(config.model_conf_path)
+        self.mgr = model_manager or ModelManager(config.model_conf_path)
         self.datasets = OmegaConf.load(Dataset_path)
         self.prediction_helper = prediction_type_helper
         self.access_token = access_token or HfFolder.get_token()

invokeai/backend/model_management/model_manager.py

@@ -151,13 +151,11 @@ import os
 import hashlib
 import textwrap
 from dataclasses import dataclass
-from packaging import version
 from pathlib import Path
-from typing import Dict, Optional, List, Tuple, Union, types
+from typing import Optional, List, Tuple, Union, Set, Callable, types
 from shutil import rmtree
 
 import torch
-from huggingface_hub import scan_cache_dir
 from omegaconf import OmegaConf
 from omegaconf.dictconfig import DictConfig
 
@@ -165,9 +163,13 @@ from pydantic import BaseModel
 
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.util import CUDA_DEVICE, download_with_resume
+from invokeai.backend.util import CUDA_DEVICE
 from .model_cache import ModelCache, ModelLocker
-from .models import BaseModelType, ModelType, SubModelType, ModelError, MODEL_CLASSES
+from .models import (
+    BaseModelType, ModelType, SubModelType,
+    ModelError, SchedulerPredictionType, MODEL_CLASSES,
+    ModelConfigBase,
+    )
 
 # We are only starting to number the config file with release 3.
 # The config file version doesn't have to start at release version, but it will help
@@ -686,3 +688,34 @@ class ModelManager(object):
 
         if new_models_found and self.config_path:
             self.commit()
+
+
+    def heuristic_import(self,
+                         items_to_import: Set[str],
+                         prediction_type_helper: Callable[[Path],SchedulerPredictionType]=None,
+                         )->Set[str]:
+        '''
+        Import a list of paths, repo_ids or URLs. Returns the 
+        set of successfully imported items. The prediction_type_helper
+        is a callback that receives the Path of a checkpoint or diffusers
+        model and returns a SchedulerPredictionType (or None).
+        '''
+        # avoid circular import here
+        from invokeai.backend.install.model_install_backend import ModelInstall
+        successfully_installed = set()
+        
+        installer = ModelInstall(config = self.globals,
+                                 prediction_type_helper = prediction_type_helper,
+                                 model_manager = self)
+        for thing in items_to_import:
+            try:
+                installer.heuristic_install(thing)
+                successfully_installed.add(thing)
+            except Exception as e:
+                self.logger.warning(f'{thing} could not be imported: {str(e)}')
+                
+        self.commit()                
+        return successfully_installed
+                
+        
+        

invokeai/backend/model_management/model_probe.py

@@ -1,17 +1,14 @@
 import json
-import traceback
 import torch
 import safetensors.torch
 
 from dataclasses import dataclass
-from enum import Enum
 
-from diffusers import ModelMixin, ConfigMixin, StableDiffusionPipeline, AutoencoderKL, ControlNetModel
+from diffusers import ModelMixin, ConfigMixin
 from pathlib import Path
 from typing import Callable, Literal, Union, Dict
 from picklescan.scanner import scan_file_path
 
-import invokeai.backend.util.logging as logger
 from .models import BaseModelType, ModelType, ModelVariantType, SchedulerPredictionType, SilenceWarnings
 
 @dataclass
@@ -102,7 +99,7 @@ class ModelProbe(object):
                                      and prediction_type==SchedulerPredictionType.VPrediction \
                                      ) else 512,
             )
-        except Exception as e:
+        except Exception:
             return None
 
         return model_info
@@ -115,6 +112,9 @@ class ModelProbe(object):
             return ModelType.TextualInversion
         checkpoint = checkpoint or cls._scan_and_load_checkpoint(model_path)
         state_dict = checkpoint.get("state_dict") or checkpoint
+        
+        if len(checkpoint) < 10 and all(isinstance(v, torch.Tensor) for v in checkpoint.values()):
+            return ModelType.TextualInversion
         if any([x.startswith("model.diffusion_model") for x in state_dict.keys()]):
             return ModelType.Pipeline
         if any([x.startswith("encoder.conv_in") for x in state_dict.keys()]):
@@ -326,13 +326,9 @@ class PipelineFolderProbe(FolderProbeBase):
     def get_base_type(self)->BaseModelType:
         if self.model:
             unet_conf = self.model.unet.config
-            scheduler_conf = self.model.scheduler.config
         else:
             with open(self.folder_path / 'unet' / 'config.json','r') as file:
                 unet_conf = json.load(file)
-            with open(self.folder_path / 'scheduler' / 'scheduler_config.json','r') as file:
-                scheduler_conf = json.load(file)
-            
         if unet_conf['cross_attention_dim'] == 768:
             return BaseModelType.StableDiffusion1  
         elif unet_conf['cross_attention_dim'] == 1024:

invokeai/backend/model_management/models/base.py

@@ -56,7 +56,6 @@ class ModelConfigBase(BaseModel):
     class Config:
         use_enum_values = True
 
-
 class EmptyConfigLoader(ConfigMixin):
     @classmethod
     def load_config(cls, *args, **kwargs):

invokeai/configs/INITIAL_MODELS.yaml

@@ -45,6 +45,7 @@ sd-1/pipeline/portraitplus:
    repo_id: wavymulder/portraitplus
    recommended: False
 sd-1/pipeline/seek.art_MEGA:
+   repo_id: coreco/seek.art_MEGA
    description: A general use SD-1.5 "anything" model that supports multiple styles (2.1 GB)
    recommended: False
 sd-1/pipeline/trinart_stable_diffusion_v2: