all files migrated; tweaks needed

.gitignore

@@ -198,7 +198,7 @@ checkpoints
 .DS_Store
 
 # Let the frontend manage its own gitignore
-!invokeai/frontend/*
+!invokeai/frontend/web/*
 
 # Scratch folder
 .scratch/

invokeai/app/services/generate_initializer.py

@@ -3,11 +3,10 @@ import os
 import sys
 import traceback
 
-from invokeai.backend.models import ModelManager
+from invokeai.backend import ModelManager, Generate
 
 from ...globals import Globals
-from ....generate import Generate
-import ldm.invoke
+import invokeai.version
 
 # TODO: most of this code should be split into individual services as the Generate.py code is deprecated
 def get_generate(args, config) -> Generate:
@@ -16,7 +15,7 @@ def get_generate(args, config) -> Generate:
         if not os.path.exists(config_file):
             report_model_error(args, FileNotFoundError(f"The file {config_file} could not be found."))
 
-    print(f'>> {ldm.invoke.__app_name__}, version {ldm.invoke.__version__}')
+    print(f'>> {invokeai.version.__app_name__}, version {invokeai.version.__version__}')
     print(f'>> InvokeAI runtime directory is "{Globals.root}"')
 
     # these two lines prevent a horrible warning message from appearing
@@ -160,7 +159,7 @@ def report_model_error(opt:Namespace, e:Exception):
 # Temporary initializer for Generate until we migrate off of it
 def old_get_generate(args, config) -> Generate:
     # TODO: Remove the need for globals
-    from ldm.invoke.globals import Globals
+    from invokeai.backend.globals import Globals
 
     # alert - setting globals here
     Globals.root = os.path.expanduser(args.root_dir or os.environ.get('INVOKEAI_ROOT') or os.path.abspath('.'))

invokeai/app/services/invocation_services.py

@@ -3,8 +3,7 @@ from .invocation_queue import InvocationQueueABC
 from .item_storage import ItemStorageABC
 from .image_storage import ImageStorageBase
 from .events import EventServiceBase
-from ....generate import Generate
-
+from invokeai.backend import Generate
 
 class InvocationServices():
     """Services that can be used by invocations"""

invokeai/backend/__init__.py

@@ -1,8 +1,8 @@
 '''
 Initialization file for invokeai.backend
 '''
-# this is causing circular import issues
-# from .invoke_ai_web_server import InvokeAIWebServer
-from .model_manager import ModelManager
+from .model_management import ModelManager
+from .generate import Generate
+
 
 

invokeai/backend/args.py

@@ -98,8 +98,8 @@ from typing import List
 from invokeai.backend.image_util import retrieve_metadata
 import invokeai.version
 
-from ldm.invoke.globals import Globals
-from invokeai.backend.prompting import split_weighted_subprompts
+from .globals import Globals
+from .prompting import split_weighted_subprompts
 
 APP_ID = invokeai.version.__app_id__
 APP_NAME = invokeai.version.__app_name__

invokeai/backend/config/invokeai_configure.py

@@ -39,14 +39,14 @@ import invokeai.configs as configs
 
 from ..args import PRECISION_CHOICES, Args
 from ..globals import Globals, global_config_dir, global_config_file, global_cache_dir
-from .model_install import addModelsForm, process_and_execute
+from ...frontend.config.model_install import addModelsForm, process_and_execute
 from .model_install_backend import (
     default_dataset,
     download_from_hf,
     recommended_datasets,
     hf_download_with_resume,
 )
-from .widgets import IntTitleSlider, CenteredButtonPress, set_min_terminal_size
+from ...frontend.config.widgets import IntTitleSlider, CenteredButtonPress, set_min_terminal_size
 
 
 warnings.filterwarnings("ignore")

invokeai/backend/config/model_install_backend.py

@@ -18,9 +18,9 @@ from tqdm import tqdm
 from typing import List
 
 import invokeai.configs as configs
-from invokeai.backend.stable_diffusion import StableDiffusionGeneratorPipeline
+from ..stable_diffusion import StableDiffusionGeneratorPipeline
 from ..globals import Globals, global_cache_dir, global_config_dir
-from invokeai.backend import ModelManager
+from ..model_management import ModelManager
 
 warnings.filterwarnings("ignore")
 

invokeai/backend/generate.py

@@ -25,17 +25,18 @@ from omegaconf import OmegaConf
 from PIL import Image, ImageOps
 from pytorch_lightning import logging, seed_everything
 
-from invokeai.backend import ModelManager
-from invokeai.backend.prompting import get_uc_and_c_and_ec
-from invokeai.backend.stable_diffusion import (DDIMSampler, KSampler, PLMSSampler)
-from invokeai.backend.generator import infill_methods
-from invokeai.backend.stable_diffusion.concepts_lib import HuggingFaceConceptsLibrary
-from invokeai.backend.devices import choose_precision, choose_torch_device
-from invokeai.backend.image_util import InitImageResizer, PngWriter, Txt2Mask
+from . import ModelManager
+from .prompting import get_uc_and_c_and_ec
+from .stable_diffusion import (DDIMSampler, KSampler, PLMSSampler, HuggingFaceConceptsLibrary)
+from .generator import infill_methods
+from .util import choose_precision, choose_torch_device
+from .image_util import (InitImageResizer,
+                                         PngWriter,
+                                         Txt2Mask,
+                                         configure_model_padding)
 
-from ldm.invoke.globals import Globals, global_cache_dir
-from ldm.invoke.args import metadata_from_png
-from ldm.invoke.seamless import configure_model_padding
+from .globals import Globals, global_cache_dir
+from .args import metadata_from_png
 
 def fix_func(orig):
     if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():

invokeai/backend/generator/base.py

@@ -23,7 +23,7 @@ from tqdm import trange
 
 import invokeai.assets.web as web_assets
 from ..stable_diffusion.diffusion.ddpm import DiffusionWrapper
-from ..util import rand_perlin_2d
+from ..util.util import rand_perlin_2d
 
 downsampling = 8
 CAUTION_IMG = 'caution.png'

invokeai/backend/globals.py

@@ -1,5 +1,5 @@
 '''
-ldm.invoke.globals defines a small number of global variables that would
+invokeai.backend.globals defines a small number of global variables that would
 otherwise have to be passed through long and complex call chains.
 
 It defines a Namespace object named "Globals" that contains

invokeai/backend/image_util/__init__.py

@@ -9,6 +9,7 @@ from .pngwriter import (PngWriter,
                         retrieve_metadata,
                         write_metadata,
                         )
+from .seamless import configure_model_padding
 
 def debug_image(
     debug_image, debug_text, debug_show=True, debug_result=False, debug_status=False

invokeai/backend/image_util/patchmatch.py

@@ -4,7 +4,7 @@ wraps the actual patchmatch object. It respects the global
 "try_patchmatch" attribute, so that patchmatch loading can
 be suppressed or deferred
 '''
-from ldm.invoke.globals import Globals
+from invokeai.backend.globals import Globals
 import numpy as  np
 
 class PatchMatch:

invokeai/backend/image_util/txt2mask.py

@@ -32,7 +32,7 @@ import numpy as  np
 from transformers import AutoProcessor, CLIPSegForImageSegmentation
 from PIL import Image, ImageOps
 from torchvision import transforms
-from ldm.invoke.globals import global_cache_dir
+from invokeai.backend.globals import global_cache_dir
 
 CLIPSEG_MODEL = 'CIDAS/clipseg-rd64-refined'
 CLIPSEG_SIZE = 352

invokeai/backend/model_management/__init__.py

@@ -0,0 +1,8 @@
+'''
+Initialization file for invokeai.backend.model_management
+'''
+from .model_manager import ModelManager
+from .convert_ckpt_to_diffusers import (load_pipeline_from_original_stable_diffusion_ckpt,
+                                        convert_ckpt_to_diffusers)
+from ...frontend.merge.merge_diffusers import (merge_diffusion_models,
+                                               merge_diffusion_models_and_commit)

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -21,11 +21,11 @@ import re
 import torch
 import warnings
 from pathlib import Path
-from ldm.invoke.globals import (
+from invokeai.backend.globals import (
     global_cache_dir,
     global_config_dir,
     )
-from invokeai.models import ModelManager, SDLegacyType
+from .model_manager import ModelManager, SDLegacyType
 from safetensors.torch import load_file
 from typing import Union
 
@@ -56,7 +56,7 @@ from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionS
 from diffusers.utils import is_safetensors_available
 from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextModel, CLIPTokenizer, CLIPVisionConfig
 
-from invokeai.generator import StableDiffusionGeneratorPipeline
+from ..stable_diffusion import StableDiffusionGeneratorPipeline
 
 def shave_segments(path, n_shave_prefix_segments=1):
     """
@@ -1014,7 +1014,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
 
     return pipe
 
-def convert_ckpt_to_diffuser(
+def convert_ckpt_to_diffusers(
         checkpoint_path:Union[str,Path],
         dump_path:Union[str,Path],
         **kwargs,

invokeai/backend/model_management/model_manager.py

@@ -31,14 +31,13 @@ from omegaconf import OmegaConf
 from omegaconf.dictconfig import DictConfig
 from picklescan.scanner import scan_file_path
 
-from .devices import CPU_DEVICE
-from ldm.invoke.globals import Globals, global_cache_dir
-from .util import (
+from ..util import CPU_DEVICE
+from invokeai.backend.globals import Globals, global_cache_dir
+from ..util import (
     ask_user,
     download_with_resume,
-    url_attachment_name,
 )
-from .stable_diffusion import StableDiffusionGeneratorPipeline
+from ..stable_diffusion import StableDiffusionGeneratorPipeline
 
 class SDLegacyType(Enum):
     V1 = 1
@@ -416,6 +415,51 @@ class ModelManager(object):
 
         return pipeline, width, height, model_hash
 
+    def _load_ckpt_model(self, model_name, mconfig):
+        config = mconfig.config
+        weights = mconfig.weights
+        vae = mconfig.get("vae")
+        width = mconfig.width
+        height = mconfig.height
+
+        if not os.path.isabs(config):
+            config = os.path.join(Globals.root, config)
+        if not os.path.isabs(weights):
+            weights = os.path.normpath(os.path.join(Globals.root, weights))
+
+        # Convert to diffusers and return a diffusers pipeline
+        print(
+            f">> Converting legacy checkpoint {model_name} into a diffusers model..."
+        )
+        
+        from . import  load_pipeline_from_original_stable_diffusion_ckpt
+
+        self.offload_model(self.current_model)
+        if vae_config := self._choose_diffusers_vae(model_name):
+            vae = self._load_vae(vae_config)
+        if self._has_cuda():
+            torch.cuda.empty_cache()
+        pipeline = load_pipeline_from_original_stable_diffusion_ckpt(
+            checkpoint_path=weights,
+            original_config_file=config,
+            vae=vae,
+            return_generator_pipeline=True,
+            precision=torch.float16
+            if self.precision == "float16"
+            else torch.float32,
+        )
+        if self.sequential_offload:
+            pipeline.enable_offload_submodels(self.device)
+        else:
+            pipeline.to(self.device)
+
+        return (
+            pipeline,
+            width,
+            height,
+            "NOHASH",
+        )
+
     def model_name_or_path(self, model_name: Union[str, DictConfig]) -> str | Path:
         if isinstance(model_name, DictConfig) or isinstance(model_name, dict):
             mconfig = model_name
@@ -519,66 +563,6 @@ class ModelManager(object):
             self.commit(commit_to_conf)
         return model_name
 
-    def import_ckpt_model(
-        self,
-        weights: Union[str, Path],
-        config: Union[str, Path] = "configs/stable-diffusion/v1-inference.yaml",
-        vae: Union[str, Path] = None,
-        model_name: str = None,
-        model_description: str = None,
-        commit_to_conf: Path = None,
-    ) -> str:
-        """
-        Attempts to install the indicated ckpt file and returns True if successful.
-
-        "weights" can be either a path-like object corresponding to a local .ckpt file
-        or a http/https URL pointing to a remote model.
-
-        "vae" is a Path or str object pointing to a ckpt or safetensors file to be used
-        as the VAE for this model.
-
-        "config" is the model config file to use with this ckpt file. It defaults to
-        v1-inference.yaml. If a URL is provided, the config will be downloaded.
-
-        You can optionally provide a model name and/or description. If not provided,
-        then these will be derived from the weight file name. If you provide a commit_to_conf
-        path to the configuration file, then the new entry will be committed to the
-        models.yaml file.
-
-        Return value is the name of the imported file, or None if an error occurred.
-        """
-        if str(weights).startswith(("http:", "https:")):
-            model_name = model_name or url_attachment_name(weights)
-
-        weights_path = self._resolve_path(weights, "models/ldm/stable-diffusion-v1")
-        config_path = self._resolve_path(config, "configs/stable-diffusion")
-
-        if weights_path is None or not weights_path.exists():
-            return
-        if config_path is None or not config_path.exists():
-            return
-
-        model_name = (
-            model_name or Path(weights).stem
-        )  # note this gives ugly pathnames if used on a URL without a Content-Disposition header
-        model_description = (
-            model_description or f"Imported stable diffusion weights file {model_name}"
-        )
-        new_config = dict(
-            weights=str(weights_path),
-            config=str(config_path),
-            description=model_description,
-            format="ckpt",
-            width=512,
-            height=512,
-        )
-        if vae:
-            new_config["vae"] = vae
-        self.add_model(model_name, new_config, True)
-        if commit_to_conf:
-            self.commit(commit_to_conf)
-        return model_name
-
     @classmethod
     def probe_model_type(self, checkpoint: dict) -> SDLegacyType:
         """
@@ -746,36 +730,18 @@ class ModelManager(object):
             )
             return
         
-        if convert:
-            diffuser_path = Path(
-                Globals.root, "models", Globals.converted_ckpts_dir, model_path.stem
-            )
-            model_name = self.convert_and_import(
-                model_path,
-                diffusers_path=diffuser_path,
-                vae=dict(repo_id="stabilityai/sd-vae-ft-mse"),
-                model_name=model_name,
-                model_description=description,
-                original_config_file=model_config_file,
-                commit_to_conf=commit_to_conf,
-            )
-        else:
-            model_name = self.import_ckpt_model(
-                model_path,
-                config=model_config_file,
-                model_name=model_name,
-                model_description=description,
-                vae=str(
-                    Path(
-                        Globals.root,
-                        "models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt",
-                    )
-                ),
-                commit_to_conf=commit_to_conf,
-            )
-
-        if commit_to_conf:
-            self.commit(commit_to_conf)
+        diffuser_path = Path(
+            Globals.root, "models", Globals.converted_ckpts_dir, model_path.stem
+        )
+        model_name = self.convert_and_import(
+            model_path,
+            diffusers_path=diffuser_path,
+            vae=dict(repo_id="stabilityai/sd-vae-ft-mse"),
+            model_name=model_name,
+            model_description=description,
+            original_config_file=model_config_file,
+            commit_to_conf=commit_to_conf,
+        )
         return model_name
 
     def convert_and_import(
@@ -800,7 +766,7 @@ class ModelManager(object):
 
         new_config = None
 
-        from ldm.invoke.ckpt_to_diffuser import convert_ckpt_to_diffuser
+        from . import convert_ckpt_to_diffusers
 
         if diffusers_path.exists():
             print(
@@ -815,7 +781,7 @@ class ModelManager(object):
             # By passing the specified VAE to the conversion function, the autoencoder
             # will be built into the model rather than tacked on afterward via the config file
             vae_model = self._load_vae(vae) if vae else None
-            convert_ckpt_to_diffuser(
+            convert_ckpt_to_diffusers (
                 ckpt_path,
                 diffusers_path,
                 extract_ema=True,

invokeai/backend/prompting/conditioning.py

@@ -13,9 +13,9 @@ from transformers import CLIPTokenizer, CLIPTextModel
 
 from compel import Compel
 from compel.prompt_parser import FlattenedPrompt, Blend, Fragment, CrossAttentionControlSubstitute, PromptParser
-from ..devices import torch_dtype
+from ..util import torch_dtype
 from ..stable_diffusion import InvokeAIDiffuserComponent
-from ldm.invoke.globals import Globals
+from invokeai.backend.globals import Globals
 
 def get_tokenizer(model) -> CLIPTokenizer:
     # TODO remove legacy ckpt fallback handling

invokeai/backend/restoration/base.py

@@ -23,16 +23,16 @@ class Restoration():
 
     # Face Restore Models
     def load_gfpgan(self, gfpgan_model_path):
-        from ldm.invoke.restoration.gfpgan import GFPGAN
+        from .gfpgan import GFPGAN
         return GFPGAN(gfpgan_model_path)
 
     def load_codeformer(self):
-        from ldm.invoke.restoration.codeformer import CodeFormerRestoration
+        from .codeformer import CodeFormerRestoration
         return CodeFormerRestoration()
 
     # Upscale Models
     def load_esrgan(self, esrgan_bg_tile=400):
-        from ldm.invoke.restoration.realesrgan import ESRGAN
+        from .realesrgan import ESRGAN
         esrgan = ESRGAN(esrgan_bg_tile)
         print('>> ESRGAN Initialized')
         return esrgan;

invokeai/backend/restoration/codeformer.py

@@ -3,7 +3,7 @@ import torch
 import numpy as np
 import warnings
 import sys
-from ldm.invoke.globals import Globals
+from invokeai.backend.globals import Globals
 
 pretrained_model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
 

invokeai/backend/restoration/codeformer_arch.py

@@ -5,7 +5,7 @@ from torch import nn, Tensor
 import torch.nn.functional as F
 from typing import Optional, List
 
-from ldm.invoke.restoration.vqgan_arch import *
+from .vqgan_arch import *
 from basicsr.utils import get_root_logger
 from basicsr.utils.registry import ARCH_REGISTRY
 
@@ -25,7 +25,6 @@ def calc_mean_std(feat, eps=1e-5):
     feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
     return feat_mean, feat_std
 
-
 def adaptive_instance_normalization(content_feat, style_feat):
     """Adaptive instance normalization.
 

invokeai/backend/restoration/gfpgan.py

@@ -3,7 +3,7 @@ import warnings
 import os
 import sys
 import numpy as np
-from ldm.invoke.globals import Globals
+from invokeai.backend.globals import Globals
 
 from PIL import Image
 

invokeai/backend/restoration/realesrgan.py

@@ -3,7 +3,7 @@ import warnings
 import numpy as np
 import os
 
-from ldm.invoke.globals import Globals
+from invokeai.backend.globals import Globals
 from PIL import Image
 from PIL.Image import Image as ImageType
 

invokeai/backend/stable_diffusion/concepts_lib.py

@@ -10,7 +10,7 @@ import traceback
 from typing import Callable
 from urllib import request, error as ul_error
 from huggingface_hub import HfFolder, hf_hub_url, ModelSearchArguments, ModelFilter, HfApi
-from ldm.invoke.globals import Globals
+from invokeai.backend.globals import Globals
 
 class HuggingFaceConceptsLibrary(object):
     def __init__(self, root=None):

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -26,11 +26,11 @@ from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 from typing_extensions import ParamSpec
 
-from ldm.invoke.globals import Globals
-from ..stable_diffusion.diffusion import InvokeAIDiffuserComponent, PostprocessingSettings, AttentionMapSaver
-from ..stable_diffusion.textual_inversion_manager import TextualInversionManager
-from ..stable_diffusion.offloading import LazilyLoadedModelGroup, FullyLoadedModelGroup, ModelGroup
-from ..devices import normalize_device, CPU_DEVICE
+from invokeai.backend.globals import Globals
+from .diffusion import InvokeAIDiffuserComponent, PostprocessingSettings, AttentionMapSaver
+from .textual_inversion_manager import TextualInversionManager
+from .offloading import LazilyLoadedModelGroup, FullyLoadedModelGroup, ModelGroup
+from ..util import normalize_device, CPU_DEVICE
 from compel import EmbeddingsProvider
 
 @dataclass

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -15,7 +15,7 @@ from torch import nn
 from compel.cross_attention_control import Arguments
 from diffusers.models.unet_2d_condition import UNet2DConditionModel
 from diffusers.models.cross_attention import AttnProcessor
-from ...devices import torch_dtype
+from ...util import torch_dtype
 
 
 class CrossAttentionType(enum.Enum):

invokeai/backend/stable_diffusion/diffusion/ddpm.py

@@ -23,7 +23,7 @@ from omegaconf import ListConfig
 import urllib
 
 from ..textual_inversion_manager import TextualInversionManager
-from ...util import (
+from ...util.util import (
     log_txt_as_img,
     exists,
     default,

invokeai/backend/stable_diffusion/diffusion/plms.py

@@ -4,7 +4,7 @@ import torch
 import numpy as np
 from tqdm import tqdm
 from functools import partial
-from ...devices import choose_torch_device
+from ...util import choose_torch_device
 from .shared_invokeai_diffusion import InvokeAIDiffuserComponent
 from .sampler import Sampler
 from ..diffusionmodules.util import noise_like

invokeai/backend/stable_diffusion/diffusion/sampler.py

@@ -7,7 +7,7 @@ import torch
 import numpy as np
 from tqdm import tqdm
 from functools import partial
-from ...devices import choose_torch_device
+from ...util import choose_torch_device
 from .shared_invokeai_diffusion import InvokeAIDiffuserComponent
 
 from ..diffusionmodules.util import (

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -8,7 +8,7 @@ import torch
 from diffusers.models.cross_attention import AttnProcessor
 from typing_extensions import TypeAlias
 
-from ldm.invoke.globals import Globals
+from invokeai.backend.globals import Globals
 from .cross_attention_control import Arguments, \
     restore_default_cross_attention, override_cross_attention, Context, get_cross_attention_modules, \
     CrossAttentionType, SwapCrossAttnContext

invokeai/backend/stable_diffusion/diffusionmodules/util.py

@@ -15,7 +15,7 @@ import torch.nn as nn
 import numpy as np
 from einops import repeat
 
-from ...util import instantiate_from_config
+from ...util.util import instantiate_from_config
 
 
 def make_beta_schedule(

invokeai/backend/stable_diffusion/encoders/modules.py

@@ -10,7 +10,7 @@ from einops import repeat
 from transformers import CLIPTokenizer, CLIPTextModel
 
 from ldm.invoke.devices import choose_torch_device
-from ldm.invoke.globals import global_cache_dir
+from invokeai.backend.globals import global_cache_dir
 from ldm.modules.x_transformer import (
     Encoder,
     TransformerWrapper,

invokeai/backend/training/__init.py__

@@ -0,0 +1,4 @@
+'''
+Initialization file for invokeai.backend.training
+'''
+from .textual_inversion_training import do_textual_inversion_training, parse_args

invokeai/backend/training/textual_inversion_training.py

@@ -48,7 +48,7 @@ from transformers import CLIPTextModel, CLIPTokenizer
 
 # invokeai stuff
 from ldm.invoke.args import ArgFormatter, PagingArgumentParser
-from ldm.invoke.globals import Globals, global_cache_dir
+from invokeai.backend.globals import Globals, global_cache_dir
 
 if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
     PIL_INTERPOLATION = {

invokeai/backend/util/__init__.py

@@ -0,0 +1,18 @@
+'''
+Initialization file for invokeai.backend.util
+'''
+from .devices import (choose_torch_device,
+                      choose_precision,
+                      normalize_device,
+                      torch_dtype,
+                      CPU_DEVICE,
+                      CUDA_DEVICE,
+                      MPS_DEVICE,
+                      )
+from .util import (ask_user,
+                   download_with_resume,
+                   instantiate_from_config,
+                   url_attachment_name,
+                   )
+from .log import write_log
+                  

invokeai/backend/util/devices.py

@@ -5,9 +5,11 @@ from contextlib import nullcontext
 import torch
 from torch import autocast
 
-from ldm.invoke.globals import Globals
+from invokeai.backend.globals import Globals
 
 CPU_DEVICE = torch.device("cpu")
+CUDA_DEVICE = torch.device("cuda")
+MPS_DEVICE = torch.device("mps")
 
 def choose_torch_device() -> torch.device:
     '''Convenience routine for guessing which GPU device to run model on'''

invokeai/backend/web/__init__.py

@@ -0,0 +1,4 @@
+'''
+Initialization file for the web backend.
+'''
+from .invoke_ai_web_server import InvokeAIWebServer

invokeai/backend/web/invoke_ai_web_server.py

@@ -12,7 +12,7 @@ from threading import Event
 from uuid import uuid4
 
 import eventlet
-import invokeai.frontend.dist as frontend
+import invokeai.frontend.web.dist as frontend
 from PIL import Image
 from PIL.Image import Image as ImageType
 from compel.prompt_parser import Blend
@@ -20,24 +20,24 @@ from flask import Flask, redirect, send_from_directory, request, make_response
 from flask_socketio import SocketIO
 from werkzeug.utils import secure_filename
 
-from invokeai.backend.modules.get_canvas_generation_mode import (
+from .modules.get_canvas_generation_mode import (
     get_canvas_generation_mode,
 )
 from .modules.parameters import parameters_to_command
-from .prompting import (get_tokens_for_prompt_object,
-                        get_prompt_structure,
-                        get_tokenizer
-                        )
-from .image_util import PngWriter, retrieve_metadata
-from .generator import infill_methods 
-from .stable_diffusion import PipelineIntermediateState
+from ..prompting import (get_tokens_for_prompt_object,
+                         get_prompt_structure,
+                         get_tokenizer
+                         )
+from ..image_util import PngWriter, retrieve_metadata
+from ..generator import infill_methods 
+from ..stable_diffusion import PipelineIntermediateState
 
-from ldm.generate import Generate
-from ldm.invoke.args import Args, APP_ID, APP_VERSION, calculate_init_img_hash
-from ldm.invoke.globals import ( Globals, global_converted_ckpts_dir,
-                                 global_models_dir
-                                )
-from ldm.invoke.merge_diffusers import merge_diffusion_models
+from .. import Generate
+from ..args import Args, APP_ID, APP_VERSION, calculate_init_img_hash
+from ..globals import ( Globals, global_converted_ckpts_dir,
+                        global_models_dir
+                       )
+from ..model_management import merge_diffusion_models
 
 # Loading Arguments
 opt = Args()
@@ -236,7 +236,7 @@ class InvokeAIWebServer:
                 sys.exit(0)
         else:
             useSSL = args.certfile or args.keyfile
-            print(">> Started Invoke AI Web Server!")
+            print(">> Started Invoke AI Web Server")
             if self.host == "0.0.0.0":
                 print(
                     f"Point your browser at http{'s' if useSSL else ''}://localhost:{self.port} or use the host's DNS name or IP address."

invokeai/backend/web/modules/parameters.py

@@ -1,4 +1,4 @@
-from invokeai.backend.modules.parse_seed_weights import parse_seed_weights
+from .parse_seed_weights import parse_seed_weights
 import argparse
 
 SAMPLER_CHOICES = [

invokeai/frontend/CLI/CLI.py

@@ -18,21 +18,22 @@ import pyparsing  # type: ignore
 
 import invokeai.version
 
-from ..generate import Generate
-from .args import (Args, dream_cmd_from_png, metadata_dumps,
+from ...backend import Generate
+from ...backend.args import (Args,
+                             dream_cmd_from_png,
+                             metadata_dumps,
                              metadata_from_png)
-from invokeai.backend.stable_diffusion import PipelineIntermediateState
-from invokeai.backend.image_util import make_grid, PngWriter, retrieve_metadata, write_metadata
-from invokeai.backend import ModelManager
-from .globals import Globals
-from .log import write_log
+from ...backend.stable_diffusion import PipelineIntermediateState
+from ...backend.image_util import make_grid, PngWriter, retrieve_metadata, write_metadata
+from ...backend import ModelManager
+from ...backend.globals import Globals
+from ...backend.util import write_log
 from .readline import Completer, get_completer
-from invokeai.backend.util import url_attachment_name
+from ...backend.util import url_attachment_name
 
 # global used in multiple functions (fix)
 infile = None
 
-
 def main():
     """Initialize command-line parsers and the diffusion model"""
     global infile
@@ -82,8 +83,6 @@ def main():
     # when the frozen CLIP tokenizer is imported
     import transformers  # type: ignore
 
-    from ldm.generate import Generate
-
     transformers.logging.set_verbosity_error()
     import diffusers
 
@@ -1021,7 +1020,7 @@ def get_next_command(infile=None, model_name="no model") -> str:  # command stri
 
 def invoke_ai_web_server_loop(gen: Generate, gfpgan, codeformer, esrgan):
     print("\n* --web was specified, starting web server...")
-    from invokeai.backend.invoke_ai_web_server import InvokeAIWebServer
+    from invokeai.backend.web import InvokeAIWebServer
 
     # Change working directory to the stable-diffusion directory
     os.chdir(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
@@ -1075,7 +1074,7 @@ def load_face_restoration(opt):
     try:
         gfpgan, codeformer, esrgan = None, None, None
         if opt.restore or opt.esrgan:
-            from ldm.invoke.restoration import Restoration
+            from invokeai.backend.restoration import Restoration
 
             restoration = Restoration()
             if opt.restore:

invokeai/frontend/CLI/__init__.py

@@ -0,0 +1,4 @@
+'''
+Initialization file for invokeai.frontend.CLI
+'''
+from .CLI import main as invokeai_command_line_interface

invokeai/frontend/CLI/readline.py

@@ -11,9 +11,9 @@ seeds:
 import os
 import re
 import atexit
-from ldm.invoke.args import Args
-from ldm.invoke.globals import Globals
-from invokeai.backend.stable_diffusion import HuggingFaceConceptsLibrary
+from ...backend.args import Args
+from ...backend.globals import Globals
+from ...backend.stable_diffusion import HuggingFaceConceptsLibrary
 
 # ---------------readline utilities---------------------
 try:

invokeai/frontend/__init__.py

@@ -0,0 +1,3 @@
+'''
+Initialization file for invokeai.frontend
+'''

invokeai/frontend/config/__init__.py

@@ -0,0 +1,7 @@
+'''
+Initialization file for invokeai.frontend.config
+'''
+from .model_install import main as invokeai_model_install
+from .invokeai_configure import main as invokeai_configure
+from .invokeai_update import main as invokeai_update
+

invokeai/frontend/config/invokeai_configure.py

@@ -0,0 +1,4 @@
+'''
+Wrapper for invokeai.backend.configure.invokeai_configure
+'''
+from ...backend.config.invokeai_configure import main

invokeai/frontend/config/invokeai_update.py

@@ -13,7 +13,7 @@ from rich.style import Style
 from rich.syntax import Syntax
 from rich.text import Text
 
-from ldm.invoke import __version__
+from invokeai.version import __version__
 
 INVOKE_AI_SRC="https://github.com/invoke-ai/InvokeAI/archive"
 INVOKE_AI_REL="https://api.github.com/repos/invoke-ai/InvokeAI/releases"

invokeai/frontend/config/model_install.py

@@ -22,13 +22,13 @@ from npyscreen import widget
 from omegaconf import OmegaConf
 from shutil import get_terminal_size
 
-from invokeai.backend.devices import choose_precision, choose_torch_device
-from ..globals import Globals, global_config_dir
-from .model_install_backend import (Dataset_path, default_config_file,
-                                    default_dataset, get_root,
-                                    install_requested_models,
-                                    recommended_datasets,
-                                    )
+from ...backend.util import choose_precision, choose_torch_device
+from invokeai.backend.globals import Globals, global_config_dir
+from ...backend.config.model_install_backend import (Dataset_path, default_config_file,
+                                                     default_dataset, get_root,
+                                                     install_requested_models,
+                                                     recommended_datasets,
+                                                     )
 from .widgets import (MultiSelectColumns, TextBox,
                       OffsetButtonPress, CenteredTitleText,
                       set_min_terminal_size,

invokeai/frontend/merge/__init__.py

@@ -0,0 +1,4 @@
+'''
+Initialization file for invokeai.frontend.merge
+'''
+from .merge_diffusers import main as invokeai_merge_diffusers

invokeai/frontend/merge/merge_diffusers.py

@@ -20,10 +20,10 @@ from diffusers import logging as dlogging
 from npyscreen import widget
 from omegaconf import OmegaConf
 
-from ldm.invoke.config.widgets import FloatTitleSlider
-from ldm.invoke.globals import (Globals, global_cache_dir, global_config_file,
+from ...frontend.config.widgets import FloatTitleSlider
+from ...backend.globals import (Globals, global_cache_dir, global_config_file,
                                 global_models_dir, global_set_root)
-from invokeai.backend import ModelManager
+from ...backend.model_management import ModelManager
 
 DEST_MERGED_MODEL_DIR = "merged_models"
 
@@ -199,13 +199,13 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
         self.add_widget_intelligent(
             npyscreen.FixedText,
             color="CONTROL",
-            value=f"Select two models to merge and optionally a third.",
+            value="Select two models to merge and optionally a third.",
             editable=False,
         )
         self.add_widget_intelligent(
             npyscreen.FixedText,
             color="CONTROL",
-            value=f"Use up and down arrows to move, <space> to select an item, <tab> and <shift-tab> to move from one field to the next.",
+            value="Use up and down arrows to move, <space> to select an item, <tab> and <shift-tab> to move from one field to the next.",
             editable=False,
         )
         self.add_widget_intelligent(
@@ -453,9 +453,9 @@ def main():
                 "** You need to have at least two diffusers models defined in models.yaml in order to merge"
             )
         else:
-            print(f"** Not enough room for the user interface. Try making this window larger.")
+            print("** Not enough room for the user interface. Try making this window larger.")
         sys.exit(-1)
-    except Exception as e:
+    except Exception:
         print(">> An error occurred:")
         traceback.print_exc()
         sys.exit(-1)

invokeai/frontend/training/__init__.py

@@ -0,0 +1,5 @@
+'''
+Initialization file for invokeai.frontend.training
+'''
+from .textual_inversion import main as invokeai_textual_inversion
+

invokeai/frontend/training/textual_inversion.py

@@ -20,8 +20,8 @@ import npyscreen
 from npyscreen import widget
 from omegaconf import OmegaConf
 
-from ldm.invoke.globals import Globals, global_set_root
-from ldm.invoke.training.textual_inversion_training import (
+from invokeai.backend.globals import Globals, global_set_root
+from ...backend.training import (
     do_textual_inversion_training,
     parse_args,
 )