Merge branch 'main' into lstein/default-model-install

invokeai/backend/training/textual_inversion_training.py

@@ -24,7 +24,7 @@ import torch.utils.checkpoint
 import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
-from accelerate.utils import set_seed
+from accelerate.utils import set_seed, ProjectConfiguration
 from diffusers import (
     AutoencoderKL,
     DDPMScheduler,
@@ -35,7 +35,6 @@ from diffusers.optimization import get_scheduler
 from diffusers.utils import check_min_version
 from diffusers.utils.import_utils import is_xformers_available
 from huggingface_hub import HfFolder, Repository, whoami
-from omegaconf import OmegaConf
 
 # TODO: remove and import from diffusers.utils when the new version of diffusers is released
 from packaging import version
@@ -47,6 +46,8 @@ from transformers import CLIPTextModel, CLIPTokenizer
 
 # invokeai stuff
 from invokeai.app.services.config import InvokeAIAppConfig,PagingArgumentParser
+from invokeai.app.services.model_manager_service import ModelManagerService
+from invokeai.backend.model_management.models import SubModelType
 
 if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
     PIL_INTERPOLATION = {
@@ -132,7 +133,7 @@ def parse_args():
     model_group.add_argument(
         "--model",
         type=str,
-        default="stable-diffusion-1.5",
+        default="sd-1/main/stable-diffusion-v1-5",
         help="Name of the diffusers model to train against, as defined in configs/models.yaml.",
     )
     model_group.add_argument(
@@ -565,7 +566,6 @@ def do_textual_inversion_training(
     checkpointing_steps: int = 500,
     resume_from_checkpoint: Path = None,
     enable_xformers_memory_efficient_attention: bool = False,
-    root_dir: Path = None,
     hub_model_id: str = None,
     **kwargs,
 ):
@@ -584,13 +584,17 @@ def do_textual_inversion_training(
 
     logging_dir = output_dir / logging_dir
 
+    accelerator_config = ProjectConfiguration()
+    accelerator_config.logging_dir = logging_dir
     accelerator = Accelerator(
         gradient_accumulation_steps=gradient_accumulation_steps,
         mixed_precision=mixed_precision,
         log_with=report_to,
-        logging_dir=logging_dir,
+        project_config=accelerator_config,
     )
 
+    model_manager = ModelManagerService(config,logger)
+
     # Make one log on every process with the configuration for debugging.
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
@@ -628,46 +632,46 @@ def do_textual_inversion_training(
         elif output_dir is not None:
             os.makedirs(output_dir, exist_ok=True)
 
-    models_conf = OmegaConf.load(config.model_conf_path)
+    known_models = model_manager.model_names()
-    model_conf = models_conf.get(model, None)
+    model_name = model.split('/')[-1]
-    assert model_conf is not None, f"Unknown model: {model}"
+    model_meta = next((mm for mm in known_models if mm[0].endswith(model_name)), None)
+    assert model_meta is not None, f"Unknown model: {model}"
+    model_info = model_manager.model_info(*model_meta)
     assert (
-        model_conf.get("format", "diffusers") == "diffusers"
+        model_info['model_format'] == "diffusers"
     ), "This script only works with models of type 'diffusers'"
-    pretrained_model_name_or_path = model_conf.get("repo_id", None) or Path(
+    tokenizer_info = model_manager.get_model(*model_meta, submodel=SubModelType.Tokenizer)
-        model_conf.get("path")
+    noise_scheduler_info = model_manager.get_model(*model_meta, submodel=SubModelType.Scheduler)
-    )
+    text_encoder_info = model_manager.get_model(*model_meta, submodel=SubModelType.TextEncoder)
-    assert (
+    vae_info = model_manager.get_model(*model_meta, submodel=SubModelType.Vae)
-        pretrained_model_name_or_path
+    unet_info = model_manager.get_model(*model_meta, submodel=SubModelType.UNet)
-    ), f"models.yaml error: neither 'repo_id' nor 'path' is defined for {model}"
-    pipeline_args = dict(cache_dir=config.cache_dir)
 
-    # Load tokenizer
+    pipeline_args = dict(local_files_only=True)
     if tokenizer_name:
         tokenizer = CLIPTokenizer.from_pretrained(tokenizer_name, **pipeline_args)
     else:
         tokenizer = CLIPTokenizer.from_pretrained(
-            pretrained_model_name_or_path, subfolder="tokenizer", **pipeline_args
+            tokenizer_info.location, subfolder='tokenizer', **pipeline_args
         )
 
     # Load scheduler and models
     noise_scheduler = DDPMScheduler.from_pretrained(
-        pretrained_model_name_or_path, subfolder="scheduler", **pipeline_args
+        noise_scheduler_info.location, subfolder="scheduler", **pipeline_args
     )
     text_encoder = CLIPTextModel.from_pretrained(
-        pretrained_model_name_or_path,
+        text_encoder_info.location,
         subfolder="text_encoder",
         revision=revision,
         **pipeline_args,
     )
     vae = AutoencoderKL.from_pretrained(
-        pretrained_model_name_or_path,
+        vae_info.location,
         subfolder="vae",
         revision=revision,
         **pipeline_args,
     )
     unet = UNet2DConditionModel.from_pretrained(
-        pretrained_model_name_or_path,
+        unet_info.location,
         subfolder="unet",
         revision=revision,
         **pipeline_args,
@@ -989,7 +993,7 @@ def do_textual_inversion_training(
             save_full_model = not only_save_embeds
         if save_full_model:
             pipeline = StableDiffusionPipeline.from_pretrained(
-                pretrained_model_name_or_path,
+                unet_info.location,
                 text_encoder=accelerator.unwrap_model(text_encoder),
                 vae=vae,
                 unet=unet,