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https://github.com/invoke-ai/InvokeAI
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migration script working well
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Lincoln Stein
4
scripts/invokeai-migrate3
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4
scripts/invokeai-migrate3
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from invokeai.backend.install.migrate_to_3 import main
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if __name__=='__main__':
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main()
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@ -1,322 +0,0 @@
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'''
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Migrate the models directory and models.yaml file from an existing
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InvokeAI 2.3 installation to 3.0.0.
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'''
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import io
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import os
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import argparse
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import shutil
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import yaml
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import transformers
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import diffusers
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import warnings
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from pathlib import Path
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from omegaconf import OmegaConf
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from diffusers import StableDiffusionPipeline, AutoencoderKL
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from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
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from transformers import (
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CLIPTextModel,
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CLIPTokenizer,
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AutoFeatureExtractor,
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BertTokenizerFast,
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)
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import invokeai.backend.util.logging as logger
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from invokeai.backend.model_management import ModelManager
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from invokeai.backend.model_management.model_probe import (
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ModelProbe, ModelType, BaseModelType, SchedulerPredictionType, ModelProbeInfo
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)
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warnings.filterwarnings("ignore")
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transformers.logging.set_verbosity_error()
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diffusers.logging.set_verbosity_error()
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model_names = set()
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def unique_name(name,info)->str:
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done = False
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key = ModelManager.create_key(name,info.base_type,info.model_type)
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unique_name = key
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counter = 1
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while not done:
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if unique_name in model_names:
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unique_name = f'{key}-{counter:0>2d}'
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counter += 1
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else:
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done = True
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model_names.add(unique_name)
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name,_,_ = ModelManager.parse_key(unique_name)
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return name
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def create_directory_structure(dest: Path):
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for model_base in [BaseModelType.StableDiffusion1,BaseModelType.StableDiffusion2]:
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for model_type in [ModelType.Pipeline, ModelType.Vae, ModelType.Lora,
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ModelType.ControlNet,ModelType.TextualInversion]:
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path = dest / model_base.value / model_type.value
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path.mkdir(parents=True, exist_ok=True)
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path = dest / 'core'
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path.mkdir(parents=True, exist_ok=True)
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def copy_file(src:Path,dest:Path):
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logger.info(f'Copying {str(src)} to {str(dest)}')
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try:
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shutil.copy(src, dest)
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except Exception as e:
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logger.error(f'COPY FAILED: {str(e)}')
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def copy_dir(src:Path,dest:Path):
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logger.info(f'Copying {str(src)} to {str(dest)}')
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try:
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shutil.copytree(src, dest)
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except Exception as e:
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logger.error(f'COPY FAILED: {str(e)}')
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def migrate_models(src_dir: Path, dest_dir: Path):
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for root, dirs, files in os.walk(src_dir):
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for f in files:
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# hack - don't copy raw learned_embeds.bin, let them
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# be copied as part of a tree copy operation
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if f == 'learned_embeds.bin':
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continue
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try:
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model = Path(root,f)
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info = ModelProbe().heuristic_probe(model)
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if not info:
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continue
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dest = Path(dest_dir, info.base_type.value, info.model_type.value, f)
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copy_file(model, dest)
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except KeyboardInterrupt:
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raise
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except Exception as e:
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logger.error(str(e))
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for d in dirs:
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try:
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model = Path(root,d)
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info = ModelProbe().heuristic_probe(model)
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if not info:
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continue
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dest = Path(dest_dir, info.base_type.value, info.model_type.value, model.name)
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copy_dir(model, dest)
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except KeyboardInterrupt:
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raise
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except Exception as e:
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logger.error(str(e))
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def migrate_support_models(dest_directory: Path):
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if Path('./models/clipseg').exists():
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copy_dir(Path('./models/clipseg'),dest_directory / 'core/misc/clipseg')
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if Path('./models/realesrgan').exists():
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copy_dir(Path('./models/realesrgan'),dest_directory / 'core/upscaling/realesrgan')
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for d in ['codeformer','gfpgan']:
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path = Path('./models',d)
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if path.exists():
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copy_dir(path,dest_directory / f'core/face_restoration/{d}')
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def migrate_conversion_models(dest_directory: Path):
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# These are needed for the conversion script
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kwargs = dict(
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cache_dir = Path('./models/hub'),
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#local_files_only = True
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)
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try:
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logger.info('Migrating core tokenizers and text encoders')
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target_dir = dest_directory / 'core' / 'convert'
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# bert
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bert = BertTokenizerFast.from_pretrained("bert-base-uncased", **kwargs)
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bert.save_pretrained(target_dir / 'bert-base-uncased', safe_serialization=True)
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# sd-1
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repo_id = 'openai/clip-vit-large-patch14'
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pipeline = CLIPTokenizer.from_pretrained(repo_id, **kwargs)
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pipeline.save_pretrained(target_dir / 'clip-vit-large-patch14' / 'tokenizer', safe_serialization=True)
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pipeline = CLIPTextModel.from_pretrained(repo_id, **kwargs)
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pipeline.save_pretrained(target_dir / 'clip-vit-large-patch14' / 'text_encoder', safe_serialization=True)
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# sd-2
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repo_id = "stabilityai/stable-diffusion-2"
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pipeline = CLIPTokenizer.from_pretrained(repo_id, subfolder="tokenizer", **kwargs)
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pipeline.save_pretrained(target_dir / 'stable-diffusion-2-clip' / 'tokenizer', safe_serialization=True)
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pipeline = CLIPTextModel.from_pretrained(repo_id, subfolder="text_encoder", **kwargs)
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pipeline.save_pretrained(target_dir / 'stable-diffusion-2-clip' / 'text_encoder', safe_serialization=True)
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# VAE
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logger.info('Migrating stable diffusion VAE')
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vae = AutoencoderKL.from_pretrained('stabilityai/sd-vae-ft-mse', **kwargs)
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vae.save_pretrained(target_dir / 'sd-vae-ft-mse', safe_serialization=True)
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# safety checking
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logger.info('Migrating safety checker')
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repo_id = "CompVis/stable-diffusion-safety-checker"
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pipeline = AutoFeatureExtractor.from_pretrained(repo_id,**kwargs)
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pipeline.save_pretrained(target_dir / 'stable-diffusion-safety-checker', safe_serialization=True)
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pipeline = StableDiffusionSafetyChecker.from_pretrained(repo_id,**kwargs)
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pipeline.save_pretrained(target_dir / 'stable-diffusion-safety-checker', safe_serialization=True)
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except KeyboardInterrupt:
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raise
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except Exception as e:
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logger.error(str(e))
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def migrate_tuning_models(dest: Path):
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for subdir in ['embeddings','loras','controlnets']:
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src = Path('.',subdir)
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if not src.is_dir():
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logger.info(f'{subdir} directory not found; skipping')
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continue
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logger.info(f'Scanning {subdir}')
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migrate_models(src, dest)
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def write_yaml(model_name: str, path:Path, info:ModelProbeInfo, dest_yaml: io.TextIOBase):
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name = unique_name(model_name, info)
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stanza = {
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f'{info.base_type.value}/{info.model_type.value}/{name}': {
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'name': model_name,
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'path': str(path),
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'description': f'A {info.base_type.value} {info.model_type.value} model',
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'format': 'diffusers',
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'image_size': info.image_size,
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'base': info.base_type.value,
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'variant': info.variant_type.value,
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'prediction_type': info.prediction_type.value,
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'upcast_attention': info.prediction_type == SchedulerPredictionType.VPrediction
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}
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}
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dest_yaml.write(yaml.dump(stanza))
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dest_yaml.flush()
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def migrate_converted(dest_dir: Path, dest_yaml: io.TextIOBase):
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for sub_dir in [Path('./models/converted_ckpts'),Path('./models/optimize-ckpts')]:
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for model in sub_dir.glob('*'):
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if not model.is_dir():
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continue
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info = ModelProbe().heuristic_probe(model)
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if not info:
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continue
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dest = Path(dest_dir, info.base_type.value, info.model_type.value, model.name)
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try:
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copy_dir(model,dest)
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rel_path = Path('models',dest.relative_to(dest_dir))
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write_yaml(model.name,path=rel_path,info=info, dest_yaml=dest_yaml)
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except KeyboardInterrupt:
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raise
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except Exception as e:
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logger.warning(f'Could not migrate the converted diffusers {model.name}: {str(e)}. Skipping.')
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def migrate_pipelines(dest_dir: Path, dest_yaml: io.TextIOBase):
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cache = Path('./models/hub')
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kwargs = dict(
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cache_dir = cache,
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safety_checker = None,
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# local_files_only = True,
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)
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for model in cache.glob('models--*'):
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if len(list(model.glob('snapshots/**/model_index.json')))==0:
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continue
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_,owner,repo_name=model.name.split('--')
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repo_id = f'{owner}/{repo_name}'
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revisions = [x.name for x in model.glob('refs/*')]
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# if an fp16 is available we use that
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revision = 'fp16' if len(revisions) > 1 and 'fp16' in revisions else revisions[0]
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logger.info(f'Migrating {repo_id}, revision {revision}')
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try:
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pipeline = StableDiffusionPipeline.from_pretrained(
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repo_id,
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revision=revision,
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**kwargs)
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info = ModelProbe().heuristic_probe(pipeline)
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if not info:
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continue
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dest = Path(dest_dir, info.base_type.value, info.model_type.value, f'{repo_name}')
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pipeline.save_pretrained(dest, safe_serialization=True)
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rel_path = Path('models',dest.relative_to(dest_dir))
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write_yaml(repo_name, path=rel_path, info=info, dest_yaml=dest_yaml)
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except KeyboardInterrupt:
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raise
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except Exception as e:
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logger.warning(f'Could not load the "{revision}" version of {repo_id}. Skipping.')
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def migrate_checkpoints(dest_dir: Path, dest_yaml: io.TextIOBase):
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# find any checkpoints referred to in old models.yaml
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conf = OmegaConf.load('./configs/models.yaml')
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orig_models_dir = Path.cwd() / 'models'
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for model_name, stanza in conf.items():
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if stanza.get('format') and stanza['format'] == 'ckpt':
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try:
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logger.info(f'Migrating checkpoint model {model_name}')
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weights = orig_models_dir.parent / stanza['weights']
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config = stanza['config']
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info = ModelProbe().heuristic_probe(weights)
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if not info:
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continue
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# uh oh, weights is in the old models directory - move it into the new one
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if Path(weights).is_relative_to(orig_models_dir):
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dest = Path(dest_dir, info.base_type.value, info.model_type.value,weights.name)
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copy_file(weights,dest)
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weights = Path('models', info.base_type.value, info.model_type.value,weights.name)
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model_name = unique_name(model_name, info)
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stanza = {
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f'{info.base_type.value}/{info.model_type.value}/{model_name}':
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{
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'name': model_name,
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'path': str(weights),
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'description': f'{info.base_type.value}-based checkpoint',
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'format': 'checkpoint',
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'image_size': info.image_size,
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'base': info.base_type.value,
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'variant': info.variant_type.value,
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'config': config
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}
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}
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print(yaml.dump(stanza),file=dest_yaml,end="")
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dest_yaml.flush()
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except KeyboardInterrupt:
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raise
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except Exception as e:
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logger.error(str(e))
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def main():
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parser = argparse.ArgumentParser(description="Model directory migrator")
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parser.add_argument('root_directory',
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help='Root directory (containing "models", "embeddings", "controlnets" and "loras")'
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)
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parser.add_argument('--dest-directory',
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default='./models-3.0',
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help='Destination for new models directory',
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)
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parser.add_argument('--dest-yaml',
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default='./models.yaml-3.0',
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help='Destination for new models.yaml file',
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)
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args = parser.parse_args()
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root_directory = Path(args.root_directory)
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assert root_directory.is_dir(), f"{root_directory} is not a valid directory"
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assert (root_directory / 'models').is_dir(), f"{root_directory} does not contain a 'models' subdirectory"
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dest_directory = Path(args.dest_directory).resolve()
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dest_yaml = Path(args.dest_yaml).resolve()
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os.chdir(root_directory)
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with open(dest_yaml,'w') as yaml_file:
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yaml_file.write(yaml.dump({'__metadata__':
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{'version':'3.0.0'}
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}
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)
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)
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create_directory_structure(dest_directory)
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migrate_support_models(dest_directory)
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migrate_conversion_models(dest_directory)
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migrate_tuning_models(dest_directory)
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migrate_converted(dest_directory,yaml_file)
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migrate_pipelines(dest_directory,yaml_file)
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migrate_checkpoints(dest_directory,yaml_file)
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if __name__ == '__main__':
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main()
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