mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
change "ialog" to "log"
This commit is contained in:
Lincoln Stein
@ -24,7 +24,7 @@ import safetensors
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import safetensors.torch
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import torch
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import transformers
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import invokeai.backend.util.logging as ialog
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import invokeai.backend.util.logging as log
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from diffusers import (
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AutoencoderKL,
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UNet2DConditionModel,
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@ -133,7 +133,7 @@ class ModelManager(object):
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)
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if not self.valid_model(model_name):
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ialog.error(
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log.error(
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f'"{model_name}" is not a known model name. Please check your models.yaml file'
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)
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return self.current_model
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@ -145,7 +145,7 @@ class ModelManager(object):
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if model_name in self.models:
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requested_model = self.models[model_name]["model"]
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ialog.info(f"Retrieving model {model_name} from system RAM cache")
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log.info(f"Retrieving model {model_name} from system RAM cache")
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requested_model.ready()
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width = self.models[model_name]["width"]
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height = self.models[model_name]["height"]
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@ -380,7 +380,7 @@ class ModelManager(object):
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"""
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omega = self.config
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if model_name not in omega:
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ialog.error(f"Unknown model {model_name}")
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log.error(f"Unknown model {model_name}")
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return
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# save these for use in deletion later
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conf = omega[model_name]
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@ -393,13 +393,13 @@ class ModelManager(object):
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self.stack.remove(model_name)
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if delete_files:
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if weights:
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ialog.info(f"Deleting file {weights}")
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log.info(f"Deleting file {weights}")
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Path(weights).unlink(missing_ok=True)
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elif path:
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ialog.info(f"Deleting directory {path}")
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log.info(f"Deleting directory {path}")
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rmtree(path, ignore_errors=True)
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elif repo_id:
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ialog.info(f"Deleting the cached model directory for {repo_id}")
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log.info(f"Deleting the cached model directory for {repo_id}")
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self._delete_model_from_cache(repo_id)
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def add_model(
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@ -440,7 +440,7 @@ class ModelManager(object):
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def _load_model(self, model_name: str):
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"""Load and initialize the model from configuration variables passed at object creation time"""
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if model_name not in self.config:
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ialog.error(
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log.error(
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f'"{model_name}" is not a known model name. Please check your models.yaml file'
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)
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return
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@ -458,7 +458,7 @@ class ModelManager(object):
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model_format = mconfig.get("format", "ckpt")
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if model_format == "ckpt":
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weights = mconfig.weights
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ialog.info(f"Loading {model_name} from {weights}")
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log.info(f"Loading {model_name} from {weights}")
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model, width, height, model_hash = self._load_ckpt_model(
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model_name, mconfig
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)
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@ -474,13 +474,13 @@ class ModelManager(object):
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# usage statistics
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toc = time.time()
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ialog.info("Model loaded in " + "%4.2fs" % (toc - tic))
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log.info("Model loaded in " + "%4.2fs" % (toc - tic))
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if self._has_cuda():
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ialog.info(
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log.info(
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"Max VRAM used to load the model: "+
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"%4.2fG" % (torch.cuda.max_memory_allocated() / 1e9)
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)
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ialog.info(
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log.info(
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"Current VRAM usage: "+
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"%4.2fG" % (torch.cuda.memory_allocated() / 1e9)
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)
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@ -490,11 +490,11 @@ class ModelManager(object):
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name_or_path = self.model_name_or_path(mconfig)
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using_fp16 = self.precision == "float16"
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ialog.info(f"Loading diffusers model from {name_or_path}")
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log.info(f"Loading diffusers model from {name_or_path}")
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if using_fp16:
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ialog.debug("Using faster float16 precision")
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log.debug("Using faster float16 precision")
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else:
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ialog.debug("Using more accurate float32 precision")
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log.debug("Using more accurate float32 precision")
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# TODO: scan weights maybe?
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pipeline_args: dict[str, Any] = dict(
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@ -526,7 +526,7 @@ class ModelManager(object):
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if str(e).startswith("fp16 is not a valid"):
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pass
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else:
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ialog.error(
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log.error(
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f"An unexpected error occurred while downloading the model: {e})"
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)
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if pipeline:
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@ -545,7 +545,7 @@ class ModelManager(object):
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# square images???
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width = pipeline.unet.config.sample_size * pipeline.vae_scale_factor
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height = width
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ialog.debug(f"Default image dimensions = {width} x {height}")
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log.debug(f"Default image dimensions = {width} x {height}")
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return pipeline, width, height, model_hash
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@ -562,7 +562,7 @@ class ModelManager(object):
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weights = os.path.normpath(os.path.join(Globals.root, weights))
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# Convert to diffusers and return a diffusers pipeline
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ialog.info(f"Converting legacy checkpoint {model_name} into a diffusers model...")
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log.info(f"Converting legacy checkpoint {model_name} into a diffusers model...")
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from . import load_pipeline_from_original_stable_diffusion_ckpt
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@ -627,7 +627,7 @@ class ModelManager(object):
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if model_name not in self.models:
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return
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ialog.info(f"Offloading {model_name} to CPU")
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log.info(f"Offloading {model_name} to CPU")
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model = self.models[model_name]["model"]
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model.offload_all()
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self.current_model = None
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@ -643,26 +643,26 @@ class ModelManager(object):
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and option to exit if an infected file is identified.
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"""
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# scan model
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ialog.debug(f"Scanning Model: {model_name}")
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log.debug(f"Scanning Model: {model_name}")
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scan_result = scan_file_path(checkpoint)
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if scan_result.infected_files != 0:
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if scan_result.infected_files == 1:
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ialog.critical(f"Issues Found In Model: {scan_result.issues_count}")
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ialog.critical("The model you are trying to load seems to be infected.")
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ialog.critical("For your safety, InvokeAI will not load this model.")
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ialog.critical("Please use checkpoints from trusted sources.")
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ialog.critical("Exiting InvokeAI")
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log.critical(f"Issues Found In Model: {scan_result.issues_count}")
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log.critical("The model you are trying to load seems to be infected.")
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log.critical("For your safety, InvokeAI will not load this model.")
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log.critical("Please use checkpoints from trusted sources.")
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log.critical("Exiting InvokeAI")
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sys.exit()
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else:
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ialog.warning("InvokeAI was unable to scan the model you are using.")
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log.warning("InvokeAI was unable to scan the model you are using.")
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model_safe_check_fail = ask_user(
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"Do you want to to continue loading the model?", ["y", "n"]
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)
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if model_safe_check_fail.lower() != "y":
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ialog.critical("Exiting InvokeAI")
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log.critical("Exiting InvokeAI")
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sys.exit()
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else:
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ialog.debug("Model scanned ok")
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log.debug("Model scanned ok")
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def import_diffuser_model(
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self,
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@ -779,24 +779,24 @@ class ModelManager(object):
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model_path: Path = None
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thing = path_url_or_repo # to save typing
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ialog.info(f"Probing {thing} for import")
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log.info(f"Probing {thing} for import")
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if thing.startswith(("http:", "https:", "ftp:")):
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ialog.info(f"{thing} appears to be a URL")
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log.info(f"{thing} appears to be a URL")
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model_path = self._resolve_path(
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thing, "models/ldm/stable-diffusion-v1"
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) # _resolve_path does a download if needed
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elif Path(thing).is_file() and thing.endswith((".ckpt", ".safetensors")):
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if Path(thing).stem in ["model", "diffusion_pytorch_model"]:
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ialog.debug(f"{Path(thing).name} appears to be part of a diffusers model. Skipping import")
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log.debug(f"{Path(thing).name} appears to be part of a diffusers model. Skipping import")
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return
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else:
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ialog.debug(f"{thing} appears to be a checkpoint file on disk")
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log.debug(f"{thing} appears to be a checkpoint file on disk")
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model_path = self._resolve_path(thing, "models/ldm/stable-diffusion-v1")
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elif Path(thing).is_dir() and Path(thing, "model_index.json").exists():
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ialog.debug(f"{thing} appears to be a diffusers file on disk")
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log.debug(f"{thing} appears to be a diffusers file on disk")
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model_name = self.import_diffuser_model(
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thing,
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vae=dict(repo_id="stabilityai/sd-vae-ft-mse"),
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@ -807,30 +807,30 @@ class ModelManager(object):
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elif Path(thing).is_dir():
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if (Path(thing) / "model_index.json").exists():
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ialog.debug(f"{thing} appears to be a diffusers model.")
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log.debug(f"{thing} appears to be a diffusers model.")
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model_name = self.import_diffuser_model(
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thing, commit_to_conf=commit_to_conf
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)
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else:
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ialog.debug(f"{thing} appears to be a directory. Will scan for models to import")
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log.debug(f"{thing} appears to be a directory. Will scan for models to import")
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for m in list(Path(thing).rglob("*.ckpt")) + list(
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Path(thing).rglob("*.safetensors")
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):
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if model_name := self.heuristic_import(
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str(m), commit_to_conf=commit_to_conf
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):
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ialog.info(f"{model_name} successfully imported")
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log.info(f"{model_name} successfully imported")
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return model_name
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elif re.match(r"^[\w.+-]+/[\w.+-]+$", thing):
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ialog.debug(f"{thing} appears to be a HuggingFace diffusers repo_id")
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log.debug(f"{thing} appears to be a HuggingFace diffusers repo_id")
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model_name = self.import_diffuser_model(
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thing, commit_to_conf=commit_to_conf
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)
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pipeline, _, _, _ = self._load_diffusers_model(self.config[model_name])
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return model_name
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else:
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ialog.warning(f"{thing}: Unknown thing. Please provide a URL, file path, directory or HuggingFace repo_id")
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log.warning(f"{thing}: Unknown thing. Please provide a URL, file path, directory or HuggingFace repo_id")
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# Model_path is set in the event of a legacy checkpoint file.
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# If not set, we're all done
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@ -838,7 +838,7 @@ class ModelManager(object):
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return
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if model_path.stem in self.config: # already imported
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ialog.debug("Already imported. Skipping")
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log.debug("Already imported. Skipping")
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return model_path.stem
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# another round of heuristics to guess the correct config file.
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@ -854,38 +854,38 @@ class ModelManager(object):
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# look for a like-named .yaml file in same directory
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if model_path.with_suffix(".yaml").exists():
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model_config_file = model_path.with_suffix(".yaml")
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ialog.debug(f"Using config file {model_config_file.name}")
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log.debug(f"Using config file {model_config_file.name}")
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else:
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model_type = self.probe_model_type(checkpoint)
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if model_type == SDLegacyType.V1:
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ialog.debug("SD-v1 model detected")
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log.debug("SD-v1 model detected")
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model_config_file = Path(
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Globals.root, "configs/stable-diffusion/v1-inference.yaml"
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)
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elif model_type == SDLegacyType.V1_INPAINT:
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ialog.debug("SD-v1 inpainting model detected")
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log.debug("SD-v1 inpainting model detected")
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model_config_file = Path(
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Globals.root,
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"configs/stable-diffusion/v1-inpainting-inference.yaml",
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)
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elif model_type == SDLegacyType.V2_v:
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ialog.debug("SD-v2-v model detected")
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log.debug("SD-v2-v model detected")
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model_config_file = Path(
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Globals.root, "configs/stable-diffusion/v2-inference-v.yaml"
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)
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elif model_type == SDLegacyType.V2_e:
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ialog.debug("SD-v2-e model detected")
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log.debug("SD-v2-e model detected")
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model_config_file = Path(
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Globals.root, "configs/stable-diffusion/v2-inference.yaml"
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)
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elif model_type == SDLegacyType.V2:
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ialog.warning(
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log.warning(
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f"{thing} is a V2 checkpoint file, but its parameterization cannot be determined. Please provide configuration file path."
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)
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return
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else:
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ialog.warning(
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log.warning(
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f"{thing} is a legacy checkpoint file but not a known Stable Diffusion model. Please provide configuration file path."
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)
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return
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@ -902,7 +902,7 @@ class ModelManager(object):
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for suffix in ["pt", "ckpt", "safetensors"]:
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if (model_path.with_suffix(f".vae.{suffix}")).exists():
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vae_path = model_path.with_suffix(f".vae.{suffix}")
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ialog.debug(f"Using VAE file {vae_path.name}")
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log.debug(f"Using VAE file {vae_path.name}")
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vae = None if vae_path else dict(repo_id="stabilityai/sd-vae-ft-mse")
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diffuser_path = Path(
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@ -948,14 +948,14 @@ class ModelManager(object):
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from . import convert_ckpt_to_diffusers
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if diffusers_path.exists():
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ialog.error(
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log.error(
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f"The path {str(diffusers_path)} already exists. Please move or remove it and try again."
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)
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return
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model_name = model_name or diffusers_path.name
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model_description = model_description or f"Converted version of {model_name}"
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ialog.debug(f"Converting {model_name} to diffusers (30-60s)")
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log.debug(f"Converting {model_name} to diffusers (30-60s)")
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try:
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# By passing the specified VAE to the conversion function, the autoencoder
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# will be built into the model rather than tacked on afterward via the config file
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@ -972,10 +972,10 @@ class ModelManager(object):
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vae_path=vae_path,
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scan_needed=scan_needed,
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)
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ialog.debug(
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log.debug(
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f"Success. Converted model is now located at {str(diffusers_path)}"
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)
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ialog.debug(f"Writing new config file entry for {model_name}")
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log.debug(f"Writing new config file entry for {model_name}")
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new_config = dict(
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path=str(diffusers_path),
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description=model_description,
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@ -986,17 +986,17 @@ class ModelManager(object):
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self.add_model(model_name, new_config, True)
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if commit_to_conf:
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self.commit(commit_to_conf)
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ialog.debug("Conversion succeeded")
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log.debug("Conversion succeeded")
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except Exception as e:
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ialog.warning(f"Conversion failed: {str(e)}")
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ialog.warning(
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log.warning(f"Conversion failed: {str(e)}")
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log.warning(
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"If you are trying to convert an inpainting or 2.X model, please indicate the correct config file (e.g. v1-inpainting-inference.yaml)"
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)
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return model_name
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def search_models(self, search_folder):
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ialog.info(f"Finding Models In: {search_folder}")
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log.info(f"Finding Models In: {search_folder}")
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||||
models_folder_ckpt = Path(search_folder).glob("**/*.ckpt")
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||||
models_folder_safetensors = Path(search_folder).glob("**/*.safetensors")
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@ -1020,7 +1020,7 @@ class ModelManager(object):
|
||||
num_loaded_models = len(self.models)
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if num_loaded_models >= self.max_loaded_models:
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least_recent_model = self._pop_oldest_model()
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ialog.info(
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||||
log.info(
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||||
f"Cache limit (max={self.max_loaded_models}) reached. Purging {least_recent_model}"
|
||||
)
|
||||
if least_recent_model is not None:
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@ -1029,7 +1029,7 @@ class ModelManager(object):
|
||||
|
||||
def print_vram_usage(self) -> None:
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if self._has_cuda:
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ialog.info(
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||||
log.info(
|
||||
"Current VRAM usage:"+
|
||||
"%4.2fG" % (torch.cuda.memory_allocated() / 1e9),
|
||||
)
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@ -1119,10 +1119,10 @@ class ModelManager(object):
|
||||
dest = hub / model.stem
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||||
if dest.exists() and not source.exists():
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||||
continue
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||||
ialog.info(f"{source} => {dest}")
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||||
log.info(f"{source} => {dest}")
|
||||
if source.exists():
|
||||
if dest.is_symlink():
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||||
ialog.warning(f"Found symlink at {dest.name}. Not migrating.")
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||||
log.warning(f"Found symlink at {dest.name}. Not migrating.")
|
||||
elif dest.exists():
|
||||
if source.is_dir():
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||||
rmtree(source)
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@ -1139,7 +1139,7 @@ class ModelManager(object):
|
||||
]
|
||||
for d in empty:
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||||
os.rmdir(d)
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||||
ialog.info("Migration is done. Continuing...")
|
||||
log.info("Migration is done. Continuing...")
|
||||
|
||||
def _resolve_path(
|
||||
self, source: Union[str, Path], dest_directory: str
|
||||
@ -1182,14 +1182,14 @@ class ModelManager(object):
|
||||
|
||||
def _add_embeddings_to_model(self, model: StableDiffusionGeneratorPipeline):
|
||||
if self.embedding_path is not None:
|
||||
ialog.info(f"Loading embeddings from {self.embedding_path}")
|
||||
log.info(f"Loading embeddings from {self.embedding_path}")
|
||||
for root, _, files in os.walk(self.embedding_path):
|
||||
for name in files:
|
||||
ti_path = os.path.join(root, name)
|
||||
model.textual_inversion_manager.load_textual_inversion(
|
||||
ti_path, defer_injecting_tokens=True
|
||||
)
|
||||
ialog.info(
|
||||
log.info(
|
||||
f'Textual inversion triggers: {", ".join(sorted(model.textual_inversion_manager.get_all_trigger_strings()))}'
|
||||
)
|
||||
|
||||
@ -1212,7 +1212,7 @@ class ModelManager(object):
|
||||
with open(hashpath) as f:
|
||||
hash = f.read()
|
||||
return hash
|
||||
ialog.debug("Calculating sha256 hash of model files")
|
||||
log.debug("Calculating sha256 hash of model files")
|
||||
tic = time.time()
|
||||
sha = hashlib.sha256()
|
||||
count = 0
|
||||
@ -1224,7 +1224,7 @@ class ModelManager(object):
|
||||
sha.update(chunk)
|
||||
hash = sha.hexdigest()
|
||||
toc = time.time()
|
||||
ialog.debug(f"sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic))
|
||||
log.debug(f"sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic))
|
||||
with open(hashpath, "w") as f:
|
||||
f.write(hash)
|
||||
return hash
|
||||
@ -1242,13 +1242,13 @@ class ModelManager(object):
|
||||
hash = f.read()
|
||||
return hash
|
||||
|
||||
ialog.debug("Calculating sha256 hash of weights file")
|
||||
log.debug("Calculating sha256 hash of weights file")
|
||||
tic = time.time()
|
||||
sha = hashlib.sha256()
|
||||
sha.update(data)
|
||||
hash = sha.hexdigest()
|
||||
toc = time.time()
|
||||
ialog.debug(f"sha256 = {hash} "+"(%4.2fs)" % (toc - tic))
|
||||
log.debug(f"sha256 = {hash} "+"(%4.2fs)" % (toc - tic))
|
||||
|
||||
with open(hashpath, "w") as f:
|
||||
f.write(hash)
|
||||
@ -1269,12 +1269,12 @@ class ModelManager(object):
|
||||
local_files_only=not Globals.internet_available,
|
||||
)
|
||||
|
||||
ialog.debug(f"Loading diffusers VAE from {name_or_path}")
|
||||
log.debug(f"Loading diffusers VAE from {name_or_path}")
|
||||
if using_fp16:
|
||||
vae_args.update(torch_dtype=torch.float16)
|
||||
fp_args_list = [{"revision": "fp16"}, {}]
|
||||
else:
|
||||
ialog.debug("Using more accurate float32 precision")
|
||||
log.debug("Using more accurate float32 precision")
|
||||
fp_args_list = [{}]
|
||||
|
||||
vae = None
|
||||
@ -1298,7 +1298,7 @@ class ModelManager(object):
|
||||
break
|
||||
|
||||
if not vae and deferred_error:
|
||||
ialog.warning(f"Could not load VAE {name_or_path}: {str(deferred_error)}")
|
||||
log.warning(f"Could not load VAE {name_or_path}: {str(deferred_error)}")
|
||||
|
||||
return vae
|
||||
|
||||
@ -1314,7 +1314,7 @@ class ModelManager(object):
|
||||
for revision in repo.revisions:
|
||||
hashes_to_delete.add(revision.commit_hash)
|
||||
strategy = cache_info.delete_revisions(*hashes_to_delete)
|
||||
ialog.warning(
|
||||
log.warning(
|
||||
f"Deletion of this model is expected to free {strategy.expected_freed_size_str}"
|
||||
)
|
||||
strategy.execute()
|
||||
|
||||
Reference in New Issue
Block a user