mirror of
https://github.com/vladmandic/automatic
synced 2026-08-26 23:20:59 +02:00
Disty0
541 lines
23 KiB
Python
541 lines
23 KiB
Python
import collections
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import os.path
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import re
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import io
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import threading
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from os import mkdir
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from urllib import request
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from rich import progress # pylint: disable=redefined-builtin
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import torch
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import safetensors.torch
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from omegaconf import OmegaConf
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import tomesd
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import ldm.modules.midas as midas
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from ldm.util import instantiate_from_config
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from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config
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from modules.sd_hijack_inpainting import do_inpainting_hijack
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from modules.timer import Timer
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from modules.memstats import memory_stats
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from modules.paths_internal import models_path
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model_dir = "Stable-diffusion"
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model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
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checkpoints_list = {}
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checkpoint_aliases = {}
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checkpoints_loaded = collections.OrderedDict()
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skip_next_load = False
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class CheckpointInfo:
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def __init__(self, filename):
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self.filename = filename
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abspath = os.path.abspath(filename)
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if shared.opts.ckpt_dir is not None and abspath.startswith(shared.opts.ckpt_dir):
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name = abspath.replace(shared.opts.ckpt_dir, '')
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elif abspath.startswith(model_path):
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name = abspath.replace(model_path, '')
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else:
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name = os.path.basename(filename)
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if name.startswith("\\") or name.startswith("/"):
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name = name[1:]
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self.name = name
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self.name_for_extra = os.path.splitext(os.path.basename(filename))[0]
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self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
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self.hash = model_hash(filename)
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self.sha256 = hashes.sha256_from_cache(self.filename, f"checkpoint/{name}")
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self.shorthash = self.sha256[0:10] if self.sha256 else None
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self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
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self.ids = [self.hash, self.model_name, self.title, name, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else [])
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self.metadata = {}
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_, ext = os.path.splitext(self.filename)
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if ext.lower() == ".safetensors":
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try:
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self.metadata = read_metadata_from_safetensors(filename)
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except Exception as e:
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errors.display(e, f"reading checkpoint metadata: {filename}")
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def register(self):
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checkpoints_list[self.title] = self
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for i in self.ids:
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checkpoint_aliases[i] = self
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def calculate_shorthash(self):
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self.sha256 = hashes.sha256(self.filename, f"checkpoint/{self.name}")
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if self.sha256 is None:
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return
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self.shorthash = self.sha256[0:10]
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if self.shorthash not in self.ids:
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self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]']
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checkpoints_list.pop(self.title)
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self.title = f'{self.name} [{self.shorthash}]'
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self.register()
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return self.shorthash
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try:
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# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
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from transformers import logging
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logging.set_verbosity_error()
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except Exception:
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pass
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def setup_model():
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if not os.path.exists(model_path):
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os.makedirs(model_path)
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list_models()
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enable_midas_autodownload()
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def checkpoint_tiles():
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def convert(name):
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return int(name) if name.isdigit() else name.lower()
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def alphanumeric_key(key):
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return [convert(c) for c in re.split('([0-9]+)', key)]
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return sorted([x.title for x in checkpoints_list.values()], key=alphanumeric_key)
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def list_models():
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checkpoints_list.clear()
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checkpoint_aliases.clear()
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model_list = modelloader.load_models(model_path=os.path.join(models_path, 'Stable-diffusion'), model_url=None, command_path=shared.opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name=None, ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
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if shared.cmd_opts.ckpt is not None:
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if not os.path.exists(shared.cmd_opts.ckpt):
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if shared.cmd_opts.ckpt.lower() != "none":
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shared.log.warning(f"Requested checkpoint not found: {shared.cmd_opts.ckpt}")
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else:
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checkpoint_info = CheckpointInfo(shared.cmd_opts.ckpt)
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checkpoint_info.register()
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shared.opts.data['sd_model_checkpoint'] = checkpoint_info.title
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elif shared.cmd_opts.ckpt != shared.default_sd_model_file and shared.cmd_opts.ckpt is not None:
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shared.log.warning(f"Checkpoint not found: {shared.cmd_opts.ckpt}")
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for filename in sorted(model_list, key=str.lower):
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checkpoint_info = CheckpointInfo(filename)
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checkpoint_info.register()
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shared.log.info(f'Available models: {shared.opts.ckpt_dir} {len(checkpoints_list)}')
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if len(checkpoints_list) == 0:
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if not shared.cmd_opts.no_download:
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key = input('Download the default model? (y/N) ')
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if key.lower().startswith('y'):
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model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
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shared.opts.data['sd_model_checkpoint'] = "v1-5-pruned-emaonly.safetensors"
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model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
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for filename in sorted(model_list, key=str.lower):
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checkpoint_info = CheckpointInfo(filename)
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checkpoint_info.register()
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def get_closet_checkpoint_match(search_string):
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checkpoint_info = checkpoint_aliases.get(search_string, None)
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if checkpoint_info is not None:
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return checkpoint_info
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found = sorted([info for info in checkpoints_list.values() if search_string in info.title], key=lambda x: len(x.title))
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if found:
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return found[0]
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return None
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def model_hash(filename):
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"""old hash that only looks at a small part of the file and is prone to collisions"""
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try:
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with open(filename, "rb") as file:
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import hashlib
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m = hashlib.sha256()
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file.seek(0x100000)
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m.update(file.read(0x10000))
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return m.hexdigest()[0:8]
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except FileNotFoundError:
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return 'NOFILE'
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except:
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return 'NOHASH'
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def select_checkpoint():
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model_checkpoint = shared.opts.sd_model_checkpoint
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checkpoint_info = checkpoint_aliases.get(model_checkpoint, None)
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if checkpoint_info is not None or shared.cmd_opts.ckpt is not None:
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shared.log.debug(f'Select checkpoint: {checkpoint_info.title if checkpoint_info is not None else None}')
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return checkpoint_info
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if len(checkpoints_list) == 0:
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shared.log.error("Cannot run without a checkpoint")
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shared.log.error("Use --ckpt <path-to-checkpoint> to force using existing checkpoint")
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exit(1)
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checkpoint_info = next(iter(checkpoints_list.values()))
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if model_checkpoint is not None:
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shared.log.warning(f"Default checkpoint not found: {model_checkpoint}")
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shared.log.warning(f"Loading fallback checkpoint: {checkpoint_info.title}")
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shared.log.debug(f'Select checkpoint: {checkpoint_info.title if checkpoint_info is not None else None}')
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return checkpoint_info
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checkpoint_dict_replacements = {
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'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
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'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
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'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
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}
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def transform_checkpoint_dict_key(k):
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for text, replacement in checkpoint_dict_replacements.items():
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if k.startswith(text):
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k = replacement + k[len(text):]
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return k
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def get_state_dict_from_checkpoint(pl_sd):
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pl_sd = pl_sd.pop("state_dict", pl_sd)
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pl_sd.pop("state_dict", None)
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sd = {}
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for k, v in pl_sd.items():
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new_key = transform_checkpoint_dict_key(k)
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if new_key is not None:
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sd[new_key] = v
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pl_sd.clear()
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pl_sd.update(sd)
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return pl_sd
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def read_metadata_from_safetensors(filename):
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import json
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with open(filename, mode="rb") as file:
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metadata_len = file.read(8)
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metadata_len = int.from_bytes(metadata_len, "little")
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json_start = file.read(2)
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assert metadata_len > 2 and json_start in (b'{"', b"{'"), f"{filename} is not a safetensors file"
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json_data = json_start + file.read(metadata_len-2)
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json_obj = json.loads(json_data)
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res = {}
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for k, v in json_obj.get("__metadata__", {}).items():
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res[k] = v
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if isinstance(v, str) and v[0:1] == '{':
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try:
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res[k] = json.loads(v)
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except Exception:
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pass
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return res
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def read_state_dict(checkpoint_file, map_location=None): # pylint: disable=unused-argument
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try:
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pl_sd = None
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with progress.open(checkpoint_file, 'rb', description=f'Loading weights: [cyan]{checkpoint_file}', auto_refresh=True) as f:
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_, extension = os.path.splitext(checkpoint_file)
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if shared.opts.stream_load:
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if extension.lower() == ".safetensors":
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# shared.log.debug('Model weights loading: type=safetensors mode=buffered')
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buffer = f.read()
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pl_sd = safetensors.torch.load(buffer)
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else:
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# shared.log.debug('Model weights loading: type=checkpoint mode=buffered')
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buffer = io.BytesIO(f.read())
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pl_sd = torch.load(buffer, map_location='cpu')
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else:
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if extension.lower() == ".safetensors":
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# shared.log.debug('Model weights loading: type=safetensors mode=mmap')
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pl_sd = safetensors.torch.load_file(checkpoint_file, device='cpu')
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else:
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# shared.log.debug('Model weights loading: type=checkpoint mode=direct')
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pl_sd = torch.load(f, map_location='cpu')
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sd = get_state_dict_from_checkpoint(pl_sd)
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del pl_sd
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except Exception as e:
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errors.display(e, f'loading model: {checkpoint_file}')
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sd = None
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return sd
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def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
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if checkpoint_info in checkpoints_loaded:
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# use checkpoint cache
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shared.log.info("Model weights loading: from cache")
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return checkpoints_loaded[checkpoint_info]
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res = read_state_dict(checkpoint_info.filename)
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timer.record("load")
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return res
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def load_model_weights(model: torch.nn.Module, checkpoint_info: CheckpointInfo, state_dict, timer):
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shared.log.debug(f'Model weights loading: {memory_stats()}')
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sd_model_hash = checkpoint_info.calculate_shorthash()
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timer.record("hash")
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shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
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if state_dict is None:
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state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
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model.load_state_dict(state_dict, strict=False)
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del state_dict
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timer.record("apply")
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if shared.opts.sd_checkpoint_cache > 0:
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# cache newly loaded model
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checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
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if shared.opts.opt_channelslast:
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model.to(memory_format=torch.channels_last)
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timer.record("channels")
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if not shared.opts.no_half:
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vae = model.first_stage_model
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depth_model = getattr(model, 'depth_model', None)
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# with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
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if shared.opts.no_half_vae:
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model.first_stage_model = None
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# with --upcast-sampling, don't convert the depth model weights to float16
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if shared.opts.upcast_sampling and depth_model:
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model.depth_model = None
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model.half()
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model.first_stage_model = vae
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if depth_model:
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model.depth_model = depth_model
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devices.dtype_unet = model.model.diffusion_model.dtype
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model.first_stage_model.to(devices.dtype_vae)
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# clean up cache if limit is reached
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while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
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checkpoints_loaded.popitem(last=False)
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model.sd_model_hash = sd_model_hash
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model.sd_model_checkpoint = checkpoint_info.filename
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model.sd_checkpoint_info = checkpoint_info
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shared.opts.data["sd_checkpoint_hash"] = checkpoint_info.sha256
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model.logvar = model.logvar.to(devices.device) # fix for training
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sd_vae.delete_base_vae()
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sd_vae.clear_loaded_vae()
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vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename)
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sd_vae.load_vae(model, vae_file, vae_source)
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timer.record("vae")
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def enable_midas_autodownload():
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"""
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Gives the ldm.modules.midas.api.load_model function automatic downloading.
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When the 512-depth-ema model, and other future models like it, is loaded,
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it calls midas.api.load_model to load the associated midas depth model.
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This function applies a wrapper to download the model to the correct
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location automatically.
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"""
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midas_path = os.path.join(paths.models_path, 'midas')
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for k, v in midas.api.ISL_PATHS.items():
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file_name = os.path.basename(v)
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midas.api.ISL_PATHS[k] = os.path.join(midas_path, file_name)
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midas_urls = {
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"dpt_large": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
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"dpt_hybrid": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt",
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"midas_v21": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21-f6b98070.pt",
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"midas_v21_small": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21_small-70d6b9c8.pt",
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}
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midas.api.load_model_inner = midas.api.load_model
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def load_model_wrapper(model_type):
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path = midas.api.ISL_PATHS[model_type]
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if not os.path.exists(path):
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if not os.path.exists(midas_path):
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mkdir(midas_path)
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shared.log.info(f"Downloading midas model weights for {model_type} to {path}")
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request.urlretrieve(midas_urls[model_type], path)
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shared.log.info(f"{model_type} downloaded")
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return midas.api.load_model_inner(model_type)
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midas.api.load_model = load_model_wrapper
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def repair_config(sd_config):
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if not "use_ema" in sd_config.model.params:
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sd_config.model.params.use_ema = False
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if shared.opts.no_half:
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sd_config.model.params.unet_config.params.use_fp16 = False
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elif shared.opts.upcast_sampling:
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sd_config.model.params.unet_config.params.use_fp16 = True
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if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
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sd_config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla"
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# For UnCLIP-L, override the hardcoded karlo directory
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if "noise_aug_config" in sd_config.model.params and "clip_stats_path" in sd_config.model.params.noise_aug_config.params:
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karlo_path = os.path.join(paths.models_path, 'karlo')
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sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
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sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
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sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
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class SdModelData:
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def __init__(self):
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self.sd_model = None
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self.lock = threading.Lock()
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def get_sd_model(self):
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if self.sd_model is None:
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with self.lock:
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try:
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load_model()
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except Exception as e:
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shared.log.error("Failed to load stable diffusion model")
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errors.display(e, "loading stable diffusion model")
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self.sd_model = None
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return self.sd_model
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def set_sd_model(self, v):
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self.sd_model = v
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model_data = SdModelData()
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def load_model(checkpoint_info=None, already_loaded_state_dict=None, timer=None):
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shared.log.debug(f'Load model: info={checkpoint_info is not None} dict={already_loaded_state_dict is not None}')
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from modules import lowvram, sd_hijack
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checkpoint_info = checkpoint_info or select_checkpoint()
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if checkpoint_info is None:
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return
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if timer is None:
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timer = Timer()
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current_checkpoint_info = None
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if model_data.sd_model is not None:
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sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
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current_checkpoint_info = model_data.sd_model.sd_checkpoint_info
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unload_model_weights()
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model_data.sd_model = None
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do_inpainting_hijack()
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devices.set_cuda_params()
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if already_loaded_state_dict is not None:
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state_dict = already_loaded_state_dict
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else:
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state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
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checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
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if state_dict is None or checkpoint_config is None:
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shared.log.error(f"Failed to load checkpooint: {checkpoint_info.filename}")
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if current_checkpoint_info is not None:
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shared.log.info(f"Restoring previous checkpoint: {current_checkpoint_info.filename}")
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load_model(current_checkpoint_info, None)
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return
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shared.log.debug(f'Model dict loaded: {memory_stats()}')
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sd_config = OmegaConf.load(checkpoint_config)
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repair_config(sd_config)
|
|
timer.record("config")
|
|
shared.log.debug(f'Model config loaded: {memory_stats()}')
|
|
sd_model = None
|
|
shared.log.debug(f'Model config: {sd_config.model.get("params", dict())}')
|
|
try:
|
|
clip_is_included_into_sd = sd1_clip_weight in state_dict or sd2_clip_weight in state_dict
|
|
with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd):
|
|
sd_model = instantiate_from_config(sd_config.model)
|
|
except Exception:
|
|
sd_model = instantiate_from_config(sd_config.model)
|
|
shared.log.info(f"Model created from config: {checkpoint_config}")
|
|
sd_model.used_config = checkpoint_config
|
|
timer.record("create")
|
|
load_model_weights(sd_model, checkpoint_info, state_dict, timer)
|
|
timer.record("load")
|
|
shared.log.debug(f'Model weights loaded: {memory_stats()}')
|
|
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
|
|
lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
|
|
else:
|
|
sd_model.to(devices.device)
|
|
timer.record("move")
|
|
shared.log.debug(f'Model weights moved: {memory_stats()}')
|
|
sd_hijack.model_hijack.hijack(sd_model)
|
|
timer.record("hijack")
|
|
sd_model.eval()
|
|
if shared.cmd_opts.use_ipex:
|
|
sd_model = torch.xpu.optimize(sd_model, dtype=devices.dtype)
|
|
shared.log.info("Applied IPEX Optimize")
|
|
model_data.sd_model = sd_model
|
|
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True) # Reload embeddings after model load as they may or may not fit the model
|
|
timer.record("embeddings")
|
|
script_callbacks.model_loaded_callback(sd_model)
|
|
timer.record("callbacks")
|
|
shared.log.info(f"Model loaded in {timer.summary()}")
|
|
current_checkpoint_info = None
|
|
devices.torch_gc(force=True)
|
|
shared.log.info(f'Model load finished: {memory_stats()}')
|
|
|
|
|
|
def reload_model_weights(sd_model=None, info=None):
|
|
global skip_next_load # pylint: disable=global-statement
|
|
if skip_next_load:
|
|
shared.log.debug('Reload model weights skip')
|
|
skip_next_load = False
|
|
return
|
|
shared.log.debug(f'Reload model weights: {sd_model is not None} {info}')
|
|
from modules import lowvram, sd_hijack
|
|
checkpoint_info = info or select_checkpoint()
|
|
if not sd_model:
|
|
sd_model = model_data.sd_model
|
|
if sd_model is None: # previous model load failed
|
|
current_checkpoint_info = None
|
|
else:
|
|
current_checkpoint_info = sd_model.sd_checkpoint_info
|
|
if checkpoint_info is not None and sd_model.sd_model_checkpoint == checkpoint_info.filename:
|
|
return
|
|
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
|
|
lowvram.send_everything_to_cpu()
|
|
elif shared.cmd_opts.use_ipex:
|
|
sd_model.to("cpu")
|
|
else:
|
|
sd_model.to(devices.cpu)
|
|
sd_hijack.model_hijack.undo_hijack(sd_model)
|
|
if shared.opts.model_reuse_dict and sd_model is not None:
|
|
shared.log.info('Reusing previous model dictionary')
|
|
else:
|
|
unload_model_weights()
|
|
sd_model = None
|
|
timer = Timer()
|
|
state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
|
|
checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
|
|
timer.record("config")
|
|
if sd_model is None or checkpoint_config != sd_model.used_config:
|
|
del sd_model
|
|
checkpoints_loaded.clear()
|
|
load_model(checkpoint_info, already_loaded_state_dict=state_dict, timer=timer)
|
|
return model_data.sd_model
|
|
try:
|
|
load_model_weights(sd_model, checkpoint_info, state_dict, timer)
|
|
except Exception:
|
|
shared.log.error("Failed to load checkpoint, restoring previous")
|
|
load_model_weights(sd_model, current_checkpoint_info, None, timer)
|
|
finally:
|
|
sd_hijack.model_hijack.hijack(sd_model)
|
|
timer.record("hijack")
|
|
script_callbacks.model_loaded_callback(sd_model)
|
|
timer.record("callbacks")
|
|
if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
|
|
sd_model.to(devices.device)
|
|
timer.record("device")
|
|
shared.log.info(f"Weights loaded in {timer.summary()}")
|
|
|
|
|
|
def unload_model_weights(sd_model=None, _info=None):
|
|
from modules import sd_hijack
|
|
if model_data.sd_model:
|
|
if shared.cmd_opts.use_ipex:
|
|
model_data.sd_model.to("cpu")
|
|
else:
|
|
model_data.sd_model.to(devices.cpu)
|
|
sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
|
|
model_data.sd_model = None
|
|
sd_model = None
|
|
devices.torch_gc(force=True)
|
|
shared.log.debug(f'Model weights unloaded: {memory_stats()}')
|
|
return sd_model
|
|
|
|
|
|
def apply_token_merging(sd_model, hr: bool):
|
|
"""
|
|
Applies speed and memory optimizations from tomesd.
|
|
|
|
Args:
|
|
hr (bool): True if called in the context of a high-res pass
|
|
"""
|
|
|
|
ratio = shared.opts.token_merging_ratio
|
|
if hr:
|
|
ratio = shared.opts.token_merging_ratio_hr
|
|
|
|
tomesd.apply_patch(
|
|
sd_model,
|
|
ratio=ratio,
|
|
max_downsample=shared.opts.token_merging_maximum_down_sampling,
|
|
sx=shared.opts.token_merging_stride_x,
|
|
sy=shared.opts.token_merging_stride_y,
|
|
use_rand=shared.opts.token_merging_random,
|
|
merge_attn=shared.opts.token_merging_merge_attention,
|
|
merge_crossattn=shared.opts.token_merging_merge_cross_attention,
|
|
merge_mlp=shared.opts.token_merging_merge_mlp
|
|
)
|