5d5dc64064
Example: Before After 11.ckpt 11.ckpt ab.ckpt ab.ckpt ade_pablo_step_1000.ckpt ade_pablo_step_500.ckpt ade_pablo_step_500.ckpt ade_pablo_step_1000.ckpt ade_step_1000.ckpt ade_step_500.ckpt ade_step_1500.ckpt ade_step_1000.ckpt ade_step_2000.ckpt ade_step_1500.ckpt ade_step_2500.ckpt ade_step_2000.ckpt ade_step_3000.ckpt ade_step_2500.ckpt ade_step_500.ckpt ade_step_3000.ckpt atp_step_5500.ckpt atp_step_5500.ckpt model1.ckpt model1.ckpt model10.ckpt model10.ckpt model1000.ckpt model33.ckpt model33.ckpt model50.ckpt model400.ckpt model400.ckpt model50.ckpt model1000.ckpt moo44.ckpt moo44.ckpt v1-4-pruned-emaonly.ckpt v1-4-pruned-emaonly.ckpt v1-5-pruned-emaonly.ckpt v1-5-pruned-emaonly.ckpt v1-5-pruned.ckpt v1-5-pruned.ckpt v1-5-vae.ckpt v1-5-vae.ckpt
281 lines
10 KiB
Python
281 lines
10 KiB
Python
import collections
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import os.path
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import sys
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from collections import namedtuple
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import torch
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import re
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from omegaconf import OmegaConf
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from ldm.util import instantiate_from_config
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from modules import shared, modelloader, devices, script_callbacks
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from modules.paths import models_path
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from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
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model_dir = "Stable-diffusion"
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model_path = os.path.abspath(os.path.join(models_path, model_dir))
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CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
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checkpoints_list = {}
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checkpoints_loaded = collections.OrderedDict()
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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, CLIPModel
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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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def checkpoint_tiles():
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convert = lambda name: int(name) if name.isdigit() else name.lower()
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alphanumeric_key = lambda key: [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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model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt"])
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def modeltitle(path, shorthash):
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abspath = os.path.abspath(path)
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if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
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name = abspath.replace(shared.cmd_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(path)
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if name.startswith("\\") or name.startswith("/"):
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name = name[1:]
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shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
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return f'{name} [{shorthash}]', shortname
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cmd_ckpt = shared.cmd_opts.ckpt
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if os.path.exists(cmd_ckpt):
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h = model_hash(cmd_ckpt)
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title, short_model_name = modeltitle(cmd_ckpt, h)
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checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config)
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shared.opts.data['sd_model_checkpoint'] = title
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elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
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print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
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for filename in model_list:
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h = model_hash(filename)
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title, short_model_name = modeltitle(filename, h)
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basename, _ = os.path.splitext(filename)
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config = basename + ".yaml"
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if not os.path.exists(config):
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config = shared.cmd_opts.config
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checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config)
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def get_closet_checkpoint_match(searchString):
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applicable = sorted([info for info in checkpoints_list.values() if searchString in info.title], key = lambda x:len(x.title))
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if len(applicable) > 0:
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return applicable[0]
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return None
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def model_hash(filename):
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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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def select_checkpoint():
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model_checkpoint = shared.opts.sd_model_checkpoint
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checkpoint_info = checkpoints_list.get(model_checkpoint, None)
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if checkpoint_info is not None:
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return checkpoint_info
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if len(checkpoints_list) == 0:
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print(f"No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
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if shared.cmd_opts.ckpt is not None:
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print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
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print(f" - directory {model_path}", file=sys.stderr)
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if shared.cmd_opts.ckpt_dir is not None:
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print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
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print(f"Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)
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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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print(f"Checkpoint {model_checkpoint} not found; loading fallback {checkpoint_info.title}", file=sys.stderr)
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return checkpoint_info
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chckpoint_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 chckpoint_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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if "state_dict" in pl_sd:
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pl_sd = pl_sd["state_dict"]
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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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vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
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def load_model_weights(model, checkpoint_info):
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checkpoint_file = checkpoint_info.filename
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sd_model_hash = checkpoint_info.hash
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if checkpoint_info not in checkpoints_loaded:
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print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
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pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
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if "global_step" in pl_sd:
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print(f"Global Step: {pl_sd['global_step']}")
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sd = get_state_dict_from_checkpoint(pl_sd)
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missing, extra = model.load_state_dict(sd, strict=False)
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if shared.cmd_opts.opt_channelslast:
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model.to(memory_format=torch.channels_last)
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if not shared.cmd_opts.no_half:
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model.half()
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devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
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devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
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vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
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if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:
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vae_file = shared.cmd_opts.vae_path
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if os.path.exists(vae_file):
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print(f"Loading VAE weights from: {vae_file}")
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vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
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vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
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model.first_stage_model.load_state_dict(vae_dict)
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model.first_stage_model.to(devices.dtype_vae)
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checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
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while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
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checkpoints_loaded.popitem(last=False) # LRU
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else:
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print(f"Loading weights [{sd_model_hash}] from cache")
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checkpoints_loaded.move_to_end(checkpoint_info)
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model.load_state_dict(checkpoints_loaded[checkpoint_info])
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model.sd_model_hash = sd_model_hash
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model.sd_model_checkpoint = checkpoint_file
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model.sd_checkpoint_info = checkpoint_info
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def load_model(checkpoint_info=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.config != shared.cmd_opts.config:
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print(f"Loading config from: {checkpoint_info.config}")
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sd_config = OmegaConf.load(checkpoint_info.config)
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if should_hijack_inpainting(checkpoint_info):
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# Hardcoded config for now...
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sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
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sd_config.model.params.use_ema = False
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sd_config.model.params.conditioning_key = "hybrid"
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sd_config.model.params.unet_config.params.in_channels = 9
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# Create a "fake" config with a different name so that we know to unload it when switching models.
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checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
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do_inpainting_hijack()
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sd_model = instantiate_from_config(sd_config.model)
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load_model_weights(sd_model, checkpoint_info)
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if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
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lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
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else:
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sd_model.to(shared.device)
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sd_hijack.model_hijack.hijack(sd_model)
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sd_model.eval()
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shared.sd_model = sd_model
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script_callbacks.model_loaded_callback(sd_model)
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print(f"Model loaded.")
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return sd_model
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def reload_model_weights(sd_model, info=None):
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from modules import lowvram, devices, sd_hijack
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checkpoint_info = info or select_checkpoint()
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if sd_model.sd_model_checkpoint == checkpoint_info.filename:
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return
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if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
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checkpoints_loaded.clear()
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load_model(checkpoint_info)
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return shared.sd_model
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if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
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lowvram.send_everything_to_cpu()
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else:
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sd_model.to(devices.cpu)
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sd_hijack.model_hijack.undo_hijack(sd_model)
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load_model_weights(sd_model, checkpoint_info)
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sd_hijack.model_hijack.hijack(sd_model)
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script_callbacks.model_loaded_callback(sd_model)
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if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
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sd_model.to(devices.device)
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print(f"Weights loaded.")
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return sd_model
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