AUTOMATIC1111
GitHub
commit
675b51ebd3
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@ -9,7 +9,7 @@ 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 import shared, modelloader, devices, script_callbacks, sd_vae
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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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@ -159,14 +159,15 @@ def get_state_dict_from_checkpoint(pl_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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def load_model_weights(model, checkpoint_info, vae_file="auto"):
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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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vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
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checkpoint_key = checkpoint_info
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if checkpoint_key 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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@ -187,32 +188,24 @@ def load_model_weights(model, checkpoint_info):
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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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if shared.opts.sd_checkpoint_cache > 0:
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checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
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# if PR #4035 were to get merged, restore base VAE first before caching
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checkpoints_loaded[checkpoint_key] = 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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vae_name = sd_vae.get_filename(vae_file)
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print(f"Loading weights [{sd_model_hash}] with {vae_name} VAE from cache")
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checkpoints_loaded.move_to_end(checkpoint_key)
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model.load_state_dict(checkpoints_loaded[checkpoint_key])
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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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sd_vae.load_vae(model, vae_file)
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def load_model(checkpoint_info=None):
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from modules import lowvram, sd_hijack
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@ -0,0 +1,207 @@
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import torch
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import os
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from collections import namedtuple
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from modules import shared, devices, script_callbacks
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from modules.paths import models_path
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import glob
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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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vae_dir = "VAE"
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vae_path = os.path.abspath(os.path.join(models_path, vae_dir))
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vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
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default_vae_dict = {"auto": "auto", "None": "None"}
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default_vae_list = ["auto", "None"]
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default_vae_values = [default_vae_dict[x] for x in default_vae_list]
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vae_dict = dict(default_vae_dict)
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vae_list = list(default_vae_list)
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first_load = True
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base_vae = None
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loaded_vae_file = None
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checkpoint_info = None
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def get_base_vae(model):
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if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model:
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return base_vae
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return None
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def store_base_vae(model):
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global base_vae, checkpoint_info
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if checkpoint_info != model.sd_checkpoint_info:
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base_vae = model.first_stage_model.state_dict().copy()
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checkpoint_info = model.sd_checkpoint_info
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def delete_base_vae():
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global base_vae, checkpoint_info
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base_vae = None
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checkpoint_info = None
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def restore_base_vae(model):
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global base_vae, checkpoint_info
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if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
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load_vae_dict(model, base_vae)
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delete_base_vae()
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def get_filename(filepath):
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return os.path.splitext(os.path.basename(filepath))[0]
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def refresh_vae_list(vae_path=vae_path, model_path=model_path):
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global vae_dict, vae_list
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res = {}
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candidates = [
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*glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True),
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*glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True),
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*glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True),
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*glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True)
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]
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if shared.cmd_opts.vae_path is not None and os.path.isfile(shared.cmd_opts.vae_path):
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candidates.append(shared.cmd_opts.vae_path)
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for filepath in candidates:
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name = get_filename(filepath)
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res[name] = filepath
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vae_list.clear()
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vae_list.extend(default_vae_list)
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vae_list.extend(list(res.keys()))
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vae_dict.clear()
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vae_dict.update(res)
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vae_dict.update(default_vae_dict)
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return vae_list
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def resolve_vae(checkpoint_file, vae_file="auto"):
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global first_load, vae_dict, vae_list
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# if vae_file argument is provided, it takes priority, but not saved
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if vae_file and vae_file not in default_vae_list:
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if not os.path.isfile(vae_file):
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vae_file = "auto"
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print("VAE provided as function argument doesn't exist")
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# for the first load, if vae-path is provided, it takes priority, saved, and failure is reported
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if first_load and shared.cmd_opts.vae_path is not None:
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if os.path.isfile(shared.cmd_opts.vae_path):
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vae_file = shared.cmd_opts.vae_path
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shared.opts.data['sd_vae'] = get_filename(vae_file)
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else:
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print("VAE provided as command line argument doesn't exist")
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# else, we load from settings
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if vae_file == "auto" and shared.opts.sd_vae is not None:
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# if saved VAE settings isn't recognized, fallback to auto
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vae_file = vae_dict.get(shared.opts.sd_vae, "auto")
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# if VAE selected but not found, fallback to auto
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if vae_file not in default_vae_values and not os.path.isfile(vae_file):
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vae_file = "auto"
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print("Selected VAE doesn't exist")
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# vae-path cmd arg takes priority for auto
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if vae_file == "auto" and shared.cmd_opts.vae_path is not None:
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if os.path.isfile(shared.cmd_opts.vae_path):
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vae_file = shared.cmd_opts.vae_path
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print("Using VAE provided as command line argument")
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# if still not found, try look for ".vae.pt" beside model
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model_path = os.path.splitext(checkpoint_file)[0]
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if vae_file == "auto":
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vae_file_try = model_path + ".vae.pt"
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if os.path.isfile(vae_file_try):
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vae_file = vae_file_try
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print("Using VAE found beside selected model")
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# if still not found, try look for ".vae.ckpt" beside model
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if vae_file == "auto":
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vae_file_try = model_path + ".vae.ckpt"
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if os.path.isfile(vae_file_try):
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vae_file = vae_file_try
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print("Using VAE found beside selected model")
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# No more fallbacks for auto
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if vae_file == "auto":
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vae_file = None
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# Last check, just because
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if vae_file and not os.path.exists(vae_file):
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vae_file = None
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return vae_file
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def load_vae(model, vae_file=None):
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global first_load, vae_dict, vae_list, loaded_vae_file
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# save_settings = False
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if 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_1 = {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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load_vae_dict(model, vae_dict_1)
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# If vae used is not in dict, update it
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# It will be removed on refresh though
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vae_opt = get_filename(vae_file)
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if vae_opt not in vae_dict:
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vae_dict[vae_opt] = vae_file
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vae_list.append(vae_opt)
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loaded_vae_file = vae_file
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"""
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# Save current VAE to VAE settings, maybe? will it work?
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if save_settings:
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if vae_file is None:
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vae_opt = "None"
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# shared.opts.sd_vae = vae_opt
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"""
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first_load = False
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# don't call this from outside
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def load_vae_dict(model, vae_dict_1=None):
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if vae_dict_1:
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store_base_vae(model)
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model.first_stage_model.load_state_dict(vae_dict_1)
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else:
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restore_base_vae()
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model.first_stage_model.to(devices.dtype_vae)
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def reload_vae_weights(sd_model=None, vae_file="auto"):
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from modules import lowvram, devices, sd_hijack
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if not sd_model:
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sd_model = shared.sd_model
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checkpoint_info = sd_model.sd_checkpoint_info
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checkpoint_file = checkpoint_info.filename
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vae_file = resolve_vae(checkpoint_file, vae_file=vae_file)
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if loaded_vae_file == vae_file:
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return
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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_vae(sd_model, vae_file)
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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"VAE Weights loaded.")
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return sd_model
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@ -15,7 +15,7 @@ import modules.memmon
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import modules.sd_models
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import modules.styles
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import modules.devices as devices
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from modules import sd_samplers, sd_models, localization
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from modules import sd_samplers, sd_models, localization, sd_vae
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from modules.hypernetworks import hypernetwork
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from modules.paths import models_path, script_path, sd_path
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@ -319,6 +319,7 @@ options_templates.update(options_section(('training', "Training"), {
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options_templates.update(options_section(('sd', "Stable Diffusion"), {
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"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
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"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
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"sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": list(sd_vae.vae_list)}, refresh=sd_vae.refresh_vae_list),
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"sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
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"sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
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"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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@ -437,10 +438,11 @@ class Options:
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if bad_settings > 0:
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print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)
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def onchange(self, key, func):
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def onchange(self, key, func, call=True):
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item = self.data_labels.get(key)
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item.onchange = func
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if call:
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func()
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def dumpjson(self):
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@ -501,7 +501,7 @@ input[type="range"]{
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padding: 0;
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}
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#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{
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#refresh_sd_model_checkpoint, #refresh_sd_vae, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{
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max-width: 2.5em;
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min-width: 2.5em;
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height: 2.4em;
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3
webui.py
3
webui.py
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@ -21,6 +21,7 @@ import modules.paths
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import modules.scripts
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import modules.sd_hijack
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import modules.sd_models
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import modules.sd_vae
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import modules.shared as shared
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import modules.txt2img
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import modules.script_callbacks
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@ -77,8 +78,10 @@ def initialize():
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modules.scripts.load_scripts()
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modules.sd_vae.refresh_vae_list()
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modules.sd_models.load_model()
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shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
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shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
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shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
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shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
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