support loading .yaml config with same name as model
support EMA weights in processing (????)
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@ -347,7 +347,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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infotexts = []
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output_images = []
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with torch.no_grad():
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with torch.no_grad(), p.sd_model.ema_scope():
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with devices.autocast():
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p.init(all_prompts, all_seeds, all_subseeds)
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@ -14,7 +14,7 @@ from modules.paths import models_path
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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'])
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CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
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checkpoints_list = {}
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try:
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@ -63,14 +63,20 @@ def list_models():
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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)
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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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checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name)
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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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@ -116,7 +122,10 @@ def select_checkpoint():
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return checkpoint_info
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def load_model_weights(model, checkpoint_file, sd_model_hash):
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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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print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
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pl_sd = torch.load(checkpoint_file, map_location="cpu")
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@ -148,15 +157,19 @@ def load_model_weights(model, checkpoint_file, sd_model_hash):
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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():
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from modules import lowvram, sd_hijack
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checkpoint_info = select_checkpoint()
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sd_config = OmegaConf.load(shared.cmd_opts.config)
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if checkpoint_info.config != shared.cmd_opts.config:
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print(f"Loading config from: {shared.cmd_opts.config}")
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sd_config = OmegaConf.load(checkpoint_info.config)
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sd_model = instantiate_from_config(sd_config.model)
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load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
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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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@ -178,6 +191,9 @@ def reload_model_weights(sd_model, info=None):
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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:
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return load_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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@ -185,7 +201,7 @@ def reload_model_weights(sd_model, info=None):
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sd_hijack.model_hijack.undo_hijack(sd_model)
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load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
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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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