Merge pull request #2573 from raefu/ckpt-cache
add --ckpt-cache option for faster model switching
This commit is contained in:
AUTOMATIC1111
GitHub
commit
d13ce89e20
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@ -1,4 +1,4 @@
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import glob
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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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@ -15,6 +15,7 @@ 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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@ -132,41 +133,45 @@ 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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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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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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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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model.load_state_dict(sd, strict=False)
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sd = get_state_dict_from_checkpoint(pl_sd)
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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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model.load_state_dict(sd, strict=False)
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if not shared.cmd_opts.no_half:
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model.half()
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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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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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if not shared.cmd_opts.no_half:
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model.half()
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vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
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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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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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vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
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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"}
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model.first_stage_model.load_state_dict(vae_dict)
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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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model.first_stage_model.to(devices.dtype_vae)
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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"}
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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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@ -205,6 +210,7 @@ def reload_model_weights(sd_model, info=None):
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return
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if sd_model.sd_checkpoint_info.config != checkpoint_info.config:
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checkpoints_loaded.clear()
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shared.sd_model = load_model()
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return shared.sd_model
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@ -242,6 +242,7 @@ 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_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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"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
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