Replace prints with logging and add --verbose argument.
This commit is contained in:
comfyanonymous
parent
4656273e72
commit
65397ce601
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@ -114,6 +114,9 @@ parser.add_argument("--disable-metadata", action="store_true", help="Disable sav
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parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.")
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parser.add_argument("--verbose", action="store_true", help="Enables more debug prints.")
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if comfy.options.args_parsing:
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args = parser.parse_args()
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else:
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@ -124,3 +127,10 @@ if args.windows_standalone_build:
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if args.disable_auto_launch:
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args.auto_launch = False
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import logging
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logging_level = logging.WARNING
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if args.verbose:
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logging_level = logging.DEBUG
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logging.basicConfig(format="%(message)s", level=logging_level)
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@ -2,6 +2,7 @@ from .utils import load_torch_file, transformers_convert, state_dict_prefix_repl
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import os
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import torch
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import json
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import logging
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import comfy.ops
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import comfy.model_patcher
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@ -99,7 +100,7 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
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clip = ClipVisionModel(json_config)
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m, u = clip.load_sd(sd)
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if len(m) > 0:
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print("missing clip vision:", m)
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logging.warning("missing clip vision: {}".format(m))
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u = set(u)
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keys = list(sd.keys())
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for k in keys:
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@ -1,6 +1,7 @@
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import torch
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import math
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import os
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import logging
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import comfy.utils
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import comfy.model_management
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import comfy.model_detection
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@ -367,7 +368,7 @@ def load_controlnet(ckpt_path, model=None):
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leftover_keys = controlnet_data.keys()
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if len(leftover_keys) > 0:
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print("leftover keys:", leftover_keys)
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logging.warning("leftover keys: {}".format(leftover_keys))
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controlnet_data = new_sd
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pth_key = 'control_model.zero_convs.0.0.weight'
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@ -382,7 +383,7 @@ def load_controlnet(ckpt_path, model=None):
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else:
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net = load_t2i_adapter(controlnet_data)
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if net is None:
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print("error checkpoint does not contain controlnet or t2i adapter data", ckpt_path)
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logging.error("error checkpoint does not contain controlnet or t2i adapter data {}".format(ckpt_path))
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return net
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if controlnet_config is None:
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@ -417,7 +418,7 @@ def load_controlnet(ckpt_path, model=None):
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cd = controlnet_data[x]
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cd += model_sd[sd_key].type(cd.dtype).to(cd.device)
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else:
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print("WARNING: Loaded a diff controlnet without a model. It will very likely not work.")
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logging.warning("WARNING: Loaded a diff controlnet without a model. It will very likely not work.")
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class WeightsLoader(torch.nn.Module):
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pass
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@ -426,7 +427,12 @@ def load_controlnet(ckpt_path, model=None):
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missing, unexpected = w.load_state_dict(controlnet_data, strict=False)
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else:
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missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False)
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print(missing, unexpected)
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if len(missing) > 0:
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logging.warning("missing controlnet keys: {}".format(missing))
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if len(unexpected) > 0:
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logging.info("unexpected controlnet keys: {}".format(unexpected))
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global_average_pooling = False
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filename = os.path.splitext(ckpt_path)[0]
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@ -536,9 +542,9 @@ def load_t2i_adapter(t2i_data):
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missing, unexpected = model_ad.load_state_dict(t2i_data)
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if len(missing) > 0:
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print("t2i missing", missing)
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logging.warning("t2i missing {}".format(missing))
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if len(unexpected) > 0:
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print("t2i unexpected", unexpected)
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logging.info("t2i unexpected {}".format(unexpected))
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return T2IAdapter(model_ad, model_ad.input_channels, compression_ratio, upscale_algorithm)
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@ -1,5 +1,6 @@
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import re
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import torch
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import logging
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# conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py
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@ -177,7 +178,7 @@ def convert_vae_state_dict(vae_state_dict):
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for k, v in new_state_dict.items():
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for weight_name in weights_to_convert:
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if f"mid.attn_1.{weight_name}.weight" in k:
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print(f"Reshaping {k} for SD format")
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logging.info(f"Reshaping {k} for SD format")
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new_state_dict[k] = reshape_weight_for_sd(v)
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return new_state_dict
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@ -1,4 +1,5 @@
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import comfy.utils
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import logging
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LORA_CLIP_MAP = {
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"mlp.fc1": "mlp_fc1",
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@ -156,7 +157,7 @@ def load_lora(lora, to_load):
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for x in lora.keys():
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if x not in loaded_keys:
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print("lora key not loaded", x)
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logging.warning("lora key not loaded: {}".format(x))
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return patch_dict
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def model_lora_keys_clip(model, key_map={}):
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@ -1,4 +1,5 @@
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import torch
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import logging
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from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
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from comfy.ldm.cascade.stage_c import StageC
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from comfy.ldm.cascade.stage_b import StageB
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@ -66,8 +67,8 @@ class BaseModel(torch.nn.Module):
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if self.adm_channels is None:
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self.adm_channels = 0
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self.inpaint_model = False
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print("model_type", model_type.name)
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print("adm", self.adm_channels)
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logging.warning("model_type {}".format(model_type.name))
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logging.info("adm {}".format(self.adm_channels))
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def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
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sigma = t
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@ -183,10 +184,10 @@ class BaseModel(torch.nn.Module):
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to_load = self.model_config.process_unet_state_dict(to_load)
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m, u = self.diffusion_model.load_state_dict(to_load, strict=False)
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if len(m) > 0:
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print("unet missing:", m)
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logging.warning("unet missing: {}".format(m))
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if len(u) > 0:
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print("unet unexpected:", u)
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logging.warning("unet unexpected: {}".format(u))
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del to_load
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return self
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@ -1,5 +1,6 @@
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import comfy.supported_models
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import comfy.supported_models_base
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import logging
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def count_blocks(state_dict_keys, prefix_string):
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count = 0
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@ -186,7 +187,7 @@ def model_config_from_unet_config(unet_config):
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if model_config.matches(unet_config):
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return model_config(unet_config)
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print("no match", unet_config)
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logging.error("no match {}".format(unet_config))
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return None
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def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False):
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@ -1,4 +1,5 @@
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import psutil
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import logging
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from enum import Enum
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from comfy.cli_args import args
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import comfy.utils
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@ -29,7 +30,7 @@ lowvram_available = True
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xpu_available = False
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if args.deterministic:
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print("Using deterministic algorithms for pytorch")
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logging.warning("Using deterministic algorithms for pytorch")
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torch.use_deterministic_algorithms(True, warn_only=True)
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directml_enabled = False
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@ -41,7 +42,7 @@ if args.directml is not None:
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directml_device = torch_directml.device()
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else:
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directml_device = torch_directml.device(device_index)
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print("Using directml with device:", torch_directml.device_name(device_index))
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logging.warning("Using directml with device: {}".format(torch_directml.device_name(device_index)))
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# torch_directml.disable_tiled_resources(True)
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lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default.
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@ -117,10 +118,10 @@ def get_total_memory(dev=None, torch_total_too=False):
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total_vram = get_total_memory(get_torch_device()) / (1024 * 1024)
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total_ram = psutil.virtual_memory().total / (1024 * 1024)
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print("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))
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logging.warning("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))
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if not args.normalvram and not args.cpu:
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if lowvram_available and total_vram <= 4096:
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print("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --normalvram")
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logging.warning("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --normalvram")
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set_vram_to = VRAMState.LOW_VRAM
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try:
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@ -143,12 +144,10 @@ else:
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pass
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try:
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XFORMERS_VERSION = xformers.version.__version__
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print("xformers version:", XFORMERS_VERSION)
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logging.warning("xformers version: {}".format(XFORMERS_VERSION))
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if XFORMERS_VERSION.startswith("0.0.18"):
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print()
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print("WARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.")
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print("Please downgrade or upgrade xformers to a different version.")
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print()
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logging.warning("\nWARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.")
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logging.warning("Please downgrade or upgrade xformers to a different version.\n")
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XFORMERS_ENABLED_VAE = False
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except:
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pass
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@ -213,11 +212,11 @@ elif args.highvram or args.gpu_only:
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FORCE_FP32 = False
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FORCE_FP16 = False
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if args.force_fp32:
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print("Forcing FP32, if this improves things please report it.")
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logging.warning("Forcing FP32, if this improves things please report it.")
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FORCE_FP32 = True
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if args.force_fp16:
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print("Forcing FP16.")
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logging.warning("Forcing FP16.")
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FORCE_FP16 = True
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if lowvram_available:
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@ -231,12 +230,12 @@ if cpu_state != CPUState.GPU:
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if cpu_state == CPUState.MPS:
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vram_state = VRAMState.SHARED
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print(f"Set vram state to: {vram_state.name}")
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logging.warning(f"Set vram state to: {vram_state.name}")
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DISABLE_SMART_MEMORY = args.disable_smart_memory
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if DISABLE_SMART_MEMORY:
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print("Disabling smart memory management")
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logging.warning("Disabling smart memory management")
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def get_torch_device_name(device):
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if hasattr(device, 'type'):
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@ -254,11 +253,11 @@ def get_torch_device_name(device):
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return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device))
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try:
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print("Device:", get_torch_device_name(get_torch_device()))
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logging.warning("Device: {}".format(get_torch_device_name(get_torch_device())))
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except:
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print("Could not pick default device.")
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logging.warning("Could not pick default device.")
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print("VAE dtype:", VAE_DTYPE)
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logging.warning("VAE dtype: {}".format(VAE_DTYPE))
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current_loaded_models = []
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@ -301,7 +300,7 @@ class LoadedModel:
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raise e
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if lowvram_model_memory > 0:
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print("loading in lowvram mode", lowvram_model_memory/(1024 * 1024))
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logging.warning("loading in lowvram mode {}".format(lowvram_model_memory/(1024 * 1024)))
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mem_counter = 0
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for m in self.real_model.modules():
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if hasattr(m, "comfy_cast_weights"):
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@ -314,7 +313,7 @@ class LoadedModel:
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elif hasattr(m, "weight"): #only modules with comfy_cast_weights can be set to lowvram mode
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m.to(self.device)
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mem_counter += module_size(m)
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print("lowvram: loaded module regularly", m)
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logging.warning("lowvram: loaded module regularly {}".format(m))
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self.model_accelerated = True
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@ -348,7 +347,7 @@ def unload_model_clones(model):
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to_unload = [i] + to_unload
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for i in to_unload:
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print("unload clone", i)
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logging.warning("unload clone {}".format(i))
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current_loaded_models.pop(i).model_unload()
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def free_memory(memory_required, device, keep_loaded=[]):
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@ -390,7 +389,7 @@ def load_models_gpu(models, memory_required=0):
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models_already_loaded.append(loaded_model)
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else:
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if hasattr(x, "model"):
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print(f"Requested to load {x.model.__class__.__name__}")
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logging.warning(f"Requested to load {x.model.__class__.__name__}")
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models_to_load.append(loaded_model)
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if len(models_to_load) == 0:
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@ -400,7 +399,7 @@ def load_models_gpu(models, memory_required=0):
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free_memory(extra_mem, d, models_already_loaded)
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return
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print(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}")
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logging.warning(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}")
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total_memory_required = {}
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for loaded_model in models_to_load:
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@ -1,6 +1,7 @@
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import torch
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import copy
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import inspect
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import logging
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import comfy.utils
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import comfy.model_management
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@ -187,7 +188,7 @@ class ModelPatcher:
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model_sd = self.model_state_dict()
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for key in self.patches:
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if key not in model_sd:
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print("could not patch. key doesn't exist in model:", key)
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logging.warning("could not patch. key doesn't exist in model: {}".format(key))
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continue
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weight = model_sd[key]
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@ -236,7 +237,7 @@ class ModelPatcher:
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w1 = v[0]
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if alpha != 0.0:
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if w1.shape != weight.shape:
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print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
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logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
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else:
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weight += alpha * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype)
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elif patch_type == "lora": #lora/locon
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@ -252,7 +253,7 @@ class ModelPatcher:
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try:
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weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype)
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except Exception as e:
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print("ERROR", key, e)
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logging.error("ERROR {} {} {}".format(patch_type, key, e))
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elif patch_type == "lokr":
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w1 = v[0]
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w2 = v[1]
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@ -291,7 +292,7 @@ class ModelPatcher:
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try:
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weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype)
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except Exception as e:
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print("ERROR", key, e)
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logging.error("ERROR {} {} {}".format(patch_type, key, e))
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elif patch_type == "loha":
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w1a = v[0]
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w1b = v[1]
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@ -320,7 +321,7 @@ class ModelPatcher:
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try:
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weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype)
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except Exception as e:
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print("ERROR", key, e)
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logging.error("ERROR {} {} {}".format(patch_type, key, e))
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elif patch_type == "glora":
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if v[4] is not None:
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alpha *= v[4] / v[0].shape[0]
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@ -330,9 +331,12 @@ class ModelPatcher:
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b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32)
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b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32)
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weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype)
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try:
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weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype)
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except Exception as e:
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logging.error("ERROR {} {} {}".format(patch_type, key, e))
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else:
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print("patch type not recognized", patch_type, key)
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logging.warning("patch type not recognized {} {}".format(patch_type, key))
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return weight
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33
comfy/sd.py
33
comfy/sd.py
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@ -1,5 +1,6 @@
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import torch
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from enum import Enum
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import logging
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from comfy import model_management
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from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine
|
||||
|
|
@ -37,7 +38,7 @@ def load_model_weights(model, sd):
|
|||
w = sd.pop(x)
|
||||
del w
|
||||
if len(m) > 0:
|
||||
print("missing", m)
|
||||
logging.warning("missing {}".format(m))
|
||||
return model
|
||||
|
||||
def load_clip_weights(model, sd):
|
||||
|
|
@ -81,7 +82,7 @@ def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
|
|||
k1 = set(k1)
|
||||
for x in loaded:
|
||||
if (x not in k) and (x not in k1):
|
||||
print("NOT LOADED", x)
|
||||
logging.warning("NOT LOADED {}".format(x))
|
||||
|
||||
return (new_modelpatcher, new_clip)
|
||||
|
||||
|
|
@ -225,10 +226,10 @@ class VAE:
|
|||
|
||||
m, u = self.first_stage_model.load_state_dict(sd, strict=False)
|
||||
if len(m) > 0:
|
||||
print("Missing VAE keys", m)
|
||||
logging.warning("Missing VAE keys {}".format(m))
|
||||
|
||||
if len(u) > 0:
|
||||
print("Leftover VAE keys", u)
|
||||
logging.info("Leftover VAE keys {}".format(u))
|
||||
|
||||
if device is None:
|
||||
device = model_management.vae_device()
|
||||
|
|
@ -291,7 +292,7 @@ class VAE:
|
|||
samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
|
||||
pixel_samples[x:x+batch_number] = self.process_output(self.first_stage_model.decode(samples).to(self.output_device).float())
|
||||
except model_management.OOM_EXCEPTION as e:
|
||||
print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
|
||||
logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
|
||||
pixel_samples = self.decode_tiled_(samples_in)
|
||||
|
||||
pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1)
|
||||
|
|
@ -317,7 +318,7 @@ class VAE:
|
|||
samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
|
||||
|
||||
except model_management.OOM_EXCEPTION as e:
|
||||
print("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
|
||||
logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
|
||||
samples = self.encode_tiled_(pixel_samples)
|
||||
|
||||
return samples
|
||||
|
|
@ -393,10 +394,10 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
|
|||
for c in clip_data:
|
||||
m, u = clip.load_sd(c)
|
||||
if len(m) > 0:
|
||||
print("clip missing:", m)
|
||||
logging.warning("clip missing: {}".format(m))
|
||||
|
||||
if len(u) > 0:
|
||||
print("clip unexpected:", u)
|
||||
logging.info("clip unexpected: {}".format(u))
|
||||
return clip
|
||||
|
||||
def load_gligen(ckpt_path):
|
||||
|
|
@ -534,21 +535,21 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
|
|||
clip = CLIP(clip_target, embedding_directory=embedding_directory)
|
||||
m, u = clip.load_sd(clip_sd, full_model=True)
|
||||
if len(m) > 0:
|
||||
print("clip missing:", m)
|
||||
logging.warning("clip missing: {}".format(m))
|
||||
|
||||
if len(u) > 0:
|
||||
print("clip unexpected:", u)
|
||||
logging.info("clip unexpected {}:".format(u))
|
||||
else:
|
||||
print("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.")
|
||||
logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.")
|
||||
|
||||
left_over = sd.keys()
|
||||
if len(left_over) > 0:
|
||||
print("left over keys:", left_over)
|
||||
logging.info("left over keys: {}".format(left_over))
|
||||
|
||||
if output_model:
|
||||
model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device(), current_device=inital_load_device)
|
||||
if inital_load_device != torch.device("cpu"):
|
||||
print("loaded straight to GPU")
|
||||
logging.warning("loaded straight to GPU")
|
||||
model_management.load_model_gpu(model_patcher)
|
||||
|
||||
return (model_patcher, clip, vae, clipvision)
|
||||
|
|
@ -577,7 +578,7 @@ def load_unet_state_dict(sd): #load unet in diffusers format
|
|||
if k in sd:
|
||||
new_sd[diffusers_keys[k]] = sd.pop(k)
|
||||
else:
|
||||
print(diffusers_keys[k], k)
|
||||
logging.warning("{} {}".format(diffusers_keys[k], k))
|
||||
|
||||
offload_device = model_management.unet_offload_device()
|
||||
unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
|
||||
|
|
@ -588,14 +589,14 @@ def load_unet_state_dict(sd): #load unet in diffusers format
|
|||
model.load_model_weights(new_sd, "")
|
||||
left_over = sd.keys()
|
||||
if len(left_over) > 0:
|
||||
print("left over keys in unet:", left_over)
|
||||
logging.warning("left over keys in unet: {}".format(left_over))
|
||||
return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)
|
||||
|
||||
def load_unet(unet_path):
|
||||
sd = comfy.utils.load_torch_file(unet_path)
|
||||
model = load_unet_state_dict(sd)
|
||||
if model is None:
|
||||
print("ERROR UNSUPPORTED UNET", unet_path)
|
||||
logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path))
|
||||
raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
|
||||
return model
|
||||
|
||||
|
|
|
|||
|
|
@ -8,6 +8,7 @@ import zipfile
|
|||
from . import model_management
|
||||
import comfy.clip_model
|
||||
import json
|
||||
import logging
|
||||
|
||||
def gen_empty_tokens(special_tokens, length):
|
||||
start_token = special_tokens.get("start", None)
|
||||
|
|
@ -137,7 +138,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
|
|||
tokens_temp += [next_new_token]
|
||||
next_new_token += 1
|
||||
else:
|
||||
print("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored", y.shape[0], current_embeds.weight.shape[1])
|
||||
logging.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(y.shape[0], current_embeds.weight.shape[1]))
|
||||
while len(tokens_temp) < len(x):
|
||||
tokens_temp += [self.special_tokens["pad"]]
|
||||
out_tokens += [tokens_temp]
|
||||
|
|
@ -329,9 +330,7 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
|
|||
else:
|
||||
embed = torch.load(embed_path, map_location="cpu")
|
||||
except Exception as e:
|
||||
print(traceback.format_exc())
|
||||
print()
|
||||
print("error loading embedding, skipping loading:", embedding_name)
|
||||
logging.warning("{}\n\nerror loading embedding, skipping loading: {}".format(traceback.format_exc(), embedding_name))
|
||||
return None
|
||||
|
||||
if embed_out is None:
|
||||
|
|
@ -422,7 +421,7 @@ class SDTokenizer:
|
|||
embedding_name = word[len(self.embedding_identifier):].strip('\n')
|
||||
embed, leftover = self._try_get_embedding(embedding_name)
|
||||
if embed is None:
|
||||
print(f"warning, embedding:{embedding_name} does not exist, ignoring")
|
||||
logging.warning(f"warning, embedding:{embedding_name} does not exist, ignoring")
|
||||
else:
|
||||
if len(embed.shape) == 1:
|
||||
tokens.append([(embed, weight)])
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@ import comfy.checkpoint_pickle
|
|||
import safetensors.torch
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
import logging
|
||||
|
||||
def load_torch_file(ckpt, safe_load=False, device=None):
|
||||
if device is None:
|
||||
|
|
@ -14,14 +15,14 @@ def load_torch_file(ckpt, safe_load=False, device=None):
|
|||
else:
|
||||
if safe_load:
|
||||
if not 'weights_only' in torch.load.__code__.co_varnames:
|
||||
print("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.")
|
||||
logging.warning("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.")
|
||||
safe_load = False
|
||||
if safe_load:
|
||||
pl_sd = torch.load(ckpt, map_location=device, weights_only=True)
|
||||
else:
|
||||
pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle)
|
||||
if "global_step" in pl_sd:
|
||||
print(f"Global Step: {pl_sd['global_step']}")
|
||||
logging.info(f"Global Step: {pl_sd['global_step']}")
|
||||
if "state_dict" in pl_sd:
|
||||
sd = pl_sd["state_dict"]
|
||||
else:
|
||||
|
|
|
|||
Loading…
Reference in New Issue