rework and speed up model loading

modules/sd_models.py

@@ -19,6 +19,7 @@ import numpy as np
 from backend.loader import forge_loader
 from backend import memory_management
 from backend.args import dynamic_args
+from backend.utils import load_torch_file
 
 
 model_dir = "Stable-diffusion"
@@ -242,45 +243,12 @@ def select_checkpoint():
     return checkpoint_info
 
 
-checkpoint_dict_replacements_sd1 = {
-    'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
-    'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
-    'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
-}
-
-checkpoint_dict_replacements_sd2_turbo = { # Converts SD 2.1 Turbo from SGM to LDM format.
-    'conditioner.embedders.0.': 'cond_stage_model.',
-}
-
-
 def transform_checkpoint_dict_key(k, replacements):
-    for text, replacement in replacements.items():
-        if k.startswith(text):
-            k = replacement + k[len(text):]
-
-    return k
+    pass
 
 
 def get_state_dict_from_checkpoint(pl_sd):
-    pl_sd = pl_sd.pop("state_dict", pl_sd)
-    pl_sd.pop("state_dict", None)
-
-    is_sd2_turbo = 'conditioner.embedders.0.model.ln_final.weight' in pl_sd and pl_sd['conditioner.embedders.0.model.ln_final.weight'].size()[0] == 1024
-
-    sd = {}
-    for k, v in pl_sd.items():
-        if is_sd2_turbo:
-            new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd2_turbo)
-        else:
-            new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd1)
-
-        if new_key is not None:
-            sd[new_key] = v
-
-    pl_sd.clear()
-    pl_sd.update(sd)
-
-    return pl_sd
+    pass
 
 
 def read_metadata_from_safetensors(filename):
@@ -312,23 +280,7 @@ def read_metadata_from_safetensors(filename):
 
 
 def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
-    _, extension = os.path.splitext(checkpoint_file)
-    if extension.lower() == ".safetensors":
-        device = map_location or shared.weight_load_location or devices.get_optimal_device_name()
-
-        if not shared.opts.disable_mmap_load_safetensors:
-            pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
-        else:
-            pl_sd = safetensors.torch.load(open(checkpoint_file, 'rb').read())
-            pl_sd = {k: v.to(device) for k, v in pl_sd.items()}
-    else:
-        pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
-
-    if print_global_state and "global_step" in pl_sd:
-        print(f"Global Step: {pl_sd['global_step']}")
-
-    sd = get_state_dict_from_checkpoint(pl_sd)
-    return sd
+    pass
 
 
 def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
@@ -343,25 +295,14 @@ def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
         return checkpoints_loaded[checkpoint_info]
 
     print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
-    res = read_state_dict(checkpoint_info.filename)
+    res = load_torch_file(checkpoint_info.filename)
     timer.record("load weights from disk")
 
     return res
 
 
 class SkipWritingToConfig:
-    """This context manager prevents load_model_weights from writing checkpoint name to the config when it loads weight."""
-
-    skip = False
-    previous = None
-
-    def __enter__(self):
-        self.previous = SkipWritingToConfig.skip
-        SkipWritingToConfig.skip = True
-        return self
-
-    def __exit__(self, exc_type, exc_value, exc_traceback):
-        SkipWritingToConfig.skip = self.previous
+    pass
 
 
 def check_fp8(model):
@@ -434,12 +375,6 @@ def apply_alpha_schedule_override(sd_model, p=None):
         sd_model.alphas_cumprod = rescale_zero_terminal_snr_abar(sd_model.alphas_cumprod).to(shared.device)
 
 
-sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
-sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
-sdxl_clip_weight = 'conditioner.embedders.1.model.ln_final.weight'
-sdxl_refiner_clip_weight = 'conditioner.embedders.0.model.ln_final.weight'
-
-
 class SdModelData:
     def __init__(self):
         self.sd_model = None
@@ -479,19 +414,7 @@ model_data = SdModelData()
 
 
 def get_empty_cond(sd_model):
-
-    p = processing.StableDiffusionProcessingTxt2Img()
-    extra_networks.activate(p, {})
-
-    if hasattr(sd_model, 'get_learned_conditioning'):
-        d = sd_model.get_learned_conditioning([""])
-    else:
-        d = sd_model.cond_stage_model([""])
-
-    if isinstance(d, dict):
-        d = d['crossattn']
-
-    return d
+    pass
 
 
 def send_model_to_cpu(m):
@@ -511,22 +434,11 @@ def send_model_to_trash(m):
 
 
 def instantiate_from_config(config, state_dict=None):
-    constructor = get_obj_from_str(config["target"])
-
-    params = {**config.get("params", {})}
-
-    if state_dict and "state_dict" in params and params["state_dict"] is None:
-        params["state_dict"] = state_dict
-
-    return constructor(**params)
+    pass
 
 
 def get_obj_from_str(string, reload=False):
-    module, cls = string.rsplit(".", 1)
-    if reload:
-        module_imp = importlib.import_module(module)
-        importlib.reload(module_imp)
-    return getattr(importlib.import_module(module, package=None), cls)
+    pass
 
 
 @torch.no_grad()
@@ -568,9 +480,6 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
     sd_model.sd_model_hash = checkpoint_info.calculate_shorthash()
     timer.record("calculate hash")
 
-    if not SkipWritingToConfig.skip:
-        shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
-
     del state_dict
 
     # clean up cache if limit is reached

modules/ui_settings.py

@@ -329,7 +329,7 @@ class UiSettings:
         button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False)
         button_set_checkpoint.click(
             fn=button_set_checkpoint_change,
-            _js="function(v){ var res = desiredCheckpointName; desiredCheckpointName = ''; return [res || v, null]; }",
+            js="function(v){ var res = desiredCheckpointName; desiredCheckpointName = ''; return [res || v, null]; }",
             inputs=[main_entry.ui_checkpoint, self.dummy_component],
             outputs=[main_entry.ui_checkpoint, self.text_settings],
         )