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modules/sd_models.py

@@ -10,7 +10,6 @@ import re
 import safetensors.torch
 from omegaconf import OmegaConf, ListConfig
 from urllib import request
-import ldm.modules.midas as midas
 import gc
 
 from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack, patches
@@ -415,89 +414,15 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
 
 
 def enable_midas_autodownload():
-    """
-    Gives the ldm.modules.midas.api.load_model function automatic downloading.
-
-    When the 512-depth-ema model, and other future models like it, is loaded,
-    it calls midas.api.load_model to load the associated midas depth model.
-    This function applies a wrapper to download the model to the correct
-    location automatically.
-    """
-
-    midas_path = os.path.join(paths.models_path, 'midas')
-
-    # stable-diffusion-stability-ai hard-codes the midas model path to
-    # a location that differs from where other scripts using this model look.
-    # HACK: Overriding the path here.
-    for k, v in midas.api.ISL_PATHS.items():
-        file_name = os.path.basename(v)
-        midas.api.ISL_PATHS[k] = os.path.join(midas_path, file_name)
-
-    midas_urls = {
-        "dpt_large": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
-        "dpt_hybrid": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt",
-        "midas_v21": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21-f6b98070.pt",
-        "midas_v21_small": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21_small-70d6b9c8.pt",
-    }
-
-    midas.api.load_model_inner = midas.api.load_model
-
-    def load_model_wrapper(model_type):
-        path = midas.api.ISL_PATHS[model_type]
-        if not os.path.exists(path):
-            if not os.path.exists(midas_path):
-                os.mkdir(midas_path)
-
-            print(f"Downloading midas model weights for {model_type} to {path}")
-            request.urlretrieve(midas_urls[model_type], path)
-            print(f"{model_type} downloaded")
-
-        return midas.api.load_model_inner(model_type)
-
-    midas.api.load_model = load_model_wrapper
+    pass
 
 
 def patch_given_betas():
-    import ldm.models.diffusion.ddpm
-
-    def patched_register_schedule(*args, **kwargs):
-        """a modified version of register_schedule function that converts plain list from Omegaconf into numpy"""
-
-        if isinstance(args[1], ListConfig):
-            args = (args[0], np.array(args[1]), *args[2:])
-
-        original_register_schedule(*args, **kwargs)
-
-    original_register_schedule = patches.patch(__name__, ldm.models.diffusion.ddpm.DDPM, 'register_schedule', patched_register_schedule)
+    pass
 
 
 def repair_config(sd_config, state_dict=None):
-    if not hasattr(sd_config.model.params, "use_ema"):
-        sd_config.model.params.use_ema = False
-
-    if hasattr(sd_config.model.params, 'unet_config'):
-        if shared.cmd_opts.no_half:
-            sd_config.model.params.unet_config.params.use_fp16 = False
-        elif shared.cmd_opts.upcast_sampling or shared.cmd_opts.precision == "half":
-            sd_config.model.params.unet_config.params.use_fp16 = True
-
-    if hasattr(sd_config.model.params, 'first_stage_config'):
-        if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
-            sd_config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla"
-
-    # For UnCLIP-L, override the hardcoded karlo directory
-    if hasattr(sd_config.model.params, "noise_aug_config") and hasattr(sd_config.model.params.noise_aug_config.params, "clip_stats_path"):
-        karlo_path = os.path.join(paths.models_path, 'karlo')
-        sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
-
-    # Do not use checkpoint for inference.
-    # This helps prevent extra performance overhead on checking parameters.
-    # The perf overhead is about 100ms/it on 4090 for SDXL.
-    if hasattr(sd_config.model.params, "network_config"):
-        sd_config.model.params.network_config.params.use_checkpoint = False
-    if hasattr(sd_config.model.params, "unet_config"):
-        sd_config.model.params.unet_config.params.use_checkpoint = False
-
+    pass
 
 
 def rescale_zero_terminal_snr_abar(alphas_cumprod):