Update model_loading.py

model_loading.py

@@ -36,24 +36,13 @@ def patched_write_atomic(
 
 import torch
 import torch.nn as nn
-from .utils import check_diffusers_version, remove_specific_blocks, log
-check_diffusers_version()
 
 from diffusers.models import AutoencoderKLCogVideoX
 from diffusers.schedulers import CogVideoXDDIMScheduler
 from .custom_cogvideox_transformer_3d import CogVideoXTransformer3DModel
 from .pipeline_cogvideox import CogVideoXPipeline
 from contextlib import nullcontext
 
-from .cogvideox_fun.transformer_3d import CogVideoXTransformer3DModel as CogVideoXTransformer3DModelFun
-from .cogvideox_fun.fun_pab_transformer_3d import CogVideoXTransformer3DModel as CogVideoXTransformer3DModelFunPAB
-from .cogvideox_fun.autoencoder_magvit import AutoencoderKLCogVideoX as AutoencoderKLCogVideoXFun
-
-from .cogvideox_fun.pipeline_cogvideox_inpaint import CogVideoX_Fun_Pipeline_Inpaint
-from .cogvideox_fun.pipeline_cogvideox_control import CogVideoX_Fun_Pipeline_Control
-
-from .videosys.cogvideox_transformer_3d import CogVideoXTransformer3DModel as CogVideoXTransformer3DModelPAB
-
 from accelerate import init_empty_weights
 from accelerate.utils import set_module_tensor_to_device
 
@@ -231,8 +220,6 @@ def loadmodel(self, model, precision, quantization="disabled", compile="disabled
 
         if block_edit is not None:
             transformer = remove_specific_blocks(transformer, block_edit)
-
-
 
         with open(scheduler_path) as f:
             scheduler_config = json.load(f)
@@ -274,22 +261,6 @@ def loadmodel(self, model, precision, quantization="disabled", compile="disabled
                 for l in lora:
                     pipe.set_adapters(adapter_list, adapter_weights=adapter_weights)
                 if fuse:
-                    pipe.fuse_lora(lora_scale=lora[-1]["strength"] / lora_rank, components=["transformer"])
-
-        #fp8
-        if fp8_transformer == "enabled" or fp8_transformer == "fastmode":
-            for name, param in pipe.transformer.named_parameters():
-                params_to_keep = {"patch_embed", "lora", "pos_embedding"}
-                if not any(keyword in name for keyword in params_to_keep):
-                    param.data = param.data.to(torch.float8_e4m3fn)
-
-            if fp8_transformer == "fastmode":
-                from .fp8_optimization import convert_fp8_linear
-                convert_fp8_linear(pipe.transformer, dtype)
-
-        if enable_sequential_cpu_offload:
-            pipe.enable_sequential_cpu_offload()
-
                     lora_scale = 1
                     dimension_loras = ["orbit", "dimensionx"] # for now dimensionx loras need scaling
                     if any(item in lora[-1]["path"].lower() for item in dimension_loras):
@@ -1057,4 +1028,4 @@ def loadmodel(self, model):
     "CogVideoLoraSelect": "CogVideo LoraSelect",
     "CogVideoXVAELoader": "CogVideoX VAE Loader",
     "CogVideoXModelLoader": "CogVideoX Model Loader",
-    }
+    }