chore: clean up gvm patch layer

Author: ALexFIlik

ec2_worker/_patch/gvm/pipelines/pipeline_gvm.py

@@ -3,21 +3,15 @@
 import numpy as np
 from diffusers import DiffusionPipeline
 from diffusers.utils import (
-    BaseOutput, 
-    USE_PEFT_BACKEND,     
-    is_peft_available,
-    is_peft_version,
-    is_torch_version,
+    BaseOutput,
     logging
 )
 from diffusers.loaders.lora_pipeline import (
-    _LOW_CPU_MEM_USAGE_DEFAULT_LORA,
     StableDiffusionLoraLoaderMixin
 )
 from peft import LoraConfig, LoraModel, set_peft_model_state_dict
 import os
 
-import matplotlib
 from typing import Union, Dict
 logger = logging.get_logger(__name__)
 
@@ -36,7 +30,7 @@ def load_lora_weights(
     ):
 
         unet_lora_config = LoraConfig.from_pretrained(pretrained_model_name_or_path_or_dict)
-        checkpoint = os.path.join(pretrained_model_name_or_path_or_dict, f"pytorch_lora_weights.pt")
+        checkpoint = os.path.join(pretrained_model_name_or_path_or_dict, "pytorch_lora_weights.pt")
         unet_lora_ckpt = torch.load(checkpoint)
         self.unet = LoraModel(self.unet, unet_lora_config, "default")
         set_peft_model_state_dict(self.unet, unet_lora_ckpt)

ec2_worker/_patch/gvm_core/wrapper.py

@@ -6,12 +6,10 @@
 import time
 from pathlib import Path
 
-from easydict import EasyDict
 import numpy as np
 import torch
 import torch.nn.functional as F
 from torch.utils.data import DataLoader
-from torchvision import transforms
 from torchvision.transforms import ToTensor, Resize, Compose
 from diffusers import AutoencoderKLTemporalDecoder, FlowMatchEulerDiscreteScheduler
 from tqdm import tqdm
@@ -151,7 +149,6 @@ def process_sequence(self, input_path, output_dir,
 
         target_h = orig_h
         if target_h < 1024:
-            scale_ratio = 1024 / target_h
             target_h = 1024
 
         # Calculate max resolution / long edge