Remove unused image enhancement functions to streamline image processing

app.py

@@ -19,7 +19,7 @@ import cv2
 import numpy as np
 import torch
 import torch.nn.functional as F
-from PIL import Image, ImageEnhance
+from PIL import Image
 import PIL.Image as PImage
 from torchvision.transforms.functional import to_tensor
 from transformers import AutoTokenizer, T5EncoderModel, T5TokenizerFast
@@ -66,16 +66,6 @@ def encode_prompt(text_tokenizer, text_encoder, prompt):
     text_cond_tuple = (kv_compact, lens, cu_seqlens_k, Ltext)
     return text_cond_tuple
 
-def enhance_image(image):
-    for t in range(1):
-        contrast_image = image.copy()
-        contrast_enhancer = ImageEnhance.Contrast(contrast_image)
-        contrast_image = contrast_enhancer.enhance(1.05)  # 增强对比度
-        color_image = contrast_image.copy()
-        color_enhancer = ImageEnhance.Color(color_image)
-        color_image = color_enhancer.enhance(1.05)  # 增强饱和度
-    return color_image
-
 def gen_one_img(
     infinity_test, 
     vae, 
@@ -141,12 +131,6 @@ def gen_one_img(
     img = img_list[0]
     return img
 
-def get_prompt_id(prompt):
-    md5 = hashlib.md5()
-    md5.update(prompt.encode('utf-8'))
-    prompt_id = md5.hexdigest()
-    return prompt_id
-
 def save_slim_model(infinity_model_path, save_file=None, device='cpu', key='gpt_fsdp'):
     print('[Save slim model]')
     full_ckpt = torch.load(infinity_model_path, map_location=device)