deployfix06

app.py

@@ -15,7 +15,6 @@ from typing import List
 
 
 import torch
-import os
 from transformers import AutoTokenizer
 
 import numpy as np
@@ -43,7 +42,6 @@ def pil_to_binary_mask(pil_image, threshold=0):
 
 
 base_path = 'yisol/IDM-VTON'
-example_path = os.path.join(os.path.dirname(__file__), 'example')
 
 unet = UNet2DConditionModel.from_pretrained(
     base_path,
@@ -267,20 +265,6 @@ def start_tryon(dict, garm_img, garment_des, is_checked, is_checked_crop, denois
         # 直接将生成的图片和mask调整到原始尺寸
         return images[0].resize(orig_size), mask_gray.resize(orig_size)
 
-garm_list = os.listdir(os.path.join(example_path,"cloth"))
-garm_list_path = [os.path.join(example_path,"cloth",garm) for garm in garm_list]
-
-human_list = os.listdir(os.path.join(example_path,"human"))
-human_list_path = [os.path.join(example_path,"human",human) for human in human_list]
-
-human_ex_list = []
-for ex_human in human_list_path:
-    ex_dict= {}
-    ex_dict['background'] = ex_human
-    ex_dict['layers'] = None
-    ex_dict['composite'] = None
-    human_ex_list.append(ex_dict)
-
 ##default human
 
 
@@ -314,21 +298,14 @@ with image_blocks as demo:
             with gr.Row():
                 is_checked_crop = gr.Checkbox(label="Yes", info="Use auto-crop & resizing",value=False)
 
-            example = gr.Examples(
-                inputs=imgs,
-                examples_per_page=15,
-                examples=human_ex_list
-            )
+
 
         with gr.Column():
             garm_img = gr.Image(label="Garment", sources='upload', type="pil")
             with gr.Row(elem_id="prompt-container"):
                 with gr.Row():
                     prompt = gr.Textbox(label="Description of garment", placeholder="Short Sleeve Round Neck T-shirts", show_label=True, elem_id="prompt")
-            example = gr.Examples(
-                inputs=garm_img,
-                examples_per_page=16,
-                examples=garm_list_path)
+
         with gr.Column():
             # image_out = gr.Image(label="Output", elem_id="output-img", height=400)
             masked_img = gr.Image(label="Masked image output", elem_id="masked-img",show_share_button=False)