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DATA/experiment.py

@@ -3,6 +3,8 @@ from PIL import Image
 import torchvision.transforms.functional as f
 from utils import load_face_generator
 from omegaconf import OmegaConf
+import random
+import sys
 
 def generate_face_image(
     anything_facemaker,
@@ -43,7 +45,8 @@ def experiment(anything_facemaker, concepts_path, face_img_path, output_dir,
     with open(concepts_path) as fr:
         concepts = fr.read().split('\n')
     concepts = [concept for concept in concepts if len(concept)!=0]
-    for concept in concepts:
+    random.shuffle(concepts)
+    for concept in concepts[:4]:
         save_path = os.path.join(output_dir, f'{concept}.png')
         if os.path.exists(save_path):
             continue
@@ -68,7 +71,9 @@ if __name__=='__main__':
     model_config = OmegaConf.load(model_config_path)['models']
     gameicon_config = model_config['GameIconInstitute_mode']
 
-    face_img_path='resources/images/faces/1.jpg'
+    # face_img_path = 'resources/images/faces/0.jpg'
+    face_img_dir='resources/images/faces'
+    faces = os.listdir(face_img_dir)
     controlnet_conditioning_scale=1.
     strength=0.95
 
@@ -80,7 +85,7 @@ if __name__=='__main__':
             prompt_template=model_info['prompt_template'],
             negative_prompt=model_info['negative_prompt']
         )
-        output_dir = os.path.join('results/concepts_test/control_inversion', model)
+        output_dir = os.path.join(sys.argv[1], model)
         os.makedirs(output_dir, exist_ok=True)
         # concept test, with control and inversion
         input_dir = 'resources/prompts'
@@ -90,6 +95,7 @@ if __name__=='__main__':
                 file_output_dir = os.path.join(output_dir, file)
                 print(f'input_file: {input_file}')
                 print(f'file_output_dir: {file_output_dir}')
+                face_img_path = os.path.join(face_img_dir, random.choice(faces))
                 experiment(anything_facemaker, input_file, face_img_path, output_dir=file_output_dir,
                         controlnet_conditioning_scale=controlnet_conditioning_scale,
                         strength=strength)