Update app.py

app.py

@@ -126,31 +126,6 @@ def calc_emb_cropped(image, app, min_bbox_ratio=0.2):
 
     return cropped_face_image
 
-def print_tree(root_dir, prefix=""):
-    """
-    Recursively prints the directory structure starting from `root_dir`.
-
-    Args:
-        root_dir (str): Path to the root directory.
-        prefix (str): Prefix for formatting the tree structure.
-    """
-    # List all files and directories in the current folder
-    items = os.listdir(root_dir)
-    items.sort()  # Sort for consistent output
-
-    for i, item in enumerate(items):
-        path = os.path.join(root_dir, item)
-        is_last = (i == len(items) - 1)
-
-        # Print item name with tree branch
-        print(f"{prefix}{'└── ' if is_last else '├── '}{item}")
-
-        # If the item is a directory, recurse into it
-        if os.path.isdir(path):
-            print_tree(path, prefix + ("    " if is_last else "│   "))
-
-
-
 def make_canny_condition(image, min_val=100, max_val=200, w_bilateral=True):
     if w_bilateral:
         image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)
@@ -164,42 +139,14 @@ def make_canny_condition(image, min_val=100, max_val=200, w_bilateral=True):
     image = Image.fromarray(image)
     return image
 
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, 99999999)
+    return seed
 
 default_negative_prompt = "Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers"
 
-# Global variable to track the currently loaded LoRA
-CURRENT_LORA_NAME = None
-
-# Load face detection and recognition package
-# app = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
-
-
-# # Define your repository, target directory, and local directory
-# repo_id = "briaai/ID_preservation_2.3"
-# target_dir = "models_aura/"  # The directory you want to download
-# local_dir = "./checkpoints"  # Local directory to save files
-
-# # Initialize the API
-# api = HfApi()
-# # List all files in the repository
-# files = api.list_repo_files(repo_id=repo_id, repo_type="model")  # Use repo_type="space" for Spaces
-
-# # Filter files that are in the target directory
-# files_in_dir = [file for file in files if file.startswith(target_dir)]
-# # Download each file in the target directory
-# for file in files_in_dir:
-#     local_path = os.path.join(local_dir, file)
-#     os.makedirs(os.path.dirname(local_path), exist_ok=True)  # Ensure local directories exist
-#     print(f"Downloading: {file}")
-#     hf_hub_download(repo_id=repo_id, filename=file, local_dir=os.path.dirname(local_path))
-
-
-# # Local directory where files were downloaded
-# local_dir = "./checkpoints/models_aura"
-
-# print(f"Folder structure of '{local_dir}':")
-# print_tree(local_dir)
-
+# Download face encoder
 snapshot_download(
     "fal/AuraFace-v1",
     local_dir="models/auraface",
@@ -211,12 +158,10 @@ app = FaceAnalysis(
     root=".",
 )
 
-# app = FaceAnalysis(name='auraface', root='./checkpoints/models_aura/', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
 app.prepare(ctx_id=0, det_size=(640, 640))
 
 
 # download checkpoints
-
 hf_hub_download(repo_id="briaai/ID_preservation_2.3", filename="controlnet/config.json", local_dir="./checkpoints")
 hf_hub_download(repo_id="briaai/ID_preservation_2.3", filename="controlnet/diffusion_pytorch_model.safetensors", local_dir="./checkpoints")
 hf_hub_download(repo_id="briaai/ID_preservation_2.3", filename="ip-adapter.bin", local_dir="./checkpoints")
@@ -229,7 +174,6 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 # ckpts paths
 face_adapter = f"./checkpoints/ip-adapter.bin"
 controlnet_path = f"./checkpoints/controlnet"
-# lora_base_path = "./checkpoints/LoRAs"
 base_model_path = f'briaai/BRIA-2.3'
 resolution = 1024
 
@@ -261,21 +205,7 @@ pipe.load_ip_adapter_instantid(face_adapter)
 
 clip_embeds=None
 
-# Loras_dict = {
-#     "":"",
-#     "Vangogh_Vanilla": "bold, dramatic brush strokes, vibrant colors, swirling patterns, intense, emotionally charged paintings of",
-#     "Avatar_internlm": "2d anime sketch avatar of",
-#     "Storyboards": "Illustration style for storyboarding.",
-#     "3D_illustration": "3D object illustration, abstract.",
-#     "Characters": "gaming vector Art."
-#     }
 
-# lora_names = Loras_dict.keys()
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, 99999999)
-    return seed
 
 
 @spaces.GPU