Merge remote-tracking branch 'upstream/main' into feature/add-metadata-to-files

modules/config.py

@@ -16,10 +16,17 @@ config_dict = {}
 always_save_keys = []
 visited_keys = []
 
+try:
+    with open(os.path.abspath(f'./presets/default.json'), "r", encoding="utf-8") as json_file:
+        config_dict.update(json.load(json_file))
+except Exception as e:
+    print(f'Load default preset failed.')
+    print(e)
+
 try:
     if os.path.exists(config_path):
         with open(config_path, "r", encoding="utf-8") as json_file:
-            config_dict = json.load(json_file)
+            config_dict.update(json.load(json_file))
             always_save_keys = list(config_dict.keys())
 except Exception as e:
     print(f'Failed to load config file "{config_path}" . The reason is: {str(e)}')
@@ -153,9 +160,14 @@ def get_config_item_or_set_default(key, default_value, validator, disable_empty_
 
 default_base_model_name = get_config_item_or_set_default(
     key='default_model',
-    default_value='juggernautXL_version6Rundiffusion.safetensors',
+    default_value='model.safetensors',
     validator=lambda x: isinstance(x, str)
 )
+previous_default_models = get_config_item_or_set_default(
+    key='previous_default_models',
+    default_value=[],
+    validator=lambda x: isinstance(x, list) and all(isinstance(k, str) for k in x)
+)
 default_refiner_model_name = get_config_item_or_set_default(
     key='default_refiner',
     default_value='None',
@@ -163,15 +175,15 @@ default_refiner_model_name = get_config_item_or_set_default(
 )
 default_refiner_switch = get_config_item_or_set_default(
     key='default_refiner_switch',
-    default_value=0.5,
+    default_value=0.8,
     validator=lambda x: isinstance(x, numbers.Number) and 0 <= x <= 1
 )
 default_loras = get_config_item_or_set_default(
     key='default_loras',
     default_value=[
         [
-            "sd_xl_offset_example-lora_1.0.safetensors",
-            0.1
+            "None",
+            1.0
         ],
         [
             "None",
@@ -194,7 +206,7 @@ default_loras = get_config_item_or_set_default(
 )
 default_cfg_scale = get_config_item_or_set_default(
     key='default_cfg_scale',
-    default_value=4.0,
+    default_value=7.0,
     validator=lambda x: isinstance(x, numbers.Number)
 )
 default_sample_sharpness = get_config_item_or_set_default(
@@ -255,16 +267,12 @@ default_image_number = get_config_item_or_set_default(
 )
 checkpoint_downloads = get_config_item_or_set_default(
     key='checkpoint_downloads',
-    default_value={
-        "juggernautXL_version6Rundiffusion.safetensors": "https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/juggernautXL_version6Rundiffusion.safetensors"
-    },
+    default_value={},
     validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items())
 )
 lora_downloads = get_config_item_or_set_default(
     key='lora_downloads',
-    default_value={
-        "sd_xl_offset_example-lora_1.0.safetensors": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors"
-    },
+    default_value={},
     validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items())
 )
 embeddings_downloads = get_config_item_or_set_default(

modules/inpaint_worker.py

@@ -4,6 +4,7 @@ import numpy as np
 from PIL import Image, ImageFilter
 from modules.util import resample_image, set_image_shape_ceil, get_image_shape_ceil
 from modules.upscaler import perform_upscale
+import cv2
 
 
 inpaint_head_model = None
@@ -28,19 +29,25 @@ def box_blur(x, k):
     return np.array(x)
 
 
-def max33(x):
-    x = Image.fromarray(x)
-    x = x.filter(ImageFilter.MaxFilter(3))
-    return np.array(x)
+def max_filter_opencv(x, ksize=3):
+    # Use OpenCV maximum filter
+    # Make sure the input type is int16
+    return cv2.dilate(x, np.ones((ksize, ksize), dtype=np.int16))
 
 
 def morphological_open(x):
-    x_int32 = np.zeros_like(x).astype(np.int32)
-    x_int32[x > 127] = 256
-    for _ in range(32):
-        maxed = max33(x_int32) - 8
-        x_int32 = np.maximum(maxed, x_int32)
-    return x_int32.clip(0, 255).astype(np.uint8)
+    # Convert array to int16 type via threshold operation
+    x_int16 = np.zeros_like(x, dtype=np.int16)
+    x_int16[x > 127] = 256
+
+    for i in range(32):
+        # Use int16 type to avoid overflow
+        maxed = max_filter_opencv(x_int16, ksize=3) - 8
+        x_int16 = np.maximum(maxed, x_int16)
+
+    # Clip negative values to 0 and convert back to uint8 type
+    x_uint8 = np.clip(x_int16, 0, 255).astype(np.uint8)
+    return x_uint8
 
 
 def up255(x, t=0):

modules/launch_util.py

@@ -5,6 +5,11 @@ import subprocess
 import sys
 import re
 import logging
+import importlib.metadata
+import packaging.version
+from packaging.requirements import Requirement
+
+
 
 
 logging.getLogger("torch.distributed.nn").setLevel(logging.ERROR)  # sshh...
@@ -73,35 +78,26 @@ def run_pip(command, desc=None, live=default_command_live):
 
 
 def requirements_met(requirements_file):
-    """
-    Does a simple parse of a requirements.txt file to determine if all rerqirements in it
-    are already installed. Returns True if so, False if not installed or parsing fails.
-    """
-
-    import importlib.metadata
-    import packaging.version
-
     with open(requirements_file, "r", encoding="utf8") as file:
         for line in file:
-            if line.strip() == "":
+            line = line.strip()
+            if line == "" or line.startswith('#'):
                 continue
 
-            m = re.match(re_requirement, line)
-            if m is None:
-                return False
-
-            package = m.group(1).strip()
-            version_required = (m.group(2) or "").strip()
-
-            if version_required == "":
-                continue
+            requirement = Requirement(line)
+            package = requirement.name
 
             try:
                 version_installed = importlib.metadata.version(package)
-            except Exception:
-                return False
+                installed_version = packaging.version.parse(version_installed)
 
-            if packaging.version.parse(version_required) != packaging.version.parse(version_installed):
+                # Check if the installed version satisfies the requirement
+                if installed_version not in requirement.specifier:
+                    print(f"Version mismatch for {package}: Installed version {version_installed} does not meet requirement {requirement}")
+                    return False
+            except Exception as e:
+                print(f"Error checking version for {package}: {e}")
                 return False
 
     return True
+