Merge pull request #185 from intel/update_sdengine_init

Update StableDiffusionEngine initialization

Author: gblong1

Docs/user_guide_for_windows_users.md

@@ -21,7 +21,7 @@
   - python 3.9-3.12
     - Note: This document will use python 3.9.13 as an example.
   - VC runtime
-  - [GIMP 3.0.2](https://download.gimp.org/gimp/v3.0/windows/gimp-3.0.2-setup-1.exe)
+  - [GIMP 3.0.4](https://download.gimp.org/gimp/v3.0/windows/gimp-3.0.4-setup.exe)
   - [GIMP AI plugins with OpenVINO™ Backend](https://github.com/intel/openvino-ai-plugins-gimp) from Github.
 
 
@@ -72,9 +72,9 @@ Please download the latest Visual C++ Redistributable package from MSFT [site](h
 ![](figs/VC_runtime_close.png) 
 
 
-### Install GIMP 3.0.2
+### Install GIMP 3.0.4
 
-Please download [GIMP 3.0.2](https://download.gimp.org/gimp/v3.0/windows/gimp-3.0.2-setup-1.exe) and follow below steps to install GIMP.
+Please download [GIMP 3.0.4](https://download.gimp.org/gimp/v3.0/windows/gimp-3.0.4-setup.exe) and follow below steps to install GIMP.
 
 - Click "Install for all users (recommended)"
 

gimpopenvino/plugins/openvino_utils/tools/model_manager.py

@@ -515,8 +515,6 @@ def is_model_installed(self, model_id):
                                 "best performance" : ["GPU","GPU","GPU"]
                         }
 
-
-
                 npu_is_available = self._npu_is_available
                 npu_arch = self._npu_arch
 

gimpopenvino/plugins/openvino_utils/tools/openvino_common/models_ov/stable_diffusion_engine.py

@@ -120,7 +120,7 @@ def __init__(self, model="runwayml/stable-diffusion-v1-5",
         if "NPU" in device: 
             try_enable_npu_turbo(device, self.core)
 
-        print("Loading models... ")
+        print("Loading models ... int8 ")
 
 
 
@@ -455,15 +455,15 @@ def __init__(
             self,
             model="bes-dev/stable-diffusion-v1-4-openvino",
             tokenizer="openai/clip-vit-large-patch14",
-            device=["CPU","CPU","CPU","CPU"]):
+            device=["CPU","CPU","CPU","CPU"], model_name="fp16"):
 
         self.core = Core()
         self.core.set_property({'CACHE_DIR': os.path.join(model, 'cache')})
 
         batch_size = 2 if device[1] == device[2] and device[1] == "GPU" else 1
 
         # if 'int8' is in model, then we are using unet_int8a16 model, and for this we will always use batch size 1.
-        if "int8" in model:
+        if "int8" in model_name:
             batch_size = 1
 
         self.batch_size = batch_size
@@ -477,21 +477,24 @@ def __init__(
             self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer)
             self.tokenizer.save_pretrained(model)
 
-        print("Loading models... ")
+    
 
         with concurrent.futures.ThreadPoolExecutor(max_workers=8) as executor:
             text_future = executor.submit(self.load_model, model, "text_encoder", device[0])
             vae_de_future = executor.submit(self.load_model, model, "vae_decoder", device[3])
             vae_en_future = executor.submit(self.load_model, model, "vae_encoder", device[3])
 
             if self.batch_size == 1:
-                if "int8" not in model:
-                    unet_future = executor.submit(self.load_model, model, "unet_bs1", device[1])
-                    unet_neg_future = executor.submit(self.load_model, model, "unet_bs1", device[2]) if device[1] != device[2] else None
-                else:
-                    unet_future = executor.submit(self.load_model, model, "unet_int8a16", device[1])
+                if "int8a16" in model_name:
+                    print("Loading models ... int8a16")
+                    unet_future     = executor.submit(self.load_model, model, "unet_int8a16", device[1])
                     unet_neg_future = executor.submit(self.load_model, model, "unet_int8a16", device[2]) if device[1] != device[2] else None
+                else:
+                    print("Loading models ... fp16 bs1")
+                    unet_future     = executor.submit(self.load_model, model, "unet_bs1", device[1])
+                    unet_neg_future = executor.submit(self.load_model, model, "unet_bs1", device[2]) if device[1] != device[2] else None
             else:
+                print("Loading models ... fp16")
                 unet_future = executor.submit(self.load_model, model, "unet", device[1])
                 unet_neg_future = None
 

gimpopenvino/plugins/openvino_utils/tools/stable_diffusion_ov_server.py

@@ -217,7 +217,7 @@ def initialize_engine(model_name, model_path, device_list):
         return controlnet_openpose.ControlNetOpenPose(model=model_path, device=device_list)
     if model_name == "controlnet_referenceonly":
         return stable_diffusion_engine.StableDiffusionEngineReferenceOnly(model=model_path, device=device_list)
-    return stable_diffusion_engine.StableDiffusionEngine(model=model_path, device=device_list)
+    return stable_diffusion_engine.StableDiffusionEngine(model=model_path, device=device_list, model_name=model_name)
 
 def handle_client_data(data, conn, engine, model_name, model_path, scheduler):
     if data.decode() == "kill":

testscases/StableDiffusion/stable_diffusion_engine_tc.py

@@ -123,27 +123,21 @@ def print_system_info():
 
 def initialize_engine(model_name, model_path, device_list):
     if model_name == "sd_1.5_square_int8":
-        log.info('Device list: %s', device_list)
         return stable_diffusion_engine.StableDiffusionEngineAdvanced(model=model_path, device=device_list)
     if model_name == "sd_3.0_square":
         device_list = ["GPU"]
-        log.info('Device list: %s', device_list)
         return stable_diffusion_3.StableDiffusionThreeEngine(model=model_path, device=device_list)
     if model_name == "sd_1.5_inpainting":
         return stable_diffusion_engine_inpainting_genai.StableDiffusionEngineInpaintingGenai(model=model_path, device=device_list[0])
     if model_name in ("sd_1.5_square_lcm","sdxl_base_1.0_square","sdxl_turbo_square","sd_3.0_med_diffuser_square","sd_3.5_med_turbo_square"):
         return stable_diffusion_engine_genai.StableDiffusionEngineGenai(model=model_path,model_name=model_name,device=device_list)
     if model_name == "sd_1.5_inpainting_int8":
-        log.info('Advanced Inpainting Device list: %s', device_list)
         return stable_diffusion_engine_inpainting_advanced.StableDiffusionEngineInpaintingAdvanced(model=model_path, device=device_list)
     if model_name == "controlnet_openpose_int8":
-        log.info('Device list: %s', device_list)
         return controlnet_openpose_advanced.ControlNetOpenPoseAdvanced(model=model_path, device=device_list)
     if model_name == "controlnet_canny_int8":
-        log.info('Device list: %s', device_list)
         return controlnet_cannyedge_advanced.ControlNetCannyEdgeAdvanced(model=model_path, device=device_list)
     if model_name == "controlnet_scribble_int8":
-        log.info('Device list: %s', device_list)
         return controlnet_scribble.ControlNetScribbleAdvanced(model=model_path, device=device_list)
     if model_name == "controlnet_canny":
         return controlnet_canny_edge.ControlNetCannyEdge(model=model_path, device=device_list)
@@ -153,14 +147,16 @@ def initialize_engine(model_name, model_path, device_list):
         return controlnet_openpose.ControlNetOpenPose(model=model_path, device=device_list)
     if model_name == "controlnet_referenceonly":
         return stable_diffusion_engine.StableDiffusionEngineReferenceOnly(model=model_path, device=device_list)
-    return stable_diffusion_engine.StableDiffusionEngine(model=model_path, device=device_list)
+    return stable_diffusion_engine.StableDiffusionEngine(model=model_path, device=device_list, model_name=model_name)
 
 def parse_args() -> argparse.Namespace:
     """Parse and return command line arguments."""
     parser = argparse.ArgumentParser(add_help=False, formatter_class=argparse.RawTextHelpFormatter)
     args = parser.add_argument_group('Options')
     args.add_argument('-h', '--help', action = 'help',
                       help='Show this help message and exit.')
+    args.add_argument('-l', '--list', action = 'store_true', 
+                      help='Show list of models currently installed.')
     # base path to models
     args.add_argument('-bp','--model_base_path',type = str, default = None, required = False,
                       help='Optional. Specify the absolute base path to model weights. \nUsage example:  -bp \\stable-diffusion\\model-weights\\')
@@ -194,8 +190,6 @@ def parse_args() -> argparse.Namespace:
     # guidance scale
     args.add_argument('-g','--guidance_scale',type = float, default = 7.5, required = False,
                       help='Optional. Affects how closely the image prompt is followed.')
-    
-    
     # power mode
     args.add_argument('-pm','--power_mode',type = str, default = "best performance", required = False,
                       help='Optional. Specify the power mode. Default is best performance')
@@ -209,6 +203,32 @@ def parse_args() -> argparse.Namespace:
 
     return parser.parse_args()
 
+def validate_model_paths(base_path: str, model_paths: dict) -> dict:
+    """
+    Check if model directories exist based on base_path and model_paths structure.
+
+    Args:
+        base_path (str): Root directory where models are stored.
+        model_paths (dict): Dictionary with model keys and relative path parts.
+
+    Returns:
+        dict: Dictionary with model names and a boolean indicating existence.
+    """
+    results = {}
+    for model_name, relative_parts in model_paths.items():
+        full_path = os.path.join(base_path, *relative_parts)
+        if os.path.isdir(full_path):
+            if "int8a16" in model_name:
+                if os.path.isfile(os.path.join(full_path, "unet_int8a16.xml")):
+                    results[model_name] = full_path
+            elif "fp8" in model_name:
+                if os.path.isfile(os.path.join(full_path, "unet_fp8.xml")):
+                    results[model_name] = full_path
+            else:
+                results[model_name] = full_path
+    return results
+
+
 def main():
     args = parse_args()
     results = []
@@ -253,6 +273,12 @@ def main():
         "controlnet_scribble_int8": ["stable-diffusion-ov", "controlnet-scribble-int8"],
     }
 
+    if args.list:
+        print(f"\nInstalled models: ")
+        for key in validate_model_paths(weight_path, model_paths).keys():
+            print(f"{key}")
+        exit()
+ 
     model_name = args.model_name
     model_path = os.path.join(weight_path, *model_paths.get(model_name))    
     model_config_file_name = os.path.join(model_path, "config.json")