init

app.py

@@ -18,34 +18,6 @@ import gc
 device = "cuda"
 dtype = torch.float16
 
-
-controlnet_depth= ControlNetModel.from_pretrained(
-    "controlnet_depth", torch_dtype=dtype, use_safetensors=True)
-controlnet_seg = ControlNetModel.from_pretrained(
-    "own_controlnet", torch_dtype=dtype, use_safetensors=True)
-
-pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-    "SG161222/Realistic_Vision_V5.1_noVAE",
-    #"models/runwayml--stable-diffusion-inpainting",
-    controlnet=[controlnet_depth, controlnet_seg],
-    safety_checker=None,
-    torch_dtype=dtype
-)
-
-pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models",
-                     weight_name="ip-adapter_sd15.bin")
-pipe.set_ip_adapter_scale(0.4)
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-pipe = pipe.to(device)
-guide_pipe = StableDiffusionXLPipeline.from_pretrained("segmind/SSD-1B",
-                                                       torch_dtype=dtype, use_safetensors=True, variant="fp16")
-guide_pipe = guide_pipe.to(device)
-   
-seg_image_processor, image_segmentor = get_segmentation_pipeline()
-depth_feature_extractor, depth_estimator = get_depth_pipeline()
-depth_estimator = depth_estimator.to(device)
-
-        
         
 css = """
 #img-display-container {
@@ -353,7 +325,34 @@ def create_demo(model):
     examples = gr.Examples(examples=[["imgs/bedroom_1.jpg", "An elegantly appointed bedroom in the Art Deco style, featuring a grand king-size bed with geometric bedding, a luxurious velvet armchair, and a mirrored nightstand that reflects the room's opulence. Art Deco-inspired artwork adds a touch of glamour"], ["imgs/bedroom_2.jpg", "A bedroom that exudes French country charm with a soft upholstered bed, walls adorned with floral wallpaper, and a vintage wooden wardrobe. A crystal chandelier casts a warm, inviting glow over the space"], ["imgs/dinning_room_1.jpg", "A cozy dining room that captures the essence of rustic charm with a solid wooden farmhouse table at its core, surrounded by an eclectic mix of mismatched chairs. An antique sideboard serves as a statement piece, and the ambiance is warmly lit by a series of quaint Edison bulbs dangling from the ceiling"], ["imgs/dinning_room_3.jpg", "A dining room that epitomizes contemporary elegance, anchored by a sleek, minimalist dining table paired with stylish modern chairs. Artistic lighting fixtures create a focal point above, while the surrounding minimalist decor ensures the space feels open, airy, and utterly modern"], ["imgs/image_1.jpg", "A glamorous master bedroom in Hollywood Regency style, boasting a plush tufted headboard, mirrored furniture reflecting elegance, luxurious fabrics in rich textures, and opulent gold accents for a touch of luxury."], ["imgs/image_2.jpg", "A vibrant living room with a tropical theme, complete with comfortable rattan furniture, large leafy plants bringing the outdoors in, bright cushions adding pops of color, and bamboo blinds for natural light control."], ["imgs/living_room_1.jpg", "A stylish living room embracing mid-century modern aesthetics, featuring a vintage teak coffee table at its center, complemented by a classic sunburst clock on the wall and a cozy shag rug underfoot, creating a warm and inviting atmosphere"]],
                            inputs=[input_image, input_text])
 
-                           
+
+controlnet_depth= ControlNetModel.from_pretrained(
+    "controlnet_depth", torch_dtype=dtype, use_safetensors=True)
+controlnet_seg = ControlNetModel.from_pretrained(
+    "own_controlnet", torch_dtype=dtype, use_safetensors=True)
+
+pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
+    "SG161222/Realistic_Vision_V5.1_noVAE",
+    #"models/runwayml--stable-diffusion-inpainting",
+    controlnet=[controlnet_depth, controlnet_seg],
+    safety_checker=None,
+    torch_dtype=dtype
+)
+
+pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models",
+                     weight_name="ip-adapter_sd15.bin")
+pipe.set_ip_adapter_scale(0.4)
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+pipe = pipe.to(device)
+guide_pipe = StableDiffusionXLPipeline.from_pretrained("segmind/SSD-1B",
+                                                       torch_dtype=dtype, use_safetensors=True, variant="fp16")
+guide_pipe = guide_pipe.to(device)
+   
+seg_image_processor, image_segmentor = get_segmentation_pipeline()
+depth_feature_extractor, depth_estimator = get_depth_pipeline()
+depth_estimator = depth_estimator.to(device)
+
+
 def main():
     model = ControlNetDepthDesignModelMulti()
     print('Models uploaded successfully')