Update app.py

app.py

@@ -1,198 +1,198 @@
-import space
-import torch
-import numpy as np
-from transformers import AutoImageProcessor, AutoModelForDepthEstimation
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
-import natten
-import gradio
-from PIL import Image
-
-
-"""
-IMPORT MODEL
-"""
-
-#model generate depth image
-depth_image_processor = AutoImageProcessor.from_pretrained("LiheYoung/depth-anything-large-hf", torch_dtype=torch.float16)
-depth_model = AutoModelForDepthEstimation.from_pretrained("LiheYoung/depth-anything-large-hf", torch_dtype=torch.float16)
-depth_model = depth_model.cuda()
-
-#model generate segment image
-from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation
-
-processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_ade20k_dinat_large")
-model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_dinat_large")
-model = model.cuda()
-
-#model generate image
-
-#load depth controlnet, segmentation controlnet
-controlnets = [
-    ControlNetModel.from_pretrained("Lam-Hung/controlnet_depth_interior", torch_dtype=torch.float16, use_safetensors=True),
-    ControlNetModel.from_pretrained("Lam-Hung/controlnet_segment_interior", torch_dtype=torch.float16, use_safetensors=True)
-]
-#load stable diffusion 1.5 and controlnets
-pipeline = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet= controlnets, torch_dtype=torch.float16, use_safetensors=True
-)
-# take UniPCMultistepScheduler for faster inference
-pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)
-pipeline.load_lora_weights('Lam-Hung/controlnet_lora_interior', weight_name= "pytorch_lora_weights.safetensors", adapter_name="interior")
-pipeline.to("cuda")
-
-
-
-"""
-IMPORT FUNCTION
-"""
-def ade_palette() -> list[list[int]]:
-    """ADE20K palette that maps each class to RGB values."""
-    return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
-            [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
-            [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
-            [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
-            [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
-            [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
-            [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
-            [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
-            [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
-            [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
-            [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
-            [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
-            [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
-            [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
-            [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
-            [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
-            [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
-            [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
-            [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
-            [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
-            [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
-            [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
-            [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
-            [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
-            [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
-            [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
-            [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
-            [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
-            [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
-            [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
-            [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
-            [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
-            [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
-            [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
-            [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
-            [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
-            [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
-            [102, 255, 0], [92, 0, 255]]
-
-
-@torch.inference_mode
-@space.GPU
-def get_depth_image(image: Image) -> Image:
-
-    """
-    create depth image
-    """
-
-    image_to_depth = depth_image_processor(images=image, return_tensors="pt").to("cuda")
-    with torch.no_grad():
-        depth_map = depth_model(**image_to_depth).predicted_depth
-
-    width, height = image.size
-    depth_map = torch.nn.functional.interpolate(
-        depth_map.unsqueeze(1).float(),
-        size=(height, width),
-        mode="bicubic",
-        align_corners=False,
-    )
-    depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
-    depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
-    depth_map = (depth_map - depth_min) / (depth_max - depth_min)
-    image = torch.cat([depth_map] * 3, dim=1)
-
-    image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
-    image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
-    return image
-
-@torch.inference_mode
-@space.GPU
-def get_segmentation_of_room(image: Image):
-#-> tuple[np.ndarray, Image]:
-
-    """
-    create instance segmentation image
-    """
-
-    # Semantic Segmentation
-    with torch.inference_mode():
-        semantic_inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt")
-        semantic_inputs = {key: value.to("cuda") for key, value in semantic_inputs.items()}
-        semantic_outputs = model(**semantic_inputs)
-        # pass through image_processor for postprocessing
-        predicted_semantic_map = \
-        processor.post_process_semantic_segmentation(semantic_outputs, target_sizes=[image.size[::-1]])[0]
-
-    predicted_semantic_map = predicted_semantic_map.cpu()
-    color_seg = np.zeros((predicted_semantic_map.shape[0], predicted_semantic_map.shape[1], 3), dtype=np.uint8)
-
-    palette = np.array(ade_palette())
-    for label, color in enumerate(palette):
-        color_seg[predicted_semantic_map == label, :] = color
-
-    color_seg = color_seg.astype(np.uint8)
-    seg_image = Image.fromarray(color_seg).convert('RGB')
-    return seg_image
-
-@torch.inference_mode
-@space.GPU
-def interior_inference(image, 
-                       prompt, 
-                       negative_prompt="window, door, low resolution, banner, logo, watermark, text, deformed, blurry, out of focus, surreal, ugly, beginner",
-                       num_inference_steps=25, 
-                       depth_weight=0.9, 
-                       segment_weight=0.9, 
-                       lora_weight=0.7,
-                       seed= 123):
-
-    depth_image = get_depth_image(image)
-    segmentation_image = get_segmentation_of_room(image)
-    prompt = prompt + " interior design, 4K, high resolution, photorealistic"
-
-    image_interior = pipeline(
-        prompt,
-        negative_prompt = negative_prompt,
-        image = [depth_image, segmentation_image],
-        num_inference_steps = num_inference_steps,
-        generator = torch.manual_seed(seed),
-
-        #lora_scale if enable_lora
-        cross_attention_kwargs={"scale": lora_weight},
-        controlnet_conditioning_scale=[depth_weight, segment_weight],
-        ).images[0]
-    
-    return image_interior
-
-interface = gr.Interface(
-    fn = interior_inference,
-    inputs = [
-        gr.Image(type = "pil", label = "Empty room image", show_label = True),
-        gr.Textbox(label = "Prompt", lines = 3, placeholder = "Enter your prompt here"),
-    ],
-    outputs=[
-        gr.Image(type = "pil", label = "Interior design", show_label = True),
-    ],
-    additional_inputs=[
-        gr.Textbox(label = "Negative prompt", lines = 3, placeholder = "Enter your negative prompt here"),
-        gr.Slider(label = "Number of inference steps", minimum = 1, maximum = 100, value = 25, step = 1),
-        gr.Slider(label = "Depth weight", minimum = 0, maximum = 1, value = 0.9, step = 0.1),
-        gr.Slider(label = "Segment weight", minimum = 0, maximum = 1, value = 0.9, step = 0.1),
-        gr.Slider(label = "Lora weight", minimum = 0, maximum = 1, value = 0.7, step = 0.1),
-        gr.Number(label = "Seed", value = 123),
-    ],
-    title="INTERIOR DESIGN",
-    description="**We will design your empty room become the beautiful room",
-)
-
-if "__name__" =="__main__":
+import spaces
+import torch
+import numpy as np
+from transformers import AutoImageProcessor, AutoModelForDepthEstimation
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
+import natten
+import gradio
+from PIL import Image
+
+
+"""
+IMPORT MODEL
+"""
+
+#model generate depth image
+depth_image_processor = AutoImageProcessor.from_pretrained("LiheYoung/depth-anything-large-hf", torch_dtype=torch.float16)
+depth_model = AutoModelForDepthEstimation.from_pretrained("LiheYoung/depth-anything-large-hf", torch_dtype=torch.float16)
+depth_model = depth_model.cuda()
+
+#model generate segment image
+from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation
+
+processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_ade20k_dinat_large")
+model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_dinat_large")
+model = model.cuda()
+
+#model generate image
+
+#load depth controlnet, segmentation controlnet
+controlnets = [
+    ControlNetModel.from_pretrained("Lam-Hung/controlnet_depth_interior", torch_dtype=torch.float16, use_safetensors=True),
+    ControlNetModel.from_pretrained("Lam-Hung/controlnet_segment_interior", torch_dtype=torch.float16, use_safetensors=True)
+]
+#load stable diffusion 1.5 and controlnets
+pipeline = StableDiffusionControlNetPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", controlnet= controlnets, torch_dtype=torch.float16, use_safetensors=True
+)
+# take UniPCMultistepScheduler for faster inference
+pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)
+pipeline.load_lora_weights('Lam-Hung/controlnet_lora_interior', weight_name= "pytorch_lora_weights.safetensors", adapter_name="interior")
+pipeline.to("cuda")
+
+
+
+"""
+IMPORT FUNCTION
+"""
+def ade_palette() -> list[list[int]]:
+    """ADE20K palette that maps each class to RGB values."""
+    return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
+            [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
+            [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
+            [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
+            [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
+            [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
+            [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
+            [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
+            [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
+            [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
+            [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
+            [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
+            [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
+            [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
+            [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
+            [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
+            [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
+            [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
+            [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
+            [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
+            [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
+            [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
+            [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
+            [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
+            [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
+            [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
+            [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
+            [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
+            [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
+            [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
+            [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
+            [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
+            [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
+            [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
+            [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
+            [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
+            [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
+            [102, 255, 0], [92, 0, 255]]
+
+
+@torch.inference_mode
+@space.GPU
+def get_depth_image(image: Image) -> Image:
+
+    """
+    create depth image
+    """
+
+    image_to_depth = depth_image_processor(images=image, return_tensors="pt").to("cuda")
+    with torch.no_grad():
+        depth_map = depth_model(**image_to_depth).predicted_depth
+
+    width, height = image.size
+    depth_map = torch.nn.functional.interpolate(
+        depth_map.unsqueeze(1).float(),
+        size=(height, width),
+        mode="bicubic",
+        align_corners=False,
+    )
+    depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
+    depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
+    depth_map = (depth_map - depth_min) / (depth_max - depth_min)
+    image = torch.cat([depth_map] * 3, dim=1)
+
+    image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
+    image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
+    return image
+
+@torch.inference_mode
+@space.GPU
+def get_segmentation_of_room(image: Image):
+#-> tuple[np.ndarray, Image]:
+
+    """
+    create instance segmentation image
+    """
+
+    # Semantic Segmentation
+    with torch.inference_mode():
+        semantic_inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt")
+        semantic_inputs = {key: value.to("cuda") for key, value in semantic_inputs.items()}
+        semantic_outputs = model(**semantic_inputs)
+        # pass through image_processor for postprocessing
+        predicted_semantic_map = \
+        processor.post_process_semantic_segmentation(semantic_outputs, target_sizes=[image.size[::-1]])[0]
+
+    predicted_semantic_map = predicted_semantic_map.cpu()
+    color_seg = np.zeros((predicted_semantic_map.shape[0], predicted_semantic_map.shape[1], 3), dtype=np.uint8)
+
+    palette = np.array(ade_palette())
+    for label, color in enumerate(palette):
+        color_seg[predicted_semantic_map == label, :] = color
+
+    color_seg = color_seg.astype(np.uint8)
+    seg_image = Image.fromarray(color_seg).convert('RGB')
+    return seg_image
+
+@torch.inference_mode
+@space.GPU
+def interior_inference(image, 
+                       prompt, 
+                       negative_prompt="window, door, low resolution, banner, logo, watermark, text, deformed, blurry, out of focus, surreal, ugly, beginner",
+                       num_inference_steps=25, 
+                       depth_weight=0.9, 
+                       segment_weight=0.9, 
+                       lora_weight=0.7,
+                       seed= 123):
+
+    depth_image = get_depth_image(image)
+    segmentation_image = get_segmentation_of_room(image)
+    prompt = prompt + " interior design, 4K, high resolution, photorealistic"
+
+    image_interior = pipeline(
+        prompt,
+        negative_prompt = negative_prompt,
+        image = [depth_image, segmentation_image],
+        num_inference_steps = num_inference_steps,
+        generator = torch.manual_seed(seed),
+
+        #lora_scale if enable_lora
+        cross_attention_kwargs={"scale": lora_weight},
+        controlnet_conditioning_scale=[depth_weight, segment_weight],
+        ).images[0]
+    
+    return image_interior
+
+interface = gr.Interface(
+    fn = interior_inference,
+    inputs = [
+        gr.Image(type = "pil", label = "Empty room image", show_label = True),
+        gr.Textbox(label = "Prompt", lines = 3, placeholder = "Enter your prompt here"),
+    ],
+    outputs=[
+        gr.Image(type = "pil", label = "Interior design", show_label = True),
+    ],
+    additional_inputs=[
+        gr.Textbox(label = "Negative prompt", lines = 3, placeholder = "Enter your negative prompt here"),
+        gr.Slider(label = "Number of inference steps", minimum = 1, maximum = 100, value = 25, step = 1),
+        gr.Slider(label = "Depth weight", minimum = 0, maximum = 1, value = 0.9, step = 0.1),
+        gr.Slider(label = "Segment weight", minimum = 0, maximum = 1, value = 0.9, step = 0.1),
+        gr.Slider(label = "Lora weight", minimum = 0, maximum = 1, value = 0.7, step = 0.1),
+        gr.Number(label = "Seed", value = 123),
+    ],
+    title="INTERIOR DESIGN",
+    description="**We will design your empty room become the beautiful room",
+)
+
+if "__name__" =="__main__":
     interface.launch()