Update app.py

app.py

@@ -1,4 +1,4 @@
-mport gradio as gr
+import gradio as gr
 import cv2
 import matplotlib
 import numpy as np
@@ -10,7 +10,11 @@ import tempfile
 from gradio_imageslider import ImageSlider
 from huggingface_hub import hf_hub_download
 
-from depth_anything_v2.dpt import DepthAnythingV2
+# from depth_anything_v2.dpt import DepthAnythingV2
+from marigold import MarigoldPipeline
+from diffusers import AutoencoderKL, DDIMScheduler, UNet2DConditionModel
+from transformers import CLIPTextModel, CLIPTokenizer
+import xformers
 
 css = """
 #img-display-container {
@@ -27,33 +31,62 @@ css = """
 }
 """
 DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
-model_configs = {
-    'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
-    'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
-    'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
-    'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
-}
-encoder2name = {
-    'vits': 'Small',
-    'vitb': 'Base',
-    'vitl': 'Large',
-    'vitg': 'Giant', # we are undergoing company review procedures to release our giant model checkpoint
-}
-encoder = 'vitl'
-model_name = encoder2name[encoder]
-model = DepthAnythingV2(**model_configs[encoder])
-filepath = hf_hub_download(repo_id=f"depth-anything/Depth-Anything-V2-{model_name}", filename=f"depth_anything_v2_{encoder}.pth", repo_type="model")
-state_dict = torch.load(filepath, map_location="cpu")
-model.load_state_dict(state_dict)
-model = model.to(DEVICE).eval()
-
-title = "# Depth Anything V2"
-description = """Official demo for **Depth Anything V2**.
-Please refer to our [paper](https://arxiv.org/abs/2406.09414), [project page](https://depth-anything-v2.github.io), and [github](https://github.com/DepthAnything/Depth-Anything-V2) for more details."""
-
+checkpoint_path = "GonzaloMG/marigold-e2e-ft-depth"
+unet         = UNet2DConditionModel.from_pretrained(checkpoint_path, subfolder="unet")   
+vae          = AutoencoderKL.from_pretrained(checkpoint_path, subfolder="vae")  
+text_encoder = CLIPTextModel.from_pretrained(checkpoint_path, subfolder="text_encoder")  
+tokenizer    = CLIPTokenizer.from_pretrained(checkpoint_path, subfolder="tokenizer") 
+scheduler    = DDIMScheduler.from_pretrained(checkpoint_path, timestep_spacing=timestep_spacing, subfolder="scheduler") 
+pipe = MarigoldPipeline.from_pretrained(pretrained_model_name_or_path = checkpoint_path,
+                                        unet=unet, 
+                                        vae=vae, 
+                                        scheduler=scheduler, 
+                                        text_encoder=text_encoder, 
+                                        tokenizer=tokenizer, 
+                                        variant=variant, 
+                                        torch_dtype=dtype, 
+                                        )
+try:
+    pipe.enable_xformers_memory_efficient_attention()
+except ImportError:
+    pass  # run without xformers
+pipe = pipe.to(DEVICE)
+pipe.unet.eval()
+
+# model_configs = {
+#     'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
+#     'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
+#     'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
+#     'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
+# }
+# encoder2name = {
+#     'vits': 'Small',
+#     'vitb': 'Base',
+#     'vitl': 'Large',
+#     'vitg': 'Giant', # we are undergoing company review procedures to release our giant model checkpoint
+# }
+# encoder = 'vitl'
+# model_name = encoder2name[encoder]
+# model = DepthAnythingV2(**model_configs[encoder])
+# filepath = hf_hub_download(repo_id=f"depth-anything/Depth-Anything-V2-{model_name}", filename=f"depth_anything_v2_{encoder}.pth", repo_type="model")
+# state_dict = torch.load(filepath, map_location="cpu")
+# model.load_state_dict(state_dict)
+# model = model.to(DEVICE).eval()
+
+title = "# ..."
+description = """... **...**"""
+
+
+# def predict_depth(image):
+#     return model.infer_image(image)
+    
 @spaces.GPU
-def predict_depth(image):
-    return model.infer_image(image)
+def predict_depth(image): #, processing_res, model_choice, current_model):
+    with torch.no_grad():
+        pipe_out = pipe(image, denoising_steps=1, ensemble_size=1, noise="zeros", normals=False, processing_res=768, match_input_res=True)
+    pred = pipe_out.depth_np
+    pred_colored = pipe_out.depth_colored
+    return pred, pred_colored
 
 with gr.Blocks(css=css) as demo:
     gr.Markdown(title)
@@ -70,25 +103,47 @@ with gr.Blocks(css=css) as demo:
     cmap = matplotlib.colormaps.get_cmap('Spectral_r')
 
     def on_submit(image):
-        original_image = image.copy()
 
-        h, w = image.shape[:2]
+        if image is None:
+            print("No image uploaded.")
+            return None
+    
+        pil_image = Image.fromarray(image.astype('uint8'))
+        depth_npy, depth_colored = predict_depth(pil_image)
+    
+        # Save the npy data (raw depth map)
+        # tmp_npy_depth = tempfile.NamedTemporaryFile(suffix='.npy', delete=False)
+        # np.save(tmp_npy_depth.name, depth_npy)
+    
+        # Save the grayscale depth map
+        depth_gray = (depth_npy * 65535.0).astype(np.uint16)
+        tmp_gray_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
+        Image.fromarray(depth_gray).save(tmp_gray_depth.name, mode="I;16")
+    
+        # Save the colored depth map
+        tmp_colored_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
+        depth_colored.save(tmp_colored_depth.name)
+    
+        print("Dummy predictions complete, returning results.")
+        return [(image, depth_colored),  tmp_gray_depth.name, tmp_colored_depth.name]
 
-        depth = predict_depth(image[:, :, ::-1])
+        # h, w = image.shape[:2]
 
-        raw_depth = Image.fromarray(depth.astype('uint16'))
-        tmp_raw_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
-        raw_depth.save(tmp_raw_depth.name)
+        # depth = predict_depth(image[:, :, ::-1])
 
-        depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
-        depth = depth.astype(np.uint8)
-        colored_depth = (cmap(depth)[:, :, :3] * 255).astype(np.uint8)
+        # raw_depth = Image.fromarray(depth.astype('uint16'))
+        # tmp_raw_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
+        # raw_depth.save(tmp_raw_depth.name)
 
-        gray_depth = Image.fromarray(depth)
-        tmp_gray_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
-        gray_depth.save(tmp_gray_depth.name)
+        # depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
+        # depth = depth.astype(np.uint8)
+        # colored_depth = (cmap(depth)[:, :, :3] * 255).astype(np.uint8)
+
+        # gray_depth = Image.fromarray(depth)
+        # tmp_gray_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
+        # gray_depth.save(tmp_gray_depth.name)
 
-        return [(original_image, colored_depth), tmp_gray_depth.name, tmp_raw_depth.name]
+        # return [(original_image, colored_depth), tmp_gray_depth.name, tmp_raw_depth.name]
 
     submit.click(on_submit, inputs=[input_image], outputs=[depth_image_slider, gray_depth_file, raw_file])