support for marigold

CHANGELOG.md

@@ -1,4 +1,6 @@
 ## Changelog
+### 0.4.5
+ * Support for [Marigold](https://marigoldmonodepth.github.io). [PR #385](https://github.com/thygate/stable-diffusion-webui-depthmap-script/pull/385).
 ### 0.4.4
  * Compatibility with stable-diffusion-webui 1.6.0
 ### 0.4.3 video processing tab

README.md

@@ -198,3 +198,16 @@ ZoeDepth :
   copyright = {arXiv.org perpetual, non-exclusive license}
 }
 ```
+
+Marigold - Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation:
+
+```
+@misc{ke2023repurposing,
+      title={Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation}, 
+      author={Bingxin Ke and Anton Obukhov and Shengyu Huang and Nando Metzger and Rodrigo Caye Daudt and Konrad Schindler},
+      year={2023},
+      eprint={2312.02145},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```

install.py

@@ -38,6 +38,11 @@ def ensure(module_name, min_version=None):
     launch.run_pip('install "moviepy==1.0.2"', "moviepy requirement for depthmap script")
 ensure('transforms3d', '0.4.1')
 
+ensure('transformers', '4.32.1')
+ensure('xformers', '0.0.21')
+ensure('accelerate', '0.22.0')
+ensure('diffusers', '0.20.1')
+
 ensure('imageio')  # 2.4.1
 try:  # Dirty hack to not reinstall every time
     importlib_metadata.version('imageio-ffmpeg')
@@ -53,4 +58,4 @@ def ensure(module_name, min_version=None):
 if platform.system() == 'Darwin':
     ensure('pyqt6')
 
-launch.git_clone("https://github.com/prs-eth/Marigold", "repositories/Marigold", "Marigold", "cc78ff3")
+launch.git_clone("https://github.com/prs-eth/Marigold", "Marigold", "Marigold", "cc78ff3")

pix2pix/models/pix2pix4depth_model.py

@@ -94,8 +94,8 @@ def set_input_train(self, input):
         self.real_A = torch.cat((self.outer, self.inner), 1)
 
     def set_input(self, outer, inner):
-        inner = torch.from_numpy(inner).unsqueeze(0).unsqueeze(0)
-        outer = torch.from_numpy(outer).unsqueeze(0).unsqueeze(0)
+        inner = torch.from_numpy(inner).unsqueeze(0).unsqueeze(0).float()
+        outer = torch.from_numpy(outer).unsqueeze(0).unsqueeze(0).float()
 
         inner = (inner - torch.min(inner))/(torch.max(inner)-torch.min(inner))
         outer = (outer - torch.min(outer))/(torch.max(outer)-torch.min(outer))

requirements.txt

@@ -16,5 +16,9 @@ transforms3d>=0.4.1
 imageio>=2.4.1,<3.0
 imageio-ffmpeg
 networkx>=2.5
+transformers>=4.32.1 # For Marigold
+xformers==0.0.21 # For Marigold
+accelerate>=0.22.0 # For Marigold
+diffusers>=0.20.1 # For Marigold
 pyqt5; sys_platform == 'windows'
 pyqt6; sys_platform != 'windows'

src/depthmap_generation.py

@@ -74,8 +74,6 @@ def load_models(self, model_type, device: torch.device, boost: bool):
         model_dir = "./models/midas"
         if model_type == 0:
             model_dir = "./models/leres"
-        if model_type == 10:
-            "./models/marigold"
         # create paths to model if not present
         os.makedirs(model_dir, exist_ok=True)
         os.makedirs('./models/pix2pix', exist_ok=True)
@@ -197,9 +195,9 @@ def load_models(self, model_type, device: torch.device, boost: bool):
             model = build_model(conf)
 
         elif model_type == 10:  # Marigold v1
-            # TODO: pass more parameters
-            model_path = f"{model_dir}/marigold_v1/"
-            from repositories.Marigold.src.model.marigold_pipeline import MarigoldPipeline
+            model_path = "Bingxin/Marigold"
+            print(model_path)
+            from Marigold.src.model.marigold_pipeline import MarigoldPipeline
             model = MarigoldPipeline.from_pretrained(model_path)
 
         model.eval()  # prepare for evaluation
@@ -301,11 +299,11 @@ def get_raw_prediction(self, input, net_width, net_height):
                                                self.resize_mode, self.normalization, self.no_half,
                                                self.precision == "autocast")
             elif self.depth_model_type == 10:
-                raw_prediction = estimatemarigold(img, self.depth_model, net_width, net_height, self.device)
+                raw_prediction = estimatemarigold(img, self.depth_model, net_width, net_height)
         else:
             raw_prediction = estimateboost(img, self.depth_model, self.depth_model_type, self.pix2pix_model,
                                            self.boost_whole_size_threshold)
-        raw_prediction_invert = self.depth_model_type in [0, 7, 8, 9]
+        raw_prediction_invert = self.depth_model_type in [0, 7, 8, 9, 10]
         return raw_prediction, raw_prediction_invert
 
 
@@ -405,11 +403,11 @@ def estimatemidas(img, model, w, h, resize_mode, normalization, no_half, precisi
     return prediction
 
 
-def estimatemarigold(image, model, w, h, device):
-    from repositories.Marigold.src.model.marigold_pipeline import MarigoldPipeline
-    from repositories.Marigold.src.util.ensemble import ensemble_depths
-    from repositories.Marigold.src.util.image_util import chw2hwc, colorize_depth_maps, resize_max_res
-    from repositories.Marigold.src.util.seed_all import seed_all
+def estimatemarigold(image, model, w, h):
+    from Marigold.src.model.marigold_pipeline import MarigoldPipeline
+    from Marigold.src.util.ensemble import ensemble_depths
+    from Marigold.src.util.image_util import chw2hwc, colorize_depth_maps, resize_max_res
+    from Marigold.src.util.seed_all import seed_all
 
     n_repeat = 10
     denoise_steps = 10
@@ -418,13 +416,18 @@ def estimatemarigold(image, model, w, h, device):
     tol = 1e-3
     reduction_method = "median"
     merging_max_res = None
+    resize_to_max_res = None
 
     # From Marigold repository run.py
     with torch.no_grad():
-        rgb = np.transpose(image, (2, 0, 1))  # [H, W, rgb] -> [rgb, H, W]
-        rgb_norm = rgb / 255.0
+        if resize_to_max_res is not None:
+            image = (image * 255).astype(np.uint8)
+            image = np.asarray(resize_max_res(
+                Image.fromarray(image), max_edge_resolution=resize_to_max_res
+            )) / 255.0
+        rgb_norm = np.transpose(image, (2, 0, 1))  # [H, W, rgb] -> [rgb, H, W]
         rgb_norm = torch.from_numpy(rgb_norm).unsqueeze(0).float()
-        rgb_norm = rgb_norm.to(device)
+        rgb_norm = rgb_norm.to(depthmap_device)
 
         model.unet.eval()
         depth_pred_ls = []
@@ -445,7 +448,7 @@ def estimatemarigold(image, model, w, h, device):
                 tol=tol,
                 reduction=reduction_method,
                 max_res=merging_max_res,
-                device=device,
+                device=depthmap_device,
             )
         else:
             depth_pred = depth_preds
@@ -942,6 +945,8 @@ def doubleestimate(img, size1, size2, pix2pixsize, model, net_type, pix2pixmodel
 def singleestimate(img, msize, model, net_type):
     if net_type == 0:
         return estimateleres(img, model, msize, msize)
+    elif net_type == 10:
+        return estimatemarigold(img, model, msize, msize)
     elif net_type >= 7:
         # np to PIL
         return estimatezoedepth(Image.fromarray(np.uint8(img * 255)).convert('RGB'), model, msize, msize)

src/misc.py

@@ -15,7 +15,7 @@ def get_commit_hash():
 
 REPOSITORY_NAME = "stable-diffusion-webui-depthmap-script"
 SCRIPT_NAME = "DepthMap"
-SCRIPT_VERSION = "v0.4.4"
+SCRIPT_VERSION = "v0.4.5"
 SCRIPT_FULL_NAME = f"{SCRIPT_NAME} {SCRIPT_VERSION} ({get_commit_hash()})"