Depth2img model support

README.md

@@ -135,6 +135,7 @@ The documentation was moved from this README over to the project's [wiki](https:
 - SwinIR - https://github.com/JingyunLiang/SwinIR
 - Swin2SR - https://github.com/mv-lab/swin2sr
 - LDSR - https://github.com/Hafiidz/latent-diffusion
+- MiDaS - https://github.com/isl-org/MiDaS
 - Ideas for optimizations - https://github.com/basujindal/stable-diffusion
 - Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
 - Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)

modules/processing.py

@@ -21,7 +21,10 @@ import modules.face_restoration
 import modules.images as images
 import modules.styles
 import logging
+from ldm.data.util import AddMiDaS
+from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
 
+from einops import repeat, rearrange
 
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
 opt_C = 4
@@ -150,11 +153,26 @@ class StableDiffusionProcessing():
 
         return image_conditioning
 
-    def img2img_image_conditioning(self, source_image, latent_image, image_mask = None):
+    def depth2img_image_conditioning(self, source_image):
-        if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
+        # Use the AddMiDaS helper to Format our source image to suit the MiDaS model
-            # Dummy zero conditioning if we're not using inpainting model.
+        transformer = AddMiDaS(model_type="dpt_hybrid")
-            return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
+        transformed = transformer({"jpg": rearrange(source_image[0], "c h w -> h w c")})
+        midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
+        midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
 
+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
+        conditioning = torch.nn.functional.interpolate(
+            self.sd_model.depth_model(midas_in),
+            size=conditioning_image.shape[2:],
+            mode="bicubic",
+            align_corners=False,
+        )
+
+        (depth_min, depth_max) = torch.aminmax(conditioning)
+        conditioning = 2. * (conditioning - depth_min) / (depth_max - depth_min) - 1.
+        return conditioning
+
+    def inpainting_image_conditioning(self, source_image, latent_image, image_mask = None):
         self.is_using_inpainting_conditioning = True
 
         # Handle the different mask inputs
@@ -191,6 +209,18 @@ class StableDiffusionProcessing():
 
         return image_conditioning
 
+    def img2img_image_conditioning(self, source_image, latent_image, image_mask=None):
+        # HACK: Using introspection as the Depth2Image model doesn't appear to uniquely
+        # identify itself with a field common to all models. The conditioning_key is also hybrid.
+        if isinstance(self.sd_model, LatentDepth2ImageDiffusion):
+            return self.depth2img_image_conditioning(source_image)
+
+        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
+            return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
+
+        # Dummy zero conditioning if we're not using inpainting or depth model.
+        return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
+
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
 

modules/sd_models.py

@@ -7,6 +7,9 @@ import torch
 import re
 import safetensors.torch
 from omegaconf import OmegaConf
+from os import mkdir
+from urllib import request
+import ldm.modules.midas as midas
 
 from ldm.util import instantiate_from_config
 
@@ -36,6 +39,7 @@ def setup_model():
         os.makedirs(model_path)
 
     list_models()
+    enable_midas_autodownload()
 
 
 def checkpoint_tiles(): 
@@ -227,6 +231,48 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     sd_vae.load_vae(model, vae_file)
 
 
+def enable_midas_autodownload():
+    """
+    Gives the ldm.modules.midas.api.load_model function automatic downloading.
+
+    When the 512-depth-ema model, and other future models like it, is loaded,
+    it calls midas.api.load_model to load the associated midas depth model.
+    This function applies a wrapper to download the model to the correct
+    location automatically.
+    """
+
+    midas_path = os.path.join(models_path, 'midas')
+
+    # stable-diffusion-stability-ai hard-codes the midas model path to
+    # a location that differs from where other scripts using this model look.
+    # HACK: Overriding the path here.
+    for k, v in midas.api.ISL_PATHS.items():
+        file_name = os.path.basename(v)
+        midas.api.ISL_PATHS[k] = os.path.join(midas_path, file_name)
+
+    midas_urls = {
+        "dpt_large": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
+        "dpt_hybrid": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt",
+        "midas_v21": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21-f6b98070.pt",
+        "midas_v21_small": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21_small-70d6b9c8.pt",
+    }
+
+    midas.api.load_model_inner = midas.api.load_model
+
+    def load_model_wrapper(model_type):
+        path = midas.api.ISL_PATHS[model_type]
+        if not os.path.exists(path):
+            if not os.path.exists(midas_path):
+                mkdir(midas_path)
+    
+            print(f"Downloading midas model weights for {model_type} to {path}")
+            request.urlretrieve(midas_urls[model_type], path)
+            print(f"{model_type} downloaded")
+
+        return midas.api.load_model_inner(model_type)
+
+    midas.api.load_model = load_model_wrapper
+
 def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()