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modules/processing.py

@@ -28,8 +28,6 @@ import modules.images as images
 import modules.styles
 import modules.sd_models as sd_models
 import modules.sd_vae as sd_vae
-from ldm.data.util import AddMiDaS
-from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
 
 from einops import repeat, rearrange
 from blendmodes.blend import blendLayers, BlendType
@@ -295,23 +293,7 @@ class StableDiffusionProcessing:
         return txt2img_image_conditioning(self.sd_model, x, width or self.width, height or self.height)
 
     def depth2img_image_conditioning(self, source_image):
-        # Use the AddMiDaS helper to Format our source image to suit the MiDaS model
-        transformer = AddMiDaS(model_type="dpt_hybrid")
-        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 = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
-        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
+        raise NotImplementedError('NotImplementedError: depth2img_image_conditioning')
 
     def edit_image_conditioning(self, source_image):
         conditioning_image = shared.sd_model.encode_first_stage(source_image).mode()
@@ -368,11 +350,6 @@ class StableDiffusionProcessing:
     def img2img_image_conditioning(self, source_image, latent_image, image_mask=None, round_image_mask=True):
         source_image = devices.cond_cast_float(source_image)
 
-        # 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.sd_model.cond_stage_key == "edit":
             return self.edit_image_conditioning(source_image)