Update ddpm.py

clean up no.1

ldm/models/diffusion/ddpm.py

@@ -461,7 +461,7 @@ class LatentDiffusion(DDPM):
         self.instantiate_cond_stage(cond_stage_config)
         self.cond_stage_forward = cond_stage_forward
         self.clip_denoised = False
-        self.bbox_tokenizer = None  # # TODO: special class?
+        self.bbox_tokenizer = None  
 
         self.restarted_from_ckpt = False
         if ckpt_path is not None:
@@ -598,7 +598,7 @@ class LatentDiffusion(DDPM):
             weighting = weighting * L_weighting
         return weighting
 
-    def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code !
+    def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code
         """
         :param x: img of size (bs, c, h, w)
         :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
@@ -793,7 +793,7 @@ class LatentDiffusion(DDPM):
                 z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
 
                 # 2. apply model loop over last dim
-                if isinstance(self.first_stage_model, VQModelInterface):  # todo ask what this is
+                if isinstance(self.first_stage_model, VQModelInterface):  
                     output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
                                                                  force_not_quantize=predict_cids or force_not_quantize)
                                    for i in range(z.shape[-1])]
@@ -901,7 +901,7 @@ class LatentDiffusion(DDPM):
 
         if hasattr(self, "split_input_params"):
             assert len(cond) == 1  # todo can only deal with one conditioning atm
-            assert not return_ids  # todo dont know what this is -> I exclude --> Good
+            assert not return_ids  
             ks = self.split_input_params["ks"]  # eg. (128, 128)
             stride = self.split_input_params["stride"]  # eg. (64, 64)