Update ddpm.py

clean up no.1

ldm/models/diffusion/ddpm.py

@@ -461,7 +461,7 @@ def __init__(self,
  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 @@ def get_weighting(self, h, w, Ly, Lx, device):
  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 @@ def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_qua
  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 @@ def apply_model(self, x_noisy, t, cond, return_ids=False):
 
  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)