Robin Rombach commited on
Commit
d2152a2
β€’
1 Parent(s): 384c3f4

Update ddpm.py

Browse files

clean up no.1

Former-commit-id: 2b46bcb98c8e8fdb250cb8ff2e20874f3ccdd768

Files changed (1) hide show
  1. ldm/models/diffusion/ddpm.py +4 -4
ldm/models/diffusion/ddpm.py CHANGED
@@ -461,7 +461,7 @@ class LatentDiffusion(DDPM):
461
  self.instantiate_cond_stage(cond_stage_config)
462
  self.cond_stage_forward = cond_stage_forward
463
  self.clip_denoised = False
464
- self.bbox_tokenizer = None # # TODO: special class?
465
 
466
  self.restarted_from_ckpt = False
467
  if ckpt_path is not None:
@@ -598,7 +598,7 @@ class LatentDiffusion(DDPM):
598
  weighting = weighting * L_weighting
599
  return weighting
600
 
601
- def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code !
602
  """
603
  :param x: img of size (bs, c, h, w)
604
  :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
@@ -793,7 +793,7 @@ class LatentDiffusion(DDPM):
793
  z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
794
 
795
  # 2. apply model loop over last dim
796
- if isinstance(self.first_stage_model, VQModelInterface): # todo ask what this is
797
  output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
798
  force_not_quantize=predict_cids or force_not_quantize)
799
  for i in range(z.shape[-1])]
@@ -901,7 +901,7 @@ class LatentDiffusion(DDPM):
901
 
902
  if hasattr(self, "split_input_params"):
903
  assert len(cond) == 1 # todo can only deal with one conditioning atm
904
- assert not return_ids # todo dont know what this is -> I exclude --> Good
905
  ks = self.split_input_params["ks"] # eg. (128, 128)
906
  stride = self.split_input_params["stride"] # eg. (64, 64)
907
 
 
461
  self.instantiate_cond_stage(cond_stage_config)
462
  self.cond_stage_forward = cond_stage_forward
463
  self.clip_denoised = False
464
+ self.bbox_tokenizer = None
465
 
466
  self.restarted_from_ckpt = False
467
  if ckpt_path is not None:
 
598
  weighting = weighting * L_weighting
599
  return weighting
600
 
601
+ def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
602
  """
603
  :param x: img of size (bs, c, h, w)
604
  :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
 
793
  z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
794
 
795
  # 2. apply model loop over last dim
796
+ if isinstance(self.first_stage_model, VQModelInterface):
797
  output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
798
  force_not_quantize=predict_cids or force_not_quantize)
799
  for i in range(z.shape[-1])]
 
901
 
902
  if hasattr(self, "split_input_params"):
903
  assert len(cond) == 1 # todo can only deal with one conditioning atm
904
+ assert not return_ids
905
  ks = self.split_input_params["ks"] # eg. (128, 128)
906
  stride = self.split_input_params["stride"] # eg. (64, 64)
907