r/stable-diffusion-finetune
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Update ddpm.py

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clean up no.1
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Robin Rombach 2022-01-17 21:24:19 +01:00 committed by GitHub
parent 417d5f3dee
commit 2b46bcb98c
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1 changed files with 4 additions and 4 deletions
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ldm/models/diffusion/ddpm.py
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@ -461,7 +461,7 @@ class LatentDiffusion(DDPM):
self.instantiate_cond_stage(cond_stage_config) self.instantiate_cond_stage(cond_stage_config)
self.cond_stage_forward = cond_stage_forward self.cond_stage_forward = cond_stage_forward
self.clip_denoised = False self.clip_denoised = False
self.bbox_tokenizer = None # # TODO: special class? self.bbox_tokenizer = None
self.restarted_from_ckpt = False self.restarted_from_ckpt = False
if ckpt_path is not None: if ckpt_path is not None:
@ -598,7 +598,7 @@ class LatentDiffusion(DDPM):
weighting = weighting * L_weighting weighting = weighting * L_weighting
return 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) :param x: img of size (bs, c, h, w)
:return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) :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 ) 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 # 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], output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
force_not_quantize=predict_cids or force_not_quantize) force_not_quantize=predict_cids or force_not_quantize)
for i in range(z.shape[-1])] for i in range(z.shape[-1])]
@ -901,7 +901,7 @@ class LatentDiffusion(DDPM):
if hasattr(self, "split_input_params"): if hasattr(self, "split_input_params"):
assert len(cond) == 1 # todo can only deal with one conditioning atm 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) ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64) stride = self.split_input_params["stride"] # eg. (64, 64)