Update ddpm.py
clean up no.1
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1 changed files with 4 additions and 4 deletions
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@ -461,7 +461,7 @@ class LatentDiffusion(DDPM):
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self.instantiate_cond_stage(cond_stage_config)
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self.cond_stage_forward = cond_stage_forward
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self.clip_denoised = False
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self.bbox_tokenizer = None # # TODO: special class?
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self.bbox_tokenizer = None
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self.restarted_from_ckpt = False
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if ckpt_path is not None:
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@ -598,7 +598,7 @@ class LatentDiffusion(DDPM):
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weighting = weighting * L_weighting
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return weighting
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def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code !
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def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
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"""
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:param x: img of size (bs, c, h, w)
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:return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
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@ -793,7 +793,7 @@ class LatentDiffusion(DDPM):
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z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
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# 2. apply model loop over last dim
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if isinstance(self.first_stage_model, VQModelInterface): # todo ask what this is
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if isinstance(self.first_stage_model, VQModelInterface):
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output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
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force_not_quantize=predict_cids or force_not_quantize)
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for i in range(z.shape[-1])]
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@ -901,7 +901,7 @@ class LatentDiffusion(DDPM):
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if hasattr(self, "split_input_params"):
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assert len(cond) == 1 # todo can only deal with one conditioning atm
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assert not return_ids # todo dont know what this is -> I exclude --> Good
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assert not return_ids
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ks = self.split_input_params["ks"] # eg. (128, 128)
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stride = self.split_input_params["stride"] # eg. (64, 64)
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