diff options
Diffstat (limited to 'modules/sd_samplers_cfg_denoiser.py')
| -rw-r--r-- | modules/sd_samplers_cfg_denoiser.py | 70 |
1 files changed, 61 insertions, 9 deletions
diff --git a/modules/sd_samplers_cfg_denoiser.py b/modules/sd_samplers_cfg_denoiser.py index 6d76aa96..ef237396 100644 --- a/modules/sd_samplers_cfg_denoiser.py +++ b/modules/sd_samplers_cfg_denoiser.py @@ -53,6 +53,7 @@ class CFGDenoiser(torch.nn.Module): self.step = 0
self.image_cfg_scale = None
self.padded_cond_uncond = False
+ self.padded_cond_uncond_v0 = False
self.sampler = sampler
self.model_wrap = None
self.p = None
@@ -91,6 +92,62 @@ class CFGDenoiser(torch.nn.Module): self.sampler.sampler_extra_args['cond'] = c
self.sampler.sampler_extra_args['uncond'] = uc
+ def pad_cond_uncond(self, cond, uncond):
+ empty = shared.sd_model.cond_stage_model_empty_prompt
+ num_repeats = (cond.shape[1] - cond.shape[1]) // empty.shape[1]
+
+ if num_repeats < 0:
+ cond = pad_cond(cond, -num_repeats, empty)
+ self.padded_cond_uncond = True
+ elif num_repeats > 0:
+ uncond = pad_cond(uncond, num_repeats, empty)
+ self.padded_cond_uncond = True
+
+ return cond, uncond
+
+ def pad_cond_uncond_v0(self, cond, uncond):
+ """
+ Pads the 'uncond' tensor to match the shape of the 'cond' tensor.
+
+ If 'uncond' is a dictionary, it is assumed that the 'crossattn' key holds the tensor to be padded.
+ If 'uncond' is a tensor, it is padded directly.
+
+ If the number of columns in 'uncond' is less than the number of columns in 'cond', the last column of 'uncond'
+ is repeated to match the number of columns in 'cond'.
+
+ If the number of columns in 'uncond' is greater than the number of columns in 'cond', 'uncond' is truncated
+ to match the number of columns in 'cond'.
+
+ Args:
+ cond (torch.Tensor or DictWithShape): The condition tensor to match the shape of 'uncond'.
+ uncond (torch.Tensor or DictWithShape): The tensor to be padded, or a dictionary containing the tensor to be padded.
+
+ Returns:
+ tuple: A tuple containing the 'cond' tensor and the padded 'uncond' tensor.
+
+ Note:
+ This is the padding that was always used in DDIM before version 1.6.0
+ """
+
+ is_dict_cond = isinstance(uncond, dict)
+ uncond_vec = uncond['crossattn'] if is_dict_cond else uncond
+
+ if uncond_vec.shape[1] < cond.shape[1]:
+ last_vector = uncond_vec[:, -1:]
+ last_vector_repeated = last_vector.repeat([1, cond.shape[1] - uncond_vec.shape[1], 1])
+ uncond_vec = torch.hstack([uncond_vec, last_vector_repeated])
+ self.padded_cond_uncond_v0 = True
+ elif uncond_vec.shape[1] > cond.shape[1]:
+ uncond_vec = uncond_vec[:, :cond.shape[1]]
+ self.padded_cond_uncond_v0 = True
+
+ if is_dict_cond:
+ uncond['crossattn'] = uncond_vec
+ else:
+ uncond = uncond_vec
+
+ return cond, uncond
+
def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
if state.interrupted or state.skipped:
raise sd_samplers_common.InterruptedException
@@ -162,16 +219,11 @@ class CFGDenoiser(torch.nn.Module): sigma_in = sigma_in[:-batch_size]
self.padded_cond_uncond = False
+ self.padded_cond_uncond_v0 = False
if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
- empty = shared.sd_model.cond_stage_model_empty_prompt
- num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
-
- if num_repeats < 0:
- tensor = pad_cond(tensor, -num_repeats, empty)
- self.padded_cond_uncond = True
- elif num_repeats > 0:
- uncond = pad_cond(uncond, num_repeats, empty)
- self.padded_cond_uncond = True
+ tensor, uncond = self.pad_cond_uncond(tensor, uncond)
+ elif shared.opts.pad_cond_uncond_v0 and tensor.shape[1] != uncond.shape[1]:
+ tensor, uncond = self.pad_cond_uncond_v0(tensor, uncond)
if tensor.shape[1] == uncond.shape[1] or skip_uncond:
if is_edit_model:
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