Add Pad conds v0 option

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: