Update app.py

app.py

@@ -59,7 +59,7 @@ class LocalBlend:
             for word in words_:
                 ind = ptp_utils.get_word_inds(prompt, word, tokenizer)
                 alpha_layers[i, :, :, :, :, ind] = 1
-        self.alpha_layers = alpha_layers.to(device)
+        self.alpha_layers = alpha_layers.to(device).to(torch_dtype)
         self.threshold = threshold
 
 
@@ -184,7 +184,7 @@ class AttentionControlEdit(AttentionStore, abc.ABC):
                  local_blend: Optional[LocalBlend]):
         super(AttentionControlEdit, self).__init__()
         self.batch_size = len(prompts)
-        self.cross_replace_alpha = ptp_utils.get_time_words_attention_alpha(prompts, num_steps, cross_replace_steps, tokenizer).to(device)
+        self.cross_replace_alpha = ptp_utils.get_time_words_attention_alpha(prompts, num_steps, cross_replace_steps, tokenizer).to(device).to(torch_dtype)
         if type(self_replace_steps) is float:
             self_replace_steps = 0, self_replace_steps
         self.num_self_replace = int(num_steps * self_replace_steps[0]), int(num_steps * self_replace_steps[1])
@@ -199,7 +199,7 @@ class AttentionReplace(AttentionControlEdit):
     def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float,
                  local_blend: Optional[LocalBlend] = None):
         super(AttentionReplace, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend)
-        self.mapper = seq_aligner.get_replacement_mapper(prompts, tokenizer).to(device)
+        self.mapper = seq_aligner.get_replacement_mapper(prompts, tokenizer).to(device).to(torch_dtype)
 
 
 class AttentionRefine(AttentionControlEdit):
@@ -213,7 +213,7 @@ class AttentionRefine(AttentionControlEdit):
                  local_blend: Optional[LocalBlend] = None):
         super(AttentionRefine, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend)
         self.mapper, alphas = seq_aligner.get_refinement_mapper(prompts, tokenizer)
-        self.mapper, alphas = self.mapper.to(device), alphas.to(device)
+        self.mapper, alphas = self.mapper.to(device).to(torch_dtype), alphas.to(device).to(torch_dtype)
         self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1])