0516_zerogpu

app.py

@@ -128,7 +128,7 @@ woman_Embedding_Manager = models.embedding_manager.EmbeddingManagerId_adain(
         loss_type = embedding_manager_config.model.personalization_config.params.loss_type,
         vit_out_dim = input_dim,
 )
-
+text_encoder.text_model.embeddings.forward = original_forward
 
 DEFAULT_STYLE_NAME = "Watercolor"
 MAX_SEED = np.iinfo(np.int32).max
@@ -208,13 +208,13 @@ def generate_image(experiment_name, label, prompts_array, chose_emb):
         print("new")
         torch.save(random_embedding, ran_emb_path)
         _, emb_dict = Embedding_Manager(tokenized_text=None, embedded_text=None, name_batch=None, random_embeddings = random_embedding, timesteps = None,)
-        text_encoder.text_model.embeddings.forward = original_forward
+        # text_encoder.text_model.embeddings.forward = original_forward
         test_emb = emb_dict["adained_total_embedding"].to(device)
         torch.save(test_emb, test_emb_path)
     elif label == "continue":
         print("old")
         test_emb = torch.load(chose_emb).cuda()
-        text_encoder.text_model.embeddings.forward = original_forward
+        # text_encoder.text_model.embeddings.forward = original_forward
     
     v1_emb = test_emb[:, 0]
     v2_emb = test_emb[:, 1]
@@ -298,7 +298,6 @@ def run_for_examples(example_emb, gender_GAN, choice, prompts_array):
     print("label:",label)
     
     test_emb = torch.load(example_emb).cuda()
-    text_encoder.text_model.embeddings.forward = original_forward
     v1_emb = test_emb[:, 0]
     v2_emb = test_emb[:, 1]
     embeddings = [v1_emb, v2_emb]