Update app.py

app.py

@@ -45,22 +45,22 @@ def train(prompt, init_image, trn_text, trn_steps):
     
     return "Training is finished !"
    
-def generate(prompt, init_image):
-    init_image = Image.open(init_image).convert("RGB")
-    init_image = init_image.resize((256, 256))
+def generate(prompt, init_image, trn_text, trn_steps):
+    #init_image = Image.open(init_image).convert("RGB")
+    #init_image = init_image.resize((256, 256))
 
    
-    res = pipe.train(
-        prompt,
-        init_image,
-        guidance_scale=7.5,
-        num_inference_steps=50,
-        generator=generator,
-        text_embedding_optimization_steps=0,
-        model_fine_tuning_optimization_steps=0)
+    #res = pipe.train(
+    #    prompt,
+    #    init_image,
+    #    guidance_scale=7.5,
+    #    num_inference_steps=50,
+    #    generator=generator,
+    #    text_embedding_optimization_steps=trn_text,
+    #    model_fine_tuning_optimization_steps=trn_steps)
     
-    with torch.no_grad():
-        torch.cuda.empty_cache()
+    #with torch.no_grad():
+    #    torch.cuda.empty_cache()
     
     
     
@@ -141,8 +141,8 @@ with gr.Blocks(css=css) as block:
         prompt_input = gr.Textbox(label="Target text", placeholder="Describe the image with what you want to change about the subject")
         image_init = gr.Image(source="upload", type="filepath",label="Input Image")
         with gr.Row():
-            trn_text = gr.Slider(100, 500, value=250, label="text embedding")
-            trn_steps = gr.Slider(250, 1000, value=500, label="finetuning steps")
+            trn_text = gr.Slider(0, 500, step=50, value=250, label="text embedding")
+            trn_steps = gr.Slider(0, 1000, step=50, value=500, label="finetuning steps")
         with gr.Row():
             train_btn = gr.Button("1.Train")
             gen_btn = gr.Button("2.Generate")