augmented training steps to test load

app.py

@@ -23,7 +23,7 @@ generator = torch.Generator("cuda").manual_seed(0)
 
 def infer(prompt, init_image):
     init_image = Image.open(init_image).convert("RGB")
-    init_image = init_image.resize((256, 256))
+    init_image = init_image.resize((128, 128))
 
    
     res = pipe.train(
@@ -32,8 +32,8 @@ def infer(prompt, init_image):
         guidance_scale=7.5,
         num_inference_steps=50,
         generator=generator,
-        text_embedding_optimization_steps=500,
-        model_fine_tuning_optimization_steps=1000)
+        text_embedding_optimization_steps=1000,
+        model_fine_tuning_optimization_steps=3000)
     
     with torch.no_grad():
         torch.cuda.empty_cache()