Update app.py

app.py

@@ -35,10 +35,10 @@ print(device)
 base_model = "black-forest-labs/FLUX.1-dev"
 
 # load pipe
-taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to_empty(device)
-good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to_empty(device)
+taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
+good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)
 
-txt2img_pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype).to_empty(device)
+txt2img_pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype).to(device)
 txt2img_pipe.__class__.load_lora_into_transformer = classmethod(load_lora_into_transformer)
 
 
@@ -165,25 +165,21 @@ def run_lora(prompt, image_url, lora_strings_json, image_strength,  cfg_scale, s
     error_message = ""
     try:
         gr.Info("Start to generate images ...")
-        with calculateDuration(f"Make a new generator: {seed}"):
-            txt2img_pipe.to(device)
-            generator = torch.Generator(device=device).manual_seed(seed)
         print(device)
-        with calculateDuration("Generating image"):
-            # Generate image
-            txt2img_pipe.to(device)
-            joint_attention_kwargs = {"scale": 1}
-            final_image = txt2img_pipe(
-                prompt=prompt,
-                num_inference_steps=steps,
-                guidance_scale=cfg_scale,
-                width=width,
-                height=height,
-                max_sequence_length=512,
-                generator=generator,
-                joint_attention_kwargs=joint_attention_kwargs
-            ).images[0]
- 
+        # Generate image
+        txt2img_pipe.to(device)
+        generator = torch.Generator(device=device).manual_seed(seed)
+        joint_attention_kwargs = {"scale": 1}
+        final_image = txt2img_pipe(
+            prompt=prompt,
+            num_inference_steps=steps,
+            guidance_scale=cfg_scale,
+            width=width,
+            height=height,
+            max_sequence_length=512,
+            generator=generator,
+            joint_attention_kwargs=joint_attention_kwargs
+        ).images[0]
     except Exception as e:
         error_message =  str(e)
         gr.Error(error_message)