Update app.py

app.py

@@ -18,7 +18,7 @@ checkpoints = {
     "4-Step" : ["sdxl_lightning_4step_unet.safetensors", 4],
     "8-Step" : ["sdxl_lightning_8step_unet.safetensors", 8],
 }
-loaded = None
+
 
 # Ensure model and scheduler are initialized in GPU-enabled function
 if torch.cuda.is_available():
@@ -49,17 +49,18 @@ if SAFETY_CHECKER:
 # Function 
 @spaces.GPU(enable_queue=True)
 def generate_image(prompt, ckpt):
-    global loaded
-    print(prompt, ckpt)
-    
+
     checkpoint = checkpoints[ckpt][0]
     num_inference_steps = checkpoints[ckpt][1] 
 
-    if loaded != num_inference_steps:
-        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample" if num_inference_steps==1 else "epsilon")
-        pipe.unet.load_state_dict(load_file(hf_hub_download(repo, checkpoint), device="cuda"))
-        loaded = num_inference_steps
+    if num_inference_steps==1:
+        # Ensure sampler uses "trailing" timesteps and "sample" prediction type for 1-step inference.
+        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample")
+    else:
+        # Ensure sampler uses "trailing" timesteps.
+        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
         
+    pipe.unet.load_state_dict(load_file(hf_hub_download(repo, checkpoint), device="cuda"))
     results = pipe(prompt, num_inference_steps=num_inference_steps, guidance_scale=0)
 
     if SAFETY_CHECKER: