Hw 4 solution

app.py

@@ -6,24 +6,32 @@ import random
 from diffusers import DiffusionPipeline
 import torch
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
+device = "cuda" if torch.cuda.is_available() \
+        else "xpu" if torch.xpu.is_available() \
+        else "cpu"
+current_model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
 
-if torch.cuda.is_available():
+if torch.cuda.is_available() or torch.xpu.is_available():
     torch_dtype = torch.float16
 else:
     torch_dtype = torch.float32
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+pipe = DiffusionPipeline.from_pretrained(current_model_repo_id, torch_dtype=torch_dtype)
 pipe = pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
+def clean_vram():
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    if torch.xpu.is_available():
+        torch.xpu.empty_cache()
 
 # @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
     prompt,
+    model_repo,
     negative_prompt,
     seed,
     randomize_seed,
@@ -33,6 +41,14 @@ def infer(
     num_inference_steps,
     progress=gr.Progress(track_tqdm=True),
 ):
+    global current_model_repo_id, pipe
+
+    if model_repo != current_model_repo_id:
+        print(f"The model changed to {model_repo}, reloading pipeline...")
+        del pipe
+        clean_vram()
+        pipe = DiffusionPipeline.from_pretrained(model_repo, torch_dtype=torch_dtype).to(device)
+
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
@@ -48,6 +64,8 @@ def infer(
         generator=generator,
     ).images[0]
 
+    clean_vram()
+
     return image, seed
 
 
@@ -68,6 +86,12 @@ with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
         gr.Markdown(" # Text-to-Image Gradio Template")
 
+        model_repo = gr.Dropdown(
+            label="Model repository path",
+            choices=["stabilityai/sdxl-turbo", "CompVis/stable-diffusion-v1-4"],
+            allow_custom_value=True
+        )
+
         with gr.Row():
             prompt = gr.Text(
                 label="Prompt",
@@ -86,7 +110,7 @@ with gr.Blocks(css=css) as demo:
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
-                visible=False,
+                visible=True,
             )
 
             seed = gr.Slider(
@@ -139,6 +163,7 @@ with gr.Blocks(css=css) as demo:
         fn=infer,
         inputs=[
             prompt,
+            model_repo,
             negative_prompt,
             seed,
             randomize_seed,