HW3

app.py

@@ -7,15 +7,30 @@ 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
+MODEL_LIST = [
+    "stabilityai/sdxl-turbo",
+    "CompVis/stable-diffusion-v1-4",
+    "runwayml/stable-diffusion-v1-5",
+    "stabilityai/stable-diffusion-2-1",
+]
 
 if torch.cuda.is_available():
     torch_dtype = torch.float16
 else:
     torch_dtype = torch.float32
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+# To avoid re-initializing pipelines repeatedly, we can cache them:
+model_cache = {}
+
+def load_pipeline(model_id: str):
+    """Loads or retrieves a cached DiffusionPipeline."""
+    if model_id in model_cache:
+        return model_cache[model_id]
+    else:
+        pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
+        pipe.to(device)
+        model_cache[model_id] = pipe
+        return pipe
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
@@ -23,6 +38,7 @@ MAX_IMAGE_SIZE = 1024
 
 # @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
+    model_id,
     prompt,
     negative_prompt,
     seed,
@@ -33,6 +49,9 @@ def infer(
     num_inference_steps,
     progress=gr.Progress(track_tqdm=True),
 ):
+    # Load the pipeline for the chosen model
+    pipe = load_pipeline(model_id)
+
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
@@ -68,6 +87,14 @@ with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
         gr.Markdown(" # Text-to-Image Gradio Template")
 
+        with gr.Row():
+            # Dropdown to select the model from Hugging Face
+            model_id = gr.Dropdown(
+                label="Model",
+                choices=MODEL_LIST,
+                value=MODEL_LIST[0],  # Default model
+            )
+
         with gr.Row():
             prompt = gr.Text(
                 label="Prompt",
@@ -138,6 +165,7 @@ with gr.Blocks(css=css) as demo:
         triggers=[run_button.click, prompt.submit],
         fn=infer,
         inputs=[
+            model_id,
             prompt,
             negative_prompt,
             seed,