Update app.py

app.py

@@ -1,27 +1,48 @@
 import gradio as gr
 import numpy as np
 import random
+from datetime import datetime
 
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
 import torch
+from diffusers import DiffusionPipeline
+from optimum.intel.openvino import OVStableDiffusionPipeline
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-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)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
+# Chọn mô hình từ dropdown
+model_choices = {
+    "SD‑Turbo (stabilityai/sd-turbo)": "stabilityai/sd-turbo",
+    "Stable Diffusion 1.5 (runwayml/stable-diffusion-1.5)": "runwayml/stable-diffusion-1.5",
+    "OpenVINO version (HARRY07979/sd-v1-5-openvino)": "HARRY07979/sd-v1-5-openvino",
+}
 
-# @spaces.GPU #[uncomment to use ZeroGPU]
+# Biến toàn cục để lưu model đang dùng
+current_model_id = None
+pipe = None
+
+# ---------------------------------------------------------
+# Hàm load mô hình
+def load_pipeline(model_id):
+    print(f"[INFO] Loading model: {model_id}")
+    if "openvino" in model_id.lower():
+        # Mô hình OpenVINO dùng OVStableDiffusionPipeline
+        pipe = OVStableDiffusionPipeline.from_pretrained(model_id)
+        pipe.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
+        pipe.compile()
+    else:
+        if torch.cuda.is_available():
+            torch_dtype = torch.float16
+        else:
+            torch_dtype = torch.float32
+        pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
+        pipe = pipe.to(device)
+    return pipe
+
+# ---------------------------------------------------------
+# Hàm infer
 def infer(
     prompt,
     negative_prompt,
@@ -31,32 +52,60 @@ def infer(
     height,
     guidance_scale,
     num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
+    model_selector,
 ):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
+    global pipe, current_model_id
 
-    generator = torch.Generator().manual_seed(seed)
+    selected_model_id = model_choices[model_selector]
 
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
+    # Nếu đổi mô hình → load lại
+    if selected_model_id != current_model_id or pipe is None:
+        pipe = load_pipeline(selected_model_id)
+        current_model_id = selected_model_id
 
-    return image, seed
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
 
+    # Thời gian bắt đầu
+    t0 = datetime.now()
+
+    # Gọi pipeline theo loại
+    if "openvino" in selected_model_id.lower():
+        image = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+        ).images[0]
+    else:
+        generator = torch.Generator().manual_seed(seed)
+        image = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator,
+        ).images[0]
+
+    # Thời gian kết thúc
+    t1 = datetime.now()
+    delta = t1 - t0
+    total_seconds = delta.total_seconds()
+    days = delta.days
+    hours, rem = divmod(delta.seconds, 3600)
+    minutes, seconds = divmod(rem, 60)
+    microsecs = delta.microseconds
+
+    print(f"Start time: {t0.isoformat(sep=' ')}")
+    print(f"End time  : {t1.isoformat(sep=' ')}")
+    print(f"Elapsed   : {days}d {hours}h {minutes}m {seconds}s {microsecs}µs")
+    print(f"Total time: {total_seconds:.3f} seconds")
 
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
+    return image, seed
 
+# ---------------------------------------------------------
 css = """
 #col-container {
     margin: 0 auto;
@@ -66,18 +115,17 @@ css = """
 
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
+        gr.Markdown("# Text-to-Image Generator (Supports SD-Turbo / SD 1.5 / OpenVINO)")
 
         with gr.Row():
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
                 max_lines=1,
-                placeholder="Enter your prompt",
+                placeholder="Enter your prompt here...",
                 container=False,
             )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
+            run_button = gr.Button("Generate", scale=0, variant="primary")
 
         result = gr.Image(label="Result", show_label=False)
 
@@ -86,54 +134,41 @@ with gr.Blocks(css=css) as demo:
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
-                visible=False,
             )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
+            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
 
             with gr.Row():
                 width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512
                 )
-
                 height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512
                 )
 
             with gr.Row():
                 guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
+                    label="Guidance scale", minimum=0.0, maximum=20.0, step=0.1, value=7.5
                 )
-
                 num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
+                    label="Inference steps", minimum=1, maximum=100, step=1, value=25
                 )
 
-        gr.Examples(examples=examples, inputs=[prompt])
+            model_selector = gr.Dropdown(
+                label="Select Model",
+                choices=list(model_choices.keys()),
+                value="SD‑Turbo (stabilityai/sd-turbo)",
+            )
+
+        gr.Examples(
+            examples=[
+                "Astronaut in a jungle, detailed, 8k",
+                "A cyberpunk dragon flying through neon city",
+                "A fantasy landscape with floating islands",
+            ],
+            inputs=[prompt],
+        )
+
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer,
@@ -146,6 +181,7 @@ with gr.Blocks(css=css) as demo:
             height,
             guidance_scale,
             num_inference_steps,
+            model_selector,
         ],
         outputs=[result, seed],
     )