Update app.py

app.py

@@ -1,12 +1,13 @@
 import gradio as gr
 import numpy as np
 import random
+
+# import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline
 import torch
-from datasets import load_dataset
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # 替換成你要使用的模型
+model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
 
 if torch.cuda.is_available():
     torch_dtype = torch.float16
@@ -19,10 +20,8 @@ pipe = pipe.to(device)
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-# 載入你的 dataset，這裡假設有一個 "prompt" 欄位
-dataset = load_dataset("jeffyuyu/labeled_images_demo_BLIP2", split="train")
-dataset_prompts = list(dataset["label"])
 
+# @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
     prompt,
     negative_prompt,
@@ -36,7 +35,9 @@ def infer(
 ):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
+
     generator = torch.Generator().manual_seed(seed)
+
     image = pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
@@ -46,8 +47,10 @@ def infer(
         height=height,
         generator=generator,
     ).images[0]
+
     return image, seed
 
+
 examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
     "An astronaut riding a green horse",
@@ -63,13 +66,8 @@ css = """
 
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown("# Text-to-Image Gradio Template")
-        # 新增下拉選單，從 dataset 中選取 prompt
-        dataset_selector = gr.Dropdown(
-            choices=dataset_prompts,
-            label="或從資料集中選取一個 prompt",
-            value=dataset_prompts[0] if dataset_prompts else "",
-        )
+        gr.Markdown(" # Text-to-Image Gradio Template")
+
         with gr.Row():
             prompt = gr.Text(
                 label="Prompt",
@@ -78,8 +76,11 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter your prompt",
                 container=False,
             )
+
             run_button = gr.Button("Run", scale=0, variant="primary")
+
         result = gr.Image(label="Result", show_label=False)
+
         with gr.Accordion("Advanced Settings", open=False):
             negative_prompt = gr.Text(
                 label="Negative prompt",
@@ -87,6 +88,7 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter a negative prompt",
                 visible=False,
             )
+
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -94,46 +96,44 @@ with gr.Blocks(css=css) as demo:
                 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,
+                    value=1024,  # Replace with defaults that work for your model
                 )
+
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,
+                    value=1024,  # Replace with defaults that work for your model
                 )
+
             with gr.Row():
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
                     maximum=10.0,
                     step=0.1,
-                    value=0.0,
+                    value=0.0,  # Replace with defaults that work for your model
                 )
+
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=2,
+                    value=2,  # Replace with defaults that work for your model
                 )
-        gr.Examples(examples=examples, inputs=[prompt])
-        
-        # 按鈕：使用下拉選單選取的 prompt 填入上面的 prompt 輸入框
-        def fill_prompt(selected_prompt):
-            return selected_prompt
-
-        dataset_btn = gr.Button("填入資料集中的 prompt")
-        dataset_btn.click(fn=fill_prompt, inputs=dataset_selector, outputs=prompt)
 
+        gr.Examples(examples=examples, inputs=[prompt])
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer,
@@ -151,4 +151,4 @@ with gr.Blocks(css=css) as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()