add examples

app.py

@@ -3,16 +3,15 @@ from PIL import Image
 import os
 import spaces
 
-# os.environ['CUDA_VISIBLE_DEVICES'] = '7'
-
 from OmniGen import OmniGenPipeline
 
-pipe = OmniGenPipeline.from_pretrained("shitao/tmp-preview")
-# pipe.to("cuda")
+pipe = OmniGenPipeline.from_pretrained(
+    "shitao/tmp-preview"
+)
 
-# 示例处理函数：生成图像
 @spaces.GPU
-def generate_image(text, img1, img2, img3, height, width, guidance_scale):
+# 示例处理函数：生成图像
+def generate_image(text, img1, img2, img3, height, width, guidance_scale, inference_steps):
     input_images = [img1, img2, img3]
     # 去除 None
     input_images = [img for img in input_images if img is not None]
@@ -24,38 +23,91 @@ def generate_image(text, img1, img2, img3, height, width, guidance_scale):
         input_images=input_images,
         height=height,
         width=width,
-        guidance_scale=guidance_scale, 
+        guidance_scale=guidance_scale,
         img_guidance_scale=1.6,
+        num_inference_steps=inference_steps,
         separate_cfg_infer=True,
-        use_kv_cache=False
+        use_kv_cache=False,
     )
     img = output[0]
     return img
+# def generate_image(text, img1, img2, img3, height, width, guidance_scale, inference_steps):
+#     input_images = []
+#     if img1:
+#         input_images.append(Image.open(img1))
+#     if img2:
+#         input_images.append(Image.open(img2))
+#     if img3:
+#         input_images.append(Image.open(img3))
+        
+#     return input_images[0] if input_images else None
+
+
+def get_example():
+    case = [
+        [
+            "A woman holds a bouquet of flowers and faces the camera. Thw woman is the one in <img><|image_1|></img>.",
+            "./imgs/test_cases/liuyifei.png",
+            None,
+            None,
+            1024,
+            1024,
+            3.0,
+            20,
+        ],
+        [
+            "Three zebras are standing side by side on a vibrant savannah, each showcasing unique patterns and characteristics that highlight their individuality. The zebra on the left has a strikingly bold black and white stripe pattern, with wider stripes that create a dramatic contrast against its sleek body. In the middle, the zebra features a more subtle stripe arrangement, with thinner stripes that blend seamlessly into a slightly sandy-colored coat, giving it a softer appearance. On the right, the zebra's stripes are more irregular, with a distinct patch of brown fur near its shoulder, adding a layer of uniqueness to its overall look. Together, these zebras create a captivating scene, each representing the diverse beauty of their species in the wild. The right zebras is the zebras from <img><|image_1|></img>. The center zebras is from <img><|image_2|></img>. The left zebras is the zebras from <img><|image_3|></img>.",
+            "./imgs/test_cases/img1.jpg",
+            "./imgs/test_cases/img2.jpg",
+            "./imgs/test_cases/img3.jpg",
+            1024,
+            1024,
+            3.0,
+            20,
+        ],
+    ]
+    return case
+
+def run_for_examples(text, img1, img2, img3, height, width, guidance_scale, inference_steps):    
+    return generate_image(text, img1, img2, img3, height, width, guidance_scale, inference_steps)
+
 
 # Gradio 接口
 with gr.Blocks() as demo:
     gr.Markdown("## Text + Multiple Images to Image Generator")
-    
     with gr.Row():
         with gr.Column():
             # 文本输入框
-            prompt_input = gr.Textbox(label="Enter your prompt", placeholder="Type your prompt here...")
-            
-            # 图片上传框
-            image_input_1 = gr.Image(label="<img><|image_1|></img>", type="filepath")
-            image_input_2 = gr.Image(label="<img><|image_2|></img>", type="filepath")
-            image_input_3 = gr.Image(label="<img><|image_3|></img>", type="filepath")
-            
+            prompt_input = gr.Textbox(
+                label="Enter your prompt", placeholder="Type your prompt here..."
+            )
+
+            with gr.Row(equal_height=True):
+                # 图片上传框
+                image_input_1 = gr.Image(label="<img><|image_1|></img>", type="filepath")
+                image_input_2 = gr.Image(label="<img><|image_2|></img>", type="filepath")
+                image_input_3 = gr.Image(label="<img><|image_3|></img>", type="filepath")
+
             # 高度和宽度滑块
-            height_input = gr.Slider(label="Height", minimum=256, maximum=2048, value=1024, step=16)
-            width_input = gr.Slider(label="Width", minimum=256, maximum=2048, value=1024, step=16)
-            
+            height_input = gr.Slider(
+                label="Height", minimum=256, maximum=2048, value=1024, step=16
+            )
+            width_input = gr.Slider(
+                label="Width", minimum=256, maximum=2048, value=1024, step=16
+            )
+
             # 引导尺度输入
-            guidance_scale_input = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, value=3.0, step=0.1)
-            
+            guidance_scale_input = gr.Slider(
+                label="Guidance Scale", minimum=1.0, maximum=10.0, value=3.0, step=0.1
+            )
+
+            num_inference_steps = gr.Slider(
+                label="Inference Steps", minimum=1, maximum=50, value=50, step=1
+            )
+
             # 生成按钮
             generate_button = gr.Button("Generate Image")
-        
+
         with gr.Column():
             # 输出图像框
             output_image = gr.Image(label="Output Image")
@@ -63,8 +115,33 @@ with gr.Blocks() as demo:
     # 按钮点击事件
     generate_button.click(
         generate_image,
-        inputs=[prompt_input, image_input_1, image_input_2, image_input_3, height_input, width_input, guidance_scale_input],
-        outputs=output_image
+        inputs=[
+            prompt_input,
+            image_input_1,
+            image_input_2,
+            image_input_3,
+            height_input,
+            width_input,
+            guidance_scale_input,
+            num_inference_steps,
+        ],
+        outputs=output_image,
+    )
+
+    gr.Examples(
+        examples=get_example(),
+        fn=run_for_examples,
+        inputs=[
+            prompt_input,
+            image_input_1,
+            image_input_2,
+            image_input_3,
+            height_input,
+            width_input,
+            guidance_scale_input,
+            num_inference_steps,
+        ],
+        outputs=output_image,
     )
 
 # 启动应用