Update app.py

app.py

@@ -1,41 +1,48 @@
+import os
+import cv2
 import gradio as gr
 import numpy as np
 import random
-import torch
-from diffusers import StableDiffusionPipeline
-
-# Load the Stable Diffusion model for text-based garment generation
-model_id = "runwayml/stable-diffusion-v1-5"
-pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-pipe = pipe.to("cuda")  # Use GPU for faster inference
-
-MAX_SEED = 999999
-
-def generate_garment(person_img, cloth_description, seed, randomize_seed):
-    if person_img is None or cloth_description is None or cloth_description.strip() == "":
-        return None, None, "Invalid input"
+import time
 
+def tryon(person_img, garment_prompt, seed, randomize_seed):
+    post_start_time = time.time()
+    
+    if person_img is None or garment_prompt.strip() == "":
+        return None, None, "Empty image or prompt"
+    
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
+        
+    # Create a copy of the person image to overlay text
+    result_img = person_img.copy()
+    
+    # Convert the image to OpenCV format (if needed)
+    if len(result_img.shape) == 2:  # Convert grayscale to RGB
+        result_img = cv2.cvtColor(result_img, cv2.COLOR_GRAY2RGB)
+    
+    # Set text position and properties
+    text_position = (10, 30)
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    font_scale = 1
+    font_color = (0, 255, 0)  # Green color for the text
+    thickness = 2
 
-    # Generate garment image from the text description
-    torch.manual_seed(seed)
-    garment_img = pipe(cloth_description).images[0]
+    # Overlay the garment description text on the image
+    cv2.putText(result_img, f'Garment: {garment_prompt}', text_position, font, font_scale, font_color, thickness, cv2.LINE_AA)
 
-    # Combine the generated garment with the person's image
-    result_img = combine_images(person_img, garment_img)
+    post_end_time = time.time()
+    print(f"post time used: {post_end_time - post_start_time}")
 
+    # Return the resulting image, used seed, and success message
     return result_img, seed, "Success"
 
-def combine_images(person_img, garment_img):
-    person_img = np.array(person_img)
-    garment_img = np.array(garment_img.resize((person_img.shape[1], person_img.shape[0])))
+MAX_SEED = 999999
 
-    # Simple overlay of garment on the person image
-    # Further improvement may require segmentation/masking
-    result_img = np.where(garment_img[:, :, 3:] > 0, garment_img[:, :, :3], person_img)
+example_path = os.path.join(os.path.dirname(__file__), 'assets')
 
-    return result_img
+human_list = os.listdir(os.path.join(example_path, "human"))
+human_list_path = [os.path.join(example_path, "human", human) for human in human_list]
 
 css = """
 #col-left {
@@ -54,32 +61,90 @@ css = """
     margin: 0 auto;
     max-width: 1100px;
 }
+#button {
+    color: blue;
+}
 """
 
+def load_description(fp):
+    with open(fp, 'r', encoding='utf-8') as f:
+        content = f.read()
+    return content
+
+
 with gr.Blocks(css=css) as Tryon:
-    gr.HTML("<h1>Virtual Try-On with Text-based Garment Generation</h1>")
-    
+    gr.HTML(load_description("assets/title.md"))
     with gr.Row():
         with gr.Column(elem_id="col-left"):
-            gr.HTML("<h3>Step 1: Upload a person image ⬇️</h3>")
-            person_img = gr.Image(label="Person Image", source='upload', type="numpy")
-        
+            gr.HTML("""
+            <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
+                <div>
+                Step 1.  Upload a person image ⬇️
+                </div>
+            </div>
+            """)
         with gr.Column(elem_id="col-mid"):
-            gr.HTML("<h3>Step 2: Describe the garment ⬇️</h3>")
-            cloth_description = gr.Textbox(label="Garment Description", placeholder="e.g., red dress with floral pattern")
-        
+            gr.HTML("""
+            <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
+                <div>
+                Step 2. Enter a text prompt for the garment ⬇️
+                </div>
+            </div>
+            """)
         with gr.Column(elem_id="col-right"):
-            gr.HTML("<h3>Step 3: Generate Try-On Image ⬇️</h3>")
-            result_img = gr.Image(label="Result", show_share_button=False)
-            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
-            randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
-            seed_used = gr.Number(label="Seed Used", interactive=False)
-            result_info = gr.Text(label="Status", interactive=False)
-            
-            generate_button = gr.Button(value="Run")
-        
-    generate_button.click(fn=generate_garment, 
-                          inputs=[person_img, cloth_description, seed, randomize_seed], 
-                          outputs=[result_img, seed_used, result_info])
+            gr.HTML("""
+            <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
+                <div>
+                Step 3. Press “Run” to get try-on results
+                </div>
+            </div>
+            """)
+    with gr.Row():
+        with gr.Column(elem_id="col-left"):
+            imgs = gr.Image(label="Person image", sources='upload', type="numpy")
+            example = gr.Examples(
+                inputs=imgs,
+                examples_per_page=12,
+                examples=human_list_path
+            )
+        with gr.Column(elem_id="col-mid"):
+            garment_prompt = gr.Textbox(label="Garment text prompt", placeholder="Describe the garment...")
+        with gr.Column(elem_id="col-right"):
+            image_out = gr.Image(label="Result", show_share_button=False)
+            with gr.Row():
+                seed = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
+                    step=1,
+                    value=0,
+                )
+                randomize_seed = gr.Checkbox(label="Random seed", value=True)
+            with gr.Row():
+                seed_used = gr.Number(label="Seed used")
+                result_info = gr.Text(label="Response")
+            test_button = gr.Button(value="Run", elem_id="button")
+
+    test_button.click(fn=tryon, inputs=[imgs, garment_prompt, seed, randomize_seed], outputs=[image_out, seed_used, result_info], concurrency_limit=40)
+
+    with gr.Column(elem_id="col-showcase"):
+        gr.HTML("""
+        <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
+            <div> </div>
+            <br>
+            <div>
+            Virtual try-on examples in pairs of person and garment images
+            </div>
+        </div>
+        """)
+        show_case = gr.Examples(
+            examples=[
+                ["assets/examples/model2.png", "assets/examples/garment2.png", "assets/examples/result2.png"],
+                ["assets/examples/model3.png", "assets/examples/garment3.png", "assets/examples/result3.png"],
+                ["assets/examples/model1.png", "assets/examples/garment1.png", "assets/examples/result1.png"],
+            ],
+            inputs=[imgs, garment_prompt, image_out],
+            label=None
+        )
 
 Tryon.launch()