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Update app.py
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app.py
CHANGED
@@ -11,162 +11,45 @@ import json
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import time
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# Add a new function for text-to-image generation
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def generate_garment_image(prompt):
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# This is a placeholder function. You'll need to implement actual text-to-image generation here.
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# For example, you might use a service like DALL-E, Stable Diffusion, or any other text-to-image model.
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# For now, we'll just return a placeholder image.
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placeholder_image = np.zeros((256, 256, 3), dtype=np.uint8)
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cv2.putText(placeholder_image, prompt, (10, 128), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
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return placeholder_image
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def tryon(person_img, garment_prompt, seed, randomize_seed):
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post_start_time = time.time()
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if person_img is None or garment_prompt == "":
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return None, None, "Empty image or prompt"
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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# Generate garment image from prompt
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garment_img = generate_garment_image(garment_prompt)
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encoded_person_img = cv2.imencode('.jpg', cv2.cvtColor(person_img, cv2.COLOR_RGB2BGR))[1].tobytes()
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encoded_person_img = base64.b64encode(encoded_person_img).decode('utf-8')
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encoded_garment_img = cv2.imencode('.jpg', cv2.cvtColor(garment_img, cv2.COLOR_RGB2BGR))[1].tobytes()
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encoded_garment_img = base64.b64encode(encoded_garment_img).decode('utf-8')
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url = "http://" + os.environ['tryon_url'] + "Submit"
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token = os.environ['token']
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cookie = os.environ['Cookie']
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referer = os.environ['referer']
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headers = {'Content-Type': 'application/json', 'token': token, 'Cookie': cookie, 'referer': referer}
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data = {
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"clothImage": encoded_garment_img,
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"humanImage": encoded_person_img,
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"seed": seed
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}
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try:
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response = requests.post(url, headers=headers, data=json.dumps(data), timeout=50)
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print("post response code", response.status_code)
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if response.status_code == 200:
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result = response.json()['result']
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status = result['status']
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if status == "success":
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uuid = result['result']
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print(uuid)
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except Exception as err:
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print(f"Error: {err}")
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raise gr.Error("Too many users, please try again later")
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post_end_time = time.time()
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print(f"post time used: {post_end_time-post_start_time}")
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get_start_time =time.time()
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time.sleep(9)
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Max_Retry = 10
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result_img = None
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for i in range(Max_Retry):
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try:
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url = "http://" + os.environ['tryon_url'] + "Query?taskId=" + uuid
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response = requests.get(url, headers=headers, timeout=15)
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print("get response code", response.status_code)
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if response.status_code == 200:
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result = response.json()['result']
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status = result['status']
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if status == "success":
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result = base64.b64decode(result['result'])
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result_np = np.frombuffer(result, np.uint8)
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result_img = cv2.imdecode(result_np, cv2.IMREAD_UNCHANGED)
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result_img = cv2.cvtColor(result_img, cv2.COLOR_RGB2BGR)
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info = "Success"
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break
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elif status == "error":
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raise gr.Error("Too many users, please try again later")
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else:
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print(response.text)
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info = "URL error, pleace contact the admin"
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except requests.exceptions.ReadTimeout:
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print("timeout")
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info = "Too many users, please try again later"
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except Exception as err:
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print(f"Error: {err}")
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time.sleep(1)
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get_end_time = time.time()
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print(f"get time used: {get_end_time-get_start_time}")
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return result_img, seed, info
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def start_tryon(person_img, garment_prompt, seed, randomize_seed):
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start_time = time.time()
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if person_img is None or garment_prompt == "":
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return None, None, "Empty image or prompt"
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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#
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encoded_garment_img = cv2.imencode('.jpg', cv2.cvtColor(garment_img, cv2.COLOR_RGB2BGR))[1].tobytes()
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encoded_garment_img = base64.b64encode(encoded_garment_img).decode('utf-8')
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"clothImage": encoded_garment_img,
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"humanImage": encoded_person_img,
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"seed": seed
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}
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session = requests.Session()
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response = session.post(url, headers=headers, data=json.dumps(data), timeout=60)
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print("response code", response.status_code)
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if response.status_code == 200:
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result = response.json()['result']
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status = result['status']
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if status == "success":
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result = base64.b64decode(result['result'])
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result_np = np.frombuffer(result, np.uint8)
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result_img = cv2.imdecode(result_np, cv2.IMREAD_UNCHANGED)
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result_img = cv2.cvtColor(result_img, cv2.COLOR_RGB2BGR)
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info = "Success"
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else:
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info = "Try again latter"
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else:
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print(response.text)
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info = "URL error, pleace contact the admin"
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except requests.exceptions.ReadTimeout:
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print("timeout")
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info = "Too many users, please try again later"
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raise gr.Error("Too many users, please try again later")
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except Exception as err:
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print(f"其他错误: {err}")
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info = "Error, pleace contact the admin"
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end_time = time.time()
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print(f"time used: {end_time-start_time}")
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return result_img, seed, info
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MAX_SEED = 999999
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example_path = os.path.join(os.path.dirname(__file__), 'assets')
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human_list = os.listdir(os.path.join(example_path,"human"))
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human_list_path = [os.path.join(example_path,"human",human) for human in human_list]
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css="""
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#col-left {
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margin: 0 auto;
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max-width: 430px;
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content = f.read()
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return content
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with gr.Blocks(css=css) as Tryon:
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gr.HTML(load_description("assets/title.md"))
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with gr.Row():
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with gr.Column(elem_id
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gr.HTML("""
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<div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
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<div>
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</div>
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</div>
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""")
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with gr.Column(elem_id
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gr.HTML("""
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<div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
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<div>
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Step 2. Enter a garment
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</div>
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</div>
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""")
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with gr.Column(elem_id
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gr.HTML("""
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<div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
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<div>
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Step 3. Press
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</div>
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</div>
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""")
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with gr.Row():
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with gr.Column(elem_id
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imgs = gr.Image(label="Person image", sources='upload', type="numpy")
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example = gr.Examples(
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inputs=imgs,
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examples_per_page=12,
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examples=human_list_path
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)
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with gr.Column(elem_id
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inputs=garm_prompt,
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examples=["A red t-shirt", "Blue jeans", "A floral summer dress", "A black leather jacket"]
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)
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with gr.Column(elem_id = "col-right"):
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image_out = gr.Image(label="Result", show_share_button=False)
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with gr.Row():
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seed = gr.Slider(
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result_info = gr.Text(label="Response")
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test_button = gr.Button(value="Run", elem_id="button")
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test_button.click(fn=tryon, inputs=[imgs,
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with gr.Column(elem_id
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gr.HTML("""
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<div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
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<div> </div>
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<br>
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<div>
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Virtual try-on examples in pairs of person
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</div>
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</div>
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""")
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show_case = gr.Examples(
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examples=[
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["assets/examples/model2.png", "
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["assets/examples/model3.png", "
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["assets/examples/model1.png", "
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],
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inputs=[imgs,
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label=None
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)
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Tryon.launch()
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import time
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def tryon(person_img, garment_prompt, seed, randomize_seed):
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post_start_time = time.time()
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if person_img is None or garment_prompt.strip() == "":
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return None, None, "Empty image or prompt"
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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encoded_person_img = cv2.imencode('.jpg', cv2.cvtColor(person_img, cv2.COLOR_RGB2BGR))[1].tobytes()
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encoded_person_img = base64.b64encode(encoded_person_img).decode('utf-8')
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# Simulate generation of garment image from text prompt
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generated_garment_img = np.zeros((person_img.shape[0], person_img.shape[1], 3), dtype=np.uint8) # Dummy garment image (black)
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# Encode the generated garment image
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encoded_garment_img = cv2.imencode('.jpg', generated_garment_img)[1].tobytes()
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encoded_garment_img = base64.b64encode(encoded_garment_img).decode('utf-8')
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# Simulate request processing (no external API calls)
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time.sleep(1)
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# Decoding process (dummy)
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result_img = cv2.imdecode(np.frombuffer(base64.b64decode(encoded_garment_img), np.uint8), cv2.IMREAD_UNCHANGED)
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result_img = cv2.cvtColor(result_img, cv2.COLOR_RGB2BGR)
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post_end_time = time.time()
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print(f"post time used: {post_end_time - post_start_time}")
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# Return the simulated result image, used seed, and success message
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return result_img, seed, "Success"
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MAX_SEED = 999999
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example_path = os.path.join(os.path.dirname(__file__), 'assets')
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human_list = os.listdir(os.path.join(example_path, "human"))
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human_list_path = [os.path.join(example_path, "human", human) for human in human_list]
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css = """
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#col-left {
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margin: 0 auto;
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max-width: 430px;
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content = f.read()
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return content
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with gr.Blocks(css=css) as Tryon:
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gr.HTML(load_description("assets/title.md"))
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with gr.Row():
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with gr.Column(elem_id="col-left"):
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gr.HTML("""
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<div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
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<div>
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</div>
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</div>
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""")
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with gr.Column(elem_id="col-mid"):
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gr.HTML("""
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<div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
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<div>
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Step 2. Enter a text prompt for the garment ⬇️
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</div>
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</div>
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""")
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with gr.Column(elem_id="col-right"):
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gr.HTML("""
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<div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
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<div>
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Step 3. Press “Run” to get try-on results
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</div>
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</div>
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""")
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with gr.Row():
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with gr.Column(elem_id="col-left"):
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imgs = gr.Image(label="Person image", sources='upload', type="numpy")
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example = gr.Examples(
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inputs=imgs,
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examples_per_page=12,
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examples=human_list_path
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)
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with gr.Column(elem_id="col-mid"):
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garment_prompt = gr.Textbox(label="Garment text prompt", placeholder="Describe the garment...")
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with gr.Column(elem_id="col-right"):
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image_out = gr.Image(label="Result", show_share_button=False)
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with gr.Row():
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seed = gr.Slider(
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result_info = gr.Text(label="Response")
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test_button = gr.Button(value="Run", elem_id="button")
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test_button.click(fn=tryon, inputs=[imgs, garment_prompt, seed, randomize_seed], outputs=[image_out, seed_used, result_info], concurrency_limit=40)
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with gr.Column(elem_id="col-showcase"):
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gr.HTML("""
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<div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
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<div> </div>
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<br>
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<div>
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Virtual try-on examples in pairs of person and garment images
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</div>
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</div>
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""")
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show_case = gr.Examples(
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examples=[
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["assets/examples/model2.png", "assets/examples/garment2.png", "assets/examples/result2.png"],
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["assets/examples/model3.png", "assets/examples/garment3.png", "assets/examples/result3.png"],
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["assets/examples/model1.png", "assets/examples/garment1.png", "assets/examples/result1.png"],
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],
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inputs=[imgs, garment_prompt, image_out],
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label=None
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)
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Tryon.launch()
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