cleanup

.gitattributes

@@ -2,3 +2,4 @@
 *.png filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,191 +1,8 @@
-import asyncio
-import base64
-import logging
-import os
-import time
-
-import cv2
 import gradio as gr
-import httpx
-import numpy as np
-import requests
 from gradio.themes.utils import sizes
 
-# LOGGING
-logger = logging.getLogger("LookSwap")
-logger.setLevel(logging.INFO)
-handler = logging.StreamHandler()
-formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
-handler.setFormatter(formatter)
-logger.addHandler(handler)
-
-# IMAGE ASSETS
-ASSETS_DIR = os.path.join(os.path.dirname(__file__), "assets")
-WATERMARK = cv2.imread(os.path.join(ASSETS_DIR, "watermark.png"), cv2.IMREAD_UNCHANGED)
-WATERMARK = cv2.resize(WATERMARK, (0, 0), fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA)
-NSFW = os.path.join(ASSETS_DIR, "nsfw.webp")
-
-# API CONFIG
-FASHN_API_URL = os.environ.get("FASHN_ENPOINT_URL")
-FASHN_API_KEY = os.environ.get("FASHN_API_KEY")
-assert FASHN_API_URL, "Please set the FASHN_ENPOINT_URL environment variable"
-assert FASHN_API_KEY, "Please set the FASHN_API_KEY environment variable"
-
-# ----------------- HELPER FUNCTIONS ----------------- #
-
-
-def add_watermark(image: np.array, watermark: np.array, offset: int = 5) -> np.array:
-    """Adds a watermark to the image at the bottom right corner with a given offset."""
-    image_height, image_width = image.shape[:2]
-    watermark_height, watermark_width = watermark.shape[:2]
-
-    # Calculate the position of the watermark in the bottom right corner, with a slight offset
-    x_offset = image_width - watermark_width - offset
-    y_offset = image_height - watermark_height - offset
-
-    # Separate the watermark into its color and alpha channels
-    overlay_color = watermark[:, :, :3]
-    overlay_mask = watermark[:, :, 3]
-
-    # Blend the watermark with the image
-    for c in range(0, 3):
-        image[y_offset : y_offset + watermark_height, x_offset : x_offset + watermark_width, c] = overlay_color[
-            :, :, c
-        ] * (overlay_mask / 255.0) + image[
-            y_offset : y_offset + watermark_height, x_offset : x_offset + watermark_width, c
-        ] * (
-            1.0 - overlay_mask / 255.0
-        )
-
-    return image
-
-
-def opencv_load_image_from_http(url: str) -> np.ndarray:
-    """Loads an image from a given URL using HTTP GET."""
-    with requests.get(url) as response:
-        response.raise_for_status()
-        image_data = np.frombuffer(response.content, np.uint8)
-        image = cv2.imdecode(image_data, cv2.IMREAD_COLOR)
-        return image
-
-
-def resize_image(img: np.array, short_axis_target: int = 512) -> np.array:
-    """Resizes an image to keep the aspect ratio with the shortest axis not exceeding a target size."""
-    height, width = img.shape[:2]
-    scale_factor = short_axis_target / min(height, width)
-    resized_img = cv2.resize(img, (0, 0), fx=scale_factor, fy=scale_factor, interpolation=cv2.INTER_AREA)
-    return resized_img
-
-
-def encode_img_to_base64(img: np.array) -> str:
-    """Encodes an image as a JPEG in Base64 format."""
-    img = cv2.imencode(".jpg", img)[1].tobytes()
-    img = base64.b64encode(img).decode("utf-8")
-    img = f"data:image/jpeg;base64,{img}"
-    return img
-
-
-def parse_checkboxes(checkboxes):
-    checkboxes = [checkbox.lower().replace(" ", "_") for checkbox in checkboxes]
-    checkboxes = {checkbox: True for checkbox in checkboxes}
-    return checkboxes
-
-
-def verify_aspect_ratio(img: np.array, prefix: str = "Model"):
-    height, width = img.shape[:2]
-    aspect_ratio = width / height
-    if aspect_ratio < 0.5:
-        raise gr.Error(f"{prefix} image W:H aspect ratio is too low. Use 2:3 or 3:4 for best results.")
-    elif aspect_ratio > 0.8:
-        raise gr.Error(f"{prefix} image W:H aspect ratio is too high. Use 2:3 or 3:4 for best results.")
-
-
-# ----------------- CORE FUNCTION ----------------- #
-
-CATEGORY_API_MAPPING = {"Top": "tops", "Bottom": "bottoms", "Full-body": "one-pieces"}
-
-
-async def get_tryon_result(model_image, garment_image, category, model_checkboxes, request: gr.Request):
-    logger.info("Starting new try-on request...")
-
-    if request:
-        client_ip = request.headers.get("x-forwarded-for") or request.client.host
-
-    # verify aspect ratio of the input images
-    verify_aspect_ratio(model_image, "Model")
-    # verify_aspect_ratio(garment_image, "Garment")
-
-    # preprocessing: convert to RGB, resize, encode to base64
-    model_image, garment_image = map(lambda x: cv2.cvtColor(x, cv2.COLOR_RGB2BGR), [model_image, garment_image])
-    model_image, garment_image = map(resize_image, [model_image, garment_image])
-    model_image, garment_image = map(encode_img_to_base64, [model_image, garment_image])
-    # prepare data for API request
-    category = CATEGORY_API_MAPPING[category]
-    data = {
-        "model_image": model_image,
-        "garment_image": garment_image,
-        "category": category,
-        **parse_checkboxes(model_checkboxes),
-    }
-    headers = {"Content-Type": "application/json", "Authorization": f"Bearer {FASHN_API_KEY}"}
-
-    # make API request
-    start_time = time.time()
-    async with httpx.AsyncClient() as client:
-        response = await client.post(f"{FASHN_API_URL}/run", headers=headers, json=data, timeout=httpx.Timeout(300.0))
-        if response.is_error:
-            raise gr.Error(f"API request failed: {response.text}")
-
-        pred_id = response.json().get("id")
-        logger.info(f"Prediction ID: {pred_id}")
-
-        # poll the status of the prediction
-        while True:
-            current_time = time.time()
-            elapsed_time = current_time - start_time
-            if elapsed_time > 180:  # 3 minutes
-                raise gr.Error("Maximum polling time exceeded.")
-
-            status_response = await client.get(
-                f"{FASHN_API_URL}/status/{pred_id}", headers=headers, timeout=httpx.Timeout(10)
-            )
-            if status_response.is_error:
-                raise Exception(f"Status polling failed: {status_response.text}")
-
-            status_data = status_response.json()
-            if status_data["status"] not in ["starting", "in_queue", "processing", "completed"]:
-                error = status_data.get("error")
-                error_msg = f"Prediction failed: {error}"
-                if "NSFW" in error:
-                    if request:
-                        gr.Warning(f"NSFW attempt IP address: {client_ip}")
-                    return NSFW
-                raise gr.Error(error_msg)
-
-            logger.info(f"Prediction status: {status_data['status']}")
-            if status_data["status"] == "completed":
-                break
-
-            await asyncio.sleep(3)
-
-        # get the result image and add a watermark
-        result_img = opencv_load_image_from_http(status_data["output"][0])
-        result_img = cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB)
-        result_img = add_watermark(result_img, WATERMARK)
-        return result_img
-
-
-# ----------------- GRADIO UI ----------------- #
-
-
 with open("banner.html", "r") as file:
     banner = file.read()
-with open("tips.html", "r") as file:
-    tips = file.read()
-with open("footer.html", "r") as file:
-    footer = file.read()
-with open("docs.html", "r") as file:
-    docs = file.read()
 
 CUSTOM_CSS = """
 .image-container  img {
@@ -198,50 +15,6 @@ CUSTOM_CSS = """
 
 with gr.Blocks(css=CUSTOM_CSS, theme=gr.themes.Monochrome(radius_size=sizes.radius_md)) as demo:
     gr.HTML(banner)
-    gr.HTML(tips)
-    with gr.Row():
-        with gr.Column():
-            model_image = gr.Image(label="Model Image", type="numpy", format="png")
-            # create a checkbox to toggle "remove accessories"
-            model_checkboxes = gr.CheckboxGroup(
-                choices=["Remove Accessories", "Restore Hands", "Cover Feet"], label="Additional Controls", type="value"
-            )
-            example_model = gr.Examples(
-                inputs=model_image,
-                examples_per_page=10,
-                examples=[
-                    os.path.join(ASSETS_DIR, "models", img) for img in os.listdir(os.path.join(ASSETS_DIR, "models"))
-                ],
-            )
-        with gr.Column():
-            garment_image = gr.Image(label="Garment Image", type="numpy", format="png")
-            category = gr.Radio(choices=["Top", "Bottom", "Full-body"], label="Select Category", value="Top")
-
-            example_garment = gr.Examples(
-                inputs=garment_image,
-                examples_per_page=10,
-                examples=[
-                    os.path.join(ASSETS_DIR, "garments", img)
-                    for img in os.listdir(os.path.join(ASSETS_DIR, "garments"))
-                ],
-            )
-
-        with gr.Column():
-            result_image = gr.Image(label="Try-on Result", format="png")
-            run_button = gr.Button("Run")
-
-            gr.HTML(docs)
-
-    run_button.click(
-        fn=get_tryon_result,
-        inputs=[model_image, garment_image, category, model_checkboxes],
-        outputs=[result_image],
-    )
-
-    gr.HTML(footer)
-
 
 if __name__ == "__main__":
-    ip = requests.get("http://ifconfig.me/ip", timeout=1).text.strip()
-    logger.info(f"VM IP address: {ip}")
-    demo.launch(share=False)
+    demo.launch(show_api=False)

banner.html

@@ -22,8 +22,6 @@
 ">
       FASHN AI: LookSwap
     </h1>
-    <p style="font-size: 14px;  margin: 0; color: #fafafa;
-     ">v0.6</p>
 
 
   </div>
@@ -35,9 +33,12 @@
           color: #fafafa;
           opacity: 0.8;
         ">
-    Welcome to the official FASHN AI demo! Using our dashboard or API, you can style any
-    model with either tops, bottoms or full outfits. <br />Our algorithm specializes in
-    swapping clothes from one person to another.
+
+    🚚 &nbsp;<b style="color: #fafafa;">WE ARE MOVING</b>&nbsp; 📦 <br>
+    Many thanks to everyone who has liked this space and given us feedback. We have decided to stop maintaining this
+    space. <br>
+    You can still try LookSwap for free on our <a href="https://fashn.ai/" target="_blank"
+      style="color:#fafafa">website</a>.
   </p>
 
   <div style="
@@ -68,4 +69,6 @@
         alt="Discord" />
     </a>
   </div>
+  <img src="https://media.tenor.com/gHygBs_JkKwAAAAi/moving-boxes.gif" alt="Moving Boxes"
+    style="width: 100%; max-width: 200px; margin-top: 24px;" />
 </div>

docs.html

@@ -1,27 +0,0 @@
-<div style="
-    padding: 12px;
-    border: 1px solid #333333;
-    border-radius: 8px;
-    text-align: left;
-    display: flex;
-    flex-direction: column;
-    gap: 8px;
-">
-  <ul style="
-        display: flex;
-        flex-direction: column;
-        gap: 12px;
-        justify-content: left;
-        align-items: left;
-        padding: 0;
-        list-style: none;
-        font-size: 14px;
-    ">
-    <li><strong>Category</strong> - Determines which part of the oufit to take from the garment image.</li>
-    <li><strong>Remove Accessories</strong> - Strips items such as belts, neckties, and handbags from the model.</li>
-    <li><strong>Restore Hands</strong> - Preserves the model's hands better, but may adjust sleeve length.
-    </li>
-    <li><strong>Cover Feet</strong> - Use when garment should cover the feet (e.g., mermaid dress) or to swap out the
-      model’s shoes.</li>
-  </ul>
-</div>

footer.html

@@ -1,54 +0,0 @@
-<div style="
-      display: flex;
-      flex-direction: column;
-      background: linear-gradient(45deg, #1a1a1a 0%, #333333 100%);
-      padding: 24px;
-      gap: 24px;
-      border-radius: 8px;
-      align-items: center;
-    ">
-  <div style="display: flex; justify-content: center; gap: 8px;">
-    <h1 style="
-      font-size: 24px;
-      color: #fafafa;
-      margin: 0;
-      font-family: 'Trebuchet MS', 'Lucida Sans Unicode', 'Lucida Grande',
-        'Lucida Sans', Arial, sans-serif;
-    ">
-      FAQ
-    </h1>
-  </div>
-  <div style="max-width: 790px; text-align: center; display: flex; flex-direction: column; gap: 12px;">
-    <div>
-      <div style="text-align: center;">
-        <strong style="color: #fafafa; font-size: 16px;">What is this model?</strong>
-      </div>
-      <p style="
-      /* margin: 0; */
-      line-height: 1.6rem;
-      font-size: 16px;
-      color: #fafafa;
-      opacity: 0.8;
-    ">
-        LookSwap is a custom 1.2B parameter diffusion architecture, designed specifically for the task of transferring
-        clothes between images. This model was trained on an internal dataset of 2M images for about 2000 A100 GPU hours
-        (sorry 🌎).
-      </p>
-    </div>
-
-    <div>
-      <div style="text-align: center;">
-        <strong style="color: #fafafa; font-size: 16px;">Can you support higher resolutions?</strong>
-      </div>
-      <p style="
-          /* margin: 0; */
-          line-height: 1.6rem;
-          font-size: 16px;
-          color: #fafafa;
-          opacity: 0.8;
-        ">
-        Yes. Image generation at 384x576 resolution is available on the FASHN platform.
-      </p>
-    </div>
-  </div>
-</div>

requirements.txt

@@ -1,4 +1 @@
-gradio
-numpy
-requests
-opencv-python
+gradio

tips.html

@@ -1,28 +0,0 @@
-<div style="
-    padding: 12px;
-    border: 1px solid #333333;
-    border-radius: 8px;
-    text-align: center;
-    display: flex;
-    flex-direction: column;
-    gap: 8px;
-  ">
-  <b style="font-size: 18px;"> ❣️ Tips for successful try-on generations</b>
-
-  <ul style="
-      display: flex;
-      gap: 12px;
-      justify-content: center;
-      li {
-        margin: 0;
-      }
-    ">
-    <li>2:3 aspect ratio</li>
-    <li>One person per image </li>
-    <li>Focus/Zoom on subject</li>
-    <li>Similar poses between images</li>
-    <li>Simple backgrounds</li>
-    <li>High-quality images</li>
-    <li>Take inspiration from the examples below</li>
-  </ul>
-</div>