Create app_3.py

app_3.py

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+import spaces
+import argparse
+import random
+
+import os
+import math
+import gradio as gr
+import numpy as np
+import torch
+import safetensors.torch as sf
+import datetime
+from pathlib import Path
+from io import BytesIO
+
+
+
+from PIL import Image
+from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
+from diffusers import AutoencoderKL, UNet2DConditionModel, DDIMScheduler, EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler
+from diffusers.models.attention_processor import AttnProcessor2_0
+from transformers import CLIPTextModel, CLIPTokenizer
+import dds_cloudapi_sdk
+from dds_cloudapi_sdk import Config, Client, TextPrompt
+from dds_cloudapi_sdk.tasks.dinox import DinoxTask
+from dds_cloudapi_sdk.tasks import DetectionTarget
+from dds_cloudapi_sdk.tasks.detection import DetectionTask
+
+from enum import Enum
+from torch.hub import download_url_to_file
+import tempfile
+
+from sam2.build_sam import build_sam2
+
+from sam2.sam2_image_predictor import SAM2ImagePredictor
+import cv2
+
+from transformers import AutoModelForImageSegmentation
+from inference_i2mv_sdxl import prepare_pipeline, remove_bg, run_pipeline
+from torchvision import transforms
+
+
+from typing import Optional
+
+from dpt import DepthAnythingV2
+
+import httpx
+
+client = httpx.Client(timeout=httpx.Timeout(10.0))  # Set timeout to 10 seconds
+NUM_VIEWS = 6
+HEIGHT = 768
+WIDTH = 768
+MAX_SEED = np.iinfo(np.int32).max
+
+
+import supervision as sv
+import torch
+from PIL import Image
+
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s')
+
+transform_image = transforms.Compose(
+    [
+        transforms.Resize((1024, 1024)),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ]
+)
+
+# Load
+
+# Model paths
+model_path = './models/iclight_sd15_fc.safetensors'
+model_path2 = './checkpoints/depth_anything_v2_vits.pth'
+model_path3 = './checkpoints/sam2_hiera_large.pt'
+model_path4 = './checkpoints/config.json'
+model_path5 = './checkpoints/preprocessor_config.json'
+model_path6 = './configs/sam2_hiera_l.yaml'
+model_path7 = './mvadapter_i2mv_sdxl.safetensors'
+
+# Base URL for the repository
+BASE_URL = 'https://huggingface.co/Ashoka74/Placement/resolve/main/'
+
+# Model URLs
+model_urls = {
+    model_path: 'iclight_sd15_fc.safetensors',
+    model_path2: 'depth_anything_v2_vits.pth',
+    model_path3: 'sam2_hiera_large.pt',
+    model_path4: 'config.json',
+    model_path5: 'preprocessor_config.json',
+    model_path6: 'sam2_hiera_l.yaml',
+    model_path7: 'mvadapter_i2mv_sdxl.safetensors'
+}
+
+# Ensure directories exist
+def ensure_directories():
+    for path in model_urls.keys():
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+
+# Download models
+def download_models():
+    for local_path, filename in model_urls.items():
+        if not os.path.exists(local_path):
+            try:
+                url = f"{BASE_URL}{filename}"
+                print(f"Downloading {filename}")
+                download_url_to_file(url, local_path)
+                print(f"Successfully downloaded {filename}")
+            except Exception as e:
+                print(f"Error downloading {filename}: {e}")
+
+ensure_directories()
+
+download_models()
+
+
+
+
+
+try:
+    import xformers
+    import xformers.ops
+    XFORMERS_AVAILABLE = True
+    print("xformers is available - Using memory efficient attention")
+except ImportError:
+    XFORMERS_AVAILABLE = False
+    print("xformers not available - Using default attention")
+
+# Memory optimizations for RTX 2070
+torch.backends.cudnn.benchmark = True
+if torch.cuda.is_available():
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+    # Set a smaller attention slice size for RTX 2070
+    torch.backends.cuda.max_split_size_mb = 512
+    device = torch.device('cuda')
+else:
+    device = torch.device('cpu')
+
+
+
+# 'stablediffusionapi/realistic-vision-v51'
+# 'runwayml/stable-diffusion-v1-5'
+sd15_name = 'stablediffusionapi/realistic-vision-v51'
+tokenizer = CLIPTokenizer.from_pretrained(sd15_name, subfolder="tokenizer")
+text_encoder = CLIPTextModel.from_pretrained(sd15_name, subfolder="text_encoder")
+vae = AutoencoderKL.from_pretrained(sd15_name, subfolder="vae")
+unet = UNet2DConditionModel.from_pretrained(sd15_name, subfolder="unet")
+# Load model directly
+from transformers import AutoModelForImageSegmentation
+rmbg = AutoModelForImageSegmentation.from_pretrained("briaai/RMBG-1.4", trust_remote_code=True)
+rmbg = rmbg.to(device=device, dtype=torch.float32)  # Keep this as float32
+
+model = DepthAnythingV2(encoder='vits', features=64, out_channels=[48, 96, 192, 384])
+model.load_state_dict(torch.load('checkpoints/depth_anything_v2_vits.pth', map_location=device))
+model = model.to(device)
+model.eval()
+
+# Change UNet
+
+
+with torch.no_grad():
+    new_conv_in = torch.nn.Conv2d(8, unet.conv_in.out_channels, unet.conv_in.kernel_size, unet.conv_in.stride, unet.conv_in.padding)
+    new_conv_in.weight.zero_()
+    new_conv_in.weight[:, :4, :, :].copy_(unet.conv_in.weight)
+    new_conv_in.bias = unet.conv_in.bias
+    unet.conv_in = new_conv_in
+
+
+unet_original_forward = unet.forward
+
+
+def enable_efficient_attention():
+    if XFORMERS_AVAILABLE:
+        try:
+            # RTX 2070 specific settings
+            unet.set_use_memory_efficient_attention_xformers(True)
+            vae.set_use_memory_efficient_attention_xformers(True)
+            print("Enabled xformers memory efficient attention")
+        except Exception as e:
+            print(f"Xformers error: {e}")
+            print("Falling back to sliced attention")
+            # Use sliced attention for RTX 2070
+            # unet.set_attention_slice_size(4)
+            # vae.set_attention_slice_size(4)
+            unet.set_attn_processor(AttnProcessor2_0())
+            vae.set_attn_processor(AttnProcessor2_0())
+    else:
+        # Fallback for when xformers is not available
+        print("Using sliced attention")
+        # unet.set_attention_slice_size(4)
+        # vae.set_attention_slice_size(4)
+        unet.set_attn_processor(AttnProcessor2_0())
+        vae.set_attn_processor(AttnProcessor2_0())
+
+# Add memory clearing function
+def clear_memory():
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+        torch.cuda.synchronize()
+
+# Enable efficient attention
+enable_efficient_attention()
+
+
+def hooked_unet_forward(sample, timestep, encoder_hidden_states, **kwargs):
+    c_concat = kwargs['cross_attention_kwargs']['concat_conds'].to(sample)
+    c_concat = torch.cat([c_concat] * (sample.shape[0] // c_concat.shape[0]), dim=0)
+    new_sample = torch.cat([sample, c_concat], dim=1)
+    kwargs['cross_attention_kwargs'] = {}
+    return unet_original_forward(new_sample, timestep, encoder_hidden_states, **kwargs)
+
+
+unet.forward = hooked_unet_forward
+
+
+
+
+sd_offset = sf.load_file(model_path)
+sd_origin = unet.state_dict()
+keys = sd_origin.keys()
+sd_merged = {k: sd_origin[k] + sd_offset[k] for k in sd_origin.keys()}
+unet.load_state_dict(sd_merged, strict=True)
+del sd_offset, sd_origin, sd_merged, keys
+
+# Device
+
+# device = torch.device('cuda')
+# text_encoder = text_encoder.to(device=device, dtype=torch.float16)
+# vae = vae.to(device=device, dtype=torch.bfloat16)
+# unet = unet.to(device=device, dtype=torch.float16)
+# rmbg = rmbg.to(device=device, dtype=torch.float32)
+
+
+# Device and dtype setup
+device = torch.device('cuda')
+dtype = torch.float16  # RTX 2070 works well with float16
+
+pipe = prepare_pipeline(
+    base_model="stabilityai/stable-diffusion-xl-base-1.0",
+    vae_model="madebyollin/sdxl-vae-fp16-fix",
+    unet_model=None,
+    lora_model=None,
+    adapter_path="huanngzh/mv-adapter",
+    scheduler=None,
+    num_views=NUM_VIEWS,
+    device=device,
+    dtype=dtype,
+)
+
+# Memory optimizations for RTX 2070
+torch.backends.cudnn.benchmark = True
+if torch.cuda.is_available():
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+    # Set a very small attention slice size for RTX 2070 to avoid OOM
+    torch.backends.cuda.max_split_size_mb = 128
+
+# Move models to device with consistent dtype
+text_encoder = text_encoder.to(device=device, dtype=dtype)
+vae = vae.to(device=device, dtype=dtype)  # Changed from bfloat16 to float16
+unet = unet.to(device=device, dtype=dtype)
+rmbg = rmbg.to(device=device, dtype=torch.float32)  # Keep this as float32
+
+
+ddim_scheduler = DDIMScheduler(
+    num_train_timesteps=1000,
+    beta_start=0.00085,
+    beta_end=0.012,
+    beta_schedule="scaled_linear",
+    clip_sample=False,
+    set_alpha_to_one=False,
+    steps_offset=1,
+)
+
+euler_a_scheduler = EulerAncestralDiscreteScheduler(
+    num_train_timesteps=1000,
+    beta_start=0.00085,
+    beta_end=0.012,
+    steps_offset=1
+)
+
+dpmpp_2m_sde_karras_scheduler = DPMSolverMultistepScheduler(
+    num_train_timesteps=1000,
+    beta_start=0.00085,
+    beta_end=0.012,
+    algorithm_type="sde-dpmsolver++",
+    use_karras_sigmas=True,
+    steps_offset=1
+)
+
+# Pipelines
+
+t2i_pipe = StableDiffusionPipeline(
+    vae=vae,
+    text_encoder=text_encoder,
+    tokenizer=tokenizer,
+    unet=unet,
+    scheduler=dpmpp_2m_sde_karras_scheduler,
+    safety_checker=None,
+    requires_safety_checker=False,
+    feature_extractor=None,
+    image_encoder=None
+)
+
+i2i_pipe = StableDiffusionImg2ImgPipeline(
+    vae=vae,
+    text_encoder=text_encoder,
+    tokenizer=tokenizer,
+    unet=unet,
+    scheduler=dpmpp_2m_sde_karras_scheduler,
+    safety_checker=None,
+    requires_safety_checker=False,
+    feature_extractor=None,
+    image_encoder=None
+)
+
+
+@torch.inference_mode()
+def encode_prompt_inner(txt: str):
+    max_length = tokenizer.model_max_length
+    chunk_length = tokenizer.model_max_length - 2
+    id_start = tokenizer.bos_token_id
+    id_end = tokenizer.eos_token_id
+    id_pad = id_end
+
+    def pad(x, p, i):
+        return x[:i] if len(x) >= i else x + [p] * (i - len(x))
+
+    tokens = tokenizer(txt, truncation=False, add_special_tokens=False)["input_ids"]
+    chunks = [[id_start] + tokens[i: i + chunk_length] + [id_end] for i in range(0, len(tokens), chunk_length)]
+    chunks = [pad(ck, id_pad, max_length) for ck in chunks]
+
+    token_ids = torch.tensor(chunks).to(device=device, dtype=torch.int64)
+    conds = text_encoder(token_ids).last_hidden_state
+
+    return conds
+
+
+@torch.inference_mode()
+def encode_prompt_pair(positive_prompt, negative_prompt):
+    c = encode_prompt_inner(positive_prompt)
+    uc = encode_prompt_inner(negative_prompt)
+
+    c_len = float(len(c))
+    uc_len = float(len(uc))
+    max_count = max(c_len, uc_len)
+    c_repeat = int(math.ceil(max_count / c_len))
+    uc_repeat = int(math.ceil(max_count / uc_len))
+    max_chunk = max(len(c), len(uc))
+
+    c = torch.cat([c] * c_repeat, dim=0)[:max_chunk]
+    uc = torch.cat([uc] * uc_repeat, dim=0)[:max_chunk]
+
+    c = torch.cat([p[None, ...] for p in c], dim=1)
+    uc = torch.cat([p[None, ...] for p in uc], dim=1)
+
+    return c, uc
+
+@spaces.GPU(duration=60)
+@torch.inference_mode()
+@spaces.GPU(duration=60)
+@torch.inference_mode()
+def infer(
+    prompt,
+    image,  # This is already RGBA with background removed
+    do_rembg=True,
+    seed=42,
+    randomize_seed=False,
+    guidance_scale=3.0,
+    num_inference_steps=50,
+    reference_conditioning_scale=1.0,
+    negative_prompt="watermark, ugly, deformed, noisy, blurry, low contrast",
+    progress=gr.Progress(track_tqdm=True),
+):
+    logging.info(f"Input image shape: {image.shape}, dtype: {image.dtype}")
+    
+    # Convert input to PIL if needed
+    if isinstance(image, np.ndarray):
+        if image.shape[-1] == 4:  # RGBA
+            image = Image.fromarray(image, 'RGBA')
+        else:  # RGB
+            image = Image.fromarray(image, 'RGB')
+    
+    logging.info(f"Converted to PIL Image mode: {image.mode}")
+    
+    # No need for remove_bg_fn since image is already processed
+    remove_bg_fn = None
+    
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+        
+    images, preprocessed_image = run_pipeline(
+        pipe,
+        num_views=NUM_VIEWS,
+        text=prompt,
+        image=image,
+        height=HEIGHT,
+        width=WIDTH,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        seed=seed,
+        remove_bg_fn=remove_bg_fn,  # Set to None since preprocessing is done
+        reference_conditioning_scale=reference_conditioning_scale,
+        negative_prompt=negative_prompt,
+        device=device,
+    )
+    
+    logging.info(f"Output images shape: {[img.shape for img in images]}")
+    logging.info(f"Preprocessed image shape: {preprocessed_image.shape if preprocessed_image is not None else None}")
+    return images
+
+
+@spaces.GPU(duration=60)
+@torch.inference_mode()
+def pytorch2numpy(imgs, quant=True):
+    results = []
+    for x in imgs:
+        y = x.movedim(0, -1)
+
+        if quant:
+            y = y * 127.5 + 127.5
+            y = y.detach().float().cpu().numpy().clip(0, 255).astype(np.uint8)
+        else:
+            y = y * 0.5 + 0.5
+            y = y.detach().float().cpu().numpy().clip(0, 1).astype(np.float32)
+
+        results.append(y)
+    return results
+
+@spaces.GPU(duration=60)
+@torch.inference_mode()
+def numpy2pytorch(imgs):
+    h = torch.from_numpy(np.stack(imgs, axis=0)).float() / 127.0 - 1.0  # so that 127 must be strictly 0.0
+    h = h.movedim(-1, 1)
+    return h
+
+
+def resize_and_center_crop(image, target_width, target_height):
+    pil_image = Image.fromarray(image)
+    original_width, original_height = pil_image.size
+    scale_factor = max(target_width / original_width, target_height / original_height)
+    resized_width = int(round(original_width * scale_factor))
+    resized_height = int(round(original_height * scale_factor))
+    resized_image = pil_image.resize((resized_width, resized_height), Image.LANCZOS)
+    left = (resized_width - target_width) / 2
+    top = (resized_height - target_height) / 2
+    right = (resized_width + target_width) / 2
+    bottom = (resized_height + target_height) / 2
+    cropped_image = resized_image.crop((left, top, right, bottom))
+    return np.array(cropped_image)
+
+
+def resize_without_crop(image, target_width, target_height):
+    pil_image = Image.fromarray(image)
+    resized_image = pil_image.resize((target_width, target_height), Image.LANCZOS)
+    return np.array(resized_image)
+
+@spaces.GPU(duration=60)
+@torch.inference_mode()
+def run_rmbg(img, sigma=0.0):
+    # Convert RGBA to RGB if needed
+    if img.shape[-1] == 4:
+        # Use white background for alpha composition
+        alpha = img[..., 3:] / 255.0
+        rgb = img[..., :3]
+        white_bg = np.ones_like(rgb) * 255
+        img = (rgb * alpha + white_bg * (1 - alpha)).astype(np.uint8)
+    
+    H, W, C = img.shape
+    assert C == 3
+    k = (256.0 / float(H * W)) ** 0.5
+    feed = resize_without_crop(img, int(64 * round(W * k)), int(64 * round(H * k)))
+    feed = numpy2pytorch([feed]).to(device=device, dtype=torch.float32)
+    alpha = rmbg(feed)[0][0]
+    alpha = torch.nn.functional.interpolate(alpha, size=(H, W), mode="bilinear")
+    alpha = alpha.movedim(1, -1)[0]
+    alpha = alpha.detach().float().cpu().numpy().clip(0, 1)
+    
+    # Create RGBA image
+    rgba = np.dstack((img, alpha * 255)).astype(np.uint8)
+    result = 127 + (img.astype(np.float32) - 127 + sigma) * alpha
+    return result.clip(0, 255).astype(np.uint8), rgba
+
+@spaces.GPU(duration=60)
+@torch.inference_mode()
+def process(input_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source):
+    clear_memory()
+
+    # Get input dimensions
+    input_height, input_width = input_fg.shape[:2]
+
+    bg_source = BGSource(bg_source)
+
+
+    if bg_source == BGSource.UPLOAD:
+        pass
+    elif bg_source == BGSource.UPLOAD_FLIP:
+        input_bg = np.fliplr(input_bg)
+    if bg_source == BGSource.GREY:
+        input_bg = np.zeros(shape=(input_height, input_width, 3), dtype=np.uint8) + 64
+    elif bg_source == BGSource.LEFT:
+        gradient = np.linspace(255, 0, input_width)
+        image = np.tile(gradient, (input_height, 1))
+        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+    elif bg_source == BGSource.RIGHT:
+        gradient = np.linspace(0, 255, input_width)
+        image = np.tile(gradient, (input_height, 1))
+        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+    elif bg_source == BGSource.TOP:
+        gradient = np.linspace(255, 0, input_height)[:, None]
+        image = np.tile(gradient, (1, input_width))
+        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+    elif bg_source == BGSource.BOTTOM:
+        gradient = np.linspace(0, 255, input_height)[:, None]
+        image = np.tile(gradient, (1, input_width))
+        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+    else:
+        raise 'Wrong initial latent!'
+
+    rng = torch.Generator(device=device).manual_seed(int(seed))
+
+    # Use input dimensions directly
+    fg = resize_without_crop(input_fg, input_width, input_height)
+
+    concat_conds = numpy2pytorch([fg]).to(device=vae.device, dtype=vae.dtype)
+    concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor
+
+    conds, unconds = encode_prompt_pair(positive_prompt=prompt + ', ' + a_prompt, negative_prompt=n_prompt)
+
+    if input_bg is None:
+        latents = t2i_pipe(
+            prompt_embeds=conds,
+            negative_prompt_embeds=unconds,
+            width=input_width,
+            height=input_height,
+            num_inference_steps=steps,
+            num_images_per_prompt=num_samples,
+            generator=rng,
+            output_type='latent',
+            guidance_scale=cfg,
+            cross_attention_kwargs={'concat_conds': concat_conds},
+        ).images.to(vae.dtype) / vae.config.scaling_factor
+    else:
+        bg = resize_without_crop(input_bg, input_width, input_height)
+        bg_latent = numpy2pytorch([bg]).to(device=vae.device, dtype=vae.dtype)
+        bg_latent = vae.encode(bg_latent).latent_dist.mode() * vae.config.scaling_factor
+        latents = i2i_pipe(
+            image=bg_latent,
+            strength=lowres_denoise,
+            prompt_embeds=conds,
+            negative_prompt_embeds=unconds,
+            width=input_width,
+            height=input_height,
+            num_inference_steps=int(round(steps / lowres_denoise)),
+            num_images_per_prompt=num_samples,
+            generator=rng,
+            output_type='latent',
+            guidance_scale=cfg,
+            cross_attention_kwargs={'concat_conds': concat_conds},
+        ).images.to(vae.dtype) / vae.config.scaling_factor
+
+    pixels = vae.decode(latents).sample
+    pixels = pytorch2numpy(pixels)
+    pixels = [resize_without_crop(
+        image=p,
+        target_width=int(round(input_width * highres_scale / 64.0) * 64),
+        target_height=int(round(input_height * highres_scale / 64.0) * 64))
+    for p in pixels]
+
+    pixels = numpy2pytorch(pixels).to(device=vae.device, dtype=vae.dtype)
+    latents = vae.encode(pixels).latent_dist.mode() * vae.config.scaling_factor
+    latents = latents.to(device=unet.device, dtype=unet.dtype)
+
+    highres_height, highres_width = latents.shape[2] * 8, latents.shape[3] * 8
+
+    fg = resize_without_crop(input_fg, highres_width, highres_height)
+    concat_conds = numpy2pytorch([fg]).to(device=vae.device, dtype=vae.dtype)
+    concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor
+
+    latents = i2i_pipe(
+        image=latents,
+        strength=highres_denoise,
+        prompt_embeds=conds,
+        negative_prompt_embeds=unconds,
+        width=highres_width,
+        height=highres_height,
+        num_inference_steps=int(round(steps / highres_denoise)),
+        num_images_per_prompt=num_samples,
+        generator=rng,
+        output_type='latent',
+        guidance_scale=cfg,
+        cross_attention_kwargs={'concat_conds': concat_conds},
+    ).images.to(vae.dtype) / vae.config.scaling_factor
+
+    pixels = vae.decode(latents).sample
+    pixels = pytorch2numpy(pixels)
+    
+    # Resize back to input dimensions
+    pixels = [resize_without_crop(p, input_width, input_height) for p in pixels]
+    pixels = np.stack(pixels)
+
+    return pixels
+
+def extract_foreground(image):
+    if image is None:
+        return None, gr.update(visible=True), gr.update(visible=True)
+    logging.info(f"Input image shape: {image.shape}, dtype: {image.dtype}")
+    result, rgba = run_rmbg(image)
+    logging.info(f"Result shape: {result.shape}, dtype: {result.dtype}")
+    logging.info(f"RGBA shape: {rgba.shape}, dtype: {rgba.dtype}")
+    return result, gr.update(visible=True), gr.update(visible=True)
+
+@torch.inference_mode()
+def process_bg(input_fg, input_bg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, bg_source):
+    clear_memory()
+    bg_source = BGSource(bg_source)
+
+    if bg_source == BGSource.UPLOAD:
+        pass
+    elif bg_source == BGSource.UPLOAD_FLIP:
+        input_bg = np.fliplr(input_bg)
+    elif bg_source == BGSource.GREY:
+        input_bg = np.zeros(shape=(image_height, image_width, 3), dtype=np.uint8) + 64
+    elif bg_source == BGSource.LEFT:
+        gradient = np.linspace(224, 32, image_width)
+        image = np.tile(gradient, (image_height, 1))
+        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+    elif bg_source == BGSource.RIGHT:
+        gradient = np.linspace(32, 224, image_width)
+        image = np.tile(gradient, (image_height, 1))
+        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+    elif bg_source == BGSource.TOP:
+        gradient = np.linspace(224, 32, image_height)[:, None]
+        image = np.tile(gradient, (1, image_width))
+        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+    elif bg_source == BGSource.BOTTOM:
+        gradient = np.linspace(32, 224, image_height)[:, None]
+        image = np.tile(gradient, (1, image_width))
+        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+    else:
+        raise 'Wrong background source!'
+
+    rng = torch.Generator(device=device).manual_seed(seed)
+
+    fg = resize_and_center_crop(input_fg, image_width, image_height)
+    bg = resize_and_center_crop(input_bg, image_width, image_height)
+    concat_conds = numpy2pytorch([fg, bg]).to(device=vae.device, dtype=vae.dtype)
+    concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor
+    concat_conds = torch.cat([c[None, ...] for c in concat_conds], dim=1)
+
+    conds, unconds = encode_prompt_pair(positive_prompt=prompt + ', ' + a_prompt, negative_prompt=n_prompt)
+
+    latents = t2i_pipe(
+        prompt_embeds=conds,
+        negative_prompt_embeds=unconds,
+        width=image_width,
+        height=image_height,
+        num_inference_steps=steps,
+        num_images_per_prompt=num_samples,
+        generator=rng,
+        output_type='latent',
+        guidance_scale=cfg,
+        cross_attention_kwargs={'concat_conds': concat_conds},
+    ).images.to(vae.dtype) / vae.config.scaling_factor
+
+    pixels = vae.decode(latents).sample
+    pixels = pytorch2numpy(pixels)
+    pixels = [resize_without_crop(
+        image=p,
+        target_width=int(round(image_width * highres_scale / 64.0) * 64),
+        target_height=int(round(image_height * highres_scale / 64.0) * 64))
+    for p in pixels]
+
+    pixels = numpy2pytorch(pixels).to(device=vae.device, dtype=vae.dtype)
+    latents = vae.encode(pixels).latent_dist.mode() * vae.config.scaling_factor
+    latents = latents.to(device=unet.device, dtype=unet.dtype)
+
+    image_height, image_width = latents.shape[2] * 8, latents.shape[3] * 8
+    fg = resize_and_center_crop(input_fg, image_width, image_height)
+    bg = resize_and_center_crop(input_bg, image_width, image_height)
+    concat_conds = numpy2pytorch([fg, bg]).to(device=vae.device, dtype=vae.dtype)
+    concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor
+    concat_conds = torch.cat([c[None, ...] for c in concat_conds], dim=1)
+
+    latents = i2i_pipe(
+        image=latents,
+        strength=highres_denoise,
+        prompt_embeds=conds,
+        negative_prompt_embeds=unconds,
+        width=image_width,
+        height=image_height,
+        num_inference_steps=int(round(steps / highres_denoise)),
+        num_images_per_prompt=num_samples,
+        generator=rng,
+        output_type='latent',
+        guidance_scale=cfg,
+        cross_attention_kwargs={'concat_conds': concat_conds},
+    ).images.to(vae.dtype) / vae.config.scaling_factor
+
+    pixels = vae.decode(latents).sample
+    pixels = pytorch2numpy(pixels, quant=False)
+
+    clear_memory()
+    return pixels, [fg, bg]
+
+
+@torch.inference_mode()
+def process_relight(input_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source):
+    logging.info(f"Input foreground shape: {input_fg.shape}, dtype: {input_fg.dtype}")
+    results = process(input_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source)
+    logging.info(f"Results shape: {results.shape}, dtype: {results.dtype}")
+    return results
+
+
+
+@torch.inference_mode()
+def process_relight_bg(input_fg, input_bg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, bg_source):
+    bg_source = BGSource(bg_source)
+
+    # bg_source = "Use Background Image"
+    
+    # Convert numerical inputs to appropriate types
+    image_width = int(image_width)
+    image_height = int(image_height)
+    num_samples = int(num_samples)
+    seed = int(seed)
+    steps = int(steps)
+    cfg = float(cfg)
+    highres_scale = float(highres_scale)
+    highres_denoise = float(highres_denoise)
+
+    if bg_source == BGSource.UPLOAD:
+        pass
+    elif bg_source == BGSource.UPLOAD_FLIP:
+        input_bg = np.fliplr(input_bg)
+    elif bg_source == BGSource.GREY:
+        input_bg = np.zeros(shape=(image_height, image_width, 3), dtype=np.uint8) + 64
+    elif bg_source == BGSource.LEFT:
+        gradient = np.linspace(224, 32, image_width)
+        image = np.tile(gradient, (image_height, 1))
+        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+    elif bg_source == BGSource.RIGHT:
+        gradient = np.linspace(32, 224, image_width)
+        image = np.tile(gradient, (image_height, 1))
+        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+    elif bg_source == BGSource.TOP:
+        gradient = np.linspace(224, 32, image_height)[:, None]
+        image = np.tile(gradient, (1, image_width))
+        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+    elif bg_source == BGSource.BOTTOM:
+        gradient = np.linspace(32, 224, image_height)[:, None]
+        image = np.tile(gradient, (1, image_width))
+        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+    else:
+        raise ValueError('Wrong background source!')
+    
+    input_fg, matting = run_rmbg(input_fg)
+    results, extra_images = process_bg(input_fg, input_bg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, bg_source)
+    results = [(x * 255.0).clip(0, 255).astype(np.uint8) for x in results]
+    final_results = results + extra_images
+
+    # Save the generated images
+    save_images(results, prefix="relight")
+    
+    return results
+
+
+quick_prompts = [
+    'sunshine from window',
+    'neon light, city',
+    'sunset over sea',
+    'golden time',
+    'sci-fi RGB glowing, cyberpunk',
+    'natural lighting',
+    'warm atmosphere, at home, bedroom',
+    'magic lit',
+    'evil, gothic, Yharnam',
+    'light and shadow',
+    'shadow from window',
+    'soft studio lighting',
+    'home atmosphere, cozy bedroom illumination',
+    'neon, Wong Kar-wai, warm'
+]
+quick_prompts = [[x] for x in quick_prompts]
+
+
+quick_subjects = [
+    'modern sofa, high quality leather',
+    'elegant dining table, polished wood',
+    'luxurious bed, premium mattress',
+    'minimalist office desk, clean design',
+    'vintage wooden cabinet, antique finish',
+]
+quick_subjects = [[x] for x in quick_subjects]
+
+
+class BGSource(Enum):
+    UPLOAD = "Use Background Image"
+    UPLOAD_FLIP = "Use Flipped Background Image"
+    LEFT = "Left Light"
+    RIGHT = "Right Light"
+    TOP = "Top Light"
+    BOTTOM = "Bottom Light"
+    GREY = "Ambient"
+
+# Add save function
+def save_images(images, prefix="relight"):
+    # Create output directory if it doesn't exist
+    output_dir = Path("outputs")
+    output_dir.mkdir(exist_ok=True)
+    
+    # Create timestamp for unique filenames
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    
+    saved_paths = []
+    for i, img in enumerate(images):
+        if isinstance(img, np.ndarray):
+            # Convert to PIL Image if numpy array
+            img = Image.fromarray(img)
+        
+        # Create filename with timestamp
+        filename = f"{prefix}_{timestamp}_{i+1}.png"
+        filepath = output_dir / filename
+        
+        # Save image
+        img.save(filepath)
+
+
+    # print(f"Saved {len(saved_paths)} images to {output_dir}")
+    return saved_paths
+
+
+class MaskMover:
+    def __init__(self):
+        self.extracted_fg = None
+        self.original_fg = None  # Store original foreground
+        
+    def set_extracted_fg(self, fg_image):
+        """Store the extracted foreground with alpha channel"""
+        if isinstance(fg_image, np.ndarray):
+            self.extracted_fg = fg_image.copy()
+            self.original_fg = fg_image.copy()
+        else:
+            self.extracted_fg = np.array(fg_image)
+            self.original_fg = np.array(fg_image)
+        return self.extracted_fg
+    
+    def create_composite(self, background, x_pos, y_pos, scale=1.0):
+        """Create composite with foreground at specified position"""
+        if self.original_fg is None or background is None:
+            return background
+        
+        # Convert inputs to PIL Images
+        if isinstance(background, np.ndarray):
+            bg = Image.fromarray(background).convert('RGBA')
+        else:
+            bg = background.convert('RGBA')
+        
+        if isinstance(self.original_fg, np.ndarray):
+            fg = Image.fromarray(self.original_fg).convert('RGBA')
+        else:
+            fg = self.original_fg.convert('RGBA')
+        
+        # Scale the foreground size
+        new_width = int(fg.width * scale)
+        new_height = int(fg.height * scale)
+        fg = fg.resize((new_width, new_height), Image.LANCZOS)
+        
+        # Center the scaled foreground at the position
+        x = int(x_pos - new_width / 2)
+        y = int(y_pos - new_height / 2)
+        
+        # Create composite
+        result = bg.copy()
+        result.paste(fg, (x, y), fg)  # Use fg as the mask (requires fg to be in 'RGBA' mode)
+        
+        return np.array(result.convert('RGB'))  # Convert back to 'RGB' if needed
+        
+def get_depth(image):
+    if image is None:
+        return None
+    # Convert from PIL/gradio format to cv2
+    raw_img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+    # Get depth map
+    depth = model.infer_image(raw_img) # HxW raw depth map
+    # Normalize depth for visualization
+    depth = ((depth - depth.min()) / (depth.max() - depth.min()) * 255).astype(np.uint8)
+    # Convert to RGB for display
+    depth_colored = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)
+    depth_colored = cv2.cvtColor(depth_colored, cv2.COLOR_BGR2RGB)
+    return Image.fromarray(depth_colored)
+
+
+from PIL import Image
+
+def compress_image(image):
+    # Convert Gradio image (numpy array) to PIL Image
+    img = Image.fromarray(image)
+    
+    # Resize image if dimensions are too large
+    max_size = 1024  # Maximum dimension size
+    if img.width > max_size or img.height > max_size:
+        ratio = min(max_size/img.width, max_size/img.height)
+        new_size = (int(img.width * ratio), int(img.height * ratio))
+        img = img.resize(new_size, Image.Resampling.LANCZOS)
+    
+    quality = 95  # Start with high quality
+    img.save("compressed_image.jpg", "JPEG", quality=quality)  # Initial save
+    
+    # Check file size and adjust quality if necessary
+    while os.path.getsize("compressed_image.jpg") > 100 * 1024:  # 100KB limit
+        quality -= 5  # Decrease quality
+        img.save("compressed_image.jpg", "JPEG", quality=quality)
+        if quality < 20:  # Prevent quality from going too low
+            break
+    
+    # Convert back to numpy array for Gradio
+    compressed_img = np.array(Image.open("compressed_image.jpg"))
+    return compressed_img
+
+def use_orientation(selected_image:gr.SelectData):
+    return selected_image.value['image']['path']
+
+@spaces.GPU(duration=60)
+@torch.inference_mode
+def process_image(input_image, input_text):
+    """Main processing function for the Gradio interface"""
+
+    # Initialize configs
+    API_TOKEN = "9c8c865e10ec1821bea79d9fa9dc8720"
+    SAM2_CHECKPOINT = "./checkpoints/sam2_hiera_large.pt"
+    SAM2_MODEL_CONFIG = os.path.join(os.path.dirname(os.path.abspath(__file__)), "configs/sam2_hiera_l.yaml")
+    DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+    OUTPUT_DIR = Path("outputs/grounded_sam2_dinox_demo")
+    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+
+
+
+    # Initialize DDS client
+    config = Config(API_TOKEN)
+    client = Client(config)
+
+    # Process classes from text prompt
+    classes = [x.strip().lower() for x in input_text.split('.') if x]
+    class_name_to_id = {name: id for id, name in enumerate(classes)}
+    class_id_to_name = {id: name for name, id in class_name_to_id.items()}
+
+    # Save input image to temp file and get URL
+    with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as tmpfile:
+        cv2.imwrite(tmpfile.name, input_image)
+        image_url = client.upload_file(tmpfile.name)
+    os.remove(tmpfile.name)
+
+    # Process detection results
+    input_boxes = []
+    masks = []
+    confidences = []
+    class_names = []
+    class_ids = []
+
+    if len(input_text) == 0:
+        task = DinoxTask(
+        image_url=image_url,
+        prompts=[TextPrompt(text="<prompt_free>")],
+        # targets=[DetectionTarget.BBox, DetectionTarget.Mask]
+        )
+        
+        client.run_task(task)
+        predictions = task.result.objects
+        classes = [pred.category for pred in predictions]
+        classes = list(set(classes))
+        class_name_to_id = {name: id for id, name in enumerate(classes)}
+        class_id_to_name = {id: name for name, id in class_name_to_id.items()}
+
+        for idx, obj in enumerate(predictions):
+            input_boxes.append(obj.bbox)
+            masks.append(DetectionTask.rle2mask(DetectionTask.string2rle(obj.mask.counts), obj.mask.size))  # convert mask to np.array using DDS API
+            confidences.append(obj.score)
+            cls_name = obj.category.lower().strip()
+            class_names.append(cls_name)
+            class_ids.append(class_name_to_id[cls_name])
+
+        boxes = np.array(input_boxes)
+        masks = np.array(masks)
+        class_ids = np.array(class_ids)
+        labels = [
+            f"{class_name} {confidence:.2f}"
+            for class_name, confidence
+            in zip(class_names, confidences)
+        ]
+        detections = sv.Detections(
+            xyxy=boxes,
+            mask=masks.astype(bool),
+            class_id=class_ids
+        )
+
+        box_annotator = sv.BoxAnnotator()
+        label_annotator = sv.LabelAnnotator()
+        mask_annotator = sv.MaskAnnotator()
+
+        annotated_frame = input_image.copy()
+        annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections)
+        annotated_frame = label_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels)
+        annotated_frame = mask_annotator.annotate(scene=annotated_frame, detections=detections)
+
+        # Create transparent mask for first detected object
+        if len(detections) > 0:
+            # Get first mask
+            first_mask = detections.mask[0]
+            
+            # Get original RGB image
+            img = input_image.copy()
+            H, W, C = img.shape
+            
+            # Create RGBA image
+            alpha = np.zeros((H, W, 1), dtype=np.uint8)
+            alpha[first_mask] = 255
+            rgba = np.dstack((img, alpha)).astype(np.uint8)
+            
+            # Crop to mask bounds to minimize image size
+            y_indices, x_indices = np.where(first_mask)
+            y_min, y_max = y_indices.min(), y_indices.max()
+            x_min, x_max = x_indices.min(), x_indices.max()
+            
+            # Crop the RGBA image
+            cropped_rgba = rgba[y_min:y_max+1, x_min:x_max+1]
+            
+            # Set extracted foreground for mask mover
+            # mask_mover.set_extracted_fg(cropped_rgba)
+            
+            return annotated_frame, cropped_rgba, gr.update(visible=False), gr.update(visible=False)
+
+    
+    else:
+        # Run DINO-X detection
+        task = DinoxTask(
+            image_url=image_url,
+            prompts=[TextPrompt(text=input_text)],
+            targets=[DetectionTarget.BBox, DetectionTarget.Mask]
+        )
+        
+        client.run_task(task)
+        result = task.result
+        objects = result.objects
+    
+    
+    
+        # for obj in objects:
+        #     input_boxes.append(obj.bbox)
+        #     confidences.append(obj.score)
+        #     cls_name = obj.category.lower().strip()
+        #     class_names.append(cls_name)
+        #     class_ids.append(class_name_to_id[cls_name])
+    
+        # input_boxes = np.array(input_boxes)
+        # class_ids = np.array(class_ids)
+
+        predictions = task.result.objects
+        classes = [x.strip().lower() for x in input_text.split('.') if x]
+        class_name_to_id = {name: id for id, name in enumerate(classes)}
+        class_id_to_name = {id: name for name, id in class_name_to_id.items()}
+        
+        boxes = []
+        masks = []
+        confidences = []
+        class_names = []
+        class_ids = []
+        
+        for idx, obj in enumerate(predictions):
+            boxes.append(obj.bbox)
+            masks.append(DetectionTask.rle2mask(DetectionTask.string2rle(obj.mask.counts), obj.mask.size))  # convert mask to np.array using DDS API
+            confidences.append(obj.score)
+            cls_name = obj.category.lower().strip()
+            class_names.append(cls_name)
+            class_ids.append(class_name_to_id[cls_name])
+
+        boxes = np.array(boxes)
+        masks = np.array(masks)
+        class_ids = np.array(class_ids)
+        labels = [
+            f"{class_name} {confidence:.2f}"
+            for class_name, confidence
+            in zip(class_names, confidences)
+        ]
+        
+        # Initialize SAM2
+        # torch.autocast(device_type=DEVICE, dtype=torch.bfloat16).__enter__()
+        # if torch.cuda.get_device_properties(0).major >= 8:
+        #     torch.backends.cuda.matmul.allow_tf32 = True
+        #     torch.backends.cudnn.allow_tf32 = True
+    
+        # sam2_model = build_sam2(SAM2_MODEL_CONFIG, SAM2_CHECKPOINT, device=DEVICE)
+        # sam2_predictor = SAM2ImagePredictor(sam2_model)
+        # sam2_predictor.set_image(input_image)
+    
+        # sam2_predictor = run_sam_inference(SAM_IMAGE_MODEL, input_image, detections)
+    
+    
+        # Get masks from SAM2
+        # masks, scores, logits = sam2_predictor.predict(
+        #     point_coords=None,
+        #     point_labels=None,
+        #     box=input_boxes,
+        #     multimask_output=False,
+        # )
+        
+        if masks.ndim == 4:
+            masks = masks.squeeze(1)
+    
+        # Create visualization
+        # labels = [f"{class_name} {confidence:.2f}" 
+        #          for class_name, confidence in zip(class_names, confidences)]
+    
+        # detections = sv.Detections(
+        #     xyxy=input_boxes,
+        #     mask=masks.astype(bool),
+        #     class_id=class_ids
+        # )
+
+        detections = sv.Detections(
+        xyxy = boxes,
+        mask = masks.astype(bool),
+        class_id = class_ids,
+    )
+        
+        box_annotator = sv.BoxAnnotator()
+        label_annotator = sv.LabelAnnotator()
+        mask_annotator = sv.MaskAnnotator()
+    
+        annotated_frame = input_image.copy()
+        annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections)
+        annotated_frame = label_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels)
+        annotated_frame = mask_annotator.annotate(scene=annotated_frame, detections=detections)
+    
+        # Create transparent mask for first detected object
+        if len(detections) > 0:
+            # Get first mask
+            first_mask = detections.mask[0]
+            
+            # Get original RGB image
+            img = input_image.copy()
+            H, W, C = img.shape
+            
+            # Create RGBA image
+            alpha = np.zeros((H, W, 1), dtype=np.uint8)
+            alpha[first_mask] = 255
+            rgba = np.dstack((img, alpha)).astype(np.uint8)
+            
+            # Crop to mask bounds to minimize image size
+            y_indices, x_indices = np.where(first_mask)
+            y_min, y_max = y_indices.min(), y_indices.max()
+            x_min, x_max = x_indices.min(), x_indices.max()
+            
+            # Crop the RGBA image
+            cropped_rgba = rgba[y_min:y_max+1, x_min:x_max+1]
+            
+            # Set extracted foreground for mask mover
+            # mask_mover.set_extracted_fg(cropped_rgba)
+            
+            return annotated_frame, cropped_rgba, gr.update(visible=False), gr.update(visible=False)
+        return annotated_frame, None, gr.update(visible=False), gr.update(visible=False)
+        
+
+block = gr.Blocks().queue()
+with block:
+    with gr.Tab("Text"):
+        with gr.Row():
+            gr.Markdown("## Product Placement from Text")
+        with gr.Row():
+            with gr.Column():
+                with gr.Row():
+                    input_fg = gr.Image(type="numpy", label="Image", height=480)
+                with gr.Row():
+                    with gr.Group():
+                        find_objects_button = gr.Button(value="(Option 1) Segment Object from text")
+                        text_prompt = gr.Textbox(
+                                label="Text Prompt", 
+                                placeholder="Enter object classes separated by periods (e.g. 'car . person .'), leave empty to get all objects",
+                                value=""
+                            )
+                    extract_button = gr.Button(value="Remove Background")
+                with gr.Row():
+                    extracted_objects = gr.Image(type="numpy", label="Extracted Foreground", height=480)
+                    extracted_fg = gr.Image(type="numpy", label="Extracted Foreground", height=480)
+                    angles_fg = gr.Image(type="pil", label="Converted Foreground", height=480, visible=False)
+
+                with gr.Row():
+                    run_button = gr.Button("Generate alternative angles")
+                    
+                    orientation_result = gr.Gallery(
+                                    label="Result",
+                                    show_label=False,
+                                    columns=[3],
+                                    rows=[2],
+                                    object_fit="contain",
+                                    height="auto",
+                                    allow_preview=False,
+                                )                
+
+                    if orientation_result:
+                        selected = gr.Number(visible=True)
+                        orientation_result.select(use_orientation, inputs=None, outputs=extracted_fg)
+                    
+                    # output_bg = gr.Image(type="numpy", label="Preprocessed Foreground", height=480)
+                with gr.Group():
+                    prompt = gr.Textbox(label="Prompt")
+                    bg_source = gr.Radio(choices=[e.value for e in list(BGSource)[2:]],
+                                        value=BGSource.LEFT.value,
+                                        label="Lighting Preference (Initial Latent)", type='value')
+                example_quick_subjects = gr.Dataset(samples=quick_subjects, label='Subject Quick List', samples_per_page=1000, components=[prompt])
+                example_quick_prompts = gr.Dataset(samples=quick_prompts, label='Lighting Quick List', samples_per_page=1000, components=[prompt])
+                relight_button = gr.Button(value="Relight")
+
+                with gr.Group(visible=False):
+                    with gr.Row():
+                        num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
+                        seed = gr.Number(label="Seed", value=12345, precision=0)
+
+                    with gr.Row():
+                        image_width = gr.Slider(label="Image Width", minimum=256, maximum=1024, value=512, step=64)
+                        image_height = gr.Slider(label="Image Height", minimum=256, maximum=1024, value=640, step=64)
+
+                with gr.Accordion("Advanced options", open=False):
+                    steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=15, step=1)
+                    cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=32.0, value=2, step=0.01, visible=False)
+                    lowres_denoise = gr.Slider(label="Lowres Denoise (for initial latent)", minimum=0.1, maximum=1.0, value=0.9, step=0.01)
+                    highres_scale = gr.Slider(label="Highres Scale", minimum=1.0, maximum=3.0, value=1.5, step=0.01)
+                    highres_denoise = gr.Slider(label="Highres Denoise", minimum=0.1, maximum=1.0, value=0.5, step=0.01)
+                    a_prompt = gr.Textbox(label="Added Prompt", value='best quality', visible=False)
+                    n_prompt = gr.Textbox(label="Negative Prompt", value='lowres, bad anatomy, bad hands, cropped, worst quality', visible=False)
+                    x_slider = gr.Slider(
+                        minimum=0,
+                        maximum=1000,
+                        label="X Position",
+                        value=500,
+                        visible=False
+                    )
+                    y_slider = gr.Slider(
+                        minimum=0,
+                        maximum=1000,
+                        label="Y Position",
+                        value=500,
+                        visible=False
+                    )
+            with gr.Column():
+                result_gallery = gr.Gallery(height=832, object_fit='contain', label='Outputs')
+        with gr.Row():
+            dummy_image_for_outputs = gr.Image(visible=False, label='Result')
+            # gr.Examples(
+            #     fn=lambda *args: ([args[-1]], None),
+            #     examples=db_examples.foreground_conditioned_examples,
+            #     inputs=[
+            #         input_fg, prompt, bg_source, image_width, image_height, seed, dummy_image_for_outputs
+            #     ],
+            #     outputs=[result_gallery, output_bg],
+            #     run_on_click=True, examples_per_page=1024
+            # )
+        ips = [extracted_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source]
+        relight_button.click(fn=process_relight, inputs=ips, outputs=[result_gallery])
+        example_quick_prompts.click(lambda x, y: ', '.join(y.split(', ')[:2] + [x[0]]), inputs=[example_quick_prompts, prompt], outputs=prompt, show_progress=False, queue=False)
+        example_quick_subjects.click(lambda x: x[0], inputs=example_quick_subjects, outputs=prompt, show_progress=False, queue=False)
+
+
+        def convert_to_pil(image):
+            try:
+                logging.info(f"Input image shape: {image.shape}, dtype: {image.dtype}")
+                image = image.astype(np.uint8)
+                logging.info(f"Converted image shape: {image.shape}, dtype: {image.dtype}")
+                return image
+            except Exception as e:
+                logging.error(f"Error converting image: {e}")
+            return image
+                      
+        run_button.click(
+            fn=convert_to_pil, 
+            inputs=extracted_fg,  # This is already RGBA with removed background
+            outputs=angles_fg
+        ).then(
+            fn=infer,
+            inputs=[
+                text_prompt,
+                extracted_fg,  # Already processed RGBA image
+            ],
+            outputs=[orientation_result],
+        )
+        
+        find_objects_button.click(
+            fn=process_image,
+            inputs=[input_fg, text_prompt],
+            outputs=[extracted_objects, extracted_fg]
+            )
+        
+        extract_button.click(
+            fn=extract_foreground,
+            inputs=[input_fg],
+            outputs=[extracted_fg, x_slider, y_slider]
+        )
+
+block.launch(server_name='0.0.0.0', share=False)