Update gradio_demo.py

gradio_demo.py

@@ -1,1103 +1,1104 @@
-import os
-import math
-import gradio as gr
-import numpy as np
-import torch
-import safetensors.torch as sf
-import db_examples
-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
-from briarmbg import BriaRMBG
-from enum import Enum
-from torch.hub import download_url_to_file
-
-from torch.hub import download_url_to_file
-import cv2
-
-from typing import Optional
-
-from Depth.depth_anything_v2.dpt import DepthAnythingV2
-
-
-
-# from FLORENCE
-import spaces
-import supervision as sv
-import torch
-from PIL import Image
-
-from utils.sam import load_sam_image_model, run_sam_inference
-
-
-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")
-rmbg = BriaRMBG.from_pretrained("briaai/RMBG-1.4")
-
-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
-
-# Load
-
-model_path = './models/iclight_sd15_fc.safetensors'
-# model_path = './models/iclight_sd15_fbc.safetensors'
-
-
-# if not os.path.exists(model_path):
-#     download_url_to_file(url='https://huggingface.co/lllyasviel/ic-light/resolve/main/iclight_sd15_fc.safetensors', dst=model_path)
-
-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
-
-# 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
-
-
-@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
-
-
-@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)
-
-
-@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
-@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)
-    elif 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
-
-@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):
-    input_fg, matting = run_rmbg(input_fg)
-    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)
-    return input_fg, 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)
-    
-    # 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
-
-
-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)
-                    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 BGSource],
-                                        value=BGSource.GREY.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():
-                    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)
-                    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')
-                    n_prompt = gr.Textbox(label="Negative Prompt", value='lowres, bad anatomy, bad hands, cropped, worst quality')
-            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 = [input_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=[output_bg, 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)
-
-    with gr.Tab("Background", visible=False):
-        mask_mover = MaskMover()
-        
-        
-        with gr.Row():
-            gr.Markdown("## IC-Light (Relighting with Foreground and Background Condition)")
-            gr.Markdown("💾 Generated images are automatically saved to 'outputs' folder")
-        
-        with gr.Row():
-            with gr.Column():
-                # Step 1: Input and Extract
-                with gr.Row():
-                    with gr.Group():
-                        gr.Markdown("### Step 1: Extract Foreground")
-                        input_image = gr.Image(type="numpy", label="Input Image", height=480)
-                        # find_objects_button = gr.Button(value="Find Objects")
-                        extract_button = gr.Button(value="Remove Background")
-                    extracted_fg = gr.Image(type="numpy", label="Extracted Foreground", height=480)
-
-                with gr.Row():    
-                # Step 2: Background and Position
-                    with gr.Group():
-                        gr.Markdown("### Step 2: Position on Background")
-                        input_bg = gr.Image(type="numpy", label="Background Image", height=480)
-                        
-                        with gr.Row():
-                            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
-                            )
-                            fg_scale_slider = gr.Slider(
-                                label="Foreground Scale", 
-                                minimum=0.01, 
-                                maximum=3.0, 
-                                value=1.0, 
-                                step=0.01
-                            )
-
-                        editor = gr.ImageEditor(
-                            type="numpy",
-                            label="Position Foreground",
-                            height=480,
-                            visible=False
-                        )
-                        get_depth_button = gr.Button(value="Get Depth")
-                        depth_image = gr.Image(type="numpy", label="Depth Image", height=480)
-                
-                # Step 3: Relighting Options
-                with gr.Group():
-                    gr.Markdown("### Step 3: Relighting Settings")
-                    prompt = gr.Textbox(label="Prompt")
-                    bg_source = gr.Radio(
-                        choices=[e.value for e in BGSource],
-                        value=BGSource.UPLOAD.value,
-                        label="Background Source", 
-                        type='value'
-                    )
-
-                    example_prompts = gr.Dataset(
-                        samples=quick_prompts, 
-                        label='Prompt Quick List', 
-                        components=[prompt]
-                    )
-                    # bg_gallery = gr.Gallery(
-                    #     height=450, 
-                    #     label='Background Quick List', 
-                    #     value=db_examples.bg_samples, 
-                    #     columns=5, 
-                    #     allow_preview=False
-                    # )
-                relight_button_bg = gr.Button(value="Relight")
-
-                # Additional settings
-                with gr.Group():
-                    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=20, step=1)
-                    cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=32.0, value=7.0, step=0.01)
-                    highres_scale = gr.Slider(label="Highres Scale", minimum=1.0, maximum=2.0, value=1.2, step=0.01)
-                    highres_denoise = gr.Slider(label="Highres Denoise", minimum=0.1, maximum=0.9, value=0.5, step=0.01)
-                    a_prompt = gr.Textbox(label="Added Prompt", value='best quality')
-                    n_prompt = gr.Textbox(
-                        label="Negative Prompt",
-                        value='lowres, bad anatomy, bad hands, cropped, worst quality'
-                    )
-                
-            with gr.Column():
-                result_gallery = gr.Image(height=832, label='Outputs')
-
-        def extract_foreground(image):
-            if image is None:
-                return None, gr.update(visible=True), gr.update(visible=True)
-            result, rgba = run_rmbg(image)
-            mask_mover.set_extracted_fg(rgba)
-            
-            return result, gr.update(visible=True), gr.update(visible=True)
-
-
-        original_bg = None
-
-        extract_button.click(
-            fn=extract_foreground,
-            inputs=[input_image],
-            outputs=[extracted_fg, x_slider, y_slider]
-        )
-
-        # find_objects_button.click(
-        #     fn=find_objects,
-        #     inputs=[input_image],
-        #     outputs=[extracted_fg]
-        # )
-        
-        get_depth_button.click(
-            fn=get_depth,
-            inputs=[input_bg],
-            outputs=[depth_image]
-        )
-
-        # def update_position(background, x_pos, y_pos, scale):
-        #     """Update composite when position changes"""
-        #     global original_bg
-        #     if background is None:
-        #         return None
-            
-        #     if original_bg is None:
-        #         original_bg = background.copy()
-            
-        #     # Convert string values to float
-        #     x_pos = float(x_pos)
-        #     y_pos = float(y_pos)
-        #     scale = float(scale)
-            
-        #     return mask_mover.create_composite(original_bg, x_pos, y_pos, scale)
-
-        class BackgroundManager:
-            def __init__(self):
-                self.original_bg = None
-
-            def update_position(self, background, x_pos, y_pos, scale):
-                """Update composite when position changes"""
-                if background is None:
-                    return None
-                
-                if self.original_bg is None:
-                    self.original_bg = background.copy()
-                
-                # Convert string values to float
-                x_pos = float(x_pos)
-                y_pos = float(y_pos)
-                scale = float(scale)
-                
-                return mask_mover.create_composite(self.original_bg, x_pos, y_pos, scale)
-
-        # Create an instance of BackgroundManager
-        bg_manager = BackgroundManager()
-
-
-        x_slider.change(
-            fn=lambda bg, x, y, scale: bg_manager.update_position(bg, x, y, scale),
-            inputs=[input_bg, x_slider, y_slider, fg_scale_slider],
-            outputs=[input_bg]
-        )
-
-        y_slider.change(
-            fn=lambda bg, x, y, scale: bg_manager.update_position(bg, x, y, scale),
-            inputs=[input_bg, x_slider, y_slider, fg_scale_slider],
-            outputs=[input_bg]
-        )
-
-        fg_scale_slider.change(
-            fn=lambda bg, x, y, scale: bg_manager.update_position(bg, x, y, scale),
-            inputs=[input_bg, x_slider, y_slider, fg_scale_slider],
-            outputs=[input_bg]
-        )
-
-        # Update inputs list to include fg_scale_slider
-
-    def process_relight_with_position(*args):
-        if mask_mover.extracted_fg is None:
-            gr.Warning("Please extract foreground first")
-            return None
-            
-        background = args[1]  # Get background image
-        x_pos = float(args[-3])  # x_slider value
-        y_pos = float(args[-2])  # y_slider value
-        scale = float(args[-1])  # fg_scale_slider value
-        
-        # Get original foreground size after scaling
-        fg = Image.fromarray(mask_mover.original_fg)
-        new_width = int(fg.width * scale)
-        new_height = int(fg.height * scale)
-        
-        # Calculate crop region around foreground position
-        crop_x = int(x_pos - new_width/2)
-        crop_y = int(y_pos - new_height/2)
-        crop_width = new_width
-        crop_height = new_height
-        
-        # Add padding for context (20% extra on each side)
-        padding = 0.2
-        crop_x = int(crop_x - crop_width * padding)
-        crop_y = int(crop_y - crop_height * padding)
-        crop_width = int(crop_width * (1 + 2 * padding))
-        crop_height = int(crop_height * (1 + 2 * padding))
-        
-        # Ensure crop dimensions are multiples of 8
-        crop_width = ((crop_width + 7) // 8) * 8
-        crop_height = ((crop_height + 7) // 8) * 8
-        
-        # Ensure crop region is within image bounds
-        bg_height, bg_width = background.shape[:2]
-        crop_x = max(0, min(crop_x, bg_width - crop_width))
-        crop_y = max(0, min(crop_y, bg_height - crop_height))
-        
-        # Get actual crop dimensions after boundary check
-        crop_width = min(crop_width, bg_width - crop_x)
-        crop_height = min(crop_height, bg_height - crop_y)
-        
-        # Ensure dimensions are multiples of 8 again
-        crop_width = (crop_width // 8) * 8
-        crop_height = (crop_height // 8) * 8
-        
-        # Crop region from background
-        crop_region = background[crop_y:crop_y+crop_height, crop_x:crop_x+crop_width]
-        
-        # Create composite in cropped region
-        fg_local_x = int(new_width/2 + crop_width*padding)
-        fg_local_y = int(new_height/2 + crop_height*padding)
-        cropped_composite = mask_mover.create_composite(crop_region, fg_local_x, fg_local_y, scale)
-        
-        # Process the cropped region
-        crop_args = list(args)
-        crop_args[0] = cropped_composite
-        crop_args[1] = crop_region
-        crop_args[3] = crop_width
-        crop_args[4] = crop_height
-        crop_args = crop_args[:-3]  # Remove position and scale arguments
-        
-        # Get relit result
-        relit_crop = process_relight_bg(*crop_args)[0]
-        
-        # Resize relit result to match crop dimensions if needed
-        if relit_crop.shape[:2] != (crop_height, crop_width):
-            relit_crop = resize_without_crop(relit_crop, crop_width, crop_height)
-        
-        # Place relit crop back into original background
-        result = background.copy()
-        result[crop_y:crop_y+crop_height, crop_x:crop_x+crop_width] = relit_crop
-        
-        return result
-    
-    ips_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]
-
-    # Update button click events with new inputs list
-    relight_button_bg.click(
-        fn=process_relight_with_position,
-        inputs=ips_bg,
-        outputs=[result_gallery]
-    )
-    
-
-    example_prompts.click(
-        fn=lambda x: x[0],
-        inputs=example_prompts,
-        outputs=prompt,
-        show_progress=False,
-        queue=False
-    )
-
-
-
-block.launch(server_name='0.0.0.0', share=True)
+import spaces
+import os
+import math
+import gradio as gr
+import numpy as np
+import torch
+import safetensors.torch as sf
+import db_examples
+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
+from briarmbg import BriaRMBG
+from enum import Enum
+from torch.hub import download_url_to_file
+
+from torch.hub import download_url_to_file
+import cv2
+
+from typing import Optional
+
+from Depth.depth_anything_v2.dpt import DepthAnythingV2
+
+
+
+# from FLORENCE
+
+import supervision as sv
+import torch
+from PIL import Image
+
+from utils.sam import load_sam_image_model, run_sam_inference
+
+
+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")
+rmbg = BriaRMBG.from_pretrained("briaai/RMBG-1.4")
+
+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
+
+# Load
+
+model_path = './models/iclight_sd15_fc.safetensors'
+# model_path = './models/iclight_sd15_fbc.safetensors'
+
+
+# if not os.path.exists(model_path):
+#     download_url_to_file(url='https://huggingface.co/lllyasviel/ic-light/resolve/main/iclight_sd15_fc.safetensors', dst=model_path)
+
+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
+
+# 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
+
+
+@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
+
+
+@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)
+
+
+@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
+@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)
+    elif 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
+
+@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):
+    input_fg, matting = run_rmbg(input_fg)
+    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)
+    return input_fg, 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)
+    
+    # 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
+
+
+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)
+                    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 BGSource],
+                                        value=BGSource.GREY.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():
+                    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)
+                    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')
+                    n_prompt = gr.Textbox(label="Negative Prompt", value='lowres, bad anatomy, bad hands, cropped, worst quality')
+            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 = [input_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=[output_bg, 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)
+
+    with gr.Tab("Background", visible=False):
+        mask_mover = MaskMover()
+        
+        
+        with gr.Row():
+            gr.Markdown("## IC-Light (Relighting with Foreground and Background Condition)")
+            gr.Markdown("💾 Generated images are automatically saved to 'outputs' folder")
+        
+        with gr.Row():
+            with gr.Column():
+                # Step 1: Input and Extract
+                with gr.Row():
+                    with gr.Group():
+                        gr.Markdown("### Step 1: Extract Foreground")
+                        input_image = gr.Image(type="numpy", label="Input Image", height=480)
+                        # find_objects_button = gr.Button(value="Find Objects")
+                        extract_button = gr.Button(value="Remove Background")
+                    extracted_fg = gr.Image(type="numpy", label="Extracted Foreground", height=480)
+
+                with gr.Row():    
+                # Step 2: Background and Position
+                    with gr.Group():
+                        gr.Markdown("### Step 2: Position on Background")
+                        input_bg = gr.Image(type="numpy", label="Background Image", height=480)
+                        
+                        with gr.Row():
+                            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
+                            )
+                            fg_scale_slider = gr.Slider(
+                                label="Foreground Scale", 
+                                minimum=0.01, 
+                                maximum=3.0, 
+                                value=1.0, 
+                                step=0.01
+                            )
+
+                        editor = gr.ImageEditor(
+                            type="numpy",
+                            label="Position Foreground",
+                            height=480,
+                            visible=False
+                        )
+                        get_depth_button = gr.Button(value="Get Depth")
+                        depth_image = gr.Image(type="numpy", label="Depth Image", height=480)
+                
+                # Step 3: Relighting Options
+                with gr.Group():
+                    gr.Markdown("### Step 3: Relighting Settings")
+                    prompt = gr.Textbox(label="Prompt")
+                    bg_source = gr.Radio(
+                        choices=[e.value for e in BGSource],
+                        value=BGSource.UPLOAD.value,
+                        label="Background Source", 
+                        type='value'
+                    )
+
+                    example_prompts = gr.Dataset(
+                        samples=quick_prompts, 
+                        label='Prompt Quick List', 
+                        components=[prompt]
+                    )
+                    # bg_gallery = gr.Gallery(
+                    #     height=450, 
+                    #     label='Background Quick List', 
+                    #     value=db_examples.bg_samples, 
+                    #     columns=5, 
+                    #     allow_preview=False
+                    # )
+                relight_button_bg = gr.Button(value="Relight")
+
+                # Additional settings
+                with gr.Group():
+                    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=20, step=1)
+                    cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=32.0, value=7.0, step=0.01)
+                    highres_scale = gr.Slider(label="Highres Scale", minimum=1.0, maximum=2.0, value=1.2, step=0.01)
+                    highres_denoise = gr.Slider(label="Highres Denoise", minimum=0.1, maximum=0.9, value=0.5, step=0.01)
+                    a_prompt = gr.Textbox(label="Added Prompt", value='best quality')
+                    n_prompt = gr.Textbox(
+                        label="Negative Prompt",
+                        value='lowres, bad anatomy, bad hands, cropped, worst quality'
+                    )
+                
+            with gr.Column():
+                result_gallery = gr.Image(height=832, label='Outputs')
+
+        def extract_foreground(image):
+            if image is None:
+                return None, gr.update(visible=True), gr.update(visible=True)
+            result, rgba = run_rmbg(image)
+            mask_mover.set_extracted_fg(rgba)
+            
+            return result, gr.update(visible=True), gr.update(visible=True)
+
+
+        original_bg = None
+
+        extract_button.click(
+            fn=extract_foreground,
+            inputs=[input_image],
+            outputs=[extracted_fg, x_slider, y_slider]
+        )
+
+        # find_objects_button.click(
+        #     fn=find_objects,
+        #     inputs=[input_image],
+        #     outputs=[extracted_fg]
+        # )
+        
+        get_depth_button.click(
+            fn=get_depth,
+            inputs=[input_bg],
+            outputs=[depth_image]
+        )
+
+        # def update_position(background, x_pos, y_pos, scale):
+        #     """Update composite when position changes"""
+        #     global original_bg
+        #     if background is None:
+        #         return None
+            
+        #     if original_bg is None:
+        #         original_bg = background.copy()
+            
+        #     # Convert string values to float
+        #     x_pos = float(x_pos)
+        #     y_pos = float(y_pos)
+        #     scale = float(scale)
+            
+        #     return mask_mover.create_composite(original_bg, x_pos, y_pos, scale)
+
+        class BackgroundManager:
+            def __init__(self):
+                self.original_bg = None
+
+            def update_position(self, background, x_pos, y_pos, scale):
+                """Update composite when position changes"""
+                if background is None:
+                    return None
+                
+                if self.original_bg is None:
+                    self.original_bg = background.copy()
+                
+                # Convert string values to float
+                x_pos = float(x_pos)
+                y_pos = float(y_pos)
+                scale = float(scale)
+                
+                return mask_mover.create_composite(self.original_bg, x_pos, y_pos, scale)
+
+        # Create an instance of BackgroundManager
+        bg_manager = BackgroundManager()
+
+
+        x_slider.change(
+            fn=lambda bg, x, y, scale: bg_manager.update_position(bg, x, y, scale),
+            inputs=[input_bg, x_slider, y_slider, fg_scale_slider],
+            outputs=[input_bg]
+        )
+
+        y_slider.change(
+            fn=lambda bg, x, y, scale: bg_manager.update_position(bg, x, y, scale),
+            inputs=[input_bg, x_slider, y_slider, fg_scale_slider],
+            outputs=[input_bg]
+        )
+
+        fg_scale_slider.change(
+            fn=lambda bg, x, y, scale: bg_manager.update_position(bg, x, y, scale),
+            inputs=[input_bg, x_slider, y_slider, fg_scale_slider],
+            outputs=[input_bg]
+        )
+
+        # Update inputs list to include fg_scale_slider
+
+    def process_relight_with_position(*args):
+        if mask_mover.extracted_fg is None:
+            gr.Warning("Please extract foreground first")
+            return None
+            
+        background = args[1]  # Get background image
+        x_pos = float(args[-3])  # x_slider value
+        y_pos = float(args[-2])  # y_slider value
+        scale = float(args[-1])  # fg_scale_slider value
+        
+        # Get original foreground size after scaling
+        fg = Image.fromarray(mask_mover.original_fg)
+        new_width = int(fg.width * scale)
+        new_height = int(fg.height * scale)
+        
+        # Calculate crop region around foreground position
+        crop_x = int(x_pos - new_width/2)
+        crop_y = int(y_pos - new_height/2)
+        crop_width = new_width
+        crop_height = new_height
+        
+        # Add padding for context (20% extra on each side)
+        padding = 0.2
+        crop_x = int(crop_x - crop_width * padding)
+        crop_y = int(crop_y - crop_height * padding)
+        crop_width = int(crop_width * (1 + 2 * padding))
+        crop_height = int(crop_height * (1 + 2 * padding))
+        
+        # Ensure crop dimensions are multiples of 8
+        crop_width = ((crop_width + 7) // 8) * 8
+        crop_height = ((crop_height + 7) // 8) * 8
+        
+        # Ensure crop region is within image bounds
+        bg_height, bg_width = background.shape[:2]
+        crop_x = max(0, min(crop_x, bg_width - crop_width))
+        crop_y = max(0, min(crop_y, bg_height - crop_height))
+        
+        # Get actual crop dimensions after boundary check
+        crop_width = min(crop_width, bg_width - crop_x)
+        crop_height = min(crop_height, bg_height - crop_y)
+        
+        # Ensure dimensions are multiples of 8 again
+        crop_width = (crop_width // 8) * 8
+        crop_height = (crop_height // 8) * 8
+        
+        # Crop region from background
+        crop_region = background[crop_y:crop_y+crop_height, crop_x:crop_x+crop_width]
+        
+        # Create composite in cropped region
+        fg_local_x = int(new_width/2 + crop_width*padding)
+        fg_local_y = int(new_height/2 + crop_height*padding)
+        cropped_composite = mask_mover.create_composite(crop_region, fg_local_x, fg_local_y, scale)
+        
+        # Process the cropped region
+        crop_args = list(args)
+        crop_args[0] = cropped_composite
+        crop_args[1] = crop_region
+        crop_args[3] = crop_width
+        crop_args[4] = crop_height
+        crop_args = crop_args[:-3]  # Remove position and scale arguments
+        
+        # Get relit result
+        relit_crop = process_relight_bg(*crop_args)[0]
+        
+        # Resize relit result to match crop dimensions if needed
+        if relit_crop.shape[:2] != (crop_height, crop_width):
+            relit_crop = resize_without_crop(relit_crop, crop_width, crop_height)
+        
+        # Place relit crop back into original background
+        result = background.copy()
+        result[crop_y:crop_y+crop_height, crop_x:crop_x+crop_width] = relit_crop
+        
+        return result
+    
+    ips_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]
+
+    # Update button click events with new inputs list
+    relight_button_bg.click(
+        fn=process_relight_with_position,
+        inputs=ips_bg,
+        outputs=[result_gallery]
+    )
+    
+
+    example_prompts.click(
+        fn=lambda x: x[0],
+        inputs=example_prompts,
+        outputs=prompt,
+        show_progress=False,
+        queue=False
+    )
+
+
+
+block.launch(server_name='0.0.0.0', share=True)