import huggingface_hub huggingface_hub.snapshot_download( repo_id='h94/IP-Adapter', allow_patterns=[ 'models/**', 'sdxl_models/**', ], local_dir='./', local_dir_use_symlinks=False, ) import gradio as gr from diffusers import StableDiffusionXLControlNetInpaintPipeline, ControlNetModel from rembg import remove from PIL import Image import torch from ip_adapter import IPAdapterXL from ip_adapter.utils import register_cross_attention_hook, get_net_attn_map, attnmaps2images from PIL import Image, ImageChops, ImageEnhance import numpy as np import os import glob import torch import cv2 import argparse import DPT.util.io from torchvision.transforms import Compose from DPT.dpt.models import DPTDepthModel from DPT.dpt.midas_net import MidasNet_large from DPT.dpt.transforms import Resize, NormalizeImage, PrepareForNet """ Get ZeST Ready """ base_model_path = "stabilityai/stable-diffusion-xl-base-1.0" image_encoder_path = "models/image_encoder" ip_ckpt = "sdxl_models/ip-adapter_sdxl_vit-h.bin" controlnet_path = "diffusers/controlnet-depth-sdxl-1.0" device = "cuda" torch.cuda.empty_cache() # load SDXL pipeline controlnet = ControlNetModel.from_pretrained(controlnet_path, variant="fp16", use_safetensors=True, torch_dtype=torch.float16).to(device) pipe = StableDiffusionXLControlNetInpaintPipeline.from_pretrained( base_model_path, controlnet=controlnet, use_safetensors=True, torch_dtype=torch.float16, add_watermarker=False, ).to(device) pipe.unet = register_cross_attention_hook(pipe.unet) ip_model = IPAdapterXL(pipe, image_encoder_path, ip_ckpt, device) """ Get Depth Model Ready """ model_path = "DPT/weights/dpt_hybrid-midas-501f0c75.pt" net_w = net_h = 384 model = DPTDepthModel( path=model_path, backbone="vitb_rn50_384", non_negative=True, enable_attention_hooks=False, ) normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) transform = Compose( [ Resize( net_w, net_h, resize_target=None, keep_aspect_ratio=True, ensure_multiple_of=32, resize_method="minimal", image_interpolation_method=cv2.INTER_CUBIC, ), normalization, PrepareForNet(), ] ) model.eval() def greet(input_image, material_exemplar): """ Compute depth map from input_image """ img = np.array(input_image) img_input = transform({"image": img})["image"] # compute with torch.no_grad(): sample = torch.from_numpy(img_input).unsqueeze(0) # if optimize == True and device == torch.device("cuda"): # sample = sample.to(memory_format=torch.channels_last) # sample = sample.half() prediction = model.forward(sample) prediction = ( torch.nn.functional.interpolate( prediction.unsqueeze(1), size=img.shape[:2], mode="bicubic", align_corners=False, ) .squeeze() .cpu() .numpy() ) depth_min = prediction.min() depth_max = prediction.max() bits = 2 max_val = (2 ** (8 * bits)) - 1 if depth_max - depth_min > np.finfo("float").eps: out = max_val * (prediction - depth_min) / (depth_max - depth_min) else: out = np.zeros(prediction.shape, dtype=depth.dtype) out = (out / 256).astype('uint8') depth_map = Image.fromarray(out).resize((1024, 1024)) """ Process foreground decolored image """ rm_bg = remove(input_image) target_mask = rm_bg.convert("RGB").point(lambda x: 0 if x < 1 else 255).convert('L').convert('RGB') mask_target_img = ImageChops.lighter(input_image, target_mask) invert_target_mask = ImageChops.invert(target_mask) gray_target_image = input_image.convert('L').convert('RGB') gray_target_image = ImageEnhance.Brightness(gray_target_image) factor = 1.0 # Try adjusting this to get the desired brightness gray_target_image = gray_target_image.enhance(factor) grayscale_img = ImageChops.darker(gray_target_image, target_mask) img_black_mask = ImageChops.darker(input_image, invert_target_mask) grayscale_init_img = ImageChops.lighter(img_black_mask, grayscale_img) init_img = grayscale_init_img """ Process material exemplar and resize all images """ ip_image = material_exemplar.resize((1024, 1024)) init_img = init_img.resize((1024,1024)) mask = target_mask.resize((1024, 1024)) num_samples = 1 images = ip_model.generate(pil_image=ip_image, image=init_img, control_image=depth_map, mask_image=mask, controlnet_conditioning_scale=0.9, num_samples=num_samples, num_inference_steps=30, seed=42) return images[0] input_image = gr.Image(type="pil") input_image2 = gr.Image(type="pil") demo = gr.Interface( fn=greet, inputs=[input_image, input_image2], title="ZeST: Zero-Shot Material Transfer from a Single Image", description="Upload two images -- input image and material exemplar. ZeST extracts the material from the exemplar and cast it onto the input image following the original lighting cues.", outputs=["image"], allow_flagging='never' ) demo.launch()