app_test.py

import spaces
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()

@spaces.GPU()
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]

css = """
#col-container{
    margin: 0 auto;
    max-width: 960px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("""
        # ZeST: Zero-Shot Material Transfer from a Single Image
        <p>Upload two images -- input image and material exemplar. (both 1024*1024 for better results) <br />
        ZeST extracts the material from the exemplar and cast it onto the input image following the original lighting cues.</p>
        """)
        with gr.Row():
            with gr.Column():
                with gr.Row():
                    input_image = gr.Image(type="pil", label="input image")
                    input_image2 = gr.Image(type="pil", label = "material examplar")
                submit_btn = gr.Button("Submit")
                gr.Examples(
                    examples = [["demo_assets/input_imgs/pumpkin.png", "demo_assets/material_exemplars/cup_glaze.png"]],
                    inputs = [input_image, input_image2]
                )
            with gr.Column():
                output_image = gr.Image(label="transfer result")
    submit_btn.click(fn=greet, inputs=[input_image, input_image2], outputs=[output_image])

demo.queue().launch()