app.py

import gradio as gr
from PIL import Image
from DAI.pipeline_all import DAIPipeline
import os
import tempfile
import numpy as np
import torch as torch
torch.backends.cuda.matmul.allow_tf32 = True
import spaces
import functools



from diffusers import (
    AutoencoderKL,
    UNet2DConditionModel,
)

from transformers import CLIPTextModel, AutoTokenizer

from DAI.controlnetvae import ControlNetVAEModel

from DAI.decoder import CustomAutoencoderKL

def process_image(pipe, vae_2, image):
    # Save the input image to a temporary file
    temp_input_path = tempfile.mktemp(suffix=".png")
    image.save(temp_input_path)

    name_base, name_ext = os.path.splitext(os.path.basename(temp_input_path))
    print(f"Processing image {name_base}{name_ext}")

    path_output_dir = tempfile.mkdtemp()
    path_out_png = os.path.join(path_output_dir, f"{name_base}_delight.png")
    resolution = None

    pipe_out = pipe(
        image=image,
        prompt="remove glass reflection",
        vae_2=vae_2,
        processing_resolution=resolution,
    )

    processed_frame = (pipe_out.prediction.clip(-1, 1) + 1) / 2
    processed_frame = (processed_frame[0] * 255).astype(np.uint8)
    processed_frame = Image.fromarray(processed_frame)
    processed_frame.save(path_out_png)

    return processed_frame

if __name__ == "__main__":
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    weight_dtype = torch.float32
    pretrained_model_name_or_path = "JichenHu/dereflection-any-image-v0"
    pretrained_model_name_or_path2 = "stabilityai/stable-diffusion-2-1"
    revision = None
    variant = None

    # Load the model
    controlnet = ControlNetVAEModel.from_pretrained(pretrained_model_name_or_path, subfolder="controlnet", torch_dtype=weight_dtype).to(device)
    unet = UNet2DConditionModel.from_pretrained(pretrained_model_name_or_path, subfolder="unet", torch_dtype=weight_dtype).to(device)
    vae_2 = CustomAutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder="vae_2", torch_dtype=weight_dtype).to(device)

    vae = AutoencoderKL.from_pretrained(
        pretrained_model_name_or_path2, subfolder="vae", revision=revision, variant=variant
    ).to(device)

    text_encoder = CLIPTextModel.from_pretrained(
        pretrained_model_name_or_path2, subfolder="text_encoder", revision=revision, variant=variant
    ).to(device)
    tokenizer = AutoTokenizer.from_pretrained(
        pretrained_model_name_or_path2,
        subfolder="tokenizer",
        revision=revision,
        use_fast=False,
    )
    pipe = DAIPipeline(
        vae=vae,
        text_encoder=text_encoder,
        tokenizer=tokenizer,
        unet=unet,
        controlnet=controlnet,
        safety_checker=None,
        scheduler=None,
        feature_extractor=None,
        t_start=0,
    ).to(device)

    try:
        import xformers
        pipe.enable_xformers_memory_efficient_attention()
    except:
        pass  # run without xformers

    # Cache example images in memory
    example_images_dir = "files/image"
    example_images = []
    for i in range(1, 9):
        image_path = os.path.join(example_images_dir, f"{i}.png")
        if os.path.exists(image_path):
            example_images.append([Image.open(image_path)])

    # Create a Gradio interface
    interface = gr.Interface(
        fn=spaces.GPU(functools.partial(process_image, pipe, vae_2)),
        inputs=gr.Image(type="pil"),
        outputs=gr.Image(type="pil"),
        title="Dereflection Any Image",
        description="Upload an image to remove glass reflections.",
        examples=example_images,
    )

    interface.launch()