app.py

import spaces

import os
import requests
import time

import torch

from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from diffusers.models import AutoencoderKL
from diffusers.models.attention_processor import AttnProcessor2_0

from PIL import Image
import cv2
import numpy as np

from RealESRGAN import RealESRGAN

import gradio as gr
from gradio_imageslider import ImageSlider

USE_TORCH_COMPILE = False
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

def download_file(url, folder_path, filename):
    if not os.path.exists(folder_path):
        os.makedirs(folder_path)
    file_path = os.path.join(folder_path, filename)

    if os.path.isfile(file_path):
        print(f"File already exists: {file_path}")
    else:
        response = requests.get(url, stream=True)
        if response.status_code == 200:
            with open(file_path, 'wb') as file:
                for chunk in response.iter_content(chunk_size=1024):
                    file.write(chunk)
            print(f"File successfully downloaded and saved: {file_path}")
        else:
            print(f"Error downloading the file. Status code: {response.status_code}")

def download_models():
    models = {
        "MODEL": ("https://huggingface.co/dantea1118/juggernaut_reborn/resolve/main/juggernaut_reborn.safetensors?download=true", "models/models/Stable-diffusion", "juggernaut_reborn.safetensors"),
        "UPSCALER_X2": ("https://huggingface.co/ai-forever/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth?download=true", "models/upscalers/", "RealESRGAN_x2.pth"),
        "UPSCALER_X4": ("https://huggingface.co/ai-forever/Real-ESRGAN/resolve/main/RealESRGAN_x4.pth?download=true", "models/upscalers/", "RealESRGAN_x4.pth"),
        "NEGATIVE_1": ("https://huggingface.co/philz1337x/embeddings/resolve/main/verybadimagenegative_v1.3.pt?download=true", "models/embeddings", "verybadimagenegative_v1.3.pt"),
        "NEGATIVE_2": ("https://huggingface.co/datasets/AddictiveFuture/sd-negative-embeddings/resolve/main/JuggernautNegative-neg.pt?download=true", "models/embeddings", "JuggernautNegative-neg.pt"),
        "LORA_1": ("https://huggingface.co/philz1337x/loras/resolve/main/SDXLrender_v2.0.safetensors?download=true", "models/Lora", "SDXLrender_v2.0.safetensors"),
        "LORA_2": ("https://huggingface.co/philz1337x/loras/resolve/main/more_details.safetensors?download=true", "models/Lora", "more_details.safetensors"),
        "CONTROLNET": ("https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile.pth?download=true", "models/ControlNet", "control_v11f1e_sd15_tile.pth"),
        "VAE": ("https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors?download=true", "models/VAE", "vae-ft-mse-840000-ema-pruned.safetensors"),
    }

    for model, (url, folder, filename) in models.items():
        download_file(url, folder, filename)

download_models()

# def timer_func(func):
#     def wrapper(*args, **kwargs):
#         start_time = time.time()
#         result = func(*args, **kwargs)
#         end_time = time.time()
#         print(f"{func.__name__} took {end_time - start_time:.2f} seconds")
#         return result
#     return wrapper
#
# class LazyLoadPipeline:
#     def __init__(self):
#         self.pipe = None
#
#     @timer_func
#     def load(self):
#         if self.pipe is None:
#             print("Starting to load the pipeline...")
#             self.pipe = self.setup_pipeline()
#             print(f"Moving pipeline to device: {device}")
#             self.pipe.to(device)
#             if USE_TORCH_COMPILE:
#                 print("Compiling the model...")
#                 self.pipe.unet = torch.compile(self.pipe.unet, mode="reduce-overhead", fullgraph=True)
#
#     @timer_func
#     def setup_pipeline(self):
#         print("Setting up the pipeline...")
#         controlnet = ControlNetModel.from_single_file(
#             "models/ControlNet/control_v11f1e_sd15_tile.pth", torch_dtype=torch.float16
#         )
#         safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
#         model_path = "models/models/Stable-diffusion/juggernaut_reborn.safetensors"
#         pipe = StableDiffusionControlNetImg2ImgPipeline.from_single_file(
#             model_path,
#             controlnet=controlnet,
#             torch_dtype=torch.float16,
#             use_safetensors=True,
#             safety_checker=safety_checker
#         )
#         vae = AutoencoderKL.from_single_file(
#             "models/VAE/vae-ft-mse-840000-ema-pruned.safetensors",
#             torch_dtype=torch.float16
#         )
#         pipe.vae = vae
#         pipe.load_textual_inversion("models/embeddings/verybadimagenegative_v1.3.pt")
#         pipe.load_textual_inversion("models/embeddings/JuggernautNegative-neg.pt")
#         pipe.load_lora_weights("models/Lora/SDXLrender_v2.0.safetensors")
#         pipe.fuse_lora(lora_scale=0.5)
#         pipe.load_lora_weights("models/Lora/more_details.safetensors")
#         pipe.fuse_lora(lora_scale=1.)
#         pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
#         pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.3, b2=1.4)
#         return pipe
#
#     def __call__(self, *args, **kwargs):
#         return self.pipe(*args, **kwargs)
#
# class LazyRealESRGAN:
#     def __init__(self, device, scale):
#         self.device = device
#         self.scale = scale
#         self.model = None
#
#     def load_model(self):
#         if self.model is None:
#             self.model = RealESRGAN(self.device, scale=self.scale)
#             self.model.load_weights(f'models/upscalers/RealESRGAN_x{self.scale}.pth', download=False)
#     def predict(self, img):
#         self.load_model()
#         return self.model.predict(img)
#
# lazy_realesrgan_x2 = LazyRealESRGAN(device, scale=2)
# lazy_realesrgan_x4 = LazyRealESRGAN(device, scale=4)
#
# @timer_func
# def resize_and_upscale(input_image, resolution):
#     scale = 2 if resolution <= 2048 else 4
#     input_image = input_image.convert("RGB")
#     W, H = input_image.size
#     k = float(resolution) / min(H, W)
#     H = int(round(H * k / 64.0)) * 64
#     W = int(round(W * k / 64.0)) * 64
#     img = input_image.resize((W, H), resample=Image.LANCZOS)
#     if scale == 2:
#         img = lazy_realesrgan_x2.predict(img)
#     else:
#         img = lazy_realesrgan_x4.predict(img)
#     return img
#
# @timer_func
# def create_hdr_effect(original_image, hdr):
#     if hdr == 0:
#         return original_image
#     cv_original = cv2.cvtColor(np.array(original_image), cv2.COLOR_RGB2BGR)
#     factors = [1.0 - 0.9 * hdr, 1.0 - 0.7 * hdr, 1.0 - 0.45 * hdr,
#                1.0 - 0.25 * hdr, 1.0, 1.0 + 0.2 * hdr,
#                1.0 + 0.4 * hdr, 1.0 + 0.6 * hdr, 1.0 + 0.8 * hdr]
#     images = [cv2.convertScaleAbs(cv_original, alpha=factor) for factor in factors]
#     merge_mertens = cv2.createMergeMertens()
#     hdr_image = merge_mertens.process(images)
#     hdr_image_8bit = np.clip(hdr_image * 255, 0, 255).astype('uint8')
#     return Image.fromarray(cv2.cvtColor(hdr_image_8bit, cv2.COLOR_BGR2RGB))
#
# lazy_pipe = LazyLoadPipeline()
# lazy_pipe.load()
#
# def prepare_image(input_image, resolution, hdr):
#     condition_image = resize_and_upscale(input_image, resolution)
#     condition_image = create_hdr_effect(condition_image, hdr)
#     return condition_image
#
# # @spaces.GPU
# @timer_func
# def gradio_process_image(input_image, resolution, num_inference_steps, strength, hdr, guidance_scale):
#     print("Starting image processing...")
#     torch.cuda.empty_cache()
#
#     condition_image = prepare_image(input_image, resolution, hdr)
#
#     prompt = "masterpiece, best quality, highres"
#     negative_prompt = "low quality, normal quality, ugly, blurry, blur, lowres, bad anatomy, bad hands, cropped, worst quality, verybadimagenegative_v1.3, JuggernautNegative-neg"
#
#     options = {
#         "prompt": prompt,
#         "negative_prompt": negative_prompt,
#         "image": condition_image,
#         "control_image": condition_image,
#         "width": condition_image.size[0],
#         "height": condition_image.size[1],
#         "strength": strength,
#         "num_inference_steps": num_inference_steps,
#         "guidance_scale": guidance_scale,
#         "generator": torch.Generator(device=device).manual_seed(0),
#     }
#
#     print("Running inference...")
#     result = lazy_pipe(**options).images[0]
#     print("Image processing completed successfully")
#
#     # Convert input_image and result to numpy arrays
#     input_array = np.array(input_image)
#     result_array = np.array(result)
#
#     return [input_array, result_array]
#
# title = """<h1 align="center">Image Upscaler with Tile Controlnet</h1>
# <p align="center">The main ideas come from</p>
# <p><center>
# <a href="https://github.com/philz1337x/clarity-upscaler" target="_blank">[philz1337x]</a>
# <a href="https://github.com/BatouResearch/controlnet-tile-upscale" target="_blank">[Pau-Lozano]</a>
# </center></p>
# """
#
# with gr.Blocks() as demo:
#     gr.HTML(title)
#     with gr.Row():
#         with gr.Column():
#             input_image = gr.Image(type="pil", label="Input Image")
#             run_button = gr.Button("Enhance Image")
#         with gr.Column():
#             output_slider = ImageSlider(label="Before / After", type="numpy")
#     with gr.Accordion("Advanced Options", open=False):
#         resolution = gr.Slider(minimum=256, maximum=2048, value=512, step=256, label="Resolution")
#         num_inference_steps = gr.Slider(minimum=1, maximum=50, value=20, step=1, label="Number of Inference Steps")
#         strength = gr.Slider(minimum=0, maximum=1, value=0.4, step=0.01, label="Strength")
#         hdr = gr.Slider(minimum=0, maximum=1, value=0, step=0.1, label="HDR Effect")
#         guidance_scale = gr.Slider(minimum=0, maximum=20, value=3, step=0.5, label="Guidance Scale")
#
#     run_button.click(fn=gradio_process_image,
#                      inputs=[input_image, resolution, num_inference_steps, strength, hdr, guidance_scale],
#                      outputs=output_slider)
#
#     # Add examples with all required inputs
#     gr.Examples(
#         examples=[
#             ["image1.jpg", 512, 20, 0.4, 0, 3],
#             ["image2.png", 512, 20, 0.4, 0, 3],
#             ["image3.png", 512, 20, 0.4, 0, 3],
#         ],
#         inputs=[input_image, resolution, num_inference_steps, strength, hdr, guidance_scale],
#         outputs=output_slider,
#         fn=gradio_process_image,
#         cache_examples=True,
#     )
#
# demo.launch(share=True)