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TileUpscalerV1.py
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TileUpscalerV1.py
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import os
import time
import torch
from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from diffusers.models import AutoencoderKL
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 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
@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)
demo.launch(share=True)