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TileUpscalerV2.py
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TileUpscalerV2.py
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import os
import time
import torch
from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler, DPMSolverMultistepScheduler
from diffusers.models import AutoencoderKL
from PIL import Image
import cv2
import numpy as np
from RealESRGAN import RealESRGAN
import random
import math
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
def get_scheduler(scheduler_name, config):
if scheduler_name == "DDIM":
return DDIMScheduler.from_config(config)
elif scheduler_name == "DPM++ 3M SDE Karras":
return DPMSolverMultistepScheduler.from_config(config, algorithm_type="sde-dpmsolver++", use_karras_sigmas=True)
elif scheduler_name == "DPM++ 3M Karras":
return DPMSolverMultistepScheduler.from_config(config, algorithm_type="dpmsolver++", use_karras_sigmas=True)
else:
raise ValueError(f"Unknown scheduler: {scheduler_name}")
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
)
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=None
)
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 set_scheduler(self, scheduler_name):
if self.pipe is not None:
self.pipe.scheduler = get_scheduler(scheduler_name, self.pipe.scheduler.config)
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()
@timer_func
def progressive_upscale(input_image, target_resolution, steps=3):
current_image = input_image.convert("RGB")
current_size = max(current_image.size)
for _ in range(steps):
if current_size >= target_resolution:
break
scale_factor = min(2, target_resolution / current_size)
new_size = (int(current_image.width * scale_factor), int(current_image.height * scale_factor))
if scale_factor <= 1.5:
current_image = current_image.resize(new_size, Image.LANCZOS)
else:
current_image = lazy_realesrgan_x2.predict(current_image)
current_size = max(current_image.size)
# Final resize to exact target resolution
if current_size != target_resolution:
aspect_ratio = current_image.width / current_image.height
if current_image.width > current_image.height:
new_size = (target_resolution, int(target_resolution / aspect_ratio))
else:
new_size = (int(target_resolution * aspect_ratio), target_resolution)
current_image = current_image.resize(new_size, Image.LANCZOS)
return current_image
def prepare_image(input_image, resolution, hdr):
upscaled_image = progressive_upscale(input_image, resolution)
return create_hdr_effect(upscaled_image, hdr)
def create_gaussian_weight(tile_size, sigma=0.3):
x = np.linspace(-1, 1, tile_size)
y = np.linspace(-1, 1, tile_size)
xx, yy = np.meshgrid(x, y)
gaussian_weight = np.exp(-(xx**2 + yy**2) / (2 * sigma**2))
return gaussian_weight
def adaptive_tile_size(image_size, base_tile_size=512, max_tile_size=1024):
w, h = image_size
aspect_ratio = w / h
if aspect_ratio > 1:
tile_w = min(w, max_tile_size)
tile_h = min(int(tile_w / aspect_ratio), max_tile_size)
else:
tile_h = min(h, max_tile_size)
tile_w = min(int(tile_h * aspect_ratio), max_tile_size)
return max(tile_w, base_tile_size), max(tile_h, base_tile_size)
def process_tile(tile, num_inference_steps, strength, guidance_scale, controlnet_strength):
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": tile,
"control_image": tile,
"num_inference_steps": num_inference_steps,
"strength": strength,
"guidance_scale": guidance_scale,
"controlnet_conditioning_scale": float(controlnet_strength),
"generator": torch.Generator(device=device).manual_seed(random.randint(0, 2147483647)),
}
return np.array(lazy_pipe(**options).images[0])
@timer_func
def gradio_process_image(input_image, resolution, num_inference_steps, strength, hdr, guidance_scale, controlnet_strength, scheduler_name):
print("Starting image processing...")
torch.cuda.empty_cache()
lazy_pipe.set_scheduler(scheduler_name)
# Convert input_image to numpy array
input_array = np.array(input_image)
# Prepare the condition image
condition_image = prepare_image(input_image, resolution, hdr)
W, H = condition_image.size
# Adaptive tiling
tile_width, tile_height = adaptive_tile_size((W, H))
# Calculate the number of tiles
overlap = min(64, tile_width // 8, tile_height // 8) # Adaptive overlap
num_tiles_x = math.ceil((W - overlap) / (tile_width - overlap))
num_tiles_y = math.ceil((H - overlap) / (tile_height - overlap))
# Create a blank canvas for the result
result = np.zeros((H, W, 3), dtype=np.float32)
weight_sum = np.zeros((H, W, 1), dtype=np.float32)
# Create gaussian weight
gaussian_weight = create_gaussian_weight(max(tile_width, tile_height))
for i in range(num_tiles_y):
for j in range(num_tiles_x):
# Calculate tile coordinates
left = j * (tile_width - overlap)
top = i * (tile_height - overlap)
right = min(left + tile_width, W)
bottom = min(top + tile_height, H)
# Adjust tile size if it's at the edge
current_tile_size = (bottom - top, right - left)
tile = condition_image.crop((left, top, right, bottom))
tile = tile.resize((tile_width, tile_height))
# Process the tile
result_tile = process_tile(tile, num_inference_steps, strength, guidance_scale, controlnet_strength)
# Apply gaussian weighting
if current_tile_size != (tile_width, tile_height):
result_tile = cv2.resize(result_tile, current_tile_size[::-1])
tile_weight = cv2.resize(gaussian_weight, current_tile_size[::-1])
else:
tile_weight = gaussian_weight[:current_tile_size[0], :current_tile_size[1]]
# Add the tile to the result with gaussian weighting
result[top:bottom, left:right] += result_tile * tile_weight[:, :, np.newaxis]
weight_sum[top:bottom, left:right] += tile_weight[:, :, np.newaxis]
# Normalize the result
final_result = (result / weight_sum).astype(np.uint8)
print("Image processing completed successfully")
return [input_array, final_result]
title = """<h1 align="center">Tile Upscaler V2</h1>
<p align="center">Creative version of Tile Upscaler. The main ideas come from</p>
<p><center>
<a href="https://huggingface.co/spaces/gokaygokay/Tile-Upscaler" target="_blank">[Tile Upscaler]</a>
<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=128, maximum=2048, value=1024, step=128, 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=0.2, 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=6, step=0.5, label="Guidance Scale")
controlnet_strength = gr.Slider(minimum=0.0, maximum=2.0, value=0.75, step=0.05, label="ControlNet Strength")
scheduler_name = gr.Dropdown(
choices=["DDIM", "DPM++ 3M SDE Karras", "DPM++ 3M Karras"],
value="DDIM",
label="Scheduler"
)
run_button.click(fn=gradio_process_image,
inputs=[input_image, resolution, num_inference_steps, strength, hdr, guidance_scale, controlnet_strength, scheduler_name],
outputs=output_slider)
demo.launch(debug=True, share=True)