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import spaces
import os
import requests
import time
import torch
from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler, DPMSolverMultistepScheduler
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 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 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
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])
@spaces.GPU
@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">Tiled Upscaler V2</h1>
<p align="center">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=1536, 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=0.4, 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=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) |