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import gradio as gr
import os
import sys
import math
from typing import List
import numpy as np
from PIL import Image
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from diffusers.utils.import_utils import is_xformers_available
from my_utils.testing_utils import parse_args_paired_testing
from de_net import DEResNet
from s3diff_tile import S3Diff
from torchvision import transforms
from utils.wavelet_color import wavelet_color_fix, adain_color_fix
tensor_transforms = transforms.Compose([
transforms.ToTensor(),
])
args = parse_args_paired_testing()
# Load scheduler, tokenizer and models.
pretrained_model_path = 'checkpoint-path/s3diff.pkl'
t2i_path = 'sd-turbo-path'
de_net_path = 'assets/mm-realsr/de_net.pth'
# initialize net_sr
net_sr = S3Diff(lora_rank_unet=args.lora_rank_unet, lora_rank_vae=args.lora_rank_vae, sd_path=t2i_path, pretrained_path=pretrained_model_path, args=args)
net_sr.set_eval()
# initalize degradation estimation network
net_de = DEResNet(num_in_ch=3, num_degradation=2)
net_de.load_model(de_net_path)
net_de = net_de.cuda()
net_de.eval()
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
net_sr.unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
if args.gradient_checkpointing:
net_sr.unet.enable_gradient_checkpointing()
weight_dtype = torch.float32
device = "cuda"
# Move text_encode and vae to gpu and cast to weight_dtype
net_sr.to(device, dtype=weight_dtype)
net_de.to(device, dtype=weight_dtype)
@torch.no_grad()
def process(
input_image: Image.Image,
scale_factor: float,
cfg_scale: float,
latent_tiled_size: int,
latent_tiled_overlap: int,
align_method: str,
) -> List[np.ndarray]:
# positive_prompt = ""
# negative_prompt = ""
net_sr._set_latent_tile(latent_tiled_size = latent_tiled_size, latent_tiled_overlap = latent_tiled_overlap)
im_lr = tensor_transforms(input_image).unsqueeze(0).to(device)
ori_h, ori_w = im_lr.shape[2:]
im_lr_resize = F.interpolate(
im_lr,
size=(int(ori_h * scale_factor),
int(ori_w * scale_factor)),
mode='bicubic',
)
im_lr_resize = im_lr_resize.contiguous()
im_lr_resize_norm = im_lr_resize * 2 - 1.0
im_lr_resize_norm = torch.clamp(im_lr_resize_norm, -1.0, 1.0)
resize_h, resize_w = im_lr_resize_norm.shape[2:]
pad_h = (math.ceil(resize_h / 64)) * 64 - resize_h
pad_w = (math.ceil(resize_w / 64)) * 64 - resize_w
im_lr_resize_norm = F.pad(im_lr_resize_norm, pad=(0, pad_w, 0, pad_h), mode='reflect')
try:
with torch.autocast("cuda"):
deg_score = net_de(im_lr)
pos_tag_prompt = [args.pos_prompt]
neg_tag_prompt = [args.neg_prompt]
x_tgt_pred = net_sr(im_lr_resize_norm, deg_score, pos_prompt=pos_tag_prompt, neg_prompt=neg_tag_prompt)
x_tgt_pred = x_tgt_pred[:, :, :resize_h, :resize_w]
out_img = (x_tgt_pred * 0.5 + 0.5).cpu().detach()
output_pil = transforms.ToPILImage()(out_img[0])
if align_method == 'no fix':
image = output_pil
else:
im_lr_resize = transforms.ToPILImage()(im_lr_resize[0])
if align_method == 'wavelet':
image = wavelet_color_fix(output_pil, im_lr_resize)
elif align_method == 'adain':
image = adain_color_fix(output_pil, im_lr_resize)
except Exception as e:
print(e)
image = Image.new(mode="RGB", size=(512, 512))
return image
#
MARKDOWN = \
"""
## Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors
[GitHub](https://github.com/ArcticHare105/S3Diff) | [Paper](https://arxiv.org/abs/2409.17058)
If S3Diff is helpful for you, please help star the GitHub Repo. Thanks!
"""
block = gr.Blocks().queue()
with block:
with gr.Row():
gr.Markdown(MARKDOWN)
with gr.Row():
with gr.Column():
input_image = gr.Image(source="upload", type="pil")
run_button = gr.Button(label="Run")
with gr.Accordion("Options", open=True):
cfg_scale = gr.Slider(label="Classifier Free Guidance Scale (Set a value larger than 1 to enable it!)", minimum=1.0, maximum=1.1, value=1.07, step=0.01)
scale_factor = gr.Number(label="SR Scale", value=4)
latent_tiled_size = gr.Slider(label="Tile Size", minimum=64, maximum=160, value=96, step=1)
latent_tiled_overlap = gr.Slider(label="Tile Overlap", minimum=16, maximum=48, value=32, step=1)
align_method = gr.Dropdown(label="Color Correction", choices=["wavelet", "adain", "no fix"], value="wavelet")
with gr.Column():
result_image = gr.Image(label="Output", show_label=False, elem_id="result_image", source="canvas", width="100%", height="auto")
inputs = [
input_image,
scale_factor,
cfg_scale,
latent_tiled_size,
latent_tiled_overlap,
align_method
]
run_button.click(fn=process, inputs=inputs, outputs=[result_image])
block.launch()
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