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on
Zero
Running
on
Zero
import os | |
import torch | |
import gradio as gr | |
import spaces | |
from PIL import Image | |
import numpy as np | |
from omegaconf import OmegaConf | |
import requests | |
from tqdm import tqdm | |
def download_file(url, filename): | |
response = requests.get(url, stream=True) | |
total_size = int(response.headers.get('content-length', 0)) | |
block_size = 1024 | |
with open(filename, 'wb') as file, tqdm( | |
desc=filename, | |
total=total_size, | |
unit='iB', | |
unit_scale=True, | |
unit_divisor=1024, | |
) as progress_bar: | |
for data in response.iter_content(block_size): | |
size = file.write(data) | |
progress_bar.update(size) | |
def setup_environment(): | |
os.makedirs("weights", exist_ok=True) | |
if not os.path.exists("weights/real-world_ccsr.ckpt"): | |
print("Downloading model checkpoint...") | |
download_file( | |
"https://huggingface.co./camenduru/CCSR/resolve/main/real-world_ccsr.ckpt", | |
"weights/real-world_ccsr.ckpt" | |
) | |
else: | |
print("Model checkpoint already exists. Skipping download.") | |
setup_environment() | |
from ccsr.models.ccsr import CCSR | |
from ccsr.utils.util import instantiate_from_config | |
config = OmegaConf.load("configs/model/ccsr_stage2.yaml") | |
model = instantiate_from_config(config.model) | |
ckpt = torch.load("weights/real-world_ccsr.ckpt", map_location="cpu") | |
model.load_state_dict(ckpt["state_dict"], strict=False) | |
model.cuda().eval() | |
def infer(image, sr_scale, t_max, t_min, color_fix_type): | |
image = Image.open(image).convert("RGB").resize((256, 256), Image.LANCZOS) | |
image = torch.from_numpy(np.array(image)).float().cuda() / 127.5 - 1 | |
image = image.permute(2, 0, 1).unsqueeze(0) | |
output = model.super_resolution( | |
image, | |
sr_scale=sr_scale, | |
t_max=t_max, | |
t_min=t_min, | |
color_fix_type=color_fix_type | |
) | |
output = ((output.squeeze().permute(1, 2, 0).cpu().numpy() + 1) * 127.5).clip(0, 255).astype(np.uint8) | |
return Image.fromarray(output) | |
interface = gr.Interface( | |
fn=infer, | |
inputs=[ | |
gr.Image(type="filepath"), | |
gr.Slider(minimum=1, maximum=8, step=1, value=4), | |
gr.Slider(minimum=0, maximum=1, step=0.0001, value=0.6667), | |
gr.Slider(minimum=0, maximum=1, step=0.0001, value=0.3333), | |
gr.Dropdown(choices=["adain", "wavelet", "none"], value="adain"), | |
], | |
outputs=gr.Image(type="pil"), | |
title="CCSR: Continuous Contrastive Super-Resolution", | |
) | |
interface.launch() |