Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,511 Bytes
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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()
@spaces.GPU
@torch.inference_mode()
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() |