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Running
on
Zero
File size: 4,569 Bytes
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import torch
from PIL import Image
from RealESRGAN import RealESRGAN
import gradio as gr
import os
from random import randint
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model2 = RealESRGAN(device, scale=2)
model2.load_weights('weights/RealESRGAN_x2.pth', download=True)
model4 = RealESRGAN(device, scale=4)
model4.load_weights('weights/RealESRGAN_x4.pth', download=True)
model8 = RealESRGAN(device, scale=8)
model8.load_weights('weights/RealESRGAN_x8.pth', download=True)
def inference_image(image, size):
global model2
global model4
global model8
if image is None:
raise gr.Error("Image not uploaded")
width, height = image.size
if width >= 5000 or height >= 5000:
raise gr.Error("The image is too large.")
if torch.cuda.is_available():
torch.cuda.empty_cache()
if size == '2x':
try:
result = model2.predict(image.convert('RGB'))
except torch.cuda.OutOfMemoryError as e:
print(e)
model2 = RealESRGAN(device, scale=2)
model2.load_weights('weights/RealESRGAN_x2.pth', download=False)
result = model2.predict(image.convert('RGB'))
elif size == '4x':
try:
result = model4.predict(image.convert('RGB'))
except torch.cuda.OutOfMemoryError as e:
print(e)
model4 = RealESRGAN(device, scale=4)
model4.load_weights('weights/RealESRGAN_x4.pth', download=False)
result = model2.predict(image.convert('RGB'))
else:
try:
result = model8.predict(image.convert('RGB'))
except torch.cuda.OutOfMemoryError as e:
print(e)
model8 = RealESRGAN(device, scale=8)
model8.load_weights('weights/RealESRGAN_x8.pth', download=False)
result = model2.predict(image.convert('RGB'))
print(f"Image size ({device}): {size} ... OK")
return result
def inference_video(video, size):
_id = randint(1, 10000)
INPUT_DIR = "/tmp/" + str(_id) + "/"
os.system("rm -rf " + INPUT_DIR)
os.system("mkdir " + INPUT_DIR)
input_image_path = os.path.join(INPUT_DIR, 'input.jpg')
try:
# Specify the desired output file path with the custom name and ".mp4" extension
output_file_path = f"/tmp/videos/{custom_name}.mp4"
# Save the video input to the specified file path
with open(output_file_path, 'wb') as output_file:
output_file.write(video)
print(f"Video input saved as {output_file_path}")
except Exception as e:
print(f"Error saving video input: {str(e)}")
os.system("python inference_video.py")
return os.path.join('/tmp/results_mp4_videos/', 'input.mp4')
input_image = gr.Image(type='pil', label='Input Image')
input_model_image = gr.Radio(['2x', '4x', '8x'], type="value", value="4x", label="Model Upscale/Enhance Type")
submit_image_button = gr.Button('Submit')
output_image = gr.Image(type="filepath", label="Output Image")
tab_img = gr.Interface(
fn=inference_image,
inputs=[input_image, input_model_image],
outputs=output_image,
title="Real-ESRGAN Pytorch",
description="Gradio UI for Real-ESRGAN Pytorch version. To use it, simply upload your image, or click one of examples and choose the model. Read more at the links below. Please click submit only once <br><p style='text-align: center'><a href='https://arxiv.org/abs/2107.10833'>Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data</a> | <a href='https://github.com/ai-forever/Real-ESRGAN'>Github Repo</a></p>"
)
input_video = gr.Video(label='Input Video')
input_model_video = gr.Radio(['2x', '4x', '8x'], type="value", value="4x", label="Model Upscale/Enhance Type")
submit_video_button = gr.Button('Submit')
output_video = gr.Video(label='Output Video')
tab_vid = gr.Interface(
fn=inference_video,
inputs=[input_video, input_model_video],
outputs=output_video,
title="Real-ESRGAN Pytorch",
description="Gradio UI for Real-ESRGAN Pytorch version. To use it, simply upload your video, or click one of examples and choose the model. Read more at the links below. Please click submit only once <br><p style='text-align: center'><a href='https://arxiv.org/abs/2107.10833'>Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data</a> | <a href='https://github.com/ai-forever/Real-ESRGAN'>Github Repo</a></p>"
)
demo = gr.TabbedInterface([tab_img, tab_vid], ["Image", "Video"])
demo.launch(debug=True, show_error=True) |