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RealESRGAN_x4plus.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4fa0d38905f75ac06eb49a7951b426670021be3018265fd191d2125df9d682f1
+size 67040989

RealESRGAN_x4plus_anime_6B.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f872d837d3c90ed2e05227bed711af5671a6fd1c9f7d7e91c911a61f155e99da
+size 17938799

app.py

@@ -1,68 +1,78 @@
-import os
-os.system("pip install gradio==2.9b23")
-import random
 import gradio as gr
-from PIL import Image
-import torch
-from random import randint
-import sys
-from subprocess import call
-import psutil
+import imageio
+import numpy as np
+from PIL import Image, ImageSequence
+from tempfile import TemporaryFile
+from pathlib import Path
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from realesrgan import RealESRGANer
 
+upsampler = RealESRGANer(
+    scale=4,
+    model_path=str(Path(__file__).parent.joinpath("RealESRGAN_x4plus_anime_6B.pth")),
+    model=RRDBNet(
+        num_in_ch=3,
+        num_out_ch=3,
+        num_feat=64,
+        num_block=6,
+        num_grow_ch=32,
+        scale=4,
+    ),
+    tile=100,
+    tile_pad=10,
+    pre_pad=0,
+    half=False,
+)
 
 
+# torch.hub.download_url_to_file('https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth', 'RealESRGAN_x4plus.pth')
 
-torch.hub.download_url_to_file('http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution_files/100075_lowres.jpg', 'bear.jpg')
-  
-    
-def run_cmd(command):
-    try:
-        print(command)
-        call(command, shell=True)
-    except KeyboardInterrupt:
-        print("Process interrupted")
-        sys.exit(1)
-run_cmd("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P .")
-run_cmd("pip install basicsr")
-run_cmd("pip freeze")
+# torch.hub.download_url_to_file('http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution_files/100075_lowres.jpg', 'bear.jpg')
 
-os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P .")
 
 
-def inference(img,mode):
-    _id = randint(1, 10000)
-    INPUT_DIR = "/tmp/input_image" + str(_id) + "/"
-    OUTPUT_DIR = "/tmp/output_image" + str(_id) + "/"
-    run_cmd("rm -rf " + INPUT_DIR)
-    run_cmd("rm -rf " + OUTPUT_DIR)
-    run_cmd("mkdir " + INPUT_DIR)
-    run_cmd("mkdir " + OUTPUT_DIR)
-    # basewidth = 256
-    # wpercent = (basewidth/float(img.size[0]))
-    # hsize = int((float(img.size[1])*float(wpercent)))
-    # img = img.resize((basewidth,hsize), Image.ANTIALIAS)
-    img.save(INPUT_DIR + "1.jpg", "JPEG")
-    if mode == "base":
-        run_cmd("python inference_realesrgan.py -n RealESRGAN_x4plus -i "+ INPUT_DIR + " -o " + OUTPUT_DIR)
+def inference(image, mode):
+    image = Image.open(image)
+    print(image.format)
+    outputs = []
+    fd = TemporaryFile(delete=False)
+    print(fd.name)
+    is_gif = getattr(image, "is_animated", False)
+    print(is_gif)
+    if is_gif:
+        for i in ImageSequence.Iterator(image):
+            image_array: np.ndarray = np.array(i)
+            output, _ = upsampler.enhance(image_array, 4)
+            outputs.append(output)
     else:
-        os.system("python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i "+ INPUT_DIR + " -o " + OUTPUT_DIR)
-    return os.path.join(OUTPUT_DIR, "1_out.jpg")
-
+        image_array: np.ndarray = np.array(image)
+        output, _ = upsampler.enhance(image_array, 4)
+    if is_gif:
+        imageio.mimsave(
+            fd,
+            outputs[1:],
+            format="gif",
+            duration=image.info["duration"] / 1000,
+        )
+    else:
+        img = Image.fromarray(output)
+        img.save(fd, format="PNG")  # format: PNG / JPEG
+    return fd.name
 
 
-        
 title = "Real-ESRGAN"
 description = "Gradio demo for Real-ESRGAN. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once"
 article = "<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/xinntao/Real-ESRGAN'>Github Repo</a></p>"
 
 gr.Interface(
-    inference, 
-    [gr.inputs.Image(type="pil", label="Input"),gr.inputs.Radio(["base","anime"], type="value", default="base", label="model type")], 
-    gr.outputs.Image(type="file", label="Output"),
+    inference,
+    [
+        gr.Image(type="filepath", label="Input"),
+        gr.Radio(["base", "anime"], type="value", label="model type"),
+    ],
+    gr.Image(type="file", label="Output"),
     title=title,
     description=description,
     article=article,
-    examples=[
-    ['bear.jpg','base'],
-    ['anime.png','anime']
-    ]).launch()
+    examples=[["bear.jpg", "base"], ["anime.png", "anime"]],
+).launch(share=True)