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A demo for my self trained models

app.py

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+# Code taken (and slightly adopted) from https://huggingface.co/spaces/havas79/Real-ESRGAN_Demo/blob/main/app.py - credit where credit is due. I am not showcasing code here, but demoing my own trained models ;)
+
+import gradio as gr
+import cv2
+import numpy
+import os
+import random
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from basicsr.utils.download_util import load_file_from_url
+
+from realesrgan import RealESRGANer
+from realesrgan.archs.srvgg_arch import SRVGGNetCompact
+
+last_file = None
+img_mode = "RGBA"
+
+def realesrgan(img, model_name, face_enhance):
+
+    if not img:
+        return
+    
+    imgwidth, imgheight = img.size
+
+    if imgwidth > 1000 or imgheight > 1000:
+        return error("Input Image too big")
+
+    # Define model parameters
+    if model_name == '4xNomos8kSC':
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+        netscale = 4
+    elif model_name == '4xHFA2k':
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+        netscale = 4
+    elif model_name == '4xLSDIR':
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+        netscale = 4
+    elif model_name == '4xLSDIRplusN':
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+        netscale = 4
+    elif model_name == '4xLSDIRplusC':
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+        netscale = 4
+    elif model_name == '4xLSDIRplusR':
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+        netscale = 4
+    elif model_name == '2xParimgCompact':
+        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=2, act_type='prelu')
+        netscale = 2
+    elif model_name == '2xHFA2kCompact':
+        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=2, act_type='prelu')
+        netscale = 2
+    elif model_name == '4xLSDIRCompactN':
+        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
+        netscale = 4
+    elif model_name == '4xLSDIRCompactC3':
+        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
+        netscale = 4
+    elif model_name == '4xLSDIRCompactR3':
+        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
+        netscale = 4
+
+    # Determine model paths
+    model_path = os.path.join('weights', model_name + '.pth')
+
+    # Restorer Class
+    upsampler = RealESRGANer(
+        scale=netscale,
+        model_path=model_path,
+        dni_weight=None,
+        model=model,
+        tile=0,
+        tile_pad=10,
+        pre_pad=10,
+        half=False,
+        gpu_id=None,
+    )
+
+    # Use GFPGAN for face enhancement
+    if face_enhance:
+        from gfpgan import GFPGANer
+        face_enhancer = GFPGANer(
+            model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth',
+            upscale=netscale,
+            arch='clean',
+            channel_multiplier=2,
+            bg_upsampler=upsampler)
+
+    # Convert the input PIL image to cv2 image, so that it can be processed by realesrgan
+    cv_img = numpy.array(img)
+    img = cv2.cvtColor(cv_img, cv2.COLOR_RGBA2BGRA)
+
+    # Apply restoration
+    try:
+        if face_enhance:
+            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
+        else:
+            output, _ = upsampler.enhance(img, netscale)
+    except RuntimeError as error:
+        print('Error', error)
+        print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
+    else:
+        # Save restored image and return it to the output Image component
+        if img_mode == 'RGBA':  # RGBA images should be saved in png format
+            extension = 'png'
+        else:
+            extension = 'jpg'
+
+        out_filename = f"output_{rnd_string(16)}.{extension}"
+        cv2.imwrite(out_filename, output)
+        global last_file
+        last_file = out_filename
+        return out_filename
+
+
+def rnd_string(x):
+    """Returns a string of 'x' random characters
+    """
+    characters = "abcdefghijklmnopqrstuvwxyz_0123456789"
+    result = "".join((random.choice(characters)) for i in range(x))
+    return result
+
+
+def reset():
+    """Resets the Image components of the Gradio interface and deletes
+    the last processed image
+    """
+    global last_file
+    if last_file:
+        print(f"Deleting {last_file} ...")
+        os.remove(last_file)
+        last_file = None
+    return gr.update(value=None), gr.update(value=None)
+
+
+def has_transparency(img):
+    """This function works by first checking to see if a "transparency" property is defined
+    in the image's info -- if so, we return "True". Then, if the image is using indexed colors
+    (such as in GIFs), it gets the index of the transparent color in the palette
+    (img.info.get("transparency", -1)) and checks if it's used anywhere in the canvas
+    (img.getcolors()). If the image is in RGBA mode, then presumably it has transparency in
+    it, but it double-checks by getting the minimum and maximum values of every color channel
+    (img.getextrema()), and checks if the alpha channel's smallest value falls below 255.
+    https://stackoverflow.com/questions/43864101/python-pil-check-if-image-is-transparent
+    """
+    if img.info.get("transparency", None) is not None:
+        return True
+    if img.mode == "P":
+        transparent = img.info.get("transparency", -1)
+        for _, index in img.getcolors():
+            if index == transparent:
+                return True
+    elif img.mode == "RGBA":
+        extrema = img.getextrema()
+        if extrema[3][0] < 255:
+            return True
+    return False
+
+
+def image_properties(img):
+    """Returns the dimensions (width and height) and color mode of the input image and
+    also sets the global img_mode variable to be used by the realesrgan function
+    """
+    global img_mode
+    if img:
+        if has_transparency(img):
+            img_mode = "RGBA"
+        else:
+            img_mode = "RGB"
+        properties = f"Width: {img.size[0]}, Height: {img.size[1]}  |  Color Mode: {img_mode}"
+        return properties
+
+
+def main():
+    # Gradio Interface
+    with gr.Blocks(title="Self-trained ESRGAN models demo", theme="dark") as demo:
+
+        gr.Markdown(
+            """# <div align="center"> Upscale image </div> 
+        Here I demo my self-trained models. The models with their corresponding infos can be found on [my github repo](https://github.com/phhofm/models).
+        """
+        )
+
+        with gr.Group():
+            with gr.Group():
+                model_name = gr.Dropdown(label="Model to be used",
+                                         choices=["2xHFA2kCompact", "2xParimgCompact", "4xLSDIRCompactN", "4xLSDIRCompactC3", "4xLSDIRCompactR3", "4xNomos8kSC", "4xHFA2k", "4xLSDIR", "4xLSDIRplusN", "4xLSDIRplusC", "4xLSDIRplusR"], value="4xLSDIRCompactC3", 
+                                         info="See model infos at the bottom of this page")
+                face_enhance = gr.Checkbox(label="Face Enhancement using GFPGAN (Doesn't work for anime images)",value=False, show_label=True)
+
+        with gr.Row():
+            with gr.Group():
+                input_image = gr.Image(label="Source Image", type="pil", image_mode="RGBA")
+                input_image_properties = gr.Textbox(label="Image Properties - Demo will throw error if input image has either width or height > 1000. Output download is jpg for smaller size. Use models locally to circument these limits.", max_lines=1)
+        output_image = gr.Image(label="Upscaled Image", image_mode="RGBA")
+        with gr.Row():
+            upscale_btn = gr.Button("Upscale")
+            reset_btn = gr.Button("Reset")
+        with gr.Group():
+            gr.Markdown(
+    """
+    **Model infos**  
+    *SRVGGNetCompact models - in general faster, but less powerful, than RRDBNet*  
+    2xHFA2kCompact - use for upscaling anime images 2x, faster than 4xHFA2k but less powerful (SRVGGNetCompact)    
+    2xParimgCompact - upscaling photos 2x, fast (SRVGGNetCompact)  
+    4xLSDIRCompactN - upscale a good quality photo (no degradations) 4x, faster than 4xLSDIRN but less powerful (SRVGGNetCompact)  
+    4xLSDIRCompactC3 - upscale a jpg compressed photo 4x, fast (SRVGGNetCompact)  
+    4xLSDIRCompactR3 - upscale a degraded photo 4x, fast (SRVGGNetCompact) (too strong, best used for interpolation like 4xLSDIRCompactN (or C) 75% 4xLSDIRCompactR3 25% to add little degradation handling to the previous one)  
+
+    *RRDBNet models - in general more powerful than SRVGGNetCompact, but very slow in this demo*  
+    4xNomos8kSC - use for upscaling photos 4x   
+    4xHFA2k - use for upscaling anime images 4x   
+    4xLSDIR - upscale a good quality photo (no degradation) 4x  
+    4xLSDIRplusN - upscale a good quality photo (no degradation) 4x  
+    4xLSDIRplusC - upscale a jpg compressed photo 4x   
+    4xLSDIRplusR - upscale a degraded photo 4x (too strong, best used for interpolation like 4xLSDIRplusN (or C) 75% 4xLSDIRplusR 25% to add little degradation handling to the previous one)    
+
+    *The following are not models I had trained, but rather interpolations I had created, they are available on my [repo](https://github.com/phhofm/models) and can be tried out locally with chaiNNer:*  
+    4xLSDIRCompact3 (4xLSDIRCompactC3 + 4xLSDIRCompactR3)  
+    4xLSDIRCompact2 (4xLSDIRCompactC2 + 4xLSDIRCompactR2)  
+    4xInt-Ultracri (UltraSharp + Remacri)  
+    4xInt-Superscri (Superscale + Remacri)  
+    4xInt-Siacri(Siax + Remacri)  
+    4xInt-RemDF2K (Remacri + RealSR_DF2K_JPEG)  
+    4xInt-RemArt (Remacri + VolArt)  
+    4xInt-RemAnime (Remacri + AnimeSharp)  
+    4xInt-RemacRestore (Remacri + UltraMix_Restore)  
+    4xInt-AnimeArt (AnimeSharp + VolArt)  
+    2xInt-LD-AnimeJaNai (LD-Anime + AnimeJaNai)  
+    """)
+
+        # Event listeners:
+        input_image.change(fn=image_properties, inputs=input_image, outputs=input_image_properties)
+        upscale_btn.click(fn=realesrgan, inputs=[input_image, model_name, face_enhance], outputs=output_image)
+        reset_btn.click(fn=reset, inputs=[], outputs=[output_image, input_image])
+
+    demo.launch()
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+torch
+torchvision
+numpy
+opencv-python
+Pillow
+basicsr
+facexlib
+gfpgan
+tqdm
+gradio
+realesrgan

weights/2xHFA2kCompact.pth

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+size 4838301

weights/2xParimgCompact.pth

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weights/4xHFA2k.pth

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weights/4xLSDIR.pth

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weights/4xLSDIRCompactC3.pth

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weights/4xLSDIRCompactN.pth

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weights/4xLSDIRCompactR3.pth

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weights/4xLSDIRplusC.pth

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weights/4xLSDIRplusR.pth

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weights/4xNomos8kSC.pth

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