Create app.py

app.py

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+import os
+from huggingface_hub import hf_hub_download
+os.system("pip -qq install facenet_pytorch")
+from facenet_pytorch import MTCNN
+from torchvision import transforms
+import torch, PIL
+from tqdm.notebook import tqdm
+import gradio as gr
+import torch
+
+modelarcanev4 = hf_hub_download(repo_id="akhaliq/ArcaneGANv0.4", filename="ArcaneGANv0.4.jit")
+modelarcanev3 = hf_hub_download(repo_id="akhaliq/ArcaneGANv0.3", filename="ArcaneGANv0.3.jit")
+modelarcanev2 = hf_hub_download(repo_id="akhaliq/ArcaneGANv0.2", filename="ArcaneGANv0.2.jit")
+
+
+mtcnn = MTCNN(image_size=256, margin=80)
+
+# simplest ye olde trustworthy MTCNN for face detection with landmarks
+def detect(img):
+ 
+        # Detect faces
+        batch_boxes, batch_probs, batch_points = mtcnn.detect(img, landmarks=True)
+        # Select faces
+        if not mtcnn.keep_all:
+            batch_boxes, batch_probs, batch_points = mtcnn.select_boxes(
+                batch_boxes, batch_probs, batch_points, img, method=mtcnn.selection_method
+            )
+ 
+        return batch_boxes, batch_points
+
+# my version of isOdd, should make a separate repo for it :D
+def makeEven(_x):
+  return _x if (_x % 2 == 0) else _x+1
+
+# the actual scaler function
+def scale(boxes, _img, max_res=1_500_000, target_face=256, fixed_ratio=0, max_upscale=2, VERBOSE=False):
+ 
+    x, y = _img.size
+ 
+    ratio = 2 #initial ratio
+ 
+    #scale to desired face size
+    if (boxes is not None):
+      if len(boxes)>0:
+        ratio = target_face/max(boxes[0][2:]-boxes[0][:2]); 
+        ratio = min(ratio, max_upscale)
+        if VERBOSE: print('up by', ratio)
+
+    if fixed_ratio>0:
+      if VERBOSE: print('fixed ratio')
+      ratio = fixed_ratio
+ 
+    x*=ratio
+    y*=ratio
+ 
+    #downscale to fit into max res 
+    res = x*y
+    if res > max_res:
+      ratio = pow(res/max_res,1/2); 
+      if VERBOSE: print(ratio)
+      x=int(x/ratio)
+      y=int(y/ratio)
+ 
+    #make dimensions even, because usually NNs fail on uneven dimensions due skip connection size mismatch
+    x = makeEven(int(x))
+    y = makeEven(int(y))
+    
+    size = (x, y)
+
+    return _img.resize(size)
+
+""" 
+    A useful scaler algorithm, based on face detection.
+    Takes PIL.Image, returns a uniformly scaled PIL.Image
+    boxes: a list of detected bboxes
+    _img: PIL.Image
+    max_res: maximum pixel area to fit into. Use to stay below the VRAM limits of your GPU.
+    target_face: desired face size. Upscale or downscale the whole image to fit the detected face into that dimension.
+    fixed_ratio: fixed scale. Ignores the face size, but doesn't ignore the max_res limit.
+    max_upscale: maximum upscale ratio. Prevents from scaling images with tiny faces to a blurry mess.
+"""
+
+def scale_by_face_size(_img, max_res=1_500_000, target_face=256, fix_ratio=0, max_upscale=2, VERBOSE=False):
+    boxes = None
+    boxes, _ = detect(_img)
+    if VERBOSE: print('boxes',boxes)
+    img_resized = scale(boxes, _img, max_res, target_face, fix_ratio, max_upscale, VERBOSE)
+    return img_resized
+
+
+size = 256
+
+means = [0.485, 0.456, 0.406]
+stds = [0.229, 0.224, 0.225]
+
+t_stds = torch.tensor(stds).cuda().half()[:,None,None]
+t_means = torch.tensor(means).cuda().half()[:,None,None]
+
+def makeEven(_x):
+  return int(_x) if (_x % 2 == 0) else int(_x+1)
+
+img_transforms = transforms.Compose([                        
+            transforms.ToTensor(),
+            transforms.Normalize(means,stds)])
+ 
+def tensor2im(var):
+     return var.mul(t_stds).add(t_means).mul(255.).clamp(0,255).permute(1,2,0)
+
+def proc_pil_img(input_image, model):
+    transformed_image = img_transforms(input_image)[None,...].cuda().half()
+            
+    with torch.no_grad():
+        result_image = model(transformed_image)[0]
+        output_image = tensor2im(result_image)
+        output_image = output_image.detach().cpu().numpy().astype('uint8')
+        output_image = PIL.Image.fromarray(output_image)
+    return output_image
+    
+    
+ 
+modelv4 = torch.jit.load(modelarcanev4).eval().cuda().half()
+modelv3 = torch.jit.load(modelarcanev3).eval().cuda().half()
+modelv2 = torch.jit.load(modelarcanev2).eval().cuda().half()
+
+def process(im, version):
+    if version == 'version 0.4':
+        im = scale_by_face_size(im, target_face=256, max_res=1_500_000, max_upscale=1)
+        res = proc_pil_img(im, modelv4)
+    elif version == 'version 0.3':
+        im = scale_by_face_size(im, target_face=256, max_res=1_500_000, max_upscale=1)
+        res = proc_pil_img(im, modelv3)
+    else:
+        im = scale_by_face_size(im, target_face=256, max_res=1_500_000, max_upscale=1)
+        res = proc_pil_img(im, modelv2)
+    return res
+        
+title = "ArcaneGAN"
+description = "Gradio demo for ArcaneGAN, portrait to Arcane style. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
+article = "<div style='text-align: center;'>ArcaneGan by <a href='https://twitter.com/devdef' target='_blank'>Alexander S</a> | <a href='https://github.com/Sxela/ArcaneGAN' target='_blank'>Github Repo</a> | <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_arcanegan' alt='visitor badge'></center></div>"
+
+gr.Interface(
+    process, 
+    [gr.inputs.Image(type="pil", label="Input"),gr.inputs.Radio(choices=['version 0.2','version 0.3','version 0.4'], type="value", default='version 0.4', label='version')
+], 
+    gr.outputs.Image(type="pil", label="Output"),
+    title=title,
+    description=description,
+    article=article,
+    examples=[['bill.png','version 0.3'],['keanu.png','version 0.4'],['will.jpeg','version 0.4']],
+    allow_flagging=False,
+    allow_screenshot=False
+    ).launch(enable_queue=True,cache_examples=True)