nightfury commited on
Commit
49dc097
1 Parent(s): 986ef15

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +10 -6
app.py CHANGED
@@ -35,27 +35,31 @@ pipe = StableDiffusionInpaintingPipeline.from_pretrained(
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  #model = CLIPDensePredT(version='ViT-B/16', reduce_dim=64)
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  model = CLIPDensePredT(version='ViT-B/16', reduce_dim=64, complex_trans_conv=True)
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  model.eval()
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  model.load_state_dict(torch.load('./clipseg/weights/rd64-uni.pth', map_location=torch.device(device)), strict=False)
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  transform = transforms.Compose([
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  transforms.ToTensor(),
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  transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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- transforms.Resize((512, 512)),
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  ])
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  def predict(radio, dict, word_mask, prompt=""):
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  if(radio == "draw a mask above"):
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  #with autocast("cuda"):
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- with autocast(enable=(False if device=='cpu' else True)):
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- init_image = dict["image"].convert("RGB").resize((512, 512))
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- mask = dict["mask"].convert("RGB").resize((512, 512))
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  else:
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  img = transform(dict["image"]).unsqueeze(0)
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  word_masks = [word_mask]
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  with torch.no_grad():
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  preds = model(img.repeat(len(word_masks),1,1,1), word_masks)[0]
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- init_image = dict['image'].convert('RGB').resize((512, 512))
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  filename = f"{uuid.uuid4()}.png"
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  plt.imsave(filename,torch.sigmoid(preds[0][0]))
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  img2 = cv2.imread(filename)
@@ -65,7 +69,7 @@ def predict(radio, dict, word_mask, prompt=""):
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  mask = Image.fromarray(np.uint8(bw_image)).convert('RGB')
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  os.remove(filename)
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  #with autocast("cuda"):
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- with autocast(enable=(False if device=='cpu' else True)):
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  images = pipe(prompt = prompt, init_image=init_image, mask_image=mask, strength=0.8)["sample"]
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  return images[0]
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  #model = CLIPDensePredT(version='ViT-B/16', reduce_dim=64)
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  model = CLIPDensePredT(version='ViT-B/16', reduce_dim=64, complex_trans_conv=True)
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+ model = model.to(torch.device(device))
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  model.eval()
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  model.load_state_dict(torch.load('./clipseg/weights/rd64-uni.pth', map_location=torch.device(device)), strict=False)
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+ print ("Torch load(model) : ", model)
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+ imgRes = 256 #512
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+
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  transform = transforms.Compose([
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  transforms.ToTensor(),
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  transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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+ transforms.Resize((imgRes, imgRes)),
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  ])
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  def predict(radio, dict, word_mask, prompt=""):
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  if(radio == "draw a mask above"):
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  #with autocast("cuda"):
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+ with autocast(device): #enable=(False if device=='cpu' else True)):
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+ init_image = dict["image"].convert("RGB").resize((imgRes, imgRes))
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+ mask = dict["mask"].convert("RGB").resize((imgRes, imgRes))
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  else:
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  img = transform(dict["image"]).unsqueeze(0)
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  word_masks = [word_mask]
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  with torch.no_grad():
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  preds = model(img.repeat(len(word_masks),1,1,1), word_masks)[0]
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+ init_image = dict['image'].convert('RGB').resize((imgRes, imgRes))
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  filename = f"{uuid.uuid4()}.png"
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  plt.imsave(filename,torch.sigmoid(preds[0][0]))
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  img2 = cv2.imread(filename)
 
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  mask = Image.fromarray(np.uint8(bw_image)).convert('RGB')
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  os.remove(filename)
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  #with autocast("cuda"):
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+ with autocast(device): #enable=(False if device=='cpu' else True)):
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  images = pipe(prompt = prompt, init_image=init_image, mask_image=mask, strength=0.8)["sample"]
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  return images[0]
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