update

app.py

@@ -17,12 +17,19 @@ _DESCRIPTION = '''
 <a style="display:inline-block; margin-left: .5em" href="https://arxiv.org/abs/2309.03453"><img src="https://img.shields.io/badge/2309.03453-f9f7f7?logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADcAAABMCAYAAADJPi9EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAuIwAALiMBeKU/dgAAABl0RVh0U29mdHdhcmUAd3d3Lmlua3NjYXBlLm9yZ5vuPBoAAAa2SURBVHja3Zt7bBRFGMAXUCDGF4rY7m7bAwuhlggKStFgLBgFEkCIIRJEEoOBYHwRFYKilUgEReVNJEGCJJpehHI3M9vZvd3bUP1DjNhEIRQQsQgSHiJgQZ5dv7krWEvvdmZ7d7vHJN+ft/f99pv5XvOtJMFCqvoCUpTdIEeRLC+L9Ox5i3Q9LACaCeK0kXoSChVcD3C/tQPHpAEsquQ73IkUcEz2kcLCknyGW5MGjkljRFVL8xJOKyi4CwCOuQAeAkfTP1+tNxLkogvgEbDgffkJqKqvuMA5ifOpqg/5qWecRstNg7xoUTI1Fovdxg8oy2s5AP8CGeYHmGngeZaOL4I4LXLcpHg4149/GDz4xqgsb+UAbMKKUpkrqHA43MUyyJpWUK0EHeG2YKRXr7tB+QMcgGewLD+ebTDbtrtbBt7UPlhS4rV4IvcDI7J8P1OeA/AcAI7LHljN7aB8XTowJmZt9EFRD/o0SDMH4HlwMhMyDWZZSAHFf3YDs3RS49WDLuaAY3IJq+qzmQKLxXAZKN7oDoYbdV3v5elPqiSpMyiOuAEVZVqHXb1OhloUH+MA+ztO0cAO/RkrfyBE7OAEbAZvO8vzVtTRWFD6DAfY5biBM3PWiaL0a4lvXICwnV8WjmE6ntYmhqX2jjp5LbMZjCw/wbYeN6CizOa2GMVzQOlmHjB4Ceuyk6LJ8huccEmR5Xddg7OOV/NAtchW+E3XbOag60QA4Qwuarca0bRuEJyr+cFQwzcY98huxhAKdQelt4kAQpj4qJ3gvFXAYn+aJumXk1yPlpQUgtIHhbYoFMUstNRRWgjnpl4A7IKlayNymqFHFaWCpV9CFry3LGxR1CgA5kB5M8OX2goApwpaz6mdOMGxtAgXWJySxb4WuQD4qTDgU+N5AAnzpr7ChSWpCyisiQJqY0Y7FtmSKpbV23b45kC0KHBxcQ9QeI8w4KgnHRPVtIU7rOtbioLVg5Hl/qDwSVFAMqLSMSObroCdZYlzIJtMRFVHCaRo/wFWPgaAXzdbBpkc2A4aKzCNd97+URQuESYGDDhIVfWOQIKZJu4D2+oXlgDTV1865gUQZDts756BArMNMoR1oa46BYqbyPixZz1ZUFV3sgwoGBajuBKATl3btIn8QYYMuezRgrsiRUWyr2BxA40EkPMpA/Hm6gbUu7fjEXA3azP6AsbKD9bxdUuhjM9W7fII52BF+daRpE4+WA3P501+jbfmHvQKyFqMuXf7Ot4mkN2fr50y+bRH61X7AXdUpHSxaPQ4GVbR5AGw3g+434XgQGKfr72I+vQRhfsu92dOx7WicInzt3CBg1RVpMm0NveWo2SqFzgmdNZMbriILD+S+zoueWf2vSdAipzacWN5nMl6XxNlUHa/J8DoJodUDE0HR8Ll5V0lPxcrLEHZPV4AzS83OLis7FowVa3RSku7BSNxJqQAlN3hBTC2apmDSkpaw22wJemGQFUG7J4MlP3JC6A+f96V7vRyX9It3nzT/GrjIU8edM7rMSnIi10f476lzbE1K7yEiEuWro0OJBguLCwDuFOJc1Na6sRWL/cCeMIwUN9ggSVbe3v/5/EgzTKWLvEAiBrYRUkgwNI2ZaFQNT75UDxEUEx97zYnzpmiLEmbaYCbNxYtFAb0/Z4AztgUrhyxuNgxPnhfHFDHz/vTgFWUQZxTRkkJhQ6YNdVUEPAfO6ZV5BRss6LcCVb7VaAma9giy0XJZBt9IQh42NY0NSdgbLIPlLUF6rEdrdt0CUCK1wsCbkcI3ZSLc7ZSwGLbmJXbPsNxnE5xilYKAobZ77LpGZ8TAIun+/iCKQoF71IxQDI3K2CCd+ARNvXg9sykBcnHAoCZG4u66hlDoQLe6QV4CRtFSxZQ+D0BwNO2jgdkzoGoah1nj3FVlSR19taTSYxI8QLut23U8dsgzqHulJNCQpcqBnpTALCuQ6NSYLHpmR5i42gZzuIdcrMMvMJbQlxe3jXxyZnLACl7ARm/FjPIDOY8ODtpM71sxwfcZpvBeUzKWmfNINM5AS+wO0Khh7dMqKccu4+qatarZjYAwDlgetzStHtEt+XedsBOQtU9XMrRgjg4KTnc5nr+dmqadit/4C4uLm8DuA9koJTj1TL7fI5nDL+qqoo/FLGAzL7dYT17PzvAcQONYSUQRxW/QMrHZVIyik0ZuQA2mzp+Ji8BW4YM3Mbzm9inaHkJCGfrUZZjujiYailfFwA8DHIy3acwUj4v9vUVa+SmgNsl5fuyDTKovW9/IAmfLV0Pi2UncA515kjYdrwC9i9rpuHiq3JwtAAAAABJRU5ErkJggg=="></a>
 <a style="display:inline-block; margin-left: .5em" href='https://github.com/liuyuan-pal/SyncDreamer'><img src='https://img.shields.io/github/stars/liuyuan-pal/SyncDreamer?style=social' /></a>
 </div>
-Given a single-view image, SyncDreamer is able to generate multiview-consistent images, which enables direct 3D reconstruction with NeuS or NeRF without SDS loss'''
+Given a single-view image, SyncDreamer is able to generate multiview-consistent images, which enables direct 3D reconstruction with NeuS or NeRF without SDS loss
+
+1. Upload the image.
+2. Predict the mask for the foreground object.
+3. Crop the foreground object.
+4. Generate multiview images.
+'''
 _USER_GUIDE0 = "Step0: Please upload an image in the block above (or choose an example above). We use alpha values as object masks if given."
 _USER_GUIDE1 = "Step1: Please select a crop size using the glider."
 _USER_GUIDE2 = "Step2: Please choose a suitable elevation angle and then click the Generate button."
 _USER_GUIDE3 = "Generated multiview images are shown below!"
 
+deployed = True
 
 def mask_prediction(mask_predictor, image_in: Image.Image):
     if image_in.mode=='RGBA':
@@ -56,11 +63,16 @@ def generate(model, batch_view_num, sample_num, cfg_scale, seed, image_input, el
     elevation_input = torch.from_numpy(np.asarray([np.deg2rad(elevation_input)], np.float32))
     data = {"input_image": image_input, "input_elevation": elevation_input}
     for k, v in data.items():
-        data[k] = v.unsqueeze(0)#.cuda()
+        if deployed:
+            data[k] = v.unsqueeze(0).cuda()
+        else:
+            data[k] = v.unsqueeze(0)
         data[k] = torch.repeat_interleave(data[k], sample_num, dim=0)
 
-    x_sample = model.sample(data, cfg_scale, batch_view_num)
-    # x_sample = torch.zeros(sample_num, 16, 3, 256, 256)
+    if deployed:
+        x_sample = model.sample(data, cfg_scale, batch_view_num)
+    else:
+        x_sample = torch.zeros(sample_num, 16, 3, 256, 256)
 
     B, N, _, H, W = x_sample.shape
     x_sample = (torch.clamp(x_sample,max=1.0,min=-1.0) + 1) * 0.5
@@ -80,12 +92,15 @@ def run_demo():
     ckpt = 'ckpt/syncdreamer-pretrain.ckpt'
     config = OmegaConf.load(cfg)
     # model = None
-    model = instantiate_from_config(config.model)
-    print(f'loading model from {ckpt} ...')
-    ckpt = torch.load(ckpt,map_location='cpu')
-    model.load_state_dict(ckpt['state_dict'], strict=True)
-    model = model.cuda().eval()
-    del ckpt
+    if deployed:
+        model = instantiate_from_config(config.model)
+        print(f'loading model from {ckpt} ...')
+        ckpt = torch.load(ckpt,map_location='cpu')
+        model.load_state_dict(ckpt['state_dict'], strict=True)
+        model = model.cuda().eval()
+        del ckpt
+    else:
+        model = None
 
     # init sam model
     mask_predictor = None # sam_init(device_idx)
@@ -121,10 +136,12 @@ def run_demo():
                     examples_per_page=40
                 )
 
+
             with gr.Column(scale=1):
                 sam_block = gr.Image(type='pil', image_mode='RGBA', label="SAM output", height=256, interactive=False)
                 crop_size_slider = gr.Slider(120, 240, 200, step=10, label='Crop size', interactive=True)
                 crop_btn = gr.Button('Crop the image', variant='primary', interactive=True)
+                fig0 = gr.Image(value=Image.open('assets/crop_size.jpg'), type='pil', image_mode='RGB', height=256, show_label=False, tool=None, interactive=False)
 
             with gr.Column(scale=1):
                 input_block = gr.Image(type='pil', image_mode='RGB', label="Input to SyncDreamer", height=256, interactive=False)
@@ -134,6 +151,7 @@ def run_demo():
                 # batch_view_num = gr.Slider(1, 16, 8, step=1, label='', interactive=True)
                 seed = gr.Number(6033, label='Random seed', interactive=True)
                 run_btn = gr.Button('Run Generation', variant='primary', interactive=True)
+                fig1 = gr.Image(value=Image.open('assets/elevation.jpg'), type='pil', image_mode='RGB', height=256, show_label=False, tool=None, interactive=False)
 
         output_block = gr.Image(type='pil', image_mode='RGB', label="Outputs of SyncDreamer", height=256, interactive=False)