HReynaud commited on
Commit
f2a9d21
·
1 Parent(s): d2217ac

lower ZeroGPU usage

Browse files
Files changed (1) hide show
  1. demo.py +6 -6
demo.py CHANGED
@@ -243,7 +243,7 @@ def preprocess_mask(mask):
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  @spaces.GPU
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- @torch.no_grad()
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  def generate_latent_image(mask, class_selection, sampling_steps=50):
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  """Generate a latent image based on mask, class selection, and sampling steps"""
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@@ -307,7 +307,7 @@ def generate_latent_image(mask, class_selection, sampling_steps=50):
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  @spaces.GPU
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- @torch.no_grad()
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  def decode_images(latents):
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  """Decode latent representations to pixel space using a VAE.
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@@ -386,7 +386,7 @@ def decode_latent_to_pixel(latent_image):
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  @spaces.GPU
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- @torch.no_grad()
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  def check_privacy(latent_image_numpy, class_selection):
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  """Check if the latent image is too similar to database images"""
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  latent_image = torch.from_numpy(latent_image_numpy).to(device, dtype=dtype)
@@ -413,7 +413,7 @@ def check_privacy(latent_image_numpy, class_selection):
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  @spaces.GPU
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- @torch.no_grad()
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  def generate_animation(
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  latent_image, ejection_fraction, sampling_steps=50, cfg_scale=1.0
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  ):
@@ -502,7 +502,7 @@ def generate_animation(
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  @spaces.GPU
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- @torch.no_grad()
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  def decode_animation(latent_animation):
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  """Decode a latent animation to pixel space"""
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  if latent_animation is None:
@@ -578,7 +578,7 @@ def convert_latent_to_display(latent_image):
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  @spaces.GPU
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- @torch.no_grad()
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  def latent_animation_to_grayscale(latent_animation):
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  """Convert multi-channel latent animation to grayscale for display"""
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  if latent_animation is None:
 
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  @spaces.GPU
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+ @torch.no_grad(duration=3)
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  def generate_latent_image(mask, class_selection, sampling_steps=50):
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  """Generate a latent image based on mask, class selection, and sampling steps"""
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  @spaces.GPU
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+ @torch.no_grad(duration=3)
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  def decode_images(latents):
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  """Decode latent representations to pixel space using a VAE.
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  @spaces.GPU
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+ @torch.no_grad(duration=3)
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  def check_privacy(latent_image_numpy, class_selection):
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  """Check if the latent image is too similar to database images"""
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  latent_image = torch.from_numpy(latent_image_numpy).to(device, dtype=dtype)
 
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  @spaces.GPU
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+ @torch.no_grad(duration=3)
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  def generate_animation(
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  latent_image, ejection_fraction, sampling_steps=50, cfg_scale=1.0
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  ):
 
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  @spaces.GPU
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+ @torch.no_grad(duration=3)
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  def decode_animation(latent_animation):
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  """Decode a latent animation to pixel space"""
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  if latent_animation is None:
 
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  @spaces.GPU
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+ @torch.no_grad(duration=3)
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  def latent_animation_to_grayscale(latent_animation):
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  """Convert multi-channel latent animation to grayscale for display"""
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  if latent_animation is None: