(x, progress, resolution_schedule, num_blocks)
return tf.add_n(x_blend)
Blends images of different resolutions according to `progress`.

  When training `progress` is at a stable stage for resolution r, returns
  image `x` downscaled to resolution r and then upscaled to `final_resolutions`,
  call it x'(r).

  Otherwise when training `progress` is at a transition stage from resolution
  r to 2r, returns a linear combination of x'(r) and x'(2r).

  Args:
    x: An image `Tensor` of NHWC format with resolution `final_resolutions`.
    progress: A scalar float `Tensor` of training progress.
    resolution_schedule: An object of `ResolutionSchedule`.
    num_blocks: An integer of number of blocks.

  Returns:
    An image `Tensor` which is a blend of images of different resolutions.
Blends images of different resolutions according to `progress`.
Blends images of different resolutions according to progress .
def blend_images(x, progress, resolution_schedule, num_blocks):
  """Blends images of different resolutions according to `progress`.

  When training `progress` is at a stable stage for resolution r, returns
  image `x` downscaled to resolution r and then upscaled to `final_resolutions`,
  call it x'(r).

  Otherwise when training `progress` is at a transition stage from resolution
  r to 2r, returns a linear combination of x'(r) and x'(2r).

  Args:
    x: An image `Tensor` of NHWC format with resolution `final_resolutions`.
    progress: A scalar float `Tensor` of training progress.
    resolution_schedule: An object of `ResolutionSchedule`.
    num_blocks: An integer of number of blocks.

  Returns:
    An image `Tensor` which is a blend of images of different resolutions.
  """
  x_blend = []
  for block_id in range(1, num_blocks + 1):
    alpha = _generator_alpha(block_id, progress)
    scale = resolution_schedule.scale_factor(block_id)
    rescaled_x = resolution_schedule.upscale(
        resolution_schedule.downscale(x, scale), scale)
    x_blend.append(alpha * rescaled_x)
  return tf.add_n(x_blend)
def blend_images ( x , progress , resolution_schedule , num_blocks ) : x_blend = [ ] for block_id in range ( 1 , num_blocks + 1 ) : alpha = _generator_alpha ( block_id , progress ) scale = resolution_schedule . scale_factor ( block_id ) rescaled_x = resolution_schedule . upscale ( resolution_schedule . downscale ( x , scale ) , scale ) x_blend . append ( alpha * rescaled_x ) return tf . add_n ( x_blend )