mirror of
https://github.com/vladmandic/automatic
synced 2026-09-18 16:54:33 +02:00
Make PixelArt run faster
This commit is contained in:
Disty0
+87
-115
@@ -11,7 +11,10 @@ from diffusers.image_processor import PipelineImageInput
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from transformers import ImageProcessingMixin
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from modules import devices
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@devices.inference_context()
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def img_to_pixelart(image: PipelineImageInput, sharpen: float = 0, block_size: int = 8, return_type: str = "pil", device: torch.device = "cpu") -> PipelineImageInput:
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block_size_sq = block_size * block_size
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processor = JPEGEncoder(block_size=block_size, cbcr_downscale=1)
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@@ -30,7 +33,7 @@ def img_to_pixelart(image: PipelineImageInput, sharpen: float = 0, block_size: i
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],
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dtype=torch.float32,
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).to(device)
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ycbcr = ycbcr - (sharpen * torch.nn.functional.conv2d(ycbcr, laplacian_kernel, padding=1, groups=3))
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ycbcr = ycbcr.sub_(torch.nn.functional.conv2d(ycbcr, laplacian_kernel, padding=1, groups=3), alpha=sharpen)
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y = ycbcr[:,0,:,:].unsqueeze(1)
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cb = ycbcr[:,1,:,:].unsqueeze(1)
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cr = ycbcr[:,2,:,:].unsqueeze(1)
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@@ -43,68 +46,72 @@ def img_to_pixelart(image: PipelineImageInput, sharpen: float = 0, block_size: i
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return new_image
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@devices.inference_context()
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def edge_detect_for_pixelart(image: PipelineImageInput, image_weight: float = 1.0, block_size: int = 8, device: torch.device = "cpu") -> torch.Tensor:
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block_size_sq = block_size * block_size
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new_image = process_image_input(image).to(device, dtype=torch.float32) / 255
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new_image = process_image_input(image).to(device).to(dtype=torch.float32) / 255
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new_image = new_image.permute(0,3,1,2)
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batch_size, _channels, height, width = new_image.shape
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block_height = height // block_size
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block_width = width // block_size
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min_pool = -torch.nn.functional.max_pool2d(-new_image, block_size, 1, block_size//2, 1, False, False)
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min_pool = min_pool[:, :, :height, :width]
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greyscale = (new_image[:,0,:,:] * 0.299) + (new_image[:,1,:,:] * 0.587) + (new_image[:,2,:,:] * 0.114)
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greyscale = (new_image[:,0,:,:] * 0.299).add_(new_image[:,1,:,:], alpha=0.587).add_(new_image[:,2,:,:], alpha=0.114)
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greyscale = greyscale[:, :(new_image.shape[-2]//block_size)*block_size, :(new_image.shape[-1]//block_size)*block_size] # crop to a multiple of block_size
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greyscale_reshaped = greyscale.reshape(batch_size, block_size, height // block_size, block_size, width // block_size)
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greyscale_reshaped = greyscale.reshape(batch_size, block_size, block_height, block_size, block_width)
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greyscale_reshaped = greyscale_reshaped.permute(0,1,3,2,4)
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greyscale_reshaped = greyscale_reshaped.reshape(batch_size, block_size_sq, height // block_size, width // block_size)
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greyscale_median = greyscale.median()
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greyscale_max = greyscale_reshaped.amax(dim=1, keepdim=True)
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greyscale_min = greyscale_reshaped.amin(dim=1, keepdim=True)
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greyscale_reshaped = greyscale_reshaped.reshape(batch_size, block_size_sq, block_height, block_width)
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greyscale_range = greyscale_reshaped.amax(dim=1, keepdim=True).sub_(greyscale_reshaped.amin(dim=1, keepdim=True))
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upsample = torchvision.transforms.Resize((height, width), interpolation=torchvision.transforms.InterpolationMode.BICUBIC)
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range_weight = upsample(greyscale_max - greyscale_min)
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range_weight = range_weight / range_weight.max()
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weight_map = upsample((greyscale > greyscale_median).to(dtype=torch.float32))
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weight_map = (weight_map / 2) + (range_weight / 2)
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weight_map = weight_map * image_weight
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new_image = (new_image * weight_map) + (min_pool * (1-weight_map))
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new_image = new_image.permute(0,2,3,1).clamp(0, 1) * 255
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range_weight = upsample(greyscale_range)
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range_weight = range_weight.div_(range_weight.max())
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weight_map = upsample((greyscale > greyscale.median()).to(dtype=torch.float32))
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weight_map = weight_map.unsqueeze(0).add_(range_weight).mul_(image_weight / 2)
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new_image = new_image.mul_(weight_map).addcmul_(min_pool, (1-weight_map))
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new_image = new_image.permute(0,2,3,1).mul_(255).clamp_(0, 255)
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return new_image
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@devices.inference_context()
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def rgb_to_ycbcr_tensor(image: torch.ByteTensor) -> torch.FloatTensor:
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img = image.float() / 255
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y = (img[:,:,:,0] * 0.299) + (img[:,:,:,1] * 0.587) + (img[:,:,:,2] * 0.114)
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cb = 0.5 + (img[:,:,:,0] * -0.168935) + (img[:,:,:,1] * -0.331665) + (img[:,:,:,2] * 0.50059)
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cr = 0.5 + (img[:,:,:,0] * 0.499813) + (img[:,:,:,1] * -0.418531) + (img[:,:,:,2] * -0.081282)
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ycbcr = torch.stack([y,cb,cr], dim=1)
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ycbcr = (ycbcr - 0.5) * 2
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if image.dtype != torch.float32:
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img = image.to(torch.float32).div_(255)
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else:
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img = image / 255
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y = (img[:,:,:,0] * 0.299).add_(img[:,:,:,1], alpha=0.587).add_(img[:,:,:,2], alpha=0.114)
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cb = (img[:,:,:,0] * -0.168935).add_(img[:,:,:,1], alpha=-0.331665).add_(img[:,:,:,2], alpha=0.50059).add_(0.5)
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cr = (img[:,:,:,0] * 0.499813).add_(img[:,:,:,1], alpha=-0.418531).add_(img[:,:,:,2], alpha=-0.081282).add_(0.5)
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ycbcr = torch.add(-1, torch.stack([y,cb,cr], dim=1), alpha=2)
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return ycbcr
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@devices.inference_context()
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def ycbcr_tensor_to_rgb(ycbcr: torch.FloatTensor) -> torch.ByteTensor:
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ycbcr_img = (ycbcr / 2) + 0.5
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y = ycbcr_img[:,0,:,:]
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cb = ycbcr_img[:,1,:,:] - 0.5
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cr = ycbcr_img[:,2,:,:] - 0.5
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ycbcr_img = (ycbcr / 2)
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y = ycbcr_img[:,0,:,:].add_(0.5)
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cb = ycbcr_img[:,1,:,:]
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cr = ycbcr_img[:,2,:,:]
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r = y + (cr * 1.402525)
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g = y + (cb * -0.343730) + (cr * -0.714401)
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b = y + (cb * 1.769905) + (cr * 0.000013)
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rgb = torch.stack([r,g,b], dim=-1).clamp(0,1)
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rgb = (rgb*255).to(torch.uint8)
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r = (cr * 1.402525).add_(y)
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g = (cb * -0.343730).add_(cr, alpha=-0.714401).add_(y)
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b = (cb * 1.769905).add_(cr, alpha=0.000013).add_(y)
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rgb = torch.stack([r,g,b], dim=-1).mul_(255).round_().clamp_(0,255).to(torch.uint8)
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return rgb
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@devices.inference_context()
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def encode_single_channel_dct_2d(img: torch.FloatTensor, block_size: int=16, norm: str='ortho') -> torch.FloatTensor:
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batch_size, height, width = img.shape
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h_blocks = int(height//block_size)
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w_blocks = int(width//block_size)
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# batch_size, h_blocks, w_blocks, block_size_h, block_size_w
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dct_tensor = img.view(batch_size, h_blocks, block_size, w_blocks, block_size).transpose(2,3).float()
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dct_tensor = img.view(batch_size, h_blocks, block_size, w_blocks, block_size).transpose(2,3).to(torch.float32)
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dct_tensor = dct_2d(dct_tensor, norm=norm)
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# batch_size, combined_block_size, h_blocks, w_blocks
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@@ -112,6 +119,7 @@ def encode_single_channel_dct_2d(img: torch.FloatTensor, block_size: int=16, nor
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return dct_tensor
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@devices.inference_context()
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def decode_single_channel_dct_2d(img: torch.FloatTensor, norm: str='ortho') -> torch.FloatTensor:
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batch_size, combined_block_size, h_blocks, w_blocks = img.shape
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block_size = int(math.sqrt(combined_block_size))
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@@ -124,24 +132,28 @@ def decode_single_channel_dct_2d(img: torch.FloatTensor, norm: str='ortho') -> t
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return img_tensor
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@devices.inference_context()
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def encode_jpeg_tensor(img: torch.FloatTensor, block_size: int=16, cbcr_downscale: int=2, norm: str='ortho') -> torch.FloatTensor:
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img = img[:, :, :(img.shape[-2]//block_size)*block_size, :(img.shape[-1]//block_size)*block_size] # crop to a multiply of block_size
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cbcr_block_size = block_size//cbcr_downscale
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_, _, height, width = img.shape
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downsample = torchvision.transforms.Resize((height//cbcr_downscale, width//cbcr_downscale), interpolation=torchvision.transforms.InterpolationMode.BICUBIC)
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down_img = downsample(img[:, 1:,:,:])
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y = encode_single_channel_dct_2d(img[:, 0, :,:], block_size=block_size, norm=norm)
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cb = encode_single_channel_dct_2d(down_img[:, 0, :,:], block_size=block_size//cbcr_downscale, norm=norm)
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cr = encode_single_channel_dct_2d(down_img[:, 1, :,:], block_size=block_size//cbcr_downscale, norm=norm)
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cb = encode_single_channel_dct_2d(down_img[:, 0, :,:], block_size=cbcr_block_size, norm=norm)
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cr = encode_single_channel_dct_2d(down_img[:, 1, :,:], block_size=cbcr_block_size, norm=norm)
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return torch.cat([y,cb,cr], dim=1)
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@devices.inference_context()
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def decode_jpeg_tensor(jpeg_img: torch.FloatTensor, block_size: int=16, cbcr_downscale: int=2, norm: str='ortho') -> torch.FloatTensor:
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_, _, h_blocks, w_blocks = jpeg_img.shape
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y_block_size = block_size*block_size
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cbcr_block_size = int((block_size//cbcr_downscale)*(block_size//cbcr_downscale))
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cbcr_block_size = int((block_size//cbcr_downscale) ** 2)
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cr_start = y_block_size + cbcr_block_size
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y = jpeg_img[:, :y_block_size]
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cb = jpeg_img[:, y_block_size:y_block_size+cbcr_block_size]
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cr = jpeg_img[:, y_block_size+cbcr_block_size:]
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cb = jpeg_img[:, y_block_size:cr_start]
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cr = jpeg_img[:, cr_start:]
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y = decode_single_channel_dct_2d(y, norm=norm)
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cb = decode_single_channel_dct_2d(cb, norm=norm)
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cr = decode_single_channel_dct_2d(cr, norm=norm)
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@@ -205,7 +217,7 @@ class JPEGEncoder(ImageProcessingMixin, ConfigMixin):
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self.latents_mean = latents_mean
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super().__init__()
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@devices.inference_context()
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def encode(self, images: PipelineImageInput, device: str="cpu") -> torch.FloatTensor:
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"""
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Encode RGB 0-255 image to JPEG Latents.
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@@ -231,11 +243,17 @@ class JPEGEncoder(ImageProcessingMixin, ConfigMixin):
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return latents
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@devices.inference_context()
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def decode(self, latents: torch.FloatTensor, return_type: str="pil") -> PipelineImageInput:
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latents = latents.to(dtype=torch.float32)
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if self.latents_std is not None:
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latents = latents * torch.tensor(self.latents_std, device=latents.device, dtype=torch.float32).view(1,-1,1,1)
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if self.latents_mean is not None:
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latents_std = torch.tensor(self.latents_std, device=latents.device, dtype=torch.float32).view(1,-1,1,1)
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if self.latents_mean is not None:
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latents_mean = torch.tensor(self.latents_mean, device=latents.device, dtype=torch.float32).view(1,-1,1,1)
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latents = torch.addcmul(latents_mean, latents, latents_std)
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else:
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latents = latents * latents_std
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elif self.latents_mean is not None:
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latents = latents + torch.tensor(self.latents_mean, device=latents.device, dtype=torch.float32).view(1,-1,1,1)
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images = decode_jpeg_tensor(latents, block_size=self.block_size, cbcr_downscale=self.cbcr_downscale, norm=self.norm)
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@@ -254,114 +272,68 @@ class JPEGEncoder(ImageProcessingMixin, ConfigMixin):
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raise RuntimeError(f"Invalid return_type! Given: {return_type} should be in ('pt', 'np', 'pil')")
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# dct functions are copied from https://github.com/zh217/torch-dct/blob/master/torch_dct/_dct.py (MIT license)
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# dct functions are modified from https://github.com/zh217/torch-dct/blob/master/torch_dct/_dct.py (MIT license)
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@devices.inference_context()
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def dct(x, norm=None):
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"""
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Discrete Cosine Transform, Type II (a.k.a. the DCT)
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For the meaning of the parameter `norm`, see:
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https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.fftpack.dct.html
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:param x: the input signal
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:param norm: the normalization, None or 'ortho'
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:return: the DCT-II of the signal over the last dimension
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"""
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x_shape = x.shape
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N = x_shape[-1]
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x = x.contiguous().view(-1, N)
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v = torch.cat([x[:, ::2], x[:, 1::2].flip([1])], dim=1)
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Vc = torch.view_as_real(torch.fft.fft(v, dim=1))
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k = - torch.arange(N, dtype=x.dtype, device=x.device)[None, :] * np.pi / (2 * N)
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k = - torch.arange(N, dtype=x.dtype, device=x.device)[None, :].mul_(math.pi / (2 * N))
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W_r = torch.cos(k)
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W_i = torch.sin(k)
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V = Vc[:, :, 0] * W_r - Vc[:, :, 1] * W_i
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n_W_i = -torch.sin(k)
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V = torch.addcmul((Vc[:, :, 0] * W_r), Vc[:, :, 1], n_W_i)
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if norm == 'ortho':
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V[:, 0] /= np.sqrt(N) * 2
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V[:, 1:] /= np.sqrt(N / 2) * 2
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V = 2 * V.view(*x_shape)
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V[:, 0].mul_(0.5 / math.sqrt(N))
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V[:, 1:].mul_(0.5 / math.sqrt(N / 2))
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V = V.view(x_shape).mul_(2)
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return V
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@devices.inference_context()
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def idct(X, norm=None):
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"""
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The inverse to DCT-II, which is a scaled Discrete Cosine Transform, Type III
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Our definition of idct is that idct(dct(x)) == x
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For the meaning of the parameter `norm`, see:
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https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.fftpack.dct.html
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:param X: the input signal
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:param norm: the normalization, None or 'ortho'
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:return: the inverse DCT-II of the signal over the last dimension
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"""
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x_shape = X.shape
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N = x_shape[-1]
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X_v = X.contiguous().view(-1, x_shape[-1]) / 2
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X_v = X.contiguous().view(-1, N).div_(2)
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if norm == 'ortho':
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X_v[:, 0] *= np.sqrt(N) * 2
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X_v[:, 1:] *= np.sqrt(N / 2) * 2
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X_v[:, 0].mul_(math.sqrt(N) * 2)
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X_v[:, 1:].mul_(math.sqrt(N / 2) * 2)
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k = torch.arange(x_shape[-1], dtype=X.dtype, device=X.device)[None, :] * np.pi / (2 * N)
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k = torch.arange(N, dtype=X.dtype, device=X.device)[None, :].mul_(math.pi / (2 * N))
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W_r = torch.cos(k)
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W_i = torch.sin(k)
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V_t_r = X_v
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V_t_i = torch.cat([X_v[:, :1] * 0, -X_v.flip([1])[:, :-1]], dim=1)
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V_r = V_t_r * W_r - V_t_i * W_i
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V_i = V_t_r * W_i + V_t_i * W_r
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V_t_i = torch.cat([X_v.new_zeros((X_v.shape[0], 1)), -(X_v.flip([1])[:, :-1])], dim=1)
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V_r = torch.addcmul((X_v * W_r), V_t_i, -W_i)
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V_i = torch.addcmul((X_v * W_i), V_t_i, W_r)
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V = torch.cat([V_r.unsqueeze(2), V_i.unsqueeze(2)], dim=2)
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v = torch.fft.irfft(torch.view_as_complex(V), n=V.shape[1], dim=1)
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x = v.new_zeros(v.shape)
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x[:, ::2] += v[:, :N - (N // 2)]
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x[:, 1::2] += v.flip([1])[:, :N // 2]
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x[:, ::2] = v[:, :N - (N // 2)]
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x[:, 1::2] = v.flip([1])[:, :N // 2]
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return x.view(*x_shape)
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x = x.view(x_shape)
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return x
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@devices.inference_context()
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def dct_2d(x, norm=None):
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"""
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2-dimentional Discrete Cosine Transform, Type II (a.k.a. the DCT)
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For the meaning of the parameter `norm`, see:
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https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.fftpack.dct.html
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:param x: the input signal
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:param norm: the normalization, None or 'ortho'
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:return: the DCT-II of the signal over the last 2 dimensions
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"""
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X1 = dct(x, norm=norm)
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X2 = dct(X1.transpose(-1, -2), norm=norm)
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return X2.transpose(-1, -2)
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X1 = dct(x, norm=norm).transpose_(-1, -2)
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X2 = dct(X1, norm=norm).transpose_(-1, -2)
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return X2
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||||
|
||||
@devices.inference_context()
|
||||
def idct_2d(X, norm=None):
|
||||
"""
|
||||
The inverse to 2D DCT-II, which is a scaled Discrete Cosine Transform, Type III
|
||||
|
||||
Our definition of idct is that idct_2d(dct_2d(x)) == x
|
||||
|
||||
For the meaning of the parameter `norm`, see:
|
||||
https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.fftpack.dct.html
|
||||
|
||||
:param X: the input signal
|
||||
:param norm: the normalization, None or 'ortho'
|
||||
:return: the DCT-II of the signal over the last 2 dimensions
|
||||
"""
|
||||
x1 = idct(X, norm=norm)
|
||||
x2 = idct(x1.transpose(-1, -2), norm=norm)
|
||||
return x2.transpose(-1, -2)
|
||||
x1 = idct(X, norm=norm).transpose_(-1, -2)
|
||||
x2 = idct(x1, norm=norm).transpose_(-1, -2)
|
||||
return x2
|
||||
|
||||
Reference in New Issue
Block a user