143 lines
4.2 KiB
Python
143 lines
4.2 KiB
Python
import torch
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import torch.nn.functional as F
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import numpy as np
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from PIL import Image
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BLOCKS = {
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'content': ['down_blocks'],
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'style': ["up_blocks"],
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}
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controlnet_BLOCKS = {
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'content': [],
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'style': ["down_blocks"],
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}
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def resize_width_height(width, height, min_short_side=512, max_long_side=1024):
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if width < height:
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if width < min_short_side:
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scale_factor = min_short_side / width
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new_width = min_short_side
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new_height = int(height * scale_factor)
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else:
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new_width, new_height = width, height
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else:
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if height < min_short_side:
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scale_factor = min_short_side / height
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new_width = int(width * scale_factor)
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new_height = min_short_side
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else:
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new_width, new_height = width, height
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if max(new_width, new_height) > max_long_side:
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scale_factor = max_long_side / max(new_width, new_height)
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new_width = int(new_width * scale_factor)
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new_height = int(new_height * scale_factor)
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return new_width, new_height
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def resize_content(content_image):
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max_long_side = 1024
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min_short_side = 1024
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new_width, new_height = resize_width_height(content_image.size[0], content_image.size[1],
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min_short_side=min_short_side, max_long_side=max_long_side)
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height = new_height // 16 * 16
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width = new_width // 16 * 16
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content_image = content_image.resize((width, height))
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return width,height,content_image
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attn_maps = {}
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def hook_fn(name):
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def forward_hook(module, input, output):
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if hasattr(module.processor, "attn_map"):
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attn_maps[name] = module.processor.attn_map
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del module.processor.attn_map
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return forward_hook
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def register_cross_attention_hook(unet):
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for name, module in unet.named_modules():
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if name.split('.')[-1].startswith('attn2'):
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module.register_forward_hook(hook_fn(name))
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return unet
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def upscale(attn_map, target_size):
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attn_map = torch.mean(attn_map, dim=0)
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attn_map = attn_map.permute(1,0)
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temp_size = None
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for i in range(0,5):
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scale = 2 ** i
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if ( target_size[0] // scale ) * ( target_size[1] // scale) == attn_map.shape[1]*64:
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temp_size = (target_size[0]//(scale*8), target_size[1]//(scale*8))
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break
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assert temp_size is not None, "temp_size cannot is None"
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attn_map = attn_map.view(attn_map.shape[0], *temp_size)
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attn_map = F.interpolate(
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attn_map.unsqueeze(0).to(dtype=torch.float32),
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size=target_size,
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mode='bilinear',
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align_corners=False
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)[0]
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attn_map = torch.softmax(attn_map, dim=0)
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return attn_map
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def get_net_attn_map(image_size, batch_size=2, instance_or_negative=False, detach=True):
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idx = 0 if instance_or_negative else 1
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net_attn_maps = []
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for name, attn_map in attn_maps.items():
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attn_map = attn_map.cpu() if detach else attn_map
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attn_map = torch.chunk(attn_map, batch_size)[idx].squeeze()
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attn_map = upscale(attn_map, image_size)
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net_attn_maps.append(attn_map)
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net_attn_maps = torch.mean(torch.stack(net_attn_maps,dim=0),dim=0)
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return net_attn_maps
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def attnmaps2images(net_attn_maps):
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#total_attn_scores = 0
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images = []
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for attn_map in net_attn_maps:
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attn_map = attn_map.cpu().numpy()
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#total_attn_scores += attn_map.mean().item()
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normalized_attn_map = (attn_map - np.min(attn_map)) / (np.max(attn_map) - np.min(attn_map)) * 255
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normalized_attn_map = normalized_attn_map.astype(np.uint8)
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#print("norm: ", normalized_attn_map.shape)
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image = Image.fromarray(normalized_attn_map)
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#image = fix_save_attn_map(attn_map)
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images.append(image)
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#print(total_attn_scores)
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return images
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def is_torch2_available():
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return hasattr(F, "scaled_dot_product_attention")
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def get_generator(seed, device):
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if seed is not None:
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if isinstance(seed, list):
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generator = [torch.Generator(device).manual_seed(seed_item) for seed_item in seed]
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else:
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generator = torch.Generator(device).manual_seed(seed)
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else:
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generator = None
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return generator
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