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on
Zero
Running
on
Zero
| import torch | |
| import torch.nn.functional as F | |
| import numpy as np | |
| from PIL import Image | |
| attn_maps = {} | |
| def hook_fn(name): | |
| def forward_hook(module, input, output): | |
| if hasattr(module.processor, "attn_map"): | |
| attn_maps[name] = module.processor.attn_map | |
| del module.processor.attn_map | |
| return forward_hook | |
| def register_cross_attention_hook(unet): | |
| for name, module in unet.named_modules(): | |
| if name.split('.')[-1].startswith('attn2'): | |
| module.register_forward_hook(hook_fn(name)) | |
| return unet | |
| def upscale(attn_map, target_size): | |
| attn_map = torch.mean(attn_map, dim=0) | |
| attn_map = attn_map.permute(1,0) | |
| temp_size = None | |
| for i in range(0,5): | |
| scale = 2 ** i | |
| if ( target_size[0] // scale ) * ( target_size[1] // scale) == attn_map.shape[1]*64: | |
| temp_size = (target_size[0]//(scale*8), target_size[1]//(scale*8)) | |
| break | |
| assert temp_size is not None, "temp_size cannot is None" | |
| attn_map = attn_map.view(attn_map.shape[0], *temp_size) | |
| attn_map = F.interpolate( | |
| attn_map.unsqueeze(0).to(dtype=torch.float32), | |
| size=target_size, | |
| mode='bilinear', | |
| align_corners=False | |
| )[0] | |
| attn_map = torch.softmax(attn_map, dim=0) | |
| return attn_map | |
| def get_net_attn_map(image_size, batch_size=2, instance_or_negative=False, detach=True): | |
| idx = 0 if instance_or_negative else 1 | |
| net_attn_maps = [] | |
| for name, attn_map in attn_maps.items(): | |
| attn_map = attn_map.cpu() if detach else attn_map | |
| attn_map = torch.chunk(attn_map, batch_size)[idx].squeeze() | |
| attn_map = upscale(attn_map, image_size) | |
| net_attn_maps.append(attn_map) | |
| net_attn_maps = torch.mean(torch.stack(net_attn_maps,dim=0),dim=0) | |
| return net_attn_maps | |
| def attnmaps2images(net_attn_maps): | |
| #total_attn_scores = 0 | |
| images = [] | |
| for attn_map in net_attn_maps: | |
| attn_map = attn_map.cpu().numpy() | |
| #total_attn_scores += attn_map.mean().item() | |
| normalized_attn_map = (attn_map - np.min(attn_map)) / (np.max(attn_map) - np.min(attn_map)) * 255 | |
| normalized_attn_map = normalized_attn_map.astype(np.uint8) | |
| #print("norm: ", normalized_attn_map.shape) | |
| image = Image.fromarray(normalized_attn_map) | |
| #image = fix_save_attn_map(attn_map) | |
| images.append(image) | |
| #print(total_attn_scores) | |
| return images | |
| def is_torch2_available(): | |
| return hasattr(F, "scaled_dot_product_attention") |