| |
| |
| |
| |
| @@ -19,7 +19,7 @@ class ModulationModule(Module): |
| |
| def forward(self, x, embedding, cut_flag): |
| x = self.fc(x) |
| - x = self.norm(x) |
| + x = self.norm(x) |
| if cut_flag == 1: |
| return x |
| gamma = self.gamma_function(embedding.float()) |
| @@ -39,20 +39,20 @@ class SubHairMapper(Module): |
| def forward(self, x, embedding, cut_flag=0): |
| x = self.pixelnorm(x) |
| for modulation_module in self.modulation_module_list: |
| - x = modulation_module(x, embedding, cut_flag) |
| + x = modulation_module(x, embedding, cut_flag) |
| return x |
| |
| -class HairMapper(Module): |
| +class HairMapper(Module): |
| def __init__(self, opts): |
| super(HairMapper, self).__init__() |
| self.opts = opts |
| - self.clip_model, self.preprocess = clip.load("ViT-B/32", device="cuda") |
| + self.clip_model, self.preprocess = clip.load("ViT-B/32", device=opts.device) |
| self.transform = transforms.Compose([transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))]) |
| self.face_pool = torch.nn.AdaptiveAvgPool2d((224, 224)) |
| self.hairstyle_cut_flag = 0 |
| self.color_cut_flag = 0 |
| |
| - if not opts.no_coarse_mapper: |
| + if not opts.no_coarse_mapper: |
| self.course_mapping = SubHairMapper(opts, 4) |
| if not opts.no_medium_mapper: |
| self.medium_mapping = SubHairMapper(opts, 4) |
| @@ -70,13 +70,13 @@ class HairMapper(Module): |
| elif hairstyle_tensor.shape[1] != 1: |
| hairstyle_embedding = self.gen_image_embedding(hairstyle_tensor, self.clip_model, self.preprocess).unsqueeze(1).repeat(1, 18, 1).detach() |
| else: |
| - hairstyle_embedding = torch.ones(x.shape[0], 18, 512).cuda() |
| + hairstyle_embedding = torch.ones(x.shape[0], 18, 512).to(self.opts.device) |
| if color_text_inputs.shape[1] != 1: |
| color_embedding = self.clip_model.encode_text(color_text_inputs).unsqueeze(1).repeat(1, 18, 1).detach() |
| elif color_tensor.shape[1] != 1: |
| color_embedding = self.gen_image_embedding(color_tensor, self.clip_model, self.preprocess).unsqueeze(1).repeat(1, 18, 1).detach() |
| else: |
| - color_embedding = torch.ones(x.shape[0], 18, 512).cuda() |
| + color_embedding = torch.ones(x.shape[0], 18, 512).to(self.opts.device) |
| |
| |
| if (hairstyle_text_inputs.shape[1] == 1) and (hairstyle_tensor.shape[1] == 1): |
| @@ -106,4 +106,4 @@ class HairMapper(Module): |
| x_fine = torch.zeros_like(x_fine) |
| |
| out = torch.cat([x_coarse, x_medium, x_fine], dim=1) |
| - return out |
| \ No newline at end of file |
| + return out |
|
|
