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