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Author: 1038lab

SDMatte/__init__.py

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+# 使 SDMatte 目录成为可导入包
+

SDMatte/modeling/SDMatte/__init__.py

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+from .meta_arch import *

SDMatte/modeling/SDMatte/meta_arch.py

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+import torch
+from transformers import CLIPTextModel, CLIPTokenizer, CLIPTextConfig
+import torch.nn as nn
+import torch.nn.functional as F
+from diffusers import DDIMScheduler, AutoencoderKL, UNet2DConditionModel
+from diffusers.models.embeddings import get_timestep_embedding
+from ...utils import replace_unet_conv_in, replace_attention_mask_method, add_aux_conv_in
+from ...utils.replace import CustomUNet
+import random
+import os
+
+# 解决离线本地目录层级差异，如存在 "subdir/subdir/config.json" 的情况
+def _resolve_nested_dir(base_dir: str, subdir: str, config_filename: str) -> str:
+    direct = os.path.join(base_dir, subdir)
+    nested = os.path.join(base_dir, subdir, subdir)
+    if os.path.exists(os.path.join(direct, config_filename)):
+        return direct
+    if os.path.exists(os.path.join(nested, config_filename)):
+        return nested
+    return direct
+
+AUX_INPUT_DIT = {
+    "auto_mask": "auto_coords",
+    "point_mask": "point_coords",
+    "bbox_mask": "bbox_coords",
+    "mask": "mask_coords",
+    "trimap": "trimap_coords",
+}
+        
+class SDMatte(nn.Module):
+    def __init__(
+        self,
+        pretrained_model_name_or_path,
+        conv_scale=3,
+        num_inference_steps=1,
+        aux_input="bbox_mask",
+        use_aux_input=False,
+        use_coor_input=True,
+        use_dis_loss=True,
+        use_attention_mask=True,
+        use_encoder_attention_mask=False,
+        add_noise=False,
+        attn_mask_aux_input=["point_mask", "bbox_mask", "mask"],
+        aux_input_list=["point_mask", "bbox_mask", "mask"],
+        use_encoder_hidden_states=True,
+        residual_connection=False,
+        use_attention_mask_list=[True, True, True],
+        use_encoder_hidden_states_list=[True, True, True],
+        load_weight = True,
+    ):
+        super().__init__()
+        self.init_submodule(pretrained_model_name_or_path, load_weight)
+        self.num_inference_steps = num_inference_steps
+        self.aux_input = aux_input
+        self.use_aux_input = use_aux_input
+        self.use_coor_input = use_coor_input
+        self.use_dis_loss = use_dis_loss
+        self.use_attention_mask = use_attention_mask
+        self.use_encoder_attention_mask = use_encoder_attention_mask
+        self.add_noise = add_noise
+        self.attn_mask_aux_input = attn_mask_aux_input
+        self.aux_input_list = aux_input_list
+        self.use_encoder_hidden_states = use_encoder_hidden_states
+        if use_encoder_hidden_states:
+            self.unet = add_aux_conv_in(self.unet)
+        if not add_noise:
+            conv_scale -= 1
+        if not use_aux_input:
+            conv_scale -= 1
+        if conv_scale > 1:
+            self.unet = replace_unet_conv_in(self.unet, conv_scale)
+        replace_attention_mask_method(self.unet, residual_connection)
+        self.text_encoder.requires_grad_(False)
+        self.vae.requires_grad_(False)
+        self.unet.train()
+        self.unet.use_attention_mask_list = use_attention_mask_list
+        self.unet.use_encoder_hidden_states_list = use_encoder_hidden_states_list
+    
+    def init_submodule(self, pretrained_model_name_or_path, load_weight):
+        if load_weight:
+            text_dir = _resolve_nested_dir(pretrained_model_name_or_path, "text_encoder", "config.json")
+            vae_dir = _resolve_nested_dir(pretrained_model_name_or_path, "vae", "config.json")
+            unet_dir = _resolve_nested_dir(pretrained_model_name_or_path, "unet", "config.json")
+            sched_dir = _resolve_nested_dir(pretrained_model_name_or_path, "scheduler", "scheduler_config.json")
+            tok_dir = _resolve_nested_dir(pretrained_model_name_or_path, "tokenizer", "tokenizer_config.json")
+
+            self.text_encoder = CLIPTextModel.from_pretrained(text_dir)
+            self.vae = AutoencoderKL.from_pretrained(vae_dir)
+            self.unet = CustomUNet.from_pretrained(
+                unet_dir, low_cpu_mem_usage=True, ignore_mismatched_sizes=False
+            )
+            self.noise_scheduler = DDIMScheduler.from_pretrained(sched_dir)
+            self.tokenizer = CLIPTokenizer.from_pretrained(tok_dir)
+        else:
+            text_dir = _resolve_nested_dir(pretrained_model_name_or_path, "text_encoder", "config.json")
+            text_config = CLIPTextConfig.from_pretrained(text_dir)
+            self.text_encoder = CLIPTextModel(text_config)
+
+            vae_path = _resolve_nested_dir(pretrained_model_name_or_path, "vae", "config.json")
+            self.vae = AutoencoderKL.from_config(AutoencoderKL.load_config(vae_path))
+
+            unet_path = _resolve_nested_dir(pretrained_model_name_or_path, "unet", "config.json")
+            self.unet = CustomUNet.from_config(
+                CustomUNet.load_config(unet_path),
+                low_cpu_mem_usage=True,
+                ignore_mismatched_sizes=False
+            )
+
+            scheduler_path = os.path.join(_resolve_nested_dir(pretrained_model_name_or_path, "scheduler", "scheduler_config.json"), "scheduler_config.json")
+            self.noise_scheduler = DDIMScheduler.from_config(DDIMScheduler.load_config(scheduler_path))
+
+            tok_dir = _resolve_nested_dir(pretrained_model_name_or_path, "tokenizer", "tokenizer_config.json")
+            self.tokenizer = CLIPTokenizer.from_pretrained(tok_dir)
+
+
+    def forward(self, data):
+        rgb = data["image"].cuda()
+        B = rgb.shape[0]
+
+        if self.aux_input is None and self.training:
+            aux_input_type = random.choice(self.aux_input_list)
+        elif self.aux_input is None:
+            aux_input_type = "point_mask"
+        else:
+            aux_input_type = self.aux_input
+
+        # get aux input latent
+        if self.use_aux_input:
+            aux_input = data[aux_input_type].cuda()
+            aux_input = aux_input.repeat(1, 3, 1, 1)
+            aux_input_h = self.vae.encoder(aux_input.to(rgb.dtype))
+            aux_input_moments = self.vae.quant_conv(aux_input_h)
+            aux_input_mean, _ = torch.chunk(aux_input_moments, 2, dim=1)
+            aux_input_latent = aux_input_mean * self.vae.config.scaling_factor
+        else:
+            aux_input_latent = None
+
+        # get aux coordinate
+        coor_name = AUX_INPUT_DIT[aux_input_type]
+        coor = data[coor_name].cuda()
+        if coor_name == "point_coords":
+            N = coor.shape[1]
+            for i in range(N, 1680):
+                if 1680 % i == 0:
+                    num_channels = 1680 // i
+                    pad_size = i - N
+                    padding = torch.zeros((B, pad_size), dtype=coor.dtype, device=coor.device)
+                    coor = torch.cat([coor, padding], dim=1)
+                    zero_coor = torch.zeros((B, pad_size + N), dtype=coor.dtype, device=coor.device)
+                    break
+            if self.use_coor_input:
+                coor = get_timestep_embedding(
+                    coor.flatten(),
+                    num_channels,
+                    flip_sin_to_cos=True,
+                    downscale_freq_shift=0,
+                )
+            else:
+                coor = get_timestep_embedding(
+                    zero_coor.flatten(),
+                    num_channels,
+                    flip_sin_to_cos=True,
+                    downscale_freq_shift=0,
+                )
+            added_cond_kwargs = {"point_coords": coor}
+        else:
+            if self.use_coor_input:
+                added_cond_kwargs = {"bbox_mask_coords": coor}
+            else:
+                coor = torch.tensor([[0, 0, 1, 1]] * B).cuda()
+                added_cond_kwargs = {"bbox_mask_coords": coor}
+
+        # get attention mask
+        if self.use_attention_mask and aux_input_type in self.attn_mask_aux_input:
+            attention_mask = data[aux_input_type].cuda()
+            attention_mask = (attention_mask + 1) / 2
+            attention_mask = F.interpolate(attention_mask, scale_factor=1 / 8, mode="nearest")
+            attention_mask = attention_mask.flatten(start_dim=1)
+        else:
+            attention_mask = None
+
+        # encode rgb to latents
+        rgb_h = self.vae.encoder(rgb)
+        rgb_moments = self.vae.quant_conv(rgb_h)
+        rgb_mean, _ = torch.chunk(rgb_moments, 2, dim=1)
+        rgb_latent = rgb_mean * self.vae.config.scaling_factor
+
+        # get encoder_hidden_states
+        if self.use_encoder_hidden_states and aux_input_latent is not None:
+            encoder_hidden_states = self.unet.aux_conv_in(aux_input_latent)
+            encoder_hidden_states = encoder_hidden_states.view(B, 1024, -1)
+            encoder_hidden_states = encoder_hidden_states.permute(0, 2, 1)
+
+        if "caption" in data:
+            prompt = data["caption"]
+        else:
+            prompt = [""] * B
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids.to("cuda")
+        text_embed = self.text_encoder(text_input_ids)[0]
+        encoder_hidden_states_2 = text_embed
+
+        # get class_label
+        is_trans = data["is_trans"].cuda()
+        trans = 1 - is_trans
+
+        # get timesteps
+        timestep = torch.tensor([1], device="cuda").long()
+
+        # unet
+        unet_input = torch.cat([rgb_latent, aux_input_latent], dim=1)
+        label_latent = self.unet(
+            sample=unet_input,
+            trans=trans,
+            timestep=None,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_hidden_states_2=encoder_hidden_states_2,
+            added_cond_kwargs=added_cond_kwargs,
+            attention_mask=attention_mask,
+        ).sample
+        label_latent = label_latent / self.vae.config.scaling_factor
+        z = self.vae.post_quant_conv(label_latent)
+        stacked = self.vae.decoder(z)
+        # mean of output channels
+        label_mean = stacked.mean(dim=1, keepdim=True)
+        output = torch.clip(label_mean, -1.0, 1.0)
+        output = (output + 1.0) / 2.0
+        return output

SDMatte/modeling/__init__.py

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+from .SDMatte import *
+# from .LiteSDMatte import *

SDMatte/utils/__init__.py

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+from .utils import *
+from .replace import *