"""This file contains the model definition of TiTok. Copyright (2024) Bytedance Ltd. and/or its affiliates Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import torch import torch.nn as nn from einops import rearrange from modeling.modules.base_model import BaseModel from modeling.modules.blocks import TiTokEncoder, TiTokDecoder from modeling.quantizer.quantizer import VectorQuantizer, DiagonalGaussianDistribution from modeling.modules.maskgit_vqgan import Encoder as Pixel_Eecoder from modeling.modules.maskgit_vqgan import Decoder as Pixel_Decoder from modeling.modules.maskgit_vqgan import VectorQuantizer as Pixel_Quantizer import json from omegaconf import OmegaConf from pathlib import Path from huggingface_hub import PyTorchModelHubMixin class PretrainedTokenizer(nn.Module): def __init__(self, pretrained_weight): super().__init__() conf = OmegaConf.create( {"channel_mult": [1, 1, 2, 2, 4], "num_resolutions": 5, "dropout": 0.0, "hidden_channels": 128, "num_channels": 3, "num_res_blocks": 2, "resolution": 256, "z_channels": 256}) self.encoder = Pixel_Eecoder(conf) self.decoder = Pixel_Decoder(conf) self.quantize = Pixel_Quantizer( num_embeddings=1024, embedding_dim=256, commitment_cost=0.25) # Load pretrained weights self.load_state_dict(torch.load(pretrained_weight, map_location=torch.device("cpu")), strict=True) self.eval() for param in self.parameters(): param.requires_grad = False @torch.no_grad() def encode(self, x): hidden_states = self.encoder(x) quantized_states, codebook_indices, codebook_loss = self.quantize(hidden_states) return codebook_indices.detach() @torch.no_grad() def decode(self, codes): quantized_states = self.quantize.get_codebook_entry(codes) rec_images = self.decoder(quantized_states) rec_images = torch.clamp(rec_images, 0.0, 1.0) return rec_images.detach() @torch.no_grad() def decode_tokens(self, codes): return self.decode(codes) class TiTok(BaseModel, PyTorchModelHubMixin, tags=["arxiv:2406.07550", "image-tokenization"], repo_url="https://github.com/bytedance/1d-tokenizer", license="apache-2.0"): def __init__(self, config): if isinstance(config, dict): config = OmegaConf.create(config) super().__init__() self.config = config # This should be False for stage1 and True for stage2. self.finetune_decoder = config.model.vq_model.get("finetune_decoder", True) self.quantize_mode = config.model.vq_model.get("quantize_mode", "vq") if self.quantize_mode not in ["vq", "vae"]: raise ValueError(f"Unsupported quantize mode {self.quantize_mode}.") if self.finetune_decoder and self.quantize_mode not in ["vq"]: raise ValueError("Only supprot finetune_decoder with vq quantization for now.") self.encoder = TiTokEncoder(config) self.decoder = TiTokDecoder(config) self.num_latent_tokens = config.model.vq_model.num_latent_tokens scale = self.encoder.width ** -0.5 self.latent_tokens = nn.Parameter( scale * torch.randn(self.num_latent_tokens, self.encoder.width)) self.apply(self._init_weights) if self.quantize_mode == "vq": self.quantize = VectorQuantizer( codebook_size=config.model.vq_model.codebook_size, token_size=config.model.vq_model.token_size, commitment_cost=config.model.vq_model.commitment_cost, use_l2_norm=config.model.vq_model.use_l2_norm,) elif self.quantize_mode == "vae": self.quantize = DiagonalGaussianDistribution else: raise NotImplementedError if self.finetune_decoder: # Freeze encoder/quantizer/latent tokens self.latent_tokens.requires_grad_(False) self.encoder.eval() self.encoder.requires_grad_(False) self.quantize.eval() self.quantize.requires_grad_(False) # Include MaskGiT-VQGAN's quantizer and decoder self.pixel_quantize = Pixel_Quantizer( num_embeddings=1024, embedding_dim=256, commitment_cost=0.25) self.pixel_decoder = Pixel_Decoder(OmegaConf.create( {"channel_mult": [1, 1, 2, 2, 4], "num_resolutions": 5, "dropout": 0.0, "hidden_channels": 128, "num_channels": 3, "num_res_blocks": 2, "resolution": 256, "z_channels": 256})) def _save_pretrained(self, save_directory: Path) -> None: """Save weights and config to a local directory.""" # Assume 'self.config' is your DictConfig object # Convert to a regular dictionary dict_config = OmegaConf.to_container(self.config) # Save as JSON file_path = Path(save_directory) / "config.json" with open(file_path, 'w') as json_file: json.dump(dict_config, json_file, indent=4) super()._save_pretrained(save_directory) def _init_weights(self, module): """ Initialize the weights. :param: module -> torch.nn.Module: module to initialize """ if isinstance(module, nn.Linear) or isinstance(module, nn.Conv1d) or isinstance(module, nn.Conv2d): module.weight.data = nn.init.trunc_normal_(module.weight.data, mean=0.0, std=0.02) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data = nn.init.trunc_normal_(module.weight.data, mean=0.0, std=0.02) elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def encode(self, x): if self.finetune_decoder: with torch.no_grad(): self.encoder.eval() self.quantize.eval() z = self.encoder(pixel_values=x, latent_tokens=self.latent_tokens) z_quantized, result_dict = self.quantize(z) result_dict["quantizer_loss"] *= 0 result_dict["commitment_loss"] *= 0 result_dict["codebook_loss"] *= 0 else: z = self.encoder(pixel_values=x, latent_tokens=self.latent_tokens) if self.quantize_mode == "vq": z_quantized, result_dict = self.quantize(z) elif self.quantize_mode == "vae": posteriors = self.quantize(z) z_quantized = posteriors.sample() result_dict = posteriors return z_quantized, result_dict def decode(self, z_quantized): decoded = self.decoder(z_quantized) if self.finetune_decoder: quantized_states = torch.einsum( 'nchw,cd->ndhw', decoded.softmax(1), self.pixel_quantize.embedding.weight) decoded = self.pixel_decoder(quantized_states) return decoded def decode_tokens(self, tokens): if self.quantize_mode == "vq": tokens = tokens.squeeze(1) batch, seq_len = tokens.shape # B x N z_quantized = self.quantize.get_codebook_entry( tokens.reshape(-1)).reshape(batch, 1, seq_len, -1) z_quantized = rearrange(z_quantized, 'b h w c -> b c h w').contiguous() elif self.quantize_mode == "vae": z_quantized = tokens decoded = self.decode(z_quantized) return decoded def forward(self, x): z_quantized, result_dict = self.encode(x) decoded = self.decode(z_quantized) return decoded, result_dict