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RAR/1d-tokenizer/README.md

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+# 1D Visual Tokenization and Generation
+
+This repo hosts the code and models for the following projects:
+
+- FlowTok: [FlowTok: Flowing Seamlessly Across Text and Image Tokens](https://tacju.github.io/projects/flowtok.html)
+
+- TA-TiTok & MaskGen: [Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens](https://tacju.github.io/projects/maskgen.html)
+
+- RAR: [Randomized Autoregressive Visual Generation](https://yucornetto.github.io/projects/rar.html)
+
+- TiTok: [An Image is Worth 32 Tokens for Reconstruction and Generation](https://yucornetto.github.io/projects/titok.html)
+
+## Updates
+- 03/16/2025: The [tech report](https://arxiv.org/abs/2503.10772) of FlowTok is available. FlowTok is a minimal yet powerful framework that seamlessly flows across text and images by encoding images into a compact 1D token representation. Code will be released soon.
+- 02/24/2025: We release the training code, inference code and model weights of MaskGen.
+- 01/17/2025: We release the training code, inference code and model weights of TA-TiTok.
+- 01/14/2025: The [tech report](https://arxiv.org/abs/2501.07730) of TA-TiTok and MaskGen is available. TA-TiTok is an innovative text-aware transformer-based 1-dimensional tokenizer designed to handle both discrete and continuous tokens. MaskGen is a powerful and efficient text-to-image masked generative model trained exclusively on open-data. For more details, refer to the [README_MaskGen](README_MaskGen.md).
+- 11/04/2024: We release the [tech report](https://arxiv.org/abs/2411.00776) and code for RAR models.
+- 10/16/2024: We update a set of TiTok tokenizer weights trained with an updated single-stage recipe, leading to easier training and better performance. We release the weight of different model size for both VQ and VAE variants TiTok, which we hope could facilitate the research in this area. More details are available in the [tech report](https://arxiv.org/abs/2501.07730) of TA-TiTok. 
+- 09/25/2024: TiTok is accepted by NeurIPS 2024.
+- 09/11/2024: Release the training codes of generator based on TiTok. 
+- 08/28/2024: Release the training codes of TiTok.
+- 08/09/2024: Better support on loading pretrained weights from huggingface models, thanks for the help from [@NielsRogge](https://github.com/NielsRogge)！
+- 07/03/2024: Evaluation scripts for reproducing the results reported in the paper, checkpoints of TiTok-B64 and TiTok-S128 are available.
+- 06/21/2024: Demo code and TiTok-L-32 checkpoints release. 
+- 06/11/2024: The [tech report](https://arxiv.org/abs/2406.07550) of TiTok is available.
+
+## Short Intro on [Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens](https://arxiv.org/abs/2501.07730) ([README](README_MaskGen.md))
+
+We introduce TA-TiTok, a novel text-aware transformer-based 1D tokenizer designed to handle both discrete and continuous tokens while effectively aligning reconstructions with textual descriptions.
+Building on TA-TiTok, we present MaskGen, a versatile text-to-image masked generative model framework. Trained exclusively on open data, MaskGen demonstrates outstanding performance: with 32 continuous tokens, it achieves a FID score of 6.53 on MJHQ-30K, and with 128 discrete tokens, it attains an overall score of 0.57 on GenEval.
+
+<p>
+<img src="assets/tatitok_overview.png" alt="teaser" width=90% height=90%>
+</p>
+<p>
+<img src="assets/maskgen_overview.png" alt="teaser" width=90% height=90%>
+</p>
+
+See more details at [README_MaskGen](README_MaskGen.md).
+
+## Short Intro on [Randomized Autoregressive Visual Generation](https://arxiv.org/abs/2411.00776) ([README](README_RAR.md))
+
+RAR is a an autoregressive (AR) image generator with full compatibility to language modeling. It introduces a randomness annealing strategy with permuted objective at no additional cost, which enhances the model's ability to learn bidirectional contexts while leaving the autoregressive framework intact. RAR sets a FID score 1.48, demonstrating state-of-the-art performance on ImageNet-256 benchmark and significantly outperforming prior AR image generators.
+
+<p>
+<img src="assets/rar_overview.png" alt="teaser" width=90% height=90%>
+</p>
+<p>
+<img src="assets/perf_comp.png" alt="teaser" width=90% height=90%>
+</p>
+
+See more details at [README_RAR](README_RAR.md).
+
+## Short Intro on [An Image is Worth 32 Tokens for Reconstruction and Generation](https://arxiv.org/abs/2406.07550) ([README](README_TiTok.md))
+
+We present a compact 1D tokenizer which can represent an image with as few as 32 discrete tokens. As a result, it leads to a substantial speed-up on the sampling process (e.g., **410 × faster** than DiT-XL/2) while obtaining a competitive generation quality.
+
+<p>
+<img src="assets/titok_teaser.png" alt="teaser" width=90% height=90%>
+</p>
+<p>
+<img src="assets/speed_vs_perf.png" alt="teaser" width=90% height=90%>
+</p>
+
+See more details at [README_TiTok](README_TiTok.md).
+
+
+## Installation
+```shell
+pip3 install -r requirements.txt
+```
+
+## Citing
+If you use our work in your research, please use the following BibTeX entry.
+
+```BibTeX
+@article{he2025flowtok,
+  author    = {Ju He and Qihang Yu and Qihao Liu and Liang-Chieh Chen},
+  title     = {FlowTok: Flowing Seamlessly Across Text and Image Tokens},
+  journal   = {arXiv preprint arXiv:2503.10772},
+  year      = {2025}
+}
+```
+
+```BibTeX
+@article{kim2025democratizing,
+  author    = {Dongwon Kim and Ju He and Qihang Yu and Chenglin Yang and Xiaohui Shen and Suha Kwak and Liang-Chieh Chen},
+  title     = {Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens},
+  journal   = {arXiv preprint arXiv:2501.07730},
+  year      = {2025}
+}
+```
+
+```BibTeX
+@article{yu2024randomized,
+  author    = {Qihang Yu and Ju He and Xueqing Deng and Xiaohui Shen and Liang-Chieh Chen},
+  title     = {Randomized Autoregressive Visual Generation},
+  journal   = {arXiv preprint arXiv:2411.00776},
+  year      = {2024}
+}
+```
+
+```BibTeX
+@article{yu2024an,
+  author    = {Qihang Yu and Mark Weber and Xueqing Deng and Xiaohui Shen and Daniel Cremers and Liang-Chieh Chen},
+  title     = {An Image is Worth 32 Tokens for Reconstruction and Generation},
+  journal   = {NeurIPS},
+  year      = {2024}
+}
+```
+
+## Acknowledgement
+
+[CrossFlow](https://github.com/qihao067/CrossFlow)
+
+[MAR](https://github.com/LTH14/mar)
+
+[MaskGIT](https://github.com/google-research/maskgit)
+
+[Taming-Transformers](https://github.com/CompVis/taming-transformers)
+
+[Open-MUSE](https://github.com/huggingface/open-muse)
+
+[MUSE-Pytorch](https://github.com/baaivision/MUSE-Pytorch)

RAR/1d-tokenizer/README_MaskGen.md

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+# Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens
+
+
+<div align="center">
+
+[![Website](https://img.shields.io/badge/Project-Website-87CEEB)](https://tacju.github.io/projects/maskgen.html)&nbsp;&nbsp;
+[![paper](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](http://arxiv.org/abs/2501.07730)&nbsp;&nbsp;
+
+</div>
+
+<!-- <p>
+<img src="assets/maskgen_teaser.png" alt="teaser" width=90% height=90%>
+</p> -->
+
+We introduce TA-TiTok, a novel text-aware transformer-based 1D tokenizer designed to handle both discrete and continuous tokens while effectively aligning reconstructions with textual descriptions.
+Building on TA-TiTok, we present MaskGen, a versatile text-to-image masked generative model framework. Trained exclusively on open data, MaskGen demonstrates outstanding performance: with 32 continuous tokens, it achieves a FID score of 6.53 on MJHQ-30K, and with 128 discrete tokens, it attains an overall score of 0.57 on GenEval.
+
+
+<p>
+<img src="assets/tatitok_overview.png" alt="teaser" width=90% height=90%>
+</p>
+<p>
+<img src="assets/maskgen_overview.png" alt="teaser" width=90% height=90%>
+</p>
+
+
+## 🚀 Contributions
+
+#### We introduce TA-TiTok, an innovative text-aware transformer-based 1-dimensional tokenizer designed to handle both discrete and continuous tokens. TA-TiTok seamlessly integrates text information during the de-tokenization stage and offers scalability to efficiently handle large-scale datasets with a simple one-stage training recipe.
+
+#### We propose MaskGen, a family of text-to-image masked generative models built upon TA-TiTok. The MaskGen VQ and MaskGen KL variants utilize compact sequences of 128 discrete tokens and 32 continuous tokens, respectively. Trained exclusively on open data, MaskGen achieves performance comparable to models trained on proprietary datasets, while offering significantly lower training cost and substantially faster inference speed.
+
+
+## TA-TiTok Model Zoo
+| arch | #tokens | Link | rFID | IS |
+| ------------- | ------------- | ------------- | ------------- | ------------- |
+| VQ | 32 | [checkpoint](https://huggingface.co/turkeyju/tokenizer_tatitok_bl32_vq) | 3.95 | 219.6 |
+| VQ | 64 | [checkpoint](https://huggingface.co/turkeyju/tokenizer_tatitok_bl64_vq) | 2.43 | 218.8 |
+| VQ | 128 | [checkpoint](https://huggingface.co/turkeyju/tokenizer_tatitok_bl128_vq) | 1.53 | 222.8 |
+| KL | 32 | [checkpoint](https://huggingface.co/turkeyju/tokenizer_tatitok_bl32_vae) | 1.53 | 222.0 |
+| KL | 64 | [checkpoint](https://huggingface.co/turkeyju/tokenizer_tatitok_bl64_vae) | 1.47 | 220.7 |
+| KL | 128 | [checkpoint](https://huggingface.co/turkeyju/tokenizer_tatitok_sl128_vae) | 0.90 | 227.7 |
+
+Please note that these models are only for research purposes.
+
+## MaskGen Model Zoo
+| Model | arch | Link | MJHQ-30K FID | GenEval Overall |
+| ------------- | ------------- | ------------- | ------------- | ------------- |
+| MaskGen-L | VQ | [checkpoint](https://huggingface.co/turkeyju/generator_maskgen_vq_l) | 7.74 | 0.53 |
+| MaskGen-XL | VQ | [checkpoint](https://huggingface.co/turkeyju/generator_maskgen_vq_xl) | 7.51 | 0.57 |
+| MaskGen-L | KL | [checkpoint](https://huggingface.co/turkeyju/generator_maskgen_kl_l) | 7.24 | 0.52 |
+| MaskGen-XL | KL | [checkpoint](https://huggingface.co/turkeyju/generator_maskgen_kl_xl) | 6.53 | 0.55 |
+
+Please note that these models are only for research purposes.
+
+## Installation
+```shell
+pip3 install -r requirements.txt
+```
+
+## Get Started - TA-TiTok
+```python
+import torch
+from PIL import Image
+import numpy as np
+import open_clip
+import demo_util
+from huggingface_hub import hf_hub_download
+from modeling.tatitok import TATiTok
+
+# Choose one from ["tokenizer_tatitok_bl32_vq", "tokenizer_tatitok_bl64_vq, tokenizer_tatitok_bl128_vq", "tokenizer_tatitok_bl32_vae", "tokenizer_tatitok_bl64_vae, tokenizer_tatitok_sl128_vae"]
+tatitok_tokenizer = TATiTok.from_pretrained("turkeyju/tokenizer_tatitok_bl32_vae")
+tatitok_tokenizer.eval()
+tatitok_tokenizer.requires_grad_(False)
+
+# or alternatively, downloads from hf
+# hf_hub_download(repo_id="fun-research/TA-TiTok", filename="tatitok_bl32_vae.bin", local_dir="./")
+
+# load config
+# config = demo_util.get_config("configs/infer/TA-TiTok/tatitok_bl32_vae.yaml")
+# tatitok_tokenizer = demo_util.get_tatitok_tokenizer(config)
+
+clip_encoder, _, _ = open_clip.create_model_and_transforms('ViT-L-14-336', pretrained='openai')
+del clip_encoder.visual
+clip_tokenizer = open_clip.get_tokenizer('ViT-L-14-336')
+clip_encoder.transformer.batch_first = False
+clip_encoder.eval()
+clip_encoder.requires_grad_(False)
+
+device = "cuda"
+tatitok_tokenizer = tatitok_tokenizer.to(device)
+clip_encoder = clip_encoder.to(device)
+
+# reconstruct an image. I.e., image -> 32 tokens -> image
+img_path = "assets/ILSVRC2012_val_00010240.png"
+image = torch.from_numpy(np.array(Image.open(img_path)).astype(np.float32)).permute(2, 0, 1).unsqueeze(0) / 255.0
+# tokenization
+if tatitok_tokenizer.quantize_mode == "vq":
+    encoded_tokens = tatitok_tokenizer.encode(image.to(device))[1]["min_encoding_indices"]
+elif tatitok_tokenizer.quantize_mode == "vae":
+    posteriors = tatitok_tokenizer.encode(image.to(device))[1]
+    encoded_tokens = posteriors.sample()
+else:
+    raise NotImplementedError
+
+text = ["A photo of a jay."]
+text_guidance = clip_tokenizer(text).to(device)
+cast_dtype = clip_encoder.transformer.get_cast_dtype()
+text_guidance = clip_encoder.token_embedding(text_guidance).to(cast_dtype)  # [batch_size, n_ctx, d_model]
+text_guidance = text_guidance + clip_encoder.positional_embedding.to(cast_dtype)
+text_guidance = text_guidance.permute(1, 0, 2)  # NLD -> LND
+text_guidance = clip_encoder.transformer(text_guidance, attn_mask=clip_encoder.attn_mask)
+text_guidance = text_guidance.permute(1, 0, 2)  # LND -> NLD
+text_guidance = clip_encoder.ln_final(text_guidance)  # [batch_size, n_ctx, transformer.width]
+
+print(f"image {img_path} is encoded into tokens {encoded_tokens}, with shape {encoded_tokens.shape}")
+
+# de-tokenization
+reconstructed_image = tatitok_tokenizer.decode_tokens(encoded_tokens, text_guidance)
+reconstructed_image = torch.clamp(reconstructed_image, 0.0, 1.0)
+reconstructed_image = (reconstructed_image * 255.0).permute(0, 2, 3, 1).to("cpu", dtype=torch.uint8).numpy()[0]
+reconstructed_image = Image.fromarray(reconstructed_image).save("assets/ILSVRC2012_val_00010240_recon.png")
+```
+
+## Get Started - MaskGen
+```python
+import torch
+from PIL import Image
+import numpy as np
+import open_clip
+import demo_util
+from huggingface_hub import hf_hub_download
+from modeling.tatitok import TATiTok
+from modeling.maskgen import MaskGen_VQ, MaskGen_KL
+
+torch.manual_seed(42)                 
+torch.cuda.manual_seed(42)            
+torch.backends.cudnn.deterministic = True
+torch.backends.cudnn.benchmark = False   
+
+# VQ Tokenizer: load tokenizer tatitok_bl128_vq
+tatitok_vq_tokenizer = TATiTok.from_pretrained("turkeyju/tokenizer_tatitok_bl128_vq")
+tatitok_vq_tokenizer.eval()
+tatitok_vq_tokenizer.requires_grad_(False)
+
+# KL Tokenizer: load tokenizer tatitok_bl32_vae
+tatitok_kl_tokenizer = TATiTok.from_pretrained("turkeyju/tokenizer_tatitok_bl32_vae")
+tatitok_kl_tokenizer.eval()
+tatitok_kl_tokenizer.requires_grad_(False)
+
+# or alternatively, downloads from hf
+# hf_hub_download(repo_id="fun-research/TA-TiTok", filename="tatitok_bl32_vae.bin", local_dir="./")
+
+# load config
+# config = demo_util.get_config("configs/infer/TA-TiTok/tatitok_bl32_vae.yaml")
+# tatitok_tokenizer = demo_util.get_tatitok_tokenizer(config)
+
+# VQ Generator: choose one from ["maskgen_vq_l", "maskgen_vq_xl"]
+maskgen_vq_generator = MaskGen_VQ.from_pretrained("turkeyju/generator_maskgen_vq_xl")
+maskgen_vq_generator.eval()
+maskgen_vq_generator.requires_grad_(False)
+
+# or alternatively, downloads from hf
+# hf_hub_download(repo_id="fun-research/TA-TiTok", filename="maskgen_vq_xl.bin", local_dir="./")
+
+# load config
+# config = demo_util.get_config("configs/infer/MaskGen/maskgen_vq_xl.yaml")
+# maskgen_vq_generator = demo_util.get_maskgen_vq_generator(config)
+
+# KL Generator: choose one from ["maskgen_kl_l", "maskgen_kl_xl"]
+maskgen_kl_generator = MaskGen_KL.from_pretrained("turkeyju/generator_maskgen_kl_xl")
+maskgen_kl_generator.eval()
+maskgen_kl_generator.requires_grad_(False)
+
+# or alternatively, downloads from hf
+# hf_hub_download(repo_id="fun-research/TA-TiTok", filename="maskgen_kl_xl.bin", local_dir="./")
+
+# load config
+# config = demo_util.get_config("configs/infer/MaskGen/maskgen_kl_xl.yaml")
+# maskgen_kl_generator = demo_util.get_maskgen_kl_generator(config)
+
+clip_encoder, _, _ = open_clip.create_model_and_transforms('ViT-L-14-336', pretrained='openai')
+del clip_encoder.visual
+clip_tokenizer = open_clip.get_tokenizer('ViT-L-14-336')
+clip_encoder.transformer.batch_first = False
+clip_encoder.eval()
+clip_encoder.requires_grad_(False)
+
+device = "cuda"
+tatitok_vq_tokenizer = tatitok_vq_tokenizer.to(device)
+tatitok_kl_tokenizer = tatitok_kl_tokenizer.to(device)
+maskgen_vq_generator = maskgen_vq_generator.to(device)
+maskgen_kl_generator = maskgen_kl_generator.to(device)
+clip_encoder = clip_encoder.to(device)
+
+# generate an image
+text = ["A cozy cabin in the middle of a snowy forest, surrounded by tall trees with lights glowing through the windows, a northern lights display visible in the sky."]
+text_guidance = clip_tokenizer(text).to(device)
+cast_dtype = clip_encoder.transformer.get_cast_dtype()
+text_guidance = clip_encoder.token_embedding(text_guidance).to(cast_dtype)  # [batch_size, n_ctx, d_model]
+text_guidance = text_guidance + clip_encoder.positional_embedding.to(cast_dtype)
+text_guidance = text_guidance.permute(1, 0, 2)  # NLD -> LND
+text_guidance = clip_encoder.transformer(text_guidance, attn_mask=clip_encoder.attn_mask)
+text_guidance = text_guidance.permute(1, 0, 2)  # LND -> NLD
+text_guidance = clip_encoder.ln_final(text_guidance)  # [batch_size, n_ctx, transformer.width]
+
+vq_generated_tokens = maskgen_vq_generator.generate(captions=text, guidance_scale=12.0, randomize_temperature=2.0, sample_aesthetic_score=6.5, clip_tokenizer=clip_tokenizer, clip_encoder=clip_encoder)
+kl_generated_tokens = maskgen_kl_generator.sample_tokens(1, clip_tokenizer, clip_encoder, num_iter=32, cfg=3.0, aes_scores=6.5, captions=text)
+
+# de-tokenization
+vq_generated_image = tatitok_vq_tokenizer.decode_tokens(vq_generated_tokens, text_guidance)
+kl_generated_image = tatitok_kl_tokenizer.decode_tokens(kl_generated_tokens, text_guidance)
+vq_generated_image = torch.clamp(vq_generated_image, 0.0, 1.0)
+kl_generated_image = torch.clamp(kl_generated_image, 0.0, 1.0)
+vq_generated_image = (vq_generated_image * 255.0).permute(0, 2, 3, 1).to("cpu", dtype=torch.uint8).numpy()[0]
+kl_generated_image = (kl_generated_image * 255.0).permute(0, 2, 3, 1).to("cpu", dtype=torch.uint8).numpy()[0]
+vq_generated_image = Image.fromarray(vq_generated_image).save("assets/maskgen_vq_generator_generated.png")
+kl_generated_image = Image.fromarray(kl_generated_image).save("assets/maskgen_kl_generator_generated.png")
+```
+
+## Training Preparation
+We use [webdataset](https://github.com/webdataset/webdataset) format for data loading. To begin with, it is needed to convert the dataset into webdataset format.
+
+## Training
+We provide example commands to train TA-TiTok as follows:
+```bash
+# Training for TiTok-BL32-VQ
+WANDB_MODE=offline accelerate launch --num_machines=4 --num_processes=8 --machine_rank=0 --main_process_ip=127.0.0.1 --main_process_port=9999 --same_network scripts/train_tatitok.py config=configs/training/TA-TiTok/tatitok_bl32_vq.yaml \
+    experiment.project="tatitok_bl32_vq" \
+    experiment.name="tatitok_bl32_vq_run1" \
+    experiment.output_dir="tatitok_bl32_vq_run1" \
+
+# Training for TiTok-BL32-VAE
+WANDB_MODE=offline accelerate launch --num_machines=4 --num_processes=8 --machine_rank=0 --main_process_ip=127.0.0.1 --main_process_port=9999 --same_network scripts/train_tatitok.py config=configs/training/TA-TiTok/tatitok_bl32_vae.yaml \
+    experiment.project="tatitok_bl32_vae" \
+    experiment.name="tatitok_bl32_vae_run1" \
+    experiment.output_dir="tatitok_bl32_vae_run1" \
+
+# Training for MaskGen-{VQ/KL}-{L/XL} Stage1
+WANDB_MODE=offline accelerate launch --num_machines=4 --num_processes=8 --machine_rank=0 --main_process_ip=127.0.0.1 --main_process_port=9999 --same_network scripts/train_maskgen.py config=configs/training/MaskGen/maskgen_{vq/kl}_{l/xl}_stage1.yaml \
+    experiment.project="maskgen_{vq/kl}_{l/xl}_stage1" \
+    experiment.name="maskgen_{vq/kl}_{l/xl}_stage1_run1" \
+    experiment.output_dir="maskgen_{vq/kl}_{l/xl}_stage1_run1" \
+
+# Training for MaskGen-{VQ/KL}-{L/XL} Stage2
+WANDB_MODE=offline accelerate launch --num_machines=4 --num_processes=8 --machine_rank=0 --main_process_ip=127.0.0.1 --main_process_port=9999 --same_network scripts/train_maskgen.py config=configs/training/MaskGen/maskgen_{vq/kl}_{l/xl}_stage2.yaml \
+    experiment.project="maskgen_{vq/kl}_{l/xl}_stage2" \
+    experiment.name="maskgen_{vq/kl}_{l/xl}_stage2_run1" \
+    experiment.output_dir="maskgen_{vq/kl}_{l/xl}_stage2_run1" \
+```
+You may remove the flag "WANDB_MODE=offline" to support online wandb logging, if you have configured it.
+
+The config can be replaced for other TA-TiTok variants.
+
+## Visualizations
+<p>
+<img src="assets/maskgen_vis1.png" alt="teaser" width=90% height=90%>
+</p>
+<p>
+<img src="assets/maskgen_vis2.png" alt="teaser" width=90% height=90%>
+</p>
+<p>
+<img src="assets/maskgen_vis3.png" alt="teaser" width=90% height=90%>
+</p>
+
+
+## Citing
+If you use our work in your research, please use the following BibTeX entry.
+
+```BibTeX
+@article{kim2025democratizing,
+  author    = {Kim, Dongwon and He, Ju and Yu, Qihang Yu and Yang, Chenglin and Shen, Xiaohui and Kwak, Suha and Chen Liang-Chieh},
+  title     = {Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens},
+  journal   = {arXiv preprint arXiv:2501.07730},
+  year      = {2025}
+}
+```

RAR/1d-tokenizer/README_RAR.md

@@ -0,0 +1,236 @@
+# Randomized Autoregressive Visual Generation
+
+
+<div align="center">
+
+[![demo](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Online_Demo-blue)](https://huggingface.co/spaces/yucornetto/RAR)&nbsp;&nbsp;
+[![Website](https://img.shields.io/badge/Project-Website-87CEEB)](https://yucornetto.github.io/projects/rar.html)&nbsp;&nbsp;
+[![paper](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2411.00776)&nbsp;&nbsp;
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/randomized-autoregressive-visual-generation/image-generation-on-imagenet-256x256)](https://paperswithcode.com/sota/image-generation-on-imagenet-256x256?p=randomized-autoregressive-visual-generation)
+
+</div>
+
+RAR is a an autoregressive (AR) image generator with full compatibility to language modeling. It introduces a randomness annealing strategy with permuted objective at no additional cost, which enhances the model's ability to learn bidirectional contexts while leaving the autoregressive framework intact. RAR sets a FID score 1.48, demonstrating state-of-the-art performance on ImageNet-256 benchmark and significantly outperforming prior AR image generators.
+
+
+<p>
+<img src="assets/rar_overview.png" alt="teaser" width=90% height=90%>
+</p>
+<p>
+<img src="assets/perf_comp.png" alt="teaser" width=90% height=90%>
+</p>
+
+
+## 🚀 Contributions
+
+#### We introduce RAR, an improved training strategy enabling standard autoregressive image generator to achieve state-of-the-art performance.
+
+#### The proposed RAR is extremly simple yet effective: During training, we randomly permute the input token sequence with probability r, where r will starts at 1.0 and linearly decays to 0.0 over the course of training. This simple strategy enbales better bidirectional representation learning which is missing in standard raster-order-based AR image generator training.
+
+#### RAR keeps the AR framework intact, and thus it is totally compatible to the LLM optimization techniques, such as KV-cache, leading to a significantly faster sampling speed compared to MAR-H or MaskBit while maintaining a better performance.
+
+## Model Zoo
+| Model | Link | FID |
+| ------------- | ------------- | ------------- |
+| RAR-B | [checkpoint](https://huggingface.co/yucornetto/RAR/blob/main/rar_b.bin)| 1.95 (generation) |
+| RAR-L | [checkpoint](https://huggingface.co/yucornetto/RAR/blob/main/rar_l.bin)| 1.70 (generation) |
+| RAR-XL | [checkpoint](https://huggingface.co/yucornetto/RAR/blob/main/rar_xl.bin)| 1.50 (generation) |
+| RAR-XXL | [checkpoint](https://huggingface.co/yucornetto/RAR/blob/main/rar_xxl.bin)| 1.48 (generation) |
+
+Please note that these models are trained only on limited academic dataset ImageNet, and they are only for research purposes.
+
+## Installation
+```shell
+pip3 install -r requirements.txt
+```
+
+## Get Started
+```python
+import torch
+from PIL import Image
+import numpy as np
+import demo_util
+from huggingface_hub import hf_hub_download
+from utils.train_utils import create_pretrained_tokenizer
+
+
+# Choose one from ["rar_b_imagenet", "rar_l_imagenet", "rar_xl_imagenet", "rar_xxl_imagenet"]
+rar_model_size = ["rar_b", "rar_l", "rar_xl", "rar_xxl"][3]
+
+# download the maskgit-vq tokenizer
+hf_hub_download(repo_id="fun-research/TiTok", filename=f"maskgit-vqgan-imagenet-f16-256.bin", local_dir="./")
+# download the rar generator weight
+hf_hub_download(repo_id="yucornetto/RAR", filename=f"{rar_model_size}.bin", local_dir="./")
+
+# load config
+config = demo_util.get_config("configs/training/generator/rar.yaml")
+config.experiment.generator_checkpoint = f"{rar_model_size}.bin"
+config.model.generator.hidden_size = {"rar_b": 768, "rar_l": 1024, "rar_xl": 1280, "rar_xxl": 1408}[rar_model_size]
+config.model.generator.num_hidden_layers = {"rar_b": 24, "rar_l": 24, "rar_xl": 32, "rar_xxl": 40}[rar_model_size]
+config.model.generator.num_attention_heads = 16
+config.model.generator.intermediate_size = {"rar_b": 3072, "rar_l": 4096, "rar_xl": 5120, "rar_xxl": 6144}[rar_model_size]
+
+
+device = "cuda"
+# maskgit-vq as tokenizer
+tokenizer = create_pretrained_tokenizer(config)
+generator = demo_util.get_rar_generator(config)
+tokenizer.to(device)
+generator.to(device)
+
+# generate an image
+sample_labels = [torch.randint(0, 999, size=(1,)).item()] # random IN-1k class
+generated_image = demo_util.sample_fn(
+    generator=generator,
+    tokenizer=tokenizer,
+    labels=sample_labels,
+    randomize_temperature=1.0,
+    guidance_scale=4.0,
+    guidance_scale_pow=0.0, # constant cfg
+    device=device
+)
+Image.fromarray(generated_image[0]).save(f"assets/rar_generated_{sample_labels[0]}.png")
+```
+
+## Testing on ImageNet-1K Benchmark
+
+We provide a [sampling script](./sample_imagenet_rar.py) for reproducing the generation results on ImageNet-1K benchmark.
+```bash
+# Prepare ADM evaluation script
+git clone https://github.com/openai/guided-diffusion.git
+
+wget https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/256/VIRTUAL_imagenet256_labeled.npz
+```
+```python
+# Reproducing RAR-B
+torchrun --nnodes=1 --nproc_per_node=8 --rdzv-endpoint=localhost:9999 sample_imagenet_rar.py config=configs/training/generator/rar.yaml \
+    experiment.output_dir="rar_b" \
+    experiment.generator_checkpoint="rar_b.bin" \
+    model.generator.hidden_size=768 \
+    model.generator.num_hidden_layers=24 \
+    model.generator.num_attention_heads=16 \
+    model.generator.intermediate_size=3072 \
+    model.generator.randomize_temperature=1.0 \
+    model.generator.guidance_scale=16.0 \
+    model.generator.guidance_scale_pow=2.75
+# Run eval script. The result FID should be ~1.95
+python3 guided-diffusion/evaluations/evaluator.py VIRTUAL_imagenet256_labeled.npz rar_b.npz
+
+# Reproducing RAR-L
+torchrun --nnodes=1 --nproc_per_node=8 --rdzv-endpoint=localhost:9999 sample_imagenet_rar.py config=configs/training/generator/rar.yaml \
+    experiment.output_dir="rar_l" \
+    experiment.generator_checkpoint="rar_l.bin" \
+    model.generator.hidden_size=1024 \
+    model.generator.num_hidden_layers=24 \
+    model.generator.num_attention_heads=16 \
+    model.generator.intermediate_size=4096 \
+    model.generator.randomize_temperature=1.02 \
+    model.generator.guidance_scale=15.5 \
+    model.generator.guidance_scale_pow=2.5
+# Run eval script. The result FID should be ~1.70
+python3 guided-diffusion/evaluations/evaluator.py VIRTUAL_imagenet256_labeled.npz rar_l.npz
+
+# Reproducing RAR-XL
+torchrun --nnodes=1 --nproc_per_node=8 --rdzv-endpoint=localhost:9999 sample_imagenet_rar.py config=configs/training/generator/rar.yaml \
+    experiment.output_dir="rar_xl" \
+    experiment.generator_checkpoint="rar_xl.bin" \
+    model.generator.hidden_size=1280 \
+    model.generator.num_hidden_layers=32 \
+    model.generator.num_attention_heads=16 \
+    model.generator.intermediate_size=5120 \
+    model.generator.randomize_temperature=1.02 \
+    model.generator.guidance_scale=6.9 \
+    model.generator.guidance_scale_pow=1.5
+# Run eval script. The result FID should be ~1.50
+python3 guided-diffusion/evaluations/evaluator.py VIRTUAL_imagenet256_labeled.npz rar_xl.npz
+
+# Reproducing RAR-XXL
+torchrun --nnodes=1 --nproc_per_node=8 --rdzv-endpoint=localhost:9999 sample_imagenet_rar.py config=configs/training/generator/rar.yaml \
+    experiment.output_dir="rar_xxl" \
+    experiment.generator_checkpoint="rar_xxl.bin" \
+    model.generator.hidden_size=1408 \
+    model.generator.num_hidden_layers=40 \
+    model.generator.num_attention_heads=16 \
+    model.generator.intermediate_size=6144 \
+    model.generator.randomize_temperature=1.02 \
+    model.generator.guidance_scale=8.0 \
+    model.generator.guidance_scale_pow=1.2
+# Run eval script. The result FID should be ~1.48
+python3 guided-diffusion/evaluations/evaluator.py VIRTUAL_imagenet256_labeled.npz rar_xxl.npz
+```
+## Training Preparation
+We pretokenize the whole dataset for speed-up the training process. We have uploaded [it](https://huggingface.co/yucornetto/RAR/blob/main/maskgitvq.jsonl) so you can train RAR directly. The training script will download the prerequisite checkpoints and dataset automatically.
+
+For pretokenization on your own tokenizer or dataset, please refer to the [example pretokenization script](scripts/pretokenization.py).
+
+## Training
+We provide example commands to train RAR as follows:
+```bash
+# Training for RAR-B
+WANDB_MODE=offline accelerate launch --num_machines=4 --num_processes=32 --machine_rank=${MACHINE_RANK} --main_process_ip=${ROOT_IP} --main_process_port=${ROOT_PORT} --same_network scripts/train_rar.py config=configs/training/generator/rar.yaml \
+    experiment.project="rar" \
+    experiment.name="rar_b" \
+    experiment.output_dir="rar_b" \
+    model.generator.hidden_size=768 \
+    model.generator.num_hidden_layers=24 \
+    model.generator.num_attention_heads=16 \
+    model.generator.intermediate_size=3072
+
+# Training for RAR-L
+WANDB_MODE=offline accelerate launch --num_machines=4 --num_processes=32 --machine_rank=${MACHINE_RANK} --main_process_ip=${ROOT_IP} --main_process_port=${ROOT_PORT} --same_network scripts/train_rar.py config=configs/training/generator/rar.yaml \
+    experiment.project="rar" \
+    experiment.name="rar_l" \
+    experiment.output_dir="rar_l" \
+    model.generator.hidden_size=1024 \
+    model.generator.num_hidden_layers=24 \
+    model.generator.num_attention_heads=16 \
+    model.generator.intermediate_size=4096
+
+# Training for RAR-XL
+WANDB_MODE=offline accelerate launch --num_machines=4 --num_processes=32 --machine_rank=${MACHINE_RANK} --main_process_ip=${ROOT_IP} --main_process_port=${ROOT_PORT} --same_network scripts/train_rar.py config=configs/training/generator/rar.yaml \
+    experiment.project="rar" \
+    experiment.name="rar_xl" \
+    experiment.output_dir="rar_xl" \
+    model.generator.hidden_size=1280 \
+    model.generator.num_hidden_layers=32 \
+    model.generator.num_attention_heads=16 \
+    model.generator.intermediate_size=5120
+
+# Training for RAR-XXL
+WANDB_MODE=offline accelerate launch --num_machines=4 --num_processes=32 --machine_rank=${MACHINE_RANK} --main_process_ip=${ROOT_IP} --main_process_port=${ROOT_PORT} --same_network scripts/train_rar.py config=configs/training/generator/rar.yaml \
+    experiment.project="rar" \
+    experiment.name="rar_xxl" \
+    experiment.output_dir="rar_xxl" \
+    model.generator.hidden_size=1408 \
+    model.generator.num_hidden_layers=40 \
+    model.generator.num_attention_heads=16 \
+    model.generator.intermediate_size=6144
+```
+You may remove the flag "WANDB_MODE=offline" to support online wandb logging, if you have configured it.
+
+Notably, you can enable grad checkpointing by adding the flag "model.generator.use_checkpoint=True" and adjust the machine number & GPU number based on your own need. All RAR checkpoints were trained with a global batchsize = 2048.
+
+
+## Visualizations
+<p>
+<img src="assets/vis1.png" alt="teaser" width=90% height=90%>
+</p>
+<p>
+<img src="assets/vis2.png" alt="teaser" width=90% height=90%>
+</p>
+<p>
+<img src="assets/vis3.png" alt="teaser" width=90% height=90%>
+</p>
+
+
+## Citing
+If you use our work in your research, please use the following BibTeX entry.
+
+```BibTeX
+@inproceedings{yu2024randomized,
+  author    = {Qihang Yu and Ju He and Xueqing Deng and Xiaohui Shen and Liang-Chieh Chen},
+  title     = {Randomized Autoregressive Visual Generation},
+  journal   = {arXiv preprint arXiv:2411.00776},
+  year      = {2024}
+}
+```

RAR/1d-tokenizer/README_TiTok.md

@@ -0,0 +1,215 @@
+# (NeurIPS 2024) Compact and Mighty - Image Tokenization with Only 32 Tokens for both Reconstruction and Generation!
+
+<div align="center">
+
+[![demo](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Online_Demo-blue)](https://huggingface.co/spaces/fun-research/TiTok)&nbsp;&nbsp;
+[![Website](https://img.shields.io/badge/Project-Website-87CEEB)](https://yucornetto.github.io/projects/titok.html)&nbsp;&nbsp;
+[![paper](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2406.07550)&nbsp;&nbsp;
+
+</div>
+
+We present a compact 1D tokenizer which can represent an image with as few as 32 discrete tokens. As a result, it leads to a substantial speed-up on the sampling process (e.g., **410 × faster** than DiT-XL/2) while obtaining a competitive generation quality.
+
+
+<p>
+<img src="assets/titok_teaser.png" alt="teaser" width=90% height=90%>
+</p>
+<p>
+<img src="assets/speed_vs_perf.png" alt="teaser" width=90% height=90%>
+</p>
+
+
+## 🚀 Contributions
+
+#### We introduce a novel 1D image tokenization framework that breaks grid constraints existing in 2D tokenization methods, leading to a much more flexible and compact image latent representation.
+
+#### The proposed 1D tokenizer can tokenize a 256 × 256 image into as few as 32 discrete tokens, leading to a significant speed-up (hundreds times faster than diffusion models) in generation process, while maintaining state-of-the-art generation quality.
+
+#### We conduct a series of experiments to probe the properties of rarely studied 1D image tokenization, paving the path towards compact latent space for efficient and effective image representation.
+
+## Model Zoo
+| Model | Link | FID |
+| ------------- | ------------- | ------------- |
+| TiTok-L-32 Tokenizer | [checkpoint](https://huggingface.co/yucornetto/tokenizer_titok_l32_imagenet)| 2.21 (reconstruction) |
+| TiTok-B-64 Tokenizer | [checkpoint](https://huggingface.co/yucornetto/tokenizer_titok_b64_imagenet) | 1.70 (reconstruction) |
+| TiTok-S-128 Tokenizer | [checkpoint](https://huggingface.co/yucornetto/tokenizer_titok_s128_imagenet) | 1.71 (reconstruction) |
+| TiTok-L-32 Generator | [checkpoint](https://huggingface.co/yucornetto/generator_titok_l32_imagenet) | 2.77 (generation) |
+| TiTok-B-64 Generator | [checkpoint](https://huggingface.co/yucornetto/generator_titok_b64_imagenet) | 2.48 (generation) |
+| TiTok-S-128 Generator | [checkpoint](https://huggingface.co/yucornetto/generator_titok_s128_imagenet) | 1.97 (generation) |
+| TiTok-BL-64 VQ Tokenizer | [checkpoint](https://huggingface.co/yucornetto/tokenizer_titok_bl64_vq8k_imagenet)| 2.06 (reconstruction) |
+| TiTok-BL-128 VQ Tokenizer | [checkpoint](https://huggingface.co/yucornetto/tokenizer_titok_bl128_vq8k_imagenet)| 1.49 (reconstruction) |
+| TiTok-SL-256 VQ Tokenizer | [checkpoint](https://huggingface.co/yucornetto/tokenizer_titok_sl256_vq8k_imagenet)| 1.03 (reconstruction) |
+| TiTok-LL-32 VAE Tokenizer | [checkpoint](https://huggingface.co/yucornetto/tokenizer_titok_ll32_vae_c16_imagenet)| 1.61 (reconstruction) |
+| TiTok-BL-64 VAE Tokenizer | [checkpoint](https://huggingface.co/yucornetto/tokenizer_titok_bl64_vae_c16_imagenet)| 1.25 (reconstruction) |
+| TiTok-BL-128 VAE Tokenizer | [checkpoint](https://huggingface.co/yucornetto/tokenizer_titok_bl128_vae_c16_imagenet)| 0.84 (reconstruction) |
+
+Please note that these models are trained only on limited academic dataset ImageNet, and they are only for research purposes.
+
+## Installation
+```shell
+pip3 install -r requirements.txt
+```
+
+## Get Started
+```python
+import torch
+from PIL import Image
+import numpy as np
+import demo_util
+from huggingface_hub import hf_hub_download
+from modeling.maskgit import ImageBert
+from modeling.titok import TiTok
+
+# Choose one from ["tokenizer_titok_l32_imagenet", "tokenizer_titok_b64_imagenet",
+#  "tokenizer_titok_s128_imagenet", "tokenizer_titok_bl128_vae_c16_imagenet", tokenizer_titok_bl64_vae_c16_imagenet",
+# "tokenizer_titok_ll32_vae_c16_imagenet", "tokenizer_titok_sl256_vq8k_imagenet", "tokenizer_titok_bl128_vq8k_imagenet",
+# "tokenizer_titok_bl64_vq8k_imagenet",]
+titok_tokenizer = TiTok.from_pretrained("yucornetto/tokenizer_titok_l32_imagenet")
+titok_tokenizer.eval()
+titok_tokenizer.requires_grad_(False)
+titok_generator = ImageBert.from_pretrained("yucornetto/generator_titok_l32_imagenet")
+titok_generator.eval()
+titok_generator.requires_grad_(False)
+
+# or alternatively, downloads from hf
+# hf_hub_download(repo_id="fun-research/TiTok", filename="tokenizer_titok_l32.bin", local_dir="./")
+# hf_hub_download(repo_id="fun-research/TiTok", filename="generator_titok_l32.bin", local_dir="./")
+
+# load config
+# config = demo_util.get_config("configs/infer/TiTok/titok_l32.yaml")
+# titok_tokenizer = demo_util.get_titok_tokenizer(config)
+# titok_generator = demo_util.get_titok_generator(config)
+
+device = "cuda"
+titok_tokenizer = titok_tokenizer.to(device)
+titok_generator = titok_generator.to(device)
+
+# reconstruct an image. I.e., image -> 32 tokens -> image
+img_path = "assets/ILSVRC2012_val_00010240.png"
+image = torch.from_numpy(np.array(Image.open(img_path)).astype(np.float32)).permute(2, 0, 1).unsqueeze(0) / 255.0
+# tokenization
+if titok_tokenizer.quantize_mode == "vq":
+    encoded_tokens = titok_tokenizer.encode(image.to(device))[1]["min_encoding_indices"]
+elif titok_tokenizer.quantize_mode == "vae":
+    posteriors = titok_tokenizer.encode(image.to(device))[1]
+    encoded_tokens = posteriors.sample()
+else:
+    raise NotImplementedError
+# image assets/ILSVRC2012_val_00010240.png is encoded into tokens tensor([[[ 887, 3979,  349,  720, 2809, 2743, 2101,  603, 2205, 1508, 1891, 4015, 1317, 2956, 3774, 2296,  484, 2612, 3472, 2330, 3140, 3113, 1056, 3779,  654, 2360, 1901, 2908, 2169,  953, 1326, 2598]]], device='cuda:0'), with shape torch.Size([1, 1, 32])
+print(f"image {img_path} is encoded into tokens {encoded_tokens}, with shape {encoded_tokens.shape}")
+# de-tokenization
+reconstructed_image = titok_tokenizer.decode_tokens(encoded_tokens)
+reconstructed_image = torch.clamp(reconstructed_image, 0.0, 1.0)
+reconstructed_image = (reconstructed_image * 255.0).permute(0, 2, 3, 1).to("cpu", dtype=torch.uint8).numpy()[0]
+reconstructed_image = Image.fromarray(reconstructed_image).save("assets/ILSVRC2012_val_00010240_recon.png")
+
+# generate an image
+sample_labels = [torch.randint(0, 999, size=(1,)).item()] # random IN-1k class
+generated_image = demo_util.sample_fn(
+    generator=titok_generator,
+    tokenizer=titok_tokenizer,
+    labels=sample_labels,
+    guidance_scale=4.5,
+    randomize_temperature=1.0,
+    num_sample_steps=8,
+    device=device
+)
+Image.fromarray(generated_image[0]).save(f"assets/generated_{sample_labels[0]}.png")
+```
+
+We also provide a [jupyter notebook](demo.ipynb) for a quick tutorial on reconstructing and generating images with TiTok-L-32.
+
+We also support TiTok with [HuggingFace 🤗 Demo](https://huggingface.co/spaces/fun-research/TiTok)!
+
+## Testing on ImageNet-1K Benchmark
+
+We provide a [sampling script](./sample_imagenet_titok.py) for reproducing the generation results on ImageNet-1K benchmark.
+```bash
+# Prepare ADM evaluation script
+git clone https://github.com/openai/guided-diffusion.git
+
+wget https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/256/VIRTUAL_imagenet256_labeled.npz
+```
+```python
+# Reproducing TiTok-L-32
+torchrun --nnodes=1 --nproc_per_node=8 --rdzv-endpoint=localhost:9999 sample_imagenet_titok.py config=configs/infer/TiTok/titok_l32.yaml experiment.output_dir="titok_l_32"
+# Run eval script. The result FID should be ~2.77
+python3 guided-diffusion/evaluations/evaluator.py VIRTUAL_imagenet256_labeled.npz titok_l_32.npz
+
+# Reproducing TiTok-B-64
+torchrun --nnodes=1 --nproc_per_node=8 --rdzv-endpoint=localhost:9999 sample_imagenet_titok.py config=configs/infer/TiTok/titok_b64.yaml experiment.output_dir="titok_b_64"
+# Run eval script. The result FID should be ~2.48
+python3 guided-diffusion/evaluations/evaluator.py VIRTUAL_imagenet256_labeled.npz titok_b_64.npz
+
+# Reproducing TiTok-S-128
+torchrun --nnodes=1 --nproc_per_node=8 --rdzv-endpoint=localhost:9999 sample_imagenet_titok.py config=configs/infer/TiTok/titok_s128.yaml experiment.output_dir="titok_s_128"
+# Run eval script. The result FID should be ~1.97
+python3 guided-diffusion/evaluations/evaluator.py VIRTUAL_imagenet256_labeled.npz titok_s_128.npz
+```
+## Training Preparation
+We use [webdataset](https://github.com/webdataset/webdataset) format for data loading. To begin with, it is needed to convert the dataset into webdataset format. An example script to convert ImageNet to wds format is provided [here](./data/convert_imagenet_to_wds.py).
+
+Furthermore, the stage1 training relies on a pre-trained MaskGIT-VQGAN to generate proxy codes as learning targets. You can convert the [official Jax weight](https://github.com/google-research/maskgit) to PyTorch version using [this script](https://github.com/huggingface/open-muse/blob/main/scripts/convert_maskgit_vqgan.py). Alternatively, we provided a converted version at [HuggingFace](https://huggingface.co/fun-research/TiTok/blob/main/maskgit-vqgan-imagenet-f16-256.bin) and [Google Drive](https://drive.google.com/file/d/1DjZqzJrUt2hwpmUPkjGSBTFEJcOkLY-Q/view?usp=sharing). The MaskGIT-VQGAN's weight will be automatically downloaded when you run the training script.
+
+You may also pretokenize the dataset for a training speedup, please refer to the [example pretokenization script](scripts/pretokenization.py).
+
+## Training
+We provide example commands to train TiTok as follows:
+```bash
+# Training for TiTok-B64
+# Stage 1
+WANDB_MODE=offline accelerate launch --num_machines=1 --num_processes=8 --machine_rank=0 --main_process_ip=127.0.0.1 --main_process_port=9999 --same_network scripts/train_titok.py config=configs/training/TiTok/stage1/titok_b64.yaml \
+    experiment.project="titok_b64_stage1" \
+    experiment.name="titok_b64_stage1_run1" \
+    experiment.output_dir="titok_b64_stage1_run1" \
+    training.per_gpu_batch_size=32
+
+# Stage 2
+WANDB_MODE=offline accelerate launch --num_machines=1 --num_processes=8 --machine_rank=0 --main_process_ip=127.0.0.1 --main_process_port=9999 --same_network scripts/train_titok.py config=configs/training/TiTok/stage2/titok_b64.yaml \
+    experiment.project="titok_b64_stage2" \
+    experiment.name="titok_b64_stage2_run1" \
+    experiment.output_dir="titok_b64_stage2_run1" \
+    training.per_gpu_batch_size=32 \
+    experiment.init_weight=${PATH_TO_STAGE1_WEIGHT}
+
+# Train Generator (TiTok-B64 as example)
+WANDB_MODE=offline accelerate launch --num_machines=4 --num_processes=32 --machine_rank=${MACHINE_RANK} --main_process_ip=${ROOT_IP}--main_process_port=${ROOT_PORT} --same_network scripts/train_maskgit.py config=configs/training/generator/maskgit.yaml \
+    experiment.project="titok_generation" \
+    experiment.name="titok_b64_maskgit" \
+    experiment.output_dir="titok_b64_maskgit" \
+    experiment.tokenizer_checkpoint=${PATH_TO_STAGE1_or_STAGE2_WEIGHT}
+```
+You may remove the flag "WANDB_MODE=offline" to support online wandb logging, if you have configured it.
+
+The config _titok_b64.yaml_ can be replaced with _titok_s128.yaml_ or _titok_l32.yaml_ for other TiTok variants.
+
+## Visualizations
+<p>
+<img src="assets/recon_w_model_size_num_token.png" alt="teaser" width=90% height=90%>
+</p>
+<p>
+<img src="assets/random_vis_l32.png" alt="teaser" width=90% height=90%>
+</p>
+
+
+## Citing
+If you use our work in your research, please use the following BibTeX entry.
+
+```BibTeX
+@inproceedings{yu2024an,
+  author    = {Qihang Yu and Mark Weber and Xueqing Deng and Xiaohui Shen and Daniel Cremers and Liang-Chieh Chen},
+  title     = {An Image is Worth 32 Tokens for Reconstruction and Generation},
+  journal   = {NeurIPS},
+  year      = {2024}
+}
+```
+
+## Acknowledgement
+
+[MaskGIT](https://github.com/google-research/maskgit)
+
+[Taming-Transformers](https://github.com/CompVis/taming-transformers)
+
+[Open-MUSE](https://github.com/huggingface/open-muse)
+
+[MUSE-Pytorch](https://github.com/baaivision/MUSE-Pytorch)

RAR/1d-tokenizer/configs/infer/MaskGen/maskgen_kl_l.yaml

@@ -0,0 +1,36 @@
+experiment:
+  tokenizer_checkpoint: "tatitok_bl32_vae.bin"
+  generator_checkpoint: "maskgen_kl_l.bin"
+  
+model:
+  vq_model:
+    quantize_mode: vae
+    token_size: 16
+    vit_enc_model_size: base
+    vit_dec_model_size: large
+    vit_enc_patch_size: 16
+    vit_dec_patch_size: 16
+    num_latent_tokens: 32
+    scale_factor: 0.7525
+    finetune_decoder: False
+    is_legacy: False
+  maskgen:
+    decoder_embed_dim: 1024
+    decoder_depth: 16
+    decoder_num_heads: 16
+    micro_condition: true
+    micro_condition_embed_dim: 256
+    text_drop_prob: 0.1
+    cfg: 3.0
+    cfg_schedule: "linear"
+    num_iter: 32
+    temperature: 1.0
+    sample_aesthetic_score: 6.5
+
+losses:
+    diffloss_d: 8
+    diffloss_w: 1024
+
+dataset:
+    preprocessing:
+        crop_size: 256

RAR/1d-tokenizer/configs/infer/MaskGen/maskgen_kl_xl.yaml

@@ -0,0 +1,36 @@
+experiment:
+  tokenizer_checkpoint: "tatitok_bl32_vae.bin"
+  generator_checkpoint: "maskgen_kl_xl.bin"
+  
+model:
+  vq_model:
+    quantize_mode: vae
+    token_size: 16
+    vit_enc_model_size: base
+    vit_dec_model_size: large
+    vit_enc_patch_size: 16
+    vit_dec_patch_size: 16
+    num_latent_tokens: 32
+    scale_factor: 0.7525
+    finetune_decoder: False
+    is_legacy: False
+  maskgen:
+    decoder_embed_dim: 1280
+    decoder_depth: 20
+    decoder_num_heads: 16
+    micro_condition: true
+    micro_condition_embed_dim: 256
+    text_drop_prob: 0.1
+    cfg: 3.0
+    cfg_schedule: "linear"
+    num_iter: 32
+    temperature: 1.0
+    sample_aesthetic_score: 6.5
+
+losses:
+    diffloss_d: 8
+    diffloss_w: 1280
+
+dataset:
+    preprocessing:
+        crop_size: 256

RAR/1d-tokenizer/configs/infer/MaskGen/maskgen_vq_l.yaml

@@ -0,0 +1,35 @@
+experiment:
+  tokenizer_checkpoint: "tatitok_bl128_vq.bin"
+  generator_checkpoint: "maskgen_vq_l.bin"
+
+model:
+  vq_model:
+    quantize_mode: vq
+    codebook_size: 8192
+    token_size: 64
+    use_l2_norm: False
+    commitment_cost: 0.25
+    clustering_vq: False
+    vit_enc_model_size: base
+    vit_dec_model_size: large
+    vit_enc_patch_size: 16
+    vit_dec_patch_size: 16
+    num_latent_tokens: 128
+    finetune_decoder: False
+    is_legacy: False
+  maskgen:
+    decoder_embed_dim: 1024
+    decoder_depth: 16
+    decoder_num_heads: 16
+    micro_condition: True
+    micro_condition_embed_dim: 256
+    text_drop_prob: 0.1
+    condition_num_classes: 1000
+    cfg: 12.0
+    num_iter: 16
+    temperature: 2.0
+    sample_aesthetic_score: 6.5
+
+dataset:
+    preprocessing:
+        crop_size: 256

RAR/1d-tokenizer/configs/infer/MaskGen/maskgen_vq_xl.yaml

@@ -0,0 +1,35 @@
+experiment:
+  tokenizer_checkpoint: "tatitok_bl128_vq.bin"
+  generator_checkpoint: "maskgen_vq_xl.bin"
+  
+model:
+  vq_model:
+    quantize_mode: vq
+    codebook_size: 8192
+    token_size: 64
+    use_l2_norm: False
+    commitment_cost: 0.25
+    clustering_vq: False
+    vit_enc_model_size: base
+    vit_dec_model_size: large
+    vit_enc_patch_size: 16
+    vit_dec_patch_size: 16
+    num_latent_tokens: 128
+    finetune_decoder: False
+    is_legacy: False
+  maskgen:
+    decoder_embed_dim: 1280
+    decoder_depth: 20
+    decoder_num_heads: 16
+    micro_condition: True
+    micro_condition_embed_dim: 256
+    text_drop_prob: 0.1
+    condition_num_classes: 1000
+    cfg: 12.0
+    num_iter: 16
+    temperature: 2.0
+    sample_aesthetic_score: 6.5
+
+dataset:
+    preprocessing:
+        crop_size: 256

RAR/1d-tokenizer/configs/infer/TA-TiTok/tatitok_bl128_vq.yaml

@@ -0,0 +1,24 @@
+experiment:
+    tokenizer_checkpoint: "tatitok_bl128_vq.bin"
+    output_dir: "tatitok_bl128_vq"
+
+model:
+    vq_model:
+        quantize_mode: vq
+        codebook_size: 8192
+        token_size: 64
+        use_l2_norm: False
+        commitment_cost: 0.25
+        clustering_vq: False
+        # vit arch
+        vit_enc_model_size: "base"
+        vit_dec_model_size: "large"
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 128
+        finetune_decoder: False
+        is_legacy: False
+
+dataset:
+    preprocessing:
+        crop_size: 256

RAR/1d-tokenizer/configs/infer/TA-TiTok/tatitok_bl32_vae.yaml

@@ -0,0 +1,20 @@
+experiment:
+    tokenizer_checkpoint: "tatitok_bl32_vae.bin"
+    output_dir: "tatitok_bl32_vae"
+
+model:
+    vq_model:
+        quantize_mode: vae
+        token_size: 16
+        # vit arch
+        vit_enc_model_size: "base"
+        vit_dec_model_size: "large"
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 32
+        finetune_decoder: False
+        is_legacy: False
+
+dataset:
+    preprocessing:
+        crop_size: 256

RAR/1d-tokenizer/configs/infer/TA-TiTok/tatitok_bl32_vq.yaml

@@ -0,0 +1,24 @@
+experiment:
+    tokenizer_checkpoint: "tatitok_bl32_vq.bin"
+    output_dir: "tatitok_bl32_vq"
+
+model:
+    vq_model:
+        quantize_mode: vq
+        codebook_size: 8192
+        token_size: 64
+        use_l2_norm: False
+        commitment_cost: 0.25
+        clustering_vq: False
+        # vit arch
+        vit_enc_model_size: "base"
+        vit_dec_model_size: "large"
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 32
+        finetune_decoder: False
+        is_legacy: False
+
+dataset:
+    preprocessing:
+        crop_size: 256

RAR/1d-tokenizer/configs/infer/TA-TiTok/tatitok_bl64_vae.yaml

@@ -0,0 +1,20 @@
+experiment:
+    tokenizer_checkpoint: "tatitok_bl64_vae.bin"
+    output_dir: "tatitok_bl64_vae"
+
+model:
+    vq_model:
+        quantize_mode: vae
+        token_size: 16
+        # vit arch
+        vit_enc_model_size: "base"
+        vit_dec_model_size: "large"
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 64
+        finetune_decoder: False
+        is_legacy: False
+
+dataset:
+    preprocessing:
+        crop_size: 256

RAR/1d-tokenizer/configs/infer/TA-TiTok/tatitok_bl64_vq.yaml

@@ -0,0 +1,24 @@
+experiment:
+    tokenizer_checkpoint: "tatitok_bl64_vq.bin"
+    output_dir: "tatitok_bl64_vq"
+
+model:
+    vq_model:
+        quantize_mode: vq
+        codebook_size: 8192
+        token_size: 64
+        use_l2_norm: False
+        commitment_cost: 0.25
+        clustering_vq: False
+        # vit arch
+        vit_enc_model_size: "base"
+        vit_dec_model_size: "large"
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 64
+        finetune_decoder: False
+        is_legacy: False
+
+dataset:
+    preprocessing:
+        crop_size: 256

RAR/1d-tokenizer/configs/infer/TA-TiTok/tatitok_sl128_vae.yaml

@@ -0,0 +1,20 @@
+experiment:
+    tokenizer_checkpoint: "tatitok_sl128_vae.bin"
+    output_dir: "tatitok_sl128_vae"
+
+model:
+    vq_model:
+        quantize_mode: vae
+        token_size: 16
+        # vit arch
+        vit_enc_model_size: "small"
+        vit_dec_model_size: "large"
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 128
+        finetune_decoder: False
+        is_legacy: False
+
+dataset:
+    preprocessing:
+        crop_size: 256

RAR/1d-tokenizer/configs/infer/TiTok/titok_b64.yaml

@@ -0,0 +1,39 @@
+experiment:
+    tokenizer_checkpoint: "tokenizer_titok_b64.bin"
+    generator_checkpoint: "generator_titok_b64.bin"
+    output_dir: "titok_b_64"
+model:
+    vq_model:
+        codebook_size: 4096
+        token_size: 12
+        use_l2_norm: True
+        commitment_cost: 0.25
+        # vit arch
+        vit_enc_model_size: "base"
+        vit_dec_model_size: "base"
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 64
+        finetune_decoder: True
+    
+    generator:
+        model_type: "ViT"
+        hidden_size: 768
+        num_hidden_layers: 24
+        num_attention_heads: 16
+        intermediate_size: 3072
+        dropout: 0.1
+        attn_drop: 0.1
+        num_steps: 8
+        class_label_dropout: 0.1
+        image_seq_len: ${model.vq_model.num_latent_tokens}
+        condition_num_classes: 1000
+
+        # sampling hyper-params
+        randomize_temperature: 11.0
+        guidance_scale: 3.0
+        guidance_decay: "linear"
+
+dataset:
+    preprocessing:
+        crop_size: 256

RAR/1d-tokenizer/configs/infer/TiTok/titok_bl128_vae_c16.yaml

@@ -0,0 +1,19 @@
+experiment:
+    tokenizer_checkpoint: "titok_bl128_vae_c16.bin"
+    output_dir: "titok_bl128_vae_c16"
+model:
+    vq_model:
+        quantize_mode: "vae"
+        token_size: 16 
+        # vit arch
+        vit_enc_model_size: "base"
+        vit_dec_model_size: "large"
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 128
+        finetune_decoder: False
+        is_legacy: False
+
+dataset:
+    preprocessing:
+        crop_size: 256

RAR/1d-tokenizer/configs/infer/TiTok/titok_bl128_vq8k.yaml

@@ -0,0 +1,21 @@
+experiment:
+    tokenizer_checkpoint: "tokenizer_titok_bl128_vq8k.bin"
+    output_dir: "titok_bl128_vq8k"
+model:
+    vq_model:
+        codebook_size: 8192
+        token_size: 64
+        use_l2_norm: False
+        commitment_cost: 0.25
+        # vit arch
+        vit_enc_model_size: "base"
+        vit_dec_model_size: "large"
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 128
+        finetune_decoder: False
+        is_legacy: False
+
+dataset:
+    preprocessing:
+        crop_size: 256

RAR/1d-tokenizer/configs/infer/TiTok/titok_bl64_vae_c16.yaml

@@ -0,0 +1,19 @@
+experiment:
+    tokenizer_checkpoint: "titok_bl64_vae_c16.bin"
+    output_dir: "titok_bl64_vae_c16"
+model:
+    vq_model:
+        quantize_mode: "vae"
+        token_size: 16 
+        # vit arch
+        vit_enc_model_size: "base"
+        vit_dec_model_size: "large"
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 64
+        finetune_decoder: False
+        is_legacy: False
+
+dataset:
+    preprocessing:
+        crop_size: 256

RAR/1d-tokenizer/configs/infer/TiTok/titok_bl64_vq8k.yaml

@@ -0,0 +1,21 @@
+experiment:
+    tokenizer_checkpoint: "tokenizer_titok_bl64_vq8k.bin"
+    output_dir: "titok_bl64_vq8k"
+model:
+    vq_model:
+        codebook_size: 8192
+        token_size: 64
+        use_l2_norm: False
+        commitment_cost: 0.25
+        # vit arch
+        vit_enc_model_size: "base"
+        vit_dec_model_size: "large"
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 64
+        finetune_decoder: False
+        is_legacy: False
+
+dataset:
+    preprocessing:
+        crop_size: 256

RAR/1d-tokenizer/configs/infer/TiTok/titok_l32.yaml

@@ -0,0 +1,40 @@
+experiment:
+    tokenizer_checkpoint: "tokenizer_titok_l32.bin"
+    generator_checkpoint: "generator_titok_l32.bin"
+    output_dir: "titok_l_32"
+model:
+    vq_model:
+        codebook_size: 4096
+        token_size: 12
+        use_l2_norm: True
+        commitment_cost: 0.25
+        # vit arch
+        vit_enc_model_size: "large"
+        vit_dec_model_size: "large"
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 32
+        finetune_decoder: True
+    
+    generator:
+        model_type: "ViT"
+        hidden_size: 768
+        num_hidden_layers: 24
+        num_attention_heads: 16
+        intermediate_size: 3072
+        dropout: 0.1
+        attn_drop: 0.1
+        num_steps: 8
+        class_label_dropout: 0.1
+        image_seq_len: ${model.vq_model.num_latent_tokens}
+        condition_num_classes: 1000
+
+        # sampling hyper-params
+        randomize_temperature: 9.5
+        guidance_scale: 4.5
+        guidance_decay: "linear"
+        
+
+dataset:
+    preprocessing:
+        crop_size: 256

RAR/1d-tokenizer/configs/infer/TiTok/titok_ll32_vae_c16.yaml

@@ -0,0 +1,19 @@
+experiment:
+    tokenizer_checkpoint: "titok_ll32_vae_c16.bin"
+    output_dir: "titok_ll32_vae_c16"
+model:
+    vq_model:
+        quantize_mode: "vae"
+        token_size: 16 
+        # vit arch
+        vit_enc_model_size: "large"
+        vit_dec_model_size: "large"
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 32
+        finetune_decoder: False
+        is_legacy: False
+
+dataset:
+    preprocessing:
+        crop_size: 256

RAR/1d-tokenizer/configs/infer/TiTok/titok_s128.yaml

@@ -0,0 +1,39 @@
+experiment:
+    tokenizer_checkpoint: "tokenizer_titok_s128.bin"
+    generator_checkpoint: "generator_titok_s128.bin"
+    output_dir: "titok_s_128"
+model:
+    vq_model:
+        codebook_size: 4096
+        token_size: 12
+        use_l2_norm: True
+        commitment_cost: 0.25
+        # vit arch
+        vit_enc_model_size: "small"
+        vit_dec_model_size: "small"
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 128
+        finetune_decoder: True
+    
+    generator:
+        model_type: "UViT"
+        hidden_size: 1024
+        num_hidden_layers: 20
+        num_attention_heads: 16
+        intermediate_size: 4096
+        dropout: 0.1
+        attn_drop: 0.1
+        num_steps: 64
+        class_label_dropout: 0.1
+        image_seq_len: ${model.vq_model.num_latent_tokens}
+        condition_num_classes: 1000
+
+        # sampling hyper-params
+        randomize_temperature: 2.8
+        guidance_scale: 6.9
+        guidance_decay: "power-cosine"
+
+dataset:
+    preprocessing:
+        crop_size: 256

RAR/1d-tokenizer/configs/infer/TiTok/titok_sl256_vq8k.yaml

@@ -0,0 +1,21 @@
+experiment:
+    tokenizer_checkpoint: "tokenizer_titok_sl256_vq8k.bin"
+    output_dir: "titok_sl256_vq8k"
+model:
+    vq_model:
+        codebook_size: 8192
+        token_size: 64
+        use_l2_norm: False
+        commitment_cost: 0.25
+        # vit arch
+        vit_enc_model_size: "small"
+        vit_dec_model_size: "large"
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 256
+        finetune_decoder: False
+        is_legacy: False
+
+dataset:
+    preprocessing:
+        crop_size: 256

RAR/1d-tokenizer/configs/training/MaskGen/maskgen_kl_l_stage1.yaml

@@ -0,0 +1,82 @@
+experiment:
+    project: "maskgen_kl_l_stage1"
+    name: "maskgen_kl_l_stage1_run1"
+    output_dir: "maskgen_kl_l_stage1_run1"
+    max_train_examples: 250_000_000
+    save_every: 50_000
+    eval_every: 50_000
+    generate_every: 5_000
+    log_every: 50
+    log_grad_norm_every: 1_000
+    resume: True
+
+model:
+    vq_model:
+        quantize_mode: vae
+        token_size: 16
+        vit_enc_model_size: base
+        vit_dec_model_size: large
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 32
+        scale_factor: 0.7525
+        finetune_decoder: False
+        is_legacy: False
+    maskgen:
+        decoder_embed_dim: 1024
+        decoder_depth: 16
+        decoder_num_heads: 16
+        micro_condition: True
+        micro_condition_embed_dim: 256
+        text_drop_prob: 0.1
+        cfg: 3.0
+        cfg_schedule: "linear"
+        num_iter: 32
+        temperature: 1.0
+        sample_aesthetic_score: 6.0
+
+losses:
+    diffloss_d: 8
+    diffloss_w: 1024
+
+dataset:
+    params:
+        train_shards_path_or_url: "datacomp5+::cc12m::laion-en-aesthetic"
+        eval_shards_path_or_url: "coco"
+        pretokenization: "true"
+        num_workers_per_gpu: 12
+        dataset_with_class_label: False
+        dataset_with_text_label: True
+    preprocessing:
+        resize_shorter_edge: 256
+        crop_size: 256
+        random_crop: True
+        random_flip: True
+        res_ratio_filtering: True
+
+optimizer:
+    name: adamw 
+    params:
+        learning_rate: 1e-4
+        beta1: 0.9
+        beta2: 0.95
+        weight_decay: 0.02
+
+lr_scheduler:
+    scheduler: "constant"
+    params:
+        learning_rate: ${optimizer.params.learning_rate}
+        warmup_steps: 50_000
+        end_lr: 1e-5
+
+training:
+    gradient_accumulation_steps: 1
+    per_gpu_batch_size: 32
+    mixed_precision: "fp16"
+    enable_tf32: True
+    enable_wandb: True
+    use_ema: True
+    seed: 42
+    max_train_steps: 1_000_000
+    num_generated_images: 2
+    max_grad_norm: 1.0

RAR/1d-tokenizer/configs/training/MaskGen/maskgen_kl_l_stage2.yaml

@@ -0,0 +1,83 @@
+experiment:
+    project: "maskgen_kl_l_stage2"
+    name: "maskgen_kl_l_stage2_run1"
+    output_dir: "maskgen_kl_l_stage2_run1"
+    init_weight: "maskgen_kl_l_stage1.bin"
+    max_train_examples: 13_000_000
+    save_every: 50_000
+    eval_every: 50_000
+    generate_every: 5_000
+    log_every: 50
+    log_grad_norm_every: 1_000
+    resume: True
+
+model:
+    vq_model:
+        quantize_mode: vae
+        token_size: 16
+        vit_enc_model_size: base
+        vit_dec_model_size: large
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 32
+        scale_factor: 0.7525
+        finetune_decoder: False
+        is_legacy: False
+    maskgen:
+        decoder_embed_dim: 1024
+        decoder_depth: 16
+        decoder_num_heads: 16
+        micro_condition: True
+        micro_condition_embed_dim: 256
+        text_drop_prob: 0.1
+        cfg: 3.0
+        cfg_schedule: "linear"
+        num_iter: 32
+        temperature: 1.0
+        sample_aesthetic_score: 6.5
+
+losses:
+    diffloss_d: 8
+    diffloss_w: 1024
+
+dataset:
+    params:
+        train_shards_path_or_url: "datacomp6+::laion-art::laion-pop::journeydb::dalle3"
+        eval_shards_path_or_url: "coco"
+        pretokenization: "true"
+        num_workers_per_gpu: 12
+        dataset_with_class_label: False
+        dataset_with_text_label: True
+    preprocessing:
+        resize_shorter_edge: 256
+        crop_size: 256
+        random_crop: True
+        random_flip: True
+        res_ratio_filtering: True
+
+optimizer:
+    name: adamw 
+    params:
+        learning_rate: 1e-4
+        beta1: 0.9
+        beta2: 0.95
+        weight_decay: 0.02
+
+lr_scheduler:
+    scheduler: "constant"
+    params:
+        learning_rate: ${optimizer.params.learning_rate}
+        warmup_steps: 50_000
+        end_lr: 1e-5
+
+training:
+    gradient_accumulation_steps: 1
+    per_gpu_batch_size: 32
+    mixed_precision: "fp16"
+    enable_tf32: True
+    enable_wandb: True
+    use_ema: True
+    seed: 42
+    max_train_steps: 500_000
+    num_generated_images: 2
+    max_grad_norm: 1.0

RAR/1d-tokenizer/configs/training/MaskGen/maskgen_kl_xl_stage1.yaml

@@ -0,0 +1,82 @@
+experiment:
+    project: "maskgen_kl_xl_stage1"
+    name: "maskgen_kl_xl_stage1_run1"
+    output_dir: "maskgen_kl_xl_stage1_run1"
+    max_train_examples: 250_000_000
+    save_every: 50_000
+    eval_every: 50_000
+    generate_every: 5_000
+    log_every: 50
+    log_grad_norm_every: 1_000
+    resume: True
+
+model:
+    vq_model:
+        quantize_mode: vae
+        token_size: 16
+        vit_enc_model_size: base
+        vit_dec_model_size: large
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 32
+        scale_factor: 0.7525
+        finetune_decoder: False
+        is_legacy: False
+    maskgen:
+        decoder_embed_dim: 1280
+        decoder_depth: 20
+        decoder_num_heads: 16
+        micro_condition: True
+        micro_condition_embed_dim: 256
+        text_drop_prob: 0.1
+        cfg: 3.0
+        cfg_schedule: "linear"
+        num_iter: 32
+        temperature: 1.0
+        sample_aesthetic_score: 6.0
+
+losses:
+    diffloss_d: 8
+    diffloss_w: 1280
+
+dataset:
+    params:
+        train_shards_path_or_url: "datacomp5+::cc12m::laion-en-aesthetic"
+        eval_shards_path_or_url: "coco"
+        pretokenization: "true"
+        num_workers_per_gpu: 12
+        dataset_with_class_label: False
+        dataset_with_text_label: True
+    preprocessing:
+        resize_shorter_edge: 256
+        crop_size: 256
+        random_crop: True
+        random_flip: True
+        res_ratio_filtering: True
+
+optimizer:
+    name: adamw 
+    params:
+        learning_rate: 1e-4
+        beta1: 0.9
+        beta2: 0.95
+        weight_decay: 0.02
+
+lr_scheduler:
+    scheduler: "constant"
+    params:
+        learning_rate: ${optimizer.params.learning_rate}
+        warmup_steps: 50_000
+        end_lr: 1e-5
+
+training:
+    gradient_accumulation_steps: 1
+    per_gpu_batch_size: 32
+    mixed_precision: "fp16"
+    enable_tf32: True
+    enable_wandb: True
+    use_ema: True
+    seed: 42
+    max_train_steps: 1_000_000
+    num_generated_images: 2
+    max_grad_norm: 1.0

RAR/1d-tokenizer/configs/training/MaskGen/maskgen_kl_xl_stage2.yaml

@@ -0,0 +1,83 @@
+experiment:
+    project: "maskgen_kl_xl_stage2"
+    name: "maskgen_kl_xl_stage2_run1"
+    output_dir: "maskgen_kl_xl_stage2_run1"
+    init_weight: "maskgen_kl_xl_stage1.bin"
+    max_train_examples: 13_000_000
+    save_every: 50_000
+    eval_every: 50_000
+    generate_every: 5_000
+    log_every: 50
+    log_grad_norm_every: 1_000
+    resume: True
+
+model:
+    vq_model:
+        quantize_mode: vae
+        token_size: 16
+        vit_enc_model_size: base
+        vit_dec_model_size: large
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 32
+        scale_factor: 0.7525
+        finetune_decoder: False
+        is_legacy: False
+    maskgen:
+        decoder_embed_dim: 1280
+        decoder_depth: 20
+        decoder_num_heads: 16
+        micro_condition: True
+        micro_condition_embed_dim: 256
+        text_drop_prob: 0.1
+        cfg: 3.0
+        cfg_schedule: "linear"
+        num_iter: 32
+        temperature: 1.0
+        sample_aesthetic_score: 6.5
+
+losses:
+    diffloss_d: 8
+    diffloss_w: 1280
+
+dataset:
+    params:
+        train_shards_path_or_url: "datacomp6+::laion-art::laion-pop::journeydb::dalle3"
+        eval_shards_path_or_url: "coco"
+        pretokenization: "true"
+        num_workers_per_gpu: 12
+        dataset_with_class_label: False
+        dataset_with_text_label: True
+    preprocessing:
+        resize_shorter_edge: 256
+        crop_size: 256
+        random_crop: True
+        random_flip: True
+        res_ratio_filtering: True
+
+optimizer:
+    name: adamw 
+    params:
+        learning_rate: 1e-4
+        beta1: 0.9
+        beta2: 0.95
+        weight_decay: 0.02
+
+lr_scheduler:
+    scheduler: "constant"
+    params:
+        learning_rate: ${optimizer.params.learning_rate}
+        warmup_steps: 50_000
+        end_lr: 1e-5
+
+training:
+    gradient_accumulation_steps: 1
+    per_gpu_batch_size: 32
+    mixed_precision: "fp16"
+    enable_tf32: True
+    enable_wandb: True
+    use_ema: True
+    seed: 42
+    max_train_steps: 500_000
+    num_generated_images: 2
+    max_grad_norm: 1.0

RAR/1d-tokenizer/configs/training/MaskGen/maskgen_vq_l_stage1.yaml

@@ -0,0 +1,86 @@
+experiment:
+    project: "maskgen_vq_l_stage1"
+    name: "maskgen_vq_l_stage1_run1"
+    output_dir: "maskgen_vq_l_stage1_run1"
+    max_train_examples: 250_000_000
+    save_every: 50_000
+    eval_every: 50_000
+    generate_every: 5_000
+    log_every: 50
+    log_grad_norm_every: 1_000
+    resume: True
+
+model:
+    vq_model:
+        quantize_mode: vq
+        codebook_size: 8192
+        token_size: 64
+        use_l2_norm: False
+        commitment_cost: 0.25
+        clustering_vq: True
+        # vit arch
+        vit_enc_model_size: "base"
+        vit_dec_model_size: "large"
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 128
+        finetune_decoder: False
+        is_legacy: False
+    maskgen:
+        decoder_embed_dim: 1024
+        decoder_depth: 16
+        decoder_num_heads: 16
+        micro_condition: True
+        micro_condition_embed_dim: 256
+        text_drop_prob: 0.1
+        condition_num_classes: 1000
+        cfg: 12.0
+        num_iter: 16
+        temperature: 2.0
+        sample_aesthetic_score: 6.0
+
+losses:
+  label_smoothing: 0.1
+  loss_weight_unmasked_token: 0.1
+
+dataset:
+    params:
+        train_shards_path_or_url: "datacomp5+::cc12m::laion-en-aesthetic"
+        eval_shards_path_or_url: "coco"
+        pretokenization: "true"
+        num_workers_per_gpu: 12
+        dataset_with_class_label: False
+        dataset_with_text_label: True
+    preprocessing:
+        resize_shorter_edge: 256
+        crop_size: 256
+        random_crop: True
+        random_flip: True
+        res_ratio_filtering: True
+
+optimizer:
+    name: adamw 
+    params:
+        learning_rate: 4e-4
+        beta1: 0.9
+        beta2: 0.96
+        weight_decay: 0.03
+
+lr_scheduler:
+    scheduler: "cosine"
+    params:
+        learning_rate: ${optimizer.params.learning_rate}
+        warmup_steps: 10_000
+        end_lr: 1e-5
+
+training:
+    gradient_accumulation_steps: 1
+    per_gpu_batch_size: 128
+    mixed_precision: "fp16"
+    enable_tf32: True
+    enable_wandb: True
+    use_ema: True
+    seed: 42
+    max_train_steps: 500_000
+    num_generated_images: 2
+    max_grad_norm: 1.0

RAR/1d-tokenizer/configs/training/MaskGen/maskgen_vq_l_stage2.yaml

@@ -0,0 +1,87 @@
+experiment:
+    project: "maskgen_vq_l_stage2"
+    name: "maskgen_vq_l_stage2_run1"
+    output_dir: "maskgen_vq_l_stage2_run1"
+    init_weight: "maskgen_vq_l_stage1.bin"
+    max_train_examples: 13_000_000
+    save_every: 50_000
+    eval_every: 50_000
+    generate_every: 5_000
+    log_every: 50
+    log_grad_norm_every: 1_000
+    resume: True
+
+model:
+    vq_model:
+        quantize_mode: vq
+        codebook_size: 8192
+        token_size: 64
+        use_l2_norm: False
+        commitment_cost: 0.25
+        clustering_vq: True
+        # vit arch
+        vit_enc_model_size: "base"
+        vit_dec_model_size: "large"
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 128
+        finetune_decoder: False
+        is_legacy: False
+    maskgen:
+        decoder_embed_dim: 1024
+        decoder_depth: 16
+        decoder_num_heads: 16
+        micro_condition: True
+        micro_condition_embed_dim: 256
+        text_drop_prob: 0.1
+        condition_num_classes: 1000
+        cfg: 12.0
+        num_iter: 16
+        temperature: 2.0
+        sample_aesthetic_score: 6.5
+
+losses:
+  label_smoothing: 0.1
+  loss_weight_unmasked_token: 0.1
+
+dataset:
+    params:
+        train_shards_path_or_url: "datacomp6+::laion-art::laion-pop::journeydb::dalle3"
+        eval_shards_path_or_url: "coco"
+        pretokenization: "true"
+        num_workers_per_gpu: 12
+        dataset_with_class_label: False
+        dataset_with_text_label: True
+    preprocessing:
+        resize_shorter_edge: 256
+        crop_size: 256
+        random_crop: True
+        random_flip: True
+        res_ratio_filtering: True
+
+optimizer:
+    name: adamw 
+    params:
+        learning_rate: 1e-4
+        beta1: 0.9
+        beta2: 0.96
+        weight_decay: 0.03
+
+lr_scheduler:
+    scheduler: "cosine"
+    params:
+        learning_rate: ${optimizer.params.learning_rate}
+        warmup_steps: 10_000
+        end_lr: 1e-5
+
+training:
+    gradient_accumulation_steps: 1
+    per_gpu_batch_size: 128
+    mixed_precision: "fp16"
+    enable_tf32: True
+    enable_wandb: True
+    use_ema: True
+    seed: 42
+    max_train_steps: 250_000
+    num_generated_images: 2
+    max_grad_norm: 1.0

RAR/1d-tokenizer/configs/training/MaskGen/maskgen_vq_xl_stage1.yaml

@@ -0,0 +1,86 @@
+experiment:
+    project: "maskgen_vq_xl_stage1"
+    name: "maskgen_vq_xl_stage1_run1"
+    output_dir: "maskgen_vq_xl_stage1_run1"
+    max_train_examples: 250_000_000
+    save_every: 50_000
+    eval_every: 50_000
+    generate_every: 5_000
+    log_every: 50
+    log_grad_norm_every: 1_000
+    resume: True
+
+model:
+    vq_model:
+        quantize_mode: vq
+        codebook_size: 8192
+        token_size: 64
+        use_l2_norm: False
+        commitment_cost: 0.25
+        clustering_vq: True
+        # vit arch
+        vit_enc_model_size: "base"
+        vit_dec_model_size: "large"
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 128
+        finetune_decoder: False
+        is_legacy: False
+    maskgen:
+        decoder_embed_dim: 1280
+        decoder_depth: 20
+        decoder_num_heads: 16
+        micro_condition: True
+        micro_condition_embed_dim: 256
+        text_drop_prob: 0.1
+        condition_num_classes: 1000
+        cfg: 12.0
+        num_iter: 16
+        temperature: 2.0
+        sample_aesthetic_score: 6.0
+
+losses:
+  label_smoothing: 0.1
+  loss_weight_unmasked_token: 0.1
+
+dataset:
+    params:
+        train_shards_path_or_url: "datacomp5+::cc12m::laion-en-aesthetic"
+        eval_shards_path_or_url: "coco"
+        pretokenization: "true"
+        num_workers_per_gpu: 12
+        dataset_with_class_label: False
+        dataset_with_text_label: True
+    preprocessing:
+        resize_shorter_edge: 256
+        crop_size: 256
+        random_crop: True
+        random_flip: True
+        res_ratio_filtering: True
+
+optimizer:
+    name: adamw 
+    params:
+        learning_rate: 4e-4
+        beta1: 0.9
+        beta2: 0.96
+        weight_decay: 0.03
+
+lr_scheduler:
+    scheduler: "cosine"
+    params:
+        learning_rate: ${optimizer.params.learning_rate}
+        warmup_steps: 10_000
+        end_lr: 1e-5
+
+training:
+    gradient_accumulation_steps: 1
+    per_gpu_batch_size: 128
+    mixed_precision: "fp16"
+    enable_tf32: True
+    enable_wandb: True
+    use_ema: True
+    seed: 42
+    max_train_steps: 500_000
+    num_generated_images: 2
+    max_grad_norm: 1.0

RAR/1d-tokenizer/configs/training/MaskGen/maskgen_vq_xl_stage2.yaml

@@ -0,0 +1,87 @@
+experiment:
+    project: "maskgen_vq_xl_stage2"
+    name: "maskgen_vq_xl_stage2_run1"
+    output_dir: "maskgen_vq_xl_stage2_run1"
+    init_weight: "maskgen_vq_xl_stage1.bin"
+    max_train_examples: 13_000_000
+    save_every: 50_000
+    eval_every: 50_000
+    generate_every: 5_000
+    log_every: 50
+    log_grad_norm_every: 1_000
+    resume: True
+
+model:
+    vq_model:
+        quantize_mode: vq
+        codebook_size: 8192
+        token_size: 64
+        use_l2_norm: False
+        commitment_cost: 0.25
+        clustering_vq: True
+        # vit arch
+        vit_enc_model_size: "base"
+        vit_dec_model_size: "large"
+        vit_enc_patch_size: 16
+        vit_dec_patch_size: 16
+        num_latent_tokens: 128
+        finetune_decoder: False
+        is_legacy: False
+    maskgen:
+        decoder_embed_dim: 1280
+        decoder_depth: 20
+        decoder_num_heads: 16
+        micro_condition: True
+        micro_condition_embed_dim: 256
+        text_drop_prob: 0.1
+        condition_num_classes: 1000
+        cfg: 12.0
+        num_iter: 16
+        temperature: 2.0
+        sample_aesthetic_score: 6.5
+
+losses:
+  label_smoothing: 0.1
+  loss_weight_unmasked_token: 0.1
+
+dataset:
+    params:
+        train_shards_path_or_url: "datacomp6+::laion-art::laion-pop::journeydb::dalle3"
+        eval_shards_path_or_url: "coco"
+        pretokenization: "true"
+        num_workers_per_gpu: 12
+        dataset_with_class_label: False
+        dataset_with_text_label: True
+    preprocessing:
+        resize_shorter_edge: 256
+        crop_size: 256
+        random_crop: True
+        random_flip: True
+        res_ratio_filtering: True
+
+optimizer:
+    name: adamw 
+    params:
+        learning_rate: 1e-4
+        beta1: 0.9
+        beta2: 0.96
+        weight_decay: 0.03
+
+lr_scheduler:
+    scheduler: "cosine"
+    params:
+        learning_rate: ${optimizer.params.learning_rate}
+        warmup_steps: 10_000
+        end_lr: 1e-5
+
+training:
+    gradient_accumulation_steps: 1
+    per_gpu_batch_size: 128
+    mixed_precision: "fp16"
+    enable_tf32: True
+    enable_wandb: True
+    use_ema: True
+    seed: 42
+    max_train_steps: 250_000
+    num_generated_images: 2
+    max_grad_norm: 1.0