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base on [NeurIPS 2024 Oral][GPT beats diffusion🔥] [scaling laws in visual generation📈] Official impl. of "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction". An *ultra-simple, user-friendly yet state-of-the-art* codebase for autoregressive image generation! # VAR: a new visual generation method elevates GPT-style models beyond diffusion🚀 & Scaling laws observed📈 <div align="center"> [![demo platform](https://img.shields.io/badge/Play%20with%20VAR%21-VAR%20demo%20platform-lightblue)](https://var.vision/demo)  [![arXiv](https://img.shields.io/badge/arXiv%20paper-2404.02905-b31b1b.svg)](https://arxiv.org/abs/2404.02905)  [![huggingface weights](https://img.shields.io/badge/%F0%9F%A4%97%20Weights-FoundationVision/var-yellow)](https://huggingface.co/FoundationVision/var)  [![SOTA](https://img.shields.io/badge/State%20of%20the%20Art-Image%20Generation%20on%20ImageNet%20%28AR%29-32B1B4?logo=data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPHN2ZyB3aWR0aD0iNjA2IiBoZWlnaHQ9IjYwNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIiB4bWxuczp4bGluaz0iaHR0cDovL3d3dy53My5vcmcvMTk5OS94bGluayIgb3ZlcmZsb3c9ImhpZGRlbiI%2BPGRlZnM%2BPGNsaXBQYXRoIGlkPSJjbGlwMCI%2BPHJlY3QgeD0iLTEiIHk9Ii0xIiB3aWR0aD0iNjA2IiBoZWlnaHQ9IjYwNiIvPjwvY2xpcFBhdGg%2BPC9kZWZzPjxnIGNsaXAtcGF0aD0idXJsKCNjbGlwMCkiIHRyYW5zZm9ybT0idHJhbnNsYXRlKDEgMSkiPjxyZWN0IHg9IjUyOSIgeT0iNjYiIHdpZHRoPSI1NiIgaGVpZ2h0PSI0NzMiIGZpbGw9IiM0NEYyRjYiLz48cmVjdCB4PSIxOSIgeT0iNjYiIHdpZHRoPSI1NyIgaGVpZ2h0PSI0NzMiIGZpbGw9IiM0NEYyRjYiLz48cmVjdCB4PSIyNzQiIHk9IjE1MSIgd2lkdGg9IjU3IiBoZWlnaHQ9IjMwMiIgZmlsbD0iIzQ0RjJGNiIvPjxyZWN0IHg9IjEwNCIgeT0iMTUxIiB3aWR0aD0iNTciIGhlaWdodD0iMzAyIiBmaWxsPSIjNDRGMkY2Ii8%2BPHJlY3QgeD0iNDQ0IiB5PSIxNTEiIHdpZHRoPSI1NyIgaGVpZ2h0PSIzMDIiIGZpbGw9IiM0NEYyRjYiLz48cmVjdCB4PSIzNTkiIHk9IjE3MCIgd2lkdGg9IjU2IiBoZWlnaHQ9IjI2NCIgZmlsbD0iIzQ0RjJGNiIvPjxyZWN0IHg9IjE4OCIgeT0iMTcwIiB3aWR0aD0iNTciIGhlaWdodD0iMjY0IiBmaWxsPSIjNDRGMkY2Ii8%2BPHJlY3QgeD0iNzYiIHk9IjY2IiB3aWR0aD0iNDciIGhlaWdodD0iNTciIGZpbGw9IiM0NEYyRjYiLz48cmVjdCB4PSI0ODIiIHk9IjY2IiB3aWR0aD0iNDciIGhlaWdodD0iNTciIGZpbGw9IiM0NEYyRjYiLz48cmVjdCB4PSI3NiIgeT0iNDgyIiB3aWR0aD0iNDciIGhlaWdodD0iNTciIGZpbGw9IiM0NEYyRjYiLz48cmVjdCB4PSI0ODIiIHk9IjQ4MiIgd2lkdGg9IjQ3IiBoZWlnaHQ9IjU3IiBmaWxsPSIjNDRGMkY2Ii8%2BPC9nPjwvc3ZnPg%3D%3D)](https://paperswithcode.com/sota/image-generation-on-imagenet-256x256?tag_filter=485&p=visual-autoregressive-modeling-scalable-image) </div> <a href="https://arxiv.org/abs/2404.02905">Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction</a> <div> NeurIPS 2024 Oral </div> <img src="https://github.com/FoundationVision/VAR/assets/39692511/9850df90-20b1-4f29-8592-e3526d16d755" width=95%> ## News * **2024-12:** We Release our Text-to-Image research based on VAR, please check [Infinity](https://arxiv.org/abs/2412.04431). * **2024-09:** VAR is accepted as **NeurIPS 2024 Oral** Presentation. * **2024-04:** [Visual AutoRegressive modeling](https://github.com/FoundationVision/VAR) is released. ## 🕹️ Try and Play with VAR! We provide a [demo website](https://var.vision/demo) for you to play with VAR models and generate images interactively. Enjoy the fun of visual autoregressive modeling! We also provide [demo_sample.ipynb](demo_sample.ipynb) for you to see more technical details about VAR. [//]: # () [//]: # (<img src="https://user-images.githubusercontent.com/39692511/226376648-3f28a1a6-275d-4f88-8f3e-cd1219882488.png" width=50%) [//]: # () ## What's New? ### 🔥 Introducing VAR: a new paradigm in autoregressive visual generation✨: Visual Autoregressive Modeling (VAR) redefines the autoregressive learning on images as coarse-to-fine "next-scale prediction" or "next-resolution prediction", diverging from the standard raster-scan "next-token prediction". <img src="https://github.com/FoundationVision/VAR/assets/39692511/3e12655c-37dc-4528-b923-ec6c4cfef178" width=93%> ### 🔥 For the first time, GPT-style autoregressive models surpass diffusion models🚀: <img src="https://github.com/FoundationVision/VAR/assets/39692511/cc30b043-fa4e-4d01-a9b1-e50650d5675d" width=55%> ### 🔥 Discovering power-law Scaling Laws in VAR transformers📈: <img src="https://github.com/FoundationVision/VAR/assets/39692511/c35fb56e-896e-4e4b-9fb9-7a1c38513804" width=85%> <img src="https://github.com/FoundationVision/VAR/assets/39692511/91d7b92c-8fc3-44d9-8fb4-73d6cdb8ec1e" width=85%> ### 🔥 Zero-shot generalizability🛠️: <img src="https://github.com/FoundationVision/VAR/assets/39692511/a54a4e52-6793-4130-bae2-9e459a08e96a" width=70%> #### For a deep dive into our analyses, discussions, and evaluations, check out our [paper](https://arxiv.org/abs/2404.02905). ## VAR zoo We provide VAR models for you to play with, which are on <a href='https://huggingface.co/FoundationVision/var'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Huggingface-FoundationVision/var-yellow'></a> or can be downloaded from the following links: | model | reso. | FID | rel. cost | #params | HF weights🤗 | |:----------:|:-----:|:--------:|:---------:|:-------:|:------------------------------------------------------------------------------------| | VAR-d16 | 256 | 3.55 | 0.4 | 310M | [var_d16.pth](https://huggingface.co/FoundationVision/var/resolve/main/var_d16.pth) | | VAR-d20 | 256 | 2.95 | 0.5 | 600M | [var_d20.pth](https://huggingface.co/FoundationVision/var/resolve/main/var_d20.pth) | | VAR-d24 | 256 | 2.33 | 0.6 | 1.0B | [var_d24.pth](https://huggingface.co/FoundationVision/var/resolve/main/var_d24.pth) | | VAR-d30 | 256 | 1.97 | 1 | 2.0B | [var_d30.pth](https://huggingface.co/FoundationVision/var/resolve/main/var_d30.pth) | | VAR-d30-re | 256 | **1.80** | 1 | 2.0B | [var_d30.pth](https://huggingface.co/FoundationVision/var/resolve/main/var_d30.pth) | You can load these models to generate images via the codes in [demo_sample.ipynb](demo_sample.ipynb). Note: you need to download [vae_ch160v4096z32.pth](https://huggingface.co/FoundationVision/var/resolve/main/vae_ch160v4096z32.pth) first. ## Installation 1. Install `torch>=2.0.0`. 2. Install other pip packages via `pip3 install -r requirements.txt`. 3. Prepare the [ImageNet](http://image-net.org/) dataset <details> <summary> assume the ImageNet is in `/path/to/imagenet`. It should be like this:</summary> ``` /path/to/imagenet/: train/: n01440764: many_images.JPEG ... n01443537: many_images.JPEG ... val/: n01440764: ILSVRC2012_val_00000293.JPEG ... n01443537: ILSVRC2012_val_00000236.JPEG ... ``` **NOTE: The arg `--data_path=/path/to/imagenet` should be passed to the training script.** </details> 5. (Optional) install and compile `flash-attn` and `xformers` for faster attention computation. Our code will automatically use them if installed. See [models/basic_var.py#L15-L30](models/basic_var.py#L15-L30). ## Training Scripts To train VAR-{d16, d20, d24, d30, d36-s} on ImageNet 256x256 or 512x512, you can run the following command: ```shell # d16, 256x256 torchrun --nproc_per_node=8 --nnodes=... --node_rank=... --master_addr=... --master_port=... train.py \ --depth=16 --bs=768 --ep=200 --fp16=1 --alng=1e-3 --wpe=0.1 # d20, 256x256 torchrun --nproc_per_node=8 --nnodes=... --node_rank=... --master_addr=... --master_port=... train.py \ --depth=20 --bs=768 --ep=250 --fp16=1 --alng=1e-3 --wpe=0.1 # d24, 256x256 torchrun --nproc_per_node=8 --nnodes=... --node_rank=... --master_addr=... --master_port=... train.py \ --depth=24 --bs=768 --ep=350 --tblr=8e-5 --fp16=1 --alng=1e-4 --wpe=0.01 # d30, 256x256 torchrun --nproc_per_node=8 --nnodes=... --node_rank=... --master_addr=... --master_port=... train.py \ --depth=30 --bs=1024 --ep=350 --tblr=8e-5 --fp16=1 --alng=1e-5 --wpe=0.01 --twde=0.08 # d36-s, 512x512 (-s means saln=1, shared AdaLN) torchrun --nproc_per_node=8 --nnodes=... --node_rank=... --master_addr=... --master_port=... train.py \ --depth=36 --saln=1 --pn=512 --bs=768 --ep=350 --tblr=8e-5 --fp16=1 --alng=5e-6 --wpe=0.01 --twde=0.08 ``` A folder named `local_output` will be created to save the checkpoints and logs. You can monitor the training process by checking the logs in `local_output/log.txt` and `local_output/stdout.txt`, or using `tensorboard --logdir=local_output/`. If your experiment is interrupted, just rerun the command, and the training will **automatically resume** from the last checkpoint in `local_output/ckpt*.pth` (see [utils/misc.py#L344-L357](utils/misc.py#L344-L357)). ## Sampling & Zero-shot Inference For FID evaluation, use `var.autoregressive_infer_cfg(..., cfg=1.5, top_p=0.96, top_k=900, more_smooth=False)` to sample 50,000 images (50 per class) and save them as PNG (not JPEG) files in a folder. Pack them into a `.npz` file via `create_npz_from_sample_folder(sample_folder)` in [utils/misc.py#L344](utils/misc.py#L360). Then use the [OpenAI's FID evaluation toolkit](https://github.com/openai/guided-diffusion/tree/main/evaluations) and reference ground truth npz file of [256x256](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/256/VIRTUAL_imagenet256_labeled.npz) or [512x512](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/512/VIRTUAL_imagenet512.npz) to evaluate FID, IS, precision, and recall. Note a relatively small `cfg=1.5` is used for trade-off between image quality and diversity. You can adjust it to `cfg=5.0`, or sample with `autoregressive_infer_cfg(..., more_smooth=True)` for **better visual quality**. We'll provide the sampling script later. ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## Citation If our work assists your research, feel free to give us a star ⭐ or cite us using: ``` @Article{VAR, title={Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction}, author={Keyu Tian and Yi Jiang and Zehuan Yuan and Bingyue Peng and Liwei Wang}, year={2024}, eprint={2404.02905}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ", Assign "at most 3 tags" to the expected json: {"id":"9129","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"

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