{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/scaling-autoregressive-multi-modal-models","title":"Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning","arxiv_id":"2309.02591","date":"2023-09-05","proceeding":null,"authors":["Lili Yu","Bowen Shi","Ramakanth Pasunuru","Benjamin Muller","Olga Golovneva","Tianlu Wang","Arun Babu","Binh Tang","Brian Karrer","Shelly Sheynin","Candace Ross","Adam Polyak","Russell Howes","Vasu Sharma","Puxin Xu","Hovhannes Tamoyan","Oron Ashual","Uriel Singer","Shang-Wen Li","Susan Zhang","Richard James","Gargi Ghosh","Yaniv Taigman","Maryam Fazel-Zarandi","Asli Celikyilmaz","Luke Zettlemoyer","Armen Aghajanyan"],"abstract":"We present CM3Leon (pronounced \"Chameleon\"), a retrieval-augmented, token-based, decoder-only multi-modal language model capable of generating and infilling both text and images. CM3Leon uses the CM3 multi-modal architecture but additionally shows the extreme benefits of scaling up and tuning on more diverse instruction-style data. It is the first multi-modal model trained with a recipe adapted from text-only language models, including a large-scale retrieval-augmented pre-training stage and a second multi-task supervised fine-tuning (SFT) stage. It is also a general-purpose model that can do both text-to-image and image-to-text generation, allowing us to introduce self-contained contrastive decoding methods that produce high-quality outputs. Extensive experiments demonstrate that this recipe is highly effective for multi-modal models. CM3Leon achieves state-of-the-art performance in text-to-image generation with 5x less training compute than comparable methods (zero-shot MS-COCO FID of 4.88). After SFT, CM3Leon can also demonstrate unprecedented levels of controllability in tasks ranging from language-guided image editing to image-controlled generation and segmentation.","url_abs":"https://arxiv.org/abs/2309.02591v1","url_pdf":"https://arxiv.org/pdf/2309.02591v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"scaling-autoregressive-multi-modal-models","repo_url":"https://github.com/kyegomez/CM3Leon","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"image-to-text","task_name":"Image to text"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"text-generation","task_name":"Text Generation"},{"task_slug":"text-to-image-generation-1","task_name":"Text to Image Generation"},{"task_slug":"text-to-image-generation","task_name":"Text-to-Image Generation"}],"methods":[{"method_slug":"sft","method_name":"SFT"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-to-image-generation-on-coco-1","task":"Text-to-Image Generation","dataset":"COCO","model":"CM3Leon-7B","rank_in_archive_order":2,"of":3,"metrics":{"FID":"4.88"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2309.02591","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}