{"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/pixart-d-fast-and-controllable-image","title":"PIXART-δ: Fast and Controllable Image Generation with Latent Consistency Models","arxiv_id":"2401.05252","date":"2024-01-10","proceeding":null,"authors":["Junsong Chen","Yue Wu","Simian Luo","Enze Xie","Sayak Paul","Ping Luo","Hang Zhao","Zhenguo Li"],"abstract":"This technical report introduces PIXART-{\\delta}, a text-to-image synthesis framework that integrates the Latent Consistency Model (LCM) and ControlNet into the advanced PIXART-{\\alpha} model. PIXART-{\\alpha} is recognized for its ability to generate high-quality images of 1024px resolution through a remarkably efficient training process. The integration of LCM in PIXART-{\\delta} significantly accelerates the inference speed, enabling the production of high-quality images in just 2-4 steps. Notably, PIXART-{\\delta} achieves a breakthrough 0.5 seconds for generating 1024x1024 pixel images, marking a 7x improvement over the PIXART-{\\alpha}. Additionally, PIXART-{\\delta} is designed to be efficiently trainable on 32GB V100 GPUs within a single day. With its 8-bit inference capability (von Platen et al., 2023), PIXART-{\\delta} can synthesize 1024px images within 8GB GPU memory constraints, greatly enhancing its usability and accessibility. Furthermore, incorporating a ControlNet-like module enables fine-grained control over text-to-image diffusion models. We introduce a novel ControlNet-Transformer architecture, specifically tailored for Transformers, achieving explicit controllability alongside high-quality image generation. As a state-of-the-art, open-source image generation model, PIXART-{\\delta} offers a promising alternative to the Stable Diffusion family of models, contributing significantly to text-to-image synthesis.","url_abs":"https://arxiv.org/abs/2401.05252v1","url_pdf":"https://arxiv.org/pdf/2401.05252v1.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":"pixart-d-fast-and-controllable-image","repo_url":"https://github.com/PixArt-alpha/PixArt-alpha","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"text-to-image-generation","task_name":"Text-to-Image Generation"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-to-image-generation-on-geneval","task":"Text-to-Image Generation","dataset":"GenEval","model":"PIXART-δ","rank_in_archive_order":20,"of":20,"metrics":{"Overall":"0"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2401.05252","atlas_url":"https://app.syntology.ai/?focus=2401.05252","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.05252"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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