{"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/cross-view-masked-diffusion-transformers-for","title":"Cross-view Masked Diffusion Transformers for Person Image Synthesis","arxiv_id":"2402.01516","date":"2024-02-02","proceeding":null,"authors":["Trung X. Pham","Zhang Kang","Chang D. Yoo"],"abstract":"We present X-MDPT ($\\underline{Cross}$-view $\\underline{M}$asked $\\underline{D}$iffusion $\\underline{P}$rediction $\\underline{T}$ransformers), a novel diffusion model designed for pose-guided human image generation. X-MDPT distinguishes itself by employing masked diffusion transformers that operate on latent patches, a departure from the commonly-used Unet structures in existing works. The model comprises three key modules: 1) a denoising diffusion Transformer, 2) an aggregation network that consolidates conditions into a single vector for the diffusion process, and 3) a mask cross-prediction module that enhances representation learning with semantic information from the reference image. X-MDPT demonstrates scalability, improving FID, SSIM, and LPIPS with larger models. Despite its simple design, our model outperforms state-of-the-art approaches on the DeepFashion dataset while exhibiting efficiency in terms of training parameters, training time, and inference speed. Our compact 33MB model achieves an FID of 7.42, surpassing a prior Unet latent diffusion approach (FID 8.07) using only $11\\times$ fewer parameters. Our best model surpasses the pixel-based diffusion with $\\frac{2}{3}$ of the parameters and achieves $5.43 \\times$ faster inference. The code is available at https://github.com/trungpx/xmdpt.","url_abs":"https://arxiv.org/abs/2402.01516v2","url_pdf":"https://arxiv.org/pdf/2402.01516v2.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":"cross-view-masked-diffusion-transformers-for","repo_url":"https://github.com/trungpx/xmdpt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"ssim","task_name":"SSIM"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"diffusion","method_name":"Diffusion"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2402.01516","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.01516"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/trungpx/xmdpt","reach":null}],"summary":{"ran":2,"unverified":3},"by_repo_kind":{"official":{"samples":5,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"b4c380658e987381","entry":"Attention_Cross","repo":"trungpx/xmdpt","repo_kind":"official","path":"masked_diffusion/models.py","file_url":"https://github.com/trungpx/xmdpt/blob/HEAD/masked_diffusion/models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"b4c380658e987381"}},{"code_sha256_prefix":"01200a9e5704de4e","entry":"FinalLayer","repo":"trungpx/xmdpt","repo_kind":"official","path":"masked_diffusion/models.py","file_url":"https://github.com/trungpx/xmdpt/blob/HEAD/masked_diffusion/models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"01200a9e5704de4e"}},{"code_sha256_prefix":"7479318d383429cf","entry":"MDTBlock","repo":"trungpx/xmdpt","repo_kind":"official","path":"masked_diffusion/models.py","file_url":"https://github.com/trungpx/xmdpt/blob/HEAD/masked_diffusion/models.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"7479318d383429cf"}},{"code_sha256_prefix":"614e0267c918b503","entry":"MDTBlock_inter","repo":"trungpx/xmdpt","repo_kind":"official","path":"masked_diffusion/models.py","file_url":"https://github.com/trungpx/xmdpt/blob/HEAD/masked_diffusion/models.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"614e0267c918b503"}},{"code_sha256_prefix":"d589628918473765","entry":"XMDPT","repo":"trungpx/xmdpt","repo_kind":"official","path":"masked_diffusion/models.py","file_url":"https://github.com/trungpx/xmdpt/blob/HEAD/masked_diffusion/models.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"d589628918473765"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}