[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85063-en":3,"doc-seo-85063-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85063,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Closing the Null Space: Guidance-Aware Quantization for Classifier-Free Diffusion","Deploying classifier-free guidance (CFG) diffusion models under real compute budgets requires post-training quantization, but existing PTQ methods treat CFG as a single-branch network and miss its paired conditional/unconditional structure. This structural blind spot causes hidden latency overhead at the system level and, algorithmically, a calibration null space where guidance-gap fidelity can be perfect while the unconditional branch drifts. The paper proves the “branch-drift trap” analytically, verifies it empirically, and proposes Guidance-Aware Mixed Precision (GAMP) to prevent drift by design and improve guided predictions.","Closing the Null Space: Guidance-Aware Quantization for Classifier-Free Diffusion  \nAbdullah Al Shafi, Sumaiya Rahim Suma  \nDepartment of Computer Science and Engineering  \nKhulna University of Engineering & Technology  \nKhulna-9203, Bangladesh  \n{abdullah.shafi99, sumaiya.rahim234}@gmail.com  \narXiv :2607 .0824 1v 1 [ cs .CV] 9 Jul 2026  \nAbstract—Deploying classifier-free guidance (CFG) diffusion models under real-world compute budgets requires quantization, yet existing post-training quantization (PTQ) methods treat CFG models as single-branch networks, ignoring the paired conditional/unconditional structure that CFG inference fundamentally relies on. This structural blind spot has two consequences. Atthe system level, the two-pass CFG execution pattern imposes a latency overhead that parameter-count and bit-operation metrics conceal entirely, and commodity INT8 inference stacks fail to realize the theoretical efficiency gains that BOPs calculations promise. At the algorithmic level, calibrating against the guidance gap alone admits an exact null space: a quantized model can achieve perfect gap-fidelity diagnostics while the unconditional branch drifts arbitrarily, corrupting every guided prediction at inference time. This paper terms this the branch-drift trap, provesits existence analytically, and confirms it empirically through a false-positive result in which the best-calibrated model by standard diagnostics simultaneously produces the worst sample quality. To close the trap, Guidance-Aware Mixed Precision (GAMP) is proposed, which calibrates directly on the guided prediction, derives per-layer activation-bit sensitivity from guidedoutput degradation, and allocates bits via a greedy knapsack—provably preventing unconditional branch drift by construction.  \nIndex Terms—post-training quantization, classifier-free guidance, diffusion models, mixed precision, branch-drift trap, null space, inference efficiency  \nI. INTRODUCTION  \nDiffusion models [1] have become the dominant framework for high-quality conditional image generation, with classifierfree guidance (CFG) [2] providing the primary control mechanism at inference time. Deploying these models under memory and latency budgets motivates post-training quantization (PTQ), which compresses a trained model by quantizing its weights and activations without retraining. CFG inference, however, differs fundamentally from the unconditional generation setting that virtually all PTQ methods assume: at every denoising step, the model executes two forward passes—one conditioned on a class label and one on a null token—whose outputs are combined as  \n˜ε = ε∅ + w(εy − ε∅), w > 1. (1)  \nThis dual-branch structure creates two deployment challenges that the existing PTQ literature has not addressed.  \nThe first is a measurement problem. Efficiency metrics such as parameter counts and bit-operations (BOPs) report  \nsingle-pass costs, invisibly discounting the two-pass per-step overhead that CFG imposes. The same measurement gap affects INT8 deployment: BOPs-predicted speedups assume hardware-matched inference stacks, but commodity ONNX Runtime without TensorRT routes quantized operators through fallback pathways that negate—and in practice reverse—the theoretical gains.  \nThe second is a calibration problem, and it is structural. A natural PTQ objective for a CFG model is to calibrate the guidance gap ∆ = εy − ε∅ , since DASH’s distillation work [5] demonstrates that preserving ∆ is central to generation quality. This paper shows, however, that a gap-only calibration objective admits an exact null space: there exist quantized models that achieve perfect gap fidelity—near-ideal ρ and cos(∆) scores—while the unconditional branch drifts by an unconstrained vector δ, corrupting the guided prediction at inference time. This phenomenon is termed the branchdrift trap. Unlike the training-time setting in DASH, where an explicit unconditional branch loss and hundreds of thousands of gradient step","cbCaimMcQQgzr275","https://ap.wps.com/l/cbCaimMcQQgzr275","pdf",373833,2,1,6,"English","en",105,"# Introduction\n## CFG as a two-pass dual-branch deployment challenge\n# Related Work\n## Post-training quantization for diffusion models\n# Method and Contributions\n## Guidance-Aware Mixed Precision (GAMP)\n# Deployment Guidelines","[{\"question\":\"Why do existing post-training quantization methods struggle with classifier-free guidance diffusion models?\",\"answer\":\"They typically treat CFG as a single-branch network, ignoring the paired conditional and unconditional branches executed at every denoising step. This omission leads to both measurement blind spots and calibration failures.\"},{\"question\":\"What is the “branch-drift trap” described in the paper?\",\"answer\":\"Gap-only calibration admits an exact null space: a quantized model can preserve guidance-gap fidelity while the unconditional branch drifts by an unconstrained vector, corrupting guided predictions at inference.\"},{\"question\":\"How does Guidance-Aware Mixed Precision (GAMP) address the trap?\",\"answer\":\"GAMP calibrates directly on the guided prediction, derives per-layer activation-bit sensitivity from guided-output degradation, and allocates bits with a greedy knapsack approach that provably prevents unconditional branch drift by construction.\"}]",1784200732,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"closing-the-null-space-guidance-aware-quantization-for-classifier-free-diffusion","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/closing-the-null-space-guidance-aware-quantization-for-classifier-free-diffusion/85063/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-22","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do existing post-training quantization methods struggle with classifier-free guidance diffusion models?","Question",{"text":75,"@type":76},"They typically treat CFG as a single-branch network, ignoring the paired conditional and unconditional branches executed at every denoising step. This omission leads to both measurement blind spots and calibration failures.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the “branch-drift trap” described in the paper?",{"text":80,"@type":76},"Gap-only calibration admits an exact null space: a quantized model can preserve guidance-gap fidelity while the unconditional branch drifts by an unconstrained vector, corrupting guided predictions at inference.",{"name":82,"@type":73,"acceptedAnswer":83},"How does Guidance-Aware Mixed Precision (GAMP) address the trap?",{"text":84,"@type":76},"GAMP calibrates directly on the guided prediction, derives per-layer activation-bit sensitivity from guided-output degradation, and allocates bits with a greedy knapsack approach that provably prevents unconditional branch drift by construction.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]